From 7fc3f6eb5665d976961b8f3b5eecd6fac6b79f45 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Sun, 10 Nov 2019 13:35:57 +0800 Subject: [PATCH 01/25] Update BT2101 Default.ipynb --- BT2101 Default.ipynb | 977 +++++++++++++++++++++++-------------------- 1 file changed, 518 insertions(+), 459 deletions(-) diff --git a/BT2101 Default.ipynb b/BT2101 Default.ipynb index cafadac..b828887 100644 --- a/BT2101 Default.ipynb +++ b/BT2101 Default.ipynb @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 114, "metadata": { "colab": {}, "colab_type": "code", @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 115, "metadata": { "colab": {}, "colab_type": "code", @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 117, "metadata": { "colab": {}, "colab_type": "code", @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 118, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -378,7 +378,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 5, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 119, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -418,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 120, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -435,7 +435,7 @@ "0" ] }, - "execution_count": 7, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -459,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 121, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -766,7 +766,7 @@ "[8 rows x 24 columns]" ] }, - "execution_count": 8, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -797,7 +797,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 122, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 123, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -848,7 +848,7 @@ "Text(0, 0.5, 'Frequency')" ] }, - "execution_count": 10, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, @@ -919,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 124, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -994,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 125, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1056,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 126, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1123,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 127, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1267,7 +1267,7 @@ "19 PAY_AMT6" ] }, - "execution_count": 14, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -1295,7 +1295,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 128, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1329,7 +1329,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 129, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1346,7 +1346,7 @@ "Text(0.5, 1.0, 'Distribution of Limit Balance')" ] }, - "execution_count": 16, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, @@ -1379,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 130, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1393,10 +1393,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" }, @@ -1429,7 +1429,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 131, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1573,7 +1573,7 @@ "BILL_AMT6 0.005372" ] }, - "execution_count": 18, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -1599,7 +1599,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 132, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1616,7 +1616,7 @@ "Text(0.5, 1.05, 'Correlation Matrix')" ] }, - "execution_count": 19, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" }, @@ -1710,7 +1710,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 133, "metadata": { "colab": {}, "colab_type": "code", @@ -1725,7 +1725,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -1734,7 +1734,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 135, "metadata": { "colab": {}, "colab_type": "code", @@ -1759,7 +1759,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -1791,7 +1791,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 137, "metadata": { "scrolled": true }, @@ -1834,7 +1834,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 138, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1852,14 +1852,14 @@ "Holdout Sample Accuracy:\n", " precision recall f1-score support\n", "\n", - " 0 0.79 0.51 0.62 4699\n", - " 1 0.23 0.53 0.32 1301\n", + " 0 0.77 0.51 0.61 4645\n", + " 1 0.22 0.49 0.31 1355\n", "\n", - " accuracy 0.51 6000\n", - " macro avg 0.51 0.52 0.47 6000\n", - "weighted avg 0.67 0.51 0.55 6000\n", + " accuracy 0.50 6000\n", + " macro avg 0.50 0.50 0.46 6000\n", + "weighted avg 0.65 0.50 0.54 6000\n", "\n", - "Kfold Average Accuracy: 0.4977333333333333\n" + "Kfold Average Accuracy: 0.4976666666666666\n" ] } ], @@ -1901,7 +1901,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 139, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1936,7 +1936,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 140, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1949,7 +1949,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1973,7 +1973,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 141, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1990,12 +1990,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.79 0.98 0.88 4699\n", - " 1 0.54 0.09 0.15 1301\n", + " 0 0.78 0.98 0.87 4645\n", + " 1 0.52 0.08 0.14 1355\n", "\n", - " accuracy 0.79 6000\n", - " macro avg 0.67 0.53 0.51 6000\n", - "weighted avg 0.74 0.79 0.72 6000\n", + " accuracy 0.78 6000\n", + " macro avg 0.65 0.53 0.50 6000\n", + "weighted avg 0.72 0.78 0.71 6000\n", "\n" ] } @@ -2022,7 +2022,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 142, "metadata": { "scrolled": true }, @@ -2031,7 +2031,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the KNN identified 113\n" + "Of 1355 Defaulters, the KNN identified 108\n" ] }, { @@ -2067,13 +2067,13 @@ " \n", " \n", " 0\n", - " 4603\n", - " 96\n", + " 4545\n", + " 100\n", " \n", " \n", " 1\n", - " 1188\n", - " 113\n", + " 1247\n", + " 108\n", " \n", " \n", "\n", @@ -2082,11 +2082,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 4603 96\n", - "1 1188 113" + "0 4545 100\n", + "1 1247 108" ] }, - "execution_count": 29, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -2123,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ @@ -2132,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -2146,7 +2146,7 @@ " random_state=None, splitter='best')" ] }, - "execution_count": 31, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -2158,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -2167,8 +2167,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 18665\n", - " 1 1.00 1.00 1.00 5335\n", + " 0 1.00 1.00 1.00 18719\n", + " 1 1.00 1.00 1.00 5281\n", "\n", " accuracy 1.00 24000\n", " macro avg 1.00 1.00 1.00 24000\n", @@ -2190,14 +2190,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the Decision Tree (GINI) identified 537\n" + "Of 1355 Defaulters, the Decision Tree (GINI) identified 560\n" ] }, { @@ -2233,13 +2233,13 @@ " \n", " \n", " 0\n", - " 3843\n", - " 856\n", + " 3801\n", + " 844\n", " \n", " \n", " 1\n", - " 764\n", - " 537\n", + " 795\n", + " 560\n", " \n", " \n", "\n", @@ -2248,11 +2248,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 3843 856\n", - "1 764 537" + "0 3801 844\n", + "1 795 560" ] }, - "execution_count": 33, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -2263,19 +2263,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.5\n" + "Optimal Threshold: 1.0\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gV1dbA4d8mgUBIQkuoISTU0DtIEVBBBUWQJoigAlcRu17sioqKermKfuC1AGIHEQtSFAsqoYP0jgmE0Et6SD3r+2MmeIghnEBOTsp6n4eHTF9TzqyZvWf2GBFBKaWUupAyng5AKaVU0aaJQimlVJ40USillMqTJgqllFJ50kShlFIqT5oolFJK5UkTRS6MMSONMcs8HYenGWNCjDFJxhivQlxmqDFGjDHehbVMdzLG7DDG9LqE6S7pGDTGVDDGfG+MiTfGzM/v9IXJ1eOrpP4ejTHljTG7jTHVC3GZjxhjXsr3hCJSpP8BB4CzQBJwDJgD+Hk6rpL4z97WvT0cQygggHcR2B4CNCxO6wyMAtYV1PYDegEO+/eXBMQAXwIdPb1/LnO9Rjqt09kc65hUSDE8DEzP0a8TsASIs//tACYDle3h44DfnMaPAY4Cvk79xgM/239728dWqN3tCxwGquUn1uJyR9FfRPyANkBb4EkPx3NJPHmVXFKu0POjlG7vesBeEcnM74R5xHzE/v35A1cAu4EVxphrLj1MzxKRz0TEz16vvtjr6NTvPG7an3cDnzgt40rgV+A3oLGIVAb62YNb5DGfcsB9rixQRFKAZVgXFK7zdGZ3IesewOkqF3gdWOzU7QNMBaKB48C7QAWn4QOAzUAC8Bdwvd2/EjALKxsfBl4CvOxhdwAR9t/vAlNzxPQd8Ij9d21gAXASiAIecBrveeAr4FN7+eNyWb9KwMf29AeBZ4AyTnGsBP4PiMf6gV6TY9q81mEl8CZwxh7WAOtAPA2cAj7j7yuVT7CuqrLv3h4jx5Uu1gE82Z5vItYBF+gUz2h7HU4Dz+bcdznWuwLwX3v8eCDC7pe9zNvtfXoKeDrHFddqrKuto8B0oJzTcAHuBfYBUXa/t4BD9j7YCFzpNL4X8JR9bCTaw+sCf9jzSra3xy32+DdiHU9xwCqgVY5j9XFgK5CGdTV3bhvYsW+w4zgOvGH3j7aXlX1F2wWnY9Aepznwk70vjwNP5bJNXwDSgQx7PmOxipefsbfzCaxjrZI9fva2HmvH8Ecu8+wFxOTSfzqwwak73Cm+PcCwfOzr7OPrDiDS3g9RwMicv0e7uyuw3p7XeqCr07DfyOMYvcCxeKF1jAEmAtuAdLtfMPANf//e73Uavwx/H0ungLlAlQsssz7WseXl1G8N8OZFYs3tjuJxe3kBdr8L3lHY/W4HfsrXefhSTt6F+Y/zf2jB9k57y2n4NGAhUBXriud7YIrTDzMe6GPvxDpAuD3sW+A9oCJQHet2/e6cBybQA+skY+zuKlgn09r2PDcCz2Fl9fr2gX6dPe7zWD/agfa4FXJZv4+xEo8/1g9nLzDWKY5MrFvUssAt9vpUdXEdMoH77YOlAtDQ3hY+QBDWyXBabts6x4nEOVH8BTS25/cb8Ko9rBnWyam7vS2m2ut+oUQxw56+DtbJuqsdV/YyP7CX0RrrpNvUnq491lWttz3uLuAhp/kK1gmravb2Bm4DqtnTPIpVhFneHpZ9ImgCGHt51Zzm1dBp3u2wTrad7Zhvt7eZj9P224yVaCrk3KZYCW6U/bcfcEVu2zmXY9AfKyk+CpS3uztfYLs+D3zq1D0G2I91bPoBXwOf5Fjux1jHUG7HZy9yP4lejXVhUdH+dwi4097G7bBOXM1d3Nfe9jwSgCb2NLWcpnfeFlWBWKwrYm9ghN2dvc9+4wLHaB7nmAutYwzW7zvYnpeXvX+fwjrGG9r79xp7/H9jJag69n6alb2tc5n3AGCLU3eAvT27XyTW3BJFL6xz4PN2v4slik7AiXydh91xci/If/aOSMK6OhDgF/6+CjZYWbmB0/hd+PtK8j1yydBADayTj/OdxwhgeS4HpsG62uphd/8L+NX+uzMQnWPeTwIfOv1o/3GV5jSulx1HM6d+d2cfCHYcR7CTlN1vnf0jcWUdoi+0bHucgcCmHNv6YoniGafhE4Af7L+fA75wGuaLdXX7j0SBlTTPAq1zGZa9zOAc6zz8AuvwEPCNU7cAV19kvWOzl4119TvgAuPlTBT/AybnGGcP0NNp+43J5fjNThR/YF31B+YY57ztnMsxOMJ5P11k3Z7n/ETxCzDBqbsJVgL3dlpu/Tzm14vcT6Lh9rR1sC5gVuQY/h4wycV9nZ0o4oDB5EhYObbFKGBdjuGrgTsudoxewjrGAKOdursBkTnGeRb4wP57X/axYHfXxfqNlsll3rdz/l1S9rZwPt7esLdJMvCE3e9CiaK1PW41Lp4ommLfIbn6r7jUUQwUEX+sDRIOBNr9g7BOSBuNMXHGmDjgB7s/WDvqr1zmVw/rCv2o03TvYV2Vn0esLTsX68cKcCtWkU32fGpnz8Oez1NYJ/Fsh/JYr0CsK5ODTv0OYv34sh22Y3AeXtvFdThv2caY6saYucaYw8aYBKwisUDy55jT3ylYV6nYMZ1bnlhloacvMI9ArCuu3PZNnssxxjQ2xiwyxhyz1+EV/rkOOdf7UWPMLvtJoDisIrvsaS50jOSmHvBojv1dF2vdc112DmOxrnR3G2PWG2NudHG5+Ykxp9r88/jyxvVj9ELqYJ2A4rC2S+cc22UkUBPX9jUikoyVcMZjHdOLjTHhLqwP/PM3c6Fj9FI4b5t6QEiO9XwMaz0BQoDvnYZtw9pGuT3VFIt1Z5jtjD1ureweIvKIWPUU32PtswsSkS3Aj3Y8F+OPtd9cVlwSBQAi8jvWU09T7V6nsK5WmotIZftfJfm7MuoQVrl8ToewMn2g03QBItL8Aov+AhhijKmHdRexwGk+UU7zqCwi/iLSz2layTkzJ6ewru7qOfULwapvyFbHGGNyDD/i4jrkXPYUu18rEQnAKpIxeYyfH0exbtEB6zFNrKub3JwCUsl931zM/7DqahrZ6/AU568DOK2HXUH4ODAMq7y4MlbxXfY0FzpGcnMIeDnH/vYVkS9yW3ZOIrJPREZgnTheA74yxlTMa5pLiDGnI/zz+MrEquc4F9olzPdm4E/7BH8I+D3HdvETkXvIx74WkR9FpA/WyXI3VvHjxdYH/vmbKUjO2+YQsC+X33t/e3gM0CfH8PIicuyfs2Ur0CD70WARScCqvxp0GbE+B9zD34nrQpoCW/Iz42KVKGzTgD7GmDYi4sA6mN7MfhbZGFPHGHOdPe4s4E5jzDXGmDL2sHAROYpVyfVfY0yAPayBMaZnbgsUkU1YlVczgR9FJDsbrwMSjDGP28+vexljWhhjOrqyIiKShfWo4cvGGH87ET2CdaWfrTrwgDGmrDFmKNZOXpLfdbD5YxXjxRlj6mCVzzs7jlWWfSm+AvobY7oaY8phFbHkPIEDYO+32cAbxpja9nbrYozxcWE5/lhl2Un2Fec9LoyfibX/vI0xz2GVB2ebCUw2xjQyllbGmOwEl3N7fACMN8Z0tsetaIy5wRjjfGV4QcaY24wxQfb6Zx9DWXZsDi687RcBNY0xDxljfOxjpbMry8S6yHnYGBNmjPHDugObJ5f2VJSxf0OTsIpAnnKKr7ExZpR9nJY1xnQ0xjR1dV8bY2oYY26yE2ca1nGalUsYS+xl3WqM8TbG3IJVP7Yov+tzCVYD6fYdanl7XVoaY9rbw98FXjHGhMC5O/ibcpuRiBzAKtJu79R7InCXMeYxY0yQPY+6/DMx5kpE9mBdxN5/kVF7AktdmWe2YpcoROQkVuXbs3avx7Eq69bYRRE/Y5XDIiLrsCrY3sS6ivydvzf6aKxin51Yt4Ff4XTbl4svgN7A506xZAH9sR7bjcK6epqJVbThqvuxyiAjsZ4G+Rzrh5VtLdDInvfLwBARyS7Sye86vIBV0RgPLMaq2HQ2BXjGvnX+dz7WARHZYa/LXKy7i0Ssit+0C0zyb6xb8/VYt92v4drx+G+s4r9ErBP3vIuM/yPWj2IvVhFFKucXJ7yBlayXYSWgWVgVl2CV939kb49hIrIBq45qOtb23o9Vfu6q64EdxpgkrCexhotIql1M9zKw0l7WFc4TiUgi1kMI/bGKVfYBV7m4zNlYT7T9gXWMpnLxE0lOte2Yk7D2V0ugl4gsc4rvWmA41hX/Maz9mZ0MXNnXZbAq64/Y4/TEql84j33s32iPexqrqOVGETmVz3XKNzu59sOqDD6A9Zt8j78vPN7AKvr+xRiTiPVUXF4Xje/h9JiqXWLSG2vf7reLr5ZindPecTHMF8ijqM2+078e6xzqsuwneVQRZIy5A+uR2u6ejiW/7KvXOKwioihPx6NUUWOMKQ9swqoAP1FIy3wYCBKRpy46spNS9xKWch9jTH+sp2wMVj3SNqwrL6VUDiKSilWUXJjLfPNSpit2RU+qSBuAVXRwBKu4bLjoLatSxZ4WPSmllMqT3lEopZTKU7GrowgMDJTQ0FBPh6GUUsXKxo0bT4lI0MXH/KdilyhCQ0PZsGGDp8NQSqlixRiT8412l2nRk1JKqTxpolBKKZUnTRRKKaXypIlCKaVUnjRRKKWUypMmCqWUUnlyW6Iwxsw2xpwwxmy/wHBjjHnbGLPfGLPVGNPOXbEopZS6dO68o5iD1ZzthfTFag+oEXAX1gdplFJKFbDUjNw+7eE6t71wJyJ/GGNC8xhlAPCx3WjcGmNMZWNMLfuDPEoppS7TycRUnv5wI8sPxV7WfDz5ZnYdzv+ATIzd7x+JwhhzF9ZdByEhIYUSnFJKFVd7jiXy1g+7WbrzOFLG4H069bLm58nK7Nw+k5lrU7Yi8r6IdBCRDkFBl9RUiVJKlWgiwm97TjBq1lqum/YHS3YcI2XHaUZVrcTu9y/nU9yevaOIAeo6dQdjfcdAKaWUi1Izsvhm02FmR0Sx70QSQX7lmHhdE1pU8KH+AwHUrZufLzPnzpOJYiFwnzFmLtAZiNf6CaWUcs3JxDQ+WXOQT9cc5ExyOv6Zwqkfohg1uAX3XtWwQJfltkRhjPkC6AUEGmNigElAWQAReRdYgvWh8v1ACnCnu2JRSqmSYvexBGatiOK7zUfIcDhoXLE8hxcfIGbXaR55pAtPPNatwJfpzqeeRlxkuAD3umv5SilVUjgcwu/7TjJrRRQR+09RoawXwzvV5eTKI7z7bARdu9bl501307JlDbcsv9h9j0IppUqLs+lZfL0phtkRUfx1MpkaAT48ck0jbgivQYO6ldgTXpN2jaoxdmw7ypTJ7fmggqGJQimlipgTCal8vPogn609SGxKBi3qBDDtljZ4H03hwfFL+b1NTRYsGEaTJoE0aRLo9ng0USilVBGx80gCsyKiWLjlMJkOoU/TGoztHkawT1kefvhH5s/fSZMm1bjvvo6FGpcmCqWU8iCHQ1i+5wSzIqJY9ddpfMt5cWunEO7sFkZoYEV++SWSpjfPIz09i8mTr2LixK74+BTuqVsThVJKecDZ9Cy++jOGDyOiiDyVTK1K5XmibzgjOoZQybcsGXb7TK1b16Rfv0a89NLVNGxY1SOxaqJQSqlCdDwhlY9XH+CztdHEpWTQKrgSbw1vQ7+WtSjrVYaEhDQefHApa9ceZuXKMQQG+jJ37hCPxqyJQimlCsH2w/HMjoji+61HyHQI1zarwbgr69OhXhWMMYgI8+fv4MEHf+DYsSQmTOhIWloWvr6e/2yQJgqllHITh0P4dfcJZkZEsibyDBXLeTGycz3u7BZKvWoVz4138mQyt9/+LUuX7qdt25p8991wOnas48HIz6eJQimlClhKeiYLNsYwe+UBok4lU7tSeZ7qF84tHUOoVKHsP8YPCPDh1KkUpk27jnvv7YS3t+fvIpxpolBKqQJyLD6Vj1Yf4PO10cSfzaB13cr834i2XN+iJmW9zj/5//HHQV5+eQULFgzDz68ca9aMc+tLc5dDE4VSSl2mbTHxzIqIZNHWozhEuK55TcZdGUa7EKv+wdmpUylMnPgTc+ZsJjS0MgcOxNGiRfUimyRAE4VSSl2SLIfwy67jzIyIYl3UGfx8vBndJZQ7u4VSt6rvP8YXET78cDMTJ/5EQkIaTz7ZnWee6YGv7z+LoooaTRRKKZUPyWmZfLUxhtkrozh4OoU6lSvwzA1NGdaxLgHl8z7pf/rpVpo1C+Ldd2+gefPqhRTx5dNEoZRSLjgaf5Y5qw7wxdpoElIzaRdSmceuC+e65jXw9sq98jklJYNXXlnB+PEdCA4OYMGCYVSqVL5IFzPlRhOFUkrlYcuhOGZFRLFkm1X/0LdFLcZ0D6N9vSp5TrdkyT7uvXcJBw7EUaeOP/fc05EqVSoUUtQFSxOFUkrlkOUQftp5nFkRkaw/EIu/jzd3dA3l9q651z84i4lJ4KGHfmDBgl00bRrI77/fQY8e9QopcvfQRKGUUraktEzmbzjEhysPEH0mheAqFXj2xmYM6xCM/0XqH7K9/PIfLF68j1deuZpHH+1KuXJebo7a/Yz1obnio0OHDrJhwwZPh6GUKkEOx53lo1UH+GJdNImpmbSvV4Vx3cPo0+zC9Q/O1q07TIUK3rRsWYPTp1OIj0+jfv28i6YKmzFmo4h0uJRp9Y5CKVVqbT4Ux8wVkSzdfgyAvi1qMrZ7GG1DXDvJx8en8tRTv/C//23gxhsbs3DhCKpV86VatbyLp4obTRRKqVIlyyEs23GMmRFRbDxo1T+M7R7G7V1DqVPZtcpmEWHevB08/PCPnDiRzP33d2Ly5KvdHLnnaKJQSpUKiakZfLkhhg9XRhETe5a6VSswqX8zhnaoi18+PwT06adbGT36Wzp0qM2iRSNo3762m6IuGjRRKKVKtJjYFOasPMC89YdITMukY2gVnrmhGX2a1cArH+8zpKVlEhkZS9OmQQwb1pzMTAejR7fGy4U6jOJOE4VSqkT6MzqWWSuiWLr9KMYYbmhZi7Hdw2hdt3K+57V8eRT33LOYlJQM9u27Hx8fb+68s60boi6aNFEopUqMzCwHP+44zsyISDZFx+Ff3pt/9ajP7V1Cqe1i/YOzEyeS+fe/l/HJJ1upX78K77/fv9C/V10UlL41VkqVOAmpGXy53nr/4XDcWepV8+WFm5ozpH0wFS/xxL5//xk6dfqApKR0nn76Sp5++koq5PItidJAE4VSqtg6dCaFD1ce4MsNh0hKy6RTWFUm9W/GNU3zV//gLCEhjYAAHxo0qMLYsW0ZM6YtTZsGFXDkxYsmCqVUsSIi/Bkdy8wVUfy44xhljOHGVrUY270+LYMrXfJ8k5PTefHF3/nggz/ZuvUegoMD+M9/ri3AyIsvTRRKqWIhM8vB0u3W+w9bDsVRqUJZ7u7ZgNu7hFKzUvnLmvf33+/hvvuWEh0dz9ixbYvFNyIKkyYKpVSRFn82g3nro5mz8gBH4lMJrebL5AHNGdw+GN9yl3cKy8x0MGzYfL75ZjfNmwexYsWddO8eUkCRlxyaKJRSRVL06RRmr4xi/oZDJKdncUX9qrw4oAVXh1/+Z0NFBGMM3t5lqFXLj1dfvYaHH+5SIhrwcwdNFEqpIkNE2HAwlpkrIlm28zhexnBT69qM6R5GizqXXv/gbM2aGO69dwkffNCfdu1qMWPGDQUy35JME4VSyuMyshws2XaUWRFRbI2Jp7JvWSb0asDoLqHUCLi8+odssbFneeqpX3jvvY3Uru1PbOzZAplvaeDWRGGMuR54C/ACZorIqzmGhwAfAZXtcZ4QkSXujEkpVXTEp2TwxfpoPlp1gKPxqdQPrMhLA1swuF0wFQqwGGjevO088MAPnDqVwkMPXcELL/TC39+nwOZf0rktURhjvIAZQB8gBlhvjFkoIjudRnsG+FJE/meMaQYsAULdFZNSqmg4cCqZD1dGMX9jDCnpWXRtUI2XBrbgqiaXX/+Qm927TxEaWpkffhhJ27a1Cnz+JZ077yg6AftFJBLAGDMXGAA4JwoBAuy/KwFH3BiPUsqDRIR1UWeYGRHFz7uO413GcFPrOozpHkrz2gVT/5AtNTWT116LoF27WvTv34SnnrqSZ57pUSoa8HMHdyaKOsAhp+4YoHOOcZ4Hlhlj7gcqAr1zm5Ex5i7gLoCQEH10TaniJCPLweKtVv3DtsPxVPEty31XNWTUFfWoXkD1D85+/jmSCRMWs2/fGR59tAv9+zehbFl9mulyuDNR5Hb/mPO7qyOAOSLyX2NMF+ATY0wLEXGcN5HI+8D7YH0K1S3RKqUKVFxKOp+vi+bjVQc5lpBKg6CKvHxzCwa1Ldj6h2zHjyfxyCPL+PzzbTRsWJVly26jT58GBb6c0sidiSIGqOvUHcw/i5bGAtcDiMhqY0x5IBA44ca4lFJuFJVd/7AhhrMZWXRvGMiUQS3p2TjILfUP2X76KZKvvtrJc8/14Mknr6R8eX2os6C4c0uuBxoZY8KAw8Bw4NYc40QD1wBzjDFNgfLASTfGpJRyAxFhTeQZZkVE8cvu45QtU4YBbaz3H5rWCrj4DC7Rli3H2LfvDEOGNGPkyJZ061aXsDDXvnetXOe2RCEimcaY+4AfsR59nS0iO4wxLwIbRGQh8CjwgTHmYaxiqTtERIuWlCom0jMdLNp6hFkRUew4kkDViuW4/+pG3HZFCNX9C77+IVtSUjqTJi3nrbfWEhpamYEDw/H2LqNJwk3cem9mvxOxJEe/55z+3gl0c2cMSqmCF5eSzmdrrfcfTiSm0bC6H68OasnAtnUo7+aK42+/3c399y8lJiaBu+5qx5QpvfH21qeZ3EkL8ZRSLvvrZBKzI6JY8GcMqRkOrmwUyOtDWtGzcRDGuK/+Idu2bce5+eZ5tGxZnXnzhtC1a92LT6QumyYKpVSeRITVf51mZkQUv+4+QTnvMtzcpg5juofRpKa/25efkZHFihXRXH11GC1b1mDx4lvp06e+PvJaiDRRKKVylZaZxfdbrPcfdh1NoFrFcjzUuxG3XVGPQL/Caf5i1apDjB+/iB07TrJnz300bFiVfv0aFcqy1d80USilznMmOZ3P1hzk4zUHOZmYRuMafrw2uCUD2ri//uFcDGfO8sQTP/PBB39St24AX389jIYNqxbKstU/aaJQSgGw/0QSs1dGsWBjDGmZDno2DmLs0DCubBRYKPUP2VJTM2nT5l2OHEnk0Ue78PzzvfDzK1doy1f/pIlCqVJMRFi5/zSzIiJZvuck5bzLMKitVf/QuIb76x+cxcQkEBwcQPny3kyefBVt2tSkdeuahRqDyp0mCqVKobTMLL7bfITZEVHsPpZIoF85Hu7dmJFXhBRa/UO2s2czmDIlgtdeW8lXXw2lf/8m3H57m0KNQeXNpURhjCkHhIjIfjfHo5Ryo9NJaXy2NpqPVx/kVFIa4TX9eX1IK25qXbvQ6h+cLVv2FxMmLOavv2K57bZWdOpUp9BjUBd30URhjLkBeAMoB4QZY9oAk0TkZncHp5QqGPuOJzJ7ZRRf/3mYtEwHvZoEMa57fbo1rFao9Q/O7r9/CdOnr6dRo6r8/PMorrmmvkfiUBfnyh3Fi1jNgy8HEJHNxpiGbo1KKXXZRISI/aeYuSKK3/eexMe7DIPaBTO2eygNqxdu/UO2rCyrYWgvrzJccUUwgYG+PP54d23Ar4hzZe9kiEhcjqsObY9JqSIqNSOLhZut9pf2HE8kyN+HR/s0ZuQV9aha0XNPD/3551HGj1/EqFGtuP/+zowc2cpjsaj8cSVR7DLGDAPK2C3BPgiscW9YSqn8OpWUxqdrDvLpmoOcSkqnaa0Apg5tTf/WtfDx9txbzImJaTz33HLefnsdQUG+1KrlmbsZdelcSRT3Ac8BDuBrrNZgn3RnUEop1+09nsisFVF8s/kw6ZkOrgmvztjuYXRp4Ln6h2zLlv3FmDHfceRIIuPHd+CVV66hcmX3tSqr3MOVRHGdiDwOPJ7dwxgzCCtpKKU8QET4fe9JZkVEsWLfKcqXLcPQ9sGM6R5GgyA/T4d3TrlyXlSvXpEFC4bRuXOwp8NRl8hc7PMPxpg/RaRdjn4bRaS9WyO7gA4dOsiGDRs8sWilPC41I4tvNh1mdkQU+04kUd3fh9u7hnJrpxCqeLD+IVtGRhZvvLGahIQ0Xn75GgAcDnHrl+2Ua+zzdodLmfaCdxTGmOuwPlNaxxjzhtOgAKxiKKVUITmZmMYndv3DmeR0mtUK4I1hrbmxVW3KFZFvMURERJ9rwG/o0GbnEoQmieIvr6KnE8B2IBXY4dQ/EXjCnUEppSy7jyUwa0UU320+QoYju/6hPlfUr+rx+odsp0+n8PjjPzNr1iZCQirx/fcjuPHGxp4OSxWgCyYKEdkEbDLGfCYiqYUYk1KlmsMh/L7vJLNWRBGx/xQVynpxS8e63NktlPpFqP4h2+nTZ5k7dzuPPdaV557rScUiUASmCpYrldl1jDEvA82Ac48riIheMihVgFIzsvj6z8PMiojkr5PJ1Ajw4bHrm3BrpxAq+xatk++uXSf58ssdTJrUi8aNqxEd/TBVq1bwdFjKTVxJFHOAl4CpQF/gTrSOQqkCcyIxlU9WW/UPsSkZtKgTwLRb2tCvZa0iU/+QLSUlg5df/oP//GcVfn7lGDu2HcHBAZokSjhXEoWviPxojJkqIn8BzxhjVrg7MKVKup1HEpgVEcXCLYfJdAi9m9ZgXPcwOoUVnfoHZz/8sJ8JExYTFRXH7be35j//6UNQUEVPh6UKgSuJIs1YR+1fxpjxwGGgunvDUqpkcjiE3/aeYOaKKFb9dZoKZb24tVMId3YLIzSw6J50k5LSGTXqG6pVq8Dy5bfTq1eop0NShciVRPEw4Ac8ALwMVALGuDMopUqas+lZLPgzhtkro4g8mUzNgPI80TecER1DqORb1tPh5Sory8EXX2xnxIgW+PmV4+efRxEeHoiPjzbgV9pcdI+LyFr7z0RgFIAxRl+xVMoFxxNS+Xj1AT5bG01cSgatgivx1nCr/qGsV9Gqf3C2ceMR7r57ERs3HqVCBW8GD26mX5srxZFFacUAACAASURBVPJMFMaYjkAdIEJEThljmmM15XE1oMlCqQvYfjie2RFRfL/1CJkO4dpmNRh3ZX061KtSJOsfssXHp/Lss8uZMWM91atXZO7cwQwa1NTTYSkPy+vN7CnAYGALVgX2N1gtx74GjC+c8JQqPhwO4dfdJ5gZEcmayDNULOfFyM71uLNbKPWqFd36B2eDB3/Jr79Gce+9HXnppaupVEkb8FN531EMAFqLyFljTFXgiN29p3BCU6p4SEnPZMHGGGavPEDUqWRqVyrPU/3CuaVjCJUqFM36B2eRkbEEBfni7+/Dyy9fTZkyho4d9ZOk6m95JYpUETkLICJnjDG7NUko9bdj8al8tPoAn6+NJv5sBq3rVub/RrTl+hY1i3T9Q7b09CymTl3F5Ml/8MADnXjttT7awqvKVV6Jor4xJrspcQOEOnUjIoPcGplSRdS2mHhmRUSyaOtRHCJc17wm464Mo11I0a5/cPbHHwcZP34Ru3adYsiQZjzwQGdPh6SKsLwSxeAc3dPdGYhSRVmWQ/hl13FmRkSxLsqqfxjdJZQ7u4VSt6qvp8PLlzffXM0jjywjNLQyixffSr9+jTwdkiri8moU8JfCDESpoig5LZOvNlrvPxw8nUKdyhV45oamDOtYl4DyRb/+IZvDISQnp+Pv78MNNzTm5MkUnnmmB75F9B0OVbRc9MNFRY1+uEgVhqPxZ5mz6gBfrI0mITWTtiGVGde9Ptc1r4F3Mah/cLZjxwnGj1987ktzqnRyy4eLCoIx5nrgLcALmCkir+YyzjDgeUCALSJyqztjUiovW2PimLkiiiXbrPqHvi1qMaZ7GO3rVfF0aPmWkpLB5Mm/M3XqaipV8mHMmDaISLGpR1FFh8uJwhjjIyJp+RjfC5gB9AFigPXGmIUistNpnEbAk0A3EYk1xmgbUqrQZTmEn3YeZ1ZEJOsPxOLn480dXUO5vWvxq3/ItmnTUQYN+pIDB+K48842vP56HwIDi+e6KM+7aKIwxnQCZmG18RRijGkNjBOR+y8yaSdgv4hE2vOZi/Vuxk6ncf4FzBCRWAAROZH/VVDq0iSlZTJ/wyE+XHmA6DMpBFepwLM3NmNYh2D8i1H9g7PsO4aQkEqEhFTio48G0qNHPU+HpYo5V+4o3gZuBL4FEJEtxpirXJiuDnDIqTsGyPkMXmMAY8xKrOKp50XkBxfmrdQlOxx3lo9WHeCLddEkpmbSvl4VnugbzrXNil/9Q7bMTAfTp69j4cI9/PTTKKpV8+X33+/wdFiqhHAlUZQRkYM5yjWzXJgut4LQnDXn3kAjoBdW21ErjDEtRCTuvBkZcxdwF0BISIgLi1bqnzYfimPmikiWbj8GQN8WNRnbPYy2IcWv/sHZunWHGT9+EZs2HaNv34YkJKRRpYp+SEgVHFcSxSG7+Enseof7gb0uTBcD1HXqDsZqBiTnOGtEJAOIMsbswUoc651HEpH3gffBeurJhWUrBVj1D8t2HGNWRBQbDsbi7+PN2O5h3N41lDqVi/fJNCkpnccf/4n//W8DtWr5M3/+UAYPbqqV1arAuZIo7sEqfgoBjgM/2/0uZj3QyBgThvWxo+FAzieavgVGAHOMMYFYRVGRroWu1IUlpmbw5YYY5qyK4tCZs9StWoFJ/ZsxtENd/ErI9xTKli3Db78d5P77OzF58tUEBPh4OiRVQrnyi8kUkeH5nbGIZBpj7gN+xKp/mC0iO4wxLwIbRGShPexaY8xOrOKsiSJyOr/LUipbTGwKH606wNx1h0hMy6RjaBWe7teMPs1q4FWm+F9p799/hhdf/J0ZM/rh7+/Dxo13Ub58yUh8qui66At3xpi/gD3APOBrEUksjMAuRF+4U7n5MzqWWRFR/GDXP9zQshZju4fRum5lD0dWMNLSMnn99ZW8/PIKypXzYvHiW7nySn2aSbnOrS/ciUgDY0xXrKKjF4wxm4G5IjL3UhaoVEHJzHLw4w7r/Yc/o+PwL+/NuCvDuL1LKLWLef2Ds+XLo7jnnsXs2XOaW25pzhtvXEft2v6eDkuVIi7ds4rIKmCVMeZ5YBrwGaCJQnlEQmoGX6633n84HHeWetV8eeGm5gxpH0zFElL/kE1EePnlFWRkOPjhh5Fcd11DT4ekSiFXXrjzw3pRbjjQFPgO6OrmuJT6h0NnUvhw5QG+3HCIpLRMOoVV5bn+zejdtGTUP2RzOIRZs/7k+usbUrduJT755GYqVy5PhWLwESRVMrly+bUd+B54XURWuDkepc4jIvwZHcvMFVH8uOMYZYzhxla1GNu9Pi2DK3k6vAK3detxxo9fxOrVMTz3XA9eeOEqatXSYiblWa4kivoi4nB7JEo5ycxysHS79f7D5kNxBJT35u6eDRjdpR61KpWc+odsSUnpvPDCb7z55hqqVKnAnDkDGD26tafDUgrII1EYY/4rIo8CC4wx/3g0Sr9wp9wh/mwG89ZH89GqgxyOO0toNV9eHNCcwe1KXv2Ds+ef/43//nc148a15dVXe1Otmjbgp4qOvH558+z/9ct2yu2iT6fw4aoovlx/iOT0LK6oX5Xnb2rONeHVKVOC6h+cHToUT3JyBuHhgTzxRHcGDgyne3dtokYVPXl94W6d/WdTETkvWdgv0ukX8NRlERE2HIxl1ooolu206h/6t67N2O5htKhT8uofsmVmOnj77bU899xy2revze+/30FgoK8mCVVkuXIvP4Z/3lWMzaWfUi7JyHKwZNtRZkdEsSUmnkoVyjK+ZwNGdwmlZqXyng7PrdasiWH8+EVs2XKcG25oxPTp/TwdklIXlVcdxS1Yj8SGGWO+dhrkD8TlPpVSFxZ/NoO566KZs+oAR+NTqR9YkckDWzC4XR18y5Xc+odsixfvpX//L6hd25+vvx7GwIHh2oCfKhby+nWuA05jtfo6w6l/IrDJnUGpkuXg6eRz7z+kpGfRtUE1XhrYgqualNz6h2wiwpEjidSpE0Dv3vV58cWrePDBzvj7awN+qvi4aFtPRY229VQ8iAjrD8Qyc0UkP+06jncZw02t6zCmeyjNa5fc+gdne/eeZsKExezde5qdO+/Fz6+cp0NSpZhb2noyxvwuIj2NMbGc/8EhA4iIVL2UBaqSLbv+YeaKKLYdjqeKb1nuu6oho66oR/WAkl3/kC01NZNXX41gypQIKlTwZsqUa6hQoeQXramSK6+jN/tzp4GFEYgq3uJS0vl8XTQfrzrIsYRUGgRV5OWbWzCobTAVynl5OrxCc+xYEj16fMi+fWcYMaIFb7xxHTVr+nk6LKUuS16Px2a/jV0XOCIi6caY7kAr4FMgoRDiU0Vc1KlkPlwZxfwNMZzNyKJ7w0CmDGpJz8ZBJb7+wVlGRhZly3pRo0ZFevSox4wZ/ejTp4Gnw1KqQLhyP/wt0NEY0wD4GFgMfA7c6M7AVNElIqyJPMOsiCh+2X2csmXKcFMb6/2HprUCPB1eoXI4hPff38grr6xg1aqxBAcHMHPmTZ4OS6kC5UqicIhIhjFmEDBNRN42xuhTT6VQeqaDRVuPMCsiih1HEqhasRz3X9WQ27rUo7p/6ah/cLZlyzHuvnsRa9ce5uqrw8jIyPJ0SEq5hUufQjXGDAVGAQPtftrecSkSl5LOZ2uj+WjVAU4kptGwuh9TBrXk5rZ1KF+29NQ/ZBMRJk78iWnT1lC1agU++eRmRo5sqe9EqBLL1TezJ2A1Mx5pjAkDvnBvWKooiDyZxOyVUXy1MYbUDAdXNgrk9SGt6NGodNU/5GSMITb2LGPHWg34ValS8lqzVcqZS+9RGGO8gexPa+0XkUy3RpUHfY/CvUSE1ZGnmbUiil92n6CcVxkGtq3NmO5hhNcsXfUPzg4ejOPBB3/gued60q5dLRwOKdXJUhU/bv1mtjHmSuAT4DDWOxQ1jTGjRGTlpSxQFU3pmQ6+33KEmRFR7DqaQLWK5XjwmkbcdkU9gkrxW8QZGVm8+eYaXnjhdwBuuaU57drV0iShShVXip7eBPqJyE4AY0xTrMRxSZlJFS1nktP5fO1BPlp9kJOJaTSq7sdrg1syoE3prH9wtmrVIe6+exHbt59gwIAmvP12X0JCSsdb5Uo5cyVRlMtOEgAisssYo20RFHP7T1j1Dws2xpCW6aBH4yD+OzSMKxsFaqWs7eefI4mPT+Xbb29hwIBwT4ejlMdctI7CGDMHSMO6iwAYCfiKyO3uDS13Wkdx6USEVX+dZuaKSJbvOUk57zIMaluHMd3DaFxDv8ssInzyyVaCgnzp27cRaWmZZGQ4tI0mVSK4tY4CGA88ADyGVUfxB/B/l7Iw5RlpmVks3Gy9/7D7WCKBfuV4uHdjRl4RQqBf6a1/cLZ79ynuuWcxv/12gKFDm9G3byN8fLzx0c2jVN6JwhjTEmgAfCMirxdOSKqgnE5K47O10Xy8+iCnktIIr+nP60NacVPr2qW+/iHb2bMZvPLKCl57bSUVK5bjvfduZNy4dp4OS6kiJa/WY5/C+pLdn1hNeLwoIrMLLTJ1yfYdT2T2yii+/vMwaZkOejUJYlz3+nRrWE3rH3L4/vu9vPTSCm67rRVTp/ahRg1twE+pnPK6oxgJtBKRZGNMELAE0ERRRIkIEftPMXNFFL/vPYmPdxkGtQtmbPdQGlbX+gdnx44lsXnzMa6/viFDhzYjNHQcnTrV8XRYShVZeSWKNBFJBhCRk8aYMoUUk8qH1Iy/6x/2HE8k0M+HR/s0ZuQV9ahaUSthnWVlOXjvvY08+eQvlCvnRXT0Q1SoUFaThFIXkVeiqO/0rWwDNHD+draIDHJrZCpPp5LS+HTNQT5dc5BTSemE1/Rn6tDW9G9dCx9vrX/I6c8/jzJ+/CLWrz9C7971eeedflSooE2WKeWKvBLF4Bzd090ZiHLN3uOJzFoRxTebD5Oe6eDq8OqM6x5GlwZa/3AhUVGxdOr0AYGBvnz++SCGD2+h20qpfMjrw0W/FGYg6sJEhD/2nWLmikhW7DtF+bJlGNo+mDu7hdGwula+5kZE2LbtBK1a1SAsrAoffjiA/v2bULly6WsOXanLpR/yLeIWbT3CWz/vY9+JJKr7+zDxuibc2imEKlr/cEFRUbHcd99SfvhhP5s23U2rVjUYNaq1p8NSqthya6IwxlwPvAV4ATNF5NULjDcEmA90FBF97dq24cAZHvhiE/WD/HhjWGtubFWbct76TMGFpKdn8cYbq3nxxd8pU8YwdWofmjUL8nRYShV7LicKY4yPiKTlY3wvYAbQB4gB1htjFjq3G2WP54/15vdaV+ddGiSkZvDQvM0EV/Hlmwld8S+vFa95ycpy0LXrLDZuPMqgQU2ZNu066tbVBvyUKggXvTw1xnQyxmwD9tndrY0xrjTh0Qnr2xWRIpIOzAUG5DLeZOB1INX1sEu+Sd/t4Gh8Km/e0kaTRB4SEqxrFy+vMowZ05bvvx/BggXDNEkoVYBcKcd4G7gROA0gIluAq1yYrg5wyKk7xu53jjGmLVBXRBblNSNjzF3GmA3GmA0nT550YdHF23ebD/PNpsM8cHUj2ter4ulwiiQRYc6czdSv/xbffbcbgAkTOnLjjY09HJlSJY8riaKMiBzM0c+Vr8jn9vzhuaZq7Rf43gQevdiMROR9EekgIh2Cgkp2mfOhMyk888122terwr1XNfB0OEXSzp0n6dXrI+688zvCwwNp0KCqp0NSqkRzpY7ikDGmEyB2vcP9wF4XposB6jp1BwNHnLr9gRbAb/Yz7TWBhcaYm0prhXaWQ3jky80IMO2WNnh7acV1Tq+/vpKnn/6VgAAfZs7sz513ttWvzSnlZq4kinuwip9CgOPAz3a/i1kPNDLGhGF9RnU4cGv2QBGJBwKzu40xvwH/Lq1JAuB/v+1n/YFY3rylNXWr+no6nCJFRDDGULOmHyNHtuQ//+lDUFBFT4elVKlw0UQhIiewTvL5IiKZxpj7gB+xHo+dLSI7jDEvAhtEZGG+oy3BNh+K482f93FT69oMbKNtD2U7ciSRBx/8gSuvDOGBBzozenRrRo/WdyKUKkwXTRTGmA9wqlvIJiJ3XWxaEVmC1eqsc7/nLjBur4vNr6RKTsvkwbmbqBlQnskDtXkJsB53feed9Tz99K9kZDjo2jXY0yEpVWq5UvT0s9Pf5YGbOf9pJnWZXvh+B4fOpDD3ri5U0obq2Lz5GOPGLWTjxqNce20D3nmnn1ZYK+VBrhQ9zXPuNsZ8AvzktohKmSXbjvLlhhjuu6ohncL0ZAgQH5/KkSOJzJs3hKFDm+kdllIedilNeIQB9Qo6kNLoaPxZnvx6G62DK/Fg70aeDsdjRIT583eyb99pnn66Bz17hhIZ+SDly2tTZEoVBa68mR1rjDlj/4vDupt4yv2hlWwOh/DIvC1kZDmYNrwtZUvpo7B//XWGfv0+55ZbvuK77/aQkWG9oqNJQqmiI89fo7Hu+VtjPd4K4BCRf1Rsq/z7YEUkqyNP8/rgVoQFlr7HPNPSMpk6dRUvvbSCsmXL8NZb1zNhQke8tdFDpYqcPBOFiIgx5hsRaV9YAZUG2w/HM3XZHvq2qMnQDqXzaZ5DhxKYPPkP+vdvwrRp11GnToCnQ1JKXYArl2/rjDHt3B5JKXE2PYsH5m6iWkUfpgxqWaoqak+eTGb69HUANGxYlZ0772X+/KGaJJQq4i54R2GM8RaRTKA78C9jzF9AMlYbTiIimjwuwUuLdxJ1KpnPxnamsm/p+PiQwyF8+OEmHnvsZxIT0+jTpz5NmgRSv742eKhUcZBX0dM6oB0wsJBiKfF+2nmcz9ZGc3eP+nRtGHjxCUqA7dtPcM89i4mIiObKK0N4990badKkdKy7UiVFXonCAIjIX4UUS4l2IiGVxxdspXntAB65tnQ0hZ2ensW1135CenoWs2ffxB13tClVRW1KlRR5JYogY8wjFxooIm+4IZ4SyeEQHp2/hZT0TN4a3gYfby9Ph+RWv/4aRc+e9ShXzosvvxxKeHgggYHayKFSxVVeldlegB9Wc+C5/VMumrPqACv2neKZG5rRsHrJ3XQxMQkMHvwl11zzMR9/vAWA7t1DNEkoVczldUdxVEReLLRISqhdRxN4deluejetzsjOIZ4Oxy0yMx1Mn76OZ59dTlaWgylTrmHkyFaeDkspVUAuWkehLl1qRhYPzd1MQIWyvDa4VYktnx816hvmzt1O374NmTGjH2Fh+jSTUiVJXonimkKLooR6delu9hxPZM6dHanm5+PpcApUXFwq3t5l8PMrx733dmTw4KYMHty0xCZDpUqzC9ZRiMiZwgykpFm+5wRzVh3gzm6h9GpS3dPhFBgRYe7c7TRtOoNnn/0VsOohhgzRVl6VKqm0YR03OJWUxsT5W2lSw5/Hrw/3dDgFZv/+M1x33aeMGLGA4OAAbrtN6yGUKg20ic4CJiI8sWArCakZfDquE+XLloxHYT//fBtjxnyHj48306f3Zfz4DniV0hZvlSptNFEUsM/WRvPzrhNM6t+M8JrFvw2jjIwsypb1okOH2gwZ0ozXX+9D7dol9xFfpdQ/aaIoQPtPJPLS4p30aBzEHV1DPR3OZTlxIplHH11GcnI6X399C40bV+PTTwd5OiyllAdo2UEBScvM4oEvNuNbzpupQ4rvo7AOh/D++xtp0mQ68+Ztp3nzILKyHJ4OSynlQXpHUUDeWLaXnUcT+GB0B6oHlPd0OJckMjKW2277mtWrY+jVK5T//e8GwsO1AT+lSjtNFAVg5f5TvPdHJCM7h9CnWQ1Ph3PJKlXyIS4ulY8+GsioUcX3rkgpVbC06OkyxSan8+iXW6gfVJFnbmjm6XDybeHCPQwaNI+sLAfVqvmyffsERo9urUlCKXWOJorLICI89c02Tien8fbwtlQoV3wehY2OjmfgwLkMGDCXvXtPc/RoEgBlymiCUEqdT4ueLsP8DTEs3X6MJ/uG06JOJU+H45LMTAfTpq1h0qTfEBFee603Dz98BWVLyPseSqmCp4niEkWdSub573fQtUE1/nVlfU+H47KsLAczZ/7J1VeH8X//15fQ0MqeDkkpVcRp0dMlyMhy8NDcTZT1KsN/h7Uu8sU1sbFnefzxn0hMTMPHx5uVK8ewcOFwTRJKKZdoorgEb/28jy0x8bw6qCW1KlXwdDgXJCJ89tlWwsNn8N//rmb58gMAVKvmq5XVSimXadFTPq2NPM2M3/YzrEMwfVvW8nQ4F7R372kmTFjML79E0alTHX788TbatKnp6bCUUsWQJop8iD+bwSNfbqFeVV8m9W/u6XDy9NBDP7BhwxHeeacfd93VXhvwU0pdMk0ULhIRnvl2O8cSUllwT1cq+hS9TffTT38RHh5I3bqV+N//bsDHx5uaNf08HZZSqphz62WmMeZ6Y8weY8x+Y8wTuQx/xBiz0xiz1RjzizGmnjvjuRzfbj7M91uO8HDvRrSpW7QqgY8dS+LWWxdw7bWf8tprKwGoV6+yJgmlVIFwW6IwxngBM4C+QDNghDEm56vLm4AOItIK+Ap43V3xXI5DZ1J49tsddAytwj29Gno6nHMcDuHddzcQHj6dBQt2MWlST6ZOvdbTYSmlShh33lF0AvaLSKSIpANzgQHOI4jIchFJsTvXAMFujOeSZGY5eGjeZgzw5i1t8CpCj8JOmbKCe+5ZTPv2tdm6dTzPP9+L8uWLXpGYUqp4c+dZpQ5wyKk7Buicx/hjgaW5DTDG3AXcBRASElJQ8blkxvK/2HgwlreGtyG4im+hLjs3iYlpnDqVQlhYFcaP70BYWBVGjGihj7sqpdzGnXcUuZ25JNcRjbkN6AD8J7fhIvK+iHQQkQ5BQUEFGGLeNh6M5e1f93Fz2zoMaFOn0JabGxHhm2920azZO9xyy1eICNWq+XLrrS01SSil3MqdiSIGqOvUHQwcyTmSMaY38DRwk4ikuTGefElMzeCheZuoVak8Lwzw7KOwBw/GcdNNcxk06EuqVq3A22/31eSglCo07ix6Wg80MsaEAYeB4cCtziMYY9oC7wHXi8gJN8aSb88v3Mnh2LN8eXcXAsqX9Vgcq1cfonfvTwCYOrUPDz54Bd7e+k6EUqrwuC1RiEimMeY+4EfAC5gtIjuMMS8CG0RkIVZRkx8w375CjhaRm9wVk6u+33KEBX/G8MA1jegQWtUjMSQkpBEQ4EO7drUYM6YNEyd2IySkeLRQq5QqWYxIrtUGRVaHDh1kw4YNbpv/4biz9J32Bw2q+zH/7i54F/IbzadPp/DEEz+zbFkkO3ZMwM+vXKEuXylVMhljNopIh0uZVp+ldJLlEB6Zt5kshzDtljaFmiREhE8+2cqjjy4jNvYsjzzSBa2GUEoVBZoonLz3x1+sjTrD1KGtqVetYqEtNz4+lYED5/Hbbwfo0iWYd9+9kVatiu+3t5VSJYsmCtvWmDjeWLaXG1rVYnC7wnkUVkQwxhAQ4ENgoC/vv38jY8e2K/Lft1BKlS76+AyQkp7Jg3M3E+TvwysDC+e9hB9/3E+7du8TE5OAMYb584fyr3+11yShlCpyNFEAkxft5MDpZN4Y1oZKvu59FPbo0USGD/+K66//jJSUDE6cSHbr8pRS6nKV+qKnH7Yf44t1h7inVwO6NKjm1mXNmLGOp576lbS0TF54oRePP94NnyLYXLlSSjkr1Wep4wmpPPH1VlrWqcTDvRu7fXkbNx6lc+c6zJjRj0aN3JuUlFKqoJTaROFwCP+ev4W0DAfThrehnBvedk5ISOO555YzalQr2revzTvv3ICPj5c2v6GUKlZKbaKYvTKKFftO8crNLWkQVLAf+BERFizYxYMP/sDRo4mEhFSiffva2gS4UqpYKpVnrp1HEnj9hz30aVaDEZ3qXnyCfIiKiuW++5ayZMk+2rSpyddfD6Nz5yL3mQ2llHJZqUsUqRlZPDh3E5V8y/La4FYFXgz02Wfb+OOPg7z55nXcd18nbcBPKVXslbpEMWXJLvadSOLjMZ2oWrFg2lFaseIgaWlZ9O5dn4kTu3LHHW0IDg4okHkrpZSnlarL3eW7T/DR6oOM7R5Gj8aX/wGkU6dSGDPmO3r0mMOLL/4OgI+PtyYJpVSJUmruKE4mpjHxqy2E1/Rn4nVNLmteIsKcOZuZOPEn4uPTePzxbjz7bI8CilSVJBkZGcTExJCamurpUFQpUb58eYKDgylbtuBeHi4ViUJEeOyrLSSmZvL5v66gfFmvy5rfkiX7GDNmId261eXdd2+kRYvqBRSpKmliYmLw9/cnNDRUH4tWbicinD59mpiYGMLCwgpsvqWi6OmTNQdZvuckT/VrSuMa/pc0j5SUDFaujAagX79GfPfdcP74405NEipPqampVKtWTZOEKhTGGKpVq1bgd7AlPlHsPZ7Iy4t3cVWTIEZ3qXdJ81i6dB8tWrxD376fEReXijGGm25qog34KZdoklCFyR3HW4lOFGmZWTzwxSb8fLx5fUjrfG/Aw4cTGDp0Pv36fY6Pjzfffz+CypXLuylapZQqmkp0ovjPD3vYfSyR/wxtRZC/T76mPXEimWbN3mHRor289NJVbNkynp49Q90TqFJu5OXlRZs2bWjRogX9+/cnLi7u3LAdO3Zw9dVX07hxYxo1asTkyZNx/jzy0qVL6dChA02bNiU8PJx///vfnliFPG3atIlx48ad12/AgAF06dLlvH533HEHX3311Xn9/Pz+bpVh79699OvXj4YNG9K0aVOGDRvG8ePHLyu2M2fO0KdPHxo1akSfPn2IjY3Ndbzo6GiuvfZamjZtSrNmzThw4AAAI0eOpEmTJrRo0YIxY8aQkZEBwKJFi5g0adJlxZYvIlKs/rVv315c8cfeE1Lv8UXy7LfbXBo/W0xM/Lm/33prjezffzpf0yvlbOfOnZ4OQSpWrHju79GjR8tLL70kIiIpKSlSv359+fHHH0VEJDk5Wa6//nqZPn26iIhsGYpl7wAAEgBJREFU27ZN6tevL7t27RIRkYyMDJkxY0aBxpaRkXHZ8xgyZIhs3rz5XHdsbKwEBwdLeHi4REZGnut/++23y/z588+bNnvbnD17Vho2bCgLFy48N+zXX3+Vbdvyd/7IaeLEiTJlyhQREZkyZYo89thjuY7Xs2dPWbZsmYiIJCYmSnJysoiILF68WBwOhzgcDhk+fLi88847IiLicDikTZs258bLKbfjDtggl3jeLZFPPZ1JTufRL7fQsLofT/Vr6tI08fGpPPPMr7z33kbWrBlHu3a1eOCBzm6OVJUmL3y/g51HEgp0ns1qBzCpf3OXx+/SpQtbt24F4PPPP6dbt25ce+21APj6+jJ9+nR69erFvffey+uvv87TTz9NeHg4AN7e3kyYMOEf80xKSuL+++9nw4YNGGOYNGkSgwcPxs/Pj6SkJAC++uorFi1axJw5c7jjjjuoWrUqmzZtok2bNnzzzTds3ryZypUrA9CwYUNWrlxJmTJlGD9+PNHR1kMk06ZNo1u3buctOzExka1bt9K6detz/RYsWED//v2pUaMGc+fO5cknn7zodvn888/p0qUL/fv3P9fvqquucnm7Xsh3/9/emUZXVWUJ+NsEkcFUVJAsEKkwiIYEwhBwQkBQsNAGzUKigAyNrQKKyGB1t9VLkC6FKtGWBouyaTvoKgIiMogKCkqBShCQmMREY4SIUQwRKQiDJCG7f9ybl5fwkrzE5GXa31pvrXvPPcO+e9139j37nLvPxo3s2LEDgIkTJzJ48GAWLVpUIk9qaioFBQXcdtttQMlRzogRIzzH/fv3JysrC3DmIQYPHszmzZsZM2bMr5azIhqcoVBVfr8uiX+cyef/JvercCmsqrJ2bSozZ27hxx9P8cgj/enS5bIASWsYgeP8+fNs376dKVOmAI7bqW/fviXydOnShVOnTnHy5ElSUlKYPXt2hfUuWLCAkJAQkpOTAcp0r3iTnp7Otm3bCAoKorCwkPXr1zN58mT27NlDWFgYoaGhjB07lscff5wBAwZw+PBhhg8fTlpaWol69u3bR2RkZIm0+Ph4nnrqKUJDQxk9erRfhiIlJeUCXfgiNzeXm2++2ee1VatW0b179xJp2dnZtGvXDoB27dpx9OjRC8qlp6dz6aWXEhMTw6FDh7j11ltZuHAhQUHFfVd+fj6vvfYaL774oictOjqaXbt2maGoCqv3fsf7qdn84Y5wItqHlJtXVYmJeZ0NG76kT592bNp0H9HR7QMkqdHYqMybf3Vy9uxZevXqRWZmJn379vW8uaq7Z7svKrPwY9u2baxevdpzftllFb9o3XPPPZ6OMDY2lqeffprJkyezevVqYmNjPfWmpqZ6ypw8eZLc3FyCg4uXuB85coQrriiOspCdnU1GRgYDBgxARGjatCkpKSlERkb6vKfKLnAJDg4mMTGxUmUqoqCggF27dnHgwAE6duxIbGwscXFxHoMOMG3aNAYOHFjCSLVt25YffvihWmUpiwY1mf1NzimefiuVAV3b8M83lf2xSX7+ecB5SAYMuIolS27n008fMCNhNEhatGhBYmIi3377LXl5eSxbtgyAiIgI9u3bVyLvwYMHueSSSwgODiYiIoL9+/dXWH9ZBsc7rfS6/latWnmOb7jhBjIyMsjJyWHDhg3ExMQAUFhYyO7du0lMTCQxMZHvv/++hJEoujfvutesWcPx48fp1KkTYWFhZGZmeoxY69atS4x2fv75Z9q0aePRhT/3mpubS69evXz+vI1aEaGhoRw5cgRwjFrbthd+d9WhQwd69+5N586dadq0KXfddRefffaZ5/r8+fPJycnh+eefL1Hul19+oUWLFhXKXB00GEORV1DIzNWJXHxRExaPiSrzG4cdOzLp2XM5Gzd+CcDs2Tfy6KPXERTUYFRhGD4JCQlhyZIlPPfcc+Tn5zNu3Dg++ugjtm3bBjgjjxkzZvDEE08AMHfuXJ555hnS09MBp+Mu3VkBDBs2jKVLl3rOizrj0NBQ0tLSPK6lshAR7r77bmbNmkV4eDitW7f2Wa+vN/nw8HAyMjI85/Hx8WzZsoXMzEwyMzPZv3+/x1AMHjyYNWvWkJeXB0BcXJxnHmLs2LF88sknvP322566tmzZ4nGnFVE0ovD1K+12Ahg5ciQrV64EYOXKlYwaNeqCPP369eP48ePk5OQA8MEHH3jqWrFiBVu3biU+Pp4mTUr2Uenp6Re43WqMqs6C19avrFVPC99N09/+frO+m3zE5/WjR0/phAnrFeZpp07/pdu3H/SZzzCqk7q26klV9c4779RXX31VVVWTkpJ00KBB2q1bN+3SpYvOmzdPCwsLPXnfeust7dOnj1577bUaHh6uc+bMuaD+3NxcnTBhgkZERGjPnj113bp1qqq6du1a7dy5sw4aNEinT5+uEydOVFXfq4/27t2rgMbFxXnScnJydMyYMdqjRw8NDw/Xhx56yOf9RUZG6smTJ/XQoUPavn37EvKrqvbu3VsTEhJUVXXevHkaGRmpUVFRGhMTo0ePHvXkS0tL0+HDh2vXrl01PDxcY2Nj9ccffyxXtxXx008/6ZAhQ7Rr1646ZMgQPXbsmOd+p0yZ4sn33nvvaY8ePTQyMlInTpyo586dU1XVoKAg7dy5s0ZFRWlUVJTOnz/fU+aOO+7QpKQkn+1W96onUa810/WB6OhoLT1c3v3NMcauSODeflfxbEzPC8rExyczffo7nDqVx9y5N/LkkwNp2bL6AmYZRlmkpaURHu7fyjujarzwwgsEBwdf8C1FQyY7O5uxY8eyfft2n9d9PXcisl9Vo6vSXr33t5w4k8+s1xPp1LoV/3HnhUM/gIKCQiIj25KY+DB//ONQMxKG0YCYOnUqF19cuQ9q6zuHDx9m8eLFAWuvXq96UlX+fX0yObnneHPajbRs5tzO6dN5LFiwk44dQ5g2rR/jx/dk/Pjq383OMIzap3nz5tx///21LUZA6devX0Dbq9cjinWffc/byUeYNawbPTs4H+ts3pxORMRLLFr0MenpxwBnssyMhFFb1Df3rlG/qYnnrd6OKL49dpqnNqZwXafLeWhgF7KyTjJjxrusX/8l3btfwc6dk7j55qpFizWM6qJ58+YcO3bMQo0bAUHV2Y+iefPqDV5aLw1F/vlCHludSFAT4YXYXgQ1EQ4ePM7Wrd/w7LNDmTXrBpo1+3WbExlGddChQweysrI8Sx8No6Yp2uGuOqmXq57GPruKJdu/Zmb/3yLfneKxx64H4NixM7Ru3bKWJTQMw6h71NlVTyJyu4h8JSIZIvKvPq5fLCJr3Ot7RCSsojrP5BWw9IOvuTIPZo1ex/PPJ3D6tPMBjRkJwzCM6qfGDIWIBAHLgN8B3YH7RKT0+tUpwHFV7Qq8ACyiAjJ/OkPBiTz2/OUAM2ZcR3LyVFq1albd4huGYRguNTlH0R/IUNWDACKyGhgFeAdEGQXMc4/fAJaKiGg5/rDzhcrlX51gw8dT6NOnXc1IbhiGYXioSUNxJfCd13kWUHqDB08eVS0QkRNAa+An70wi8iDwoHt67vPsySl+RARuDLShlK4aMaaLYkwXxZguirmmqgVr0lD4WgtYeqTgTx5U9WXgZQAR2VfVCZmGhumiGNNFMaaLYkwXxYjIvopz+aYmJ7OzgKu8zjsApYOne/KISFMgBPi5BmUyDMMwKklNGoq9wNUi0klEmgH3AptK5dkETHSPRwMflDc/YRiGYQSeGnM9uXMOjwBbgSDgFVX9QkSexgl3uwn4X+A1EcnAGUnc60fVL9eUzPUQ00UxpotiTBfFmC6KqbIu6t0Hd4ZhGEZgqddBAQ3DMIyaxwyFYRiGUS511lDURPiP+oofupglIqkikiQi20WkwYbNrUgXXvlGi4iKSINdGumPLkRkjPtsfCEiqwItY6Dw4z/SUUQ+FJED7v9kRG3IWdOIyCsiclREUsq4LiKyxNVTkoj08aviqu6hWpM/nMnvb4DOQDPgc6B7qTzTgOXu8b3AmtqWuxZ1cQvQ0j2e2ph14eYLBnYCCUB0bctdi8/F1cAB4DL3vG1ty12LungZmOoedwcya1vuGtLFQKAPkFLG9RHAuzjfsF0P7PGn3ro6ovCE/1DVPKAo/Ic3o4CV7vEbwFBpmAH/K9SFqn6oqmfc0wScb1YaIv48FwALgD8BvwRSuADjjy7+BVimqscBVPVogGUMFP7oQoHfuMchXPhNV4NAVXdS/rdoo4BX1SEBuFREKoyFVFcNha/wH1eWlUdVC4Ci8B8NDX904c0UnDeGhkiFuhCR3sBVqro5kILVAv48F92AbiLysYgkiMjtAZMusPiji3nAeBHJAt4BHg2MaHWOyvYnQN3duKjawn80APy+TxEZD0QDg2pUotqjXF2ISBOcKMSTAiVQLeLPc9EUx/00GGeUuUtEIlX1HzUsW6DxRxf3AXGqulhEbsD5fitSVQtrXrw6RZX6zbo6orDwH8X4owtE5FbgSWCkqp4LkGyBpiJdBAORwA4RycTxwW5qoBPa/v5HNqpqvqoeAr7CMRwNDX90MQV4HUBVdwPNcQIGNjb86k9KU1cNhYX/KKZCXbjulr/iGImG6oeGCnShqidUtY2qhqlqGM58zUhVrXIwtDqMP/+RDTgLHRCRNjiuqIMBlTIw+KOLw8BQABEJxzEUjXF/2k3ABHf10/XACVU9UlGhOul60poL/1Hv8FMXfwYuAda68/mHVXVkrQldQ/ipi0aBn7rYCgwTkVTgPDBXVY/VntQ1g5+6mA38j4g8juNqmdQQXyxFJB7H1djGnY95CrgIQFWX48zPjAAygDPAZL/qbYC6MgzDMKqRuup6MgzDMOoIZigMwzCMcjFDYRiGYZSLGQrDMAyjXMxQGIZhGOVihsKoc4jIeRFJ9PqFlZM3rKxImZVsc4cbffRzN+TFNVWo42ERmeAeTxKR9l7XVohI92qWc6+I9PKjzEwRaflr2zYaL2YojLrIWVXt5fXLDFC741Q1CifY5J8rW1hVl6vqq+7pJKC917UHVDW1WqQslvMl/JNzJmCGwqgyZiiMeoE7ctglIp+5vxt95IkQkU/dUUiSiFztpo/3Sv+riARV0NxOoKtbdqi7h0GyG+v/Yjd9oRTvAfKcmzZPROaIyGicmFt/c9ts4Y4EokVkqoj8yUvmSSLy31WUczdeAd1E5C8isk+cvSfmu2kzcAzWhyLyoZs2TER2u3pcKyKXVNCO0cgxQ2HURVp4uZ3Wu2lHgdtUtQ8QCyzxUe5h4EVV7YXTUWe54RpigZvc9PPAuAra/ycgWUSaA3FArKr2wIlkMFVELgfuBiJUtSfwn96FVfUNYB/Om38vVT3rdfkNIMbrPBZYU0U5b8cJ01HEk6oaDfQEBolIT1VdghPL5xZVvcUN5fEH4FZXl/uAWRW0YzRy6mQID6PRc9btLL25CFjq+uTP48QtKs1u4EkR6QC8qapfi8hQoC+w1w1v0gLH6PjibyJyFsjECUN9DXBIVdPd6yuB6cBSnL0uVojI24DfIc1VNUdEDrpxdr522/jYrbcycrbCCVfhvUPZGBF5EOd/3Q5ng56kUmWvd9M/dttphqM3wygTMxRGfeFxIBuIwhkJX7ApkaquEpE9wB3AVhF5ACes8kpV/Tc/2hjnHUBQRHzub+LGFuqPE2TuXuARYEgl7mUNMAb4ElivqipOr+23nDi7uC0ElgExItIJmAP0U9XjIhKHE/iuNAK8r6r3VUJeo5FjriejvhACHHH3D7gf5226BCLSGTjouls24bhgtgOjRaStm+dy8X9P8S+BMBHp6p7fD/zd9emHqOo7OBPFvlYe5eKEPffFm8BdOHskrHHTKiWnqubjuJCud91WvwFOAydEJBT4XRmyJAA3Fd2TiLQUEV+jM8PwYIbCqC+8BEwUkQQct9NpH3ligRQRSQSuxdnyMRWnQ31PRJKA93HcMhWiqr/gRNdcKyLJQCGwHKfT3ezW93ec0U5p4oDlRZPZpeo9DqQCv1XVT920Ssvpzn0sBuao6uc4+2N/AbyC484q4mXgXRH5UFVzcFZkxbvtJODoyjDKxKLHGoZhGOViIwrDMAyjXMxQGIZhGOVihsIwDMMoFzMUhmEYRrmYoTAMwzDKxQyFYRiGUS5mKAzDMIxy+X9QDtUb4DyozwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -2292,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -2301,12 +2301,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.82 0.83 4699\n", - " 1 0.39 0.41 0.40 1301\n", + " 0 0.83 0.82 0.82 4645\n", + " 1 0.40 0.41 0.41 1355\n", "\n", " accuracy 0.73 6000\n", " macro avg 0.61 0.62 0.61 6000\n", - "weighted avg 0.74 0.73 0.73 6000\n", + "weighted avg 0.73 0.73 0.73 6000\n", "\n" ] } @@ -2333,7 +2333,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -2343,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -2358,7 +2358,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 37, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -2369,7 +2369,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -2378,8 +2378,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 18665\n", - " 1 1.00 1.00 1.00 5335\n", + " 0 1.00 1.00 1.00 18719\n", + " 1 1.00 1.00 1.00 5281\n", "\n", " accuracy 1.00 24000\n", " macro avg 1.00 1.00 1.00 24000\n", @@ -2401,7 +2401,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -2410,12 +2410,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.85 0.95 0.89 4699\n", - " 1 0.66 0.37 0.48 1301\n", + " 0 0.84 0.94 0.89 4645\n", + " 1 0.65 0.37 0.47 1355\n", "\n", - " accuracy 0.82 6000\n", - " macro avg 0.75 0.66 0.69 6000\n", - "weighted avg 0.81 0.82 0.80 6000\n", + " accuracy 0.81 6000\n", + " macro avg 0.74 0.66 0.68 6000\n", + "weighted avg 0.79 0.81 0.79 6000\n", "\n" ] } @@ -2426,14 +2426,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 153, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the Decision Tree (Random Forest) identified 487\n" + "Of 1355 Defaulters, the Decision Tree (Random Forest) identified 501\n" ] }, { @@ -2469,13 +2469,13 @@ " \n", " \n", " 0\n", - " 4453\n", - " 246\n", + " 4376\n", + " 269\n", " \n", " \n", " 1\n", - " 814\n", - " 487\n", + " 854\n", + " 501\n", " \n", " \n", "\n", @@ -2484,11 +2484,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 4453 246\n", - "1 814 487" + "0 4376 269\n", + "1 854 501" ] }, - "execution_count": 40, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -2499,19 +2499,19 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.30333333333333334\n" + "Optimal Threshold: 0.25666666666666665\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2550,7 +2550,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -2568,7 +2568,7 @@ " warm_start=False)" ] }, - "execution_count": 42, + "execution_count": 155, "metadata": {}, "output_type": "execute_result" } @@ -2581,7 +2581,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -2590,12 +2590,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 18665\n", - " 1 0.77 0.44 0.56 5335\n", + " 0 0.86 0.96 0.91 18719\n", + " 1 0.77 0.44 0.56 5281\n", "\n", - " accuracy 0.84 24000\n", + " accuracy 0.85 24000\n", " macro avg 0.81 0.70 0.73 24000\n", - "weighted avg 0.84 0.84 0.83 24000\n", + "weighted avg 0.84 0.85 0.83 24000\n", "\n" ] } @@ -2613,7 +2613,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -2622,12 +2622,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.85 0.95 0.90 4699\n", - " 1 0.68 0.38 0.49 1301\n", + " 0 0.84 0.94 0.89 4645\n", + " 1 0.66 0.37 0.48 1355\n", "\n", - " accuracy 0.83 6000\n", - " macro avg 0.77 0.66 0.69 6000\n", - "weighted avg 0.81 0.83 0.81 6000\n", + " accuracy 0.82 6000\n", + " macro avg 0.75 0.66 0.68 6000\n", + "weighted avg 0.80 0.82 0.79 6000\n", "\n" ] } @@ -2638,19 +2638,19 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 158, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2669358254079571\n" + "Optimal Threshold: 0.23767613676167343\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2667,14 +2667,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 159, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 490\n" + "Of 1355 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 503\n" ] }, { @@ -2710,13 +2710,13 @@ " \n", " \n", " 0\n", - " 4473\n", - " 226\n", + " 4389\n", + " 256\n", " \n", " \n", " 1\n", - " 811\n", - " 490\n", + " 852\n", + " 503\n", " \n", " \n", "\n", @@ -2725,11 +2725,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 4473 226\n", - "1 811 490" + "0 4389 256\n", + "1 852 503" ] }, - "execution_count": 46, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } @@ -2776,7 +2776,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 160, "metadata": {}, "outputs": [], "source": [ @@ -2785,7 +2785,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -2793,7 +2793,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.468240\n", + " Current function value: 0.464583\n", " Iterations 7\n" ] }, @@ -2812,13 +2812,13 @@ " Method: MLE Df Model: 22 \n", "\n", "\n", - " Date: Tue, 05 Nov 2019 Pseudo R-squ.: 0.1162 \n", + " Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1184 \n", "\n", "\n", - " Time: 00:42:24 Log-Likelihood: -11238.\n", + " Time: 10:37:26 Log-Likelihood: -11150.\n", "\n", "\n", - " converged: True LL-Null: -12715.\n", + " converged: True LL-Null: -12647.\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -2829,73 +2829,73 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -8.002e-07 1.72e-07 -4.655 0.000 -1.14e-06 -4.63e-07\n", + " LIMIT_BAL -9.464e-07 1.74e-07 -5.433 0.000 -1.29e-06 -6.05e-07\n", "\n", "\n", - " SEX -0.2234 0.030 -7.547 0.000 -0.281 -0.165\n", + " SEX -0.2012 0.030 -6.782 0.000 -0.259 -0.143\n", "\n", "\n", - " EDUCATION -0.1334 0.022 -5.996 0.000 -0.177 -0.090\n", + " EDUCATION -0.1390 0.023 -6.168 0.000 -0.183 -0.095\n", "\n", "\n", - " MARRIAGE -0.2692 0.025 -10.647 0.000 -0.319 -0.220\n", + " MARRIAGE -0.2880 0.025 -11.330 0.000 -0.338 -0.238\n", "\n", "\n", - " AGE 0.0006 0.001 0.400 0.689 -0.002 0.003\n", + " AGE 0.0014 0.001 0.907 0.364 -0.002 0.004\n", "\n", "\n", - " PAY_0 0.5692 0.020 28.920 0.000 0.531 0.608\n", + " PAY_0 0.5527 0.020 28.033 0.000 0.514 0.591\n", "\n", "\n", - " PAY_2 0.0768 0.023 3.389 0.001 0.032 0.121\n", + " PAY_2 0.0684 0.023 3.012 0.003 0.024 0.113\n", "\n", "\n", - " PAY_3 0.0588 0.025 2.318 0.020 0.009 0.109\n", + " PAY_3 0.0833 0.025 3.305 0.001 0.034 0.133\n", "\n", "\n", - " PAY_4 0.0397 0.028 1.430 0.153 -0.015 0.094\n", + " PAY_4 0.0143 0.028 0.515 0.606 -0.040 0.069\n", "\n", "\n", - " PAY_5 0.0114 0.030 0.381 0.703 -0.047 0.070\n", + " PAY_5 0.0325 0.030 1.078 0.281 -0.027 0.092\n", "\n", "\n", - " PAY_6 0.0171 0.025 0.691 0.490 -0.031 0.066\n", + " PAY_6 0.0203 0.025 0.817 0.414 -0.028 0.069\n", "\n", "\n", - " BILL_AMT1 -6.268e-06 1.3e-06 -4.839 0.000 -8.81e-06 -3.73e-06\n", + " BILL_AMT1 -6.352e-06 1.31e-06 -4.848 0.000 -8.92e-06 -3.78e-06\n", "\n", "\n", - " BILL_AMT2 1.858e-06 1.73e-06 1.077 0.282 -1.52e-06 5.24e-06\n", + " BILL_AMT2 3.958e-06 1.67e-06 2.365 0.018 6.77e-07 7.24e-06\n", "\n", "\n", - " BILL_AMT3 2.57e-06 1.47e-06 1.745 0.081 -3.16e-07 5.46e-06\n", + " BILL_AMT3 6.543e-07 1.45e-06 0.451 0.652 -2.19e-06 3.5e-06\n", "\n", "\n", - " BILL_AMT4 -3.253e-07 1.48e-06 -0.220 0.826 -3.23e-06 2.58e-06\n", + " BILL_AMT4 -2.436e-07 1.52e-06 -0.161 0.872 -3.22e-06 2.73e-06\n", "\n", "\n", - " BILL_AMT5 1.381e-06 1.63e-06 0.845 0.398 -1.82e-06 4.58e-06\n", + " BILL_AMT5 1.356e-06 1.68e-06 0.808 0.419 -1.93e-06 4.65e-06\n", "\n", "\n", - " BILL_AMT6 9.318e-08 1.29e-06 0.072 0.943 -2.44e-06 2.63e-06\n", + " BILL_AMT6 -7.978e-09 1.32e-06 -0.006 0.995 -2.6e-06 2.58e-06\n", "\n", "\n", - " PAY_AMT1 -1.444e-05 2.59e-06 -5.567 0.000 -1.95e-05 -9.36e-06\n", + " PAY_AMT1 -1.643e-05 2.74e-06 -5.990 0.000 -2.18e-05 -1.11e-05\n", "\n", "\n", - " PAY_AMT2 -1.203e-05 2.46e-06 -4.893 0.000 -1.69e-05 -7.21e-06\n", + " PAY_AMT2 -8.733e-06 2.31e-06 -3.788 0.000 -1.33e-05 -4.21e-06\n", "\n", "\n", - " PAY_AMT3 -2.361e-06 1.87e-06 -1.263 0.206 -6.02e-06 1.3e-06\n", + " PAY_AMT3 -2.995e-06 1.95e-06 -1.538 0.124 -6.81e-06 8.23e-07\n", "\n", "\n", - " PAY_AMT4 -4.493e-06 1.97e-06 -2.286 0.022 -8.35e-06 -6.41e-07\n", + " PAY_AMT4 -5.848e-06 2.12e-06 -2.759 0.006 -1e-05 -1.69e-06\n", "\n", "\n", - " PAY_AMT5 -1.38e-06 1.84e-06 -0.749 0.454 -4.99e-06 2.23e-06\n", + " PAY_AMT5 -3.815e-06 2.01e-06 -1.894 0.058 -7.76e-06 1.34e-07\n", "\n", "\n", - " PAY_AMT6 -2.049e-06 1.46e-06 -1.403 0.161 -4.91e-06 8.14e-07\n", + " PAY_AMT6 -3.507e-06 1.53e-06 -2.292 0.022 -6.51e-06 -5.08e-07\n", "\n", "" ], @@ -2907,41 +2907,41 @@ "Dep. Variable: Y No. Observations: 24000\n", "Model: Logit Df Residuals: 23977\n", "Method: MLE Df Model: 22\n", - "Date: Tue, 05 Nov 2019 Pseudo R-squ.: 0.1162\n", - "Time: 00:42:24 Log-Likelihood: -11238.\n", - "converged: True LL-Null: -12715.\n", + "Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1184\n", + "Time: 10:37:26 Log-Likelihood: -11150.\n", + "converged: True LL-Null: -12647.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "LIMIT_BAL -8.002e-07 1.72e-07 -4.655 0.000 -1.14e-06 -4.63e-07\n", - "SEX -0.2234 0.030 -7.547 0.000 -0.281 -0.165\n", - "EDUCATION -0.1334 0.022 -5.996 0.000 -0.177 -0.090\n", - "MARRIAGE -0.2692 0.025 -10.647 0.000 -0.319 -0.220\n", - "AGE 0.0006 0.001 0.400 0.689 -0.002 0.003\n", - "PAY_0 0.5692 0.020 28.920 0.000 0.531 0.608\n", - "PAY_2 0.0768 0.023 3.389 0.001 0.032 0.121\n", - "PAY_3 0.0588 0.025 2.318 0.020 0.009 0.109\n", - "PAY_4 0.0397 0.028 1.430 0.153 -0.015 0.094\n", - "PAY_5 0.0114 0.030 0.381 0.703 -0.047 0.070\n", - "PAY_6 0.0171 0.025 0.691 0.490 -0.031 0.066\n", - "BILL_AMT1 -6.268e-06 1.3e-06 -4.839 0.000 -8.81e-06 -3.73e-06\n", - "BILL_AMT2 1.858e-06 1.73e-06 1.077 0.282 -1.52e-06 5.24e-06\n", - "BILL_AMT3 2.57e-06 1.47e-06 1.745 0.081 -3.16e-07 5.46e-06\n", - "BILL_AMT4 -3.253e-07 1.48e-06 -0.220 0.826 -3.23e-06 2.58e-06\n", - "BILL_AMT5 1.381e-06 1.63e-06 0.845 0.398 -1.82e-06 4.58e-06\n", - "BILL_AMT6 9.318e-08 1.29e-06 0.072 0.943 -2.44e-06 2.63e-06\n", - "PAY_AMT1 -1.444e-05 2.59e-06 -5.567 0.000 -1.95e-05 -9.36e-06\n", - "PAY_AMT2 -1.203e-05 2.46e-06 -4.893 0.000 -1.69e-05 -7.21e-06\n", - "PAY_AMT3 -2.361e-06 1.87e-06 -1.263 0.206 -6.02e-06 1.3e-06\n", - "PAY_AMT4 -4.493e-06 1.97e-06 -2.286 0.022 -8.35e-06 -6.41e-07\n", - "PAY_AMT5 -1.38e-06 1.84e-06 -0.749 0.454 -4.99e-06 2.23e-06\n", - "PAY_AMT6 -2.049e-06 1.46e-06 -1.403 0.161 -4.91e-06 8.14e-07\n", + "LIMIT_BAL -9.464e-07 1.74e-07 -5.433 0.000 -1.29e-06 -6.05e-07\n", + "SEX -0.2012 0.030 -6.782 0.000 -0.259 -0.143\n", + "EDUCATION -0.1390 0.023 -6.168 0.000 -0.183 -0.095\n", + "MARRIAGE -0.2880 0.025 -11.330 0.000 -0.338 -0.238\n", + "AGE 0.0014 0.001 0.907 0.364 -0.002 0.004\n", + "PAY_0 0.5527 0.020 28.033 0.000 0.514 0.591\n", + "PAY_2 0.0684 0.023 3.012 0.003 0.024 0.113\n", + "PAY_3 0.0833 0.025 3.305 0.001 0.034 0.133\n", + "PAY_4 0.0143 0.028 0.515 0.606 -0.040 0.069\n", + "PAY_5 0.0325 0.030 1.078 0.281 -0.027 0.092\n", + "PAY_6 0.0203 0.025 0.817 0.414 -0.028 0.069\n", + "BILL_AMT1 -6.352e-06 1.31e-06 -4.848 0.000 -8.92e-06 -3.78e-06\n", + "BILL_AMT2 3.958e-06 1.67e-06 2.365 0.018 6.77e-07 7.24e-06\n", + "BILL_AMT3 6.543e-07 1.45e-06 0.451 0.652 -2.19e-06 3.5e-06\n", + "BILL_AMT4 -2.436e-07 1.52e-06 -0.161 0.872 -3.22e-06 2.73e-06\n", + "BILL_AMT5 1.356e-06 1.68e-06 0.808 0.419 -1.93e-06 4.65e-06\n", + "BILL_AMT6 -7.978e-09 1.32e-06 -0.006 0.995 -2.6e-06 2.58e-06\n", + "PAY_AMT1 -1.643e-05 2.74e-06 -5.990 0.000 -2.18e-05 -1.11e-05\n", + "PAY_AMT2 -8.733e-06 2.31e-06 -3.788 0.000 -1.33e-05 -4.21e-06\n", + "PAY_AMT3 -2.995e-06 1.95e-06 -1.538 0.124 -6.81e-06 8.23e-07\n", + "PAY_AMT4 -5.848e-06 2.12e-06 -2.759 0.006 -1e-05 -1.69e-06\n", + "PAY_AMT5 -3.815e-06 2.01e-06 -1.894 0.058 -7.76e-06 1.34e-07\n", + "PAY_AMT6 -3.507e-06 1.53e-06 -2.292 0.022 -6.51e-06 -5.08e-07\n", "==============================================================================\n", "\"\"\"" ] }, - "execution_count": 48, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -2953,7 +2953,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 162, "metadata": {}, "outputs": [ { @@ -2962,8 +2962,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.97 0.89 18665\n", - " 1 0.71 0.24 0.35 5335\n", + " 0 0.82 0.97 0.89 18719\n", + " 1 0.71 0.23 0.35 5281\n", "\n", " accuracy 0.81 24000\n", " macro avg 0.76 0.60 0.62 24000\n", @@ -2978,7 +2978,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -2987,12 +2987,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.97 0.89 4699\n", - " 1 0.71 0.24 0.36 1301\n", + " 0 0.81 0.97 0.89 4645\n", + " 1 0.71 0.23 0.35 1355\n", "\n", - " accuracy 0.81 6000\n", - " macro avg 0.77 0.61 0.63 6000\n", - "weighted avg 0.80 0.81 0.78 6000\n", + " accuracy 0.80 6000\n", + " macro avg 0.76 0.60 0.62 6000\n", + "weighted avg 0.79 0.80 0.76 6000\n", "\n" ] } @@ -3010,7 +3010,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -3018,7 +3018,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.469260\n", + " Current function value: 0.465111\n", " Iterations 7\n" ] }, @@ -3031,19 +3031,19 @@ " Dep. Variable: Y No. Observations: 24000 \n", "\n", "\n", - " Model: Logit Df Residuals: 23989 \n", + " Model: Logit Df Residuals: 23987 \n", "\n", "\n", - " Method: MLE Df Model: 10 \n", + " Method: MLE Df Model: 12 \n", "\n", "\n", - " Date: Tue, 05 Nov 2019 Pseudo R-squ.: 0.1143 \n", + " Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1174 \n", "\n", "\n", - " Time: 00:42:24 Log-Likelihood: -11262.\n", + " Time: 10:37:26 Log-Likelihood: -11163.\n", "\n", "\n", - " converged: True LL-Null: -12715.\n", + " converged: True LL-Null: -12647.\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3054,37 +3054,43 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -8.953e-07 1.58e-07 -5.665 0.000 -1.21e-06 -5.86e-07\n", + " LIMIT_BAL -1.007e-06 1.62e-07 -6.220 0.000 -1.32e-06 -6.89e-07\n", + "\n", + "\n", + " SEX -0.1916 0.028 -6.803 0.000 -0.247 -0.136\n", "\n", "\n", - " SEX -0.2169 0.028 -7.732 0.000 -0.272 -0.162\n", + " EDUCATION -0.1319 0.019 -6.927 0.000 -0.169 -0.095\n", "\n", "\n", - " EDUCATION -0.1340 0.019 -7.079 0.000 -0.171 -0.097\n", + " MARRIAGE -0.2889 0.025 -11.519 0.000 -0.338 -0.240\n", "\n", "\n", - " MARRIAGE -0.2734 0.025 -10.972 0.000 -0.322 -0.225\n", + " PAY_0 0.5635 0.020 28.857 0.000 0.525 0.602\n", "\n", "\n", - " PAY_0 0.5856 0.019 30.041 0.000 0.547 0.624\n", + " PAY_2 0.0744 0.022 3.313 0.001 0.030 0.118\n", "\n", "\n", - " PAY_2 0.0766 0.022 3.423 0.001 0.033 0.120\n", + " PAY_3 0.1204 0.021 5.799 0.000 0.080 0.161\n", "\n", "\n", - " PAY_3 0.1143 0.021 5.545 0.000 0.074 0.155\n", + " BILL_AMT1 -6.803e-06 1.3e-06 -5.223 0.000 -9.36e-06 -4.25e-06\n", "\n", "\n", - " BILL_AMT1 -1.738e-06 2.93e-07 -5.933 0.000 -2.31e-06 -1.16e-06\n", + " BILL_AMT2 5.763e-06 1.36e-06 4.249 0.000 3.1e-06 8.42e-06\n", "\n", "\n", - " PAY_AMT1 -1.19e-05 2.33e-06 -5.106 0.000 -1.65e-05 -7.33e-06\n", + " PAY_AMT1 -1.804e-05 2.75e-06 -6.564 0.000 -2.34e-05 -1.27e-05\n", "\n", "\n", - " PAY_AMT2 -9.201e-06 2.09e-06 -4.396 0.000 -1.33e-05 -5.1e-06\n", + " PAY_AMT2 -7.366e-06 2.01e-06 -3.665 0.000 -1.13e-05 -3.43e-06\n", "\n", "\n", - " PAY_AMT4 -4.244e-06 1.7e-06 -2.502 0.012 -7.57e-06 -9.19e-07\n", + " PAY_AMT4 -5.439e-06 1.86e-06 -2.926 0.003 -9.08e-06 -1.8e-06\n", + "\n", + "\n", + " PAY_AMT6 -3.948e-06 1.52e-06 -2.598 0.009 -6.93e-06 -9.7e-07\n", "\n", "" ], @@ -3094,31 +3100,33 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 24000\n", - "Model: Logit Df Residuals: 23989\n", - "Method: MLE Df Model: 10\n", - "Date: Tue, 05 Nov 2019 Pseudo R-squ.: 0.1143\n", - "Time: 00:42:24 Log-Likelihood: -11262.\n", - "converged: True LL-Null: -12715.\n", + "Model: Logit Df Residuals: 23987\n", + "Method: MLE Df Model: 12\n", + "Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1174\n", + "Time: 10:37:26 Log-Likelihood: -11163.\n", + "converged: True LL-Null: -12647.\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", - "LIMIT_BAL -8.953e-07 1.58e-07 -5.665 0.000 -1.21e-06 -5.86e-07\n", - "SEX -0.2169 0.028 -7.732 0.000 -0.272 -0.162\n", - "EDUCATION -0.1340 0.019 -7.079 0.000 -0.171 -0.097\n", - "MARRIAGE -0.2734 0.025 -10.972 0.000 -0.322 -0.225\n", - "PAY_0 0.5856 0.019 30.041 0.000 0.547 0.624\n", - "PAY_2 0.0766 0.022 3.423 0.001 0.033 0.120\n", - "PAY_3 0.1143 0.021 5.545 0.000 0.074 0.155\n", - "BILL_AMT1 -1.738e-06 2.93e-07 -5.933 0.000 -2.31e-06 -1.16e-06\n", - "PAY_AMT1 -1.19e-05 2.33e-06 -5.106 0.000 -1.65e-05 -7.33e-06\n", - "PAY_AMT2 -9.201e-06 2.09e-06 -4.396 0.000 -1.33e-05 -5.1e-06\n", - "PAY_AMT4 -4.244e-06 1.7e-06 -2.502 0.012 -7.57e-06 -9.19e-07\n", + "LIMIT_BAL -1.007e-06 1.62e-07 -6.220 0.000 -1.32e-06 -6.89e-07\n", + "SEX -0.1916 0.028 -6.803 0.000 -0.247 -0.136\n", + "EDUCATION -0.1319 0.019 -6.927 0.000 -0.169 -0.095\n", + "MARRIAGE -0.2889 0.025 -11.519 0.000 -0.338 -0.240\n", + "PAY_0 0.5635 0.020 28.857 0.000 0.525 0.602\n", + "PAY_2 0.0744 0.022 3.313 0.001 0.030 0.118\n", + "PAY_3 0.1204 0.021 5.799 0.000 0.080 0.161\n", + "BILL_AMT1 -6.803e-06 1.3e-06 -5.223 0.000 -9.36e-06 -4.25e-06\n", + "BILL_AMT2 5.763e-06 1.36e-06 4.249 0.000 3.1e-06 8.42e-06\n", + "PAY_AMT1 -1.804e-05 2.75e-06 -6.564 0.000 -2.34e-05 -1.27e-05\n", + "PAY_AMT2 -7.366e-06 2.01e-06 -3.665 0.000 -1.13e-05 -3.43e-06\n", + "PAY_AMT4 -5.439e-06 1.86e-06 -2.926 0.003 -9.08e-06 -1.8e-06\n", + "PAY_AMT6 -3.948e-06 1.52e-06 -2.598 0.009 -6.93e-06 -9.7e-07\n", "==============================================================================\n", "\"\"\"" ] }, - "execution_count": 51, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -3132,7 +3140,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -3141,8 +3149,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.97 0.89 18665\n", - " 1 0.71 0.24 0.35 5335\n", + " 0 0.82 0.97 0.89 18719\n", + " 1 0.71 0.23 0.35 5281\n", "\n", " accuracy 0.81 24000\n", " macro avg 0.76 0.60 0.62 24000\n", @@ -3157,7 +3165,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 166, "metadata": {}, "outputs": [ { @@ -3166,12 +3174,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.97 0.89 4699\n", - " 1 0.71 0.23 0.35 1301\n", + " 0 0.81 0.97 0.89 4645\n", + " 1 0.72 0.23 0.35 1355\n", "\n", " accuracy 0.81 6000\n", - " macro avg 0.77 0.60 0.62 6000\n", - "weighted avg 0.80 0.81 0.77 6000\n", + " macro avg 0.76 0.60 0.62 6000\n", + "weighted avg 0.79 0.81 0.76 6000\n", "\n" ] } @@ -3191,19 +3199,19 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 167, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.27015063587368277\n" + "Optimal Threshold: 0.27420941443503294\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gUVffA8e8lgUAgtCTUEAIkhCZdkA4C0kSUjooF1BexF+S1gYqKvf3AXl+UIiCKgAgICKJ0kN5LCIEAISEJIf38/phN3MSUBbLZbHI+z8NDdmZ25kzZOTP3ztxrRASllFIqN6VcHYBSSqmiTROFUkqpPGmiUEoplSdNFEoppfKkiUIppVSeNFEopZTKkyaKPBhjbjPGLHN1HK5mjAk0xsQbYzwKcZlBxhgxxngW1jKdyRiz2xjT/Qq+d0XHoDGmnDHmZ2PMBWPM3Mv9fmExxvxijLnzCr7XxRiz3xkxFWWu+C0CGHd5j8IYcwyoDqQB8cBS4EERiXdlXMWRbVvfIyIrXBhDEHAUKC0iqa6KwxaLACEicsjJywmigNbZGDMaeAjoWBDbz5bkvhWRgKud1xUuv0D2gTHmBeBZIAlIBfYAT4jIX1cdZDHmbncUA0WkAtASaAU87eJ4rogrr5KLyxX65Sih27sucOBKkkQJOEbm2M4jfsAqwCl3XMVqO4qIW/wDjgG97D6/ASy2++wFvAWEAZHAx0A5u/GDgO1ALHAY6GsbXgn4AjgFnAReBjxs4+4C/rD9/THwVraYfgIet/1dC5gPnMW6KnzYbroXgHnAt7bl35PD+lUC/mf7/nHgOaCUXRzrgP8DLgD7gJ7ZvpvXOqwD3gXO28Y1AFYCUcA54Dugsm36GUA6cAnrzu0pIAgQwNM2zWpgim2+ccAywM8unjts6xAFPJ9932Vb73LA27bpLwB/2IZlLPNO2z49Bzxr9712wF9AjG29pwFl7MYL8ABwEDhqG/Y+cMK2D7YAXeym9wCesR0bcbbxdYA1tnldtG2PEbbpb8Q6nmKAP4Hm2Y7VicAOrCtXT/ttYIt9sy2OSOAd2/Aw27Libf86YHcM2qZpCiy37ctI4JkctumLQDKQYpvPWKyLwuds2/kM1rFWyTZ9xrYea4thTQ7z7A6E57IP8zp2PWz79xzW7+JB/n0s3WP7Oxj43XYcnMM6oZPTPsgej21f/WCLIQqYlkusL2DdGWV8bmKbt7/dsLz2bWtgm+0YmQvMAV6230a2fX8amOHA/CZi/WbjgP3Yftd5HCMZ+ypj+9UCFtqOh0PAvdnW9XvbvokDdgNtr+j8W9gn/Cv9R9YfWgCwE3jfbvx7tg1WFfABfgam2m30C0BvrB9MbaCRbdyPwCdAeaAasBH4j91JNiNRdMU6yWQU11XBOpnWss1zCzAJKAPUB44Afex2WApws23acjms3/+wEo+P7WA4AIy1iyMVeAwojfVDuQBUdXAdUrGKITyxTsLBtm3hBfhj/RDfy2lb53JwrsY6oTa0zW818JrdDy8e6GzbFm/Z1j23RDHd9v3aWCeVjra4Mpb5mW0ZLbBOuo1t32sDXGdbpyBgL/Co3XwF64RaNWN7A7cDvrbvPIH1Yy5rGzcB65gKBYxteb528wrOdrI4A7S3xXynbZt52W2/7Vgnr3LZtylWghtt+7sCcF1O2zmHY9AHKyk+AZS1fW7v4AlxDNaJpL5tmT/wz4ksY7n/wzqGcjo+u5N7osjr2B2HVbwTgPWbWUHuiWIWVrFQKdv6dc62P4Nzise2D/7Guhgqn/27uW0XrOPzNayklBFPrvvWNv1x4BGs3+FgrIRsnyhSgddt05fLZ36hWOeUWnb7ocHlHCNYifVD2zq3xEqUPe3WNRHob1v2VGD9FZ1/XZ0AHA7U2rjxWJlRgN/45yrYYF1tNLCbvgP/XEl+ArybwzyrY5187O88RgGrcviRGqyrra62z/cCK21/twfCss37aeArux32r6s0u2k9bHE0sRv2H2C1XRwR2JKUbdhGYLSD6xCW27Jt09wMbMu2rfNLFM/ZjR8PLLX9PQmYZTfOG+vH9K9EgXVCuAS0yGFcxjIDsq3zyFzW4VFggd1nAa7PZ72jM5aNdTU3KJfpsp+kPgKmZJtmP9DNbvuNyeH4zUgUa7Cu+v2yTZNlO+dwDI6y30/5rNsLZE0UvwHj7T6HYiVwT7vl1s9jft3JIVE4cOyuxHbRYvvcK4djKSNR/A/41H6f57EPMuPB+q2ftd9u+WyXZKyr+zSsu4/ujuxbrIvFk2T9Hf5B1kSRjO3iw4H5BWMlkV5Y9VL20+R7jGBdiKQBPnbjpwJf263rCrtxTYBLjhw/2f+5Wx3FzSLig7VDGmGVMYJ1VewNbDHGxBhjYrAqu/1t4+tgXQFnVxfryuCU3fc+wboqz0KsLT0b68cKcCtWkU3GfGplzMM2n2ewTuIZTuSxXn78c7WS4TjWVXaGk7YY7MfXcnAdsizbGFPNGDPbGHPSGBOLVSTmx+U5bfd3AtZVD7aYMpcnIglYP8ac+GFdCeW0b/JcjjGmoTFmkTHmtG0dXuXf65B9vZ8wxuy1PQkUg1VkkvGd3I6RnNQFnsi2v+tgrXuOy85mLNbd2D5jzCZjzI0OLvdyYsyuFv8+vjxx/BjNTX7HbpbjIZ9lPIV1QbbR9pTYGAdjqAMcF8frY74XkcpY674L6+40Q177thb//h1mX5+zIpLoyPzEqph/FOuEfsb2m8w4hhw5RmoB50Ukzm5Y9vNG9t9P2SupO3G3RAGAiPwOfI1VrAHWreMloKmIVLb9qyRWhRVYO7NBDrM6gXU15Gf3vYoi0jSXRc8Chhpj6mLdRcy3m89Ru3lUFhEfEelvH3Yeq3QO6+qurt2wQKyrlwy1jTEm2/gIB9ch+7Kn2oY1F5GKWEUyJo/pL8cprGIGwHpME6u4JyfnsG6Nc9o3+fkIq64mxLYOz5B1HcBuPYwxXbDKg4cDVWwnigt238ntGMnJCeCVbPvbW0Rm5bTs7ETkoIiMwkrmrwPzjDHl8/rOFcSYXQT/Pr5Sscq/M0O7gvnmd+xmOR6wTpI5EpHTInKviNTCuiv50BgT7EAMJ4DAyz0Bisg523JeMMbUtJtXbvv2FP/+HWZfn+zbMM9jRURmikhnrO0nWMdDXseIvQigqjHGx25Y9vNGgXDLRGHzHtDbGNNSRNKxyrLfNcZUAzDG1DbG9LFN+wVwtzGmpzGmlG1cIxE5hVUR+7YxpqJtXANjTLecFigi27BucT8HfhWRGNuojUCsMWai7fl1D2NMM2PMtY6siIikYVU6vWKM8bElosexrvQzVAMeNsaUNsYMAxoDSy53HWx8sIrxYowxtbHK5+1FYpVlX4l5wEBjTEdjTBms2+fsJ3AAbPvtS+AdY0wt23brYIzxcmA5PlgVffHGmEbA/Q5Mn4qtiMIYMwmoaDf+c2CKMSbEWJobYzISXPbt8RkwzhjT3jZteWPMgGw/2FwZY243xvjb1j/jGEqzxZZO7tt+EVDDGPOoMcbLdqy0d2SZWBc5jxlj6hljKmDdgc25jKvwjNjL2v+zxZvXsfs98IjtN1cZK1nnNu9hxpiMpBKNdeJMs33O65jciHUSf822L8oaYzo5sj4isg/4FetuBvLet3/Z4nnQGONpjBmEVf+Zl1znZ4wJNcZcbzveE7EudtNs2yK3Y8Q+9hNYleNTbevcHOtO5DsKmNsmChE5i1Wm+bxt0ESsyrr1xiqKWIFVDouIbATuxqrsuoBVAZRxBXQH1q3zHqyDcx6QcXWRk1lYZYoz7WJJAwZiVSYdxbrK+hyraMNRD2HVsxzBKveciXUSzbABCLHN+xVgqIhkFOlc7jq8iFXJdgFYjFWxaW8q8JztVvnJy1gHRGS3bV1mY/1447DKYZNy+cqTWJXIm7Ce3Hgdx47LJ7GK/+Kwfoxz8pn+V+AXrIrW41g/TPtig3ewTmrLsBLQF1iVkWAVDXxj2x7DRWQzVh3VNKztfQirLsFRfYHdxph4rCexRopIoq2Y7hVgnW1Z19l/yVbE0BvrWDuN9URXDweX+SXWE21rsI7RRKz9dDlqY53M7P81IO9j9zOsbboD62mhJVgJO8tJz+ZaYINtuywEHhGRo7ZxL2C3D+y/ZPf7C8aqRwzHeuDDUW8C9xljquW1b0UkGasCeyzWyft2rOSd27FNPseKF/9Upp/Guhh8xjYux2Mkh0WMwqq3iAAWAJNFZPllrLtD3OaFu5LMGHMXVoVfZ1fHcrlsV68xWEVER/ObXhVvxph+wMciUjffid2AMWYD1vp85epYnMlt7yhU0WWMGWiM8baVqb6FdcdwzLVRKVewFcX2txXV1AYmY135uiVjTDdjTA3b+twJNMd6cKZY00ShnGEQ1q1wBFZx2UjRW9eSymAVdUZjFT3txXqE2l2FYr2zcQHrfZahtnrCYk2LnpRSSuVJ7yiUUkrlye0arfLz85OgoCBXh6GUUm5ly5Yt50TEP/8p/83tEkVQUBCbN292dRhKKeVWjDHH858qZ1r0pJRSKk+aKJRSSuVJE4VSSqk8aaJQSimVJ00USiml8qSJQimlVJ6cliiMMV8aY84YY3blMt4YYz4wxhwyxuwwxrR2VixKKaWunDPvKL7Gaio3N/2w2gEKAe7D6ohGKaVUAUtJS7+q7zvthTsRWWOMCcpjkkHA/2yNxa03xlQ2xtQsCQ1sKaWUs6SnC3tOxbInIpZtJ6KZtfFKerjNypVvZtcma8cx4bZh/0oUxpj7sO46CAwMLJTglFLKHYgIv+09w6Zj5/l+8wmiE1KyjE8Mj6NCmas71bsyUeTUPWaOTdmKyKfApwBt27bV5m6VUiVaZGwiq/ad4es/j7HvdFyWcVW8S/Ofbg34cMpajm6P5MVnuvLww+0pfRVdK7kyUYSTtWPyAKz+C5RSStlcTEplw9Eo1h48x95Tsaw/cj5zXCkDHRv40rC6D+N7NODgjjM0b14dHx8v2lf0xs/Pmzp1LqdH5py5MlEsxOqkfDbQHrig9RNKKWUVJ20Ni+b7TeEs2H6S5NR/KqMb16xIPT9vbmpRm+6h/pQt7UFUVAL/fXwZn3++jcmTu/HCC91p1apmgcXjtERhjJkFdAf8jDHhWF0glgYQkY+xOlnvj9XZeAJwt7NiUUqpouzw2XiW7jrN+iNRRCcks+tkbOa4RjV8uKdLfZoHVKKBfwU8Sv1Tai8ifPPNdp58cjnR0ZeYMKEjEyZ0LPD4nPnU06h8xgvwgLOWr5RSRVlSahrbw2J4ePY2ImOTsoxrV68qjWv4MLpDXYKr+eQ6j4kTV/Dmm3/SsWMdPv54ANdcU90psbpdfxRKKeWudkdc4Nv1Yfy47SSXUtIyh/cI9ef26+rSraE/nh55v9526VIKFy+m4OfnzdixrQgJqcrYsa0pVSqn54MKhiYKpZRykrR04bsNx9lw5Dwr953JkhwqeHlyV8cghretQ6Cvt0PzW7r0EA88sISWLWswf/5wQkP9CA31c1b4mTRRKKVUAYuIucS0VYeYuSEMAM9ShrZBVWgeUJkhrQMIrZF7cVKO84uI49FHlzJ37h5CQ3158MFrnRF2rjRRKKVUAUhNS2flvjN8tvYIm45FZw7vUN+Xmfe2x5grKxr67bcj3HLLHJKT05gypQcTJnTEy6twT92aKJRS6iocPXeRT9ccztJURu8m1bm1fSDdQvyvuO4gJSWN0qU9aNGiBv37h/Dyy9cTHFy1oMK+LJoolFLqMqSlCwci41i66zSzNoZxJs56Yqlq+TLc3LI2j/QKoVK50lc8/9jYJJ5/fiUbNpxk3box+Pl5M3v20IIK/4poolBKqVyICPsj41h3KIqj5+LZExHLwch44pJSAQjy9aZv0xo8eH0wzWpf3RvQIsK8eXt45JGlnD4dz/jx15KUlIa3t+u7DdJEoZRS2cQmpjBx3g5+2XX6X+OCq1Xg0XaB9Aj1p75/hQJZ3tmzF7nzzh/55ZdDtGpVg59+Gsm119YukHkXBE0USikF7Dp5gflbw4mMTWT5nkhS0oQG/uXp2bg6A66pScPqPpQtXeqKK6XzUrGiF+fOJfDee3144IF2eHq6/i7CniYKpVSJtPdULCv3nWFbWAx7T8VyMuZS5rguIX6M7x5Mhwa+Tlv+mjXHeeWVtcyfP5wKFcqwfv09Tn1p7mpoolBKlRgz1h/nmz+Pcf5iMucvJgNQxqMUxsDgVrW5r1t9GtWo6NQYzp1LYMKE5Xz99XaCgipz7FgMzZpVK7JJAjRRKKWKuT0RsfywNZw5m05kVkK3qVuF+7s1oG+zGtSp6thb0VdLRPjqq+1MmLCc2Ngknn66M8891xVv7yt/QqqwaKJQShUriSlp7D0Vy+Idp/hy3VHSbV2d1ahYloEtazF5YBO8PD1cEtu33+6gSRN/Pv54AE2bVnNJDFdCE4VSyu0dORvPnE0n+G5DGPG2uwawOvbp07Q6T/VtRH2/8k6piM5LQkIKr766lnHj2hIQUJH584dTqVLZIl3MlBNNFEopt5SQnMqy3ZG8sXQfERcSAahTtRzB1Sow4to6tKxTmcY1nVvfkJclSw7ywANLOHYshtq1fbj//mupUqWcy+K5GpoolFJuITEljd0Rsby34gB/HY4i1VamVMpAt4b+/KdrfToGO78l1fyEh8fy6KNLmT9/L40b+/H773fRtWtdV4d1VTRRKKWKLBFh3aEoZm8KY9GOrD0l39CkOl1C/BjcOoDyhdxIXl5eeWUNixcf5NVXr+eJJzpSpoxr6kMKkrE6mnMfbdu2lc2bN7s6DKWUk1xMSuXnvyNYvieS3/adyRzeqIYPA66pyS2taxNQpXCeVHLUxo0nKVfOk2uuqU5UVAIXLiRRv34VV4eVhTFmi4i0vZLvFp00rJQq0eISUxj/3VbWHjyXOaxdUFWuCajEvV3qU6NSWRdGl7MLFxJ55pnf+Oijzdx4Y0MWLhyFr683vg52ROQuNFEopVxGRDgWlcAXfxxh7uZwklLTqV7Ri0d6NmRIm9oue4w1PyLCnDm7eeyxXzlz5iIPPdSOKVOud3VYTqOJQilVqESEX3dH8vuBs8zaGJY5vFntijzWqyE9G1d3YXSO+fbbHdxxx4+0bVuLRYtG0aZNLVeH5FSaKJRSTpeUmsaGI+eZtvIQh8/GE2VrPqNh9Qq0qVuFW9vVpVntioX+nsPlSEpK5ciRaBo39mf48KakpqZzxx0t8PAoWg34OYMmCqWUU5yNS+KHreGs2BuZpWtQfx8vXr3lGvo2q0HV8mVcGKHjVq06yv33LyYhIYWDBx/Cy8uTu+9u5eqwCo0mCqVUgUlLF7aGRfPK4r1sPxGTObxLiB+9m1jNdftW8HJhhJfnzJmLPPnkMmbM2EH9+lX49NOBhd5fdVFQ8tZYKVXgNh49z4JtJ7PUOQRUKcdjvRoyoHlNypYumpXSeTl06Dzt2n1GfHwyzz7bhWef7UK5q+ji1J1polBKXZEVeyKZs/kEv+2NzGx4r3blctx2XSAj2tZxqzsHe7GxSVSs6EWDBlUYO7YVY8a0onFjf1eH5VKaKJRSDktMSeONpftZsTeSsPMJAIRW96FbqD/D2wbQwL9Cka6QzsvFi8m89NLvfPbZVnbsuJ+AgIq8+eYNrg6rSNBEoZTK10/bTzJ/60l2hMcQk5ACwKQbmzDi2jpFqvmMK/Xzz/t58MFfCAu7wNixrdyij4jC5P57WCnlFBExl1iy8xQz1h/neJR191DZuzRvD2vBkDYBLo6uYKSmpjN8+FwWLNhH06b+rF17N507B7o6rCJHE4VSKotLyWm8vWx/Zqc/1Xy8GNI6gMk3NaFi2eJxpS0iGGPw9CxFzZoVeO21njz2WIdi0YCfM2iiUEqRkpbOh6sOs/1ENKv2nwWgvn95PhjZisY1K+LhZh3t5GX9+nAeeGAJn302kNatazJ9+gBXh1TkaaJQqoQSEX7ZdZoft51k2Z7IzOEDmtfkhibVualFLbetmM5JdPQlnnnmNz75ZAu1avkQHX3J1SG5DacmCmNMX+B9wAP4XEReyzY+EPgGqGyb5r8issSZMSlVkp2/mMysjWHM3XyC4+cTyOhlILS6D90b+fPw9SHFonI6uzlzdvHww0s5dy6BRx+9jhdf7I6Pj3s+vusKTjsijDEewHSgNxAObDLGLBSRPXaTPQd8LyIfGWOaAEuAIGfFpFRJtfdULN+uP853G/55IS6wqjcj29Vh9HV18SkmdQ+52bfvHEFBlVm69DZatarp6nDcjjMvHdoBh0TkCIAxZjYwCLBPFAJkdGpbCYhwYjxKlSjh0Ql8vvYoC7ad5MIl65HWVoGVuatjEP2a1aSMZ/FtzC4xMZXXX/+D1q1rMnBgKM8804XnnutaIhrwcwZnJorawAm7z+FA+2zTvAAsM8Y8BJQHeuU0I2PMfcB9AIGB+uiaUjkREbadiOGLP46y2K7b0PJlPOjbtAZP929EXd/yLoywcKxYcYTx4xdz8OB5nniiAwMHhlLaDZsQKUqcmShyqgXL3u/qKOBrEXnbGNMBmGGMaSYi6Vm+JPIp8ClYXaE6JVql3FB8UiobjkQxd3M4q/afISnV+uk08C9P01qVGNetAU1qVcxnLsVDZGQ8jz++jJkzdxIcXJVly26nd+8Grg6rWHBmoggH6th9DuDfRUtjgb4AIvKXMaYs4AecQSmVowuXUpi28iDfbQgjITkty7i7OwUxpHUAzWpXclF0rrN8+RHmzdvDpEldefrpLpQtW/wq5V3FmVtyExBijKkHnARGArdmmyYM6Al8bYxpDJQFzjoxJqXc0vmLyfy2N5Kftkfwx6F/+pTuEuLH6Ovq0iqwCv4l8Cmev/8+zcGD5xk6tAm33XYNnTrVoV69Kq4Oq9hxWqIQkVRjzIPAr1iPvn4pIruNMS8Bm0VkIfAE8Jkx5jGsYqm7RESLlpSyOR51kZd+3sPvB86Smi54eZai/zU16N6wGkPbBFCqGL0Idzni45OZPHkV77+/gaCgytx8cyM8PUtpknASp96b2d6JWJJt2CS7v/cAnZwZg1Lu6KftJ/nmz2NsDbM6/+na0J/7uzWgdd3KeHmW7IrZH3/cx0MP/UJ4eCz33deaqVN74VmMn+AqCrQQT6kiIjk1nR+3neT/Vh3kxHnrreFuDf2Z0Ce0RNY55GTnzkhuuWUO11xTjTlzhtKxY538v6SumiYKpVxk18kLrDl4lq3HY4hNTGHj0fOZ48Z0qsdTfUPdsme4gpaSksbatWFcf309rrmmOosX30rv3vX1kddCpIlCqUIgIpyOTWT5nkg2HYtm/+lYDkTGZ473LuPBTS1q0bGBLwOa1yz2b0o76s8/TzBu3CJ27z7L/v0PEhxclf79Q1wdVomjiUIpJ0hPF/46EsX8LeFsOHqekzFZG6ArY6uUfrhnCA2r+ZTYSuncnD9/if/+dwWffbaVOnUq8sMPwwkOrurqsEosTRRKFZC9p2KZs+kE+07Hsv7IP8VI/j5edA/1J8i3PNfV9+WGJtU1MeQhMTGVli0/JiIijiee6MALL3SnQoUyrg6rRNNEodRVOBefxPwt4Xy/+QSHz17MHN491J8G/hUYeW0dQqr7uDBC9xEeHktAQEXKlvVkypQetGxZgxYtarg6LIUmCqUuS3q6EHY+gY9WH+bv8Bj2nY4DoFxpDwa1rMVdHYNoFajP8l+OS5dSmDr1D15/fR3z5g1j4MBQ7ryzpavDUnYcShTGmDJAoIgccnI8ShUpaenC4bPxbD0ezYq9kazYm7V1mQHNazLq2kA6BfsWq05+CsuyZYcZP34xhw9Hc/vtzWnXrrarQ1I5yDdRGGMGAO8AZYB6xpiWwGQRucXZwSnlCiLC4bMXeWf5fpbtjiQ1/Z/GAsp4lmL0dXXpFOxLj9BqmhyuwkMPLWHatE2EhFRlxYrR9OxZ39UhqVw4ckfxElbz4KsARGS7MSbYqVEpVcgSU9KYuyWclXsjM/uMBqhTtRwDm9eieUBluof663sNVyktzWrd1sOjFNddF4CfnzcTJ3bWBvyKOEf2ToqIxGS7ctL2mJTb23XyAl+uO8rxqAS2n4ghzXbn0MC/PH2a1uCWVrW1IroAbd16inHjFjF6dHMeeqg9t93W3NUhKQc5kij2GmOGA6VsLcE+Aqx3blhKFTwR4e/wC8zaEMa2E9FZXnir6+vNPZ3rcUvrACoUwz6jXSkuLolJk1bxwQcb8ff3pmZNTb7uxpFfxIPAJCAd+AGrNdinnRmUUgUlKj6Jr9YdY9bGMKIuJmcZd0eHutzdqR71/Ip/r2+usmzZYcaM+YmIiDjGjWvLq6/2pHLlsq4OS10mRxJFHxGZCEzMGGCMGYyVNJQqUkSErWExvLpkL9EJyRyxe7chyNeb/tfUZFDL2oTW0KvawlCmjAfVqpVn/vzhtG8f4Opw1BUy+XX/YIzZKiKtsw3bIiJtnBpZLtq2bSubN292xaJVEXU86iKfrT3CifOX+P3APxXRZTxLMfLaOrSrV5V+zWrioW9DO11KShrvvPMXsbFJvPJKT8B690TfRHc923m77ZV8N9c7CmNMH6xuSmsbY96xG1URqxhKKZdac+Asc7eE8/Pf//Sw27ZuFUKq+/BorxCqV9QijsL0xx9hmQ34DRvWJDNBaJJwf3kVPZ0BdgGJwG674XHAf50ZlFK5OXw2npcX7eGPQ+dISbPuhpsHVOKx3g3pEVrNxdGVTFFRCUycuIIvvthGYGAlfv55FDfe2NDVYakClGuiEJFtwDZjzHcikliIMSmVxcHIOBbvPMXczeFZWmEd370BYzrXw69CyesruiiJirrE7Nm7eOqpjkya1I3y5bUBv+LGkcrs2saYV4AmQOa9vIjoJYNyisSUNDYcPc/2sBhmbQzjdKx1nWKM1djefV3r07GBn4ujLNn27j3L99/vZvLk7jRs6EtY2GNUrVrO1WEpJ3EkUXwNvAy8BfQD7kbrKFQBS01LZ+W+M/z0dwSLd5zKHF7GoxS3tKrN8LZ16NDA14URKoCEhBReeWUNb775JxUqlGHs2NYEBFTUJFHMOZIovEXkV2PMWyJyGHjOGLPW2YGpkj8OqsgAACAASURBVGHBtnB+3BaR5Wkl3/JlGN2hLn2b1SCkmo8+rVRELF16iPHjF3P0aAx33tmCN9/sjb+/voNSEjiSKJKM1X7HYWPMOOAkoLWG6opcSk5j/ZEo/joSxcp9Zzh0xno7ukWdytzQpDqDWtYioIq3i6NU2cXHJzN69AJ8fcuxatWddO8e5OqQVCFyJFE8BlQAHgZeASoBY5wZlCp+RISlu04zfuZW7F/dub97A8Z3b6B9RBdBaWnpzJq1i1GjmlGhQhlWrBhNo0Z+eGkTJyVOvntcRDbY/owDRgMYY/QVS5UvEeFAZDwfrT7Ej9v/edfhsV4N6dOsuvYVXYRt2RLBf/6ziC1bTlGunCdDhjTR3uZKsDwThTHmWqA28IeInDPGNMVqyuN6QJOFylF6ujBx/g5+3X2a2MRUAJrVrkjzgMpM7NuISuX07qGounAhkeefX8X06ZuoVq08s2cPYfDgxq4OS7lYXm9mTwWGAH9jVWAvwGo59nVgXOGEp9xJYkoaX/xxlK/WHeNcfBIA47o1YGib2gRX07aV3MGQId+zcuVRHnjgWl5++XoqVdK321XedxSDgBYicskYUxWIsH3eXzihKXeQmJLG7I1hzNsazu6IWESgekUvnhvQmDs6BFHGs5SrQ1T5OHIkGn9/b3x8vHjllespVcpw7bXaJan6R16JIlFELgGIyHljzD5NEirD/tNx/Lj9JB+tPpw5rH29qozuUJcB19TULkLdQHJyGm+99SdTpqzh4Yfb8frrvbWFV5WjvBJFfWNMRlPiBgiy+4yIDHZqZKrIuZScxiOzt/HXkSjibHUPrQIrM6hFLUZ3CNL3HdzImjXHGTduEXv3nmPo0CY8/HB7V4ekirC8EsWQbJ+nOTMQVTSJCOHRl5i/NZz/W3kos7vQ268L5Lb2dWlcs6KLI1SX6913/+Lxx5cRFFSZxYtvpX//EFeHpIq4vBoF/K0wA1FFz7Ldp5n00+7MtpYAHu4ZwuO9tZkvd5OeLly8mIyPjxcDBjTk7NkEnnuuK97e+gSayp++OaP+Zd2hczwyexvn4pMxxnoprkuIH+2CquLpoZXT7mb37jOMG7c4s6e5hg19efXVnq4OS7kRpyYKY0xf4H3AA/hcRF7LYZrhwAuAAH+LyK3OjEnlLDYxhXeWHeDrP49lDuva0J+3h7XA30eb8XZHCQkpTJnyO2+99ReVKnkxZkxLREQfNFCXzeFEYYzxEpGky5jeA5gO9AbCgU3GmIUissdumhDgaaCTiEQbY7QNqUIWm5jCxHk7+GXX6cxhQ9sE8OQNodTQZ+jd1rZtpxg8+HuOHYvh7rtb8sYbvfHz0za01JXJN1EYY9oBX2C18RRojGkB3CMiD+Xz1XbAIRE5YpvPbKx3M/bYTXMvMF1EogFE5Mzlr4K6EiLCzztO8fCsbQDU8yvPIz1DuLmVPj/vzjLuGAIDKxEYWIlvvrmZrl3rujos5eYcuaP4ALgR+BFARP42xvRw4Hu1gRN2n8OB7M/gNQQwxqzDKp56QUSWOjBvdYVOxlzizaX7srS9dG+Xejw7oIkLo1JXKzU1nWnTNrJw4X6WLx+Nr683v/9+l6vDUsWEI4milIgcz1aumebA93IqCJVsnz2BEKA7VttRa40xzUQkJsuMjLkPuA8gMDDQgUWr7PadjuXb9cf5dn0YAN5lPBjaJoAHegRTvaIWMbmzjRtPMm7cIrZtO02/fsHExiZRpYp2JKQKjiOJ4oSt+Els9Q4PAQcc+F44UMfucwBWMyDZp1kvIinAUWPMfqzEscl+IhH5FPgUoG3bttmTjcpFalo6y/ZE8t2G46w7FAXA9Y2q8eQNoTSppe8/uLv4+GQmTlzORx9tpmZNH+bOHcaQIY21sloVOEcSxf1YxU+BQCSwwjYsP5uAEGNMPazOjkYC2Z9o+hEYBXxtjPHDKoo64ljoKje7Iy4wZdEeDkbGE3UxGYCODXx5sk8orQOruDg6VVBKly7F6tXHeeihdkyZcj0VK+rTaco5HEkUqSIy8nJnLCKpxpgHgV+x6h++FJHdxpiXgM0istA27gZjzB6s4qwJIhJ1uctSlqPnLvL499vZFmaV3NWoWJaJfRtxa7tAKumLVcXCoUPneeml35k+vT8+Pl5s2XIfZcvq61DKuYxI3iU5xpjDwH5gDvCDiMQVRmC5adu2rWzevNmVIRQ56enCpIW7Musfujb056Hrg7k2qKqLI1MFJSkplTfeWMcrr6ylTBkPFi++lS5d9Gkm5ThjzBYRaXsl33Wkh7sGxpiOWEVHLxpjtgOzRWT2lSxQFZyUtHQ+XHWYd1f8U2U0//4OtKmrCaI4WbXqKPffv5j9+6MYMaIp77zTh1q1tH8PVXgcumcVkT+BP40xLwDvAd8BmihcKDEljVs+/JO9p2KpVaksnYL9mHJzM8qW9nB1aKoAiQivvLKWlJR0li69jT59gl0dkiqBHHnhrgLWi3IjgcbAT0BHJ8elciEifPHHUV5evBeAOzrU5aVBzVwclSpI6enCF19spW/fYOrUqcSMGbdQuXJZymkXsspFHLmj2AX8DLwhImudHI/KRUpaOq8u2cuSnaeIjE2ifBkP7uoUxIQ+jVwdmipAO3ZEMm7cIv76K5xJk7ry4os9qFlTi5mUazmSKOqLSLrTI1E5SksXZm4M4/kfd2UOe6J3Q8Z1b0Bpbcm12IiPT+bFF1fz7rvrqVKlHF9/PYg77mjh6rCUAvJIFMaYt0XkCWC+MeZfj0ZpD3fOd/hsPI/N2c6O8Av4+3hxZ4e6PNAjWF+oKoZeeGE1b7/9F/fc04rXXuuFr6824KeKjrzuKObY/tee7QpZUmoaby87wKdrrHcPH+/dkAd7BFNKuxotVk6cuMDFiyk0auTHf//bmZtvbkTnztpEjSp68urhbqPtz8YikiVZ2F6k0x7wClhGMdMnvx8mPPoS3UP9mdAnlKa1Krk6NFWAUlPT+eCDDUyatIo2bWrx++934efnrUlCFVmO1FGM4d93FWNzGKauwvojUYz8dH3m53eGt2Bw6wAXRqScYf36cMaNW8Tff0cyYEAI06b1d3VISuUrrzqKEViPxNYzxvxgN8oHiMn5W+pyrT8SxZRFe9gdEUspAzc2r8Vbw1pQxlMrqoubxYsPMHDgLGrV8uGHH4Zz882NtL5JuYW87ig2AlFYrb5OtxseB2xzZlAlgYjw9rIDTFt1CC/PUvynW33Gdq5HNR9t8rs4EREiIuKoXbsivXrV56WXevDII+3x0e5llRvJt62noqY4tPV05Gw8t3++gYgLiXSo78u0W1vhW0FPHMXNgQNRjB+/mAMHotiz5wEqVCjj6pBUCeaUtp6MMb+LSDdjTDRZOxwygIiINih0Bb784ygvLbJ6gx3cujZvDW2hTzMVM4mJqbz22h9MnfoH5cp5MnVqT8qV0xZelfvK6+jN6O7UrzACKe5EhKd/2MnsTScwBhY/1EU7DyqGTp+Op2vXrzh48DyjRjXjnXf6UKNGBVeHpdRVyevx2Iy3sesAESKSbIzpDDQHvgViCyG+YiEmIZl7/7eZTceiaVi9AnPHdaSStttTrKSkpFG6tAfVq5ena9e6TJ/en969G7g6LKUKhCOP1vyI1Q1qA+B/WA0DznRqVMXI5mPn6f7WajYdi+buTkH88khXTRLFSHq68PHHm2nQ4APCw2MxxvD55zdpklDFiiMFp+kikmKMGQy8JyIfGGP0qad8pKcLry7Zy+d/HKV8GQ+mDr6GUe30hari5O+/T/Of/yxiw4aTXH99PVJS0lwdklJO4VBXqMaYYcBo4GbbML0kzkVqWjpvLz/AvC3hnI1LonHNinw7tp0+1VSMiAgTJiznvffWU7VqOWbMuIXbbrtG34lQxZajb2aPx2pm/Igxph4wy7lhuacT5xO49fP1nDh/CYBHeobwaK8QPYEUM8YYoqMvMXas1YBflSrlXB2SUk7l0HsUxhhPIKNrrUMikurUqPJQVN+j+HztEV77ZR+p6cKDPYJ5vHdDfey1GDl+PIZHHlnKpEndaN26JunpovtXuRWn9pltjOkCzABOYr1DUcMYM1pE1l3JAoub1LR07v3fZlbtP0t9v/J8dHsbQmtoRzPFRUpKGu++u54XX/wdgBEjmtK6dU1NEqpEcaTo6V2gv4jsATDGNMZKHFeUmYqbu7/exNqD57ihSXWm3dpa22gqRv788wT/+c8idu06w6BBoXzwQT8CA7UlX1XyOJIoymQkCQAR2WuMKfFtEcz46xjztp7k7xMx3NKqNu+OaOnqkFQBW7HiCBcuJPLjjyMYNEi7nFUlV751FMaYr4EkrLsIgNsAbxG507mh5czVdRQiwjvLD/B/Kw8BVoX1wz1D8NCiCLcnIsyYsQN/f2/69QshKSmVlJR0baNJFQtOraMAxgEPA09h1VGsAf7vShbm7kSEZxbsZNbGEwxpHcCUm5viXUbb8CkO9u07x/33L2b16mMMG9aEfv1C8PLyxEufalYq70RhjLkGaAAsEJE3CiekoklEuG/GFpbviWTktXWYOlifmy8OLl1K4dVX1/L66+soX74Mn3xyI/fc09rVYSlVpORa82qMeQar+Y7bgOXGmDGFFlURk5YujP1mM8v3RNI8oBKv3KJJorj4+ecDvPzyWkaMaMa+fQ9w331t9IkmpbLJ647iNqC5iFw0xvgDS4AvCyesouWtZftZue8Mw9oE8PqQ5noicXOnT8ezfftp+vYNZtiwJgQF3UO7drVdHZZSRVZez3ImichFABE5m8+0xdZP20/y0erDVK/opUnCzaWlpfPhh5sIDZ3G6NELuHQpBWOMJgml8pHXHUV9u76yDdDAvu9sERns1MiKgO0nYnhk9nZqVSrLL4921SThxrZuPcW4cYvYtCmCXr3q8+GH/Smnrfgq5ZC8EsWQbJ+nOTOQoujx77djDMy4p702De7Gjh6Npl27z/Dz82bmzMGMHNlM65iUugx5dVz0W2EGUtTMWH+cI2cv0qtxdRr4aw9l7kZE2LnzDM2bV6devSp89dUgBg4MpXLlsq4OTSm3UyLrHfKz/kgULyzcTbPaFZl2aytXh6Mu09Gj0dx44yxatfqEHTsiARg9uoUmCaWukFMThTGmrzFmvzHmkDHmv3lMN9QYI8YYl7cflZyazhPf/413aQ++G3sdZUt7uDok5aDk5DRee+0Pmjb9kN9/P8Zbb/WmSRN/V4ellNtz+LViY4yXiCRdxvQewHSgNxAObDLGLLRvN8o2nQ/Wm98bHJ23M729bD8nYy7xbP/GVPLWegl3kZaWTseOX7BlyykGD27Me+/1oU4dbcBPqYKQ7x2FMaadMWYncND2uYUxxpEmPNph9V1xRESSgdnAoBymmwK8ASQ6HrZzHD4bzydrjtCohg/3dq3v6nCUA2JjrWsXD49SjBnTip9/HsX8+cM1SShVgBwpevoAuBGIAhCRv4EeDnyvNnDC7nO4bVgmY0wroI6ILMprRsaY+4wxm40xm8+ePevAoi9fTEIy/d5bC8CzAxo7ZRmq4IgIX3+9nfr13+enn/YBMH78tdx4Y0MXR6ZU8eNIoiglIsezDXOkF/mcnj/MbKrWGFMKq6+LJ/KbkYh8KiJtRaStv79zypwfnr2d5LR0Hu/dkC4hWq5dlO3Zc5bu3b/h7rt/olEjPxo0qOrqkJQq1hypozhhjGkHiK3e4SHggAPfCwfq2H0OACLsPvsAzYDVtmfaawALjTE3iUihtiP+898RrDlwlg71fXm4Z0hhLlpdpjfeWMezz66kYkUvPv98IHff3UpfhFTKyRxJFPdjFT8FApHACtuw/GwCQowx9bC6UR0J3JoxUkQuAH4Zn40xq4EnCztJHDoTx0OztlHNx4vpt2mroUWViGCMoUaNCtx22zW8+WZv/P3LuzospUqEfBOFiJzBOslfFhFJNcY8CPwKeABfishuY8xLwGYRWXjZ0TrBiz9bD2HNGNuequW1g5qiJiIijkceWUqXLoE8/HB77rijBXfc0cLVYSlVouSbKIwxn2FXt5BBRO7L77sisgSr1Vn7YZNymbZ7fvMraOsOnWPtwXP0aVqd0Bo+hb14lYeMBvyefXYlKSnpdOwY4OqQlCqxHCl6WmH3d1ngFrI+zeSWLialcueXGylX2oPnb2zi6nCUne3bT3PPPQvZsuUUN9zQgA8/7K8V1kq5kCNFT3PsPxtjZgDLnRZRIflo9WFS04U3hjYjoIq3q8NRdi5cSCQiIo45c4YybFgTbcBPKRe7kg6f6wF1CzqQwiQiLPw7gnp+5bmllfZF4Goiwty5ezh4MIpnn+1Kt25BHDnyCGXLan/kShUFjryZHW2MOW/7F4N1N/GM80Nznl93RxJ2PoEHegTr1aqLHT58nv79ZzJixDx++mk/KSnWKzqaJJQqOvL8NRrrLNoC6/FWgHQR+VfFtjsREaavOkStSmUZ1LKWq8MpsZKSUnnrrT95+eW1lC5divff78v48dfi6akNGitV1OSZKEREjDELRKRNYQXkbBuOnmfnyQtMurEJpT30pOQqJ07EMmXKGgYODOW99/pQu3ZFV4eklMqFI2fKjcaYYvMm2v+tPEjZ0qUYcW2d/CdWBers2YtMm7YRgODgquzZ8wBz5w7TJKFUEZfrHYUxxlNEUoHOwL3GmMPARaw2nERE3C55bDl+nnWHohjaJoDyXloGXljS04WvvtrGU0+tIC4uid696xMa6kf9+lVcHZpSygF5nS03Aq2BmwspFqd7ZfFeAB7oEeziSEqOXbvOcP/9i/njjzC6dAnk449vJDTUL/8vKqWKjLwShQEQkcOFFItTRcRcYmtYDH2b1qCen7YRVBiSk9O44YYZJCen8eWXN3HXXS31KTOl3FBeicLfGPN4biNF5B0nxOM0X/5xFIAn+2h/Bc62cuVRunWrS5kyHnz//TAaNfLDz09falTKXeVVme0BVMBqDjynf24jJS2dH7dHULtyOYKruVXobiU8PJYhQ76nZ8//8b///Q1A586BmiSUcnN53VGcEpGXCi0SJ/pt7xnOxScxSdt0corU1HSmTdvI88+vIi0tnalTe3Lbbc1dHZZSqoDkW0dRHPx5+BwAnYK1EtUZRo9ewOzZu+jXL5jp0/tTr54+zaRUcZJXouhZaFE42Zbj0XiWMtqUeAGKiUnE07MUFSqU4YEHrmXIkMYMGdJYK6uVKoZyraMQkfOFGYizJKaksTsiloEttLmOgiAizJ69i8aNp/P88ysBqx5i6FBt5VWp4qrYt2Gxat8ZAFoHVnZxJO7v0KHz9OnzLaNGzScgoCK33671EEqVBMX+9eSv/jxGKQPD2mqTHVdj5sydjBnzE15enkyb1o9x49rioW1lKVUiFOtEceJ8AhuPnufG5jUpW9rD1eG4pZSUNEqX9qBt21oMHdqEN97oTa1aWtejVElSrBPFx78fxhh4tJe+ZHe5zpy5yBNPLOPixWR++GEEDRv68u23g10dllLKBYpt2UFCcirfbQhjwDU1Ca5WwdXhuI30dOHTT7cQGjqNOXN20bSpP2lp6a4OSynlQsX2jmLxjlMA9G5S3cWRuI8jR6K5/fYf+OuvcLp3D+KjjwbQqJG+e6JUSVdsE8XczeFUKleaAdfUdHUobqNSJS9iYhL55pubGT26uT7uqpQCimnRU1R8EhuPnWdgi5p46pM5eVq4cD+DB88hLS0dX19vdu0azx13tNAkoZTKVCzPosv2RALQtFYlF0dSdIWFXeDmm2czaNBsDhyI4tSpeABKldIEoZTKqlgWPW0PiwGgX7MaLo6k6ElNTee999YzefJqRITXX+/FY49dR2l9fFgplYtimSh+3XOa6hW9qOxdxtWhFDlpael8/vlWrr++Hv/3f/0ICtI31pVSeSt2RU+7Tl4gJiGF4fomdqbo6EtMnLicuLgkvLw8WbduDAsXjtQkoZRySLFLFIt3Wo/FjmwX6OJIXE9E+O67HTRqNJ233/6LVauOAeDr662V1UophxW7oqfdEbEA1K5czsWRuNaBA1GMH7+Y3347Srt2tfn119tp2VLrbJRSl6/YJYotx87TSluK5dFHl7J5cwQfftif++5row34KaWuWLFKFGdiE7mYnEbrwJLZw9ry5Ydp1MiPOnUq8dFHA/Dy8qRGDW2+RCl1dZx6mWmM6WuM2W+MOWSM+W8O4x83xuwxxuwwxvxmjKl7NcvbcjwaoMQlitOn47n11vnccMO3vP76OgDq1q2sSUIpVSCcliiMMR7AdKAf0AQYZYxpkm2ybUBbEWkOzAPeuJplLth2EmOgRyP/q5mN20hPFz7+eDONGk1j/vy9TJ7cjbfeusHVYSmlihln3lG0Aw6JyBERSQZmA4PsJxCRVSKSYPu4Hgi40oWlpKWzbE8kfZrUwLtMsSpRy9XUqWu5//7FtGlTix07xvHCC90pW7ZkrLtSqvA486xSGzhh9zkcaJ/H9GOBX3IaYYy5D7gPIDAw58deNx2zuvjuFFK8WzuNi0vi3LkE6tWrwrhxbalXrwqjRjXTx12VUk7jzDuKnM5ckuOExtwOtAXezGm8iHwqIm1FpK2/f87FSn+fuABAj9DiWewkIixYsJcmTT5kxIh5iAi+vt7ceus1miSUUk7lzEQRDti/Hh0ARGSfyBjTC3gWuElEkq50YTtPxlC1fJli+f7E8eMx3HTTbAYP/p6qVcvxwQf9NDkopQqNM4ueNgEhxph6wElgJHCr/QTGmFbAJ0BfETlzNQtbsvM0PUL9i90J9K+/TtCr1wwA3nqrN488ch2envpOhFKq8DgtUYhIqjHmQeBXwAP4UkR2G2NeAjaLyEKsoqYKwFzbCT5MRG663GUdOmM1kV2nqndBhe9ysbFJVKzoRevWNRkzpiUTJnQiMFCbTVdKFT6nPiIjIkuAJdmGTbL7u1dBLGftwbMADGvj/g0BRkUl8N//rmDZsiPs3j2eChXK8H//19/VYSmlSrBi8Szl5uPRlCvtQbPaFV0dyhUTEWbM2METTywjOvoSjz/egWJWiqaUclNunyhEhPWHo+gc4ue29RMXLiRy881zWL36GB06BPDxxzfSvHl1V4ellFJAMUgUpy4kEnUxmQ71fV0dymUTEYwxVKzohZ+fN59+eiNjx7bW7kiVUkWK2z8+E3beerE7yM+9KrJ//fUQrVt/Snh4LMYY5s4dxr33ttEkoZQqctw+URw+az3xFFDFPRLFqVNxjBw5j759vyMhIYUzZy66OiSllMqT2xc9xSWmAuBbvuj3jz19+kaeeWYlSUmpvPhidyZO7ISXl9vvAqVUMef2Z6nTFxIp7WGo4l30E8WWLado374206f3JyTE/epUlFIlk9sninWHztEqsEqRLNuPjU1i0qRVjB7dnDZtavHhhwPw8vJw26ezlFIlk1vXUSQkp3LobDztgqq6OpQsRIR58/bQuPF0PvhgA7//fhyAsmU9NUkopdyOW99R7Ay/gAi0rFN0+sg+ejSaBx/8hSVLDtKyZQ1++GE47dtfcTcbSinlcm6dKLaEWV2fhtbwcXEk//juu52sWXOcd9/tw4MPttMG/JRSbs+tE0X0xWQAqlcs69I41q49TlJSGr161WfChI7cdVdLAgLctzkRpZSy59aXu2sPnqOClydlXHTVfu5cAmPG/ETXrl/z0ku/A+Dl5alJQilVrLjtHUVaunAuPpmalQr/bkJE+Prr7UyYsJwLF5KYOLETzz/ftdDjUEVfSkoK4eHhJCYmujoUVUKULVuWgIAASpcuXWDzdNtEseFoFOfik3i6X6NCX/aSJQcZM2YhnTrV4eOPb6RZs2qFHoNyD+Hh4fj4+BAUFKRPvCmnExGioqIIDw+nXr16BTZfty162hYWA0CLQnriKSEhhXXrwgDo3z+En34ayZo1d2uSUHlKTEzE19dXk4QqFMYYfH19C/wO1m0TxXlbRXY9v/JOX9YvvxykWbMP6dfvO2JiEjHGcNNNoUXyJT9V9GiSUIXJGceb2yaK41EJhFSrgIcTT9YnT8YybNhc+vefiZeXJz//PIrKlV37hJVSShU2t00UG45EObWP7DNnLtKkyYcsWnSAl1/uwd9/j6NbtyCnLU8pZ/Hw8KBly5Y0a9aMgQMHEhMTkzlu9+7dXH/99TRs2JCQkBCmTJmCiGSO/+WXX2jbti2NGzemUaNGPPnkk65YhTxt27aNe+65J8uwQYMG0aFDhyzD7rrrLubNm5dlWIUKFTL/PnDgAP379yc4OJjGjRszfPhwIiMjryq28+fP07t3b0JCQujduzfR0dH/mmbVqlW0bNky81/ZsmX58ccfAbjtttsIDQ2lWbNmjBkzhpSUFAAWLVrE5MmTryq2yyIibvWvTZs2kpiSKnUnLpJHZm2VghYefiHz7/ffXy+HDkUV+DJUybFnzx5XhyDly5fP/PuOO+6Ql19+WUREEhISpH79+vLrr7+KiMjFixelb9++Mm3aNBER2blzp9SvX1/27t0rIiIpKSkyffr0Ao0tJSXlqucxdOhQ2b59e+bn6OhoCQgIkEaNGsmRI0cyh995550yd+7cLN/N2DaXLl2S4OBgWbhwYea4lStXys6dO68qtgkTJsjUqVNFRGTq1Kny1FNP5Tl9VFSUVKlSRS5evCgiIosXL5b09HRJT0+XkSNHyocffigiIunp6dKyZcvM6bLL6bgDNssVnnfd8qmn7baK7K4N/QtsnhcuJPLccyv55JMtrF9/D61b1+Thh9sX2PyVevHn3eyJiC3QeTapVZHJA5s6PH2HDh3YsWMHADNnzqRTp07ccMMNAHh7ezNt2jS6d+/OAw88wBtvvMGzzz5Lo0bWk4Wenp6MHz/+X/OMj4/noYceYvPmzRhjmDx5MkOGDKFChQrEx1v9xcybN49Fixbx9ddfc9ddd1G1alW2bdtGy5Yt0rMibwAAEABJREFUWbBgAdu3b6dyZevBlODgYNatW0epUqUYN24cYWHWQyTvvfcenTp1yrLsuLg4duzYQYsWLTKHzZ8/n4EDB1K9enVmz57N008/ne92mTlzJh06dGDgwIGZw3r06OHwds3NTz/9xOrVqwG488476d69O6+//nqu08+bN49+/frh7W2VlvTv3z9zXLt27QgPDweseoju3buzaNEihg8fftVx5sctE8W6Q+cAuLYAGgMUEebO3cOjjy7l9Ol4HnywHQ0aVLnq+SpV1KSlpfHbb78xduxYwCp2atOmTZZpGjRoQHx8PLGxsezatYsnnngi3/lOmTKFSpUqsXPnToAci1eyO3DgACtWrMDDw4P09HQWLFjA3XffzYYNGwgKCqJ69erceuutPPbYY3Tu3JmwsDD69OnD3r17s8xn8+bNNGvWLMuwWbNmMXnyZKpXr87QoUMdShS7du3617bISVxcHF26dMlx3MyZM2nSpEmWYZGRkdSsWROAmjVrcubMmTznP3v2bB5//PF/DU9JSWHGjBm8//77mcPatm3L2rVrNVHkJiktHYDalctd1XxEhMGDv+fHH/fRunVNFi4cRdu2tQoiRKX+5XKu/AvSpUuXaNmyJceOHaNNmzb07t0b+KfP9pxczpMzK1asYPbs2Zmfq1TJ/0Jr2LBheHh4ADBixAheeukl7r77bmbPns2IESMy57tnz57M78TGxhIXF4ePzz9tu506dQp//39KFiIjIzl06BCdO3fGGIOnpye7du2iWbNmOa7T5T4h5OPjw/bt2y/rO446deoUO3fupE+fPv8aN378eLp27ZolSVWrVo2IiAinxJKdW1Zmbw+Lwbd8mSt+PDUlJQ2wDpLOnevwwQd92bjxHk0Sqlj6//buPriq+s7j+PsjAgkQ2DYUZpUqdKSYB8OD0IJdRKTLWKVQGEfkqbJj6wAitSgOO7izPlBrW0EXwUWWdaC1jVGnAgMKRTaWioCGbQQFm1J6DTgsZjFLKeWZ7/5xTpJLyMNJzL3JTb6vmTtz7znnnvO737n3fO/vd875nvT0dIqLi/n44485c+YMy5YtAyAnJ4eioqKLlj1w4ABdunQhIyODnJwcdu3aVe/6a0s48dOqn9ffuXPVae3Dhg1j//79lJWVsWbNGiZMmADAhQsX2L59O8XFxRQXF/PJJ59clCQqPlv8ugsKCigvL6dPnz707t2bWCxWmcQyMzMv6u189tlndO/evTIWUT7r8ePHLzrwHP+IT2oVevbsyeHDh4EgEfToUft1Vy+//DLjx4+/5IrqRx99lLKyMhYvXnzR9FOnTpGe/vn+LEeVkomi4hqKxnjrrRh5ectZu/YjAB544Abuu+/rtGuXkqFwLrJu3bqxZMkSnnrqKc6ePcuUKVN4++23efPNN4Gg5zFnzhweeughAObNm8cTTzxBSUkJEOy4q++sAEaPHs3SpUsrX1fsjHv27Mm+ffsqh5ZqI4nx48czd+5csrKyyMzMrHG9Nf2Tz8rKYv/+/ZWv8/Pz2bhxI7FYjFgsxq5duyoTxU033URBQQFnzgT7j1WrVlUeh5g8eTLvvPMOGzZsqFzXxo0bK4fTKlT0KGp6VB92Ahg7diyrV68GYPXq1YwbN67WOOTn5zNp0qSLpq1cuZJNmzaRn5/PZZddvI8qKSm5ZNgtUVJy73js5Fmyr2hY4b2yshPcddcaRo5czenT58jI6Jig1jnXcg0cOJD+/fvz0ksvkZ6eztq1a1m4cCH9+vXjuuuuY8iQIcyePRuAvLw8nnnmGSZNmkRWVha5ubmV/47jPfzww5SXl5Obm0v//v0pLCwE4Mknn2TMmDHcfPPNleP0tZk4cSIvvvhi5bATwJIlSygqKiIvL4/s7GyWL19+yfuuvfZajh07xvHjx4nFYpSWljJ06NDK+X369KFr167s3LmTMWPGMHz4cK6//noGDBjAtm3bKg8sp6ens379ep599ln69u1LdnY2q1atqrMHEMX8+fPZvHkzffv2ZfPmzcyfPx8Ijq3En9Ibi8U4ePAgI0aMuOj9M2bM4MiRIwwbNowBAwbw2GOPVc4rLCzktttu+1zti0oWd850KsjJG2gnbl3Iv4zJ5u5/iFbLJD9/D/fe+zp//esZ5s27gQULbqRTp6YrmOVcbfbt20dWVlZzN6NVe/rpp8nIyLjkWorW7MiRI0yePJktW7bUOL+m752kXWY2uDHbS7kexV9OBRecfLt/3f9Q4p07d4Hc3B4UF8/gRz8a5UnCuVZk5syZdOzYtkYISktLWbRoUdK2l3JnPZ27YHypcwd6ZNReSuPEiTM8/vhWrrqqG7NmDWHq1DymTs3zmjvOtUJpaWlMmzatuZuRVEOGDEnq9lKuR3H+gtGpQ7ta569fX0JOznP85CfbKCk5CgQHyzxJuOaSasO7LrUl4vuWcj2KU2fPc3XmpTWeDh36C3PmvMFrr31EdvaX2Lp1OsOHX90MLXSuSlpaGkePHvVS4y4pLLwfRVpa0xYvTblEcfrcBTI7XzoeeeBAOZs2/Ykf/3gUc+cOo0MdvQ7nkqVXr14cOnSIsrKy5m6KayMq7nDXlFIuUQB07xIkinff/YTt2w/ygx8M5cYbr6a09H4ya+htONdc2rdv36R3GnOuOST0GIWkWyT9QdJ+SfNrmN9RUkE4f6ek3lHW+4WOlzNr1gaGDl3J4sU7OBFegOdJwjnnml7CEoWkdsAy4FtANjBJUvVLF+8Gys3sGuBpoPayinGemPsbnn9+F3PmfJ09e2bSuXOHpmy6c865OIkcevoasN/MDgBIegkYB8QXRBkHPBI+fxVYKklWz2H7K9M7suG97zNoUPRrKZxzzjVOIhPFlcDBuNeHgOo3eKhcxszOSToGZAL/G7+QpHuAe8KXp3f9zz0fRKgI3BZ0p1qs2jCPRRWPRRWPRZV+jX1jIhNFTecCVu8pRFkGM1sBrACQVNTYy9BbG49FFY9FFY9FFY9FFUlF9S9Vs0QezD4EfDnudS+gevH0ymUkXQ50Az5LYJucc841UCITxXtAX0l9JHUA7gTWVVtmHXBX+Px24L/qOz7hnHMuuRI29BQec5gNbALaAS+Y2YeSHiO4yfc64D+BX0jaT9CTuDPCqlckqs0pyGNRxWNRxWNRxWNRpdGxSLky484555Ir5YoCOuecSy5PFM455+rUYhNFosp/pKIIsZgraa+k3ZK2SGq1ZXPri0XccrdLMkmt9tTIKLGQdEf43fhQ0q+S3cZkifAbuUpSoaTfh7+TW5ujnYkm6QVJn0r6oJb5krQkjNNuSYMirdjMWtyD4OD3n4CvAB2A94HsasvMApaHz+8ECpq73c0Yi5FAp/D5zLYci3C5DGArsAMY3NztbsbvRV/g98AXwtc9mrvdzRiLFcDM8Hk2EGvudicoFjcCg4APapl/K/AGwTVsQ4GdUdbbUnsUleU/zOwMUFH+I944YHX4/FVglFpnwf96Y2FmhWb2t/DlDoJrVlqjKN8LgMeBnwKnktm4JIsSi+8Dy8ysHMDMPk1yG5MlSiwM6Bo+78al13S1Cma2lbqvRRsH/NwCO4C/k1RvLaSWmihqKv9xZW3LmNk5oKL8R2sTJRbx7ib4x9Aa1RsLSQOBL5vZ+mQ2rBlE+V58FfiqpG2Sdki6JWmtS64osXgEmCrpEPA6cF9ymtbiNHR/ArTc+1E0WfmPViDy55Q0FRgMjEhoi5pPnbGQdBlBFeLpyWpQM4ryvbicYPjpJoJe5u8k5ZrZ/yW4bckWJRaTgFVmtkjSMILrt3LN7ELim9eiNGq/2VJ7FF7+o0qUWCDpm8ACYKyZnU5S25KtvlhkALnAW5JiBGOw61rpAe2ov5G1ZnbWzP4M/IEgcbQ2UWJxN/AygJltB9IICga2NZH2J9W11ETh5T+q1BuLcLjleYIk0VrHoaGeWJjZMTPrbma9zaw3wfGasWbW6GJoLViU38gaghMdkNSdYCjqQFJbmRxRYlEKjAKQlEWQKNri/WnXAd8Nz34aChwzs8P1valFDj1Z4sp/pJyIsfgZ0AV4JTyeX2pmY5ut0QkSMRZtQsRYbAJGS9oLnAfmmdnR5mt1YkSMxQPAf0j6IcFQy/TW+MdSUj7BUGP38HjMvwLtAcxsOcHxmVuB/cDfgH+KtN5WGCvnnHNNqKUOPTnnnGshPFE455yrkycK55xzdfJE4Zxzrk6eKJxzztXJE4VrcSSdl1Qc9+hdx7K9a6uU2cBtvhVWH30/LHnRrxHrmCHpu+Hz6ZKuiJu3UlJ2E7fzPUkDIrznfkmdPu+2XdvlicK1RCfNbEDcI5ak7U4xs/4ExSZ/1tA3m9lyM/t5+HI6cEXcvO+Z2d4maWVVO58jWjvvBzxRuEbzROFSQthz+J2k/w4fN9SwTI6kd8NeyG5JfcPpU+OmPy+pXT2b2wpcE753VHgPgz1hrf+O4fQnVXUPkKfCaY9IelDS7QQ1t34ZbjM97AkMljRT0k/j2jxd0rONbOd24gq6Sfp3SUUK7j3xaDhtDkHCKpRUGE4bLWl7GMdXJHWpZzuujfNE4Vqi9Lhhp9fCaZ8C/2hmg4CJwJIa3jcD+DczG0Cwoz4UlmuYCHwjnH4emFLP9r8N7JGUBqwCJprZdQSVDGZK+iIwHsgxszxgYfybzexVoIjgn/8AMzsZN/tVYELc64lAQSPbeQtBmY4KC8xsMJAHjJCUZ2ZLCGr5jDSzkWEpj4eBb4axLALm1rMd18a1yBIers07Ge4s47UHloZj8ucJ6hZVtx1YIKkX8Gsz+6OkUcD1wHtheZN0gqRTk19KOgnECMpQ9wP+bGYl4fzVwL3AUoJ7XayUtAGIXNLczMokHQjr7Pwx3Ma2cL0NaWdngnIV8Xcou0PSPQS/678nuEHP7mrvHRpO3xZupwNB3JyrlScKlyp+CBwB+hP0hC+5KZGZ/UrSTuA2YJOk7xGUVV5tZv8cYRtT4gsISqrx/iZhbaGvERSZuxOYDdzcgM9SANwBfAS8ZmamYK8duZ0Ed3F7ElgGTJDUB3gQGGJm5ZJWERS+q07AZjOb1ID2ujbOh55cqugGHA7vHzCN4N/0RSR9BTgQDresIxiC2QLcLqlHuMwXFf2e4h8BvSVdE76eBvw2HNPvZmavExworunMo+MEZc9r8mvgOwT3SCgIpzWonWZ2lmAIaWg4bNUVOAEck9QT+FYtbdkBfKPiM0nqJKmm3plzlTxRuFTxHHCXpB0Ew04nalhmIvCBpGLgWoJbPu4l2KH+RtJuYDPBsEy9zOwUQXXNVyTtAS4Aywl2uuvD9f2WoLdT3SpgecXB7GrrLQf2Aleb2bvhtAa3Mzz2sQh40MzeJ7g/9ofACwTDWRVWAG9IKjSzMoIzsvLD7ewgiJVztfLqsc455+rkPQrnnHN18kThnHOuTp4onHPO1ckThXPOuTp5onDOOVcnTxTOOefq5InCOedcnf4fr9KfbSPoEFgAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3227,7 +3235,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 168, "metadata": { "scrolled": true }, @@ -3238,12 +3246,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.86 0.86 4699\n", - " 1 0.51 0.53 0.52 1301\n", + " 0 0.86 0.86 0.86 4645\n", + " 1 0.52 0.53 0.52 1355\n", "\n", - " accuracy 0.79 6000\n", - " macro avg 0.69 0.70 0.69 6000\n", - "weighted avg 0.79 0.79 0.79 6000\n", + " accuracy 0.78 6000\n", + " macro avg 0.69 0.69 0.69 6000\n", + "weighted avg 0.78 0.78 0.78 6000\n", "\n" ] } @@ -3264,14 +3272,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 169, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the Logistic Regression identified 693\n" + "Of 1355 Defaulters, the Logistic Regression identified 717\n" ] }, { @@ -3307,13 +3315,13 @@ " \n", " \n", " 0\n", - " 4035\n", - " 664\n", + " 3979\n", + " 666\n", " \n", " \n", " 1\n", - " 608\n", - " 693\n", + " 638\n", + " 717\n", " \n", " \n", "\n", @@ -3322,11 +3330,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 4035 664\n", - "1 608 693" + "0 3979 666\n", + "1 638 717" ] }, - "execution_count": 56, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -3337,14 +3345,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 170, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the Logistic Regression identified 301\n" + "Of 1355 Defaulters, the Logistic Regression identified 310\n" ] }, { @@ -3380,13 +3388,13 @@ " \n", " \n", " 0\n", - " 4577\n", - " 122\n", + " 4522\n", + " 123\n", " \n", " \n", " 1\n", - " 1000\n", - " 301\n", + " 1045\n", + " 310\n", " \n", " \n", "\n", @@ -3395,11 +3403,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 4577 122\n", - "1 1000 301" + "0 4522 123\n", + "1 1045 310" ] }, - "execution_count": 57, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -3417,19 +3425,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 171, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2921264496155599\n" + "Optimal Threshold: 0.26257360578023514\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3471,7 +3479,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 172, "metadata": { "colab": {}, "colab_type": "code", @@ -3493,7 +3501,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -3567,27 +3575,27 @@ " \n", " \n", " mean\n", - " -5.214347e-17\n", - " -2.641868e-16\n", - " 1.104219e-15\n", - " -3.598464e-16\n", - " -3.119727e-16\n", - " 8.520976e-16\n", - " -2.162159e-16\n", - " 6.565651e-16\n", - " 1.393564e-15\n", - " -1.218796e-15\n", + " 1.017149e-16\n", + " 7.708834e-16\n", + " 4.951317e-16\n", + " 5.376625e-16\n", + " -1.626419e-17\n", + " 8.471233e-16\n", + " -1.830735e-15\n", + " 1.871623e-15\n", + " -1.919451e-15\n", + " 2.199158e-15\n", " ...\n", - " -1.731092e-16\n", - " -1.989473e-16\n", - " -2.074590e-16\n", - " -6.902812e-17\n", - " -1.543453e-16\n", - " -1.330689e-16\n", - " -2.335655e-16\n", - " 2.194894e-16\n", - " -1.635659e-16\n", - " 5.870304e-17\n", + " -2.152861e-16\n", + " -2.414226e-16\n", + " -1.714184e-16\n", + " 2.617582e-16\n", + " 6.972201e-17\n", + " -1.275126e-16\n", + " -1.808068e-16\n", + " 2.455443e-16\n", + " 2.328091e-16\n", + " -2.814427e-16\n", " \n", " \n", " std\n", @@ -3615,123 +3623,123 @@ " \n", " \n", " min\n", - " -1.211291e+00\n", - " -1.230719e+00\n", - " -2.336141e+00\n", - " -2.984610e+00\n", - " -1.568638e+00\n", - " -1.761812e+00\n", - " -1.552760e+00\n", - " -1.525774e+00\n", - " -1.517458e+00\n", - " -1.527986e+00\n", + " -1.215668e+00\n", + " -1.237489e+00\n", + " -2.356076e+00\n", + " -2.978518e+00\n", + " -1.576658e+00\n", + " -1.762383e+00\n", + " -1.557417e+00\n", + " -1.530280e+00\n", + " -1.517284e+00\n", + " -1.527848e+00\n", " ...\n", - " -2.916838e+00\n", - " -3.289651e+00\n", - " -1.989248e+00\n", - " -6.330582e+00\n", - " -3.348315e-01\n", - " -2.459503e-01\n", - " -2.891836e-01\n", - " -3.034297e-01\n", - " -3.105793e-01\n", - " -2.995681e-01\n", + " -2.944276e+00\n", + " -3.312793e+00\n", + " -1.998575e+00\n", + " -6.347299e+00\n", + " -3.586451e-01\n", + " -2.583330e-01\n", + " -3.011661e-01\n", + " -3.101540e-01\n", + " -3.120793e-01\n", + " -2.887985e-01\n", " \n", " \n", " 25%\n", - " -9.030887e-01\n", - " -1.230719e+00\n", - " -1.073760e+00\n", - " -1.061020e+00\n", - " -8.068131e-01\n", - " -8.730929e-01\n", - " -7.203110e-01\n", - " -6.935713e-01\n", - " -6.651928e-01\n", - " -6.478307e-01\n", + " -9.079055e-01\n", + " -1.237489e+00\n", + " -1.085669e+00\n", + " -1.059473e+00\n", + " -8.151410e-01\n", + " -8.730392e-01\n", + " -7.232169e-01\n", + " -6.965670e-01\n", + " -6.662719e-01\n", + " -6.466090e-01\n", " ...\n", - " -6.329098e-01\n", - " -6.298894e-01\n", - " -6.287115e-01\n", - " -6.277382e-01\n", - " -2.754925e-01\n", - " -2.115053e-01\n", - " -2.677142e-01\n", - " -2.850539e-01\n", - " -2.949365e-01\n", - " -2.929913e-01\n", + " -6.394791e-01\n", + " -6.366044e-01\n", + " -6.344794e-01\n", + " -6.321678e-01\n", + " -2.953392e-01\n", + " -2.217062e-01\n", + " -2.787466e-01\n", + " -2.911114e-01\n", + " -2.962091e-01\n", + " -2.827191e-01\n", " \n", " \n", " 50%\n", - " -2.096324e-01\n", - " 8.125333e-01\n", - " 1.886207e-01\n", - " 8.625697e-01\n", - " -1.538206e-01\n", - " 1.562665e-02\n", - " 1.121378e-01\n", - " 1.386310e-01\n", - " 1.870721e-01\n", - " 2.323242e-01\n", + " -2.154410e-01\n", + " 8.080881e-01\n", + " 1.847384e-01\n", + " 8.595724e-01\n", + " -1.624117e-01\n", + " 1.630464e-02\n", + " 1.109834e-01\n", + " 1.371457e-01\n", + " 1.847406e-01\n", + " 2.346298e-01\n", " ...\n", - " -3.852846e-01\n", - " -3.749672e-01\n", - " -3.628172e-01\n", - " -3.649372e-01\n", - " -2.102197e-01\n", - " -1.623161e-01\n", - " -1.900938e-01\n", - " -2.087093e-01\n", - " -2.135193e-01\n", - " -2.122660e-01\n", + " -3.894941e-01\n", + " -3.783304e-01\n", + " -3.666160e-01\n", + " -3.669621e-01\n", + " -2.240251e-01\n", + " -1.711519e-01\n", + " -1.964268e-01\n", + " -2.146227e-01\n", + " -2.153100e-01\n", + " -2.064582e-01\n", " \n", " \n", " 75%\n", - " 5.608746e-01\n", - " 8.125333e-01\n", - " 1.886207e-01\n", - " 8.625697e-01\n", - " 6.080041e-01\n", - " 1.562665e-02\n", - " 1.121378e-01\n", - " 1.386310e-01\n", - " 1.870721e-01\n", - " 2.323242e-01\n", + " 5.539640e-01\n", + " 8.080881e-01\n", + " 1.847384e-01\n", + " 8.595724e-01\n", + " 5.991058e-01\n", + " 1.630464e-02\n", + " 1.109834e-01\n", + " 1.371457e-01\n", + " 1.847406e-01\n", + " 2.346298e-01\n", " ...\n", - " 1.809200e-01\n", - " 1.635999e-01\n", - " 1.557510e-01\n", - " 1.693108e-01\n", - " -3.801802e-02\n", - " -3.769773e-02\n", - " -4.145918e-02\n", - " -5.051058e-02\n", - " -5.168802e-02\n", - " -6.676252e-02\n", + " 1.938462e-01\n", + " 1.769007e-01\n", + " 1.652917e-01\n", + " 1.768375e-01\n", + " -4.065942e-02\n", + " -4.307087e-02\n", + " -3.828314e-02\n", + " -5.129604e-02\n", + " -4.786688e-02\n", + " -6.899112e-02\n", " \n", " \n", " max\n", - " 6.416728e+00\n", - " 8.125333e-01\n", - " 5.238144e+00\n", - " 2.786160e+00\n", - " 4.743624e+00\n", - " 7.125383e+00\n", - " 6.771728e+00\n", - " 6.796250e+00\n", - " 7.005191e+00\n", - " 6.393409e+00\n", + " 6.401442e+00\n", + " 8.080881e-01\n", + " 5.266367e+00\n", + " 2.778618e+00\n", + " 4.733058e+00\n", + " 7.131055e+00\n", + " 6.784586e+00\n", + " 6.806847e+00\n", + " 6.992841e+00\n", + " 7.284540e+00\n", " ...\n", - " 2.309614e+01\n", - " 1.308834e+01\n", - " 1.451696e+01\n", - " 1.544185e+01\n", - " 5.150083e+01\n", - " 6.990430e+01\n", - " 4.903771e+01\n", - " 3.891079e+01\n", - " 2.673615e+01\n", - " 3.046953e+01\n", + " 2.329162e+01\n", + " 1.317106e+01\n", + " 1.455042e+01\n", + " 1.547053e+01\n", + " 3.161085e+01\n", + " 7.225311e+01\n", + " 5.120848e+01\n", + " 3.337398e+01\n", + " 2.720452e+01\n", + " 2.873154e+01\n", " \n", " \n", "\n", @@ -3741,58 +3749,58 @@ "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean -5.214347e-17 -2.641868e-16 1.104219e-15 -3.598464e-16 -3.119727e-16 \n", + "mean 1.017149e-16 7.708834e-16 4.951317e-16 5.376625e-16 -1.626419e-17 \n", "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -1.211291e+00 -1.230719e+00 -2.336141e+00 -2.984610e+00 -1.568638e+00 \n", - "25% -9.030887e-01 -1.230719e+00 -1.073760e+00 -1.061020e+00 -8.068131e-01 \n", - "50% -2.096324e-01 8.125333e-01 1.886207e-01 8.625697e-01 -1.538206e-01 \n", - "75% 5.608746e-01 8.125333e-01 1.886207e-01 8.625697e-01 6.080041e-01 \n", - "max 6.416728e+00 8.125333e-01 5.238144e+00 2.786160e+00 4.743624e+00 \n", + "min -1.215668e+00 -1.237489e+00 -2.356076e+00 -2.978518e+00 -1.576658e+00 \n", + "25% -9.079055e-01 -1.237489e+00 -1.085669e+00 -1.059473e+00 -8.151410e-01 \n", + "50% -2.154410e-01 8.080881e-01 1.847384e-01 8.595724e-01 -1.624117e-01 \n", + "75% 5.539640e-01 8.080881e-01 1.847384e-01 8.595724e-01 5.991058e-01 \n", + "max 6.401442e+00 8.080881e-01 5.266367e+00 2.778618e+00 4.733058e+00 \n", "\n", " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean 8.520976e-16 -2.162159e-16 6.565651e-16 1.393564e-15 -1.218796e-15 \n", + "mean 8.471233e-16 -1.830735e-15 1.871623e-15 -1.919451e-15 2.199158e-15 \n", "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -1.761812e+00 -1.552760e+00 -1.525774e+00 -1.517458e+00 -1.527986e+00 \n", - "25% -8.730929e-01 -7.203110e-01 -6.935713e-01 -6.651928e-01 -6.478307e-01 \n", - "50% 1.562665e-02 1.121378e-01 1.386310e-01 1.870721e-01 2.323242e-01 \n", - "75% 1.562665e-02 1.121378e-01 1.386310e-01 1.870721e-01 2.323242e-01 \n", - "max 7.125383e+00 6.771728e+00 6.796250e+00 7.005191e+00 6.393409e+00 \n", + "min -1.762383e+00 -1.557417e+00 -1.530280e+00 -1.517284e+00 -1.527848e+00 \n", + "25% -8.730392e-01 -7.232169e-01 -6.965670e-01 -6.662719e-01 -6.466090e-01 \n", + "50% 1.630464e-02 1.109834e-01 1.371457e-01 1.847406e-01 2.346298e-01 \n", + "75% 1.630464e-02 1.109834e-01 1.371457e-01 1.847406e-01 2.346298e-01 \n", + "max 7.131055e+00 6.784586e+00 6.806847e+00 6.992841e+00 7.284540e+00 \n", "\n", " ... BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", "count ... 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean ... -1.731092e-16 -1.989473e-16 -2.074590e-16 -6.902812e-17 \n", + "mean ... -2.152861e-16 -2.414226e-16 -1.714184e-16 2.617582e-16 \n", "std ... 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min ... -2.916838e+00 -3.289651e+00 -1.989248e+00 -6.330582e+00 \n", - "25% ... -6.329098e-01 -6.298894e-01 -6.287115e-01 -6.277382e-01 \n", - "50% ... -3.852846e-01 -3.749672e-01 -3.628172e-01 -3.649372e-01 \n", - "75% ... 1.809200e-01 1.635999e-01 1.557510e-01 1.693108e-01 \n", - "max ... 2.309614e+01 1.308834e+01 1.451696e+01 1.544185e+01 \n", + "min ... -2.944276e+00 -3.312793e+00 -1.998575e+00 -6.347299e+00 \n", + "25% ... -6.394791e-01 -6.366044e-01 -6.344794e-01 -6.321678e-01 \n", + "50% ... -3.894941e-01 -3.783304e-01 -3.666160e-01 -3.669621e-01 \n", + "75% ... 1.938462e-01 1.769007e-01 1.652917e-01 1.768375e-01 \n", + "max ... 2.329162e+01 1.317106e+01 1.455042e+01 1.547053e+01 \n", "\n", " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean -1.543453e-16 -1.330689e-16 -2.335655e-16 2.194894e-16 -1.635659e-16 \n", + "mean 6.972201e-17 -1.275126e-16 -1.808068e-16 2.455443e-16 2.328091e-16 \n", "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -3.348315e-01 -2.459503e-01 -2.891836e-01 -3.034297e-01 -3.105793e-01 \n", - "25% -2.754925e-01 -2.115053e-01 -2.677142e-01 -2.850539e-01 -2.949365e-01 \n", - "50% -2.102197e-01 -1.623161e-01 -1.900938e-01 -2.087093e-01 -2.135193e-01 \n", - "75% -3.801802e-02 -3.769773e-02 -4.145918e-02 -5.051058e-02 -5.168802e-02 \n", - "max 5.150083e+01 6.990430e+01 4.903771e+01 3.891079e+01 2.673615e+01 \n", + "min -3.586451e-01 -2.583330e-01 -3.011661e-01 -3.101540e-01 -3.120793e-01 \n", + "25% -2.953392e-01 -2.217062e-01 -2.787466e-01 -2.911114e-01 -2.962091e-01 \n", + "50% -2.240251e-01 -1.711519e-01 -1.964268e-01 -2.146227e-01 -2.153100e-01 \n", + "75% -4.065942e-02 -4.307087e-02 -3.828314e-02 -5.129604e-02 -4.786688e-02 \n", + "max 3.161085e+01 7.225311e+01 5.120848e+01 3.337398e+01 2.720452e+01 \n", "\n", " PAY_AMT6 \n", "count 2.400000e+04 \n", - "mean 5.870304e-17 \n", + "mean -2.814427e-16 \n", "std 1.000021e+00 \n", - "min -2.995681e-01 \n", - "25% -2.929913e-01 \n", - "50% -2.122660e-01 \n", - "75% -6.676252e-02 \n", - "max 3.046953e+01 \n", + "min -2.887985e-01 \n", + "25% -2.827191e-01 \n", + "50% -2.064582e-01 \n", + "75% -6.899112e-02 \n", + "max 2.873154e+01 \n", "\n", "[8 rows x 23 columns]" ] }, - "execution_count": 60, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -3812,7 +3820,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 174, "metadata": {}, "outputs": [], "source": [ @@ -3826,14 +3834,14 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 175, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.28496002 0.17873312]\n" + "Explained variation per principal component: [0.28322138 0.17768244]\n" ] } ], @@ -3851,12 +3859,12 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 176, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3904,7 +3912,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -3914,7 +3922,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -3924,7 +3932,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 65, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -3935,7 +3943,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 179, "metadata": {}, "outputs": [ { @@ -3962,7 +3970,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 180, "metadata": {}, "outputs": [], "source": [ @@ -4141,7 +4149,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -4153,12 +4161,13 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 111, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "from sklearn import svm\n", "#train svm model with feature reduction and no parameter tuning\n", "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", "clf_reduced.fit(X_train_pca, y_train)" @@ -4166,19 +4175,19 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.18521737456115206\n" + "Optimal Threshold: 0.17778938528613197\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4197,14 +4206,14 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the SVM reduced features no tuning RBF kernal identified 408\n" + "Of 1355 Defaulters, the SVM reduced features no tuning RBF kernal identified 401\n" ] }, { @@ -4240,13 +4249,13 @@ " \n", " \n", " 0\n", - " 4493\n", - " 206\n", + " 4425\n", + " 220\n", " \n", " \n", " 1\n", - " 893\n", - " 408\n", + " 954\n", + " 401\n", " \n", " \n", "\n", @@ -4255,11 +4264,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 4493 206\n", - "1 893 408" + "0 4425 220\n", + "1 954 401" ] }, - "execution_count": 113, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -4269,6 +4278,31 @@ "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.95 0.88 4645\n", + " 1 0.65 0.30 0.41 1355\n", + "\n", + " accuracy 0.80 6000\n", + " macro avg 0.73 0.62 0.64 6000\n", + "weighted avg 0.78 0.80 0.78 6000\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,clf_reduced.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4314,7 +4348,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -4326,7 +4360,7 @@ " verbose=False)" ] }, - "execution_count": 78, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" } @@ -4339,19 +4373,19 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.17635908428846214\n" + "Optimal Threshold: 0.16880724430144842\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4369,14 +4403,14 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 187, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1301 Defaulters, the SVM reduced features and tuning RBF kernal identified 374\n" + "Of 1355 Defaulters, the SVM reduced features and tuning RBF kernal identified 365\n" ] }, { @@ -4412,13 +4446,13 @@ " \n", " \n", " 0\n", - " 4525\n", - " 174\n", + " 4450\n", + " 195\n", " \n", " \n", " 1\n", - " 927\n", - " 374\n", + " 990\n", + " 365\n", " \n", " \n", "\n", @@ -4427,11 +4461,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 4525 174\n", - "1 927 374" + "0 4450 195\n", + "1 990 365" ] }, - "execution_count": 104, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" } @@ -4441,6 +4475,31 @@ "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.96 0.88 4645\n", + " 1 0.65 0.27 0.38 1355\n", + "\n", + " accuracy 0.80 6000\n", + " macro avg 0.73 0.61 0.63 6000\n", + "weighted avg 0.78 0.80 0.77 6000\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,clf_reduced_tuned.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, From c37542d3ea4c42c667aa41c87b8c98897c8a1498 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Sun, 10 Nov 2019 13:46:10 +0800 Subject: [PATCH 02/25] Create .DS_Store --- .DS_Store | Bin 0 -> 6148 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..0a0d48ff58bdc2685db6319af7d54679a85e5216 GIT binary patch literal 6148 zcmeHKJx>Bb5PgFrLTre!usk~}BDOXsfx^N@?T?Q`B_bFB6T55t4<`Nsf1f`9-|TJ( zcZiiS#>^zMZ+CX*-R!&M_7;FN=V=3|0jSUwOI0>sn9i%OSt&%eiJ}~5qe;Ivn)EnU zLbJcGfWEsj>bSuWv%>p3;oRZEncK)N*A4N2XN=K9fDEvvXd8>g)MoLI*_(n5wRx0=DBnJHijm;yhf z0MBf-%8o~CO#xHD6j&;t--n2<7zfNex=#licLgAp8Ft3L`}HSA8X(32GmnhW#HSK{ zs)Q?sgqwr-bk4_Nedf`pL)??e6P_&Lh9cqOA(2}Si9K3t3YY?U1-AUKrsscq^Zh@c zWFu3+6!=#PNV#>{YH~~AY+c)&p0yFZLs!#vna5=cJMJjvT0M&Q>CPCBv_p&oW*!-# P*^hve!5UNGR~7gKcBFYA literal 0 HcmV?d00001 From 7db75f71135ec6b4fb3ae641aab40fc89667a30e Mon Sep 17 00:00:00 2001 From: sgxcj777 <42569747+sgxcj777@users.noreply.github.com> Date: Sun, 10 Nov 2019 13:53:33 +0800 Subject: [PATCH 03/25] Delete .DS_Store --- .DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 0a0d48ff58bdc2685db6319af7d54679a85e5216..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJx>Bb5PgFrLTre!usk~}BDOXsfx^N@?T?Q`B_bFB6T55t4<`Nsf1f`9-|TJ( zcZiiS#>^zMZ+CX*-R!&M_7;FN=V=3|0jSUwOI0>sn9i%OSt&%eiJ}~5qe;Ivn)EnU zLbJcGfWEsj>bSuWv%>p3;oRZEncK)N*A4N2XN=K9fDEvvXd8>g)MoLI*_(n5wRx0=DBnJHijm;yhf z0MBf-%8o~CO#xHD6j&;t--n2<7zfNex=#licLgAp8Ft3L`}HSA8X(32GmnhW#HSK{ zs)Q?sgqwr-bk4_Nedf`pL)??e6P_&Lh9cqOA(2}Si9K3t3YY?U1-AUKrsscq^Zh@c zWFu3+6!=#PNV#>{YH~~AY+c)&p0yFZLs!#vna5=cJMJjvT0M&Q>CPCBv_p&oW*!-# P*^hve!5UNGR~7gKcBFYA From 2696862b71799aa5b9ded27b06739b02d6d9ed9f Mon Sep 17 00:00:00 2001 From: sgxcj777 <42569747+sgxcj777@users.noreply.github.com> Date: Sun, 10 Nov 2019 13:53:58 +0800 Subject: [PATCH 04/25] Delete BT2101 Default.ipynb --- BT2101 Default.ipynb | 4551 ------------------------------------------ 1 file changed, 4551 deletions(-) delete mode 100644 BT2101 Default.ipynb diff --git a/BT2101 Default.ipynb b/BT2101 Default.ipynb deleted file mode 100644 index b828887..0000000 --- a/BT2101 Default.ipynb +++ /dev/null @@ -1,4551 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-4Rm0wjQMUHi" - }, - "source": [ - "# BUILDING A DEFUALT DETECTION MODEL\n", - "\n", - "---\n", - "\n", - "\n", - "\n", - "## Table of Contents\n", - "1. Problem Description (Brief Write Up)\n", - "2. Exploratory Data Analysis (EDA)\n", - "3. Data Pre-processing\n", - "4. Model Selection\n", - "5. Evaluation\n", - "6. Discussion and Possible Improvements\n", - "\n", - "## 1. Problem Description\n", - "\n", - "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan. \n", - "\n", - "Each data sample is described by 23 feature attributes and a binary target feature (default or not) valued 0 (= not default) or 1 (= default). \n", - "\n", - "The 23 explanatory attributes are:\n", - "\n", - "### X1 - X5: Indivual attributes of customer (should this be dummified)\n", - "\n", - "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", - "\n", - "X2: Gender (1 = male; 2 = female). \n", - "\n", - "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", - "\n", - "X4: Marital status (1 = married; 2 = single; 3 = others). \n", - "\n", - "X5: Age (year). \n", - "\n", - "### X6 - X11: Repayment history from April to Septemeber 2005\n", - "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", - "\n", - "\n", - "X6 = the repayment status in September, 2005\n", - "\n", - "X7 = the repayment status in August, 2005\n", - "\n", - "X8 = the repayment status in July, 2005\n", - "\n", - "X9 = the repayment status in June, 2005\n", - "\n", - "X10 = the repayment status in May, 2005\n", - "\n", - "X11 = the repayment status in April, 2005. \n", - "\n", - "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", - "\n", - "X12 = amount of bill statement in September, 2005; \n", - "\n", - "X13 = amount of bill statement in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X17 = amount of bill statement in April, 2005. \n", - "\n", - "### X18 - X23: Amount of previous payment (NT dollar)\n", - "X18 = amount paid in September, 2005\n", - "\n", - "X19 = amount paid in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X23 = amount paid in April, 2005. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aM_aIU6UPHe4" - }, - "source": [ - "## EDA\n", - "\n", - "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", - "\n", - "### Importing packages and the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Is0wEkk3LJCt" - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "x_Z7u_9vRC5m" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KhmX9KWWyrUW" - }, - "outputs": [], - "source": [ - "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", - "df = pd.read_csv(url, header = 1, index_col = 0)\n", - "# Dataset is now stored in a Pandas Dataframe\n", - "df0 = df" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "colab_type": "code", - "id": "FhJ2eAxVQhBm", - "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", - "

5 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 20000 2 2 1 24 2 2 -1 -1 \n", - "2 120000 2 2 2 26 -1 2 0 0 \n", - "3 90000 2 2 2 34 0 0 0 0 \n", - "4 50000 2 2 1 37 0 0 0 0 \n", - "5 50000 1 2 1 57 -1 0 -1 0 \n", - "\n", - " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", - "ID ... \n", - "1 -2 ... 0 0 0 0 689 0 \n", - "2 0 ... 3272 3455 3261 0 1000 1000 \n", - "3 0 ... 14331 14948 15549 1518 1500 1000 \n", - "4 0 ... 28314 28959 29547 2000 2019 1200 \n", - "5 0 ... 20940 19146 19131 2000 36681 10000 \n", - "\n", - " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 0 0 0 1 \n", - "2 1000 0 2000 1 \n", - "3 1000 1000 5000 0 \n", - "4 1100 1069 1000 0 \n", - "5 9000 689 679 0 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#rename the target variable to \"Y\" for convenience\n", - "df[\"Y\"] = df[\"default payment next month\"] \n", - "df = df.drop(\"default payment next month\", axis = 1)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "zcuPyfM86AKj", - "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 24 Columns and 30000 Rows\n" - ] - } - ], - "source": [ - "size = df.shape\n", - "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "QVaSnvJP3VbO", - "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check for null values\n", - "df.isnull().any().sum() " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eVYXnIGH9Zq6" - }, - "source": [ - "There are no null values in the data.\n", - "\n", - "We can also calculate some summary statistics for each attribute." - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 317 - }, - "colab_type": "code", - "id": "HgdgYfpR6hUM", - "outputId": "0e6655d1-3872-448d-864b-786a54b7cf70" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
count30000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.000000...30000.00000030000.00000030000.00000030000.0000003.000000e+0430000.0000030000.00000030000.00000030000.00000030000.000000
mean167484.3226671.6037331.8531331.55186735.485500-0.016700-0.133767-0.166200-0.220667-0.266200...43262.94896740311.40096738871.7604005663.5805005.921163e+035225.681504826.0768674799.3876335215.5025670.221200
std129747.6615670.4891290.7903490.5219709.2179041.1238021.1971861.1968681.1691391.133187...64332.85613460797.15577059554.10753716563.2803542.304087e+0417606.9614715666.15974415278.30567917777.4657750.415062
min10000.0000001.0000000.0000000.00000021.000000-2.000000-2.000000-2.000000-2.000000-2.000000...-170000.000000-81334.000000-339603.0000000.0000000.000000e+000.000000.0000000.0000000.0000000.000000
25%50000.0000001.0000001.0000001.00000028.000000-1.000000-1.000000-1.000000-1.000000-1.000000...2326.7500001763.0000001256.0000001000.0000008.330000e+02390.00000296.000000252.500000117.7500000.000000
50%140000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...19052.00000018104.50000017071.0000002100.0000002.009000e+031800.000001500.0000001500.0000001500.0000000.000000
75%240000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...54506.00000050190.50000049198.2500005006.0000005.000000e+034505.000004013.2500004031.5000004000.0000000.000000
max1000000.0000002.0000006.0000003.00000079.0000008.0000008.0000008.0000008.0000008.000000...891586.000000927171.000000961664.000000873552.0000001.684259e+06896040.00000621000.000000426529.000000528666.0000001.000000
\n", - "

8 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", - "mean 167484.322667 1.603733 1.853133 1.551867 35.485500 \n", - "std 129747.661567 0.489129 0.790349 0.521970 9.217904 \n", - "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", - "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 140000.000000 2.000000 2.000000 2.000000 34.000000 \n", - "75% 240000.000000 2.000000 2.000000 2.000000 41.000000 \n", - "max 1000000.000000 2.000000 6.000000 3.000000 79.000000 \n", - "\n", - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", - "mean -0.016700 -0.133767 -0.166200 -0.220667 -0.266200 \n", - "std 1.123802 1.197186 1.196868 1.169139 1.133187 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", - "\n", - " ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 \\\n", - "count ... 30000.000000 30000.000000 30000.000000 30000.000000 \n", - "mean ... 43262.948967 40311.400967 38871.760400 5663.580500 \n", - "std ... 64332.856134 60797.155770 59554.107537 16563.280354 \n", - "min ... -170000.000000 -81334.000000 -339603.000000 0.000000 \n", - "25% ... 2326.750000 1763.000000 1256.000000 1000.000000 \n", - "50% ... 19052.000000 18104.500000 17071.000000 2100.000000 \n", - "75% ... 54506.000000 50190.500000 49198.250000 5006.000000 \n", - "max ... 891586.000000 927171.000000 961664.000000 873552.000000 \n", - "\n", - " PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", - "count 3.000000e+04 30000.00000 30000.000000 30000.000000 \n", - "mean 5.921163e+03 5225.68150 4826.076867 4799.387633 \n", - "std 2.304087e+04 17606.96147 15666.159744 15278.305679 \n", - "min 0.000000e+00 0.00000 0.000000 0.000000 \n", - "25% 8.330000e+02 390.00000 296.000000 252.500000 \n", - "50% 2.009000e+03 1800.00000 1500.000000 1500.000000 \n", - "75% 5.000000e+03 4505.00000 4013.250000 4031.500000 \n", - "max 1.684259e+06 896040.00000 621000.000000 426529.000000 \n", - "\n", - " PAY_AMT6 Y \n", - "count 30000.000000 30000.000000 \n", - "mean 5215.502567 0.221200 \n", - "std 17777.465775 0.415062 \n", - "min 0.000000 0.000000 \n", - "25% 117.750000 0.000000 \n", - "50% 1500.000000 0.000000 \n", - "75% 4000.000000 0.000000 \n", - "max 528666.000000 1.000000 \n", - "\n", - "[8 rows x 24 columns]" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "W6hhPNl1Slau" - }, - "source": [ - "### Exploring the features" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1Sp2F3gzXX2F" - }, - "source": [ - "**1) Exploring target attribute:**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 - }, - "colab_type": "code", - "id": "DCSEICWwXWgX", - "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "defaults : 22.12 %\n", - "non defaults : 77.88000000000001 %\n" - ] - } - ], - "source": [ - "All = df.shape[0]\n", - "default = df[df['Y'] == 1]\n", - "nondefault = df[df['Y'] == 0]\n", - "\n", - "x = len(default)/All\n", - "y = len(nondefault)/All\n", - "\n", - "print('defaults :',x*100,'%')\n", - "print('non defaults :',y*100,'%')" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 312 - }, - "colab_type": "code", - "id": "W4TWo-gkYTql", - "outputId": "0f7d6129-f6f2-448a-9236-9f9ef7ae1bb4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Frequency')" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plotting target attribute against frequency\n", - "labels = ['non default','default']\n", - "classes = pd.value_counts(df['Y'], sort = True)\n", - "classes.plot(kind = 'bar', rot=0)\n", - "plt.title(\"Target attribute distribution\")\n", - "plt.xticks(range(2), labels)\n", - "plt.xlabel(\"Class\")\n", - "plt.ylabel(\"Frequency\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tysR0WHw4SGU" - }, - "source": [ - "**2) Exploring categorical attributes**\n", - "\n", - "Categorical attributes are:\n", - "- Sex\n", - "- Education\n", - "- Marriage" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "oxsZ8GTGarMC" - }, - "source": [ - "**2a) Checking formatting for categorical attributes:**\n", - "\n", - "Since all categorical attributes are in numerical format, there is no need to convert them into numerical factors." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TSiH-BxjyJ_e" - }, - "source": [ - "**2b) Analysis of categorical data groups**\n", - "\n", - "- Sex\n", - "- Education\n", - "- Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 323 - }, - "colab_type": "code", - "id": "s61SSRII00UB", - "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 60.373333\n", - "1 39.626667\n", - "Name: SEX, dtype: float64\n", - "--------------------------------------------------------\n", - "2 46.766667\n", - "1 35.283333\n", - "3 16.390000\n", - "5 0.933333\n", - "4 0.410000\n", - "6 0.170000\n", - "0 0.046667\n", - "Name: EDUCATION, dtype: float64\n", - "--------------------------------------------------------\n", - "2 53.213333\n", - "1 45.530000\n", - "3 1.076667\n", - "0 0.180000\n", - "Name: MARRIAGE, dtype: float64\n" - ] - } - ], - "source": [ - "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Uudv5XE828nb" - }, - "source": [ - "**Conclusion**\n", - "\n", - "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", - "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", - "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Z92LGXPKetjL" - }, - "source": [ - "**2c) Analysing the relationship between categorical attributes and default paymment (target attribute)**\n", - "\n", - "- Sex\n", - "- Education\n", - "- Marriage\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 357 - }, - "colab_type": "code", - "id": "U3IJzhwwe5KK", - "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total target attributes:\n", - "non defaults : 77.88000000000001 %\n", - "defaults : 22.12 %\n", - "--------------------------------------------------------\n", - "SEX Male Female\n", - "Y \n", - "non defaults 75.832773 79.223719\n", - "defaults 24.167227 20.776281\n", - "--------------------------------------------------------\n", - "EDUCATION 0 1 2 3 4 5 \\\n", - "Y \n", - "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", - "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", - "\n", - "EDUCATION 6 \n", - "Y \n", - "non defaults 84.313725 \n", - "defaults 15.686275 \n", - "--------------------------------------------------------\n", - "MARRIAGE unknown married single others\n", - "Y \n", - "non defaults 90.740741 76.528296 79.071661 73.993808\n", - "defaults 9.259259 23.471704 20.928339 26.006192\n" - ] - } - ], - "source": [ - "#proportion of target attribute (for reference)\n", - "print('Total target attributes:')\n", - "print('non defaults :',y*100,'%')\n", - "print('defaults :',x*100,'%')\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with Sex\n", - "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(sex_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with education\n", - "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(education_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with marriage\n", - "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(marriage_target)" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - }, - "colab_type": "code", - "id": "apWUtjyHPWcE", - "outputId": "a7e40c77-64d1-4ef3-f31c-b7d1b7f24878" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SEX: 0.6854422910010494\n", - "EDUCATION: 1.0276815835411287e-08 *** [Significant at the 95% Level]\n", - "MARRIAGE: 0.016161318279331434 *** [Significant at the 95% Level]\n" - ] - } - ], - "source": [ - "# we would like to conduct a chi square test of independence using the contingency tables above\n", - "import scipy.stats as sp_stat\n", - "\n", - "def sigf(p, confidence):\n", - " return str(p) + \" *** [Significant at the \" + str(int(confidence*100)) + \"% Level]\" if p < 1-confidence else p\n", - "\n", - "#print the p values for the test statistic of each chi-sq test\n", - "print(\"SEX:\", sigf(sp_stat.chi2_contingency(sex_target)[1], 0.95))\n", - "print(\"EDUCATION:\", sigf(sp_stat.chi2_contingency(education_target)[1],0.95))\n", - "print(\"MARRIAGE:\", sigf(sp_stat.chi2_contingency(marriage_target)[1],0.95))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kOriUQ0wxbhD" - }, - "source": [ - "**Conclusion**\n", - "\n", - "Categorical attributes EDUCATION and MARRIAGE are associated with the target variable.\n", - "\n", - "We will omit SEX from our models as it is statistically insignificant.\n", - "\n", - "Categorical attributes SEX and MARRIAGE have approximately uniform distributions throughout groups of categories." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "77GAylGWnPJO" - }, - "source": [ - "**3) Analysis of Numerical Attributes**\n", - "\n", - "The numerical attributes are:\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "HEcCl5Rj-N0T", - "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0
0LIMIT_BAL
1AGE
2PAY_0
3PAY_2
4PAY_3
5PAY_4
6PAY_5
7PAY_6
8BILL_AMT1
9BILL_AMT2
10BILL_AMT3
11BILL_AMT4
12BILL_AMT5
13BILL_AMT6
14PAY_AMT1
15PAY_AMT2
16PAY_AMT3
17PAY_AMT4
18PAY_AMT5
19PAY_AMT6
\n", - "
" - ], - "text/plain": [ - " 0\n", - "0 LIMIT_BAL\n", - "1 AGE\n", - "2 PAY_0\n", - "3 PAY_2\n", - "4 PAY_3\n", - "5 PAY_4\n", - "6 PAY_5\n", - "7 PAY_6\n", - "8 BILL_AMT1\n", - "9 BILL_AMT2\n", - "10 BILL_AMT3\n", - "11 BILL_AMT4\n", - "12 BILL_AMT5\n", - "13 BILL_AMT6\n", - "14 PAY_AMT1\n", - "15 PAY_AMT2\n", - "16 PAY_AMT3\n", - "17 PAY_AMT4\n", - "18 PAY_AMT5\n", - "19 PAY_AMT6" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#printing numerical attributes\n", - "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "EUl9bX7k_nxw" - }, - "source": [ - "**Meaning of attributes PAY_0 to PAY_6**\n", - "\n", - "The numeric value in these attributes shows the past history of a credit card holder, example -2 means: No consumption of credit card, -1 means that holder paid the full balance, 0 means the use of revolving credit; 1= paymentdelay of one month; 2= payment delay of two months and so on.\n", - "\n", - "\n", - "\n", - "**3a) Limit Balance**" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 170 - }, - "colab_type": "code", - "id": "Csm29blenaJT", - "outputId": "e84fd570-639f-4a4b-a57f-af2d000d6730" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "count 30000.000000\n", - "mean 167484.322667\n", - "std 129747.661567\n", - "min 10000.000000\n", - "25% 50000.000000\n", - "50% 140000.000000\n", - "75% 240000.000000\n", - "max 1000000.000000\n", - "Name: LIMIT_BAL, dtype: float64\n" - ] - } - ], - "source": [ - "#Find out min and max value of LIMIT BALANCE\n", - "print(df[\"LIMIT_BAL\"].describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 313 - }, - "colab_type": "code", - "id": "eHrdunFN7Sj0", - "outputId": "1c087f10-d56c-493a-8f44-45f6c685b8d4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Distribution of Limit Balance')" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.distplot(df[\"LIMIT_BAL\"]).set_title('Distribution of Limit Balance')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "xlDZf8ru7Xp6" - }, - "source": [ - "**3b) Analysis of Age**" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - }, - "colab_type": "code", - "id": "Iwal1Lhb6ryG", - "outputId": "b4f48898-27c5-4455-d8d1-9e344f3ddb7d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.distplot(df.AGE)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "MewzQ1N6e6sf" - }, - "source": [ - "To find out the relationships between the features, we calculate the absolute value of R with the target for all attributes. (R = Correlation Coefficient)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "awXnqvLOS-wB", - "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0
PAY_00.324794
PAY_20.263551
PAY_30.235253
PAY_40.216614
PAY_50.204149
PAY_60.186866
LIMIT_BAL0.153520
PAY_AMT10.072929
PAY_AMT20.058579
PAY_AMT40.056827
PAY_AMT30.056250
PAY_AMT50.055124
PAY_AMT60.053183
BILL_AMT10.019644
BILL_AMT20.014193
BILL_AMT30.014076
AGE0.013890
BILL_AMT40.010156
BILL_AMT50.006760
BILL_AMT60.005372
\n", - "
" - ], - "text/plain": [ - " 0\n", - "PAY_0 0.324794\n", - "PAY_2 0.263551\n", - "PAY_3 0.235253\n", - "PAY_4 0.216614\n", - "PAY_5 0.204149\n", - "PAY_6 0.186866\n", - "LIMIT_BAL 0.153520\n", - "PAY_AMT1 0.072929\n", - "PAY_AMT2 0.058579\n", - "PAY_AMT4 0.056827\n", - "PAY_AMT3 0.056250\n", - "PAY_AMT5 0.055124\n", - "PAY_AMT6 0.053183\n", - "BILL_AMT1 0.019644\n", - "BILL_AMT2 0.014193\n", - "BILL_AMT3 0.014076\n", - "AGE 0.013890\n", - "BILL_AMT4 0.010156\n", - "BILL_AMT5 0.006760\n", - "BILL_AMT6 0.005372" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#absolute correlation coefficient\n", - "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).apply(lambda x: x.corr(df.Y) if x.corr(df.Y) > 0 else -x.corr(df.Y)).sort_values(ascending = False))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "C6c_Gz6wUrJ8" - }, - "source": [ - "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status, the more correlated it is.\n", - "\n", - "The least correlated attributes are the bill amounts.\n", - "\n", - "We can also generate a correlation matrix (heatmap) to see which features are the most correlated with each other." - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 964 - }, - "colab_type": "code", - "id": "C2Mg6Zi_Q_rf", - "outputId": "a06ab39f-82c5-43a7-cc4f-a267b2a967f9" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.05, 'Correlation Matrix')" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f = plt.figure(figsize=(19, 15))\n", - "plt.matshow(df.corr(), fignum=f.number)\n", - "plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=45)\n", - "plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\n", - "cb = plt.colorbar()\n", - "cb.ax.tick_params(labelsize=14)\n", - "plt.title('Correlation Matrix', fontsize=16)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "gXQQgn7kU0Uc" - }, - "source": [ - "The heatmap reveals that the payment and billed amounts across the 5 months are highly correlated with each other. Highly Correlated factors should be removed before carrying out any regression to prevent multicollinearity." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "usBpD2aFLDDz" - }, - "source": [ - "- Linear regression\n", - "- Scatterplot matrix\n", - "- Boxplot matrix" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AQBksEyEf4Sf" - }, - "source": [ - "## Data Preprocessing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AG82bczx53gl" - }, - "source": [ - "???\n", - "1. get rid of unknown attributes\n", - "2. normalise\n", - "3. pca?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mbhlIlQzZz7c" - }, - "source": [ - "## Model Selection\n", - "\n", - "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", - "\n", - "We will be attempting to fit the following models:\n", - "- K-Nearest Neighbour\n", - "- Support Vector Machine\n", - "- Decision Tree \n", - "- Naive Bayes Classifier\n", - "- Logistic Regression\n", - "- Neural Network\n" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "cdg_TpPwZyua" - }, - "outputs": [], - "source": [ - "#using kfold to create train test splits\n", - "import sklearn.model_selection as skm\n", - "kf = skm.KFold(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import *" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "VOB68z_hM1jW" - }, - "outputs": [], - "source": [ - "#using holdout sampling for train test split\n", - "ft = df.drop(\"Y\", axis = 1)\n", - "target = df[\"Y\"]\n", - "X_train,X_test,y_train,y_test = skm.train_test_split(ft,target,test_size=0.20)\n", - "#make the results reproducible\n", - "np.random.seed(2101) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [], - "source": [ - "def get_roc(model, y_test, X_test, name):\n", - " try:\n", - " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", - " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", - " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", - " except:\n", - " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", - " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", - " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", - " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", - " plt.xlim([0.0, 1.0])\n", - " plt.ylim([0.0, 1.05])\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver operating characteristic for ' + name)\n", - " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", - " plt.legend(loc=\"lower right\")\n", - " \n", - " #find- best threshold\n", - " optimal_idx = np.argmax(tpr - fpr)\n", - " optimal_threshold = thresholds[optimal_idx]\n", - " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", - " \n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "def confusion(y_test, predictions, name):\n", - " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", - " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", - " return conf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluation \n", - "We will select the model based on the model evaluation. The key metrics we will compute are:\n", - "(Need someone to fill in the formula)\n", - "\n", - "1. Accuracy\n", - "2. Precision\n", - "3. Recall\n", - "4. F1 Measure\n", - "5. AUROC\n", - "\n", - "Because of the nature of a default detection problem, we would like to prioritise recall for defaults. \n", - "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "z--cWiwmc0yv" - }, - "source": [ - "### Baseline - Random Classifier (Test)\n", - "This part aims to provide a benchmark accuracy for our models, i.e. a random classifier with (expected) accuracy of 0.5." - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - }, - "colab_type": "code", - "id": "q_AFJtiNc0LB", - "outputId": "35a03ab9-3b59-459a-e09e-1ca1fea7b96c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Holdout Sample Accuracy:\n", - " precision recall f1-score support\n", - "\n", - " 0 0.77 0.51 0.61 4645\n", - " 1 0.22 0.49 0.31 1355\n", - "\n", - " accuracy 0.50 6000\n", - " macro avg 0.50 0.50 0.46 6000\n", - "weighted avg 0.65 0.50 0.54 6000\n", - "\n", - "Kfold Average Accuracy: 0.4976666666666666\n" - ] - } - ], - "source": [ - "from random import *\n", - "import sklearn.metrics as skmt\n", - "\n", - "def rnjezus(i):\n", - " return randint(0, 1)\n", - "\n", - "# Holdout sampling\n", - "print(\"Holdout Sample Accuracy:\")\n", - "print(skmt.classification_report(y_test, list(map(lambda x : rnjezus(x), range(len(X_test))))))\n", - "\n", - "# K-fold\n", - "accuracies = []\n", - "for train,test in kf.split(df):\n", - " prediction = list(map(lambda x : rnjezus(x), test))\n", - " actual = list(df[\"Y\"][test+1])\n", - " \n", - " #By definition the columns correspond to the predicted values and rows are the actuals\n", - " conf_mat = skmt.confusion_matrix(actual, prediction)\n", - " #print(pd.DataFrame(conf_mat))\n", - "\n", - " accuracies.append(skmt.accuracy_score(actual, prediction))\n", - "\n", - "print(\"Kfold Average Accuracy:\", sum(accuracies)/len(accuracies))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "PEyK8hM1BHj3" - }, - "source": [ - "### Model 1 - K-Nearest Neighbour" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "colab_type": "code", - "id": "mgcr1jtzcVpL", - "outputId": "669a1298-dc36-4dfc-e9fd-298089a515dc" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "\n", - "neighbours = np.arange(1,25)\n", - "train_accuracy =np.empty(len(neighbours))\n", - "test_accuracy = np.empty(len(neighbours))\n", - " \n", - "for i,k in enumerate(neighbours):\n", - " #Setup a knn classifier with k neighbors\n", - " knn=KNeighborsClassifier(n_neighbors=k,algorithm=\"kd_tree\",n_jobs=-1)\n", - " \n", - " #Fit the model\n", - " knn.fit(X_train,y_train.ravel())\n", - " \n", - " #Compute accuracy on the training set\n", - " train_accuracy[i] = knn.score(X_train, y_train.ravel())\n", - " \n", - " #Compute accuracy on the test set\n", - " test_accuracy[i] = knn.score(X_test, y_test.ravel())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - }, - "colab_type": "code", - "id": "OUhQMYZhPKUS", - "outputId": "ea071750-1327-4907-8934-c329416798f2" - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEWCAYAAAB1xKBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3xV9fnA8c+TPciEALISloMRECNWxdUiLtwL3NZRVKy1tY5qq2Jrta3VWq1WrasqFPVHi1ZFUNG6GSIKiCAghBlGBtnj+f3xPTfchJvkZtzckDzv1+u87tnnuYdwn3O+3+/5HlFVjDHGmKZEhDsAY4wx+wZLGMYYY4JiCcMYY0xQLGEYY4wJiiUMY4wxQbGEYYwxJiiWMDoJEVknIuPDHUd7EpHdIjIo3HG0hojcJSIvhPH4vxWR7SKypY33e5SIrAxy3WNFJLeR5c+KyG/bLjrTUpYwuiDvR0pF5Fy/eVHevCxv+llveqzfOkNEJOCDOyIyR0SmBZh/uohsEZGotv4eqtpNVde09X67ChHpD/wCGKaqvdty36r6P1U9oC33acLPEkbXtROYJiKRTawT7JXds8DFIiL15l8MvKiqVc0JLhQJprNrwTnLBHao6rZQxNPRiGO/ea1gJ68TEpEDRWStiExqZLW3gArgokbWeQ7IFpFjgjjsv4F04Ci/ONKAicDz3vQpIvKFiBSKyAYRuctv3SzvjuYKEVkPvCsi/xWR6+t9t6UicoY3riIyxBt/VkQe9bYpEpHPRGSw33YTRGSliBSIyN9E5H0RuTLQF/HuwGaKyPPevpaJSI7f8trj+h37t974sSKSKyI3i8g2EdksImeIyMki8q2I7BSRX9U7ZJyI/Ms71mIRGeW37z4i8qqI5Hn/pj+tF+crIvKCiBQClwX4Line98gTke9F5A4RifCKL+cCfbyivWcDbOv7Lr/w+y6X+y2PFZE/ich6EdkqIo+LSLz/tn7rjvH+7YtE5GXv+/623vECHsfTQ0Tmetu/LyKZftsdISILvH/bBSJyhN+y+SLyOxH5CCgBBonIZSKyxtvXWhG5sP53N4FZwuhkRGQM8DZwvarOaGRVBX4N3Cki0Q2sUwLcC/yuqeOqaikwE7jEb/Z5wDeq+qU3XewtTwVOAa7x/fj7OQY4CDgBl7BqE5r3Q9oXeKOBMCYDdwNpwGpf3CLSA3gFuA3oDqwEjmhgHz6nATO8WGcDjzSxvr/eQJwX62+AJ73vcQguof5G6ta9nA68jEu4LwH/FpFo72r4NeBLb18/An4mIifU2/YVL84XA8TyVyAFGIQ7t5cAl6vqPOAkYJNXtHdZI98lxTv+FcCj3oUAwP3A/sBoYIjf961DRGKAWbi70HRgOnBmM44DcCFwD9ADWOL7riKSDvwXeBj3b/tn4L8i0t1v24uBq4EkIM9b9yRVTcL9HSxp4Lub+lTVhk4wAOtwP5a5wHFNrHsX8II3/hlwDRCFSyJZ3vxnccVRscB63I/LEPcn0+B+xwEFQLw3/RFwYyPrPwQ86I1neccf5Lc8FlcsNtSb/hPwN7/lCgzxi/cpv2Un45IVuB/JT/yWCbABuLKR8zPPb3oYUBrouP7nyhs/FigFIr3pJG/9w/zWXwSc4XesT/2WRQCbcYnlMGB9vdhuA57x2/aDRs5vJFCOq6PwzfsJMN8v1txGtvd9lyi/eduAH3jnsBgY7LfscGBt/X0DRwMbAfFb98MA52yv4/id3xl+y7oB1UB/XDL4vF7cnwCXeePzgWl+yxKBfOBsvL9TG4If7A6jc5kCfKyq7/lmiMiFXpHDbhF5M8A2dwC3466I96Kq5bgru3twPxINUtUPcVdwp3tX0Ifirph9sRwmIu95xSMFXrw96u1mQ71jzwQu8q62JwP/bCQE/5Y+JbgfFoA+9faruMTamPr7ipPg6wh2qGq1N17qfW71W17qFxv1YqvxYuuDq2PoIyL5vgH4FdAr0LYB9ABigO/95n2Pu4oP1g6tW//kO68ZQAKwyC+2t7z59fUBNnrnvaG4GzrOXuur6m7chUQfb/D/frD3d/Tfthg4H/e3t9krwjwwQMwmAEsYncsUYICIPOiboaovqity6KaqJ9XfQFXn4opvrm1kv8/gigvqFyME8jzuiv5i4G1V9f+hfAlXvNNfVVOAx9k7CdVvhfUcrjjiR0CJqn4SRAz1bQb6+SZERPynW6AE92Pp09oWRv19I15i7Adswv3QrVXVVL8hSVVP9tu2se6mtwOVuMTjMwB3td9a23GJb7hfbCmq2i3AupuBvt559+kfYL3G+J+jbriirU3ekFlv3frfsc45UtU5qno8sB/wDa7I0ATBEkbnUgScCBwtIvc1Y7vbgZsbWuhd+d0F3BLEvp4HxgNX4X7s/SUBO1W1TFxz3Qua2pmXIGqAB2j87qIx/wVGepXPUcB1tO5HfglwgYhEisiJuLqB1jhERM7yYvsZrhjpU+BzoFBEbhGReO94I0Tk0GB26t3lzAR+JyJJXkXxz4FWP/fh3Qk9CTwoIj0BRKRvvfoVn09wRUhTxTXfPh0YG2C9xpwsIuO8+pB7gM9UdQOuPmt/EbnA2/f5uCLE1wPtRER6ichpIpKIO8+7vdhMECxhdDKqmg8cD5wkIvcEuc1HuB+nxkzHXSk2ta91wMe4suLZ9RZfi2vKW4SrHJ0ZTHy4JDSSFv7Qqep24FzgD8AO3A/KQtwPRkvcAJyKKwu/ENdCrDX+gysm2YW7MztLVSu9H/xTcZXKa3FX9U/h7vaCdT2urmENrt7gJeDpVsbrcwvu7vRTr5XWPGCvZy9UtQI4C1eZnY9rAPA6zTv/LwF34oqiDsGdd1R1B64l3i9w/7Y3AxO9f/NAIrx1N3n7OobG766NH6lbrGhMxyMilwBXq+q4NtpfBK6e4EL/+h7TfkTkM+BxVX0m3LGY4NkdhunQRCQBdwX4RCv3c4KIpIpILK7iWHDFPqYdiMgxItLbKza6FMjGVZKbfYglDNNheeXhebgWRi81sXpTDge+wxXrnIpr1lra+CamDR2Ae56kAFckdI6qNlnEaToWK5IyxhgTFLvDMMYYE5RO08Fbjx49NCsrK9xhGGPMPmXRokXbVTXQA5d76TQJIysri4ULF4Y7DGOM2aeISP0n5RtkRVLGGGOCYgnDGGNMUCxhGGOMCUqnqcMwxjRPZWUlubm5lJWVhTsU0w7i4uLo168f0dENvf6maZYwjOmicnNzSUpKIisrC9nrzbqmM1FVduzYQW5uLgMHDmzxfkJWJCUiT3uvW/y6geUiIg+LyGpxr90c47fsUhFZ5Q2XhipGY7qysrIyunfvbsmiCxARunfv3uq7yVDWYTyL62q7IScBQ73hauAxqH3l4p24t42Nxb1CNK2hnRhjWs6SRdfRFv/WIUsYqvoBrvvghpwOPK/Op0CqiOyHe5fzXFXdqaq7cC+qbyzxtE5pPsy/HzYuCtkhjDGmMwhnK6m+1H1NY643r6H5exGRq0VkoYgszMvLa1kUIjD/Xlj3Ycu2N8a0yI4dOxg9ejSjR4+md+/e9O3bt3a6oqIi6P08/fTTbNmy5426l19+OStXrmyzOF9++WVEhNWrV7fZPvdV4UwYge6PtJH5e89UfUJVc1Q1JyMjqCfb9xaXAnGpsGtdy7Y3xrRI9+7dWbJkCUuWLGHKlCnceOONtdMxMTFB76d+wnjmmWc44IC93uPUYtOnT2fcuHHMmDGjzfYZSFVVVdMrhVk4E0Yudd/r63uPcUPzQyctC3YF/XS8MSbEnnvuOcaOHcvo0aO59tprqampoaqqiosvvpiRI0cyYsQIHn74Yf71r3+xZMkSzj///No7k3HjxrFkyRKqqqpITU3l1ltvZdSoURx++OFs27YNgFWrVnHYYYcxduxYfv3rX5OamhowjsLCQj777DOefPJJpk+fXmfZvffey8iRIxk1ahS33347AN9++y0//OEPGTVqFGPGjGHdunXMmzePM844o3a7KVOm8MIL7uWR/fr145577uHII49k1qxZPP744xx66KGMGjWKc889l9JS1wP/li1bOP3008nOzmbUqFF89tln3HbbbTz66KO1+73lllv429/+1nb/CAGEs1ntbNw7fmfgKrgLVHWziMwB7vWr6J4A3BbSSNIyYeuykB7CmI7s7teWsXxTYZvuc1ifZO48dXizt/v666+ZNWsWH3/8MVFRUVx99dXMmDGDwYMHs337dr766isA8vPzSU1N5a9//SuPPPIIo0eP3mtfBQUFHHPMMdx33338/Oc/5+mnn+bWW2/l+uuv56abbuLcc8/lkUceaTCW//u//2PixIkceOCBJCYmsnTpUrKzs3nttdd48803+fzzz4mPj2fnTlddO3nyZO666y5OPfVUysrKqKmpabIoKzExkY8++ghwxXRTpkwB4NZbb+XZZ5/lmmuu4brrruP4449n6tSpVFVVUVJSQo8ePZg0aRLXXXcd1dXVvPzyyyxaFNq62FA2q52Oe/n7ASKSKyJXiMgUEZnirfIG7j3Dq3Evk78WQFV34l7yvsAbpnnzQic1E/LXQ01NSA9jjGnavHnzWLBgATk5OYwePZr333+f7777jiFDhrBy5UpuuOEG5syZQ0pK0682j4+P56STTgLgkEMOYd26dQB89tlnnH322QBccMEFDW4/ffp0Jk2aBMCkSZNq7zLmzZvHj3/8Y+Lj4wFIT09n165dbN++nVNPPRVwD8olJCQ0GeP5559fO7506VKOOuooRo4cyYwZM1i2zF3Izp8/n5/85CcAREVFkZyczODBg0lKSuKrr77izTffZOzYsaSlhbZBacjuMFR1chPLFbiugWVP03Yvqm9aWhZUV0DRZkgJWL9uTKfWkjuBUFFVfvzjH3PPPffstWzp0qW8+eabPPzww7z66qs88UTjb+71rwuJjIxsVj1BXl4e77//Pt988w0iQlVVFdHR0dx7772oasBmqoHmRUVFUeN3MVr/WYjExMTa8UsuuYQ333yTESNG8NRTT/Hpp3veIhxo31dccQXPPvss69atq00ooWR9SYErkgLIt3oMY8Jt/PjxzJw5k+3btwOumGb9+vXk5eWhqpx77rncfffdLF68GICkpCSKioqadYyxY8cya9YsgAYrs2fOnMkVV1zB999/z7p168jNzaVPnz58+umnTJgwgX/84x+1dQw7d+4kLS2NHj168NprrwEuMZSUlJCZmcmyZcuoqKhg165dvPvuuw3GVVxcTO/evamsrOSll/a8lfi4447j8ccfB6C6uprCQld8ePbZZ/Paa6+xZMkSxo8f36xz0BKWMABSs9ynVXwbE3YjR47kzjvvZPz48WRnZzNhwgS2bt3Khg0bOProoxk9ejRXXXUV9957L+Ca0V555ZXNao778MMPc//99zN27Fi2bdsWsHhr+vTpnHnmmXXmnX322bz00ktMnDiRE088sbbY7MEHHwTgxRdf5IEHHiA7O5tx48aRl5fHwIEDOeOMMxg5ciSXXHIJY8aM2etYPtOmTWPs2LEcf/zxDBs2rHb+I488wpw5cxg5ciQ5OTl88803gCv2Ovroo5k8eTIREaH/Oe807/TOycnRFr9AqaocftsLjrkFjgtt/boxHcWKFSs46KCDwh1GWBQXF5OQkICI8MILLzBr1ixeffXVcIfVbDU1NYwePZp///vfDBo0qMn1A/2bi8giVc0J5njW+SBAVCwk97EiKWO6iAULFvCzn/2Mmpoa0tLSeOaZZ8IdUrN99dVXnHbaaZx77rlBJYu2YAnDJzXTiqSM6SKOPfZYlixZEu4wWmXkyJGsXbu2XY9pdRg+aZl2h2GMMY2whOGTmgmFm1x9hjHGmL1YwvBJywIU8jc0taYxxnRJljB8ap/FWBfWMIwxpqOyhOGT6iUMq/g2pl20RffmwXRl/uijj/Liiy+2RchdnrWS8knaDyJjrJtzY9qJr3tzgLvuuotu3bpx00031VlHVVHVBh9KC6Y57HXXBeyBqEOrqqoiKqrj/TzbHYZPRASkDrCWUsaE2erVqxkxYgRTpkxhzJgxbN68mauvvpqcnByGDx/OtGnTatcNpivzO+64g4ceeqh2/VtvvZWxY8dywAEH8PHHHwPuQb6zzz6bUaNGMXnyZHJycgI2u73zzjs59NBDa+PzPfgcqFtzCNwFui9mcN2WDxkyBICnnnqKSZMmMXHiRE466SQKCwv54Q9/yJgxY8jOzub111+vjeOZZ56p7er88ssvJz8/n0GDBtX2lZWfn8/AgQOprq5us38XsDuMuuxZDNNVvXkrbPmqbffZeyScdF+LNl2+fDnPPPNMbf9J9913H+np6VRVVXHcccdxzjnn1Ok6Axruyrw+VeXzzz9n9uzZTJs2jbfeeou//vWv9O7dm1dffZUvv/yywe47brjhBu6++25UlQsuuIC33nqLk046KWC35g11gd6YTz75hCVLlpCWlkZlZSX/+c9/SEpKYtu2bRx55JFMnDiRL7/8kvvvv5+PP/6Y9PR0du7cSWpqKkceeSRvvfUWEydO5KWXXuK8884jMjKyBWe/YXaH4S8t04qkjOkABg8ezKGHHlo7PX36dMaMGcOYMWNYsWIFy5cv32ubhroyr++ss87aa50PP/ywthvzUaNGMXx44N5733nnHcaOHcuoUaN4//33WbZsWYPdmgfqAr0pEyZMqO2iXFW55ZZbavvT2rBhA9u3b+fdd9/l/PPPr92f7/PKK6+sLaJ75plnuPzyy5s8XnPZHYa/tCwoy4eyAvfqVmO6ihbeCYSKf5ffq1at4i9/+Quff/45qampXHTRRXt1EQ7Bd2UeGxu71zrB9KlXUlLC1KlTWbx4MX379uWOO+6ojSNQ1+MNdYHu3915Y12dP//88xQUFLB48WKioqLo168fZWVlDe73mGOOYerUqbz33ntER0dz4IEHNvmdmsvuMPxZSyljOpzCwkKSkpJITk5m8+bNzJkzp82PMW7cOGbOnAm4PpoC3cGUlpYSERFBjx49KCoqqu2ssKFuzQN1gQ6QlZVV+2a8V155pcGYCgoK6NmzJ1FRUcydO5eNGzcCrvv3GTNm1O7Pv6jroosu4sILLwzJ3QVYwqjL3othTIczZswYhg0bxogRI7jqqqs48sgj2/wY119/PRs3biQ7O5sHHniAESNG7NXleffu3bn00ksZMWIEZ555JocddljtskDdmjfUBfovf/lL/vKXv3DEEUewa9euBmO6+OKL+fjjj8nJyeHll19m6NChAGRnZ3PzzTfXdvX+y1/+snabCy+8kIKCgjpv8WtL1r25v5Kd8IeBMOG3cMT1bROYMR1UV+7evL6qqiqqqqqIi4tj1apVTJgwgVWrVnXIpq2NmTFjBnPmzGmwubF1b96W4tMgNsWKpIzpYnbv3s2PfvQjqqqqUFX+/ve/73PJ4pprrmHevHm89dZbITvGvnVGQk0E0uxZDGO6mtTU1Np6hX3VY489FvJjWB1GffYshulCOkuRtGlaW/xbW8KoLy3L3WHYfyTTycXFxbFjxw5LGl2AqrJjxw7i4uJatR8rkqovLQuqymD3VkjqHe5ojAmZfv36kZubS15eXrhDMe0gLi6Ofv36tWofIU0YInIi8BcgEnhKVe+rtzwTeBrIAHYCF6lqrresGvD1VbBeVU8LZay1/J/FsIRhOrHo6GgGDhwY7jDMPiRkRVIiEgk8CpwEDAMmi8iweqv9CXheVbOBacDv/ZaVqupob2ifZAF7nsWwLkKMMaaOUNZhjAVWq+oaVa0AZgCn11tnGPCON/5egOXtL3WA+7SWUsYYU0coE0ZfwP99p7nePH9fAmd742cCSSLS3ZuOE5GFIvKpiJwRwjjrio6Hbr2tpZQxxtQTyoSxd+9YUL85xk3AMSLyBXAMsBHw9Rg2wHv68ALgIREZvNcBRK72ksrCNq24S8u0OwxjjKknlAkjF+jvN90P2OS/gqpuUtWzVPVg4HZvXoFvmfe5BpgPHFz/AKr6hKrmqGpORkZG20Weat2cG2NMfaFMGAuAoSIyUERigEnAbP8VRKSHiPhiuA3XYgoRSRORWN86wJHA3t1HhkpaFhRuhOrKdjukMcZ0dCFLGKpaBUwF5gArgJmqukxEpomIr9XTscBKEfkW6AX8zpt/ELBQRL7EVYbfp6rtmDAyQWugYEPT6xpjTBcR0ucwVPUN4I16837jN/4KsFeH8Kr6MTAylLE1yv9ZjPRBYQvDGGM6EusaJJC0LPdp9RjGGFPLEkYgyX0gItpaShljjB9LGIFEREJKP3sWwxhj/FjCaEiaNa01xhh/ljAa4uvm3BhjDGAJo2GpmVCyA8p3hzsSY4zpECxhNMTXa63dZRhjDGAJo2GpWe7T6jGMMQawhNGw2mcx7A7DGGPAEkbDEtIhppsVSRljjMcSRkNEvF5rLWEYYwxYwmhcWpbVYRhjjMcSRmN8L1LS+u99MsaYrscSRmNSM6GyBIq3hzsSY4wJO0sYjfE9i2HFUsYYYwmjUb6mtdZSyhhjLGE0KnWA+7Q7DGOMsYTRqJhESMywOwxjjMESRtNSrZtzY4wBSxhNS8uyh/eMMQZLGE1Ly4SCXKiuCnckxhgTVpYwmpKaCVoNhRvDHYkxxoSVJYym1PZauy6cURhjTNhZwmiKvUjJGGOAECcMETlRRFaKyGoRuTXA8kwReUdElorIfBHp57fsUhFZ5Q2XhjLORiX3A4m0im9jTJcXsoQhIpHAo8BJwDBgsogMq7fan4DnVTUbmAb83ts2HbgTOAwYC9wpImmhirVRkVGQ0teKpIwxXV4o7zDGAqtVdY2qVgAzgNPrrTMMeMcbf89v+QnAXFXdqaq7gLnAiSGMtXFpWVYkZYzp8kKZMPoCG/ymc715/r4EzvbGzwSSRKR7kNsiIleLyEIRWZiXl9dmge/FXqRkjDEhTRgSYF79F0vcBBwjIl8AxwAbgaogt0VVn1DVHFXNycjIaG28DUvLhOJtUFESumMYY0wHF8qEkQv095vuB2zyX0FVN6nqWap6MHC7N68gmG3bVWqW+7RiKWNMFxbKhLEAGCoiA0UkBpgEzPZfQUR6iIgvhtuAp73xOcAEEUnzKrsnePPCo/ZZDEsYxpiuK2QJQ1WrgKm4H/oVwExVXSYi00TkNG+1Y4GVIvIt0Av4nbftTuAeXNJZAEzz5oWHPYthjDFEhXLnqvoG8Ea9eb/xG38FeKWBbZ9mzx1HeCVmQHSC3WEYY7o0e9I7GCLWzbkxpsuzhBGstEwrkjLGdGmWMILlexZD92rda4wxXYIljGClZUJFEZSEr+7dGGPCyRJGsHxNa/PXhTMKY4wJG0sYwUr1mtZaSyljTBdlCSNY9iyGMaaLs4QRrNgkiE+3prXGmC6ryYQhIlPD9i6KjiYty4qkjDFdVjB3GL2BBSIy03uDXqCeZLsGexbDGNOFNZkwVPUOYCjwD+AyYJWI3Csig0McW8eTmgn5G6CmOtyRGGNMuwuqDkNVFdjiDVVAGvCKiPwhhLF1PGlZUFMJheHrad0YY8IlmDqMn4rIIuAPwEfASFW9BjiEPW/L6xqspZQxpgsLprfaHsBZqlrnV1JVa0RkYmjC6qD8n8XIGhfeWIwxpp0FUyT1BlDbH4aIJInIYQCquiJUgXVIKf0Bsaa1xpguKZiE8Riw22+62JvX9UTFQEo/K5IyxnRJwSQM8Sq9AVcURYhfvNSh+XqtNcaYLiaYhLHGq/iO9oYbgDWhDqzDsmcxjDFdVDAJYwpwBLARyAUOA64OZVAdWmomFG2GytJwR2KMMe2qyaIlVd0GTGqHWPYNtd2cb4CM/cMaijHGtKcmE4aIxAFXAMOBON98Vf1xCOPquPyfxbCEYYzpQoIpkvonrj+pE4D3gX5AUSiD6tBqn8VYF9YwjDGmvQWTMIao6q+BYlV9DjgFGBnasDqwbr0gKs4ShjGmywkmYVR6n/kiMgJIAbJCFlFHFxEBqQOspZQxpssJJmE84b0P4w5gNrAcuD+YnXvdoa8UkdUicmuA5QNE5D0R+UJElorIyd78LBEpFZEl3vB4M75T6NmzGMaYLqjRSm8RiQAKVXUX8AEwKNgdi0gk8ChwPK457gIRma2qy/1WuwOYqaqPicgwXDckWd6y71R1dNDfpD2lZcKGz8MdhTHGtKtG7zC8p7qntnDfY4HVqrpGVSuAGcDp9Q8BJHvjKcC+0W94WhaUF0DprnBHYowx7SaYIqm5InKTiPQXkXTfEMR2fYENftO53jx/dwEXiUgu7u7ier9lA72iqvdF5KhABxCRq0VkoYgszMvLCyKkNuLfa60xxnQRwSSMHwPX4YqkFnnDwiC2C/QqV603PRl4VlX7AScD//SKwTYDA1T1YODnwEsiklxvW1T1CVXNUdWcjIyMIEJqIy19L0ZlKVRXtX08xhjTDoJ50ntgC/edC/T3m+7H3kVOVwAnesf5xHtIsIf3dHm5N3+RiHwH7E9wiSr06j+LUVMDJTugaBMUbq77WbRlz3jpLug+FK6cB/GpYQvfGGNaIpgnvS8JNF9Vn29i0wXAUBEZiOuHahJwQb111gM/Ap4VkYNwT5LniUgGsFNVq0VkEO6d4h2nw8P4VIhLhY8fgc+fcn1L1VTWXUciILEnJO/n6jwyD3fbfPgg/PcXcM4/whK6Mca0VDDdlB/qNx6H+4FfDDSaMFS1SkSmAnOASOBpVV0mItOAhao6G/gF8KSI3IgrrrpMVVVEjgamiUgVUA1MUdWdDRwqPA69EnI/h6Q+LinUfnpDt14QGeD0RsfBu7+F/U+A7PPaP25jjGkh8XvVRXAbiKQA/1TV00ITUsvk5OTowoUdo8SqUTXV8OwpsHUZTPlwT32IMcaEgYgsUtWcYNYNptK7vhJcEZFpiYhIOPPvbnzWT1wCMcaYfUCTCUNEXhOR2d7wOrAS+E/oQ+vE0jLhlAdg/Sfw4Z/DHY0xxgQlmDqMP/mNVwHfq2puiOLpOrLPg2/nwHu/h0E/hH6HhDsiY4xpVDBFUuuBz1T1fVX9CNghIlkhjaqrOOUBSO4D/3cllO8OdzTGGNOoYBLGy0CN33S1N+pgVmsAACAASURBVM+0Vnyqq8/YuRbe2qtvRmOM6VCCSRhRXl9QAHjjMaELqYvJOhLG3Qhf/BNWvBbuaIwxpkHBJIw8EaltQisipwPbQxdSF3TsbbDfaJh9vXsq3BhjOqBgEsYU4Fcisl5E1gO3AD8JbVhdTFQMnP0UVJXDv6e4rkaMMaaDaTJhqOp3qvoDYBgwXFWPUNXVoQ+ti+kxFE64F9bMh88eC3c0xhizl2Cew7hXRFJVdbeqFolImoj8tj2C63IOuQwOOAXm3QVbvgp3NMYYU0cwRVInqWq+b8J7+97JoQupCxOB0x6G+DR49SrXHboxxnQQwSSMSBGJ9U2ISDwQ28j6pjUSe8AZf4O8Fe5OwxhjOohgEsYLwDsicoWIXAHMBZ4LbVhd3JDxcNgU+OxxWDUv3NEYYwwQ3AuU/iAiS4HxuLfovQVYF6uhNv5uWPsB/PsauPYTd+dRnypU7Ibd26B4OxRv2zNeUQSjL4KeB7Z/7MaYTimYvqQAtuCe9j4PWAu8GrKIjBMdB2c9CU8eBzMvhQGHeckgzw27vc+qBuo5IqLgs7/DcbfDEde7XnKNMaYVGkwYIrI/7i15k4EdwL9w7884rp1iM71HuKa2b97serZN7OHe4pfYA7oPgcQMN3TruWd+t56Q0APK8uH1G2HenfDNf+GMx6DHkHB/I2PMPqzBFyiJSA3wP+AK33MXIrJGVQe1Y3xB22deoNQSFcUQFQ8RzXx9iSp89Qq8cZN7KHD8nTD2J83fjzGm02qrFyidjSuKek9EnhSRH+HqMEx7i0ls2Y+8CGSfC9d+CgOPdh0cPneq6+zQGGOaqcFfIVWdparnAwcC84EbgV4i8piITGin+ExbSN4PLvgXnP4obFkKjx0JC/7h7kCMMSZIwXQNUqyqL6rqRKAfsASwvrj3NSJw8EWuxVX/sfDfn8M/z4D8DeGOzBizj2hWOYeq7lTVv6vqD0MVkAmxlH5w8SyY+CBsWACPHQGL/2l3G8aYJgXbrNZ0JiKQ82MY/EP4z1SYPRVWzIZTH3bFVz41NVC602vOu81ryrsNdm/1G9/mWmblXAH7n2DNd43pxBpsJbWv6dStpEKppgYWPAlz73TdrPfN8UsOeaDVe28TEe2SRLeerlnv1mVQuBFSM2HsVa7oKz6t/b+LMabZmtNKKqQJQ0ROBP4CRAJPqep99ZYPwHUzkuqtc6uqvuEtuw24AvdK2J+q6pzGjmUJo5V2fAdv3eaShC8RdOsJ3XrVfdajWwbEpbq7FJ/qKvjmdfeg4PqPIToBss+Hw34CPQ8K33cyxjSpQyQMEYkEvgWOB3KBBcBkVV3ut84TwBeq+piIDAPeUNUsb3w6MBboA8wD9lcNdLnrWMLoIDYvhc//7p7/qCpzzXkPmwL7n9iy4qrqKtixGrYth/zvoXc2DDgcYhLaPnZjuqDmJIxQ1mGMBVar6hovqBnA6cByv3UUSPbGU4BN3vjpwAxVLQfWishqb3+fhDBe0xb2y3bNd8dPg8XPwYKnYMYFkDoADr0KxlwcuLhK1RVrbV3uksO25W58+0qorqi7bmQM9D8MBh0Lg46DPqOt7sSYdhDKhNEX8G+zmQscVm+du4C3ReR6IBHXwaFv20/rbds3NGGakEjsDkf9HI74Kaz8ryuumvtreO9eGHU+HHgq7FoL21bsSRBlBXu2T+oDvYbB4OOg13BXtJXSHzYtdm8lXDMf3r3HDXEpkHXUngTSfXDdIjNjTJsIZcII9D+2fvnXZOBZVX1ARA4H/ikiI4LcFhG5GrgaYMCAAa0M14REZBQMO90NW75yiePLGbDoWbc8NsUlhhFnQ89he5JDQ5XmQ8a7AVzF/LoPXPL4br6rRwFI7uclj2Nh0DGu/kXVG2oA79M3HWhebBJERofstBizLwplHcbhwF2qeoI3fRuAqv7eb51lwImqusGbXgP8AFfZXbuuiMzx9tVgkZTVYexDSna6J867D4Hkvm1zN6Dq7lh8dx9rP4DSXS3fX2SMS177jYL9Rrtir57DIMreHWY6l45S6R2Fq/T+EbARV+l9gaou81vnTeBfqvqsiBwEvIMrehoGvMSeSu93gKFW6W2CVlPtktK6D6GsECTCG8RLUFJvXoTfPHH1KZu/dIOvqCwi2t397DfKJZD9RrukEh0fzm9qTKt0iEpvVa0SkanAHFyT2adVdZmITAMWqups4BfAkyJyI67I6TJ1GWyZiMzEVZBXAdc1liyM2UtEJPQ52A2toQq71sHmJS55bFriir6++KdbLpGQcaBLIOkDXauu6nKoqnCtxKrLXU/BVeWu8r6qzC3zzdcaV2SW3A+S+0BKX3fXldzXTcelWH2M6TDswT1jmksVCjbsSSCbl7jPku1ueUS0K7qKjIGoOPdAZFScNx1bdxxxT84XboLdW7z6FD/RiV4S6bMnqST3cS/IqqkKPFQ3MD8iyj0jE5Pg9lvnM8H1ihyTuGc8OsFtU1UKlWUBPr2hsrTuZ3WlqwOKTXafcclu3PcZm+zqtppzvqsrobLEHaP2s9S9aMz3LhhrKdciHeIOw5hOS8Q1E04dAAed6uapujuGyJiWv2+kutIlj4KNrkiscKNLJAW57vO7d6BoCwHafwSIMdL92NcOka6YrmJ34Kf321t0Qr2EkuQSXaCkUFnSdMwSAQnd9zxcmtjTrzcC/3m9ILZb44k1UOJFvZiT/BJrYvMSX1NU3V2oql9xaUSHen+NJQxj2oKIu9ptjcho1zlkSr+G16mudP13aU3dZBAZvWdaIhv+kfH9KFUUux/iihKoLPY+S/zmF7uhpsrV0UTFBf8ZEeW2LS909T/lhVBe5OqSygv3fNYZ3+2SbUJ3t5/ohHqfDcyrKPZeWez1ceYb37nGfVaVte7fpClRcRDTzUsi3fYkE99QXekVSZYF/qyuN68h/gmkzhDp/vb6HAyX/Du03xVLGMbsWyKjXRFVS4l4xWKxQHqbhbWXmAR3VR9Oqi5R+ZKIr7PMiuK6Cbb+nVhElN9ybxpcUq0odndpvoS617g37N7qHcdXLOkVRcal1C2m9M33L6qUCL8m3tV7mn7XH2r8xhu7yGhDljCMMZ2TiCvuikt2D3OaVus4hWPGGGM6NEsYxhhjgmIJwxhjTFAsYRhjjAmKJQxjjDFBsVZSxhjThlSVovIqisqq6BYTRVJcFBERnaN7F0sYxphOTVXZVlTOmrxi1m4vJr+0gpjICKIjI4iJcp/RkUJs7fieZTGREURHuR/7gpJKdpVUkl9S4fdZsde8/JJKqmr2PI0fIZCaEENqQjSp8dGkJcSQmhBDWkI0aYluflrCns+oCKGyWqmuUapqarxPN11ZXXfafdaQGh/DcQf2DPm5tIRhjGlX1TXKjuJythWWs62ojG2F5eworiA2KoK0hBjSEqNJTYghPSGGtISYoK/QC0oqWbujmLXbd7M2r5g1212CWLu9mJKKtu8OJSYqwv3oez/2Q3t225MIvLh3l1eRX1LJLi+R7CqpYFNBGcs3F7KrpIKyypqmDxSE0f1TLWEYY8JPValRAlzdetPVe650q2qUssoa8or2JIM9n+VsLSxjR3EF1TXBd3rqf4We5v0gpybEkBofTX5pZW1S2FlcUWeb/ukJDOyRyNiB6QzqkcjAHt0YmJFI98QYKqtrqKxWKqpqqKyuoaK6pna8srqG8iq3vLLKLatRJTXeiyHRxRAfHYm0sifhssrq2kTiSyo1qkRFCFEREURGClERQqRvOsJNR0XWnY6PaZ+OFy1hmHa1fXc5f3//Oz5cvYPM9ASG9urGkJ7dGNoziUEZicRF7xs9jlZW17BySxFfbMjnyw35LNmQz67iCkb1T+WQzDRyMtMY1T+1zb5PWWU132wpYmluPss3FVJdoyTERBIXE0lCdBTxMRHEx0SREB1JfIw3REeS4H3Gx0RSWa0UlFZSWFpJgd+w13SZN15SSUlFdZ3ileYSge6JsfRMiqVnciwH7ZdEz6Q4eiXHkpEUR89kt6xHt1jKKqvZVXs1XsGu4rpX5vmlrshnU34ZyzcVsqukkqS4KAb2SOSE4b0Y6EsKPRIZkJ5ATFTDbXo6yt9ZXHQkvVMi6Z3Syn7I2oklDNMudhVX8MT/1vDcx+soq6zmsIHd+XZbEXNXbK292owQGJCewNBeSQzt2Y2hvVwiGZzRba8rqJoaZWdJxZ4r2KJythX6Pt28rYXlbN9dTkp8NFndE8nsnuANiWR1T2RA9wRS4pt+DauqkrurlCVeYvhyQz5fbSygvMoVJ6QnxjC6fyqj+6fyxfpdvPvNNgCiI4XhfVLIyUwjJyuNQzLTyUhq+o19VdU1fLt1N0tz81m6sYClufms3FJEZbU7T2kJ0cRGRVJaWU1pRTUV1a0r1oiJiiAlPrp26JkUx5CMbqTER5MYG0VUZETtVW50pBAZEeF31St1lkdFCDFREWQkxdIzKY4e3WKIigyuMWZcdCSpCTEMJLFV38eEjr0Pw4RUQWkl//jfGp7+aB3FFVWcmt2HG8YPZXBGNwDKq6pZt72EVduK+HbrblZvK2LV1t2s3V5ce2UrAv3S4slMT6SorJJtReXkFZUHvPJNjouiV7LvytX9YBWUVrJuRwnf7yhma2F5nfXTE2MYkJ5Ali+R9EhgQHoiJRVVLFnvJYjcfLbvdsUdsVERjOibwqh+qYwekMrB/VPplxZfp2hiZ3EFi7/fxcLvd7Ho+518mVtAhZdcMrsneHcg6eRkpTE4oxtrtxe75JDrksOyTYW1ySgpLorsfilk90slu28K2f1T6ZMSV+d4VdU1Lnl4CaS0spqSimrKKtynb35UpNQmhWS/BNFRrrZNeHSIV7S2t86cMApKKklJaPpKuLV2l1chQGJs6288i8oqeeajdTz5vzUUlVVx8sje/Gz8/uzfKymo7Sura/h+RzGrtu5m1TY3rN9RTLJ3BdwzOZZeSbH0TI6jZ1IsvZLjyEiKbfLHr6SiivU7S1i3vYT1O4trE8m67SVsKiil/n+HwRmJjOrvEsPo/mkcuF8S0UFeMfuUV1Xz9cZCFn2/k4XrdrHo+13s8MrbIyOk9g4rPjqSEX2TGdk3lVH9XZLITE/oNE0yTcdkCaMT+dv81fxxzkqmHjeEG340NOjb++Z6dVEuv/7P15RX1XDQfknkZKa7K+GsNPZLCf6d1cXlVTz3yTqe+GAN+SWVHD+sFz8bP5ThfVJCEndbKq+qZsPOUr7fUUxMVATZ/VKDKrJqLlVl3Y4SFq7byeq83QzO6EZ2vxSGZHQL2b+vMQ2xhNFJLNtUwBmPfkTPpDg25peSk5nGXyYfTN/U4H/Am7K7vIpf//trZn2xkbED0xmblc7C73eyZEN+bZO/vqnxtcnjkMw0DuydTGS9q97Simpe/Ox7Hpv/HTuKKzj2gAx+fvz+ZPdLbbNYjTFtz17R2g6ueHYBPZNjuffMka1uWhdIeVU1v5j5JakJMbx+/Tg+WJXH7bO+5qSHPuAP52Rz4oj9Wn2Mr3ILuH76YtbvLOFn44dy/Q+H1iaCyuoalm8qrC2H/3TNDmZ/uQmAbrFRHDwgtbYsfvW2Ih6d/x15ReWMG9KDG4/fn0My01odnzGmY7GE0QK5u0p4x2sJM6xPChf/ILPNj/HQvFV8s6WIZy47lLTEGE4f3ZfR/VO5fvoXTHlhMRf9YAB3nDKsRRWWqsrTH63jvjdX0D0xlulX/YDDBnWvs050ZASj+qcyqn8qV4wbWNtSaKFfOfxf3llVW+Z/2MB0Hpl88F77McZ0HpYwWmDu8q0AjOqfyj2vLSe7bwqj+rdd0cui73fy9/e/Y9Kh/es8vZnZPZFXphzBH+d8w5P/W8vCdbt45IKDGdIzuIpkgB27y/nlK0t595ttjD+oF388J5u0xJgmtxMR+qcn0D89gTMPdq+DLCitZMmGfBJjIjkkMy0kd1rGmI7Datha4O1lW9m/VzeevexQMpJiufbFxezye8q0NUoqqvjFzC/pkxrPHROH7bU8JiqC208ZxjOXH0peUTkT//oh/1qwnmDqoj75bgcnP/w/Ply1nbtPG86TlxwSVLJoSEp8NMfsn0FOVrolC2O6AEsYzbSruILP1+3khOG9SUuM4W8XjiGvqJwbZy6hphVPxPrc9+Y3rNtRwh/PGUW3Rpq3HndAT9684SjGDEjjlle/4vrpX1BYVhlw3arqGv789koueOpTEmOjmHXdEVx6RJb9yBtjmiWkCUNEThSRlSKyWkRuDbD8QRFZ4g3fiki+37Jqv2WzQxlnc7zzzTaqa5QJw3oDrljq16cOY/7KPB59b3Wr9v3hqu08/8n3/PjIgRw+uOm6gJ7JcfzzisP45QkH8ObXWzjl4f+xZEN+nXU25pcy+clPefjd1Zw9ph+vTR23TzRxNcZ0PCGrwxCRSOBR4HggF1ggIrNVdblvHVW90W/964GD/XZRqqqjQxVfS81ZtoU+KXGM6JtcO++iwwawaN1O/jzvWw4ekMa4oT2avd/CskpufuVLBmckcvOJBwS9XWSEcN1xQ/jBoHR+On0J5zz2Mb884QCuOmoQc1ds5eZXllJVXcND54/mjIP7NjsuY4zxCeUdxlhgtaquUdUKYAZweiPrTwamhzCeViutqOZ/q/KYMLx3neIcEeHes0YytGc3fjrjCzYXlDZ733fPXs7WonIeOG90i1o+HZKZzhs/PYrjh/Xi929+w4SHPuAn/1zEgPQE/vvToyxZGGNaLZQJoy+wwW8615u3FxHJBAYC7/rNjhORhSLyqYic0cB2V3vrLMzLy2uruBv0wao8yiprmDCs117LEmKi+NuFh1BeWc11Ly6u7TsoGG8v28Kri3O59tjBjG5Fa6uUhGj+duEYfnvGCLYWlHHluIG8es0RZPWwztyMMa0XyoQRqEa1oVrhScArqur/lpMB3tOHFwAPicjgvXam+oSq5qhqTkZGRusjbsKcZVtIiY9m7MD0gMuH9OzG/edks3h9Pr9/c0VQ+9yxu5xfzfqK4X2Suf6HQ1sdo4hw0Q8yWXrXBO6YOKzRLp6NMaY5Qvlrkgv095vuB2xqYN1J1CuOUtVN3ucaYD516zfaXVV1De+s2MaPDurZaH8/E7P7cNkRWTzz0TpeX9rQ13VUldtnfU1haRV/Pm90m/64WwsoY0xbC2XCWAAMFZGBIhKDSwp7tXYSkQOANOATv3lpIhLrjfcAjgSW19+2PX2+dicFpZW1raMa86uTD2LMgFRueWUp3+XtbnC9/yzZxFvLtvDzCftzQO/gH74zxphwCFnCUNUqYCowB1gBzFTVZSIyTURO81t1MjBD6z55dhCwUES+BN4D7vNvXRUOby/fSlx0BMfs33TRV0xUBI9eOIbY6EiueWERJRVVe62zpaCM3/znaw7JTOOqowaFImRjjGlTIe0aRFXfAN6oN+839abvCrDdx8DIUMbWHKrK28u2cNTQjKDfnbtfSjx/mTSaS57+nNtnfc2fzxtVW0ykqtz86lIqq5UHzh21V8+vxhjTEVmNaBC+3ljIpoKygK2jGnPU0AxuHL8/s77YyIufra+d/+Jn6/ng2zx+dfKB1oLJGLPPsM4Hg/D28i1ECIw/qHkJA2DqcUNYvH4X015bTna/FFLio7n3jRUcNbQHF4Wgl1tjjAkVu8MIwpxlWxg7ML1FHfVFRAgPnjeajKRYrnlhMTf+awmREcL9Z2dbSyZjzD7FEkYT1m4v5tutu4NqHdWQtMQYHr1wDNuKyli8Pp+7Th1OnzZ8a54xxrQHK5JqwtzlWwCYMLz5xVH+RvdP5eFJB/PNliLOGmPddBhj9j2WMJowZ9lWhvdJpl9aQqv3ddLI/ThpZOtfrWqMMeFgRVKNcEVIu1pVHGWMMZ2FJYxGzFu+DVU4YUTriqOMMaYzsITRiLeXb2FAegIH9LJuO4wxxhJGA4rKKvl49Q5OGN7Lmr8aYwyWMBo0f2UeFdU1TBhu9RfGGAOWMBr09vKtdE+MYcyAtHCHYowxHYIljADKq6p575ttHD+sl3UMaIwxHksYAXzy3Q52l1e1+mE9Y4zpTCxhBPD28q0kxkRyxOAe4Q7FGGM6DEsY9dTUKHOXb+XYA3oSFx3cuy+MMaYrsIRRzxcb8skrKrfiKGOMqccSRj1vL99CVIRw7AE9wx2KMcZ0KJYw/LhXsW7l8MHdSYmPDnc4xhjToVjC8LN6227Wbi+2h/WMMSYASxh+5izz3n3RzHd3G2NMV2AJw8/by7cyun8qvZLjwh2KMcZ0OJYwPJvyS1maW2Cto4wxpgGWMDxzl28F4ASrvzDGmIBCmjBE5EQRWSkiq0Xk1gDLHxSRJd7wrYjk+y27VERWecOloYwTXHPawRmJDM7oFupDGWPMPilk7/QWkUjgUeB4IBdYICKzVXW5bx1VvdFv/euBg73xdOBOIAdQYJG37a5QxJpfUsGna3Zy9dGDQrF7Y4zpFEJ5hzEWWK2qa1S1ApgBnN7I+pOB6d74CcBcVd3pJYm5wImhCvTdb7ZRXaNWHGWMMY0IZcLoC2zwm8715u1FRDKBgcC7zdlWRK4WkYUisjAvL6/Fgb69bCu9kmPJ7pvS4n0YY0xnF8qEEehFEtrAupOAV1S1ujnbquoTqpqjqjkZGRktCrKsspr3v83j+GG9iLB3XxhjTINCmTBygf5+0/2ATQ2sO4k9xVHN3bZVCksrOX5YL04Z2ScUuzfGmE4jlAljATBURAaKSAwuKcyuv5KIHACkAZ/4zZ4DTBCRNBFJAyZ489pcz+Q4Hp58MIcP7h6K3RtjTKcRslZSqlolIlNxP/SRwNOqukxEpgELVdWXPCYDM1RV/bbdKSL34JIOwDRV3RmqWI0xxjRN/H6n92k5OTm6cOHCcIdhjDH7FBFZpKo5waxrT3obY4wJiiUMY4wxQbGEYYwxJiiWMIwxxgTFEoYxxpigWMIwxhgTlE7TrFZE8oDvgR7A9jCH01HYuXDsPDh2Hhw7D47vPGSqalB9K3WahOEjIguDbVPc2dm5cOw8OHYeHDsPTkvOgxVJGWOMCYolDGOMMUHpjAnjiXAH0IHYuXDsPDh2Hhw7D06zz0Onq8MwxhgTGp3xDsMYY0wIWMIwxhgTlE6VMETkRBFZKSKrReTWcMcTLiKyTkS+EpElItKl+nwXkadFZJuIfO03L11E5orIKu8zLZwxtocGzsNdIrLR+7tYIiInhzPG9iAi/UXkPRFZISLLROQGb36X+pto5Dw062+i09RhiEgk8C1wPO4VrwuAyaq6PKyBhYGIrANyVLXLPZwkIkcDu4HnVXWEN+8PwE5Vvc+7kEhT1VvCGWeoNXAe7gJ2q+qfwhlbexKR/YD9VHWxiCQBi4AzgMvoQn8TjZyH82jG30RnusMYC6xW1TWqWgHMAE4Pc0ymnanqB0D9tzOeDjznjT+H+4/SqTVwHrocVd2sqou98SJgBdCXLvY30ch5aJbOlDD6Ahv8pnNpwQnpJBR4W0QWicjV4Q6mA+ilqpvB/ccBeoY5nnCaKiJLvSKrTl0MU5+IZAEHA5/Rhf8m6p0HaMbfRGdKGBJgXucob2u+I1V1DHAScJ1XPGHMY8BgYDSwGXggvOG0HxHpBrwK/ExVC8MdT7gEOA/N+pvoTAkjF+jvN90P2BSmWMJKVTd5n9uAWbjiuq5sq1eG6yvL3RbmeMJCVbeqarWq1gBP0kX+LkQkGvcj+aKq/p83u8v9TQQ6D839m+hMCWMBMFREBopIDDAJmB3mmNqdiCR6lVqISCIwAfi68a06vdnApd74pcB/whhL2Ph+ID1n0gX+LkREgH8AK1T1z36LutTfREPnobl/E52mlRSA1yTsISASeFpVfxfmkNqdiAzC3VUARAEvdaXzICLTgWNxXTdvBe4E/g3MBAYA64FzVbVTVwg3cB6OxRU9KLAO+ImvHL+zEpFxwP+Ar4Aab/avcOX3XeZvopHzMJlm/E10qoRhjDEmdDpTkZQxxpgQsoRhjDEmKJYwjDHGBMUShjHGmKBYwjDGGBMUSximQxIRFZEH/KZv8jrPa4t9Pysi57TFvpo4zrle76DvtcG+ponI+CbWuUtEbgowP8u/11pjWsoShumoyoGzRKRHuAPx5/WKHKwrgGtV9bjWHldVf6Oq81q7n5Zo5nc2nZglDNNRVeHeOXxj/QX17xBEZLf3eayIvC8iM0XkWxG5T0QuFJHPvfeDDPbbzXgR+Z+33kRv+0gR+aOILPA6Y/uJ337fE5GXcA8+1Y9nsrf/r0Xkfm/eb4BxwOMi8sd66x8rIvNF5BUR+UZEXvSexEVEDvG+wyIRmePXfUXtdxaRk73tPhSRh0Xkdb/dD/P2vUZEfuo3P0pEnvO+1ysikuDt60ci8oUX/9MiEuvNXycivxGRD4FzReSnIrLc235GEP9+pjNSVRts6HAD7l0OybinT1OAm4C7vGXPAuf4r+t9HgvkA/sBscBG4G5v2Q3AQ37bv4W7YBqK64csDrgauMNbJxZYCAz09lsMDAwQZx/ck8IZuCfr3wXO8JbNx72XpP42xwIFuP7OIoBPcMklGvgYyPDWOx/XY0Htd/bi3OCLBZgOvO6N3+VtH4t7wnuHt88s3JO8R3rrPe2dT9++9vfmP4/rlA7vvN/sF/MmINYbTw3334cN4RnsDsN0WOp603we+GlT6/pZoK7v/3LgO+Btb/5XuB9On5mqWqOqq4A1wIG4frcuEZEluK4juuMSCsDnqro2wPEOBearap6qVgEvAsH0Dvy5quaq6/RtiRfbAcAIYK4Xwx24pOLvQGCNXyzT6y3/r6qWq3t51jaglzd/g6p+5I2/gEtQBwBrVfVbb/5z9WL/l9/4UuBFEbkId/dnuqCocAdgTBMeAhYDz/jNq8IrTvWKcmL8lpX7jdf4TddQ9++9fp84iusi/3pVneO/QESOxd1hBBKoW/1gP3Sq9wAAAYhJREFU+MdZ7cUmwDJVPbyR7Zo6XqD9QsPftzH+3/kUXDI5Dfi1iAz3EqTpQuwOw3Ro6jqEm4mrQPZZBxzijZ+OK3ZprnNFJMKr1xgErATmANd43UAjIvt7Pf425jPgGBHp4VUOTwbeb0E8eDFkiMjh3vGjRWR4vXW+AQaJewkOuGKrYAzw7deL8UNvX1kiMsSbf3Gg2EUkAuivqu8BNwOpQLcgj2s6EbvDMPuCB4CpftNPAv8Rkc+Bd2j46r8xK3E/jr2AKapaJiJP4YqGFnt3Lnk08epOVd0sIrcB7+Gu2N9Q1RZ1la2qFV7F9sMikoL7//kQsMxvnVIRuRZ4S0S2A58HufsVwKUi8ndgFfCY950vB14WkSjcKwIeD7BtJPCCF5MAD6pqfku+o9m3WW+1xuxjRKSbqu72ktqjwCpVfTDccZnOz4qkjNn3XOVVii/DtSD7e5jjMV2E3WEYY4wJit1hGGOMCYolDPP/7dWBAAAAAIAgf+sVBiiJABZhALAIA4BFGAAsAfIZGW/RYCY+AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Generate plot\n", - "plt.title('k-NN Varying number of neighbors')\n", - "plt.plot(neighbours, test_accuracy, label='Testing Accuracy')\n", - "plt.plot(neighbours, train_accuracy, label='Training accuracy')\n", - "plt.legend()\n", - "plt.xlabel('Number of neighbors')\n", - "plt.ylabel('Accuracy')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 170 - }, - "colab_type": "code", - "id": "AKG1zgjxPcPE", - "outputId": "eac2fe99-1db9-4160-e3ff-da85bb9c2459" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.78 0.98 0.87 4645\n", - " 1 0.52 0.08 0.14 1355\n", - "\n", - " accuracy 0.78 6000\n", - " macro avg 0.65 0.53 0.50 6000\n", - "weighted avg 0.72 0.78 0.71 6000\n", - "\n" - ] - } - ], - "source": [ - "# best k:\n", - "idx = np.where(test_accuracy == max(test_accuracy))\n", - "k = neighbours[idx][0]\n", - "knn = KNeighborsClassifier(n_neighbors=k,algorithm=\"kd_tree\",n_jobs=-1)\n", - "knn.fit(X_train,y_train.ravel())\n", - "print(skmt.classification_report(y_test,knn.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0LZH_AAXUL72" - }, - "source": [ - "Despite a reasonably high accuracy, the KNN model has very low recall for defaults." - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the KNN identified 108\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
04545100
11247108
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4545 100\n", - "1 1247 108" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, knn.predict(X_test), \"KNN\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "H89tM6NvaN17" - }, - "source": [ - "### Model 2 - Decision Trees\n", - "\n", - "#### Theory:\n", - "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", - "\n", - "Below is a binary decision tree that has been split for a few iterations.\n", - "\n", - "![image.png](https://elf11.github.io/images/decisionTree.png)\n", - "\n", - "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", - "\n", - "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", - "\n", - "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", - "\n", - "#### Training\n", - "We will now construct a simple decision tree using the GINI index." - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False,\n", - " random_state=None, splitter='best')" - ] - }, - "execution_count": 144, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree = DecisionTreeClassifier()\n", - "tree.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 18719\n", - " 1 1.00 1.00 1.00 5281\n", - "\n", - " accuracy 1.00 24000\n", - " macro avg 1.00 1.00 1.00 24000\n", - "weighted avg 1.00 1.00 1.00 24000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, tree.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the Decision Tree (GINI) identified 560\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03801844
1795560
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3801 844\n", - "1 795 560" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 1.0\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.82 0.82 4645\n", - " 1 0.40 0.41 0.41 1355\n", - "\n", - " accuracy 0.73 6000\n", - " macro avg 0.61 0.62 0.61 6000\n", - "weighted avg 0.73 0.73 0.73 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, tree.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random Forest Classifier\n", - "\n", - "#### Theory\n", - "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", - "\n", - "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", - "\n", - "#### Training\n", - "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "randf = RandomForestClassifier(n_estimators=300)" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_jobs=None, oob_score=False, random_state=None,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "randf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 18719\n", - " 1 1.00 1.00 1.00 5281\n", - "\n", - " accuracy 1.00 24000\n", - " macro avg 1.00 1.00 1.00 24000\n", - "weighted avg 1.00 1.00 1.00 24000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, randf.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.84 0.94 0.89 4645\n", - " 1 0.65 0.37 0.47 1355\n", - "\n", - " accuracy 0.81 6000\n", - " macro avg 0.74 0.66 0.68 6000\n", - "weighted avg 0.79 0.81 0.79 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, randf.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the Decision Tree (Random Forest) identified 501\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
04376269
1854501
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4376 269\n", - "1 854 501" - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.25666666666666665\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradient Boosted Trees Classifier\n", - "\n", - "#### Theory\n", - "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", - "\n", - "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n", - " \n", - "#### Training\n", - "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=4,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_iter_no_change=None, presort='auto',\n", - " random_state=None, subsample=1.0, tol=0.0001,\n", - " validation_fraction=0.1, verbose=0,\n", - " warm_start=False)" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import GradientBoostingClassifier\n", - "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", - "xgb.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.96 0.91 18719\n", - " 1 0.77 0.44 0.56 5281\n", - "\n", - " accuracy 0.85 24000\n", - " macro avg 0.81 0.70 0.73 24000\n", - "weighted avg 0.84 0.85 0.83 24000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, xgb.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.84 0.94 0.89 4645\n", - " 1 0.66 0.37 0.48 1355\n", - "\n", - " accuracy 0.82 6000\n", - " macro avg 0.75 0.66 0.68 6000\n", - "weighted avg 0.80 0.82 0.79 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, xgb.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.23767613676167343\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gU1dfA8e9JAgkhCS2hh957E5AuRYogihRBURFfRbAjdkVFRdSfFRQ7NgQFFQREqoJYKIL0XkMvgfR+3z9mEjY9QDa7m5zP8/CQ6WfKzpm5c+eOGGNQSimlrpSXqwNQSilVOGhCUUoplS80oSillMoXmlCUUkrlC00oSiml8oUmFKWUUvmi0CcUEblFRJa4Og5XE5FqIhIlIt4FuMwaImJExKeglulMIrJNRLpdxnSXdQyKSAkR+VlELojI95c6fUHK6/FVWH+PIuInIjtFpLyrY8lvItJKRFbnZdwCTSgiclBEYu0D74SIzBCRAGcu0xjzjTHmWmcuwx3Z27pnarcx5rAxJsAYk+zKuFzFTmx1rmQexpjGxpjfcllOpiR6BcfgYKACUM4YM+Qyps8YWzcRSbF/f1EiEiYi34nIVVc677weX874PdpJKnWdYjOsY1R+LisH9wLLjDGn7Jjmicj7GeL8WUTeduiuLCKfisgxO9Z9IvKZiNS3h9exj6XUdTkhIlOdeYEmIj72Mmuk9jPG/AvEikjf3KZ3xR3KAGNMANACaAk86YIYrpgrr7oLyxX/pSii27s6sNsYk3SpE+YQ8zH79xcItAd2AqtFpMflh+ladpIKsNerL/Y6OvRLx0n78x7gK4fue4GbRaSLvcxbgMbA03Z3CPA3UBzohLU/2gBrgJ4O88FhPVoAXYAxTog/N99grWPOjDEF9g84CPR06H4NWOjQ7Qu8ARwGTgLTgRIOwwcCm4AIYB/Qx+5fCvgUOA4cBV4CvO1hdwB/2H9PB97IENM84BH778rAXOA0cAB4wGG854E5wNf28u/KYv1KAV/a0x8CngG8HOJYA7wHXMD6IffIMG1O67AGeAs4Zw+rDawAzgJn7B1e2h7/KyAFiAWigMeAGoABfOxxfgMm2fONBJYAwQ7x3Gavw1ng2Yz7LsN6lwD+Z49/AfjD7pe6zNvtfXoGeNphurbAX8B5e72nAsUdhhtgHLAHOGD3ewc4Yu+DDUBnh/G9gafsYyPSHh4KrLLnFW1vj2H2+P2xjqfzwJ9AswzH6uPAZiAe8HHcBnbs6+04TgJv2v0P28uKsv9djcMxaI/TGFhq78uTwFNZbNMXgAQg0Z7PaKwLwGfs7XwK61grZY+fuq1H2zGsymKe3YCwLPpPBdY7dDdwiG8XMPQS9nXq8XUHsN/eDweAWzL+Hu3uDsA6e17rgA4Ow34jh2M0m2Mxu3UMAyYAW4AEu19V4Ecu/t7HOYzvxcVj6QwwCyiTzTJrYR1b3hn63wXsxrowOE36c9+rWMen5LAudQCTod+bwPsZjqXfsY7hLcB1DsNKY52vTmMdu0+mLg+oh/W7uGCv30y7/5+k/63cZPevbvcrluP2z2syyI9/pP9BVrU3wDsOw98G5gNlsTL2z8Bkhx/wBaCXvbOrAA3sYT8BHwIlgfLAWuCejAcwVnY/4rBRy2CddCvb89wAPId11VAL6wfR2x73eawf9w32uCWyWL8vsRJUINYPbDcw2iGOJOBhoBgwzF6fsnlchyTgfqwTWwn7YOuFlYRD7IPj7ay2dYYTjmNC2WcfWCXs7lftYY3sg6mTvS3esNc9u4QyzZ6+CtZJvYMdV+oyP7aX0Rzr5NzQnq411lWyjz3uDuAhh/karBNb2dTtDdwKlLOnGQ+cAPzsYaknjPqA2Msr5zCvOg7zboV1Um5nx3y7vc18HbbfJqyEVCLjNsVKhCPtvwOA9llt5yyOwUCs5Dke8LO722WzXZ8HvnbovhPYi3VsBgA/AF9lWO6XWMdQVsdnN7I+2XbHugApaf87Aoyyt3ErrBNO4zzuax97HhFAfXuaSg7TO26LskA4MNKebrjdnbrPfiObYzSHc0x26xiG9fuuas/L296/T2Ed43Xs/dvDHv9RrERWxd5Pn6Zu6yzmPRD4L5thy+zt92mG/uuBZ3JZl3QJxY5lK3Cb3V0cKxE+hnVO6Yn1u61jD59pHyOB9jGzF7jdHvY91gWTl71+He3+PvZ+rJFFPDFAoxxjvpzEcLn/7B0WhXW1YYDlXLyqFqwMWNth/Ku5eGX6IfBWFvOsgHWScryTGQ6szOIAFqyrty529/8BK+y/2wGHM8z7SeBzhx93pqs+h3G97TgaOfS7B/jNIY5jOFyRYCWNkXlch8PZLdse5wZgY4ZtnVtCecZh+Fhgsf33c8C3DsP8sa6WMyUU+4CMBZpnMSx1mVUzrPPN2azDQ8CPDt0G6J7LeoenLhvranpgNuNlTCgfAJMyjLML6Oqw/e7M4vhNTSirsO4igjOMk247Z3EMDnfcT7ms2/OkTyjLgbEO3fWxEr2Pw3Jr5TC/bmR9sm1gT1sF60JndYbhHwIT87ivUxPKeeAmMiS2DNtiJLA2w/C/gDtyO0YvYx3DsE/EdndHYH+GcZ4FPrb/3pN6LNjdoVi/Ua8s5n07DnddWexDA1yTxbF0l0P3IHubRQKL7H517GnP2/8MsBoIsIdfg1Wa4XhO+R7rLrYY1kVoPYdh47Ce84CVbD4AqmSIK6eEchKHO8is/rniGcoNxphArB3fAAi2+4dgnbg2iMh5ETkPLLb7g7VD92Uxv+pYG++4w3QfYl3lp2OsrTIL60cNMAKrqCh1PpVT52HP5ymsk32qIzmsVzDWFcMhh36HsH6kqY7aMTgOr5zHdUi3bBEpLyKzROSoiERg3doGc2lOOPwdg3XVix1T2vKMMTFYRV9ZCca6wslq3+S4HBGpJyIL7IeNEcArZF6HjOs9XkR22DWfzmMVFaZOk90xkpXqwPgM+zsUa92zXHYGo7GunHeKyDoR6Z/H5V5KjBlVJvPx5UPej9HsVOHiias60C7DdrkFqEje9jXGmGisxDQG65heKCIN8rA+kPk3k90xejkct011oFqG9XwMaz0BqgE/OwzbgrWNsqrFFY51F5COvc4PYZ2438zw7OYs1p0bAMaYH4wxpbHusos7zscYU9oeVhKrWPAXe1BlrAvNjOeUKnac3mR/PhqPdc5ZLyJbROT2LNYro0CsYyRbLqs2bIz5HZiBVZwC1m1hLNatcWn7Xylz8aHaEaznBhkdwbpyCHaYLsgY0zibRX8LDBaR6lh3JXMd5nPAYR6ljTGBxph+jmHnsEpnsK4Wqzv0q4Z1BZGqiohIhuHH8rgOGZc92e7XzBgThFUUJDmMfymOYxUNAFb1VaxipqycAeLIet/k5gOsZ0l17XV4ivTrAA7rISKdsW7Th2KVZ5fGKjZMnSa7YyQrR4CXM+xvf2PMt1ktOyNjzB5jzHCsH+4UYI6IlMxpmsuIMaNjZD6+krCuHNNCu4z53gj8ayeCI8DvGbZLgDHmXi5hXxtjfjXG9MI6ae7EKvbMbX0g828mPzlumyPAnix+7wPs4WFArwzD/YwxJzLPls1Abccq0yLiBXyCdX67H+vc8KjDNMuBGzOcD3IO3rqw+wLoJCKlsbZfaBbnlKNYxbnJZHM+MsYcN8bcZYyphHXn8pGI1CSb48c+X4J155YtV7+H8jbQS0RaGGNSsA66t1LrcotIFRHpbY/7KTBKRHqIiJc9rIEx5jjWw7r/iUiQPay2iHTNaoHGmI1YD6k+AX41xqRm3LVAhIg8Llb9f28RaZLXKpXGqi75HfCyiATaO+ARrDuHVOWBB0SkmIgMARpi3d5e0jrYArGKD8+LSBWsKxtHJ7HKTS/HHGCAiHQQkeJYRTtZHvj2fvsM6wqssr3drhYR3zwsJxCrrD3Kvpq7Nw/jJ2HtPx8ReQ4Ichj+CTBJROqKpZmIpCbCjNvjY2CMiLSzxy0pIteJSKYrzayIyK0iEmKvf+oxlGzHlkL2234BUFFEHhIRX/tYaZeXZWJdDD0sIjXFqm7/CjDbXF4tMLF/QxOxHh4/5RBfPREZaR+nxUTkKhFpmNd9LSIVROR6O8HGYx2nWVUnXmQva4RY1VWHYT2/W3Cp63MZ/gIS7DteP3tdmopIa3v4dOAVEakGaSUC12c1I2PMQayi9NYOve/DOjan2OeG0cCTIlLXHv4G1vngS3t/iogEYT33y5K9nUdilXSkViRJwrrTLiYi3YF+wHfGmESs3/ErIhJgJ4uHsc9HIjLUPm/AxeK0ZDvWs2Q+frtiFZclZhcfuDihGGNOYz1EfNbu9TjWg6O/xSoCWYZVTowxZi3Wg8K3sK5Kf+di9r0N6zZxO9bt5xwcbiez8C3WA6yZDrEkAwOwquYdwLoa+wSrSCWv7sd6DrQfq/bLTKwfYKp/gLr2vF8GBhtjUouSLnUdXsB6YHoBWIj18M3RZOAZ+5b90YwT58QYs81el1lYdyuRWFc88dlM8ihWkcA6rJpBU8jbsfUoVrFjJNYJfnYu4/+Kdbu/G+v2PY70xRhvYiX1JViJ6lOsB7BglWV/YW+PocaY9VjP0KZibe+9WOX7edUH2CbWew7vYD0XirOvIl8G1tjLau84kTEmEqsyxQCs4pw9WGXhefEZVg2+VVjHaBzWfroUle2Yo7D2V1OgmzFmiUN81wI3Y10Bn8Dan6lJIy/72gurSOWYPU5XrOcf6djHfn973LNYRU79jTFnLnGdLpmdhPthVfY5iPWb/JCLFyhvYhW5LxeRSKyTd04Xlx9inewR6x2OSVgVchLt5W0B3gU+FhEx1vsq7bESwl9Yv4F/sYoUxznOWC6+T3MCK2kNtOcZj3UcDbTjfxcYYYzZbU86FuvZ5wGs8+UXWOdbsEpn1olINNa5Y5wx5rA9bCIw0z5+B9n9bsFKsjlKre2knExE7sB6CNfJ1bFcKvtq+DxW0dQBV8ejlLsRET9gI9aD/FOujic/iUhL4L28nLtcXeSl3JSIDBARf7vY4g2sq9KDro1KKfdk3502LGzJBKzHBHm9ENaEorIzEKvI4hhWMd3NRm9nlVI50CIvpZRS+ULvUJRSSuULj2tkMDg42NSoUcPVYSillEfZsGHDGWNMSO5jXj6PSyg1atRg/fr1rg5DKaU8iohkbJUg32mRl1JKqXyhCUUppVS+0ISilFIqX2hCUUoplS80oSillMoXmlCUUkrlC6clFBH5TEROicjWbIaLiLwrIntFZLOItHJWLEoppZzPmXcoM7Ca+M5OX6w2ouoCd2N9bEkppVQ+MsYQl5jV52jyn9NebDTGrLK/C5CdgcCXdoODf4tIaRGpZH9sSimlVA6i45P4ZesJzsck4O0lrD8Yjm8xL5ZuO4kIlCjujbcIxy7EFVhMrnxTvgrpP44UZvfLlFBE5G6suxiqVatWIMEppZQ7WLnrFGv2nMG/uDcfrtpPzeCS7DwRme343l6Ct5fQpmpp/l13jKgjEZQu718gsboyoWT1Sdksmz42xnwEfATQpk0bbR5ZKVVo7T8dxcpdpzkVEceHq/ZnGr7zRCS9GlXgbFQ8LULLcHPbUEICfPH2FoL8igFWMVebNh+zd9cZXnzxGh54oB3FvnB+7K5MKGFAqEN3VaxvbyilVJFwKiKOfaej+WnjUfaejmLbsQvEJaZkGm/23e1pV6tcrvP7888jNG1ansBAXz75ZADBwf6Ehl7KV8yvjCsTynzgPhGZhfV94wv6/EQpVRgdPR9LQlIK8UnJfP7HQTYfvcD5mASOZ/F8w0vguf6NuLZxRcoFFMfXxzvX+Z89G8MTTyzjk082MnFiV55/vhstW1ZyxqrkyGkJRUS+BboBwSIShvXh+2IAxpjpwCKgH7AXiAFGOSsWpZRytvDoBBZuOc6ek5H4Fffmn/3nCA4ozrId2X8VuHloaa5rWpHGlUvRoXY5RLJ6EpA9Ywxffvkfjz66lPDwWCZM6MCECR2udFUumzNreQ3PZbgBxjlr+UoplZ+MMZyNTuC/I+dJSjH8te8sS7adQEQ4ej422+lqh5QkOj6Zx/rUx0usB+Zd64ekPe+4Eo8/vozXX/+TDh1CmT79Opo2rXDF87wSHvc9FKWUym8JSSlMW7mXC7GJFPO27hL2n45m+c5TVC/nz6GzMdlOW6KYN/UrBFK9nD+NK5fihpaVCS3jj5fXpd1t5FVsbCLR0YkEB/szenRL6tYty+jRrZy2vEuhCUUpVeglJlsPuo2BNXvP8OGqfRw4E03FUiUQYNOR82nj+hf3TjdNgK8P1zevzOnIeFpUK01wgC9NKgcR4OdD5VIlKFOyeIGtx+LFexk3bhEtWlRk7tyh1K8fTP36wQW2/NxoQlFKebyUFMPpqHi+/Osgy3ecokKQH2v2nqFCkF+OxVE+Xl7ULh9Al3ohxMQn8cGtrQkJ9C24wPPo2LFIHnpoMd9/v5369ctx331XuTqkLGlCUUp5nJQUwz8HzvHB7/vYfuwCZ6IS0g0/Gh5L/YqBGAPtapblfGwiraqVBiA8JpH+zSrRIrT0JT8Ed4Xly/dz442zSUhIZtKka5gwoQO+vu556nbPqJRSypacYpj/31EuxCSy9uA59pyMYs+pqHTjVAzyo0GlQLrWC2Fom1BKuukJ91IkJiZTrJg3zZtXpF+/urz0Unfq1Cnr6rBy5PlbXSlVqExdsYdVu8+w9uA5RKznHhmFBPpStUwJ7rumDt0blPeIO428ioiI59lnV/DPP0dZs+ZOgoP9mTVrsKvDyhNNKEqpAnXsfCzbj0Ww/XgEf+w9Q1xiMgG+PiQlG9YePJc2XqCvD6X8i3Fd00pEJyRxY8uq1AkJoJT/lVe3dUfGGObM2c6DDy7mxIkoxo69ivj4ZPz9PeezVZpQlFJOcex8LH/sOcMPG8MI8C3Gsh0nsx23YaUgShb3plnVUhTz9uKRXvXoWMd9ai852+nT0dx++0/88steWrasyLx5N3PVVVVcHdYl04SilMo3H6/azwe/7+NcdEKmYaFlSxCbkEyvRhVpXDmIplVKUTOkZL684OfpgoJ8OXMmhrff7s24cW3x8fGcuxJHmlCUUpdl54kIZv5zmC//OpRpWKCfD74+XjzQoy7ta5WjXoVAF0To3latOsTLL69m7tyhBAQU5++/73KLlxOvhCYUpdQl2X4sgnmbjmZqWn3cNbWJjEti2FWhNK5ccC3cepozZ2KYMGEpM2ZsokaN0hw8eJ4mTcp7fDIBTShKqTw6H5PA43M38+u2i89C7ulSi/u61yFQi61yZYzh8883MWHCUiIi4nnyyU4880wX/AtRJQNNKEqpLBljNYA44pN/Mg17rE99hrQOdcu3yt3Z119vplGjEKZPv47Gjcu7Opx8pwlFKYUxVtMlxsDirSf493A48zZd/N5dxSA/2tcqS6PKQfRtUonQsgXzSVlPFxOTyCuvrGbMmDZUrRrE3LlDKVXKr1AUb2VFE4pSRVhEXCLLd5zkk9UH2HYsItPw0v7FeLpfQ4a0Cc1iapWTRYv2MG7cIg4ePE+VKoHce+9VlClTwtVhOZUmFKWKmOQUw8mIOJ6bty3TuyEv39iEmPhkrmtWicqlC/fJz1nCwiJ46KHFzJ27g4YNg/n99zvo0qW6q8MqEJpQlCrEEpNTeG/FXgT493A4q/ecyTRO13ohPH1dQyqXLkFAIWgDy9VefnkVCxfu4ZVXujN+fAeKF8/9E76FhZisGspxY23atDHr1693dRhKuaU/953h41X78fby4nRUPP85fOcjVYvQ0rQILU21sv7c2LJKgX7Po7Bau/YoJUr40LRpBc6ejeHChXhq1Srj6rDSEZENxpg2zlyGXo4o5YHiEpPZePg8Ly/azrmoBAL8fNh9MirTeA0qBpKQlMLy8V0LVQOK7uLChTieemo5H3ywnv796zF//nDKlfOnXLmiWWlBE4pSHiI5xXDdu6vZeSIy07AyicXo3bgCJyPief76xrQILe2CCIsOYwyzZ2/j4Yd/5dSpaO6/vy2TJnV3dVgupwlFKTcXm5DMSwu3880/h9P6Bfr5MLh1VXo1qsDVtcrp3UcB+/rrzdx220+0aVOZBQuG07p1ZVeH5BY0oSjlhowxfPD7Pn7deoL/wi6k9b+qRhm+/b/2+Hh7ZuOBniw+Pon9+8Np2DCEoUMbk5SUwm23Ncdb90UaTShKuYHo+CTiEpOJTUxm8i87Wbj5eLrhE3rXp1v9EG0jy0VWrjzAvfcuJCYmkT177sfX14dRo1q6Oiy3owlFKRdITE7hk9UH+PdwOEu3Z/+dkH+f7UWpEsXwLqRvVru7U6eiefTRJXz11WZq1SrDRx8NcNvvubsD3TJKOVlKimHSwu18vuYgoWVL4CXCobMxmcYb36te2tcIR7avrs9FXGzv3nO0bfsxUVEJPP10Z55+ujMlShSehhydQROKUk5gjOHg2Ri++fsQn/xxIK1/YpKhfa0yNKtampQUw/+GNsevWNF58c0TRETEExTkS+3aZRg9uiV33tmShg1DXB2WR9CEolQ+Wrb9JHd9mfnF2yqlSzDr7vbaqKIbi45O4MUXf+fjj/9l8+Z7qVo1iNdfv9bVYXkUTShKXQZjDGHhsczZEMbe01Es3HycYt5CYrLV8kT1cv7UqxBI57rBDG5dFf/i+lNzZz//vIv77vuFw4cvMHp0y0L1jZKCpEe5Unm0cPNxTkbEMW/T0XRVeVM1qBhE+1plaVKlFANbVHFBhOpSJSWlMHTo9/z4404aNw5h9epRdOpUzdVheSxNKErlIC4xmYNno3nw203sOpn+DfVaISW5+apQhretRoCvjz5E9yDGGEQEHx8vKlUK4NVXe/Dww1cXqYYcnUETilIZfLf+CPM2HWXN3rOZhs38v3Y0rVJKP3nrwf7+O4xx4xbx8ccDaNWqEtOmXefqkAoNTShKAfFJyby5ZDcfrtqfrr9fMS/u6VKbmsEl6d24IiX0CtZjhYfH8tRTy/nwww1UrhxIeHisq0MqdJyaUESkD/AO4A18Yox5NcPwasAXQGl7nCeMMYucGZNSAEnJKWw7FsEdn68lPCYx0/AlD3ehXoVAF0SmnGH27K088MBizpyJ4aGH2vPCC90IDPR1dViFjtMSioh4A9OAXkAYsE5E5htjtjuM9gzwnTHmAxFpBCwCajgrJqUAJi/akelOpHloaRpUCOSlG5tQTNtmKnR27jxDjRqlWbz4Flq2rOTqcAotZ96htAX2GmP2A4jILGAg4JhQDBBk/10KOObEeFQRFhWfxJDpf7Hj+MXvpnesU46R7avTu3FFfaBeyMTFJTFlyh+0alWJAQPq89RTnXnmmS7akKOTOTOhVAGOOHSHAe0yjPM8sERE7gdKAj2zmpGI3A3cDVCtmlbpU3ljjGHvqShe/3UXSxzay6pXIYCJAxrTsU6wC6NTzrJs2X7Gjl3Inj3nGD/+agYMqE8xbY2gQDgzoWR1yZfxe8PDgRnGmP+JyNXAVyLSxBiTkm4iYz4CPgLrE8BOiVYVCsYYYhOTOXIult5vr0o3rFejCrw3vKU2dVJInTwZxSOPLGHmzC3UqVOWJUtupVev2q4Oq0hxZkIJA0IduquSuUhrNNAHwBjzl4j4AcHAKSfGpQqp5BRD7acy1+l4bXAzrqlfnhB9CFuoLV26nzlztvPcc1148snO+PlpJdaC5swtvg6oKyI1gaPAzcCIDOMcBnoAM0SkIeAHnHZiTKoQOh+TwLSVe/l49cVGGB/oUZc65QPo3qA8AdrceKH1338n2LPnHIMHN+KWW5rSsWMoNWuWcXVYRZbTfmnGmCQRuQ/4FatK8GfGmG0i8iKw3hgzHxgPfCwiD2MVh91hjNEiLZUngz/4k81hF0hITldCyq6X+uDro8VahVlUVAITJ67knXf+oUaN0txwQwN8fLw0mbiYUy/d7HdKFmXo95zD39uBjs6MQRUekXGJLN56gm3HIpjx58G0/l3qhVCjnD+P9q5PkL7BXuj99NNO7r//F8LCIrj77lZMntwTHx+tveUOtCxAub3kFMPaA+cY/vHfmYb999y1aR+lUoXfli0nufHG2TRtWp7ZswfToUNo7hOpAqMJRbm1rB60rxjflYql/LRJ+CIiMTGZ1asP0717TZo2rcDChSPo1auWVgV2Q/qLVG7r1k/+4Y+9Z9K6f76vE02qBOlLiEXIn38eYcyYBWzbdppdu+6jTp2y9OtX19VhqWxoQlFuZ+XOU4yasS6t++pa5fj0jjZ6R1KEnDsXyxNPLOPjj/8lNDSIH34YSp06ZV0dlsqF/kKV2zDGsPtkVLpksmrCNVQrp5/NLUri4pJo0WI6x45FMn781Tz/fDcCAoq7OiyVB5pQlMudiozjf7/uZvb6iy31DGhemfeGt3RhVKqghYVFULVqEH5+PkyadA0tWlSkefOKrg5LXQJNKMolDp6J5vUlu1i4+Xi6/sV9vHh7WAv6NdUWYYuK2NhEJk/+gylT1jBnzhAGDKjP7be3cHVY6jLkKaGISHGgmjFmr5PjUYVUcoph1rrD/PjvUdYfCk/r7yXgX9yHh3rW5db21bWdrSJmyZJ9jB27kH37wrn11ma0bVvF1SGpK5BrQhGR64A3geJATRFpAUw0xtzo7OCUZ4tLTObLvw7yyqKdmYaVD/TlxYFN6N24gtbaKqLuv38RU6euo27dsixbNpIePWq5OiR1hfJyh/IiVrPzKwGMMZtEpI5To1Ie71x0Aq0mLU3Xr0eD8jx9XUNqBpfUJFJEJdvN5Hh7e9G+fVWCg/15/PFO2pBjIZGXvZhojDmf4QSg7W2pLBlj+HXbScZ8vSGt39qne1A+0M+FUSl38O+/xxkzZgEjRzbj/vvbccstzVwdkspneUkoO0RkKOBltxz8IJC5DQxV5G09eoH+7/2Rrt+Byf30bqSIi4yM57nnVvLuu2sJCfGnUqVAV4eknCQvCeU+4DkgBfgBq/XgJ50ZlPIsJy7E0X7y8nT95o3rSPPQ0i6KSLmLJUv2ceed8zh2LJIxY9rwyis9KF1a71YLq7wklN7GmMeBx1N7iMggrOSiirjNYee5fuqatO5pI3i6vocAACAASURBVFrRo2F5ra2lAChe3Jvy5Usyd+5Q2rWr6upwlJNJbp8fEZF/jTGtMvTbYIxp7dTIstGmTRuzfv16Vyxa2eKTknlt8S5m/HmQ5BTr+KlTPoBlj3R1cWTK1RITk3nzzb+IiIjn5Zd7AJCSYvDy0mJPV7PP222cuYxs71BEpDfW53mriMibDoOCsIq/VBH0zE9b+Prvw+n6TbqhCSPbV3dRRMpd/PHH4bSGHIcMaZSWSDSZFB05FXmdArYCccA2h/6RwBPODEq5lx3HI4iKT+Ld5XtYvcdq/Te0bAl+f/QaPVkozp6N4fHHl/HppxupVq0UP/88nP7967k6LOUC2SYUY8xGYKOIfGOMiSvAmJSbyOpbJAD/PNWDCkH6YFVZzp6NZdasrTz2WAeee64rJUtqQ45FVV4eylcRkZeBRkDaWcQYo5cghdTeU5F8+sdBvl17sWhr2ohWBPr50KBSoL5Totix4zTffbeNiRO7Ua9eOQ4ffpiyZUu4OizlYnlJKDOAl4A3gL7AKPQZSqEUl5jMVS8tIzI+Ka1fkJ8Pa5/uqbW2FAAxMYm8/PIqXn/9TwICijN6dCuqVg3SZKKAvCUUf2PMryLyhjFmH/CMiKx2dmCqYJyKjGPH8Uj+74v1JCRfvE545+YWtKtZjoql9G5EWRYv3svYsQs5cOA8t9/enNdf70VISElXh6XcSF4SSrxYrzrvE5ExwFGgvHPDUs52LjqBT//Yz7SV+9L1H9y6Ks9f35gAX21bSV0UFZXAyJE/Uq5cCVauvJ1u3Wq4OiTlhvJy1ngYCAAeAF4GSgF3OjMo5TwT523li78Opet3d5da9GlSkZahpbWZFJUmOTmFb7/dyvDhTQgIKM6yZSNp0CAYX73YUNnI9cgwxvxj/xkJjAQQEX3l1QO9uXR3WjJpXDmI65pVok/jitQKCXBxZMrdbNhwjHvuWcCGDccpUcKHm25qpF9PVLnKMaGIyFVAFeAPY8wZEWmM1QRLd0CTigdZd/Ac7y7fA8DihzrToGKQiyNS7ujChTiefXYl06ato3z5ksyadRODBjV0dVjKQ+T0pvxk4CbgP6wH8T9itTQ8BRhTMOGp/JCcYhgy/S8AXrmxqSYTla2bbvqOFSsOMG7cVbz0UndKaaUMdQlyukMZCDQ3xsSKSFngmN29q2BCU/khLDyGTlNWAlCjnD8j2lVzcUTK3ezfH05IiD+Bgb68/HJ3vLyEq67ST/GqS5dTQokzxsQCGGPOichOTSae4Z/9Z5m6ci+Hz8Vw6GxMWv9fHuziwqiUu0lISOaNN/5k0qRVPPBAW6ZM6aUtAqsrklNCqSUiqU3UC1DDoRtjzCCnRqYuyenIeGavO8y6g+H8vvt0Wv8y/sUY2b46j1xb34XRKXezatUhxoxZwI4dZxg8uBEPPNDO1SGpQiCnhHJThu6pzgxEXZ7jF2K5evKKTP3HXVObCb0buCAi5e7eeusvHnlkCTVqlGbhwhH061fX1SGpQiKnxiGXZzdMuQdjTLpk8lDPuozpWpvi3l7aCrBKJyXFEB2dQGCgL9ddV4/Tp2N45pku+PsXc3VoqhDRN5Q8VEqKoZZDS8AHX73OhdEod7Zt2ynGjFmY9uXEevXK8corPVwdliqEvJw5cxHpIyK7RGSviGT5DRURGSoi20Vkm4jMdGY8hcEHv+2jxhML0yWTrS/0dmFEyl3FxCTy5JPLaNHiQ3bsOE3//nXJ7QutSl2JPN+hiIivMSb+Esb3BqYBvYAwYJ2IzDfGbHcYpy7wJNDRGBMuItpGWDYWbj7OlMU7OXzOqrXlX9yb65tX5r7udbTdLZXJxo3HGTToOw4ePM+oUS147bVeBAf7uzosVcjleiYSkbbAp1hteFUTkebAXcaY+3OZtC2w1xiz357PLKx3W7Y7jPN/wDRjTDiAMebUpa9C4bZy1ylGfb4urbtO+QAm9K5P78baDIbKzBiDiFCtWimqVSvFF1/cQJcu+nlmVTDycmn7LtAf+AnAGPOfiFyTh+mqAEccusOAjHUT6wGIyBrAG3jeGLM4D/MuEiYv2sGHq/andX8+6iquqa83cSqzpKQUpk5dy/z5u1i6dCTlyvnz++93uDosVcTkJaF4GWMOZWiFNjkP02VVzShjAa4PUBfohtU22GoRaWKMOZ9uRiJ3A3cDVKtWNN703nAoPC2ZfHBLK/o2reTiiJS7Wrv2KGPGLGDjxhP07VuHiIh4ypTRD16pgpeXh/JH7GIvIyLeIvIQsDsP04UBoQ7dVbGab8k4zjxjTKIx5gCwCyvBpGOM+cgY08YY0yYkJCQPi/ZspyLiuOmDPwH4enQ7TSYqS1FRCYwbt5D27T/h5Mlovv9+CAsXjtBkolwmL3co92IVe1UDTgLL7H65WQfUFZGaWB/luhkYkWGcn4DhwAwRCcYqAttPEXUqMo4Hvt3I3/vPAdC8aik61Q12cVTKXRUr5sVvvx3i/vvbMmlSd4KCfF0dkiri8pJQkowxN1/qjI0xSSJyH/Ar1vORz4wx20TkRWC9MWa+PexaEdmOVYw2wRhz9lKX5enORsUzbua/aYkE4MEedXmop77BrNLbu/ccL774O9Om9SMw0JcNG+7Gz09r+Sn3ILnVSxeRfVhFUbOBH4wxkQURWHbatGlj1q9f78oQ8sXxC7E88O1G1h0MT9d/bLfajL1GqwKr9OLjk3jttTW8/PJqihf3ZuHCEXTurLW3VN6JyAZjTBtnLiMvX2ysLSIdsIqsXhCRTcAsY8wsZwZWmGWsClwzuCTXNa3EXZ1rUtq/uAsjU+5o5coD3HvvQnbtOsuwYY15883eVK4c6OqwlMokT5fBxpg/gT9F5HngbeAbQBPKZdh69EJaMmlVrTQ/jO3o4oiUOzPG8PLLq0lMTGHx4lvo3buOq0NSKlt5ebExAOuFxJuBhsA8oIOT4yqUklMM/d/7A4Bv7mpHxzr6wF1llpJi+PTTf+nTpw6hoaX46qsbKV3ajxIltCFH5d7yUm14K9AeeM0YU8cYM94Y84+T4yp0Vu46RW27/a2u9UI0magsbd58kk6dPuPuuxfwySf/AlCpUqAmE+UR8lLkVcsYk+L0SAqps1HxdJyygrhEaxPWCinJR7e1dnFUyt1ERSXwwgu/8dZbf1OmTAlmzBjIbbc1d3VYSl2SbBOKiPzPGDMemCsimaqC6Rcbc5eUnELfd1YTl5hC48pBTBzQmLY1y7o6LOWGnn/+N/73v7+4666WvPpqT8qV04YclefJ6Q5ltv2/fqnxEq09cI6hH/6V1u3jJfx8Xyf96JVK58iRC0RHJ9KgQTBPPNGJG25oQKdORaNpIVU4ZfsMxRiz1v6zoTFmueM/rIfzKgsnLsSlJZM65QMY2b4621/so8lEpUlKSuHNN/+iYcNp3HPPAgCCg/01mSiPl5dnKHeS+S5ldBb9irxXf9nJ9N/3ATC8bSiTBzVzcUTK3fz9dxhjxizgv/9Oct11dZk6tZ+rQ1Iq3+T0DGUYVlXhmiLyg8OgQOB81lMVXYfORqclk96NK/DSDU1dHJFyNwsX7mbAgG+pXDmQH34Yyg03NCBDK95KebSc7lDWAmexWgme5tA/EtjozKA8zTM/beHrvw8D8HDPejyobXApmzGGY8ciqVIliJ49a/Hii9fw4IPtCAzUhhxV4ZNtQrGbkz+A1bqwysaek5FpyaRXowqaTFSa3bvPMnbsQnbvPsv27eMICCjOM890cXVYSjlNTkVevxtjuopIOOk/jCWAMcYU+fqvf+47w4iPrXc8597bgdbVy7g4IuUO4uKSePXVP5g8+Q9KlPBh8uQelCihjX2qwi+nozz1M7/6SncWUlJMWjJ55cammkwUACdORNGly+fs2XOO4cOb8OabvalYMcDVYSlVIHIq8kp9Oz4UOGaMSRCRTkAz4GsgogDic1vX/O+3tL9HtNPqnkVdYmIyxYp5U6FCSbp0qc60af3o1au2q8NSqkDlpS2vn7A+/1sb+BLrHZSZTo3KzSWnGA6djQFg3yta7bMoS0kxTJ++ntq13yUsLAIR4ZNPrtdkooqkvCSUFGNMIjAIeNsYcz9Qxblhua+UFEOX11YCcG+32njrC4tF1n//naBDh0+5996F1K1bjsTEZFeHpJRL5ekTwCIyBBgJ3GD3K5JNn6akGGrZLQaD9ZleVfQYY5gwYSlvv/03ZcuW4KuvbuSWW5rqOyWqyMvrm/JjsZqv3y8iNYFvnRuWe3r6p61pf//5RHf8inm7MBrlKiJCeHgso0dbDTmWKVPC1SEp5RZy/aY8gIj4AKmfittrjElyalQ5cMU35Y0xzPjzIC/8vB2AZY90oU55/QRrUXLo0HkefHAxzz3XlVatKpGSYrR9NuVRCuKb8rk+QxGRzsBe4FPgM2C3iBSp79Y+NHtTWjJ5sEddTSZFSGJiMq+9toZGjd5n6dL97Np1BkCTiVJZyEuR11tAP2PMdgARaQh8BTg107mLuMRk5m06BsBvj3ajRnBJF0ekCsqffx7hnnsWsHXrKQYOrM+77/alWrVSrg5LKbeVl4RSPDWZABhjdohIcSfG5FZ+2XocgL5NKmoyKWKWLdvPhQtx/PTTMAYObODqcJRye7k+QxGRGUA81l0JwC2AvzHmdueGlrWCfIZy9HwsHV9dAcCK8V2pFaJvPBdmxhi++mozISH+9O1bl/j4JBITUwgIKDLXT6oQc4tnKMAYYB/wGPA4sB+4x5lBuYsn5m4GoE/jippMCrmdO8/QvfuX3H77T3z++SYAfH19NJkodQlyLPISkaZAbeBHY8xrBROSe4hNSGb1njMU8xamj2zt6nCUk8TGJvLKK6uZMmUNJUsW58MP+3PXXa1cHZZSHinbOxQReQqr2ZVbgKUicmeBReUGDp+zmlbpWi/ExZEoZ/r559289NJqhg1rws6d47j77tZag0upy5TTHcotQDNjTLSIhACLsKoNFwmz1lnfOLmlfXUXR6Ly24kTUWzadII+feowZEgjatS4i7Zti2xrQkrlm5yeocQbY6IBjDGncxm30Pl8zUEAOtQu59pAVL5JTk7h/ffXUb/+VEaO/JHY2ERERJOJUvkkpzuUWg7fkhegtuO35Y0xg5wamQu9vWx32t++Ptq8SmHw77/HGTNmAevWHaNnz1q8/34/SpQokk3SKeU0OSWUmzJ0T3VmIO5iz8lI3l62B4B/n+3l4mhUfjhwIJy2bT8mONifmTMHcfPNTbQhR6WcIKcPbC0vyEDcwardp7nts7UADGpVhbIltcqopzLGsGXLKZo1q0DNmmX4/POBDBhQn9Kl/VwdmlKFVpF6LpKTCzGJacmka70Q3hzawsURqct14EA4/ft/S8uWH7J580kARo5srslEKSdzakIRkT4isktE9orIEzmMN1hEjIi4pH0wYwydplhvxHepF8IXd7Z1RRjqCiUkJPPqq3/QuPH7/P77Qd54oxeNGmm1b6UKSl7a8gJARHyNMfGXML43MA3oBYQB60RkvmO7YPZ4gcADwD95nXd+azVpKZHxVov802/Vl9o8UXJyCh06fMqGDccZNKghb7/dm9BQbchRqYKUl+br24rIFmCP3d1cRN7Lw7zbYn07Zb8xJgGYBQzMYrxJwGtAXN7Dzj/xScmExyQCsHNSH/yL5znHKjcQEWFd43h7e3HnnS35+efhzJ07VJOJUi6QlyKvd4H+wFkAY8x/wDV5mK4KcMShO4wM36IXkZZAqDFmQU4zEpG7RWS9iKw/ffp0Hhadd9+tDwNgUMsq+gVGD2KMYcaMTdSq9Q7z5u0EYOzYq+jfv56LI1Oq6MpLQvEyxhzK0C85D9NlVS8zrWljEfHC+tbK+NxmZIz5yBjTxhjTJiQkf8vEn7U/6zuhT/18na9ynu3bT9Ot2xeMGjWPBg2CqV27rKtDUkqRt2coR0SkLWDs5yL3A7tzmQasO5JQh+6qwDGH7kCgCfCb/U5ARWC+iFxvjCmQ9ulX7jyV9nelUvpdcE/w2mtrePrpFQQF+fLJJwMYNaqltr2llJvIS0K5F6vYqxpwElhm98vNOqCuiNQEjgI3AyNSBxpjLgDBqd0i8hvwaEElE4BRM9YBsPCBTgW1SHWZjDGICBUrBnDLLU15/fVehIToB8+Ucie5JhRjzCmsZHBJjDFJInIf8CvgDXxmjNkmIi8C640x8y852nxijKH+s4vTuhtX1ge47urYsUgefHAxnTtX44EH2nHbbc257bbmrg5LKZWFXBOKiHyMw7OPVMaYu3Ob1hizCKuVYsd+z2Uzbrfc5pdfftp0lISkFAA2PafNq7ij1IYcn356BYmJKXToUNXVISmlcpGXIq9lDn/7ATeSvvaWR0lKTuHh2f8B8P2Yqyntr82ruJtNm05w113z2bDhONdeW5v33++nD96V8gB5KfKa7dgtIl8BS50WkZOt3GVVO65fIZCrauhJyh1duBDHsWORzJ49mCFDGmlDjkp5iMt5i68m4LFfndp5PAKAl25s4uJIVCpjDN9/v509e87y9NNd6Nq1Bvv3P4ifn75kqpQnycub8uEics7+dx7r7uQp54fmHNvthNKmehkXR6IA9u07R79+Mxk2bA7z5u0iMdF6xUmTiVKeJ8dfrVhlDc2xqv0CpBhjMj2g9xSRcYn8svUEgBajuFh8fBJvvPEnL720mmLFvHjnnT6MHXsVPj7aALZSnirHhGKMMSLyozGmdUEF5ExNn18CWM9PlGsdORLBpEmrGDCgPm+/3ZsqVYJcHZJS6grl5XJwrYh4fBO8q3ZfbANs8UOdXRhJ0XX6dDRTp1rfnKlTpyzbt4/j+++HaDJRqpDI9g5FRHyMMUlAJ+D/RGQfEI3VRpcxxnhUktlwKByARQ901uKuApaSYvj884089tgyIiPj6dWrFvXrB1Orlj7HUqowyanIay3QCrihgGJxqneWW9+Jb1RZr4YL0tatp7j33oX88cdhOneuxvTp/alfPzj3CZVSHienhCIAxph9BRSL0/x35DwAJbR5+gKVkJDMtdd+RUJCMp99dj133NFC7w6VKsRySighIvJIdgONMW86IR6nGDhtDQAvDmzs4kiKhhUrDtC1a3WKF/fmu++G0KBBMMHB/q4OSynlZDk9lPcGArCamc/qn0f4a9/ZtL+HtAnNYUx1pcLCIrjppu/o0eNLvvzSat6mU6dqmkyUKiJyukM5box5scAicZLv11vNjs26u72LIym8kpJSmDp1Lc8+u5Lk5BQmT+7BLbc0c3VYSqkCluszFE+3Kcx6ftKuprbb5SwjR/7IrFlb6du3DtOm9aNmTa29pVRRlFNC6VFgUTjJhdhE9p+OBvTN+Px2/nwcPj5eBAQUZ9y4q7jppobcdFND3c5KFWHZPkMxxpwryECcof97qwF4vE8DF0dSeBhjmDVrKw0bTuPZZ1cA1nOSwYO1VWClirpC3XDSkXOxAPxf55oujqRw2Lv3HL17f83w4XOpWjWIW2/V5yRKqYsKbZOuF2ITAejZsDw+3oU6bxaImTO3cOed8/D19WHq1L6MGdMGb92uSikHhTahvPHrLgB6NKzg4kg8W2JiMsWKedOmTWUGD27Ea6/1onJlj6k1rpQqQIU2oXz19yEAbmxZxcWReKZTp6IZP34J0dEJ/PDDMOrVK8fXXw9ydVhKKTdWKMssTkXGAVA+0Bc/bW7lkqSkGD76aAP1609l9uytNG4cQnJyiqvDUkp5gEJ5h/L5moMAPNCjrmsD8TD794dz660/8NdfYXTrVoMPPriOBg20IUelVN4UyoTywW9We5Y3X6VNrVyKUqV8OX8+ji++uIGRI5tpNWCl1CUpdEVe79rN1IcE+mrtrjyYP38XgwbNJjk5hXLl/Nm6dSy33dZck4lS6pIVqjOuMYY3l+4GYOZd7VwcjXs7fPgCN9wwi4EDZ7F791mOH48CwMtLE4lS6vIUqiKvVXvOAFC2ZHHq6nfjs5SUlMLbb//NxIm/YYxhypSePPxwe4pp5QWl1BUqVAnlh3/DAHhrWAsXR+K+kpNT+OSTf+nevSbvvdeXGjVKuzokpVQhUaiKvFIbguxSV2smOQoPj+Xxx5cSGRmPr68Pa9bcyfz5N2syUUrlq0KVULYcvUDN4JL6QNlmjOGbbzbToME0/ve/v1i58iAA5cr56zZSSuW7QlPkdTLCepmxceUgF0fiHnbvPsvYsQtZvvwAbdtW4ddfb6VFi4quDkspVYgVmoSyavdpALrVL+/iSNzDQw8tZv36Y7z/fj/uvru1NuSolHK6QpNQnv5xK1C0n58sXbqPBg2CCQ0txQcfXIevrw8VKwa4OiylVBHh1MtWEekjIrtEZK+IPJHF8EdEZLuIbBaR5SJS/XKWE5OQRILd3lT5IL8rjNrznDgRxYgRc7n22q+ZMmUNANWrl9ZkopQqUE5LKCLiDUwD+gKNgOEi0ijDaBuBNsaYZsAc4LXLWdapiHgAbmlX7bLj9UQpKYbp09fToMFU5s7dwcSJXXnjjWtdHZZSqohy5h1KW2CvMWa/MSYBmAUMdBzBGLPSGBNjd/4NVL2cBe09Zb3l3bFO0Srumjx5Nffeu5DWrSuzefMYnn++G35+haYUUynlYZx59qkCHHHoDgNyag9lNPBLVgNE5G7gboBq1TLfhWw/HgFA1TIlLi9SDxIZGc+ZMzHUrFmGMWPaULNmGYYPb6LVgJVSLufMO5SsznAmyxFFbgXaAK9nNdwY85Expo0xpk1ISEim4dN/t1oXblCx8FYZNsbw4487aNTofYYNm4MxhnLl/BkxoqkmE6WUW3BmQgkDHNuPrwocyziSiPQEngauN8bEX+pCzkUnEJOQDEBxn8JZNfbQofNcf/0sBg36jrJlS/Duu301iSil3I4zi7zWAXVFpCZwFLgZGOE4goi0BD4E+hhjTl3OQtYfPAfAM9c1vKJg3dVffx2hZ8+vAHjjjV48+GB7fApp4lRKeTanJRRjTJKI3Af8CngDnxljtonIi8B6Y8x8rCKuAOB7+4r7sDHm+ktZzqd/HACgZ8MK+Rm+y0VExBMU5EurVpW4884WTJjQkWrVSrk6LKWUypZTqwQZYxYBizL0e87h755Xuox19h1KjeCSVzort3D2bAxPPLGMJUv2s23bWAICivPee/1cHZZSSuXKo+uYnoqII8VArRDPTybGGL76ajPjxy8hPDyWRx65Gn1MopTyJB6dUJbvtB67jGx/WS/Yu40LF+K44YbZ/PbbQa6+uirTp/enWbPCVYSnlCr8PDqhbDwcDkD/ZpVdHMnlMcYgIgQF+RIc7M9HH/Vn9OhW+hlepZRH8tjqQikphu/WW19oDAn0dXE0l+7XX/fSqtVHhIVFICJ8//0Q/u//WmsyUUp5LI9NKBFxiQDULe9ZDSAePx7JzTfPoU+fb4iJSeTUqWhXh6SUUvnCY4u8IuOSABh2VWguY7qPadPW8tRTK4iPT+KFF7rx+OMd8fX12F2glFLpeOzZbOOR8wAElSjm4kjybsOG47RrV4Vp0/pRt245V4ejlFL5ymMTyp6TkQC0rVHWxZFkLyIinueeW8nIkc1o3boy779/Hb6+3tpsilKqUPLYhJKcYrUzWcUNWxg2xjB37g4efHAxx49HUq1aKVq3rqxNyyulCjWPPcMdPBuNr48XxdzsW+kHDoRz332/sGjRHlq0qMgPPwylXbvL+syLUkp5FI9NKCkpEJ+U4uowMvnmmy2sWnWIt97qzX33tdWGHJVSRYbHJpRkY6hfIdDVYQCwevUh4uOT6dmzFhMmdOCOO1pQtWrh/TaLUkplxWMvn5duP0kxH9c+3D5zJoY775xHly4zePHF3wHw9fXRZKKUKpI89g4FIDw60SXLNcYwY8YmJkxYyoUL8Tz+eEeefbaLS2JR7i0xMZGwsDDi4uJcHYoqIvz8/KhatSrFihX8KxUemVC2H7O+Id+/WSWXLH/Roj3ceed8OnYMZfr0/jRpUt4lcSj3FxYWRmBgIDVq1NDq4srpjDGcPXuWsLAwatasWeDL98girx/+tdrwurp2wb0cGBOTyJo1hwHo168u8+bdzKpVozSZqBzFxcVRrlw5TSaqQIgI5cqVc9kdsUcmlNNR1qfnu9QNKZDl/fLLHpo0eZ++fb/h/Pk4RITrr6+vDTmqPNFkogqSK483j0woJYp5U8xbnH5CP3o0giFDvqdfv5n4+vrw88/DKV3az6nLVEopT+WRCSUx2VA+0Lkn9lOnomnU6H0WLNjNSy9dw3//jaFr1xpOXaZSzuDt7U2LFi1o0qQJAwYM4Pz582nDtm3bRvfu3alXrx5169Zl0qRJGGPShv/yyy+0adOGhg0b0qBBAx599FFXrEKONm7cyF133ZWu38CBA7n66qvT9bvjjjuYM2dOun4BARdbK9+9ezf9+vWjTp06NGzYkKFDh3Ly5Mkriu3cuXP06tWLunXr0qtXL8LDwzONs3LlSlq0aJH2z8/Pj59++gmAzp07p/WvXLkyN9xwAwALFixg4sSJVxSbUxhjPOpf69atzY3T/jCdp6wwzhAWdiHt73fe+dvs3XvWKctRRcP27dtdHYIpWbJk2t+33Xabeemll4wxxsTExJhatWqZX3/91RhjTHR0tOnTp4+ZOnWqMcaYLVu2mFq1apkdO3YYY4xJTEw006ZNy9fYEhMTr3gegwcPNps2bUrrDg8PN1WrVjUNGjQw+/fvT+t/++23m++//z7dtKnbJjY21tSpU8fMnz8/bdiKFSvMli1brii2CRMmmMmTJxtjjJk8ebJ57LHHchz/7NmzpkyZMiY6OjrTsEGDBpkvvvjCGGNMSkqKadGiRZbjGZP1cQesN04+P3tkLa/zsYmERyfk6zwvXIjjmWdW8OGHG/j777to1aoSDzzQLl+XoYq2F37ellZDMb80qhzExAGN8zz+1VdfzebNEKFdOwAAEEZJREFUmwGYOXMmHTt25NprrwXA39+fqVOn0q1bN8aNG8drr73G008/TYMGDQDw8fFh7NixmeYZFRXF/fffz/r16xERJk6cyE033URAQABRUVEAzJkzhwULFjBjxgzuuOMOypYty8aNG2nRogU//vgjmzZtonTp0gDUqVOHNWvW4OXlxZgxYzh82KoM8/bbb9OxY8d0y46MjGTz5s00b948rd/cuXMZMGAAFSpUYNasWTz55JO5bpeZM2dy9dVXM2DAgLR+11xzTZ63a3bmzZvHb7/9BsDtt99Ot27dmDJlSrbjz5kzh759++Lv75+uf2RkJCtWrODzzz8HrOck3bp1Y8GCBQwdOvSK48wvHplQvERoVyt/Whk2xvD999t56KHFnDgRxX33taV27TL5Mm+l3ElycjLLly9n9OjRgFXc1bp163Tj1K5dm6ioKCIiIti6dSvjx4/Pdb6TJk2iVKlSbNmyBSDLYp2Mdu/ezbJly/D29iYlJYUff/yRUaNG8c8//1CjRg0qVKjAiBEjePjhh+nUqROHDx+md+/e7NixI9181q9fT5MmTdL1+/bbb5k4cSIVKlRg8ODBeUooW7duzbQtshIZGUnnzp2zHDZz5kwaNWqUrt/JkyepVMl6vaFSpUqcOnUqx/nPmjWLRx55JFP/H3/8kR49ehAUdPGl6TZt2rB69WpNKFfqwJloGle+8rfRjTEMGvQdP/20k1atKjF//nDatPHM79Mr93cpdxL5KTY2lhYtWnDw4EFat25Nr169AOv4z65G0KXUFFq2bBmzZs1K6y5TJvcLsiFDhuDt7Q3AsGHDePHFFxk1ahSzZs1i2LBhafPdvn172jQRERFERkYSGHixyaXjx48TEnKxtufJkyfZu3cvnTp1QkTw8fFh69atNGnSJMt1utQaUYGBgWzatOmSpsmr48ePs2XLFnr37p1p2LfffpvpOVH58uU5duyYU2K5XB75UD45xRAdn3zZ0ycmWtOKCJ06hfLuu31Yu/YuTSaqUPr/9u4/uKr6zOP4+1MqJEBgtyCsLWpojZAQAyK0YIcC0mX8wcLCOCK/Cjt2HUGkLYrDDs6su1JLW0GL4CJrnaRrGyJOBQYQFtlYKvLDsI2goCmlEXAYTWPKIoKAPPvHOUku+UFu8P4Mz2vmztx77rnnPPeZm/PN+X7Peb6ZmZmUl5fz/vvvc+bMGZYvXw5Av379KCsru2DdQ4cO0blzZ7KysujXrx979uxpcfvNNUyRyxreF9GpU6e650OHDuXgwYNUVVWxZs0aJkyYAMD58+fZsWMH5eXllJeX88EHH1zQmNR+t8htl5SUUFNTQ+/evcnOzqaysrKusevWrdsFZ08ff/wx3bt3r8tFNN/1xIkTFwygRz4iG79aPXv25NixY0DQYPTo0fx9ay+++CLjx49vdId7dXU1u3fv5o477rhg+enTp8nMTK3pO9KuQTkdNgbX97y0ueRfe62SgoIVrF37LgAPPngzDzzwLdqlWBl852Kta9euLF26lCeeeIKzZ88yZcoUXn/9dV599VUgOJOZM2cODz/8MADz5s3j8ccfp6KiAggO8EuWLGm03dGjR7Ns2bK617UH7Z49e3LgwIG6Lq3mSGL8+PHMnTuX3NxcunXr1uR2mzozyM3N5eDBg3Wvi4uL2bRpE5WVlVRWVrJnz566BmXEiBGUlJRw5kww/lpYWFg3TjJ58mTeeOMNNmzYULetTZs21XXj1ao9Q2nq0bC7C2Ds2LEUFRUBUFRUxLhx45rNQ3FxMZMmTWq0fPXq1YwZM4aMjAuvbK2oqGjU3ZdsaXcU/eupoH7XwGtaN85RVXWS6dPXMHJkEZ99do6srA7xCM+5lHbjjTfSv39/Vq1aRWZmJmvXrmXhwoX06dOHG264gcGDBzN79mwACgoKeOqpp5g0aRK5ubnk5+fX/bcd6ZFHHqGmpob8/Hz69+9PaWkpAIsWLWLMmDHccsstdeMIzZk4cSIvvPBCXXcXwNKlSykrK6OgoIC8vDxWrFjR6HN9+/bl+PHjnDhxgsrKSg4fPsyQIUPq3u/duzddunRh165djBkzhmHDhnHTTTcxYMAAtm/fXjdAnpmZyfr163n66afJyckhLy+PwsLCi55RRGP+/Pls2bKFnJwctmzZwvz584Fg7CeyC6uyspIjR44wfPjwRttYtWpVkw1NaWlpo7OWZJNFXHOeDjKuyrG/m/4UFQtvo32Uc40UF+/j/vs38sknZ5g372YWLPgOHTumz1z0Ln0dOHCA3NzcZIfRpj355JNkZWU1GmNoyz788EMmT57M1q1bm3y/qd+dpD1mNiiecaXdGUpt8xdtYwJw7tx58vN7UF5+Hz/+8ShvTJxrQ2bOnEmHDpdXj8Phw4dZvHhxssNoJC2v8po54hsXff/kyTM89tg2rrmmK7NmDWbq1AKmTi3wmkrOtUEZGRlMmzYt2WEk1ODBg5MdQpPS7gwF4Lormx+QX7++gn79nuGnP91ORUU1EAz6eWPikiXdupVdekvm7y0tz1CuaKK76+jR/2POnFd4+eV3ycu7km3bZjBs2LVJiM65ehkZGVRXV3sJe5cQFs6H0vCKsERJywblys6N+0sPHaph8+Y/8ZOfjGLu3KG0b98uCZE5d6FevXpx9OhRqqqqkh2Ku0zUztiYDGl3lVeHq3Lsk6PvcUW7L7F79wfs2HGEH/wguEywuvpTunXr2MIWnHPu8pP2V3lJulXSe5IOSprfxPsdJJWE7++SlB3Ndk+eOMOsWRsYMuQ5lizZycmwUKQ3Js45lzxxa1AktQOWA7cBecAkSQ1vJb0HqDGz64AngebLcEbo23cZzz67hzlzvsW+fTPp1Kl9LEN3zjl3CeI5hvJN4KCZHQKQtAoYB0QWvBkHPBo+fwlYJkl2kX44O3eeq7O7snHjFAYOvPjdt8455xInng3K14AjEa+PAg0nGKlbx8zOSToOdAP+ErmSpHuBe8OXn5X95d63o6g0fTnoToNcXcY8F/U8F/U8F/X6xHsH8WxQmrpGsuGZRzTrYGYrgZUAksriPbCULjwX9TwX9TwX9TwX9SSVtbzWFxPPQfmjwNURr3sBDYv3160j6ctAV+DjOMbknHMuTuLZoLwJ5EjqLak9cDewrsE664Dp4fM7gf+52PiJc8651BW3Lq9wTGQ2sBloBzxvZu9I+negzMzWAb8E/kvSQYIzk7uj2PTKeMWchjwX9TwX9TwX9TwX9eKei7S7sdE551xqSsvikM4551KPNyjOOediImUblHiVbUlHUeRirqT9kvZK2iqpzZZZbikXEevdKckktdlLRqPJhaS7wt/GO5J+k+gYEyWKv5FrJJVK+kP4d3J7MuKMN0nPS/pI0tvNvC9JS8M87ZU0MKYBmFnKPQgG8f8EfB1oD7wF5DVYZxawInx+N1CS7LiTmIuRQMfw+czLORfhelnANmAnMCjZcSfxd5ED/AH42/B1j2THncRcrARmhs/zgMpkxx2nXHwHGAi83cz7twOvENwDOATYFcv9p+oZSl3ZFjM7A9SWbYk0DigKn78EjFLbnHCixVyYWamZfRq+3Elwz09bFM3vAuAx4GfA6UQGl2DR5OKfgeVmVgNgZh8lOMZEiSYXBnQJn3el8T1xbYKZbePi9/KNA35lgZ3A30iKWQ2rVG1Qmirb8rXm1jGzc0Bt2Za2JppcRLqH4D+QtqjFXEi6EbjazNYnMrAkiOZ3cT1wvaTtknZKujVh0SVWNLl4FJgq6SiwEXggMaGlnNYeT1olVSfYilnZljYg6u8paSowCBge14iS56K5kPQlgqrVMxIVUBJF87v4MkG31wiCs9bfS8o3s7/GObZEiyYXk4BCM1ssaSjB/W/5ZnY+/uGllLgeN1P1DMXLttSLJhdI+i6wABhrZp8lKLZEaykXWUA+8JqkSoI+4nVtdGA+2r+RtWZ21sz+DLxH0MC0NdHk4h7gRQAz2wFkEBSOvNxEdTy5VKnaoHjZlnot5iLs5nmWoDFpq/3k0EIuzOy4mXU3s2wzyyYYTxprZnEvipcE0fyNrCG4YANJ3Qm6wA4lNMrEiCYXh4FRAJJyCRqUy3Fe5nXA98KrvYYAx83sWKw2npJdXha/si1pJ8pc/BzoDKwOr0s4bGZjkxZ0nESZi8tClLnYDIyWtB/4HJhnZtXJizo+oszFg8B/SvoRQRfPjLb4D6ikYoIuzu7heNG/AlcAmNkKgvGj24GDwKfAP8V0/20wp84555IgVbu8nHPOpRlvUJxzzsWENyjOOediwhsU55xzMeENinPOuZjwBsWlHEmfSyqPeGRfZN3s5iqrtnKfr4XVat8KS5X0uYRt3Cfpe+HzGZK+GvHec5LyYhznm5IGRPGZH0rq+EX37VxLvEFxqeiUmQ2IeFQmaL9TzKw/QdHRn7f2w2a2wsx+Fb6cAXw14r3vm9n+mERZH+czRBfnDwFvUFzceYPi0kJ4JvJ7Sf8bPm5uYp1+knaHZzV7JeWEy6dGLH9WUrsWdrcNuC787KhwDo194VwTHcLli1Q/B80T4bJHJT0k6U6Cmmq/DveZGZ5ZDJI0U9LPImKeIenpS4xzBxGF/ST9h6QyBXOf/Fu4bA5Bw1YqqTRcNlrSjjCPqyV1bmE/zkXFGxSXijIjurteDpd9BPy9mQ0EJgJLm/jcfcAvzGwAwQH9aFhmYyLw7XD558CUFvb/D8A+SRlAITDRzG4gqCwxU9JXgPFAPzMrABZGftjMXgLKCM4kBpjZqYi3XwImRLyeCJRcYpy3EpRXqbXAzAYBBcBwSQVmtpSgVtNIMxsZlmB5BPhumMsyYG4L+3EuKilZesVd9k6FB9VIVwDLwjGDzwnqUjW0A1ggqRfwWzP7o6RRwE3Am2FZmkyCxqkpv5Z0CqgkKG/eB/izmVWE7xcB9wPLCOZaeU7SBiDqUvlmViXpUFhH6Y/hPraH221NnJ0IyoxEzrh3l6R7Cf6uryKYSGpvg88OCZdvD/fTniBvzn1h3qC4dPEj4EOgP8GZdaPJs8zsN5J2AXcAmyV9n6Bcd5GZ/UsU+5gSWUhSUpPz64S1o75JUGzwbmA2cEsrvksJcBfwLvCymZmCo3vUcRLMSrgIWA5MkNQbeAgYbGY1kgoJCiA2JGCLmU1qRbzORcW7vFy66AocC+evmEbw3/kFJH0dOBR286wj6PrZCtwpqUe4zlckXRvlPt8FsiVdF76eBvwuHHPoamYbCQa8m7rS6gRBOf2m/Bb4R4I5OkrCZa2K08zOEnRdDQm7y7oAJ4HjknoCtzUTy07g27XfSVJHSU2d7TnXat6guHTxDDBd0k6C7q6TTawzEXhbUjnQl2Cq0/0EB97/lrQX2ELQHdQiMztNUI11taR9wHlgBcHBeX24vd8RnD01VAisqB2Ub7DdGmA/cK2Z7Q6XtTrOcGxmMfCQmb1FMH/8O8DzBN1otVYCr0gqNbMqgivQisP97CTIlXNfmFcbds45FxN+huKccy4mvEFxzjkXE96gOOeciwlvUJxzzsWENyjOOediwhsU55xzMeENinPOuZj4f8LdiXudhzTSAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 503\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
04389256
1852503
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4389 256\n", - "1 852 503" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model 3 - Logistic Regression\n", - "\n", - "#### Theory\n", - "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", - "\n", - "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", - "\n", - "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", - "\n", - "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", - "\n", - "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", - "\n", - "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", - "\n", - "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", - "\n", - "#### Training\n", - "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": {}, - "outputs": [], - "source": [ - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.464583\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 24000
Model: Logit Df Residuals: 23977
Method: MLE Df Model: 22
Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1184
Time: 10:37:26 Log-Likelihood: -11150.
converged: True LL-Null: -12647.
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -9.464e-07 1.74e-07 -5.433 0.000 -1.29e-06 -6.05e-07
SEX -0.2012 0.030 -6.782 0.000 -0.259 -0.143
EDUCATION -0.1390 0.023 -6.168 0.000 -0.183 -0.095
MARRIAGE -0.2880 0.025 -11.330 0.000 -0.338 -0.238
AGE 0.0014 0.001 0.907 0.364 -0.002 0.004
PAY_0 0.5527 0.020 28.033 0.000 0.514 0.591
PAY_2 0.0684 0.023 3.012 0.003 0.024 0.113
PAY_3 0.0833 0.025 3.305 0.001 0.034 0.133
PAY_4 0.0143 0.028 0.515 0.606 -0.040 0.069
PAY_5 0.0325 0.030 1.078 0.281 -0.027 0.092
PAY_6 0.0203 0.025 0.817 0.414 -0.028 0.069
BILL_AMT1 -6.352e-06 1.31e-06 -4.848 0.000 -8.92e-06 -3.78e-06
BILL_AMT2 3.958e-06 1.67e-06 2.365 0.018 6.77e-07 7.24e-06
BILL_AMT3 6.543e-07 1.45e-06 0.451 0.652 -2.19e-06 3.5e-06
BILL_AMT4 -2.436e-07 1.52e-06 -0.161 0.872 -3.22e-06 2.73e-06
BILL_AMT5 1.356e-06 1.68e-06 0.808 0.419 -1.93e-06 4.65e-06
BILL_AMT6 -7.978e-09 1.32e-06 -0.006 0.995 -2.6e-06 2.58e-06
PAY_AMT1 -1.643e-05 2.74e-06 -5.990 0.000 -2.18e-05 -1.11e-05
PAY_AMT2 -8.733e-06 2.31e-06 -3.788 0.000 -1.33e-05 -4.21e-06
PAY_AMT3 -2.995e-06 1.95e-06 -1.538 0.124 -6.81e-06 8.23e-07
PAY_AMT4 -5.848e-06 2.12e-06 -2.759 0.006 -1e-05 -1.69e-06
PAY_AMT5 -3.815e-06 2.01e-06 -1.894 0.058 -7.76e-06 1.34e-07
PAY_AMT6 -3.507e-06 1.53e-06 -2.292 0.022 -6.51e-06 -5.08e-07
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 24000\n", - "Model: Logit Df Residuals: 23977\n", - "Method: MLE Df Model: 22\n", - "Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1184\n", - "Time: 10:37:26 Log-Likelihood: -11150.\n", - "converged: True LL-Null: -12647.\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==============================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "LIMIT_BAL -9.464e-07 1.74e-07 -5.433 0.000 -1.29e-06 -6.05e-07\n", - "SEX -0.2012 0.030 -6.782 0.000 -0.259 -0.143\n", - "EDUCATION -0.1390 0.023 -6.168 0.000 -0.183 -0.095\n", - "MARRIAGE -0.2880 0.025 -11.330 0.000 -0.338 -0.238\n", - "AGE 0.0014 0.001 0.907 0.364 -0.002 0.004\n", - "PAY_0 0.5527 0.020 28.033 0.000 0.514 0.591\n", - "PAY_2 0.0684 0.023 3.012 0.003 0.024 0.113\n", - "PAY_3 0.0833 0.025 3.305 0.001 0.034 0.133\n", - "PAY_4 0.0143 0.028 0.515 0.606 -0.040 0.069\n", - "PAY_5 0.0325 0.030 1.078 0.281 -0.027 0.092\n", - "PAY_6 0.0203 0.025 0.817 0.414 -0.028 0.069\n", - "BILL_AMT1 -6.352e-06 1.31e-06 -4.848 0.000 -8.92e-06 -3.78e-06\n", - "BILL_AMT2 3.958e-06 1.67e-06 2.365 0.018 6.77e-07 7.24e-06\n", - "BILL_AMT3 6.543e-07 1.45e-06 0.451 0.652 -2.19e-06 3.5e-06\n", - "BILL_AMT4 -2.436e-07 1.52e-06 -0.161 0.872 -3.22e-06 2.73e-06\n", - "BILL_AMT5 1.356e-06 1.68e-06 0.808 0.419 -1.93e-06 4.65e-06\n", - "BILL_AMT6 -7.978e-09 1.32e-06 -0.006 0.995 -2.6e-06 2.58e-06\n", - "PAY_AMT1 -1.643e-05 2.74e-06 -5.990 0.000 -2.18e-05 -1.11e-05\n", - "PAY_AMT2 -8.733e-06 2.31e-06 -3.788 0.000 -1.33e-05 -4.21e-06\n", - "PAY_AMT3 -2.995e-06 1.95e-06 -1.538 0.124 -6.81e-06 8.23e-07\n", - "PAY_AMT4 -5.848e-06 2.12e-06 -2.759 0.006 -1e-05 -1.69e-06\n", - "PAY_AMT5 -3.815e-06 2.01e-06 -1.894 0.058 -7.76e-06 1.34e-07\n", - "PAY_AMT6 -3.507e-06 1.53e-06 -2.292 0.022 -6.51e-06 -5.08e-07\n", - "==============================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "glm = sm.Logit(y_train,X_train).fit()\n", - "glm.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.97 0.89 18719\n", - " 1 0.71 0.23 0.35 5281\n", - "\n", - " accuracy 0.81 24000\n", - " macro avg 0.76 0.60 0.62 24000\n", - "weighted avg 0.79 0.81 0.77 24000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.81 0.97 0.89 4645\n", - " 1 0.71 0.23 0.35 1355\n", - "\n", - " accuracy 0.80 6000\n", - " macro avg 0.76 0.60 0.62 6000\n", - "weighted avg 0.79 0.80 0.76 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logisitc model with all features performs quite well on both the train and test set with an accuracy of 0.81. We will now try removing all the insignificant features to see how that affects the model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.465111\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 24000
Model: Logit Df Residuals: 23987
Method: MLE Df Model: 12
Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1174
Time: 10:37:26 Log-Likelihood: -11163.
converged: True LL-Null: -12647.
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -1.007e-06 1.62e-07 -6.220 0.000 -1.32e-06 -6.89e-07
SEX -0.1916 0.028 -6.803 0.000 -0.247 -0.136
EDUCATION -0.1319 0.019 -6.927 0.000 -0.169 -0.095
MARRIAGE -0.2889 0.025 -11.519 0.000 -0.338 -0.240
PAY_0 0.5635 0.020 28.857 0.000 0.525 0.602
PAY_2 0.0744 0.022 3.313 0.001 0.030 0.118
PAY_3 0.1204 0.021 5.799 0.000 0.080 0.161
BILL_AMT1 -6.803e-06 1.3e-06 -5.223 0.000 -9.36e-06 -4.25e-06
BILL_AMT2 5.763e-06 1.36e-06 4.249 0.000 3.1e-06 8.42e-06
PAY_AMT1 -1.804e-05 2.75e-06 -6.564 0.000 -2.34e-05 -1.27e-05
PAY_AMT2 -7.366e-06 2.01e-06 -3.665 0.000 -1.13e-05 -3.43e-06
PAY_AMT4 -5.439e-06 1.86e-06 -2.926 0.003 -9.08e-06 -1.8e-06
PAY_AMT6 -3.948e-06 1.52e-06 -2.598 0.009 -6.93e-06 -9.7e-07
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 24000\n", - "Model: Logit Df Residuals: 23987\n", - "Method: MLE Df Model: 12\n", - "Date: Wed, 06 Nov 2019 Pseudo R-squ.: 0.1174\n", - "Time: 10:37:26 Log-Likelihood: -11163.\n", - "converged: True LL-Null: -12647.\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==============================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "LIMIT_BAL -1.007e-06 1.62e-07 -6.220 0.000 -1.32e-06 -6.89e-07\n", - "SEX -0.1916 0.028 -6.803 0.000 -0.247 -0.136\n", - "EDUCATION -0.1319 0.019 -6.927 0.000 -0.169 -0.095\n", - "MARRIAGE -0.2889 0.025 -11.519 0.000 -0.338 -0.240\n", - "PAY_0 0.5635 0.020 28.857 0.000 0.525 0.602\n", - "PAY_2 0.0744 0.022 3.313 0.001 0.030 0.118\n", - "PAY_3 0.1204 0.021 5.799 0.000 0.080 0.161\n", - "BILL_AMT1 -6.803e-06 1.3e-06 -5.223 0.000 -9.36e-06 -4.25e-06\n", - "BILL_AMT2 5.763e-06 1.36e-06 4.249 0.000 3.1e-06 8.42e-06\n", - "PAY_AMT1 -1.804e-05 2.75e-06 -6.564 0.000 -2.34e-05 -1.27e-05\n", - "PAY_AMT2 -7.366e-06 2.01e-06 -3.665 0.000 -1.13e-05 -3.43e-06\n", - "PAY_AMT4 -5.439e-06 1.86e-06 -2.926 0.003 -9.08e-06 -1.8e-06\n", - "PAY_AMT6 -3.948e-06 1.52e-06 -2.598 0.009 -6.93e-06 -9.7e-07\n", - "==============================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#remove all insignificant attributes\n", - "sig = glm.pvalues[glm.pvalues < 0.05]\n", - "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n", - "glm_2.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.97 0.89 18719\n", - " 1 0.71 0.23 0.35 5281\n", - "\n", - " accuracy 0.81 24000\n", - " macro avg 0.76 0.60 0.62 24000\n", - "weighted avg 0.79 0.81 0.77 24000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.81 0.97 0.89 4645\n", - " 1 0.72 0.23 0.35 1355\n", - "\n", - " accuracy 0.81 6000\n", - " macro avg 0.76 0.60 0.62 6000\n", - "weighted avg 0.79 0.81 0.76 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", - "\n", - "We now Calculate the AUROC for the train set." - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.27420941443503294\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the optimal cut off was found to be 0.276 using the training data, we will use that as our cut off for our evaluation of the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.86 0.86 4645\n", - " 1 0.52 0.53 0.52 1355\n", - "\n", - " accuracy 0.78 6000\n", - " macro avg 0.69 0.69 0.69 6000\n", - "weighted avg 0.78 0.78 0.78 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.276)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. The recall here is more important since we prioritise detecting defaulters at the expense of misclassifying non-defaulters. Looking at recall, our lower cutoff is able to correctly catch more defaulters.\n", - "\n", - "\n", - "Calculate the confusion matrices for both cut offs to better compare their performance." - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the Logistic Regression identified 717\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
03979666
1638717
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 3979 666\n", - "1 638 717" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.276, \"Logistic Regression\")" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the Logistic Regression identified 310\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
04522123
11045310
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 4522 123\n", - "1 1045 310" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.5, \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is evident that the cutoff of 0.276 is better able to detect defualts, with 688 detected in the test set for 0.276 but only 346 detected for 0.5" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.26257360578023514\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "iCxBcin11EI8" - }, - "source": [ - "### Model x - Support Vector Machine\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Standardizing attributes to prep for PCA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using training set to determine parameters of standardization\n", - "apply the parameters to standardize all attributes in training and test set" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "3LfxfeMZ1Kol" - }, - "outputs": [], - "source": [ - "#Standardising data\n", - "from sklearn.preprocessing import StandardScaler\n", - "# Standardizing the features\n", - "names = X_train.columns\n", - "scalar = StandardScaler()\n", - "scalar = scalar.fit(X_train)\n", - "X_train_standardize = scalar.transform(X_train)\n", - "X_train_standardize = pd.DataFrame(data = X_train_standardize, columns = names)\n", - "X_test_standardize = scalar.transform(X_test)\n", - "X_test_standardize = pd.DataFrame(data = X_test_standardize, columns = names)" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
count2.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+04...2.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+042.400000e+04
mean1.017149e-167.708834e-164.951317e-165.376625e-16-1.626419e-178.471233e-16-1.830735e-151.871623e-15-1.919451e-152.199158e-15...-2.152861e-16-2.414226e-16-1.714184e-162.617582e-166.972201e-17-1.275126e-16-1.808068e-162.455443e-162.328091e-16-2.814427e-16
std1.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+00...1.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+001.000021e+00
min-1.215668e+00-1.237489e+00-2.356076e+00-2.978518e+00-1.576658e+00-1.762383e+00-1.557417e+00-1.530280e+00-1.517284e+00-1.527848e+00...-2.944276e+00-3.312793e+00-1.998575e+00-6.347299e+00-3.586451e-01-2.583330e-01-3.011661e-01-3.101540e-01-3.120793e-01-2.887985e-01
25%-9.079055e-01-1.237489e+00-1.085669e+00-1.059473e+00-8.151410e-01-8.730392e-01-7.232169e-01-6.965670e-01-6.662719e-01-6.466090e-01...-6.394791e-01-6.366044e-01-6.344794e-01-6.321678e-01-2.953392e-01-2.217062e-01-2.787466e-01-2.911114e-01-2.962091e-01-2.827191e-01
50%-2.154410e-018.080881e-011.847384e-018.595724e-01-1.624117e-011.630464e-021.109834e-011.371457e-011.847406e-012.346298e-01...-3.894941e-01-3.783304e-01-3.666160e-01-3.669621e-01-2.240251e-01-1.711519e-01-1.964268e-01-2.146227e-01-2.153100e-01-2.064582e-01
75%5.539640e-018.080881e-011.847384e-018.595724e-015.991058e-011.630464e-021.109834e-011.371457e-011.847406e-012.346298e-01...1.938462e-011.769007e-011.652917e-011.768375e-01-4.065942e-02-4.307087e-02-3.828314e-02-5.129604e-02-4.786688e-02-6.899112e-02
max6.401442e+008.080881e-015.266367e+002.778618e+004.733058e+007.131055e+006.784586e+006.806847e+006.992841e+007.284540e+00...2.329162e+011.317106e+011.455042e+011.547053e+013.161085e+017.225311e+015.120848e+013.337398e+012.720452e+012.873154e+01
\n", - "

8 rows × 23 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean 1.017149e-16 7.708834e-16 4.951317e-16 5.376625e-16 -1.626419e-17 \n", - "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -1.215668e+00 -1.237489e+00 -2.356076e+00 -2.978518e+00 -1.576658e+00 \n", - "25% -9.079055e-01 -1.237489e+00 -1.085669e+00 -1.059473e+00 -8.151410e-01 \n", - "50% -2.154410e-01 8.080881e-01 1.847384e-01 8.595724e-01 -1.624117e-01 \n", - "75% 5.539640e-01 8.080881e-01 1.847384e-01 8.595724e-01 5.991058e-01 \n", - "max 6.401442e+00 8.080881e-01 5.266367e+00 2.778618e+00 4.733058e+00 \n", - "\n", - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean 8.471233e-16 -1.830735e-15 1.871623e-15 -1.919451e-15 2.199158e-15 \n", - "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -1.762383e+00 -1.557417e+00 -1.530280e+00 -1.517284e+00 -1.527848e+00 \n", - "25% -8.730392e-01 -7.232169e-01 -6.965670e-01 -6.662719e-01 -6.466090e-01 \n", - "50% 1.630464e-02 1.109834e-01 1.371457e-01 1.847406e-01 2.346298e-01 \n", - "75% 1.630464e-02 1.109834e-01 1.371457e-01 1.847406e-01 2.346298e-01 \n", - "max 7.131055e+00 6.784586e+00 6.806847e+00 6.992841e+00 7.284540e+00 \n", - "\n", - " ... BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", - "count ... 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean ... -2.152861e-16 -2.414226e-16 -1.714184e-16 2.617582e-16 \n", - "std ... 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min ... -2.944276e+00 -3.312793e+00 -1.998575e+00 -6.347299e+00 \n", - "25% ... -6.394791e-01 -6.366044e-01 -6.344794e-01 -6.321678e-01 \n", - "50% ... -3.894941e-01 -3.783304e-01 -3.666160e-01 -3.669621e-01 \n", - "75% ... 1.938462e-01 1.769007e-01 1.652917e-01 1.768375e-01 \n", - "max ... 2.329162e+01 1.317106e+01 1.455042e+01 1.547053e+01 \n", - "\n", - " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 \\\n", - "count 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 2.400000e+04 \n", - "mean 6.972201e-17 -1.275126e-16 -1.808068e-16 2.455443e-16 2.328091e-16 \n", - "std 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 1.000021e+00 \n", - "min -3.586451e-01 -2.583330e-01 -3.011661e-01 -3.101540e-01 -3.120793e-01 \n", - "25% -2.953392e-01 -2.217062e-01 -2.787466e-01 -2.911114e-01 -2.962091e-01 \n", - "50% -2.240251e-01 -1.711519e-01 -1.964268e-01 -2.146227e-01 -2.153100e-01 \n", - "75% -4.065942e-02 -4.307087e-02 -3.828314e-02 -5.129604e-02 -4.786688e-02 \n", - "max 3.161085e+01 7.225311e+01 5.120848e+01 3.337398e+01 2.720452e+01 \n", - "\n", - " PAY_AMT6 \n", - "count 2.400000e+04 \n", - "mean -2.814427e-16 \n", - "std 1.000021e+00 \n", - "min -2.887985e-01 \n", - "25% -2.827191e-01 \n", - "50% -2.064582e-01 \n", - "75% -6.899112e-02 \n", - "max 2.873154e+01 \n", - "\n", - "[8 rows x 23 columns]" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check if numerical attributes are normailised with mean = 0 and sd = 1\n", - "X_train_standardize.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perfrom initial PCA for 2 components for visualization\n", - "giving 2 pca components, we will visualize the information retained after performing 2 pca components" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [], - "source": [ - "#perform pca\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "principalComponents = pca.fit_transform(X_train_standardize)\n", - "principalDf = pd.DataFrame(data = principalComponents\n", - " , columns = ['principal component 1', 'principal component 2'])" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Explained variation per principal component: [0.28322138 0.17768244]\n" - ] - } - ], - "source": [ - "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", - "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#visualizing pca\n", - "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", - "fig = plt.figure(figsize = (8,8))\n", - "ax = fig.add_subplot(1,1,1) \n", - "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", - "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", - "ax.set_title('2 component PCA', fontsize = 20)\n", - "targets = [1,0]\n", - "colors = ['r', 'g']\n", - "for target, color in zip(targets,colors):\n", - " indicesToKeep = pca_visualize_df['Y'] == target\n", - " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", - " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", - " , c = color\n", - " , s = 50)\n", - "ax.legend(targets)\n", - "ax.grid()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform PCA to retain 90% of information\n", - "perform PCA to reduce components so we can run SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "#setting pca threshold to 90%\n", - "pca = PCA(0.9)" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pca.fit(X_train_standardize)" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No. of components before pca: 23\n", - "No. of components after pca: 13\n" - ] - } - ], - "source": [ - "#get number of components after pca\n", - "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", - "print('No. of components after pca: {}'.format(pca.n_components_))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the number of components is reduced from 23 components to 13 components" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "#perform pca on training and test attributes\n", - "X_train_pca = pca.transform(X_train_standardize)\n", - "X_test_pca = pca.transform(X_test_standardize)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply SVM model\n", - "Next, we will used the reduced attributes train set to train our SVM model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we train our SVM model without parameter tuning\n", - "nor pca reduction" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "//anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", - " \"avoid this warning.\", FutureWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", - " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import svm\n", - "#train svm model without standardization and no parameter tuning\n", - "clf_original = svm.SVC(kernel = 'rbf', probability = True)\n", - "clf_original.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.2207190875203909\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1301 Defaulters, the SVM original data RBF kernal identified 17\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
0468118
1128417
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4681 18\n", - "1 1284 17" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernal\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "note that the default values of gamma = 1/13 and c= 1" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", - " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import svm\n", - "#train svm model with feature reduction and no parameter tuning\n", - "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", - "clf_reduced.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.17778938528613197\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_reduced, y_test, X_test_pca, \n", - " \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the SVM reduced features no tuning RBF kernal identified 401\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
04425220
1954401
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4425 220\n", - "1 954 401" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.95 0.88 4645\n", - " 1 0.65 0.30 0.41 1355\n", - "\n", - " accuracy 0.80 6000\n", - " macro avg 0.73 0.62 0.64 6000\n", - "weighted avg 0.78 0.80 0.78 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,clf_reduced.predict(X_test_pca)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, by reducing features through pca, the AUROC curve improves, suggesting a better prediction model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now try to find best parameters for SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 10, 'gamma': 0.01}" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001, 0.01, 0.1, 1, 10]\n", - " gammas = [0.001, 0.01, 0.1, 1,10]\n", - " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", - " grid_search.fit(X, y)\n", - " grid_search.best_params_\n", - " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.01)\n", - "clf_reduced_tuned.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.16880724430144842\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", - " \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1355 Defaulters, the SVM reduced features and tuning RBF kernal identified 365\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
04450195
1990365
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 4450 195\n", - "1 990 365" - ] - }, - "execution_count": 187, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.96 0.88 4645\n", - " 1 0.65 0.27 0.38 1355\n", - "\n", - " accuracy 0.80 6000\n", - " macro avg 0.73 0.61 0.63 6000\n", - "weighted avg 0.78 0.80 0.77 6000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,clf_reduced_tuned.predict(X_test_pca)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "BT2101 disrudy ", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From b5cccfcbfad9f605f74b6023c9b1b1d9e25b7d94 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Sun, 10 Nov 2019 17:09:43 +0800 Subject: [PATCH 05/25] Update BT2101 Default.ipynb --- BT2101 Default.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BT2101 Default.ipynb b/BT2101 Default.ipynb index 72a44da..01864ed 100644 --- a/BT2101 Default.ipynb +++ b/BT2101 Default.ipynb @@ -3892,7 +3892,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 7ee6597fb99044fb2d061c346d6197da95ccaf0a Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Mon, 18 Nov 2019 16:34:56 +0800 Subject: [PATCH 06/25] Change data --- .../BT2101_Default - EDIT-checkpoint.ipynb | 1890 ++++++++++++----- BT2101_Default - EDIT.ipynb | 1890 ++++++++++++----- BT2101_Default.ipynb | 2 +- 3 files changed, 2659 insertions(+), 1123 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-checkpoint.ipynb index a39ffdd..849d9b3 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-checkpoint.ipynb @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 169, "metadata": { "colab": {}, "colab_type": "code", @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 170, "metadata": { "colab": {}, "colab_type": "code", @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 171, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 172, "metadata": { "colab": {}, "colab_type": "code", @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 173, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -377,7 +377,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 5, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 174, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -419,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 175, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -436,7 +436,7 @@ "0" ] }, - "execution_count": 7, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 176, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -507,13 +507,13 @@ "Text(0, 0.5, 'Frequency')" ] }, - "execution_count": 8, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 177, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -623,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 178, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -693,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 179, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -761,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 180, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -798,83 +798,83 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " \n", " \n", - " 1\n", + " 1\n", " AGE\n", " \n", " \n", - " 2\n", + " 2\n", " PAY_0\n", " \n", " \n", - " 3\n", + " 3\n", " PAY_2\n", " \n", " \n", - " 4\n", + " 4\n", " PAY_3\n", " \n", " \n", - " 5\n", + " 5\n", " PAY_4\n", " \n", " \n", - " 6\n", + " 6\n", " PAY_5\n", " \n", " \n", - " 7\n", + " 7\n", " PAY_6\n", " \n", " \n", - " 8\n", + " 8\n", " BILL_AMT1\n", " \n", " \n", - " 9\n", + " 9\n", " BILL_AMT2\n", " \n", " \n", - " 10\n", + " 10\n", " BILL_AMT3\n", " \n", " \n", - " 11\n", + " 11\n", " BILL_AMT4\n", " \n", " \n", - " 12\n", + " 12\n", " BILL_AMT5\n", " \n", " \n", - " 13\n", + " 13\n", " BILL_AMT6\n", " \n", " \n", - " 14\n", + " 14\n", " PAY_AMT1\n", " \n", " \n", - " 15\n", + " 15\n", " PAY_AMT2\n", " \n", " \n", - " 16\n", + " 16\n", " PAY_AMT3\n", " \n", " \n", - " 17\n", + " 17\n", " PAY_AMT4\n", " \n", " \n", - " 18\n", + " 18\n", " PAY_AMT5\n", " \n", " \n", - " 19\n", + " 19\n", " PAY_AMT6\n", " \n", " \n", @@ -905,7 +905,7 @@ "19 PAY_AMT6" ] }, - "execution_count": 12, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -917,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -965,7 +965,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 30000.000000\n", " 30000.000000\n", " 30000.000000\n", @@ -988,7 +988,7 @@ " 30000.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 167484.322667\n", " 35.485500\n", " -0.016700\n", @@ -1011,7 +1011,7 @@ " 5215.502567\n", " \n", " \n", - " std\n", + " std\n", " 129747.661567\n", " 9.217904\n", " 1.123802\n", @@ -1034,7 +1034,7 @@ " 17777.465775\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 21.000000\n", " -2.000000\n", @@ -1057,7 +1057,7 @@ " 0.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 28.000000\n", " -1.000000\n", @@ -1080,7 +1080,7 @@ " 117.750000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 140000.000000\n", " 34.000000\n", " 0.000000\n", @@ -1103,7 +1103,7 @@ " 1500.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 240000.000000\n", " 41.000000\n", " 0.000000\n", @@ -1126,7 +1126,7 @@ " 4000.000000\n", " \n", " \n", - " max\n", + " max\n", " 1000000.000000\n", " 79.000000\n", " 8.000000\n", @@ -1204,7 +1204,7 @@ "max 426529.000000 528666.000000 " ] }, - "execution_count": 13, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -1233,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 182, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1271,83 +1271,83 @@ " \n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 0.324794\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 0.263551\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 0.235253\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 0.216614\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 0.204149\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 0.186866\n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 0.153520\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 0.072929\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 0.058579\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 0.056827\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 0.056250\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 0.055124\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 0.053183\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 0.019644\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 0.014193\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 0.014076\n", " \n", " \n", - " AGE\n", + " AGE\n", " 0.013890\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 0.010156\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 0.006760\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 0.005372\n", " \n", " \n", @@ -1378,7 +1378,7 @@ "BILL_AMT6 0.005372" ] }, - "execution_count": 14, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" } @@ -1426,16 +1426,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 183, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, - "execution_count": 17, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -1447,16 +1447,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, - "execution_count": 18, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -1470,13 +1470,189 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Outliers\n", + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6\n", + "ID \n", + "1 2 2 0 0 0 0\n", + "2 0 2 0 0 0 2\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 0 0 0 0 0 0\n" + ] + } + ], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)\n", + "\n", + "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].head())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -1511,142 +1687,142 @@ " PAY_4\n", " PAY_5\n", " ...\n", - " BILL_AMT4\n", - " BILL_AMT5\n", - " BILL_AMT6\n", - " PAY_AMT1\n", - " PAY_AMT2\n", - " PAY_AMT3\n", " PAY_AMT4\n", " PAY_AMT5\n", " PAY_AMT6\n", " Y\n", + " S_0\n", + " S_2\n", + " S_3\n", + " S_4\n", + " S_5\n", + " S_6\n", " \n", " \n", " \n", " \n", - " count\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", + " count\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", " ...\n", - " 25128.00000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " \n", - " \n", - " mean\n", - " 152888.092964\n", - " 1.613937\n", - " 1.844675\n", - " 1.563873\n", - " 35.024554\n", - " -0.085840\n", - " -0.225127\n", - " -0.260148\n", - " -0.313913\n", - " -0.358445\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " \n", + " \n", + " mean\n", + " 149324.899981\n", + " 1.608954\n", + " 1.852734\n", + " 1.564717\n", + " 35.006592\n", + " 0.372109\n", + " 0.337321\n", + " 0.324633\n", + " 0.278224\n", + " 0.238750\n", " ...\n", - " 31879.61712\n", - " 29394.608286\n", - " 28196.281280\n", - " 3606.968641\n", - " 3696.252467\n", - " 3201.249522\n", - " 2850.484479\n", - " 2840.468959\n", - " 2882.267869\n", - " 0.212472\n", - " \n", - " \n", - " std\n", - " 116895.631153\n", - " 0.486855\n", - " 0.740839\n", - " 0.521725\n", - " 8.782188\n", - " 1.020072\n", - " 1.092299\n", - " 1.094282\n", - " 1.056713\n", - " 1.025571\n", + " 2787.425071\n", + " 2778.830673\n", + " 2822.285007\n", + " 0.230177\n", + " -0.133587\n", + " -0.300438\n", + " -0.327300\n", + " -0.364412\n", + " -0.395999\n", + " -0.428158\n", + " \n", + " \n", + " std\n", + " 116558.616530\n", + " 0.487994\n", + " 0.738572\n", + " 0.521936\n", + " 8.832028\n", + " 0.765730\n", + " 0.814878\n", + " 0.811491\n", + " 0.786314\n", + " 0.743923\n", " ...\n", - " 40923.95966\n", - " 38977.042600\n", - " 38392.329148\n", - " 5182.549076\n", - " 6111.658249\n", - " 5406.757131\n", - " 4899.719901\n", - " 4810.352492\n", - " 5317.958689\n", - " 0.409065\n", - " \n", - " \n", - " min\n", + " 4835.081906\n", + " 4751.263287\n", + " 5271.198100\n", + " 0.420954\n", + " 0.879876\n", + " 0.883472\n", + " 0.895264\n", + " 0.886115\n", + " 0.877789\n", + " 0.900723\n", + " \n", + " \n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 21.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " ...\n", - " -50616.00000\n", - " -53007.000000\n", - " -94625.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " 0.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 28.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " ...\n", + " 150.000000\n", + " 82.000000\n", + " 0.000000\n", + " 0.000000\n", + " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", - " ...\n", - " 1623.00000\n", - " 1178.000000\n", - " 814.000000\n", - " 996.500000\n", - " 785.000000\n", - " 390.000000\n", - " 225.000000\n", - " 165.000000\n", - " 0.000000\n", - " 0.000000\n", " \n", " \n", - " 50%\n", - " 130000.000000\n", + " 50%\n", + " 120000.000000\n", " 2.000000\n", " 2.000000\n", " 2.000000\n", @@ -1657,19 +1833,19 @@ " 0.000000\n", " 0.000000\n", " ...\n", - " 16941.50000\n", - " 15029.500000\n", - " 13313.000000\n", - " 2000.000000\n", - " 2000.000000\n", - " 1600.000000\n", - " 1265.500000\n", - " 1300.000000\n", " 1200.000000\n", + " 1218.000000\n", + " 1143.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1681,101 +1857,101 @@ " 0.000000\n", " 0.000000\n", " ...\n", - " 45781.75000\n", - " 40615.500000\n", - " 38585.750000\n", - " 4404.250000\n", - " 4136.250000\n", - " 3638.250000\n", - " 3224.500000\n", - " 3290.500000\n", - " 3179.250000\n", + " 3118.000000\n", + " 3140.000000\n", + " 3069.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", " 3.000000\n", " 59.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", " ...\n", - " 208931.00000\n", - " 196698.000000\n", - " 192499.000000\n", - " 48329.000000\n", - " 65280.000000\n", - " 50551.000000\n", " 45171.000000\n", " 44197.000000\n", " 51000.000000\n", " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", "\n", - "

8 rows × 24 columns

\n", + "

8 rows × 30 columns

\n", "" ], "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean 152888.092964 1.613937 1.844675 1.563873 35.024554 \n", - "std 116895.631153 0.486855 0.740839 0.521725 8.782188 \n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 130000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", "\n", " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean -0.085840 -0.225127 -0.260148 -0.313913 -0.358445 \n", - "std 1.020072 1.092299 1.094282 1.056713 1.025571 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 2.000000 2.000000 2.000000 2.000000 2.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", "\n", - " ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 \\\n", - "count ... 25128.00000 25128.000000 25128.000000 25128.000000 \n", - "mean ... 31879.61712 29394.608286 28196.281280 3606.968641 \n", - "std ... 40923.95966 38977.042600 38392.329148 5182.549076 \n", - "min ... -50616.00000 -53007.000000 -94625.000000 0.000000 \n", - "25% ... 1623.00000 1178.000000 814.000000 996.500000 \n", - "50% ... 16941.50000 15029.500000 13313.000000 2000.000000 \n", - "75% ... 45781.75000 40615.500000 38585.750000 4404.250000 \n", - "max ... 208931.00000 196698.000000 192499.000000 48329.000000 \n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", "\n", - " PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean 3696.252467 3201.249522 2850.484479 2840.468959 2882.267869 \n", - "std 6111.658249 5406.757131 4899.719901 4810.352492 5317.958689 \n", - "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25% 785.000000 390.000000 225.000000 165.000000 0.000000 \n", - "50% 2000.000000 1600.000000 1265.500000 1300.000000 1200.000000 \n", - "75% 4136.250000 3638.250000 3224.500000 3290.500000 3179.250000 \n", - "max 65280.000000 50551.000000 45171.000000 44197.000000 51000.000000 \n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", - " Y \n", - "count 25128.000000 \n", - "mean 0.212472 \n", - "std 0.409065 \n", - "min 0.000000 \n", - "25% 0.000000 \n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 \n", "\n", - "[8 rows x 24 columns]" + "[8 rows x 30 columns]" ] }, - "execution_count": 19, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } @@ -1783,7 +1959,7 @@ "source": [ "from scipy import stats\n", "#we are only concerned with the ordinal data\n", - "o = pd.DataFrame(df.drop(['EDUCATION', 'MARRIAGE', \"SEX\"], axis=1))\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", "df = df[rows]\n", @@ -1801,21 +1977,189 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 189, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALAGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
ID
120000243913310268900006890000
212000026268217252682327234553261010001000100002000
39000034292391402713559143311494815549151815001000100010005000
45000037469904823349291283142895929547200020191200110010691000
550000578617567035835209401914619131200036681100009000689679
\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL AGE BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 \\\n", + "ID \n", + "1 20000 24 3913 3102 689 0 0 \n", + "2 120000 26 2682 1725 2682 3272 3455 \n", + "3 90000 34 29239 14027 13559 14331 14948 \n", + "4 50000 37 46990 48233 49291 28314 28959 \n", + "5 50000 57 8617 5670 35835 20940 19146 \n", + "\n", + " BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \n", + "ID \n", + "1 0 0 689 0 0 0 0 \n", + "2 3261 0 1000 1000 1000 0 2000 \n", + "3 15549 1518 1500 1000 1000 1000 5000 \n", + "4 29547 2000 2019 1200 1100 1069 1000 \n", + "5 19131 2000 36681 10000 9000 689 679 " + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", - "cols = df.drop([\"Y\", \"SEX\", \"MARRIAGE\", \"EDUCATION\"], axis =1)\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", "df1 = df.copy()\n", "df1[cols.columns] = scaler.fit_transform(cols)\n", - "df = df1" + "df = df1\n", + "cols.head()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 190, "metadata": {}, "outputs": [ { @@ -1850,16 +2194,16 @@ " PAY_4\n", " PAY_5\n", " ...\n", - " BILL_AMT4\n", - " BILL_AMT5\n", - " BILL_AMT6\n", - " PAY_AMT1\n", - " PAY_AMT2\n", - " PAY_AMT3\n", " PAY_AMT4\n", " PAY_AMT5\n", " PAY_AMT6\n", " Y\n", + " S_0\n", + " S_2\n", + " S_3\n", + " S_4\n", + " S_5\n", + " S_6\n", " \n", " \n", " ID\n", @@ -1888,159 +2232,151 @@ " \n", " \n", " \n", - " 1\n", - " -1.136832\n", + " 1\n", + " -1.109548\n", " 2\n", " 2\n", " 1\n", - " -1.255356\n", - " 2.044837\n", - " 2.037145\n", - " -0.676121\n", - " -0.649279\n", - " -1.600657\n", + " -1.246237\n", + " 2\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.779012\n", - " -0.754167\n", - " -0.734439\n", - " -0.695997\n", - " -0.492062\n", - " -0.592095\n", - " -0.581776\n", - " -0.590503\n", - " -0.541998\n", + " -0.576511\n", + " -0.584873\n", + " -0.535426\n", " 1\n", + " 1\n", + " 1\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", " \n", " \n", + " 2\n", + " -0.251594\n", " 2\n", - " -0.281351\n", " 2\n", " 2\n", + " -1.019784\n", + " 0\n", " 2\n", - " -1.027618\n", - " -0.896189\n", - " 2.037145\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.699057\n", - " -0.665523\n", - " -0.649499\n", - " -0.695997\n", - " -0.441174\n", - " -0.407137\n", - " -0.377679\n", - " -0.590503\n", - " -0.165907\n", + " -0.369685\n", + " -0.584873\n", + " -0.155999\n", + " 1\n", + " -1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", " 1\n", " \n", " \n", - " 3\n", - " -0.537996\n", + " 3\n", + " -0.508980\n", " 2\n", " 2\n", " 2\n", - " -0.116665\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " -0.113973\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.428819\n", - " -0.370651\n", - " -0.329429\n", - " -0.403085\n", - " -0.359362\n", - " -0.407137\n", - " -0.377679\n", - " -0.382613\n", - " 0.398231\n", + " -0.369685\n", + " -0.374398\n", + " 0.413143\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", - " 4\n", - " -0.880188\n", + " 4\n", + " -0.852162\n", " 2\n", " 2\n", " 1\n", - " 0.224942\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " 0.225707\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.087130\n", - " -0.011176\n", - " 0.035183\n", - " -0.310079\n", - " -0.274440\n", - " -0.370146\n", - " -0.357269\n", - " -0.368269\n", - " -0.353953\n", + " -0.349003\n", + " -0.359875\n", + " -0.345713\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", - " 5\n", - " -0.880188\n", + " 5\n", + " -0.852162\n", " 1\n", " 2\n", " 1\n", - " 2.502324\n", - " -0.896189\n", - " 0.206108\n", - " -0.676121\n", - " 0.297071\n", - " 0.349514\n", + " 2.490235\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.267321\n", - " -0.262945\n", - " -0.236127\n", - " -0.310079\n", - " 5.397128\n", - " 1.257479\n", - " 1.255100\n", - " -0.447267\n", - " -0.414315\n", + " 1.284920\n", + " -0.439856\n", + " -0.406611\n", + " 0\n", + " -1\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", "\n", - "

5 rows × 24 columns

\n", + "

5 rows × 30 columns

\n", "" ], "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 \\\n", - "ID \n", - "1 -1.136832 2 2 1 -1.255356 2.044837 2.037145 \n", - "2 -0.281351 2 2 2 -1.027618 -0.896189 2.037145 \n", - "3 -0.537996 2 2 2 -0.116665 0.084153 0.206108 \n", - "4 -0.880188 2 2 1 0.224942 0.084153 0.206108 \n", - "5 -0.880188 1 2 1 2.502324 -0.896189 0.206108 \n", + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 -1.109548 2 2 1 -1.246237 2 2 0 0 \n", + "2 -0.251594 2 2 2 -1.019784 0 2 0 0 \n", + "3 -0.508980 2 2 2 -0.113973 0 0 0 0 \n", + "4 -0.852162 2 2 1 0.225707 0 0 0 0 \n", + "5 -0.852162 1 2 1 2.490235 0 0 0 0 \n", "\n", - " PAY_3 PAY_4 PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", - "ID ... \n", - "1 -0.676121 -0.649279 -1.600657 ... -0.779012 -0.754167 -0.734439 \n", - "2 0.237739 0.297071 0.349514 ... -0.699057 -0.665523 -0.649499 \n", - "3 0.237739 0.297071 0.349514 ... -0.428819 -0.370651 -0.329429 \n", - "4 0.237739 0.297071 0.349514 ... -0.087130 -0.011176 0.035183 \n", - "5 -0.676121 0.297071 0.349514 ... -0.267321 -0.262945 -0.236127 \n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... -0.576511 -0.584873 -0.535426 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... -0.369685 -0.584873 -0.155999 1 -1 1 0 0 0 1 \n", + "3 0 ... -0.369685 -0.374398 0.413143 0 0 0 0 0 0 0 \n", + "4 0 ... -0.349003 -0.359875 -0.345713 0 0 0 0 0 0 0 \n", + "5 0 ... 1.284920 -0.439856 -0.406611 0 -1 0 -1 0 0 0 \n", "\n", - " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 -0.695997 -0.492062 -0.592095 -0.581776 -0.590503 -0.541998 1 \n", - "2 -0.695997 -0.441174 -0.407137 -0.377679 -0.590503 -0.165907 1 \n", - "3 -0.403085 -0.359362 -0.407137 -0.377679 -0.382613 0.398231 0 \n", - "4 -0.310079 -0.274440 -0.370146 -0.357269 -0.368269 -0.353953 0 \n", - "5 -0.310079 5.397128 1.257479 1.255100 -0.447267 -0.414315 0 \n", - "\n", - "[5 rows x 24 columns]" + "[5 rows x 30 columns]" ] }, - "execution_count": 21, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -2053,7 +2389,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### One-Hot Encoding" + "### One-Hot Encoding for Categorical attributes" ] }, { @@ -2074,7 +2410,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -2083,7 +2419,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ @@ -2092,7 +2428,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -2125,7 +2461,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2133,7 +2469,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2141,7 +2477,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2149,7 +2485,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2157,7 +2493,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2177,12 +2513,13 @@ "4 0.0 1.0 0.0 1.0 0.0" ] }, - "execution_count": 24, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", "#drop one of each category to prevent dummy variable trap\n", @@ -2192,7 +2529,233 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, "metadata": {}, "outputs": [ { @@ -2227,85 +2790,85 @@ " PAY_6\n", " BILL_AMT1\n", " ...\n", - " PAY_AMT3\n", - " PAY_AMT4\n", - " PAY_AMT5\n", - " PAY_AMT6\n", - " Y\n", - " GRAD\n", - " UNI\n", - " HS\n", - " MARRIED\n", - " SINGLE\n", + " PAY_3_Revolving_Credit\n", + " PAY_4_No_Transactions\n", + " PAY_4_Pay_Duly\n", + " PAY_4_Revolving_Credit\n", + " PAY_5_No_Transactions\n", + " PAY_5_Pay_Duly\n", + " PAY_5_Revolving_Credit\n", + " PAY_6_No_Transactions\n", + " PAY_6_Pay_Duly\n", + " PAY_6_Revolving_Credit\n", " \n", " \n", " \n", " \n", + " 0\n", + " -1.109548\n", + " 2\n", + " -1.246237\n", + " 2\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " -1.136832\n", - " 2\n", - " -1.255356\n", - " 2.044837\n", - " 2.037145\n", - " -0.676121\n", - " -0.649279\n", - " -1.600657\n", - " -1.528474\n", - " -0.734773\n", + " -0.734016\n", " ...\n", - " -0.592095\n", - " -0.581776\n", - " -0.590503\n", - " -0.541998\n", - " 1\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", - " 1\n", - " -0.281351\n", + " 1\n", + " -0.251594\n", + " 2\n", + " -1.019784\n", + " 0\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " 2\n", - " -1.027618\n", - " -0.896189\n", - " 2.037145\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 2.253746\n", - " -0.760267\n", + " -0.759736\n", " ...\n", - " -0.407137\n", - " -0.377679\n", - " -0.590503\n", - " -0.165907\n", - " 1\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", + " 2\n", + " -0.508980\n", " 2\n", - " -0.537996\n", - " 2\n", - " -0.116665\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " -0.210263\n", - " ...\n", - " -0.407137\n", - " -0.377679\n", - " -0.382613\n", - " 0.398231\n", + " -0.113973\n", + " 0\n", " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -0.204872\n", + " ...\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2313,97 +2876,112 @@ " 1.0\n", " \n", " \n", - " 3\n", - " -0.880188\n", + " 3\n", + " -0.852162\n", " 2\n", - " 0.224942\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " 0.157367\n", - " ...\n", - " -0.370146\n", - " -0.357269\n", - " -0.368269\n", - " -0.353953\n", + " 0.225707\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", + " 0\n", + " 0.166005\n", + " ...\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 4\n", - " -0.880188\n", + " 4\n", + " -0.852162\n", " 1\n", - " 2.502324\n", - " -0.896189\n", - " 0.206108\n", - " -0.676121\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " -0.637351\n", - " ...\n", - " 1.257479\n", - " 1.255100\n", - " -0.447267\n", - " -0.414315\n", + " 2.490235\n", " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -0.635734\n", + " ...\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " \n", " \n", "\n", - "

5 rows × 27 columns

\n", + "

5 rows × 45 columns

\n", "" ], "text/plain": [ - " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "0 -1.136832 2 -1.255356 2.044837 2.037145 -0.676121 -0.649279 -1.600657 \n", - "1 -0.281351 2 -1.027618 -0.896189 2.037145 0.237739 0.297071 0.349514 \n", - "2 -0.537996 2 -0.116665 0.084153 0.206108 0.237739 0.297071 0.349514 \n", - "3 -0.880188 2 0.224942 0.084153 0.206108 0.237739 0.297071 0.349514 \n", - "4 -0.880188 1 2.502324 -0.896189 0.206108 -0.676121 0.297071 0.349514 \n", + " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 \\\n", + "0 -1.109548 2 -1.246237 2 2 0 0 0 0 \n", + "1 -0.251594 2 -1.019784 0 2 0 0 0 2 \n", + "2 -0.508980 2 -0.113973 0 0 0 0 0 0 \n", + "3 -0.852162 2 0.225707 0 0 0 0 0 0 \n", + "4 -0.852162 1 2.490235 0 0 0 0 0 0 \n", + "\n", + " BILL_AMT1 ... PAY_3_Revolving_Credit PAY_4_No_Transactions \\\n", + "0 -0.734016 ... 0.0 0.0 \n", + "1 -0.759736 ... 1.0 0.0 \n", + "2 -0.204872 ... 1.0 0.0 \n", + "3 0.166005 ... 1.0 0.0 \n", + "4 -0.635734 ... 0.0 0.0 \n", "\n", - " PAY_6 BILL_AMT1 ... PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 Y GRAD \\\n", - "0 -1.528474 -0.734773 ... -0.592095 -0.581776 -0.590503 -0.541998 1 0.0 \n", - "1 2.253746 -0.760267 ... -0.407137 -0.377679 -0.590503 -0.165907 1 0.0 \n", - "2 0.362636 -0.210263 ... -0.407137 -0.377679 -0.382613 0.398231 0 0.0 \n", - "3 0.362636 0.157367 ... -0.370146 -0.357269 -0.368269 -0.353953 0 0.0 \n", - "4 0.362636 -0.637351 ... 1.257479 1.255100 -0.447267 -0.414315 0 0.0 \n", + " PAY_4_Pay_Duly PAY_4_Revolving_Credit PAY_5_No_Transactions \\\n", + "0 1.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", "\n", - " UNI HS MARRIED SINGLE \n", - "0 1.0 0.0 1.0 0.0 \n", - "1 1.0 0.0 0.0 1.0 \n", - "2 1.0 0.0 0.0 1.0 \n", - "3 1.0 0.0 1.0 0.0 \n", - "4 1.0 0.0 1.0 0.0 \n", + " PAY_5_Pay_Duly PAY_5_Revolving_Credit PAY_6_No_Transactions \\\n", + "0 0.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", "\n", - "[5 rows x 27 columns]" + " PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + "\n", + "[5 rows x 45 columns]" ] }, - "execution_count": 25, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df1 = df.drop(['EDUCATION', 'MARRIAGE'], axis = 1)\n", + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", "df1.head()" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -2464,7 +3042,7 @@ "[0 rows x 27 columns]" ] }, - "execution_count": 26, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -2480,14 +3058,14 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 220, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data has 27 Columns and 25128 Rows\n" + "Data has 45 Columns and 26245 Rows\n" ] } ], @@ -2506,7 +3084,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -2528,14 +3106,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 117, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2576,7 +3154,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -2586,7 +3164,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 119, "metadata": { "colab": {}, "colab_type": "code", @@ -2611,7 +3189,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -2645,7 +3223,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 121, "metadata": { "scrolled": true }, @@ -2676,7 +3254,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -4223,7 +4801,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -4237,14 +4815,14 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.34110585 0.16477455]\n" + "Explained variation per principal component: [0.33957018 0.16512418]\n" ] } ], @@ -4262,12 +4840,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4315,7 +4893,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -4325,7 +4903,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -4335,7 +4913,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 72, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -4346,7 +4924,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -4368,12 +4946,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the number of components is reduced from 23 components to 13 components" + "As we can see, the number of components is reduced from 26 components to 13 components" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -4400,7 +4978,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -4412,7 +4990,7 @@ " verbose=False)" ] }, - "execution_count": 144, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -4426,19 +5004,19 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16392572029200445\n" + "Optimal Threshold: 0.16930806252855038\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4451,10 +5029,10 @@ { "data": { "text/plain": [ - "0.722588777021613" + "0.7218839449278682" ] }, - "execution_count": 145, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -4466,14 +5044,14 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1017 Defaulters, the SVM original data RBF kernel identified 280\n" + "Of 1056 Defaulters, the SVM original data RBF kernel identified 297\n" ] }, { @@ -4508,14 +5086,14 @@ " \n", " \n", " \n", - " 0\n", - " 3913\n", - " 96\n", + " 0\n", + " 3869\n", + " 101\n", " \n", " \n", - " 1\n", - " 737\n", - " 280\n", + " 1\n", + " 759\n", + " 297\n", " \n", " \n", "\n", @@ -4524,11 +5102,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 3913 96\n", - "1 737 280" + "0 3869 101\n", + "1 759 297" ] }, - "execution_count": 146, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -4538,6 +5116,31 @@ "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.97 0.90 3970\n", + " 1 0.75 0.28 0.41 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.79 0.63 0.65 5026\n", + "weighted avg 0.82 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4554,7 +5157,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -4566,7 +5169,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 147, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -4579,19 +5182,19 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.18731494599976836\n" + "Optimal Threshold: 0.17464839093863194\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4604,10 +5207,10 @@ { "data": { "text/plain": [ - "0.7062615751726756" + "0.7171996651019006" ] }, - "execution_count": 148, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -4620,14 +5223,14 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1017 Defaulters, the SVM reduced features no tuning RBF kernal identified 296\n" + "Of 1056 Defaulters, the SVM reduced features no tuning RBF kernal identified 307\n" ] }, { @@ -4662,14 +5265,14 @@ " \n", " \n", " \n", - " 0\n", - " 3904\n", - " 105\n", + " 0\n", + " 3872\n", + " 98\n", " \n", " \n", - " 1\n", - " 721\n", - " 296\n", + " 1\n", + " 749\n", + " 307\n", " \n", " \n", "\n", @@ -4678,11 +5281,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 3904 105\n", - "1 721 296" + "0 3872 98\n", + "1 749 307" ] }, - "execution_count": 149, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -4692,11 +5295,36 @@ "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.98 0.90 3970\n", + " 1 0.76 0.29 0.42 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.80 0.63 0.66 5026\n", + "weighted avg 0.82 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, by reducing features through pca, the AUROC curve improves, suggesting a better prediction model." + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." ] }, { @@ -4708,38 +5336,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 10, 'gamma': 0.01}" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001, 0.01, 0.1, 1, 10]\n", - " gammas = [0.001, 0.01, 0.1, 1,10]\n", + " Cs = [0.01, 0.1, 1,10]\n", + " gammas = [0.01, 0.1, 10]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,10)\n" + "svc_param_selection(X_train_pca, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.01)\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", "clf_reduced_tuned.fit(X_train_pca, y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.17824393299122973\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", " \"SVM reduced features and tuning RBF kernal\")" @@ -4747,14 +5427,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1056 Defaulters, the SVM reduced features and tuning RBF kernal identified 325\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03835135
1731325
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3835 135\n", + "1 731 325" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.97 0.90 3970\n", + " 1 0.71 0.31 0.43 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.77 0.64 0.66 5026\n", + "weighted avg 0.81 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4781,7 +5550,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -5212,7 +5980,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/BT2101_Default - EDIT.ipynb b/BT2101_Default - EDIT.ipynb index a39ffdd..849d9b3 100644 --- a/BT2101_Default - EDIT.ipynb +++ b/BT2101_Default - EDIT.ipynb @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 169, "metadata": { "colab": {}, "colab_type": "code", @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 170, "metadata": { "colab": {}, "colab_type": "code", @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 171, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 172, "metadata": { "colab": {}, "colab_type": "code", @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 173, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -377,7 +377,7 @@ "[5 rows x 24 columns]" ] }, - "execution_count": 5, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 174, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -419,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 175, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -436,7 +436,7 @@ "0" ] }, - "execution_count": 7, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 176, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -507,13 +507,13 @@ "Text(0, 0.5, 'Frequency')" ] }, - "execution_count": 8, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 177, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -623,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 178, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -693,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 179, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -761,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 180, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -798,83 +798,83 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " \n", " \n", - " 1\n", + " 1\n", " AGE\n", " \n", " \n", - " 2\n", + " 2\n", " PAY_0\n", " \n", " \n", - " 3\n", + " 3\n", " PAY_2\n", " \n", " \n", - " 4\n", + " 4\n", " PAY_3\n", " \n", " \n", - " 5\n", + " 5\n", " PAY_4\n", " \n", " \n", - " 6\n", + " 6\n", " PAY_5\n", " \n", " \n", - " 7\n", + " 7\n", " PAY_6\n", " \n", " \n", - " 8\n", + " 8\n", " BILL_AMT1\n", " \n", " \n", - " 9\n", + " 9\n", " BILL_AMT2\n", " \n", " \n", - " 10\n", + " 10\n", " BILL_AMT3\n", " \n", " \n", - " 11\n", + " 11\n", " BILL_AMT4\n", " \n", " \n", - " 12\n", + " 12\n", " BILL_AMT5\n", " \n", " \n", - " 13\n", + " 13\n", " BILL_AMT6\n", " \n", " \n", - " 14\n", + " 14\n", " PAY_AMT1\n", " \n", " \n", - " 15\n", + " 15\n", " PAY_AMT2\n", " \n", " \n", - " 16\n", + " 16\n", " PAY_AMT3\n", " \n", " \n", - " 17\n", + " 17\n", " PAY_AMT4\n", " \n", " \n", - " 18\n", + " 18\n", " PAY_AMT5\n", " \n", " \n", - " 19\n", + " 19\n", " PAY_AMT6\n", " \n", " \n", @@ -905,7 +905,7 @@ "19 PAY_AMT6" ] }, - "execution_count": 12, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -917,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -965,7 +965,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 30000.000000\n", " 30000.000000\n", " 30000.000000\n", @@ -988,7 +988,7 @@ " 30000.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 167484.322667\n", " 35.485500\n", " -0.016700\n", @@ -1011,7 +1011,7 @@ " 5215.502567\n", " \n", " \n", - " std\n", + " std\n", " 129747.661567\n", " 9.217904\n", " 1.123802\n", @@ -1034,7 +1034,7 @@ " 17777.465775\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 21.000000\n", " -2.000000\n", @@ -1057,7 +1057,7 @@ " 0.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 28.000000\n", " -1.000000\n", @@ -1080,7 +1080,7 @@ " 117.750000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 140000.000000\n", " 34.000000\n", " 0.000000\n", @@ -1103,7 +1103,7 @@ " 1500.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 240000.000000\n", " 41.000000\n", " 0.000000\n", @@ -1126,7 +1126,7 @@ " 4000.000000\n", " \n", " \n", - " max\n", + " max\n", " 1000000.000000\n", " 79.000000\n", " 8.000000\n", @@ -1204,7 +1204,7 @@ "max 426529.000000 528666.000000 " ] }, - "execution_count": 13, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -1233,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 182, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1271,83 +1271,83 @@ " \n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 0.324794\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 0.263551\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 0.235253\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 0.216614\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 0.204149\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 0.186866\n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 0.153520\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 0.072929\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 0.058579\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 0.056827\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 0.056250\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 0.055124\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 0.053183\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 0.019644\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 0.014193\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 0.014076\n", " \n", " \n", - " AGE\n", + " AGE\n", " 0.013890\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 0.010156\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 0.006760\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 0.005372\n", " \n", " \n", @@ -1378,7 +1378,7 @@ "BILL_AMT6 0.005372" ] }, - "execution_count": 14, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" } @@ -1426,16 +1426,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 183, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, - "execution_count": 17, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } @@ -1447,16 +1447,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, - "execution_count": 18, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -1470,13 +1470,189 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Outliers\n", + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6\n", + "ID \n", + "1 2 2 0 0 0 0\n", + "2 0 2 0 0 0 2\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 0 0 0 0 0 0\n" + ] + } + ], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)\n", + "\n", + "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].head())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -1511,142 +1687,142 @@ " PAY_4\n", " PAY_5\n", " ...\n", - " BILL_AMT4\n", - " BILL_AMT5\n", - " BILL_AMT6\n", - " PAY_AMT1\n", - " PAY_AMT2\n", - " PAY_AMT3\n", " PAY_AMT4\n", " PAY_AMT5\n", " PAY_AMT6\n", " Y\n", + " S_0\n", + " S_2\n", + " S_3\n", + " S_4\n", + " S_5\n", + " S_6\n", " \n", " \n", " \n", " \n", - " count\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", + " count\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", " ...\n", - " 25128.00000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " 25128.000000\n", - " \n", - " \n", - " mean\n", - " 152888.092964\n", - " 1.613937\n", - " 1.844675\n", - " 1.563873\n", - " 35.024554\n", - " -0.085840\n", - " -0.225127\n", - " -0.260148\n", - " -0.313913\n", - " -0.358445\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " 26245.000000\n", + " \n", + " \n", + " mean\n", + " 149324.899981\n", + " 1.608954\n", + " 1.852734\n", + " 1.564717\n", + " 35.006592\n", + " 0.372109\n", + " 0.337321\n", + " 0.324633\n", + " 0.278224\n", + " 0.238750\n", " ...\n", - " 31879.61712\n", - " 29394.608286\n", - " 28196.281280\n", - " 3606.968641\n", - " 3696.252467\n", - " 3201.249522\n", - " 2850.484479\n", - " 2840.468959\n", - " 2882.267869\n", - " 0.212472\n", - " \n", - " \n", - " std\n", - " 116895.631153\n", - " 0.486855\n", - " 0.740839\n", - " 0.521725\n", - " 8.782188\n", - " 1.020072\n", - " 1.092299\n", - " 1.094282\n", - " 1.056713\n", - " 1.025571\n", + " 2787.425071\n", + " 2778.830673\n", + " 2822.285007\n", + " 0.230177\n", + " -0.133587\n", + " -0.300438\n", + " -0.327300\n", + " -0.364412\n", + " -0.395999\n", + " -0.428158\n", + " \n", + " \n", + " std\n", + " 116558.616530\n", + " 0.487994\n", + " 0.738572\n", + " 0.521936\n", + " 8.832028\n", + " 0.765730\n", + " 0.814878\n", + " 0.811491\n", + " 0.786314\n", + " 0.743923\n", " ...\n", - " 40923.95966\n", - " 38977.042600\n", - " 38392.329148\n", - " 5182.549076\n", - " 6111.658249\n", - " 5406.757131\n", - " 4899.719901\n", - " 4810.352492\n", - " 5317.958689\n", - " 0.409065\n", - " \n", - " \n", - " min\n", + " 4835.081906\n", + " 4751.263287\n", + " 5271.198100\n", + " 0.420954\n", + " 0.879876\n", + " 0.883472\n", + " 0.895264\n", + " 0.886115\n", + " 0.877789\n", + " 0.900723\n", + " \n", + " \n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 21.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " -2.000000\n", - " ...\n", - " -50616.00000\n", - " -53007.000000\n", - " -94625.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", + " ...\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " 0.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", + " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", " 28.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " ...\n", + " 150.000000\n", + " 82.000000\n", + " 0.000000\n", + " 0.000000\n", + " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", " -1.000000\n", - " ...\n", - " 1623.00000\n", - " 1178.000000\n", - " 814.000000\n", - " 996.500000\n", - " 785.000000\n", - " 390.000000\n", - " 225.000000\n", - " 165.000000\n", - " 0.000000\n", - " 0.000000\n", " \n", " \n", - " 50%\n", - " 130000.000000\n", + " 50%\n", + " 120000.000000\n", " 2.000000\n", " 2.000000\n", " 2.000000\n", @@ -1657,19 +1833,19 @@ " 0.000000\n", " 0.000000\n", " ...\n", - " 16941.50000\n", - " 15029.500000\n", - " 13313.000000\n", - " 2000.000000\n", - " 2000.000000\n", - " 1600.000000\n", - " 1265.500000\n", - " 1300.000000\n", " 1200.000000\n", + " 1218.000000\n", + " 1143.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1681,101 +1857,101 @@ " 0.000000\n", " 0.000000\n", " ...\n", - " 45781.75000\n", - " 40615.500000\n", - " 38585.750000\n", - " 4404.250000\n", - " 4136.250000\n", - " 3638.250000\n", - " 3224.500000\n", - " 3290.500000\n", - " 3179.250000\n", + " 3118.000000\n", + " 3140.000000\n", + " 3069.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", " 3.000000\n", " 59.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", - " 2.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", + " 8.000000\n", " ...\n", - " 208931.00000\n", - " 196698.000000\n", - " 192499.000000\n", - " 48329.000000\n", - " 65280.000000\n", - " 50551.000000\n", " 45171.000000\n", " 44197.000000\n", " 51000.000000\n", " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", "\n", - "

8 rows × 24 columns

\n", + "

8 rows × 30 columns

\n", "" ], "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean 152888.092964 1.613937 1.844675 1.563873 35.024554 \n", - "std 116895.631153 0.486855 0.740839 0.521725 8.782188 \n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 130000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", "\n", " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean -0.085840 -0.225127 -0.260148 -0.313913 -0.358445 \n", - "std 1.020072 1.092299 1.094282 1.056713 1.025571 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 2.000000 2.000000 2.000000 2.000000 2.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", "\n", - " ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 \\\n", - "count ... 25128.00000 25128.000000 25128.000000 25128.000000 \n", - "mean ... 31879.61712 29394.608286 28196.281280 3606.968641 \n", - "std ... 40923.95966 38977.042600 38392.329148 5182.549076 \n", - "min ... -50616.00000 -53007.000000 -94625.000000 0.000000 \n", - "25% ... 1623.00000 1178.000000 814.000000 996.500000 \n", - "50% ... 16941.50000 15029.500000 13313.000000 2000.000000 \n", - "75% ... 45781.75000 40615.500000 38585.750000 4404.250000 \n", - "max ... 208931.00000 196698.000000 192499.000000 48329.000000 \n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", "\n", - " PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \\\n", - "count 25128.000000 25128.000000 25128.000000 25128.000000 25128.000000 \n", - "mean 3696.252467 3201.249522 2850.484479 2840.468959 2882.267869 \n", - "std 6111.658249 5406.757131 4899.719901 4810.352492 5317.958689 \n", - "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25% 785.000000 390.000000 225.000000 165.000000 0.000000 \n", - "50% 2000.000000 1600.000000 1265.500000 1300.000000 1200.000000 \n", - "75% 4136.250000 3638.250000 3224.500000 3290.500000 3179.250000 \n", - "max 65280.000000 50551.000000 45171.000000 44197.000000 51000.000000 \n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", - " Y \n", - "count 25128.000000 \n", - "mean 0.212472 \n", - "std 0.409065 \n", - "min 0.000000 \n", - "25% 0.000000 \n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 \n", "\n", - "[8 rows x 24 columns]" + "[8 rows x 30 columns]" ] }, - "execution_count": 19, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } @@ -1783,7 +1959,7 @@ "source": [ "from scipy import stats\n", "#we are only concerned with the ordinal data\n", - "o = pd.DataFrame(df.drop(['EDUCATION', 'MARRIAGE', \"SEX\"], axis=1))\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", "df = df[rows]\n", @@ -1801,21 +1977,189 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 189, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALAGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
ID
120000243913310268900006890000
212000026268217252682327234553261010001000100002000
39000034292391402713559143311494815549151815001000100010005000
45000037469904823349291283142895929547200020191200110010691000
550000578617567035835209401914619131200036681100009000689679
\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL AGE BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 \\\n", + "ID \n", + "1 20000 24 3913 3102 689 0 0 \n", + "2 120000 26 2682 1725 2682 3272 3455 \n", + "3 90000 34 29239 14027 13559 14331 14948 \n", + "4 50000 37 46990 48233 49291 28314 28959 \n", + "5 50000 57 8617 5670 35835 20940 19146 \n", + "\n", + " BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \n", + "ID \n", + "1 0 0 689 0 0 0 0 \n", + "2 3261 0 1000 1000 1000 0 2000 \n", + "3 15549 1518 1500 1000 1000 1000 5000 \n", + "4 29547 2000 2019 1200 1100 1069 1000 \n", + "5 19131 2000 36681 10000 9000 689 679 " + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", - "cols = df.drop([\"Y\", \"SEX\", \"MARRIAGE\", \"EDUCATION\"], axis =1)\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", "df1 = df.copy()\n", "df1[cols.columns] = scaler.fit_transform(cols)\n", - "df = df1" + "df = df1\n", + "cols.head()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 190, "metadata": {}, "outputs": [ { @@ -1850,16 +2194,16 @@ " PAY_4\n", " PAY_5\n", " ...\n", - " BILL_AMT4\n", - " BILL_AMT5\n", - " BILL_AMT6\n", - " PAY_AMT1\n", - " PAY_AMT2\n", - " PAY_AMT3\n", " PAY_AMT4\n", " PAY_AMT5\n", " PAY_AMT6\n", " Y\n", + " S_0\n", + " S_2\n", + " S_3\n", + " S_4\n", + " S_5\n", + " S_6\n", " \n", " \n", " ID\n", @@ -1888,159 +2232,151 @@ " \n", " \n", " \n", - " 1\n", - " -1.136832\n", + " 1\n", + " -1.109548\n", " 2\n", " 2\n", " 1\n", - " -1.255356\n", - " 2.044837\n", - " 2.037145\n", - " -0.676121\n", - " -0.649279\n", - " -1.600657\n", + " -1.246237\n", + " 2\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.779012\n", - " -0.754167\n", - " -0.734439\n", - " -0.695997\n", - " -0.492062\n", - " -0.592095\n", - " -0.581776\n", - " -0.590503\n", - " -0.541998\n", + " -0.576511\n", + " -0.584873\n", + " -0.535426\n", " 1\n", + " 1\n", + " 1\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", " \n", " \n", + " 2\n", + " -0.251594\n", " 2\n", - " -0.281351\n", " 2\n", " 2\n", + " -1.019784\n", + " 0\n", " 2\n", - " -1.027618\n", - " -0.896189\n", - " 2.037145\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.699057\n", - " -0.665523\n", - " -0.649499\n", - " -0.695997\n", - " -0.441174\n", - " -0.407137\n", - " -0.377679\n", - " -0.590503\n", - " -0.165907\n", + " -0.369685\n", + " -0.584873\n", + " -0.155999\n", + " 1\n", + " -1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", " 1\n", " \n", " \n", - " 3\n", - " -0.537996\n", + " 3\n", + " -0.508980\n", " 2\n", " 2\n", " 2\n", - " -0.116665\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " -0.113973\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.428819\n", - " -0.370651\n", - " -0.329429\n", - " -0.403085\n", - " -0.359362\n", - " -0.407137\n", - " -0.377679\n", - " -0.382613\n", - " 0.398231\n", + " -0.369685\n", + " -0.374398\n", + " 0.413143\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", - " 4\n", - " -0.880188\n", + " 4\n", + " -0.852162\n", " 2\n", " 2\n", " 1\n", - " 0.224942\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", + " 0.225707\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.087130\n", - " -0.011176\n", - " 0.035183\n", - " -0.310079\n", - " -0.274440\n", - " -0.370146\n", - " -0.357269\n", - " -0.368269\n", - " -0.353953\n", + " -0.349003\n", + " -0.359875\n", + " -0.345713\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", - " 5\n", - " -0.880188\n", + " 5\n", + " -0.852162\n", " 1\n", " 2\n", " 1\n", - " 2.502324\n", - " -0.896189\n", - " 0.206108\n", - " -0.676121\n", - " 0.297071\n", - " 0.349514\n", + " 2.490235\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " ...\n", - " -0.267321\n", - " -0.262945\n", - " -0.236127\n", - " -0.310079\n", - " 5.397128\n", - " 1.257479\n", - " 1.255100\n", - " -0.447267\n", - " -0.414315\n", + " 1.284920\n", + " -0.439856\n", + " -0.406611\n", + " 0\n", + " -1\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", " 0\n", " \n", " \n", "\n", - "

5 rows × 24 columns

\n", + "

5 rows × 30 columns

\n", "" ], "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 \\\n", - "ID \n", - "1 -1.136832 2 2 1 -1.255356 2.044837 2.037145 \n", - "2 -0.281351 2 2 2 -1.027618 -0.896189 2.037145 \n", - "3 -0.537996 2 2 2 -0.116665 0.084153 0.206108 \n", - "4 -0.880188 2 2 1 0.224942 0.084153 0.206108 \n", - "5 -0.880188 1 2 1 2.502324 -0.896189 0.206108 \n", + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 -1.109548 2 2 1 -1.246237 2 2 0 0 \n", + "2 -0.251594 2 2 2 -1.019784 0 2 0 0 \n", + "3 -0.508980 2 2 2 -0.113973 0 0 0 0 \n", + "4 -0.852162 2 2 1 0.225707 0 0 0 0 \n", + "5 -0.852162 1 2 1 2.490235 0 0 0 0 \n", "\n", - " PAY_3 PAY_4 PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", - "ID ... \n", - "1 -0.676121 -0.649279 -1.600657 ... -0.779012 -0.754167 -0.734439 \n", - "2 0.237739 0.297071 0.349514 ... -0.699057 -0.665523 -0.649499 \n", - "3 0.237739 0.297071 0.349514 ... -0.428819 -0.370651 -0.329429 \n", - "4 0.237739 0.297071 0.349514 ... -0.087130 -0.011176 0.035183 \n", - "5 -0.676121 0.297071 0.349514 ... -0.267321 -0.262945 -0.236127 \n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... -0.576511 -0.584873 -0.535426 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... -0.369685 -0.584873 -0.155999 1 -1 1 0 0 0 1 \n", + "3 0 ... -0.369685 -0.374398 0.413143 0 0 0 0 0 0 0 \n", + "4 0 ... -0.349003 -0.359875 -0.345713 0 0 0 0 0 0 0 \n", + "5 0 ... 1.284920 -0.439856 -0.406611 0 -1 0 -1 0 0 0 \n", "\n", - " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 -0.695997 -0.492062 -0.592095 -0.581776 -0.590503 -0.541998 1 \n", - "2 -0.695997 -0.441174 -0.407137 -0.377679 -0.590503 -0.165907 1 \n", - "3 -0.403085 -0.359362 -0.407137 -0.377679 -0.382613 0.398231 0 \n", - "4 -0.310079 -0.274440 -0.370146 -0.357269 -0.368269 -0.353953 0 \n", - "5 -0.310079 5.397128 1.257479 1.255100 -0.447267 -0.414315 0 \n", - "\n", - "[5 rows x 24 columns]" + "[5 rows x 30 columns]" ] }, - "execution_count": 21, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -2053,7 +2389,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### One-Hot Encoding" + "### One-Hot Encoding for Categorical attributes" ] }, { @@ -2074,7 +2410,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -2083,7 +2419,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ @@ -2092,7 +2428,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -2125,7 +2461,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2133,7 +2469,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2141,7 +2477,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2149,7 +2485,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2157,7 +2493,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2177,12 +2513,13 @@ "4 0.0 1.0 0.0 1.0 0.0" ] }, - "execution_count": 24, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", "#drop one of each category to prevent dummy variable trap\n", @@ -2192,7 +2529,233 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, "metadata": {}, "outputs": [ { @@ -2227,85 +2790,85 @@ " PAY_6\n", " BILL_AMT1\n", " ...\n", - " PAY_AMT3\n", - " PAY_AMT4\n", - " PAY_AMT5\n", - " PAY_AMT6\n", - " Y\n", - " GRAD\n", - " UNI\n", - " HS\n", - " MARRIED\n", - " SINGLE\n", + " PAY_3_Revolving_Credit\n", + " PAY_4_No_Transactions\n", + " PAY_4_Pay_Duly\n", + " PAY_4_Revolving_Credit\n", + " PAY_5_No_Transactions\n", + " PAY_5_Pay_Duly\n", + " PAY_5_Revolving_Credit\n", + " PAY_6_No_Transactions\n", + " PAY_6_Pay_Duly\n", + " PAY_6_Revolving_Credit\n", " \n", " \n", " \n", " \n", + " 0\n", + " -1.109548\n", + " 2\n", + " -1.246237\n", + " 2\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " -1.136832\n", - " 2\n", - " -1.255356\n", - " 2.044837\n", - " 2.037145\n", - " -0.676121\n", - " -0.649279\n", - " -1.600657\n", - " -1.528474\n", - " -0.734773\n", + " -0.734016\n", " ...\n", - " -0.592095\n", - " -0.581776\n", - " -0.590503\n", - " -0.541998\n", - " 1\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", - " 1\n", - " -0.281351\n", + " 1\n", + " -0.251594\n", + " 2\n", + " -1.019784\n", + " 0\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", " 2\n", - " -1.027618\n", - " -0.896189\n", - " 2.037145\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 2.253746\n", - " -0.760267\n", + " -0.759736\n", " ...\n", - " -0.407137\n", - " -0.377679\n", - " -0.590503\n", - " -0.165907\n", - " 1\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 1.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", + " 2\n", + " -0.508980\n", " 2\n", - " -0.537996\n", - " 2\n", - " -0.116665\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " -0.210263\n", - " ...\n", - " -0.407137\n", - " -0.377679\n", - " -0.382613\n", - " 0.398231\n", + " -0.113973\n", + " 0\n", " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -0.204872\n", + " ...\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2313,97 +2876,112 @@ " 1.0\n", " \n", " \n", - " 3\n", - " -0.880188\n", + " 3\n", + " -0.852162\n", " 2\n", - " 0.224942\n", - " 0.084153\n", - " 0.206108\n", - " 0.237739\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " 0.157367\n", - " ...\n", - " -0.370146\n", - " -0.357269\n", - " -0.368269\n", - " -0.353953\n", + " 0.225707\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", + " 0\n", + " 0.166005\n", + " ...\n", + " 1.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " \n", " \n", - " 4\n", - " -0.880188\n", + " 4\n", + " -0.852162\n", " 1\n", - " 2.502324\n", - " -0.896189\n", - " 0.206108\n", - " -0.676121\n", - " 0.297071\n", - " 0.349514\n", - " 0.362636\n", - " -0.637351\n", - " ...\n", - " 1.257479\n", - " 1.255100\n", - " -0.447267\n", - " -0.414315\n", + " 2.490235\n", " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -0.635734\n", + " ...\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " \n", " \n", "\n", - "

5 rows × 27 columns

\n", + "

5 rows × 45 columns

\n", "" ], "text/plain": [ - " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "0 -1.136832 2 -1.255356 2.044837 2.037145 -0.676121 -0.649279 -1.600657 \n", - "1 -0.281351 2 -1.027618 -0.896189 2.037145 0.237739 0.297071 0.349514 \n", - "2 -0.537996 2 -0.116665 0.084153 0.206108 0.237739 0.297071 0.349514 \n", - "3 -0.880188 2 0.224942 0.084153 0.206108 0.237739 0.297071 0.349514 \n", - "4 -0.880188 1 2.502324 -0.896189 0.206108 -0.676121 0.297071 0.349514 \n", + " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 \\\n", + "0 -1.109548 2 -1.246237 2 2 0 0 0 0 \n", + "1 -0.251594 2 -1.019784 0 2 0 0 0 2 \n", + "2 -0.508980 2 -0.113973 0 0 0 0 0 0 \n", + "3 -0.852162 2 0.225707 0 0 0 0 0 0 \n", + "4 -0.852162 1 2.490235 0 0 0 0 0 0 \n", + "\n", + " BILL_AMT1 ... PAY_3_Revolving_Credit PAY_4_No_Transactions \\\n", + "0 -0.734016 ... 0.0 0.0 \n", + "1 -0.759736 ... 1.0 0.0 \n", + "2 -0.204872 ... 1.0 0.0 \n", + "3 0.166005 ... 1.0 0.0 \n", + "4 -0.635734 ... 0.0 0.0 \n", "\n", - " PAY_6 BILL_AMT1 ... PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 Y GRAD \\\n", - "0 -1.528474 -0.734773 ... -0.592095 -0.581776 -0.590503 -0.541998 1 0.0 \n", - "1 2.253746 -0.760267 ... -0.407137 -0.377679 -0.590503 -0.165907 1 0.0 \n", - "2 0.362636 -0.210263 ... -0.407137 -0.377679 -0.382613 0.398231 0 0.0 \n", - "3 0.362636 0.157367 ... -0.370146 -0.357269 -0.368269 -0.353953 0 0.0 \n", - "4 0.362636 -0.637351 ... 1.257479 1.255100 -0.447267 -0.414315 0 0.0 \n", + " PAY_4_Pay_Duly PAY_4_Revolving_Credit PAY_5_No_Transactions \\\n", + "0 1.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", "\n", - " UNI HS MARRIED SINGLE \n", - "0 1.0 0.0 1.0 0.0 \n", - "1 1.0 0.0 0.0 1.0 \n", - "2 1.0 0.0 0.0 1.0 \n", - "3 1.0 0.0 1.0 0.0 \n", - "4 1.0 0.0 1.0 0.0 \n", + " PAY_5_Pay_Duly PAY_5_Revolving_Credit PAY_6_No_Transactions \\\n", + "0 0.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", "\n", - "[5 rows x 27 columns]" + " PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + "\n", + "[5 rows x 45 columns]" ] }, - "execution_count": 25, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df1 = df.drop(['EDUCATION', 'MARRIAGE'], axis = 1)\n", + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", "df1.head()" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -2464,7 +3042,7 @@ "[0 rows x 27 columns]" ] }, - "execution_count": 26, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -2480,14 +3058,14 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 220, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data has 27 Columns and 25128 Rows\n" + "Data has 45 Columns and 26245 Rows\n" ] } ], @@ -2506,7 +3084,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -2528,14 +3106,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 117, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2576,7 +3154,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -2586,7 +3164,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 119, "metadata": { "colab": {}, "colab_type": "code", @@ -2611,7 +3189,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -2645,7 +3223,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 121, "metadata": { "scrolled": true }, @@ -2676,7 +3254,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -4223,7 +4801,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -4237,14 +4815,14 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.34110585 0.16477455]\n" + "Explained variation per principal component: [0.33957018 0.16512418]\n" ] } ], @@ -4262,12 +4840,12 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4315,7 +4893,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -4325,7 +4903,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -4335,7 +4913,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 72, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -4346,7 +4924,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -4368,12 +4946,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the number of components is reduced from 23 components to 13 components" + "As we can see, the number of components is reduced from 26 components to 13 components" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -4400,7 +4978,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -4412,7 +4990,7 @@ " verbose=False)" ] }, - "execution_count": 144, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -4426,19 +5004,19 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16392572029200445\n" + "Optimal Threshold: 0.16930806252855038\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEWCAYAAAAkUJMMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gU1dfA8e8hgUAINaFDDD0UqQEEQQFBiiCIgCBiAfVFEDt2BUXFgh3s+sMOAhYEREEEESmC9N4h9JJKeva+f8wkbkLKBrLZbHI+z8NDpp/ZnZ0zc+fOvWKMQSmllPKUEp4OQCmlVPGmiUgppZRHaSJSSinlUZqIlFJKeZQmIqWUUh6liUgppZRHeU0iEpERIvKbp+PwNBEJFpFYEfEpwG2GiIgREd+C2qY7icg2Eel6Ectd1DEoImVE5GcRiRKR2Xld3lvl5VjNz+NaRGaIyAsuzlukjm1n9n41KARx5Pp9XFQiEpGDIhJvHzgn7A0FXFyYrjHGfG2Mudad2yiM7M+6R9qwMeawMSbAGJPqybg8JT9+XMaYZsaYZbls54IT1CUcg4OBakCgMWbIRSyfVXxPisgB+zcYLiKz7PEfisgXWczfQkQSRaSyiEyy9+2+TPM8YI+flB8x5uVY9YbjWkS6ikj4JSyfdkzF2v8OisjjmeZxPrdGiMgCEanjNH2GiCQ5rSNWRG66lP0qDC7ljqi/MSYAaAW0Bp7In5AKlievhIriVVhuiunnfRmw2xiTktcFs4pZRG4DRgI97N9gGPC7PXkGMEhEymZa7FZgvjHmnD28G7gti3l25zVGV+NW6Sra39tg4BkR6Zlpetq5tQZwEng30/RX7aSd9m+WO4MtiNKXSy6aM8acAH7FSkgAiIifiEwVkcMiclJEPhCRMk7TB4jIRhGJFpF9ItLbHl9BRD4VkeMiclREXkj7EETkdhH5y/77AxGZ6hyHiPwkIg/Zf9cUkbkictq+arzPab5JIjJHRL4SkWjg9sz7ZMfxhb38IRF5WkRKOMWxUkTetYtadorINZmWzWkfVorImyJyDpgkIvVFZKmInBWRMyLytYhUtOf/EggGfravfB7NfKUuIstEZLK93hgR+U1EgpziudXeh7Mi8oxkusPKtN9lROR1e/4oEfnL+XsDRtjf6RkRecppufYiskpEIu39niYipZymGxEZJyJ7gD32uLdF5Ih9DKwXkS5O8/uIdcW/z96n9SJSR0T+tGfZ5HwlKCL97OMpUkT+FpEWTus6KCKPichm4LyI+Dp/Bnbs6+w4TorIG/aiaduKtLfV0fkYtJdtJiKLReScveyTWXymzwHPAjfZ6xktIiXsY+qQiJyyj7UK9vxp3+9oETkMLM3iq2oH/GqM2QfWb9AY85H99yrgKHCj8+cJ3Ax87rSOfwB/EWmWti9AGXt8lvIadxbHal0R+dP+TpeIyHQR+SrT8q4e17PFKo2JstfZLLu4M+2Dj1jnpjMish+4LtP0O0Rkh73N/SLyf/b4ssAvQE35706kZm7Hfk6MMeuAbTidOzNNTwDmAE1dWV9ORKSz/XvrZg+HOh27u0RkqNO8M0TkfRFZKCLngW72uOli3aHFiMgaEanvtEy263OJMSbP/4CDWFdjALWBLcDbTtPfAuYBlYFywM/AFHtaeyAK6ImVCGsBofa0H4EPgbJAVWAt8H/2tNuBv+y/rwKOAGIPVwLigZr2Otdj/fhLAfWA/UAve95JQDIw0J63TBb79wXwkx17CNZV4minOFKAB4GSwE32/lR2cR9SgPGAL9YPv4H9WfgBVbBOgG9l9VnbwyGAAXzt4WXAPqCRvb5lwMv2tKZALNDZ/iym2vveI5vvdbq9fC3AB+hkx5W2zY/tbbQEEoEm9nJtgSvsfQoBdgAPOK3XAIuxjocy9rhbgEB7mYeBE0Bpe9oErGOqMSD29gKd1tXAad1tgFNABzvm2+zPzM/p89sI1HHadvpnCqwCRtp/BwBXZPU5Z3EMlgOO27GXtoc7ZPO5TgK+choeBezFOjYDgO+BLzNt9wusYyir4/MW4Jz9OYUBPpmmPwUscRruBZwGSjrHAzwJvGKPexWrVOMrYFI2+5GnuDN/hvZnPRXrWOwMRKd9LlnMu4xsjmunWMphHZ9vARudps0AXshmH8YAO+3joTLwR6btXgfUxzrurgbigDb2tK5AeKb15XjsZ5o38z5eYa//hmzOrf5YFw9fuLJvWWzPYJ1femGdL9vb48vaw3fYcbcBzgDNnLYRBVyJdY4sbY87h3X+9gW+BmbmYX05xnwpiSgWiLF39nes203sL/A8UN9p/o7AAfvvD4E3s1hnNayTWxmnccOBP7I4CQhwGLjKHr4LWGr/3QE4nGndTwD/c/oR/pnDvvnYcTR1Gvd/wDKnOI5hJ0F73FqsohJX9uFwdtu25xkIbMjqwMzhB/u00/SxwCL772eBb52m+QNJZJGI7AMuHmiZww+odqZ9HpbNPjwA/JDpB9E9l/2OSNs2sAsYkNOPy2n4fWBypnl2AVc7fX6jsjh+037sfwLPAUHZ7HN2iWi48/eUy75NImMi+h0Y6zTcGOsCwddpu/VyWecIYAnWb+0s8LjTtGB7fbXt4a/JeKE4CSvhBGP9jkra/9ch50SUp7idP0N7WymAv9P0r8g5EWV5XGcRV0V72Qr28AyyT0RLgTFOw9dm/p4zzf8jcL/9d1cyJaLcjv1sjqlIrN+awUrMzueSg1jn1kj78zoGXO40fQaQYE+PBM7kEIvBOvcdyrSOm4AVmeb9EJjotI0vMk2fAXziNNwX2JmH9eWYiC6laG6gMaac/eWEAmm3zVWwTnjr7dvVSGCRPR6sg31fFuu7DOsHcdxpuQ+x7ioyMNbezcQ6GYBV7PC103pqpq3DXs+TWEkizZEc9isI64rtkNO4Q1h3CWmO2jE4T6/p4j5k2LaIVBWRmWIV40Vj/TiDyJsTTn/HYV2tYseUvj1jTBzWSSsrQVhXPll9NzluR0Qaich8u6gkGniJC/ch834/bBeBRNmfUwWnZbI7RrJyGfBwpu+7Dta+Z7ntTEZjXXXvFJF/RKSfi9vNS4yZ1eTC48sX149RjFVxogfWSXgM8LyI9LKnHcZKsLeIVYloIBmL5XCaby/W97XHGJPjNi8x7prAOfsYzG3eNNkdbz4i8rJYRbfRWCdvcO13k+E3Qcb9QUT6iMhqu4gpEuuEm+16XTz2Mwuy9+URrPNnyUzTBxpjKmLd7d0LLBeR6k7TpxpjKtr/ctvWA8B3xpgtTuMuAzpk+s2MAJy3kdV3k915xpX15Sg/nhEtx8p4ac9szmBl+2ZOH1YFYz18A2sH61+4Jo5g3U0EOS1X3hiTXdnvt8BgEbkM6y5ortN6Djito6Ixppwxpq9z2Dns0hmsq7zLnMYFY5W7p6klIpJp+jEX9yHztqfY41oYY8pjFbtIDvPnxXGsolPAegaEVRyWlTNYV1pZfTe5eR+ruKOhvQ9PknEfwGk/xHoe9BgwFKhk/+iinJbJ7hjJyhHgxUzft78x5tustp2ZMWaPMWY41sXCK8Ac+3lAbp97XmLM7BgXHl8pWA+m00NzZUXGmGRjzGxgM9DcadLnWJUPbsT6PfybzSq+wCpevKCmXT7HfRyoLCL+TuPqZDNvbm4GBgA9sC5gQuzxmY+57OJw3m5w2h8i4od1HpkKVLOPy4VO681q31w59i9gjEk1xryO9Zsbm8M83wOpWEWZF2MIMFBEHnAadwRYnuk3E2CMucd583nYhivry1F+vUf0FtBTRFoZYxxYzxLeFJGqACJSK+1qDfgUuENErhHr4WctEQk1xhwHfgNeF5Hy9rT6InJ1Vhs0xmzAKvf+BOvBbaQ9aS0QLdYD6jL21VNzEWnnyo4Yq/rod8CLIlLOTnQPYd2ppKkK3CciJUVkCNAEWJjXfbCVw74VF5FaWOX+zk5ilclfjDlAfxHpZD9AfY5sfiT29/YZ8Ib9ENZHrAf0fi5spxxWeX+siIQCuR2A5bBOYKcBXxF5FijvNP0TYLKINBRLCxFJS6CZP4+PgTEi0sGet6yIXCci5VyIGxG5RUSq2Pufdgyl2rE5yP6znw9UF6vKs599rHRwZZtYF1EPivXwPgDrKnqWcbFWnViVJq6zt1lCRPoAzYA1TrPNxTrhPkcWd0NOZmEVT33nzriNMYeAdVgVdEqJSEegvwvbzEo5rAu+s1ilLy/lYdnvsH67tUWkEuBcfboU1l3IaSDF/lydq+ufBALFrqDhFEtejv3MXgYeFZHSmSfYx/MArGfgO/K43jTHgGuw9jkt4c0HGonISPscVlJE2olIk4vcxiWvL18SkTHmNNYV1TP2qMewbvlX27erS7DKkzHGrMV6qPUm1lXwcv67yroV62DYjvXMYA5WFcbsfIt1VfSNUyypWAd4K+AA1pX+J1hXTq4aj1X2vh/4y17/Z07T1wAN7XW/CAw2xqQVeeV1H57DergXBSzAegDsbArwtH3L+0ge9gFjzDZ7X2ZiXQnGYD3YT8xmkUewKgn8g/Vg8hVcO0YewbpKjcFKDLlVJ/0VqwbSbqyikQQyFgW8gXXC+A3rR/4p1gNrsJ5vfG5/HkONVfPoLmAa1ue9lyxqQuagN7BNRGKBt7GeeyXYRUgvAivtbV3hvJAxJgarkkl/rCKLPUA3F7f5GfAlVvHZAaz9H5+HmKOxrrwPYyXPV4F7jDHpNfqMMef5Lxl9ndVK7PnijTFLjDHxBRD3CKznxWeBF7COk+yOxZx8gXXcHMX6na3Ow7IfYx1/m4B/cfq92d/pfVjHXgTWMT3PafpOrHPOfvuYqEnej/3MFtjbustp3M/28RiNdQzeZv+WL4pdBHsN8JiI3Gnv57XAMKxEdQLrt+7KRWdW67/k9aXVOlMuEpHbgTuNMRd7q+wx9lVsJFYxwgFPx6OKN7Fewt1pjJno6ViUZ3lNEz/q4ohIfxHxt597TMW64zno2ahUcWQX19S3ixN7Yz3n+dHTcSnP00RU9A3Aul0+hlWcOMzobbDyjOpY1bJjgXewihM3eDQiVSho0ZxSSimP0jsipZRSHuV1DRMGBQWZkJAQT4ehlFJeZf369WeMMVVyn7PgeV0iCgkJYd26dZ4OQymlvIqIHMp9Ls/QojmllFIepYlIKaWUR2kiUkop5VGaiJRSSnmUJiKllFIepYlIKaWUR7ktEYnIZ2L1a781m+kiIu+IyF4R2SwibdwVi1JKqcLLnXdEM7Ca2M9OH6y2zxoCd2N1MKWUUiofGWNISE71dBg5ctsLrcaYP0UkJIdZBmD1i26w+i2qKCI17M7llFJK5VFyqoOE5FRSHYa5/x7l0xX7ORaV4OmwcuXJlhVqkbEztHB73AWJSETuxrprIjg4OPNkpZQqtowxRMYl8+PGozz38/Ys5/E97Uq/h57jyUSUVZfVWTYFboz5CPgIICwsTJsLV0oVa+sPneP5+Ts4E5PI0ciMSWZQm1o0qV6ON95eQ/jyIzz35FXc90IHSn6WzcoKAU8monCsbozT1MbqM0cppVQ2TsUkcOP7qwDwLSFUKFOSzg2CaFG7AuXjUunX+TLKlfMjrJw/QW/5U6dOBQ9HnDtPJqJ5wL0iMhPoAETp8yGllLqQMYb3l+9j2c7TrD14DoCujasw4472AJw9G8fjjy/hk082MHHi1Uya1JXWrWt4MuQ8cVsiEpFvga5AkIiEAxOBkgDGmA+AhUBfYC8QB9zhrliUUspbJSSnEvrMogzjejWrxocjwzDG8MUXm3jkkcVERMQzYUInJkzo5KFIL547a80Nz2W6Aca5a/tKKeWNklMdvPzLTjaHR5KUath0JDJ92oZnelKpbKn04cceW8Jrr/1Np051+OCD67j88mqeCPmSeV1/REopVdQ4HIbvNxzlkdmbMozvVD+QNsEVqVGxDNOGt0ZEiI9P5vz5ZIKC/Bk9ujUNG1Zm9Og2lCiRVf0v76CJSCmlPCgl1UGr5xcTm5gCQP0qZenWuCp3dqlH9QqlM8y7aNFexo1bSKtW1Zk7dyiNGwfRuHGQJ8LOV5qIlFKqABlj2Bwexd/7znI8Kp4vVv3Xcerfj3enZsUyFyxz7FgMDzywiNmzt9O4cSD33tuuIEN2O01ESilVQPaeiqXHG8svGN8muCLf3HUFpUv6XDDt99/3c8MNs0hKSmXy5G5MmNAJP7+ideouWnujlFKFzL7TsVzz+nJEwDi9jv/l6PY0rVEe/1K+lCl1YQJKTk6lZEkfWrasTt++DXnhhe40aFC5ACMvOJqIlFLKDeKTUlm26xT3fP0vABXLlCS0enmGtqtNvxY1KemTdZvT0dGJPPPMUtasOcrKlaMICvJn5szBBRl6gdNEpJRS+WDbsSiORsTjMPDG4l3sPhmbPq1MSR82PHttjssbY5gzZzv337+IEydiGTu2HYmJqfj7F/1u4zQRKaXURYhLSuGfgxG8sXh3hnd9nPW9vDr3X9OIRtUCclzX6dPnue22H/nll720bl2dn34aRrt2tdwRdqGkiUgppXKw7VgUn/99kO/WhVOmpA++Ptb7OjEJKenzBAX44VtCeLpfE0ICy1LKtwQNqwYg4tq7PeXL+3HmTBxvvdWLcePa4+tb9O+CnGkiUkqpbMxed4QJczanD8cnp3JH+xDAqnhQuqQPHepVplvjqnle959/HuLFF1cwd+5QAgJKsXr1nV79Uuql0ESklCqWjDHsO32e9YfOZRj/976zrDsYQVKqg9MxiQCM61afCb1C82W7Z87EMWHCYmbM2EhISEUOHoykefOqxTYJgSYipVQxkNZ53Nx/w/lt+0mORcYTHpFzZ3GXBfrTOrgi91xdn2ubVc+XGP73v41MmLCY6OhEnniiM08/fRX+/iUved3eThORUqpIOnjmPCeiExj39b+cPZ90wfQWtSuQ6jCM69aA0OrlMrxMWr18abfcoXz11WaaNq3CBx9cR7NmeS/OK6o0ESmlvF5MQjJbj0az9WgUe0/FMmvdkQvmua5FDdpdVolBbWtTvnTB3IXExSXz0ksrGDMmjNq1yzN37lAqVHBPkvNmmoiUUl5n69EoVuw5w+mYRBZsOcbJ6MQL5gnw8+W565tRq1IZOtSt7HINtvyycOEexo1byMGDkdSqVY577mlHpUoXtiOnNBEppQqpuKQU/rfyIPM3H2fvqRiqBPilJ5OjkRmf7/iWEO6+qh6dGwbRsGo5KvqXzLblAncLD4/mgQcWMXfuDpo0CWL58tu56qrLPBKLt9BEpJQqNNbsP8vvO08xe90RIuKSM0yrXdmfOpX8AXAYw5UNgujXogY+JcRjSScrL774JwsW7OGll7rz8MOdKJVFO3IqIzHOrfB5gbCwMLNu3TpPh6GUyier959l2Eers5w2rF0dnujbhAplCnfNsrVrj1KmjC+XX16Ns2fjiIpKpF69Sp4OKwMRWW+MCfN0HFnROyKlVIGKT0rleFQ8X64+xL+HIzM0j3Pd5TUY1r4OYZdVzrJF6sImKiqBJ5/8nfffX0e/fo2YN284gYH+BAb6ezo0r6KJSCnlVuERcazZb700+sWqg2wKj8owvXmt8tzWMYQhYXU8EN3FMcYwa9Y2HnzwV06dOs/48e2ZPLm7p8PyWpqIlFJuEZ+UyhPfb+bHjccumDawVU26hValY/1AqpYrncXShdtXX23m1lt/JCysJvPnD6dt25qeDsmraSJSSuWLVIfh/WV72Xf6PPvPnM9Q5NalYRAvDrwcgOoVSlPKCxv1TExMYf/+CJo0qcLQoc1ISXFw660t8SlEFSW8lSYipdRFW38ogk1HInl+/vYM48uXtk4t/VvW5OVBl1PWy7u2/uOPA9xzzwLi4pLZs2c8fn6+3HFHa0+HVWR499GhlPKIpBQHt362htX7MzYYenunEMZ3b0BggJ+HIstfp06d55FHfuPLLzdTr14lPvqoP35enlQLI/1ElVJ58sHyfbz8y8704Sf6hDKsfXChr2KdV3v3nqN9+4+JjU3iqae68NRTXShTxPaxsNBEpJTKUVKKgz2nYpizPpzfd5zi8Lk4AHo2rcbrQ1sWWLttBSU6OpHy5f2oX78So0e3ZtSo1jRpUsXTYRVpmoiUUhcwxvDv4QiGfLAKR6Z33suW8mHGqPa0C6nsmeDc5Pz5JJ5/fjkff/wvmzffQ+3a5XnttWs9HVaxoIlIKYUxhi1Ho/hw+X42HI7gWFRChukjOgTTvm5lejWrnqG7hKLi5593ce+9v3D4cBSjR7fWPoIKmCYipYohYwyfrTzI5Pnb8S/lQ1xSaobp5fx8aVmnIqO71OWqhlXwKaLdFqSkOBg6dDY//LCTZs2qsGLFHXTuHOzpsIodTURKFQPGGA6ejSM2IYVv1h5i3cEI9pyKBSAuKZXbO4UQnZBM/xbWi6ZFnTEGEcHXtwQ1agTw8svX8OCDHbWBUg/RRKRUEZeQnEroM4uynLbi0W7UqVy82kVbvTqcceMW8vHH/WnTpgbTp1/n6ZCKPU1EShVRxhiufm1Zei03gGk3t8ZHhGubVS+yxW3ZiYiI58knf+fDD9dTs2Y5IiLic19IFQi3JiIR6Q28DfgAnxhjXs40PRj4HKhoz/O4MWahO2NSqihKTnVw6Gwcp2ISePqHrQDsP3M+fXqPJlV5fWirIveuj6tmzdrKffct4syZOB544Aqee64r5coVjZduiwK3JSIR8QGmAz2BcOAfEZlnjHFuC+Rp4DtjzPsi0hRYCIS4KyaliooVe05z5+frqF2pDOER8SSmOC6Y55rQqiSmOPj09jD8fIv3s4+dO88QElKRRYtG0Lp1DU+HozJx5x1Re2CvMWY/gIjMBAYAzonIAOXtvysAFzbTq1Qxtjk8kuj4FMBq1+3fwxEs3306ffrJ6ER6NKnG6dhE2oVUolG1clQp58cVdQMpUcyK3pwlJKTwyit/0aZNDfr3b8yTT3bh6aev0gZKCyl3JqJawBGn4XCgQ6Z5JgG/ich4oCzQI6sVicjdwN0AwcFatVIVPRHnk1i07QSHzsbx974zbD8WjcFq0TorJX2ET29rx1WN9I3/zJYs2c/YsQvYs+ccDz/ckf79G1OyCL77VJS4MxFldTmW+Vc1HJhhjHldRDoCX4pIc2NMhnIGY8xHwEdgdRXulmiVKmDGGD5Yvp9XFu28YFop3xK0qFWBauVLM6BVTSqVLUUJgcbVyxOgjW5m6eTJWB566De++WYLDRpU5rffbqFnz/qeDku5wJ1HdDjg3OVibS4sehsN9AYwxqwSkdJAEHDKjXEp5TFJKQ6+XnOId5fu5dz5pAzTJg9oRq9m1QkM8Ct2Ndryw+LF+5kzZzvPPnsVTzzRhdKlNWF7C3d+U/8ADUWkLnAUGAbcnGmew8A1wAwRaQKUBk6jVBHz08ajvLBgB6djEtPHlRDo1rgqb9xUfGuzXapNm06wZ885Bg9uyogRl3PllXWoW7eSp8NSeeS2RGSMSRGRe4Ffsapmf2aM2SYizwPrjDHzgIeBj0XkQaxiu9uNMVr0poqEM7GJ/L3vLA/N2kiK07OeKxsE8urgltSqWMaD0Xm32NgkJk78g7ffXkNISEUGDgzF17eEJiEv5dZ7V/udoIWZxj3r9Pd24Ep3xqCUJ9z9xTp+234yw7hv77qCjvUDPRRR0fHjjzsZP/4XwsOjufvuNkyZ0gNfL+x6XP1HC1GVygdnYhPp/+5fHI9KQATS7uufvq4JXRpWoVG1AET0uc+l2rLlJDfcMIvLL6/KrFmD6dSpTu4LqUJPE5FSl8jhMIS9sCR9eHy3BiDC9S1r0KBqOQ9GVjQkJ6eyYsVhunevy+WXV2PBgpvp2bOeVskuQjQRKXWRzsQm8tlfB3hv2T4AKvqXZP3TPbXGWz76++8jjBkzn23bTrNr1700aFCZvn0bejoslc80ESmVB8ej4lm+6zTrDkUwZ314+vjQ6uX45q4rNAnlk3Pn4nn88SV8/PG/1KlTnu+/H0qDBkWrR1j1H01ESrkgLimFfu/+xf7T5zOM79eiBhP7N6OKNqCZbxISUmjV6gOOHYvh4Yc7MmlSVwICSnk6LOVGmoiUysEXqw7y+m+7iYpPTh83oVdj+rWoQa2KZfDVtsvyTXh4NLVrl6d0aV8mT+5Gq1bVadmyuqfDUgVAE5FSmazad5bHv9/MobP/9ePTp3l1ypTyYerglsW6MVF3iI9PZsqUv3jllZXMmTOE/v0bc9ttrTwdlipALiUiESkFBBtj9ro5HqU8ZvuxaG54b2V6lwqlS5YgwK8k741oQ/u6+nzCHX77bR9jxy5g374IbrmlBe3b1/J0SMoDck1EInId8AZQCqgrIq2AicaYG9wdnFL5LS4phVX7zqYXtTkMrNx7hh82HM0w36e3hXFNk2qeCLHYGD9+IdOm/UPDhpVZsmQk11xTz9MhKQ9x5Y7oeazuG/4AMMZsFJEGbo1KqXx2MjqBzq8sJTk15xakXhjYnJva1aGkPvtxi9RU627Tx6cEV1xRm6Agfx57rLM2UFrMufLtJxtjIjO9Fa7twalCz+EwLN15imd+2srxqATAKm67s3M9OjcMokaF0gD4+fpQ3f5buc+//x5nzJj5jBzZgvHjOzBiRAtPh6QKCVcS0Q4RGQqUsFvSvh9Y7d6wlLo4h86e5/ppK0lKcRCfnJph2tiu9Xm0d6iHIiu+YmISefbZP3jnnbVUqeJPjRra2oTKyJVEdC/wLOAAvsdqTfsJdwalVF6lpDp48octfLfuv5dMW9apSLvLKnFdixq0DtZWmT3ht9/2MWrUTxw7FsOYMWG89NI1VKyod58qI1cSUS9jzGPAY2kjRGQQVlJSyiOSUx0ci4xn/aEIdp2I4cM/96dPG9+9AQ/1bKSNjBYCpUr5ULVqWebOHUqHDrU9HY4qpCS37n9E5F9jTJtM49YbY9q6NbJshIWFmXXr1nli06oQ2BIexc0fryYmMeWCafWrlOW7/+tIYIC2cuApycmpvPHGKqKjE3nxxWsA61mdvnvlefZ5O8zTcWQl2zsiEemF1Y13LRF5w2lSeaxiOqUKzP7TsQz7aDWn7B5OL69Vgea1KtC8Vnk61A0kuFh5Qs0AACAASURBVLI/pbRPGo/666/D6Q2UDhnSND0BaRJSucmpaO4UsBVIALY5jY8BHndnUEpl1v315QB0qh9IWEhlHurZyMMRqTRnz8bx2GNL+PTTDQQHV+Dnn4fTr59+P8p12SYiY8wGYIOIfG2MSSjAmJRKl5LqoNnEX9OHv7nrCg9Go7Jy9mw8M2du5dFHO/Hss1dTtqw2UKryxpXKCrVE5EWgKZBe3cUYo5c8yq02h0dy/bSV6cObJl7rwWiUsx07TvPdd9uYOLErjRoFcvjwg1SuXMbTYSkv5Uqh+gzgf4AAfYDvgJlujEkpPv3rQIYktOuF3lQoU9KDESmAuLhknnrqd1q2/IC3315DeHg0gCYhdUlcuSPyN8b8KiJTjTH7gKdFZIW7A1PF0+mYRNq9+F+32y8Pupxh7YM9GJFKs2jRXsaOXcCBA5HcdltLXnutJ1WqlPV0WKoIcCURJYr1QsY+ERkDHAWqujcsVdxExiVx9WvLMvT7M3tMR9qFaKvXhUFsbBIjR/5AYGAZ/vjjNrp2DfF0SKoIcSURPQgEAPcBLwIVgFHuDEoVfUkpDs7EJrLuUARTf93F4XP/9f3zUM9G3HdNQw9Gp8BqoPTbb7cyfHhzAgJKsWTJSEJDg/Dz0wZKVf7K9Ygyxqyx/4wBRgKIiL4irfIsOdXB3lOxvLRwByv2nLlg+ogOwbx4w+UeiExltn79Mf7v/+azfv1xypTx5cYbm2pvqcptckxEItIOqAX8ZYw5IyLNsJr66Q5oMlIu2Xc6lnFf/8vOEzEZxt/eKYSQQH86NQiiUTVtCLMwiIpK4Jln/mD69H+oWrUsM2feyKBBTTwdliricmpZYQpwI7AJq4LCD1gtb78CjCmY8JS3Wr77NGv2n+W9ZfsyjL+uRQ2ubVqNjvUDqVpOG78sbG688TuWLj3AuHHteOGF7lTQ7jFUAcjpjmgA0NIYEy8ilYFj9vCugglNeaOE5FTaTF5MXNJ/XTAE+PkyZdDl9G9Z04ORqezs3x9BlSr+lCvnx4svdqdECaFdO+2yWxWcnBJRgjEmHsAYc05EdmoSUjn55+A5hnywKn142s2tue7yGtoKdiGVlJTK1Kl/M3nyn9x3X3teeaWntpCtPCKnRFRPRNK6ehAgxGkYY8wgt0amvEJyqoP7vt3A7pMx7Dt9HgDfEsKWSb0oU8rHw9Gp7Pz55yHGjJnPjh1nGDy4Kffd18HTIaliLKdEdGOm4WnuDER5l7UHzvH7jpMZ+gFqVrM8d3Wpx8DWWqxTmL355ioeeug3QkIqsmDBzfTtq1XllWfl1Ojp7wUZiPIOqQ7D8I9Xs/bAufRxpXxLsOP53vhoc/+FlsNhOH8+iXLl/LjuukacPh3H009fhb+/NpukPE/fTFMuSU518O7Svbzz+570cW/e1JLezWpoEVwht23bKcaMWZDeU2qjRoG89NI1ng5LqXRuTUQi0ht4G/ABPjHGvJzFPEOBSYABNhljbnZnTCpvjpyLo/+0v4iM+6/pncplS7HogS5a/bqQi4tLZvLk5UyduooKFfwYNaoVxhitPKIKHZcTkYj4GWMS8zC/DzAd6AmEA/+IyDxjzHaneRoCTwBXGmMiRETbsCsEjDGsPxTB3lOxPP79lvTxT/YN5aZ2wdoKthfYsOE4gwZ9x8GDkdxxRytefbUnQUH+ng5LqSzlmohEpD3wKVYbc8Ei0hK40xgzPpdF2wN7jTH77fXMxHo3abvTPHcB040xEQDGmFN53wWVn9YfOseN76/KMG5Q61q8cVMrD0Wk8iLtjic4uALBwRX4/POBXHXVZZ4OS6kcuXJH9A7QD/gRwBizSUS6ubBcLeCI03A4kLmOaCMAEVmJVXw3yRizyIV1q3xkjGHNgXNExiUz5qv1AFQr78fUIS2pUaE0Dapq8zuFXUqKg2nT1jJv3i4WLx5JYKA/y5ff7umwlHKJK4mohDHmUKZy5dTsZnaSVUG0yWL7DYGuWG3XrRCR5saYyAwrErkbuBsgOFj7pslPxhiaT/yV804tIZQuWYI1T/bwYFQqL9auPcqYMfPZsOEEffo0IDo6kUqVtKM65T1cSURH7OI5Yz/3GQ/sdmG5cKCO03BtrGaCMs+z2hiTDBwQkV1Yiekf55mMMR8BHwGEhYVlTmbqIs1dH87DszelD8+8+wqCAvyor52deYXY2CQee2wx77+/jho1yjF79hBuvLGJVkZQXseVRHQPVvFcMHASWGKPy80/QEMRqYvVmd4wIHONuB+B4cAMEQnCKqrbj3KbxJRUNh6O5KaPVqePa1mnIh+PbEvV8loLzpuULFmCZcsOMX58eyZP7k758n6eDkmpi+JKIkoxxgzL64qNMSkici/wK9bzn8+MMdtE5HlgnTFmnj3tWhHZjlXcN8EYczav21I5S3UYftl6nCkLd3I0Mj7DtDljOhKmvaB6jb17z/H888uZPr0v5cr5sX793ZQura8DKu8mxuRc0iUi+4BdwCzge2NMTI4LuFlYWJhZt26dJ0PwKokpqXR46fcM7wF1rBfIQ9c20m64vUhiYgqvvrqSF19cQalSPixYcDNdumhtOOU6EVlvjAnzdBxZcaWH1voi0gmraO05EdkIzDTGzHR7dOqipKQ6cBh4ZdFOPv3rQPr4JQ9dpTXgvNAffxzgnnsWsGvXWW66qRlvvNGLmjX1e1RFh0v39MaYv4G/RWQS8BbwNaCJqJAJj4ij8yt/XDC+c4MgPr41TJvi8ULGGF58cQXJyQ4WLRpBr14NPB2SUvnOlRdaA7BeRB0GNAF+Ajq5OS7lopRUB6/9uotjUQn8vOm/Son3dW9AisNwQ+taNNRuuL2Kw2H49NN/6d27AXXqVODLL2+gYsXSlNEWLVQR5cod0VbgZ+BVY8wKN8ej8sDhMDR46pf04dqVytCsZnk+HFkoi4GVCzZvPsmYMfNZtSqcZ5+9iuee60aNGnohoYo2VxJRPWOMw+2RqDyJjEui1fOL04f/faYnlcuW8mBE6lLExibx3HPLePPN1VSqVIYZMwZw660tPR2WUgUi20QkIq8bYx4G5orIBVXrtIdWz0hOddD/3b/YecKqvFjKpwT/PNWDCtqvjFebNGkZr7++ijvvbM3LL/cgMFAbKFXFR053RLPs/7VnVg9LTnXw1epDbA6P4ocNR9PH39m5Lk9dp2/Se6sjR6I4fz6Z0NAgHn+8MwMHhtK5szZhpYqfnHpoXWv/2cQYkyEZ2S+qag+ubmaM4bt1R3hs7pYM45vWKM93YzoS4KcvMnqjlBQH77yzhmef/YO2bWuyfPntBAX5axJSxZYrZ7JRXHhXNDqLcSofnY5JpN2LSzKMW/FoN+pU1iIbb7Z6dThjxsxn06aTXHddQ6ZN6+vpkJTyuJyeEd2EVWW7roh87zSpHBCZ9VLqUjkchg/+3Meri3alj1v1RHdqVNDWlL3dggW76d//W2rWLMf33w9l4MBQLVZVipzviNYCZ7FazZ7uND4G2ODOoIojYwzHoxLo9PLS9HGP9wllzNX1PRiVulTGGI4di6FWrfL06FGP55/vxv33d6BcOW2gVKk0ubY1V9gUtbbmjkbG033qMhJTMtaQX3BfZ5rVrOChqFR+2L37LGPHLmD37rNs3z6OgACtXq88xyvbmhOR5caYq0Ukgowd2glgjDHaYuYlOHc+iXeX7uF/Kw8CUEKgW+OqdGkYxO1X1vVscOqSJCSk8PLLfzFlyl+UKePLlCnXUKaMVixRKjs5/TrSugMPKohAioP4pFQW7zjJfd9mLNlsWbsCP467Up8XFAEnTsRy1VX/Y8+ecwwf3pw33uhF9eoBng5LqUItp+rbaWVFdYBjxpgkEekMtAC+AqILIL4iwRjDp38d4IUFOzKMf6pvEwa2rkUVfV7g9ZKTUylZ0odq1cpy1VWXMX16X3r21Od7SrnClfKCH4F2IlIf+AJYAHwD9HNnYEVBQnIqt366lrUHz6WPE4HfH7qaelX0KrkocDgMH320npdeWsHff4+mdu3yfPLJ9Z4OSymv4koichhjkkVkEPCWMeYdEdFac7nYeyqWHm8sTx++oXUtXhjYnLL6EmqRsWnTCf7v/+azZs1RunevS3JyqqdDUsorudRVuIgMAUYCA+1x2rBZDk5FJ6QnoZ5Nq/HRyLb6/KcIMcYwYcJi3nprNZUrl+HLL29gxIjL9TtW6iK52rLCWKxuIPaLSF3gW/eG5b1e/20X7y7dC8DgtrWZOkRbUC5qRISIiHhGj7YaKK1USV82VupSuPQekYj4AmldQ+41xqS4NaocFOb3iL5de5gnvrfahXuoZyPuu6ahhyNS+eXQoUjuv38Rzz57NW3a1MDhMJQooXdAynsU5veISuQ2g4h0AfYCnwKfAbtF5Ep3B+ZttoRHpSehe7s10CRURCQnp/Lqqytp2vQ9Fi/ez65dZwA0CSmVj1wpmnsT6GuM2Q4gIk2AL4FCmVkLmsNhaPncb8QkWjeJD/dsxHhNQkXC338f4f/+bz5bt55iwIDGvPNOH4KDtbULpfKbK4moVFoSAjDG7BARbasESEpx0Ojp/7rqnjLocoa316b8i4olS/YTFZXAjz/exIABoZ4OR6kiK9dnRCIyA0jEugsCGAH4G2Nuc29oWStMz4gGTF/JpiNWQ+Rbn+ul/QN5OWMMX365mSpV/OnTpyGJiSkkJzu0jThVJBTmZ0SunDnHAPcBj2K1M/cn8K47gyrsHA5DvScXpg/vnNyb0iV9PBiRulQ7d57hnnsWsGzZQYYMaUqfPg3x8/PFTxu9UMrtckxEInI5UB/4wRjzasGEVHgZY7jz83X8vvNU+riF93XRJOTF4uOTeemlFbzyykrKli3Fhx/2484723g6LKWKlZxa334SqyfWf7Ga+HneGPNZgUVWCE1bujc9CTWrWZ6fxl2Jr0+uFQ9VIfbzz7t54YUV3HJLC6ZO7Um1atr0klIFLac7ohFAC2PMeRGpAizEqr5dLP299wyvL94NwOonrqF6hdIejkhdrBMnYtm48QS9ezdgyJCmhITcSfv2tTwdllLFVk6X84nGmPMAxpjTucxbpKU6DDd/sgaAd4e31iTkpVJTHbz33j80bjyNkSN/ID4+GRHRJKSUh+V0R1RPRL63/xagvtMwxphBbo2skDDGUN+umFDSR+jfsqaHI1IX499/jzNmzHz++ecYPXrU4733+lKmjDaZqFRhkFMiujHT8DR3BlIYxSam8JZdHAew/fneHoxGXawDByJo3/5jgoL8+eabQQwb1lwbKFWqEMmpY7zfCzKQwmbx9pPc9cV/7yvNH9+ZkloxwWsYY9iy5RQtWlSjbt1K/O9/A+jfvzEVK2qxqlKFjZ5ZM0l1GJY4JaG2l1Vi5ePdaV5Lm3bxFgcORNCv37e0bv0hmzefBGDkyJaahJQqpNzaFICI9AbeBnyAT4wxL2cz32BgNtDOGOOxZhOMMbSY9Cvnk6wOzsZ1q8+EXtq0i7dISkrljTdW8fzzyylRQpg6tSdNm1bxdFhKqVy4nIhExM8Yk5iH+X2A6UBPIBz4R0TmObdbZ89XDqvlhjWurttdZv1zJD0Jzbv3SlrUrujhiJSrUlMddOr0KevXH2fQoCa89VYv6tTRu1ilvIEr3UC0F5EtwB57uKWIuNLET3usvov2G2OSgJnAgCzmmwy8CiS4Hnb+e2T2Jh63u3H4c0I3TUJeIjraujby8SnBqFGt+fnn4cydO1STkFJexJVnRO8A/YCzAMaYTUA3F5arBRxxGg63x6UTkdZAHWPM/JxWJCJ3i8g6EVl3+vRpFzbtuqj4ZEIeX8Cc9eEAvDq4BcGB/vm6DZX/jDHMmLGRevXe5qefdgIwdmw7+vVr5OHIlFJ55UoiKmGMOZRpXKoLy2VVPza9qW8RKYHV19HDua3IGPORMSbMGBNWpUr+lfkbY+jyytL04a9Gd2BoWJ18W79yj+3bT9O16+fcccdPhIYGUb9+ZU+HpJS6BK48IzoiIu0BYz/3GQ/szmUZsO6AnM/qtYFjTsPlgObAMvudjurAPBG5vqAqLDz941aiE6wO7fa+2EfbjfMCr766kqeeWkr58n588kl/7rijtfaWqpSXcyUR3YNVPBcMnASW2ONy8w/QUETqAkeBYcDNaRONMVFAUNqwiCwDHimoJBSdkMzXaw4DsOiBLpqECjljDCJC9eoBjBhxOa+91pMqVcp6OiylVD7INREZY05hJZE8McakiMi9wK9Y1bc/M8ZsE5HngXXGmHl5jvYSGWP4as1hZq49zLZj0QD0a1GD0OrlCzoU5aJjx2K4//5FdOkSzH33deDWW1ty660tPR2WUiof5ZqIRORjnJ7tpDHG3J3bssaYhVitdjuPezabebvmtr5Lsf1YNH3fWZFh3A2ta/HGUD2pFUZpDZQ+9dRSkpMddOpU29MhKaXcxJWiuSVOf5cGbiBjbTiv4JyE5t7TibaXVfJgNConGzee4M4757F+/XGuvbY+773XVyskKFWEuVI0N8t5WES+BBa7LSI3+PdwBAAVypRk08RrPRyNyk1UVALHjsUwa9Zghgxpqg2UKlXEXUwTP3WBy/I7EHca9N7fAEy/WbuALoyMMcyevZ09e87y1FNXcfXVIezffz+lS7u1BSqlVCHhSssKESJyzv4XiXU39KT7Q8sfKamO9L+vbBDowUhUVvbtO0ffvt9w001z+OmnXSQnW6+oaRJSqvjI8dcuVplIS6zq1wAOY8wFFRcKsxV7zwAw5ur6WsRTiCQmpjB16t+88MIKSpYswdtv92bs2Hb4+mo1eqWKmxwTkTHGiMgPxpi2BRVQfkpMSeWer9YDMCRMa10VJkeORDN58p/079+Yt97qRa1aWoVeqeLKlcvPtSLilQ9XFm09QUKyVTRXv0qAh6NRp0+fZ9q0tQA0aFCZ7dvHMXv2EE1CShVz2d4RiYivMSYF6AzcJSL7gPNYbcgZY0yhT05vLdkDwIZneno4kuLN4TD8738bePTRJcTEJNKzZz0aNw6iXj2tQq+Uyrlobi3QBhhYQLHkq32nYzlw5jwAlcqW8nA0xdfWrae4554F/PXXYbp0CeaDD/rRuHFQ7gsqpYqNnBKRABhj9hVQLPnq69VWO3JvD2vl4UiKr6SkVK699kuSklL57LPruf32VlphRCl1gZwSURUReSi7icaYN9wQT76Zt8lq6LtzA736LmhLlx7g6qsvo1QpH777bgihoUEEBWkfT0qprOVUWcEHCMDqriGrf4XWtmNRnIlNpF+LGgQG+Hk6nGIjPDyaG2/8jmuu+YIvvtgEQOfOwZqElFI5yumO6Lgx5vkCiySfnI5J5Lp3/gKgSQ2tjVUQUlIcTJu2lmee+YPUVAdTplzDiBEtPB2WUspL5PqMyNs89N1GALqHVmVctwYejqZ4GDnyB2bO3EqfPg2YPr0vdetqbTillOtySkTXFFgU+SjVYTX88Nnt7TwcSdEWGZmAr28JAgJKMW5cO268sQk33thEKyMopfIs22dExphzBRlIfklITtUKCm5kjGHmzK00aTKdZ55ZCljPgQYP1laylVIXp8g17LX1WDSltL0yt9i79xy9en3F8OFzqV27PLfcos+BlFKXrkg1cRwZl0RSioP4pFRPh1LkfPPNFkaN+gk/P1+mTevDmDFh+PhowldKXboilYi2HI0CYECrmh6OpOhITk6lZEkfwsJqMnhwU159tSc1axbq2vtKKS9TpBLRrH+sHsy1G/BLd+rUeR5++DfOn0/i++9volGjQL76apCnw1JKFUFFpmzFGMP8zccBaFhNr9gvlsNh+Oij9TRuPI1Zs7bSrFkVUp06F1RKqfxWZO6IFmyxklBIoL7Ff7H274/gllu+Z9WqcLp2DeH9968jNFRrICql3KvIJKKzsUkAfHKbvj90sSpU8CMyMoHPPx/IyJEttDq2UqpAFJmiuZ/tRk6rVyjt4Ui8y7x5uxg0aBapqQ4CA/3ZunUst97aUpOQUqrAFIlE5HAY1h2KACDAr8jc5LnV4cNRDBw4kwEDZrJ791mOH48FoEQJTUBKqYJVJM7axv5/UOtaHo3DG6SkOHjrrdVMnLgMYwyvvNKDBx+8gpIlfTwdmlKqmCoSiSg2IQWAGhW1WC43qakOPvnkX7p3r8u77/YhJKSip0NSShVzRaJobu9pq1ipZsUyHo6kcIqIiOexxxYTE5OIn58vK1eOYt68YZqElFKFQpFIRL/vOAlAvaAAD0dSuBhj+PrrzYSGTuf111fxxx8HAQgM9NfKCEqpQqNIFM2t2HMGgCvqVfZwJIXH7t1nGTt2Ab//foD27Wvx66+30KpVdU+HpZRSFygSiWjL0Sgqly2lV/lOHnhgEevWHeO99/py991ttYFSpVSh5fWJKCHZamm7sTbrw+LF+wgNDaJOnQq8//51+Pn5Ur26FlcqpQo3t14mi0hvEdklIntF5PEspj8kIttFZLOI/C4il+V1G4kpVjto1zSpmg8Re6cTJ2K5+ea5XHvtV7zyykoALrusoiYhpZRXcFsiEhEfYDrQB2gKDBeRpplm2wCEGWNaAHOAV/O6nSQ7EfkVw87wHA7DBx+sIzR0GnPn7mDixKuZOvVaT4ellFJ54s6zd3tgrzFmvzEmCZgJDHCewRjzhzEmzh5cDdTO60bWHfTKHs3zxZQpK7jnngW0bVuTzZvHMGlSV0qX9vrSVqVUMePOs1Yt4IjTcDjQIYf5RwO/ZDVBRO4G7gYIDg7OMO395fsA6Nq4eBTNxcQkcuZMHHXrVmLMmDDq1q3E8OHNtaKGUsprufOOKKszo8liHCJyCxAGvJbVdGPMR8aYMGNMWJUqVTJM2xxu9cpap3LR7v7BGMMPP+ygadP3uOmmORhjCAz05+abL9ckpJTyau5MROFAHafh2sCxzDOJSA/gKeB6Y0xiXjaQVmOua+Mquczp3Q4diuT662cyaNB3VK5chnfe6aPJRylVZLizaO4foKGI1AWOAsOAm51nEJHWwIdAb2PMqbxuYO8pq2mfNsFFt2vwVauO0KPHlwBMndqT+++/At9iWDFDKVV0uS0RGWNSRORe4FfAB/jMGLNNRJ4H1hlj5mEVxQUAs+0r/MPGmOtd3ca2Y1axXOvgotdmWnR0IuXL+9GmTQ1GjWrFhAlXEhxcwdNhKaVUvnNrFStjzEJgYaZxzzr93eNS1r/7pHVHVJReZj17No7HH1/Cb7/tZ9u2sQQElOLdd/t6OiyllHIbr67ru/FIJABVy3t/9w/GGL78cjMPP/wbERHxPPRQR/QxkFKqOPDqRFS6ZAlCAr2/tlxUVAIDB85i2bKDdOxYmw8+6EeLFtU8HZZSShUIr05EyamGal58N2SMQUQoX96PoCB/PvqoH6NHt9HuupVSxYpXV79KSXVQ0ktblf711720afMR4eHRiAizZw/hrrvaahJSShU73nkWt209Go2Pl524jx+PYdiwOfTu/TVxccmcOnXe0yEppZRHeW3R3JnYRJJSHSSnOjwdisumT1/Lk08uJTExheee68pjj12Jn5/XfgVKKZUvvPYs+Obi3QB0qh/o4Uhct379cTp0qMX06X1p2NB74lZKKXfy2kSUZszV9T0dQraioxN59tk/GDmyBW3b1uS9967Dz89Hm+dRSiknXpuIDp61nq34FsLKCsYY5s7dwf33L+L48RiCgyvQtm1N7aJBKaWy4LVnRv9SvlQp5+fpMC5w4EAE9977CwsX7qFVq+p8//1QOnTIczdLSilVbHhtItpwOIIq5QrfO0Rff72FP/88xJtv9uLee9trA6VKKZULr01ElfxLUVhK5VasOERiYio9etRjwoRO3H57K2rXLu/psJRSyisUklN53qU4DPWCAjwaw5kzcYwa9RNXXTWD559fDoCfn68mIaWUygOvvSM6cOa8x/ohMsYwY8ZGJkxYTFRUIo89diXPPHOVR2JRhVtycjLh4eEkJCR4OhRVTJQuXZratWtTsmRJT4fiMq9MRGkvsSakpHpk+wsX7mHUqHlceWUdPvigH82bV/VIHKrwCw8Pp1y5coSEhGi1feV2xhjOnj1LeHg4devW9XQ4LvPKormDZ6yq201rFFwRWFxcMitXHgagb9+G/PTTMP788w5NQipHCQkJBAYGahJSBUJECAwM9Lo7cK9MRPtOW4kotHrBdIj3yy97aN78Pfr0+ZrIyAREhOuvb6wNlCqXaBJSBckbjzevTERbj1pdhNesWMat2zl6NJohQ2bTt+83+Pn58vPPw6lYsfBVGVdKKW/mlYnogF00VzeorNu2cerUeZo2fY/583fzwgvd2LRpDFdfHeK27SnlLj4+PrRq1YrmzZvTv39/IiMj06dt27aN7t2706hRIxo2bMjkyZMxxqRP/+WXXwgLC6NJkyaEhobyyCOPeGIXcrRhwwbuvPPODOMGDBhAx44dM4y7/fbbmTNnToZxAQH/1bzdvXs3ffv2pUGDBjRp0oShQ4dy8uTJS4rt3Llz9OzZk4YNG9KzZ08iIiIumOePP/6gVatW6f9Kly7Njz/+CMCIESNo3LgxzZs3Z9SoUSQnJwMwf/58Jk6ceEmxFSrGGK/617ZtW3PZY/NNi0m/GncID49K//vtt1ebvXvPumU7qnjYvn27p0MwZcuWTf/71ltvNS+88IIxxpi4uDhTr1498+uv1m/p/Pnzpnfv3mbatGnGGGO2bNli6tWrZ3bs2GGMMSY5OdlMnz49X2NLTk6+5HUMHjzYbNy4MX04IiLC1K5d24SGhpr9+/enj7/tttvM7NmzMyyb9tnEx8ebBg0amHnz5qVPW7p0qdmyZcslxTZhwgQzZcoUY4wxU6ZMMY8++miO8589e9ZUqlTJnD9/3hhjzIIFC4zD4TAOh8MMGzbMvPfee8YYYxwOh2nVqlX6fJllddwB60whOIdn9c8ra82V8No5MwAAELVJREFUK+1LYNlS+brOqKgEnn56KR9+uJ7Vq++kTZsa3Hdfh3zdhirenvt5G9uPRefrOpvWLM/E/s1cnr9jx45s3rwZgG+++YYrr7ySa6+9FgB/f3+mTZtG165dGTduHK+++ipPPfUUoaGhAPj6+jJ27NgL1hkbG8v48eNZt24dIsLEiRO58cYbCQgIIDY2FoA5c+Ywf/58ZsyYwe23307lypXZsGEDrVq14ocffmDjxo1UrFgRgAYNGrBy5UpKlCjBmDFjOHzYqiT01ltvceWVV2bYdkxMDJs3b6Zly5bp4+bOnUv//v2pVq0aM2fO5Iknnsj1c/nmm2/o2LEj/fv3Tx/XrVs3lz/X7Pz0008sW7YMgNtuu42uXbvyyiuvZDv/nDlz6NOnD/7+/gD07ds3fVr79u0JDw8HrOdAXbt2Zf78+QwdOvSS4/Q0r0xEMQkpDGtXJ1/WZYxh9uztPPDAIk6ciOXee9tTv75n3k9Syp1SU1P5/fffGT16NGAVy7Vt2zbDPPXr1yc2Npbo6Gi2bt3Kww8/nOt6J0+eTIUKFdiyZQtAlsVPme3evZslS5bg4+ODw+Hghx9+4I477mDNmjWEhIRQrVo1br75Zh588EE6d+7M4cOH6dWrFzt27MiwnnXr1tG8efMM47799lsmTpxItWrVGDx4sEuJaOvWrRd8FlmJiYmhS5cuWU775ptvaNq0aYZxJ0+epEaNGgDUqFGDU6dO5bj+mTNn8tBDD10wPjk5mS+//JK33347fVxYWBgrVqzQROQJDrv8Oj750t8hMsYwaNB3/PjjTtq0qcG8ecMJC6t5yetVKit5uXPJT/Hx8bRq1YqDBw/Stm1bevbsCVjHf3Y1rPJS82rJkiX8f3t3HxxVfe9x/P0RxQSNXOVp2qqEjhETQkARC1Z8oletUiiOGnmw4uh1AJW2KA53dOYqWktbnxrBi17qQGsbok4VBhEucmOpyIPhGgXB0pRGRDOYYi5SfADM9/5xTjabkJANsnt2k+9rZmd2z56z57u/2ew3v9/57fe3aNGi2OOTT277H7lrr72WLl26AFBcXMysWbO46aabWLRoEcXFxbHX3bJlS+yYTz/9lL1795KT0zhbtqamhl69esUe79q1i6qqKi644AIkceyxx7J582YKCwtbfE/tnWGWk5NDZWVlu45JVE1NDZs2beLyyy8/5LmpU6dy4YUXNkmCvXv35qOPPkpKLKmWcZMVGhZkPbPPkU/dPhAmMUlccMFplJRcwYYNt3gSch1SdnY2lZWVvP/+++zfv5+5c+cCMGDAACoqKprsu337dk488URycnIYMGAAGzdubPP1W0to8dua/67lhBMaJxoNHz6cqqoqamtreemll7j66qsBqK+vZ+3atVRWVlJZWcmHH37YJAk1vLf41y4rK6Ouro5+/fqRm5tLdXV1LEn26NGjSW/tk08+oWfPnrG2SOS97t27t8nEgvhbfNJs0KdPH2pqaoAg0fTu3frvDp977jnGjh17SEWE+++/n9raWh599NEm27/44guys5M7czhVMi4RWdgjyj6uyxEd/9pr1RQVzWPx4vcAuPPO87njju/QJV0qqDqXJN27d6ekpISHH36YAwcOMGHCBF5//XVeffVVIOg5TZs2jbvvvhuAGTNm8NBDD7FtW7Aacn19/SFfhgCXXXYZc+bMiT1u+LLv06cPW7dujQ29tUYSY8eOZfr06eTn59OjR48WX7elnkh+fj5VVVWxx6WlpSxfvpzq6mqqq6vZuHFjLBFdfPHFlJWVsX//fgAWLFgQuw40fvx43njjDV5++eXYay1fvjw23NigoUfU0q35sBzA6NGjWbhwIQALFy5kzJgxrbZDaWkp48aNa7Jt/vz5rFixgtLSUo45pul31LZt2w4ZlsxUGffte7A+6BId187EUVu7jxtvfIlLLlnIl18eJCcN1zJyLtnOPvtsBg0axKJFi8jOzmbx4sU8+OCD9O/fn4EDBzJ06FBuv/12AIqKinj88ccZN24c+fn5FBYWxv67j3fvvfdSV1dHYWEhgwYNory8HIDZs2czatQoLr300th1ktYUFxfz7LPPxoblAEpKSqioqKCoqIiCggLmzZt3yHFnnXUWe/bsYe/evVRXV7Njxw6GDRsWe75fv36cdNJJrF+/nlGjRjFixAiGDBnC4MGDWbNmTWziQHZ2NkuXLuWJJ54gLy+PgoICFixYcNgeTCJmzpzJypUrycvLY+XKlcycORMIrm3FTzmvrq7mgw8+4KKLLmpy/OTJk9m1axfDhw9n8ODBzJo1K/ZceXk5V1111deKL12ooYeRKXLPGmj8cDa/u/k8RuT1avsAoLR0E7fdtox//nM/M2aczz33XEi3bplTENBlrq1bt5Kfnx91GB3aY489Rk5OziG/JerIdu3axfjx41m1alWLz7f0uZO00czOTUV87ZVxPaKGSQpD+iY+s+3gwXoKC3tTWTmZn/1spCch5zqQKVOmcPzxnWuEY8eOHTzyyCNRh3HUZN6suXCyQreurYe+b99+HnhgNaef3p2pU4cycWIREycWZWQNJufc4WVlZXHDDTdEHUZKDR06NOoQjqqM6xF9tv8gvQ9zfWfp0m0MGPAkv/jFGrZt2w0EF0M9CbmoZNrwt8tsmfh5y7ge0cF6I7+F5R927vyUadNe4cUX36OgoBerV09ixIi+EUToXKOsrCx2797tS0G4lLBwPaKsrMwqzpxxiQige/ah13i2b69jxYq/8fOfj2T69OF07Xpk07udO5pOPfVUdu7cSW1tbdShuE6iYYXWTJJxs+aO/0aevbFuPUP6nsKGDR+ydu0H/PjHwXTN3bs/o0ePbhFH6Jxz6afTzpqTdIWkv0iqkjSzheePl1QWPr9eUm4ir9s3J4upU19m2LD5PProOvbtC36g5knIOecyT9ISkaQuwFzg+0ABME5S858e3wzUmdkZwGNA62VpQ10kCgue5KmnNjJt2nfYtGkKJxzlStzOOedSJ5nXiM4DqsxsO4CkRcAYIL4g0xjgvvD+C8AcSbLDjBcePPAVp+V2Z9myCZxzzuF/re2ccy79JTMRfQv4IO7xTqD5Aj+xfczsoKQ9QA/gH/E7SboVuDV8+GXFP27dnEDF9s6gJ83aqhPztmjkbdHI26JR/6gDaE0yE1FLc1Wb93QS2Qczexp4GkBSRbpecEs1b4tG3haNvC0aeVs0klTR9l7RSOZkhZ1A/Op1pwLNF8+I7SPpWKA78EkSY3LOOZdmkpmI3gTyJPWT1BW4HljSbJ8lwI3h/WuA/znc9SHnnHMdT9KG5sJrPrcDK4AuwDNm9q6kWUCFmS0BfgP8TlIVQU/o+gRe+ulkxZyBvC0aeVs08rZo5G3RKG3bIuN+0Oqcc65jybiip8455zoWT0TOOecilbaJKFnlgTJRAm0xXdIWSe9IWiWpw5Ydb6st4va7RpJJ6rBTdxNpC0nXhZ+NdyX9IdUxpkoCfyOnSyqX9Fb4d3JlFHEmm6RnJH0saXMrz0tSSdhO70g6J9UxtsjM0u5GMLnhb8C3ga7A20BBs32mAvPC+9cDZVHHHWFbXAJ0C+9P6cxtEe6XA6wG1gHnRh13hJ+LPOAt4OTwce+o446wLZ4GpoT3C4DqqONOUltcCJwDbG7l+SuBVwh+wzkMWB91zGaWtj2iWHkgM9sPNJQHijcGWBjefwEYqY654EubbWFm5Wb2WfhwHcFvtjqiRD4XAA8AvwS+SGVwKZZIW/wbMNfM6gDM7OMUx5gqibSFAQ0LmXXn0N80dghmtprD/xZzDPBbC6wD/kVS5LXS0jURtVQe6Fut7WNmB4GG8kAdTSJtEe9mgv94OqI220LS2cBpZrY0lYFFIJHPxZnAmZLWSFon6YqURZdaibTFfcBESTuBZcAdqQkt7bT3+yQl0nVhvKNWHqgDSPh9SpoInAtclNSIonPYtpB0DEEV90mpCihCiXwujiUYnruYoJf8Z0mFZvZ/SY4t1RJpi3HAAjN7RNJwgt8vFppZffLDSytp+b2Zrj0iLw/UKJG2QNL3gHuA0Wb2ZYpiS7W22iIHKARek1RNMAa+pINOWEj0b2SxmR0ws78DfyFITB1NIm1xM/AcgJmtBbIICqJ2Ngl9n6RauiYiLw/UqM22CIejniJIQh31OgC00RZmtsfMeppZrpnlElwvG21maVvs8WtI5G/kJYKJLEjqSTBUtz2lUaZGIm2xAxgJICmfIBF1xvXblwA/CmfPDQP2mFlN1EGl5dCcJa88UMZJsC1+BZwIPB/O19hhZqMjCzpJEmyLTiHBtlgBXCZpC/AVMMPMdkcXdXIk2BZ3Av8l6acEQ1GTOuI/rpJKCYZie4bXw/4DOA7AzOYRXB+7EqgCPgNuiibSprzEj3POuUil69Ccc865TsITkXPOuUh5InLOORcpT0TOOeci5YnIOedcpDwRubQj6StJlXG33MPsm9tapeF2nvO1sHrz22FJnP5H8BqTJf0ovD9J0jfjnpsvqeAox/mmpMEJHPMTSd2+7rmdSxZPRC4dfW5mg+Nu1Sk67wQzG0RQTPdX7T3YzOaZ2W/Dh5OAb8Y9d4uZbTkqUTbG+SSJxfkTwBORS1ueiFxGCHs+f5b0v+Ht/Bb2GSBpQ9iLekdSXrh9Ytz2pyR1aeN0q4EzwmNHhmvYbArXejk+3D5bjWtAPRxuu0/SXZKuIaj59/vwnNlhT+ZcSVMk/TIu5kmSnjjCONcSV7BS0n9KqlCw9tD94bZpBAmxXFJ5uO0ySWvDdnxe0oltnMe5pPJE5NJRdtyw3Ivhto+BfzWzc4BioKSF4yYDvzazwQSJYGdYzqUY+G64/StgQhvn/wGwSVIWsAAoNrOBBJVIpkg6BRgLDDCzIuDB+IPN7AWggqDnMtjMPo97+gXg6rjHxUDZEcZ5BUEZnwb3mNm5QBFwkaQiMyshqCV2iZldEpb6uRf4XtiWFcD0Ns7jXFKlZYkf1+l9Hn4ZxzsOmBNeE/mKoG5ac2uBeySdCvzRzP4qaSQwBHgzLH+UTZDUWvJ7SZ8D1QTLBPQH/m5m28LnFwK3AXMI1jqaL+llIOElJ8ysVtL2sM7XX8NzrAlftz1xnkBQziZ+hc3rJN1K8Hf9DYIF4N5pduywcPua8DxdCdrNuch4InKZ4qfALmAQQU/+kEXvzOwPktYDVwErJN1CUPZ+oZn9ewLnmBBfIFVSi+tbhbXNziMoonk9cDtwaTveSxlwHfAe8KKZmYKskHCcBKuQzgbmAldL6gfcBQw1szpJCwgKezYnYKWZjWtHvM4llQ/NuUzRHagJ14+5gaA30ISkbwPbw+GoJQRDVKuAayT1Dvc5RVLfBM/5HpAr6Yzw8Q3An8JrKt3NbBnBRICWZq7tJViWoiV/BH5IsEZOWbitXXGa2QGCIbZh4bDeScA+YI+kPsD3W4llHfDdhvckqZuklnqXzqWMJyKXKZ4EbpS0jmBYbl8L+xQDmyVVAmcRLIm8heAL+78lvQOsJBi2apOZfUFQnfh5SZuAemAewZf60vD1/kTQW2tuATCvYbJCs9etA7YAfc1sQ7it3XGG154eAe4ys7eBt4B3gWcIhvsaPA28IqnczGoJZvSVhudZR9BWzkXGq28755yLlPeInHPORcoTkXPOuUh5InLOORcpT0TOOeci5YnIOedcpDwROeeci5QnIuecc5H6f8SnahLb9pRpAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -4451,10 +5029,10 @@ { "data": { "text/plain": [ - "0.722588777021613" + "0.7218839449278682" ] }, - "execution_count": 145, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -4466,14 +5044,14 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1017 Defaulters, the SVM original data RBF kernel identified 280\n" + "Of 1056 Defaulters, the SVM original data RBF kernel identified 297\n" ] }, { @@ -4508,14 +5086,14 @@ " \n", " \n", " \n", - " 0\n", - " 3913\n", - " 96\n", + " 0\n", + " 3869\n", + " 101\n", " \n", " \n", - " 1\n", - " 737\n", - " 280\n", + " 1\n", + " 759\n", + " 297\n", " \n", " \n", "\n", @@ -4524,11 +5102,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 3913 96\n", - "1 737 280" + "0 3869 101\n", + "1 759 297" ] }, - "execution_count": 146, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -4538,6 +5116,31 @@ "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.97 0.90 3970\n", + " 1 0.75 0.28 0.41 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.79 0.63 0.65 5026\n", + "weighted avg 0.82 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4554,7 +5157,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -4566,7 +5169,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 147, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -4579,19 +5182,19 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.18731494599976836\n" + "Optimal Threshold: 0.17464839093863194\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4604,10 +5207,10 @@ { "data": { "text/plain": [ - "0.7062615751726756" + "0.7171996651019006" ] }, - "execution_count": 148, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -4620,14 +5223,14 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 1017 Defaulters, the SVM reduced features no tuning RBF kernal identified 296\n" + "Of 1056 Defaulters, the SVM reduced features no tuning RBF kernal identified 307\n" ] }, { @@ -4662,14 +5265,14 @@ " \n", " \n", " \n", - " 0\n", - " 3904\n", - " 105\n", + " 0\n", + " 3872\n", + " 98\n", " \n", " \n", - " 1\n", - " 721\n", - " 296\n", + " 1\n", + " 749\n", + " 307\n", " \n", " \n", "\n", @@ -4678,11 +5281,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 3904 105\n", - "1 721 296" + "0 3872 98\n", + "1 749 307" ] }, - "execution_count": 149, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -4692,11 +5295,36 @@ "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.98 0.90 3970\n", + " 1 0.76 0.29 0.42 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.80 0.63 0.66 5026\n", + "weighted avg 0.82 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, by reducing features through pca, the AUROC curve improves, suggesting a better prediction model." + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." ] }, { @@ -4708,38 +5336,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 10, 'gamma': 0.01}" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001, 0.01, 0.1, 1, 10]\n", - " gammas = [0.001, 0.01, 0.1, 1,10]\n", + " Cs = [0.01, 0.1, 1,10]\n", + " gammas = [0.01, 0.1, 10]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,10)\n" + "svc_param_selection(X_train_pca, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.01)\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", "clf_reduced_tuned.fit(X_train_pca, y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.17824393299122973\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", " \"SVM reduced features and tuning RBF kernal\")" @@ -4747,14 +5427,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1056 Defaulters, the SVM reduced features and tuning RBF kernal identified 325\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03835135
1731325
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3835 135\n", + "1 731 325" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" ] }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.97 0.90 3970\n", + " 1 0.71 0.31 0.43 1056\n", + "\n", + " accuracy 0.83 5026\n", + " macro avg 0.77 0.64 0.66 5026\n", + "weighted avg 0.81 0.83 0.80 5026\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -4781,7 +5550,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -5212,7 +5980,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/BT2101_Default.ipynb b/BT2101_Default.ipynb index 1bdbfc8..08bdc8e 100644 --- a/BT2101_Default.ipynb +++ b/BT2101_Default.ipynb @@ -6441,7 +6441,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 330e1764a4e2589328c1ed0d47ee3110354389dd Mon Sep 17 00:00:00 2001 From: Janson-Chew Date: Mon, 18 Nov 2019 19:00:20 +0800 Subject: [PATCH 07/25] hi push --- .DS_Store | Bin 0 -> 8196 bytes BT2101_Default - EDIT-Copy1.ipynb | 5882 +++++++++++++++++++++++++++++ 2 files changed, 5882 insertions(+) create mode 100644 .DS_Store create mode 100644 BT2101_Default - EDIT-Copy1.ipynb diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d6901fc33840474cf27bd83ef26c6925ea54fe2a GIT binary patch literal 8196 zcmeHMzl#$=6#gccA|f174m=Q!)yn1$W2;4sd&PmGc!F5PWH;UgliOet!EPJ>0jq0m z1Uvm>ECl}nYYPPn{oc+HcCzlV5l#i)gPCubnfGJ9yq)E30GQ4^ZULJB8f=2iv+RB$ zvM#M4H8-aS_r6`#UKvmZ{!0er zehAnE+YVEU+10@=D*=dgPG`b#QwGGqVcTJ9kt-BqRf$&BxDvxybPUX(|Qak1XqsyQF zWIJ7QJ0!CMOqY0k`@43$+^fZ}Xs2=vx%~V3{oLm_tLZLsaAjRD4{Pt}7F$dF$K+{> zXUxNx`616;Lf(4WGbp3DJYyhS9_KQqLG2!GiB3eT&wcLAOYYDAhG%e}IeL>>dWX1! zG2$hSFP?_J|4E06GH{9vtXbQeQvdIkfB!$l2-LJPpbY$N224HN4?B#))z*c0q}C4D s9IDEc8Fqd_}m;Eyu!6T!\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n", + "2. Categorical attributes EDUCATION and MARRIAGE are likely to be associated with the target variable\n", + "3. SEX is not expected to be an important factor in our models as it appears to be statistically insignificant\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
0LIMIT_BAL
1AGE
2PAY_0
3PAY_2
4PAY_3
5PAY_4
6PAY_5
7PAY_6
8BILL_AMT1
9BILL_AMT2
10BILL_AMT3
11BILL_AMT4
12BILL_AMT5
13BILL_AMT6
14PAY_AMT1
15PAY_AMT2
16PAY_AMT3
17PAY_AMT4
18PAY_AMT5
19PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0\n", + "0 LIMIT_BAL\n", + "1 AGE\n", + "2 PAY_0\n", + "3 PAY_2\n", + "4 PAY_3\n", + "5 PAY_4\n", + "6 PAY_5\n", + "7 PAY_6\n", + "8 BILL_AMT1\n", + "9 BILL_AMT2\n", + "10 BILL_AMT3\n", + "11 BILL_AMT4\n", + "12 BILL_AMT5\n", + "13 BILL_AMT6\n", + "14 PAY_AMT1\n", + "15 PAY_AMT2\n", + "16 PAY_AMT3\n", + "17 PAY_AMT4\n", + "18 PAY_AMT5\n", + "19 PAY_AMT6" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
count30000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.0000003.000000e+0430000.00000030000.00000030000.00000030000.0000003.000000e+0430000.0000030000.00000030000.00000030000.000000
mean167484.32266735.485500-0.016700-0.133767-0.166200-0.220667-0.266200-0.29110051223.33090049179.0751674.701315e+0443262.94896740311.40096738871.7604005663.5805005.921163e+035225.681504826.0768674799.3876335215.502567
std129747.6615679.2179041.1238021.1971861.1968681.1691391.1331871.14998873635.86057671173.7687836.934939e+0464332.85613460797.15577059554.10753716563.2803542.304087e+0417606.9614715666.15974415278.30567917777.465775
min10000.00000021.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-165580.000000-69777.000000-1.572640e+05-170000.000000-81334.000000-339603.0000000.0000000.000000e+000.000000.0000000.0000000.000000
25%50000.00000028.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0000003558.7500002984.7500002.666250e+032326.7500001763.0000001256.0000001000.0000008.330000e+02390.00000296.000000252.500000117.750000
50%140000.00000034.0000000.0000000.0000000.0000000.0000000.0000000.00000022381.50000021200.0000002.008850e+0419052.00000018104.50000017071.0000002100.0000002.009000e+031800.000001500.0000001500.0000001500.000000
75%240000.00000041.0000000.0000000.0000000.0000000.0000000.0000000.00000067091.00000064006.2500006.016475e+0454506.00000050190.50000049198.2500005006.0000005.000000e+034505.000004013.2500004031.5000004000.000000
max1000000.00000079.0000008.0000008.0000008.0000008.0000008.0000008.000000964511.000000983931.0000001.664089e+06891586.000000927171.000000961664.000000873552.0000001.684259e+06896040.00000621000.000000426529.000000528666.000000
\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 \\\n", + "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", + "mean 167484.322667 35.485500 -0.016700 -0.133767 -0.166200 \n", + "std 129747.661567 9.217904 1.123802 1.197186 1.196868 \n", + "min 10000.000000 21.000000 -2.000000 -2.000000 -2.000000 \n", + "25% 50000.000000 28.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 140000.000000 34.000000 0.000000 0.000000 0.000000 \n", + "75% 240000.000000 41.000000 0.000000 0.000000 0.000000 \n", + "max 1000000.000000 79.000000 8.000000 8.000000 8.000000 \n", + "\n", + " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 \\\n", + "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", + "mean -0.220667 -0.266200 -0.291100 51223.330900 49179.075167 \n", + "std 1.169139 1.133187 1.149988 73635.860576 71173.768783 \n", + "min -2.000000 -2.000000 -2.000000 -165580.000000 -69777.000000 \n", + "25% -1.000000 -1.000000 -1.000000 3558.750000 2984.750000 \n", + "50% 0.000000 0.000000 0.000000 22381.500000 21200.000000 \n", + "75% 0.000000 0.000000 0.000000 67091.000000 64006.250000 \n", + "max 8.000000 8.000000 8.000000 964511.000000 983931.000000 \n", + "\n", + " BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", + "count 3.000000e+04 30000.000000 30000.000000 30000.000000 \n", + "mean 4.701315e+04 43262.948967 40311.400967 38871.760400 \n", + "std 6.934939e+04 64332.856134 60797.155770 59554.107537 \n", + "min -1.572640e+05 -170000.000000 -81334.000000 -339603.000000 \n", + "25% 2.666250e+03 2326.750000 1763.000000 1256.000000 \n", + "50% 2.008850e+04 19052.000000 18104.500000 17071.000000 \n", + "75% 6.016475e+04 54506.000000 50190.500000 49198.250000 \n", + "max 1.664089e+06 891586.000000 927171.000000 961664.000000 \n", + "\n", + " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 \\\n", + "count 30000.000000 3.000000e+04 30000.00000 30000.000000 \n", + "mean 5663.580500 5.921163e+03 5225.68150 4826.076867 \n", + "std 16563.280354 2.304087e+04 17606.96147 15666.159744 \n", + "min 0.000000 0.000000e+00 0.00000 0.000000 \n", + "25% 1000.000000 8.330000e+02 390.00000 296.000000 \n", + "50% 2100.000000 2.009000e+03 1800.00000 1500.000000 \n", + "75% 5006.000000 5.000000e+03 4505.00000 4013.250000 \n", + "max 873552.000000 1.684259e+06 896040.00000 621000.000000 \n", + "\n", + " PAY_AMT5 PAY_AMT6 \n", + "count 30000.000000 30000.000000 \n", + "mean 4799.387633 5215.502567 \n", + "std 15278.305679 17777.465775 \n", + "min 0.000000 0.000000 \n", + "25% 252.500000 117.750000 \n", + "50% 1500.000000 1500.000000 \n", + "75% 4031.500000 4000.000000 \n", + "max 426529.000000 528666.000000 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3df7BcZZ3n8ffHIC4VpEiM3IoEDboZZ/mhCHcIrlUz17EIAZwKlj+WHyXBQWMpKWdXtoowyxYK6Ga2lnXBHzhRY0ItENmpYZNRNGZZe3FnRZMoS4KARLzKJUiEQJaLOBj87h/naWgufW/f7j7d/Zx7P6+qru5++ulznpP+5vme55znnqOIwMzMLDevGHQDzMzMmnGCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5AQ1TZJGJT0raVzSY5K+Lunwhs83SDoo6XUNZaenugsayl4l6T5JH22xvpMk7ZT02/R8Um+2zHqlnzEj6Y8kbZb0G0n7JW2V9ObebZ2Vrc/xskDSP0p6QtJTkn4g6R2927rOOEG15y8i4nDgZOBPgCsAJM0F3gscAC6oV46IbcA3gesalnEF8CiwbrKVSDoU2Az8V2AesBHYnMqtWvoSM8CRwBbgzcAQ8COKGLJq6Ve8jAN/CbyWoo/5G+AfJB1S2paUwAmqAxHxCPBt4IRU9F7gKeAqYOWE6p8E/kzS2ZJOAFYDH4mprzE1AhwC/JeI+KeIuB4Q8OflbYX1U69jJiJ+FBFfi4j9EfF74HPAmyW9puxtsd7rQ7z8LiIeiIg/UPQtz1Mkqvnlbkl3nKA6IOkY4CzgJ6loJXALsAn4Y0kn1+tGxAHgY8CXgfXApyPi5y1WcTxwz4QAuyeVWwX1IWYm+lPg1xHxRLdtt/7rV7xIugf4HcXo+6sRsa+0jSiBE1R7/rukp4D/Dfwv4LOSXg+8E7g5Ih4D7mDCHk5E/ANwF8W/9/XTWM/hFEP5RgeAV3fXfBuAfsXMCyQtAr5IsWdt1dLXeImItwBHAOendWbFCao950TEkRHxhoj4eEQ8C3wQuC8i7k51bgLOl/TKCd+9F7g/DalbGacImkZHAE9303gbiH7FDACSXgt8F/hSRNxSxgZYX/U1XuCFw323AGskvbXrLShRVifEKupC4PWSfp3eHwK8BjiTYtjciXuBSyWp4TDfWyj2iq36ehEzSJpHkZy2RMRnum6l5aIn8dLEK4E3Av+3xGV2xQmqC5LeDrwJeBvwm4aPrqUYgncaPDWKk5afkPRl4COp/H92uDzLRK9iRtIRwFbgHyNiTbfttDz0MF5Oo+j/fwTMAT5BMfvzh920t2xOUN1ZCWyOiF2NhZKuA74vaX5E7G93oRHxnKRzgK8Ca4H7KIb+z5XRaBuonsQM8B6KacnHS7qoofy4iPhVx621QetVvLyK4lzVG4HfA7uAsyNib7cNLpN8R10zM8uRJ0mYmVmWnKAGRNIF6ZImEx/3DrptlifHjLVjJsSLD/GZmVmWWk6SkLQeeDewLyJOSGWfophZVp9V8tcRcXv67HLgYtIstIjYmsqXU1wvag7FXyyvTeXHUvx19Hzgx8AHpzMZYMGCBbF48eJpb2grzzzzDHPnzi1teb0yiHbu3Lnz8Yh4bV9XWrKy4wWqETOOl865j+mfSWMmIqZ8UFwy5WRgd0PZp4B/26TucRRz6F8FHAv8nCIhzUmv3wgcmuocl75zK3Buev1l4GOt2hQRnHLKKVGm733ve6Uur1cG0U5gR0zjN8n5UXa8RFQjZhwv+cRMFeIlIq+YaXkOKiLuBKY7jXEFsCmKC5z+AtgDnJoeeyLioShGR5uAFZLqF0D9u/T9jcA501yXmZnNYN38HdRqSRcCO4BLI+JJ4GiK60HVjaUygIcnlC+l+GvopyLiYJP6LyNpFbAKYGhoiFqt1kXzX2p8fLzU5fVKVdppZtatThPUDcDVQKTnaynuLaImdYPmswVjivpNRcQ60j1OhoeHY2RkpK1GT6VWq1Hm8nqlKu00M+tWRwkqiivqAiDpKxQ3zIJiBHRMQ9VFQP0vk5uVPw4cKemQNIpqrN9Xux45wEVrvjVlndG1Z/epNVYFrWLG8WKNHC/t6+jvoCQtbHj7HmB3er0FODfdcvhYYAnFtZ62A0skHZvuCnsuxQUtA/ge8L70/ZX4LqCVJ2m9pH2SdjeUfUrSI5LuTo+zGj67XNIeSQ9IOqOhfHkq2yNpTUP5sZJ+KOlBSd/wnYarzfFik2mZoCTdAvyA4u6cY5IuBv6jpF3pZlfvBP4NQETcSzEr76fAd4BLIuL5NDpaTXExy/uAW1NdgMuAT0raQ3FO6mulbqENwgZgeZPyz0XESelR/7OE4yh2WI5P3/mSpDmS5lBcvf1Mitmh56W6UNye+nMRsQR4kuLPGqy6NuB4sSZaHuKLiPOaFE+aRKK4zP/LLvWfAuz2JuUPUczysxkiIu6UtHia1V+Y+Qn8Iu2o1ONhT4oPJNVnft5HMfPz/FRnI8WfPdxQTuut3xwvNhlfzdz6qa8zP3s56xNg6DC49MSDk36ew2zLis/6nFEzhasQL5BXzDhBWb/0feZnL2d9Anz+ps1cu2vy/0KjF5S7vk5UeNbnjJspXIV4gbxixgnK+mImzvy03nG8GPhq5tYnnvlp7XC8GHgEZT2QZn6OAAskjQFXAiOSTqI4vDIKfBSKmZ+S6jM/D5Jmfqbl1Gd+zgHWT5j5uUnSNcBP8MzPSnO82GScoKx0nvlp7XC82GR8iM/MzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllqWWCkrRe0j5JuxvK5kvaJunB9DwvlUvS9ZL2SLpH0skN31mZ6j8oaWVD+SmSdqXvXC9JZW+kmZlVz3RGUBuA5RPK1gB3RMQS4I70HuBMYEl6rAJugCKhUdzGeSnFnS2vrCe1VGdVw/cmrsvMzGahlgkqIu4E9k8oXgFsTK83Auc0lN8YhbuAIyUtBM4AtkXE/oh4EtgGLE+fHRERP4iIAG5sWJaZmc1ih3T4vaGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRjLYYGhqiVqt12PyXGzoMLj3x4JR1ylxfp8bHx7Noh5lZr3WaoCbT7PxRdFDeVESsA9YBDA8Px8jISAdNbO7zN23m2l1T/3OMXlDe+jpVq9Uoc7t7QdJ64N3Avog4IZXNB74BLAZGgQ9ExJPpnON1wFnAb4GLIuLH6TsrgSvSYq+JiI2p/BSKQ8+HAbcDf5VG4FZBjhebTKez+B5Lh+dIz/tS+RhwTEO9RcDeFuWLmpRbtW3A5y1t+jbgeLEmOk1QW4D6TLyVwOaG8gvTbL7TgAPpUOBWYJmkeSlolgFb02dPSzot7Rld2LAsqyift7R2OF5sMi0P8Um6BRgBFkgao9hLWQvcKuli4FfA+1P12ymG3nsoht8fAoiI/ZKuBraneldFRD0gP8aLw+9vp4fNPH0/b9nLc5bQ+rxlDucKK3zOctad587ld8opZlomqIg4b5KP3tWkbgCXTLKc9cD6JuU7gBNatcNmrJ6dt+zlOUtofd7S5yx7Ysae584hXiCvmPGVJKxffN7S2uF4MSco6xuft7R2OF6s9GnmZj5vaW1xvNhknKCsdD5vae1wvNhkfIjPzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZWnW3LBw8ZpvTfn5pSf2qSFWGY4Zs8HqKkFJGgWeBp4HDkbEsKT5wDeAxcAo8IGIeFKSgOsobtf8W+CiiPhxWs5K4Iq02GsiYmM37TIz6zfv0JSvjEN874yIkyJiOL1fA9wREUuAO9J7gDOBJemxCrgBICW0K4GlwKnAlZLmldAuMzOrsF6cg1oB1EdAG4FzGspvjMJdwJGSFgJnANsiYn9EPAlsA5b3oF2WAUmjknZJulvSjlQ2X9I2SQ+m53mpXJKul7RH0j2STm5YzspU/8E0ArcZyPEyu3WboAL4rqSdklalsqGIeBQgPR+Vyo8GHm747lgqm6zcZi6Puq0djpdZqttJEu+IiL2SjgK2Sbp/irpqUhZTlL98AUUSXAUwNDRErVabdkMvPfHglJ8PHda6Tjvr65Xx8fEs2lGyFcBIer0RqAGX0TDqBu6SVB91j5BG3QCS6qPuW/rbbBsQx8ss0VWCioi96XmfpNso9k4ek7QwIh5NwbEvVR8Djmn4+iJgbyofmVBem2R964B1AMPDwzEyMtKsWlMXtTyBeZBrd039zzF6wfTX1yu1Wo12tjtD9VF3AH+bftOXjLrTDg941G2Ol1mt4wQlaS7wioh4Or1eBlwFbAFWAmvT8+b0lS3AakmbKIbaB1JwbQU+2zDkXgZc3mm7LHt9G3V3M+KG7kfdOYx0Z8CIe9Ycpcnld8opZroZQQ0BtxWzxzkEuDkiviNpO3CrpIuBXwHvT/Vvp5hivodimvmHACJiv6Srge2p3lX1objNPP0cdXcz4obuR90ecXdvNh2lySFeIK+Y6XiSREQ8FBFvTY/jI+IzqfyJiHhXRCxJz/tTeUTEJRHxpog4MSJ2NCxrfUT88/T4evebZTmSNFfSq+uvKUbLu3lx1A0vH3VfmGZnnUYadQNbgWWS5qWR97JUZjOI48VmzZUkLAsedVs7HC+znBOU9U1EPAS8tUn5E8C7mpQHcMkky1oPrC+7jZYPx4s5QZmZTUOrSxlZ+Zyg2tAqQEfXnt2nlli3cuhsptMGx5Q1mm19kG+3YWZmWXKCMjOzLDlBmZlZlpygzMwsSzNmkkQOJ73NzKw8MyZB5WC2zbAxM+slH+IzM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmlqVsEpSk5ZIekLRH0ppBt8fy5nixdjlmqieLq5lLmgN8ETgdGAO2S9oSET8dbMvKNZ1bgviK563NlngBXyG/LLMpZmaSLBIUcCqwJyIeApC0CVgBOHisGceLtWtWxMxM26HJJUEdDTzc8H4MWDqxkqRVwKr0dlzSA2U14BOwAHi8rOV1Sn/Tssog2vmGPq+vlYHHC7SOmWn8ll1zvEzbwGOmIvECGcVMLglKTcriZQUR64B1PWmAtCMihnux7DJVpZ09NvB4gWr8FlVoY58MPGaq8lvk1M5cJkmMAcc0vF8E7B1QWyx/jhdrl2OmgnJJUNuBJZKOlXQocC6wZcBtsnw5XqxdjpkKyuIQX0QclLQa2ArMAdZHxL19bkbPDgWVrCrt7JlM4gWq8VtUoY09l0nMVOW3yKadinjZYVgzM7OBy+UQn5mZ2Us4QZmZWZacoKjGJVAkjUraJeluSTsG3Z7ZrArxAo6ZnFQhZnKMl1l/DipdAuVnNFwCBTgvt0ugSBoFhiNi4H9MPJtVJV7AMZOLqsRMjvHiEVTDJVAi4jmgfgkUs2YcL9Yux0yHnKCaXwLl6AG1ZSoBfFfSznQ5FhuMqsQLOGZyUZWYyS5esvg7qAGb1iVQMvCOiNgr6Shgm6T7I+LOQTdqFqpKvIBjJhdViZns4sUjqIpcAiUi9qbnfcBtFIcNrP8qES/gmMlIJWImx3hxgqrAJVAkzZX06vprYBmwe7CtmrWyjxdwzGQm+5jJNV5m/SG+TC6B0soQcJskKH6zmyPiO4Nt0uxUkXgBx0w2KhIzWcbLrJ9mbmZmefIhPjMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKCmSdKopGcljUt6TNLXJR3e8PkGSQclva6h7PRUd0FD2ask3Sfpo9Nc70pJIenD5W6R9Vq/YybFyTNpfeOSvtqbLbNeGEC8zJF0jaS9kp6W9BNJR/Zm6zrjBNWev4iIw4GTgT8BroAX7kD5XuAAcEG9ckRsA74JXNewjCuAR4F1rVYmaR5wOZDbzc1s+voaM8BbI+Lw9PBOTfX0M14+DfxL4O3AEcAHgd+VshUlcYLqQEQ8AnwbOCEVvRd4CrgKWDmh+ieBP5N0tqQTgNXAR2J6d4r8D8D1wOOlNNwGpo8xYzNAr+Ml7fz+61Tvl1HYHRFOUFUn6RjgLOAnqWglcAuwCfhjSSfX60bEAeBjwJeB9cCnI+Ln01jHqcBw+p5VXD9iJrlT0q8l/b2kxSU13/qsD/FyInAQeF+Kl59JuqTkzeheRPgxjQcwCoxT7MX8EvgScBjweuAPwEmp3lbguibf/2/ADuAV01jXnFT37el9DfjwoP8N/Mg3ZlL9PwUOBY4EvgDsBg4Z9L+DH/nFC3A+EMDX0jreAvwGOH3Q/w6ND4+g2nNORBwZEW+IiI9HxLMUx23vi4i7U52bgPMlvXLCd+8F7o+IP0xjPR8H7omIH5TXdBuQfsUMEXFnRDwXEU8BfwUcC/yLkrbD+qNf8fJser4qIp6NiHsoRmdnlbERZTlk0A2YAS4EXi/p1+n9IcBrgDOBLR0u810Ux5TrwTIfeJukkyJidVettRz0ImaaCUAlLs8Goxfxck96zvq8phNUFyS9HXgT8DaK4XHdtRTHjDsNnouAf9bw/u+Bv6MYjluF9SpmJB0PvBLYRXHI5hrgEeC+btprg9WreImIn0v6PvDvJH0CeCPwr4DzumtxuZygurMS2BwRuxoLJV0HfF/S/IjY3+5C0yGaxuU9B/y/KE6GWrX1JGaAIeAGYBHwDPB/gHdHxO+7bbANVK/iBYpk9DXgCWAf8O8j4o6uWlsypRNmZmZmWfEkCTMzy5IT1IBIuqDhkjSND181wppyzFg7ZkK8+BCfmZllqbKTJBYsWBCLFy8ubXnPPPMMc+fOLW15vTKIdu7cufPxiHhtX1dasrLjBaoRM46XzrmP6Z/JYqayCWrx4sXs2LGjtOXVajVGRkZKW16vDKKdkn7Z1xX2QNnxAtWIGcdL59zH9M9kMeNzUGZmliUnKDMzy5ITlJmZZamy56DKtuuRA1y05ltT1hlde3afWmNV0CpmHC/WyPHSPo+gzMwsS05QZmaWJScoMxsoSesl7ZO0u6HsU5IekXR3epzV8NnlkvZIekDSGQ3ly1PZHklrGsqPlfRDSQ9K+oakQ/u3ddaNlgnKwWNmPbYBWN6k/HMRcVJ63A4g6TjgXOD49J0vSZojaQ7wRYp7JB0HnJfqAvxNWtYS4Eng4p5ujZVmOiOoDTh4zKxHIuJOYLq3jFgBbIqIf4qIXwB7gFPTY09EPBQRz1HcHXaFJAF/TnE/NYCNwDmlboD1TMsE5eAxswFZLemedBRnXio7Gni4oc5YKpus/DXAUxFxcEK5VUA308xXS7oQ2AFcGhFPUvzwdzXUaQyGicGzlDaDR9IqYBXA0NAQtVqti+a/1NBhcOmJB6esU+b6OjU+Pp5FO6YiaT3wbmBfRJyQyj4FfIQX7wr61w0j78spRs7PA5+IiK2pfDlwHTAH+GpErE3lx1Ls5MwHfgx8MO342MxxA3A1xS3Jr6a4g+xf0vwW9kHzne3Jbnk/6RWyB9nH5PL/Oqc+ptMENZDgiYh1wDqA4eHhKPN6UZ+/aTPX7pr6n2P0gvLW16mKXM9rA/AF4MYJ5Z+LiP/UWDDhsPDrgP8h6Y/Sx18ETqfYcdkuaUtE/JQXDwtvkvRliuR2Q682xvovIh6rv5b0FeCb6e0YcExD1UXA3vS6WfnjwJGSDkk7wo31m613YH1MDv0L5NXHdDSLLyIei4jnI+IPwFcoDuHB5MEzWfkLwTOh3CrMh4WtW5IWNrx9D1CfpLUFOFfSq9JIegnwI2A7sCRNujqUYqdnSxT3E/oe8L70/ZXA5n5sg3WvoxGUpIUR8Wh6OzF4bpb0nyn2huvBI1LwAI9QBM/5ERGS6sGzCQfPTNfXw8K9PFwD1Thkk9PhmslIugUYARZIGgOuBEYknURxRGUU+ChARNwr6Vbgp8BB4JKIeD4tZzWwleKQ8PqIqN+Y7zJgk6RrgJ8AX+vTplmXWiYoB4+VpO+HhXt5uAaqccgmp8M1k4mI85oUT9oPRMRngM80Kb8duL1J+UO8eJTHKqRlgnLwWBkGdU7BzKrLV5KwvvA5BTNrl69mbqXzYWEzK4MTlJXOh4XNrAw+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzAZK0npJ+yTtbiibL2mbpAfT87xULknXS9oj6R5JJzd8Z2Wq/6CklQ3lp0jalb5zvST1dwutUy0TlIPHzHpsA7B8Qtka4I6IWALckd4DnAksSY9VwA1Q9EkUd25eSnEzyyvr/VKqs6rhexPXZZmazghqAw4ea4N3aqwdEXEnsH9C8QpgY3q9ETinofzGKNwFHClpIXAGsC0i9kfEk8A2YHn67IiI+EFEBHBjw7Iscy1v+R4Rd0paPKF4BTCSXm8EasBlNAQPcJekevCMkIIHQFI9eGqk4Enl9eD5djcbZQO3AfgCRWdQV9+pWStpTXp/GS/dqVlKscOytGGnZhgIYKekLanzqe/U3EVxS/jlOGZmmqGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRxBZDQ0PUarXutqLB0GFw6YkHJ/28zHV1Y3x8PJu2tExQk5h1wQN5BFBOwTMZ79RYDzUbLUcH5U1FxDpgHcDw8HCMjIx00MTmPn/TZq7dNXmXO3pBeevqRq1Wo8zt7kanCWoyMzZ4II8Ayil42jSQnRqrrMckLUyxshDYl8rHgGMa6i0C9qbykQnltVS+qEl9q4BOE5SDx8rSs52aXo64oRqHbKow4p7EFmAlsDY9b24oXy1pE8Uh4QOpH9oKfLbh3PYy4PKI2C/paUmnAT8ELgQ+388Nsc51mqAcPNauvu/U9HLEDdU4ZFOFEbekWyh+6wWSxijOPa4FbpV0MfAr4P2p+u3AWcAe4LfAhwBSX3I1sD3Vu6p+eBj4GMV50cMoDgX7cHBFtExQDh4riXdqrKmIOG+Sj97VpG4Al0yynPXA+iblO4ATummjDcZ0ZvE5eKwt3qkxszKUPUnCzDs1ZlYKX+rIzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZck3LDQzK8HiNd+a8vNLT+xTQ2aQrhKUpFHgaeB54GBEDEuaD3wDWAyMAh+IiCclCbiO4vbevwUuiogfp+WsBK5Ii70mIjZ2065mHDxm1VOlPsbKV8YI6p0R8XjD+zXAHRGxVtKa9P4y4ExgSXosBW4AlqZguxIYBgLYKWlLRDxZQtssM1XqcLxTkw33MbNUL85BrQDqncVG4JyG8hujcBdwpKSFwBnAtojYnwJmG7C8B+2yfLwzIk6KiOH0vt7hLAHuSO/hpR3OKooOh4YOZylwKnClpHl9bL8NlvuYWaLbEVQA35UUwN9GxDpgKCIeBYiIRyUdleoeDTzc8N2xVDZZ+ctIWkXRUTE0NEStVpt2Qy898eCUnw8d1rpOO+vrlfHx8SzaUbIVwEh6vRGoUewRv9DhAHdJqnc4I6QOB0BSvcO5pb/Ntj6YNX1MLv+vc+pjuk1Q74iIvSlAtkm6f4q6alIWU5S/vLAIznUAw8PDMTIyMu2GXtTycM1Brt019T/H6AXTX1+v1Go12tnuDPWtw+mms4GZ0eHk1Nl0KJs+ptUh31bdaas+Jof+BfLqY7pKUBGxNz3vk3QbxeGWxyQtTB3NQmBfqj4GHNPw9UXA3lQ+MqG81k27LGt963C62aGB7ndqcuhwcupsOuE+Znbr+ByUpLmSXl1/DSwDdgNbgJWp2kpgc3q9BbhQhdOAA2mveSuwTNK8dB5hWSqzGaixwwFe0uEAtNHhNCu3GcR9jHUzghoCbismWnEIcHNEfEfSduBWSRcDvwLen+rfTjEbaw/FjKwPAUTEfklXA9tTvavq5xba0Xr4bYOWOplXRMTTDR3OVbzY4azl5R3OakmbKCZEHEh7zVuBzzZMjFgGXN5OWxwvlZBVH2P913GCioiHgLc2KX8CeFeT8gAumWRZ64H1nbbFKsMdjk2b+xjzlSSsb9zhmFk7fC0+MzPLkhOUmZllyQnKzMyy5HNQJWo1M2x07dl9aomZWfV5BGVmZlnyCMrMrCJm21Eaj6DMzCxLHkGZDch0rmYx0/aIzdrhEZSZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMspRNgpK0XNIDkvZIWjPo9ljeHC/WLsdM9WSRoCTNAb4InAkcB5wn6bjBtspy5XixdjlmqimXi8WeCuyJiIcAJG0CVgA/HWirSjadi4O2smH53J63oQIXKJ0V8QLdx0y38TKdNlQgXmCWxMwM+a1eoIgYdBuQ9D5geUR8OL3/ILA0IlZPqLcKWJXevhl4oMRmLAAeL3F5vTKIdr4hIl7b53VOKpN4gWrEzKyPF8gmZqoQL5BRzOQyglKTspdlzohYB6zrSQOkHREx3Itll6kq7eyxgccLVOO3qEIb+2TgMVOV3yKndmZxDgoYA45peL8I2Dugtlj+HC/WLsdMBeWSoLYDSyQdK+lQ4Fxgy4DbZPlyvFi7HDMVlMUhvog4KGk1sBWYA6yPiHv73IyeHQoqWVXa2TOZxAtU47eoQht7LpOYqcpvkU07s5gkYWZmNlEuh/jMzMxewgnKzMyy5ARFNS6BImlU0i5Jd0vaMej2zGZViBdwzOSkCjGTY7zM+sWNACwAAAEnSURBVHNQ6RIoPwNOp5iKuh04LyKy+gtzSaPAcERU4Q/9ZqyqxAs4ZnJRlZjJMV48gmq4BEpEPAfUL4Fi1ozjxdrlmOmQExQcDTzc8H4sleUmgO9K2pkux2KDUZV4AcdMLqoSM9nFSxZ/BzVg07oESgbeERF7JR0FbJN0f0TcOehGzUJViRdwzOSiKjGTXbx4BFWRS6BExN70vA+4jeKwgfVfJeIFHDMZqUTM5BgvTlAVuASKpLmSXl1/DSwDdg+2VbNW9vECjpnMZB8zucbLrD/El8klUFoZAm6TBMVvdnNEfGewTZqdKhIv4JjJRkViJst4mfXTzM3MLE8+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZen/A6K07c+5e7/dAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies and Outliers\n", + "2. One Hot Encoding\n", + "3. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "#### Inconsistency\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6\n", + "ID \n", + "1 2 2 0 0 0 0\n", + "2 0 2 0 0 0 2\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 0 0 0 0 0 0\n" + ] + } + ], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)\n", + "\n", + "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].head())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.02040820.0789472200000.061163...0.00.01.00.01.00.00.01.00.00.0
10.22449020.1315790200020.056292...1.00.00.01.00.00.01.00.00.00.0
20.16326520.3421050000000.161370...1.00.00.01.00.00.01.00.00.01.0
30.08163320.4210530000000.231605...1.00.00.01.00.00.01.00.00.01.0
40.08163310.9473680000000.079775...0.00.00.01.00.00.01.00.00.01.0
\n", + "

5 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 \\\n", + "0 0.020408 2 0.078947 2 2 0 0 0 0 \n", + "1 0.224490 2 0.131579 0 2 0 0 0 2 \n", + "2 0.163265 2 0.342105 0 0 0 0 0 0 \n", + "3 0.081633 2 0.421053 0 0 0 0 0 0 \n", + "4 0.081633 1 0.947368 0 0 0 0 0 0 \n", + "\n", + " BILL_AMT1 ... PAY_3_Revolving_Credit PAY_4_No_Transactions \\\n", + "0 0.061163 ... 0.0 0.0 \n", + "1 0.056292 ... 1.0 0.0 \n", + "2 0.161370 ... 1.0 0.0 \n", + "3 0.231605 ... 1.0 0.0 \n", + "4 0.079775 ... 0.0 0.0 \n", + "\n", + " PAY_4_Pay_Duly PAY_4_Revolving_Credit PAY_5_No_Transactions \\\n", + "0 1.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_5_Pay_Duly PAY_5_Revolving_Credit PAY_6_No_Transactions \\\n", + "0 0.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 1.0 0.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + "\n", + "[5 rows x 45 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Chew/anaconda3/lib/python3.7/site-packages/statsmodels/compat/pandas.py:23: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n", + " data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)\n" + ] + } + ], + "source": [ + "#importing libraries\n", + "from sklearn.datasets import load_boston\n", + "import statsmodels.api as sm\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the Pearson Correlation between each input attribute and the target attribute. Those with correlations below a threshold are excluded from the learning phase.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABCQAAAOECAYAAAB5LeBpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd3gU1d6A37MJ6YUkkARCT5ASQhXpvddQFBCkiRQRFEURsWBEwQYWbHBFRQGVFgLSSYCEEqpA6D3UFBJSSELK7nx/zLIluykoWT/vPe/z+NzLzJnz7u83kzlnzpyZEYqiIJFIJBKJRCKRSCQSiURiSzT/9A+QSCQSiUQikUgkEolE8r+HHJCQSCQSiUQikUgkEolEYnPkgIREIpFIJBKJRCKRSCQSmyMHJCQSiUQikUgkEolEIpHYHDkgIZFIJBKJRCKRSCQSicTmyAEJiUQikUgkEolEIpFIJDZHDkhIJBKJRCKRSCQSiUTyP4AQ4gchRJIQ4mQR64UQ4kshxEUhxAkhRFOTdaOFEBf0/41+FL9HDkhIJBKJRCKRSCQSiUTyv8FPQM9i1vcCauv/mwB8CyCE8AZmAy2AJ4DZQgivv/tj5ICERCKRSCQSiUQikUgk/wMoihINpBZTJBT4WVGJBcoLISoBPYDtiqKkKopyF9hO8QMbpUIOSEgkEolEIpFIJBKJRCIBCACum/z7hn5ZUcv/FvZ/t4L/BSYJD8WWvq9/nmlLHZoO/W3qA1BuXrKt78BOm/pE10E29QEIR2fbCnVam+oUrW19to4PAEVnW92BSJv6sPsfaHL+iePGloh/4D7Gtau29WlsG6OSn29Tn7D/B/4OFZt2o1B0tj2XagY/kseoS42oFGhTH4BwsHEfw8bt4T9x7la0BbYV/gMxCv9AYXNpGWPr68KHZRGZE1Efs3jAYkVRFj9kNdb2m1LM8r/F/0DvUCKRSCQSiUQikUgkkv9u9IMPDzsAUZgbQFWTf1cBbumXdyy0fNffdMlHNiQSiUQikUgkEolEIpEAsB4Ypf/aRksgXVGU28BWoLsQwkv/Msvu+mV/CzlDQiKRSCQSiUQikUgkkv8BhBC/os50qCCEuIH65YxyAIqifAdsAnoDF4FsYKx+XaoQYg5wSF/Ve4qiFPdyzFIhByQkEolEIpFIJBKJRCIpgf+GxwsURXm6hPUK8EIR634AfniUv+e/IacSiUQikUgkEolEIpFI/mXIAQmJRCKRSCQSiUQikUgkNkc+siGRSCQSiUQikUgkEkkJaMR/3ZdM/3HkDAmJRCKRSCQSiUQikUgkNkfOkCgjRi75mpC+PclMSmZOSMu/VEfMpZvM23oQraLwZOPajG8TYrY+r0DLzPV7OHU7hfLOjiwY1IGA8m6G9bfS79HvuwheaN+IZ1s1AGDpgVOs/vMCQggeq1ieD/q3xbkIv6IofPDND0QfPIqTowPzXptKcO1aZmVy7ucybc6nXLudgJ1GQ6eWjzP9uZFmZbZE72fanE9Z9dVHhNQJKjJeRVGYu3w90SfO4uRQjrnPDSG4RhWLcp+v3kLEviNkZOVwZNH7huWHzl1m3or1nL+ewPznh9OjecMiXQAx15KZt+eMmt96VRjfNNBs/eFbqczbe4bzKZl82q0RPQIrAXAzM4eXthxFqygU6BRGhFRnWHC1Yl2G+H74jeijcTg5ODB36liCa1W3jG95OBG795ORlc2R5V8Zls/78XcOnjwLQE5uHqnpmRz85csSvab+Dxb/QvThYzg5OjJv2gSCg2qalcm5n8u0D7/kWkKSuj+faML0McMezvGf5UQfPq4eM9PGExxYw6LcZ7+sJmLnXjLuZXF0pfFTyTeT7vDml0tITc/A092NT16ZiH8F72J9c5esIPpIHE6ODsydOo7gQCs5XbaGiF371Jz++q1h+a3kFN74cgmZWdlodTpeGfkkHZoVf9zYej+qvt+J/vOk6psyhuBalsfb5yvWEbE7VvUtM9Y378eVHDx1ztz38+dm28Zcuc28yGPq30LDmoxvUc9sfV6BlpmbDnIq8S7lnR1Y0K8VAZ6u5Gt1vLP1EKcT09DqdPQPrsGElvXILdAy6ted5Gm1FOgUuj9WhaltGxh9l28xb8cRtDqFJxsFMr5VsKXvj/2cSkhVz22hbQzntnNJd3l3y0Hu5RWgEbBydE8KtDqeWb7dsH1iZg79gmvwRtdmZeJztLcjT6vlg22HOXgtCY0QvNS+Id3rGvdLzOXbzIv8U5/TWoxvaSWnGw8Yc9q/NQGermw4dZUfDp0zlDuflMbq0d2p5+dlWPbCmhiup99j/bO9ytSXp9XywfajHLyuj7FdCN3rVjfmNPKoMact61vxxZrktDUBnm6q7+AZc9+YntTz82LCyp0k37tPgU5Hs6q+vN2tGXaaUtw3CQpB03M4aDQoR6NR9mw0Wy1a9UA0bQ86HWRlootYAukpJddrSmADND2Gg0ag/BmDsneT+fpqj6Hp8TT4VUG35js4c8To7/IkorZ6XlGiN6CcPkRJiKAQNH1GgtCgO7ILJeYP8/Wte6Jp1hF0WpSsTHTh/zHEpBn1GqJKIMq18+iWLXi4OE3jNc1poXhFy+7mOV3/w8Pn1BQb7EMRFIKm9zNqTo/utp7Tph3UnGZnogv/3pjTka/qc3oB3fLS5VRRFOb+uIroP0/h5FiOuZNHWT93/xpBRPQBMu7lcOSXzwzLw3ft55NfwvHzLg/A8J4deKpLmxKdH3zxLdGxB3FydGLerOkE16ltUe656bNITklFq9XSrFED3nl5CnZ2dmzZGc1XP/zCpfjrrFz8JSF1HyvZt+BLdu+LxcnJkQ/ffoPgunUsyo176VWS76SovsYNmf3ay9jZ2QHwy8o1LFu1Fns7Ozq0acWMqc+X4FvI7v2xODk68eHbMwm28hvHTXuN5Dv6+BqHMPvVadjZ2bHwPz+ycv1GvMt7AvDK8+Pp0Lr4PrqiKHzw2dfs3n9AjfGtGQTXseJ8eSbJKfoYG4Uwe/qL2NnZ8fniH4mM2YtGo8GnfHnmvTUDv4oVivfZeh9+uYjoA4fUvuEbrxD8mHlfPef+fabNnse1W7fVvmHrFkyfOBaAmwmJvPnR56SmpePp4c4nb76Gv2/R8Ukk1pADEmXE/p+Ws+urxYz5edFf2l6r0/H+5li+H9EdPw8Xhi7ZSKfHqhJUsbyhzJpjF/BwcmDrC4PYdOoK86OOsGBQB8P6j7Yfol1QgOHfiRlZLDt4lg2TQnEqZ8/La3ax6dQVBnex/huiDx4l/uZttv70FcfPXCDsy8WsXPihRbmxT/WnZeMQ8vLzGTsjjOiDR2n/RFMA7mXnsGzdRhrVtTyZWvhOnCU+8Q5bPprB8UvXeO/ncH5/Z6pFuY6N6zG8a2t6vf6x2fLK3uWZ99xQfti8u0SXVqfwfswpvu/3BH6uTgxds49ONXwJ8nY3lKnk5sTcziH8eOyK2bYVXRxZMaglDnZ2ZOUXEPr7HjrX8MXX1an4+I6eJP52Elu++oDjFy7z3uLl/P7hLMv4mjdkeO9O9JryltnyN8YONfz/ZZsiOXPleolxmvkPHyf+VgJbF8/n+LlLhH3zEysXhFmUGzuoDy0b1icvv4Cxb84l+vBx2j/eqHSOIydUx6KPVce3S1n56WyLcp2aN2ZEn670nDTDbPnHP/xGaKc2DOzSltjjp1nw8yo+fmVi0b6jccTfSmTLN/M4fv4y7y36md8/ftuiXMfmjRneuwu9XnjDbPl3qzbQs01znu7ZiYvXbzJxzudELv6k+BhtvB+j/9T7Fs7h+IUret8bFuU6Pt6Q4b060WuqefxvjB1i4ouy8Gl1Ot7ffpTvh3TAz92Zob/soFNgZYIqeBrKrIm7godTObaO782mM9eYv/sEC/q3Yuu56+RpdUSM7UFOfgH9fthCn3rVqOzhwg9DO+DqUI58rY5nfo2ifa1KNKrqp/q2Heb7YZ1V309b6VS7irnvxCX13DapP5tOX2X+rmMsGNCWAp2O1zfs58O+rajr50VaTi72GoGjfTnCn+1t2P7JHzfT7bGqxvgesQ9g0b5TeLs6sXliP3SKQnpOrnlOdxzh+yEdVefP2+kUVDinl1XnhD5qTncdZ0Foa/oF16BfcA0AzienMWXtHrPBiO3nb+DiYN50l5Vv0f4zaozj++hjzDM5Zo7w/dBOqm/pNjoFBRTKqd43sR+bTsfrfW0sfWuiDb4FoW1xcyyHoihMW7eHrWev07u+5WCfGUKg6T0S3S+fQEYqmvGzUc79Ccm3DEWU2/Eoi8MgPw/xeCdEtyEoq78tplIrjl7PoFs2X3U89w7KuWNwx+ggPQVdxBJEq57m29ZuiKhUHd2id8HeHs3omSgX4yDvfvG+fqPR/vQRZKRiN+k9tGePmsXE7Xi0372jxtS8C5oew9Ct/FqNd89GlHKOiOadSh9jYX/vkeh++VSfU8t4lYRrKIvfgwJ9TrsOQVnzEDm16ivjfdh3FNqlH6s5nRhmPaeLZutz2hlN92HoVulzuncTSjkHxOOdS62M/vMU8QlJbPnyXY5fuMp73//G73NnWJTr2Kwhw3t2pNeL71qs69W6GW+PG2qxvEhn7CHib9xk668/cvz0WcLmL2TlYssB78/fexM3V1cUReHFt+ewZWcMfbp2pHbNGnz5wTvM/qR0Nzui98Vy9foNtq1ewfGTp3n34wWs+sGy3/vFB2G4uel9M99mS+Qu+nTvQuzho0RG72HD8h9xcHAgJfVu8b79B1TfquUcP3Wadz/+jFU/WB4HX3zwrjG+N2azJWoXfbqpnd0xw55k3IjS32iJ3n+QqzdusG3lzxw/dYZ3P/mCVd9/bel8/22j880wtkTtpk+3zjw3YgjTJqgX7z+vXMvXP/7CezNeLtpn63144LDqW/49x0+fI2zBV6z87nOLcmOHDqJl00ZqX//lWUTHHqJ9y+Z8/M0SQnt0YWDPrsQePcaCxT/y8Vuvlcr9b0U+XvDoeSQ5FULcs7LsXSHEq/r//5MQIlsI4W6y/gshhCKEqPCgDiFEiBDimP6/VCHEFf3/31GEt4YQIkdf5rgQYp8Qok6hMl8IIW4KITQmy8YIIb6yrPHRcTFmH9klnFiLI+7WHap5e1DVyx0HOzt6Bdck6rz5hUTU+esMaKje1e9erzqxV26jfqUFdpy7RpXy7gRVKG+2jVan436BlgKdjvv5WnzdipofAZH7DxHatQNCCBrXf4yMe1kkpZjH5OzkSMvG6swNh3LlqB9Uk4Q7xrsWX/70K+OGDMDBwaHEmKP+PE1om6aqL6g6Gdk5JKVlWJRrHFQd3/IeFssDKnpTp2qlUj3bFZeURjVPV6p6uOBgp6FXUCWiriaZ1+fhQh0fD4v6HOw0OOhH9vO1OnT6nJcY36FjhHZoqcb3WCAZWdkk3U2zjO+xQHy9ylupwcjGPYfo3faJUnkfEHngCKGd26r+ukFkZGWRlGplfzZU73Q6lLOnfmANEu6U/vPCkQeOEtqpjYkjm6RUKzHWDcLX2zLGS9dv0qqR6m/RsB6RB44W64s6+CehnVqrvjqBRfvqBFr1CSG4l50DQGZWjtUyFk4b78eoQ8cJ7fjAV0v9u7ibbsVXC18vTys1FPY1N1sWdzuVal5uVC3vpp5r6lYj6uItszJRF28yQH8R2b1OFWKvJaIoCkIIcvILKNDpyC3QUs5Og6uDPUIIXB3KAVCg01Gg1Zn4Usx99asTdeGGue/CDQaEqLN3utetRmy86tt75TaP+Zanrv4Ctryzo8Ud9KupGaRm59KsasUy9YWfuMz4lupMC40QeLkYByTjbqdSrby70VmvGlEXbxZy3mJAA8ucmu2vM9foXc94RzUrL5+lh84xsZX5bISy8oXHXTbMllFjdDTxuZn7rOW0wYOcViU2PsHSdzrebMDBzfHBMaOQr9VRqsd0A2pBaiLcTQatFuXkAUSdJuZlrp6FfHUwRblxCeFR9KyrIh13kyAtWb17fuoAok5j8zLpKZB0AxSd2WJRoTJK/Dl1eX4eSuJ1RJD5bEcLqgSipBhj0sXFIuo1MyuiXDljEtNFs5iUy6chN+fhYiwcb6ppvAcRda3ktMA0p15WKnoYXxnvwyqBKKlJ5jmt29SsiFlOr19CeBpjUnNazCCSFaIOnyC0fQv9ubumvq2wdu6uWeK5u7RE7tlPaM+uqjO4ntpvu2M5k8TN1RWAAq2W/PwCw99aYI1q1KpWtfS+6D0M6NVD9YUEk5F5j6Q7dyx9bia+AqPv17URTBg1wtBH9PEu/jiKjN7LgN56X4NgMu7dK0V8+Qj++jP/kTF7GdCzu95Zv/ROfZAPloM600CUcGKz+T7cE0tojy56X119X9+83+fs5ETLpuqNKYdy5aj/WCAJyepvuhR/jVZN1fNhiyaNiNwbW2q3RPIAWw7yXARCAfSDA50Asx6ToihxiqI0VhSlMbAeeE3/767F1HtJX6YRsBQw3KrUewYC14H2jzSaMiYxMxt/D+NJzN/dhaTMrCLL2Gs0uDuWIy0nl+y8fJbsO8nk9uZ3tf08XBnbKpguX66mw+crcXMsR5vAAIoi8U4qlUymXflX8CHRyknxARn3stgZe5hWTdTO1umLl7mdfIdOLR8vXcx30/E3uSD09ypvtfF+FCRm3cffZEaDv6sTSVml72zcvpfDgN/30PmXnTzXpFaJsyMAElPvmj1+4O/jRVKK5YVsSdxMSuFG4h1aNqj7UNslptylUgUfE783iSlFD5pl3Mti58E/adU4uMgyVh0VS+8oTJ2a1di27zAA2/cfISvnPnczLMY7zXz+PqY59bYYZCmOF4aGsmH3fjo+N51J73/OW+NHlLiNrfdjYkqaeYze5S0GBkvlS07hRpKlL/FeDv7uLsb63Z1JupdjWcZDLWOv0eDuUI60nDy6P1YF53L2dPhmA10W/cHY5nUo76xetGp1Ogb+tI22X6+ndQ0/GlVWj4vEzBz83Quf27LNfSZlTM9t8amZCGD871EM/nEzS2JPW8S56XQ8PetVM3T6ysKXcV+9aFkYc5zBP25mWngMd7KMOVNzahzsVZ2Fc5ptnlNHNaembDl7jT4mAwQLY04ypnkdnMvZF6rr0fsMMe6JY/BPW5kWsZc7+nOk2vaYHjMu1o8Zd1OfQxE+8xkQ43/fSbuFa3F1KEf3OqXoVHt4oWSYdJ4z7kIxF8eiaXuUiydKrtcU9/Io6YUc7qW7ADcMQNg7gLMbokZdKOFiWnh4gakvPRVRjE/TtAO6Cw8ZU3G4F85parHxiibt1VkffxUb7EPh7mX+iEdGarGDKJpm7f92ThNT0/CvYHT4+3hZHTAvjm0H/iT01fd5af5/uF2KmwOJyXeo5FvR6KxYoch+27hXZtGm31BcXZzp0bHdQ/0uU5+/n6/R51uRxGTLAQmAcS9Op3XP/ri6uNCjc0cArl67zuFjJ3jq2Yk8M2kqJ06fsbqt0ZeMv2l8vhVJTE627nvpNVr3GoCrqws9OhtnDy9fFU6/Ec/yxvsfkZ6RWcoYTXNaTIzTXqd1n8FqjJ2Mlx2ffbeEDgOGsWFrJC89N6ZEn0334R0rviLiA8jIvMfOfQdp1Uy9xqgTWJNt0XsA2B6zj6zsHO6mW95MlEiKw5YDEr8CD+addQT2AgWP2OEBmPbUOwEngW+Bpx+xq0yxftNdFCpjWUgAX0UfY1SL+oY7lA9Iz8kl6tx1tk8ZzK6XhpCTX8D6uEsP9SOKGtkt0GqZPvczRg7sQ9VK/uh0OuZ9+xOvTxxTdP0l60ocSf6rWJ/TUHpXJTdn1g1ty5bhHYg4d5M72bklbvOo4tu09yA9WjXFzu4h/3wfdn9+8jUj+/egqr+v1TKl5WFCnDF2GIdOnmXgS29z6NRZ/Hy8sC8mTmv78WFyuinmAAM7t2HX9/P57q1pvP75f9DpdMVuY+v9qFiJ8i/59hyy6ivN/B7rMat3yjVCsOv5fmwb34efDp3nepo6gGSn0RA+pjs7J/Ul7nYqF5LTi/EVOrdZKyEEBTqFozeS+bhfa5Y9040d56+z/2qCeZxn4ulTv0YJ8f09n1anIyEzmyYBFVkztheNAyrwSdSfxvqsVmj+z6Jy+oDjt1Jwsrentv4xvTOJd7mWdo+uj1m+V6csfFqdQkJmDk0CKrBmTA8aV67AJzuPqXVZ0Vn+JmvHranvDk72dgbfA/4ztBO7pwwkT6vlQHxiKUxW/haK+IGiYStE5ZooezeXot4SHKXKAnD5FMqFODTPzkIzeCLKjYug0z4yn2jUGhFQ0+KdC38Lq6eXIvwhrRCVa6Dse9icliB81PvwIXahaNhadezZZL1AKbH+N1D6c3fHZiFEfj2HiE/folVIHd74+udSSC0XFeVcsmAuMet+JS8/n9ijx0r9u8x0VvuhRfi+nM+ejeHk5eURe1id/ajVasnIzGTlku+YMfV5ps2abbVOo89yWZHxffEJe/5YQ15ePrGH1fPz04NC2b5mBRG/fI+vjw8ffvlNSSE+nPPzj9izfpWa0yPGNuHlSePYve43+vXowrI160oQPoTvEezDh/EVFGiZ/t5HjBzcn6qV1feqzZj8HIeOnWTguCkcOhaHX0Uf7PWziP9b0Yj/3//9G7HlOyQuAKFCCC/UwYFlQK/iNykVgUKIY4A74AK0MFn3NOpASAQwVwhRTlGU/NJUKoSYAEwAaIcj9Sn5kYNHib+HCwkZxhkRCZnZ+JrcxVTLuJKQkYW/hysFOh2Zufl4Ojty4uYdtp2JZ37kYTLv5yGEwNHeDh9XZwLKu+Gtv5vfrW51jt1IZoBJncsjNrNqk/qETEidIG4nGUdJE+6k4Otj/c7OO599R/WASowe1BeArJwcLly9xqhX3wHgTmoak9/5kG/em2n2YsvlO/axevcBABrUrEqCyd2DhLtpVLTyaMajwN/ViQSTGREJWffxdXV86Hp8XZ0I9HLjyO1Uw0svTVm+eSerd0QD0CCoptnjDwkpd6no/fDTNDfvPcTbzw0vVdnlf2xn1dadAITUrsVtk1H2hJTUIh9ReGfhEqpX9md0aE+r680cG3ewattuvaMmt5MLO0o/ldfPx4uFs14EICvnPtv2Hcbd1fy4X74pktXbTXKaYprTVCqW8JiEKasjY/jPO68A0KRuELn5+dzNuIdPoePO1vtx+eadrI5U7zg0CKxhHmNqGhVL8WiJpe8wbz9nOS7r7+ZMgsmMgYTMHItHufzdnUnIyMbf3UU91+Tl4+nkwMYz8bSr6U85Ow0+rk40CfDhZMJdqpq8XNfDyYHmVX2JuXKb2v4+al2Zhc9tVnyZWfh7uBjPbU4O+Ls707yqr+HxiPaBlTmdmEqrGv4AnE28i1anEOzvbVHXo/S1rO6Hczk7uurv4PeoW401Jy4Xqi/H3GmRUxfznOqdhv1V6PGJ47dSOJWQStfvNqDVKaRk5zL61yiWPt25THzlnR3UGPUDID3qVDXE+KCuEn36mRSqL8/SV8T7IRzt7egUFEDUxZu0rml5XjUjIxXh4W3sU3t4QaaVGUS16iPa9UP30zzQPuS9kMy7CM/CjtLf6Vb2/IGyR32Bohg4ASW1+IEWJSMV4WnS1np6o1jxiVrBaDr0R7tk7sPHVBwZdwvl1Nt6vDXrI9r1RffTh3/Pb4N9qGTcRXgaZ+/h4Y1ixWHI6Q8f/KWYlm/ZzerIvQA0CKxOwh2jIyHlLhUf4tEML3fjefSprm2Zv9z6hezytetZtUEdoAmp+xi3k4wzBhKS7xTZbwNwdHSgc5tWRO7ZT5vmzYosZ+ZbtZaVEerxHFK/LgmJxsddE5KS8TWZJWnpc6Rz+zZERu+hTYvm+PlWpFvH9gghaBhcH41Gw920dLxN2vHlq8ONvnp1STCNLykZ3wpFv0DR0dGRzu1aExmzhzYtHqeCSS6eCu3DpFct38cEsHzNOlauVwekQurWISHRNKfJ+FYoLkYHOrdtRWTMPto8YT5DuG+3Lkx8dRYvFpolYfN9GL6BVX9sVX11alv6iojvnU+/pHqVAEY/Zbxy8Kvgw8L31XdlZWXnsC16L+5urla3l0iKwtbv5VgLDEMdNIh5RHU+eGQjEJgGLAYQQjgAvYF1iqJkAAeA7qWtVFGUxYqiPK4oyuO2HowAaFC5AvGpGdy4m0meVsvmU1foVOjOWKfHqrLuhDrDYduZeFrU8EcIwbLRvdgx9Ul2TH2SkU/UZ0KbEEY0r0clT1eO30wmJ78ARVGIvXKbWhXMG8cRob1Yt2g+6xbNp0ubJ4jYsRtFUTh2+jzuri74+lheXH7+4woys7KY9fxYwzJ3V1di1/xE1LLviFr2HY3qPWYxGAEwomtrwue8TPicl+nSNJiIvUdV38V43J2drb4r4lHQwNeT+LQsbmRkk6fVsfnibTrVKN1MgIR7OdwvUO9wpefm82fCXWqaXICZMqJXJ8LnzyZ8/my6PNGYiN2xanznL+Hu4lziOwYKc+VmAun3smlcJ7DkwsCIvt1Yt3Au6xbOpUurZkRE7VH9Zy/i7uJidbDg819WkZmdw6zxz5TO0acr676Yw7ov5tClRVMidu41cTiX6r0MD7ibkWmYobB49R8M7mr5pNWI3l0I/yyM8M/C6NKiCRE796m+c5f0MZXeV7mCN7En1Gn4l67fIjcvH29Pd4tytt6PI3p1IvzTtwn/9G3Vt+uB77Le93ADIFduJpCelU3jOrUs1jWo5E383XvcSLunnmvOXqNTUGWzMp0CK7Pu1FUAtp27QYtqvgghqOThQuy1JBRFITuvgOO3U6nl7U5q9n3DlP/7+QXsj0+klo+H3udDfGqm0Xc6nk5B5o+OdQqqwro49WWy285eo0V1P4QQtKlVmXPJaYb3Vhy6lkSQjzEXm87EW1zkloVPCEHHoAAO6u/gx15NJNDHw8TpTfxdE+eZa1aclVl30jSnfoa7UjpFYeu562YDBMOaBLH7hVB2TOrHshFdqOHtxtKnO5eZTwhBx8DKHLymXmzExicSWMGjGF+h9ql2AOtOPsjpdUvf2Wv0NnlcIysvn2T9Yx8FOh3Rl29T07sU5/9bV8DHD8pXADs7RIMW6gsRTfGvhqbvGHS/fidB8JgAACAASURBVAFZJU/RtuDmFfDWOzR2iOAWKOdLeTdSCHDWd859qyD8qsClUyX4LiN8/KF8RbCzQxPSEuVsoffpVKqOJnQs2mWfQdYjnh598wr4+JrE+0QROR2N7rcvIfsv5NQUm+zDywhvo0PNaWFHdTT9x6Bd/tlfcwAjenYg/JNZhH8yiy5PNCQi+oD+3H3loc/dpo+sRh0+Qa0q/tadg/qz7sdvWffjt3Rp15qILTtU56kzuLu5WFxcZmXnGN5JUFCgJTr24EO9c2DEU4OIWPYDEct+oGv7dqzbvFX1xZ3C3c3VYoAgKzvb8F6JgoICdu+LpVYN9VzTtUM7w2yJK9euk5+fj1f5Qv3SJwcS8csSIn5ZQtcObVm3Se87+cBXOL5sk/gK2L3vALWqqz7TdzHs2L2H2rXMvzRmcA4eQMTSxUQsXUzX9m1Yt2Wb3nkad1drTvOc7t5/0OC8et34fp2oPfuoVd0y1zbfhwP7sW7JV6xb8hVd2rUiYmuk3ndWjc/KAMjn3y9V+/pTJ5gtv5uWbuy3LV/J4F6lvtSSSAyI4qZGlboSIe4piuJWaNm7wD1FUT4VQvwE/AEcBI4CSxVFmS6EuAo8rijKncJ1PNhGUZTVxXhr6Ms00P/bGUhRFMVFCNEfdXbEg2E/F2CboijPCCHG6L1TShPfJOHx0Ekat+IHHuvYFrcKPmQkJrFh9lz2/fBLqbb9+ueZAOy+eIMPtx1Cp9MxsHFtJrVtyMJdfxJc2YfOj1Ujt0DL6xExnElIpbyzA58O7EBVL/OLqa92H8PFwd7w2c+Fu4+x5fQV7DQa6vl5M6dva5y6DLT6OxRFYc7C74k5/CdOjo7MffUFw4DCgInTWbdoPgnJKXQcPoFaVQNwKKc+IjIitBdP9TZ/7cfI6e8wY8Iow/bKTctHRRRFYc4v69gTd079hOO4p2hQUz3BDnz7M8LnqG8l/uT3jWyMPUZSWga+5T14sn1zpgzsTtzl60xd+DMZWdk4lCtHBU93/pg7Xa37wE4L3+74JD7cewadojCwbhUmNQti4cHzBFf0pHNNP+KS0nhxy1EycgtwsNNQwcWRDcPase/6HT7edxYh1Kl8w0OqMaS++ae8RNdB1uP7fgV7/jylxvfCGBoE1VDjmx5G+Hz1axSf/LyajTEHSLqbjq+XJ092bceUof3V/fn7enLz8pk+crBF/cKx6BeUGvzfLSXmyAnVP20CIfrPuA6YOot1C+eScCeFjmNeolaVyjjon1Mf0bcbT/Ww8rZ2K9OOFUVhzqJfiDl6Qj1mXnyOkNpqgz/gpbdZ98UcNcYff+eP6P0kpabh612eJ7t1YOrwgWzZe4jPfl4FApoH1+GdSaMMx5WiLcK3eBl7/jyp/+znszTQf8p04MuzCf9M/YrIJ0tXqjl94OvajinDBnDx+k3e+WYp2ffvIxC8Ovop2jRuUGR8BmdZ7UfF8nER1fcre47pfZNHG32vziH8U/WrGp/8soaNMQeNvi5tmTK0n963gdz8fKY/Y35cKgciAdh9+TYfRv2JTqcwMKQmk1rVZ+GekwT7e9E5KEA912w8wJmkNMo7OfBpv5ZULe9GVl4+b24+xKWUDBRgYIMajHuiLueS0nhj80F0OgUdCj3rVGVy62CwU4+p3Zdu8uGOo+rfXsNaTGrdgIXRJwiu5E3n2lVU34Z9nNF/ovLT0LaGWRfrT17hP7GnEagzFl7tZHz5XfdvI/huSEdq+Zh3aMvCdzM9i5kb9pGZm4+XiyMf9G5JZU9Xw3Gz+9ItNaeKwsCQWmpOY+II9vemc+0HOY3lTKI+p/1bGZwHryWxYPdxfhvZzeoxeDM9i+fXRJt99rMsfDfTs5i58QCZuXl4OTvyQe8nqKwfsNt96RYfRh41+loHszDmhN6nz+kf+4057d/GxJfIgl3H+W2UseN6JyuHyaujydNq0eoUWlT3Y2aXpthrNHDtqtU8GKjdUP1kpNCon+SM2YDoNBDl1hU4dwzNqNfAtwrc01/gpaeoF7ZFYe1To0Eh6mc9hQbl2B6UPX8gOg5AuXUVzh+DyjXQDJkCTq5QkA/30tF99zbY2aOZ8K5aR24Ouo0/Q6L5C6qVfMuJm6J2IzS9R4BGg+5oNMru9Wg6D0K5dQXl7J9oxryO8KtqmLmgpKegW65+MtJu3FtQsRI4OEH2PXTrvjd7x4OwL8Xk2KCGaHo+iDcGJcY8Xs3IVy1z+lsxb/Uvqa/5iPehYuXRO1G7IZpez4BGqDmN3qDm9OYVlHN/ohn9ujpgZJrTFerXBuzGvQkVTHIascQsp5rBo62ErDBnye/sOX5a/WTz5JE00H+WeuBrcwn/RH3l2SfL1rJxz2Hjubtza6YM6cuCFeuIOhyHvZ0GTzcXZj/3NLUC1EEJUcn6YLaiKMz57GtiDhzGycmRuW9MN3z2ccDY51n347fcSb3LpNffIS8vH51OS4umjXlj6iTs7e3YHr2X9z//htS0dDzcXKkbFMiSBXNVp4NlH0NRFN775DNiYg/i7OTI3LffIKSe+p6i0GeeJWLZD9xJSWXi9Jnk5eeh0+po+XhT3pg2BXt7e/Ly85n1/oecPX+RcuXsmfHiZFo93uzBTrTu+/QLo++t142+keOI+GWJ6nv1DX18Olo2a8Ib017A3t6e1979gLMXLgKCgEr+vDdzuvFiv5g2/735XxITewhnJyfmvvkaIfXU9+eHjp5AxNLF3ElNZeKrb6kxPnC+OBl7ezumznqXK/HXERpBgL8fYTOm4VdRfWeDYmUWTlnuwyL7bZ9/Q8zBI2q/bebLRt+4Kaxb8hUJSXfo+NQoalWrioP+cfARA/vyVN+ebNm1h88W/6T22xo14J1pLxjKAAj/wH/pQwRFM83O8+9fPJchn2vT/3U5t+mAhKIoq4UQE4EdiqJcKoMBiW7AAkVRQoQQvwLrFUX5Vb/OFbgC1ACGUMYDEn+HBwMStkLTob9NfWB9QKJMfVYGJMoSawMSZe4sYUDikVPic9CPFmsDEmWKjeMDrHbAylSnH5CwGXb/A1+a/ieOG1sibD2xkpIHJB411gYkyhBrAxJlSakGJB41j6Cv+VC6Et4F9KixNiBRlhQ1IFGmTisDEmWKjdvDf+LcbW1Aokz5B2L8bxyQeOX/+YDEgn/hgMSjapVchBCm3/xaUFRBRVEsP1D893jwDgkB5AHPCSFcgB7ARBNvlhBiD9BPv2iMEML09QktFUUx/26ZRCKRSCQSiUQikUgkkjLhkQxIKIpS7O0ERVHGFLG8hsn/dyu0zuo2hcpcBYoasrV4AEpRFNPb1j+VVL9EIpFIJBKJRCKRSCSSsuF/YP6sRCKRSCQSiUQikUgkfw/NX/j0uqR4/hUDEkKIEKDwGyFzFUVpYa28RCKRSCQSiUQikUgkkv/f/CsGJBRFiQMa/9O/QyKRSCQSiUQikUgkEsmj4V8xICGRSCQSiUQikUgkEsk/yT/wran/emROJRKJRCKRSCQSiUQikdgcOSAhkUgkEolEIpFIJBKJxObIRzYkEolEIpFIJBKJRCIpAY38yMYjRw5IlIKvf55pU98Loz60qe/rNa429QHg4mZbX9NWNtUpNy7Y1AegCDnh6ZGi6P4Bp2Jbn19l2/psjc7G+QTbHze2Pmb+CTzK29Zn430otDY+Zv6J3rSNP5Nn65wqCdds60u+aVMfAHY2vmSw9bntn2jztVrb+v6B9sLOP9DmTsm/D3kFI5FIJBKJRCKRSCQSicTmyBkSEolEIpFIJBKJRCKRlIC8m//okTmVSCQSiUQikUgkEolEYnPkgIREIpFIJBKJRCKRSCQSmyMf2ZBIJBKJRCKRSCQSiaQEhI1f0vu/gJwhIZFIJBKJRCKRSCQSicTmyAEJiUQikUgkEolEIpFIJDZHDkhIJBKJRCKRSCQSiUQisTnyHRIPQcylm8zbehCtovBk49qMbxNitj6vQMvM9Xs4dTuF8s6OLBjUgYDybob1t9Lv0e+7CF5o34hnWzUAYOmBU6z+8wJCCB6rWJ4P+rf9S79t5JKvCenbk8ykZOaEtPzrMZ67xrw/9qDV6XiyeX3Gd2xqGePKHZy6mUx5FycWDO9OgJcHAIt3HWHNoTPYaTTM6teWto9VAyAjJ5d31u7kQmIqAnh/cGca1wsCQFEU5q6NJPr0ZZzLlWPuiF7Ur+pv8btOXU9g1vJN3M8voH39Wswa1AUhBF9ujCEq7iJCI/Bxc2HuiF74erqTmZPL67/8we27GRTodIzt2Y5BbZqqvt82ER13AWeHcswdO5D61Stb+uJvMevHtdzPK6B9SG1mDeuNEIIz124TtmwDufkF2NtpeHtEXxrWrMLBc1eY8vUKAny8AOgWEsjknm3KLL707Pu8tWIz1++k4VjOjvef7kXtAD9jTtfsIPr0JTXGEX2sO68lMGv5Ru7n59O+fiCzBnc1ey7uh8gDfBqxk71zX8TLzYXMnPu8/vMGfU4VxnZ+gkEtG5aZ7wFx8bd5esHPzB8TSo8mdcvMF3niPAs3xSCEwF6jYebAzjQLrPJIjlODM+ogn0bsYu8HU/Byc+FyYgpvrtjM6euJvNS3Hc92am7iiyL6zGWcy9kzd3hv6lf1s+5bsVn11avFrEGd1eNm0x6i4tTzio+7C3OH98bX042DF64xZUk4Ad6eAHRrEMjk7i1VX8Ruos9cUXM6tDv1q/ha+m4kMuu3bXpfTWaFdkAIwZbj5/l6WyyXk1L5/cWnaaD/rXkFWt5dHcmpG4lohOCN0A48EVTVdr7+HXjCdB+u30302atqTocU41y5XXXWrcGs/nrniQt8vV3vnDLM4DxxLYHZayINDn9PN67cSdM7ulI/wJojiVmrdqiOOtWZ1b89QgjSsu8zffkWbt7NIMDLgwUjeuLp4sTBSzeYsnQjAd4exv3W9QkURWHUd2s4fl2Nt3nNynw1qg+O5eyt+wq0qq9fO6NvxVajb3gPPF2c2PDnOZbsPgpAgVbH/fwC7Ow0PNm8PleT09h99irebs6sf3m4vk3YbmwTnu5h+J2Ldx5mzeEz2AnBrP7taPtYdQBizsUzb0OM2pY2r8/4js0AmLVyB4eu3MTNyRGAp5rXZ9m+E+TmF6AAKfeyealbC55t19iwv2eujjS6h3Uztke7j6pujWBW37a0ra22R7/sO8GqQ6dRgKcer8eoNo0A2BJ3ia+jDnE5+S6/TxpEg4CKxJy/xrxN+9DqFJ5sVpfxHZqY5VX1R3Hq1h3VP7QrAV7upGXfZ9qv24m7mcTAJnV4q59lu/7Csi1cT81g/YtDDMtiLlxn3ub9al6a1mG8Pk4z39pdnLp9R+1jPNWFAC939l26wYLth8jXailnZ8er3Z+gZa0Ac9+KrVy/m8n6F540923cp/qa1WV8eyu+NTv18TmyYIga376LN1iw/SD5BVrK2dvxao8WBt/n2w+y/tgF0u/ncuTtZy3iLouc5hVo+eCPPRy8chuNELzUrTnd69Yok5xm5ebxzA8bDNsnZmTRr2Ft3hjeFyi79vfghXim/GctAT76c3fjukzu3c7oXLWN6FN658i+1K9WyYrzNrN+2aD2a4IDmfVUd4QQfLUxmtV7/zS0u9P6d6JDgyDytVreWb6R09cT0Gp19G/ViAk926m+lVuIPqnvR40eYN0Xf4tZSyPUGBvUZtaQnggheOU/q7mSeAeAzOz7uLs4Ef7WJG7eSaNv2NfU8PMBoFHNKrw7vI9Znap7K9Gn9O5RoUW7f16vuoNrM2tID9X9/WquJKaYu9+caOlYtY3oUxfV9n5Uv6Lz+fN69TweHGTIJ8CynYdYsfsQdnYaOgTX5tVBXdh35jIL1kUZj6lBXWgZVM3kuNlu3IfP9C3iuLnNrGX64yY4kFmDu6n7cFMMq/cdM+7Dfh3oEBzEiau3mP3bZn1g8EKvNnRtVMd2/ahBXXjCotZ/P/Ju/qPnvyKnQog3hRCnhBAnhBDHhBAthBC7hBDn9P8+JoRYrS/7pRDi7ULbfl2SQ6vT8f7mWBY93ZUNk0LZdOoKF5PTzMqsOXYBDycHtr4wiNEt6jM/6ojZ+o+2H6JdkLGTkJiRxbKDZ1k1ri/rJ4aiVRQ2nbryl3Kw/6flLOw56C9t+wCtTsf766NZNLYPG15+mk3HL3AxMdWszJpDZ/BwdmTra88wum0j5m/eD8DFxFQ2H7/IhpefZvHYvsyJiEar0wEwb8Me2j5WjY2vDGfti0Op5etlqC/69GXik++y5a3xhA3rQdiq7VZ/23srtxE2tAdb3hpPfPJdYs6oeXq2yxOsmzmW8Blj6BAcyDdb9gGwIuYogf4+hL8+lqVTn+bjlVvJKygg+uQF4pNS2PLBS4SN7E/Y8g3Wfcs2EDayP1s+eIn4pBRiTl4AYP6abUzu15Hw2ZOZEtqZ+au3GbZpFlSd8NmTCZ89mck925RpfIu376dugC/rZo5l3jN9mLs20jKnb08kbGhPwlZuLcK5lbBhPdny9kS987Jh3e27Gew/d5VK+s69MacVCJ85jqVTh/PxuijyCrRl5gP1mFywfidt6tUs8/ha1qlB+OvPEv76s7w/vDfv/LbF0vkX92NRTk8XJ2YN6sLYzs3N6ok+c0X1vfkcYUOL8a3aTtiQ7mx58znz46Zzc9a9rj9u6gfyzdZ9hm2a1apC+IwxhM8Yw+Tu6uBl9Nmrqm/mGMKe7ELYmkjrvjVRhD3ZhS0zx6i+s1cBqO1fgS9H9+XxmuYXQasPnAQg4tWRfD9hEB9viEGnU2zn+0P1GWK8k8aWGaMJG9yFsPAo687wnYQN7sKWGaOJv5NGzLl41ennw5cjLZ21/X1Y9eLThL88gnEdH2f/xev88coIwgZ1Jix8V9GOQZ3Y8tpIM8f3u47QMqgKW2aMomVQFb7fZWxDmtWsTPi0pwmf9jSTu6pdvPVHz3LyRhIHZo9n6YSBnLyRxKbjFyx963apvlefUX3nr5n7Xhup+vSDEFW8PVg6YSBrXhxKxv1c3J0d2PDycDYdO88TtSqz+Nl+hrrXHDqtbxNGqm2C/hyltgkX2PDycBY/258563aj1enUdiZiN4vG9jPUadrOvNq7DeEvDWP11CEs3XOMRWP7sXziABzs7RjQpI5ZXGsOn8HDyZGt00cwuk1D5m+NVd1JqWw+cZENLw1j8ei+zFkfg1an40JiCqsOneb35wcTPmUIu87Fc/VOmn7/evPl8B48XkMdoNbqdLy/YS+LRvVmw4tD2BR3kYtJd839R86qsb/yNKNbhxj8DvZ2TO3yOK/1bGV1/28/dRkXh3Jmy7Q6He9v3MuiZ3qy4YUn2RR3ydJ39Bwezg5sfWkoo1uFMH/7QQDKuzjxzfDuRLzwJPMGdmDm2l3mvtNXrPs27GHRqF5smPoUm04UE9/Lw1TftgNG34geREx9inmDOjJz9U7DNp3qVuf3SQOtxl1WOV20+yjers5sfnkYG14cQvMalcosp66ODoQ/P9jwX2VPd7rVq2Goryzbw2aBVQxt1IPBCIDoU5eIT05ly7vPEza8N2EmbZeZ87fNhD3dmy3vPk98cioxpy8Z1o3q3ILwWeMJnzWeDg3UG0Zbj54hr0BLxJsTWDVzHCujj3DzThrRJy8Sn5TKlvemEjaiH2ErNlr3rdhI2DN92fLeVOKTUok5dRGABeOfJPytSYS/NYluTevRrUk9wzZVK3oZ1hUejFBjvaj24cKmEDa8L2G/FuH+dRNhI/qwJWyK2od74H7uScLfnEj4mxPp1qQe3RrXteK4pMb37mTCRvQm7MEFvYVjM2HD+7Dl3clqfPp8Hjh3lagT51j35gQ2vD2Jsd3Udra8mwvfPD+UiLcmMm90f2b+tN7oPH2J+KS7bHlnEmHDehH2exH78PethD3dky3vTCI+6S4xp43HzahOTxA+cxzhM8fRIVjdh7UrV2TVa2MJnzmOxZOH8u7vWynQ6mzXj/rVeu4kksL86wckhBCtgL5AU0VRGgJdgev61SMURWms/+/BbYG3gLFCiFpCiJrAc8CbJXnibt2hmrcHVb3ccbCzo1dwTaLOXzcrE3X+OgMaBgLQvV51Yq/cRlHUzvCOc9eoUt6doArlzbbR6nTcL9BSoNNxP1+Lr5vzX8rDxZh9ZKfeLblgMcRdT6KajydVvT1xsLejV6Mgos6YD5BEnbnCgKbqCbx7g0BiL91EURSizlyhV6MgHOztqOLtQTUfT+KuJ3Hvfh6Hr95i8ONqg+Ngb4eHs6OxvpMXCW0ejBCCRjUqk5lzn+T0e2bO5PR73LufR+OaAQghCG0eTGSc2ul+cCcNICcv3zByK4Qg634eiqKQnZuHp6sz9hoNUcfOEtqyseoLrEpm9n2S0zLNfWmZ3LufS+PAaqqvZWMij51V6wWy7ucCcC/7Pr7l3YvNaVnFdykhhZb6u421/Hy4lZrBnYws1Rl3gdAnGqjOmgFk5uQW4cw1Op9oQOQJ44XMR2sjmR7aEdMXCQsEWbn6nObl4enipOa0jHwAy3cfoVujOviYzJgoK5+ro4Mhv2qujev+7n4E+Cg8iun9zZ0+7q6EVK+EvZ35qTgq7sKjPW4onqhTlwh9vJ7qq16JzPt5JOuPJ4MvI0v11ais+h6vR+QptQMW6OdNTV9vi3ovJabQsnZVfawuuDs7cvJGog19Dpy8kajGePoyoU1NnDm5RTurV1KdTQs7vSiMs0M5w/7bffYKjvZ2eod/0Y5cE0ezekSeuqzfD5cZ0Ew9Vw4wWV4Ue89fw6mcPbkFWoIDfMnTanGwtyve17Su0XfaeD4fYLK8SfVKeLo4EXc9kcCKXtzNuq9vE2qTmJGFp7OTof6o05dN2oQgYi/eUNuE05fp1ah2oTYhkbjriWo74+NpqDPqtGWcpuX8Pd0Z1Kwu11IzzMpEnbnKgKbqIEX3YNP26Cq9Gpq0R96exN1I4lJSGo2q+hn2WfMalYk8rbZxgb5e1Kxo3L9xN5Ko5uNBVW8P9XeGBBF15qqlv8ljen8tYi/fQlEUXBzK0axGJRwL7QuArNx8lu6NY2Kh2YdxN5PVPsYDX4NAos7Gm/vOXmVAY72vfk1ir6jx1q9UAV8PVwCCfL3ILdCSV6A1+vbHMbG9+UyEuBvJ+vb+QXyBlvGdjTf6gmsRe1nvq1y0r1FVPyq6u2CNsspp+JFzhpkWGo3Ay9W5THP6gKsp6aRm5dCsuvHOclm2h0URdeI8oS0amjjvk5xeqF+Tnqme22pVUZ0tGhJ5/Hyx9QohyMnNo0CrIzcvn3L2drg6OxJ14iyhLfW+WlWK8eXSuFZVfT+qIZHHz5qVURSFrUdO0/vxBqULFIg6fo7Qlo2M7uzcUrgbEXn8nKX76Gl6N7d0R504R2iLEH0+q6j9xCIdD/IZYnD8FnOE53q0xkE/U83HXT2O6lf1N/QZgypVJLeggLz8AtX5UMdNFeNxE1f8PjRtn3LzCwzHlO36UfJrFJLS8d/wyEYl4I6iKLkAiqLcgaI/yaIoSoYQ4k3gK/2idxRFSbNa2ITEzGz89Y0TgL+7CyduJRdZxl6jwd2xHGk5uTja27Fk30m+H9GNH/efMpT383BlbKtguny5GqdydrSuWZk2gQGsKH3sj5TEjCz8PY2PmPh7uHHieqJlGf1jKPZ2GtydHEjLvk9SehYNqxmnlPt5upKYkYVjOXu8XZ15c3UUZ2+nEBxQkTf6tcVF319JSsvEv7yHyXbuJKZnUtHkdySmZ+JncuHvV96dJJNBhM//iGb9oVO4OTny09RhAIxo14QX/rOWDu98Q9b9PBZMHIJGoyHpbgb++unqAH5eHiSmZVDRpP7EtAz8TEZ8/bw8SLqrdoRnDuvN+M9/5pNVW9EpCstnjjeUO3b5OgPDvqZieXde69ue2pUqlFl8dSr7suPEeZoFVuFE/G1u3U0nMS2TCh6uJKVn4l+ovlI59Q1uVNwFfMu7UTfA/BGBEe2b8sLiNXR4+ys1p2ND0WhEmfkS0zLZceI8P059mpMrbhuWl5UPYMfxc3y2YTcp97L5bsJgo/Nv7seouAv4erpT18r0fWskpd/D36twjPcK+e7hZ/JImBqjsUPx+cYY43EzZahh+bGrtxj48U9U9HDjtb5tqe3vQ1J6lnlOPd1Un8k5z8Ln6U5SuvnFdmHqVK5I1KnL9G5ch4S0TE7fSCQhLdOGviQS0jNpiL+aU7N8FeH0NC+TVKiTZo3j1xJ4a9V2riTdZWr3FoYOoJ+nG4kZhRwZhRyeriTpBy1S7mUbylb0cCU1K8dQ7ti1BAZ+voKK7q681kfdb5n38+jXpA5dPlyKUzk7XBzKUb2C8fxm3edGUsY96757OYW2zSI7L592+sFPf8+i2gR1X5q1CRlZNKxmvFBTc5Glr8e47wvX+cXWWL6NPEQlTzfDBSGAv4crWbl5FrE9aLNK0x7V9vPmi+0HSMu+j6O9HdHnrxEcUBFrJGZkF2oPXTlxI8kydlO/o+p/cEFsjYWRhxjTtiHOhR6rsWh/Pa34TPsYBl8uXq7GAaJtp69Qr5KPYWBqYdRhxrQOKcJnkl9rPpMyRfpOXaFepQoWA2HWKIucZuSoNwgW7jjEwSu3qertwVv92lDB2anMcvqATXGX6Nmglll/syzbp2NXbjLwwyVU9HTntUFdqV25oonTpH0q70FiWiYVPU37NUU7AVbsPsz6A3EEV/NnxuCueLo4071JXaJOnKfDrC+4n5fP60/1oLyrs9oeenmW7PMy/01JhW7+HLl4DR93V8MjGgA376Qx6INFuDk58mK/jjxeu7rZNqrbtH/mXkSspm73ot2+PhTG0lGKfHoZ47ualMqRi9f5Yv0uHO3teW1QV0JqmD8avO3Ps9Sr4mcYtLBwFnncFJ3TFdFHWH8wjuBqlZgxsDOeLurfzPGrN3lr+SZupabz0ci+2NtpbNePmviUxfr/BjRynOWR86+fu28n8AAAIABJREFUIQFsA6oKIc4LIb4RQnQwWbfc5JGNTx4sVBTlV8AL8FAU5ZfSSPQTHQohCpWxLCSAr6KPMapFfVwLTZlMz8kl6tx1tk8ZzK6XhpCTX8D6uEsWddgKBStBitLEKKxtiRDqDJDTt5IZ2qIBa18cgrODPd/vOmritLZdYWfxZab1bU9U2PP0fbw+y6PVuvecvUrdAF92vzeZtTPG8P6KjdzLuW81xtL51P/9bddBZg7pSdTHr/L6kF68vXQdAPWrVWLHh68QPvsFRnRuydTv15ZpfOO7tSA9+z4DP/6J5dFHqBfgh52dKLo+i2PVWhl1RHvRtn1MNZkO+oA9Z65Qt4ovu+dMYe3rz/L+qu3cy8ktM9+8tTuY3r8jdhrz01RZ+QC6NqrDxrcm8NVzg/hy0x5jfVbKlnY/5uTls2h7LFN7l/79MEX9PZmXKf5YntanHVHvTqJvs3osj1GPm/pV/dgxeyLhM8Ywon1Tpv60Qf/brdVVyFfE+a04BjUPxt/Tjae+WMG89btpXKMydhqN7XzVKxmOn0eR06JoVM2fDdNH0rRGZTYcPUuu/q6X+ptLf34pivoBvuyYOZrwacMZ0aYRU39Wpynna7UcvXqL7TNGsWvWWPVxmHPXSvaVmEmVCwkpXL2TxvRe1h89KLr+h1v+gJd7tmLj9BGsnDKErLx8zt1OMd++8N+clTqEENb3I+osiOfaN2HcDxuYsHQjdfx9sNdY7wZZbw8Ll7HuL4ozt+9wLSWdrvVrWqwrJi3GMiX83VxISmXB9oO826+d3pfCtdQMutYrre/hjtULiaks2HaAd0Otn08tnY8+p1qdjoSMLJpU92fNC4NpXM2PTzbHFlmXxW96yJyasunkJfqEBBaqz8rvfwTtU/0q/uwIm0z4zHGMaN+MqYtXlRBDKf5W9P87rF1TtoZNZu0bz1HR042P1+wAIO7qLTRCsGvui2x77wV+2rGf68l3i/z95jFa7yuasvFQnNkMhYqebkTOncbaNyfy+pPdmfFjOPf0A07GOEpuF0rTvmw8dNLq7Ah1e8tlpWrv9b9Eq9WRkX2f314by6uDuvDKkjVmv+nCrWQWrIvk3eG9S6iv8O8qej8Pa9uUrbMnsfb1cVT0cONjk0cSG9UIYMOb41n52hj+sz1WfSePrfpRG6OtlpFICvOvnyGhKMo9IUQzoB3QCfhdCDFTv3qEoiiHC28jhKgC+AOKEMJNURSLW2BCiAnABIBvx4b+H3vnHV5Fsf//15703kghIaGkQRKqSFFa6L0jImBBUbEXFAQLKMXrBbz3YkHFSpGeRDqEFnqVACH0ECCQ3kNIOWd/f+xySnIC8V5y/OpvXs/D85Czs/Pez8xnZ2an8nBoI9KNpt6mF93Gp8q0RD9XJ9ILS/BzdaJSp6OorAI3BztOpWWzLTmV+TuOUXSnHEmSsLO2wsvJgQB3ZzzVnvheTRty8obprAtL4ufqTLrRaGB6YTE+rlVsdHMmPV8ZlarU6ii6U46box2+bk4m92YUlODj4oSvmzO+rs60VEerekcF83HsHnapy12aB/mRnl9odF8RPq7OppruSg+4Pky+aS/uXQY81IxJ36zl1f6diDl8mud6tkeSJPafSyGv+DajZn3Dw+GNSM8tMMSVV4iPUa83gJ+HKxl5hSZhvNVe6biDJ5n2uFKJ9G0byYe/xAHgbDSFOS07j5t5hQye+wOtGvvXiX3O9nbMGas8hyzLPDJtIVN+2YBGI9E8qD7pVeLzcauNpgvXs/NIyylg2D9+0P8+4p8/sfLtJ5U07dVBSdPkK0qazvuJh0OC6kQv6Vo6b/+spG9WQTHrjyUxL24XHcMb1Ymet1G+tA0JIvnGOgbP/QErK83/5KfXs/MVzc9+NNL8mZVvjzfR/P1KGsOOKjOomgfVJz2vio1V9dxcyMgvNgljPBJ/lwEPNWPSt+t4tV8nk6UcaTn5ip/OW0KrhlV8pqC4BvuM3/EivN2q6xljbaVh6hClj3j5/kTmbdjLjZwCHmoSUOd6AL3m/MCCTfv5Ytshmgf6km6SXmY03ZSZKMZhzKWpMcsPJLJa3buieaAveSW3uZiRQ1QDX9Uu0/v91NkgBrtK8Fan9Ho5O5JVWIK3qxNZhSV4qqPCzva2+vBpuYXczC9i8IJleDk74mRni6e61M9Ko+F6jumEv+p6Bpuq6RktGTx/K5s1R5II9fXCXX2OdLP2OJGeX1SlTrBX6oRqeeykxmP43TjOu89la21F3+YhfLnjiCFcYUm1fRDu1lkm2g52+Lo5V6+P1LhHtG2mX0L4+bZD+LlWL2uVuJ2q1IdKnWYujF6/TNGvicRrGSTdzKbnvGVodTI5JaU8tfg3fn5mYHW9ghr01FH/qnrpBcW8tmI7c4d3I0jdVDTxegZJt7Lp+fmvBr0fN/Dzs4NUvZIqelXreyVMjXq/bmfuiGi93v2oizR1d7THwcZa3+nSJ7IJa4+fM6/3ANL0LufSc9DqdET6e7P8cBKrE5U9Cuqq/jWuK7pGBjN1yXoGz/oWK41E84ZV2hn5hbXWBKhnFPeoR1sz6etVAGw8lkTniGBsrKzYeiKZvKISnvn8Zx5p1oT0PKN2VH5hteWr1dpR+YV4G81Qq9TqiP/9HKunPa//zdbGWj9jILKhP4H1PLiamcOplDRW71c61Zs39CfdpH1WZF7bJD1MZzdUanXEnzzH6vcMM1yX7z5qpFG/ioaZ9PSokp55Bvv8PFzo1SocSZJo0SgAjSSRV3wbTxcn0vMKee3b1cx9agj7zl5h9T5VM6iKZn5R9bape1W7CvVtxXpG5fKoR1oy6ZvVVOXwhVQup2cz9NPv66zdVrUddT17I+Hh4fXOnz+fXe2BBAIj/g4zJJBlWSvL8m5Zlj8CXgFG3OeWfwMzgFXARzXE+a0sy21lWW47MbodUf71SM0t5EZeEeVaLZuTUogOa2ByT3RYILGnlBkO25JTad/ID0mSWPpUP+JfHUn8qyMZ3y6C5x9tztiHm1HfzYnEtCxKKyqRZZlDKbdoUmW6rSWJauBDanYBN3ILKa/UsjnxEtFVRlaimzUi9oRS2W87c5n2wcrasuhmjdmceInySi03cgtJzS6geaAP3i6O+Lk7k5Kl7G9x6PINOoc31G+q16N5KHFHk5BlmcSrN3Gxt6v2Me7t5oyTnS2JV5W1pHFHk+iubrp0NdOwGdquM5do4qusK6/v4cqhC8pa0d4tw3F2sGP51Ofo0aopcYdOKnqXr+PiYG+yXAPA291F0bt8XdE7dJLu6sZHPm4uHL1wVbHl3BUaquvYswqK9L3XUY0CqOfiRNzUZ+rMvsLbd/TrWdccPEXXyGDi3nuWmCkT6NEilLgjZxTNlLSaNe1tSVTXy8YdOUP35qGE+fuwb85rxM94ifgZL+Hr7sLad57G29VZSdPziu29WzVV0vTN8XWmt33GJP3vAx6KYN5TQ9g+Y1Kd6aVm5enz8Oz1dOxsrIlTNxT9X/IxzN+bfbNfIf6jF4n/6EVV8ymTihugdZMAo/cixFTPoZZ6zUMVv8ky7Cez68xlvd9kFRYb/DSovuKnb4+jR2QwcceSFb3UW7jY21b7EPd2dVL0UpW9ceKOJdM90nR0sCql5RXcLqsAoJG3O5ENfNk45SmL6B24kIqfmzMb33mSmDfHKponjDQd7GrQtDFonkime0STe2p2adpIv6llq4b1uZZTiL+7C4mp6fexK13ROJ5M90hFIzqiMbHHkwGINfo9q6jEkG+BPtRzdiTuzSfo0rQRZ9KyuF1WzsnUW1TqdERWWRak17um6p04R3d1hD46orG+PI81+v1mfhGvLd3MvCf6kFlUYlQnXCS6yui+cRzbzlyifbCyvjk6ojGbEy8a6oScApoH+hLVwJfUnAKzcd7db0OWZVKy86nU6gzhTl2icZU9mJT6SFm3vS3pMu2bqPVR00ZsPmVUH+Xk01w9USWn+LbexvikFPq3DDWbr1EBPqbPefoS0U1Np49HN21I7O8XVP0rtG/if8/R/MfbR7JnynjiJ49l6cQhNPJy4+fnBit6/t5qG0PVO3OZ6KZBpnrhDYk9qeqdTaF9Y0WvsLSMScu28mbPdrQxWibzeLsI9kweS/ybY1g6YZCi98xA1T5vxb67eqcvm7fvpJF96lrywtIyJi3Zwpu9HqZNw+o789dEXaSpJEl0a9qQIyk3ATh0JY1gdS+QukjTu2w6fZn+aj39RPtI/UZ+dVU/GZfdp1Jv4mBnS9x0ZRPKHi3DiDt8yqDpYGfyAa5oqu2au5qHT9G9hbJ3hvH+CPGJ5/VLQZR21FVkWWZohxZ4uTrz5Utj1HaUqnflhmqjGT17OxKv3FDbUafo3sKwgeTBc1do7FfPZJlCblGJfjP061l5pGbm0qCeB090e1i/EWWPluHEHUo0aNdkq4l2It1bhlfR9jLRfqLbw/pNPXu0CCfu8Gk1PW8o7cQa01PVOHya7i0Uje4twjmstpWuZuRQUanFw9mRwtt3mPTVCt4cEk2b4ECe6NpWvwlljxZh/7XfKHlo6HiLT7xAaH0lD2+o5ShA16gQnOzrtt1WtR1VodUCmE51+xug+T/+76+IZG4K0F8JSZLCAZ0syxfVv2cB7kAUMLnqDAlJkvoB04AugCOQCAyWZflsTRraJXNkgD2XbvDptqPodDqGtQrlxU4tWLj7dyL9vegeFkRZpZYpcXtJTs/F3cGWecO6EuhhWoh9seckjrbW+mM/F+45yZazKVhpNDTz9eSTgY/w5oR/Vn+I+/Ds8h8I69YJ53peFGZksv6jORz4oVarUfhy7Sf6/+85l8qnG/ahk2WGtW3Ki9FtWbj9CJEB3nSPaExZRSVTVu0g+aZyzNq8Mb0IVPdkWLTrGDHHzmGl0TB14KN0CVcaGsk3s/lw3S4qtFoaeLoxe2Q0bl71AKXhOWtNPPuSU7C3tWb2E/2IUo9XGvbZT8S8+zQAZ67dYtqyzZRVVNI5ojHT1aOHXv8+lpTMXDSShL+nKx891lu/zm3ass1qJQ7PDejK4A4tFb3lG9mXdBF7WxtmPz2MqEbKrvnDZn5FzEcvKXpX05j2YwxlFRV0jgpl+pgBSJLE8YupzF2xCa1Oh62NNR+OHURkQ3+W7TzMit1HsLbSYGdjw5QhXWndOKDO7DuZksbUpRux0mgI9vPikzH9cHNyNKTp6u3sS76i2Di2v0HzHz8QM2WCkeZGysor6RzRhOkje1Vr+PWc8RWrJz+Nh7OjkqZLN5JVWIKMzHM9OzD44ag60zNm2tINdI0M0R/7WRd6i7cfIu7oGaytNNjbWDN5cDeTYz//l3w00Zy5iNVvP4mHsyNZhcU8Nu8Xiu+Uo9FIONrasP69CTjZ2TJr7V09G2aP6UeU2ig21VOO/SyrqKBzsyZMH6EcM/r6D7GkZOahkcDf042PRvXC192FZXtPsGL/Saw1GuxsrJkyqDOtG/kr9sXsYt/5VOxtrJk9urf+WMthC5YS89Y4Re+6cgxnWWUlncMbMX1YNyRJIv70JWbH7ia3uBRXBzua+tfju+eHk5ZbwMTvYtFI4OPmzCejehHg6Wo5vZE99cdAyrLMrNjdiqatNbNH9TJofr6MmDfHGjRXbVfysGlDpg9RNc9cYnbcHlXTlqb+3nz33DB+O57Md7uPYa3RIAH1XB1JzcpX8m1UD6IaqBr/+pWYN8YoGjcymLYqXtEIb8h09TjT/JJS3ly2hVv5RdR3d+Hzcf1wd7Rn2YFEVhw8g7WVhJ21NVMGdqZ1o/rIssxjC1dx/lY2Go1E++AGLBzfH1trK4b9ewUxrz9u0Fu9w6B395jRklLeXL7VoDe2L+6O9nywZifbz1ymvocLxeqGo75uTgxrG8GljBx2JadQVqHF28WRF7q35dDlGyTfVI5NnDemD4Hq0YSLdh4j5thZpU4Y1FlfJ+w5d5VP1RNQhrWN4MXubQF45tsYcktKkYGm9evRM7IJn289SEWllgJ16nZFpRYHWxu2Tx6HjZWGKWt2qNr2zHu8F4HqSPaiXceJOXEOK43E1P6G+mjctzHk3y7DxkrDu/0foaP6jscnXWH2hn3klpTiam9H0/pejOvYnE83HVCe86FwXuzWhoXxR5X6sFkjpT5cs4tk9cjIeaN76vV7zltGcVkFFVotrvZ2fPf0AEKMNkVNyyti0pLNyrGf6gfDngvX+HTLQUWvdTgvdm3Nwp3HiPT3pnvThoreut0kpytHi88b2Z1AT1cW7TnBd3sTCfIyfGAtHt8fL6MZL2l5RUxavlU59lNdAL3nwjU+3XRQadO0Ue3bcYxI/3oG+9buIlk9ynzeYz0Uvd0n+C7hJEFehgGUxU8pevO2HmLjqctkFimzEUY8FM4r3dvq58zvOX/tgadpWl4RU9fspOhOOR5O9swe3g1/dSZEXaVp73+tYNHYvjTxVjvJPLwM5Uwd1E/LEo6zYt/vWGskpY0xshetmxjVT6u2su/sZUVz3ECi1OPMh835jphpykyAM6k3mbZkg1JfRAQzXT0Kc8pPcZxLy0BCIsDLjRlj+uHt5kLJnXKmL13P5VvZyMCwR1rxbG/lOPNZKzaxL0nVe2qIQW/WImLef9Gg93OsYmNkCNMf76e3cdpPsbRo0oDHu7TV27vtxFkWrt+NtUaDRiPxysCuRLcwPVlH0d5ssPXJwQbt2d/oj/A8ox45Wlahao/ua9D+OY4WjQNMtJXIdQaNlVsMGuMH1Zyev6xX0jMyRJ+e5ZVa3l+ynnM3MrCxtuKd4T3oEN6YRZv38t3WAwQZbci8+KXReLk4qX6zTfEbGxtmjxtg8JtPvydm6rMGv1m6QbGrWROmq0eNTvnlN87dyESSIMDTjRmP98PbzZnfjpzmu+2HsLbSoJEkJvV9lJ4twizXjhoSTbuXPvzb7bgw087j//TH80dleX+5NP87dEg8BCxE6YSoBC6hLLVYg7Lh5d1durJRTuNIBEbKsnxavX848Iosy91r0rjbIWEpXn7yU0vKmXRIWAxH89Nk/zZ6t++/Ed4DR/qr9ov+H0VtnFhW08LlcWWFZfUsje5PqN8s7Td/8Tq8Vmi19w/zILF0HmotrPdn7Mhm6d32LZ2mHtU3R6xTrO6/ieiD17TwKm9Ll21/Rp1v8bLN8vWFVZ9n/nIfx/dDdEg8eP4Oe0gcBx4xc6lbDbeYdLfKsrwOWPeAH0sgEAgEAoFAIBAIBH8jNOI40weOGFIVCAQCgUAgEAgEAoFAYHFEh4RAIBAIBAKBQCAQCAQCi/OXX7IhEAgEAoFAIBAIBAJBXSNG8x88Ik0FAoFAIBAIBAKBQCAQWBzRISEQCAQCgUAgEAgEAoHA4ogOCYFAIBAIBAKBQCAQCAQWR+whIRAIBAKBQCAQCAQCwX3QiFM/HzhihoRAIBAIBAKBQCAQCAQCiyNmSNQCTdfBFtX7cq2TRfUAXh7xgUX1vjq+xqJ68oYVFtWjYWPL6gHodJbVs7KyrJ6l+TPs01hYs6LcsnqWRvoT+twt7TeW9pk/A896ltWTZcvq/d3L0j8DS9eHOZl/bz2A0lLL6ln6vZD+hGFvjYXrKOs/4bOvj+UlBX89RIeEwOKdEQKBQCAQCAQCgUDwV0MsL3jwiDQVCAQCgUAgEAgEAoFAYHFEh4RAIBAIBAKBQCAQCAQCiyOWbAgEAoFAIBAIBAKBQHAfNIhjNh40YoaEQCAQCAQCgUAgEAgEAosjOiQEAoFAIBAIBAKBQCAQWByxZEMgEAgEAoFAIBAIBIL7oBErNh44YoaEQCAQCAQCgUAgEAgEAosjZkj8l8iyzOyvfiDhyAns7WyZ+86rRIY2MQlTeqeMNz6Zx7Vb6VhpNER3aMvbz403CbMl4SBvfDKP1V/8g+bhISbX9p6/xtwN+9DqdIx8OIKJ3dqYXC+v1DJ1VTxJaVm4O9qz4IneBHi4AvDt7uOsPZqMlUbDtEGd6BQWBEBhaRkfrtvFxYxcJGDWiO7/lf3jv/+S5gP7UpSZxSfNO/xXcVRFlmXm/LyWhJNJ2NvaMmfSOCIbB1YL96+V64lLOEJhyW2O/zS/2vWth3/njX/9wOpZ7xAVHFSzYHAUmj5PgEZC/n0v8v5NpteDwtD0GQO+DdCtXQTJx/WXpB4jkUJbKM+dsB757FGzEnsv32Tu9qNoZZmRLUOY+EiUyfXySi1T1x8gKT0Hdwc7FgztTIC7M2n5xQz8dj2NPJX8bBlQjxn92lNSVsG4Jdv092cU3WZQVGPe69XWoHnlJnPjj6PVyYxsGczEjpHVNTccJCk9V9Ec8igB7s4AnM/MY8aWIxSXV6KRYNVTfbGztqJcq2X2tmMcuZaJRpJ4vUsLekc2Vm1MY+62Y4qNrWqw8bf9qp4tC4Z1Mdj4zW+mNvZXfOn5X3eQVVxKpU7HQ4E+fNC3HVYaTZ3olVZU8ubaBK7nF6GRJKJDG/BWd8O7tvfSDeZuPaKkZ+tQJnZqUV0vdi9Jt9Q8HNmVAHcX/fWbBcUM+iqWl7u2YoL6rNN/28eeCzfwdLLnt0lDzfqOXv/iDeZuOaSUA23Cmdi5ZXX9mD0k3cxWyoGR0QR4uHDqRhYfrd+nD/dyt9b0bNbIvIal09RSPmOlMdXcekTVDGXio83NaO4z5OPwrvr3AtR8XBTHy11aMqGj8rxLjpxl9e8XkWWZUa3DeLJ9hEHv0t18kxnZJoyJnczkW2yCmm92Sr65u3AqLYuP1u9XQ8m83NWQb0sOJbH6xHlkYFSbcJ7sYHi39168ztzNBxX72oQzsXOr6nrrdpN0K1uxb1QPAjxcOHD5Bgu2H6VCq8XGyorJvdvRoUkAAJvPXOabhJNodTq6hgUxuXf7OtMrKStn3A/r9fdnFJYwqEUo7/XrqP9NlmXmrN1OQtJlHGxtmDNuIBGBflQl6dotpi3dyJ2KCrpEBjNtRC8kSeKLTXtZc+AkHs6OALwxqCtdI0PIL7nNG9/HcDr1FsPaN+f9Ub2N9OJJOKvqjR1Qg14605apehHBTBvRE0kyDKH9sOMw8+J2sX/Oa3ptgNOptxiz4BfmTxhGnzbNFL3V2wz2jR9IRFB98/YtWc+d8krFvlG9Ffs2JrBm/+8G+wZH0zUqhPVHzvBD/EH9/RduZrJmyrM0C/SrM8273MwtYNAn3/DygC5M6NmhzvQqtFo+XLaRs9fT0Wp1DG4XxfO9H7Gcz/RoDTz48vpWQTHvxSSQXXwbSZJ47KFwxncwLS+N2ZuaydyEM8p7GRHExLahJtePpeUwd+8ZLmQXMa9vG/qE+OuvPR93iMT0PNr4e/L1oPZVozZPowg0PUaCpEE+tR/5yHbT6w1C0HQfAd4B6Nb/CBd+11/SvL0Qsm8qfxTmoov5phZ6zdB0Gw4aDfLpg8hH402vBwQr17390W38GS6eNL1ua4/m6WnIl04h71xzX7m917KYuz8ZrQwjmzVgYmvTdv6xm7nMPXCOCzlFzOvZkj7Bpr5VXF7JwBV76dnYl/c7R1Ab9l7LYu6+s0rdERHIxDbB1TX3nVU0e7eiT7Dy/qQVlfL6FqXtV6mTGdu8IY9HNby/3tVM5iacVnwmsqF5n0k4w4XsQub1fYg+oUY+E3tQ9Rkvvh5cS58RCMxw3w4JSZK0wGmjn1bIsvypJEm7gfpAGWALxAPvy7KcL0lSI2CDLMtRRvHMAIplWZ6n/j0ZeA6oBLTAfFmWf1GveQM3gVdkWf5G/e0wYAd4Ag5Amhr1UGA30FaW5WxJkhoAXwIRKDNANgDvyLJcLklSN2AXMFiW5fVqvBuAebIs765ViqkkHDlBatottv70BYnJF5n5n29ZtfDTauGeGTWYDq2aU15RwTPvziThyAm6tFMa5sW3S1kau5GWTUOr3afV6Zj1WwKLnx2Er6szo79cQ3SzRoT4eurDrD2ajKuDHVvfGcemxIvM33yQBU/04VJGLpsTL7H+zTFkFpbw7Pe/sentJ7DSaJi7fh+dwoL419i+lFdquVNR+UfM1nPwp2Xs/uJbnv6lFhVILUk4eZbU9Ey2fP4hiZeu8vH3K1k5a3K1cN3aRPFE7y70e/PjatdKSu+wZMseWoQ0ureYJKHpNw7d0vlQmIvmuQ+Rz580VI4ABTno4r5H6tjX9N7QFkj1G6L7ZgZYW6N5airypdNQfsckmFanY9bWIywe0wNfV0dG/7iZ6NAGhHi768OsTbyEq70tWycNZVPSVebv+p0FwzoDEOjuTMxzA0zidLKzMflt5A+b6BVu6LTR6nTM2naMxY93x9fFgdE/bVU067kZNE9dVjRfHMyms1eZv/skC4Z2olKnY8r6g3w6sCNNfT3ILy3DWp2X9s2BJDyd7Nn8wiB0skxBaZlBb8sRFj/RU7HxBzM2nlRtfGkom5JSmL/zBAuGd1Fs9HAmZuLAatmzYHhnnO1skWWZN9YmsDU5lf6RjetM75kOEbRv5Ee5VsuEZfEkXEqjS3iQorf5MIvH9Vb0Fm8gOjzIVO/3i7g62LL11RFsOnOF+fHHWTCym/76P7YepXNIgInesJYhjH24GVNj91Z7FmO0Oh2zNh1g8fi++Lo6Mfq73xR9Hw+D/onzuNrbsfX1x9h0+jLz44+yYFR3Qn08WP38EKytNGQV3WbY1zF0CwvC2kpTXcNSaRoSYFmfiQo22Lj5EIvHqvn4/UaiwwKraF5UNF8ermoeZ8HwroZ83G6ajxcz81j9+0VWThiAjZWG55fH0yW0AY28PdR8O8ji8X1M883bKN9+v6DovTZK9ZtjLBgZrebbYKw1ar4tiqVbeBAp2QWsPnGelRMHK3pLt6p6norexv0sfrK/ovdtLNHhDav7iYMtW18frfjJ9iMseKwH7o72fPVEb3xcnbiYkcvEJZvZPXks+bfv8M9th1nzwjA8nRx4b91uDl5Jo2OTgDrRc7KzJWbSCP39IxfF0KtKB1pI37EGAAAgAElEQVTC2cukZuax5cMXOXX1JjNXbmHl5Ker+cLHK7cyc0xfWjYK4IWvV7H37BW6RCq+8GR0Oyb0MG0421pb8+qALly8lcWlm1lGeldIzcpjywcvKHqrtrLy7aeq663ayszH+9KykT8vLFrN3uQrdIlQ9G7lFXLw/FXqq4MFd9HqdCz4bRePNmts0Eu6TGpWLltmTFL0Vmxh5bvPVNdbsZmZY/rTsnEAL3y1gr1nL9MlUukEeLJ7eyb0NB0kGNQuikHtlObYhbRMXvlmNc3Uj/K60rzLP9Zup3Ok4YOqrvS2nkimvFJL3PTnKS2vYNAn3zDgoQgupWdbzGfqpLzWaHi3dzsi/OtRUlbOyG/i6NgkgBCr6mmt1cnM2n2axUM74OvswOiVe4lu4keIp6GDvL6LA3N6tubHE5er3f9Mm2DuVGpZdSa1euTmkCQ0vR5Dt2ohFOWjGf8u8uXTkJNuCFOYi27zEqSHe1a/v7IC3c9za6d1V6/7KHRrv1T0xk5GvnwGco30ivLQbV2G1Nb8YJv0SH/kG5dqJafVyczad5bFAx/G18me0esOEt3QhxBPQ0d1fWd75kQ358fEFLNx/OfIRR729zR7rUbNhCQWD2qHr7M9o9fsJ7qRj2keOtszp3sLfjxpquntaMfy4R2xtbKipKKSISv20r2xLz5O9vfW232KxcM6qj6TQHRjP0K8qvhMr1bmfeahEO5U/AGf+Zsglhc8eGqTpqWyLLcy+mf81T1WluUWQAuUjom42ohKkvQi0Atop3ZadAGTM1RGAYeAMXd/kGW5vSzLrYAPgZVGz3PVKF4JWAfEyrIcCoQBzsBso7hvANNr85z3YsfBowzp2RVJkmgVEUZhcQmZOXkmYRzs7ejQShmFs7WxISKkMenZOfrr//npV559bCi2trbV4j99PZMgLzcCPd2wtbaiX8sQdiabFj47k1MY2qYpAL2jgjl0OQ1ZltmZnEK/liHYWlvRwNOVIC83Tl/PpPhOOceu3mRE22bKM1lb4epg91/Zf2nvAW7n5t0/4B9g5/HTDOncTknT0MYU3i4lM6+gWrhWoY3x8XAzEwP8e9VGnh3UEzub+/S1BTSBvEzIzwKdFjnpMFK46QgfBTmQeQNkncnPUj1/5NTzyu8V5cgZ15FCTEdbAU7fzCHIw4VADxdsrazoF9GInRdvmNp84QZDmys97r2bBXHoajqyLN/72VWu5haSW3KHhwJ9DJq3cgjycCbQ3VnVbFhd8+INhjZXGsG9mwZxKDUDWZbZn3KLMB93mvoqjSd3Bzv9rISYU1eYqI7GaiQJD0d7g42exjY2ZOeF61X0rjO0RbBqY8Na2ehsp7wTlTqZCq1WP9pYF3oONta0b6Q0zm2trIjw8ySj6Lail5ZtmoeRjdl5/pqp3vlrDG2hNJR7RzTiUMotvV78uVQaeDibfPgCtG3oh5tD9fe+KqfTsgjydCXQ01UpB6KamNdvdVe/MYeu3ESWZRxsrfWdD2WVWqQa1jxaPE0t7DOKZraSjsb5WFXzQhVN43w8f40G7i6E1DPk4+XsAloGeONgY421RsPDDX3ZcU7Jm9Npd/VcVb0m7DxnJt9aKp3RvSMaGfJNjQ9M8+1yVj4tG/gY6dVnx7lUVa+qnwSz85xpA3HnuasMbRWm6jXmUIpSX0TUr4ePqxMAIT4elFVqKa/Ucj2viEZebng6OQDQMTiA7WdT6kzPmKs5BeSWlPJQQ9PRxp2nLzKkXRSSJNGycQBFpWVkFRSbhMkqKKb4ThmtGjdAkiSGtItix+kL3AtHO1seCg7Eztq03vhjegEGvVMX9df/sW4Hbw/pVu39W7bnOL1ahuNlNGNi56kLDGnfwkjvDlkFRVX0iii+U06rJqp97VuwI/He9hmz8VgS/dsaRmrrUjM+8TwNvDwIqe9d53qSJFFaVk6lVkdZeQU2Vhqc7O0s6jN1UV57uzgS4V8PACc7W5p4u5OplqVVOZ2RR5C7E4FuTthaaegX5s/OK+kmYQJcHQmv52p2DXzHQG+c7td2MqZ+I8jLUtpKOi3yueNIIaYzCCnMhaybUMt2zT3xa6i02fR6J5CCq7S9CnOVgSVzej6B4OgCV8/VSu50Zj5Bro4Eujoq6Rnsx86rGSZhAlwdCfdyMfsxlZRVQE5pGY808KqlgaqmmyOBbqpmSH12ppjRNJOHtlYabK2UnqoKrQ5dLdK8ms+EBtTgM25ozDQiOgZ642QrJtsL/nceSCePLMvlwLtAkCRJLe8XHpgGvCTLcqF6f4Esyz8bXR8DvA00kCQpwFwENdAduCPL8o9qvFrgTWCCJEl3a/1EoECSpF5/IN5qZGTnUt+nnv5vv3peZBh1NlSlsLiEXYeO0bG1UnievXSFW1nZRHdoazZ8RmEJfm6GXlg/V2cyC0qqh1GnFFtbaXCxtyX/9h0yC0zv9XVzIqOwhOu5hXg6OTB9zU6G/2cVH6zdxe3yij9ufB2RkZuPn5dhJMHP053M3OodEjVxNuU66bl5RLepeTqjHhd35IJcw9+FeeDiUXN4I/QdENa24OCM1KgpuFbvAc8ouo2fq6Gx6efiWK0hYRzGWqPBxc6GfHX2QVpBMcO/38iTS7Zx7Fpmtfg3JV2lb0RDkw+vjKJS/Fyc7qNpCGOsmZpbhARMXLmTET9u5vtDZ5WkuVMOwMK9iYz4cTNvxOwlu6TU8PzGeq5OZBaV1t7G/GKGL97Ak0u2cuyaaaU78dd4Ov9rNU52NvRuGlTnendt3X3xBh3Uj+mMotv4uVXVM5OHbkbpaW9LfmkZt8sr+H7/GV7qWqWj6w+QUXgbP1djfUcyC82UA65VywHF3sQbmQz6ci1DvlrHRwMfrTY7Qv/8lk5TC/qMIb6q70WVdDQKY6x5u7yC7w+c4aUuplVbqI87x65lkH/7DqUVlSRcSuOWmjcZRSVV8s2M3xSWmPUbUPPtq3UM+TqGjwY8grVGQ6iPB8dS0430rnNLrROq1RduTve2z0qDi53BT+6y7WwKzep7YWttRZCnKynZBaTlFVGp1bEj+SrpdahnzKbTl+kb1cSkbAPIzC/Cz2imga+7CxlVPmYzCorwdTcO40pmviHM8oTjDJ27mOnLNlJw29TvqpJZUISf0fKrmvVMw2SqYXaevoiPuzNNA3xN78kvIv7UBUZ3am1Gz/TZM/KLqt1bkx7A8j3HGDr7O6YvWW/Wvi0nzjKgrWGpT11p3i4r5/vtB3mpf2eL2Ni7dVMc7GzpOu3f9PjgC57p0R53JweL+kxdl9dpeUUk38qhRYA35sgouYOfs4NB39mezOI7ZsM+EJzdkYuMBqaK8sHZvebwVbG2RjP+XTRjJ0PVjowa9fINfxfng4v5wanqSGi6DkVOqNW4KQAZJWXV07Ok7B53GNDJMp8dOMfkjuG11lM07+DnbJjR4OfsUGtNgFtFpQxdsZfuv+zkudZN7jk7AiCj2IzPlNy7XBQI6oLadEg4SJJ00ujfaHOB1I//RKDpvSKTJMkFcJFlufrcH+V6IOAny/IRYBVgVq8GIoHjxj+onR7XAOMNGmYB7/+BeKtjpuexauPpLpVaLW/P+ZzxwwYQWN8PnU7H3K9/YsoLT9ccPWZ6NqvEb27EUEIydyeSpEwnPHszi9Hto1j32mM42FqzePeJGp/B0pi1p5Y72ep0Oj5dso4p44bVUs1cxLXswb+ShHzxNJoJ09CMeEGZ/qfTVgtmPjbpvmEkJLydHdjx8nDWPTuAKT0f4t24fRSXlZuE23Q2lQERjR6MpiRRqZM5cSOLzwY9wtJxvYi/cJ2DV9PR6nSkF92mdYA3a5/pR6uAevxz5+8161VJWnOd9HobXxnBuucGMqVnW96NNbXxuzE92fP6SMordRy+ml7nepU6HZNj9jLu4aYEerjUrFcF83kIX+w+yZMdInCytalFLLWPu1o5cI8gLRv4sP7lEax6fgjf7U2kzMwSrf8TaVqHPlNTfNXeC7PlKXyRcJIn21fPx+B67jzXMYpnl2/n+eXbCff10C9xqs1gYE1+A2q+vTScVRMH892+U5RVVhLs7c5zj7bg2SVbeX7pVsJ9PfUzKWrlp/cpXy9m5rJg+xFmDFI+IN0c7Phw4KO8tXoH439Yj7+7C1Z37asDPWM2nbnMgObB1X437xe10VVCPd6pDVs/epF1U57F29WZz2J23scGc3pV/cb8M5WWV/DNtgO82r+6fXPXxfP24G76GWi1eXZ9GDPPeTfE453bsHXmS6x77zm83Zz5bK3p+vrElDTsbW0I9TfMqqsrzS82JvBkdDuc7E1ngtWV3umrN9FIErvnvMa2j1/mp51HuJ6dZ1GfqcvyuqSsgtdX7eC9vh1wtjc/u85suWPx0wBqPxNCt+gDdEs+Q7fhRzTdR4J7vfvfVE2udnpSq07IKWeVTozaRm02otrd+2vSNboEeVPf6GO/Vpr/Yx7Wd3Eg9vHObBnbjbjzaWTfvndnRm3ajAKBJajNPJtSdalEbbjrxTWVELIa5l4lyOMoHREAK4DvgQV/QL+m11n/uyzLeyVJQpKk6i2FuzdI0vPA8wCL5n7I80+MYlncZlZvUiq/5uEh3MrM1odPz87Bx8v8OrEPP19Ew4D6PDVcWfdcUlrKxavXeHLyhwBk5+bz0oef8tXHU/UbW/q5OpNuNK0wvbAYH6PRdgA/N2fS84vxc3OmUquj6E45bo52+Lo5mdybUVCCj4sTvm7O+Lo60zJIGa3pHRXM4j0nMLMU0WIs25bAmp0HAIhqEkS60bKX9Nx8vGtYmlGVkjtlXLx+iyc//g8A2QWFvDTvG76a/IL5jS2L8pDcPA1O4eqh9O7XEnnfBuR9GwCQhj2PnFt9ZNjPxZH0QsOoaHrRbXxcHMyG8XN1olKno6isAjcHWyRJ0o8aRtb3ItDDmau5RUTVV6b+ncvIQyvriKzvVSU+B9KNRirNayph/FwdDZr2tvi5OPBwoI9+OUaXYH/OZuTSoaEvDjZW9FT3qujTNIi1p64Ynt9Yr7AEnyoVsJ9rbW104WpOEVH+BpvsrK2IDmvAzgs3eKSJf53qfbTxEA09XXiyXTPT/CmooudS5T1Uw+j17pTj5mDHqbQstiVfZX78MYrulCNJEnbWVow1iv9+KLYY69+uru/qRHphMX5uToZyoMpSrGBvdxxsbbiYmUdUldG1PyVNLeUzwQ3Mp2NRTelYUkXTjlNp2WxLTmX+jir5+HAzRrQOZURrZdnF5ztP6Gd13I3LxEZzemb8pnq+WXMxM58o/3qMaBPGiDbKMojPdxzTj8YqcRnVF2qZb9a+u/VFmUEvvaCY11ZsZ+7wbgR5GkaKo8MbEh2ubIq26liyvkOirvQAzqXnoNXpiPRX/HT54SRWJyprvpsH1Sc9r1AfNiO/CB83F5P7/dxdycg3DlOItzqbo57R6PWoR1oy6ZvVVOXCzSyG/eMHg57RSLmi52wS3s/dxWSEPyO/CG83F65n55GWU6CPKyO/iBH//ImVbz9J0rV03v5ZGanNKihm/bEk5sXuoGN4Y9KrPHtt9RT7DGFHPdqaSV+vMrl38/Gz9H8okuV7jrF6v9Kp3Lyhf51onrp6k22/n2N+7E5yikqoqNTyy84jdIkMrhO9jceS6BwRjI2VFVtPJJNXfJtnFi7nkfDGde4z+njqqLyu0Op4Y9UOBjYPpleVQQiTuJ3tSS82jG6nF9+57wj5/0RxPpKLh6Ed5eIOxbWf1UqJGrYgB/n6RWVJRX52zeGL85Fc3A16zu5QXFhzeGPqN0YKaILUshPY2oHGGsrLkPetr/EWPye76unpWLtlzifT8zmensevSde4XamlQqvD0caKtzrce8aEkoeGWS3pxaW11jTGx8meYE9njt/K1W96WbOeBX3mb4I49vPB88D25ZAkyQpoDiQDOUDV+e+eQLY6Y6FEkqQmmGcM8LQkSVeB34CWkiRV3/XRPEmAyRoISZJcgUCg6oyM2dxjLwlZlr+VZbmtLMttn39iFABjh/Qj9pv5xH4znx6PtiMufg+yLHPy7AVcnBzx8ao+5f9fPy6nqKSEaZMMmza5ODlxaO1P7Fy6iJ1LF9GyWZhJZwRAVAMfUrMLuJFbSHmlls2Jl4g22vwKILpZI2JPKGvhtp25TPtgZQ1rdLPGbE68RHmllhu5haRmF9A80AdvF0f83J1JyVI++g9dvkGwT+0326kLxvbuQsynU4n5dCo92rYgbu8RJU0vpuDiaF/jXhFVcXF04OB3n7Jj4Ux2LJxJy5BGNXdGAKSlgKev0iOvsUKKbI984aT5sFWRJHBQGyk+DZB8G8DlpGrBovy9SM0r4kZ+MeVaLZvPXiU6tIFJmOjQBsSeVj7utyVfo31DXyRJIrfkDlqdsnfF9bwiUnOLaGC04/+ms1fpb6ZhElXfi9RcY81UoqtsqBgd0oDY08pa8G3nDJqPNvHnfFY+pRWVVOp0HL2WSYiXG5Ik0S0kgCOpSqfLoasZBHu5GmzMLeJGfpFBL8z0ZJTo0EBiT11WbUylfSO/GmwspIGHMyXlFWSp09srdToSLqXRuA71AP69+3eKyyp4r/fDpukZUI/U3EJu5Kl6SSnV9cIDiT2lfDBtO3uV9o3rI0kSS5/pT/zro4h/fRTj20fwfKcWf6gzQrHXm9QcVb9Sy+YzV4gON/Xp6PAgYk/e1U+hfWN/JEnihjrVHiAtv4iU7AKT0z8MGhZOUwv7jKJpLh+rvIth5jWXPtWP+FdHEv/qSMa3i+D5R5sz9mElH3PUqa03C4qJP69svAqq3+QUGOmZybewQGITL6r5ZvCbG3lFVOru5luxmm/O1fWSU+kf1US1z1u1T60vzlwmumlVP2lI7MkLqp7BTwpLy5i0bCtv9mxHmyDTPRty1IZqQWkZvx49y8g24XWqB8pyjf5GpzQ80T6SmKnPEjP1WXq0CCPuyBlkWSYxJQ0Xezv9h+NdvN2ccbK3JVHdsyLuyBm6N1eaEMZ7B8QnXiC0fvWp72H+3sRMmUDMlAn0aBH6X+uF+fuwb85rxM94ifgZL+Hr7sLad57G29WZ7TMm6X8f8FAE854ZxvaPX6FHyzDiDp8y6DnY6T/EDXouONkZ6R0+RfcWYap9ho/4+MTzhPob7NPpZLb+nkz/thE80bUtMdMmEjNtYp1pLn3rSeI/eYX4T17h2V4deWtod3bPea3O9Op7uHLowlVkWWZohxZ4uTrx5fOjLOIzd6mL8lqWZT6I20uTeu48/Uj1vapM9H3dSc0v4UbBbcq1OjZfuEl04+rv2APjVip4+ICbl9KOavqQssF3bbBzACt1TNTBCSmgCeTcuvc96dfA3VtZIquxQmraBvlK7fTkzb+gWzwD3fczkffEIicfuWdnBECUjxupBbe5Uaim5+V0ohv53POeu/yzZ0t2jutG/LhuvNMhnCFhAfftjDBolhg0L90iurHvfe8DpfPijrofT8GdCn6/lUdjd6d73mPwmRJF72Ia0U1qpycQPEgeyE4kkiTZoHzgX5dl+ZT62y1JknrIsrxDkiRPoC/wb/WWucCXkiSNlmW5UO00eBzYAzjJshxgFPdM9dontXiUHcCnkiQ9KcvyL2onyXzgJ1mWbxtPC5RleZskSZ8A/jXEdU+6tmtDwuET9H7qZezt7Jgz+WX9taEvvE3sN/NJz8ph0fK1NAkMYPikdwClU2NUfzO7DVfB2krD9MGdmfjDenSyzLC2TQn19WTh9iNEBnjTPaIxI9o2Y8qqHfT551LcHe2ZN0bZFiPU15M+LYIZ9PmvWGk0vD+ks35q6PRBnXl3ZTwVWi0NPN2YPTKaad9v/sP2P7v8B8K6dcK5nhdzryez/qM5HPhhyR+Ox5iurSNJOHmWPm98jL2dDXNeGKe/Nmzqp8R8OhWAfy6LZeOB45SWV9Dt5Q8YGd2RV0b2/2Nisg7d5qVoxr6lHFd1ch9k3UTqNhT55lW4cBL8G6F57BWwd0IKawVdh6Jb9AForNA8/Z4ST1kpupjvqm18Ccq68Om9H2biih3odDLDWgYT6u3Owj2JRNb3pHtYICNahTDlt/30+ToWd3s75g3tBMCx65ksTEjEWiOhkSQ+6tced6NRlC3JqSx6LLoGzbZMXLlL8ZsWTRTNhFOKZmgDRrQMZsr6A/RZ9BvuDrbMG6Joutnb8tTDTXns561IKDMkuqqdGW91a83U9Qf4dMcJPBztmK0etWit0TC9Tzsm/nrXxhDVxpNE1vcy2Bi3jz5fxeJub8s89RSRY9czWLgnEWuNxsTG7OJSXl69m3KtFq1Opn0jP0Y/FFZneumFJXyz/wxNvFwZsXgjAGPbhjOybVNFr18HJi7brqRnqxBCfTxYuOt3Iv296B4exIjWoUyJ2UufhWtxd7Bj3oiu3I/Ja/dwRN0PIPrzVbzSrRUjWodVz08rDdP7d2Tiki2KfuswRX/ncSL969G9aUNGtA5jSswe+vx7laI/UvGLE9fS+W7fKb29HwzoiIeZkQ+LpmnrUIv7jN7Gvu2Z+Gs8Op2OYa1CCfX2YOFuNR/DghjRKpQpcXvp8+U65b0Ydv98fH3NbvJLy7DRaHi/bwf9SKe1Rs23pVtVvwlV/eaEkm/hQYxoE8aUmAT6/Ge1mm/d1HzL4Lv9xvn2iH7W0uurdpJ/uwwbK4n3+3c06FlpmN7/ESYu2aykaetwQn08WbjzGJH+3oqftAlnyrrd9Pn3SlVP2YV++ZEkruUW8vWeE3y9R1nCt3h8f7ycHZi7+SDnMpS9kV7q2oZG6qaedaUHsCXpCovGVjnZSKVLZDAJZy/T9+NF2NvYMHuc4cShYZ9+T8zUZwH4cHRfpi3dQFlFJZ2bNdGfeDEvbifnbmQiSRDg6caMx/vp7+/50VcU3ymjolLLjlMX+e6l0XSJCCYh6Qp9P/4Ge1sbZo811DPD/vEDMVMmKHqP9WHaso2UlVfSOaIJXSJqGm+5N10iQ0hIukzfGV8peuMMp8kMm/MdMdMmKnqP92Xakg2UVVTQOSJYfxrEvJidnEvLQEIiwMuNGWMM9h27dA1fdxcC63lYTNOSNo7p0pbpS9czeNa3yMCw9i0ID/AhzN/bMj5z8hzfje/7wMvr46np/HbqEmE+Hgz7OgaAN3q0patX9VFza42G6V2jmPjbIeW9jAgk1MuFhYfOEenjTvcmfpzOyOe1jUcpLKtg19UMvjh8nvVjlWcYt2Y/KXnF3K6oJPqH7XzSoyWdGt7jA1zWoYtfhWbky/pjOMm5hfToAOT0a3D5NPgFoRn6PNg5IgVHwaMD0P04C7z80PQeo6xRkCTkw9tMT+eoSW/XGjQjXlLabWcOQU66cnJG+jW4cgZ8g9AMfg7sHZCaREHHfuh++QMneVRNz04RTNx4TMnP8AaEerqw8OhFIr3d6N7Ih9OZBby29QSFZZXsSs3ii2OXWD+603+lp9fsHMnE9UfQyTCsqap55IKi2dhXycMtJ9Q8zOSLIxdZP6YLV/KK+Wz/OSRJSdZnWjUhzMv1/nrdmjMxTvWZyCBCvVyr+Ewer21QfSYlXfGZcXd9Zh8puarPfL+NT3q2urfPCAQ1IN1v53Izx35ukWV5apVjP+1Qjv2cLstyvnpfBMrxm3drv3/KsrxMvSYB7wDPAhXqv/ko+zzYy7I81Ui/BcpRoxHq30+jHPH5ilGYqxiO/QwEvkLZy0IDbAImy7Jcph77OVmW5YHqfYNRTgaJvtexn/K1Mw9ge+Daozu2w5JyvDziA4vqAXx1/P7nPz9I5A0rLKpHw8b3D/Og0VXvFKlTrP7MxT4W4M+wT2NhzYry+4f5KyP9CYdzWdpvLO0zfwae/8Xa8v+FB3EiwB/h716W/hlYuj7Mqb7x9N9KD6DUwpsdWvq9qO2mZQ8SjYXrKGvLn4hh9fI//3YLHBY61bNwJfHHeLUk+y+X5vf1TFmWzZYIsix3u899Z4HqQ7jKNRn4TP13P/1TQITR3z8BP1UJ08jo/9eBQTXEtRvYbfT3b4jdWwQCgUAgEAgEAoFAILA4f8LwkUAgEAgEAoFAIBAIBIL/37H83B2BQCAQCAQCgUAgEAj+YohTNh48YoaEQCAQCAQCgUAgEAgEAosjOiQEAoFAIBAIBAKBQCAQWByxZEMgEAgEAoFAIBAIBIL7IEbzHzwiTQUCgUAgEAgEAoFAIBBYHNEhIRAIBAKBQCAQCAQCgcDiiCUbAoFAIBAIBAKBQCAQ3AdxysaDR3RI1AI57bJlBR2dLSr35ebPkXwCLar50kMjLaq3qOS6RfW025dZVA8AWWdZPZ3WsnrS/wcTuiydh84ultXTyZbVE/w9KL1tWT1Lv4dWVpbVq6y0rB6AxsI2WhpX9z/7CeoeS9f5or4QCP6/4f+DFr7gfli6M0IgEAgEAoFAIBAIBAIxQ0IgEAgEAoFAIBAIBIL7oEGs2XjQiBkSAoFAIBAIBAKBQCAQCCyO6JAQCAQCgUAgEAgEAoFAYHFEh4RAIBAIBAKBQCAQCAQCiyP2kBAIBAKBQCAQCAQCgeA+iGM/HzxihoRAIBAIBAKBQCAQCAQCiyM6JAQCgUAgEAgEAoFAIBBYHLFk479ElmXmLPuNhFPnsLe1Yc5zjxHZqEG1cP9as4W4A8cpLCnl+Dez9L8fPX+Fuct/48L1dOZPeoI+D7cwr7FuBwlnr+BgY8Ocsf2ICPSrFi7pejrTlm3iTkUlXSKaMG14DyRJ4j8b97Lz9CUkjYSXsyNzxvbDx82FotIypizZwK28Qip1OiYM6cvwbh3M6/+8loSTSdjb2jJn0jgiGwdWt3HleuISjlBYcpvjP82vdn3r4d95418/sHrWO0QFB903bc0x/vsvaT6wL0WZWXzSvPqz/jfIsszsz+azZ/8B7O3t+XTmh0Q2a1pj+Bdff5sbaWlsWLMCgM3b4/li0XdcTrnK6iU/0jwywqzGnAfxVJQAACAASURBVN/2kHDuKg421sx5rDcRDXyqhUu6kcG0VduVPGzaiGmDuyJJEltOXeTL7Ye4kpnLylceJyrQF4D8klLeWLKJ0zcyGNa2GdOHdGNO3B4SzqeqOj2JCDCnk8m01fGKTnhDpg3ugiRJ5N++w9vLtpCWV0iAhysLxvbFzdGe7/ecYMPv5wHQ6nRcycxj34fPcfp6Bu+t3EbRnXJc7O14vlsbnuzUUq9TXqll6qp4ktKycHe0Z8ETvQnwcAXg293HWXs0GSuNhmmDOtEpzOATWp2OUV+swdfVia+fHgDAwUs3+Oem/aQXlFBaXkF9dxfmjel9b/sqtYp9gzob7Fu+1WDfE31wc7TnSmYe09fEczYti9f7dGBClzYA3MwrZOzXa8kpLkUjSYx9pAXvDHi0zvQAZqzbRczxZGTg0ZBAvf11labzNh9ABzjZ2jJrdC+W7U8kITkFB1sb5oy+h5+u2Kb4T7PGTBui+mniBb7cpvrpa2P0fpqWW8DAz36hkY8HAC0D/ZgxokedvRenrqXz0dod+vtf7tWenlEhQN29iwcupLJg8wEqtFpsrKyYPKATHUICLW7j38W+mvSqUlf6sizXSVlacPsO76/ZwfWcAuysrZg1qiehfl76+LQ6HSM/X07xnXI0GumBvocA529mMWPtDopKy8gpuo2rg52iMapHnZalKVl5vLV8qz7e6zkFNAvwJrOw5IHm211u5hUyaP5SXu7VngldH7Kcnw7uSofQIEUvZpdallozZ0xfIhr4Vte7nsG0X7cY8nBYNJIk8c/f9rD77GVsrKwI9HJn9pg+uDrYs/54Mj/sOqq//8LNLNa8MJTs4lLmbjmk+E+bcCZ2bmmiU16pZWrMHpJuZiv1xshoAjxcOHA5jQXxR6nQ6rCx0jC5Vzs6NPEHYPOZK3yTcBKtLNM1NJDJvdtVe/677L10g7lbj6DVyYxsHcrETqZt2fJKLVNj95J0Kwd3BzsWjOxKgLuLIb8Kihn0VSwvd23FhEeizGtcTlM0ZJmRrUKZ+Gjz6hq/7TNoDO9KgLuzqcaiOF7u0pIJHaNIySngrXV79Ndv5BXzatdWPNk+ok70AH4+nMSa3y8iSRJh3u7MHtwJO2urOrMRYMmRs6z+/SKyLDOqdZjevrrSK7xTzocbDnAxKw8JiVmDHqGVmXft74BYsfHg+a9nSEiSJEuStMTob2tJkrIkSdpQJVycJEkHq/w2Q5KkNEmSTkqSdFaSpDFG136SJClFvZYoSVIPo2u7JUlqa/R3a/U5+lSJ31eSpOWSJF2RJOm4JEkHJUkapl7rJklSgRr/3X89/6j9CafOkZqRzZZ/vMvMp0fw8S8xZsN1a9WMlR++Wu13f0935j43mgEdWtWscfYKqVl5bHl/IjMf78PM1dvNhvt41TZmju7DlvcnkpqVx97kFAAm9GhH7NRniHn3abpGBvPVlgMALN97gmA/L2KmPMPPr47hs6UxlFdWVtc/eZbU9Ey2fP4hMyc+zsffrzRvY5soVs6abPZaSekdlmzZQ4uQRjXaWRsO/rSMhX2H/09xVCVh3wGuXrvOtri1fPL+e8yY848aw27bsQsnRweT38KCg1k4/zMebtO6Zo1zV0nNzmfLu08xc0QPZsbsNBvu45hdzBzRgy3vPkVqdj57z6cCEOrrxX/GD6Rt4wCT8LY21rzapwPvDOhkqvPOeGYO787MmN016wyPZss74010Fu8+ToeQBmx590k6hDRg8e7jADzbtQ0xb4wh5o0xvNn3ER5uEoCLvS0frd2Ji70de6c9TT0XRzadusTV7Hy9ztqjybg62LH1nXE81akl8zcrRcCljFw2J15i/Ztj+PaZgXwSl4BWp9Pft2T/KYLVj1f9M8fuYXT7SKIa+PBO/0cJ8nJjZuwezPFx7G7FvsnjFPsuXDO1753xin17TgDg5mjHtEFdeKaLaR4eT7lJfXcXEmdPYtEzA1l28BSXMnLrTO/czSzWHU9mzSujOPzBsxxNuUmCmjd1laafje5JzFvjGNA6nI/X7lTKmqlPM3NkD2YaffCa3Ld2JzNH9mDL1KeVsubcVQBC/erxn6eq+ylAoJc7MW+NI+atccwYoRTndfVehPp5sfq1McS8OZZvnx3KjLU7qdTq6lTT3cmBr54eRNxb45g7uhdTV2z9U2z8u9hXk15V6kq/rsrSb3cdo2n9esS++QRzR/dizm8JJvEt2XsSVwc7bpdXPPD3sFKrY8qvW/loRA/eGdSFZv7ebHx7LDOHR9d5WdrY24OY1x8n5s2xrHl9DFYaDRpJeuD5dpd/rE+gc3hD/d8W89NlmxW95BRSs/PYMm0CM0f1YuaaePN6a+KZ+VgvtkybQGq2IQ//H3vnHVbFlf7xz1x6b1KVJiiCva0mdhSNRmOv2FJMjIlJdE3immQTU002MbtrNmtiutFYg9hFRbF3RUBAQaQpRSmXeil3fn/M5Ra4KInAbvY3n+fhAWbOzPe8p82Z95R5NMiXyFfns+PVefi5OrHu0DkAxvUOJmLZXCKWzeXjWaNp62hHR3cn3t97iq/CR7Lrhcnsjb9JSl6hgc72S8nYW1pw4OVpzOvfmc8OSU4NR2sLvpwZRuSiSXw0YTDLI6RyUFReyd+izvHdvNHsemEy98oqOH3ztlEbatVq3t93lq9mhbFr0QT2JqSRkl9kEGb75RvYW5lzYPFk5vUP4bNDFw3z68B5BgUaz0udxhm+mjmCXQvHG9e4cgN7S3MOvDCJef1C+Cy6nsZBQw1/FwciFjxBxIIn2Pb0WCzNTBge5NNiernKMn4+l8TWp8ey87nx1IoiexPSWtTGG3mFbL18g81PPU7Es09w9EYWtwqULaYH8NGBcwwM8GLP8xP59dlxtG/jiIxMU3mYJRtlQBdBEOre0sKAbP0AgiA4Ar0AR0EQ/Otd/7koij2A8cBXgiCY6Z17VXPuFWDtfeIwEzih+V2nKQA7gGOiKLYXRbE3MAPQn75wXBTFHno/xp8Y9yH68jXGD+iFIAj0CPRFWV5BXpGyQbgegb64Odo3ON7W1Zkgb08UQuN+tuj4FMb37YwgCHT386KkopL84lKDMPnFpZRWVtHDvy2CIDC+b2cOx90AwNbSQhuuoqoaQaMlCAJllVWIoki5qgoHW2tMFQ2LQvTFOMYP+pNkYwd/ycbC4oY2dvDHzcnBqA3/2LKHp8eNwMLs4SbjpBw/RXlB4YMD/gYOxxxjwtgxkn3duqIsKSEv/26DcGXl5Xz/80aef+Ypg+MB7f1p7+fbILw+0dduMr5XsJSHvp6UVKjIV5YZhMlXlkl56Osp5WGvYA4npEoa7s7413uZBLA2N6O3f1ttukZfu8n43nU6Ho3rqPR0egdzOOGmdH3CTSb0DgZggt5xffbGXmdM9w7EZebiYG1BH38v7K0tGdM9EBtzMw7rPWCjE9OY0EuabTKySwBnUrMRRZHoxDRGdw/E3NSEds72+Lg4EJeZB0BOcSkxyelM7htsoCsIAieuZzK+VydKVVUEebZpmn29Ounsu6aLzwS94y621nT1dm9Q/i/cukP4o90QBIH+gd6YKBRcz7nbYnpHE2/hbGNFBw8XrCzM6O7tzs8n4wzCNHeallZWA1BSqaKovJLxffTKaWVV4+XUz0uyt0/9cupMU2mpemFlboapiZS2qpoa9JvXltIMaeuGm4M0chTo7oKqppaqmppWt/F/xb7G9OrTUvot1Zam5hVoZ3q0d3PmdqGSuyXlAOQUlRCTmIa1hTkeDrbNXg9PXk+no2cbOnm5Ep2QyuS+IZiamNDdp4n2PUTbps+ZlEzMTU2Y1q9rs+cbwKH4VNo5OxDorpt50nrltEaqF/GpjO8TotdvU5GvrNdvU5ZSqlLp5WEIh+NSABgQ5Ket3919PckpLmmgvedyEmO6ticuOx8fZ3u8ne0xNzVhdJf2RCdnGISNTs5gQg9pltjIEH/O3LyNKIqEeLbBzd5Gir+bk6ae1ZJZWIKfiwPONlLX/pH2Xhy8loYx4rLv4uNkh7eTHeYmJozu7G9cv1udvh9n0u4giqKUX0nptHOyJdC18RfXuNt3JRv1Na5nGmpcz2RCtwBJI9jXUCM5g3aOdgQ28nJ8Ju0OPk522tH/ltKrVauprKmlRq2msroWN1vdAFdLaKbeLaZ7W1eszEwxVSjo6+vO4aSMFtMrVVVxISOXyT06AGBuYoK9pbnRNJeRMcbD7iGxD6ibVzwT+KXe+cnALmATklOgAaIo3gDKAWNPmNOAUdepxvEwBZgPjBQEwVJzKhSoEkVR68gQRTFdFMU1TbCnyeQWFuPhrKuMHk6ORl/WH4a8ohI89JwZ7g525NZ7OOUWl+CuN/3N3dGOvCJdmL/vPkbo2/9m94VrLB4jjaaHD+rJzdx7DPnrl4xf9T1/mTsZhZFORG5BER4uumzxcHYkr6DpNl5LyySnoJBhvYxPw/tPk5uXh4eHbiqlh7sbuXl5DcL948u1PDVnFpZWlg3OPYi84lI89Ka5uTvaklvPqZRbXIq7g2GYvHphmqSjfw8HW3LrdYJylfV0HGzI03TM7pWW46rpnLja21BQVmFwbUVVNceT0wnrGkhucRk+Lo5cSLtNUVklzjZWXM+9xx29OOcqy7R2m5oosLM0p6i8krzisnrxtCFXE4dVu0+wbPQjDZx0704aSkxSOh/uPMbOy8ksGNq7ifbZkqcJ08C+UkP7GqSnUpdv2QVKamvVtLG1aTE9awszylVVFJVVUlFVTVah0oh9zZumC3/YzbD3vmHnxSRc7Kzx0G9HHBopp/pl2cGOvGLDjr0xsguKmbR6A3O/3MqFNMln3ZL1IjYjh3GfrWf86g28PSlU27lvjboYFZdCsJcr5qamrW7j/5J9xvTq01L6LdWWBnm24VC89JJ7NTOH20Ul2viuioxh2diBFJVVGDjvm6sepucXIgALvv6VfZevcyFNN+Ld0m2pPnuvXMfJxrJF8q28qppvj15gUVg/g+OtVk7bukn1Qllq2JY62jWiV6/fpmyo9+u5eAZ1qj+WB/uvJPN4l/bkKsvxsNc9lzzsrbXlUKulLMPDvv5zQ2UY/2u3CPZwwdzUBB9ne9LuFpFdWEJNrZrDSRnkKI2Xr9yScjwc9PVtyNM42YyFMVVo9CtUUn6djGfRkMZnCWuv17fRzpq8krJGw5gqFNhZmOk0TsWzaLDhMhZ99l67xZjOujRuCT13exuefKQzw/+5jSF/34KthRkDAtoavV9zaXZwc+RCRi5F5ZVUVNdwLCWbO5p8bAm9zMJSnG0seGPXSSat28Vbu09RXlXdIL3/V1AI/90/f0Qe1iGxCZihcQZ0A87WO1/npPgFvVkM+giC0Au4IYpiwzdBeAxptoMxBgBpoiimAkeBMZrjnYFLD4j3oHpLNgIeEL4BGkehAYLQvKXAiEQDjQfF45Wxg4le+Txj+4Sw4ZiULCeSbtGprRsx7y7i19fm8/4PWyktb9ipEI3cvKkmqtVqVq3/lddnT2zaBf8BmpKHicnXycjMIix02O/TMHKsfhqKRkL91rJkVIemlJWm3f9oYhq9/DxxtLZERMTeyoJnhvTk6e928t2xyzhZW2Kq1woaLTsIjaZH3QyBzkbWMv90MpYu7dz459wxTOwdzMe7TzTdvt+50q/uXmWqKl7esA9fV0dsLMyMhmkOPTd7W4I82/D0dzt59vvdeDraolDUt69503Tt/LEceesZJvYNITXnntF7PFj//rja23D4zaf5dWk4rz8xmNc27qe0UtWi9aK7jwe7/jyHLYtnsO7IBVTVNZr7tZwmwI2ce6zee5J3Joe2uJ4xG/+X7DOmV5+W0m+ptnTB0D4UV6iY+Pdf2HDyKsFerpgoBI4mpuFsa03ndu5Ns+l31MMatciltNt8Ej6a7n6eXEjL5nSKbkS0JdvSOqpqajly7SZt7GwanGuOfPsi6gxzB/XExsJwVLbVyunUMOlejbTTBnpG09eQtQfPYKJQMK634ey22PQ7WJqZ0cHd2aht9Y17kP038gpZfeg874yT9khysLLgr2MHsHTbEeZ8vxsvR1tMGpn1YlS/CWEE4IujV5jbPwQbczMjIfSuN25kvTDG68QXx64wt1/jGlW1tRy5nsmoYL8W1SuuUBGdnMnBFydz9OVpVFTXsDMutUU1A9o48swjXXh640Ge3XiQIHcnbR+tJfRq1Wqu3Slgeu8gfl0wDiszU745FW9MSEbGKA81j14UxauCIPghORv26p8TBMEdCAROiKIoCoJQIwhCF1EU60roEkEQFgDtkRwP+vxNEIRPADegsR0MZyI5RND8ngP8Wj+QIAj/AgYizZroqzl8XBTFsfezTRCEZ4FnAf792vM8O2EUGw6dYluM5HPp4u9NToFuzVVOYRGuRpZm/FY2HDrF1iPSXg9dfTzI0VsGkltcgpu9rUF4D0c7cvVmROQWleDqYBgG4PHewTz/1XYWjxlIxNk4nhnRD0EQ8HV1op2rCzdv59It0I8NUcfYFi3pd2nvQ8493TKJnIIiXBtZmlGfskoVNzLvMPfdfwJwt1jJok+/4stlz/3ujS2bgw2bt7LlV8nH1bVzCDk5udpzObl5uLm6GoS/HHuV+GtJhI4ZT01tLQUFBcx5ZiHrv2l8JdGGzVvZ/OMGScPbnZwivZkDRaUN89DBcAQlt6hUOwJ1PzaeiuWboxeorKphRJcAcvTvUVyqnY6p0zEcGcotLsNV0zl0sbUmX1mGq70N+coy7XTNOvbG3mBM947a++QUlTJ50lAm9w3h66MXOXk9E18XRwOtnCJppLGmVk1JZRUO1ha4O9jUi2cZbnY2RCemcSTxFseSM1DV1FCmqmbaF1uprK4l/V4R43sFkVNUyujuHXj2u52o1WIT7NOlYwP7bA3tA7h86w4TL0tNStd2bmQXKFl7+Dxje3Rky9mEZtczvJcNlmamrH9OcuDN+3oHXnqjbM2Vpq9tPsjyxweSfOceCdl5vBMZQ02NmjJVNTn67UixkXLqaEeuflkuLsHV4f7l1NzUVDu6HZueQ3GFiqn/3ETf9m1brF7UEeDujLJcxYTPN2BpZtqidTGnqISXftpNaOf2vPzTHqBl634dZ1MzSc0tYMLnG1o0TVvbvjq9j2aMxEevXdl4KpatZ+ObXX//1Ru8pVnr39XbvUXaUltLcz6cJm1XJYoiYR//SDtnBz7de5JzqdlsOxePiUJArRZ5beN+Ppn1WLPVw9Sce5RUqnjqq+109fbA1syUa9n5PBLo3UT7fn/bBrDx9FW+P3aFqppa2jnbt0i5uZqRQ1TcDT7be4J7JeVU16r56fhlBnfya5168cNOALp6exi2pUUluNXLHw9HW4PZrrlFJbjqxWnH+QRirt3ku+enNnCM7LucxBjNkhkPe2uD2Qs5ynLc7KwNtextyFGW4uFgo3tuWEnLeXOKy3hp0yE+mjgEH2dd/3VYkA/DNHsqbLmQhEkjzhkPO2tyivX1yxrqa8J42NtQo9bpX83OJyrxFp8dukBJZRWCIGBhakL4nwwdMA1sLGnMRj0NVbVG4y5Riel8driehmYJ4/GUbEI8nGmjV4ZbQs/Fxoq2jrY420izbMM6+XIlK58nuga0qI2Te3Zgck9pCcXn0ZfwsLduMb2RwX6421vTva3Uhx4Z7Ms3pwyXncrI3I/m+MrGTuBTYCjgond8OtIyjDRNg2qPtGzjTc35z0VR/FQQhEnAT4IgBIiiWKk59yqSc+El4Eegt76gIAgmSMtBnhAE4Q0kx52LIAh2QILmHACiKL4gCEIb4MJvMUoUxa+BrwHUpyNFgPARjxI+4lEAjl5JZOPhU4zp14PY1AzsrKyM7hXxWwkf8Siz+kovfjEJqWw4fokxvYK5mn4HO0uLBs4GVwdbbCzMib11m26+nkSeTyB8kLS79a28Avw060mPxKfQ3l3629PJnjPX0+kT4M1dZRlpd/Lwdmsj6Y8cTPjIwZKNl+LZGHWMMY/2JjblFnbWlo3uFVEfO2srTq9bpf1/7rv/4LXwif9RZwRA+PSphE+fCsDR4yf4edNWHn9sJLFx8djZ2uLm2sYg/KxpU5g1bQoAWbdvs/Clpfd1RtRpzHCuAiAmMY0Np2IZ06MjVzNysLOyaNDRcbW3wcbCjNj0O3Tz8SDyUiLhjzY+xVAbt0e7Y2VuRkJWLoOC/Nhw8gpjunfgakYudpbmjeiYE5ueQzcfdyIvJhI+QNIZFuLPjouJLBjWhx0XEwnt3F57XUmFivM3s/l4xkgAurRzJ/1eEXFZuQR5tGHnpeuoamoYo1mnCjAs2I8dl5Lo4etBVHwq/QKkPU6GBfvz2qaDzB/YgzxlGel3i+nq7UYPXw+WPvYIAOduZvP9sSv8e/7j1NSqGfzhD3Rp60bkpSQqqqpxsbGisrqmcfsycujm7U7kpSTCH+2ms+9SEguG9mbHpSRCQxpOg+3p58maudIKtKOJabzz61FGdg2gp68n+2JvNLuePl3auXMzv5CsAiW1oprYjBy+XzDeIExzpOkn08O0ndJHA72ZNbg328/Gs/1cApEXEhnTI0gqp/ctP5pyeiGR8IH3n25bUFqOg7UlJgoFgzr58fXhc/zy4nRi0++0SL3IKijGw8EOUxMF2YVKyquq2bE0HCcbqxari8oKFc9/v5Mlox9lZNcO2uMtpadv45Bgf746fJ6NL0zjakbO/4R9+nq9/LwMzs16tDuzNNc3p/5j3TqwTLNBcExiWou0pcoKFZZmppibmrDtXAJ9/L2wtTTnm2cmgIm02/7Xh8/x47HLfDxzFLHpd5qtHr72xGCS7uTz8wvTOH09gze2RDG+dydiH1TXm6FtA5j1SDcu3sphQEcfXGytW6Tc/LxoqvbvL6LOYG1hxlNDerdevdc4XmOu3WTDicuM6dlJ12+r5wBxta/Xb7twjfCB0magxxPT+Cb6HD+9MB2reqPQarXIgdjr/PTidAC6eLmSfk9JVmEJbnbW7Iu/ySeThxpcMyzIhx1XUujh7U7UtTT6+Uv7VigrVDy/MYolI/rQy8fwKyD3SitwsbWiuELFL+cT+Xyq8VlKXdq2Ib1Ao29vzb6END6ZOLievjc7rqbQw9uNqGu36Ocv7dPx85NjtGG+OHoZa3OzBs4IyUZjGoMMNTp6s+NqKj3auRGVmE4/Pw9JY95onUbMFazNTbXOCIC9CWkGyzVaSi82O5/Y7HwqqmuwNDXhTNodOnu5tKgmwL2yClxsrLhdXMqh5HQ2zh/Tonoe9jak3SvG38WBM2l3CPgf3tRSIX9no9kRjE3LadKFglAqiqKtIAjtgMmiKP5DEIShwDJRFMcK0pc1loqieFoT3h84KIpioCAI7wCloih+qjkXCewVRfErQRB+AHaLorhNs0/EJWC5KIoHBEE4CixDcnwsFUVxlF58fgQOAT8DZ4AfRFH8t+acD9Iml376cWyqrXUOCX1EUeS99Ts4EZeMpYU5Hz49lS6aT2JOfOtzIt5bAsDfNu9hz5kr5BUpcXO0Z8rgvrw4cSRxNzNZvOYnlGXlmJuZ0cbBjt0f/lm6d/Fdrcb72w5xIjENS3NTPpg1mi4+npLGJz8Q8dp8AOIz7rBiwz5U1TUMCvHnjckjEASBl7/dQVpeAQpBwMvZnrenjZTWKhaXsGLDPvKVpYgiLJg0hicG9aU+oijy3vdbORGbiKWFGR8+N1vrUJi4fBURq5ZLNm7YwZ5TF8krLMbNyYEpwx7hxSljDO5V3yGxqPeUpiY/AE9v/I6OQwdi28YFZW4eu97+kFPfrX/whRrWlmU2OCaKIu+u+hvHT53GytKSD995S/vpzvHTw4ncvMEgfJ1Dou6znwejj/Dex59RUFiIvZ0dwUEd+PZLaauS2oMbtBrv7zjKieR0KQ+nhmk/Gzbx8w1ELAkHID5T+uSYqrqGQZ18eWP8UARB4FB8Ch9ExlBQWoG9lTmdvFxZ94w0ij7io+8orayiulaNnaU5ffy9iMvMxdLcjA+mDqeL5lNjE//+CxGvSCum4rNyWbHlkKQT5Msbms/FFZVVsGTDfu4UleDpaMfns0fjaC158yMuJHIiOZ3PwnUTmWKSbvHK+r3UqkUcrS34eHoYF9Ju07mtK6Eh/qiqa3h9y2ESb0ufqPx0ZhjezpIza+2RC0RcSMJEoWD52AEM1tsRHQwdEgCHEm7yz6iz3C0tp6KqBk9HOz6ZEaaz7x+biHh5hs6+rYd19tV9iq+sgiUbD+jsC38MR2tL8kvKmLZmC6WqKhSCgLW5GbuWhpN0J5+5X0VgZqJAEAQ8HWxZPm4QQzr5tYieraU541ZvIOOetEfL2B4d+WBKKGsOnmuxNF1z8BwKhQJ7KwvemxbGj8cuSeXUzJQPpo/UldPVPxOxdLaunG6KQlVTw6AgP96YqCmncSl8sOOoppxa0MmrDeuenUTU1RusOXAaU4UChULgxbD+DAtp32L1YufFRNYdvSDpCQLPj+jHiC4BLVoX1x4+x7ro8/jodb6+WTARZxurVrXxf8W+xvRcbA1H8FrKXum+RzT3bb629Er6HZZvPoiJQiDAzZn3pgzHQdPG1jkkzt7I4I0tBzFRKJq1HgJSuYk+DyKYmSgoU1VJGvr2tVDbVlFVTeiqH4l6fT62luYt9kysQ98h0Wr1QhD45rkpONta8f6vhzmRdAtLMzM+mDmKLprPtU/89Ccils3V6OWw4pf9Gj1/3pgUiiAIjPrgW6pra3DQfNWru6+ndjnIuZRMVu8+zqZXZoFSmqEbcz2TVfvPoBZFJvbsyMLBPVgTfZHOXm0I7eQrPTciYkjUfL7x0ynD8Ha2Z23MZdaduGowM+KbOY/hYmvFsm1HSMqRviq1aEgPxmhG8lHXUp+YG1msOnBO0u8RyMJB3Vlz5DKdvVwIDfJBVVPD6xHHScwpkPQnD8Hb0h+juQAAIABJREFUyXD2X51DosFnP9VS1zsmJYtVUedRq9VM7NGBhQO7seaoRqOjD6qaWl6PrNMw59OJRjQ0L891n6isqK4h9J/biHphEnb1Nl9sCb01MVfYfy0NE4WCYHdn3hv7KOZ6n/1sCc3ZP+6jqEKFmULBa2F9ecTfs0X1EnMK+OueU1TXqmnnaMsH4wbgYGWByZwV/3Nv7z85uP2+l+dWYm5x3h8uzR/aIVHv2FAkh8GLwEmgnagnIAjCJeB5YDSGDonewEYgGPgOjUNCc24ysEgUxeF6DokXgTP6G1cKgvAE8LwoiqMFQfAEPgf6AflIXwRZK4riZk0cIwH9bYPfr9MzhjGHREtS55BoLQS3ht94b2l+q0PiYTHmkGhJ6hwSrYqofnCY5sRI56RFER52y5s/AK2dh2atvAu2+r/6GS4jI9Ha9dDE5MFhmhMjXy1pcRStbGNrY2Tj1RZFWfTgMM1Naz/z5efF/wSyQ6L1+SM6JH53C1rfGaE5dhRpg0kw8nUMURR7af48W+/4RSBI8+/8eue2A9s1fw81FkZzbifS8hFEUbxD41/1OAo0bd2BjIyMjIyMjIyMjIyMjAx/3C9Z/Dfz/2DIUUZGRkZGRkZGRkZGRkZG5r8N2SEhIyMjIyMjIyMjIyMjIyPT6sgOCRkZGRkZGRkZGRkZGRkZmVanlXfhkZGRkZGRkZGRkZGRkZH54yGP5jc/cprKyMjIyMjIyMjIyMjIyMi0OrJDQkZGRkZGRkZGRkZGRkZGptWRl2zIyMjIyMjIyMjIyMjIyDwA+aufzY88Q0JGRkZGRkZGRkZGRkZGRqbVkWdINAHx7JHWFez1SKvKiaWFcPRAq2quLctsVb2FNt6tqveviA9bVQ8AoZX9iyatrCeqW1evtdMTQNHKTbJK1bp6/x9QtPLYyX+inLY21VX/6Ri0LEIrlxlRbF29/wSmrdyWtnYZNTMHR+fW1VSYtK7e/wdau+6ra1tXT0amicgOCZlWd0bIyMjIyMjIyMj8TlrbGSEjI6NF0dqOpP8H/D8YWpGRkZGRkZGRkZGRkZGRkflvQ3ZIyMjIyMjIyMjIyMjIyMjItDrykg0ZGRkZGRkZGRkZGRkZmQcgL9hofuQZEjIyMjIyMjIyMjIyMjIyMq2O7JCQkZGRkZGRkZGRkZGRkZFpdeQlGzIyMjIyMjIyMjIyMjIyD0BestH8yDMkZGRkZGRkZGRkZGRkZGRkWh3ZISEjIyMjIyMjIyMjIyMjI9PqyEs2fifHM/L56EQitaLIlOB2LOgVYHD+wu0CPjqZyPV7JXwa1p1RAZ4AZJdU8PL+S9SKIjVqkfCuvszo7GNUQxRFPty0l2NxN7AyN+PDJycS4uvVIFxC+m1WfP8rlVU1DO7agRUzxiAIAokZd1j58y5U1TWYmih4K3ws3fzbcS45jRf/tZG2Lk4AhDmbsairkTgEdEExahYoBMTLxxFP7jU879MRxaiZ4N4O9fa1kHhRe0oYPgWhQzfJjmO7EK+df2CaiqLIB598RszJU1haWrJq5V/pHNyp0fALX/4zWdnZ7N62CYB9Bw/xxdp1pKbdYuv67+naOeSBmvdjzrf/ouvYxyjJy+e9rv2bfJ0IfBAZw7HkdKzMTPlw2ghC2ro1CJeQlceKrYeorK5hcJAvK54YjCAIFJVX8ucN+8kuVNLWyZ7V4Y/hYG3JudQsXvxxD22d7QEI6xLAohF/Ii2/kKUb9mvvm1WgZPHI/swd1FMqQztjOJZ0SxOXkYS0MxaXXFZsOSjFpZMfK54YgiAI7L96g38dPMPNvAI2vziDLt7uDe1tYY3bhUrGfrqeYC9X7paWN2ua1hGXmcvMf23ls1mPMapbIACf7jlBTFIaarUac1NTyquqm9W+U9fTWb3vFNW1tZiZmLDs8YH07+ijS9PIGI4lpkl1f/p9NDdFSZrB/qwYr9GMvc6/ojSaL83UalbX1vLXLYe4lp1HrVrNEz068uyQXhy/nsFHe05QqxaZ0ieYBUN6GehU1dSyfNthErLzcbS2ZPWMMNo62VNUXskrGw8Ql53HxJ6dePOJQdpr/h51lp1XkimuUHHx7QW/WwPg65hLbL+QiIlCYMXYgQzsIKXTjydj2XYhEQHo6OHCB5OGYWFmiiiKLNt8kIMJaQAM7ODNl3PHPJReWn4hSzcd1F6fVahk8fC+zB3QXXvsu+NX+HT/aU6++STxWfl8tPsEtWo1U/qGsGCoEXu3HNLpzxqp0z96ke3nEzFRKFgxbiADO/pwp6iEv2w9TL6yjIKySgRBwM3emg+nPqAu1NRKdWHcIF1d2HhAVxdmjcLB2lIqc7uO69qtqcMJaevG8eR0Ptp1nJpaNYXlFYzv1Yk3xw+R8vjAaSIvJnG3tBwvJ3vJjpmjtG3U10cuSOkoCKx4YhADO/oCaO9ZK4qatOkNwOmUTD7dewq1KGJjbsYHU4fj28aR20WlrPj1CLeLSslVluFobcns/l1YMLhHwzTdfoSE23dxtLZg9bQRtHWyk8rppoPEZeczsWdH3hw7UHvNnqspfB1zWUpPO2s+nhKKk40l9Tl+I5OP9pyS4ty7U5O1T6VksfrgOaprajEzNWHZqH70b9+2wf0BqY7sPSXVkd6dWDCkZ0ONbdEaDUtWT9ez75eDmnoYxJvj9OyLTeHrY5cRADd7jX3WDe1rNRt/p8ZvycOJvYJYE32x2eoewIiP12NjYYZCIWCqULD1xakAfHHoHNvOJ2rTNKxze3bHXm+2trSiqpolv0SRWaBEoRAY1smPpaMM+yOiKPLh1iiOJaRKz4s5Ywnx8WyQ9gkZd1ixfpfUV+wcwIqpIxEEgS/2HGPbycs42VoD8MoTwxjSRXoOJmfn8s4v+yitUKFQKNiy/BnMTU34cMsBjiVo+qZzxxvXS7/Nip92UlldzeDOHVgxbRSCIE12//nIOTYePY+JiYIhXQJZNimMqppa3tm4m4T0OygEgb9MG8WfOvo1tPUhtJd+s4203HsAlJRXYmdtScQbzzWrRmP27ToXx3cHT2mvv56dy7YVzxHs7aHR3M+xeI3mvAmNa/4YKWl26cCKaY9Jdq3bRlruXUO73lzIqWuprN5xWFc3J4bSP8hfr8ykYGVmxodzxzVeZn7aKfUxOgdqy4xk43k2xmhs7NyBZZOGa6+7XVDMuPfW8sKYwSwYPqfBff/oyEs2mp8//AwJQRAmCoIgCoLQSe9YB0EQdguCkCoIwkVBEI4IgjBYc26+IAj5giBc0fv5TW+utWqR948n8NXYPuyaMYi9KXdIKSgxCONpa8mHoV15vINhBXe1tmDjpP5ETBvIpsmP8M3lm+SVVRrVORZ/g/S8e+z/4GVWznmClRt2GQ337s+7WDnnCfZ/8DLpefc4Hn8DgM+2R7Fo3FAi3l7Ei+ND+WxblPaa3oG+RLy9iIi3Fxl3RggCitGzUW/8HPWXbyJ07gdt6jlDiu+hjvwWMe6s4fEO3RA8fVF/9Q7qb99HeHQ0mBvvABnYe+IUtzIyiYrczntv/oV3Pvy40bBRh49gY21lcKxjQABrPvuEvr16NnLVb+P0DxtY89ik33xdgY0F6XeL2P/qHFZOCmVlxFGj4d6NOMLKScPY/+oc0u8WcTw5HYBvjl6kf2A79r82l/6B7fjmqM7R09vfi4hXZhLxykwWjfgTAP6uTtKxJeFse3kmlmamDO8iOciOJd2S4vLaPFZOHs7KiOjG4zJ5OPtfm2cQlw7uLvxzzlj6+BvvXLaGxse7jhHs5UpheUWLpGmtWs3qfacY0FFXDy7fusPlW7fZsSScZY8P4nahkncnD29W+xxtrPhy/jgil87mo+lhLN90wDBN8wvZv3w+K6cMZ+X2w8Y1t0ezcspw9i+fT3p+IceTbkmaHm3457yGmgdib1BVW0vksjlsfWUWW85dI+NeEe/vOs5X88ay6+UZ7L2aQkpegcF12y8kYm9pwYE/hzNvQDc+O3AGAHNTExaP+BOvPvZog7gN6+TL5oWTtWn8ezVS8grYdzWFXS/P4Ot5Y3lv53Fq1Wpyi0v5+XQcWxdNYefLM6hVi+yNS5HudTGJmOQMdr08gwtvP0NmofKh9fxdnYhYPI2IxdPY9sIUqZ6FtNfe705RKadTsvB0tJXs3XmMr558nF1LZrI39gYpufX0zydib2XBgVdnM29gdz7bd1rSzy1gX2wKu5bM5Osnx/Je5DFq1WpMFQpeGzOA1x4fSEhbV+wtzXlmSC9W7ogxXjZ2HJXqwrLZUnm8ngHo1YVX50h1IeYSAMeS06V6vGw2KycNY+WOGMmOyBi+enIcg4J8UAgCxeW659WwYH9mPtIVEDjw6hzJjv2n9Oy4wa4ls/j6qSd4T3M//XvuWjKLvVeua9Pm3R1H+WRGGBEvz+DxHh35KvoCAF/FXGJk5/YIwL/CR2GqEDRlqNAwTS8mSWm6ZAbzHunKZ1FndeV0eF9erfciV1Or5qO9p/jhqXHseHEKHT2c2XA2vkFaSuX3BF/NHc2uxVN/k7ajtSVfho8icvFUPpo0lOXbjhjNL0njJF/NHcOul6axN+4+GktnMu/Rrob1cHgfXn3sESP2neSHp8ayY/FUOrq7sOFMwn30W8PG36fR1DwMdHdi5c7jzVr36vhhwXgiXpqudUbUMXdAN227EHk5udnb0icH9WDPkplsf2Eql9LvcEzzbKnjWEIq6fkF7H/neVbOGsPKTfsb3APg3U37WDlzDPvfeZ70/AKOX0vV2RDaj4gVC4hYsUDrjKipVfP6Dzt5e8Zodr31HD8umYupiYJjCSlS33Tli6ycNZaVv+wxrvfLXlaGP87+lS9KfdMEqX0+m5xGdGwyO958jl1/fZ4nR0g2bzshtUWRby3km5dm88m2g6jVYj1bH0579TNTiHjjOSLeeI6wnsGE9Wg46NVS9o37U1et9sfzJ9DW2ZFgbw9JMz6F9LwC9r+7mJXh41i5sRHNjXtYOXss+99dTHpegc6uBVOIeHMhEW8uJKxXMGE9gwFwtLXmy0Uzifzr83w0bwLLf9ipsTFV0ntnESvDx7By075GbNzHylmPs/+dRZKepsycTb5F9NVkdrzxLLveWsiTYYb18uNtBxkUEmj0njIyxvjDOySAmcAJYAaAIAiWwB7ga1EUA0RR7A0sBtrrXbNZFMUeej/XfotgXF4RPg42eNtbY26iYHSgJ9G38gzCtLW3JsjFHoVg6EczN1FgbmICQHWtGrVo2NjqE30lifH9eyAIAt0DvCkpryS/yNDxkV9UQmmlih4BPgiCwPj+PTh8JQmQPHhllSoASssrcXO0a7qRbdtDYR4U5YO6FjHhLEKQ4UgGxfcgLwtEtcFhoY0XYnqydLy6CjE3EyGw6wMlD8ccY8JYaXZHj25dUZaUkJd/t0G4svJyvv95I88/85TB8YD2/rT38226jQ8g5fgpygsKHxywHndtLBjfO1jKN18PSipU5CvLDMLkK8soVVXRw9dTyrfewRxOuAlAdMJNJvSWHiYT9I43hTMpmfi4OGhHe6Kv3WR8r7q4eDYel0q9uPQK5nCC9NAJcHfG383pvpotqXEoPpV2zg6oamrp3NatRdJ0w8mrhHUJwMVW5+ASBFDV1FJdq+ZwfCoOVpa0sbNpVvtC2rrh5mALQKC7C6qaWqpqajTxTWV8H700raxqXNPPS9LsU1/TuYGmIECFqpqaWjWq6hrMTBSk31Pi4+yAt7M95qYmjO4WSHTiLYProhNvMaFXEAAjOwdwJjUbURSxNjejt58nFmYmDbS6+3jgam8DQFxW3u/WiE68xehugZibmtDO2R4fZwfisqT2tlatprK6hppa6bebnaT3w/FYOnm64NvGAXNTE8b16NgsenWcSc3Gx9mBtk66NvXjvSf582P9ERBIvH0XHxcHvJ0l/dHdA4lOTKunn8aEXlJneGQXff00RnfX03dxIC4zD1d7G0LauhKdmMak3sEEuDvjbGPVtLrQq5OuLlzT6U6od3x8r05SmfOR6tixpHR8XBxQVqgoKlfxSKA3WYVKgzw+fzMbE4WgsSOQMylZkh3XbjK6e4d6duQSl5krpY1LXdp0IPqaFAcBgdLKKgBKKlXa8gPSy6KPiwO2Fua429syumtAwzxNSmdCj46aPG3PmZt65dTXAwtTw3IqAqII5dXViKJIqapaW4b0icvK1+Snpvz+Bu0Qrza4aewIdHPS1PNaIxp5+LjY62k0Ukd66mvcNqyHDewTJfuqajT2VeFmb91Au/Vs/P0aTc3DjAIlrnbWzVr3msrDtHONtaVW5mb008w2MTc1IcTLldx69T366nXG9+sm1V3/tpRUVJJfXK+vWFwiPS/at5PahH7dOBx7/b72nEy8Sce2bnRqJ82wc7S1xkShIDo2mfH9u0t67dtRUq5qRE9Fj/bemr5pdw7HJgOw6dhFnhk1AHMzaZK2i6bspN7Jp3+Qv/aYnbUF8Rm3DW19SO06RFHkwKVrjOnbpYHdLWWfPnvOxxtoR19NYnz/bjrNRvNQX7Mbh2OTGtp18Rpj+kj3DvHx1Pb9A71cUdXUUFVdQ/TVZMb366opM+2k94tG9erKTFedjccv8syoR3U26rWbh64k066NI4GebRrYLSPTGH9oh4QgCLbAAOBpNA4JIBw4LYrizrpwoijGi6L4Q3Pp5pZV4qE3pdPDxrLRWQ7GuFNawYTNJwhdf4RnerbHzcj0UIC8QiUezg7a/92d7MktUhqEyS1S4q55+awLk6fpMC6fMYa/bYsi9LVP+du2A7wyKUwb7srNTCau/BfP/uMnbhSVNxS3c0Qs1vPsKwvB7v4vpnVoHRCm5mBli+DXCewbvhzVJzcvDw8P3XR9D3c3cvMadgb+8eVanpozC0urB8+6+E+gMjXBQ/OiCeDuYEuustQgTK6yFHeDMDbkaToZ90rLtR1xV3sbCsoqtOGuZOQw8e8befbbSG7k3GugvffKdcb0CNL+n1dcioejno6jLbnF9eJSXC8ujrbk1QtzP1pKo7yqmm+PXmBRWD/KVFXYWVrorm+mNM0tLuVQQirT+xt2Snr4evKngHYMeW8dkZcS6eXvRYC7c7Pap09UXArBXq6Ym0oP97ziMjz0HIjuDo1o6qe7gx15xYYd1fqM7NYBKwszhry7juHvf8uTA3tQXlWNh4OuM+Fhb9PgPrnKUm2ZNjVRYGdpTlF509u8XGXZ79bIKy6rV59syFWW4e5gy5MDezD8b+sZsupHbC3NGdDBG4AcZQmV1bVM/dc2nv1hNyaC8NB6+uy9msKYbrrRn+jENNzsbeik6YDll5Qb3MPD3taIfpm23vwW/bziMkxNJKdHN2/3JtYFW/I0YRrUhVKpLuQp69VjB1tS8wpwd7Dlkz0nWDbmUeytLChXVTewo87xbmCH0kgZVpZpyoLuuIeDrbaevjs5lIU/7GLYh9+z83KydinHi6F9OJqcwcX0HBau38cbjz+Kh4MNeSVG0lRTzkxNFNhZmFNUrqIxzEwU/HXcQCZ8sY0hn/xMal4hk3sHNQjXoPz+Tu2ohDSCPdtgbtrQgZerrF9mdO2XoYZembG4fz00MzHhr08MYsIXWxnycZ19xpdBto6NzaNhaKNhHt7KL6a7t24JU3PVPUGAZ77bxZQ1W9lyznCWycbT8Uz452b+HnUWZz3HdnO3pcoKFUeTbtE/oJ3B8bziEjwc9fqBjvbk1hu8yi0qwV2/Pjrakaf3Arox5gITPljHG+t3UVwutQnpeQUIwIIvfmHyqm/4NuqkpFdUgodBv9OuET39ONmRpwlzK+8eF1MymP7xN8xd/QNxt7IBCGrnTvTVZGpq1WTdLeRaxh1yCgz7vA+rXcfFlAxc7Gzwc3OhPi1lnz77L17j8T66foekqdffbywPnQzzuVG73BvaFXUpkeB27pibmRqxsQllxslez8YCLqZkMv2T75i7+ifibkmOo3JVFd8ePMWiMYMb6MvI3I8/tEMCmADsF0XxOlAgCEIvoDNw6QHXTa+3ZMOqfgBBEJ4VBOGCIAgX1p2KMzhnfE5D01cUedpasWP6QPbPGkJkcjZ3G3nYikaU6tZuacMYiUxdkE1Hz7F82mNEf7KM16eN5q0fdwCSx/TQqqVEvP0C4aH9WXwssYn2ND6bw4CbCYg34lA8tQLF5OcQs1JA3XC0pMHdjdpiGI/E5OtkZGYRFjqsaXH5D2AslQSanm+NEdLWjUPL5xHxyizCB3Rn8U+GU/qqamo5cu2mdg+ERuNST6cp5ex+tJTGF1FnmDuoJzYW5sbTqxnS9KNdx/nz6AGYKAybwvS7RdzMKyD6jafpH+hNQlYeF27qOhXNmYY3cu6xeu9J3pkcqrufEWMaaBoL8wCtuIxcFILA0b8+Q9SKp/jh5BXulhhxSDawz0iQ31JGjFeKJmkYTVuguEJFdGIaB5fN5ujyuVRUVbPzijTiV6NWY6IQ2PrCFKb2DWHbhcSH1qujqqaWI0m3GNVVWhZVUVXNV0cvsXhEXz17H1wYjeef8MD6VKNW848D5/jL2AHYWpprrzO8t5F7PKB0NDZZLzWvkMGd/PDUdEyb9Az6jcfr+OnEFdbOH8eRFU8ysXcwH+8+AUh7BPypvRePdwtg7ZzRvL79CNJM7odrA6pr1Ww6f43tiyYT89psgjycWXfsSoNwTXnmP0j7Rm4Bq6PO8s74QQ0DYrwNedh6WF1by6ZzCZJ9r88myMOFdTEN7Wvs3s1v48Nr1Kd+Hno52pKUY7hMojnq3oaFk9i+eBpfPfk4v5yO50Ka9AI2o18XDrwazq8vTsPeyoKrmbn3M+93t6U1tWqWbT7I7Ee64u1sb3DO+POins3GdDW/ZwzqxYGVi/j1L8/g6mDLJ9sPSZpqNZduZvLJ/PH8vHQeh64kcTrp5gPbyMbjJP2urVWjLK9k02tPs2xSGEu/2Y4oikx6tCcejvZMXbWOj7YeoEd7b0xMDJ/ND6tdR/0ZCs2p0Zh9dcSmZWFpbkYHvb1/GmsvH6hZL9Se83FG7bpxO4/VEYd4Z9aYxvWa1LZLYbQ2vvokyyYNZ+m3ko1f7D7G3NB+2GieTf+rCILwX/3zR+SPvqnlTODvmr83af43QBCECKADcF0UxboNATaLovji/W4siuLXwNcAtX9/xaBaethYkqM3IyKnrBI3Gwt+K242lgQ42XLxToF208uN8els3XUZgK7+bckpKNaGzy1U4uZguOzCw8meXL0ptLmFSlw1XtvI01dYMUNqfB7r05m//hQJgK3ezIIhXTvynlqksLIaJ0sz3Y1LChEcnHXNsr0TlBQ12TbxxG7EE7sBECY+i1iQazTchs1b2fKr5Cjp2jmEnBxduJzcPNxcXQ3CX469Svy1JELHjKemtpaCggLmPLOQ9d+sbXLcWoIsB2vuOEhTYe1U1eTojWbnFpdqp7PW4VFvxDu3uAxXzZQ3F1tr8pVluNrbkK8sw9lG8pfZ6jXwQzr58d6OoxSWVeCkOX88+RYhbd2Iikthq2YddFdvd3KK9HSKSnGz140ASXGxM4xLUanBVGljbDwV2+Iah+NT+fnkFV7fdAABiLyYSIC7E+GPdm+2NE3IyuPPv0jrbQvLKjmWlI6JicCOC0lcy85j9pdb6ertjo25ObEZd+jTvm2z2QeQU1TCSz/t5qMZIzmRnM7W9ZKTqau3Bzl6oxWSvfU0He3I1U/34hJcHe6vuedyEoM6+WFmYoKLnTU9fTxRVqrI0RvFy1GWNUxbe1tyiqWRvZpaNSWVVThYNb3N83Cw+d0a7g629eqTdO3plCzaOtlr8zKsc3uupOfwRI+OuNhYa0doR4T48+rmQw+tV8fx6xmEeLWhjWYDuMwCJdmFSiau2SqFV5by+YGzBOgt08lRljaYKu/hYEtOUT19awvcHWwa6F9Jz+Hf0RdAhKLySvq29yJMs09M0+qCrjw2qAuaEV13TXz0rwlwc+ank7FsOHWVX07HUVBWod1zZenoR7V5m6l5TunssJTsaFCGpTjk6I3M5miOF5RWkHznLt19pDXVo7t34NnvpImO2y8m83JYHzafS6SHjztVNbXculuEm139NJXKmTZNVfcvp0l3pCWBPpoXvMe6BBh1SHjY1yu/xWW/STunuJSXfjnIR5OHabWMa+jSP0dZ1mD5SF2YptsnzaLzcXHQ2NeedceNOyRaz8bfr2HcRsM8HNbJlzXRuj2CHrbu1eWBm179Gd7Zn6uZufTx96KNNv4i43p0YPk23R5DzdmWvr0jBt82jtpNdDfGXGDrSU1f0deLHL3Zs7lFSu1yQK2uo+Eof25RCa6a/mQbvWfL1AE9ef7fW7TX9A30xcnWmo0xF8i+V8TyH3YwpEsHcgz6nSUNlgR71JvRq6/n4WRPWE9peVg3v7YoBIHC0nKc7WxYPnWU9ppZf/sOXzdnNh49z9aTl3S2PoQ2SO3UoStJbP3LAu2x5tS4n30A+y4kMKZPZyOaev39IqVxzULDfHbVm9VWU6vm0OUktq541uC6nEIlL63dTGj3IF5et12j51nPRiNlpt7MEOn9wlZ7LqxHUAMbr97KJupyIp9FHKakQtp8+dOIoBeTk5O/QEbmPvxhZ0gIguAChALfCIJwC3gVmA4kANptjUVRnAjMBx68ZqCJdHFzIL2ojCxlOVW1aval3GGYX8Ndzo2RU1pBpWZtZbGqmss5hfjrNSizuug2mxzeoxORZ64giiKxqZnYWVniWq+BcnW0w8bCnNjUTERRJPLMFUI1m/S4Odhx/votAM4k3cRXs6Y8v7hE62m9mpaFWhRxtKjnm8pOA2d3cGwDChOEzv0QrxvvyDRAEMBK8xB2a4fg3g5SjW+kFT59KpGbNxC5eQMjhg1hx+69iKLIlatx2Nna4uZquAZt1rQpnDi4l+i9kWz8/mv8fH3+484IgHbF5fTNuEvfjLu0Ka2HryhSAAAgAElEQVQk8mKilG/pOdhZmjd4OXW1t5HyLT1HyreLiYR2lrY5GRbiz46L0qyVHXrH80vKdPmWmYNaLeKot1u6tFyjI7Me7U7EknAiloQzvHMAkZfq4nIHOyuLRuJiRmz6HSkulxIJDWnP/WgNjQPL53P1o8Vc/WgxYV0DcXe0ZdYj3Zo1TQ8un8eh5fM5tHw+o7oG8NaEoYzoHMDjPToQ6O7C1pdmMiTYn7M3M/F3dWpW+5QVKp7/fidLRj9KLz8vKU2XziZi6WwpTS/opel97dVoXkgktHNAI2oSno52nLkhtRXlqmpiM3MZEuRL+r0isgqUVNXUsu9qCsM6+RlcNyzYjx2XpLWjUQmp9Gvf9jd54bu0dfvdGsM6+bHvagpVNbVkFShJv1dE13ZueDraEpuZS0WVtHb8TGoW7TVOgNHdArmZV0hWgZLTKVkoBB5arw5puUYH7f8dPVw4seJJDr06m0Ovzsbd3paIl6dxp6hUZ29sCsOC/Y3oS+t/o+JT6Reg0Q/2Z1+snv7dYv48+hF+XTyNIE8XQrxcKSpXSWUj4wF1IUNTFy4lERoi6Q8L8dfq7tA7HhriT+SlJIP7DgryxdLMlJ+em8TeZbNxtLYkrEuA1hlRd79azcZzUfEp9AuQ1hsPC/FnX+wNvXQspqu3O13auZN+r1gvbW4wLMQfeysLSiqruJUv7dtz+kYmAa5Sfno62lJUpiL9XjEnb2RRWV3D0eQMhnUy3DNoWCdfdmhmyUQl3KSf//3Lqbu9Dal5hdolXKdSsmjv6tggXJe2rlKcCzVxjkttsrayQsXz6/ezJKwvvXw9Go2LVEf00iUuxbjGZT2N9l5NsK9IZ19qNu1djS+9bB0bf7/G/W3U5WGusoya2tpmq3tdvd0or6qmTCXtbVJeVc2pG5l00EyJ19+/JaugBESavS39x8GzlKpU/GXMAO2xWUP6aDehHN69I5Fnr0p1Ny1bekbVG7xyddD0FdOkPSsiz14ltJu0V4f+3gGHYpPp4CUNBA0IaU/ybamNnTawF4GebrwbPpbh3YOIPBMr6d3MalzP0oLYm1mavmksod2l5VCh3YM4myzt63Er9x7VtbU42VpTUVVNuSadTyWmYqJQEOjpyqyhfbWbQT6sNsDppJv4e7gYLFloTo3G7ANQqzV7V/TpImlqNqKU+vtXdZqWTdG8Smg33RIsya42BnYpyyt5/ouNLJkwnNenjtKVmW5BRJ6N05SZLOn9otEyo9E7G0doN42N3YI4m3xLZ2ONZOPPf57HofcXc+j9xcwZ9ieeHTUA2Rkh0xQEo1NL/wAIgvAc0EsUxef0jsUAbwLfA0vr9pHQfGHjXVEUhwqCMB/o86AZEvrUnyEBEJOex6qTiahFkYmd2rGwdyBrzl2ns6sDof7uxOUV8dL+SyhVNZibKGhjbcGuGYM4lXmXT04lIQjSdKhZXX2YFlLvKxe9pJ2yRVHk/Y17OJFwA0tzMz6YP5EuftLmRhNXfknE24sAiL+VzYrvI1BVVzOoSwfemPk4giBw8UY6H23aS61ajbmZKX8NH0dnXy82RJ9l09FzmJoosDAz4/UOTvR0NTKiEdhV+qynoEC8cgLxxG6EoRMQb9+C61fAyw/FtBfB0gZqqqG0GPXat8DEFMWz70j3UFWg3vMT5GYa3Npk2ecN5ERR5N1Vf+P4qdNYWVry4TtvaT/dOX56OJGbNxiEz7p9m4UvLdV+9vNg9BHe+/gzCgoLsbezIzioA99+uQaAhTbe98lh4zy98Ts6Dh2IbRsXlLl57Hr7Q059t/6B14mA5zxpxNtS8+m6LppNoSb+/RciXpEm8sRn5bJiyyFU1TUMCvLlDc0nG4vKKliyYT93ikrwdLTj89mjcbS2ZMOpWDadjsfURMDC1JTXxw6ip580s6aiqprQj34g6vX52OmNtoiiyPs7jmriYsoHU8O0n4Cc+PkGIpaES3HJlD5ZqaquYVAnX94YPxRBEDgUn8IHkTEUlFZgb2VOJy9X1j0z0dDeVtBYc+A0p29kcK+0vFnTVJ8VWw4ypJM/o7oFUqtW8+6OGC6mZSOK0mZi5arqZrVv7eFzrIs+j08b3QvQN89OwsXOWkrTiCNSmpqZ8sH0kTrN1T8TsXS2TnNTFKqaGgYF+fHGRI1mXAof7Diq0bSgk1cb1j07iTJVFW9sPkhq7j1EESb26sjTg3oSk5zOqj0npfasVycWDuvNmkPn6NzWldBgf1TVNby+7TCJt+/iaGXJpzPCtNOGR/ztZ0pVVVTX1mJvacG6J8cS6ObMp/tPsyf2Bnkl0ihjHz9P4rPzf5fG2iMXibiUhIlCYPmYAQwOkl5i1hw6x/64VEwUAsFerrw3cSjmpiYoK1Q8890uknPvISAwpW8wb44b9NB6FVXVhH6ynqhl4QZ7mugz4m8/s/XFKVzNzGPV7hOSvX06sXBYH9Yc1OiHaPS3HCbxtvT5v09nhuGt2TNo7ZELRFxIwkShYPlYSf/irTvM+SqCDu5O5JeUU6aqxtXOhr/PfkxXF/6xiYiXZ+jqwtbDurpQ9wncsgqWbDygqwvhj+Go+ezn+5HHOHFdU+Y0dSwm6Rardh9HrRYJ8pQ2L3SwsqRzOzcu3brN7svJ5JWUYyIIuNhZs/65SXhrRuTXRl8g4sI1yY5xg7TpqH/PiX1CWBjaB5A2sV1z8CwKQcDeyoL3pwzH28WBlOxc3o48Rp6yjLySchytLJnZL4SFQ3ux5vAFOnu1ITTYT0rT7UdIvHMPRysLPp02XFdOP9tIqapaV07njSHQzYlN567x8+l4TE0UeDna8uGkoQ3aBoCY6xms2nsatVrNxF5BTdZee/QS645d0c5SAPhm3hiDTXTr5nrHJGewau8pKV16azQOndeUWY3GtiMk3rkraUwfobPv0w2G9s1/XM++OEwVGvsmD8OxkRH5FrXxITV+Sx6O7RbImuiLzVb3MguKeWm9NJOuRq3m8R4dWDhMKrOvbz5E0p27CEBbJzse6xLAl0cuNltbamthTugn62nv6oiZZkP08P5dmBKm++ypKIq8v+UAJ66lSs/H2WPpovlE/MQP1xGxQpoJEJ9+mxXrd0t9xZAA3tB8pvL1HyJJys5FQKCtiwPvzBytfTndeS6OdQdOIQgCgzWfrxRFkfc37dPpzX1Cp/fBV9rPaMZrPlGpqq5hUOdA3pgufaKyqqaWN9fvJCkzBzNTE16dFEb/Tv5k3ytiwT83oFAIuDnY8d6ccbR1MXQQPqw2wIofI+nm35YZg/sYrQctZR/Aueu3WB1xmE2vPy2JaeIkae7lRIJGc954neb7a4l4c6Ge5g5UVRrNGaN1dv2wg27t2xnYtXbvMdbtP4GP3ibX3yyehbOtNe9v3q+zcc64xsvMT7ukMtM5UFtmJBt3kZSVq7FxuHZD0jq+2B2DtYU5C1Z9+cdcQ3AffnX2+K9+eZ5UkPOHS/M/skPiKLBKFMX9esdeAoKBfwCrgf9j77zjoyjeP/7eu8uld1LoSSAEQi8qqJTQewcVRLEgFrDB14IioBQb6BesiAhiUEAMoUOoQYo0aUmAkEAIkEZ6T+5uf3/smbvLpal46vc3b1954c0+O5+ZLTO7M88z2xJIA/KB92VZ3mMckPgAMF9l5llZlo9QDVUNSPyldOpWu82d5MCu2m3uMFUNSPyV/JEBiT/DpxELbaoHgPSvdXiqG5W+5vKX83ccT5WN+5DS6heLE/xBbH0O/9fve4Dysr+7BH8tto75/Zc+9/0uNDaOSDbY+Jh63DGn37qjsl6oVPAnsfW9X4f13O406j6T/nUvx7UhBiTuPP/aNSRkWe5VRdpSs5+Dq9lvFbDqLymUQCAQCAQCgUAgEAgEgjrxrx2QEAgEAoFAIBAIBAKBwFb8P/BLtDnimAoEAoFAIBAIBAKBQCCwOWJAQiAQCAQCgUAgEAgEAoHNESEbAoFAIBAIBAKBQCAQ1IKt1yL9/4DwkBAIBAKBQCAQCAQCgUBgc8SAhEAgEAgEAoFAIBAIBAKbI0I2BAKBQCAQCAQCgUAgqAUJEbNxpxEeEgKBQCAQCAQCgUAgEAhsjiTL8t9dhn88hgvRNj1I8o14W8pB2i3b6gF4eNtWTzbYVO65UbNsqgcw0dfNpnotmthWz8HZzqZ6KgetTfUANG4ONtXTPjTepnroym2rp7ftfa9o2rqOetvq/Q3PDFLbe2wrWFpsUznZYOtz+DfcFzqd7TVtSMnHS22qF3/K9s9tLXsF2VRP4+liUz20tu/zUattq6exvWO8ZkH4/5w7wWbv+v/ol+fhmSn/umMuPCQEAoFAIBAIBAKBQCAQ2ByxhoRAIBAIBAKBQCAQCAS18K9zP/gXIDwkBAKBQCAQCAQCgUAgENgcMSAhEAgEAoFAIBAIBAKBwOaIAQmBQCAQCAQCgUAgEAhqQfqH/9WpDpI0UJKkS5IkXZEk6bUqtn8kSdIZ499lSZJyzLbpzbZtrqNkjYg1JAQCgUAgEAgEAoFAIPgfR5IkNfAp0A+4AZyQJGmzLMuxv9nIsvySmf10oKNZFsWyLHe4k2USHhICgUAgEAgEAoFAIBD873M3cEWW5URZlsuAH4ARNdg/BHz/VxZIDEgIBAKBQCAQCAQCgUBQCyrpn/0nSdJTkiSdNPt7qlIVGgLJZr9vGNOskCSpKRAI7DNLdjDme0ySpJF34piKkI0/iCzLLFz5A9Gnz+Og1bJw+mO0DmpqZfdxeASRB4+SV1jEqfBPKtIXfbOO4xcuAlBcWkZWbj7H1yy11vhpL9GxiTja2bFw4iBCG/tbacQkpzIrfDsl5Tp6hAYxa3QfJEli6bZD7Dt/BUkl4e3ixMKJg/B1dyW3qIQ31+4g+XYO9nZq5vfvQrCvBwCHEm6xKOoEellmbPvmTLm3jYVWmU7Pa1uOEJOaiYejPUtGdqehhws3cwoYunwLAV5uALRvWI+5g+6hsLSch9fsrtg/Lb+IYW0CeX3cAFMdNx8k+uI1HO00LBzfn9BGvtZ1vJHGrPVRSh1bBjBreE8kSWLnuXg+jTpGYnoW66Y9SJvGfgDkFBbz4prtnL+RxsjOLVFJEtGXkowafQltWJVGOrM27FE0Qpoya3gPJEkip6iEGeE7uZmdR0NPN5ZMHIi7kwPHE24wbfU2Ghrr3K9NM57tezdXM7I50aReRb7FdmoCM/NpnFNkpWnOpK8/pe3QgeSnZ/BO26412tYVz7BeBM2fh6RWkxr+PTeWfWplU2/4UJrOfBlZlimMjePSM9MACHhzFl79egNwfcl/uR25pVY9bbf7cZ3xOqjUFEf+SNHqFRbbHYaOxPX5megz0gEoXh9OceRGADyWfoldm/aUnzlNzsvP1ql+mrvvxWHaq6BWUb4tgtK1K6u269kX53mLKZj6EPpLikeaKigYxxmzkZxcQDZQ8PQEKCurVVPduRsOz8wAlYrynZGUrV9dteb9vXF88z0Kpz+CIT4Odce7sX98GmjsQFdO6Yql6M+erFVP1f5u7B57AVQq9Hu3oosMtyxPvxFoBowCgwG5pJjyLz9AvnkN9f390Ax/qMJOatKMfY+N5r1fL2OQYZSXA09X0irT6XltfRQxNzPwcHJgyUMDKq7v5ftPsvFkHGpJYtbw7tzfQmnvDl1KYtGWQ0qbcVcoU3p1BmDW+j2cuHoTFwd7ABaO60MrYzuzIvoMy/aeQAb8XJ2JmjnBuhw/7iPm1m2lHA/0paGnKzlFJbz4fRTnb6YzqmMIbw67v2KfbWevsDz6VyTA182J98b2xtOoDXAoPplFO44q5ewUwpTuHaw1fzpATMptpW0b14eGnq4cSbjBkqgTlOv12KnVzOx/N12DlH572/krLI8+gyRJ+Lo68d7oMDwd1IrelRss2nUcvUFmbMdgptzfzlpv0yFiUoxt6dieNPRwrdh+K7eAYZ9t4rmeHXjcrB3WGwyMW7EVP1cnPn+or6l+CTdZtPukUr8O1bTdmw8Tk5qFh6OWJaN6mNruLzdbtt2DLduf59bvJzknn81PDTfTu7N9BcCO2Gt8efgCelmmZ/OGzOzdieqQZZmFqzcSfSZG6YOfeZjWgY2t7D5et4XI6ONKH7xqcUV6xMFjfBAeiZ+XOwAT+vdgXO97a9YL30z0uYs4aO1Y+OR4Wgc0stb7cSeRR06RV1jMqS/nV6SfuJTIorWbuZycyuJnJjDgrnZW+9aovXYL0ecv4ajVsvCJsYQ2tX52/HjjLjYf+ZXcomJOfT6v7nl/v82Ytx0LHx9TZd4x124ya+VGSsrL6dE2hFkPDUGSJC4mpzDv20iKSstoWM+D96eMx8XRgXOJycz5dpNRBJ4b2ou+HVspeut2EH0hXtGbPJLQJg2s9ZJuMWtVhNIftwlm1gODkCSJl5ev52paJgD5xSW4OjoQMfsZynQ65n63hZikW6hUEq+PH8TdIYHWdf0T2nHJKcwL30ppuQ6NSsXsCUNoF2h9DVSHuuM9aJ98EVRqdFFbKP9pTdV23cJweHUBxTMex5Bwsc75A7j26knDuW8hqdVkfr+O9M8+t7LxGDoE/5deRJZlSuLiSJr+AnYNGxK4/AsktRo0Gm6vWk3md+FVKFiiansXmknTlP7pwHb0Wy0nUdW9h6HuOwIMBigppnzlEuRbSUj1/NC+two5RXknMlyJRbfq41r1pFadUI1+ElRqDEd3I+/ZaLk9bASqbv1Ab0AuyMWwdilkZyhlHT4ZqXUXkCTkS2cwbPyqdr2QDqhGPAYqFYZf9iLv32S5vcdQVPf0UfQK8zCs/xSybyt1f38dpFwHQM65jeGb92rVA5BatEc1fDJIKgwn9iEfiLTc3n0Iqrt6g0GvaG74AnJumwzsHVHPWIIccxxD5De16wW3QzVkklLHkweQoy2f9aT7BqHqEmbS++krRc+jHuoJL4JKpZyPY7uRj++tUx0Ffy2yLC8HltdgUtVSE3I1tg8CP8qyrDdLayLL8i1JkoKAfZIknZdlOeEPFhcQAxJ/mOjTF0hKSWfnJws4G5/I28vDWffuLCu7Xne1Y8LgMAZNe9Mi/fXHHqj4/++27yXuanLlXYmOTSQpI5udb07hXFIK8zZEse7lSVZ2b6/fzbwHBtA+oAFTv/yRQ3FX6REaxON97ub5Id0BWHPwFJ/tPMLcBwawPOooLRv6suzJUSSmZfJO+Da+mdgXvcHA/F3HWfFQH/zcnHjgmx2EBTeiuY9HhdbGs1dwc9Cy65mRbI+5xuL9v7JklKLR2MOFiCeHWJTN2d7OIm3syu30CzE9NEZfvEbS7Rx2vvIo566nMi9iH+umP2hdx4j9zBvTh/ZN/Jm6MpJDl5Lo0TKAYD9vlk4aytyfLBtBrZ2G6QO6Ep+aycGLV9HrDez8zyTOXU9jXsQB1k0bX7XG6DCjxuYKjRUHTtG1eSOmhHXhq/0nWXHgFDMG3wdA58AGfP7YMIt8An08ueu60jnIwJEgX3wKSq30KnN0VTgHPlnO5G+/rNW2TqhUNHt3PhfGT6D0Vgoddm0ja9duii7HV5g4BAbS+PlpnB02Cl1uLnb1vAHw7Nsbl3ZtON17ACp7Le0ifiR77370BQU16rm+8iY5055En5aG1+p1lEbvR3/Vso0qidpB/gcLrHYvWvMNODjgNMr63FSn5/DCLApnTkXOSMPli7WUHz6AISnR0s7RCfvRE9DFnjOlqdU4vbGQooVvYEi4jOTmDjpd3TSfe4WiWdOQb6fhtHQ1umPRGK5ftdK0G/EA+rjzFUlyXg7Fc15GzrqNqmkzHBcspfDhIdSIpMLuiZcpm/8ScmYG9ou+Qn/yMPLNaxUm+p+j0EcpDyyqzvdh9+g0yhbOVNJ/jlKyaRyEesZ8Fp6+xBdBnvjZqZgYn03ftCya+3lV5LXxRCxujvbs+s8ktp+9zOKdR1gyYSBX0rLYcTaeLS9NID2vkCdWbGL7zIcBmB95kBVPjMDP3YUHPllPWKvAijxnDr6PAW2bm+qjKye7sJile06w8vGhtGvky5hPN3IlPZvmvp6mcpy6qJTj5YfYfu4Ki3cdY8mD/dBq1Ezv04X49GyupGWZstUbWLT9MFueH4+nsyMf7jxG+LEYpvVSXmj1BgPztx1mxSOD8XNz5oHlmwgLaWqpefoSbo5adr3wANvPJ7A46jhLxvfBw8mBzyb0x9fNmfi0LKas2cGBmRMVzR1H2fLcODydHfhw9y+EH49hWo92it6OX1jxcH+lLV2xlbCQJpZt6a/xit70MWy/kMjiPadYMrZXxfb3dp2ge3PrF8M1v8TRrJ47BaXlpmvAYGD+zuOsmNBX0VtZRdt9xth2PzuS7TFXWbzvNEtG9wCgsacLEVOGVnkJRl28jpPW8lHhr+grcopK+WDfaX58bDBezg68vuUIR6+m0C2wfpXlij4TS1JqOjs/eouzV67x9tfrWDd/ppVdr05tmNC/B4Neettq26BuHZn9WN3am+hzF0lKu83O917hbMJ13v42gnVvTbfW69CKCX3vZdCr71ukN/DyYNGTD7Byx8E66Vlon79EUlomOxfN5FxiMvO+3cS62c9Z2YV1aMXEPt0Y+PriKnKpLu/LSr0WvqzkvWYz6958xsru7e8imffISNo3a8zUj1dz6MJlerQN4a1VEfxn/CDuCglk46GTrNx5iOdH9SO4oR8bZj+LRq0mIyePUXM/oVe7FhyOTSApPZOd7zzPuas3mBe+lXWvV568g7fXbmXew8NpH9SIqcu+41DMFXq0CWbJU6bz9d6Gnbg6OgDw46FTAETOeY7MvAKmLvuO9a8/hUplcgSOvhD/p7QXb4zi2aG96NEmmIPnL7P4pyhWz3isbgdapUI7dSYlc15AzkzH4YOv0R0/hHzjmqWdgxN2Q8ehv3ShbvlW0mg0/20SJjxMeUoqLbZuJjcqitL4KxUm2oAAfJ97lvjRY9Dn5qHxVvp8XXo68aPGIJeVoXJyouWe3eRGRaFLS69eT1KhefQFyt/7D3JWBtq3P8dw+gjyraQKE/2Rvej3KS+4qo73opn4DOUfKOvnyem3KHvT+vjXpKcaNxX9p29BTibqmYvRXzgOqWbPzzcS0X/wMpSXId0/CNWIyRhWfQCBLZGCWqF/93kA1C++i9S8DfKVGo6zpEI16kn0y9+G3CzUL7yLPvYkpN0w2dy8iv7jVxW9bv1RDZmE4buPlG3lZeg/+k/d6wcgSahGPo5+xQLIzUQ9bZGimX7TTPMa+mOvK5pd+6EaPBHD2v9WbFb1H4+cGFtF5tXoDZuM/ptFkJeF+pl30MedhgwzvVtJ6D97U9G7uw+qAQ9hWLcM8rPRfzkX9DrQ2qN+/j30cacgP6daOcE/hhuA+Qh+I+BWNbYPAhYdjizLt4z/JkqSdABlfYk/NSDxjwzZMFu984IkSRskSXIy2zZKkiRZkqSWxt8OkiRdlCSprZnNK5IkfVFD/o9KkhRv/Hv0j5Rx34kzjOjZFUmS6NCiGXmFRaRnW9+EHVo0w9fTo4ocTGz7+QSD77/bWuPCFUbc1RpJkmgf0ID84hIyci1fCDNyCygoKaNDYEMkSWLEXa3Ze1554XQxmx0sLitHkpQBsYTUTLoaZzeD/Ly5lVvA7YJizt/KpImnK409XdGq1QwKDWBf/A0LvX2XbzCybRAA/Vs14di1VGS5ukE1S65l5ZFVWELnxibvhH2xiYzo1EqpY9P65BeXkpFXaFnHvEKljk3rK3Xs1Iq9Mcp138zPi0Czl4rfcNLa0TmwIfZ2Gm5m5TGi828a/tVrlJppdG7F3hjlxXZfTCIjO7cCYKRZel3IdtLiUK7HQaev1fbKoSMUZWXXOe/acO3UgZKr1yhJuo5cXk7Gpki8Bva3sPF/eAK3vlmNLjcXgPLbysyTU4sW5B49Bno9hqJiCmPj8Ozdq0Y9u9Zt0SdfR3/zBujKKYnagX3P3nUub9mJY8iFhbUbGlG3bIPhZjJyyk3Q6SjftxO7+6zL6PDEc5T+sArKTINCmi7d0CfGY0i4DICcl6vM4NSCKqQ1hpRk5FRFU3cwCk23nlZ29o88TdmGNcjlJo8LQ8Jl5CxloMqQlICk1YKdXc16zVshp95ETk8BvQ79kb2o77rf0qjY5HkjOThAFfej+v6+nFm3hsZaDY3s1dipJAZ42LMv1vJa3hebyMhOLQHo36Y5x67cQJZl9sUmMqh9MFqNmkZebjTxdud8chrnk9No4u1OY293tBo1g9oHW+VZmRXRZ/B3d6ZLQH20GjXDOgSzL+6aZTnirjGyYwulHK2DOJZ4C1mWlfs6oD72GrWFvYyMLENRmQ5ZlikoLcPXraLb4PzNDJp4udHYy00pZ5tm7LuYZKl58RojOxg1QwM5dvUmsiwTWr8evm7OADT39aRUp6dMp0dGOdRF5eUmTVcno95ty7a0dSD7Ll231Lt0nZHtmhv1Ajh2NaWiLd1zMYlGni4WL/gAqXmFHIy/wRjjsamo361MmniZt91N2XfZcpB7X3wyI9s1U/RaNa1T211YVs7qX2KZel9bi/S/oq9IzsknwMsNL2flBbNbgD9Rl6wH6ivyP3WeEd3vVvrg4EDyiopJz861susQHIivp3uN9awL+36NZcR9nRS95k0VvZw8a73mTfH1cLNKb+jjRUjj+qikuq6Bbq4dx4h7Oyp9WLMm5BeVkFGFdvtmTfCpQrvGvM/UnndGTh4FxaV0aN5E6R/v7cjeX+MAuJp6my4tAgC4t3Vzdp+KAcDRXotGrdynpeW6ium4fWcvMqJrB0UvqLHxuSbfUi83X9Fr1ljR69qBvWfiLGxkWWbXqRgG36VcmwkpGXRtqVxv3m4uuDo6cCHJ8vn6z2pLEhQWK/1IQXEpvu6u1BVVcCiGlBvIabdAp0P/8x4093S3stNOnEJ5xHdQXru3XmWcOnSg9FoSZdeTkcvLyd68Bff+ln2+9wT4ep0AACAASURBVIQHub36W/S5yjnWZSp9vlxejmz0EJS0WsXvuxakZi2R024iZxj7p2P7UHWu5GVUYuYZal91/1RnmgYrWplpoNdhOH0Iqe09FiZy/PmKYydfu4TkYfRWlWWlv9VolD+1Grm2F+cmzZEzUyErXdE7cxip9V2WegkxJr2keCR37z9eP4DGzZEz04yaegxnjyCFVtJMNNO8XkmzYSC4eiDHn6NONGqGnJWmeJHo9RjOHUNq1dlS72qsSS/5CpK7cRJDr1cGIwDUdsoN8v8E6R/+Xx04AQRLkhQoSZIWZdDB6msZkiSFAJ7AUbM0T0mS7I3/Xw+4D6jjCFj1/CMHJDCu3inLchugDCw8ix8CfkY5eMiyXAK8CHwmKTQEpgKvV5WxJElewBzgHpRFPeZIkmT9RlsLaVnZ+NczzSz6e3uSnvn7RwVvpmdyI+02Xdu0tNqWnpOPv9nDhZ+7K2mVOs+03Hz8zNx8/TxcSc8x2Xy8NZrecz5n68lYpg9WXmRCGviy55zyInYuKYVbuYWk5ReRll+Ev9lDvL+rE+n5lmEG5jYalQpXeztyjB30zdwCRn+9jUfW7ObkdetR9e0x1xgY2rRiYAQgPbcAfw8Xs/K7kFZp0CUttwA/d0ub9NwaZuorUVRWjr/5/u4upOVV0sirpOHuTLpx0CKzoAgf4wuJj5szWYXFFXZnrqcy6uO1PPV1JPGpmVbaaa6O+OUXW6XbAnv/+pTeSqn4XXYrFXt/y9lGx2aBOAYF0W5LBO23b8YzrBcAhTGxePYOQ+XogMbLE/f7umHfwNq11RyVjx+GtNSK34a0VNQ+1qEx9r3747U2Avd3P0LlZx2CVFckH1/kDDO9jHQkHz/LMjVvicrHH93RaMv0xk1BlnF6/3Nclv+A9sHJddJUeftgyEgzad5OQ/L2sbRp1gLJxw/98Z+rzUdzf2/0CZehvLxaGwC8fJAzTfeSnJmB5FXPykw9YBT2S39QZp+++a/19m69ST1+GH+tqcn3s1NVXOO/kZZXiL+xPdGoVbg6aMkpKiHdLB1+u4cKFXuzh3J/dxeLPP+76xgjP/6ed7ccosw4KJeQkY1Wo+bRFZsZ+9lGbmbnV10O4/2oUatwtVfKUR12ajVvDe/OyE820PO970hIz2ZMZ1Obap6fUk5n0vMraeYX4W+8z02alp5Nu2Ov0qq+N1qNGju1ireG3sfIzzbS88NwEjJyGNMpxJSXu7NJz8256rbUaKNRGY91cSlFZeV8ffgCz/a0XsD63V3Hmdm3s9W7Qlp+Ef6ulfWKrW2qa7tzChi9YiuPrNnFyeum63vZwTNMvicURztNtXnBnekrmni6cjUzj5s5BegMBvZeTiY1r/oByrSsHPy9TV23v5cH6VnWAxI1sfv4WUa8sogXPvqalMyaB4PTsnPx9zINEPl7elQ5APJXkF5J28/LnbRs6wGJP5Z3Hv5epgEbP0830ioNSKTl5OHnaW7jTrpRP7ihH/uML+y7Tlwg1ewcnE1MZtjs/zJizjLmTByGRq1Wnmu8zJ5rPNys6pKWnYefp5mNp5vFcw3AqfgkvF1dCPBTXsZCGvmz7+xFdHo9N25nE3s9hdRK+f5Z7dfGD+KDjbvp/dpiPti4ixdH9aWuSF4+yLdN95bSllfqOwJbINXzRX/ySJ3zNcfO34/yW6ZBmPKUFOz8LftEh6Ag7IMCaf7TjwRHRuDayzSgble/PiG7d9D6+FHSP/+iZu8IQPKsh5xl1j9l3Uby9LGyU/cdgfbD77B78Cl0a0yhy5KPP9p3vkT7xkdILdpa7Wel5+FtGZqQc7vGAQBV134YYhXPGa5dQr58HvU7q1DPX40c96ulp0NVeu5elfQyTS/jVend0xvDxV9NCRot6hfeQz19odVARs2aZs+SuZlI7tW/oqjuCsNw6YxxZwn1kEkYtn1XJy0Ayc0Lcs308rJq1uvSC8Pls6YEdy/U0xehfmUphuitwjviX4IsyzpgGrALiAPWy7IcI0nS25IkDTczfQj4QbacTWgFnJQk6SywH3jX/Oscf5R/6oCEOYeA5gCSJLmgjMQ8gXFAAkCW5Z1ACvAI8BEwV5bl6p4uBgBRsixnGW2igIG/t1BVDfJKf2B0cPvh4wzo1gm12vpUVDWOXFmjtnK8OLQH++Y9w9AuoYRHnwZgSr97yC0qYdT7qwiPPkUrf0/UKlU1wUOV9Kq0kPBxcWTvc6P56YkhvNq3M69E/kxBqeUI//bYJIaEBtShjlSysbb6Xce6qmNUuV5VHseasw1t6Mue1x4l4sUJTLyvPdO/3Wax3QBkujjgm1/9i9RfSpXlt6yopNHgGBTI+VHjuPj0cwQv+QC1mxs5B6PJ3ruP9lsjafnFp+SfPI2sr8XLo6oDVum4lh7az+3hfcmaMIqy48dwn7Pwd1WpkmAVemaCkoTjtJkUf16F+7JajaZtR4oXvE7B9MnYde+NupO1l5K1ZO2a9lNfpvSr6mNhVU2DsH98OiVL61D3Okb56XdFUPr8g+jCv0Az5hHLLJqHQlkJhsyaHy6hmvvgd6b/xksDu7FtxkTWTxtPbnEJKw4oD4YGg0x2YQmfPzKIrx4dzL6LV8kvsXzxr0vbZ065Xs8Px2PY+OwYDr76MCH+3nx18EyN+VWmqtl7c8n49CyWRB1n7rDuRk0DP5yIY+PTozk4cyIhfl58dehM3fWqSJOATw6c4ZGuoThrLb1nDlxOxsvZgdYNrAekqtSr3I5W0w76uDiyd9oYfnpyKK/27cIrm5S2Oy41i+tZ+fRt2aRuen+yr3B3tOetgXfz8qZDTFqzmwbuLqhV1T+i1Ha+aqNXp7bsXTqXyPdfp1ubEF7/rOp4fpNeFfWx0Yzg770fflfeVR3H39E/zn9sNN/v+4Wxb39KYUkpdmbeS+2DGrPlnRdY/+YzfLXzEKVGbyLrvOpy7Viy7cR5Bt9tWrdk9H0d8fd0Y9zC5Sxav4MOzRpbXT9/VvuHgyd4bfxA9r07g1fHDWT2t5FVWFdDbf2xJKF94nnKvllW9zytNGrpnwDUauwDA7ky/kGSpk2n8fvvonZTBmDKU1K41H8Qsd174jl2DJp61m3N79YD9HsiKZv5MOXrlqMZoYT6yTlZlL74EGWzp1Ie/hnaZ98AByerfSsJ1kkPQOrSC6lJc+R9PykJ9eqDfyP0bz2OfvZjSC3aQbPWd06vU3ekRs0s1nvQL3ga/X9fRR/+sbIOhbdflfvWrlmNZcf7Fc2DysS21LW/MjiRaz059nvkqq1j+/uQGgQhH9pqSszNQr/sdfRLXkbVqTs4/z4PLcHfhyzL22VZbiHLcjNZlhcY096SZXmzmc1cWZZfq7TfEVmW28qy3N7479d3ojz/6DUkJEnSAIOAncakkcBOWZYvS5KUJUlSJ1mWTxu3vQgcB+JlWa7pyaJOK4saVyR9CuDzt2bw1LjhhO/Yz497lJnWNs0DSb1timFOzczGx+v3u4XuOHyC2U+aFnQL37GfDTv2ANC2iT+pZjMVabn5+Lq5WOzv7+FKmtnMQVpOPj7uljYAQzq34pkvNzJ98P24ONizcOJgQOmg+835jEYezpSU60jNM81ypeYX4evqaKnn6kRqnjKTqDMYyC8tx91RiyRJaI0PIq3re9PY04VrWfm0qa+MXl9My0YvG2hd35u1Jy+x4bwSc9+2sR+pOSZvhbScAus6urtaeE2k5RRUeCxUx9ojZ9nwywVyi0tw1mpJNd8/t6DCBdukYemZkZZbiI9xxtHbxYmMvEJ83JzJyCvEy1k5Ji4O2gr7ni0DeGfTAbILi/E0bs90tselpBytvvZQgL+C0pQU7BuYPCK0DfwpTU21sCm7lULeqdPIOh2l15MpTkjAMSiQgjNnSf54GckfKw9HIZ9/QnFipXUSKmFIT7XweFD5+aO/bfkSLOeaZs+KN23AZfrLf7h+ckYako+Zno8vsrmekzOqwOa4fKwsrCl51cNpwX8peuMF5Ix0dGdPIucqo/m6Yz+jDm6F/vTxmut4Ox07My8MVT2/ijAMABydUDVthtP7SsSY5OmN49zFFM+dgSE+DqmeL46z36fkwzlKqEltZGYgeZu8TCRvH+Ts29Wa64/sxW7KDMz9LjT39UF/eC9+dmpSy0zXYlq5oYr7wJnUnHz83V3Q6Q3kl5Th7uSAnzG9Yl+zeyjVzGsr1Sz9t3tUq1EzqnMrvjmkzBw19HQlKTMPJ60dTlo7/N1c0Fd6APJ3cyY1t8BUDuMLa3VcTFEewJp4K23wwDZBFYMD5vmZylmIr2ulurs5k2r0pKismZpbwPM/RLFodC+aGGdYLxo9on77PbB1EF/9rMwc+bs6kZprmt1PzSusCOeo0DPaVLSlJYreuZsZ7I67xuI9J8kvKUOSJOw1atLyi9h/KZno+BuU6vQUlpbzSkQ07w+/T8krv5KeS6W2262ubbcr1zLzuZBym5jULPp+8hN6g0xmYQmPrtnN6of7VfQDFXp3qK8IC25EWLCySOD6X+NRV3IFCd8dzY/7lNnjNkFNSDXzakjNysHnd4RmeJqd/3F97mXx99Yvl+F7jvDjwV8UvcDGpGaZZv9Ss3N+d3jE72Ht3qNsiD4BQNvARhbaaVm5+HrUPVzAKu99x0x5BzSy8GpIy86zytvf0420bHOb3Iq6B9X3YYVxHYVrqbeJPn/JSu+Xi4kkpKQz8u3PuatFU1KzzJ5rcqrTM7PJzsPHzEan17Pn1zg2vDG1Ik2jVvPa+EEVvye8t4Kmvl6s3f8LG34+baxrgz+lHXn0DLMeUDQGdm7NW2usPJyrRc7MQKpn6jskbx/rvqNJEA7zlYWnJQ8v7N94j9IFr9Z5YcvylFTszDwZ7erXp7ySl0N5SipFv/4KOh1lyTcoTUhEGxhA8VmTi78uLZ2Sy/E4330Xudt3VF+nrAwkL7P+yaseck71/ZPh2H7sJr9oFCmHAqWnkq/FI6ffQqrfCPnq5er1cm6bQjAAPOoh52VZ2Ukt2qPqPw790lkVa0NJ7boiX7sMZcoEkSHuFFJAiBJyUZ1ebmYlPW/kPOu5Tim4Lao+Y9B//pYphAHgN9usdOSEGKSGgUo4Rg0ommZeH+7VaDZvi6r3aPRfzK3QlJq2QApsCV37KeExag2q0hIMO6v/WqOcm2XpZeLmhZxn7eUgNWuNqtcI9CvmW9bxN/JzkNNuIAW0RI6p+Tnqf4H/P8EptuOf6iHhKEnSGeAkcB34bfTlIZRvpWL8t2IJeeMCG/sA6yWFLanTnKMsy8tlWe4iy3KXp8Yp3isTB4URsXgOEYvn0OfuDkQePIYsy5y5nICrk2Ota0VU5urNVHILiugQ0qwibeKgMCJemUzEK5Pp0zaYyBMxyLLM2Wu3cHWwtxps8HF3wdley9lrSox15IkYerdR4pKvpZsa6v0XrhBkXGgur6ikwn36x6Pn6NLYFxd7LW0aeJOUnc+NnALK9Hp2xF6reDj8jbDgRmw6r8SI7467zj1N/ZAkiazCEvTGGPzk7HySsvJpZBaKsT32GoON3hETuoQQ8dJEIl6aSJ/WzYg8HafUMSkFV0d7q8EGHzdnnO3tOJukxFhHno6jd2hQjcd2wr3tiXhpItP7d6ORlxuRp37TSMXVQVuNhpazSUqcc+SpOHq3VjTCQgPZdEpxSd1klp6RX1gx43IuORWDQcbDyaEiz/S/MVwDIP/XszgEBWLfpDGSnR0+I0eQtSvKwiZzxy487lNiPjVenjgGBVGSlAQqFRrj9ewU2grn0JZkH6h5Mbby2AuomzRF1aAhaOxw6DeI0uj9FjYqb1Pnbt8jDN3Vuq/HURn9pRjUjZog+TcEjQa73gMpP2JWxsIC8kf0Iv/BweQ/OBh97DmK3ngB/aVYyo8fRh3Uwthpq9F06Gy9GGYVGC7FomrQBMmvAWg0aHr2Q3fMLBykqJDCB/pR+OgICh8dgf7ihYrBCJxdcHz7I0q/+RR9bN3iOw0JF5HqN0LyqQ9qDep7+6A/aRkKIvmb7lFVp27IKWZuqJKEumsv9If30NpJw/UyHTdL9ZQbZHbllBIWarkKfVhoIJtOKw+/uy9c4Z5mjZAkibDQQHacjadMp+dGVh5Jmbm0bexHm0Z+JGXmciMrjzKdnh1n4yvy/G2dFlmW2Rt7lWCja/VD97QmPb+Qa7dzyCsuIT49m0FtmlmWo2VTNv2qPJjujknknqAGNc4I+7k5k5CeUxFOdSThJkE+JrfTNg18SMrK40a2sZwXEgirNPMfFtKUTWeMmrFXuSdQ0cwrLuWZ8F281PduOjUxDYD5uTqRkJFtqVlPuWfaNKxn1MtX2tKYq4S1aFxJrzGbzl0x6l3jnkBl/ZrvHhvMnhfGseeFcUy6J5Sn7m/HxLtb8XKfzux/aTx7XhjH4jE9uSewPu+P6mGsnzdJWfncyDHqxSZZ6wU3ZtM5Ze2d3XFJ3BPgX03bnUcjTxce7BzCwRfGsmfaaL57ZAAB3q6sntTfpPcX9BWZhcrLQm5xKd+fuszY9s0t8pzYvwcR775GxLuv0adLOyIPHVf64PiruDo5/K61IszDLfadOk9QQ+vwsYl97yXinZeIeOcl+nRqTeTh04relSRcHR2rXCviTjGhTzci5j1PxLzn6dMxlMgjvyp9WMJ1XJ0c/tRgyITeXYmYO52IudPp07FVpbztrfL28XDD2cGeswnXlf7xyK/07qCsq5RpDH80GAx8sXU/43sqnmY3MrLQGb3qerYLwdnBgbWvPkGfDq2IPHZG0UtMxtXRAZ9KazH4uLvi7KDlbGKyonfsDL3bm0KwjsYlEuhfD3+z811cVkaR0SvzSGwCapWK5g18mRB2DxGznyFi9jN/WtvXw5UTl68BcOziVZr6Vu++XxlDfByq+o2QfOuDRoP6/r7ozMP6igopemQwxU+NofipMRgux/yuwQiAorNnsQ8IQNu4EZKdHZ7Dh5EXZdnn5+7ejUu3bgCoPT2xDwqkLOk6dv7+SMZ1x9Tubjh36UxpYs19opx4Ecm/oTIxoNag7tobw+mjFjaSn2m+T9Whq7L+EoCrO0jKK4jkUx/Jr5GyVlJNXI9H8mkAXn7Ky3an7sjnf7G0aRSE6sFn0X81HwrMQqqyM5Cat674IoTUrA2kVb9GDQDJV5Dq1QcvX0Wvw33IMScsbRoEohozFf0370KBWfiPozOojXO+Tq5IAS2RawkRAeBGApK3P3j6gFqNqv29yHGVvsbVIADV6CfRr3ofCk2ahh+WoV/0HPr3pmPY9h3y6egaByMAuJloqdeuK/LFU5Y29ZuiGvEE+u8WW+jh5qV8NQzAwQmpaQvk27WcQ4GgGv6pHhLFsixbBNBKkuQN9AbaSJIkA2pAliTpFbPYFoPxryZuAL3MfjcCDvzeAvbs1Jbo0+cZ8NwbONhrWfjc5Ipto2bMI2LxHAA++PZHth36heLSMnpN+Q9j+3Zn2gPKAMe2n48z+L67qn3Q7hEaRHRsIgPf+QoHrYYFE0yj/6PeX0XEK4rmW+P7MSt8B6XlOrqHBtLD+LL+0ZZorqZnoZIkGni5MWe88jCZmJbJa99tQ61S0czfm3f6dQGUON83+t/FlB/2YjDIjGrfjGAfD5YdPEvr+l70btGYMR2a8+rmwwz4fBMeDvZ8OFJZl+JkcjrLos+iUUmoJIk5g+7Bw2xGc2dcEl+MD7OuY8sAoi9eY+B7q5U6jutnquNH4US8NFGp46jezFofpdSxZVN6tAwAYM+FKyyIPEhWQTHPfBNJywY+fPXkKAD6LlpJQUkZZTo9EtBn0SpcHe1ZMK6PSePj74l48SGjRi9mrd+jaIQ0pUeIsvDnlF6deSl8JxtPxFLfw5WPHlbOw+7zV/jh6AU0agl7jYbFEwZWnEu9BFnO9oSk1z3G+Im1K2nR635c6nmzKDmOLXMWcmRlzW7ENaLXk/D6bNr8EI6kVpH2/TqKLl2m6SszyT97lqxdUWTvP4BHrx50jt6HbDBw9e356LJzkOztaR+puDrqCgq49OzzygJGtejlv78Az6VfgVpFyeYI9IlXcJ46DV1cDKXR+3F6cBL2PcKQdTrkvFzy5pm+TOO5fA2agEAkRyfqbd1H3vzZlB07XKNe8X8X4fzB58onOHdswnAtAfvHnkV/KQbdkRoGUAryKd2wBpcv1gIyumOH0B07VPsxNegp+ex9nBYsBZWa8t2bMSQlop00FX18HPpj0dXuqh0+HlWDxmgnPIl2wpMAFM+ahpxbQ+y6QU/5yo/QvrFY+aza/m3IN66hGf8EhoSLGE4dRjNwNKq2XUCvQy7Ip+xT0xdMVK3aI2dmIKenoJEkXmvoyjOJORiQGeHlSLCfN8t2/0LrRr70Dg1kTJdQXl0fxYAP1uDhaM+HDymf5w3282ZAu2CGLQlHrVLx5oieFe7QbwzvwZSVkUqb0SW0YuDhlR92k1VYjAy0rF+POaN6AdDC35vB7ZozYtkGZBnubd6QPqGBLNtzgtYNfejdKoAxnVvy6o/7GbDke6UcD5hitft+GE5BaTnlej17467x1eQhNPf15NnenXlkxWY0KhUNPFxYOMbU3mjUKt4YfC9T1uxQytkxhGBfL5btO0nrBj70btmUMZ1CePWnAwz47zpFc6yyIOva4zFcz8rj84On+fygMtO6YtJgfN2cebZXJx5ZuRWNWkUDdxcWjlLisTUqFW8M6sqU8CgMssyoDs0J9vVk2f5fad3Am94hTRjTMZhXIw4xYNlGRW+M9eKodUWjUvHGgLuZ8v1vbXdzY9t9htb1vU1td+TPDPhsEx4OWj40fvHiZHIayw6eRaNSVdl2V6v3F/QVi6JOcDFNmZ179v62BHhX/9Lds2Nros/EMuDFt3Gwt2Ph1Icrto167V0i3lW8TD8I38S2I6coLiun13OzGRvWjWljB/PdzoPsO3UejVqFu4szi56eWGOde7ZvSfS5iwx45T2lz39inElv9kdEvPOSorduG9uOnVH0XlrA2B53MW1Uf84nJjN92bfkFRax/0wcyyKi2LpwRo2av9GjXQjR5y4x8LUPcdDaseDxsSbtOUuJmKd8OeDD9TvY9ssZSsrKCZuxiDHd72LayJrXOejRLoTo85cZ+PoSY96jTXnPXUbEXOVLIm9NGs6srzcq/WPbYHq0VRZW3f7LOdbuPwZAv06tGX2/siDe6fgkvtoRjUatXFezJwzB08WZHm2CFb03/6voPWr6hP2odz4nYrbyhY+3Jgxl1upNlJaV071NMD3aBFfY7Th5oWIxy9/IyitkytI1qCQJXw833jWrR0Vd/6T2vEnDWbRuB3qDAa1Gw7yHh1tpVItBT9lXS3CY8xGo1ej2bEVOvordQ09iuHIR/Ynq1xyqM3o9N2a/RdB33yKp1WStW0/J5Xj8Z7xE0bnz5EXtIf/AQVx7dKfl3ihkg55bCxahz8nBsfv9BM1+Q5mekyDjy68ouWjt7WJZJwO6b5dh95/3QKVGH70D+eY1NKMnY7h6GcOvR1D3G4mqdWelfyrMp3y58ulLVUg7NGMeA4MeDAbKV30Ehfm16hl+/BL1s3OVT1Qe2wOpyagGT0C+fgX5wnFUIyaD1hH1Y68CIGdnYPhqAfKZI0gt2qF+bRkgI8edRr5woiY1RS9iBeopb1Z8gpO0G6gGPICcnIAcexLV0Elg74B6knIvV3ze07cR6rFPKeEPkoRhf0Sta1ZUaEauRP3ELKWOJw4omv3GId9IRI47hWrww6B1QP3wSybN1R/Unnd1eltWoZ78qlLH0wch/SaqPmOQb15Fvnga1cAJSh0fesGk990SJJ8GqAZPNNXx5221D/IIBNUg1fULCbZEkqQCWZZdKqVNBTrJsjzVLO0g8KYsy4eMv1cBW2VZ/rGGvL2AU8BvHzg/DXSWZdna78uI4UK0TQ+SfCO+dqM7SVp1X3r5C/H4kysR/15k24ZNPDfK+hOwfzUTfW0bu9eiiW31HJxr/iLFnUZlFpJjKzRuDrUb3UG0D9XxE6t3Cl0ti3jeaf6OcCm9retY+xd87ih/wzND5ZX0/3JKbevZJhtsfQ7/hvuiLp9V/hdT8vFSm+rFn7L9c1vLXjV7pt5pNJ7W4cd/KVrb9/mo1bXb3Ek0tp+H1iwI/5+LcNhZr8E/7+XZjIG3b/3rjvk/1UOiKh4C3q2UthGYgLLwZZ2QZTlLkqR3UD55AvB2TYMRAoFAIBAIBAKBQCAQ/D/6wqnN+EcOSFT2jjCm9aoibWml35PrmP9KYOUfLJ5AIBAIBAKBQCAQCASCP8k/dVFLgUAgEAgEAoFAIBAIBP/D/CM9JO4EkiS1BSqvCFgqy7KNg1EFAoFAIBAIBAKBQPBvR0Rs3Hn+ZwckZFk+D3So1VAgEAgEAoFAIBAIBAKBzREhGwKBQCAQCAQCgUAgEAhszv+sh4RAIBAIBAKBQCAQCAR3CpUI2rjjCA8JgUAgEAgEAoFAIBAIBDZHDEgIBAKBQCAQCAQCgUAgsDkiZKMOSPaONtWTJRuPExkMttUDkG2saeNjOtHXzaZ6AOHpeTbVe0yyrctaQGNXm+o52FRNQW+ntq1gaalt9Qx62+rpbawHoCu3rZ6tj6ks21YPQGvbu1Eusm1bavNzaPgbzqHexveFjbHzdLapXlBLb5vqAcQfumpTvYAQ29bRzse2zxgAKjvbvoZJDnY21ftfRQRs3HmEh4RAIBAIBAKBQCAQCAQCmyMGJAQCgUAgEAgEAoFAIBDYHBGyIRAIBAKBQCAQCAQCQS3YOGL5/wXCQ0IgEAgEAoFAIBAIBAKBzREDEgKBQCAQCAQCgUAgEAhsjgjZEAgEAoFAIBAIBAKBoBZExMadR3hICAQCgUAgEAgEAoFAILA5YkBCIBAIBAKBQCAQCAQCgc0RIRt3AFmWWbB8DdEnz+Bgb8+iF5+idfNAC5viklJefHcp11PTUatUj2BSHQAAIABJREFUhN3dkRmTH6w134Ub9xAdm4Cj1o6FE4cQ2tjfyi7meiqzwrdRUl5Oj9BmzBrTF8lsCdiVe3/hw8j9HF74PJ4uTuQXl/Dqt1tIyc5DZ5B5rFMzRrdrBsChxFss2nMKvUFmbPtmTOnW2kKrTKfnta1HiUnNwsPRniUj7qOhhwsAl9KzmbvzOAVlOlQSrH90IPYaNWV6PQt2n+T49XRUksTz3dtyMuMC0ZeScLTTsHB8X0Ib+lrX60Y6szbsoaRcR4+Qpswa3gNJksgpKmFG+E5uZufR0NONJRMH4u7kwNcHT7P110sA6A0GEtOz+fmtJ/FwcmDNz7+y4ZcYZGTG3d2GR7p3VI7v5oNEX7xmLEd/QhtVVY40Zq2PUsrRMoBZw3siSRI7z8XzadQxEtOzWDftQdo09qvxfHqG9SJo/jwktZrU8O+5sexTK5t6w4fSdObLyLJMYWwcl56ZBkDAm7Pw6tcbgOtL/svtyC01atWFSV9/StuhA8lPz+Cdtl3/dH4A7mG9aPrOXCS1mvTw70n55DMrG6/hQ2k08yVkWaYoJo6EZ6cDoG3YgKDFH6BtUB+QuTjxUcqSb9Sop+12P64zXgeVmuLIHylavcJiu8PQkbg+PxN9RjoAxevDKY7cCIDH0i+xa9Oe8jOnyXn52TrXUXP3vThMexXUKsq3RVC6dmXVdj374jxvMQVTH0J/KRYAVVAwjjNmIzm5gGyg4OkJUFZWo566U1fsp7wEKhXlUZsp/3FN1Xb3huH4+iKKXpqM4cpFcHXD4bVFqINbUb53G2VfLq5W41B8Mot2HEUvy4ztFMKU7h0stpfp9Lz20wFiUm4r9/24PjT0dOVIwg2WRJ2gXK/HTq1mZv+76RrUEIBHv9lKRn4R9nZqAFZMGoy3k1bRu3KDRTuPKe1MpxZMub+9td6maGJu3cbDyZ4lY8No6OHKuZsZzNly2Ggl81zPjvRtFcDV27m8/OP+iv1vZOczPawTj9zVUtFLuMmiXceV+nUIZsp9ba31Nv9MTEqmUr/RPWno4cLNnAKGfrGJAG83ANo39GHu4G4AfLz/NJvPJZBbUsapVydaH9M73Jbq9AYeDo+q2D8tv5hhrQN4vXcHo14Ki/b+qtSxXRBTuray1tv2CzFp2Xg4alky/F4aujuzJeYaK09cqrC7nJ7Dj4/2p4mHCw9/v89Mr4hhoU15vXdHRe9qCov2njHqBTLlnir0th836Q3rRkN3Z8r1Bt7adYLYtBz0BgPDWwfwVNdWpOQV8fr2X7hdWIIkSYxvH8Skzi2sjutvyLLMwpU/EH36PA5aLQunP0broKZWdh+HRxB58Ch5hUWcCv+kIn3RN+s4fuEiAMWlZWTl5nN8zVJrje+3EX3+ktIHPz6G0KYNrTRirt1k1sqNSh/cNoRZDw1BkiQuJqcw79tIikrLaFjPg/enjMfF0UE5x8mpzP12EwUlpco5fmMqWo2GhT9sJ/p8vKL32ChCmzaw1ku6xaxvfqKkTEePtsHMenAwkiQRdz2Fed9tobRch0atYvbEobQLbATA8UtXWfTDDnR6PZ4uTnw783Glfut2EH3BqDd5JKFNqtFbFaH0gW2CmfXAICRJ4uXl67malglAfnEJro4ORMx+hi2/nGPl7sMV+1++mcaPrz1Bq8b+iuaG3UTHXMHRzo6FjwwjtEl9a83rKcz6drOi2bo5s8b1r3iu+W7/CdYePIFaraJn62Bmju7DuWs3mbN2+28njueG9KBvh5Y203vGxUAfbyeLPKU2XdBMeBZUKvTROzBsX2exXdVrKKo+w8FggJJidKs/glvXkQJDUE9+qcJOH7kG+fRh6oK6Szccnp2JpFJTtmMTZetWVWmn6d4Hp7fep+C5hzFcjjOV2ccfl683UPrtcsqq6WvMcenZgwZvzQG1iux168j4/AsrG/chQ/B98QWQZUri4kh+4UUAAlavwqljRwpPnCDpiSfrXr+nZyr9745NlK1fXXX97u+D4+z3KJw2CUN8HKqQ1ji8MMtYSYmyNcvRHTlQq56q3V1oJk0DlRr9gW3ot3xvWZ7ew1D3G1lxDsu/Xox8Kwmpnh/a91cjpyQDYLgSi+6bj+pUx7/juqnQDu2MevxUkFQYDu/CsHuDpXb3wah6DgWDHrm0BH34UkhN/l0aAkFViAGJO0D0ybMk3Upl1/LFnL2UwLzPVrF+yTwru8dGD6Fru1DKynU89sZCok+epUeX9lXkaMw3NpGkjGx2zp7KuWu3mLd+F+tmPGpl9/b6Xcx7cCDtAxow9YsNHIpLpEeoMsCQkp3H0UvXqO/pVmG/9tBpmvnX47Op48jKL2LwO18wtHUAakli/u6TrHiwN36ujjywahdhwY1oXs+9Yt+N5xJwc9Cy6+nhbI+9xuIDZ1gy8n50BgOvbjnKu0O70dLPk5ziUjQqpTP/8kgMXs4O7Jg6DIMssyMuiaTbOez8zyTOXU9jXsQB1k0bb12viP3MGx1G+yb+TF25mUOXkujRMoAVB07RtXkjpoR14av9J1lx4BQzBt/HEz078UTPTgDsj73Ktz+fwcPJgfjUTDb8EsO66Q9gp1bz1Neb6NEygKTbOUo5XnmUc9dTmRexj3XTrQeJ3o7Yz7wxfYzliKwoR7CfN0snDWXuT3urPYcVqFQ0e3c+F8ZPoPRWCh12bSNr126KLsdXmDgEBtL4+WmcHTYKXW4udvW8AfDs2xuXdm043XsAKnst7SJ+JHvvfvQFBbXr1sDRVeEc+GQ5k7/98k/lU4FKRcCi+VwcP4GylBRa79xKzu4ois3qaB8YQIPpzxEzbDT63Fw0xjoCNFv2MTc/XkZe9CFUTk4gG2rVc33lTXKmPYk+LQ2v1esojd6P/mqChVlJ1A7yP1hgtXvRmm/AwQGnUdbXXk2aDi/MonDmVOSMNFy+WEv54QMYkhIt7RydsB89AV3sOVOaWo3TGwspWvgGhoTLSG7uoNPVqmf/9EyKZz+PnJmO45Jv0P1yCDn5mpWedth49BcvmNLKyigLX46qSRCqpkHVSugNBuZvO8yKRwbj5+bMA8s3ERbSlOa+nhU2G09fws1Ry64XHmD7+QQWRx1nyfg+eDg58NmE/vi6OROflsWUNTs4MNP0cv7+mDDaNPQxiRn0it72o6yYNEDR+2ozYSFNaO5jpvfrZaWdeX4c2y8ksnjPSZaMDSPY15MNTw1Ho1KRkV/EqC820SukCYH13Il4emRFfXotWUeflk1N9dtxjBUT++Pn5sQDX28jrEVjmvt4mPTOxCt6z41me8xVFu87xZLRPQFo7OlKxJThVsctLLgxE7u0ZOBnEVUf0zvcltpr7Ih4fHDF/mO/2UG/Fo1NentOsWJ8L0Xv2yjCmjew1DufqOg9NYTtcddZfOAsS0bcy7DWAQxrHQDA5Ywcpv30M638lHMRMXmASW/1bvq1aGTSizrNivE9Fb01ewhrVlnvKm4OduyaMljRO3iOJcO7setSMmV6A5GPDaC4XMewlTsZ0qoJWrWKV8I6EOrnSWFZOWO/jaJbUz+LPM2JPn2BpJR0dn6ygLPxiby9PJx1786ysut1VzsmDA5j0LQ3LdJff+yBiv//bvte4q5aP1RHn79MUtptdi58mXOJycxbs5l1bz5jZff2d5HMe2Qk7Zs1ZurHqzl04TI92obw1qoI/jN+EHeFBLLx0ElW7jzE86P6odP/H3vnHR5Fuf7ve3Y3m95JIxBSaQkkgEjTQECqQCgKigX1iNi7omABC1jRI1ZUFBCk914CJEoHCSGEECCNlE3vdXfn98cs2d3sksA5Gv2e39zXxUV25pn3M8+UtzxvGR2zfljDB4/eTdeOfpRVVKJSKok/l0ZmQTG73n+Os1euMm/FVlbPnmlFbyvzHhhPZHBHZn6xnIRzaUT36Myn6/fw5LghRPfozKGki3y6bg9LX3mEippa3lmxjcXPPUB7TzeKyyol/67pvfssZ9OvMm/FNla//pil3sptzLt/PJHBHZi56BcSki8RHRHGwseMeeeHa3fhbAi2jOvXk3H9egJSMOLpr3+lm6EzJT75MpkFJeya+yRnM3KYt2onq199xFLz153Mm3YnkUH+zPxqFQnnLxMdHsqx1Azizqayac5jqG1UFFdWAxDW3pu1s/6FSqmgsLySie9/z5Aenfk95Urb6L25iMEe9qiudQYJClQPPEPjJ7OgpAjVW1+iP3MEcrOaNPVH49Af3CaZRw1Adc/jaBfORszJQDvvSanB6eqBzTvf0njmiPS7JRQK7J95jepZTyIWaXD8cjnaI4fQZ6Wb29k7oJ5wD9qUJIsk7J54Ee2Jwy3rmOi1f+cd0u9/AG1+PiFbNlOxdx/1ly41magDA/F68gkuT74LfUUFSk9jmV/43WIU9vZ4TLv3hvXsnppFzetPIRZpcFi0DO3ReKv+2UyYis7EP33GJWqefhD0OgQPTxy++RXt0QTQ666vJyhQTX+Oxg9eQSwpRP3Ot+hPHUbMzWwy0R3Zjy5O6iBS9B6I6v4nafxoFgCiJpeGOTNuzDdTzbZ+bky0lfc8ifaLOVBahOq1z9GfPWoWcNCfOIA+QQrECT37obxrBrov37o5H/8HEORVJP50/pFTNgRB0AmCcEYQhHOCIKwVBMHBZN9EQRBEQRC6Gn7bCYJwQRCEHiY2rwqCYBmmlfZFCYJwRBCEZEEQzgqCMNWa3c2w/9gpYofehiAIRHUNpaK6moKSUjMbeztb+vfsDoDaRkX3kEDyi0paTDcuKY3YWyMQBIHIIH8qa+spLDdvhBaWV1FVV09UkD+CIBB7awT7zxobgB9u2M9LsUPMvpkrIFBd3yD1UDc04GqnRqVQkJRXTIC7Ex3dnFArlYzu3om4NPMe6ri0q0zoIY3+GNE1gKOZGkRR5Pf0PDp7u9HVUJF1s7dFqZAer41nrzCjv9Q7qBAEjmcVENunm+RXJ1/Jr4pqc78qqqmqbyCqk5/kV59u7E+WGn1xyVeY0EfqjZtgst2UHYkXGRMZBsDlghIiA3yxV9ugUiroG+zP/uTLxJ2/Qmzva+fhd/3zqDM5j97d2J8sNXhDfDwIMmm0tYRz7yjq0jOoy8xCbGykcNNmPEaNMLPxvX8auT8tRVteDkBjkdTz5NC5M+VHjoJOh76mlurzKbgPHXJDui1xKeEwNc2e0/8Gp16Sj/VZko8lm7bgPtLcR+/7p6H5aSk6g49ag4/2ncMQlEoq4hMA0NfUoK+ta1HPJrwHuuwsdDlXQdtI3d6d2A4eesPn23DiKGJ1deuGJii7RqDPyUbMywGtlsa4XdgMGmJhZ/evp6hf9TM01DdtU90yAN2VNPSXLwIgVpS3WlFQhHVHn3cVUZMLWi3a+L2o+kVb2Knve4yGDb9Ao8loi/o69OcTzbdZISmnkAAPFzp6uKBWKRkdEULchUwzm7gLGUyIknqrR3QP4mh6DqIo0t2vHd4ujgCEertTr9XRoG2hggck5RRJeu4uUj4THkzchSwzm7jULCYY3t8R3QM5eiUXURSxt1GhMuQr9Vqd1W+BH03PI8DDuWm0QVLuNT1ng14QcRfNG59xF7OZYBglNqJbJ46m5yGKYot+RHbwwsvZweq+vyovvUZGSQUlNfX06ehl0CshwM3ZqNctgLhLOc30cpkQESjpdenA0SyNhY/bU7IY0y3Awp+MkkpKauro08FEz9S/rgHEXco117uUw4RwSz1BEKht1KLV66nX6rBRKnBUq/Bysqe7wWdHtQ3Bni4UVNVavb4AcSfOEDu4v1Tudg6horqGgtIyC7uoziF4u7tZScHE799OMOa2Wy01zqQQO7CXVEaEBFBZU0dhWYWZTWFZBVW19USFBkhlxMBe7P9D6nFOzy/ils7SNRgYHsqeU8kA/J58ic4dfOnaUeqpd3NyQKlQEHfmArH9owx6HQ16lc30KqUyP8Sg1z+K/WekkR4CUF0n5TlVNXV4uzlL/h1LYnivbrT3lK6Dp4v0bsQlmugFd6Syto7C8mZ65ZWSfyEdTfRSzGxEUWT3qWTG9DUfeQSw/XgSY/pGGK/p2VRi+/Uw1Gs6SD5a06yrJyq4g6TZrwf7E6VRPKsSTvHoyIGobaT+NE9nKf+5Vr4D1Ddqm0Y3tJVe8/aJENwFsSAXCvNBp0V//CCKXgPNjepqjPa2dnDtfWyoN5YNNmpoOStqQtklHH1uNmK+oXw6uAfVwCEWdrYPPUHDmmVm5ROAauAQ9Hk56DMuWxxjDYeoSBoyM2nMzkZsbKR861ZcRgw3s/G45x6Kly1HXyG9N7ri4qZ91YcPo6++8Y4VRTP/tAf3oBow2NK/6Y/TsHYZounow/p6Y/DBxtZ4rVtACOmKqMlFLMwDnRbd0TgUfQaZG9Ua7yGm9/A/5O94bprSCuyMWJgLRQbtk/EoIgc00zbJk9V2NycgI9MC/8iABFArimKUKIoRQAPwuMm+e4HfgHsARFGsA54HvhYk/IGZwOvXSbsGeFAUxXBgFPC5IAgt11ZaQVNcip9JT6+vpwea4us39Cqqqjlw/A8GRIVf1wagoLwSX0OFAsDHzRlNs4JUU16JTzObAoNNXFIa3m5OdPU3n0pwX3RvruQXM/jNL4ld8COz7+iDQhDQVNbiayhsAXydHSiorDE71tRGpVDgbGtDWW09mSWVCMCM1XFM/mknPx6VhqhX1EkFwqKERCb/tJPnNyZwtawSX1cn4zm7OqGpMC+UNBVV+JjZOFJgCBYUV9XgZWgEebk4UlJtXmmtbWgkITWT4T1CAQjz8eRkeg5l1bXUNjQSfyGDvLIqCsqr8HUz0XBzQtMs4KMpb3Yebk4UlN/8yARbXz/qc/Oafjfk5mPraz5s1D4kCPvgYHpu3Ujkji24xwwBoDr5PO5DY1DY26HycMd10ABs21sOq/27Ufv50pBrbJQ05OVh42c+xcguOBi7kGC6b9lA+PbNuBp8tAsORltRQdiPi4nYu5OOb80BRcvZk8LLB70mv+m3XpOP0styyo3t0BF4rNyI6wefofCxnPJ0Mwhe3oiFJpqFBQhe5u+XIrQrCi9ftEfizbd37ASiiMNH3+C0eBXqex5qXc/TC7GooOm3WFyA4OllZqMI7ozCywfdiZsbmnkNTUW12fvo6+pIQaV5oEZTWYOv4Z1TKRU426opqzGvzO45n043P0/UKmXTtjmbDjHxm/V8c/B0U+NXU1ndlBaAr4ujZT5TUY2vq0k+Y6emrFbSS7xawLivNxD7zUbevnNgU4DiGjvOXWFMhHFEiOm5w7V8rQX/TPI1gJyyKiZ9v5UHl+3iZJbG+kVsxl+Rl5r5eD6TUd0CmhpdmqpafJ3tm+mZ54uaqhp8XRya6ZkHq3ZdyOJOKwGJHSlZjOraXM8YjPF1trcIHmiqas311JLeiM4dsLdRMfjrrQz7bhsP9+2Cm72t2bE55dWkaMro6efJ9dCUlOLbzsN4Dp7uFBRbBiRaI6egmKuaIvpHdLXYV1Baga+HcYSGj7sLmmYBCU1ZBT7upjauFJRKNmH+PsQZGu+7T5wjv0QKxGZqihAQmLHwJybP+5IfdyXcpJ6Lmc01vdfuGcPH6/Yw9NVP+Hjdbp6fJDUOMzRFVNTUMf3jJdz17jdsPnJG0iurxNfDJC03FzSlzfRKreg1C5KcSsvE09mJQB/L+7Xr5DnuNAlUFJRV4tssPU2z9DRlzeo1JpoZBSWcupTN1I+W8ODCZSRlGMucxPQcxr37LbHvL+bte0ejUiraTO+tYA/j6AgA93aIJYXG3yVFCO7tLK6PYuh4bD5cinLKo2hXGqc4CsFdUb33PTbvLka77N831MsttPNGX2jMo8QiDYp2zcqLkC4ovHzQHkswP9jODvXU6dQvX9yqzjVUPr40mtRrGvPysWlWxqqDg7ANCiJ43VpCNm7AabBlQP1GUXia+6cvKkBoZ17mK0K6IHj5ojv2m+XxXcJxWLwax+9WUf/FgpZHRwCCezvEEpPyt6TQ6j1U3jEB9ae/YHPPTLTLFhmP9/JF/d5i1HM+R+hiGayzyt/w3DTh5gmlRU0/xdIiaVtz7cFjUb3zI8qJj6BbbbXvV0bmpvm/MGUjAegJIAiCEzAIiAG2AHMBRFHcJQjCI8CDwJ3AXFEUrUYERFG8aPJ3riAIBYAXcPM1GWNCFpsEa113gFan46WPv+KB8SPp6GvZeGolWYthQtZtpEb5d3sO88OTlgNAfktJp2sHb3565l6yisp49Itf6NPR+zrB1GZ61iwEAa1e5PTVQtZMH4mdjYpHft1Pd18Punq7kV9ZQy9/L2YN68PPx1P4+fiF/8yvGxwhdTAlnd6Bfrg5SNHbEB8PHh3Sh399vxEHWxu6+LVDpRCu44v5b9GK1fXubYtYPcQ8bUGlwj44iKSJd6Nu70fk5g2cGjyMskPxOPeKJHLbZhqLi6k8eRpR13JB+rdg7bqIzX1UYhcURMqkKajb+9F903rODrkDQaXEud+tnLtjNPU5OYR99zVeU++m8NfVlmm2qGf+sz7hAHW7t0NjI/aTpuL69nxKn7QcrnvjtOKjIGD/9MvUfGBlCKNSiapHL6oen4ZYV4fjwsXoLp5Hd/p4C3Kt+CgI2D76PHWfv3vDHrSU3HVtrOZxxr/TCkpYuPc43z9onFLw0eQYfFwcqa5v4LnV+9iSmEZsz5Ab6kCy+m4a/o/s4M3WJydxubCM2ZviuT2sA7YqqShr0Ok4kJrFC8NuMTl3awrN8xsr/gFeTvbsf2Yybg52JOcV88yaOLY8HouTrfqmz/+/zUsHBBor+jtSMvlwnLHXzKqPzfOyVvLUxNxi7FQqwrws4/M7LmTx4Z39Wjz35lxPLymvBIUgcPCJcVTUNfDArwcY0MmHjobgcHVDI89tPszrQ6NwsrW5yfRvPm/e8ftxRg7ojVJpGQC1/lzceFn13sOTmL9yG99sPUBMZFdsDME6rU7P6UuZrHnjCezUNjzyyY9079T+hsqblvRWHTzOa1NGMaJPODtPnOPNpZtY8uJD6PR6kjNzWfLSQ9Q3NHLvB98TGdzhOu/1DTynzX5vP5HEmFsjLOwS069ip7YhzN8HdI0tnP+N1GskG51OT0VNHateeZikzFxe/HE9e955umkk6dY3H+dyXhGzl23h9vDQNtN7/bMl3OZuj61CaErBAitC+rgt6OO2oOgfg3LcNHQ/fCyZXrmA9o0Z4BeA6tFX0J49DtpGyzTNHWtZUxCwe+JFaj+ea2Fm++DjNKxfad4D3hpW9Jo/U4JSiTookCv33IuNry8ha9dwceRI9BWVFse2rmdlWzP/bGe+SN2nc60erk9NpuaxqSg6BmL3yjxpakpLIwit5ieW91C3bxO6fZtQDBiGasIDNH73AWJZCfXP3wNVFQiBnVG/8C71rz1sPqLCuqgVyb/4uWmSvkHtQ9vQH9qG0HcIyjH3oFu68MbS/x9CcfNFjUwr/FNHSAAgCIIKGA1cmwg2AdhlCCqUCILQ28T8eeB9wEsUxdZX4pHSvxVQAxbj0wRBeEwQhJOCIJxcvMpyjvCKbXuZ8MxsJjwzG28Pd/KKjMPQ8otL8PawPujirUU/0qm9L9NjR1ndv2LbXiZ+uISJHy7B29WJfJNIvqasEm+TnkwAXzdns2i/pqwSL1dnsotKySkuZ+KHS7hj7tdoyiqZ/PHPFFZUsfFYEndEdkEQBDp5udPB1YkrxeX4OtuTb9JzmF9Zg7dJrxtgZqPV66msb8TVTo2vsz19O3rj7mCHvY2K6JD2nNdIi7XZ2ygprK5l4pIdrE+8TG2jlnyTUQaa8qqmYd9NOq7moxU05dV4GXoTPZ0cmqZWFFZU4+Fofo47EtMYE2m+GNrkWyNY//w0Rkd2Zl/yZXafTcPbxZH8MhONsiq8XZpdX1dn8/Moq2oanXEz1OflYdveOCJC3d6X+vx8M5uG3DyKd+1G1Gqpz8qm9vJl7IOlId3Zny/ij2EjOTdlGggCtVeazZn8B9CQm4faZOSG2s+PxnyNhU3p7j1mPtoFB9GQm0fNuWTqs7JAp6N0124ce7bco6AvyDcb8aDw8UVnMpoAQCwvh0apMK7dtBZVt5ZHJbWGWKhB8DLR9PI2G8GAgyOKoFCcPv8B51U7UHbvicP7/0bZpTtiYQHaxJOI5WVQX4f26G8ow7pZUTHRa9YDJHh6m/ee2Dug6BSM/fyvcfhho7Rw1xsfowi17O29Hr4ujmbvY355Nd7OjpY2hndOq9NTWd+Aq6FXO7+8imdX7WXBpCEEmPa2Gt4TR1s1d/YIJSmn0CItgPyKarybTX2Qzskkn6kz6l0jxMsNe7WKtAJjLDkh7Srd/Txp52QyWsDFwVyvssa6XkWzfM3eFrVK2RTYDPfzpKO7MxnF5j3I1vgr8tJrXNCUotOLhPt6NEvL2JDIr6zB26m5ngP5FTUWetfYeZ3pGhcKStHp9eZ6Tvbkm4z4yK+staJnb67XIOltT8nk9iBfbJQKPB3t6OXvybl8qf+gUafn+c2HGdstoGm9ClNW7DzAxJfmMfGleXh7uJlNe8wvLsXLw/p6Ey2x8/cT3GkyXWPFzgNMnLuIiXMX4e3m0jSqAaTRAt4mPekAvu4uaEpNbcrxcpPeg2A/L3546WHWvfUUd/aLJMDbw3CMK307B+Lu7MjG30+TU1TGaz9usK7nak2vwszmmt7mI2cY3luaGjrqlnCS0qVpOz7uLtwWEYqDrZqdJ89RXl3Lo/9ejrebM/klJmmVXc+/5npGG61Ox74/Uhh9i2VAYueJJMb07cHKA8eYOP97Js7/Hm83J/KbpWdRr3FvVq8prcDLELDydXdmeJRUf+kZ6I9CECitMm/kHbuYweW8Qia8v7hN9EL82mGvELhUY9K4LS1E8DAZneDRDrGsmOuhP3YQRa9BljvysqC+DqFDkOW+ZoiFGhQmI/aEdj7oi4093tiSFluhAAAgAElEQVQ7oggMxfGTxTgt34qyWw8c3vkMReduKLtGYDfjWZyWb0U9aRq29z6MTWzL6ytp8/OwManX2Pj5oi0wL/Mb8/Op2LtXmkJy9Sr1V65gG9i6L9bQFxWY+ado541Y3Kw8DAzB4aPvcFy6BWW3COznLUTRrJzVZ2cg1tWiCAxpUU8sKUTwMCl/PbwQS1u4h6ZTOrSNUCU9d2LGRcSCXARfyzzNgr/huTFqF4HJaAzBvR2UX39quXjyEELzKR0yMv8h/9SAhL0gCGeAk0AW8KNh+73AKsPfqwy/AWm0AxAHfHMjAoIg+AHLgYdF0XL1PFEUF4uieIsoirc8ds9Ei+PvGzucTYvms2nRfIYN6MPmuN8QRZEzFy7h7OCAt4fl2gKfL19LZU0ts2fcf93zum/scDbOeoSNsx5hWM8wNh8/hyiKJKbn4Gxni1ezgtTL1QlHOzWJhjndm4+fY2iPMDq39+a3+c+yb+6T7Jv7JD5uzqx/5SG8XJzwc3fhaGoGAEUV1aSXVNDRzYkIP08ySyq5WlZFg07HzvOZxISarygeE9qBTUlSY3jPhSz6dfJBEAQGBbcntbCsaW7wiawCQj1dEQSBIaH+hHi6svGRMTzSrzuh7VzZfCpF8iszH2c7tUUj38vFEUdbNYmZ+ZJfp1IYGi4Nw47pHsSmU9Iw2E0m2wEqa+s5cSXHbBtI0zwAhnQLwtXejk0v3s+w8BA2n752Hnk429te5zxsSMyU5pRvPp3C0O7XXyDwelT+kYhdcBC2AR0RbGzwmhBLye69ZjbFO3fjNkjq9VR5uGMfHExdZiYoFKgMc6AdunfDsXtXSg8euulz+KupOpOIXXBgk48eE8ZTusfcx9Jde3AZJBVgKg937IKDqc/MpOpMIkpXV1SeUoXd5bZBZothWqPx/DmUAZ1QtPcHlQ12w0dTH3/AzEbhaSxcbaNj0KZbrjdyM+hSk1F2CEDw9QeVCpuho2g8bHIvqquojB1C5T1jqLxnDLrzZ6mZ8xy61PM0Hv8dZXBnaZ6pUokqqo/lYpjN0KeloGjfEcHHD1QqVNHD0R03GWpbU031faOoeXQiNY9ORJ+aTN17r0hf2bhBItp7kVlSwdXSChq0Onaeu0xMV/OGaUyXTmw6Iw0u23M+nX5B7REEgYraep5YsZsX7riV3gHGQI1Wp6e0WloDpFGn59DFrKZFMiP825FZXM7V0kopn0m+QkyXZnqdO7IpMc2gl0G/IGkNl6ullWgNQ1BzyqpILypvWisCLKdrSP61M/h3TS+dmGaN3ZjOHdl0VopL70nJpF+gL4IgUFJdh86gl11aSWZpBR3czRtsVq/pX5CXNvmYksmY7p2a6XmQWWqil5JlRa89m85lSHqpV+kX4NPUU6wXRXanZlsNSOxIyWJMN2t6VUa9C1nEhJpPI4sJac+mZFM9bwRBwM/FgaNZBYY1jLQk5pUQ7OGMKIq8uesEwZ4uPNS3i9Xret/oGDZ++jYbP32bYbdGsfnQUancvXgZZwf7VteKaE56Tj7lVTVEdTE2TO4bHcPGuc+wce4zDOvVjc2H/5DKiMtZODvYNjX+r+Hl5oKjnS2Jl7OkMuLwHwyNkhpAxYapiHq9nm+3HWDKYCnwMSgijNSr+dTWNzBlcF9C/b1558HxDIvqyuajZwx62Tjb25k1/iU9Z6lsvJwt6R09w9AoKQDp7erMiYvSNT964QqdDAGQoVHdOJWWiVanY+LAXni5OvPNU9MYFtXNqHfFoNcsAOLl6izVMa6Y6EUaA55HUq4Q5NsOX3fzYJBer2f3qfOM6RvBtJh+bJw9g42zZzCsZxc2H0sy1GuuXl/TVk1i+lVJ81gSQ3tKz8TQnl04Zqi/ZGiKadRKXw25WlSKVie9q4MjQnG0t2Xlyw+1iV5OcRkZtVra2xoHHYvpqQje/tDOF5QqFLcOQfzjiJkuPsZ3VOjZD1FjWPelna9xyqKnN4JvR8Qi8w4Ma+hSz6Pw74jg214qn4aMQHvEpHyqqaLqrmFUPTCOqgfGoUtJouatF9BfTKHmxUebtjdsWEn9rz/RuHlNi3o1iWexDQzEpkMHBBsbXMeNo2LvPjObij17cBoglflKd3fUQUE0ZGVZS65V9Nf885H8Uw0ZgfaoydTImmqqp9xB9fTxVE8fjy7lHLVvv4g+LUU6RiGNUBK8fVF06CStzdQC4pULCL7+UieEUoWy/1D0p80X/BRM7qEiqr+0vgWAsysI0j0UvPwQfPwRC/Jojb/juWnSzryI4N0ePH0k7VuipUUtTfEy5vNCRF9pvQsZmT+Bf+qUjVpRFM2+OycIgicwFIgQBEEElIAoCMKronGMmN7wr0UEQXABtgNviKJ4tDX71hh8SxTxJxMZMeMl7GzVzH/euEr1hGdms2nRfPKLivl29WaCO7Rn0nPSat/3jR3O3SNjrptudPcQ4pOvMOqd77BT2/D+fcbh0BM/XMLGWdLQ87emjGT2iu3UN2i5vXsw0a00mJ8YNZDZv2wndsGPiIi8OCQKd0Mv4JwRtzBj9QH0osjEnsGEebmxKP4s4X4eDA3rwOTIEGZtPczIb7fgZq/mk9jbAHC1UzO9b1emLN2NAESHtGewoUL84pBevLb1MB/sP427gy0fjRvIksR0Rn20TPLr7mFGvz7/lY3PS3GmtyYOYfaafdQ3arm9Syeiu0iV4hlD+vDCil2sP3EePzdnPrt/dNPx+5KvMCgsAAe1+VDf55Ztp6ymDhulgjcmDMHVwY7oroHEX8hg1IdLsVOreP9u42JMEz9bwcYX7jOcx1Bmr9krnUfXTkR3DZS0zl3i/c2HKKmq5YmfNtO1vRffP2oZvAJAp+Py628SsWoFglKB5tfV1KRepNOrL1OZmEjJ7r2UHjiI25Bo+sTHIer1pL/zHtrSMgRbWyI3bwBAW1VF6pPPwp8wZeNfK5fQechtOLXzZEF2Clvfns/hJTc0uOi6PmbMfpMuv/6CoFRS+OtqalMv4v/qS1SfOUvZnr2UHziI6+BoesbvR9TpyXrnfbSGheiy5r1Ht7WrQBCoPptEwS8rW9Wr/Oh93L/4HpQK6rZsRHflEo4zn0abkkx9/AEc7nkA2+gYRK0WsaKcinnGVfjdFy9HFRiEYO9Au21xVLz3Jg1HW1mHQaej9t8LcPz4G+kznDs3oc+4jO3DT6JLTUZ7uIVAUVUl9WuX4/TtSkBEezRBWuW7JfQ66r/9BPt5/5b09m1Dn5WO+r4Z6NIumAcnrODww0YEBwdQ2aDqP5jat561+EKHSqlgzpiBzFi+E71eZGKvLoR5e7Ao7iTh7b0Y2rUTk3t3YdaGg4z892rc7G355C5p8dCVx5PJKqngm0On+ebQaUD6vKe9WsWM5TvR6vXo9HoGBPtzd5+ugIhKoWDOmAHM+GW3lM9EhRHm7c6iA6cJb9+OoV0CmNy7M7M2xjPyi7UGvSEAnM7S8P3vZ1EpFCgEgTfvHNiUd9U2ajl8JZe5Y817i1QKBXNG9WPGr/vQ6/WSnpc7iw7+QXh7T4Z2DmByVBizNicw8qsNUr42UVoo7WSWhkWH/pD0FAJvjx7QtN7BJ/tPsv1cOnWNWmL+vZbJUWE8PTjKqPkX5KUAu1Iy+XbKEEsf7+jNjLWHJL0ewYS1c2VRQhLhvh4MDfNncs9gZm0/ysjF23GzU/PJeGPP1snsQnyc7ZumTZiy60I2395lPu+7SW9dvPTM9AiS9H47R7ivO0NDr+kdY+T3OyS9cdKnhe/tFcqcnScY/9NuRGBiRCBdvN04dbWQLecz6dzOlYk/7wHg+egeDA62/EQjwODePYg/ncTIp+ZI5e5TDzXtm/jSPDZ++jYAHy9bx/aEY9TWNzBkxivcdcftPD1V+mrK9t+OM2ZQ3+tO9Yju2YX4pIuMen2hVFY9MsmoMXcRG+dKnyx+64HxzP5xvVRG9Agjuoc0Qm/HsbOsPCBVMYb3DmfSbX0AcHW0Z/qI25jy3jfSPe4RxuCeXRBFkfikNEbN+VzSe8hYnkyc9zUb35Y+T/zW/eOY/dNG6hsbuT0ijOgIaQHYeQ/GsmDVDnR6PWobFfMejAUgxM+L2yLCmDDvaxSCwF2DehPm70Noe2/Jvzf+LelNn2DUe/cbNr4pfVHkrWljmb10E/UN5noAO0+es7qY5cm0THzcXejo5WG2PToilPjkS4x6+ytJ84FxRs3537NxtvRVgrfuHc3sZVslH8NDiQ6XgkaTBkbxxvKtjH/3O2xUSuZPH48gCJy+nM33e1ajUiqlvGHqaNydHNpMb06wO+42xvVz0OvRrvgSm5cWSJ9vTNiNmJuJcsJ09BkXEc8cQTksFqF7L6k8r65E98NHACjCIlDcOVXaLurRLv+iqbe9RfQ66r78CIcFX0qf/dy9GX3mFWynP47u4nmLdY3+a3Q6ct96m6Bly6TPfq5ZS31aGt4vvEBtUhKV+/ZRdSge59tvJ2zvHtDpyF+wAF2ZVOYHr1mDbUgwCkdHuh45zNVZr1EV38I56nXUffUxDvMXgUJJ454t6DOvoH5wJrqLKeiOXv9YZUQU6qnTpS9b6UXqF30gLSzdEno92qVfYPPqR9I9PLQTMScD1eSH0aenoj99GOWIiSjC+4BOi1hdSeN3HwCg6BqJavLDhnuoo/Gnz6D6Bqap/B3PjYm2btU3qJ55DxQK9If3QF4WirH3I2alIZ49hmLIOBRdoyR/a6rQLb3+58T/l5FnbPz5CK2tJP53IAhClSiKTs22zQR6i6I402TbIaSgQoLh98/ANlEU17WQthrYCWwVRfHzGzkfMe1Em14k/ZVzrRv9meT+Z9Hq/wr36y9W9pcgtO1goMOPzW9TPYAVBTdR8PwJPOxz88Oj/xsCO7beM/1nYud4/fnrfxXKZsP6/2rs77urTfVaW0TsT+fvWGvlRufr/lm09TX9G+oMQv/hrRv9iYhlBa0b/Zm09T3U/w31Pl0bvxdtjO6XZW2qV3e15a+0/RVkprWtZmCXtq0n2ni1bR0DQGHTtv3Cgl3b12tsvtnxP9d+T/Dp8M9rPJtwu+bq/7lr/k+dsmGNe4HmizmsB6bdZDpTgGjgIcOnRc8IghDV2kEyMjIyMjIyMjIyMjIyMjJ/Hv/IKRvNR0cYtg2xsu2LZr8fuoG0fwF++S9OT0ZGRkZGRkZGRkZGRub/M/7PDT/4P8D/pRESMjIyMjIyMjIyMjIyMjIy/yP8I0dI/BkIgtAD6SsaptSLotjPmr2MjIyMjIyMjIyMjIyMjEzb8T8bkBBFMQmQ14aQkZGRkZGRkZGRkZGR+a8R5EkbfzrylA0ZGRkZGRkZGRkZGRkZGZk2Rw5IyMjIyMjIyMjIyMjIyMjItDlyQEJGRkZGRkZGRkZGRkZGRqbN+Z9dQ0JGRkZGRkZGRkZGRkZG5s9CkJeQ+NORAxI3gl73d5/BX4tS2faabX1NlW07GKhzgEub6gE83MY55E+a8jbVe0BsUznae9m3rSDgVKttUz17W9s21aO+vm31/o5ag6hvY702fjFoY/8AMftim+oJvoFtqidWlbapHmJj2+r9HQhtW+YrnOzaVM8uwLNN9QCCbNq2rpiVWtSmegHqtm8S2bg7tqmeoGv7/FtG5kaQp2zIyMjIyMjIyMjIyMjIyMi0OfIICRkZGRkZGRkZGRkZGRmZVpB78/985GsqIyMjIyMjIyMjIyMjIyPT5sgBCRkZGRkZGRkZGRkZGRkZmTZHnrIhIyMjIyMjIyMjIyMjI9MK8kc2/nzkERIyMjIyMjIyMjIyMjIyMjJtjhyQkJGRkZGRkZGRkZGRkZGRaXPkKRsyMjIyMjIyMjIyMjIyMq0gCPKkjT8bOSDxHyKKIu9/v4L4k4nY2apZ8PwMwkMCLew+W76OzQd+p6KqmtNrFjdtzykoYs4XP1JSXoGrsxMfvzgT33YeFhrz1+8j/vxl7NU2zL/vTrp39LXQSM7KZ/aK7dQ1NhLdPYTZk+8we1mW7D/GJ5sP8Pv8Z3F3cmjanpSZx70Ll/HpxNsZ2a0TAAmXc1iw5yQ6UeSuqFBmDIww02rQ6nhty+8k55fgZq9m4cRo/N2cyCmrYux3Wwj0cAEg0r8dc8f0B+CxX/dTWFWLVq+nT0dvYsI68GHcH+j0eu7q250ZQ3pbaqzZR3JOIW4OdiycNgJ/dyndxQdPsf5ECkqFgtnjbuO2zgFNx+n0eu7+ch0+Lo5889CdABy5dJVPdh5GL4KjrQ3v3T2cFYcTib+Qgb2NivlTRtC9g7flNb2qYfaavdQ1aonuGsjs8YMRBIFdZ9P4au9RrhSUsPrpe4jo6GN2XG5pBeM+/YWHBD332poPQFIPuA3nl14HhZLazeuoWfqD2X67sRNwfvZldIUFANSuWUHt5vUAuH3xHTYRkTSeOU3Zi09anO/1cI0ZQqd35yIolRSs+JW8L7+2sPEYP5YOL7+AKIrUJKdw+clnpPP1b0/wpx+jbu8HiFy4bzoN2VdvWLs5D/z4FT3GjqKyoJB3e/T/j9MxxS1mCMHvzQWlEs2KX8lZZOmf5/ixBLz8Aogi1edTuPiE5F+nN2fjccdQUCgoi08gfc7bN6RpP+h2PF6bg6BUUrl+LeU/Ljbb7xQ7EY+XZqEt0ABQ8esvVK1fC0BgYgoNaRcB0OblUvDME63q2fS/DacXX0NQKKndsp7aZT9YtVMPHYHrgs8onT4F7YVkUNng/PrbqLqGgyhStXABjadPNNmLosj7W+KJT82U3oW7h9Hd39q7UMDstfuo0+qI7tKJ2eNuRxAEymrqeGnlbnJKK/B3d2HhtJG4Otg1HZeUreHer9fx6bSRjOwRCsBjy3eSeLWAQE8XKusapXymdxdm3B5lptmg1fHahoMk5xXhZm/LwruH4e/uzOHLV1m49wSNOh02SiUvj7iV/sH+Zsc+tXI32aWVbHnqLrPtCWlXWbDrqJTv9O7CjNsjLTU3HiI5t0jKd+6Kwd/dmbNXC3l762/G9If04o5ugU2/dXo9dy/ejI+zI9/cN8Ko9xfkpQ06He/vOs7xLA0KQeC5IVGMCJX8T7iSy4L9p9HpRe6KDGFG/+6WetuPGvRsWRg7EH9XJwBSC0qZu/sEVfWNKASBNdNHYqtSsjMlk++OJKPTiwwOac/LMb2M/l3JY8H+PyT/egYzo383K3rHSNaUSv6NH4i/qyNbkzNYciK1ye5iQRnrpo+gm4+78RqvTyC7vIotj4w2S1MUReZviCM+5Yr0zE4bQ/dmeTBAcnY+s1fulPLvbsHMnjQUQRD4YsdvxCWlIQgCns4OzJ82Bm9XJ46nZfH0jxvx93AFYPigW3lq8hhL7aVrif8jGTtbNfOfeIDwoAAL7c9XbWFz/DEqqms4tfSzpu0bDx7h4xWb8DFoTBs5mLuHDrI43kzv1+3EJ12Uyv9HJtO9U3tLXzNymP3TBuoaGonu0ZnZ996JIAhcyM5j3vIt1NQ34O/pxkcz7sbJ3s5SY80u4s+lSRrTJ9A9wM9SIzOX2Us3S3WMiDBmTxmFIAi8+P060jVFAFTW1OHsYMfGNx6nrKqG5xevJSkzh4n9o3jj3jHmmmv3EJ98CXsbG+Y/OM66ZlYes5dtke5heCiz7x7RVK/55cAJVh46gVKpYHB4GC9PGtZ0XG5JOePe/Zan7hzMI8MHGnzcTXyywccHY6/v47Itko/hYcyeMtJE7zgrDxr0IkJ5edJwGrQ65q7cRnJmHgpB4DU/G271cDBLU+jWG8XkGaBQoD+yF3HvOvP9MbEoBowAvQ6xqgL9in9DaSEAivHTEcL7AqDfvQrx9G/cCELELajufQIEBbqEXeh3rjbbrxh8J4qh40Gvh/patEs/h7wshO69UU7+F6hUoNWiW/s94oUzreop+/THduZLoFDQuHszjWuXWbcbNBT7OR9Q89x09GkpKHvdivqhp8DGBhobaViyCF3iyVb1HKOj8X3zLQSlgtLVayj+7luz/a6TJ+Mz6zW0Gqn8LVm+jLI1awDwfnUWTjFDACj68ksqtm9v3b/e/bGd8YLk394tNK5bbt1uYAz2ry+g5oWH0F+6AM4u2L22AGVYNxr3b6fhu09b1bqG0KMvqvufQlAo0B3agW7bKrP9ipixKO+INd7DJZ8h5mYaDTy9US9Ygm7jUnQ717auF94H5dQnpOf0t13od60x14segyJmHOj1iPV16Jb/W3pmuvVCOekR4zOz7gfE1MQb9lNGxhQ5IPEfEn/qLJm5+ez+7iMSUy8z75ulrPnEskET0zeK++68g1GPv2q2/aMlq4iNGcTEYbdxNPE8C5et5aMXZ5prnL9CZmEpu96cydmMXOat2c3ql6ZbaLyzZjfz7hlFZGB7Zn67loSUK0R3DwEgr7SCI6kZ+Bka9NfQ6fUs3HKAQd2CzLa9t+s4P0y7Ax8XB6Yu2UlMWAdCvdyabNafuYSLnZrdT05gR3I6n8adZuGkaAA6ujuxccZYi/NbOOl2nGzViKLIc+sO8ca2I6x8ajI+Lk5M/WodMd0CCfUxBmPWn0jBxd6W3a/cz47END7deYSF00ZySVPCzsRLbH3hXgoqqvnXj1vY8dI0lAqp4b/897OEeLtTVddgvDabDvHlg6MJ8fPh18OJvLPxACqlgl2vTudsVj7zNsax+pl7LK/pxgPMmzyMyABfZi7ZTEJqJtFdAwnz8eSLB8Yyd8N+i2MAPtwaz+1dOsHFy+Y7FAqcX32DsqcfRafR4LF0NfXxB9Clm9vV7d1J5cfvW6Rbs/wnsLPDYeIUq7pWUSgIXPAeF6ZMoyEvj/Bd2yjbs5fai2lNJrZBgbR/5imSx01CV16Oqp1n076QRZ+T8/kiKuITUDg4gKi/cW0rHPl5BQe/XMxDy777r9JpQqEg+IP3SJ4yjYbcPCJ3b6Nkt7l/dkGBdHj2Kc4a/LMx+Od8Sx9cbr2FP2KkBmSPrRtwGdifisNHW9X0fONt8mc8jDY/n/ar11NzYD+NV8zvY/WuHRTPf8ficLG+jty7Ym/KR+dX5lD2zAz0BRrcf15NQ4LlcyM4OGA/5T4azxkrA3YTpAZ56X0TEdw9cP38W8oemgqiCMBRrUhmURm7Xr6fs9ka5m06xOqn7rY4hXc2HWTepBjpXfhpKwkXs4ju0okfDp6if2gHZgzpw/cHT/HDodO8NHogYMhfdh5mUGfzBtvDg3pSU9/IK+vj2PzkXfi4ODJ18SZiunQi1NvYIF1/OhUXezW7n5vKjqTLfLr3OAunDMPNwY6vp43A28WRNE0JM5bv5ODL9zUdt/d8Og5qGwsfdHo97+04zA8PjJI0v99CTJcAS007W3Y/N0XS3HeChXcPJczbnbWPxaJSKiisrGHiNxsZ0jkAldKQ7xxNJqSdG1X1jeZ6f0Fe+t1v5/BwtGPnExPQiyLltfVGvb2n+GFqDD7O9kxduoeYUH9C27ka9c5ekfRmjmPH+Uw+PZjIwthBaPV6Zm07wgdjB9DV252y2npUCoGy2no+PnCGdQ+NxMPBjte3H+FIRj4DOnlLevtO8cOUIZLesr3EhLY310sy6D12JztSsgx6AxkXHsi48EAALhaW8fSG38yCEXsvXsVBbb1qEp+SLpWJcx7lbGYe89buZfWL91vYvbN2L/OmjJDKxO/Wk5CSTnT3YB4Z2pdnx9wm3bdDp/h692HmTpHygD7BHfjmsckACL6BltpnksnMK2TX53NJvJTBOz+sYvX7r1rYDenTg2kjBzP6+bkW+0YP6M2bj0y16puFXtJFMguK2TX/Bc5eucq8X7awes7jlr7+soV5D8YSGdyRmf9eRsK5NKJ7dOatpZt45e5R9O0SxPrfTrFk9288O+EOc41zl8gsKGHXO89wNj2HeSu3s/q1Ry01Vm5n3v1jiQzqwMwvV5KQfInoiDAWzjAG/T5ctxtnQ8BDbaPimfExpOUWcCmnwFwz+bKkOfdJzmbkMG/VTla/+oil5q87mTftTiKD/Jn51SoSzl8mOjyUY6kZxJ1NZdOcx1DbqCiurDY77sN1e7m9e6iJ3iXpOs57WvLx1+2snmXFx193MO++Oy18PJaaTlxiKpvemCnpVUh66347DcDmNx+nuKKame99yer+ASiudQYJChR3P47uqzehrBjlKwvRJR2D/Gyj6NUr6D5+ERrrEW4bjWLCw+h/+ggh/BboGILuw2dBZYPyuQXozp+CulqL8zZDUKC672kaP30NSotQvbkI/ZkjkJfVZKI/dgD9IakhLkT2RzV1JtrP50BVOdpFb0JZCYJ/IKoX5tP48rSW9RQKbJ98ldo5TyMWFWD/+VK0RxMQs9PN7ewdUMdORXchqWmTWF5G3byXEEuKUHQKxu7dL6h50DLPa67nN3cemdMfpDE/n+CNm6jcv4+GS5fMzCq2byd/3lyzbU5DYrALD+fK2LEIajWBv66i6tAh9FVVLfv3+MvUvvksYnEB9gt/QnssATE7w9K/cVPQXThn3NbQQMOKxSgCglF0Cm7ZL1MEBTYPPkvDR69CSSE2875Gf/qIWcBBfyQO/YFt0in2GoBq2uM0fvJ6037VtCfQnz1+w3rKaU+h/Wy29MzM/gJ94lHzZ+b4QfTxOyTzyP4o734M3RdvIFZVoP3ybSgvgfadUD33PtpZlvmxjMyN8I9cQ0IQBJ0gCGcEQTgnCMJaQRAcTPZNFARBFAShq+G3nSAIFwRB6GFi86ogCN9eJ+1OgiCcMqSfLAiCZQl/A+w/dprYmEEIgkBU11AqqmsoKCmzsIvqGoq3h5vF9svZOQyIlHqw+vXsxv5jpy1s4pLSiL01AkEQiAzyp7K2nsJy88yzsLyKqrp6ooL8EQSB2Fsj2H/W2CD7cMN+XoodQvPRRSsOnWJ4ZBc8TUdM5BYT4OFMR3dn1Eolo7t3Iu5ittlxcWnZTOgpBTtGdOvE0Yx8REMD53o42aoB0OpFiqtr8WkMFW0AACAASURBVHSyo6OHK2qVktGRocSlmBdecSnpTOjdVdKICOHo5RxEUSQuJZ3RkaGoVUo6eLgQ4OlKUrZU2ckvr+JQaiaT+5r30gmCQFWd1FCorGugrKaW2N7dpGvayU+6phXmFZrCimqq6hqI6uQnXdPe3difLDUAQ3w8CDJpxJiy79xlOni4EurjabHPJrwHuuwsdDlXQdtI3d6d2A4e2uJ1M6XhxFHE6urWDU1w6hVFXXoG9VlZiI2NlGzagvvIEWY23vdPQ/PTUnTl5QBoi4oBsO8chqBUUhGfAIC+pgZ9bd1N6TfnUsJhakpK/6s0THHubfAvU/KvcNMWPEaZ++dz/zTyTfxrNPgHIgpbWxRqNQpbNQqVDY2FRa1q2vboSWNWJtqr2aBtpHrndhyG3tHqcf8pqu490F3NRp977bnZgTo6xsLOYeaz1C5fglhfbzw2KISGE1KARSwtQaysRNXN2Euf0CgS27ur9C4E+F7/Xag3fRe6sj/5CgBx543v6QST7QArDp9leI8QPB3tzdIbEOyPpqIaexsbOnq4SHlARAhxFzLN7OIuZDAhqjMAI7oHcTRdygO6+7XD28URgFBvd+q1Ohq0OgCq6xtZeiSJmdG9aE5STiEBHi4mmsHEpWaZ2cSlZjEhKtSoeSUXURSxV6uagg/1Wp1ZXppfXs2htGwm9+5irvcX5aUbEy81jbRQCALuhhEpSXklBLg50dHNSdLrFkBcmvlopri0q0yIkALQI7p25GimpPd7ej6dvdzoasjX3OxtUSoUZJdVEejhjIdBY0AnX/YafJD0nM31LuU008tlQkSgpNelA0ezNBb+bU/JYkw3Y9CquqGRpSdSmTnAfHRHU5pJacT2DZee2cD2VNbWXadMbDCWiX3D2Z8klYlOdrZNdrUNjTe1UnrcybPERveTyvywICpqaikoLbewiwoLwtvd1UoKN0fcmRRiB0RJvoZ0pLKmjsKySjObwrJKqfwPCZB8HRDF/j/OA5CeX8QtnQMBGNg9hD2nki01zl4gtn9PSSO4g+F6NtMoN2gEd5Q0+vdkf+IFMxtRFNl96jxjbpGeTQdbNX1CA7BVWQaW4s6mEtuvh6Fe00Hy67qaHSTNfj3YnyiNqlmVcIpHRw5EbSOl7ens2HTcvjOpdGjnRqhfO6NeYiqx/SONPtbU34CPkUa9+FM8OnKQUc+Q/1zOK6R/l6Cmbc42Cs5VmJSRncIQi/KgWAM6LfpT8Qg9+plft7QkaJTybDEjFcHNUHfw7Yh46ZzUA95Qj5iTjtCtj8W1bI4Q3AWxIBeK8iXN44dQ9BpoblRXY7S3NY6YEbMuQ1mJ9HdOBtioQWUZ3DVF0Tkcfe5VxPxc0GrRxu9BNSDawk79wEwa1i2HBmNnkf7KRcQSqczVZ15BUNu2qmcfGUlDZiaN2dnQ2Ej5tm043zG8xWOuYRsWSs3xY6DTIdbWUpeSglO05bma+RfWHX3eVUTNNf/2oupnxb/7HqNhwy/QaPSP+jr05xPNt90AQkhXxIIcKMyT7uHRAyh6X/8eYmsHJtmqovcgxMI86R7eiF5QF8SCPOMzc+IQisgB19dT29EkmH1ZCkYA5Gbe0DPzv4LwD//3f5F/ZEACqBVFMUoUxQigATANGtwL/AbcAyCKYh3wPPC1IOEPzARexzp5wEBRFKOAfsBrgiBYjoNsBU1xKX5exoanr6cHmuIbb2x1CQpgz2FpeNreI6eorq2jtMK8YlVQXomvm3PTbx83ZzTNClJNeSU+zWwKDDZxSWl4uznR1d98SKumrJJ9Zy8y9TbzirumsgZfk8Ld18WRgspaSxsXKYihUihwtrWhzNBLl1NWxaQftvHg8t2czNKYHTfj133c/vlaBEEgws/kurk4UVBu3gjSVFTj6yYNJVYpFTjbqSmrqaOgvBpfwxBjAB9XRzSGBtQH237j5dEDjL0TBt6ZNITHf95GzPs/suX0BTydHJrSlq6XE5pmFVpNeRU+ruY2BeUtRNGBmoZGfjx4kieH97O6X+Hlg16T3/Rbr8lH6WU5PN526Ag8Vm7E9YPPUPhYTs+5GdR+vjTk5jb9bsjLw8bPPE274GDsQoLpvmUD4ds342oYzmgXHIy2ooKwHxcTsXcnHd+aA4p/Vnah9m3mX24etr7m/tmHBGMXHEyPrRvouWMzbgb/Kk+epvz3I/Q9e5K+Z09RevAQtWnmvSzWUHr7oMs33kedJh+Vt+WQcYfhI/DfsAXvhV+gNDknQW1L+9Xr8Vux5oYCGQpvH3SavKbf+gINSi9zPVXnrih9fGn4/ZDZdm1aKrbRQ0GpROHnj6prd7NnqkiP+bvg6oSmWR6kqWj2Lrg6UWCwKa6qwctQOfdycaSkSsorNOVV7Eu+wtR+5lMUrlFaU4etStn029fVkYJmvZxSPiOlrVIqcLZVU1ZTb2az53w63fw8URvSWhR3kocG9sDexrIRpKkwpgfg6+JAQYWVfMeleb4jaSZeLWDcV+uJ/XoDb48d1BSg+GDXUV4efqtFvvNX5KUVhpFfiw4lMvmH7Ty//hBF1665SVoAvs4OFFQ106uqxdfZVE9NWW0DmSUVCILAjNUHmPzzLn48JjVoA9ydSS+uIKe8Cq1ez/60q+RX1JikZW+u19y/Kmv+mVfOd13I4k6TgMSihHM81LeL1XsIUFBeha978zLRSv5tlsc7m+Xfn29PYOjcb9l2KoVnDKMlAM5k5DLxo5957Nt1pGXn0hxNSTm+nsbOBV8PN6udEC2x5/gZYl99n+cWfk9eUcv1hYKySnw9jIENH3cXNGUV5udUVoGPyehHH3dXCgxBizB/b+LOSIGD3SeTyS+xDJ4UlFXiaxI88XFzQdMs6KEpqzTXcHNp0rjGqUtZeDo7EmglGG9d0/Scr6NpWq9xN2pmFJRw6lI2Uz9awoMLl5GUId2rmvoGftx7mCfHmDcYLfWcr6Nn6qOziV4xpy5lMfXDH3hw4c8kZUiBty4dfIg7m4pWp+dqUSnnK+rJr9M2pSG4eUKpSZC7rNgYcLCCYsBw9OdPST9yMlB07wM2tuDoghDWE9zbXffYJtzaIZYUGn+XFlrVVMSMw2bBzyjvnoF25VcW+4U+tyNmXQJto8U+MztPL8QiY11PLCpA8PQy1wrujMLLB93x6085UQ4aiu5yaqt6Kh9fGvOM5aE2Pw8bH8vy13nUKIK376DDl1+h8pOm59SlpOA0eDCCnR1Kd3cc+/dv2teyf8YRPmJxC/6d+L3FtG4Uwb0dYrHxHoolhQhW7r1iWCzqj5ejmvoY2l++lDaq7VCOvQfdRuvTZqzi5gkmz4xYVgTuVp6ZIeNQvb8E5eR/oVv1jeV5974NMftyq/dQRuZ6/LNaGNZJAEIBBEFwAgYB/8IQkAAQRXEXUqDhQeAzYK4oilZLe1EUG0RRvFazteVPvAY3s8bJqw/fw4lzF5j43JucSL6Aj6d7UyXXeK5WNJrFvqzbSL0/3+05zDNjbrfYv2DDPl4aP6RpqkNTWtZOtJlP1zsnLyd79j89mQ2PjmXWHbfw6qbfqKo3Vj6/v/cODj13F1q9aFFxbX7hrPUSCghWz08Q4GBKBh6O9oRbmf++7PdEvn1oLAfm/IuJt3TnsqbEahpm+laUWlvA5ss9R3nw9l44GkaDtCoiCZlRn3CAovF3UDJtIg3Hj+L69vwWNVvFqqa5qKBSYhcURMqkKVx64mmCP/0IpYsLgkqJc79byZr3HudGjcUuIACvqZbD+f9WrPjX/N4JKiX2wUGcmziF1MefJnSh5J9dYCD2YaGciLqVE5F9cb1tIC79rQeTWtVsdk1rDh4ge0QMOZPGU3v0MF7vf9i0L3v4EHKnTqZw1kt4zJqNqmPHG3TWTNDsfByfn0XVvz+yMKvbugFdgQb3n9fg9OJrNCadAZ2xwmz1fbqh/KXld2HBtgReGj3QIn9pSdfCxloeYCKbVlDCwr3HmTtOyt9S8orJKqngDpMpaK1qNs93WjCJ7ODN1qcms+axWL5PSKS+UcvB1Cw8HO0Ib29ZWfwr8lKdXk9+ZQ29Onqx/tE7iergxcf7T11fr/k5Xeeaav8fe+cdHkXV/u97dje9J6RDSEJooTdBkEDoRUqw0ESxAHYREBRQRAUrWF8LVpReDBHpoQVUOiQQQiiphPTeN7s7vz9m2ZJNw5LX7/ub+7pyXdmZM+czz5mZc86c85xndCLnbuby3rj+rJs+jOirN/kjJQsXW2teG9mHeVG/M2N9NH4uDoZrWqcjR1PsM0kTeysfW5WKtvplLAnZhaQVlTGsXcv6bahL9g7r77ljB3Lo9Se5t1dH1h+TPBNDW3kTvWwOkQtnMj2sJ8+uWmORx59pF0wZ3KsLBz99g6j3lnB3lw688kXDLw11t4NNT/PWzElsPHyC+9/4nPKqaqxMBgGNx1vqNk3DPNWu0xcZ06fuAcgmaVr0Aeo6LymNVqujpKKKTS89yoJJQ5n37XZEUeSzX2N4eEhfHGzN2+A6r5vFOdVf3xj0Fj7OgknDmfeNpDepfw98XJ154J2veXvrPrq72qIys6Px9teQsvdghFYhiAd/lpJdOY8u/izKee+hmLkAMfkKaLV1HtugYVJuFlt0h3dS88pMtNu+QXnvdLN9gl9rVPc/jubHj5ug10i/RhCwmf0i1V/Xn5ciIBibx56l+tO3m6BX10Zz+8oOHuT6oDCSxo6h/Lff8H//fQDKjx+n7MgRgrZuw/+jj6k8f77xMm2KfU/MpfrbTxo/979EHdfwYBTql2ag2fI1ygnSMgnlpEfQ7t0G1XfgzdqEfiKA7shONEseQ/vztyjHTDXf6dsa5X2PoV33T5eDzP8y/+oYEoIgqIDRwF79ponAXlEUrwqCUCAIQk9RFG+vdZgLnAKuiaJYd9QZY76tgF1IAx0viaJoMR0iCMJsYDbAl8sXMXvyRNbvimbrfmkWskvbIDJz8w3ps/IL8HKv25W/Lrw93Ph08fMAlFdWsf/3Mzg52LN+VzRbft0naQT4kmUykp9dVIqXyWwlgI+r+Wh/dlEpni5OpOcVkpFfTMS73xm23/f+D2ye/zDxaVnMXxsFQGFZJTGXFCgVAj5O9mSZzFRmlZTj5Wjucu3jbE+WfrZRo9NRWl2Di501giAYZio7+XrQys2JlPxSOvsZR1ptVEr6B/my4+INE40yvJzNA0H5uDiSVVSGj4sjGq2O0io1LvY2eLs4kGUy05VdXI6XkwOHEpI5nJBCTGIa1RoN5dU1LNx8gJfH3kNiZj7xGTm8vuMoGq2O8uoasopM8igqw8u5Vpm6mM+6ZReVGWaC6yMuLYv9F6+xavdxSiuroVrEWtBxn7XUgdflZJnNTiu8fdDmma+tFYuNM1iVO7bi+Ny8BjUbQ30rE2s/o/OPta8vNVnZFmnKzp1H1GioTkun8sYNbIODUN/KpOJSPNVpklt74d59OPbqSe5G8wBZ/03UmbXs8/NFXYd9pWfN7bMLDsKlfz9Kz55HVyHN+BYdPIxTrx6UnDjZoKY2O8vM40Hp7WMIQnobXbFx1rR02xbcX3zJeLw+reZmOlWnT2HdIRRNurkrv1leOdkovY2zOAovb7P7RrB3QNWmLa6f/yDt92iB8wefUbLgWTRX4in/6F1uP9GuX69j87lYokqkQYmOKsH8WSguMyyHuI2Pi7kHUXax8VnwcLQnt6QcT2cHckvKcdfXFfE3c5i/QarDCiuqiElMRalQMKyTtI7W3d6Wao2xI5ilf47NdJ0dyCopN9YB1Wpc7Gz06ct4ftMB3p40mAB94MfY9GziM/MY9uFGtPqlYY98/ytrZ47R52dPVolp3VaBl5N9HZpl+Lg4GOsdOxuzNG08XbGztuJaTiHn0rM5nJhGzLWbVGu0lFerWbj9CO+N7/+P1KWdfN2xs1IyrL3kUTCyY2u2X5C8enyc7A3eCwBZpRWWek72ZOk9KSQ9NS621vg42dOnlRdu9pKtYcF+XM4u5O5AH8JD/AnXB83ccuE6Sn3n1cfJjiyTgeV69Uoq8HGyN9pn8rK4p9Zyjdhb+cRnFTDsy53SNayo5pGNhxjZvhVb1x0C9G1iYa02sa7626yOL62z/h7bqyNPrfmZ50bfY7aUY1BoMG/+fJjCkjJ2/3GWbYekmc/ObVqTlW98trMKivC8g6UZbk7G83xg6ABWbdhhkWbDoRNsPSZ5TnYJ9DfzasguLMHL1TwWlI+bC9mFJSZpivHUexYE+3ryzbxHAUjJyiMmLtGoESMFt+3S2o8sk2Un2UUleJl4JkgazuYaRSV4mnigaLQ6os9fYevi2fXavuHIKbbqYy50ae1Lltk5l1j2a2p5MWQXGjV93JwY3r09giDQNdAfhSBQWFZBXEoG+88nsCryIPml5dRotPx46CRhnUJq6ZXWbWORqY1SP+r2vuE9OljouTs58PIDIw3HTHv5PQLsje7qYlGe+cy2qwdicR0TIu27oRj5INqPXwGNyYDx/i1o90vBBRWPLIBcS68dCwrzENxNZvDdPBGLLDVvozt1BKuHnsdQG7u1QPXMMjTfvictGWgEMS8HoYXRQ0Fo4WXuoWFnj6J1G+zelWbUBTcPbF/7gKo3FqC7loDg4YXtq+9Rtep1xKyM2tlboMnKwsrEq0Hl40tNtnn7qy0yPqOFmzfhtWiR4Xfe55+T97kU+Nr/w49Qp6Q0wT7jRJfgUZd9wdit/Fxvnzu2S9+n6q2XpMCWfwKxMM/MC0Nw90QszK83ve7EYVSPvACAok1HhD5hMHk22DuCqEOsUaOLjqpfsDAPTO4ZwbWFYelOned3+ijC9OfgB32QTtcWqJ5+Fe13HzTpnvlf4f/qsoh/M/9WDwk7QRAuAGeANOBb/fapwO1ws5v0vwHQDyocAix9iWohimK6KIpdkQYkHhEEwcLnSxTFNaIo9hZFsffsyRMBmD52GDs+fpMdH7/J0L49iTr8G6IocuHKdZzs7eqMFVEfhSWl6HRSkMA1237lvmFhBo3IRY8RuegxhnZtS9SpS4iiSGxyBk62NnjWarg9XRxxsLUmVr/GOurUJYZ0aUs7Py+Or3ye6NefJvr1p/F2dWL7SzPxdHbkwOtPGbaP7N6eV0f1ZVj7ADr7eZBaUMrNolLUWi17LqcS3s58Bje8bSt2xEkDCvsTUukb6IMgCBSUV6HV25NeWEpqQQkt3RwpV9eQWyp1lDU6HUn5xVTVaLlZUIJao2VP7HXCa81qhncMZMc5qTLff+kGfdtIa4HDOwaxJ/Y6ao10fGpeMV1aeTFv1N0cfuURohfNYNXUEfQN9ue9ycNxtrOhtEpN/5BWRL44nZlhPWjj7UbUuQSpTFMzcbKzseisejo74GBjRWxqplSm5xIYEtpwUKJ1Tz9A9CuPEf3KY8y4pwcP2QiGwQiAmsuXUAa0RuHnDyorbIePpjrmsFkeCg9j58UmLBxNchJ/hbILsdgGB2IT0ArBygr3ieMp3H/ALE3h3v04D5DWC6rc3bANDqY6NZWyC7EoXVxQeUjBRp3vGWAWLPLfQOn5WOxM7POcOJ6Cfeb25e/Zj4uJfXbBwVSlplKdcQuX/n1BqURQqXDu34+KJizZqL50EauAQFT+LUFlhcPosVQcNg9wqmxhbNztw4ei1ge8VDg7SxHFAYWrG7Y9elJzo2FNTcIllK0CUPjevm/GoDa5b8TyMvJH3kNBxAgKIkZQcynWMBiBjS3YSi+JVnfdjajVElFWwA/OKn5wVjHQSiDq3BXpWUjLwsnWup5nwZrYtCz9s3CFIaHS8xoeGmR4TneYbD+w6BGiX5b+RnZuw6sTBxkGIwCCWrhQWVPDzUJ9HXDpBuEdzINfhrdvzY4L0tdI9l9Opm+QH4IgUFJZzVPr9/HisLvoGWAcGJpyVyhHF0wn+sWprHtsHIEeLqx91BggrbOfJ6n5JdwsLNVrJhHevrZmADv0L/immjcLS9Fopboto6iU5Lxi/F2dmDesD4fnTyX6xcmsuj+cvkF+vHffYL3e31+XCoLA4LYtOZUqLRk6kZxFG30Qyc6+7qQWlnKzqEzSS0gjPKRlLT1/dlyS4vXsv5JO3wBvBEFgQLAviblFVNZo0Oh0nE7PIaSF9OKbXy7NtBVXqdl4/hr3d2vTgJ75107CQ/zYcSlF0ku8adAD0Iki+xLTzQYkpvQI4egzE4h+chzrpg8l0N2RtVOHMK1nWyIXziRy4UyGdgkh6nS8dM+m3JLq77raRBtrYlOkGCBRp+MZ0qUtACm5RsfJw5duEKwPppxbUmaYJY/T1/uuTg5MHzmIyHcXE/nuYob27kZUzEmpzb+WLLX5dzAgYRpv4tCZOIL9LZfkTRvSj8hlzxK57FmG9ggl6o8Lkq030iVba71Ie7o64WBrQ+yNdMnWPy4wpLsURylfv7RKp9Px5a4jPDj4LqPG0ieJXPokQ7t3IOpEnKSRdFPfx6il4aLXSLopaZyIY0jXDob9f1xJIsinhdmyCAu7Bt9F5OJZRC6exdCu7Yk6eVHfr7mJk51t3Zo21sQm6zVPXmRIVylOy5Cu7TmZmAJASnY+NRotbo72rJv/CNFvPUf0W8/x+PC7mRcxlCNvv8jQbu2JOhFrtNGuKTbGMqSbXq9be04mJhv1tJJepbqGCr0X6O8JN1AKAiGOJgOYadcQPP3AwxuUKhS9whAv1go02DIYxeRn0K55E8pMltQICrDXn6NfIIJfIOKV8/WW723E5EQEb39o4SNp3jUI8cIf5om8jIP4Qte+UrwCADsHVC+8iebn7xCvX25UC0B39TIKv1YI3n6gUqEKG4H2xDFjgopyyqeOoOLRiVQ8OhHdlUuGwQgcHLFd/iHVP/wH3eW4JulVxsVhHRiIVcuWYGWFy733UnYw2iyNytPY/joNG0b17YCXCgVKV6mPbtO+AzYd2lN27BgNobuWoLfPV2/fcLSnatk3fRQVT0RQ8UQEusT4vzQYASAmXTG/hv3C0Z3/3SyN4G2saxXd+iFmS9ewZsVc1POno54/He3+7Wh3bmh4MAJ97BIvk/u0zyApqKUppvdMl7sMetg5oHruDbSR3yPeaNo9IyNTH/9WD4lKfYwHA4IgeABDgM6CIIiAEhAFQVgoGv3tdPq/JiGK4i1BEOKBgcC2xtKbMqh3N2LOxjFizkvY2tiw8nlj1OaJL7zKjo/fBOD97zfza8wfVFarGfToXO4fPojnpkVw8uIVPvxxKwjQp1N7XnvyYQuNsNA2xMQnMeqNr7C1tmLFdOOnsyLe/Y7IRVJk6tceHMni9buoVmsYGBpMWCMvz/WhUihYMvIuZm08iE4nEtEthLaernx69AKdfD0Y0q4V93UPYVHUcUZ+vgNXW2s+iJBcps+kZ/Pp0VhUCgUKQWDZ6L642tmQV1bJM1uPoNZq0epE+gb6sOLeu5n13U50okhE7w609Xbn0wOn6OTvyZDQIO7r3ZFFWw4y8v11uNrb8sFUKWhRW293RnZtw7gPN6JUKFg6YWC9buEgrQN/Y9JgXli/F4VCgbOdDe9MHsnaY+cZ9e5abK1VrHjAGBAp4sP1RL4ouS++FjGExVsOUF2jYWCH1oR1CAQg+tJ1VkQdpaCskqe+j6KDnydfPxHReOFqtZS+twK3T74GpYKqXyLRJl3HYc6zaBLiqY45jP2UGdiEhSNqNIglxZQsX2w43G3NT6gCgxDs7Gnx6yFK3noV9YlG1ixqtaQsfpX2G9chKJXkbtxMZeJV/BfOp/xCHEX7D1B8+Agug8LoGnMQUasj7Y0VaAqlGYa05W/RcesmEATK4y6Ss25D43Y2wOMbvqPd4HtwbOHB2+kJ7Fy2kt+/a9CZqVH7kl55lU6b1oFSSY7evoCF8ymLjaNg3wGKDh/BdXAYPWIOIup0pOjty9u5C5d7+tPjyAEQRQoPH6Vwf3STNPNXvoHPV9+CUklp5DZqblzH9ZnnUcdfouLIIZwfehj7wUMQtVp0xUXkLX0ZAKvgNrR47Q1EUZQ+m/ntGouvc9SlV/bBClw+WYOgUFC1MxJt8g3sZ0v3jfrY4XoPVbi74/LxGtDp0OXmUPr6y2b771YJnHd3YdT7P2FrpWLFA8ZP50V8vInIF6RVca9NHMTirQelZ6F9a8LaS58InjWoJy9u2Mf205fxdXXiw+mjGi2+h779heS8YjRakdGfbMHN3pZpd3WirZc7nx46Qyc/T4Z0aM19Pduz6OcjjPx4M652NnxwvxQAdsOpeNIKSvji6Dm+OCrNuH4zYwwetWbna6NSKlgy5m5m/bRXqnd6tKOtlxufHjpLJ78WkmaPdiyKPMrIj7foNaXgoefSsvj6eJyhbnt17N24Odg2rPcP1KUA84b05OWo33jnwBnc7G1ZcW9/o97w3szackSyr0swbT1d+PRYHJ183BnStiX3dW3Dol//YORXO3G1s+aD8dInJ11srXmkT3seXLsPQRAIC/ZlUBupw/v2wbNcyZHqg6f7d5Y+RSrqJL1hPZm19ahRr4ULnx67qNfz576uwSzadYKRa3ZJ9o03Bko7k56Lt5MdrVzNBxMaIyw0mJiEJEa99bXUJk41fhY04r0fiFw4E4DXHhjO4g17qK6pYWDHYML0g94f7jxKck4hCgH83F1Ypq//98deZdNvF1ApFNhYqVj1/GMWywgG9ehEzIV4Rr7wuvTZzyeN0eQjFq0k8l2pvn5/fSS7fjtDpbqGwU8v4f7w/jz7wFjW7T3CobNxqBRKXBztefupGQ3b2qUdMRevMmrxamytrVnx6CSj3vLPiFz2rGTrQ+NZ/N12ydbO7QjrIgWD3X0qjg2HJY+v4T1CmTSgp6VG57bEXLrGqFc/lcrzEeMXgCLe+pLIpVL4rtemjWXx2h1SH6NTCGGdjV+x2HP6Up3LNYYt/oiyqmpqtFoOxl7h6+emEeLrSVjnEGLirzNq2X8kzRnjNFZWLAAAIABJREFUjJorvyZy8SxJc+poFv+4U7KrUwhhnaTBsEn9u7P0p52Mf/MrrFRKVj4yvsGlM5KN1xn12meS3sPjjXorviJyyRy93hgWr42S6rlOIYR1CtHr9WDpT78w/o0vJL2HJ0gDh6XlzPpkPQqFgJeLE+90qTXApNOh2/olyqeXg6BAdyIastJQjJmOmHYN8dIpFBMfBRtblI9JdbNYmItuzVugVKKc+46UT1UF2h9XSQEuG0OnQ7P+M6xeXAkKBdrj+xBvpaKc8DC6lKuIsSdQDp2A0LGHtFyhohTtt9KSBsXQCQhe/tISDv0yDs3qV6C0gTgpOi3VX7yP3VufSJ/F3L8TXVoS1g/NRnstAe3J+l/4rcY9iMKvJdZTHocpj0umLn0OsbiB2CpaLVnLXyfgh7UICgVF27ZSfe0annPnUnnxImUHD+L+yEwchw4FrRZtcRG3FkoeioJKReAmycNTW1ZGxrx5jS/Z0Gmp/vID7JZ/LNkX/Su6tGSsp89Ce+2K+eBEHdh/E4lgbw8qK1T9BlH52vOWX+iw0NSh+fFTrBa+iyAo0MbsQcxIRTlpJmJyIrrzf6AYNhFFp57SEszyMjRr3m04z0b0tBs/RzV3hfTZz9/2Q2YqivEzEFOvIcaeQBE+HkXHHqDVIFaUof1e8o5QhI8HLz+UY6fBWOmLLJqPFkOpZbwaGZnGEBqL6v3fQBCEMlEUHWttmwP0FEVxjsm2o8BSURSP6X//APwqimK9gwuCILQE8kVRrBQEwQ04CdwniuLF+o4RE080ayHpUhKaUw6yG3eV+9txdGo8zd+Jsnkj/+a99Vmz6gGkpJc2nuhv5Pvs5m10Znj99cj1d4KfZ8Mvuf8Ejg7Ne5+6v/ynPjL056mubjzN38lf/FTtn+IOo6r/ZUzcvJuF/0aZ+gU0nuZvpK7Pfv6TiGV/39eHmsR/I/CcrgkxEP5OhOZ1ABYjNzWe6G9EV9HMdSmgzryzIK5/lbTExr989XcSEGoZh+yfxsqt4eXAfzcK2+b/CobVmr3/cysczvgG/Ptenk3onZn2f67M/60eEnUxFXin1rbtwDSkwJdNpSOwSu9lIQAfNDQYISMjIyMjIyMjIyMjIyNzJwGNZZrGv3JAorZ3hH7b4Dq2fVLr98wm5H0A6PoXTk9GRkZGRkZGRkZGRkZGRuYv8m8NaikjIyMjIyMjIyMjIyMjI/M/zL/SQ+LvQBCELkDtiHnVoij2/W+cj4yMjIyMjIyMjIyMjMz/XRTyio2/nf/ZAQl9XIjujSaUkZGRkZGRkZGRkZGRkZFpduQlGzIyMjIyMjIyMjIyMjIyMs3O/6yHhIyMjIyMjIyMjIyMjIzM34Ugr9n425E9JGRkZGRkZGRkZGRkZGRkZJodeUBCRkZGRkZGRkZGRkZGRkam2ZGXbDQBUav9b5/CP4u3P2RnNK+m0MxjYaKuWeVsHayaVQ8gsJVTs+rNEJtVjp9yiptVb7xW06x6AAFOts2q5y40s9uhspmf+5r/8br7v4HYzA8+gI1ds8qJ2anNqid4+Darnlhe0qx6Ev/jz6Kiees2hU3z9zFUHo7NqhcQ2rxlmp9S2Kx6AC2auQ1WujRvXQrQ/HfqP09zd53+f0D2kJBp/sEIGRkZGRkZGRkZGRkZmf/vkQckZGRkZGRkZGRkZGRkZGRkmh15yYaMjIyMjIyMjIyMjIyMTCPISzb+fmQPCRkZGRkZGRkZGRkZGRkZmWZHHpCQkZGRkZGRkZGRkZGRkZFpduQBCRkZGRkZGRkZGRkZGRkZmWZHjiEhIyMjIyMjIyMjIyMjI9MIghxE4m9H9pCQkZGRkZGRkZGRkZGRkZFpdmQPiT+JKIqs/HYDMWcvYmtjzcrnHqdTm9YW6T5at52oI79TUl7B2Y1fGLbfys3nlU++pbS8Aq1Ox7wZ9zOoV1dLje3RxFy+gZ21FSunjyW0lY+FRnxaFovX76Kqpoaw0DYsvm+Y2ejddwdP8kHUYX5b+TxujvYcjLvKp7uPIQgCKoWCl8O70auVFwDHbmTw9v4zaEWR+7uHMKt/ZzMttUbLy7/8RnxWAa521qyOCMPf1ZGMojLu/eoXAt2dAejm34LXx/SjskbDi9tjSC8qRSEIhLdtyYvhPVj5SwwxianYWalY+cBQQv29LO26mcPirdFUabSEtW/N4nEDEQSBoooq5m/YR0ZhCf5uzqyeNhIXe1uScgpZsi2ayxm5vDCyH4+F9ZTKurCE6V9sJ7+sEoUgML1/V16695769Wo0kt74MKPe+r1GvemjcLG3NRx3MT2bqf/ZyqppoxjZNQSAT0pq+K1ahyjCXTYK5jurpPK+qz+2zy4CpYKaXZFUb/jO4jwAVIOG4bB8FWVzpqJNvAyAIrgtdvNfRbB3BFFH2ZPTQK2u8/jbWN99D07zXwGFksqobVSs/cZsv+29E3F6fgHa3BwAKrespzJqOwCun3yFVedu1Fw4R9G8pxvUuY1r+GCC33odlEqy128k49PPLdJ4jL+XgAUvgihSfjmBq089B0DrVxfjPmwIKBQUxRwjecmyJmk2xIxv/0OXe0dRmpPLm136/eX8AFoMCSd05RsICiXp6zaQ9MlnZvs7vrUcjwH9AVDa22HdogUH2nQAYHT2TUovJwBQmZHB2YdmNqrnGBaG77JloFBQuHkzeV9+aZHGeexYvF54AUSRqoQEbs6dC0DrH37AvkcPyk+fJu2JJ8yOEYEVUUeNz+GDwxp+Dpv4XHx79By/nk8EQKvTkZRTyPHXnsDV3pYlPx/haGIqNioVNlZKtDqR+3t1YNagHmaaao2Wl7cdIv5WHq72tqyePAx/NyeKKqqYu/EAFzNyiOjRnqXjjM/x7LW7yC2tQKMT6dXah1fH3YPSJM9j12/y9t4TkmbPdsy6p5ul5o4YvaYNq+8Px9/VibiMXJbt/M1Qas8M6sGwjoEk5xUzb9thw/E3C0t5LrwnD/duJ+n9zXVpeXUND/24z3B8dmkF4zoH8coQqeyOJd3i7YPnJPu6tWFWv1BLvV0n9Ho2rJ7QH38XR3bGp/DdqQRDuqs5RWybOYqO3m7M3nKY3LIqNDodvVp58erwXoYyPZacydsHL0j2dQ1iVt+Olnq7TxGfXSjZN+5u/F0cqNHqeG3faS5nF6HV6RjfKZDZ/TqSWVLBK7tPkldehSAIPNgtmBm92pnlKYoiK7fsIyb+mtQmPjyB0ABfahOfeovFP/4itYmd2rL4wZGGNnHd4VNsOHIapVLBoM4hLJg0HIDEm9m8vuFXyqrUKBDZMv9hrFVKVv58kJjLSdhZWbFy+ui62+D0LBav3y09I6HBLJ401LwNPnSKD6KO8NuKZ3FztGfnmXi+jT4FgL2NFctmTaNDa39LW3+KJOZCArY2VqycPZVOQa0stD/asouo42ekPsa37xq2bzr4GxsO/IZSIWBva8Pyxx8kxN/y3EVRZOXmPcRc0pfpzImEBvjVXaY/REo2dm7L4smjEQSBeWu2kJydD0BpZRVOdrZEvvoUNVotr/0YxeW0TOk639WF2aMGSHpb9xMTf10q04fH1X0N0zL111BDWKcQFj8wwuQanmbDUf017NSWBZOGUlRWwdyvt3Mx7RYR/bqxdMoYo33Ncc+UlrD57tbYKI3zfEKHHigmzQKFAt2JA4jR2800hcHjUdw9AnRaxLJidBs+hcJchJAuKCIeMyb0bolu7QeIF09anHdthNBeKB6YDYIC3e/7EfdvNd8/ZCKKASMlzdJidOs+goJcABQTH0Xo3BsA3Z5NiGePNaqn6NIH1YxnQaFAe2Q32l83mu1XDhmHctgE0OmgqpKa71Yj3ko1JvDwwuad79FErkW7e0ujesqe/bCZ9SIoFNQc+IWabT/Vna5/OHavvE3FizPRXb8CTs7Yvvw2yrYdqTm4C/VXqxrVArAdMBD3RUtAqaDs562UfPu12X6HCRG4zVuINicbgNKN6yj7eZthv+DggF/UHioOHaBw5ZtN0qxtr/UTc0GpRLP/F2q212+v7csrqZz3qGTvHfBnr6HQwhvrd39AzEwHQHf9MpofPrpjG2VkQB6Q+NPEnLtI6q1s9n7+NrFXk3jjqx/Z/N6rFukG9+nOtDFDGf3MK2bbv9y6k1ED+jB1VDjX0zOY8+ZHHFzzvrnG5SRScwvZ++oc4lJusXzLPjbPf8RC440t+1g+ZRTdAv2Y8+VWjiUkERbaBoDMwhL+SEzB183ZkL5f+0CGdGmLIAgkZuQw75tt7HpyAlqdjrf2nuKbacPwdrZn8nd7CG/bkhBPV8Ox2y9cx9nWmn1PT2R3fDKrDp1j9aQwAFq5ORI5616L83u0Xyh9A31Qa7U8tj6az4/FkZpXxN4FDxGXns3yHUfZ/MwDlnbtOMLySeF0C/Bhzvc7OXY1jbD2rfnmyFn6hbRk1uBefH3kLN8cPcf80f1xsbdh8bgwDl5OMsvnbPItfN2cOLT4UU7euMmT3+8kok8oId7u5nqRh4163/3CscRUwjoEGvXCe/P14TN8c+Qs88cMAKQXrtV7fmdAuwBDPudTMolT69jQwhqAWflqzql19LKzwvaFxZQvmIOYm43jlxuo+e0IulTz88XOHptJ09BcjjNuUyqxX7KSipVL0N24iuDsAhqNRZmZoVDgtHApRc8+gTY7G/e1m6mOOYw2+YZZsqoDeyh9f4XF4RU/fQ+2tthHPNiwjole8DtvEf/gNNS3Mum271cK9h2g8uo1QxLboEBaPv8MceMmoS0uxqqFBwBOvXvhfFdvzoePAKDLzp9x7t+Pkt9PNE27Hv74YT1HPlvDzB+/+kv5GFAo6PTuSk7dP5mqW5kMOLCHnL37Kbt61ZAkYalxIKX1E4/h3MX4IqqtrOJ4+PA70vN74w2SZ8xAk5VFcFQUpdHRVF+/bkhiHRiI51NPkXT//ehKSlB6eBj25a1Zg8LODrepUy2yPicI0nP40gzi0rJZHnmEzc9aXus7fS4eH9STxwdJg4GHLyfz4/ELuOoH8CJ6tGPKXaFMXxPFrrmT8XZ2YPKXPxPeMZAQLzeD5vazV3C2s2HfvKnsjrvOqn0nWD1lONYqJc8N7c21nEKuZxeYnefqycNxtLVGFEXmbjzAvktJjOkoDRJrdTre2v0H38wYKWl+/Qvh7QMI8TTRPH9Vqtuef4Ddl5JYFX2G1feH09bLja2zx6NSKMgtrSDiyx0Mbh9AUAsXIp+caMh/8OrNDO1govc316UONlZm2+7/dhfDOwQY9Q6c5ZvJ4Xg72TF57X7CQ/wJaeFi1ItLkvTmjGP35VRWHYll9YQBjOsUyLhOgQBczS3i2e0xdPSWymX1hHtwtLGSynTHcfZdSWdMh5Z6vXN88+AgSe+naMLb+JnrXUzG2daKfbPGsDshjVVH41g9/m72Jaaj1uqIenQklTUaxn23l7EdA7BWKlgY3p1QbzfK1TXc/+MB7m7tbZZnTPx1UnPy2bv8WeKSM1i+cRebF5kPtAG8sXE3y6ePpVtQS+Z8toFj8dcJ69yWk4nJHIpNZMfSOVhbqcgvKQdAo9Wx6IdI3pk5kQ4tfSgqLESlVBjb4KWziEvNZPnWA2yeN8NSb8t+lk8eKbXBX23jWEIyYaHBQN1tcEsPV9Y+PxUXe1tiLiex7LstbF7+olmeMbEJpGblsnfVYmJvpPLGD9ss0gAM7tmJacPvYfSClWbb7727F1OGSu3UobOXeHddFF8vmmNxfMyla1KZvvk8cck3Wb7+Vza/MtvSxg2/svyh8XQLbsmcT9cZynT1bGOd8e7WvTjZSc/6vrPxqDVaopY9Q6VazbhlnzG2TyeuZ+aRmlPA3tefJi4lg+Wb9rB54WOWehv3sHzaWLoF+TPnP5s4dvkGYZ1COJmYwqG4RHYsmS1dw1LpGlpbqXhu3CCu3crlemau0b5mumcKt25EpTBx4xYUKB6Yg/bzZVCUj3L+B2gvnoLsdGOam8loP5gHNWqEAaNQjJ+Jbu37iNcvon1ff63tHVEu/RLxynmLc7ZAUKCY/BTaT5ZCUR7KRR+ijTsBWaaaSWjfmQs11QgDx6CIeAzdt+8idO4DrdqgXfkcqKxQvvgu2vgzUFXZoJ7qkReoefclxIJcrN/4At25380GHLS/H0R7aCcAih79UU1/ipr3Xzbst5r+NLq4U43bBqBQYPPkAipffR4xPwe71d+jOXkMMT3FPJ2dPdbjHkR75ZJxm1qNev0aFAHBKFoHN1nPfclr5Mx+FE1WNr6btlF5+BA1SeZ9qPJ9u+sdbHB9di7VZ5toXx361nPmU/XaC4j5Odiu+g7NqbrttRr3ANrES3Vm0yB/8RqKObdQL7WsL/7XkVds/P38K5dsCIKgFQThgiAIlwRB2CoIgr3JvghBEERBEDrof9sKgnBFEIQuJmkWCoJgOY1oruEsCEKGIAifNZSuPg6dOs+E8P4IgkD39m0oKa8gp6DIIl339m3wcne12C4IAmUVUkVfWl5ZZ5pDF68x4a7OCIJAtyB/SiuryS0uM0uTW1xGWVU13YP8EQSBCXd15mCc8QXw3Z8PMn/CYLOHx8HG2jDyX6mu4faui7fyCXB3opWbE9ZKJaNDW3PoqklDBhy6ls7ErtJgx4iOrTmRkoUoivWWk52Vir6B0qyMtVJJqI87J1KymNCzg2RXgI9kl76RN9hVUk5ZtZrurX0lu3p24GC89OJ+6HIyE3tKM84TTbZ7ONrTpZU3KoX5bX0mJZPp/bshCAL9QlqhVCi4mpnXsF6vjka9+CQm9pJm/yaabAdY/1scwzu3wcPRzrBNEEAtQg3SnwZwVwgoO3RGl5GOmJkBGg01h/ZiNWCwRZnZPv4M1Zt+AHW1YZuq991ok66huyG9+IolxdJodQNYdeqCNj0NbcZN0NRQdWAPNoOGNHiMKerTJxDLyxtPqMepZ3eqklOoTk1DrKkhd8cvuI8aYZbG+6FpZH2/Fm1xMQA1efn6PSIKGxsU1tYobKxRqKyoyc3jr3L92O9UFBT+5Xxu49qzBxXJKVTqbcyMjMJ79Mh60/tNmkjmzzv+tJ5dt25Up6ZSk56OWFND8c6dOA03H9BwmzKFgp9+QldSAoA2P9+wr/z339GVmdcZtzmtUDChV0fpOWzdxOewic/FbXbHXmVMt7aG372D/MgqLsNKpaCVuzPWKiWju4RwKCHF7LhDCSlM7CHNjo/oFMyJpFuIooi9tRW9An2xUSmpjaOtNACo0emo0WrN6ryLGXkEuDvTys1Zqts6BXPoSpq5ZmIaE/XnOiI00KBpZ6Uy1CnVGm2dHZETyZkEuDvh7+oo6f0DdakpKQUlFJRXGTzbLmYWEODqSCtXR0mvYwCHrt2spXeTiZ2DJL0OrTiRaqm363IqY0KNnn6ONlYAaHQiNVqdwfaLmQUEuJnodQjg0PVb5nrXM5ioH+gY0b4lJ9KyEUURQRCorNGg0emo1mixUipwsFbh6WhHqH4gxMHaimAPZ3LKzF+GDsUmMqGfVJd3C25JaUU1ucWlZmlyi0ulNjG4lXTP9uvGwVjJY2dTzFmeGDkAaytpLsbD2QGA3xJu0M7fmw4tpbbK1cEOpULBoUvXmdCnk6QX6EdpZVU9bbDa2Ab36cTBiyZtcOQh5o83b4N7BPkbvOy6BfqRVVBMbQ6dvcSEe/pIfYyQQErKK8kptEzXPSQQLzcXi+2OJl58ldXqejvQh2KvMKFfd32ZttLbWEeZVlbTvc3tMu3OwQsJZmlEUWTf2XjG9JG6YgICldVqNFot1WoNViolDrY2HIpLZELfLvp+TUtKK+rRq6qme3BLSa9vF+M1PHaWJ0b2N15DJ+ka2ttY0yskABsr83m2ZrtnrJUoTQu5dVvE3CzIzwatBt25Ywhd7jIvs+sXoUbychRTEhFcPaiN0K0/YsI5Q7oGCWyHmHsL8rMkzbMxCN3MPQPFq3FQI/UtxOQrCK4tpB0+rRCvXZT6FepqxIxkhNBeDcoJbTogZmcg5maCVoP2xCEUvfqbJ6qqMP5vYwsmdY6i1wDEnEzEmymN2wYo2oaiy7yJmH0LNBo0MQdQ9Q2zSGc9fTbqn9eZl1l1FbrLsU0rx9v5dOmKJi0VzU2pD1W+Zxd24UObfnxoJ5QeHlT+/lvjieugtr3aY9H12luzfX2jHrN18VevoYzM38W/ckACqBRFsbsoip0BNfCkyb6pwHFgCoAoilXAXOBzQcIfmAO8QsO8CRz9syeYnV+Ij4dxht3Hw52cO3jxeWbyBHYe/YPBT8znybc+Yums6RZpcopL8XF1Mvz2dnUiu1ZDml1cinetNDn6NIcuXsPL1ZEO/t4WeUfHJjL2rTU8+dVW3rpXqnyySyvw0TfuAD7ODuSUmncIs0sr8HGWxodUCgVONlYUVUqNW0ZRGZO++ZWHf9rHmbRsC82SKjVHrt1EqRDw0XfcAbxdHMkuMe/kZZeU4e1iniZHnya/rAJPfafA09mBgrIGRvCBnJIyfPR5ZRSUoNXqaGFiZ916DuToX84s9MolveziMqLjbzC5n7krdvfWvvSyUTAmu5rR2dX0s1YQZKVA8PSSOih6dLk5CJ7m10YR0gGFpw+aP2LMt7dqDaKI/Xtf4LhmE9ZTZjZoM4DC0xtdtoledhZKT0uXfJshI3DfEInLOx+i8LZ06W0q1j4+qG8ZX0rUtzKx8THPz65NMLbBwXTZ+TNdd0fhGj4YgNIz5yj+7Q/6xJ2hT9xZCo8cpfLadf5t2Pr6UHUrw/C78lYmNr51l5lty5bYtQ4g79hxwzaFrQ0Dovdy995f8R49qlE9Kx8fajIzDb81WVlY1SpTm6AgrIOCCNq6leCff8YxzLLDUhcFgmB4LqCpz2Hjz8VtKtU1HEtMZXiXELPteWWVWJkMGvo4G/M06pYbzk2lVOBkY01RRVWjNs36YRcD3/4RBxtrRnQyzoJll5bj41y7bqswO1bSlNKoFAqcbK0NdVvszRzGff4zE76IZNnY/haDnrsvJTGms6neP1uX7o5PYVRooGFg2TQvAB8ne4uX+eyySnycTPWsKao078DuvZLG2I7mSw9nbT7MwE9/xsHaihHtW1nkJenZ1a1nap+1FUWVaka0a4mdlYpBn+9k6Fe/8mif9rja2Zgdm1FcTkJ2EV19zV/QcopK8THxNPB2cyK7qFabWFSKt6tJGlcncvRpUnLyOXs9jcnvfsPDq3/gYor0LKdm5yMAsz5Zx30r1/DtwZNGPdO8XJrYBheZtMEuTnSoYynUbbafiGNg1w4W27MLi/HxME5U+Li71jkg0RDrDxxnxLy3+GDTThY/PKnONDlFpfi4m5aXM9mFJbXOpQRvs3J3Nth4m7PXUvFwciTQW7pmI3qFYmdjzaCFHzD0ldU8Oqwfrg52dVxD53quoZNZGuM1LODs9XQmv/cdD6/+kYsp5gNhddrXHPdMUr5ZnoKLBxSZDKoX5Uvb6kHRbzi6hLMW24WeAxHPxdRxhCWCqwcUmmgW5jWs2X8Euvgz0o+MZBSdeoOVDTg4I7TrCm6eDeu5tUAsyDH8FgvyEOo4RjlsAtYfrMNqymw0P+nnAG1sUY2dgiZybZNsAxA8PBHzTPTycxA8zPUUwe1QeHqjPf3nBgFMUXl5o8ky9qG02dkovS370/bDRuC7/RdarPoY5e0+lCDgtmARhave+9P6Fvbm1W2v0MIL7Zk/Z+9fuoaA4OmD9ZtfYb3kQ4R2XSyOk5FpKv/WAQlTjgEhAIIgOAIDgMfRD0gAiKK4F8gEHgY+BF4XRbHe0QFBEHoB3sD+P3tSdY0P3knU1d3HThIxZABHvlnFl0vnsuijr9HVmvGuaxBSQGhCGull4Kv9v/PcmIF16g/r1p5dS2fz2ROT+OToBSmvuhLWMqm+c/J0tOPgs/fx8xP3smhYbxbuOE5ZtbGzq9HpWBB5jIf6dMBWZblSqGl2/Tkfqdt5lVereWHdblp7uuKgn/lrUK8Rubd3HmP+6AEoa72cpOYVkaIR+dXLhl1eNpxR6zhXrcOiMGsLCwJ2zy6g8os61jYqlai69KByxSuUPTcTq4FDUPa8yzJdYwbUsrP62GHyxg+jYFoE6lMncFm20vKYplKHnlhLUFApsQsO4lLEgyQ++Swhq99D6eyMbWAgdm1DON39Lk5364PLPf1x7tf3z5/LP0WdZVr3bIFfxASyfvnVzJPlcPfe/DZsFBfmPE3HFW9gH2gZd+aO9ZRKbAIDSZ46lfTnn8f/nXdQODlZHlc7m7rkmvIcNvExPJKQTM9AX8NyjYbytKhn6krSBOGvZ47l6KIZqDVaTiYZX1SaMqHT0Gl1a+nFzqcnsWXWeL4+Hke1yXIptVbL4cQ0RoYGNZjX31WXAuy+nMJYvfdBvXq1qMv7wrRIY2/lYatS0tbT3Fvv68nhHH02ArVWy8nU7DvQs9wmCJJ3hUIQOPLUOPbPGssPp6+SXmQcCCtX1/BC1O+8MqS7wUPDkGcdyrXviobs1Gp1lFRUsWnh4yyYNJx532xHFEU0Oh3nbqTz3mOTWLfgUaLjrvFHYmqT7sO67RSkNvjACZ4bYxmv6DYnr6Xy84k45k8ZZ7GvbjvurA2cPvwe9q9eyvwp9/Lljrq7O03RacIjy67TFxlzl3Fw/mJyBgqFwJH3FrB/xVx+iD5Bel5hveVlfk516UlpDNfwpUdZMGko877d3qBnUXPdMwezSzmRbzKwWuelqvs8hd6DEAJCEA9Gmu9wdkPwa42Y0ITlGvWL1p3yrnCE1m0NcS3EhPPo4s+gXPABiscWIiYlgE7bSCZNaw+10VGoFzxEzeY1qCY8BIBq0kw0e7dBdeMDzQ3rme+3eWIu1d9+0vQ871jP3L7KI4fJGDmEzPvGU3XiD1qskOK4OE2ZRuWxGLQmk0J/u74gYP34C6i/+wv2/oVrKBYVUD13KupX51Cz/nOsn14CtvagO0vXAAAgAElEQVQWx/4vIgjCv/rv/yL/6hgSgiCogNHAXv2micBeURSvCoJQIAhCT1EUz+n3zQVOAddEUaw76ouUpwJYBcwA6vW9EgRhNjAb4ItlLzH7wQms332QbQekkerOIUFk5RvXMGflF+DpZrnsoj62HTzG16/NA6BHhxCqa2ooLClj7++n2br7AABdAnzJMhnJzy4qxctkthLAx9V8tD+7qBRPFyfS8wrJyC8m4t3vDNvve/8HNs9/GE9nYx69QwJILyylsKIKHyd7skqNDWpWSTleJksRAHyc7ckqqcDH2QGNTkdpdQ0udtISEGu9G3UnXw9auTmRkl9KZz9pdH7G2n2kFZaSW1ZJFz8Pskw6oNnFZXg5m3ss+Lg4kl1snub2bKyHoz25JeV4OjuQW1KOe61zBCmOQ8T5TVI5tvQio7CELw+e5t7u7dly8lIT9MrxdKpHz0HSi7+Zw/yN0q1ZWF5FzJVUlEqB1LxiOlspsNevJ+1vo+RSjY4+udkInsbZbYWnl9noN/YOKIJCcPxICjwpuLfAfsXHVCx5ATE3B03sGcRiaVmQ5sRxlG07oj1X/9pEXU6WmceDwtsHrakeIBYbZ9wqd2zF8bl59ebXGOrMTKz9jMHQrP18UWeZz+6qb2VSevY8okZDdVo6lTduYBcchEv/fpSePY+uQpq1Ljp4GKdePSg50XgQr+ak6lYmtn7G4HN2fr5UZ1nOYAP4RkwgftFis22301amplHw2+84d+lMRUpqXYcDUJOZiZWvMQCbyseHmmxzPU1WFhXnz0vLgG7epDopCZugICrj4mpnxx6Fgmj9AFqIKJJVfKfPYePPxW12x15jTDfzoIQAnk521JgM0mSVlONVy2PJx9mBrGLJs0mj1VFarcal1ix6fdhYqQjv0JpDCSn0b+1tzK+kVt3mZN5xkjTLjXVblaVmG09X7KxVXMsporOf5Op87NpNQn09aGFSD/2TdemV7AK0Oh2dTLwHfJykvAx6pRWWek72ZOk9KSQ9NS76ZS4AexLSzJZrmJWpSkl4iD+HrmfQv7UXPo52ZJWa6lXWoWcn2eek11PX4GJrza6EVAYG+WClVODhYEsPfw8uZRXSytWRGq2OuVG/c2/HAIa3awnAhnPX2LpRcmbs0tqPLJPZ++zCUrxczQfffNycyS4ySaNvE2/vG95DWi7YNdAfhSBQWFaBj6szfdq2xs3Rng1HTpORX8zL635lUKc2ZJnmVVyKl3NT2mBH0vOKpDb4ve8N2+97fy2b58/A09mRxIwcXtu4j6+evB83/f2//sBxth3+A4DOwQFk5RuXgWYVFOFpMot/J4zp14Pl3xsD7W04fJKtx6WuU5dAP7IKTMurpO4yNSv3EjxN0mi0WqLPJ7B1iTFGxa5TcQzs1BYrpZJ9Z+MpLKvg0Y/W0b9jUK1rWGLZr6nlxSDpORr2De/e3uIaupvUIVczcohYIcUNao57BmCgpyOXS6ro5yGdh1iUb1wOAeDqgVhsHvcGQGjXDcXwB9B+ugS05nGhhB4DEONOND4woEcsykNwM9F0a4FYnG+RTmjfHcWoyWhXLzKLRSXu3Yx272YAFI++BDkNe5+IBbkI7kbvH8G9BWJR/UstdScOYzVTCrisaNMBZZ8wmDIH9IG6UavRRte/xFHMy0FoYaLn4YVYYIwXgp09itbB2K2UgmkLbu7YLn2fqrdeuuNAjwCa7CxUJh6JSm9vtDnmfShdsfEZLdu+BbcXFwBg060HNj174TR5KoK9A4KVFWJFBUUfNS2YJtRhbwsvxAKT8tXba7vCaK/NkveoXrGwyfb+lWuIpgbKaqR8Uq4h5txC8G2JmHy13uNlZOrj3+ohYScIwgXgDJAGfKvfPhXYpP9/k/43AKIo3gIOAV/QME8Du0VRTG8okSiKa0RR7C2KYu/ZD04AYPqYoUR+uJzID5cztG8Pog7/jiiKXEi8gZO9fZ1xIOrDr4U7J+KkryfcSL9FtboGdxcnSWPRY0QueoyhXdsSdeoSoigSm5yBk60NnrUabk8XRxxsrYlNzkAURaJOXWJIl7a08/Pi+MrniX79aaJffxpvVye2vzQTT2dHUnMLDTMCl9OzqNHqcLWzobOfB6kFpdwsKkWt1bLncirh7cyjeoe3bcWOOCmgz/6EVPoG+iAIAgXlVWj1LxnphaWkFpTQ0k0614+PnMfLyZ5jLz5A5Kx7GdquFVHnrkh2pWXhZGttGGww2OXsgIONNbFp0jrnqHNXGKKfgQwPDWLHOamy3WGy3ZQegb5EvjCFyBemMCQ0iNV7fifI05UerX0b1tOvq446m8AQvct3eGgQO85K62V3mGw/8PIjRL88k+iXZzKySxtenTiYYZ3a4OfqyDm1Do0oohFFzql1BKkEtInxKFsGIPj4g0qF1ZBR1PxusmqovIzSCYMpnTKG0ilj0F6Oo2LJC2gTL1Nz6jeUwe2k9XtKJaruvSyDYdai5vIllAGtUfj5g8oK2+GjqY45bJZG4WHsvNiEhaNJbjjPhig9H4tdcCA2Aa0QrKzwnDiegn0HzNLk79mPy4C7AVC5u2EXHExVairVGbdw6d8XlEoElQrn/v2o+Bcu2Sg+fwGH4CDs9Db6Rkwge+8+i3QOIW2wcnWl6PQZwzaViwsKa+kF0MrdHbe+fShLvGZxrCmVcXHYBAZi1bIlgpUVLuPGURodbZamZP9+HO6WylTp5oZNUBDqtLS6smO0TscqjYZVGg136XREnU2QnsPURp7DO3guAEorqzmdlGG27TbtfTyo0ei4WVCCWqNlz8XrhHcwfxEO79CaHeelTs3++CT6Bvs1OOpfXl1Dbqkx2FzM1TSCTGb6O/u3IDW/mJuF+rotPonw9gFmeYS3a8WOWOl67L+cQt8gKW7GzcJSNPq6LaOojOS8YkOsCLBcrgH8Y3UpSMs1xtSq8zr7upNaWMrNojJJLyGN8JCWtfT82XEpWdK7kk7fAG9DmepEkX1X0gxBQEHyVMjVL8PQ6HTEJGUSpHftl/TKjHpX0ggPMf8yQ3gbP3bEp0h6iTfpG+CFIAj4OttzIi0HURSpUGuIzSwg2N0JURR5de9pgj2cmdmnvSGfaT3bErlkDpFL5jC0W3uiTsRK92zSTZzsbAwvjrfxdHHCwdaG2KSb0j17IpYh3aT8hnRrz8lEqQxSsvOp0Wpxc7RnQGgbEjOyqVTX8ODAXoT4tuCNKaMY2qUtUafjJb2UW/W3wTbWxKZIMUeiTsczpHMI7fw8Ob7iWaKXPUn0sif1bfAjeDo7cqughOe/28E7M8YS6GVc+jl9+D1ErnyJyJUvMbRXZ6KOn5b6GNdTcLK3qzNWRH2kZBlf1I5euExrH2NdPy28L5GvPkXkq08xtHtHok5c0JdpOk52tvWUqTWxSen6Mr3AkG7GZSZ/JCQR5NMCH5Pz83V34cSVJERRZGL/Hng4O/KfJx9kaNf2RJ28qO/X3Kxfz8aa2GT9NTx5kSFd9dewa3tOJqYYr6FGaxgUuE07f69mvWc0Wh1nCito42AygJl2DcHTF9y9QKlC0XMg4qVaEwj+QVIQym9WQJnlchxFz7AmfenCQOpVBC9/8PCWNHuFIcbVGtRvGYxi2rNov3jDXFNQgIO+XPwDEfwDpdgVDSAmXUHw8ZcmWpQqlP2GoDv3h1kawds4gK/o3g8xS1ryon5rLtXzplE9bxrafdvR7NzQ4GAEgO5aAgq/VgjevqBSoQobjvaUSflUlFM+fRQVT0RQ8UQEusT4Pz0YAaC+dBFV60BU/i1BZYXD6LFUHjlklkbZwri8wW7wEEPAy7yXF5AxIpyMUUMpXPUuZTt33NFgRF32KgcOQ3PS3N6Kh0ZTOWsSlbMmoUuMv6PBCPhr1xAnF+m+AQRPXwTvlog5mcjI/Bn+rR4SlaIodjfdIAiCBzAE6CwIgggoAVEQhIWi0d9Op/9riLuBgYIgPA04AtaCIJSJovhyI8eZMahXV2LOxjHyqZf1n/00RomOeHEZkR8uB+D9tVvYdewkldVqBj8xn/uHDeTZKRNZ+OhkXvt8LWt37kdA4O3nH7focIeFtiEmPolRb3yFrbUVK6aPMWq8+x2RiyTN1x4cyeL1u6hWaxgYGmyI7l0fBy4kEnX6EiqlAlsrFasmSZ/xUwkCS0bexayNB9HpRCK6hdDW05VPj16gk68HQ9q14r7uISyKOs7Iz3fgamvNBxHSkpAz6dl8ejQWlUKBQhBYNrovrnY2ZJWU89Vvlwj2cOa+b3YBMK1XO1q5uzDq/Z+wtVKx4gGjo0rEx5uIfEFajfPaxEEs3nqQ6hoNA9u3Jqy91FmeNagnL27Yx/bTl/F1deLD6dJa/NzSch78dAtl1WoUgsBPx2PZOW86jrbW5JSUs/nkJbacisfXxZGjV1IY1CGQiI82EjlXGtd6LWIwi7dEW+oN7sWL6/ca9R4a3WD5jugSwvEd0fw/9s47vIpq68PvzDnpPYQkJBAgJPRQRRBpCb13UVTEEtFr7xhQQAWsWLCDDQSlGXoNAUIVBKUECDWBACmE9H5m5vtjQk5OTgpe47ne++33eXjImVmzf7P2zOw9s2ftNRPT9URi3RxkejoaQFEo/HguLu99oX+yatNq1MTzODz4L5SEeEwVBycqk5dL8YrFuH65FNAwHdiN6UAtNyqKQu67s/H6ZAEYZIrWRqNcOIfLlCcxnYqnOG4Hznffj0OvcDSTCS0nm5xZ5jf6Xl8vxtikKZKTMz7rY8l56zVKDtQwT1FRuPDqa7T5+UcwGEj7aRmFCWcIevkF8o4e48aWbWTt2Ilnn150jNuOpqokvjEbU2YW19dtwKNHdzru3AaaRuaOXWRujale6xZ5eOm3NO/TA1efesy9fIp1M+aw79tqA6hqRVMU4qdGcfuKn0A2kLz0Z/ISzhA69SWy/zhK2mY9LDpgzCiuRVveXLk2DyXsg3fRVBVJljn/8acWX+eoEkXh6owZNFm0CEmWyVyxguKzZ/F97jkKjx8nNyaGvLg4XHv2JGTrVlAUUubORcnS39o0Xb4ch+BgZBcXWuzbx5WpU8mL06O8OmkaF+t5MOjdRXr7UvE6/IvXRUz8Be4MDcLZ3jLk/sVlMRy8eA1V0xj80c+4Ozpwf/cwQv28mR9ziDaB9Ylo1YSxnVvyysodDJz3E55ODrw/oV95Gf3eX0JecSmlisL2U4ksmDwUT2cHnvhxCyUmBUXT6BocwIQurcvfLBplmWlD7iDyxy2omsboDqGE+noxf8cR2gT4ENEiiLGdmvNKdBwDP1mha47rA8CRS6ks2HusvG17bWh3vMqmoRSWmth34Sozh91p4adRluu8Lb3J5lNJfDkhwlqv/21ELt+p+xcWTGh9D+bvPkYbf28iQhsytl0zXlm/n4FfrcPTyZ73R5j3+bfLafi5OdOowkBLYamJJ1bFUaIoKKpG18Z+TOgYAmi6Xr9ORK6M0/0La0qojwfz95ygjb8XESGBjG0XzCsbfmXggo26f8P15Hr3dAxh2qZDjPhuCxowum0TWvh6cjg5nbUnk2ju48Ho7/Xr6NleYfQONkcI9WobStyJcwx6/VP9nJ00wnzOzv6K6LI39K/fM4SoH9bo52ybEHq10fOYjOnekemL1zLijS+wMxqYM2kkkiTh4eLEA327cdfbC5GAXq2a0rtNMzRNI+7kBQa9uQBHeyOzJ5rP8dHvfk/0y5N1vbv6E7Vkk67XummtffAXW/aSnV/IGyv0AVujvT0r33zBwqZ3h9bEHT3FwBdm42hvz5xHy2epMjrqPaLnvATAez+tZcO+IxSWlNLnqZmM69ONJ8cOYunW3eyLP4OdwYC7izNzp0yscl96tQ0l7vgZBk3/WK/TB0aZdd78gujXHtd9nDiMqB9WU1xSSs+2ofRqa05Wu+m3E+XJLG9yT5/bmfbDakbM+kw/zne0p0VDP5oH+hIXf45BMz7T9e43T1cZPWcB0VGRZcdwMFGL1lFcWlp2DJuVHcMOTF+8jhFvfqUfwwdGlN879Zs+n7yiYr1tOJrAgqfvs9k509Pdkd6+FQarVBV11dcYHp9Z9tnP7ZByGXnwRLTL59BOHEQe+SA4OGGY/DIAWuZ11IVlX7zy9gVPH7Tzf+LLCaqKuuwLDE++qWvu3wbXLiEPuw8t6Sza8V+RxzwMDo4YHnm1TDMd9cs3wGDA8HxZvoOiApTvP6g1cTaqimnRfOxeegdkA0rcJrQriRjHTEa9eAb1930Y+o9CbtMZFBNafi6lX79Tc5k16ikUf/k+TrM+1u+hYtajXrqI/b2RKGdPWw5OVIHzwmgkZ2cw2mHs1pvC15+2/mJFRRSFG3PewPfLhWAwkBe9itLz5/B44mlK4k9QuDMWt3vvx6lPBCgKanY211+rLX3dn/O35KsPcJz5Ecgyppj1aJcvYjcxEvXcKZSDe2ovo1aNf/8Yyi3aYRz7oN7Pqiql338I+bm1CP5v8F86K+IfjXSrWb1tSdkAgWulZVOATpqmTamwbBcwXdO03WW/vwfWa5q2kltAkqTJwG2apj1Zk516cq9NK0m7bONwp9QrtdvUNW63/qanTrBx65H30YLajeqYovxSm+qdu2Tbjmdx2p9L6PZXGVHPpXajOibIzbF2ozqk1Ycv1G5Ul/yJDOd1o2fbawLQw1htqlfL53/rmlsMH69Tgq2TPv6tlPyJee11gFSvQe1GdYiWn1O7UV2j2Pi6kGwbAKytWW5TPUptfN0DptyaE4jXuV5WQe1GdUhGYt19ketW8WnqXbtRHWLwsJ7i/HfjuDj2f+7x/WSz4H/ew3MFWp+/8F9X5//UKRtVcQ9QKeMPq4Cqh/0FAoFAIBAIBAKBQCAQ/GP5R07ZqBwdUbasTxXLPqn0e/Kf1Pke+P5P7ZxAIBAIBAKBQCAQCP7fIYs5G3XOf1OEhEAgEAgEAoFAIBAIBIL/Ef6RERJ1gSRJYUDl7HXFmqZ1/U/sj0AgEAgEAoFAIBAIBAIz/7MDEpqmHQc61GooEAgEAoFAIBAIBAKBwOb8zw5ICAQCgUAgEAgEAoFAUFeIFBJ1j8ghIRAIBAKBQCAQCAQCgcDmiAEJgUAgEAgEAoFAIBAIBDZHTNkQCAQCgUAgEAgEAoGgFiQxZ6POEQMSt4Kq2FZPU22r59sAMtJsq2lrJNsGA8mO9jbVA3C0sV5AfSeb6o1QTDbVW5uRb1M9gJHYtpNrpWk21bM5dnZQWmpbTcXG/YWt+ydb+/efwM627bd2/apN9XD3tq0egGLj69DWGAz/6T3425HtbfvIYOflYlM9n//AQ2bO1Syb6rnL4kFa8M9ETNkQ/O8PRggEgv+f2HowQiAQCAQCgUDwpxAREgKBQCAQCAQCgUAgENSCjYOu/18gqlQgEAgEAoFAIBAIBAKBzREDEgKBQCAQCAQCgUAgEAhsjpiyIRAIBAKBQCAQCAQCQS2Ir2zUPSJCQiAQCAQCgUAgEAgEAoHNEQMSAoFAIBAIBAKBQCAQCGyOmLIhEAgEAoFAIBAIBAJBLYgZG3WPGJD4N9E0jTnf/kzckeM42tsz56kHaRPc2MruoyXRrNm1n5z8Ag4v+bR8+dzvlnHwxGkACotLuJGdy8HFn1hr/LKduJMXcLKzY869g2ndyN9KI/5yClFLNlJUaqJX62CixvS1mN/0bexB3l+zk72zn8TL1ZkLqRlMW7qJk5dTeWZYTx5q36Tcdve5ZOZuOYiiaozrGEpkj3YWWiUmhamrdxN/LQNPJwfmjetNoKdb+fqr2XkM/3w1T/TuwEPd2wIwbe0edp1JxtvFkbWPj9L9WhtHXEISTnZG5ozvS+tAX2u/ktOIWhFDkUmhV4vGRA3viSRJZBUU8cLSLVzJzCHQy515Ewfi4ezIhbRMpq2M4eSVdJ4Z2I2HenUqL2vxnt9Z8Ws8qqbi5eJEWk6+rn3XAFo3rEo7lajl2/Q6bdmEqBG9kSSJzcfO8tm2A1xIu8GyJ++mbSM/APadSWLepn2UKgp2BgNPKSa6OFlfXobOd+D4+Asgy5RuXkPJ8h+sbACMPSJwmv4O+U9NQj17CkPH23F46Ekw2oGplOKFn6Ac/a3KbS3Kub07jk++AgaZ0g3RFC/9tmq73v1wmfUBeVPuQUk4CYAcHIrTC68hObuCppL32EQoKalRz+nOnnhPnYZkMJC7agXZ33xtsd515Gi8X3gFU1oqADk//UjeqhUANDl6ipKzZwAwXbtK2lOP1+qfT0Q4ree8gSQbuPzjUi588qnF+lZvzaLend0BMDg7Ye/jw7ZmLQEYnJpM7slTABReucLh+ybXqlcb93/zGWHDBpGbls6bYd3+cnkAPhF9aDXnTSRZJvnHn6x8bPnWTOrdeScABmdH7H18iGnWqny90dWVnvt3kbphMyenTitfrgGzbXgdFpeamPTlL5QoCjmFJRSUlOLu5MC4zi2J7N3RQrPEpDB1ZSzxV6/j6ezIvAn9CPRyI6ugiGd/2sbxK2mM7tiC6cN7WGwze/0eDl68hixJPNO/CwOaNypfv/tcMnM3H9Dbtk7NiezR3lpzdVyZpgPzxoUT6OnGsSvpzFi3t7zWnujdkX6tmgDQ76PluDjYIUsSRllixaMjzXoXrjI35rCu174ZkXe0sdZbv5/4lBt6WzryTgI9XQFISMtk5uaD5JWYkCVY/sAgHIwGShSF2Vt/4+ClNN3HXu0YEBqg6128xtztf6BoGuPaNSWyaytrvY0HiU/NxNPJnnnD7yDQw4VSReX1LYc4mZqFoqqMaNOER7u1otikMOmnHZQoCiZVY0DzhjzVo63Zv4spzN15VPcvrCmRt7ew1tv8m1lvaFcCPVwoUVRmxhwhPiUTWZJ4Nbw9tzeqD8Cjq/aQnl+ESVPpHOjDaxEdMcjmvkzTNOYs30Jc/Fmc7O2YM2kkrYMaWJ+zSVeJWrSWotJSerUJJequgeV94o87DrJ05yEMBpnebUN4cUx/SkwKM5euJz5JP3deHd+f25s30fVWbCUu/pzeB08aXrXepWtleiZ6tQkhavwAJEni0/W7WLn3D7zcnAF4dkQ4vduGlOltIP5Smd6ocG4PDSrr82OJO3VBvyYnDqF1WR9joXc5hailm3S9VsFEjYlAkiQ+2biH2ONnkSSJem7OzJk4BF8PV76JPcj63/R2XVE1LqRmsOfj6Xi6OluVbVHXS9cRdzwBJ3t75jw8jtaNA63sPlq1hbX7fie7oJDDX8yqtjyLcv9CnerH8BBLd5UdwzahvDimL1l5BTy7YBXHL11ldLf2TL97iFnvbzhnrmRkMWzW5zTxqwdAe7mEGWEBFmVKLTsij3oIZBn1QAxabLTl+t7Dkbv2A1VBy8tBXfYZZKbrKz19kCf8C8nTBzQNZcFb5nU1ILXsiDwmskxzG1rMKsv1fUYg3zGgTDMbden88nLlEQ8gtb4NJAkt4SjqLwtq12t7G8Z7HgdJRtm9GXXTMov1cu+hyBEjQFWhuBDTDx/BtUtIrTthGPswGI1gMqGsWIB2+o/a9cK6YLzvCSRZRtm1EWX9z5Z64cMw9Btp1vv2Q7SrSWaDer7Yz/0WJfoHlE0ratWrjKFTN+wfeRYMBkxb11K6anHVdt3DcZw6h8LnH0Q9d/pPaTjc0QP3F6NAlilYvZL8HxZarHcaNgq3Z15CLbuPyl++lMI1KwHw+uRr7MPaU/LHETKfq/0eymq/O3bV/ZMNmLato/SXavy7IxzHV2ZT+MJDqOf/nH8CQVWIAYl/k7gjJ0i6lsbmT2dz9OwF3vh6CcvejrKy69OlHROHhDP4yekWy199cEL53z9u3M6pi5etNU5eICk9k83TIzmWdI1ZK7ax7Pn7rezeWL6VWRMG0r5JAFO+WsnuUxfp1ToYgGuZOexPSKSBl3u5vYezI1Fj+rL9+FmLchRV5a1Nv7LwvgH4uTszYeF6wlsEEVLfs9xm1e9ncXeyZ8tTY9l44gIfxBxm3rg+5evf2XKIniGWNy2j24dwb5dWTF29W/fr3BWSrmex+cX7OHY5lVmrd7HsifHWfq3eyawx4bQP8mfKd+vYfeYSvVo0ZuHOw3QLaUhkn84s2HmYhbuO8MLg7ng4OxA1vBfbT16wKOdsSgYrfo1n2VMTOHD2MlErtrH0X+PJKSxmVnQsy56621o7egezxvbVtb9dw+6EJHq1bEKoXz0+uX8YM3/ZbmHv6eLE55OH4+vhytmU6zzy8VI2BblZFirLOD7xMgVRT6JdT8X5kx8wHYhDvXTR0s7JGbuRE1BOHS9fpOVkUTjjebQb15EbN8Np9ifk3zfUar+t9J6JIv/FKWjpqbh+uZTSvTtRkyzrBydnHMZMxHTymHmZwYDztDkUzJmGev4MkrsHmEy16tWbPoOUyAcxpaQQsGwVBTu2U3rhvIVZ/uaNZMx5w2pzrbiIq+NGWi2vSa/NO3M4OG4CRVevcee2TaRt3kremTPlJqemzyj/u/EjD+EeZn6gUgqL2BPe/9b1boH93y9h56dfM3nRV3VTYLmPd1N09Rrdt20kbfMW8s6Yr93T02eW/13ZR4DQV1/mxr4DVkWfsjPY9Dq0Nxr49qHhONoZGPzhz/h7uDJ92J28tX4P4a2aEOLrVW676vBp3J0c2PL8PWw8do4Pthxg3t39sTcaeKrvbZxNy+Rc6g2L8r/adQRvFyc2PXc3qqqRXVhUvk5RVd7auJ+F9w/Ez92FCQvWlrVtFTR/P4O7oz1bnh5f1rb9xrxx4YT6erHi0REYZZn03AJGf7maPi2CMMr6jMfvHxiMl7Ojxb4oqspbW39j4d0R+Lk5MeH7LYSHNiTEx8Osd+y8rvfYCDaeTOSDnX8wb1QPTKrKK+v28/awO2jp50VWYTHGsofyr/bF4+3iyKYpw1E1jezCYrPetiMsvKu3rrc4hvBmAZZ6xy/i7mjHlsghbDx1iQ92HWPeiIBoM8UAACAASURBVDvYknCZEkVlzYMDKSw1MfzbzQxtFUSAuzPfTuiNi70dpYrKfT/F0iu4Ae39PFFUjbdi/2Dh2B74uTkzYUks4c0aEFLP3M+sOpGo6z08iI2nL/PB7hPMG9aVlcf19m7NA/3JKChiyi97WX5vBLIkMW9YV1wd7NA0jWfXHWDLmWSGtDQPKsXFnyMpLYPNs57k2MUrzPppA8teecT6nP1pI7PuHUr7pg2Z8ulSdsefo1fbUH5NuEjs0QRWT5+CvZ2RjJx8AFbuOaLv02uPkZGTz5RPl7D8lYfZffI8SWk32DzzXxxLvMKsnzex7OWHqtDbxKyJQ2nfNJApn/3M7pPn6dUmBIBJEbfzUP87LOxX7v1d15s+hYzcfKbMX8ry5+9n9+mLep8/7ZEKff591nortjHrrgFlff6q8j7/oYguPD1EH6RbvOswn2/Zx8y7BvBwxO08HHE7ADtOnGPRnqM1DkYAxB1PICk1g81zX+TYhcvMWrSaZa89YWUX3qEV9/a9g0GvflBjeeXlxv+1Ov01IZHYYwmsnvaofgxz9WNob2fkqeG9OXs1nXPX0ivo/T3nDEAjHy+ip00BQFtv+eCPJCOPiUT5chZkZ2B47l2U+EOQmmy2uXIR5cOXoLQEqftA5GGTUBfr9WiY+DRqzCrUM0fB3hE0tfbKlWTk8VNQPp8BWRkYXngf5fhBSK1wf5l8EeX953XNOwchj5iM+sN70KQlUtNWKO88o+s/MxcppC3auRM16hnvfZLSD6ZC5nWMr81H/WM/XLtUbqL+ugN11wbdvH03jBOmYPpoGuRlY5r/GmTdQApsgvG5OZS+OLFW/+wmPU3Juy/DjXTsZn2OemS/xYCDuj8Wdcd6AOSOd2Cc+Bil779avt448XHUYwdrr8uqkGXsp7xA0evPoGWk4fjBt5gO7ka7nGhp5+SM3fDxKAk11F0NGu6vvMaNJx5GSU3FZ9FyiuN2YLpoeR9VtG0TOe++ZbV5/uJvKXB0xHnMBKt1t6JtP+VFimaU+ffeN7p/yYmWdo7O2A37N/0TCKrhH5lDQpIkRZKkPyRJOiFJ0gpJkpwrrBstSZImSVLLst+OkiSdliQprILNy5IkfXkL5f8hSdLaf2cfYw/9wcje3ZAkiQ7Nm5GTX0BaZpaVXYfmzfD18qyiBDMb9hxiSI/brTVOnGNklzZIkkT7JgHkFhaRnp1nYZOenUdeUQkdmgYiSRIju7SxGGh4JzqWF0b0sQgvqufmQljjBhgNlof/+JXrBHm50cjLDXuDgcFtmhKbcMnCJjbhEqPa6TdaA1o34cDFa2iaBkDM6SQaerlaDGAA3NbYHw8ne4syRnZqqfsV5E9uYTHpFTp5gPScfPKKS+jQuIHuV6eWbI/XH3BiT15kVCf9LfeoCsvruToT1siv/EHhJufTMmkf5I+TvR07T1+kW7OGxJ68QPvGDarXLqqo3Yrt8Xpn0MzPm6YVHpxu0jrQF18P/e1miF89SjQoKauXm8gt2qBeu4yWcgVMJky7tmG8o7dVWQ6THqNkxWK0UnM0gnr+DNqN6/rfSeeR7O3Bzs5q24oYWrZFvXIZ7ZquVxq7Gbs7+1jZOT78BMU/fw8lxeXLjLfdgXLhLOp5/eFey8nW3zjUgENYO0ovJWFKvgymUvI3bcA5ol+N2/wVPDt1pOBiIoVJl9BKS7kWvQa/wQOrtQ8YM4prv6z+2/YH4NzufRTcyKyz8jw7dSS/ko++NfjYYMworlbw0b19GPa+9bm+Y5eV7XF7o02vQ0mScHGw43hyGg293PWoAoPM4LAQYk8lWtjGnkpkVMfmAAxoE8yBC1fRNA1nezs6N2mAg9Fg5U/04YTySAtZlvBycTL7euU6Qd7uNPJyL2vbgok9XUXb1j5U12zdpFzTyc5Y7kuxSbmlUM3j1zII8nKlkaerrte6MbFnky1sYs8mMyqsqa7XMogDSalomsbei9do7utJSz+9nfF0csBQph997AKR3fRIC1mSygdCjl+7YanXMojYc1ct9c5dYVSbJrpei4YcuKTrSZJEYakJk6pSbFKwM8i42Bv142WvtzEmVcWkmK//4yk3CPJ0KdOTGdyyIbHnK+mdv8qo1nrU4IDmgRy4lIamaZzPyKFbIz0Sp56zI24OdpxI0a8ZV4ebehqlqmpV17FHExjZrb1+zgY3JLegmPTsXAub9Oxc8oqK6RDcSD9nu7Vn+9EEAH6OO8wjA+/E3k5/F1PP3QWA89fS6daiafkyN2dHTly6SuyxBEZ2DdP1mjYkt6CoBr2Gul7XsHK96rDQc3PBzcmBE5dTiD1+9i/1+a6ODuV2hSWlVHWqbjxymiFd21exxpLY308xsntHfV+aBem+Z+VY2bVvFkR9T/cqSqim3L9Ypz/vPswjA7ubj6GbfgydHezpHBKEg53le7a/65yplaAQtOvX4EYqKCbU3/cgtbW8z9POnYCyfl5LOoPkqUdb4NcQZAPamaP675KicrsaaRyKlp4CGWWaR3YjhVXWPG7WTEwwa6Lp9xRGo/7PYETLtb6nrYgU3AIt7SpcT9H1Du5C7tjd0qiowGzvYB641S6dhyx9UFm7kgh29noEaE16zVqipV2B9Gu63oEdyJ2q18PBUQ8FLEPudCda+jVd799ADm2Nei0ZLfWqHtWxOwZj115Wdvb3PkrpqiW1RpRWhV2bdiiXL6FcSQZTKYVbN+LQO+KWty85dACtIL92wyqw8m9PDMauPa3s7O+NpDT6x1s7J/9HkSTpH/3vv5F/5IAEUKhpWgdN09oCJcBjFdbdA+wB7gbQNK0IeBb4XNIJBKYAr1I9N8vvoGnaiH9nB1NvZOLv413+27+eF2kZNTfeVXElLYPk1Ot0a9vSal1aVi7+FTp6Pw83Uit1pKnZufhVmDLh5+lGWpZuE3v8LL4ebrSsIgy7KlJzC/D3MHe2/u4upOUWVGtjlGXcHO3JKiymoKSUb/ae4F+9O9Sqk5ZbgH9ZaLLulyupOZY3Xak5efh5WNqkldlk5BVQv+ymoL67CzfyCmvUC/X35reLV8jKL+RaVi7nUm9wLUsvy8/TldRKN3yp2ZW0PV1Jq2RTE1uPn6OFvYx9pUZBrlcfNT21/Ld6PRWpXn1Lm2bNker7oRzcU235xh4RKOfPQGlpjfsh1ffVb05u6qWnIdW3DP+VQ1oi1/fHtD/OcnmjxqBpOL/7Ba5f/4z93ZNr1AIw+PqhpJj1lNQUjL7W4cbO/QcQ+MtafOd9gsHfPAVJsncgYNkqGixZfksDGY4N/Cm6eqX8d+HVazg0sJ7SBODYsCFOjYO4vttcr7KjA3fGbOaOzevxGzyoVr3/BLqP5ge9oqvXcGxgHW4M4NgwEKfGjci46aMk0fKNGSTMeLNK+2xZtul1CPqb/BeXb+dQ4lW6hwTSvpGf3s5UGghJzcnHv0zXaJBxc7Anq6CoqiIByCmLFJgfc4ixn63i2Z+2cT3P3Hal5ubj715L25aTX2XbBnA0OY3hn//CyC+imTG0e/kAhSTBI4u3MO7rNSw/bA5bTc0txN+tgp6bcxVtqdnGKMu4OdiRVVhM0o1cJCByWSxjv9vENwf0UPucIv3mb/7uo4z9bhPPRu/mer5e56l5hfi7OVfQcyKt0vFIzSvE393ZrGdvR1ZhCQOaN8TJzkjvz9fR96v1PNilBZ5O+oOtoqqM/n4rPT5bS/cmfrQPqFe1nqsTabmV9Yrwd3Oy9K+ohBb1PYg9fxWTqpKcnc/JtCxSKmwbuWo3Pb9cj4udHQNCG1qUmZaVi3+FaD8/LzdSsyr1iVm5+FXsNyv0iYlpGRw+d4kJ7yxk0rzvOZ6otx8tGvoReywBk6KSfD2Tk5eukZKZU4WeezV6bhY2aRVslu76jVFvfc20xevILiisoHfGrHc5lZSsHNKy8/D3suzPq+yfKl63nm4W/dNHG3YTMfNL1h8+xVNDelhsW1hSyu7TF+nf2TKKqirSMrPx9za/XPDz9iA103pA4s/yV+s0Me0Gh89dZsK73zJp3iKOJ1oOhNWuVzfnDMCVjCzGzP6aSfO+5/ANyzZM8qgHWRnmBVkZSB7eVIfctS/qKT1SR6ofgFaYjzz5ZQzPv488fBJItd+u65rXK2nWq9Ze7tYf9dRh/UdiAtrZ4xje+A7Dm9+jnf7dMpqjKjx90G5UmEaSmV5hgKOCTvhw7OZ+j2F8JKaln1nvd+eeaJfOgamWexovH7QMs552Ix3Jy8dar+9I7N9bjHHCo5h+LJviaO+IYdjdKNGLavapJv169dGup5n1r6dZ38MFN0fy8UX5bW/lzW8Jg68vSmqF+7a0VAxV3Ec5RgzA56fVeL7zEbJf1fc9fxbJuz7adfM9qpaRjuRdyb+mN/3bVyeaAsFN/qkDEhXZDYQASJLkCtwJPEzZgASApmmbgWvAJOBDYKamaXX3mrIKKr38pmz//nQ5G/ceZOAdnTAYrA9FFRJWGtXtR2FJKV9tO2B1Q1ITVend0j4Bn+78g0ndWpe/UauxjKr2mVvwq8r3PbXTzNebR/p05uEF0Ry7lEKQj0d5CDRYJ6fRqvDyVo/t2ZQM5m3cS5SPk/XKqsqo6Kgk4TDleYoXfFRt+XLjYBweeoqiT+bcwt7Uruf05IsUflFFqK3BgDGsI4WzXyXvqcnY9YzA0Mk6isdSzlpPq3QgC3bu4PKAcK6MGUHhgX3Un/1O+brL/ftwdcJY0l95Ae9XojA2alS5uFr1qjxxgIDRI0lZu94iymNHh9vY228Qf0z5F61mv4FzE+scMP9xbqFObxIwehQpazeU+xj00GTSY2ItBjRqlfsbr0MAgyzz4qBuDAkL4XhyOmdvTruwugar0K3hGlRUlZScfDo29mfVE2PpEOTHe5vM01SqqbLaNcv+b9/Ql3X/GsPyyBEs2HOM4rLpS0seGsaqKSP56t4B/HToFL8lpVRbVmUnq/PRpGocSU7n3eHd+fG+/sScucz+xBTdx9wCOgbWZ9WDg+kQ6MN7sb/XoFfJvyr7Cj26QpYkdj4+nK2RQ/n+0Bkulw3YGmSZ6MkD2PHYMI5fu8HZ9Ozq9Sr3TVW1o8CYtk3wd3Ni/JJY5u48SocG3hZ5IhaM7cmuKUMpURR+vZxmsX11ZVr6WVX7rf+vKCo5BUX8/PLDvDimP88vXIWmaYzp3hF/T3fGv72AuSu20CG4IQZZvqV+vqZr5O5endnyxhP8EhVJfXdX3l0VA8CYOzrg7+XG+He+Ye7KbXRoGqDrWRf1p/unZ4f2JHbmYwzr3Iolu49Y2O08cZ5OTQNrna6h61S1L3/9zdtfrdPyY/jSg7w4pi/Pf7Oq2jYR/r5zpr67K9tnP8Mv0x7llbEDePn3ZPJKlepF9J2pEqlzL6RGIWg7yqLbZANScCvUtT+gfPQy1PNDuj28Wh9r1KyuNbqtN1JQCNr2srwWPv7g1whlxsMorz+EFBoGzVrXiZ66Yx2lr05GWbkQw7B7LYsIaIxx3MOYFn1cs1a1VKG3fQ0lL92PafkCDCP1KU+GMQ+gbF4JxdUPbNfKLdzD2T/8DCXffmJtd+siNWsARbt3kja8L9fvGUXJwf14zpz7F/RqlraoX0nC/uGnKfluft3oCQQV+EfnkJAkyQgMBjaXLRoFbNY07YwkSTckSeqkadrNHvdZ4CBwVtO0qrOwmHGUJOk3wAS8rWmaVRy3JEmPAo8CfPH6Czw6fgRLNu1gZYz+JrltSFNSrpvnMKdkZFLf26NyMbWyae8hXnvEPG9uyaYdrNik37SEBfmTUiFEMjU7F193V4vt/T0tR/tTs3Kp7+HK5etZXMnIZvS735UvH/veDyx74X7qVyqjvCw3Z1KyzaP8KTn5+Lo5V2nj7+6CSVXJLSrBw8mBY1fS2XoqkQ9ifiO3qARJknAwGrj3dj2x2pqj57l0I4fRX60hLMCHlCzzG53U7Dx8K4VB+ntYRi6kZueVv42t5+pMek4+9d1dSM/Jx9u1iof/Cizdf4wVB/W3jAPCQsjIK6Cxj2dZveRZ16mH5Vup1Cyzdk2kZOXy9KL1zL17AA03bbFar15Pw65ChILs41c+DQMAJ2fkxs1wflefbSR51cNp5gcUznwB9ewpJB9fnF57l6L3Z+jTMGpBS09Fqm8eOZfr+1qM7uPsgtw0BNeP9IRJkrcPzrM/pmDaM2jpaZiO/oaWrUf9mA7swRDaCuVI9XMvldQUi4gHg58/SrrlA4WabY4iyl25HO/nXjJvX2ZrSr5M0aGD2LdsjemydW6VmxRdvYZjgDlfiVNAA4pTUqu0bTB6JPGvWOZ4uWlbmHSJG3v34R7WloLEpKo2/4+h+2hOlOYY0IDiClEoFanso1eXznh160rQgw9gdHFBtrcj+sB+1uyIBSDIpNj0Oiwv092FG/mFdGnagN1nL2NSVHzdXKxsUrLz8PdwxaSo5Bbr7Ux1eDo74mRnpF8rPQx+YJtgVlWIWPB3dyElp5a2zd2lyratIs3qe+Jkb+RsWhZtA3zKy6jn4kTflo05diWd2/w98XdzIiW3gl5uAb5ulvVz08bf3VnXKy7Fw9EefzcnujTyLZ+O0atZACdTb9CtsR9Odgb6tdAH6ga2DGLVMX2ajL+rEykVIjBScgvxda1CL6cAf7cyvRJdb8OpJHo29cfOIFPPxZGOgfU4kZJJowpv4d0d7enSyJfdF68R6h1qrZdXiK+rZR4N3abQrFfmnyRJTO1jnjIw8acdNPaybIMdjAbCmwUQe+4qiZl5rFiuR/2ENQ4gpcJb+tTMXHw9LXP1+Hu5k1qx38zKpb6HW/m6/h31aUrtmgQiSxKZeQV4u7kwdbw+FWrpzkO8Hx1D8vUsOoc0qqSXUz41z6xXqQ/OzKF+Wd35VOhbxvfoyOOf6wn/jAaZqeMG6Hq7fuP9X2JIzsimc3BDUjIt+/Mq+6eK121WbpX909DOrXj86194arD5pcTG308xpJN1NOZNlm7fz4q4QwCENW1Iyg1ze516I9uqrm+Vpdv3s2LXr3q5jRv8pTr193Kjf4cWVR7Dm5y5ksbo2V+V6f1958zNaRxtGgfQyNmexPwS2nrq152WlWEZLeBZDy3HMu8NgBTaDrnfOJTPXgNFH+jUsjPgykV9ugegHT+I1Lg5Gtuttq+IrlkhYsCzHlp2FZrN2yP3H48yf1q5ptTuDrTEBH16CKCeOoLUuAXa+ZPVC2Zet3yD7lUfLcta7ybqwZ3Y3fc05cM2Xj4Yn5iB6Zt39WkYtaBlXreISJC866NlZlRrrx7YgfEBPSeG3KwVUpdeMOFRKEvUrZWWoMasqVW3XP96GpKPOeJY8vGt4h4uGMfZn+vrvbxxmPYuxbNfvuXElkpaKoYKEQ+yr5/VfZRW4T6qIHoFbk+9cMs+1ISWkY7kY75HlerVt/YvKBjHt/QoF8nTG4dp71A8+xWR2FLwl/mnRkg4SZL0B/AbcAn4pmz5PcDNlLo/l/0GQNO0q0As8MUtlB+kadptwETgI0mSmlU20DTta03TbtM07bZHx+uzOu4dHE70BzOI/mAGfW/vwJpdB9A0jT/OnMfN2anWXBGVuXglhey8Ajq0MMvfOzic6JcnE/3yZPqGhbLmUDyapnE08Spujg7Ur9Rx1/dwxcXBnqOJ+nznNYfiiWgbQvOA+uyZ/SQxMx4jZsZj+Hm6seqlB6odjABoG+hD0o0ckjNzKVEUNsVfJLy55Vvq8BaNWH3sHABbTybStak+t/zHB4cQ88x4Yp4Zz/1dW/Noj3blgxEAI9s3I8jbnegpI+nbIog1R07rfl1Kwc3R3uqGqr67i+7XpRTdryOniWitP2yEt27K6iN647e6wvLqmHhHOxZGjib6uXvp0LgBB85eZnD75hxNuoabk0M12nYcTbpWpn2KiLIkodWRU1jM49+t5bnB3enUJKBKGzXhJHJAEJJfABiNGHv3x3SgwlSJgnzyJ/Qn/4GR5D8wEuX0ifLBCFxccXrjQ4q/+wylYvLJGlAS4jE0DELyDwSjEbuIQZTuq5BLID+P3JF9yL17CLl3D0E5eYyCac+gJJyk9OBeDMHN9TmYBgPGDp2tk2FWovjEceyCmmAMbAhGO1wGD6Vgh+UNlMHHfDPhHN6XkrKEl7K7e3lODNnTC8eOnSg9f65Gvezf/8AluClOQY2Q7OxoMHokqZutB4JcQpph5+lJ1iHzV0mMHh7I9npeEztvb7y6diEv4azVtv9pqvIxbfNWKzuXkGYYPT0sfDz62JPs7NCFXZ26cnrGG1xZtpIWq9fzcnYBL2cXEFZisul1eCOvkJzCYtoG+pJ4PZudp5MI8nJn0/FzhLe0jE4Jb9mY1b/r+Uu2xl+ga3BAjW9nJUmiT8vGHLyoR4McuHCFZhUSVrYN9CEpI7tC23aB8BZBlprNG7H6qH4OVGzbkjNzMZVFnVzJyuPi9WwCPV0pKCklv1gPMS4oKWXf+auEluWXadugHkk3cknOytP1TiYRXinZb3hIQ1aXJXjcevoSXRv7IUkSdwYHkJCeVZ7X4dClNELqeeg+hgRyMEl/SDmQmEqzsiSSbRt4k5SZZ9Y7fYnwEMt2KLxZAKvjE3W9hGS6BvkiSRIN3J3L8zsUlJg4eu0Gwd5u3CgoKp8mUlRqYn9SKsE39fy9SMrKIzk7nxJFZdPpZMKDq9A7qQ/wbT1zha5B9cvzVRSU6g9B+5JSMcgyIfXcyS8xkV42zcSkqsRdTKGptxsTOzQjetoUoqdNoW/7Fqw5cFQ/Zy8k6+23h+XDZX0PN1wcHTh6IVk/Zw8cJaK9/gWQiPYt+DVBr/PE1AxKFQUvV2cKS0opKNZ9beLnTZsgfzbMeJy+7Vqw5tfjut7FZNycHKvWc7Dn6MUyvV+PE9FO16uYqyDmjwRCA/T2z0LP15s2jfzZEPUwfcNCLPt8p1vs88P03CeJ6ebA0B0nzhPsZ54ikFtYzKHzyUS0DaE6Jva9g+hZTxM962n6dmzNmn2/6/ty/hJuzo5/KleEVblRkURHRf7lOo1o14JfExJ1f1MzKDXpx7AizQN9//Zz5kZuPkpZu3A5PZOk/BIaOleIEL18Dql+A/D2BYMRuWMPtBOHLCsmsCny+MdQvpkLednm5ZfO6Q/NLnp9S6FhaKnVD86btztrqdmpJ9qJSi8RApsiT3gcZeFsS83MdKSQtiDLeoRGSJtap2xoFxOQ/AL16AqDEfn23mh/7Lc08jW3C1K7rnoOCAAnF4zPvInpl2/RztUw6FFR78JpS71u4ai/W04dkPzM7azcvhtaqq5XOvtZSl64l5IX7kXZugpl3dI/NRgBoJ49hRzQCMmvARiNGHr2w/TrbrNBQT4F9w2mMHIMhZFjUBPi/9RgBEDpyeMYGjXGEBAIRjucBgyhOG6HhY1cYVDGoVcEpos135vdKurZU8gNGiL5lvnXox+mitOHC/IpmDSEwkfHUvjoWNQz8f9vByMk6Z/977+Rf2qERKGmaRbJCCRJqgdEAG0lSdIAA6BJkvSyZo63U8v+1UjZ4AWapl2QJGkn0BE4X+NGlejdKYy4I8cZ+MQ0HB3smfPE5PJ1o1+YRfQHenb/9xatZMPuXyksLqFP5EuM69eTJyfoAxwb9hxkyJ1dqr3R7tU6mLiTFxj05gIc7Y3MnjjYrPHu90S/rGu+fld/opZsorjURM/WTcu/sFEd6Tl53PX+IvKKSpBlicWxB1n3r1G4OtgzbXA3IpdsQ9U0RncIIdTXi/k7fqdNQD0iWgQxtmMor0TvZuD8VXg6OfD+WOukjJV5cdUuDialkFVQRPiHy3mid3saeXsw6L3FONoZmT2+r9mvj38m+hl9Ns7ro3oTtWK77leLxvRqoT+0RPbuxHNLt7Dq0EkaeLrx4b36/P/03Hzumr+cvOISZEli8Z6jrHv+Xlwd7Xlm0QayCoowyhLdQhsxYf4yvU7Hm7+yMPrDJUQ/p4cTvj46gqjl23Ttlo3p1bIJADEnzjF7zS5u5BXy+HdraBlQnwWPjGbpvqNcup7FFzEH+SLmIFpGHp/6O+NdcSqOqlD0+bs4z/4EZAOlW9eiJl3A/v4pKGdPoRywzONQEfsRdyEHNMJ+4iPYT9QzhBdGPYmWXcPMJEWh8OO5uLz3hf6Z0U2rURPP4/Dgv1AS4jFVHJyoTF4uxSsW4/rlUkDDdGA3pgO7q7cv08uY8wb+X30DBgO50SspPX8OzyeepiT+BAU7Y3G/bxLOfSLQFAU1O4vr06cCYBfcDJ/X3yhPspf1zddWX+eojKYoxE+N4vYVP4FsIHnpz+QlnCF06ktk/3G0/ME9YMworkVbBkG5Ng8l7IN30VQVSZY5//GnFl/n+Hd5eOm3NO/TA1efesy9fIp1M+aw79vaAraqR1MUTk6dRpcVS5Fq8LHBmFFci/5zN1etSxXybHgdpufm8+rybaiqhqppJGfm8t7mA4zu3IJQP2/mxxyiTWB9Ilo1YWznlryycgcD5/2ktzMTzDlF+r2/hLziUkoVhe2nElkweSghvl48P6ArU1fG8vbGfXi5ODJ7TJ/ybYyyzLQhdxD545ayti20rG07QpsAH71t69ScV6LjGPjJCl2z7OtBRy6lsmDvMYyyjCxJvDa0O17OjlzOzOHpZfqAm0nVGNo2mJ4hDaG4SNcbcBuRy3boeu2CCa3vyfy4Y7Rp4E1EaEPGtm/GK+v2MfDLtXg62fP+SP1NtoejPQ90acldP2xBQo+Q6F02mPF8n45MXbePt7cfwcvZgdlDupn969eJyJVxqKrG6LCmhPp4MH/PCdr4exEREsjYdsG8suFXBi7YiKejPe8P17e9p2MI0zYdYsR3W9CA0W2b0MLXk4S0LF7ddFA/XmgMatGIPs0CQFF0vfAORK7ao/vXtgmhPu7M3xuv6zULYGzbJryy6RADv9ms6w3Vp3zdKCgm8pc9yJKEr6sjbw++ytdgHwAAIABJREFUDYDCUhNPrNlPiaKgaBpdG/kyob1lX9arbShxJ84x6PVPcbS3Y/Ykcwqo0bO/Kv/iwev3DCHqhzX6OdsmpPyLF2O6d2T64rWMeOML7IwG5kwaiSRJ3MjNJ/KTJciyhK+HG28/MLJML4S4+HMMmvGZrnf/cLPenAVER0WW6Q0matE6iktLy/T0lwzvR2/ndHIqEhKB9TyYOVH/FOWN3Hwi5y/V68DTjbfv05f3ah1M3KkLDHprga53TzV9/vj+RC3dpOu1CqZXWWTQh+t2cTEtE1mCAG8PZlTo32KOneXOFk1wdjAnmK6JXu1aEHcsgUFT39f35aFx5n2Z8QnRs57WfVy+iQ2//kFRSSnhL8xlbM8uPDmq+hxAf7VOx3TvwPTF6xjx5lf6MXxgRPk9VL/p88krKtbbhqMJLHj6vr/tnPnt7CXmr9+ptwuyxOthAXjaV7ilVlXUXxZiePR1/ROcB7dD6mXkQXejXT6PFn9Izw3h4IjhgRcBPQJA/XYuaCrq2h8wPD4TJEm3PxBT+0FTVdRVX+vbyTLqge2Qchl58ES0y+fQThxEHvkgODhhmPyyWXPhbLQ/9iGFhmF4RZ9uoJ06ghZ/qHqtMj3Tkk+xe24OyDLKni1oV5MwjJyEmngG7egBDH1HIrXqCIoCBbko37wH6HkeJN9AfQpH2TQO07xXoaZEmqqKadF87F5+B0mSUeI2oV1JwjBmMtrFBNTf9yP3G4XcppMe+ZGfh+nrd6ov78+iKpR89QGOMz8CWcYUsx7t8kXsJkainjtVY+6vW0ZRyHnvLbznLwSDTOHaXzBdOIfrlKcoPXWC4rgduNx9Hw69IvTEnjnZZM00p8yrt2AxhibByE7O+G7YQdab0yk5cIv5LFSFkgXzcJzxof5Z05v+3fMI6rnTKIfqwD+BoBqkmube/aeQJClP0zTXSsumAJ00TZtSYdkuYLqmabvLfn8PrNc0bWUNZXsBBZqmFUuS5APsB0ZqmlbtEK16Is6mlaQl2/hNbUZa7TZ1jXP1kRp/C7J1Vv6/k4IvvrOpHoBaZNuMxzeu157EsC45lZJbu1Edsjbj38tU/VcYWc+218WABTXl/v0bqCVpWZ1TS+LXv4W/Mkf530Gp5XO8da6n1G5T14S2sa3erXxisS6p8IUjm+BefXLFv40iG7ent5AEsi6x+uzn381/4DpUbyFxcV2ildrWR1NWQe1GdUzO1T+fDP+v4N7Q+itxfzcuq/f9l76zr56LbZv/8x6eK9D0xJn/ujr/p0ZIVMU9wNuVlq1Cn3ZRy6tbC1oBX0mSpKJPWXm7psEIgUAgEAgEAoFAIBAI5P/WeRH/YP6RAxKVoyPKlvWpYtknlX5PvoWy9wFhf2H3BAKBQCAQCAQCgUAgEPxF/qlJLQUCgUAgEAgEAoFAIBD8D/OPjJCoCyRJCgMqZ5Mr1jSt639ifwQCgUAgEAgEAoFA8N+LmLFR9/zPDkhomnYc6FCroUAgEAgEAoFAIBAIBAKbI6ZsCAQCgUAgEAgEAoFAILA5/7MREgKBQCAQCAQCgUAgENQVkpizUeeICAmBQCAQCAQCgUAgEAgENkcMSAgEAoFAIBAIBAKBQCCwOWLKxq2gqTbW02yr510fsm7YVtPWdSrb9lQ3ujvaVA9AsTPYVM+10GRTvSA329bpSGwfkrcmI8+megNsqvYfwM4OTLY9T5FtPM5fauv+ycZ6AKpiWz3Ztm0pBhvfil1Psa0egI+/bfWK8m2rZ7TxMTQaQbHtdSHZ29hHG4fFGz2dbaoH4G6wbX+RlZhhUz0AF5sr/v2IGRt1j4iQENh+MEIgEAhsga0HIwQCgcAW2HgwQiAQCP5OxICEQCAQCAQCgUAgEAgEApsjBiQEAoFAIBAIBAKBQCAQ2ByRQ0IgEAgEAoFAIBAIBIJaEDkk6h4RISEQCAQCgUAgEAgEAoHA5ogBCYFAIBAIBAKBQCAQCAQ2R0zZEAgEAoFAIBAIBAKBoBYkWczZqGtEhIRAIBAIBAKBQCAQCAQCmyMGJAQCgUAgEAgEAoFAIBDYHDFl499E0zTmfLuMuN9P4Ghvz5wnJ9MmOMjK7qOlq1mz6wA5+QUc/vGT8uVzv1vOwfgEAAqLS7iRncvBRR9Za/wSS9ypCzjZGZkzcQitG/lZacRfTiFq6SaKSk30ahVM1JgIJEnik417iD1+FkmSqOfmzJyJQ/D1cOXg2Us8+U00gd4eAPRv3oh/9eloVe7us8nM3XwARVUZ16kFkT3bW6wvMSlMjd5F/NXreDo7Mm9cOIFebhxLTmfGuj3ldk/06Ui/Vk0sy93yq15ul9ZE9ulkXe7yGOKvpOvlThxAoJc7AF/vPMyqQ6cwyDJRw3vQo7m5zhVVZfynK/Fzd+GLyUMB2H8umfc37UMFXOzteWtCf5bsPUrcqYs42dsxZ8IAWjf0ta7T5FSift5aVqdNiRrZG0mS2Hz0DJ9tPcCFtBsse/oe2pYdj1JF4fXlMZy8koaiqgyVS3jYz8WqXLn97dg9+AzIMsr29ZjWLLFYb+g/EuPA0aCqaEWFlH71HtqVRAw9+mMccU+5nRTUjOJXHkZLOmelYVFep244RD4HskzptrWUrlxctV33cJxenUvBc5NRz50GN3ccp87FENqK0u0bKPnqgxp1bmLXrQeuz09Fkg0Url1F4aKFVdrZRwzAY+6HZD5wF6bT8WC0w+3VGRhbtgFNI2/eXEqPHKpVz7VXLxrMmAGyTOayZVz/8ksrG/ehQ/F95hnQNIpOnSL52WcBaPz99zh37Ej+oUNceuSRW/IPwCeiD63mvIkkyyT/+BMXPvnUYn3Lt2ZS7847ATA4O2Lv40NMs1bl642urvTcv4vUDZs5OXXaLetWxf3ffEbYsEHkpqXzZli3P7WtBsxeG0dcQpLevozvS+vAqq6FNKJWxFBkUujVojFRw3siSRJZBUW8sHQLVzJzCPRyZ97EgXg4O3IhLZNpK2M4eSWdZwZ246Felte3oqoM/vBnMvIK8XFzZlznlkT2tmx/SkwKU1fGmtuWCf0I9HIjq6CIZ3/axvEraYzu2ILpw3uUb/PAwrWk5xXgYNS7tYWTh1LP0a58/d/Vnimqyviv1+Dn5sIX9w6osq53n7/K3G2HUDSNce1DiOze1lp73T7iUzLwdHJg3qieBHq6ApCQlsnMTb+SV1yKLEksf3AwDkaDtcbFFObu+EPXaNuUyK4trTU2HSI+LRNPR3vmDetGoIcLpYrK61sPczItE0XVGNG6MY+Wbbv4yFlWHLuIBowPa8qkzqFmvcRU5u48hqJqjGvbmMjbW1jrbTlMfGoWnk72zBvShUAPF0oUlZkxvxOfmoUswat92nF7o/r6MVyxm/T8onL/Fo65k3rODuVlaprGnBVbiYs/r7ff9w+jdVADq7qIv3SNqMXrKCox0atNM6LGD0CSJD7dEMfKvb/j5eoMwLMjwundNkRvv5ds4OTlFBRFZUS39jw6qIeut3wLcfFndb1JI6vWS7pK1KK1FJWW0qtNKFF3DUQqS8P+446DLN15CINBpnfbEF4c058rGVkMm/U5TfzqAdC+cQAz7x6k663aZvbvvmG0buRftX8/bijTa0bU2P7legDfbv+V91fHsnfuM3i5OpNdUMj0JRu5fD0TB6ORt8ZFEOrvY67TtbuIO52otwN31dAnLt+m94ktmxA1oqxPPHaWz7aV9YlP3l3eJ97kamYOwz/4kSdG9eOhQb10vaXriDuegJO9PXMeHkfrxoHWeolXiPpmhe5jWAuiJg5HkiROX7rGrMXRFBSVEOjjxbuPTsDVyZESk4mZP6wmPjEZWZJ4dVw/bm/epMI5cw4nOzvmTBpe/TmzaK3uX5uQ8nNGP4aHWLqr7Bi2CeXFMX3Jyivg2QWrOH7pKqO7tWeap1WRSM3bI498ECQZ9eB2tJ1rLNf3HIp8e19QFbS8HNQVX0DWdQAMb/8MKZf0Y5R1HfX7d60FqkBq0QF51EMgy6i/bkeLjbZc32s4cte++j1Gfjbqss8hM11f6emDfNfjSJ4+oGkoC2eb192KduvOGO6aovu7dwvq1hUW6+WeQ5B7D9P9LS5CWfIJpFy+5fIBpDa3YbznMZANKLs3oW5abqnReyhy+HBQVSguxLToY7h2Cal1JwxjHwKDERQTyooFaKeP3ppmWBeM9z2BJMsouzairP/ZUjN8GIZ+I82a336IdjXJbFDPF/u536JE/4CyaQW1YejYFfuHnwVZxhSzjtJffrRYbxw4CrvBY9BUFYoKKP78XbTkRDAasX/sZQwhLdFUlZJvPkaN/71WPYfuPfB4aRqSLJO/eiV53y2wWO88fDTuz72EkpYKQP6yJRREr8SueUs8p81EcnEBRSX3my8p3LqpVr3/BcRXNuqef+SAhCRJCnAcff9OAQ9omlZQtm408AvQStO005IkOQJ/AOM1TTteZvMyEKxp2mPVlB8ELAQaod+XD9E0LfHP7GPc7ydIupbG5vlvcvTsRd74egnL3n7Vyq7Pbe2YODicwU+9ZrH81QfvKv/7x42xnLpo3SjHnbpIUnomm6c9wrGka8xasY1lz99nZffGim3MumsA7ZsEMOWrVew+dZFerYN5KKILTw/Rb9gX7zrM51v2MfMu/Ya5c3BDvnh0rF5A1g2rMhVV5a2N+1h4/yD83F2YsGAt4S2CCPH1KrdZdSQBd0cHtjxzFxuPn+eDmEPMGx9BqK8XKx4didEgk55bwOgvounTPAijQTaX+8gI/NxdmfDZSsJbNSHEz/v/2Hvv8Ciq9v//dWY3m947CSUQem/SJJRIlV5ElKqAD/aCgKAIdlFQsSNgV1AhRJBO6IiCaAihBtJJI71vdnd+f8yS3U1CEnwwXz/Pb17XxUV25p7znnNm5pR77nPGku7J87g52rP72WnsiL7Mqp2/svq+YcRl5LAzOo5tT00ls6CYB9f/zI5n7kMjKYE+Xx87Qws/T4rK9Jay2XqID2aMoEVQAN8fi+alzVFoNRK7Fs/iTFI6KzbvZ9MTloF+5XGbo1gxKZzOTQN5aN1WjlxIIKxtCC0DfFgzcxTLf9pvY787+jJ6o5HIBdMp1Vcw6sWPGe7pQJDOauAgJOwefBr9K08hZ2dh//pnGE8dQ05NsJT70b0Y9yqdFql7P+xmPor+tQXK9qN7lWQaN0e38PU6nRFIEvb/WUDpC48jZ2fiuPpzDL8dQU5OsLVzdEI3+h6MF85atun16L9di9SkOVLT5rXrWOm5PruUvMfmYsrMwPOLTeiPHMAYf8XGTDg54XjP/VSctXQGHMZNAiD3/vEITy/c3/2EvFlTQJZr1Wv00kvET5+OIT2d5pGRFO7bR3mcpVx0zZrhO38+VydNwlRQgMbbu3Lf9bVrkRwd8Zxa/frXptn+zdf4fdK9lF1Lo+/eHWTu2k3RpcuVJheeX175d9M5D+DW0Xbw2fK5heQcP1F/zVr49YtvOfjBWmZ99ektH5vjbI/heh67FkzjTHIGK7YeYtMjk6vZvbT1ICsmDKJzkwAe+nwbRy4lEda6KesO/kHv0GDmDuzOZwf/YN2h0zwzoi/uTvYsGR3G/nNXa9T98ngMOcWldAz2Y+3MkUz5ZItSB1jXLX9cUOqAp6ey40wcq3afYPW9Q9BpNTwW3oPLmbnEZVSvt1ZODqdDkK9lg8EA/HP1GcDXJ2Jp4eNBUXlFjfk1mky8svt31k0Nx9/NiSmf72RQy2BCfS0jmM3Rcbg56Ng9fxw7YhNYdeBPVo/vj8FkYlHkMd4Y0482/p7klZSjrWHuqtEk88r+P1k3qT/+rk5M+XY/g0IbEertZtE4m6BoPDiCHReSWXU4htWje7P7UopSd80cSmmFgdFf7OHuNo0pqTDw45l4Nt0/GDuNxLzNRwlrHkAzdydFLyqadRP64e/qyJTvDjCoRaCtXmwibvZ27H5gKDsuprDqaCyr776Dn2ISAIicEU52STkPRRznh/sGIpl7eCuH96BDgCc1cTj2ColZOexaPp8zCddYsXEXmxbOrmb30sadrJg6ks4hQTz00UaOnLtCWPtQAGYM7sUDd9k673afPo/eYCRy6TxK9RWMfvlT7u7Zgbi0LBIzs9m14lHOxKey4vtf2LSouvPype93sOL+u+kcEsxDH3zHkdg4wjq05LeL8URFX2Tr8w+hs9OSXVBceUxjH08ilj6k/DAo987hc1dIzMxl17L/KPnbtItNC2ZV19u0mxVTh9O5WRAPffwDR85dJax9CwDScgv49UI8gZ6Wa7F2z6+0CfLj/bkTuZqezcubdvC5uf0/fCGBxOt57Fo4U2kTI6LY9Ni91TUjDrBiYrhSD2yI5MjFRMLaNKOlvzdrpo9i+Zb91Y4BeHPbYfq3bmq5hjEXSczIZtfrCzhzNZkVX21l0wuPVNf7eisrZo6nc4smPPTOFxyJuURYp9Ys+2Izz04ZSc/Wzdl85BQbdh7m8QlD+emQ4sCOfPlJsguKeGjVen5Y9CBHzl0hMTOHXcsf5kxCKis27mTTwgdquIY7WXHf3co986HlnvntYgJRZy6ydek85RoWKtdQZ6flsdEDuHwti7i0GgbtQiCNfxDjZ69Afjaax17HeO4UZKZabK4lYFyzGCr0iN5DkO6ehulb84upCj3GdxfWWKY3RUhIE+Zi/PQlRfPJNzHGnoSMFItNarySboUe0WcY0qjpmL5eDYBm6mOY9m/GdOkM6BxANt2StubehzGsWQq519EufhfTmRM2DgfTyQOYjuxQzDv1QjNpLsYPlt2Shvb+R6hY/Zyi8fz7mP46AWlJFo3fDmA69Iti3rk32ikPYXh3KRTmY1izDPJzEI2aon3qNSqevb9emnYzHke/ciHkZGG34iNMp3+1cTiYfo3CdGA7AFLXPmjv+w8Vb1vGA9r75mM683v98ihJ6OY9Q9nyJ5GzM3FYuQ7D70cVh4MZw+E9GHZvBUDT8050sx+j/OVn0A4ZA0DpkzPA3QOHF1ZR9uycOvtRHouXcX3+AxgzMvD79kfKDkVhuGrbbyvdvZP8N1+22SaXlZHzwiKMSYlIvn74ffsTZcePIhcV1i+vKipW/FunbJTKstxFluUOgB6wdixMBY4C9wLIslwGPAl8JBSCgIeA6t4BC18Bb8my3Ba4A8i81ROMOhnN2IG9EULQpVVzCkpKyczNr2bXpVVz/Dzda03rl6MnGXlnz+oaMZcZ27M9Qgg6N2tEYWkZWflFNjZZ+UUUlenpEhKEEIKxPduzP0YZHLk4WN4sleoruBWHXkxqFk283Gjs5YZOq2FEh+ZEXUyysYm6mMS4Lkonb2i7EE5cvYYsyzjqtJWd9XKD0caTaEnXXUm3cyhR5+Nt0z0fz7huyhu6oR1acOJKKrIsE3U+nhGdQ9FpNQR7udHE252YZOXSpecXcehiIhN7trVJSwhBUZnS2SssKyevpIyxPdoqZdo0kMIyPVlWnUSArIJipUybNVLKtEdb9scqlXMLfy9C/LyoihBQWl6BwWiivMKAnRC4VBk4SKFtkdNTkTPTFA/98f1oet5pm1BpiSVNB4caGxLNnXdhPLav2vaqSC3bYUpLQc64BgYDhsN70fYKq2anu38e+i3fQIXFkUN5GaZz0bbb6kDbriPGlGRM11LAUEHZ3h3owgZVs3N66HFKv96AXF5uOTakBfqTyiBdzs1BLixE27ZDtWOtcezcmfLERCqSk5ErKsjftg3XIUNsbDzvvZecr7/GVFAAgDE7u3Jf8fHjmIpsn6e68OjWleL4BEoTk5ArKkiLiMRvxLCb2gdOGMe1LVsrf7t17ojOz5frBw7dku7NiDtynJKc3L917HVne8Z2a6M8C00CKCwtr/lZKNfTpWmg8ix0a8P+WMXREHXO8pyOs9ru7eJEx8b+aKXqzUt6fhE7zsQR4uOJo06r1AEdQ4k6n2BjF3U+gXFdWwEwtH3zyrrFSWdH92aBNUYI1MY/VZ+l5xdz6HIyE7vZRgfYaF/LpomnK409XdFpNIxo14yoyyk2NlGXUhjXUXH8DW3bhBMJ6ciyzLGrabTy86CNvzJA93Cyr3TA2mik59DEw4XGHi7oNBIjWjcmKu6arUbcNca1VwaGQ1sFcSIpE1mWEUBphRGDyUS5wYidRsJZZ8eV7EI6B3rhaKdFK0n0DPZh/+VrVnrONPZwNusFE3UlzVbvShrj2ikRbENbNuJEUhayLHMlp4DeTRSnkbeTPa72dpzNqN89HHXmEmN7dVLu2ZAgc5to2/nNyi9U6u/mwco926sT+6Mv1ZquEILScr1Sf+srsNNqcHawJyr6ImN7d1b0mgdTWFJ+E71yujRvrOj17sz+aCX6cePhP5gzrB86O+Xdj7db9ag5m/zFXGbsHR2s8ld+kza/nC4h5vzd0YH9MZb8vbllH8+MHWQTMXEl7Tq9WzcDoHmAN9dyCrluHlhHnbvK2G5WbeLN6oEy63qgaptYswNp39krBHu5E+pvcQRH/XmesX27KnotmlBYUkZWXoGtXl4BRaXldAltquj17cr+P88BEJ9+nR6tQgDo2z6UPX/EKnm8lknvdi3M5eyCq5MDZ5OuEXXmImN7dTSXabCid9NreOOe6Wi5hkf+YM6wvpZr6KpcQyd7Hd1Dm2Bvd5P3eo1Dka+nQ04mGI2Yoo8j2tv28+QrsZVtrJx0GeFevW9xSzQJRc5Oh5wMMBow/Xm0Bs2zVpqXEO7ma+MfDBoN8qUzym992S21/6JZK+Ssa3A9XdE+dRipcx9bo7JSy986h1vNHSKkNXKmlcbvB5G6VNWw6kPZW/pQcvIVyFec2PK1RLDTgdaOuhAt2iBnpkKW0m8znTiA1K3vTTWxd1Bec5qRuvVDzkqzefFUG1LLtjb9NuPR/Wjv6G9rVFpFzywoNW6GMeaUsj0/D4qLkEJtI+WqouvQCUNyEsZUpd9WsnsHDgPD63WuhqQEjEmKY8aUlYkpNwfJ67+8h1X+f8u/1SFhzREgFEAI4QL0Ax7E7JAAkGV5F5AGzADeAZbLslxjD0cI0Q7QyrK813xs0Y3oi1shIzuPAG/Lgxfg5UFm9q0PDFKzsknJvE7vDtUrjcz8IgI8XSt/+3u4klGlc5KRX4S/ObT3hk2mlc27vxxh8PJP2P7HeR4baRn8/pVwjfErv2DeJz9xObP6eWcUlBBg1XkKcHMis0onJaOgmAA3RVurkXB10JFXogwyo1MyGf3hZsZ+tIUXR/Wr7NBXT9eFzPwa0vWomm4ZmfnFBLhb5dXdmQzzOb2x/SgLRvSpfMt2g5cmDOQ/X2xn0Mvr+PmPC3i7OhHgYVWm7i51l6m7a7VzrMrQTi1xtLdjwEufEf7Kemb4OeGurfJ4efkiZ1t8X3J2FsLLp1pammHjsV+zEe3986n4/L3q+/sMrpdDQnj7Il+31stEePva2EjNWyH5+mM8eazO9OpC8vPHmGEZlJgyM9D42obvalu1QeMfgP6Y7YDccPki9mGDQaNBCgxC26Ydkn/1UGVr7AICqEiz6BnS07ELsD3GPiQEXUgIIT/+SPMtW3AJq+6QuRUcAgMou2YZ6JVdS8MhsHr4L4BDcBCOTRuTfcQc7i8EbV56kYsvvlyjfUNTrtVUPmdgfhYKqjwLBUX4u9vaZJptsotK8DU/y75uzuQUlVIXb+w4zvAOLfB2cazcFuDmXHPd4m5VB9grdUBdLN1ykPEf/MTHB/5AtnLm/VP12Ru7TrBgyB3V6h2bdAtLCHBzsmi7OpFZWHJTG60k4WpvR15pOYk5BQghmPv9fiau/4X1v8bWrFFUSoCrVZm6OpJZ5XpY21g09AxtFYyjnYYBn2wnfO0OZvdohYejjpY+bpxKvU5eaTmlFQYOx6eTZj7vjKIyWz0XRzKLymrQq5KnMj2tfdyJupKGwWQiJb+Yc5l5pBdaznXpntOM/yaKj09csLmGAJn5hQR4WN78+3u4kZFnO7jMyCvE38O23cy0GoB+d+gU4179jKVfbyO/RNEd2rUNjvY6Bix5j/AXPmD2XX3wcHYkM6+QAKtIA39P15voWZ+TK5lmm4TMbP6IS2LKm+uYsfoLYhIsb8dTs/OY8OpaZqz+glNxylvkanoermRUGTxn5FfVc6vUi4q5jJ+7K22Cbevd1kF+7DMPsM8kXONaXkFlu5eZX2RbD3jcpE10t7XJzK/dmVuir2D9wVM8PKSXzfbM3HwCvCzRQf5e7mTk2jokMnIL8LcuBy/3yhc+LYP8ifrrPAC7T8aQnpOn5LFxIFF/nsNgNJKSlcO5pDTScwtquIb1uGc83ayuYQ5/xCUzZeUGZqz+ipgEW0ffzRDuXpBvcYCTn41wu/lgTeo5GNOFvywbtHZoHn8dzSOvVHMq1KppnvKhaOZYHA41ad4RjunCaeVY30bIpcVIM59F8/RbSKNmgLiFIYKHN+RatOXc68q2qpoDRqF9aT2a8Q9g3FR9imWteHojW08hyb2O8Kzeh5IGjcbutc/RTJqD4fuPqu0X3e9ETrpSGZlUG8LTBznboinnZNWsGT4W3Vtfo50yD8M35mmcOgc0o+7FGPFVPTJn1vOqu98GoB0xAcePf0A382H065SoGlN8nOK8kDQIv0CkFq0R3tWneducd5V+mzEjvVq/DcAxfAh+myLxeus9NDX0zezadwStHcbkpGr7/heRhPhX//u/yL/aISGE0AIjUKZvAIwDdsmyfAnIEUJYT05+EngV8JVlueaJ8gqtgDwhxBYhxJ9CiLeEELf2ug2Qqf7mWvyNm2DH0ZMM69MNjab6pagpyKqqRF3n8eTd/Yla/h9GdW/Lt0eUhqddY3/2vfgQEQtncX9YNx7bWH1wW2OAVxXx2s6vc7Af2x6ZyA/zxvLZkWjKKwz1T7eGqACBuKnewfMJeDk70r6G+e9fHYvmk1mjOPDCHMb3bMeV9Owa06gzHpEHAAAgAElEQVRbv3ZikjKQhODgsjnsWfIAX2eVkFJurDuRGjJl3B1B+eP3Yvj2E7QTZ9gmEdoO9GXIyfHVD6x20jUIyrb77ec8Sfn6NdXtbhfWZSkEzk8uoui96nNhy7ZtwZiZgecXP+Dy9GIqYv4Co6H2tGvMX5UC1Wiwb9aM+KlTSX78cYLeeAPJ1bX6cfWlBs2a7heARuPHkf7zL8q8UqDJA7PI2hdl49D4f0mNzxNVn8W6berLwfPxeDk7EuxVQ/lXq9dqMKmjfl15TziRj03mm7lj+CMhnZ//skyj+Sfqs4MXk/BydqB9o+qd07rSrZrhm10Lg0nmdHImK8f245sZw9h3KZlf49Oq2dYWkVurhlCiHSQhOPjQKPbMHcEXpy6RnFdEC2835vRszYM/HWHe5qO09vWonC5Sc3nWfU4CmNChKQEujkz+7iCvHzxDl0AvNOZ0V47oQeSMcL65pz9/pGbz8/nkKmnW3e7WXJYK9/bvxu4VD7PluTn4uruwcrPS9sUkXFPK4LXH2fPSI3yx7wTJWbk1t69Vftd8Tsr/RqOJgpIyNi58kAUThvD0us3Isoyvmwv7X32CLUvnsWjiUBZ+GUlRaflNy6w+ZVCqr+DT3cd47O7+1fbPHdKH/JIyxr+xnm8Pn6JtI9/KSJvb0c+oiQ/2nGBG/6442+uqpFWTXtVreHO9Vx6YyPdRvzJpxfsUl5VjZ46WmtC/OwGe7kx+6UNe/347XZoHo5Gkmsu0Wp+jBhtzyVdew2dns2BCOE+v33zTOr9qCtW5SW3QtT8iuDnyoZ8rtxlffxjjmucwfr8GafRM8Kp9YHnLmt3CEI1bIB8wr2shSYiQtpi2fYXx3UXg7Y/oWT3C8ebS9WiPAdOh7RiWPYhx6+doRlafGlSHSP00DmyjYslsjD+tRzPqPtsUGjVFO/FBDF9Xf9lTf2rQ3B+J/tnpGH74DM1YZWq1ZsJMjLt+gvK6nemWE6xfHg07t1A6/x70X32M3eRZyrb9vyBfz8Lh7fXoHnxCmYZrqqMfVSO2emWHD5B+dziZU8ZS9ttxPF96w2a/5OOL5ysryV2+pH6Nkcq/AiHEcCHERSFEnBBicQ37ZwkhsoQQf5n/zbHaN1MIcdn8b+btOJ9/5RoSgKMQ4oar+Aiw3vz3VODGyo8bzb9PA8iyfE0IEQVsryNtLdAf6AokAZuAWVYaAAgh5gHzAD5e9jTzJo3m250H+Gm/8razQ4tmpGdb5jCn5+Th61XDqkZ1sPPYKV6YY5nD/u3OA/y4U5mL2bFJIOm5Fk9+Rl4hfm4uNscHuLuSkVdkY+NbQ1jo3d3bMn/tFh4bcafNVI4B7ZrzstFEbnEZns6WELoANyfSrd4gpheU4OfqZJNmgJsz6QVFBLg7YzCaKCzT4+5ob2PTwtcDR50dlzNz6RDkW0O6Rfi5VUnX3YX0vCIC3F0s6TrZ4+/uTLrVW5mM/GL8XJ2JOh/PgfMJHL6YRLnBQHF5BQs37WXx3XdyMS2b2NRMlkcewmAwUVxeQbrV25GM/KLqZepRpUzzC/F1rz3U9pc/L9C/TTPsNBq8XZ3o4mRHbGkFwfZWvq7sLIS3xWkivH2Vtwg3wXh8P3Zzn8Hah6/tF47xWM1zdasiX89E+Fjr+SHnWL1dcHRCatocx9eUNwjC0wuH59+i7JVnlYUtbxFTZgYaf0u0gOTnj9HK0y+cnNG2aInHR18o+719cHv7AwoWPIrhQizF777JjTvD47Nv6vS0V6SlYWcVnaANCKAiI8PGxpCeTsmff4LBQEVKCuVXr2IfEkLpmTO3nD8wR0Q0alT526FRIOXp6TXaBo4fS+yiJZW/PXt2x7N3L5rMnonW2RlJZ4ehuJhLL7/2t87l75Di7kSau/K8uZZXkG5znxfhV6XuCKgSQZSRX1RZv3i7OJFVUIyvmzNZBcV4WUU91MTpxDQOXEhk37l48kvL0UgSC3/cT6ifF36uVXTdlGe9sg4or163VMXffF7O9jru7hxKTEomY83TIP6J+ux0cgYHLiZx+HIK5QYjxeV6Fm4+yMrRtiHEAa5OpBdYIiLSC0vwc3Ws0SbAzRmDyURheQXujjoCXJ3o2cQfTyelbg5r0YhzGTn0CQmscryjTZRBemEpflWuR4CLYhPg6mTRcNDxy/lk+ocEYKeR8HZyoGsjH85m5NLYw4WJHUOY2FEJj3/nSExlxEOAi4OtXlEpfs62IdjKOZUQ4OpooyeEYPHATpV29208RFPzG3p/8zk76+y4u00wMem5FOsN/PiDEsHVsWkj0q3C+zPyCvBzr6n+tm03fd0VJ5iPVV0/uV9X5n+sLIj3y6lY+rdrgZ1Gw+7T58ktKmb2u1/Rt21z0q3e3mfkFuLnYetQC/B0I8PmnCx6AZ5uDOmqTIvq1CwISQhyi0rwcnWunAIQHZ9KfkkZk9/6nJ6hTWz18grxc6+i51FVrwBfdxeSr+eSmp3P+Dc2VG6fuPJzNi2Yia+bC69NGwUoDo2+i95h0cbdSELQsbG/bT2QV0Ob6G4bnZmRV1RjP8OaM0np7Im5zKodR8kuLKHCZOKrvccI69i6MqoBICMnv4YytY2ayMjJx9ccFdI80I91zzwIQEJ6FofPKJEfWo2GxVOVPH63/1fe/nEnKdfz6B7auMo1rOGeqRL5kpFbgK/5ngzwdGVIl9Y1XsPakPOzbaMT3L2RC6pHo4rQjkiDx2P8ZLmtE/6GbU4m8tVziKBmyDkZ1Y6vpulh5SB190LOr77WjmjZCemuiRg/eqFSU87LhtR4ZboHIJ/9HdG0JXI9lz4g9zpYRQ4IT5/KKRI1nuupQ4ip1dcOqUtDeFpFC3j6KOd9E0wnD2I37TGMVvbah5dh2PCWMgWjHsi5120iFISXL3JuLZonDqCd+QQAUou2iJ5hMGUeOLmAbEKu0GPaF3nT4+XsmvpttfQTj+7D/qEF6AFMRvSfW14wObz+iTKFthaq9ts0/gEYs2xnsZvyLc9ryZYfcX98geX8nJ3xXvMJBR++S0VM/RYJVfl/j/lF/IfAECAFOCmE+FmW5XNVTDfJsvxolWO9gBeBHijeqz/Mx/69+cNm/q0REjfWkOgiy/JjsizrhRDewGBgnRAiAXgWmCJsXd0m87/aSAH+lGX5qizLBmAr0K2qkSzLa2VZ7iHLco95k0YDcP+IQUS8/QIRb79A+B1diDx4AlmW+evSVVydHOtcK6Iq8anp5BeX0KW1ZdHA+0cMImLhLCIWziK8YyiRJ2ORZZnohGu4OtrjW6Uh9XV3wdleR3SCMt858mQsgzsqK6EnZFnujQNnr9DcvHBkVkFRpYf/TGIaJlnGw8m2492hkS+J2QWk5BaiNxjZefYqg1rbfkVkUOsmbP1LWUBwz7l4eoUoay6k5BZiMCqXITWvkPjr+QSZOxyV6eYUKOlGxzGobYhtum2bsfW0MiDec/YKvVoo62MMahvCzug49AYjKTkFJF7Pp2NjP54e3ocDz81k36LprJo6lF7Ng1g5ZQhujvYUlunpG9qYiKenMWtAN1r4exF56rxSpolpuDroqnWsfN2clTJNTFPK9NR5BpsXDLsZgR6unLicjCzLlJRXEFNSQYi9rb/PdOUCIjAY4RsIGi2avuEYTx21sREBwZV/S936IKdZNSZCoOk9sF7TNQBMl88jNWqM8A8ErRZt2BCMvx+xGJQUU3z/cErmjKdkznhMF2P/tjMCwHD+LJrGTZACg0Brh8OQkegPH6jcLxcXkT3sTnLGDyVn/FAqzkZXOiOwdwAHZTBid0cfZKOx2mKYVSk9cwb7Zs2wCw5G2NnhPno0hftsy6Zgzx6c+ygDRI2nJ/YhIeiT/n5IYf6ff+HcPATHJo0RdnYEjh9L5q491eycQ1ug9XAn7+Spym3R/3mUg116cqhbLy68+BKpm35qUGcEQHB+CT2TrtMz6To+RWVEnlbC4qOT0mt/FpKUNQ0iT19gcDvleR3ULqTyOd1qtf1mPD28LwcWTiPq2Wl4ODrQKdiPV8YPZGdMHIPaNLWxHdSmKVv/VObF74m9Sq/mjWp9I2swmsgtVgbIFUYjhy4m2iyU+0/UZ0/f1ZMDz0xl31NTWDVpEL1CGrFy4sBq59ahkTeJuYWk5BWhNxrZeS6BQS2DbWwGtQxma4yyBsee80n0auqPEIJ+zQO5mJlLaYUBg8nEyaRMQn2qtzMdAjxJzCsiJb8YvdHEzovJDGph67QY1CKQrbHKfN89l1Lp1cQPIQSBbo6V60mUVBiITsumuTmKJds8TeZaQQn7Ll9jZJvGFr1ca70UBjWvotc8kK3nlGdtz+Vr9Grsq7zJrzBQYo6YO56YiUYShHq7YTCZyC0tN19DE4euphPq7cZ9XZoTsWQuEUvmEt65FZG/nVHu2fhUc5toO5j1dXdV7tl4Ze2hyN/OMLiTsh6J9doB+6Iv0rKRMtAI9HTjxKUEZFlmXO9OeLu58OH8ewnv3JrIE9GK3tWUm+s52BN9NUXROxHN4M7KmiKDO7fmt4tKNFtCRjYVRiOeLk7kFBZjNEdO9W8fiqujPd8/M5PwTq2I/P2sJX8ON2nzHazy9/tZBndsSatGfhx9/Qn2rXiYfSsext/Djc0LZ+Pr5kJBSRl6gzIs++l4NAPahhD59DQinrqf8PYtiDxt1SY62t+kHrCztImnzzO4Xe2LHX/z8GT2PfcA+557gAcH9uDpScM5uOo5wru2I/L4n4relSRcnRwqnQ2Veh5uSh6vJCl6x/9kcFdlfahs85Qxk8nEJ9sOcM9AZUpIabmeknJlzYNmAT60bxLALy/OJ7xTayJ/izGXaQqujg613DPma/hbDIM7ma9hp9b8djHBcg0NxsqvtNRKyhWETyB4+ipTETv3RT53ytamUTOkiXMxfrkSiq2mrTg6K1+DAHByRTRrjZxR+8ASgOQ4RdPLDzRapK53IsdW0QwKQZr0EMYNb0CRlWbyFWXQ7KxcCxHaoX6aZuTESwi/RuDtr2j3CFMWtbTG1+LMFx16KutB3AJywkWEfxD4mDXuGIgcXUXDz0qj0x3K+g8Ajs5oH38Zw5bPkeOqjrlq0bx6wawZoGj2HoTpz+M2NsLf8pUYqXNv5AxFs+LVJ9E/cz/6Z+7HuGczxm3f1eqMADBdvoAUGIzwU/ptmjvDMZys0k8MtLQfmu59Md3oJ+rszWtKgNS5JxiNNoth1oQ+NgZtk6ZoGin9NqdhIyk7GGVjI/lYHDIOAwZjuNE309rhteoDSrZHUrZvd606/2sI8e/+Vw/uAOLMY2E9ykv+sfXM/jBgryzLOWYnxF5g+N8pR2v+rRESNTEJ+EqW5YdubBBCHALuRImiqC8nAU8hhK8sy1koTo5TdRxTjQHdOnD4dAzDHn0eB3sdrz1siVgZv+BlIt5Wvqrx1teb+eXI75SW6xk4bxGTwu/k0SmKg+OXoycZ2a/HTTvaYe2ac/j8VYa/8hkOOjtenTrCorHyCyIWzgJg2eQhLPluJ+UVFfRv25ww8wD/nW2HiM/MRRLQyMudFycrC/7tib7ExmN/oZUk7O20rJo0qNo5aDUSS0f2Ye7XuzDJMuO7tqKlnyfvR/1B+0Y+DG7TlIldW7Eo4hDD3vsBD0d73p6khPedTkrns6Nn0EoSkhC8cHefyuiLynQ3bFPS7dGGlv5evL/3d9oH+TK4XQgTe7Rl0Q/7GfbWN3g4OfD2VOW8W/p7MaxTC0a/8z0aSeL5sf1rXODNOg8vTRjIE9/uQpIk3BzteWPqML48fJrhb3yBg52WV6dYPtM3fvU3RJi/YrJs4mCWbNxDucFA/9bNCGvTDIB9MXG8uvUgOUWlzF8fSZtGPnw2bwJT+3Vm6aa9jHn7a2QZxng50MqxyuNlMlKx4R10S1cpn/088AtySgLaex7EdOUCpj+OoR0+AaljDzAakIsK0X/4auXhUtvOyNlZyqKY9cFkpPyTt3Fc8Z7y2c992zElxaO7fy7GyxdsnRM14LQuAuHkBFo7tL0HULrs8epf6LDGaKTo7VdxX7MWIUmUbYvAGH8Fp3mPYjgfi/7IgZseKnl54f7eWjCZMGVlUri8WvRYjXrXXnyRZl99hZAkcn/8kfLLl/F76ilKY2Io3LePosOHcenfn9A9e8BoJP311zHmKd7+kB9+wL55cyRnZ1ofP07q4sUUHT5cq6RsNHJu8VJ6/vgdQtKQ8t1Gii5eouXiZ8n/K7rSORE4YRxpEbV3PG4HD363gVYD78TFx5vXk8+z7cXXOL6hthlrFryLywn0cmf4W18rz8Jky0JW49/bSMQTSjjtsnEDWPLjfsorDPRv3ZQw84r5cwd046nvdrP55DkCPVx5536lPcoqLOae93+gqFyPJARfH41m29P34+KghG5rNRLT+nTgk4OnGf3eD4zv3lqpA/adVOqAts2Y2L0Ni346wLDV3yt1y5S7Ks/trre/pai8ggqjkf3nE/hs1t008nBh7pc7MBhNGGWZPi2CmNyjTeV0mX+qPqsPWkli6dCezN24H5NJZnznFrT09eD9Q9G0D/RicKvGTOwSyqKfjzHs4614ONjz9jhlvR93R3tm9mrLPZ/vRABhoUEMCA2uWWNwF+ZuPqJodGhGSx933j8WS3t/TwaHNmJixxAW7fydYet34uGg4+27lYHc1C6hLN19kjFf7kWWlWNbm78A8sTPv5JXqsdOI/F8eBfcHXRgMpr1OjN3yzFMMoxv35SWPm68f/ycotcikIkdmrJo1ymGbdij6I1U5sHnlJQzN+I4kgA/Z0feGN4DAL3RxNwtxzGYTBhNMn2a+DG5YzObfIa1D+Vw7BWGL/9IaRPNb/0Bxr/2GRFL5ir37L3DWfL1dqVNbNei8gsUb0dEcSE1A4EgyNud5eY2dWpYD5Z+s40xr6xFBsb36ULrYH9aBflx+Gwcw5d9oOjNGGPRe/XTyq9kLJs6kiVfRirPSPvQyi96TOjblee//pkxL32MnVbDazPGIoTg1OUk3t9+ULmnJMGLU4bj4exIWPsWHD53heEvfYKDnR2vTrvbovfGeiIWK5EBy6YMZ8k32xW9ts0Ja1e7w/xqxnUWf70djRC0CPDh5QmWUPywNs04fCGB4W9+iYNOy6uTLQsDj3/nWyKeUr5EsGz8YJb8sFfRbNPU0iaejePVyENKm/h5JG0a+fLZnPE3PZewTq05fOYiwxe/rZTpA5Msei+uIWLF44re9HEs2fAT5foK+ndsRVhHxUGw47dovov6FYAh3Tow4c7uAOQUFjN31QYkSeDn4cYbM5W+dViHUA7HxjH8xQ8VvemjLXrW98zUESz5aptyz7QPrbxnJvTtwvNfb2PMy58q13DmmMr+0l3Pv09RWblSFwlY26c5oW7musFkwhS5Ac2cpconOE8egIwUpKH3IKdcQT73B9Ld05R1BqY9DVh93tMvCM2EecpXLoSE6cBW269z3AyTCdOWdWjmvaAc93sUZCQjDbsXOSUOOfaUsjaEvQOaGc9YNDe8AbIJ07Yv0fxnOQiQU64in6jfy48b2saNH6N97BUlv8f3QFoS0qhpyEmXkc/8hjRwNFKbLkr/pqQI45f1+5y4tYbhuw+xe/I1pQ91bA/ytUQ0Y2dgSriEHH0CzeAxiLbdlMiPkiKMG94GQBo8BuHXSJnCYZ7GYXjnOSisvhh9Nc2v3sdu4ZsIIWE8vBM5NRHNhFnI8Rcx/fkr0l3jkNqbNYuLMKx989byZaNnRP/ZOzi8uBokDYb925GT47GbOgdT3AWMJ49iN3Iimk49kY0GKCqkfM0rAAh3TxxefEeJxMjOovy9l+rWMxrJe/NlfD5aD5JEceRmDFfjcJ3/GBXnzlJ26AAuU6fjMGCQsjhrfj65LyrfDHAcOhz7bj2QPDxwGqM883nLnqPi0t97qaXSoAQB1nMiU4BeNdhNFEKEAZeAp2RZTr7JsdW/3XyLiPrNhWtYhBBFsiy7VNl2EHjDvIDljW2Po3z+c7759xfAdlmWf6oj/SHAKpQJaX8A88weohoxxRxs0EKSU+r4nOPtpobPfv7j2Ncefn3bsdPVbXMb0X/9XYPqARhL616g6XZSmtmwn3bKyLzltWf/K5ILy+s2us1EZt/aVz/+Wz7c8mrdRreTeiwidnv1/s782f8SfQPfN7cyP/l2YDLWbXO7CW3XsHrSLS8r9d/R0M9FacPWpYDyhrkhKat9Ierbjbx7W4PqYWz451AuqXvh4tuqV9GweZT1Dd9eGAobtv7OS7j5dJN/iqA/L/zfXGWxFjLvaPfvGzxb4X/y/EOYlx0ws1aW5bU3fgghJgPDZFmeY/49HbhDluXHrGy8gSJZlsuFEP8B7pFlebAQ4lnAXpblV8x2LwAlsizfoofRln9lhERVZ4R528Aatq2p8ntWPdPfC3Sq01BFRUVFRUVFRUVFRUVFhb/3EYOGxOx8WFuLSQrQ2Op3MGAzh0qWZWvv1WfAjdCfFGBglWMP/s1TreTfuoaEioqKioqKioqKioqKiorK7eMk0FIIESKE0AH3Aj9bGwghrBeGGgOcN/+9GxgqhPAUQngCQ83b/iv+lREStwMhREeg6mTqclmWa5ojo6KioqKioqKioqKioqLyP4ssywYhxKMojgQNsEGW5VghxEvAKVmWfwYeF0KMAQxADsoXKZFlOUcI8TKKUwPgJVmW/+u5//+zDglZlmOALv+vz0NFRUVFRUVFRUVFRUVF5d+ALMs7gB1Vti2z+vs54LmbHLsB2HA7z+d/1iGhoqKioqKioqKioqKionK7+JcvIfF/EnUNCRUVFRUVFRUVFRUVFRUVlQZHdUioqKioqKioqKioqKioqKg0OOqUDRUVFRUVFRUVFRUVFRWVOvi3f/bz/yJqhISKioqKioqKioqKioqKikqDo0ZI1AP5t/0NK+jfqGH1KvQNqwfg4tqweuXlDSqnm3pPg+oBDZ5HR3v7BtXzamCPdFtZblA9UD7m3JA8MmFpg+q5aRrWB+6qbXifu0cD59FZ07DPhaPU8G+GJs8Pb1A94e7eoHoNjtHY8JoN/UZRatjnUAxq2Npb3rezQfUAKrIKGlRP2GkaVq+B624AjUvD9qN8OgY1qJ6KSn1RHRIqKioqKioqKioqKioqKnWgzti4/ahTNlRUVFRUVFRUVFRUVFRUVBoc1SGhoqKioqKioqKioqKioqLS4KhTNlRUVFRUVFRUVFRUVFRU6kD9ysbtR42QUFFRUVFRUVFRUVFRUVFRaXBUh4SKioqKioqKioqKioqKikqDo07ZUFFRUVFRUVFRUVFRUVGpA6G+zr/tqEWqoqKioqKioqKioqKioqLS4KgRErfAkfg0Xt//F0ZZZlKnEOb2amuzX28wsnjH78Rm5OLhqGP16D4EuTtTYTSxbPdJzmXkYTSZGNO+GfN6t6XcYGTG9wfQG40YTDJDWwXz2J0dKtOTZZnXIg9x+Hw8jjo7XpsylHbBftXOKzYlgyUb91BWYSCsbQhLxg5ACMGu6Et8uOcEVzNz2PT4VDo09q88z+U/7Sc2JQNJCJ4L78odTQOUPF5J5fU9p5Q8dgllbt8ONlp6g5HFPx8jNj1HyeP4MII8XEjNK2LUpz/TzMsNgM5BPiwf2ZvSCgNPbT5Mcl4hkhAMahnM04O7/eN5TM3JZ9TKr2jm52k+Hz+WjxvAkUtJvP7LUYwmmUk92jJ3QDcbHb3ByOKf9hObmoWHkwOr7x1CkKcbeSVlPPndbmJSMxnftQ3Pj+lfecy7e37jx5Ox5JWUE+zlpqQ7uGf1dH/Ya0l36jCCzGW19sApNp86j0YIlozpz52tmirX4mIir287olyLnu2YO7A7AEt+2MfJ+FRcHOwBeG1yOG0b+QLwe/w1no88REZBCZIQPDywG3P7d6l+LlsOEpt2HQ9He1ZPDifI05XjV1JYvfckFUYjdhoNC4beQe/mQQDM/Hw7WYUl2NtpAPhs2gg+3f0bhy8m4min5bXJ4bQLqum6ZbLkx32UGYyEtW7KktH9EUKQV1LGM9/tJjW3gCBPN1bfNwx3J4fK42KSM5j60U+sum8YwzqGIssyd73xBen5RTjp7PjyoQm161UYFL0xYRa9b3dZ9O4fjruTA+sPnWb7nxcBMJpMXM3M5eiyObg72jN61bckXM9Dq5H4bv7E25K/q5m5LP1pH+dSs3hiWG8eCFPuv/IKA9M/2UxKbiFF5XrcHO1ZO3v0P6p5Axk41cQHe4ORTtdyq+lVZfr6D+k4ajiFmVm83LF3nfZ10WpYOGNXv4HQaPh9w1ccXPmuzX6PJo2ZvO4DXHx8KMnNZeOMeeSnXlP2NQ5m0to1uAcHgSyzYfQ95CYm1anZYkg4w1a9htBo+PPzrzn+9ns2+92bBDP60/dx8vGhNDeXrbP/Q2HqNfw7dWDkmlXYu7liMho5+uZqzv0UUadekyGDCVv5GkIjce7Lb/hj1Rqb/S7BQQxZ+yH2Hm4IjYbjy14mcfc+JK2WwR+9i2+XTkgaLRe+38QfVc61JoLuGkzvN19FaDRc+vIbzrxjq+ccHETYJx+gc3dHaCROLX+FlD37bPZP+P0Yf76+krPvf1SnHkBg+CC6vaFoXvnqG86/+77NfqfgIHp//L5ZU8Nfy18mbe9+AgYOoPPy55Hs7DBVVPDXshVkHD5ap55o1Rlp1AyQJEwnDyAf+tl2/50jkXoMApMJubgA0+ZPIe86ePigmfaU8opLo8V0fDfy7/tuomJFSDuku+4BSUKOPoZ8Yrft/sahSOH3gF8Qpsj1cPG0sr1JK6TwyRY77wBMkevgcvQ/owfg5ok0Yjq4Ku2f6ccPID+77jw2b4809F4QEvJfR5B/3VVFsyXS0CngF4wpYi1csGiKwRMRoZ1ACOT4c8h7NtZPb8gURS/6aM16Q6Yoedz6ma3eoImI0I4Wvb2b6tZrgDKVZZnXtkRx+DogP10AACAASURBVPxVpW28byTtzP0Sa2KT01ny3U5zn6Y5SyYMRgjBmh1HiYq5jBACb1cnXrtvJH7uLvx+OYlH10cQ5OUOwF0ugodbVW8fROsuSOMeUJ6L3/YjR9nWTyJsNFKvcPNzkY9p00eQm6Xs9PBBumc+wsMHZBnjulct+26C1KUXutlPgCRh2L8dw9ZvbPZrh45FO2yColdWiv7TlcgpCaDRoJu/GCmkFWg0GA7twhDxTc0i1nqd78BuxmMgSRgP/ILh5+9s9mvuGoN2yHgwGZHLSqlY9zZyaqKS9ybN0T24AJycwCRT/vxDUKGvXa9TT7TTHwVJg/HgLxi3fW+rN3g0miHjwGSCslIq1q9CvpaI8PFHt/JL5LRkAExx5zB8/k6d+QOQOt7QlDAe3IFxew2ad421aG5YjXwt0WLg7Yf9G59jiPgS444f6tQTHXuinfYIQpIwHtqBcbvtsysNGmXRKy/FsOGdanq61zdgjPgS484f65VHFZWqqBES9cRoMvHK3tN8Oqk/2x4Yxo7zScRdz7ex2RwTj5uDHbvnjmRm91asOnQGgN0Xk9EbTUTOHsaPM4bwQ/QVUvOL0WkkNkwZQMSsYWyZOZSjCelEX7M0cIcvJJCYlcuuxbNYMSmcFZv313huL22OYsWkcHYtnkViVi5HLiQA0DLAhzUzR9EjJMjG/qffzgIQuWA66+ZNYOW+PzDJspLHXb/z6b2D2fbQaHbEJhCXlWebx7/icHPQsfvhccy8oy2roiwNdmNPFyLmjiJi7iiWj7QMVGb3bscv/xnL5jl3czoli8Nxqf94HgEae3sQ8fQ0Ip6exvJxA5T8bTvCpzNHse2Je9lxJo64zBzb/J06j5uDPbufuZ+Z/TqxavcJAHRaDY/ddQfPDu9bTWdAqyY46uzQ2Wks6WZUSffkOdwc7dn97HRm3tmZVbuOAxCXkcPO6Mtse+o+1j4whpe3HsJoMinnGnmIT2ePZttT97Hjr0s2aS4Y2Y+IJ+4l4ol7K50RBaXlrNh+FKMJtj86mZ2P38OOmCvEZdoOMjefvoibo47dT0xhZp+OrNr7OwAeTg58dN9QIh+ZxOvjB7B4y0Gb41ZOHETE/IlEzJ9IbNp1Eq/nsWvBNFZMGMSKrYdqvm5bD7JiwiB2LZhG4vU8jlxSBovrDv5B79Bgdj07nd6hwaw7ZLmPjCYTq3cep1+rJpXbDl9MxNvFifen302rAG9WRBysKqXoRRxQ9J6druhdTLTVWzhD0Tv4BwAPDuhGxJNTiXhyKk8N70vP5kF4ODlw+GIiLg46Nj0yCT9X59uWP3cne5aMDmN2WFebdHRaDXMGdqdDsB+nVszD08mBRZv2/qOaN0j2cMZJb6hxX038+sW3vD98Qr3ta0NIEuPXvM36UZNY1bEXXaZMwq9taxubUStf5vTXG3mnWz/2vbKS4a++WLlvyhefcGjVGlZ17MX7fcIpyqy983xDc/h7K/lu7D183KUPHe6ZiE8bW827Xn+ZM99uYm3P/hx57S0Gv/wCABUlpUQ+OJ9PuvXluzGTGfrWq9i7u9WpN3D1m/w8fgrfdu9Hq8kT8GzTysam56JnuLwlko19B7Nr5lwGvrMSgNAJY9Ho7Pn+jjA23RlOhwdm4tqkcZ16fVa9wZ6J97KlZz+aTxqPR2tbvS7PPk18RCSR/QdzcPY8+qx602Z/r9dfIWVvzXXxzTS7v/0mBydNZUevO2k6aQJuVTTbL3iKpIif2RUWzrEH5tHDrFmek83he6exs99ATsx/jN6ffFgPQYE0ZjbGz9/E+M4CpM59wa9KG3AtAeOHSzGuWYR89jekEfcp2wtzMX78Isb3n8P40fNIA8dUDjJr1Rs6FdMPH2D6bAWiXU/wDrS1KcjF9MuXyOdO2m5PuoTp81eVf9+/owyA4s/9c3qANGo2pt/2Ylq3AtOXb0BxQe16NzSH34dp43uYPl2GaH8H+FTVzMG07XPks7/bbg9qgQgOxfTZckxrX0QENoMmtte/Rr1h92HatEY5pl3Pm+vFVtVrjghuoeTvs+X112uAMj18Pl7p0yydw4opw1jx403q8R/3suKeoexaOkfp05yPB+CBwT3Zumg2EQtnMaBdCz7afbzymO7Ng4lYOIuIhbNqdEYgJKQJczF+9irGlU8idb0T/INtbVLjMb67EOOqp5GjTyCNml65SzP1MeSDkRhXPoHxvcVQlE+tSBK6OU9T/uoCyp6ahvbOuxDBzWxMDEf2UvbMTMqenY0h8lt0Mx9TtPoMBjs7Zd/CB9EOGYvwDahdT0jYzX4S/ZsLKV8wE03fcERQUxsT47F9lC+aTflzczBs/x676Y+Yz1WD7pHn0a9fRfmzsyh/+Qkw1NHuCQntzCeoWLkY/cJZaHqHIxpV0ft1P/rnHkS/dC6GXzainfZw5T454xr6pXOVffV0RlRqvrUY/aLZaPoMrq55fD/6JXPQPz8Pwy+b0N4/32a/3f0PYzpT5ZmpRc9uxuNUvP0c+sUPIPWurmf6NYqKpXOpeOEhjL9sQnvff2z2a++bX389FZWb8LcdEkIIoxDiLyFEtBDitBCir3l7MyHEWfPfA4UQ22s49qAQosctaL0nhEgVwjJrRwgxSwghCyHCrbaNN2+bJISIMJ9fnBAi3/z3X0KIvkKIR83bZSGET33OISYthyaeLjT2cEGn0TCiTROi4q7Z2ETFpTKufTMAhrYO5kRSBrIsI4SgtMKAwWSi3GDETiPhrNMihMBZZweAwWTCYDTZphd7hbE92iKEoHPTQArL9GQVFNvYZBUUU1Smp0uzRgghGNujLftjrwDQwt+LED+vanm5kpFN75ZKp9bb1QlXBx1nr2UTcy2bJl6uNPZ0VfLYrilRl5Jtz+lyMuM6tVDy2LYpJxLSkWX5puXmaKelVzOlkdFpNLQL8CKjsOQfz2NNxKRk0sTLncZebui0GkZ0CiXqfIJt/s4nMK6bMjgZ2r4FJ66kIssyTjo7ujcLrIwQsEZIghAfTyQhLOmeu2qb7rmrjOvWRkm3Qygn4lKQZZmoc1cZ0bklOq2GYC83mni7E5OcQUxyBk283Wns7a6k2blltTSr8kvMFToF+9HcR8ljgLsLIzq0IOpCoo1d1IUExnVROm9D24VwIl7JY7tAH/zcnAEI9fOk3GBEbzDWqBV1IZGx3doo161JAIWl5TVft3I9XZoGKtetWxv2x141l0d8ZXmMs9oO8O3xMwzp2AJvZ0er8otnZv+uuDjocHN0qJ9e97YWvdirjOuuRDSNs9puzY7oS4zs3LLSfnq/zng4OeKg0962/Hm7ONGxsT9aybbqFUJw7HIyY7u1wWiS0Wm1lOgr/lFNgDKtRLaLPY3yS6rtuxlxR45TklN3JEV9aHxHd65fuUpOfCLGigqif9hM+zEjbWz82rYmLkpxCF05cJj2Y0ZUbpe0Gi7vOwiAvriYitLSOjUb9exO7pV48uITMVVUEPvjFlqPHmFj49u2NfEHDgOQcPAIrUcp55QTd4WcK0q5FqWlU5J1HWef2psQ/x7dyLsaT0GConfppwiaj7LVQ5bRubkAYO/mRnFaunmzjJ2zE0KjQevogFFfgb6wsFY9nx7dKLiaQKFZ7+rmrTS521ZPlmXsXF0BsHN3oyQ9vXJfk7tHUJiQQN6FC7XqWOPVvRtFV+MpTlQ0kzZHEDxyeJU8YtF0c6M0LQOA3DNnKU1X/s4/fwGNgz2STle7YONQ5Ox0yM0EoxFT9K+ItrZdCvnqucq3n3JSHMLN3E4YjWA0D0S0dlCfz7cFNlO08q8rb17PnUS07GRrk58NWalQS3soWndDvhoLhop/Ts87UIn+SDiv/K4or1sPoFEI5GQpUSQ3NFvZRteRnw2ZNeVRVspSowWNHWg0dTtBGoUoebTWa9m5fnmEW9droDKNirnM2J7tlbaxWSMKS8vIyi+yscnKL1L6NCFBSj3esz37Yy4DVEY+ApTqK7iljws2MT8XORlgNGD68yiivW20pnzlrNVzcQnh7q3s8A8GjQb5kvIiDX1Z3dEDoW2R01OQM6+BwYDh2D40Pe+0NSq1alvsHVFi8gBZRtg7gqQBnT0YDMiltu1dzXqpyJlpYDRg/DUKTY+b6wl7x0o5qVMPTElXkJOUviNFBSDb9rmrIlq0Qc64hpxl1jsRhdS9Xy35c6j1+a8PimZqFc0qL8LKbq4pde+HnJmmRKHUVy8zFcx6phMHkLrVpWf5KXXrh5yVhpxaP73/FYQQ/+p//xf5b6ZslMqy3AVACDEMeB0YcFvOygqzE2I8kAyEAQetdscAU4Ebr3LuBaIBZFkebz5+ILBAluVRVmmWAturpFUrGUWlBLg6Vf4OcHXkTFpOdRs3xUYrSbjq7Mgr1TO0VTBRcakM+GgbZQYDiwZ1wcNRaXSMJhOTvtpHUl4R93VtQedG3pXpZeYXE+DhWvnb392FjPwifM2DRoCM/CL8PVysbFzJzK+9Um/dyJeo2KuM7NKa9LxCzqVlk15YjCxDgKsl7QA3Z86kXrfNY2GJbR7t7cgrLQcgNa+ICeu242Jvx+MDutCjiW2YYkGZnoOXU5jes80/nkdQpm1MWP0tLg46Hg/vQXZRKQHuVfKXnGmbv4IiAtwVLa1GwtVBR15JGZ5Wg+OqZBQUV083Nau6jTmf1ulmFhTTqYnlrYC/uwsZ5kFogLulXALcXTiTnFH5+73dJ/h4/0l6twjm6RF90Wk1JGTnc72wlIsZOUz6JILpvdsT4O7MmZQqeSwsIcBcvlqNhKu9jryScjydLVMm9pyLp22gNzqtxQGzdOshJEkwtG0ImQXFBNhcExcyCqpct4Ii/N1tbTILlI5ZdlFJpa2vmzM5RcpAMiO/iH2xV/l87jjOJkdVHptpvi5Gk+kW9JzJNJdlNb1i24Frqb6CIxcTWTp2gFnvn8lfbWTkF7Fm728s23KA+/p05GxK5j+uGefrRmhWAYYanBUNgXujQPKTLRFT+SnXaHxHdxubtDNn6TBhDMfe/4QO40bz/7F33vFVFen/f8+5N733hJBCCElIQFoQUUBCB0EIRVAsWFDsoi6oa9tdFddFLKy7KqisAioKoUjvHek99BBIJ70Qktx75/fHueTem9wUVkC/+zvv14sXuefMzOc8c9qcZ2aecfb0xNXXh4A20VwpLuGBn77DNzKCMxs2seLVt5GmxhuZni1CKM2waJZmZhHa1VYz98hR2o4Yxu7PviBu+FCcPD1w8fWh0soR0yKxMzpHRwrPpTWq59YihPIMi/O6PDOL4ERbvV/f+4DhS3+iw6SJ6F1dWTx0FABnU5YSdddgHj17DL2rC1unvkFVke2otXp6ISFUWNlXkZVFQB29A9P+wcDFC4h/4jH0rq6sGq7q6V1duWXys6waPob2zz1Fc3ENCeZypkXzclY2fl1spwYdef8DkhYtIObxR9G7ubJh+Oh65YTdPZSiw0cxVTf+ISQ8fWyHy5cWIMKiaehTQOnaG9MpqykSXr7oHpoKfkGYVs6DsiYcbB4+SOs0ZcXqB/U1IuITMe1uxsiT36LnGwhVl1GSnwBvP+T5E8hNKU1/KHl4I8us2jSlRRDaTM3Mc8j0EyjPTwdA7tsIBTmN5/HwRpZa6V2LjZnnkOknUZ77ByCaqXdz6jSvpJxgH6s2jbeH2qaxem7Xa9N4e5Bn5bT4ePlWlu45hruzE3OeGVu7/eD5LJI/mEOApzt/CtIR7WF5ZwMIL1/VwXOVkkJEeJuG74tb+2IyT4MRAS2QlRUoD/0J4ReIPHUE0/K5jX60C98AZL6lfSELLqG0ia+XTj9oJPqhY0Gvp+rt5wEw7tqI7tYeuMxaDE7OVM+ZCeWNO1vx8UcW1NGLblsvma7/CPR33QN6B6rfeUE91pAwkOD4yj8Qnt4Yd27AUGf6RT37fPyRhVZ6hZdQWtvR6zcC3eDRCL0D1e+9aMkfEIzjO19C5WVqfv4KefJI4/bZ1cxvQHM4ukFjEHo91dNeUjc6OaO/axzVf/8T+iFj6+VpUK/A0l5tyEal73D0g0aDXk/N+y+rGx2d0Q0dR83f/4RuyD3N0tPQaIjr1Qr1BK5Pl1l9koCjwL9RnQ/WbAVuFUI4CCHcgWjgYFMFSikPSCnPX8tBNMfnae99L4Q6ukIRgk1PDmPNxLuYs+cUF4vVl49OUUiZMICNk4ZyJLuQ05dKrMqrX2Bdx5fdNE0c58iuCQR7uTPmk/lMW7qZji0D0AnFvo319OwlEQS4u7D+mVEsemwoU/slMmXxNsqrLI1Kg8nEyylbub9rHGFWL+sbZWOApxvrX3+URS+OZ+rdvZiyYB2V9oal19WyU1ZT3sbmOMTt19u1bb/K5EHdWf7SeBY8cw8llVdqpx8YTSYuFpVyR+uWzHpgMP/efIBLZfV7vpuq89N5hcxYu5u3h1niZHwwKoklT49m7iPD2Hchh5zS+g4hUacyG7pWGmPaL1t5afDt6Op8IDenrIbuv+awKTWNzpEheJvjWEg7V8L1sK8p3hvTl42vTuDIxVxz79iN09yUmoaD0YRHVfOna1x37J2gOjYun/IGUb3u4Pk9W4jqdQfFGZmYDEYUvY7IHt1ZPuV1Zt6WhG+rSBIfGv9fada9J9a+8iYRPW9n4q5NhPe8g9KMLExWw3vdg4MY8fW/Wfr4M00+AOw9P+rqxYwZyYm5P/BNzC0sGzmOAbP/BUIQlNgZaTLydXQ7/pPQhU7PPYVnZES98pqyr+4xRo1O5sy8H/ixbQfWjLmXXl+qep1em8Kxz77AUNG0w7dJzTonMmL0SNK+/5ElCR3ZNOY+un/xmU0+z7hYOvzlTfa88HJzBJuSs6Ts2AMRGoXcssyysaQQ46dTMU6fjNK5F7h7NUOzmYIN4eYJAaGQduy/0LoGPUUHLdtg2rAQ05z3Ed7+iPbdm5Gx+XVaD58AhH8Ipk+nYPp0CiIiDsLaXLtecwWv6s2cimnmFEREbDP07HH969R+G6JuGnvvYEuiF+7qyYa3JzG0S1vmbVUdBvFhQax76wlSpkxgfK/OPLvHXqyc5tep6NwLEdYauXGJukFREK3aYlr2LcaPVWed6JpkN2+DhoHd56Fh1SKuPDOWmrmf4zD6IVUuOh5MJiofH0HlU2NwGDYOEdji2vXsYFy7mKoX7sMw/wv0yQ+qWRUdSmx7qj97h6q3n0GX2BMloXPjBTXjuQZgXLeY6pfup+aHL9GPUKfAyOJCql4YR/Xrj1Mz7184PvU6uLjWy9ssTTt1aly3hOqX76fmxy/RD78fAP3ICRhW/QxVV5rWaZT6eqb1S6j+0wMYFsxCZ9bTjXwI43XR09D4bSMkXIQQBwFnIAToc30OqR73At8DS4D3hBAOUsqrY+UksA4YCHgBS4Fr77awgxDiceBxgH8/MISurcPJsfq4yymrJNDdttc82MOFnNLLBHu4YjCZKKuuwcvZkeWp6fRsFYyDTsHPzZlOoX4czSkizMpD7unsSNewQD7ddoSMNeoHZvuwYHKKLR7j3JJyAj3dbTW9PcgtLrdKU0aAVW+9PfQ6hVeGWwaz3PfRPCJ8PSivqiGnzNIQzSmtqG+jp6tqo6ebamNVDV4ujgjzdAWAhBA/wnw8OF9QRjvziI+3lu8iwteDB29ty/y9J/npyLkbaqOjXo+jXr28E1oGEebrhclkIqekjn2etuUEe7qTU6L2xhuMJsquVOPl4kRjBHu5NV2ulxs5xWW25bo6E2Tebmu/mjenxLI9x2r71V5wR72O5C5t+WbrAQCCPN1IjAgmt7QCHzdnEiOCSc0pIKbOlJZgTzdySissx1JlsTGnpJznfljLtJG9Cfe1zI0P8nRj/q/H+Gn/CUouV+Hm5ECOzTkpt2OzOtrFOs3VY/dzd+VSaQUBnm5cKq3A13ydHcvI46X5aqCxS2UVLDt4kukrttM9OoycknICzKOUmqdXQYBHA3p1RrysOHQab1dnkj9We0zatwwkp7icYPO1eL3sq8v8nYf5afdxG80ukS3oGhXK/J2Hb4jmVfanZ1Pg5szOVk6YhMCgKBwP9iY+p/Ee+OtJSWYWXmGWuf9eLVtQmp1tk6Y0O4fvxqiNPEc3N9qNHMaV0lJKMrPIOniEwjR1StKxJcsJ75bInm8a1yzNzMKzpUXTM7QF5dm2vavl2Tn8NE5tODu4udF2xDCqStX70dHDg3EpP7Dx7ffI3L23SRvLM7Nwb2lpaLuHtqAix1Yv/sHxLB2h9jDl7N6LztkJF38/Yu4ZRfra9ZgMBiov5ZO961cCO3ek9LztNCxrKrKycLOyz61FCy7XsS/mwfGsGan2oF3avRe9kxPOfn4EJHYhcvgwEv/6Jo5eXiBNGKuqSP3yq0ZtvJyVjWuoRdO1RQiVdTRb338fm0aPA6Bgz150zs44+flRlZ+PS4sQes6dw65Jz1B+/nyjWgCytNAy1BzA0w9ZWr9PRLRuh5I0AuOXf7VM07CmrAiZm4GIjK0fF6FOOuHhY2mme3irPezXgGibiDx1UA0M1xS/Ra+sCPIuqlMTAHnqkHkkwI4m8wkPX4umpw+UN09TxHZCZp5TpzKgTgsQoVHIi6cb1/P0/a9sFDF19M41U+8G1en81Ax+Wq3G5mofHkJOkdU7vbisfpvGq06bprjMZiTcVe7q0pYnv1zEs4N72EzluDM+ir9JSVG1AR9HSzNelhSoASmv4uWLLLEdyQsg2tyC0m8Uxn+9UXtfyOICyExTp3sA8uhuREQbZCO3hSzIQ/hbYlkIvwBkUX6D6Y3b1+E4Ue3N1/Xsj/HAr+oUqtJiTCePoLSOw5iX1WB+Ci8h/K5Bb+d6HB6dTA1qz78p9SCUqZ1+xoO7UFrFYDq2v8H8svASwtdKzzcAWdRwcFjTrg04PKyOyMBQA+Xqp4o8fwqZl4UIbolMO9WwfXY1/ZHFDdto2rURhwmqptI6Dl3XXjDuCXB1V0e3VFdjXLe4Yb2ifIRfwDXYuBH9Q8+b9doiuvaCsY/X6smaakzrljRq4/8Eyv/NaRF/ZH7LCIlKKWVHKWUcMAj4VlzniStCCEdgCLBYSlkK/AoMqJPsB9SpGuNQHRfXBSnll1LKRCll4sRenWkX4kt6UTkZxeVUG42sPHGBpGhbb25S6xYsPnYegDUnM+gWHogQghBPV3ZdyENKyeVqA4eyC4ny9aDw8hVKr6ijCK7UGNiZnsuoW6JqAzH2TWjNkr2pSCk5lJ6Nh7NjvZdWgKcbbk6OHErPRkrJkr2p9Elo3ahtldU1XK5SH5Q7TqWjUwTRAd60a+FHemEZGcVlqo3H00mKsQ2gltQmjMWH1Tl4a1LT6RYZjBCCwoortcPpLxaVkV5YSksf9SX8yaYDlFfV8OoAdS7jfYmxN9zGwvLLluMpKCE9v4RecRGkFxSTUVhKtcHIysNnSIqLtLWvbSSL96urLqw5dpZuUaFNjpBoFxpIekExJikt5cbb+sWS4luxeL86J3vN0TN0a90SIQRJ8a1Yeeg01QYjGYWlpBeU0D4siHYtg0gvKLEc66HTtWVejSsgpWT98TTaBKkN8z5xEWQUlXG+oJQzeUUcysjjeFY+SXHhtscSG8Hig+pLcc3xNLq1UmNzlFZW8eS81UzudyudraaRGIwmiiqucF+3BBY8nswtLQPpGhnCkv0n1PN2Iafx83ZBjTOyZP8J+phtsK6PxVbb1059iHWvqP/u6hDD9HsHsnbqQ/RNiGLJPvU6Ka280sR1Ytbbl0qfhCiL3j51/u9iq+0AZZVV7DmXyesjetcGuOybEFVr35Vqw3Wzry73db+FlOfH8dWjw+keHcaS/SeorK5h3dGzeDo73RDNq7w46HZuT8uje9ol4rOL8blcdVOdEQAZe/bjH90an8gIdA4OdLhnFMeXrbRJ4+rnW3sPJr0ymb1z5gFwcc9+XLy9cfNXr//WSb3ITT3ZpGbW3v34RkfhHRmO4uBAwpiRnPrFNrq/i59vbU9VjykvcPBbVVNxcOCeBd9yeN6PpC5qXqMrd98BvFtH4Rmh6sWMTiZtua1eeUYGLZN6AeAT2wadszOVl/Ipy8ig5Z3qSCW9qyvBXRMpOtXIRxeQv+8AXlGtcDfrRY0awYUVtnoVGZmE3KnqecWoelfy81kxaBg/te/CT+27cPzfX3Bo+sdNOiMACvcfwKN1FG5mzfBRyWSstF3BoCIjkyCzLZ4xbVCcnKjKz8fBy5M7F8zn0F/fJf/XZgZGyziL8A8GnwDQ6VA6dEem7rNNExKJkvwYxm+n28YX8PRV4w8AOLshImPV+dqNkZ2uDtv38gNFh4jvijxzuHnHaka0TbQbLPG662WfB2dXcDF/BEfEQkET9gFknTdr+ls0TzWxEshVSgoR4TFqnAVFhwiPQTalmXUefOrY2NTKI1cptaOXfwPPYfb5Ruv0vraWYJN920ezZM8x9d14PgsPFyeb6RoAAV7u6nP8fJb6HN9zjD7t1REe5y9ZHGsbj54lKkjtULhUWl47supwejYmCd51Y1pdPIPwD1Ht1OlROvVAHqvjNA1thTL6CYxfv6/GUajNe1b9qHRTOyJEdDtkbkaj1WI6cwIREoYIDAG9Hv0d/TDu2W6TRgRbgmrqOt+OKUctU+bnomtnHqHg5IzSJh5TVsOOVgDT2ROI4JZq8EudHl33Phj31dWzOEaVTt2RZj3j4d0o4a3VeBWKDqVtB0xNxD2Q504ggkMterf1wbTf1rEngqz0Ot6GzDFPXfPwUq9PQASEIIJC1dgXTWBfc2ezNKvfeYGqF++j6sX7MK5eiGHZ/EadEbV6QaHgr+optyVhOtCIjR1uQ+aqejXvvkD1S+Opfmk8xjULMS6b//+HM0LjhiAaC0jYaEYhyqWU7la/c4H2gCvwi5Synb34Dea0m8zbG+1eEkLcjepkuDrByRVYI6W8XwgxAUiUUj5jHqlRKaXsiB7zVAAAIABJREFULoSYY9b/2VyG3WMw7ztvLqNh9yNgnP2GBNh8Lpv3NxzAZJIkt2/FpO7xzNx2lIRgH/pEh1JlMDJ1+a+k5hXj7ezI9GG3EebtTkV1DX9euYezBaVIILldJI/eGsfJvGJeXbkbk0liQjIoNoynbk+AINXRIaXknZSNbDuZjrODnnfHDqhd1jJ5xlxSXlSHTR29qC6JWWUw0DM2kj8n90YIwbojZ3h38SYKyyvxdHEiroU/sx4fSWZhCRNnLUYREOjlzt8GJhJqfmFuPpPJ+2v3qDZ2iGZSj/bM3HyQhBA/+sSEqTYu2UZqbpFqY3JPwnw8WHMinZmbD6FXFBQheKbXLSTFhJFTWkGfmYuI8vPEQae+PMcnxjK6z2031MY1h08zc/VO9XgUwTNJiSS1jWTzyXTeX74dk5Qkd45jUlIXZq7bTUJoAH3atqKqxsDUn9eTmpWPt4sz08f1J8w8UqDfP+ZSXlVNjdGIp7MTsx4eSnSgL9NX7WTR3lSKK6vQCUGXViHMeXwkM9f8SkLLQPrEm8tdsNZcrhPT7x1ImJ86TPjzDXtJ2XscnaLwyrCe9IpVh2RvPnGe93/Zqp6LxHgm9VGDtj38ZQqFFZVIIC7En7eSe+Pm5AhVVXy17RDzfj1GQUUlbo4OPNi9PZPu7MTMDXtJaBFAn7gI9VgWbSI1p0A9ltF9CPP15PPN+5m19RDhfpaREbMfGIKLo54Hv/4Fg3n1j+5RoUwZ2I1pa/aw7ZT5vI3pS7uW5vP2yQ+kPK/2hB7NyOW1n9ZTVWOgZ2wEf766DGdFJZPnrya7uIwQbw8+Gj+odrrEVV5bsI4720bWLvt553vfUFheiZQSHzdn3h87gB6xESR//D0pL9xr0VuwzqJnXh62uKKSyfNWWfTuH1yrl7I3lW0n0/lwvCUIn5SSwf/4jozCUkxS4uvmzORBtzOqa/xvsu9SWQX3zFxAeVU1ihC4Ojqw7MXxZBaV8sqPa8kpLaeiqgZPZyc+f3jodanThjTdnR15euSfAShyceSij1uzlv18dP7XxPTugbu/H6W5eSx76z12fP1dk/kAPHX1feBxg/sz7MNpKDode+bMZcO0Dxnw9mtk7D3A8V9W0n7k3Qx+9y2klKRt3UHKsy9jNMcYaNOvN0M/eBcEZO4/xMJJz2OssQSb89Db97lHD+zHgOnqsp+H/jOPbX+fwZ1vvkr2vgOcWr6Ktsl3k/S3N0BKLmzbycrn/4Sxupr2945h2Jf/5NJxS8DHpROfJvfw0drf3nZsjBjYj55/fxdFp3D82/ns/cdHdHv9FfL2HyRtxSp84mLo88+PcHR3Q0rJ9tf/wsX1m3Bwc6Pv55/iGxeLEILjc7/nwMf/tCnbTVffYdpyQD+6vf8OQqdw+rvvOTT9Izr9eSr5+w9yceVqvGNjuGPmRzi4uSIl7HnzL2Rt2GRTRqdX/0RNeUW9ZT9dGugZCunfl87T3kHodJybO5/jH35M+9emUnjgIJkrV+MZG8Otn8xA7+4GUnLwzb+Ss3ETCS9PJn7yc5RZxeLYmHwPVfmW1/KYJ/vW0xOxHdVlP4WCae8m5KbFKP1GIzPTkKn7UB59DREUXhsfQhYXYPpuOiK6PcqQ+1EHWApMO1cj92ywLdvLzhSOqHYo/caAUJCHdyB3rkT0HIbMToczhyE4AmXkJPXD1VgD5aWYvvqrmtfLD+X+P2H67FWaPU3gt+hFtkXpMwoQyNwLyJVzwWQVoNhoP1gxrduh9B8HilCXxdy+AtHrblXz9CHVyTP6KVXTUAMVpZi+fAuEQAwarzoJpESeO4ZcV2e5QXuO/dbtUPqNtSzDuaOuXgTKqDp6s9626IW1ASTy7DHk+jrLDdqLi3Mj67SD+n6WUvLOwnVsS03D2dGBd+8dTDuzgz/5gzmkTJkAwNEL6rKfVTU19GwbxZ9H9UUIwfNfLyYtrwhFQAtfL94a058gbw/mbd3PD9sPolcUnBz0TAl1oZNv/SkAIq4zyoiH1fti9wbk+oUoA8chM84gj+1FeeItREi4GiMEkMX5mL5+X80bcwvKsAkgQGacw/TT5zYji6rTc+vpKZ1usyz7uWE5hkXf4jD2UUxnT2Dcux2Hh59Hd0uiGrSyoozq2R8hM9LA2QXHp19DMa/KYdi4AsNS2z5FYSeIuNKxm2XZz00rMCyei370I5jSTmDatwOHB59Fad/FrFdOzZyPawM86nr0Rz98vLqk6cFfMcz/3FbPzrNb6dAN/f1Pq3qbV2JcOg/9qIcxpZ3EtH8H+geeQUnoAkbVPsN/PkVmnkfp2gv9qIfVe00aMSycg+nAznrl27svlA7d0I9/Sl1qdItZc+QETGmnMB3Ygf7+p201v51ZL6ikPvkhZFVlvWU/7XWwKbfciu7+pxFCUfWWzUc3cgIy7SSmAzvRjX9and5iNEBFOYbvPq1dSvUquuQH4Uql3WU/nb5d/z83nKCkd4ffFr30BuO16dD/uTq/Lg4JIUQcsA0IAsK4fg6J74GlUsrvzb/dgDQgErgHi0NiMHBFSrnxRjokbhpBTcyju940MjzrhuHj13Sa60lV1c3V09V/kd5wbraNTo1PZbnu3OzIwb8xWvb/Ba46JG4W9hwSN5KGHBI3EnsOiRuJPYfEjaQhh8SNxJ5D4kZi1yHxv0RDDokbyc1+ft/sQL0dmr1w3HVBrlvZdKLrjD2HxI3EnkPihurd5Ge3Knpz74vfYwWG/0mHRFLHP3QD0Wvjwf9zdX49YkiAGknnISml0c7F3lcIYT3ua4z5/+VCiKtdWTullGOsMwkhXFFjQzxxdZuUskIIsQ0YZp1WSnlNT2YhxHPAFCAYOCyEWCGlfOxaytDQ0NDQ0NDQ0NDQ0NDQ0Pjv+a8dElJKu65L8+oV7cx/bwLsRVbr3YzyLwO+draPtPo5x87+CXV+b6LO8p5Syk+BT5s6Bg0NDQ0NDQ0NDQ0NDQ0NjRvDbxkhoaGhoaGhoaGhoaGhoaHx/wW/x9SX/3X+EA4JIcRA4O91NqdJKZN/j+PR0NDQ0NDQ0NDQ0NDQ0NC4sfwhHBJSytXA6iYTamhoaGhoaGhoaGhoaGho/E/wh3BIaGhoaGhoaGhoaGhoaGj8ofkdVpv6X+d3WONGQ0NDQ0NDQ0NDQ0NDQ0Pj/3c0h4SGhoaGhoaGhoaGhoaGhsZNR3NIaGhoaGhoaGhoaGhoaGho3HS0GBLNQadV03XHJH/vI7ixGGpuvqbJeHP1qqpurp5O859ebzxvcp2WGk03Ve/34GZfpSZu7lxWo/wd7sOS0pur5+V1c/X+f0De5He+cpOvU+NNfv+abv6zVBpv7jmUN7sd9Tss5Sh0N1dTaLEPrg/asp/XHa2Fr6GhoaGhoaGhoaGhoaGhcdPRHBIaGhoaGhoaGhoaGhoaGho3HW0ugoaGhoaGhoaGhoaGhoZGE2hTX64/2ggJDQ0NDQ0NDQ0NDQ0NDQ2Nm47mkNDQ0NDQ0NDQ0NDQ0NDQ0LjpaFM2NDQ0NDQ0NDQ0NDQ0NDSaQltl47qjjZDQ0NDQ0NDQ0NDQ0NDQ0NC46WgOCQ0NDQ0NDQ0NDQ0NDQ0NjZuONmXjGth6Lotp6/ZhNElGd2jNxO4JNvurDUZe+WUnx3IK8XZxYsbwOwj1dgfgZF4Rb6/aTXm1AUXAgocGYTCauH/e2tr8uWWVDEuI5NVxgwGQUvLeks1sSU3DxdGB98YOIL5lYL3jOpaRy2s/rOFKjYFebVvx2vA7EUKw6tApPluzi3N5hfz43L20CwuqPc63f17PsYxcFCF4tW8nbo0IVm08m8m0NXsxSsnojtFMvL1dfRuXbjfb6MiM5F6EeruTWVzO0C+WEunrCUCHUH/eHnIbAI9/v55L5ZUYTCa6hAXyxqBb0ZnLk1Ly3tLNbDlxHhcHPe/d04iNC9aqNsZF8trdZhsPn+aztWYbnxlXa+PhCzm8tXA9ABVXqqk2GHBy0DM6sS0T7+xc36af13Ms8xLers7MGNefUB/Vji8372fh3lR0iuC1oT3o0SYcgP9sP8TPe1MRQEywH++OTMLJQY+Ukk/W7mb10bPoFEFiZAi707LUa6ZLHBPv7GRHewPHsvJV7bH9CPXxoPjyFV74fi1HMvNI7hTL68N61OZZfugMX245gAACPV35++g++Li51O7feiaDaat2qZqdY5jYo0N9zcVbzJpOzBidRKi3B4czL/HWsu1XzwxP39mJfm0jScsv4cWfN9bmzygq49mkzjx4+y2q3umLTFu5U71mOscysWfH+nqLNnEsO1+9L8b0JdTHgx1nM5ixdg81RiMOOh0vD7iV26JCbfI+PX81F4vKWPr0aIt9py4wbcWO61qnj/9nOZfKLmMwSbpEBPPGsB7oFOWG6VUbjLz7yzZ2p2WjCMHz/bsyICHqhtr40OylXCq/jJNefezPnnAX9ogZ2JfhM95H6HTs/vpbNn3wsc1+7/Awxsz+J+7+/lwuKuKHBx+nJDNL3RfWktFffopXy1CQkq+H3UNR+gW7Os3lga8+o/3QQZTlXeJv7W/7TWVd5WbbGNW/LwM+fA+h03Hwm+/YOf0Tm/2e4S0Z+sVMXP39uVJUxJKHJ1Fm1hu39CdCb03k4o5dLBh5b7Psi+jfhzs/mIbQKRz7z1z2fmir59EylP5ffoaTtxeKTsf2N//K+dXrUPR6+v7rEwI73oKi05P6/Y/snf5xAyq2tOzXh+4fvItQdJz8di6HZnxqs9+tZSi9v/gnjl5eCJ3Cnrfe4eKadQD4JsTT49PpOHp4IE0mFt85AGNVVbN0AUR8F5Qxj4NQMO1Yg1zzk+3+noNReg0FkwmqKjHOnwk5F5tdPgCt4lH63QOKgjy0Hblrte3+sGiUvvdAYCimJV/Byf3q9vAYlL5jLOn8gjEtmQ2nD90YPQBPH5TBD4CHDwCmn/4JJQVN2xiVgDJgHAgFeXArcueqOpptUAaMhcCWmFK+hBMWTdFnFCL6FhACmXYcueaHP55eq3j1XAiBPLwD+esa2/0to1H6joaAUExLv4ZTByz7PHxQBt0Pnj4gJaafP4PSwnoSUkreW7zR0m4bN5D4lkH10h27mMtrP6yytNtGJCGE4B/LNrPp2Dkc9DrC/Lx4d9xAPF2cOXwhm7d+WndVhKdCnOkX7FmvXBHXCSX5UfVe+HUdcv0i2/133o1yWz8wGZHlpZh++CcUXUJEt0MZ8YglYWAopm8/RB7d3WiV6jp1w/HRF0BRMKxbRs2iuTb79QNH4DB4JNJkgiuXqfrXB8iM86DX4zhpCrroOKTJRPVXn2A6dsC+SGPaj70Aig7D2mXULPrOfrruSThPfZfKlx7BdPbEtWvcZPuUjt1wfPh5VXP9LxgW19EcMBz9wJFgMiGvVFL9hVlTp8PxyVdQWsWATodh8yoMKXPti1jrdbgVB7Oecf0vGJbMs62D/sPRD0yu1av54h/ITFXPYdJUVU/RYdyyut6x/q+irbJx/dEcEs3EaDLxzpq9zB7XhyAPF8bOWU1Sm5ZE+3vVpll4+Cyezo6snnQ3K46f58NNB5kxogcGk4mpy3by/tDuxAX5UFxZhV4ROOkdSHlkSG3+0d+spH9MWO3vLSfOk36piFWvTODwhRz+snA9Pz5fv0H614Ub+MvovnSICOGJ2YvZeuI8vdq2ok2wP58+NJS3f15vk/7nX48CsOTlBygou8wTX/zMgkeGIKXknVW7mX1fP4I8XRn79UrVxgBvi40Hz6g2PjWCFcfS+HDDfmaM7AVAmI87KROH1ju+GSN74u7kiJSSFxZuYXVqOkNu97fYmF/MqikPqTambODHZ8fVtzFlI38Z1ZcO4cE88fUStp5Mp1dcJG2C/Pj0gaG8vcjWxjbBfvz03L0IAQPfn0OVwcDKyfdx35cpJLWNJDrQ12LT3lQ8nZ1Y/dJ4Vhw+zYerdzFj3ADO5BWy8vAZlj0/jrzSCh79ZhkrJt9Lftll5u48wrLnx+HsoGfy92tYceQMyZ3jSNl/kpyScpY/PxaJZOCM7/nmkWEEebox9vNFZm0fi/a+E3i6OLH6xXtZcfiMWbs/jnodz/ZN5HReEWdyLY0eg9HEtBXbWfbcPfi4uTB91S7m7TrGM30TLdfpip3MfmCgqjlrKUmx4UQHWGkeOKWew+fGsOLoOT5ct5cZo5NoE+jDT4/fjV5RuFR2meTPF9M7NpxW/l6kTBpRW37vGT/SNy7Cord8O7MfHKLqfbmYpNgIWxv3n8TTxZHVz49lxZGzfLh2NzPu6Yu3qzP/um8AgZ5unM4tZOJ3K9n08vjafGuPp+Hq6GBzXo0mE+8s287sh++6bnUKMGNsf9ydzdfo92tZffQcQ26JvmF6X2zej6+bCysnj8NkkpRUXrnhNgJ8MKYv7UID6m2/ilAUkj+dzqxBIyjJyOLZXRs5vmwleakna9MM/eBv7P/uB/Z99z2tk3ox6N23+HHCEwCMnfM5G6ZN5/S6TTi6uamNtN/Izjnz2PTPL5nw7Re/uSy4+TYKRWHQJx8w/66RlGZk8cj29Zz+ZRX5Jyx6/ab9jSPzfuTI3B+I6N2TpL+9wdJHngRg10czcXBxodNjE5ptX+8ZH5AybBTlmVmM27qOc8tXUWil13XqS5xetIQjs7/BNy6W4Yt+4Jv4TrQZORydoyPzbu2J3sWFB/bt4OSChZRdaPzjXSgKd3z4PiuGj6EiM4sRm9eQvnwVxSdP1abpNOVFzi1aQupXc/COjWHQwu/5oV0XhE5H79n/YtPEpyk8egwnXx9MNTXNstUsjjL2SYyfvg7F+eimfoTx8C4bh4Pcswnj1pVq8vbdUEZNxPTZm9egIVAG3Ivph0+grAhlwqvI04ehINuSprQI0/L/ILr1t8174RSmb95V/3Z2RXnib5B2/MbpAcrQhzHtWAnnU8HBCWQz7kMhUAbdh2n+R1BahPLIn5GnD0G+tWYhpmXfILoNtM0b2hrRMhrTrLdV/QenIsNj4MIpGuT30Os3FtOCT6GsWM1z5jAU5NjqrfgO0bVfvezKXQ9h2rkK0k80WqdbTqSpbZpXH+HwhWxzu+2+eun+unAdfxnT39xuS6ltt90eE8HkIT3R6xQ+/GULs9bv5qWhvWgT7M9PL4xHr1O4VFpO8rSv6B3ogd76w0goKKMex/j521BcgG7yBxiP7obcDEuazHMYZ7wMNdWI2weiDHtQdTycOYpx+otqGld3dK/9C3nyYMP1CaAoOD7+ElfefgFZkIfzB7Mx7N6mfhybMWxZg2H1YgB0XXvg+PCzVP3tJfT97wag8oUHwcsb5zc+5MqfHgMpG9e01n7iZa689byq/Y+vMOzeaqMNgLMrDkPHYDx5tHnl/t72KQqOj71I1V8nIwvzcH5/Nsa9dTS3rsWwZomqmXgHjg89S9W7L6Hr3gccHLjy0kPg6ITzx3MxbluHvJTTgBggFBwefZHqdyYjCy7hNG0Wxr3bVYeDGeO2tRjXqnpKlztweOgZqt97Gd1tSaB3pOrlCeDohNOM7zBub0JPQ6MB/uspG0IIoxDioBDikBBivxDidvP2SCHEUfPfvYUQv9jJu0kIkXgNWp8IITKFEIrVtglCCCmE6Gu1Ldm8bbQQIsV8fGeEECXmvw8KIW4XQswTQpwUQhwVQnwthHCwr2zhSHYB4T7uhHm746jTMTg+gg2nM2zSbDidwYj2rQAYEBfOrvRcpJRsT8smJtCbuCD1g8Lbxam29/Uq5wtLKbxcRZcwy8fChmNnGZ7YFiEEHSJCKLtSzaXSCpt8l0orKL9STcfIFgghGJ7YlvXHzgLQOsiXVlYf3lc5m1vAbW1Ux4efhysezo4czSrgSFYB4b4ehPl4WGw8ZdsQ3XD6IiNuaa3a2DaCXedzkE08YN2dHAEwmCQ1RiPCKhjMhuPnGN7ZysbKqoZtjAhRbexc10Yf6uLi6IBep3DkYi4hPh7oFR0Oeh2Db4lmQ+p5W5tSzzOic6xqU0Jrdp3NRErJhtTzDL4lGke9jpa+noT7enEkIw9QPxqv1BgwGNX/Az3cAPjx12M82ScRRREcycijlb83Yb6eOOp1DG7fgHanGLN2FLvOZSGlxNXRgS6RITjpdTbpJRIp4XK1ASkl5VXVBHq61u4/kplPuK8nYT6e6jlMiGLDCdve2w0nLzCiQxtVMz6yVtPFQY/efF1WGYx2Y/bsSssm3NejduTPkcxLqt5VG9u1ZsOJdFu9E+cZ0dFsY3wrdqWp9Rsf4k+gp1pv0YE+VBmMVBuMAFRU1fCfnUd4opftyIAjGXmE+3le1zoFcHe+eo2azNfojdVL2XeydtSDogibES43SrM5hN3ahfyz5yhMS8dYU8OhBQtJuHuITZrAtrGc2bAZgLMbt5Bw9+Da7Ypex+l1mwCorqigprLyvzoOa85s3cHlwqLfXM5VbraNLbp2ofBsGsVp6Zhqajj+0yJihg22SePfNpbzG7cAkL5pKzFDLcdzfuMWqsrLm21fUGJnSs6lUXpe1Tv1cwpRQ231kBJHTw8AHD09KM/OMW+WOLi5InQ69C7OGKurqS4ra1IzILEzpefOU2bWPLtwMRGNaXp5cjlH1WzZN4nCo8cpPHoMgKrComtzZEXGIC9lqR+WRgOmfVsQHeqMpLlidY6cnIFmfvRcJSQSivKgJF/tWT6+B9HmFts0JQVwKbPRDw4R2xl57hgYmnC4/BY9vxAQiuqMAKipaloPoEUrKLwExVaaMbaj3SgpgDx7NkrQO4BODzoH0OmgovSPpRcSCcWX1DJNRmTqPkS07ehBSgvNdVrn+vMLBkVRnRHQaJ1uOHqW4V3izW2aFuY2je39e6m03Lbd1iWe9UfPAHBHbCR6nfoe7hARQk6xmvdqmwagqsaI3f7Z8DbI/GwoyFXvhQPbEO1uta25M0ehplr9O/0UwtuvXjGiQ3fkif216RpCadMWU3YGMjcLDAaM29ajv7WnbaLKy5a/re49JSwS45G96vaSYqgoR4mOa1TPVju+jvY69N161kvnOH4iNSlzm7TFvsbNt0+JbovMyUDmqZqG7evQde1hm8hG06VWEykRTi6g6MDRCQwGZKVte9q+XiYyLxuMBow71jeqJ5ydre5Hqf621rvcuJ6GRkP8lhESlVLKjgBCiIHANODO63JUVpidEMnARaAXsMlq9xHgXuBq9/g44BCAlDLZnL838LKUsrbrXgjhDdxv/jkfeAz4d2PHkVtWSbD5oxMg2MOVw1n5DabRKwoeTg4UV1aRXliGACb+uIHCy1UMaRvBo7fF2+RdcTydQW3DbT7W80oqCPb2qP0d5OVObkk5AZ6W48gtKSfI/HGopvEgr6TxB0JsiwA2HDvHkI6x5BSXcTy7gJyyCqTE1kZPNw5n1rXxMsHmD2BrGwEyi8sZOfsX3J0ceO7OjiSGW4YpTvx+HUeyCujZugUD4sKtbCwn2Pr4vRuw0cs2TV5J0w30QxdymDx3BfllFXw0bgB6naLadDHP1qbScoLN5et1Ch7OjhRfvkJeSQW3WNkQ5OVGbmkFHcODebhHR/r+4zuc9XpubxPGHWYHz4XCElYePsP64+cwmiThfpYRNMGebhzOqKtdYavtpGpbf6Ba46DT8ebdPRnxz59wcXAgws+TN6yG5eeWVRDsWfccXrKjaXWdOjtSXFmFj6szhzLyeH3pNrKKy/l7cq9aB8VVVhw9x5B2UXXKspybYC87NpZdrj0mi41V+Lg516ZZczyNtiF+OJo/pGdu2MuE29vj4mD7iMotvWyrdx3q9CoT5yznSEYePWPCa6dP3Ai9UvP9MnPdHnanZRPm68nrw+7A3931htv450WbUIRgQEIrJvXuXG+/V4sQSi5m1v4uycgi7NYuNmmyDx+l3ci72T7zc9qNGIazpyeuvj4EtInmSnEJD/z0Hb6REZzZsIkVr759XUZJXE9uto0eLUIoy7DolWZmEdrVVi/3yFHiRgxjz2dfEDt8KE6eHrj4+lD5Xzhi3OvolWdmEZxoq7frvQ9IXvozHSZNxMHVlZShIwE4k7KUqLsG89jZ4zi4urBl6utUFRU3qekWEkJ5pkWzIjOLwDqa+977B0OWLCD+icdwcHVlxd2jAPCKbg1SMjhlAc7+fpxdmMLhj//ZbHuFtx8UWb2nivIRkbH1XA6i110ofZNBr8f48WvNLh8ADx9kmdW5KCtWP6ivERGfiGn3+qYT/hY930CouoyS/AR4+yHPn0BuSmm6Z9bDG1lmNaqqtAhCm6mZeQ6ZfgLl+ekAyH0bbUce/BH03L3r1GkRtIhsnp5PEFRVoox4HLz8kOknkJsX261TtU1jr91meaar7TarNA20aRbtPsYgszMf4FB6Nq//uIasolLebxdiOzoCEN6+qoPnKiUFiPCYBt1vSrd+mFL319suOvVEblraQC6rdL4ByHzLu0kW5KHEJNRLpx88Eoe7x4Fez5U3nwPAlHYG/a09MW5dj/APRGkdi/ALgtOpTepatHOttC+htLFtVyutYhD+gRj37sBhRP1RKn9E++pr1rcLQD9oJPqhY0Gvp+rt5wEw7tqI7tYeuMxaDE7OVM+ZCeVNOJR9A5AFdfXa1kumG5iM/i5Vr/qvL5j1NqEk9sT5y8Xg6ETNf2ZCRdMO7P8JtFU2rjvXK6ilJ3D9urBsSQKOojoM6s5X2ArcKoRwEEK4A9FAE2PMQEq5QpoBdgMtm8xjd6toMo0QAoNJsj/jEh8Mu5259/dn3amL7Dxv+/JckZrOXfGRdY/TTnk0ncbusVoY2TWBYC93xnwyn2lLN9OxZQA6odi3sZ6evSSCAHcX1j8zikWPDWVqv0SmLN5GeZXFIz3r3n5sfn401QYTv1rZbr/O6mjaSSWa8TDoEB7M1GG96BPfmlmbD1BVY7Bvk91jEPZ1gZLKKjakprH25fvZ9MqDVFbXsPSgOlS02mjEyUHHT0+N4rYeWalEAAAgAElEQVTWoexLz65fQDO0G6LGaOSH3cdY+NQoNk+9n9hgP2ZttlzyzRntaFfT/H+HloEse2okCybezaxth6kyGGrTVBuNbDx5gYHxlkZjc/oZm7qOT+cVMmPtbt4epvY8pGYXcKGwlH5t6zdO7Z2T31qnV5k14S42T32AaoORX89l3TA9o8lETmkFnSKCWfj0KDqGB/GPlbusyrsxNn5wT1+WPDuGuRPvZt/5HJYePG2vkPrb6ogtn/IGUb3u4Pk9W4jqdQfFGZmYDEYUvY7IHt1ZPuV1Zt6WhG+rSBIfGl+/vN+bm22jHb2698T6V94kvOftPLprE+E976A0IwuT1b13TTRDL3bMSI7P/Z6vY9qzZORYBsz+NwhBUGJnpMnIV9EJfJPQmc7PPY1nZMR/pVn3YRQ9JplT837g+7gOrBp9L71n/QuEQOh1BHfvxobHJrF0wFAihw2hxZ31ezobEW9WKrllOca3HsOU8g3K4LHXUH6DJV5bcjdPCAiFtGM3Vk/RQcs2mDYsxDTnfYS3P6J992ZkbPq+aBCfAIR/CKZPp2D6dAoiIg7C2vyx9OxdJs3VUxRoGY1p40JM3/4d4eWPaGe/Tu2/X+u0Fe094uvcQ5+v+xWdIhjW2fJx2CEihGVTHmLBC/cx62w+Vca6jtDmGym63IkIa43csNh2h6cPIiQceaIZ8Q6acd8DGFYuovLJe6j+9t84jJmgblu/HJl/CefpX+H46PMYTxwF0zU88+ze9lbaQuD46HNUfzOz+WXW0/gd7Guu5qpFXHlmLDVzP8dh9EMAKNHxYDJR+fgIKp8ag8OwcYjAFk3o2dlm55Ixrk6h6rlxGOZ9jn7Ug1Z6Rq48MYIrz9yDftg4RGBIUxZqaNjltzgkXMxTIE4As4G/Xadjqsu9wPdACjC0zvQKCawDBgLDgaZdulaYy3oAWGVn3+NCiL1CiL2zNu0l2MOFnDLLyIOcsssEetj2RlqnMZhMlFXV4OXsSLCHC13DAvFxdcbFQU+v1i04bjW/+0RuEUaTJCHYl/n7TpE8Yy7JM+YS6OVOTrHF25hbUk6glZcdINjbg9zicqs0ZQR4udEYep3CK8PvJOXF+/ns4bspu1JDhK8HwR6utjaWVhDoXsdGT1dySi/b2ujiiKNeh7erEwAJIX6E+XhwvsDWU+qk15EU05LPtx0h+aN5JH80j0BPt9ohiQC5xXZs9PIgt8Q2jfUIisYI9nLncnU1Lo56TucWqjbVyRvs6U6OuXyD0UTZlWq8XJwI8rJsB8gtUfPuPJNBqI8nvm4uOOh09E+I4mB6Tm1ZV3vX+8e3orjCEhsgp7SidmqHRdvNVrtK1W6IE9lqYLJwPy+EEAxqF8WBixYHT7CnGzmldc6hh6tNGaqm1XV6pb5m6wBvtc7yLL2jW09nEB/ih7/VNWF9/AA5JQ3YaD6mujbmlJTz3A9rmTayN+HmgKiHLuZyLDuffh99z/1fL+N8QQkPffOLfb3rUKfWODnoSYqLqJ0icSP0vM3PgasOl4EJURzPtvRq3Sgbg8zXvZuTI3d1iK6dfmRNSWYWXmGWwKJeLVtQmm3rVCvNzuG7MQ/wSdderHpDfexfKS2lJDOLrINHKExLx2Q0cmzJckI71Rlm/gfgZttYlpmFR0uLnmdoi9opElcpz85h4biH+Oq23mx66x0Aqkr/u56m8jp67qEtqMix1Ut48H5OL1Q/RHJ270Xv7ISLvx+x94wmfe0GTAYDlZfyydr1K0Gd6wyjt0NFVhbuoRZNNzuasQ+O59widR5y3u696JyccPbzoyIzi+ztO6kqKMRYWcnF1evw79j860YW54OPv2WDjz+ykQCOct8WRIfmfKBbUVaE8LCaGujhrY5auAZE20TkqYNqYM0bqVdWBHkX1eke0oQ8dQiCwpuVT3hYTfH09IHy5mmK2E7IzHPqVIaaKuTZo4jQqMYz3XS94jp16gPlJc3So6wIci+q0z2kSY11EWSJ9zX/+EWSP/yO5A+/M7dp6rTb6rTJgr3dybVOU6dNs3jPMTYfP8cH44fYdTS3DvLDRadwusw28KssLgBvq3vByw9ZUj+WkIi5BaX/aIxfTQOj7Uey6HgH8sivYDI2USnqiAHhbwlCLvwCkYX5DaY3bltnmfJgMlL9zadceXECVdNeQbi5Y8rKaDBvfe1LCH/LCFbhF2Cr7eKKEh6F8zuf4fLlQpSYBJz+/HeU1s2fFvJ72FdfMwBZ1Ijm9nXouqqaup79MR74FYxGKC3GdPJI0/YWXEL4XYPejvUWvR79MB3cfW16GhoN8FscEpVSyo5SyjhgEPCtaE435DUghHAEhgCLpZSlwK/AgDrJfkCdqjEO1XFxLfwL2CKl3Fp3h5TySyllopQycWLvRNqF+JFeWEZGcTnVRiMrj6eTFG27IkBSdEsWH0kDYM2JC3SLCEIIwR1RLTh5qZjKGgMGk4k9F/KIthrKvyI1nSHxai/UfV1iSHnxflJevJ++Ca1ZsjcVKSWH0rPxcHas9yEe4OmGm5Mjh9KzkVKyZG8qfRJaN2p0ZXUNl6vU+Y87TqWjUwTRAd60a3HVxjKLjVZBNgGS2oSx+LAav2FNajrdIoMRQlBYcQWjuaF1saiM9MJSWvq4U1Fdw6UyiwNjy5lMBraNIGXyeFImj1dt3G9lo4tTAzY6WGzcn0qf+MYbHxmFJRiMJtq1DOJsXiFn8goJ9HRl5eEzJMVF2trUNpLF+9WAb2uOnaVbVChCCJLiIll5+AzVBiMZhaWkFxTTvmUgId7uHLqYS2V1DVJKdp3NIMocx6JvfCt2nVWHLl+pMaAogozCUqoNRlYeOUNSnG1vY1JcBIsPnDJrn6NbVItGe7qDPN04m1dMYYU6J3rH2UyirAJWtgv1J72ghIwi8zk8do6kWNvGaFJMGIsPqb3ja46fp1srNTZHRlEZBvM5zCwuJy2/pDZWBNSfrgHQrkUA6YWlZBSZbTx6lqS4OnqxESw2jyBZczyNbq1UG0srq3hy3mom97uVzuHBtenH3RrP5pfHs27yvcx9ZBiRfl785+GhZvsCVfuuY51WVNVwqcziMNly6gKtzIFcb4SeEILecRHsTlNHYew6l0lrm3N4/TUNRhNF5mumxmhk88l0ooPqx5fJ2LMf/+jW+ERGoHNwoMM9ozi+bKVNGlc/31qtpFcms3eOGpH74p79uHh74+avzklundSLXKtAkX8UbraNWXv34xsdhVdkOIqDA/FjRnLqF1sfuIufb23P2B1TXuDQt/PsFdUscvcdwLt1FJ4Rql7M6GTOLbe1rywjg7AkdYalT2wMOmdnKi/lq9vNoxP0rq4Ed02k6JSdkTR1uLTvAJ6tW+Fh1mw9agQXltvaWH4xkxa91QDI3rFt0Dk7cyU/n4z1G/FNiEfn4oLQ6QjpcTtFJxoJTliX9FOIwFDwCwKdHqVLL+ThX23TBFh6CUW7rpCX1fzyAbLT1akQXn6g6BDxXdWAiNeAaJuIPL7nxutlnwdnV3AxP7sjYm2DYTZE1nmzpr9F81QTK4FcpaQQER6jxq5QdOo0gaY0b7Zedjr4WNVp2y7Nr9Oc9Ebr9L74MFJeeoCUlx6gb7toluw7bm7TZJnbbbadLAGe7rg5O3IoXY39s2Tfcfq0U9ttW0+kMXvjHj57ZDguVkGdMwrUNg1AZmEp5yuqCXWtE/7s4mlEQIharzo9SqceyGN1rrnQVihjnsQ4+z27DhmlUw/k/npNYruYTp9ACWmp9orr9eh69MWwZ5tNGhFiGYCs63I7pmzzR7mjkznmAigduoLRWD8gZaPaqXW0+2HYbaV9uYLLDw6h8vFRVD4+CtOpY1S9O/WaVtn4PewznTmBCAmr1dTf0Q/jnu02aUSwlWbn2zHlqJoyPxddO/NUTCdnNc5Glm1Mr3p6Z08gQlqq141Oj+72vhj31rHRSk/p3B2ZbdFTbPQSMGX+tlW1/s+giD/2v/+DiKYCEjaYUYhyKaW71e9coD3gCvwipWxnL36DOe0m8/a9TWjcjepkuDoJ3hVYI6W8XwgxAUiUUj4jhDiI6iDpLoSYY9b/2VxGQ8fwFtAJGCll4yGojd/8RQJsPpvJ++v2Y5KS5FuimHR7O2ZuOUxCiC992rSkymBk6rIdpOYW4e3iyPThPQgzf9AtPZrGrF3HEUCv1i14OckSrG/Av5fw+T29ibrqpDB7R6WUvJOykW0n03F20PPu2AG1y1omz5hLyotqGIyjF9VlP6sMBnrGRvLn5N4IIVh35AzvLt5EYXklni5OxLXwZ9bjI8ksLGHirMUoAgK93PnbwERCzfPRN5/J5P21ezCZJMkdopnUoz0zNx8kIcSPPjFhqo1Ltqk2OjsyPbknYT4erDmRzszNh9ArCooQPNPrFpJiwsgvr+SpBRupNhoxmiTdIoN5pX8ienOPlpSSdxZvUm101PPumP4WGz+aR8rk8RYbF6ylqsZAz7gI/jzcbOPRM7y7ZLPZRkfiWgQw67Fklu5LZdamvegVhctV1VypMeDsoCe5cxyTkrowc91uEkID6NO2FVU1Bqb+vJ7UrHy8XZyZPq4/Yebe+s837iNl/wl0iuCVIXfQK1b9MJy5bjerjqhLe7ZtEcDfknvjqNdRWlnFlAXryC4px9VRz9Bb2jB311G1PrvEMql3Z2au22PWjjRrbyTVvCTm9LH9arX7TZ9HeVUNNUYjns5OzJpwF9GBPvyw+zhzdx5Bryi08HbnvVFJeLs6gzky/ebTF3l/1a/qddqxDZN6dWTmxv0ktPCnT2w4VQYDU1O2kJpdoGqO7k2YjydLD51h1vbDtefwyTs70s/8IVxZY6DPRz+y5rkxeJgDQKKo8R42n7rA+6t2qjZ2imXSnZ2YuWEvCS0C6BMXodq4aBOpOVf1+hDm68nnm/cza+shwv0sS5fNfmAIflYjMDKLynhy/mp12U9zQK/NJy/w/ood161OvV2deOq7VVQbjBilpFtUC14ZfHttALHrrRcd6ENmURmv/LyBsivV+Lg58+7I3rSwmlN8vTVbeLvz4OylGIwmjFLSvXUoUwd35/Xx79R73sUN7s+wD6eh6HTsmTOXDdM+ZMDbr5Gx9wDHf1lJ+5F3M/jdt5BSkrZ1BynPvoyxWp2e1aZfb4Z+8C4IyNx/iIWTnsdotWJCab0hxk3z6PyviendA3d/P0pz81j21nvs+Nr+8m518dTZ97nfSBu99PU1Ww/sR//p76HodBz6zzy2/30Gvd58lex9Bzi9fBVxyf+PvfMOi+pY//hnztIRRARRUUQFe2zRqIkae4vErjHGmKbpMYk3Mcm96cU0Ta+2xJJYYu8NiRo19oaogIiCUqSDCOye+f1xVtilCDbMvb/5PA8Pu3PmzPe875ndnfNOu5/u77+JlJJzO3axfuIrhXpjt6yheqNgnKq4k5uSxpqnXuD05tDCsquYSjY+Avv2ousnHyJMJo7P+Y29n02j439eI/HAIWLWrse7SWN6fvsFjlXcQUp2/Ocdzm4Jw9Hdnd4/foN3k8YgBMfn/caBYus5uGml+7Run150+uQDhKZxcu7vHPr8C+7892SSDx7i7NoNeDVuRJdvv8DR3Q0k/P3mu8SHhgEQNGo4rSdNNOzfuJk9b75nV/ajY+8qRbEI0bwd2vAJoGnouzYh1y9EG/gQMjYSefRvtBETEI1bGw8FudnoC3+AC2U3nEVA3ZKJDVqg9RphbFF5ZCdy1zpElxDkhViIOgI166ENfcp4cLUUQHYm+kyrHVWroz30Cvp3r1PheQI3ohfYFK3HMEAgE88i182z7/G2lNH73bAFWu8HQBPGVqN/rUV0vd/QjDwMtQLRhj9jaJoLICcT/ee3jak3/cYYQQIpkafDkZsXlW/jrdRzLGWt8gbN0XoMN3x6dBdy93pE54HIhFiIOmr4dMgEcLb6NCcTfZb1O7JeE7Tuw0CATDiL3PCbvU+bGQtkSin5YGkoO06eMdptD/SlRV0j4D5k6lyWTRoLwLFzCbyxYIO1TRPIv4f0QAhB349mUmC2UNW6HlCrerV4Z3gvVu47zvTQvTiYrL/Pfk70LG3bz6Zt0QY/bnwW/t6C3PwHWr/RyHNRyPC9aE+/g6hVz1izA5BpyegzpxgnV/PF9MIULO+NL3WaQN6ZkqPqTG074fT4C8bWm1tWU/DHHBxHP4EedQLL3h04PT4RU8v2SIsZsrPImz4NeS4G4VsTl7e/MEacpCST990UZHKifeHlPJ+Y7uyE02MTjS0uN6+m4I9f7bRtcfngW/Jnf3v1gEQpQf1bah8gSvn+1tp0LNr2M3QN5qVzcBz1OHr0CSz7/sLx0YmYWrYzFpHMySJ/xhfIuBhwccXp2TfQ6gQCYN66FvNK+37a0rar1Np0xHHcC8a2n1vXYF42F4eRhp6+/y8cH3kB7Y52YDEjs7MomPWFEVxxdsXpmdcRdQJBCCxb12JeVbJf2HXR9v/OJ+SrkB3S8foeniuJKqt2/9f5/KYEJIQQTYAdgB9Ql5sXkPgdWCml/N363h2IAQKBkRQFJPoDl6WUWysSkBBCPAE8BvSUUpa7DPyVgESlYTNcq1JIq8D+5DebqiV7Z28p17HC8g1RkW3WbjbXslXezUC7vp0crpsyHi4V18/ro98rP9NN5HoCEjdCWQGJW0lpAYlbSWkBiVtJWQGJW0l5AYmbTakBif8lygpI/C9RWkDiVtKsVfl5biJyS4mZxrec0gISt5TrfD65bm7DQoWlBSRuqd5t6D1XAYnK578xIHEju2y4WkcmgLEsyjgppaWUYcM9hRC2E6dGWP+vEUJceYLaJaUcYXuSEMINY22IJ6+kSSlzhBA7gBDbvFJK+/Go5fMjEAvssl7vUill5bbMFQqFQqFQKBQKhUKh+H/MdQckpJSldo9KKc8ALayvw4DS9qHrVoHyLwElutGllENt3v5SyvFHir0Pw36rUKSUNxKIUSgUCoVCoVAoFArF/zNu8pKJCm7etp8KhUKhUCgUCoVCoVAoFBXmHzFSQAjRF/ikWHKMlHLI7bgehUKhUCgUCoVCoVAoFLeWf0RAQkq5Adhwu69DoVAoFAqFQqFQKBSKUvkv3Vrzn4yasqFQKBQKhUKhUCgUCoWi0lEBCYVCoVAoFAqFQqFQKBSVzj9iyoZCoVAoFAqFQqFQKBT/aNQuGzcdNUJCoVAoFAqFQqFQKBQKRaWjRkj8E9Fl5eoJFZe66Vj026BpqVy9yo4QF1SyfbeDSvaph8P/9mc/8zZ8Dit7rSu9kvsVzPI2fLe5ulSqnExOrlQ94etbqXqV/lsBICu5XWMyVa5eZX8ubkMPbWU3FWUlV1OZV1C5goA0Va5TherZV/xDUQEJhUKhUCgUCoVCoVAoykH14958lEsVCoVCoVAoFAqFQqFQVDoqIKFQKBQKhUKhUCgUCoWi0lFTNhQKhUKhUCgUCoVCoSgPtRbHTUeNkFAoFAqFQqFQKBQKhUJR6aiAhEKhUCgUCoVCoVAoFIpKRwUkFAqFQqFQKBQKhUKhUFQ6ag0JhUKhUCgUCoVCoVAoykFoag2Jm40KSFwD20+fZ8rm/Vh0yfBWDRnfqbnd8XyzhddW7yI8IRUvV2emDboHf68qAJxMSuOd9XvIzjejCVg0rh/ODibyLRY+3LiPPWeT0IRgYteW9LnbFwApJR+t/JNtJ87g6ujARyP70KxOjRLXFR6XyBuLNnG5wEzXJoG8cf+9CCFYfySS7zbt5nRSKgufe4AWdf0AOHI2gbeXbCk8/9l7WtCrST3Dxuh4pmzYg0VKhrcOZvw9d5S0ceUOwi+kGDYOvbfQRoDzGdmE/LiCZ7u24rFOLQCYu+c4iw9GIqVkRJtGPNyhWWH+W2XjzlOxTFu3kwKLhbwCM3kFZpwcTAxv15Tx97YtadMfWwiPT8bLzYVpD/TGv5onAD//eYAl+yIwaYI3Bnamc3AAMclpvLxgU+H5cWmZPN+zPQ/f06owbdZfR/h8499MHd6Db7buN/zZtjHju7Quqb00jPALFw1/juiJfzUPdkbHMW3TXgosFhxNJv7V5y46NvAHYM3RKH7edgghBDU83PhkaHequbsUlnm99zA+PZuBPy4nsLpheyt/X94Z0AmAL7ceYOWRaDIu57N/8hi78rZHxjFl/W4sum61sVVJvWV/En7+ouHf4d3xr+bBkbhk3l61ozDfs93a0KtpYOF7i64z4ucV+Hm488OYPkV6UVf0JMPbNmJ851L0lm+z6jkbel4eHIlP5u1Vf1lzSZ6919CLuZjBy39stbmfWTzfvS0Pd2x+S/QAen25CHdnRzQhcNAEiycMuq0+taVh7570nfoRwmTi4Oy57Pz8K7vjVQPqEPLTN7j5+JCblsbyR58iK/48fi1bMODrqTh7eqBbLOz4ZBrH/1hWqoYtjfr2ZNC0jxEmE3tmzSHs0y/tjnsF1GXEjG+p4uPDpbQ0Fjw8gYz488axunUY/vPXVK3jD1IyK2QkabFny9W8GmNnfscdA/uRlZTM+3d0vKGyrtCoT09CrDbunTWHPz8raePw6d/i7utDbmoaC8ZNINNq40eXU0g4dhyA9LNxzBk6uly9yr6HAIG9e9D9sykIk8axX+axZ6q9pkcdf/pN/w4Xr6oIzcT2t94jZsNmNEdHen87Db82rZG6ztZX3iBu+19lqBQhGrdGG/wYaBr631uQofbXKbqGoHXoCbqOzMlAX/g9pCUbB7180EY+jfDyASmxzPiw6Ng/RI/6zdB6jQRNQx7+C7l7g/3xukFoPUdCDX/0FTPh5IGiY57V0PqPBY9qAOiLv4WMlKvrATRsgdb3QdAE8uB25F9r7Y8HNELrOxr86qAv+REi9hfZ33M4IrglAHLbKuTxveXrFdfu96Bh74FtJbRFxz6Itl1B1yEnC33lrIrZZMv1+jSgEVrPEUX5qtdEXzEDIg+XkJBS8tGKP9kWEYOrkyMfjbpKm2bBRqNN07Q+bwyytmkOn+K7jdY2zQujC9s08akZDPx0DoE1jHvaysHC23fULlGuaNKmqJ7u3lyynt4bgtahF+gWZHYm+sLvIC0ZEdQCbdCjRRlr+KPPnYY8tueqLtVad8DpsRdB0zBvWYV52Ty74w59BuPQb6jxubh8ifwfP0XGnQGTCaenX0dr0AhMJsxh6zEvm3tVreKY2nTA6YkXQTNh3rSKgqWln2/q1B2XyR+SO+kx9OgT16ZxZ0ecn5wEmkbBhhUULJ5Ter57euD674+5NHEcemQEpjZ34fTIs+DoCAUF5M/6BsvhfRXTbNsR5/EvGZqbVlLwRxl23d0d19encOmlR9CjToCHJy6vTcEU3JSCLWvI/2nqNdl6RdvpiReNe7JxJQVLytZ2ee0jcl9+1NBWKG4QFZCoIBZd54ON+5jxQA/8PFwZ9csGugfXIcinamGeJUei8XRxYsNT97P2+Bmmhh1i2uDOmHWdyat28fHATjTxq0Z6bh4O1ujaTzvD8XZ3Yd2TIehSkpGbV1jethNniL2YzvpXx3HkbALvLgtl4fMPlLi295Zt5d1hPWkVUJMnZ61g+8lYujYJJNivOl+PHcg7S7fY5Q+uWZ3FL4zGwaSRnJnDkGnz6NaoLgL4YN1uZozpg5+nG6NmrqF7o7oE+XoV2Xgo0rDx2aGsDY9hauh+pg29t/D4J5v20iXIv/B9ZFIaiw9GsvCx+3A0aUz4bTNdg+sQ6Ol1S230cnfl+0dCqO7hRu8pszFbdDZMGsOoH5bQvWkgQTW8i2zaF4GnizMbJo1h7ZFIpm7YzbQH+hCVlMq6I1GsmvgASZk5PD57FWtfGk1932ose35kYb3o9skcejZrUFjehfRsdkXHUdPTnamb9zB73H34eboz6ufldG9cjyBrgwJgyYGTeLo6sWHiKNYejWbqpj1MG9kTLzcXvn+wDzU83YlMTGX83HWE/WsMZovOlHW7WPXsCKq5u/D5xr+Zvyec57rfWVRPb+Ae1q3mwbLx95fwf/fguoxp14R+39s3biy6zgdrdzJjbD/Dxukr6d44oKSNLs5smDjSsHHzXqaN6EFwjWosnjDIqIdZlxjywzK6NQrAwWTMJJu7O5yGPl5k5xUU09vFjLF97fV8bfQOnjLse2EEa4+dZurmfUwb3t2qdz8OmlXvx+V0axxAfZ+qLHtqcNH9nLaQntYA3a3Qc9AM+34Z159qbkWBpNvlU1uEptHvq0+Zf99QMuPO88RfWzi1ej0XT5wszNNryvscmb+QI/MWENitCz3ef5MVjz1NwaVcVjz+NKnRp6lSqyZP7AwletMW8jIyS9W6ojfk68+Z3m8wGXHneX73Vo6vWkdSRJHewE/f58DcBeyf+zsNu3el34dvs/CRJwEY9cuPhE75nMjNYTi5uyN1vUytirLrl/mEffszj8z56YbLAsPGQV9/zsz+ho3P7d5KxGp7Gwd88j4H5i3gwNzfadjNsHGR1caC3Fy+btflmvQq8x5e0ez5xaf8MXAYWfHnGbN9M1Fr1pNqo9nxtUmcWrqCw9Nn492kMUOXLWBG0za0fOxhAObc1QVXXx+GLV/IvM69QMqrCaINHY/lp/cgIwXTi59gCd8LiXFFeeJjsHz5KhTkIzr1RRs4Fn3uNABMo59H37IE/dQRcHIBWU69qXQ9gdZnNPqCryArDe2R15GRRyDlQlGezDT0Nb8iOvQucbo28FH0nevgTAQ4Opevd0Wz/0Po86ZCZiraE28hTx6Ci+eL8mSkoK+YiejUz/7c4JaIWvXQf3oHHBzQxr2GjDoK+ZfL172iPWAs+tzPDe3xJbVlwlnkz++BOR/Rrjui10jkkh8qVv4Vjev16dlT6LM/NF67uKE9+T7EHC9VZtuJM8Qmp7H+tUeMNs2SLSycWDKI+N6SUN4d3pNW9Wrx5IzlbD9xhq5N6xNc04evxw3knT+2lDinbnUvlmj8gpIAACAASURBVL38kOGPrZtKHC+spz++a9TTlz4tvZ5+8YpRT+/uizbwYfS5U5FRx7BMnWTkcauC6Y3vjHtwNTQNp/GTyHvvRWRKEi6fzMCyd4cRcLBi3r4R88blAJjadcbpkefJ+2ASpk49wNGRyy8/DE7OuHw1H8uOTcjkhKtr2mo/+S8uvz3R0P5sJuY92+20AXBxw3HgCCwnj1Ws3GIazs+8Su6/n0NeTML1y18x796OPBdjn8/VDadBo7CcOFqYJDPSufzuJGTqRbR6DXB5/2suPTywYppP/YvcN19ApiThOm025r+3I88Vs8vVDaeQkVhO2NiVn0/+/J/RAhqg1WvANaNpOD05ictvWX06dZbh01K0HUOu06cKRRlc9xoSQgiLEOKQEOKwEOKAEOJua3qgEOKY9XU3IcTqUs4NE0K0uwatr4QQ8UIIzSbtESGEFEL0tEkbYk0bLoRYZr2+KCFEhvX1ISHE3UKImdbrPiKE+EMIUaV05SKOXkghoFoV6npVwclkon+zeoRGxtnlCY2MY/Ad9QHo0ySA3bGJSCn5K+YCjWp40cTPeKDwcnXGZH0oWXbkNOOtPbGaEHYPKKHHTzOobVOEELSqV4us3DySM3PsNJMzc8i+nE/rerUQQjCobVO2hEcD0NDPm/o2DzFXcHVyLHxAyTObC3evOXr+IgHentSt5mHY2Lw+oafO2dt46hyDWzY0bGxaj90xF5DWhuPmk2ep4+VBkE/Rw2/0xQxa+fvi6uiAg6bRvp4fW04U9WDeKhub+degRtUqHD2XSIMa3pitDyr9WwYRGnHG3qaIMwxu29iwqXlDdkfHI6UkNOIM/VsG4eRgoo63JwHeVTkal2R37u7oeAK8q+JfzaMw7ZO1fzGpTwfMuk4dLw/qenvi5GCif4uGhJ6Itdc+cYbBrRsZ2s3qszvG0G5Wy4canu4ABNWoRp7ZQr7ZgsRop18qKEBKSXZePjU83ArLu9F7WBat6vjia6NTqBefbOgV2tiA0JP2PdShJ88yuHVQkY2nzyOlxNXJwaYeWux2UUrIyOHPyHMMs96XIr0r9nla7WtA6IlS9FoFW/UCi/SsdbA0vSvsjrlAgLdH4aifW61XGpXtU1tqt7+TtOgY0mNi0QsKCF+8lMYh/e3y+DZtTMzWbQCcCdtO44EDAEiNiiY1+jQA2RcSuJR8EXcfn6vaWveuO7kYfZrUmFgsBQUcXrSE5vcPsMtTo2ljokL/BCB66zaa39+/MF1zMBG5OQyA/JwcCnJzr6pXEaK27+RSatoNl3OFunfdSYqtjQuX0CzE3kY/WxvDttGsmM+vhcq+hwA127UlPTqGjDOG5sk/lhE00F5TSomTh/E96ezpQc4F48GjepPGnLVeS27yRS6nZ1LzzjZXFwwIQqYkQGoiWMzoB3cgmre314s+BgX5xuuzpxBVqxsH/OqAyYQ8dcR4n3+5MN8/Rq9WIKQlQcZFoyf7+N7C0QeFZKRAcnzJwE31WiA0IxgBUJAH5tIDkHb4NzA005MNzfC/EY3tR/SRkQJJcSUCHMKnNjL2pJFekI9MPIcIsh+ZV652qq32HkSTYnXgzAkwW/0bF43wLPm7f1VuxKc2iMZtkafDy/RpaHg0g9rZtGku55fdpgmsbbRp2hVv03iXVnT5BAQhL16wr6ct7rLLIqNs6mnsKYRX9ZI2tuyEjDhYbj3VgpoiE+KQiefBbMa8Ywum9sWCp7mXil67uNj4ViJcXEAzgZMzmAuQufZ+uqp2cDP0C0Xalh2bcehQMnDrNGY8Bcvmlf+ZK02jUXP083HIBKt92zbi0KlrSY2xT5L/x1zIL9LQT59Cpl40XseeRjg5g4PjNdtl3rYJhw6laI6ZQP7SYnblXUY/fvi6bC1N27J9c5naBUvm29n7/w4h/tl//4XcyKKWuVLK1lLKVsDrwJSbdE12WIMQQ4BzQPFPxlHANvT8AHAYQEo5RErZGngC2G691tZSyp3AS1LKVlLKlsBZ4LnyriMxK5eaHu6F72t6uJGUdanMPA6ahoezI+m5ecSmZiGA8QtDGTZ7HTN3G5H1zMvGh/mb7YcZNnsdLy7bzsWcogZ1UkY2NW2mQ/h5VSExI9teMyMbv6r2eZKK5SmNw2cTCJk6l0HT5vN2/444aBqJWZeo6VncRvsfCNs8tjZeyi9g5s5jPNPVfmh5cA0v9p1NJP3SZXILzGyLiueCzY/zrbTxyrkWXadpbR+cHEzU9HQnKaOYTZnZ1LSW72DS8HBxIv3SZZIycgrTAfyqupNYrGGx9kgUA1oGFb4PjYihhqc7TWpWx6JLatjWmaruV/enScPD2Yn0S3l2eTYej6Fpreo4OZhwNGm8NfAeBn+/hHs/n090crrdA+aN3EOA+PRshk5fxcNz1rPvbOLVXGv1XTE9TzeSMov7N4eansX9a+gdjksi5LslDPp+KW8PvKfwYfrj9bv5V++70Ip9sSZm5RTTcy/5OczMoWZVG/tcnArtOxyXRMj3Sxn0wzLevu/uwoDBFdYeO82AFkU9C7dKTwh4Yu4Ghv+8gkX7TxQrr3J9aotn7VpkxsUXvs+MP49H7Vr22keP0XRwCABNBg3E2dMDV2/7h4Pa7dpicnIi9XSxnqRiVK1di4xzRXoZcefxLKZ34cgxWgw1Ru20GByCi6cnbt7V8A0O4nJ6BmMXz2Xi3m3c98l7CO1GftJuDZ61a5Fh49OM+PN4+pe08Q6rjc1tbARwcHHhud1beWbHJprdf1+F9CrzHgJUqV2LrPgizaz481Qpprnrw09p+sAIJkQeZeiyhWyZ9BoASUeP0XBgf4TJhGe9APzatMLD35+rIap6Q/rFooSM1KIAQClod/VEP2EMvxe+tZG5OWjjXsH08mdoAx82HuD/QXp4VENm2QTFstILp1+Ui3cNyLuENuRJtEffQHQfWrEGqocXMiO16H1mWoU1CwMQDk7gWgUR2AQ8r+Gh2qMaMtNWO/Wq2qJNV2MExrVwIz611W7W7qrTUZIycqjpVdRB4Ve1jDaNbbunqkeJdklpxKdmMHTafB7+fjH7U0vmF1WrQ7rNNJb0FKPuloHWoSd6xIES6aJNZ+TB7eVej/D2RV4s6qSRqUmI6r4l8jn0G4rLd4twHPsM+bOM6WqWXVuRly/jOmMFrj8tpWDl75CdVa6mvXZRG0WmJCO87bW1+o0QPjWw7NtZ4XLtNKoX07hY0j6tQSM0Xz8se3YUP70Q0z09sESfrFBg0NC08WnKVTT3lj+17VoooV2GvYZPb662QnGzWm+ewM3rUrKnO3AM+AH74APAduAuIYSjdZRDEFDOGDOQUmYCCCEE4ApcvXu4zAyi3DxCCMy65EBcMp+G3M28h3qz+dQ5dp1JwKLrJGRdoo2/L0se7U9rfx8+Cz1YTnnFr6tkLlGBxkergJqsmjSWRc8/wPSdR8kzW8roFChmYymZBPDttkM83KEZ7k72EeCGPl480akFj/+2iQm/baKxX7XC6SrG9ZeieJNsBLiQnsWxuETeGVQ0rYQS5Zd2DaJ0XZvX+WYLW0+coe8dxmiD3PwCfgo7wPO92pc4ryxK9aeNSGRSKtM27eGdECPyX2DRWbA3giVPDeXPf42hsZ8307cXVfkbuYe+VVzZ8vwwlo4PYXLv9ry6bBvZeVePgJcuV5HPhfG/VZ0arHp2GIsmDGL69sPkFZgJO3kWb3cXmtcu2TNbzkCOsvWw0XtmKIvG38/0HUfIM5sL8+RbLGw9eZa+zerfcr35jw1kyZOD+GlMH37fG8G+2ISrlncrfXo1HShZXza99hb1utzN+N1hBHS5h8y48+g2fqxS04/Bs35g5YTnyndgaZ/jYqesefVNGnS9h4l7t9Gg6z2kx8Wjmy1oDiYCO3dizav/4ZuO3fGuH0i7cWNKlnebKfW7qriNk9+kfpd7eMFqY0ZcPBazBYCPG7Tg247dWTD2CUKmTsG7QWB5giXlbuU9LNNG+/OajBhK+Lzf+Tn4DpYOGcWAGT+AEBz7dT7Z8ed56K8tdP/sI87/vQfdYi5Znr1iKWll/Eq37Yqo2xC5dYWRoGmI+k3RV83B8uVkqO6HaN/9H6ZXGhX4MgKjx7lOMHroEvRfPkZ4+SDu6FSBEytuYwlOhyMjj6I99gbasCeRcVGgWyp2blnSZfn3jk6I2oHInesqXn6ZVNC+K7h7gq8/xISXXWI5v+ll5ilH2tfTnS3/eZylL49h8v1defVgHNkFxXx8DbdQ3NkVUTcIuXW5/QGPaohaAcgT5Taly/j+LiloXr+Uy8+OpGDuDzgOewQALagZ6Dq54weR+/RwHENGI/xKrolRtnZpiTbaQuD0+Avkz/6m4mWW0Cjnu1sInCe8RN70r0rms6IFNMD5sefI+6aCfbYV0XziRfJmfl2x8q6F8u6nEDg9PpH8WbdAW/H/nhtZQ8JVCHEIcAFqAT1uziWVYDTwO7AC+EgI4SilvBJmlMBmoC9QFVgJ1C+1lGIIIWYDA4DjwKRSjk8AJgD88PBA2gfXI8Gmpzkh6xI1PFztzqnp4UpCVg41Pd0w6zpZeQVUdXGipocr7evWKJyO0bVhbY4nptKxnh+ujiZ6Na4LQN8mAcz6O4IhX8wH4I66fiSkF0XWE9OzqeFpP7ukZlUPu+h7Yno2vja9q+XR0M8bV0dHIpPSqOnpRkJmcRvth+nX9HQnIdPoNS600dWZI/EX2RgRy9Qt+8i6nI8QAmcHE2PaN2VYm2CGtTGGtH8ReoBzaZmVYmNCehZzth+kQQ1vAqoba30kZOYUToUosqkKCRnGKAmzRSfrcj5VXZ3xq2qkF+pm2J+7/dRZmtX2waeK4aNzqZnEp2Uy5JvFgCQj9zIbI2J4JasDvh5uJGTk2I2YsPPnFe08QxsgISObFxZsYsrQbgR4GwtNnkgwej+uvO/XvAHTdxy2Ke/676EQAicHEwDNa1WnbjUPzqRk0uIqD7El9DLL0sumZlV3O//a0tDXC1cnox4eOJfI1pNn2RYZR57ZQk5ePq8uCePTofcWXnuRXk7pehk29pWp50BkUnqhfdsj42hWqzo+VVzty7oFelfKqO7uSs8m9TgSn0y7ejUr3ade2JMZfx7POkW9057+tcm+YD+nN/tCAosfGAeAo7s7TQeHkJdp9Gw5eXjwwLIFbH3nI+L3lL+AV0b8earWLdKrWqc2mRcu2OXJvJDA3BFjjfLd3WkxNITLmZlkxJ/n/KGjpMYY06DCV6whoEM79s4uV7ZSyYg/byy6aaWqf20yz9vbmHUhgXkjbWwcEkJeZmbhMYDUmFhOb9tB7dYtST19pky9yr6HYIyIsB3V4FGKZotxD7F0kLEw4IU9+zC5OOPqU53c5IuETf5PYb7RoetIizp9VT2ZkWIsEHmFqt72vftWRHBLtF7DsHz/JliDHDI9BeJjjGHtgDy2B1EvGHmVtfsqW4+sNIRHtaLnEA8vo0e/ImSlQdI5Y2oCIE8dhtr1gXJ6ibPSEFW9izQ9q1VcE5A7ViN3GDN0xZAJyNTyR9gVkpmG8LTV9i5du34zRJeB6L98XOjfCnMjPrUimrZDnjpkLKxpw28RcSzeYIzYuKNuTRLSi3r6EzNKadN4eZBo2+7JyMK36tXbNE4ODjg5GE325nX8qOvmxJmcfFp4Ff1eyfQU+ykYXtXtR55csSO4JVqv4Vi+e7OEH0Xru5FH/65QQEmmJCF8ihbsFN41CqcplIblr804TfgXAKYuvbEc2g0WC2Smo584gtawCZbE82Web6+djPDxK9Ku7muv7eqGFtAAlw++M457eeP870/I+3ByhRe2lBeT7DV8aiBTbRajdXVDq9cQ10+MtUxEteq4vPU5l9/7F3pkBKJ6DVze/JTLU99BJsQXL/4qmjY+rV6aZgNcP/requmNy38+4/IHr9zw4pIltH1qlPRpvQa4fFik7fzvT8n78NX/fwtbql02bjo3Y8pGE6AfMEdUtNu6ggghnDCCBsutoxr+BoovDb8AY6rGAxiBiwohpXwUqA1EAKNKOf6zlLKdlLLd+G7taFGrOrGpWcSlZ5NvsbDueCzdg+yHlXYPqsPyo8bw1o0nztKhnh9CCO5pUJuTyenkFpgx6zp7zyYRVL0qQgi6BfmzJ9b44d59JpG2dXxZ9tIYlr00hp7NG7LiQARSSg7HXsDD1bnEg7ivpzvuzo4cjjXWAVhxIIIeNgsslkZcagZmi/GDGp+WSUxKBv5eVWhR24fY1Ezi0rIMG8Nj6N6ojr2Njeqy/Igx13FjRCwdAmsihGDeuP5sfn44m58fzti7mjHhnjsY074pACnWaSjnM7LZfDKWtwd0uuU2Zubm8fTslbwysDNpObnEpWaSb7aw7kgU3ZsE2tvUNJDlB4zF1zaGR9OhgT9CCLo3CWTdkSjyzRbiUjOJTUnnDpvVso3pGsGF7xvVrM6ONx5l8ysPsfml0fh5uuPj7kqe2WxoH4ume5MAe+3G9Vh+6JShfTyGDvWNOaWZuXk8PX8DL/W6i7YBNQvz+3m4EZ2cRqrVpzuj42lgs2bHjdzD1JzLWKwNrXNpWcSmZVLHZm2M0mhR25fYFKue2cK6Y6fp3ri4jQEsPxRVwsa4tKyiepieRczFDPy9PHi5V3u2ThrN5pdGMXV4dzrUr82nw7oZev4+xKZk2NhXil6juiw/HGnVO0OH+rWK9PQretlWvaKGYvHpGrdK71J+ATnWRSUv5RewM/o8wTbroFS2T205v+8A3kEN8AoMQHN0pPmIoZxavd4uj2t178KelM6vvsihOUZwUXN0ZOSiORyZv5CIpStKlF0acXsP4BPUkGqB9TA5OtJq5DCOr7Lv+XSr7l3YA9/9tZfY94uhd27vAVy9vHD3MRrgDbt3JdFmoch/CnF7D1Dd1sZRwzi+umwbu00ustHVqyomJ6fCPPU6dbBbDLM0KvseAiTsP4hXUAM86xmajYcPIXqNvY1ZcXEEdDdGqnk3boSDiwu5yRdxcHXFwc0IuNXr0Q3dbLZbDLNUzkUhfGoZ0xNMDmhtOiPDiwVP/OujDX8Sy6yPIdtmUc5z0eBWxejtBkRQC2Si/XpQt13vQqyhVbU6aCZEs/bIqCNXP6fw3DPg4gau1u+2eo3tF24si/gY8PYDLx9Ds3kH4+G7IggBrtbf7Rp1EH51ILrsUQSlalevYaN9F/LkQfs8NQPQBo5DX/A1XKr40P5CbsSnVkTT0qdrPNi0DstefohlLz9ktGn22bRpXJzKaNM4FbVp9kXQo3nDq2qnZl8q+n1OySA2J586bsXWJDgXhfAtVk+PFbte//poI57CMnMKZGeU0NHadkEeLHv6gS161AlErTqIGrXAwQGHzj2x7LM/V9Qqan+Y7rwb/YJR9+XFREwtjMW4cXYx1muIt19j66rakRFoNtqmzr0w206buJTDpYcHkDthGLkThqGfCr+mYASAfuo4Wu26xsgNBwccuvbBsttmKsulHHJG9+HSo4O59Ohg9BPHCoMRuFfB5d0vyPvlO/TjFa9nemSEVdPq0669sewppjmmH5eeGMKlJ4agnwy/KcGI0rRNXXph/tte+9JD/ckdP5Tc8UPRT4b//wxGKG4JN2WXDSnlLiGED1By8tiN0Q9j5MNRa2PNDbgErLHR3iOEaIERIDl1LTERKaVFCLEQeAW4ar+ag6bx7z7tGL9wK7qUDGnZgGBfL77ZdoTmtbzpEVyHYa0aMnnVTvr+uBIvVyc+H9QZgKouToxr34SRv25AYIyQuNcazHi5WxteW7WTj7ccoJqbMx8OKNpmrmuTQLadOEO/T37FxcmBD0cUrfw85Iv5LHvJGJ781pAevLFoE3kFZro0qUdX6wP35mNRfLjiT1Kzc3l69gqa1PZl+hNDOBBznulh+3DQNDQheLN/x8LRG//u14Hxv29G13WGtA4m2Lca34QdpHnt6vRoFMCw1sFMXrGdvt8tNWwcYjMVogwm/hFGem4ejprGf/p1tOvJvVU2/rbzMGcvpvNz6D6khPu+/B1fDzeGt2tGsJ8332zeQ3N/X3o0rc+wO5sw+Y8t9J06Hy9XFz5/wLiGYD9v+rZoSMhXCzBpgv+EdClcjDQ3v4CdUed4Z3DJBX+uIITgpZ53MX7uOnRdMqRNY4JrePNN6D6a1/alR5N6DGvbmMlLw+j71UK8XJ35fLgx0Oi3PeGcTc3khz8P8MOfxhzPGWMHUMPTnWe6teXhWatxMGnUrlqFj2zugYOmXfc93Hc2kW/+PGjUC03wdv9OeFnv1edb9rHmWAyXC8x0/2oxw1oH81zPdjiYNP49oBPj5643PhdtGhFcoxrfhO6neW0fw8Y2jZi87E/6frXIaqMxXPnA2QSm7zhSVA/v62S3fWlpOGhWvXkbDL3WwYbe1gOGXuMAhrVtxORl2+j79WKrXjerXiLT/7LVu7uw3ucWmNl5+jzvDLznluudS8vkhYXGCupmXXJfiwZ0CSpqtFW2T22RFgvrX3yVB1f9gTCZOPzrfJIjTnDvW69zYf9BTq1ZT2DXznR//02QkrM7drFu4isANB8+mIDOd+Pq7U2rscbsupXjnyXxSNkrcesWCysmvsITa5egmUzs/WUeicdP0OedN4jbd5Djq9fR8N7O9P/wbaSUxGzfybLnjR42qeusmfwfJmxcCQLiDxxmz4xfK2xrWTz+2ywadetMFZ/qTDkXwaq3P2LnrGvbjq64jSsnvsJjawwb9/0yj6TjJ+j99hvE7T9IxOp1NLi3M/0+MGw8s2Mny602+jZtzNDvv0DqEqEJwj77styARGXfwyuaoS9PZtjKxWgmE8fm/EZKxEnufvM1Eg8cInrNesJee4s+331B2+eeAiTrJzwLgJuvD8NW/oHUdbLPX2Dt409XwKk6+tIZmCa8CUJD3xMKiefQ+j6AjItChu8z1mpwdsH0sDEAUqZfRJ/1MUgdfdWvmJ56BwTIuNPI3Zv/WXpSR9+4EG3UCyA05JGdcPECoksI8kIsRB2BmvXQhj4FLm7G+g2dB6LPfA+kRA9dgjb6RUAgE88iD1XgAVPq6OvmoY152dA8tAOSzyO6DUaePwOnDkHtQLSRz4GLO6JRa7h3MPqPb4JmQnvkdaOcvFz0ZdMrtrOHrfba+WgPTbJqby+hrfUeCU7OaCOeMc7JSDGCE9eicb0+BSOQ4ekNZyOvKtO1aSDbTsTQ7+NfcHF04MNRRX1oQ6bNK9wl461hPXhjwUbyzGa6NA4satMcjeLD5WFGm2bmCprU9mH6hKHsOx3PNxt2Ff4+v3VHbbycijXhC+vpW8a2n3u2GPW03wPIc9HI8L1oIdZ6Os76PZp2EX2WdTpBNV9jVEVFg0m6hfwZX+D85jRj683Q1chzMTg+8AR61Aks+3bg0H8YppbtwWxG5mSR/+0HgDGNw+nZN3D50tgm1Lx1LTI2umK6V7SnT8Pl7S+MLSo3W7VHW7X3ViyoUp5G3g+f4frB18YWnBtXoZ89jdNDE7BERmD5u+x1NhxDRqLVroPTA4/DA48DcPk/zyMzypndrlvI+/FzXN/9ytDcvBr9bAxOY8ZjiTxhH5woBbcZyxBubuDgiEPHe8l964WSu2RcRTv/p6m4vPOlsY3rFZ8+OB49KuKq62QoFDeKKG91/TJPFCJbSlnF+roJsAPwA+oCq6WULYQQ3YB/SSkHFjs3zJp+1fGgQojfgZVSyt+t792BGCAQGAm0k1I+J4ToD1yWUm4VQvxi1f/Deo7dNVhHcTSUUkZZX38GIKX8V1nXYZn97vU56XrxvtlxnXLIvLZhizcFz+KDxW8x17nq8HVjvsbhpDeDyrbRoZJ3Db7O76r/Kip5deSPHv24UvWyzDe+Lee1kGmpXD0AL4fKXVjT3VS5em63YajqxKe7VbpmZSJ8K/k3/3asjl/Z39/OzuXnuZk0b1V+nptIqdt+3mLyY69hGs5NQFoqt87I/NvQbqvk7++bPJC9Qriv3PU/N7/h8oPd/tENUpffwv7rfH4z1pAAY3mZcdYRB8Xz9RRC2I5PHGH9v0YIcWUtiF1SyhG2Jwkh3DDWhnjySpqUMkcIsQMIsc0rpbyW1Y0E8KsQwtP6+jBQgS4ZhUKhUCgUCoVCoVAoFDeL6w5ISClNZaSfAVpYX4dh7GJRnG4VKP8SUGK/IinlUJu3v5Ry/JFi78OAMJv3OmA/LluhUCgUCoVCoVAoFApFpVLJY64VCoVCoVAoFAqFQqH4L0TtsnHT+UcEJIQQfYFPiiXHSCmH3I7rUSgUCoVCoVAoFAqFQnFr+UcEJKSUG4ANt/s6FAqFQqFQKBQKhUKhUFQO/4iAhEKhUCgUCoVCoVAoFP9obsNuJf/rVO5+MwqFQqFQKBQKhUKhUCgUqICEQqFQKBQKhUKhUCgUituACkgoFAqFQqFQKBQKhUKhqHTUGhIVQbdUrp7UK1fPwxMu5VSuZmVT2Vv0WAoqVw/AXMmalV1PKxtLJX/uAbTKjRF7mSpXr7Ij4LdjZ650c+V+LsyyUuXIvx1OdajkpoqjY6XKyYsXK1VPeHpWqh4AspIrql7Jv0+34/eisqnsefP/D6bp67n5laonKvk3/38VodaQuOmomqn43w9GKBQKhUKhUCgUCoXiH4cKSCgUCoVCoVAoFAqFQqGodNSUDYVCoVAoFAqFQqFQKMrjdkxd/B9HjZBQKBQKhUKhUCgUCoVCUemogIRCoVAoFAqFQqFQKBSKSkdN2VAoFAqFQqFQKBQKhaIc1C4bNx81QkKhUCgUCoVCoVAoFApFpaMCEgqFQqFQKBQKhUKhUCgqHTVl4xrYfvoCU7YcxCIlw1s2YHzHpnbH880WXlvzN+GJaXi5OjHt/rvxr+rOqvAzzNp7sjDfqaR0/hjXh6Z+1QrTnl2ynXMZ2ax4tB8frfiTbSdjcXV04KORvWjmX6PEtYTHJfHG4s1cLjDTp2G0dAAAIABJREFUtXE93ri/K0II0i9dZtL89cSnZeJfzZNpY/pR1c2FPdFxPPfrGvy9PQHo3aIhz/S6C4Bfdx3jj4OnEEA1N2eSsnLRpWR420aM79yqpI3LtxF+/iJebs5MG94dfy8PjsQn8/aqv6y5JM/e24ZeTQMBmLs7nMUHTiKBEW0b83DH5oXlSSn5aOWfbDtxxmpvH5rVKc3eRN5YtMmwt0kgb9x/L0II1h+J5LtNuzmdlMrC5x6gRV0/AI6cTeDtJVuK/NuzHb2aN2D7ybNMWb0Di64zvH0zxndrW9K+RZsJj0/Gy82FaQ/2wb+a4bOfw/azZG8EJk3jjZDOdG4UwIX0LF5fvIWLWZcQQjDyrmaMvafIZ9uj4piyYQ8WXTK8TTDjO7csxZ/bCb+QgperM9OG34u/l0fh8fMZ2YR8v5xn723NY3e3KEy36DojZqzGz8ONH0b3sitz++nzTNm839Bs1ZDxnZrbHc83W3ht9S7CE1INzUH34O9VBYCTSWm8s34P2flmNAGLxvXDbNF5aP6mwvMTs3IJaR7I632N+rM9Op4pG/cZn4vWQYy3uc5CvZV/WfWcmDakK/5eVYhPz2bgTysJtNbJVv4+vDOgo3GOxcKH6/ew52wimhBM7NaaPk3q3RK9nLwCHpqzwca+S4S0qM/rPdveEn86O5gM+zbuY8/ZJMO+ri3p0ySA0tgefZ4pm/Ya9rYqw95VOwlPsNahwV3s9df9TXZeAZoQLHq0P84OplJ1rhDQuwddP/0IYdI4/us89k/92u54lTr+9P75O5y9PBEmEzvfep/YDZvRHBzo8f2X+LZuiWZy4MTvC9n/+VdX1QJo0LsnfaZ+hDCZODR7LruKneMZUIeBP32Dm48Pl9PSWPHoU2TFnwfggZWL8b+rHed27mbR0NHlal2hUZ+ehEz7GGEysXfWHP787Eu7414BdRk+/VvcfX3ITU1jwbgJZFo1P7qcQsKx4wCkn41jzjXolsbYmd9xx8B+ZCUl8/4dHW+orCs07tuTQdM+RjOZ+HvWHLZ+am9ftYC6jJzxLe4+PuSmpfHbwxPIsNrnVbcOI37+Gq86/iAlM0JGkhZ7tlzNoD49uW/aFIRmYv/suWwv5tOqAXUZ8vM3hT7945EnC30K4OzhwQtHdnN8xRrWvPhquXqiUSu0+x8BoaHvDUWGrbA/3uU+tPY9QLcgczLRF/8I6ReLMji7Ypo0DRm+B33F7PL1gluiDRgLmoa+Pwy5bZX98bv7o7XrZtXLQl/2M6SnQM0ATPc/Cs6uIHX0sBXIY3+Xr9e4NdqgRw29v7cgty63P951IFqHnmDRDfsWfQdphn2mTxfCBeOeyfSL6LM/KVcPgIYt0Po+CJpAHtyO/Gut/fGARmh9R4NfHfQlP0LE/qLr6TkcEWz8vsltq5DH91ZM8wpBd6D1exA0DXlgG3LHGnt7O/VFtO0Kug45WegrZkJGyrVpNGiO1nsUCA15eAdy13r743WDjeM1/NGXT4cTB4z0eo3Reo0syle9pnH81CHDXilvSbtt5p8HWH3QaDtadJ3TSWnseOsJvNxcjDQpGbnjNH4uDnzf3vh9FE3aoA1+zKg3uzcjQ5fZ+/HeELQOvYx6mp2JvvA7SEs2Dnr5oI16BuHlA1Jimf5B0bEy0Fp3wOnRiaBpmLesxrx8nt1xhz6DcOg7FHQdeTmX/J8+RcadAZMJp6dfQ6vfCEwmzH+ux7xsXukiNpjadMDp8RcNvc2rKFhaTK/vYBz7D0XqOly+RN73Vj0HB5yeehVTUBOkrpM/8yv08IPl693ZEecnJ4GmUbBhBQWL55Se754euP77Yy5NHIceGYGpzV04PfIsODpCQQH5s77BcnhfuXoApvZ34/LMvxCaifx1y8hf8Eup+Ry69MTt7c/IfmYM+qmIwnRRoyZVZv5B3pyfyF88t3y9dp1weepfYNIoWLec/EW/lq7XuSeub35CznNj0SMj0Bo3x2XiG1ZRQf7cnzHvDKuQjf/1qF02bjoqIFFBLLrOB5v3M2NkN/w8XBk1ZxPdg2oT5FO1MM+So6fxdHFiw4T7WBtxlqlhh5k26G5CmgcS0jwQgFPJ6Ty3dIddMGLTqTjcnIxbse30BWIvprP+lbEcOZvIu8vCWPjcSIrz3rKtvDu0O60CavLkrJVsPxlL1yaBzAjbT8egOozv3o7pW/cxI2w/kwbcA8Cd9Wvzw6MhduUkZmQzb89xVj0zFEeTxl0fz+OZe1sztmNzRk1fSffGAQT5Fl3rkoOnDBtfGMHaY6eZunkf04Z3J7hGNRZPuB8HTSM56xJDflxOt8YBxFzMYPGBkywcfz+OJo0J8zbQNbgOge7GQ/e2E2cMe18dx5GzCby7LJSFzz9Qur3DelrtXVFob7Bfdb4eO5B3lm6xyx9cszqLXxiNg0kjOTOHIV/Mp0ujAD5YuY0Zj4fg51mFUd/9QfemgQT5eRfZtzcCT1dnNrzyEGsPRzJ13S6mPdiXqMRU1h2OYtVLo0nKzOHxmStZO+lBHDSNVwfcQzN/X3Ly8hn+zWI6BdUlyNPFqDPr/mbGQ33w83Rj1IzVVn962fgzEk9XJzY8P8zqz/1MG96t8PgnG/bSJci/hD/m/h1BQ5+qZOcV2KVbdJ0PNu5jxgM9jHr6ywa6B9exr6dHoo17+NT9rD1+hqlhh5g2uDNmXWfyql18PLATTfyqkZ6bh4MmcHZwZNljAwrPHz57Hb0b1S3SW7+HGQ/2Mmyctc7Qs7XxUJSh98xg1obHMDX0ANOGdgWgbrUqLBs/sIR9P+04hre7C+ueHowuJRm5ebdMz93Z0S5t+Mw19LYGB26FPwF+2hlu2PdkiJ19xbHoOh9s2MOM0T0Ne2eXYu9hq71PD2Zt+Bmmbj3ItCFdDP0Vf/Hx/fcY+peK9MtCaBrdpn3C8pDhZMefZ9T2TZxes560E6cK87SfPInIpSs4NmM21Zo04v6lC/i1WVuChg7C5OTM73d1xcHVlTH7/+LUoqVknT13Vb1+X33Kb/cNJTPuPI/9tYXI1eu5eKIogNtryvscnb+Qo/MWUK9bF7q//yYrH3sagN1ffIOjqyttnnjkqnYV1xz09efM7D+YjLjzPLd7KxGr15EUUaQ54JP3OTBvAQfm/k7Dbl3p9+HbLHrkSQAKcnP5ul2XCuuVx65f5hP27c88Muenm1Ke0DSGfP05P/cz7Ju4eyvHV60j0ca+gZ++z/65C9g393eCundlwIdv87vVvtG//MjmKZ8TuTkMJ3d3o2FfAc2Qrz7jlwFDyIw7z1O7Qjmxeh3JNpr9PnmPQ/MXcGjuAup360LvD95iyaNPFR7v+c4bnNm+s4JGCrTBj2GZ8SFkpGB6bgqW4/sgKb4oT/wZLLtfh4J8RMfeaAPGoP9WFOzS+oxEnj5ecb2QcVhmfwyZqZieeg9LxH5ILgqocOEMlh/eNPTu6onWdzT6wm+hIB/Lkh8hJRE8vDA98wGWqKNw+dJV9DS0IU9g+fk9yEjFNPFjw77EOBv7YrB8OdnQ69QH7b6x6PO+MI4V5GP54pWK2WZrY/+H0OdNhcxUtCfeQp48BBdtbMxIQV8xE9Gpn/25wS0Rteqh//QOODigjXsNGXUU8i9XXHvAWPS5nxna499Gnjxo5195IRb587uGve26I3qPRP7xw7XZ1/dB9N+/gMw0tEffQEYehosXivJkpqKvmo3o2Mf+3NiT6DPfN167uKE9/SHY1J1t8anEXsy86e22x+9ty+P3GoHxrcdjmLPjUGEwAmBuTAoNqjiTY7ZYbdTQho7H8uO7xufipU+xhO8tWW++eMXw49190QY+jD53KgCmB19A37wE/dRhcHIBWc5nX9NweuJl8t57CZmahMvHM7Ds22EEAKyYt2/CvNEIFpra3YPTuOfJ+3ASpk49wNGRy5PGgZMzLl/Ow7JjMzI54ep6EyZx+Z0XkSlJuHw6A/OeYnrbNmLeYATvTO074/To8+S9PwmH3vcDkPviw1DVC5c3p3L5lSdAyqvqOT/zKrn/fg55MQnXL3/FvHs78lyMfT5XN5wGjcJy4mhhksxI5/K7k5CpF9HqNcDl/a+59HDJtk5pmq7PTyZn8jPI5ETcv5uHeeef6GdL0RwyGnPE0RJFuDw9CfOev0qkl6Xn8uxkLr3+LPJiIm7fzMG8e1upeo6DR2Gx0dPPRHHpuYdBtyC8q+P2w++Yd28H3VIxbYXChuuesiGEsAghDgkhDgshDggh7ramBwohjllfdxNCrC7l3DAhRLtr0PpKCBEvhNBs0h4RQkghRE+btCHWtOFCiGXW64sSQmRYXx+6cp3W/N8IIbIrcg1HL6QS4OVBXa8qOJlM9G8aQGhUvF2e0MjzDG4RCECfxnXYfTYRWezLbk3EWQY0LeoJzckv4Ne9J3myUzOjjKh4Bt3ZFCEErerVJCs3j+TMHLsykjNzyM7Lp3W9WgghGHRnU7aEnzbODz/N4DuNkRuDbdKvhkWXXDZbOBSXhIujA01qVjdsbN6A0BP2PWOhJ88yuFWwYWOzQHafPo+UEldHBxw04/bkmS1cWe8lOjmdVnVqFB5vX68WW07EFpV3/DSD2l6xt1bZ9l62sbdtU7aERwPQ0M+b+jWqURxXJ0ccTFeux4wQcCwuiYDqVanrXRUnBxP9WwURGmH/pRsaEcPgtk0M+1o0ZHd0PFJKQiNi6N8qCCcHE3W8PQmoXpWj55Lw9XSnmb8vAO7OTjSoUY0k6/Ufjb9IQDUP6lbzsPqzPqEnS/Fny6Aif8ZcKKwzm0/EUqdaFbuHT4CEzBz+jIxjWJtGJew+eiGFgGpViupps3qERsbZ5QmNjGPwHfUNzSYB7I416ulfMRdoVMOLJtZgmZerMybN/iviTGomqZfyuLOuYfPR8ykEeNvY2KweoafOFdM7x+CWDQ29pvXYfSahxOeiOMsORxWOBNCEoJq1EXar9Ozsy7nMnXWN3q1b5c9lR04z3jpSyNa+4hw9n2Jfh5oFltQ/FcfgOxpY7Q0otPev08X03Urez+L4tWtL+ukYMs/EohcUcOqPZTQY2N8+k5Q4eRojMJw9Pcm5kGBNlji6uyFMJhxcXbDkF5CflXVVvdrt7yQ1Oob0GEPv+OKlNAqx1/Np2pgzW7cBEBu2nUYDi4JjZ7ZuIy+7Ql/hhdS9605Sok+TGhOLpaCAwwuX0CxkgF0ev6aNiQr9E4DosG00K3ZNN5Oo7Tu5lJp208oLKGbfoUVLaH5/SfsirfZFbd1G8/v7F6ZrDiYiN4cBkJ+TQ0FubrmaddobmmlWzaOLltK0mE9rNG3M6VDjPsaEbaeJjU9rt2lFFb8aRG0KrZiRdYOQKYmQmgQWC/rhnYhm7e2yyNPhUJBvvD4biahaveigf33w8EJGHqmYXp2Ghl5asqF3dDei6Z32ejERRXrnohCe1kB3SoIRjADISofsDHD34KoEBCFTEqz2mdEP/YVoXsy+aBv7YovZdz34N4C0JEhPNnrPw/9GNG5tnycjBZLiSjyoCp/ayNiTRnpBPjLxHCLojmvTTi3yrzz2N6JxG/s8Z04U2RsXXeTfilK7vtW+i4Z9x/cigu1HgJKRAsnxV31IFU3uREYfA3N+YVrouZRb3m5be/gUA6xtL4CE/2PvvOOjqNb//z6z6Z30RggkkNC7NCkBBKQXQSwIFrBcGzbUq6j3qigqFq4dCwoXG733rghIJwk1Cem9952Z3x+zbHaTTUEh937vb96vFy+yM2fO5zzPnNmdec45z5RXsy+rhCktLe4PwiJRc9I1X8pGlOMHEJ1usqpHvXjGot+cR3iZ+k1AKEgG1PMntc9VFeZy9SFFtkfNSEHNSgOjEePBHRh632xdqNwi8OboDJh8q6oIR2eQDODgCEYjarm1z+rotW2Pkp6CmqnpyQd2YndTreCwlZ6TWU9qGY582jRDobAASkuQIqMb1mvXESUtBTXDZN++bdj1G1SnnMOMB6n65XuoqvGXcvk8ap42Y0lJuoxwcAQ7+wb1AAxRnTTN9FQwGqnesxW7AUPqlHOc9QhVPy6FKuvBDLv+Q1DSU1GSGr/3B5CiOqKkJaNmaHrGPduw6ze4rt7Mh6j6+TtUCxuprKwJPtg7Nhzc0dFphL+SQ6JcVdVuqqp2BV4AFlynNllhCkJMApKB2t8EpwHL+bLTgZMAqqpOUlW1G/AAsN/U1m6qqv5qqrcX4EUTySwpJ9Dd2fw50N2FrOLyWmXKCPRwAcBOknB3tKeg3PoLfUv8FcZYBCQW7z/DrN5RONtrMySyissJ9HQz7w/wdCOzyPqGO7OohACrMq7mh+DckjL8PFwB8PNwJa+0po0nrmQw6YN/M+ertVzIyDXXf2+/Tgx7/0ceWr4dN0d7BkRoI/KBHq5kFVuP4mQWlRLo6Vpjo5MDBabR3ZMpWYz7ZBUTPl3NK2P6YydJtPVvwdGkDArKKiivNrLvYjLphTU/OlmFJQR6Wdji5UZmYS17C2vZ6+VGVmHjDyEnr2Qw7r3vmbBoOa9MHExOibVvAz3cyCq0/gHMLCo1t8fOYLKvrIKswtJa58WVzFo3HKn5RcSl5dDFtGwks7jM7CtNz4Y/LcpY+rOsqpqvDp7hkcG1bgiBt7Ye5pnhPW3OGMssLifQ3ULT3cWGZk2Zmn5aSVJeMQKY/eMupnyzma8O1R093BSbxKj2YeYMw5nFZdZ6Hq51r4tiW9eF1mdSC0qYvGQD93y/laNXtJv2ogrtmlm89yRTlmzkyZV7ySkpv2F6VvadTWRUh3AL+66/P8327T/JlG828+Tq/eSU2n7os7Slfn3b9iblFSGEYPaKnUz5aiNf/XbWpoYlrsFBlKTUjEqWpKbhFhRkVeb3NxcSNX0q954/xbhVP7D36RcAuLR6HdWlZdx/6Syz4k9w/MOPqcwvaFDPPTiI4pSawG5RahruwdZ6mafPED1Rm9kVNWEsjh7uOHvXDUI2FY/gIAotNAtT0/AIsdZMP3WGzpO1EbWOE8fh5OGBi0nTzsmJRw/t5pED2+kwfsyfbseNwjM4iILkGvsKUtLwrOXTNAv7OlnY59s2kvKCQmb+/D1zj+xj7Nv/QDQSxALwCKnr09rnMePUWTpM0s5jh4ljcfLwwNm7BUIIRi18nS3Pz2+yjcLTW1sOYRbMRXjW3yek3jEo506YDhYYxsxA2dj49HCznkcLKMyr2VCUp22rT6/nYJQLJ+vuCGkDBjst0NCQnqe39fKSglxtW316fYaixFtMP7dzwPDE2xgee7NOIKNe3L1QrWzMB/emXWfmAISdAzi7IcKj4VoCBh4tUItqaTfgX9FjEOrFJgaTruLuZa1RXNBk+6y0O/RGjT1stS2rrPKG3bcBlFdVs/9cErd0jjRveys2g6fbByBZZPsXnj7W10Wj/WYYSpy2LEX4BaOWlyLNeg7DU+8ijbsHRMPXvvD2Q82p6ctqbjbC269OObtRk3H614/Yz3iYqq+0pVzyod2oleU4f7kG589WUr1uBZQ0HMCuq5eF8LGhd+tknD/9CYeZj1C1RNNTEi5qwQvJgPAPQoqIQvgENKzn44eaU3OfoObU1ZPatEPyC0A+fKDeegwDhiJfOgfG6nrLmDV9/VCyamaJqNlZSD7Wy3+kyCgk/wCMv++3PtjJCYfps6i8htl2ko8/SnaNjUpOFsK3ll5EFMIvEPn3ujZKUR1x+eJHXD//gcqPFvz/MztCiP/uf/8HuV5JLT2A6zfEY00McAb4FOvgA8B+4CYhhL0Qwg2IBE40VqEQwgC8AzS+UNWEzcCfaLyMZb84mZaLk50dbU0j3nGZ+VwpKGF4u9CaOmzKWAs1pmOLDiH+7Hh+JqufvJO7BnTlse+09ZmFZRXsOneF7U9M5dWxAzAqCutOXay3nobc0DXUn/WPTOan2eP58sApKo1GIvy8eGBAF+7/fitzlm0lKsDbPJOi3vpq+9VGqaa8cqdrWCDrn57BT49N58s9x6g22viiFLV9a0ML0Wg7SyureWLZVl4YOwA3JwdTuxunPn/+a88J7unbAVcH64j6nvPJeLs60THYt8n11e6otm0RGBWVYynZLBzXn2V338KO88n8lmg9fXJTXBJjOoQ3rNeU6wKBn5szOx+dwqoHxjJveC+eW3OAksoqZEUho7iM7i39WPnAGLqF+vHOzj9umJ6VfbGJjOnYiH1/0Z9m+0L8WHnvrXQL8eWdXbbXsv5pfUz6yVksnDCAZfeM1PQT0m2Utm53nTbUcmi7qZOJX/YD37TrwvrJ0xmx5BMQgoBePVAVma8jO7G0Y0+6P/4IHuGtGtSz9cVVW2/n8/MJG9if+w/tIWzgAIpS0lCMxobrbVDSxndHLSdunPcyrQcO4PEj+2gzaACFKanIpu+Pt9p04l99Y/hhxgOMe28B3m3C/3Rbbgg2fWr9ecNzLxMxaABzj+wjYtAAClJSUYwyBjsDrW/ux/rnXuLDvjF4tw6n98y7/pRmbdEt816m9aABPHJ4L+EDNZ8qRpmbHnqA81u2U5SSWreO+gVt6NVTsvvNiNAI1L3rtM99R2jBiWvKP3ANel0HIELaoO63zoGAmxeG2x5GXvVFE0YSG/enuWSPgZp9Fjk05DceQv5wHvLyD7Q8FI08eNWr2aRfMeDyWdQLp5HuexFpyoOoKRev8cHkGvzbpR8iuDXqwc3XUH89Gk217yqunuAfYrVcA+r/zWm0TBOfG/bEJdAjPMi8XGNPXALeDgY6ejpbF7wGE0XPQYiWkTW5SSQDok17lHVLkT94DnwCEDfFNNywJlz3AMYtq6h49Haql32G/W0zNbnIDqAolM+ZSPkjU7EfNx3hH3x99DavovzhaVR99yn2U2dp23ZuRM3Jxundr3C4/wnk+DOgNPI70thvhRA4zplL5Zf150qSwtrgeN+jVC5u4pitzU6hWu13evhpKj5bVKeU4z0PUbVyOVQ0Pqutpj5bctZ6jg8+ReUX79s8XDl3lrI5t1P22D04TL8X7B2arq2jY8FfySHhLIQ4ATgBQcDQ69OkOtwBrADWAm8KIexVVb0aZlSBHcBIwBNYB7RuQp2PAutUVU2v78FWCDEHmAPw6Ywx9I4MI8NiJDajuAx/N+sfg0B3FzKKygh0d8GoKBRXVuPpVHNxbq61XONkWi5nM/Lo99EqSquMGBWVgvIqMixG/zMLS/D3cLXW8bSeRZBZWIqfaYTWx82F7KJS/DxcyS4qxdtVa6ObRTsGR4fzzzV7yC8t5/dLKYR4ueHt6kyIlxtujvacSM5ifJdIMopK8Xd3sdb2cCWjsJRAD1fNxooqPJ0drcpE+Hnh7GDHhawCOgX7MqVHO6b00JYXvL/zKMl5xUx6fzkAnVsGkFFgYUtBCf4eblb1BXq6W9tbUGIeTWgKEQHeODvYUSXLVr7NKCrB36OWfZ5uZBSUEOjphlE22efiSICna63zUoq/yefVssyTy7cwtltbbukUUVOXuwsZFjMwbPrTVKa2P0+lZrMtLpH3dhyluKIKIQSOdgYyi8vYfS6ZfRdSqDTKlFZW89zqfSycNMhUnzMZxRaaxWX4u9fup1qZQA/rfhro7kzvlv7m5QODIoKJzcyjX3ggAPGZ+ciKSsdAb4u6XKz1ikrrXhcepuviqo2V1Xg6OyCEwMGUYLFjkA8tW7iTmFtMxyBvnO0NDI/SrpWR7Vux8sTFG6bXKdjHZF8esqLQMahm6vON8GffVgEm+7Q8HCOjw1h5yvb0yqvfKQ3r27Y30N2F3mEBdc9na+uRa0tKUtNwC625KXQLCaY0wzoo1eGeu1g3UVsfnXH4KAYnR5x9fWg3bQpJ23eiGI2UZ+eQfuh3/Ht0oygxifooTk3DPbQmR4pHSDAl6dZ6JekZrJyu3cTau7oSPXEclUUNj6Q1RGFqGp4Wmp4hwRSlWQdqitMzWDZtBgAOrq50mjSOyqIi8z6AvIQkLu87QHC3LuRdTvzT7bneFKam4dWyxj6v0GCK0q3tK0rPYOnUGvs6Tx5HRVERBalppJ04TV6Cds7OrN1Iqz69oJGcj0UpdX1aXOs8FqdnsGLaPWbNDiaftuzbm1YD+nHTg/fj4OaKwcGeqtJStv/9tXr11MLcmqnmAJ4+qEV1x0REZGekoZORP3sVZO3hQ7Rqh2gdDX1v0aZ0G+yQKitQtqyoX68oz3qk2cMbtdiGXkRHpMHjkb96w6wHaAk073kGZcfPkHKpXh1r+yyCzl712Ne2M9KwKcifzrfWu1o2Lwv10llESGttyUlDFOcjPL1rHn08WmizCJqIemAD6gFtha6YNAc1rxE9S4ryEB61tW2McbXpgBg4DuXbBdb2NoXifGsNd69rsg9AdOip5bZQZP4dn8rP57XrqrOv+w25b7vKppMXGN21ZonmscR09mQVs3/XeSoVldJqmXnHU3i7Va3rwsvHelbIVTvadkEafhvyxy+b/agW5kJqgrbcA1BPH0a0aofKzjrHX0XNtR5NFz5+qPk59ZaXD+7AYfbTABgG3oJ8/HeQZSgqQDl3GikiGjkrrd7j6+r5m5dF2NQ7sAPHB5+hCkCRqfqmJkGz04LPUNJS6j0WTDMifGuCecLXHzXPIsmnswtSqwic39ZymYgWPjjNf5eKfzyDciEO4eOP08sLqXjvVW1JRBNQs7OQ/ANrNP38UXItNF1ckcIjcH3vS22/tw8u//iAsvlPYmjfGftBw2H2Ewg3dy2RaFUV1Wt/rFdPycnC3q/GRsnXHzW3lo3hEbgs/Nys5/zaIspfeQrlQk0iTSU5EbWiHCk8wmq7jk5TuR5LNqKBUcB3oinD1teAEMIBGA2sUVW1CPgdqJVtiB/QlmpMRwtcNFZnMDAVWNxQOVVVv1BVtZeqqr1mD+5BpyBvkvKLSSkooUqW2Rx3hZhayQZjIoNZcyYRgG3nUugTFmAejVNUla3nkq0CEtN32Mc2AAAgAElEQVS7R7L3bxP47fHJbJ49hkhfD/45qjdr/4hDVVVOJmXg7uRQ5+Hbz8MVV0cHTiZpa8XX/hHH0I7aGvKYDq1Z84f2ZbDGYnt2cal55PFUcgaKouLl4kSQlzsnU7MprzbSMdiH1IISvF2cNBvPXiYmyjrzf0y7lqw5eUGzMTaRPq219ZAp+cUYTcnPUgtKSMgpNGf6zzVNP0wrLGFHXBKvjO3P6rl3sXruXQzrGMHaY1ftTcfd2bEee+05maTlV1h7LI6hHdo0dPpIySvEKJvak19EQnYBA6PCSMopJCWviCqjzOaTF4lpbx2/imkfzppj8Zp9Zy7RJyIEIQQx7Vuz+eRFqowyKXlFJOUU0rmlP6qq8vLK3bTxa8GsgdbLKzqF+JKUV0RKfrHJnwnEmJJBmvWiWrLGNCPF0p/L7h3NjiemsuOJqczo04E5N3fhrpva89SwnuyeO40dT0zlvSmD6dM6yByMAOgU5ENSnkU/jU2y0U9DWXNay52xLf4KfVpp/XRAm2DOZRdQXm3EqCgcuZJFpE9N8sZNcUmM7mA94t0p+KpecY1ebRvbtmTNKe0mfFtcEn3CAxFCkFdagWzqM8n5xSTlFRHawg0hBEPahnI4SXugOZSQQYQpieSN0DPbdzaR0R2s+8ON8KcQgiGRIRxO0m78DiVmEuHjgS06BftYf+/EJhLTNtSqTEzbUNacvmyy11I/iHNZ+db6Fsk4bZH5x3G8Itrg0SoMyd6edrdNImGjdSb6kpQUQmO0Ptciqi0GJyfKs3MoTkkhdLC2ntfOxYXA3r3IP3+hQb20o8fwjmyDZ7im12HqZM5vsNZz9vE2jxoNeO5JTn63vME6GyPlyDF8IiNoEd4Kg709XW+fQuwG69FWFx9v83f3kHlzOfqtpuns5YnBwcFcplW/PlbJMP8bSD5yDN/ICLxN9nWbNoWz6+u3b+jzczlisi/5yDGcvbxw9dUeatrGDLJKhlkfqUc1n3qFh2Gwt6fztMnEN+DTQfPmcmyppvnLzDm8F9mZRe26snXey5xY9mODwQgAUi4hfAKhhR8YDEhd+6PG1cpgHxyONPkB5G8XQmmRebPyw2LkBX9DfvsxlI3LUI/tazAYoRl42Vqvc1/Uq29guEpQK6QJ9yEvX2Slh8GAdOeTKMf3o561nupfL8kXEb5B4O2vBUy6DUA9W+utFcGtkaY8qCXaLLHQc3bVloUAuLgjwqNRMxt+8NJsTADvAPDy1UbLO/ZBPd/ohFMNITRdAP9QREAoXGp8iZiZtARtFoeXLxgMiE59tAd/SwLDkMbOQlnxIZT+iYBkWiK08AdPH82+Dr21pJbXgOhwk/ntIXdGh7B6fC9Wj+/FsDDfG3LfBlBcXsmRy6lW2566tT+7hkWxfWg73u0eSh9fV97uHqr1Gz+LftP9ZtQztfpNSGukqQ8hf7VAy2dylSsXwcUNXLXfItG2M2pm/QmJAZSL8Yiglgj/ILCzw27AcOQj1skURWDN75WhR3+UDK0vqjmZGDqZ3nLm6ITUtgNKWv3BawDlQjxSUKhZz3DzMIxHrJcRiCALvZ79UdJNfd/B0ZRTAqSuvbVcJRbJMG3qnY9FCm6JCAjW7Bs0AvmQxTKJslJK7xhB2b0TKbt3Ikr8GXMwAlc3nF57n8pvP0aJbfryIvncWaSQlohATdN+yEiMv+6tKVBaQsmUYZTcPZaSu8cix52mbP6TKOfjKJt7v3l71ap/U7ni6waDEQDKuVhN76qNQ0ZgPLTP2sZpwymdOZ7SmeOR486YgxEiIFjLAQII/0Ck0FZafg8dnT/BdXnLhqqqvwkhfIG6i7n+GqPQZj6cNt3IuABlgHkupKqqh4UQndACJOebEBPpjra04+LVOoUQF1VVjWzoIDtJ4u/DezD7570oqsqkzm1o6+vJ4v2n6RjozdC2IUzp0oZ5Gw8x8ouNeDk58O74fubjjyZnE+DuTEsvtwZUYFCbIPalFTBq4Xc4OdjzxlRzzk4mfbCC1U9qq1bmTxrCiz/toLLayMCoVgyK0h4UZw/pydzlW1h5JJYgL3fev1tLHLbt9EV++O0MdgaBo50d7905SkvAFBbIiPbh3Pb5WgySoFOwLxvOXGb96UtM6taWtv4tWLz7GB2DfRkaFcaUHu2Yt3ofIz/6GS9nR941vRHi2JVMvjx4CjtJQhKCl8f0N4/MPvHTLgrKKrE3CF4a3c9qRsWg6HD2xScy6u2lODnY8cbUW2rsfX85q+feZbJ3KC/+tF2zN7oVg6LDAdhx5iJvrN1LXkk5D3+zluhgP758YBLHEtL4cs/RmvZMGISfuyt/Hz+Q2V+v185hr2jaBnizePthOob4MbRDa6b0as+8n3Yy8p1leLk48e4dWnvaBngzsksE495fgUGSeGnCQAySxB+J6aw7fp52gd5M+kj74n9yRF8Gt/LT+sytfZm9fLum1y3S5M/jdAz20fzZvS3zVu9n5OKVmj+n1E0mdC3YSRJ/H9GL2T/u1jS7tKGtnxeL952iY5A3Q9uGMqVrBPPW/8rIz9bh5ezAuxO0JFSeTg7M7B3NtKVbEWgj6oMtHr63xCXx2bQhdfVG3sTsFTtRFJVJXSM1vb0n6Bjkw9B2LZnSLZJ5aw8w8pM12nUxSXtoPZqcyeK9J83n6JVb++Bl6htPDe3B82sP8tb2o7RwceKNsf1vqJ7ZvtuH1rXvBvjzqSHdeX79r7y18xgtXBx5Y7Tt1z1q+r2Z/cNVeyNM9p7U9K/au+4gIz9dg5eTI+9ONOk7OzKzT3umfbNZ048MYXBkqE2dq6iyzN6nn2f82p+RDBKx3/2bvLhz9HnpebKOnSBh0xb2vzCfof96n+6PPoSqqux48FEATn/+NcM++4g7jxxACEHsshXknmn4LQaqLLP1yee4Y/0vSAYDJ5cuJycunkHzXyD9j+Nc2LiFVoNuJuafL6OqKskHfmPLEzVvD5ixcyM+7dri4ObKYxfPsPGhx7m8o+HEiIoss+6JZ7lv40okg4Gj3y4jKzaeW155kZQ/jhO3YTNtBt/MqNdfQVVVEg/8yprHngHAr30Ukz95H1VREZJgzzsf/OWAxP3//pp2Q27GzdeHBclxrH/lTX79uvFXtTVk3+onnmX2ppXaa02/XUZmbDwjX32R5KPHid2wmcjBN3PrG6+AqnJ5/6+sMtmnKgrr573Eg9vWIQSkHDvJ70tsvwKutuaGJ59j5saVSJKBY0uXkxUbz9BXXiDtjxPEb9hM68E3c8s/56Oikrj/VzY8fo1vgbASVFDWfo3h/he11xse2QOZKUi3TEVNuYwa9wfS6LvBwQnD3XM12wpyUJa+8+f1NizFMPM502s/90JWKtKwKaipCajxx5BG3aHpTX/cpJeLsnwRolNfRHgUwsUNemiBPHnl55DRwKtUFQVl9RIMs1/i6mtNyUxBGnk7avIl1NijSGNngKMThhlP19j3zdvgH4rhtjnatGshUHavtn7LQn2oCsrmZUh3PQVCQj1xALLTEEMmoqYlaq+4DA5HmvYoOLki2nWDwRNRPnsZJAPSLC2XDJXlKKu/bPwNDbXt3bQMacYzmvbx/Zp2zCTUtAQ4dwJpxO3g4Ig07W/aMYW5WnCiqagKyrYVSNO1V0aqJw9CTjpi0HjU9CS4cFILKk15BJxcEJFdYOB4lC9f1Y739NFmbiSdr1P1oBBv9pWJ637fBrDj7GUGtA3DxaHxhIgoCsqqJRjmzNf66eGdkJmMNGq61m/OHtFyQzg6YZhpuubzc1C+XqD5Z91SDA+/CkJo5Q/taERPpmrJIhxfWqS9hnPXRtSUBOxvvx/lUjzy0YPY3ToFQ5deWtLK0mKqFr8BaMs4HP72Ik7va991xt2bUJMamT2kyFR9+T5OrywCyYBx5wbU5ATs73gA5WI88pED2I+egqFLb1TZCCXFVH70OgDCswVOr7wPqoKam03lh/9ogj9lKj99B+fXP9Je+7ltPcqVyzjcPQf5Qhxy7RwOFtiPm4YUHIrD9Pth+v0AVLz0GGphI6vbFZmKxW/j8tbHCEmiass6lKTLOM58CPl8LMbf9jV8/LWiyFR8/A4uby4GyUD1Nk3P4Z4Hkc/HIR+qX8/QqRsOt88EoxEUlcrFb6EWFdZb/n8Job/287ojmpp9vs6BQpSoqupm+jsaOAAEAC2BDaqqdhJCDAGeUVV1bK1j95i2N/hSXiHECrSlFStMn12BBCAcmAb0UlX1USHErUCFqqq7hRDfmvR/MR1jsw227KgP+av5zZs6tlYCmxtOWcOZjW8IjWUZv97IjScTuq6UlzVe5npT1XBG7OuOwdC8es2N/B9IztSEBILXk08euYYb+utAkXwNDynXgeJm1gMoMDavppuhefuM83/gRuyVubUnRt5g7Jvw8Hc9qWji6zGvE8LD9oysG0oTXh97XXFo5rXsHbo0q5y6r4lvpLmOVF1pOBnr9UaVm/fWW61s5vtEQKn68/mQ/gyimX8vANy3Hv2fe3qvnjPqv/qVIvZfbPk/5/PrkUMCtLQoM1VVlW3MUBgmhLAMz081/b9RCHH16v9NVdWplgcJIVzQckM8eHWbqqqlQogDwDjLsqqqXmt2Ix0dHR0dHR0dHR0dHR0dnf8gfzogoaqqzeFRVVUTgU6mv/cAzjaKDWlC/WVAnfcVqao62eLjtzb2z6r1eQ+wpwGdhtdQ6Ojo6Ojo6Ojo6Ojo6Oj8H3215n8zzT93R0dHR0dHR0dHR0dHR0dH5/97rktSy7+KEGIk8HatzQmqqk76T7RHR0dHR0dHR0dHR0dHR0fnxvJfEZBQVXUrsPU/3Q4dHR0dHR0dHR0dHR0dHZvob9m47uhLNnR0dHR0dHR0dHR0dHR0dJodPSCho6Ojo6Ojo6Ojo6Ojo6PT7PxXLNnQ0dHR0dHR0dHR0dHR0flvRuhv2bju6DMkdHR0dHR0dHR0dHR0dHR0mh19hsR/I6ravHrOLlBZ2byazY1o5tibLDevHoDSzJrN3U+bm+b2J0C10qxyrobmjfIrNK+em8FAkdy8/dTYzJdFidy8fcao/gdGhuya91ZFNLOe6uLSvHq5uc2qByBatGh2zWalmX8PxcAY1P27m1ezuRP5NbNP1f/EqHczaypVxmbV09FpKnpAQud/Pxiho6Pz/yXNHYzQ0dHRaQ6aOxiho6Njgf6WjeuOvmRDR0dHR0dHR0dHR0dHR0en2dEDEjo6Ojo6Ojo6Ojo6Ojo6Os2OHpDQ0dHR0dHR0dHR0dHR0dFpdvQcEjo6Ojo6Ojo6Ojo6Ojo6jaG/9vO6o8+Q0NHR0dHR0dHR0dHR0dHRaXb0gISOjo6Ojo6Ojo6Ojo6Ojk6zoy/Z0NHR0dHR0dHR0dHR0dFpDH3JxnVHD0hcA/svp7Ng53FkVeW2Lm2Y3be91f4qo8zzG3/nbGY+Xs4OLBrfnxBPV9afTeTrI+fM5c5nFfDLzBG0D2hBlSzzxvZjHE7OQhKCx2/uxNGcOPadS8LZ3o43pw6jQ4h/nbacTcnixZ93UGGUGRTVihfHDUQIQUFZBU//eyup+UWEtPBg0Z0j8XRxYv3xc3y19xgALg72zJ84hOhgXwD+vmYve89fwdvVmWdH9GHB5t80G3tEMXtgt7o2rtrD2fQcvJwdWTR1GCEt3Pn1UgqLth+hWpaxNxh4ZsRN9G0TAsDmM5f4fN8JZEVhcLswnhnRx1yfqqq8uW4v++ITNXunjaBDqC17M3nxp+1UVBsZFB3Oi+MHI4Rgy6kLfLz9EJez8vjx0el0ahkAwMFzicz7YRvFFZVIQvD82IHc3rfTX/ajqqq8uX5/veenpKKKsYuWM7xtKC+Nuon9l1J5bs0BSiqrEUJw6oW76vpz3UHOZuRpfWbSIEK83EgtKGHs5+sI9/YAoGuIL6+O7mt17N9+2k1yQTHr5oy32n69+2mYlxt3r9hl3p5ZXMa4Dq14YXgvk14aC3YeQ1ZUbusawey+HWzoHTLZ6MiiCf0J8XQD4FxWPq9uPUJJZTWSEPw0cySOdgY2xyXx+W9nkRWVwRHBPBPT3cK+P6e3/mwiXx+Os7Zv1ijaB7Rgzk+7yS6pwKgo9Gzpz8u39MRg6dOEdBbsPGHyaWtm97Hh002Ha3w6rh8hnq5Uywrztx4hNrMAWVEY3zGcOX3bU2mUuWfFbqpkGaOiMqJdKI/d3MlCL4MFu016nVozu090Xb3NRziblY+XkwOLxvat0dv2B7FZ+ciKyvgOrZhjOvb7Yxf4+VQCKjC1c2vu6dkWW4QMH0rft99AGAycX7qMU+9/ZLXfNTSEQZ/9CwdPT4RB4uirr5OybYfV/smHD3J8wULOLP7EpoYlrW4ZyuCFCxAGibNLl3H0vQ+t9ruHhnDLFx/j6OWJZDBwcP4/SNy6A8nOjmGffIh/ty5IBjviVvzI0Xc/aFQPIOKWYYx8702EwcDxb77n13etNT3DQhn3+WJcfH0pz89nzb0PUZyaRkCXToz+6D0cPdxRZJkDby8i9pfVjepFjRzGhEVvIRkM/P71d+xeaN3OFmEtmbbkX7ia9P59zxwKU9MA8GoZytQvPsIrNARUlSXjppGfdKVJdtbHjK8+pvPYURRnZfPPzn0bP6ARokcOY/L7byMMBg599R07F75vtb9FWEvuWPIxbn4+lOXl873JvsghA5n03pvmcv7R7fjuzvs4vXZjo5qibRekMTNAklCO7kHdt956/4BbkXrFgCKjlhahrPoSCnLAyxfDnU+CJIFkQDm0DfXwzmszOLIz0qg7QZJQj+1DPWDdXtFvJKLHIFAUKC1GWfsVFOZek4SI7Iw0+m4QEsqxvaj7N1jv7z8Kqcdgzb6yYpTVS8wa0oxnEKERqFcuoCxf1HTN6O5Ik2drPj20HXXHSuv9Q8Yj9RuhaZYUovx7MeRna22ddF9NwYBQlKXvop7+vekGN4NPadMR6ZbbQUioJw+g/rbFen/Lttp+/xCUNV9CvHbPRKsopOHTasr5BGr7z58Art7H7Lvu922Xs/L5+y87iE3N5omRfblvUA9zXUsv57AyuQABtPVw5I0uITgaJO0cTrzPdA53oO6y/n4Sg8ch9RluOodFKD9+DPnZ2k4vX6TbH0F4+YKqIn/5es2+epC63oT9vU+AJCHv3IBx7XKr/YZbJmA3chIoCmpFOdWfv4Oamojh5luwG39HTbvCIqicdz9q0sWG9br1weE+7fo17lyPcfUyq/12IyZiN2qySa+Mqs8WoqYkgp0dDg8+hxQRDapC1dcfopw93qAWgKFnXxznPAWSRPW2dVT//J3tcgOG4vziAsqemIlyMR7cPXB68S0MbdtTvWMjVZ+926iWua5e/XB65BmEZKBq8xqqfvzWZjm7gcNwmb+Qkr/djXI+DimqI85z/27aK6j8/guMB3c3qmfXuz9Ojz4HBonqjaupXPGN7XKDhuP62ruUPHgn8vlYREAw7ktXoSQnAWCMPUXF+2802U4dHUv0gEQTkRWF13f8wZJpQwhwd+b277YTExlMpK+nuczK05fxcHJg65wxbIq7wnt7TrJoQn/GdQxnXMdwAM5nF/DoqgO0D2gBwOe/xeHt6sTm2WNQVJXNcVdIyilgyzN3cyo5k9fW7OXHv02t055/rNnDa5Nj6BoWyIPfrGf/+SsMimrFkj1/0DcylNlDevLlnj9YsvcYT9/an1BvD5bOmYSnixP7ziXxyurd5nondWvHXTd1ZN6q3by+8SBL7hlNgIcrt3+xhpioVkT6t6ix8dg5PJwd2PrE7Ww6fYn3th9m0bRheLk48cmdI/D3cOVCZh6zv9/MnmfuoqCsgne2/c4vD07C29WZF1bt4bfLqfTrqD0M7YtP1Ox9bianrmTw2upd/PjY9Lr2rt7Na1OGafZ+vZb955IYFB1O2wAfPpoxlldXWd9QJmQXEBngwzcPTmbj8XO8+NN2mwGJa/XjvnNJDZ6fj7Ydonfr4Jo+s+Uwr43pS6dAH275eDUXswuI9POq8eeJi1qfeWQim84m8N6uYyyaPAiAli3cWD17rM3+uD3+Ci4OdS/fG9VPV88aaT7+tqXbuKVdaI3e9j9YcnuMprd0GzGRIdZ6p0x6D45jU2ySSW8ARkVh3obfeGtsP6L9W1BQXomdJCgor+Sd3Sf4ZdZIvF2ceGHjb/yWmEG/8MC/pFfHvpX7zPYtmnAzbo72qKrKk2sOsDU+mdHRljYeY8m0wZrm9zuIiajt0wQ8nOzZOnu05tO9p1g0vh9bzyVTJSusvXck5dVGxn29hTHtwwj2cOHr2wfj6mBPtaxw94pdDGoTRNfAFsiKyus7j7PktoEEuLtw+/Kd2jn08ajRO5Oo2Xj/rWyKT+a9fadZNK4vW8+nUCXLrJ05QtP7dhtjoltSVm3k51MJ/HjXUOwNEnNWHmBQm8A6/UdIEv3ee4utE6ZSmprG+D3buLJpCwXnzpvLdHv2KRJWryX+q2/ximrHLb+s4OfOPc37+yx4nZTtTXvAE5LEkEULWT1uCiWpaUzfv4PLG7eQF18TGOs972kurFrL6SXf4B0dxYRVP/BNh+60nTwBg4MDy28aiJ2zMzP++JVzP62k+Epyo5qjPlzI8jGTKUpJ44GDOzm/YQs5FprDF/yTU8t/5NSyHwgfMpCh/3yZtfc9THVZOWvvf5i8S5dxCwrkgV93cWn7TioLixrUm/TRu3wxaiKFKWk8cWg3ses3kxlXozd24T/54/sfOPr9CiJjBjH6jVdYMetBAO749jN2LHiXCzv24ODqiqooTfJtQ/z27XL2/OsLZn33+V+uS0gSty1+j09HTqQgJZWnft/NmfWbrOyb8M7rHFm2giPfraBtzCDGvvkKy2c+yMU9+3mn50AAXFq04O/njxO/bVd9UhaiAmncLORvFkBRHoaH/4kcdwyyU2vKpCUhf/ISVFchbhqGNPIOlB8XQ3E+8uevgmwEB0cMj7+NHPcHFBc00WCBNHoGyvfvQFEe0uxXUM8dh+w0cxE1PQn1i9c07V4xiFumof7yadPqv6ox9h7kpQs1+x58DTn+mJUG6UnIn7+iafQeijRiOsrPH2v6Bzeh2jsgeg29Bk0JaeqDyJ+8AgW5GJ5+F/n0Yci0uJ5SEpDffUrTHDAKafwslKXvoF48jfzOXK2MixuGlz5DjW/8Yc/K3ubw6cg7UVa8D0X5SPe+iHrhJOSk15QpykNZ/w2i7wjrY5POoXz1T+1vJxekh9+Ay7Hm3ftS80jKKbru922eLo68OG4QO2MvW9WTWVjC8sQ81g2OxMkg8dSxZDalFTIpzAdp8mzkz16DwlwMcxcinz0CmSk1B6cmIL//rObH/iORxt6D8v17ABjufBxlx0qU8yfBwQnURr5rhIT9/U9R9fpc1NxsHBd8iXz0IGpqormIfGA78va1AEg9B2A/81Gq3nxG235gu1ZNyzY4PLeg0WAEkoTD7Kep/MeTqLlZOL29BPnIAS3gYMK4fxvGbWs0e3rdjMOsx6h8/WnshmsDNxVP3QMeXji99B4V8x4AVW1Qz/HhZyl/6THUnCyc3/8W46H9qMkJ1uWcXXAYPw05/kzNtqoqqr7/HKlVG6RWEQ3bVUvT+bHnKZ33CGpOJq7/+h7jb3tRrtjQnDgdY9xp8yYl8RKlj8wARUZ4++L62QpKftsHitygntMTL1D67EOo2Zm4fbac6l/3oiRZ9zmcXXCcfAfG2FNWm5W0FEpm3950+3R06uFP55AQQshCiBNCiJNCiGNCiP6m7eFCiDOmv4cIITbYOHaPEKLXNWh9KIRIFUJIFttmCSFUIcQwi22TTNtuE0KsNrXvohCi0PT3CSFEfyHEt0KIBItt3Wwr13A6PY8wL3daernhYDBwa/swdl1MtSqz60IaEzuFAzAiKpRDVzJRa33ZbYy7wuj2YebPq09fNo+2SkJwODmLCT2iEULQNSyQ4vJKsotKrerILiqlpLKKbq2CEEIwoUc0O89qXx67YhOY2EMbEZ1osb17qyA8XZwA6NoygMzCEnN9vcKD8HR2pMIoE+btQUtvDxzsDNzaKYJd8UnWNsYnMrFbO83GDq05lJCKqqp0CPLF38MVgEj/FlQaZaqMMsn5xYT7eOLt6gxAv4gQtsfWfLHuir3MhB7tNXtbBdVvb4Wlve3ZefYSABEB3rS2CJhc5UJmLlP7dEIIwZjuUSiqSmp+Ud16r9GPu2IT6j0/Z1OyyC0pp39b7fyeTsslzNudEdGtCPZywyBJ7Dpv/bC060IyE7toP1Yj2rfiUGJGnT5Tm9Kqapb+HsuDAzrX2Xej+ulVEvOKySuroGeon4Wem7XehRSrY3ZdSGFip9aaXnRLDiVpNh5MyKCdnxfRpvPn5eyIQZJILigh3Nsdb1N/7dcqkO0mv/0VPSv7YpMY3aGV+bOboz0ARkWlWlasZuOdTs8jrIWFZnQYuy6mWdW362IqEzvW9akQgvJqI0ZFodIoY2+QcHWwQwiBq8NVTQWjXHPjdzrD0kaJW6Na2tBLY2JHrf0j2oVw6EqWpgeUV8u19Oy5lFtM1yBvnO3tsJMkeof6svOCdZ0Avr16UHQ5keLEJJTqai6vXEPYmFutyqiqir27OwD2nh6UZWSY94WNuZXixEQK4uPr1G2LgF49KLycQJFJ7/wvq2kz1loPVcXBQ9Nz8HCnJD2jph2uLgiDATtnJ+SqKqqKixvVDO7dk/xLCRQkaJpnf15F1DhrTb/2USTs3gdA4p79RI0dDUDexUvkXdK+C0rSMyjLzsHV17dBvbCbepJ76TJ5CUnI1dWc+GklHcePtvZD+ygu7NoLwMXd++g4/lbzdsnOwIUdewCoKi2lury8URsb4+L+XynLy//L9QC0uqknOZcuk5uQiFxdzfEfV9F5/BirMgHtozi/U7Pvwu59dK5lP0DX2yYQt2V70+wLjUDNy9RGb2UZ5dQhRPueVg9fuJcAACAASURBVEXUhFiortL+Tr6I8PTWdsiyFowAMNhf+9TbkDZgoa2e+R0R1d26TGJ8jXbKJYSH97VphEag5mXV2Hf6ECK6h1URNSHOwr5LCM+a30H1cixUVlybZqu2qNkZkJsJshHl2H5E55usNS+ertFMPIfw8qlTjejaHzXumLlck2gOnwa3hvwsbZaMIqPGHkG07WpdpjBXC2o18BssonuiXjoDxhr7diXn3pD7Nh83Fzq3DMBOqnurLqsqFbKCUdH+93eyh7BI1Jx0zZeyEeX4AUSn2ufwTI0fk87XnMOAUJAMqOdPap+rKho9h1Jke9SMVNSsdJCNyL/uxND7ZutC5WU1vnNysulbw83DkQ/uqLPdtl4KamYaGI0YD+zE0HtgvXpY6InQcOTTR7XtRQWopSXabImG9Np1QElLQc0w6e3bjl3fQXXKOdz9IFW/fA9VlTUbKytQYk9e23UAGKI6oqQlo2akgtFI9Z5t2PUfUqec46yHqfrpuzqa5uCDgwPQ8L0kgCG6k6aXbtLbtRX7AXX1nO77G5U/fAtV12bP/yxC/Hf/+z/IX0lqWa6qajdVVbsCLwALrlObrDAFISYByUDtb4LTwB0Wn6cDJwFUVZ2kqmo34AFgv6mt3VRV/dVU9lmLbScaa0dmSTmB7s7mz4HuLmQVl9cqU0aghwsAdpKEu6M9BeXWF++W+CuMMT3oFVVo+xYfOM2Ub7fy5NqDpBSUEujlZi4f4OlGZlGJVR2ZRSUEeFqXyTKVyS0pw88UGPDzcCWvpO7N3cqjsQxs16rOdllWCLSoN9DTlaxi6x/VzOIyAk312xkk3B0dKCirtCqzLTaB9kE+ONgZCPP2ICGnkNT8Yoyyws64RDIKa+rMKiyxttfLzSpYAtpogJW9Xm5k1SpTG8t6t52+iJuTA/mltc7Xn/BjVlGJzfOjKCoLNx7gmdH9rX3l7mr+LAR1+0yxrT6j+TO1oITJSzZwz/dbOXol03zM4r0nmNWnA872dWdI3Ih+asmmuCuMig5DmL7wLNtv1iuprVdOoLulngMF5VUk5RUhhGD2j7uZ8u0WvvpdG3EKa+FOQm4RqYUlGBWFnRdSyCgq+8t6de2zvgZm/7ibgYtX4epgz4ioljbr0zSdbWta+tRB8+mIdqE429sx+JP1DPt8A/f2jsLL2RHQZl5M+nYbN3+8jv7hAXQN9rHQc25cz1TG8hxqegYGf7aBYV9s4t5e7fBydqCtrwdHU3MoKK+kvNrIvoQM0ovLqI1rUBClKTUBrNK0NFyCg6zKHF/wDhG338btcScZ8fMKDj37gtYOFxe6zH2M4281fWqqW3AQxRZ6JalpuAVZ6x16cyHR06dy3/nTTFj1I3uffh6Ai6vXUV1axgOXYrkv/iTHPvyYyvzGR7k9goMostAsSk3DvZaNmafP0H7iOACiJ4zF0cMdZ2/rwGdwrx4YHBzIu1xr5KoWnsFBFCTX6BWkpOFZSy/t1Bk6T9ZG8DpNHIeThwcu3i3wbRtJeUEhM3/+nrlH9jH27X8gbDyc/CfxDAkm39K+1FQ8Q+ra19VkX5dJNfZZ0n3aFI798EuTNIWHt/V0/aI8qwfy2ki9hmijvuZGe2N4bAGG5z5C2beh6bMjADxaoBblWWjng0f92qLHINSLp+rdb/MY9xZ17WtAQ+o5COXCtWnU0fT00R7Wr1KQq22rT7PvLShxf9Stp8dA1GP7rk28GXyKu5e1RnEBuNevUa92h96osYettmWVVd7w+zZLAjzdmNXGl+G7LjBk5znc7AwM8HMznUOLflOQWxOIs4HUZxhKnLYsRfgFo5aXIs16DsNT7yKNuwdEI9813n6ouVnmj2puNsK7boDWMHISjh/9gN1dD1P9zYd19/cb2qSAhPD2Q82x0MvLQvj41SlnN2oyTh//hP2MR6j6WlsepyRd1IIXkgHhH4QUEYXwDWhYz8cfNafm3kvNqasntWmH5BeAfORgo+1vCsLXHyXbUjMTybeWZkQUkl8Axt/31zneEN0J1y9/wu2LH6n4cEHDsyNMempWzaCCkp2J8LVebiRFRiH5B2A8VFdPCgzB7YsfcP1gCYbO3evs19FpKtfrzsYDuD7DLXWJAc4An2IdfADYD9wkhLAXQrgBkUCjwYU/g82AuWi8jGWg6mRaLk52drQ1TdmXFZWM4nK6h/iyctZIugX7cim37tRfUUvIpk7txtTD75dSWHUkjqdv7dek8rWxNXpvaeOFrDwWbT/Mq+O0qLWnsyPzxw7gqZ93MuPr9QR7uWOQag6w6dbafrVRSjQSAbx6xIWMXBZtOkgbvxbXxY/1HbPi0GkGRYcT5OVepw21CjepPj83Z3Y+OoVVD4xl3vBepjwUVcRl5HElr5jh0XWDBfXV91f7qSWbagUqGo+/199njIrKsZRsFo7rz7K7hrPjfAq/JWbg6eTA/JG9eWrtr8xYvoNgT1cMpoewv6J3lZNpOTjZGerY9+XtMex9dBJVsszvSRY3BE3SrLtNCG12hSQEex4ex7bZY/j2yHmSC7SbUIMksXrWCHY/NJbT6XlcyC6st646eja2CaHNrpCEYM+DY9k2+1a+ParpRfh48EDvKO7/ZT9zVh4gys8LO8lGX7d1XdVqUJvbJnFx+Q/82L4r26bewaAvPgEh6P7ic5z9+HOMpaV166gPG3q1z1/U1MnELlvB1+06s3by7YxY8ikIQUCvHqiKzFeRHfmmYw96PP43PMLrBlr/jOb25+fTamB/Zh/aQ9jAARSlpKEYjeb9boEBTPz6U9bNebTxE2ZTz/rzhudeJmLQAOYe2UfEoAEUpKSiGGUMdgZa39yP9c+9xId9Y/BuHU7vmXfVqe8/ShP8ufbZl4gYfDPPHN1vZd9VPAIDCO7cgfitTczlYOtrup7zILoOQAS3sc7BUJiHvPgF5EVPIfUYCK4eNo9tsng9XUB06YcIbo16cPM11G9bon6N/prGgU3XptEUzXpERa/BiLBI1J218qd4tEAEt0KNu4blGvWJX2+fXotT68PVE/xDrJZrQNPuJf7KfVttCssq2JVZzLaYtuweFkW5rLA+peDa+k3PQYiWkai7teUNSAZEm/Yo65Yif/Ac+AQgboppuCFN1JO3rqby8ekYl3+G3ZR7rKuI7ABVFXWXQdjUa/z3CcC4ZRUVf5tG9fefYj9lltaGnRtRc7NxWvgV9vc+gXLuTM1MqXr1bG200BMCx9lPUrmkbpDlT9OYjULg9PBTVHz+ft1ygBx/htLZ0yh9dAaO02eBvcNf1nP+27OUf1I3F42al03x9FGUzJlO+Sfv4fLSAnBxrVNOR6cp/JUcEs5CiBOAExAEXMNixWviDmAFsBZ4Uwhhr6pqtWmfCuwARgKewDqgdRPrfUMIMR/YCTyvqqrVML8QYg4wB+DTGWPoHRlGhsVIc0ZxGf5uzpaHEOjuQkZRGYHuLhgVheLKajydar4MNteaBu/l7ICzvYHsknImfbsVo6xQXm0ko6Amsp5ZWGJeCmHW8bSeRZBZWGKOrvu4uZBdVIqfhyvZRaV4W7TxXHoO81fu4vN7x+Hlat12AINBIsOi3ozCUvzda2l7uJJRVEqgpxtGWaG4sgpP04hvRmEJj/+wnQWThxDmXXODFxPVipgo7UHhp6NxnMvMZdL7WuKjzi0DrO0tKMHfww1LAj3dre0tqLHXki2nLvDyLzvM9canZbPs4EkWTB/BSz9tvy5+DPB0s3l+TlzJ4I+ENFb8dpqyqmqqjTLFlVXkldVMm1VV6vYZD1Of8XCt6TPODgghcLDT0ip2DPKhZQt3EnOLOZOew9mMPIb/axWyopJbWsHM77exdIa25jXQ3fm699OrxGflIysKHQO969TVqJ5pZoOmV4WnkwOB7i70bulPCxet/wxqE0xsZj79wgOJiQwhJlJLivrTiYsYTD+af0XPyr4Oth9cHe0MxESGsOtiKv1baaMEgW7OZBRbapbb0HS29mmV5tONcUkMbB2IvUHCx9WJ7iE+nMnIp6XFaJqHkwO9W/qzPyGdtj7uNs6hDT03rUztc7gxLrlGz8WJ7sG+nMnU9KZ0bs2UztrX4/v7TxPo7kLtyZelaWm4hoaYP7sGB1OWnmFVpt09d7FtsrZmNPvwUewcHXHy8cGvV0/CJ4yj1z/m4+DpCaqCXFlJ3Bdf2fQ1aDMi3C303EKCKc2w1ut4z92smaitx844fBQ7J0ecfX2ImnYbSdt3oRiNlGfnkHbodwJ6dKMo0XqZWW2KUtPwsND0CAk2LwMxtys9g5+nzwTA3tWV9hPHUVmkLQdxcHdn+uof2P3qm6QePtqgFkBhahpeLWv0vEKDKUpPtypTlJ7B0qkztPpdXek8eRwVRUUUpKaRduI0eQmaTWfWbqRVn15gO+fYf4TClFRaWNoXEkJRmrU/i9Iz+Oa2uwHNvq6Tx1NRVBN87zZ1EqfWbLAK+jSEWphnPXrv4Y1aVHeWg4joiDRkAvKS120/fBQXoGamIMKjUc8errvfFkV5CA/vmscSjxZQbGM8pk0HxMBxKN8uaPzBpxZqUX5d+2xoiDYdkQaPR/76jWvWqKNZkKslM7yKlw9qYV6dcqJdV6RbpiIv/nsdTdF9AOqpQ42OytahGXxKcb61hrvXtc2MAUSHnlpuC0Xm3/Gp/Hxeu447+7rfsPs2W/x2MYVQZ3u8HbVb+OGBHhzPL2NcQa71MhovH+tZIVftaNsFafhtyB+/bPajWpgLqQnacg9APX0Y0aodKg0ECXOzET41o+nCxw81P6fe4vKvO7Gf/TTVFtvsBgxDPti0QKSam2U1ei+8/VHzGtA7uAOHOc9oHxSZ6m8/Mms7vvEZSnpKvceCaUaExSwK4euPmmuh5+yC1CoC57e05M2ihQ9O89+l4h/PaIkt/wRqdiaSn6VmAIqVpitSeCSu736h7ff2weUf71M2fy7K+Zqk3cqVRNSKCqTWEVbbbekJ/5p8UpJfAGquRSJTF1ek1hG4fbCkRu+NDyj7+5PI52NRq7WBFOV8HEpaCobQVsjnrQN2/5P8l81U/F/geizZiAZGAd+JxoatrxEhhAMwGlijqmoR8DtQK9sQP6At1ZiOFrhoCi8A0UBvwBuYV7uAqqpfqKraS1XVXrMH96BTkDdJ+cWkFJRQJctsjrtifmC6SkxkMGvOJAKw7VwKfcICzCP5iqqy9Vyy1YOeEIIhEcFE+HqyetZI7rspmkhfD9Yei0dVVU5eycDdyaHOw7efhyuujg6cvKKtjV97LJ6hHbQHjZgOrVlzTPsiXGOxPa2gmMeXbeat228h3M/2NEUnOwNJeUWk5BdRZZTZfOYSMbVG4mOiWrHmhJbgbltsAn1aByOEoKi8koeXb2Xu8JvoEWadLC/XNP2wsLySFUdimT9mAKvn3sXquXcxrGMEa4/FafYmpePu7FiPvfacTEo32RvH0A5t6rR/VJe25nr7tQ3jgy2/8uSofhiEuG5+HNqhtc3z8870Eex6YRY7np/Js6MHMKFzG94Y15+kvGJSCoqpkmVkRSGmXUurNsS0bcmaU1o+jG1xSfQJD0QIQV5pBbIpeV1yfjFJeUWEtnBjes8o9j5xGzsencyye0YS7uNuDkYAN6SfXmVT3BVG11rmYFsvtJaNIaw5o41+bItPNusNaBPEuewCc46FI8lZRPpqgazcUi2QU1hRxYrjF7ita8Rf1jPbF29tR2lVNdmmPmpUFPZdTqe1RUBN0yyp0Yy/QkxksLVmRDBrzlr61B8hBEEeLub8DmVVRk6m59HG2528sgrzkq2KaiO/JWXSxpS0slNgC5IKSkgpLKVKVth8LpmYiKBaekGsOas9pG47n2qh51yjV23kZHoubby1WTu5puBYWlEZOy6kMTraui8C5PxxHM82rXFrFYZkb0+bKRO5ssk6E31pSipBg7XVc57t2mJwcqIiJ4dNo8bxc+ee/Ny5J7Gffs7Jdz9oMBgBkPnHcbwi2uBh0mt32yQub7Qe+SxOSaFlzGAAWkS1w+DkRHl2jrZ98P9j77yjqyrWPvzMPumdBEhCIAkk9NBBivQO0kITsIAFFHtBwXLV6xVRLKjYsGADUZTeQToC0pEWSgiQQkIgpPe95/tjH5JzkpCAxui93zxrscjZe2Z+M7PbzDsz75gzsRzc3Aho15arp06XqweQsO8AvuH18Ak1NZuOGs6plfZldPXzLRo56vzsExz61jSgao6OjF74Lb/P/5ETi5dVqAUQu/cA1cPD8A0NweLoSMvRIzi2wr6Mbn6+Rfdoz2lPsvfr+UVxXX18cK9udjLq9+hq5yzyn8CFEuVrdftwjq6wH613tylf72lP8dtX9p7xW48ZecPLNQCIP4vwC4BqNcBiQWveARlVYvlAYAja0PvQ570DWTYzD718wcH034KLm9npumxvICqXhBjw8wef6mCxICLam51UWwKC0QZNwFjwPmRV7NekzPL5FmtozTqUdhIZEII2ZAL6/Fl/TKMkF04jagSCb02wOKC17oI8WsJIE1QX7fbJ6F9Mh8y0Uklorbsi95ee1l0hVVGnCeegWk3w9jNnAzRpZzq1vAlEk1uQx/cCMK5REEuGtGXJkLb0Cq7+l7TbrkegjweHU3PI0Q2klOy+nEmYhzPEnrG/hq06I4/utY8cVBdt1IPoX86wv4YXzoCbR9FsIVG/GTKpfAfBRnQUIrC2qWlxwNKpF/q+HfZ1FlD8fdZad0TaGgGEwNKh+w0t1wAwzlj1agaCgwMOncvQCyzWs7TpVGx0cHIGZ9Mvlda8nelHxMYZZpl6p06gBdVB+Fv1uvZB/81mOVJ2Flnj+pF9byTZ90ZiRB39U8YIAP3kcVMzoBY4OODYvS+Fu7baaGaSObIXmXcNJvOuwegnjhQZI0RALdDMgSxRMwCtTggysfx3mx51DEtQcLFez34U7LTRy8okY1gPMsYOJGPsQPTjR4qMEcK7WlHHXAQGoQUFV2jkUSiuR6XssiGl3CWEqA6UXsz15+iPOfPhiLUx4wZkA0X7QUkp9wghIjANJKduxCYipbz2hOYJIb4CplQUx0HTeKF3ayb+tBVDSiKb1aN+dW9mbz9C0wBfetYPYkTzekxdtZt+n63Cx8WJt4cUL4vYF5uMv6er3cgowFPdWjBt1W+8sekg1VydmTmoA3MPX6D/W9/h4ujA9FFFPjuJfP8Hljxu7kDx0rBuPP/TRvIKCunSMISu1hkIE7u15snv17Fo73ECfTyZdUd/AD75ZS9pWbm8unSrtTyCnx41Rzmn/LSJPecSSM3OxcPZibGfL8fNyYHIVg2pX9OX2Zv20bRWDXo2CmFE64ZMXbyFfu//iI+rM2+PNCfGfL/nGBdS0vlk6wE+sW4v+sVdA/HzcGXGml1EJZnrGh/q1prQ6sVT5bs2CmVb1Dn6v/kNLk4OTB/Vp7i8s+az5ElzevJLkT15fuEGs7yNQujaKBSAX46eYfqyraRk5jD5q2U0qlWDz++P5FzyVXILCnl2wTqzo+btwZXMbPw83P5UPXZtGMK2qPNlXp8y75l+tzDyy9Vk5RUggUkLNlLHx4MJHZrQs0EdRrQMZ+qyHfT7eKl5z0R2sd4vSczeehgHTUMTgpcHtC/yPVAef9V9CrA2KpZPR9q7cXHQNF7o05aJC7cU69XwZvb23616tRnRPIypK3fRb84KfFydeHvIrQB4uzgxvl1DRn9jXqOu9QLpFmYaT2Zs3E/UJXP06qFOEUXbn/4ZPbN8l/D3dLMrX05BIQ8v2mY1Gknah/hze6twrk3NLKrTn7dhGJLIZnXNOt1xlKYB1egZfq1Of6Pf56vNOh1sbqU4tlU4L6zZy5Cv1iGByIhQGtb04eSlVJ5bswfDkBhI+jesQ/ewWmAYpl7PlkxctN3Uiwg19X49RlP/avQMr8WIZnWZumYP/b5cY+rdZm6lO7ZlOC+s28uQbzYgpRm3oXVpyuPLd5Gak4+jRePFXi3tZo1cQ+o6u555jn5LFiIsGqe/W0Bq1ElavTCVywcOEbtmHXuef4lbZ88i4uEHkBK2TX60wvvyekhdZ8vTUxm27CeExcLxb78n5cRJOrw4jaQDh4hZvZbtz71Erw9n0eqRB0FKNjzwMAC/z/mSPp/O5s69v4IQHJ/3PZePVjwyI3WdtU88y7gVPyMsFg5/M5/kE1F0e+k5Lu4/yKlVawnt2pke//kXSMmFHbtY8/gzADQdOYzgzp1w9fWlxV3mCsLlEx8m6fej19UzdJ0ljz/DxNWLEBYLe7+eR9LxKPq98jyx+w5yfOUawrt1ZsD0l0FKzm7fyeJHzU+SNAxWTH2RB9YvRwiIO3CY37745g/X9zXu+34uDbp3xqO6HzNiT7Di5dfZOfe7P5SWoessemwKD65ZbG5r+tU8Eo9HMeCV57mw/yDHVqwhvHsXBk1/GSkl0dt38vMjTxfF9w0JxqdOENFbd5SjUlLUwFjxNZYJU7m2LSaX4tF6jUDGxyCjDphbSDq7YBn7OAAy9TLGvHcRNWqhDbzDnLImBMaOVfY7SdyI9up5aHdNAaEhD26H5AREj0hkQgycPITW93ZwckYbbd6rpF0xO9I3o7HqWyx3PwuawDiwDZLj0XoON8t38iBavzHg5ILl9kfM8qVdwfjeXC9vue8FqB5onn/6PYxlX5oOKSvSXPQZlsmvWLeM3AiJsWgDxiFjzyCP7kEbeg84u2KZ8KypefUyxhfWbf58a4JPddPh481SFXUqDYz1C9DGmFtGysO/wuWLiK5DkBfPw+nDphFrxEOmoSq8OXQZgvH5K2Z8bz9z5sb5U6WS7hrky7YsUenttuSMLEbPXkhmXj6aEHy34zArnrqDFsEB9A30YtT2aCxC0NjbhVHB1cx6XPwFlkkvmddwz0ZIikXrPwYZG408ttf0DeHsgmW89R1z9TLG3Blm/Sz/xrz+Qpjhd1dgKDB0CubOwumFd8xtPzevQsadw2H0fRjRURj7f8Wh/3C0Zm1BL0RmZpD/UfG2kFrjFsgryaZTzBvB0Mn/YhbO/3oXNAuFm1YiY2NwHHM/xpko9H07cBgwAkvzdlBYiMzKIP/D1wAQ3tVw/tcskAYyJZn8D169Ib28T97G9T8fmNt+bliBcSEGpzsnoZ8+gV6GDwdb3OYuQbi5g4MjDh27kfPiYxUvTTF0cj+ciduMD81tP9ctwzh/FufxD6KfOk7hruv7Z3GIaInT7RPMWS+GJPeDN8qcOVZSL+eDN3Cf+YlZxjXLMM5F43zPZPSTxym0NU6UwNKiNS73PGTq6QY5s15DZlx/xymFojxERR79rxtRiEwppYf170bADsAfqAOslFJGCCG6A1OklINKxN1iPV7ufFchxAJguZRygfW3OxADhAKjgbZSykeEEAOAXCnlZiHE11b9n61xSuVBCBEopbxondExyxp32vXyoX/50h+rpD+Kb2XbdSogL6/iMJWNq1vFYSqTm51C+mfJKD169Jdzk96c/zQVObz6b6eq7xmAStjS8Wb4ZspnVaqXoVdt+dL1qn11A6QVVm0ZM6u4Tl3K8j3yF/P2tLK3P/6rEI6OVapXGVu53hRpVf99EtVu3oHkn8KpgrXzlU3j0rte/ZXI7ZurVA+gIC654kCViKzid5uR++eWPf0hzfyq1ZSFVd+u8d586L9z24dyKHxqeNU3Lm4Ch3cX/9fVeWX4kADT9ct4KaVexgyFXkII2zk81zZnXiWEuLaca5eU0m7TZiGEG6ZviAeuHZNSZgkhdgCDbcNKKW/Wu9F8IUQNa74PAQ/eZHyFQqFQKBQKhUKhUCgUf4I/bJCQUlquc/wcEGH9ewtQlnee7jeQfjamf4eSx4fb/Py6jPMTSvzeAmwpceyvcsCpUCgUCoVCoVAoFAqF4gaoFB8SCoVCoVAoFAqFQqFQ/E9TuXs4KPiHGCSEEP2AN0scjpFSRv4d+VEoFAqFQqFQKBQKhULx1/KPMEhIKdcB6/7ufCgUCoVCoVAoFAqFQvG/ihCiP/A+YAG+kFK+UeL8U8D9QCGQDNwrpTxvPacD17ZuuiClHPJn8/OPMEgoFAqFQqFQKBQKhUKh+OsQQliAj4A+QBywVwixXEppu3f6QczdLLOFEJOBmcDt1nM5UsqWlZmn//F9+xQKhUKhUCgUCoVCoagEhPhn/6uYW4AzUsqzUsp84AdgqG0AKeVm6wYTALuB2pVahyVQBgmFQqFQKBQKhUKhUCj+9wkCYm1+x1mPXY/7gDU2v12EEPuEELuFEMMqI0NqyYZCoVAoFAqFQqFQKBT/5QghJgGTbA59JqX8zDZIGdHkddK6E2gLdLM5HCylTBBC1AM2CSGOSCmj/0yelUFCoVAoFAqFQqFQKBSKitD+2QsMrMaHz8oJEgfUsfldG0goGUgI0Rt4AegmpcyzST/B+v9ZIcQWoBWgDBJ/OeKffeMp/oHIMg2N/2OaRtXKVXX5dL1q9QBk1dapq1a1e2nrsmrfpYVVXJ8A+VVcp4WyavVyjb/h3VbVjT+LpUrlRBXvaS/d3KpUD0BevVqleiIwsEr1qvrdfYPrxCsXSxU/h1X8rhFVXb6/Q1Ov+m+i4h/JXqC+EKIuEA+MAcbZBhBCtALmAP2llJdsjlcDsqWUeUKI6sCtmA4v/xTKIKFQKBQKhUKhUCgUCsX/OFLKQiHEI8A6zG0/50opjwkhXgX2SSmXA28BHsBPVqP5te09GwNzhBAGpi/KN0rszvGHUAYJhUKhUCgUCoVCoVAoKuLvmKFUyUgpVwOrSxx7yebv3teJtxNoVtn5UWsRFAqFQqFQKBQKhUKhUFQ5yiChUCgUCoVCoVAoFAqFospRSzYUCoVCoVAoFAqFQqGoiP+BJRv/NNQMCYVCoVAoFAqFQqFQKBRVjjJIKBQKhUKhUCgUCoVCoahy1JINhUKhUCgUCoVCoVAoKkIt2ah0lEHipDynLAAAIABJREFUJth+NoEZGw+gG5KRLcKY2KGJ3fn8Qp1pq3ZzLDEFH1dn3h3aiSBvD1YcO8fcPSeKwp26lMrPE/rT2L8akxZu5lxKBkkZOTg7WLivQ2MeGNi1dLoLN3AsPhkfNxfeHduPIF8vAD7bvI9F+05gEYLnh3Shc4MQM68nzzNjxXZ0KRnZrgkTu7cB4PmFv7A3Jh4PF2cAXh/Vi8Z+XpxNTuWFpVs5mpCMl4szHi5OjGzdkIldWpbOy+ItHLt42SzjqF4EVfNkZ3Qc727YS4Gu42ixMKXvLXSoF0RWXj53zl1RFD8pPYvBzevz3PBeAEgpeX35VrZFncPV0YHXR/elSe2aper+WFwSzy/cQG5BIV0bhfL8kG4IIVj7+2k+2rCbs5dS+PGRMUTU8TfrZeMePt64Bykl1dxdeWN0HzqE1/5L6nTXmVjeXr0TQ0rcnRyZPqoXIU6QkJbFQws3cy4lHSnhtqahvD64U+n6XLGTY4lXzPoc1oUgHw/iUzMZ9NkKQq15ahFUnVcGtAdgzfFzzPn1KLqUdAsPYkrP1vb3acxFZmw8ZOazeV0mtm9cWnP1Ho4lXcXH1Yl3B3ckyNudAt3gpXV7OZ6Uim4YDGkayqQOjbmYns1zq3/jclYuQghGt6jHXW0aFOudvciMjQetevWY2KEMvVW/FesN6USQt7v5XOw9WRTu1KVUfh7fl8b+1YqOPbxoO7FpmSy/d8DfV76YRGZsOWw+983qMvGWhqX11u4r1rutPUHe7uTrBq/8coBjiVfRhOC5Hi24pU4NACYt2kFyVi6F0qBNUHX+1bMVFq34A7f9XBIztvxuakaElK25bj/HklJNzYHtbDQPciwpFU3Ac92bF2mO/2k7yVm5ODtYAPhi+K2URWCvHrR+YzrCYiH623mceG+23Xm32kF0+GQ2Tt7eCIuFQ6/8h4sbNhLQvRstXnkRzdERo6CAQy/9m6RtO8rUsKV27550nDkdoVk4+e08Dr/7gd1599pBdJ/zoVVPY+/LrxG7/hcAfJs2ofMHb+Pk6Yk0DJZ264uel1ehZmifnvR4awbConH063nseed9u/OetYPo//lHuPh4IzQL2196lZh1v6A5OtLnw3fxb9USaRhsfuZ54rb/WqFeeN9e3PbuDIRmYf9X37H9rffsznsH1yHys9m416hOTspVfp7wAOnxCUXnnT09eez33RxftopVTzxboV6jfr0YPutNhMXC7i+/ZePMWXbnqwXXYewXH+FRw4/slKt8d/ck0uITCO/ehch3Xi8KV7NRA74ddy9Hlq2qULM87vryI5oN6k/GpWT+06zDn0rrGiK8Gdptd4HQMPZvQW5faX++U3+0Nt3B0JFZGRhLPoe0KxAQjGXwBHBxBcPA2LocefS3igXDItD6jQUhkAe3I3eusT8f3ACt7xjwr42xeA6c2F+cl14jEeHNAZDbVyCP7725woZFoPUfB5qGPLAN+avdTm2IDn0RrbuCYUBWBsbyuWZZbxLRoAXaoLtB0zD2bkZuXW5/vvNAtLY9wDCQWekYi+ZA6mXwqY7lzidBaGBxwNi5Drnnl4r1GrVCGz7R1Nu9AfnLIvvz3YegdexrXsPMNIzvZ8PVZAC0IeMRTdqa1+PkYYzFn1dcwLpN0HqPNuvx8K/I3evsz9cJR+s1GmoGYSz7Ek4eMI8HN0DrNao4nF8AxrIv4PRhALbHXWHGiu/RDcPaNrD/JpvtjV+K2xvj+hJUzdre2LKfRXtPYNE0nh/cmc4NgskrKOTuz5aSX6hTaBj0jQjj0T63APDMD2a7xSEvh2Y+rrzcLAhHm2+HaNgSbdi9Zp3+thG5aYl9nXYdjNa+l/UapmH8+HFRneJTHW30ZIRPdZAS/Yvpxeeug9biFhzvfhQ0DX3zKgqXf2933tJ7CA59Is1rmJtDwRdvI+PPY7m1Nw6DxhTnKziMvOcnIs+fKV+vZXuc7nkcNI3CjSspXDrP7rxD36E49Btuli83h/w5M5Fx58BiwWnyNLS6DcBioXDrWgqXzCtbpBwsrTvgdP8TZhrrl1Ow6Luyw3Xqgcu018l56h6MM1E3p9GmIy6TnwZNo2DtMvIXflNmOIfOPXF98U2yHr0b4/QJLK1uwfneR8DBEQoLyPviA/TD+yrWa9cJl0eeQWga+auXkr/gq7L1uvbG7ZW3yHzwDoxTx4uOi5oBeHy1iLxvPiV/Ydn1oVBUhFqycYPohsFrG/YzZ1R3Vtw/kNXHz3PmcppdmEW/n8XLxYl1DwxmfNuGvLPF/FgNbhrKknsGsOSeAbw5yOwcXet0vTX4VoQQrLh/IO1DarLw0BnOJKXYp7v3OF6uzqx75i7Gd27BO2t3AnAmKYU1h0+z4slxfHbvEP6zdCu6YZh5XbaVOfcMZsWT41h96JRdmlMG3sqSx8ew5PExNK5ldla8XZ2Z1r8jbk6OjGjdkBUPj2T1kWjOXLpqn5cDJ/FydWLd47czvmMz3tmwBwAfNxc+HteXZQ+PZEZkN6Yt3gKAu7MTSyaPKPpXy9uTPo1Di9LbFnWO85dTWfvseP49ohf/XrKpzPp/dclm/j2iF2ufHc/5y6lsP3kegPr+fnxw1yDa1g2yu1YLdh/h60nD2fufh3BzcmTKAvuGR2XW6atLtzBzTB+WPD6G21o2YM4m8wPw6Y7fSc7MYcWkwfwwoT8rj53jTHKqfT4OnzHvmcnDGN+uMe9sPlh0ro6PB0vuv40l999WZIxIzc7jrU0HmDuuNysmDeZKVi67Yi7alf21DQeYM7ILK+7tx+oTF0rfp0di8HJxZN3EgYxv04B3tv4OwLqTseTrBsvu6cdPd/dh4eFo4tOycNAEz/Zoycr7BvDDnb34/uCZojR1w+C1X/YzZ1RXVtzXn9Unyngujlifi0m3lX4uJvRjyYR+vGntxNsaIzacisPNyd5mWvXlk7y26RBzIm9lxYS+rI6K5cyVdHu9o+dMvfv6M751fd7ZfhSAn4/EALBsfB++GNmZmVt/x5ASgHcHtWfJ3b1Zfncfrmbnse5UnE0ZJa9tOsycYZ1YMb43q0/GldY8dh4vZ0fW3duX8a3DeWfHMavmOVPz7l58MaIzM7cdLdIEmNm/LUvu7MmSO3vi5+ZMSYSm0ebtN9kyciyr23cmZORwvBo2sAvTdMqTXFiynLVde/HrvZNo+86bAOSlXGHbmDtZc2t3dk9+lA6fflQq/bL0bn3nDdYOH8PP7W4lbGQkPiX0Wj37FGcXL2NJ555smjCJW9819YTFQvcvPmbH48/w8y1dWDlwGEZBwQ1p9po1k8XDRvN16040HDUc30b2Bp8O057m1OJlfNexByvHT6TXe28B0PzeuwH49pYu/Dx4BN3feLXCkRKhaQx+/y2+HTyK2S060Pz2EdRobK/X/81XOTT/Bz5q05nN02fS57WX7M73euV5zm3fWWHZrumNnP0Oc24byRsRt9B6zAj8S+gNfes19s5bwMxWt7LutZkMev1lAM5s2c5bbbrwVpsufNR7CPnZOUStL/udfDPs+no+s/sP/9PpFCEE2uDx6N++hT57KlrzjlCjln2Yi+fRP30J/aMXkMf2ovWzdn4K8tEXzUGf/Rz6t2+hDbwTXNwq1ut/B8b3szA++Rcioj1UD7QPk3YFY/nc0saN8OaIgGCMz17BmPsaomN/cHK5ubIOvAtj/iyMj16watuXVSZewPjsVYxPX0Ke2IfoPfrG07fVGXIP+ldvos+agtaiE9QMsg+TcA79oxfQP5iKPPob2oBx5vGMq+ifvGzW6ccvonUfAp7VSknY62loox5An/Nv9BmPoLXuAv517MPExaC//RT6m48jD+1EGzLBPB7aCFG3Mfqbj6O/8RgiOBwRHlFx+fqOxVj4Icbn/0Y0aQd+Ja5h+lWMVd+UNhhdOIXx1XTz34JZUJAPMWaHTDckr+0+xZx7bmPFk2NZffh0GW24E9b2xp1me2PNLuBae+MMK54cy2f3DOI/y7ahGwZODhbm3j+UJY/fzuLHRrPj1AUOX0gEYFDLBqx6aixLu4aTq0sWXbDREhra8Inon09Hn/kEWqvO4F/bLi/Ex6C/9yz6O08hD+9GG3RX0SnL2EeRW5ahz3wc/f1pkGn/bS1dpxqO9zxB/pvPkjdlPJZOvRBBIXZB9F9/IW/qPeQ9dz+FKxfgeNfDxcefu5+85+4n/+PXkcmJFRoj0DSc7n+KvOlTyH3yThw690bUDrULUrh9A7lPjyf3mXsoXDYfp/GPmmXr2BMcHc1zz96HQ5+hiBoB5euVpf/A0+T++ylyHh6LpWsfRJ3Q0uFc3XAcPAr95NGbS9+q4fLws2S/+DhZk0bj0L0vWnDdsjWG3o5+4kjRIZmeSs7LT5E9eSy5b/8bl2f+fUN6ro9PI3vaI2TeMwLHnv3RQuqVqec0fCyFx38vdcrloSkU7qnYMK9QlMcfNkgIIXQhxCEhxGEhxAEhRCfr8VAhxFHr392FECvLiLtFCNH2JrTeF0LECyE0m2MThBBSCNHL5lik9dhIIcQSa/7OCCHSrH8fEkJ0EibThRCnhBAnhBCPVZSHIxdTCPbxoI6PB04WCwMaB7PpdJxdmE2n4xgWYb44+jaqw+7ziUibzgDAquPnGdik+IUdk5JOsI8HgV7u6IakVVB1Nh0/a5/u8bMMa93ITDcinN1n4pBSsun4WQa0qI+Tg4Xavl4E+3lzJDaJI7FJBPt5U8fPGycHCwNa1C+VZkn8PFyRSHxcnfFxczHjRYSxKeq8fV6izjGspdlh6NukLrtj4pFS0iSwOjW93AEIr1mNvEKd/ELdLu65K2mkZOXQJqT4I7Dp+FmGtm6MEIIWIYFk5OSRnJ5lFy85PYvM3HxahgQihGBo68ZsPBYNQJi/L3Vr2jd8jsQmEe7vS8vQWjg5WBjWtjFZ+fl2+anMOhUIMnPzAcjIzaOGtR5SsnPxcnGiTjVP8gp1ani4lL5nTsUxrJn58u/bOJjd50rfM7bEpmYQ6uuFr7vZoO0YGsCGk7HFZb+YQnA1m/u0UTCbziTYpbHpTDzDmoaamg1rs/tCElJKhBDkFBRSaBjkFeo4WjTcnRyo4eFKE6uhwN3JkXp+XlzKzCnW8/G0fy7OxNvrnU5gWERpPVtWnbjAwMbBRb+z8gv4Zu9JHuhoPwupysuXmEKwj7tVT2NAo9psii6hF53AMOsz3bdBELsvXEJKSfSVdDrUMWf7+Lm54OnsyNFE08Dn4ewIQKEhKTAMuz5tsaa7qdmwNpuiL5bQvMiwJmZ99a1fi90Xkk3NlHQ6BNewajqbmkn2RsXy8G3TmsyzMWSdP49RUMCFRUuoPbC/fSAJjp6eADh6eZFzMQmAq78fJSfR/DvtRBQWF2c0J6dy9Wq0bU362XNknDP1ohctJWTQAPtAUuLkZeo5eXuRnWg2zGv36kHK0eOkHDWNMXkpV5GGUWEZA9q2JjU6hjSr5smflxBeQlNKiZO1jM5enmRdNDX9GjXkwuZtAOQkXyY3NZ2ANq3K1avdrg1Xos9yNeY8ekEBRxYupvHggXZhajZuyNlNZroxW7bTaHBxfmq1aoGHf03ObLgxw0DILW24HH2WKzHn0AsKOPjjYpoNuc0ujH/jhpzauBWA05u30WzIwFLptBg5lBNrN1CQk3NDuuVxZvtOslNu/D6skNphyCtJ5uitrmMc2Y1o3MYuiIw5YXYcARl3BuHla564kggp5n1KRipkpYObZ/l6terB1UvmbABDRx7bg2hY4rqnXYFLcVDi3SZqBCLPnwJpQEE+Mim24s6zLUH1IOUSpCYXazcqoX0uCgqvlTUa4VWBMaAs6oQjrySa5dR1jMO7EI3tm2ny7PHiOr1gU6e6Dnqh+beD441NZw6pj0xOhCtJoBdiHNiOaHaLvd6ZI8V6504ifPyunQFHR3BwMP9ZHJAZqZRLYKhZtjTrNTy+F1G/uX2YtCuQHF/qGtoiGrZGnj0Ghabx88jldII9Xanje61tEM6mEzF2cTadiLFpb4SxO9psN206EcOAFuEl2huXEELgfu0boRsUGgZg1mm3RiEIIRBC0MzHlaTcwmKhYOs1TLHW6cEdiKbt7Os0+qjNNTyF8LbWqX9tsFiQp6wdzvzconDXQwtvjEyMR166CHoh+q5NWNp2tg+Uk11cd86uUEbVWjr1Qt+5sVytYr045KUEKCyk8NdfsLS7vh7OrhQJSmnqaxZwcobCQmSOfVuzQv36TTAuxiGTTH19+y84tO9aKpzTHZMoWDQf8suvvzI1GjbFuBiLTIw3y7h1Aw4du5UK53z3g+T/9B3S5hoZ0aeQKZfNv89HI5yczOekHCyNIjDiY5EXTb2CTetw6NS9tN69D5H/w9elyuRwa3eMi3EY56JvuqwKhS1/ZoZEjpSypZSyBfAcMKOS8mSH1QgRCcQCJZ/8I8BYm99jgMMAUspIKWVL4H5guzWvLaWUO4EJQB2gkZSyMfBDRflIysgmwKt4FCXA062o01IUJjOHAE8zjIOm4ensRGqO/cO7NuoCtzUuNkgkZWRz+nIaXWYvxt3Jkc51A7lUokOelJ5FgI/ZYHKwaHi6OJGancslm+MA/t4eJKVnmeG9i48HeHvYpfn+ut0Me28Bb6zYbtdJT0rPwsPZySaeO5cySuQlI5sAa4fbwWItY7b9FOn1x2NoHOiHk3Vq+DVWH4mmf0Q9hE1j5VJaJgE+HsVl8PEgKS3TXjMtE39v+zCXSoQpGd62/JfSs4uMLLZlraw6fXVETx78egU9Xv+K5QdPFi3l6BZem6s5efSYvZgHF25mRItwLmXYfCyxv6/Me8aR1ByzPuPTMhn+5Sru/m49+y5cAiC4micxV9KJT82k0DDYeCqWRJtra3sPAgR4upZ9n9pqOjmSmpNP3wa1cXV0oNvHK+g1ZyX3tGuIj6v9KHp8WhYnklJpHuhno+dqo+fGpYySemWVsaznotggMXv7USa0a4iro0OJtP6O8tnoebiWUb7cojooKl9uPg1reLMpOoFCwyAuLYvjl1JJtIk7cdF2uny6EndHR/rWr11mekWambnXrQc7zerebIq+eF3NF9YfIHLeJj7ZHVWm4cstMIDs+GKDUnbCRVwD7UcRj7wxk9DRIxh67BDdf/qe/c8+VyqdOkMGcfX3oxgVNMjcAwPJtNHLik/AvYTe/tffIvz2kYyNOkz/nxewc4qp5x0eBlIyYMlCIrdvpPkTj5SrdQ2PWoFk2GhmxCfgUctec9f0mTQeM4pJp48wfMmPbHx6GgCXjhwlbNAAhMWCV0gw/q1a4BlUYhS5BF5BgaTFFeulxSfgWUIv8fdjNIkcDECTYYNw8fLC1bcaQgj6z3yNtdPsZ0yUh3dQLa7GFuulxsfjHWSvl/D7UVoMHwJA88jBuHh54eZr34ltNXoEB374+YZ1qxLhVQ3SbEaG01IQ5YzIa627YZwuPbJHUD2wWMyOanl4+SDTbfTSr4Knzw3lVSbFmQYIBydw9UCENoJrHfkbwbNaCe2UcmcfiFZdzY78TWLWqc0yj/QrCO9y6rRdd4xTh4sPePtieexNLFM/xNi6HDLKN0AJbz/TwHON1CvFneOy9Dr0wbi2DObcSeTpI1he/QrLf75GRh2EpLjrxgXMerTNU0ZqxbM4ysp3k7Z2MyiSsvMIcC+e8RLg5cGltLLacGYbxq69kZZFgG3bxtudJOv3XDcMIj/4kc7Tv6JTeB1aBPvbpVlgSFbEpdK5RnF84e1rX6dpKeXX6S29MKLMZSmiRi1kThba+GewPPWWuXRHVNBFqFYdeaX42ZFXkhHVqpcKZukzDOf3vsdh3IMUfPN+6fMde9yQQUL41kBeLqHnW6NUOIf+w3H58Ecc75pM/pfm8jh992ZkXg6uny/F9dNFFCxfAJkZFWra6fuV0L98CeFnr6/Va4CoXhN93x+bMaD51cBITir6bVxOKq0R1gBRwx99z/WXRDp07okefQoqmDUoqtfEuFSsJy8nodUooRfeEK1GAIW7t9tHdnHBacw95H0zp6Ji/e+haf/sf/+FVFauvYBKHP6wowdwFPgEe+MDwHbgFiGEoxDCAwgHDt1AmpOBV6WUBoCUsoLWSJlG3dJhymjg2w4UHE64jIuDhfo1ihsyEuhSL5Ctj0SSr+tEXy49Ra4sY724yePXeLJ/R1Y9fQcLHxlNWk4uX2wpXudaGWU8fSmFdzfs4ZXBXUqFW300mtuahdmnV4ZGycEVWUYoUc4IjG3o04lXWHv4FO3r2U9brMw6/XbHIT6dMJjNz99DZJvGvLnS/Ejsj71EiK8nmx8dzqeje/DjwdPl5rVYT1DDw5WNDw9n8X23MbV3G55dtoPMvHy8XZ15qf8tPLV0O3d9t55a3h5YbF4+N3YNy9AU5uwDTQi2TB7M+om38fXeU8SmFht+svILeHzZTp7r2bJohL/Meil5/a6jd43DCVdwcXAoei5OJF3lQmomvRvULhWvystXlkCJe6/M+xMYHhFKgKcro+ZvYsaWw7QM9LXzE/H5iC5sfeA28nWd32JtGjllat5AGYHhESEEeLgy6vstzNjyu53mzAFtWXZ3L+aN7sL++CssPxFbRiJlPVf2YiEjhxOz4EeWNW3JllHj6DjnI7t4Xo0a0uLfL7H3iSlllaRivRKFCx8Vyan5P7CgUQvWjhxL988/BiEQDhYCOrZn0/0PsrzvIEIHD6RWt9LvndKSFWs2GjWcY/MW8Fn9ZiyOvJ2BX3wCQnD0m/lkxidw568b6fHW6yT8tgdDLyyd3k2Wce3Uf1G36608tGcroV1uJS0uHqNQ55YH7+fU2g2kx8WXTuMm9Eq+t5c98yJh3TozZd92wrreSqpV7xpeAf7UataEqHUVdxL+Hiq+T4tCtuiECKqL3FHCD4aHN5aRD6Iv/rz8F/z19G7kZQRw9hjyzBG0e55DGz4JGRdt+nq4Ucr81F2nrM06ImqFlvZv8UeFrlNG0bIzIqgecluxfyjSUtA/mIr+9pNorbuCh/dNy123XG27IYLDkRut/hCqB4B/HfSX70N/6V5E/WYQ1qTMuOVzoxfRirsX1AiCmGPFKdxAI6bMdhOi3PaPRdNY8tjtbJ42niNxSZxOtPcJ8p8jCbTxc6eNn7tdqqW5Tp227oqoE4bcvMw8oGmIuo0xVnyL/t5U8PNHtOtRZtxSma0AfcNS8p4YR+H3c3CIvNs+ibDGkJeHjIu5TuwK9Mqo28K1i8l95HYK5n2K48jxAGjhTcAwyJk0jJyHRuE4eAyiZq1Scf+UvhA43fc4+XM/KB2uEjWcH3iKvM/fKx3OihZSD+d7HyX3g9evG6Y4vTKO2VapELg8NIXcT94pFcx5wmTyf54HuX9+Fp1C8WecWroKIQ4BLkAg0LNyslSKscACYBnwuhDCUUp5zeQngV+AfoA3sBwoY7FVKcKA24UQkUAy8JiU0q63KISYBEwC+OTuQbQLDyExvXh0OzEjm5oerrZRCPB0I9E64l1oGGTk5ePtUjzjYM2JC3bLNYripGfj7GChR3gQPx46Q49m9e3DeLuTmJpBgLcHhbpBRm4+3m4u+FuPXyMpLbNo2URiWvHxRJvj15YTODlYiGzTmK+2F/ssCPByJzMv3yZeFjU9bT92ZpjE9KzivFg7ydd0HvthAzOGdyfY6ozxGlGJV9ANg6a1avD9b8f46eApAJrV8SfRplOYlJpJTS8Pu7gB3p52syaSUjOLylEWAd4eJKZlkJiawWPfrqR3RBi1fEqmWTl1mpKZw8mLl2kRbC5DGdCiPpPmmo7A9sdeItAat2XtGuQV6nYzUKD4+gd4uVvvmQK8XZ0QQhTN6Gga6Eedah6cS8kgItCPHvVr08M6or7w4Gm7Tm6AhyuJGbb3aU4Z96mrqelpvU/zC/B2cWLVifN0qRuAo0XDz92FVkF+HE28Sh0fDwp0gyeW7WRQ42D62BgKAjxd7Ubgr/tc2OrlFZR+LmxmRxxOuMKxxBR6f7oC3ZBcyc5j/IJNfDOmR9WXr6ReZg41PezXf5thckqVTwjBtO4tisKNW7CZkGr296Gzg4UeYbXYdCaBTiH+1vRc7Os0M4ea7iU0Pc18BXi6lqFZPA153A9bCbHe+/7WenJ3cuS2RrU5kngVe28N5owIN5sRf7dageRYlytcI+zOcWwZaa7Hv7J3HxYXF5z9/Mi7fBnXWoF0mfc1ux98hMxz56iIrIQEPGz03INqkZVor9fw7jtYE3k7AJf27MPi7IyLnx9Z8Qlc/HUXeVfM0ePYdb9QvWVzEraWGMUpQUZ8gt2sBs+gWmSWKGPE+DtZPNR0Yndxzz4sLs64VvcjJ/kyW6a+WBRu7KY1XD1T/nK49LgEvGsX63kH1SKjhF7GxUQWjDYb6k7u7jSJHExeejp1OrQj5NaO3PLAfTh5uGNxciQ/K4sNL1x/bXBaXDzV6hTr+QQFkZ5gr5d+MZGvRt5ZpNdi+BBy04v9lLQcFcnvS1diFFZgbPmbkOkp5mjwNbx9y5yyL+o1Res2BP3L14uXFAA4u2C5awrGLz9D3A1MNU6/ivDyLW6ne1WDzAqWCNjmd8eqIoOIiJyITEmqIEZ52r7m6H5J6jZBdBmE8fUb9mW90TymlxhN9/JDppceZxJhEWg9hqF/9mrZOhlXzVkhoQ2RR/dcXy/1iuk88Ro+fsi0lFLhRIMWaH1Goc9+oUhPNO+IPHfSXFYAGCcOIEIaIqOPl4pvmy/hWa24Hj19yq7HchCN2yJPHbIzKAW4O5OYVTyDLTE9k5pe9j5JArw9SEzNLNHecDbbG7ZtmzLaXF6uzrSrG8T2UxeoH2Ben49+2cvV/EJeaR5sF1amlahTb9+y67R+c7TeI9A//ldRncrUKxAfU7ScSR7dgwipj7z+JYSUZIRfsRNy4VcDefXydYPruzbieN+T2I7ZO3TqeUOzIwB9CYupAAAgAElEQVTklUuI6jeh9+svOE18GgBLlz7oB38zlxelp2KcPIIW1gj9UsJ145fSv1xCv3rNoiUSALi6oYXUw2X6x+b5ar44vzCTvOnP3rBjS+PyJRxrFM+G0ar7l6ERhtvMT60afri+8g45rzyNcfoEonpNXP81k9y3XzaXYVRUpuRLaDWL9UR1f4zLNo5M3dzR6obhPusL87yvH26vvUf2i09gaRSBY9fe8MATCA9P05Fofj4FS3+8obIqFLZUxpKNRkB/4FtR3rD1H0AI4QQMBJZKKdOB34C+JYL9gLlUYwym4eJGcAZypZRtgc+BuSUDSCk/k1K2lVK2nditDRGBvpy/mkFcaib5us6aExfoEW4/gtujfhBLj5pW3vVRsbQP9i8ajTOkZF3UBQbaLNfIyi/A39OV81czOJeSzpboBC6mZ9Ojib1NpUeTuiw9YL7M1h89Q/uw2ggh6NGkLmsOnya/UCcuJZ3zV9JoVsefiNr+nL+SRlxKOvmFOmsOny5K85p/BiklG4/HUN+/uAESUasGaTl5pGbnmvGORtOjkf0Hr0fDEJYeMo0J64/H0L5uLYQQpOfkMXn+Op7sfQutg0s7Clp9JJqBEeEAjGvflCVP3sGSJ++gV9Mwlh04gZSSw+cv4unqXMrYUMPLHXdnRw6fv4iUkmUHTtCzSRlOd66Vo7Y/MclXue/zJTzatwOHLyT+ZXXq5epMRm4+55LNhtuu07GE1TCngYb6ehF9JY241Eyikq6SlV/AbSUMUj3q12bpEbNDs/7EBdqHmPdMSlYuurXRE3s1g/MpGdS2diyvWBs/aTl5LNh/ipEtwovLHujL+auZxfdp1AV6hNuPAvQIq8XSY+dMzZNxtA+uiRCCQC+3Iv8H2fmFHL6YQj1fT6SU/GvtXur5eTGhnb1zvLKfC/sp7D3Ca7H0qK1eiefiZKydQWJMq3C2PjyUXx4czLw7ehHq68E3Y3v+PeULqMb51Ezi0rLI1w3WRMXRo14ZesdNXyvrT8XTPrhGkb+K7AKzsbfzfBIWTSPcz4us/EKSrctMCg2DbTGJ1PX1tNe8aqN5Mo4e9eyn3PeoF8jS4xdMzdMJtK9TluYlLJog3M+LQsPgqnUpUIFusPVsIuF+9kZDgJQDB/EMq4d7SDCaoyPBIyKJW2PvEDYrLh5/60wErwb10Zydybt8GUdvL7ot/J7Dr07n8m/ltWKLSd5/EK+wunha9cJGDOPCqrV2YTJj46nV3Vyt59OwPhYXF3IvXyZu42Z8mzbB4uqKsFgI7NyJq1GnKtRM3H8Qn/B6eFk1G46MJHqV/YhyRlwcwT3Mdbu+DRvg4OJCTvJlHFxdcXAzOxshPbtjFBaSEnWypIQd8fsO4Bcehk9oMBZHR5qNHk7USns9Nz/fomei69QnOfDNfAB+Hj+Jd8Kb8W6DFqyb+i8OzfuxXGMEwIW9B6geHoZvaAgWR0da3T6coyvsd2Vwt9HrPe0pfvvK3tt86zEj/7HLNQCIP4vwCwCfGmCxoDXrgLROPS8iMARt6D3o82aZfiKuYbGgjX0C49AO5LEbu09JiAFff/CpDpoF0fQWs2N6IwgBrtZvWs3aiJp1IPpY+XFsiY8Bv5r22icP2ocJCEYbNB7jhw8g++amoRcRF42oHgDVrHXaoiPSZqcQAAJD0SLvR//2bfs69fI1fUcAuLgjQhsik+393pTiwmlEjUDwrQkWB7TWXUobMILqot0+2dztwdbB4tVkcxmMppl1Et604iUbF8+bWt5+Zpwm7ZBnyljGUw6icdtSDi8jqntyPj3Hpm1whh6NS7Q3GofatDeiaR8WZLY3GtdlzeEzxe2Ny2k0q1OTlMwc0q3v69yCQnZFx1HP2q74ee9xfj19gbda10Er2eSOPYOoblOnrTojj5XYZSGoLtrIB9DnvgGZNtcwNhrcPMxZIIAIj0BWUKdGdBQioLbpHNLigKVjT/T99ksVREBxe0Br1RGZaJOmEFjad0ffdWMGCeNMFCKwDqJmIDg44HBrb/S9JfWK2+WW1p0wrHrychKWCOvuJ84upj+IBHsfaRXqnz6BVqsOwt/Ut3TpTeFvNgbw7Cyy7xxAzsTh5EwcjnHy2E0ZIwCMk8fRagUj/GuZZezWh8Ld2+w0sm7vQ9b4oWSNH4oedbTIGIG7B66vziLvq4/Qy3A+WRZ61DG0oGBEgKnn2LMfhbu2FAfIyiQzsieZ424jc9xt6MePkP3iExinjpP9xH1Fx/MXzSfv+y///xgjhPhn//svpFK2/ZRS7hJCVAdKL+b6c/THnPlwxNp4cgOygaK5l1LKPUKICEwDyakbtInEAdf2l1oClL3HjQ0OmsYLfdoyceEWDCmJbFaP+jW8mb39d5oG+NKzfm1GNA9j6spd9JuzAh9XJ94eUryt3r7YS/h7ulHHZqQ+p6CQx5fswJCSoV+uwdnBwoRbGlHf34/Z63+jae2a9GxSlxFtmzB14Qb6vfUdPq7OvD22H2DuMNGveX0Gvzsfi6bx4tBuRdP3XxjSlYlzl2EYksi2TYoMD8/+sJ6UrBwk0CiwOi9HdgckyRnZjP5sKXmFOl/+epivfv2diV1aUr+mL7M37aNprRr0bBTCiNYNmbp4C/3e/9HMy0izo/j9nmNcSEnnk60H+GSr2TD84q6B+FlHZdceO8und5Rwjgd0bRTKtqhz9H/zG1ycHJg+qk/RuchZ81ny5B0AvBTZk+cXbiCvoJAujULo2igUgF+OnmH6sq2kZOYw+atlNKpVg8/vj6Rt3SBWHIjiuR/X4+3qwrM/bKBDWG3a1Quq9Dp9dXgPHp+3Bk0IvFydeW1kL0AytXcbnli8jdvmLAcJg5vWpX7Naszeepimgb70bFCHES3Dmbr8V/p9shQfF2feHta56H6Zve0wDppAE4KXB7Qv8ncwY8NeopLMkZ2HOjcj1KZj6aBpvNC7NRN/3mbms1ld6lf3ZvaOozQNqEbP8CBGNK/H1FW/0e/z1fi4OPH2YHMbvrGtwnlhzV6GfLUOCURGhNKwpg/745JZfvw8Dap7E/n1egCe6NqMbmG1ivV+2lr8XFT3Zvb2I9bn4prebvp9tsrUG9LR5rlIxt/T1e65KI8qLV9ITVOvR0smLjKf08iIUOpX92L2r8dMvbBajIgIZeqavfT7cq2pd5vpmC0lO4+Ji3egCUFNDxfeGGA6iMspKOThZbvI13V0KWlfpya3t6hnX8aeLZi4+FcMCZFNQ0zNncdp6l+NnmGBjIgIYeraffSbu97UHNiuWHPJTjQBNd1deaO/qZmvG0xcvJNCw0A3JB2DazKqWSiLSphwpa6z75lpdF/0I8Ji4ey870mPOkmz56eScvAQ8WvWcfDFl7nl/Xdp+NCDICW/PWT6BG4w8T4864YS8cxTRDzzFACbI0eTd/n6I1hS19k55TkGLF2I0DROfreAq1EnafPCVJIPHuLC6nXsfv4lunw4i2YPPwAStj5oek3PT03jyIefELl1PVJKYtf/Quy6DRXeQ1LX2fTUVEYs/wnNYuHot99z5cRJOv1rGkkHDhG9ai1bpr1E349m0fqRBwHJ2kmmZ3i3GtUZsfxnpGGQmXCR1fdNrlDP0HVWPvEs41ctQtMsHPhmPpeOR9Hz5edI2H+IqJVrqNutM33+8xISybntO1n52DMVplue3qLHpvDgmsVoFgu/fTWPxONRDHjleS7sP8ixFWsI796FQdNfNh2hbt/Jz488XRTfNyQYnzpBRG+teMvWG+W+7+fSoHtnPKr7MSP2BCtefp2dc//E9nCGgbHyWyzjnzG3NzywDS7Fo/UcjkyIQUYdNHfVcHLBMsa8X2TaFYz5sxAR7RGhDRFuHtDKNKzpiz+DxAvX15MGxtr5aOPMrS3l4R2QnIDoNhR58RycOmx21kc/bHbI67eAbkMxPn0JNAvaeNMHCXk5GEs/Nx1c3ijSwFg9H+3Op03tQ9tN7e7DkAnn4NQhtD6jwckZbdRDZpy0K6Zx4mbrdPnXWO59DoSGsW8LXIpD6z0SGR+DPLEfbeA4s07HPW5mLfUKxndvI2oGmbuVIAGBsW0lJJWxJKyk3qLPsEx+xbrt50ZIjEUbMA4ZewZ5dA/a0HvA2RXLBHOrW3n1MsYX05GHdiLqN8My1SyjPHEAeayCrVSlgbH+R7TbHzPr8fedcPkiostg5MXzcOZ3CAhBG/4guLghwptB50EYX75qxvf2Mw0vF+yXXjpoGi90aMDEuSvMb0TbRtT392X2hj00DaphbW80ZurCjfR7ax4+bi68PdZs59T396Vf8zAGz1pgbW90waJpJGdk8dxPmzCkgSGhf7Mwult3J/v30q3U8vFk3K/mQEbvAC8ealCzuE4Xf4Fl0r/Ma7hnEyTFovUbg4w7gzy2z/QN4eyC5e6nrdfwMsbcN8z6WfENlgdfAQEy7ixydwVbtxo6BV+/h9Nzb5vbfm5ZjYw7h8PIezFiojD278Sh73C0Zm1MJ5JZmeR/UuxuTmvUApmSbDrFvBEMnfwv3sX5xXfNbT83rULGxeB4+30Y0VHo+37FYcAILM3bWvUyyJ89HTCXcTg9/Dwus8z3TuHm1cjzN+mI0dDJn/MOLq+8Z+r/shIZG4PjuIkYZ06U69PhZjRyP56J2/QPQLNQsH45xvmzON31APrpE+i2xokSOA0ZjVarDk7j7sdp3P0A5Dz/CDKtnBX1hk7u7Ddxe/NjhEUjf80yjHNncZ4wGf3UcQp3bv3zZVIobgBRnkf/ciMKkSml9LD+3QjYAfhjOotcKaWMEEJ0B6ZIKQeViLvFerzcDXKFEAuA5VLKBdbf7kAMEAqMBtpKKR8RQgzAnPGwWQjxtVX/Z2ucUnkQQrwBnJJSzrWef0tKae+K2AZ97it/rJL+KNWu74ToLyEvr+IwlY1rBdusVTaGXnGYyiT95qaCVgoVeMSudKraCvsH31V/GL2K7xm4uY5KJbBwWqnJYX8pmXrVXsN0vWrrEyC1sGo1M6q4jLlGFT+HwIcvDq1SPVHB7jCVzs34lKgEZG5uxYEqm+zsisNUIqKEc9y/nAaNq1RO/nr9julfRUHC9Y3LfwWyoGq/wTK/6r/5Rn7VLouTVawH4LXp4H/nkH05FL54Z9V/CG8Ch9fm/dfVeWX4kADTLcp4KaVexgyFXkII23lfo6z/rxJCXFtKtktKOco2khDCDdM3xAPXjkkps4QQO4DBtmGllDfrwekNYL4Q4kkgE3MnDoVCoVAoFAqFQqFQKMrmv3RZxD+ZP2yQkFJarnP8HBBh/XsL4FpGsO43kH42UGpvLCnlcJufX5dxfkKJ31uALSWOpQL2G7MrFAqFQqFQKBQKhUKhqDL+OzcrVSgUCoVCoVAoFAqFQvFfTaU4tfyzCCH6AW+WOBwjpYz8O/KjUCgUCoVCoVAoFAqFHWrJRqXzjzBISCnXAesqDKhQKBQKhUKhUCgUCoXifwK1ZEOhUCgUCoVCoVAoFApFlfOPmCGhUCgUCoVCoVAoFArFPxmhqfH8ykbVqEKhUCgUCoVCoVAoFIoqRxkkFAqFQqFQKBQKhUKhUFQ5asnGjXDhXNXqeflUrZ5v9arVA8jJrlq9gvwqlRPN2lepHgBOLlUqJ2NPVakezq5Vq/d3YOhVKjdqcq8q1SMtvWr1XKv2mQDAoYo/q1Wt9zdMVX3ktWVVqvfBvR2qVE9YqrZOhYtTleqZolXrlV4mJ1epntZtQJXqiVH1qlQPwCnzatUKxp+vWj3H//3nAimrVu9/FbXLRqWjZkgoFAqFQqFQKBQKhUKhqHKUQUKhUCgUCoVCoVAoFApFlaMMEgqFQqFQKBQKhUKhUCiqHOVDQqFQKBQKhUKhUCgUiopQPiQqHTVDQqFQKBQKhUKhUCgUCkWVowwSCoVCoVAoFAqFQqFQKKoctWRDoVAoFAqFQqFQKBSKilBLNiodNUNCoVAoFAqFQqFQKBQKRZWjZkhUBuHN0PqPA01DHtiG3LHK7rTo2A/RuisYBmRlYCz7EtKulJnU9oSrzNjwPbohGdm2MRO7tbY7n1+oM+3njRyLT8bHzYV3x/QhqJoXAJ9tPcCifSewaILnB3Wmc/1gAL7b+Ts/7T2OBEa1bczdt7YAYO2RaD7atJezyVf5ccoEIoIDkVLy+qINbDsWjauTI6/fOYgmdQJK5fPYhYv/x955h0V17P//NWeXItJVQBFERVFBsbcoijX23mNiosb0m+QmJjFNUzRNk5ueaIwpGo0aRGONooK901QQERSUIh2Rtnt+f5yV3WUX0W8iyb2/83oeH9k5c+Z9pp2Z+ZwpLPh5K6UVFYQEtmTBhMEIIfh8WxQbDp3BzdEBgGdH9aNfoD/5N0p49rswYlOvMa5He14b1dciTFmWWbx5P5HnU6hno2Xx5CG0a+phqZ2WyYJf/6C0opKQNn4sGN0PIQQ7Yi7wxR9HSM7KZd1TUwny8bRM3wtXWLL1EDpZZmKXNswN6WiZvhv3En/1Oq4OdiybPAhvNyfyS0p5du0fxKZnM65Ta14b2afqnq0xSXy7/zRCCDycHHh/4gDcreStLMss/mEjkWfisbe1ZfHjDxDY3MfC3yfrthAeeYzCGyWcXLW0yj1s/xE+XB2Op7sLANOHhDBpQG8rStU0V64l8lSsovn0wwS2aGapuTqM8P2HFc3Vn1e5L/l+HcfizgNws6yc3IIijv30qaXGbxFEnktW8m36cNpZSfv4KxksWLNdybe2LVgwfgBCCD7ddoCI2AsIIWjg5MDi6cPxcHHk2IXLPPVdGN6G+A7u3I4nRvRT9H7dSWT8BaWMPjiGdr6NLfVSr7Lgx82GMtqKBZOHIgxW7Z/3HmPNvuNoNBL9gvx5YfxgABLSMlm45neKS8uRhODXl+dgq9XcE73ySh0L1/xOfOo1JCF4ZfJQuvv7GNN0/S5jPZw50rrm5Wss+GkLpeWVSj2cNESph1sj2XDwtLEejg6lX5A/FTodb6zeytkrGeh0ekY3tGNugHn9Fq2DkUY+CJKE/vhe5P2bza/3GY7UNRT0euQbheg3fgP518G1IZoHngMhgUaL/tBO5GO7LZ65NkS7LkiTHgUhoT+0C3nXevPrfYchhYxU3qdlN9Gt+QwyrtydRkBHpLGPKHE8ugc5Isz8esgopB4DDXEsQL/uS8jLVi66NkSa/DjCtSHIMroV7xqv1aTXOhhp9CwlTscjkPeFV4vTCKRuA0CvU9J0/ddKmt7Crh6afy9Djj+GPvz72uPXqgPSiJlK/E7sQ47cYn79vmGGPDTo/bbcmIfTnwVJAkmD/sgu5GN7atfzb6/oCQn9yX3IUb+bX+99P1KX/ga9IvRhy5U20MsXzahZYF8P9Hr0+zcjxx2tVa82Zn73Be1H3k9RVjZvt+/5p8MDEEFd0U57HISELmoH+u3rzK5L/UYgDRhdVS4rf/gErl1GtOuMZsJs0GqhshLd+uXI58/UrhfYBc2Ux5U8PLAD/Y5fzfVChiOFjlLKaFkpup/+A9cuQ30nNI+9hmjWGv3hP9D/8uWdx7FtZ6Txc5S8P7wLefdG8+uhY5B6DQadHrm4AP2aT6vKvjR6FiKwKwiBnHAG/cblteu16YQ0fq4SxyN/WOr1H43Ua4hSbooL0K/5zETvIUS7W3rRShmuTa+O670syyxevZnImPPY29qweM5kAv2aWvj7ZMMOwg+dpPDGTU5+806V+/GEZJas2UzilQyWPj6dod061BpHRTOcyOhzSps/d0oNmtsJP3hC0fx2cZX7qh372bD/KBpJg7tzfd6ZPRnvhuY9mnvVPm05FsfK3Yer7k9Mz2LD7OFcLy5lyR/HlX5bsD9zeweZ6ZRX6nh5yyHiM3JwrWfHsrF98XZ1JD2/mJHfbsHPXekjB3s3ZOGwHgBsP5vCNwfj0Mky/fy9eWGAsa8dlZTGkh1HlH5459bM7RNsqbcp0thPnBiKt6sTMenZvLnl4K1U4sl+nRjU1g+AHw7HseF0IgJo7enGu2P6Yqc1DsGiLtzS1DOxcwBz+1rRDNtv0LRXNN2ciEnL5s0tB6r8Pdlf0SyrqOTB77dSrtNTqdczpF1zng69t3EsLC3jjc0HuZCVhxDwzui+dPSx7MOrqFhDNUj8WYRAGj4T/U8fQmEu0tw3kRNOQ/bVKi/ytVTkbxdBRTmiayhi8GTkDV9ZBKXTy7xz8hIr5k3E07k+U77aSGhbP/w9jI3BxhPncLa3Y+e/Z7At5gJLdx5h2dQhJGXlsj0miS3/mkpW4Q1mf7+Fbc9NIzk7j/XHz7Lu8QnYaDQ8+sPvhAQ0w6+hK6083fl0+lAW/n6oKvzIsxdJzcpjxxuPEZNylUXrdrDuhVkWz/rWup0smnY/wX7ezPvqV6LOJhMS2BKAB0O788jAHmb+bbVanh4RwoVr2SRdtd6IR55PIfV6PjvmP0TM5QwWhUWw7umpltphe1k0YSDBvl7MWxlOVEIqIW38aOXZgE9njmThb9Y70Dq9nne2HGDFrBFK+n4dRmibZvh7uBnT9+R5nOvZsfO5qWyLSWLprqMsmzIIW62Gpwd240JmLklZuVX+K3V6lmw7xJanJ+NW356Pdh5h9dE4ng7uZhm/M2dJzchix8dvEJ2UwlvfrWPdOy9Y+OvfOYjpQ0IY9txbFteG9erE6w9Ptho/a0SeiiP1WhY7Pn+X6AvJvPXtata9t8BSs1sHpg8PZdhTr5m5v/LwlKq/f962h3OXLAd+kecukZqdx45X5xCTeo1F6/9g3fMPWPh7a/0fLJo8hGC/Jsz7ZiNR5y4R0q4FjwzoxjPDFQPPT/tP8uXOQyycPASALi2a8tWjE5QA7OopevFJpGblsGPRU8RcSmfRL1tZ99IcS71ftrFoxgiCmzdl3udriIpPIiSoFUcTLhERncCm1+Zha6Mlp/AGoOTlS6vCeG/WWNo09SK/uAStRrpnehsOnAIg/PXHyCm8wbzP1/Dr/IeRJEFk/EVSs3PZsfBxpR6u3cG6+Q9baq7dzqJpwwlu7s28L9cSdfYiIYH+ADw4oAePDDIfjO08dY7ySh3hrz7KzfIKRr36CcObuuFd307xIATS6IfRfbcYCnPQPPkuunMnISvdGMjVFHRfvKq8z3oMQho2Hf0vn0JRHrqv3gRdJdjaoXn2Q+XeojyL564RISFNeRzdp69B/nU0L32MLuaImcFBPr4PXdR2xXv7HkgT5qL/4o270xg/F903b0FBDppn30cXfxwy04x+0i+h+2S+EsdeQ5FGzkT/0zIANNOeRr9nI/rEGLC1B1lfi55AGvuIMoApyEHz1BJ0Z0+Yp2l6Crojryh6PQcjDZ+Bfs1/qi5LQyYjJ5+9w/gJpFGz0H2/BApz0Tz+NrpzpyDbNA9T0X35mqLXfSDS0Gno132m5OE3C415+Mz7hjzMr0XvIXSr3lf0HnsL3flTZm0g11LRff2GotdtINLQqeh//QIqytFt/AZyM8HJVXnWpFgoLbmzuNbA4VWr2ff5t8z68Zs/FU4VQkI74ykqlr4MedfRvv4Z+jOHFQOAAf3Rvej3Kx8jRHBPtFPmUfnJq1BcQOVnr0N+LsLbD+1zi6l4YXqteprpT1L58QJFb8Gn6KOPmOsd24c+cluVnmbSo0q9qShHH/4jokkz8Pa7qzhKk+ah++INyM9B88JSdHHHzI19acnoPnxeycc+w5DGzEK/6kNo3gbRoi26954BQPPsewj/IOSkuNr1vnxT0fv3R+hij0Gmqd4ldB8Z9O67H2n0LPQ/fAh+bRDN26J7/1+K3r+W3JleXdZ7IDLmPKmZ19nx/nyiL17mrR/DWPfG0xb++ndsy/RBvRn20gdm7k3cXVkyZwort++vVctMMyObHR+8rGj+sJF1b/7LimY7pg+6j2Hz3zNzb9vMm/ULn6WenS2/7DnER+u28vGTM8017lH7NKp7EKO6K8aGxPQsnvryF1o3cuXZjZtZMW0gns4OTPl+O6GtmuLfyLXqvo3RSTjb27Lz8bFsi09h6d7TLBunfPTycXUkbM4IM538kjI+jDjFhoeH417fnle2HOLwpWv0at1M6SduO8yKmUOVfuLyzYQG+OLfyKSfeDpR0XtmEtviklm6+wTLJobSysON9Y+ORitJZBeVMO7rTfQP8CWn+CY/HzvLlifGY2+j5bn1EWyLu8S4jq0AQ9902yFWzLzfXNO0b3oqQen7/2sy22IvsnT3cZZNGmDQHINWY9D8Koz+rX2x1WpY+dBw6tvZUKHT88DK3wnxb0pw00b3JI5aSWLJjqP08ffmk8kDKNfpKK2otCgT/zNI6gKDv5o/naJCCJ0Q4owQIk4IsV4I4WBybZwQQhZCtDH8thdCnBdCtDfxM18I8XUtGs8JIUqFEC4mbv0NYc82cetkcHtBCPGF4bnOCiFuGv4+I4SYKISYJISIF0LohRBd/1QCeLdQOlN52aDTIccdRQR0MveTch4qygGQ0y4inK19P4fY3GJ8He3xcXfGVqthWAd/Is6lmPmJOJfC2M4BAAwJbMmRi+nIskzEuRSGdfDHVquhqbszvu4uxKZlcTErn2AfT+rZ2qDVSHTza8Kes5cAaOnhRnOTFxBAROwFxnQPQghBcHNvim6WkV1QbOYnu6CY4tIyOjZvihCCMd2D2BObeNtkcrCzpUtLHzOLcHUiziYzpnNbRbtZY0XbMHir0i68QXFpOR2bNVa0O7dlT/xFJT6e7jT3cLMWtJK+adn4NnAxpm/7lpbpez6VsR1bG9K3BUeSlfR1sLWhSzMv7LQaM/8yIMtQUlGBLMsUl1Xg4VTfevxOxjKmb3eEEHRs1ZzCkptk5RVY+OvYqjkebi5WQrh7Io6fYUy/nopm65YU3ighK89ycNGxdUs83FythGBk64HjDO/T3VIj9gJjugUq+ebXhKKbpTWUmXI6NvdW8q1bIHtiLwDgaG9X5e9meXti8UAAACAASURBVAW1rcyLiE5gTM9gRa9FU4pKysguKKqmV6SU0RY+il7PYPZEJwCwNvIkc4beh62NUhYbOCv5dfDcRVp7e9KmqTJjwNXRAY0k3TO9i9ey6RnQvMrNycGOuMvKIC4iJpExPTqY1MPSGjTL6djCUA97dGBP9O3roRCCm2XlVOr0lJVXYCMJ6tuYlGkff+ScDMjLAp0OffRhRFvzV6ScfNb4PrucZHyf6XTKQBZAa/N/W2Pp1xo5+yrkZICuEv3JSERwtS/cpTeNf9vZo9TCu8DXEMfcTEXj9AFEoLkBUb4YZxLHRIRLA+WCZ1PQaJATY5Tf5aVV/mrExx85JxNyb6XpIUS7anrJ8SZ6F4x6AN7NwckV+ULMncWvaUtkkzZJH3ME0baLud4lkzy8koRwsZKHmjvMw6Ytlfjd0ou1pnfOpA00KTO38gEUo8eNQnBwurN43oakqEOU5N6FIawWRIsA5KyrcN1QLo/tR+pUbXaaiRFF2NlX/S1fvgj5ihFbTk8BG1ulftxOr3kActY1o97x/UjBvWrUw9akHpSXISfFI1dU3F0km7VCzr4GOYZ6cSoK0d78w4J8IdaYjykJymwBUBpBGxtlFohWq9SR2xmxqvQyqumZty9yUnW9W/Wiup62dr26rvdAxOmzjLmvs9L++jdT2vz8Qgt/Hf2b4eHqbOHu3cidAJ/GSHfxLo04Fc+Y+7qaaJbelWaPtv7Us7MFINi/GZm5ln2Ue9U+mbL1RDzD2/kRezUHXzcnfNycsNVoGNbOj4gLaWZ+IxLTGNu+BQBD2vpyJCUDWa65XbiSX4SfuzPu9ZV62svPiz8SFENYbPp1fN2d8XFzVvQCWxBx/rLZ/REJlxkbrBgThrTz40jyVWRZpp6NFq1hoFpWqTN7fer0MqWVOir1ekordHg4VQ2ViE3PVjRv9U2DWhCRYEWzo79Bs7lR01aLVmOpKYSgvp3ynqnU6anU6bnVuboXcSwuK+dEagYTOin9Z1uNBmeTvp2KSm38FTMkbsqy3BFACLEaeAxYZrg2DTgATAUWyrJcKoR4FvhSCBECNAHmAbUZBaYBx4FxwCoT91hgCvCd4fdUIBpAluUnDc/kB/x+6xkNbm2B8cCf/3zi7IZcaPxiTmEeNG1Ro3fROQQ5yXrHMrOkHC8HYwX2cq5PzJUscz+FxXi5OAKg1Ug42duSX1JKVsENOvgap8l7utQns/AGrTzd+c8fR8kvKcVOqyEy8TKB3o1qfL6s/CK83IyNlKerE5kFRTQyaAJkFhTh6Wrqx5msfGNjtCbyJJuPxRLo25j54wbg4lCvRj0z7YJivFyNOp6ujmQWFNPI2TjAzywoxtPF3E9WtcFvTWQW3sDLxRiWl0t9YtKqp6/Rj1Yj4WRnS35JGW717bGGjUbijVF9GPv5BurZaGnWwIXXR95nXT83H68GRoOJl7srWbkFd2V82HUsmhPnLuLX2IOXHxxP4wY1G2AUzTy8TKZbejVwIysnv1bjQ3XSs3JIy7xOz6A2FteyCorxcjMOIpQyU1ytzBTjaZa3Tmb59snWKDYfj8fR3o5VTxlnZZxJucq4D1bRyNmRFycNpVUTD8sy6uZEZn4RjVyMz5CZX72MOlWV0ZSsHE4mXeY/myOws9Hy4vjBtPfzJjUzBwHM/fRncotLGN41kNlD7rtnegFNPYmISWB41yAy8go4e/kaGXlFdPCDrIIivKrVMeua5umeZdIpXLP/BJuPxhLo68X8CYNwcajHkE5tiIhJpN+C/1BaXsH8wMa42hqbAeHsZr6crDAH4eNf45Bf6tYffWK00cHFHc1DL0EDT/TbV9/d7AhQBhx5JksV8q4j/AIs9EXICKSB40CrRfeJ5Yyf22q4uJsvhyjIRfi2qjmO3QeiP6/MZhGNmiDfvIH00IuIBh7IibHot/5826+lip5JmhbkIHxvl6ah6BMMU/qFQDNiJrp1XyD8g2q4o5qes3u1PMxF+LSsWa+rlTx88EVw90S/45fbz47gVpkxaQMLchFNb6PXuR96a8YV7xag0SjGsH8arg2Rc01m9uVlI5pbvgul0FFohkwArQ0VH75ocV106Yt8OQkqazEWuDYAEz05/zqieYClXv9RSIPHgcaGymUv3XF0rCFcG5jXi/zriGaWda9Ku+dg9GdPKj9SEpATY9G8vUpZQhG51XzmgTU9l+p6OYhmrW+vd85E70Ismre+V/Sitt2BXt3We4DMvAK83I1trZebK1l5BVYNAX8VmXkFeDUw0XR3+T9rbtx/lL4drLX596Z9MmXHqbN8PrYPyTkFeDkbB+9eTg7EXL1u5jezqKTKj1aScLKzIf9mGQDpBcWM/24rjrY2PNOvI119PfB1c+JSTiHp+cV4OjuwJ/EKFTq9IawbeJn0Ob2c6xOTbj6r16yfKBn64TfLcHOwJzoti9c2H+BqfjHvjwtBK0l4Otfn4V5BDPx4HfY2Wnq3bMJ9Lb1NwiuppulATJoVTefqfX+lbxqdlsVr4VGK5vh+VQYKnV7PxG/CuZxbyPTubQlu6gGyfE/ieCWvCHcHe14Nj+J8Zi6BjRvyyv09cLC9vfFVReUWf/WckyjAH0AI4QjcB8xGMRQAIMvyDuAa8CDwMYqhosZeqxCiJeAIvIZimDDlMmAvhPAUymLt+4HttT2kLMvnZFlOuIt43QYrlusaWjjRoReiSXPkg9Yf0eptonY/QghkK1cEyiyIOSGdmL1yC4/+sJUArwZV1k2rz2BFoHoMrVmeb62Vn9qnMzvffIzfXppNI2dHPgiLqFHLQtuKW/UPA1bjeYdfD6xni/m9VuN/m+ArdHrWHj/LxicmsH/+AwR4ubM80vr6YOvpVnPY1enfuT17Pl1I+Aev0CsogFe+/KnWe6zH5+6/XG87eIyhvTqj0ViWnb8i354d0ZeIhY8xsktbVkcpncB2Pp7sfnMeYfNnMSOkM09//WvNYVV/ptuktU6np7CklLXzZ/PC+ME8v2IjsixTqddz6uIVPnhkPD+/8DC7z5zn8Pnke6Y3vncnvFydmfTecpas30nHFj5oJHGb8KqVVQsfxuea2rczOxc9wW+vzKGRiyMfbFT2cohNuYokBPsWP8Out57khwuZXLlRdpuY1SAEiI59EN4tzPcnKMhF9+lL6D56DqlzCDje7UyfO6zLkVvRvTkHfdj3SMOm1H5DrRo1vB06hyiD+b2GPR8kCdG8LfotP6L7RDG8iG6hd69XU5p26qMM5g37doieQxTjRA17Dt2pnNUXASCC70M0aWG+50NBLrrPXkG37Hmkzn2hfm0DmbtIz+DeCO/mFvss4eiCZuJj6H5bXuOz/q1YLZaWz6nfu4WKV2ah27ACzcgZ5kE0aYZ24mwqf/yPxX2WetbKjBW9fVuofPURdL99h2Z49e7R3XJnmgCia3/FqBbxm+LQsDF4NUX3xiPoXn8Y0boDtAy8a7kay03XforeHsOeDw29wNMH3Zuz0b3xCKJVe2jZ7vZ6dV7v/7r2926w3l7dvebmgyeJS0lj9vD+lhr3qH26RfSldOxtbWjl4Xpn/TarPgSNHOux58nx/DZ7BC8N6sL88AMUl5XjUs+ON+7vzvObopj50y6auDiiMfSL7+T1c7u4BTf1YMsT4/l17miWH4ihrLKSgptlRCRc5o9/TWLf81O5WV7J5pik24ZX/R1wu35WcFMPtjw5gV8fHcPyqGjKDEslNJJE2OPj2Pv8VGLTr3MhM/eexVGnlzl7LYcpXdvw27yx1LPRsuLAHc7q+29EiH/2v/9C/rI9JIQQWmAYsMPgNBbYIctyohAiVwjRWZblU4ZrzwLHgAuyLNc2qpoG/IJi7AgQQnjIsmz6CWUDMAk4DZwCyiyD+D/F51HgUYCvRvZibpfW1j0W5iKc3Y2V19nN+lfBFu0QfUehX7XEOCW2Gl4OtmSUGB8/o/AGHs7m0/+9nB3JKFBmSVTq9BSVKi9XTxfF/RaZBcZ7J3Rty4SubQH4eNeRKitrld/8Il5cFY69rQ3tfRuTkVdods3DxXwKrZerM5n5pn4Kq76GNzR53km9g3n8G/MN6aqz5lA0648q6z7b+3iSkW8Sh/xiPKo9q5eL8vXd1E8jZ+tLJKrj5VyfjALjEpCMghtm0+aU8BU/VelraLxq4vw1xVLva9g06f6glmYGidW7ItkQoezREdTCl4wcY9nIyM2n0V3MjnAzWQoyaWBvlv4SbtXf6u172bA7UtH0b07GdePXy4ycPBq53/1ykO0Hj/P6HOO659Xb97J+2x8AhjJj/PKRmV9kPd/M8rbIar6N6NKWx7/9jaeH9TFbypGek8/V3HxGv/UVHVs0NS+jeUV4uFYro27Vy6jx642XmzODO7VBCEEHP28kIcgrLsHL1ZlurZrh5ujAmn3HSc/J5+VVm+gX1Oqe6Lk71eflSUOr7hn82qcsC9/L51sjad+sCRnV6piHS7U0dVVmaljTbGiS/pPu68TjXynGnK0n4unbriU2Gg0NnOrTqYEj8Xkl+Bj2kJALc82XCzg3QC60fJ+JlkFIoWPRffuW9fdZUR5yZhrCLwA57pjl9RqQ868j3BoaHdwaIt9mMC6fjESa9iSKbfsONQpyjFPNAVzckU2/8BsQrTogDZqA7svXq+Io5+dA+qWqZQZy3DFEs1bIt4miomeSpi41pKl/e6QB49F9vbBKTzRrrXyJ7zlYWZ6i0SKVlSozF2rUq56H7siFlrMcRMtApP5j0K14p4Y8zDfkYRvk+JojqJQZk2WILu5Wp8+LFoFI/UYr+5OY6tnZo5n5AvrdGyDtYo06fyt51xHuJjML3Roh51uWmVvoj+3D5oFn0FX5b4j2yTep/O4DyL52R3qY6AnXhlXLPqwhH9+PmPE0mGyCfLfI+dfN64VrQ/PZn7eepXUw0pBJ6D5dAJWGctqhJ3JKorKUAdCfO4nwC0C+GH8bvWr10LWB9XrYOhhp8CR0n71qrBcdeiGnJJjonVJmc1yseZ+Vuqr3q3cfYsN+ZWPWoOY+ZOQa60JGXj6N7sHsiNW7D5pr5pho5hbQyO3uNA/FJ/LNlj38uODxqqWGq3cfZH2EspnhvWqfbrH95FmGd1EMWl5ODmQUGpcnZRSV4OFkPpvilh8v5/pU6vUUlVXgUs8WIQS2hmW2gY0b4OPmSEpuEUGNGxDaqimhrZTNPn89faHqY4CXc30yTJYKZxRa6Sca+pJVeqWW/cSWjVypZ6vlQlY+aXlFeLs64l5fee7BbZtx5koWozv4G8JzqKZZYl2zsBgvl/pmfX9LTRsuZOURZDIT2rmeHd38vIhKSqeVh9s9iaOnswOezvWVWRgoyzxWHPwfNkio/OX8FTMk6gkhzgAnUGYs3Fo+MQ1Ya/h7LSazG2RZvgpEAJY7O1oyFVgry7Ie+A3F+GDKrwa3W4aLvwRZlr+VZbmrLMtdazRGAFy9BA08wbUhaDSIoB7KppamePkijZyF/pf/wI0i6+EAQe6OpBaVkpZbSHmlju0xSYS28TPzE9rWj02nlMkdu+Iv0qOFsiY/tI0f22OSKK/UkZZbSGpOPu0NL4acYuVlfjW/iN3xlxhuWBd2C09XJz6cNYawl2czsENrwo/FIcsy0ZfScbK3M5t6D9DIxZH69rZEX1L2Vwg/FseA9kqYpnsH7I5OpFXjmpeHAEzvHUzYczMIe24GAwNbEn7qnKKdeg2nenYWg9ZGzvWpb2dDdOo1RfvUOQa0q3mJjFn6ejciNaeAtDxD+sZeJLSN+YkToW2aselMoiF9k+lh2POgJjyd63MxK4/cG8q69kNJabQw2WxpxpAQwt57mbD3XmZg1w6ERx1DlmXOXLiEk4P9XS3XMN1vIuJkLC28LU8/AZgxLJSwpW8StvRNBnbvSPj+I4pm4kWcHOrd9XKNS+kZFBSX0DGgpbnG/FmEzZ/FwPb+hB+PV/It5aqSb9bKjJ0t0SnKOsTw4/FVZSYl2zg42xt3kRaeyuAmu7C46ktMkG9jGjo7Ev76YwwMDiD8SLSil5xm0HOqpudEfXs7opPTFL0j0QwIVqY8DwgO4GiCso9KSmYOFTodbo4O3NeuJQnpmdwsr2By3y74N/bgrRkj75nezfIKSsqUdciHzl3Ey82ZrW88RtiCuQwMbk340RhjPaxJ086kHh6NYUAH5V1lup53d3QCrZoo9bCxmzNHElOQZZmSsnKic2/Q3Mmkk5F2EdHQC9wagUaDFNwL+dY06Vs09kMaNwfdjx8pa/5v4exuXBtvXx/hF6CsSb8bUhMRHt7KO1WjReoSghxT7dSFRk2q/hRB3SDrKnfFlSREw8bg7qFodOqDHH/C3I93c6SJ89CtfA+KTeJ45SI4OFbNGhD+Qci1TBUn7SKigWma9kY+V02viR/S+DnoVn1glqb6tZ+hW/IkuvefRr/1Z+RTkbc1RgCQnmyu16En8vnqedgMacxsdD8vvU0eOihT6K/Xkoe39FwNeu17Ip8/Ze6ncTOkMQ+j+/ljcz2NBmnas+jPHLit0ePvRr6UgPD0Vr7Ma7RI3fshnzls7snDpFx26IF8a9PSevXR/uttKn9biZx0ZxuTyikJCI8mxnrQrZ+yqWVNeu27I2em86e4fAHRqAm4GzQ790WOrVb3mrZAmvoEuuXvQLHJ3gJ52Qj/wKrTWUTLIPPNKWvUM6mHnftaGi+9myub3K5414pekFHPP7DWJRt1Ve9nDOpN2NvPEfb2cwzsHEj4wVNK+5uUilO9evdkucaMQfcR9vbzhL39vEHzhImm/V1pnk1NZ+H3G/ni2Ydp4Gxsc2YMuo+wBXPvafsEoNfL7Dx9juFdlRkvQU0akJpXRFp+MeU6HdvPplQZEm4R2qopm2KTAdh17jI9mnkihCD3Rik6vbIU40peEam5RTQ1LB3NuaEYswpulvHLyUQmBivGgSDvhoZ+YpGiF59MaICvuV5rHzZFK3tg7TqbQo/myp5maXlFVBr00vOLuXS9AG9XRxq71Cc6PZubFZXIssyRS9do0dDYDwtq0ojUnEJFs1LH9jgrmgG+bDqTZNC8RI/mTYyauluaRQZNJ3Jv3KTQsGyltKKSw8lXadHQ5Z7FsZGjA14u9bl0XamnRy5dpWXDu+trqvz/jbjdxi93FIAQxbIsO1ZzawCkAVkoM380hv+byQZBIcQqlL0dNtwm7A4oe0fc6hHZAsmyLPcRQvQHXpBleaQQYjfgA7QDXgeKZVn+yBCGn0HHYgGuEGKfIYwT1a+Zols46/aJ1KqDcuynkJBPRyFHbUGEjkO+egkSziA9+CJ4NDU2qAU5inHCCvuv5vFe3DX0ssy4zm14LLQLn+0+RqB3Iwa0bU5ZRSUvbdjDuavXca1nz0dTB+Nj+Dr/9d6ThJ06j0YSvDz8PkIClMH2A9+GkV9Sho1GYv7w3vRqqbzMd8cn8+7vB8gtKcW5nh1tvD359okpvLN+FwfOJWNvY8O7D4wgyHCc07j3viPsZWUP0bjL11jw8++UVVTSt20LXjUc5/TSj5s5n5aFEODt7sLCqcOqBqeD3vyS4tIyKip1ONezY/mcsfh7Gr/kybLMO5v2cSAhFXtbLe9OGlx1dOe4j1cT9pwyBTbuinLsZ1lFJX3bNOPVMf0RQrA7Lol3w/eTW3wT53q2tGnSiOVzximBlykGg/2Jl3lv22H0ej3jOgfwWP/OfLbnBIFNGjLAcFTSSxv3cu6acnzUR5MHVqXvoKVrKC6roEKnw9nejuUPDcffw421x87y8+E4tBqJJq6OLB7fH7cOlqdsyLLM29+v50D0OeztbFg87wGCWiqNwLiX3yPsvZcB+HD1JrYeOqms+3RzYWJoL56aOJxlv2wm4mQsWo2Ei2N93nxksrlRwtZynwtZlnl7xRoOnI7H3s6WxU/OIsjfT9H89yLClr6paP64ga1RR42ag/ry1JTRAHy+bjNl5RX8e+YE87CvJBrzbeNuDpy7hL2tDe9OG0aQr/Jc4z5YRdj8WYYyoxz7WVZRoZSZCQMRQvCvlZu4lJWHJKCJuwtvThqMp6sTq6NOsfbgGbSShJ2Nlpcm3U+nlj6K3trtHDh7UdF7cDRBzZTO+bh3vyHs1XmKXupVFvwQrpSTQH9enXI/QgjKK3W89tNmzl/JwEar4cXxg+nZRtlccvPRGJbvPIgAQgzHc94rvfScfOZ+uhpJEni4OPH2zFF4G/bikGWZd37dadR8YKRRc/FywhbMNWr+9LuSpu1a8qrhqNGXVoVzPj0TgcC7gQsLpw2jkYsTN0rLefXnLVy8dh0ZGNfQjkdamx/RKgI6Ksd+CsORkfs2IQ2aiJx+CfncSaTZCxCevlUzweT8HPQ/faR84R/+AMqrXqA/vBP5eLUlWwWWm6tVRwR2RZr4qHI03+E/kHesQxr5AHLqBeTYo0iTHkUEdFQ2Eb5ZjH7dV2anD5hRz/reL6JNZ6SxDytxPBaBvGcj0tCpyGlJyPEnkOa9iWjsq+wJhPL1WL9S2Y1etO6ANGoWCJDTkpUjOk2/+FvZuFcEdEQa9ZDhKNV9yHvDkAZPQk5LVtJ0zmsIL5+q/Rrk/OvKaQKmYXTph2jawvLYT2t6rYONx3Ce2o+8Lxxp4AQlD8+fQnr4FUu9n5cpM1+Gz1Dm9AqhHPt5fK954FaW/IlWwcp9koT+VCTy/s1IA8YjX72EfP400qyXEJ4megU56Fd/jAjujTRurtmJI7rfvoUM8/x86h3rs8FqYvaalbTu3wfHhg0ozMxiy5uLObSy9iVut/j0EcujQkX7bminKsdw6g7sRL/1FzRjHkSfkogcfQTNtMcRbTspG4OWFKFb/QXy1VSkkdPRDJ9qZjCoXPaK2d4cwspSOBHUDc2UeUqaHtyFfttapNEzlXoQfQRpymNIbTuBrhK5pBjdmi/hWioA2sU/QD0H0GjhZrFy2odJHRH2tlbjLdp1MRz7KaE/sht513qk4dORLychxx1DevItRGM/MMyckPOy0S9/F4SENPkxRMtAQEY+dwp92MpqgVsa9kW7LkjjZhv09iD/sR5p2HTkKwa9J95STgspuKV3Hf0Kg96keQY9FL1N1fSs1Yt7WO+liQ9a6MmyzNs/beJAbILS/s6eRJDhqO9xr39M2NvPAfDhuq1sPXKGrPxCPFydmRjSjafGDSE2+QpPf/YjhTdKsLWxoaGLE78v/rchMta/JyqaYRyISVD6GXOmmGguI+zt5w2av7P18GmjZr/uPDVuKA+//w0X0q5VzeRo7O7Kl889ooRdnFelcS/aJ4BjiaksC49g7YsPQ7pSnvcnpfPe7hPo9TLjglvy2H3t+Wx/NIGN3RnQ2oeySh0vbT7IucxcXO3t+GhsH3zcnNh1/jKfRUajlQSSEDwVElxlzHhhUxTnM5U6+ESf9gwP9FM2nAX2X7jCezuOKv3wjq14LKQjn+09pfQTA3wpq6zkpbBIYz9xYn983JzZHJ3E8oMxaCUJSQge79eRQYaPXp/tPcWO+EtoJEHbxg14e1QfZfaGoV7sT7zCezuOKJqdWiuaEScVzTbNlL5p2H4TzVB83J3ZHH2B5Qeqabb1IyEjl1c27Uevl9HLMvcHtuCJ/p2q1mvciziey8jhjc0HqNDpaermxLtj+uJSzw7N9Jf+O9cQ3Abdknn/wLWFRjSvfPNfl+b3yiAxD+gsy/I8E7f9wGuyLEcZfq+idoPEEqBQluUlJm6XgP5Ac4wGid6AhyzLm4QQC6lrg8RfTVDH2v38lTj/DVbMm3/uWLe7puxm7X7+QoR/+9o9/dVYMUjcS24ZJOoMuzvbHPW/Gr2udj9/IfKeWrfc+Wu5A4PEX0oNBol7ym1OEvqf0Psbjju7W4PEn8WaQeJeYs0gcU/1ajBI3FvROu4f13G9sGaQuKfUYJC4l9wySNQZBoNEnWHz/0G9+Bv26FENEnXPf6NB4l690aYBYdXcNgK1HL5twVQr4YRhskkmgCzLh2RZ3nSngRqOI00DegFbhRA77/K5VFRUVFRUVFRUVFRUVFRU/gR/2oRcfXaEwa2/FbdPq/2edQdhN7fi9rzJz31Wri+s9jsFCKrmFoaloUNFRUVFRUVFRUVFRUVFRaWOqOO5nioqKioqKioqKioqKioq/4X8lx6t+U/mH2GQEEK0B6rvOFUmy3KPv+N5VFRUVFRUVFRUVFRUVFRU7i3/CIOELMuxQB3v5KiioqKioqKioqKioqKiovJ38Y8wSKioqKioqKioqKioqKio/KP5G06b+l9HTVEVFRUVFRUVFRUVFRUVFZU6RzVIqKioqKioqKioqKioqKio1Dnqkg0VFRUVFRUVFRUVFRUVldpQT9n4y1ENEndCXa8VkvV1rCfXrR7UfRzrmrKbdS4plxTWqZ7w8qtTPTkztU71sLGtWz0ASVOncsLFpU71qGM9OTu7TvUAsLGpUzmhreNmXFO3ZRTg00d61qneMyuP1Kne50/3r1O9v4W67kfVdb2o4z6NrNfVqR4AlRV1q9fYp2710lLqVg/qvpxq67Z9UlG5U9QlGyoqKioqKioqKioqKioqKnWOOkNCRUVFRUVFRUVFRUVFRaU21CUbfznqDAkVFRUVFRUVFRUVFRUVFZU6RzVIqKioqKioqKioqKioqKio1Dnqkg0VFRUVFRUVFRUVFRUVldqo6016/z9ATVEVFRUVFRUVFRUVFRUVFZU6RzVIqKioqKioqKioqKioqKio1DmqQUJFRUVFRUVFRUVFRUVFRaXOUfeQ+L/SMghp6HSQBPLpKOSD28yv+7ZGGjoNPJui3/g1nDtZdUkMnIho1QEAOXIL8tnjViWiEi+zZNshdHqZiV3aMLdfJ7Pr5ZU6Xt4QQfzV67g62LNsyiC83ZzILynl2V/+IDY9i3GdAnhtVB+LsJ/8eQdXcgvZ/MxkM3dZllm8cTeRZy9Sz9aGxTNG0M7Hy+L+ILbhcwAAIABJREFU+MsZLFi9ldKKCkLatWTBhEEIk2NwVu45ykfhezm4+BncHB2q3GNTrzF16Q/0CWhGyvV86tloWTx5EO28PSw10rJYsH43pRWVhAQ0Y8HoEIQQ5JeU8u/VO0jPK8TbzZllM+7HxcGegpJSXtuwhys5BdhpNbwzaRCtvBpUhafT65n0VRiezvX5aub9RF24wpKth9DJhvQN6WiZvhv3GtLXjmWTlfQ9lJTGsj+OUVGpw0ar4YWhPejZwttqHlqk7erNRMacx97WhsVzJhPo19TC3ycbdhB+6CSFN25y8pt3qtyPJySzZM1mEq9ksPTx6Qzt1qFmnV+2EhmboOThIxNo18zy+eJT0lmwcqOSh+0DWDBtBEIIzl+5xqIfwykpK8e7oSsfzJ2MYz17ABKuZLDwx00Ul5YhabSsf3c+drY25to/rCfydDz2drYsfnwmgc19LeO4djPhkUcpvFHCyR8+rnIP23eYD1dvwtPdBYDpQ/sxacB9lvH7bQ+RZ5OpZ2PD4hnDrJfRKxksWL1NKT/tWrBg/EDzMhpxjI/C93Hw3adwc3Rgy4l4vtt9DAAHOxvemD6CNk09Fb31u4iMT1L0HhxFO9/GlnqXr7Hgx82KXqA/CyYNQQjB57/vZ8PBM7g5KfXg2dGh9Avyp7xSx8I1W4m/fA1JCF6ZNITuAS2Mcfx1J5HxF5Q8fHCMdc3UqwbNCkICW7Fg8tCqOP689xhr9h1Ho5HoF+TPC+MHk56Tz8hFX+LnqdSL4PoaFvYOMA+0eTukQZNBkpCjDyIf2Wl+3ccfaeBk8PBGH/4dJJxS3H1bIw2cZPTXwAt9+Aq4EG3x3H+rHiACOiKNfQQkCf3RPcgRYebXQ0Yh9RgIej3yjQL0676EvGzlomtDpMmPI1wbgiyjW/Gu8VpNeq06IA2fqeid3IccucX8eu9hSF37g16HfKMIfdi3kJ8DXr5oRj8MdvVA1qPfF44cd7TW+Jnh3x7p/ulK+p6KRD6w1Vy711BE5xDQ6+FGkZLGBTl3p9EySGnvhKE9PLTd/Lpva6QhU5X28LdvLNtDf0N7GFVze1gdEdQV7bTHQUjoonag377O7LrUbwTSgNFKvMpuUvnDJ3DtMqJdZzQTZoNWC5WV6NYvRz5/5u7iW42Z331B+5H3U5SVzdvte/6psG4h2nZGGj8HJA36w7uQd280vx46BqnXYNDpkYsL0K/5tKocSqNnIQK7KvmRcAb9xuX/SE3RphPSuNkgJPRHdyPv+c38er/RSD0HKfWiuBD92s8hLxvhH6TU31t4eKP/cSly3LHb67UORho9S9E7HoG8L9z8et8RSN0GGOphIfr1X0P+daMHu3po/r0MOf4Y+vDva42fLMssXrOFyJgEpc2fPYlAP8u2+JONOwk/eIrCkpuc/PqtKnelzf+dxLQMlj42jaHd2teqaVU/NoF6trYsnj3Ral/gk4072XzoNAUlNzn51aI7C/dPtMGfbo0iIjYJIQkaODqweMYwPFyc+G7PUX4/eQ4AnU5PcmYOB5b8CxcHexZv/IPIeEO/9IGRNfRLr7HgZ0O/NLAlCyYMVtrgbVFsOHSmqi/67Kh+9Av0JyblKm+uNbyrZHiyR2sGtfYhKvkqS/acUvrewS2Z27OdmU55pY6Xtx4hPiMX13p2LBvTG28XR7bEp7Dy2Lkqf4lZ+WyYdT9tPd34JDKazXEpFJSWc/L5SVQn6mI6S3adUPqjHf2Z2zvIUnPzQYOmLcvGheDt6kh6fjEjv9mMn7szAMHeDVk4XHkHlet0vLvjGMcuZyIJwb/6d2RIkL+il5TGkh1HlDh2bs3cPsGWepsijf3fiaF4uzoRk57Nm1sO3ioJPNmvE4Pa+gHww+E4NpxORACtPd14d0xf7LT/o8NM9djPv5z/0ZJyjxECadgD6H9eCoW5SHPeQE44A9evGv0U5KAP/w7R637ze1t1QDRuhv6bhaDVIj30MnJSLJSXmnnT6fW8s+UgKx4egadzfaZ8/Ruhbf3w93Cr8rPx5Hmc69mx8/lpbItJYunOIyybOhhbrYanB3blQlYeSZm5Fo//R3wyDiaDSFMizyaTmp3HjtfnEZNylUW/7mTdvx+y8PfWrztZNPV+gv2aMO/r9USdSyakXUsAruUVcjghhcZuzhZxWrZ5L+18vMguvMGOF2cSczmTRWH7WPfUZEuNsL0sGh9KsK8X81ZuJiohlZA2fqzYd5Ke/k2ZG9qV5XtPsGLfSf49/D6+3XuCNo0b8tmDI0jOyuXtTfv5/tFxVeH9dDiOlo1cKS6rMKTvAVbMupW+YYS2aWY9fZ+bqqTvrqMsmzIIVwd7vpwxFA/n+lzIzGXuD9vYN/8Bq+lplrYx50nNvM6O9+cTffEyb/0Yxro3nrbw179jW6YP6s2wlz4wc2/i7sqSOVNYuX3/7XViExWdxc8Tk3yFRT9tZt1rj1um78/hLHpwLMEtfZj3yQ9ExSUS0j6AN1aF8eLkYXQLaM7GqBOs3BHFM+MGU6nT8dKKX3lvziTa+DQmX69Fq9WYa5+JJ/VaNjs+WUh0UgpvrVjLunfnW8axS3umD+3HsGcXWlwb1qszrz8ypeb43Sqjr80lJvUai9b/wbrnZ1rG79ddLJoyVCmj32wg6twlQtopA35rZbRpA1d+eGYaLg72RJ5N5s01W1k3/xEi4y+SmpXLjoVPEJOSzqK121k3/xFLvV+2s2j6CIKbezPvi7VEnb1ISKDS+D84oDuPDO5l5n/DwdMAhL82j5yiG8z7/Bd+fXkukiSIjE8iNSuHHYueIuZSOot+2cq6l+ZY0dzGohkjCG7elHmfryEqPomQoFYcTbhERHQCm16bh62NlpzCG1X3+DR0I+zVecqPY1HmAQqBNGQa+rX/gaI8pFmvIF+IgZxrRj+Feei3/oDoMdj83suJ6L9/V/nb3gFp3ttw6azFM/+tegBCQho/F903b0FBDppn30cXfxwy04x+0i+h+2Q+VJQjeg1FGjkT/U/LANBMexr9no3oE2PA1h5kfe1xHPUQuu/fg8JcNI+9he7cScg2aS+upaD76nVFr/tApKHT0K/7HCrK0W38GnIywckVzRPvoEuKhdKS2uN5S3v4TPQ/fai0VXPfRE44baYtX0tF/naRot01FDF4MvKGr+4s/Fsa989Av3opFOYhzXkdOfEMXDfJw4Ic9JtXInoNNb/XvwPCyxf9twuV9vDBl6y2h5aaEtoZT1Gx9GXIu4729c/QnzkM1y5XedEf3Yt+v2J8EcE90U6ZR+Unr0JxAZWfvQ75uQhvP7TPLabihel3Hl8rHF61mn2ff8usH7/5U+FUISSkSfPQffEG5OegeWEpurhjkHHF6CctGd2Hzyv51mcY0phZ6Fd9CM3bIFq0RffeMwBonn0P4R+EnBT3z9IUEtKER9F9vVDRe+4DRc+sHiajW/aCotd7KNKoBxXDQ1Icuo+eV/w4OKJZ8KXSB7tt/ATS2EcUA2JBDpqnlqA7ewKy0k30UtAdeUXR6zkYafgM9Gv+U3VZGjIZOfkO3jEGImMSlLb4vReITr7CWz9tYt3rT1r469+xLdMH9mLYyx+ZuTdp4MqSOZNYuSPyjjXN9GMTSM3MYceSF5S+wI/W9UM7tmXGwF7c/8rSOwv3T7bBjwzszjMj+gLw0/6TfLnjEAunDGX2wB7MHtgDgL1xSfy47ySu9euxPz6J1Kw8drzxmNIvXbeDdS/MstRbt5NF0+4n2M+beV/9StTZZEIClX7pg6HdecQQ9i1aNWnE+hcfRquRyC4oZtzib+nbojHv/HGSFVNC8XSqx5QfdhHq741/Q5eq+zbGJONsb8vOeaPYdjaVpfuiWTbmPkYF+jEq0A+AxOx8ntoYSVtPpU8Z2tKbGZ1bc/+3v1s8t06v550dx1gxfRCezg5MWbmd0FZN8W/katQ8k6RoPjGWbfGXWBpximXjQwDwcXMkbO5Ii3C/ORCHe317tj8+Fr0sU3CzzKi37TArZg5V+r/LNxMa4It/I5P+7+lERe+ZSWyLS2bp7hMsmxhKKw831j86Gq0kkV1UwrivN9E/wJec4pv8fOwsW54Yj72NlufWR7At7hLjOrayeC4VFWv86SUbQgidEOKMECJOCLFeCOFgcm2cEEIWQrQx/LYXQpwXQrQ38TNfCPF1LRrPCSFKhRAuJm79DWHPNnHrZHB7QQjxheG5zgohbhr+PiOEmCiE+NDwHDFCiDAhhKt15RrwbgF5WZCfrVjS448iAsy/rlOQA1lpFp1V0bAJcmqC4l5Rjpx5BeFvafWOTcvCt4EzPu7O2Go1DGvvT8S5FDM/EedSGNupNQBDAltwJPkqsizjYGtDF7/G2FUbLALcKKvgh4OxzOvf2WrUImIvMKZ7EEIIgpt7U3SzjOyCYjM/2QXFFJeW0bG5N0IIxnQPYk/Mharr7/+2h3+P6W9hQFy9/ySDgwO4UVpGtxbKvcHNvBQNkwETQHbhDYrLyunYrLGi0aUte+KTlWeMT2Zsl7YAjDVxv5iVS09/HwBaeLhzNa+Q60VKxz2joJj9iZeZ0LWNIX2z8W3gYpK+LS3T93wqYzuapm86sizTrklDPJzrA+Dv4UZZpY7ySp3V9DQL7/RZxtzXGSEEHf2bUVhyk6z8Qgt/Hf2b4eHqbOHu3cidAJ/GSLVYZiPOnGNM705K+rb0paiklOxqOtn5hRTfLKOjv6+Svr07see0Ytm/lHGdrq39AOgd6M+uk/EAHIxPonVTL9r4KF/q3Zwc0VTbaTjiRAxjQnoocWzVXIljXoFlHFs1x8PNxcL9ToiIS2JMt0Alfn5NKLpZWkMZLTeW0W6B7Ik1KaNhEfx7tHkZ7dTcGxcHZSZIsF8TMvOKFL2YBMb0aG+oE02V9CwoqqZXpNSJFk0VvR7t2ROdcNt4XLyWTc+A5gA0cKqPk4M9cZeVgWJEdAJjegYrmi2aUlRSdhtNH0WzZ3CV5trIk8wZeh+2NorNuYGhvNZKYz/l3VZwXXm3nT1eNZurioIcyE4HWa4xGBHQGTk5Hior/ll6AL7+yDkZkJsJukr0pw8gAruZeZEvxkFFufL35USEi2GmlWdT0GiQE2OU3+WlVf5qpGlL5JxM5UuyToc+9giibRdzvUvnjHpXkhDO7sqFnAzFGAFQlA/FBVDfqfY43sK7hRJPg7YcdxQRYD7TjpTzRu20i0btO6XJrfbQkIfxxyw1qtpD8zwUjRojpyZWaw/NvwxaQ7QIQM66CtczlDw8th+pU29zTyZGG2FnX/W3fPki5CuGejk9BWxsQWvdQH+nJEUdoiQ370+FYUazVsjZ15S811WiPxWFaG8+mJIvxBrzLSVBmbEDShrb2CgzQLRapbwW5f/zNH1bIV830Tt9ABHU3VwvyaQepiYiXBtYBCOCeyGfP1V7PfTxV+phbpZSD6MPIdpVq/fJ8Sb1/oKx3gN4NwcnV8VgeodEnD7LmN6GNr+lb81tfktf621+wztr82vWr70vABDc0pdGVvRrDPdPtsGO9nZV/m6WV5jNXLzFtpPnGN5FmZlwd/3SpsZ+aWzibeNRz9YGrUbpw5RVVCKAuGu5+Lo64uPqiK1Gw7C2vkRcSDO7L+JCGmODlLZ7SBsfjqRmIFd7t209m8rwds2qfgd7N6SRYz2rzxF7NQdfdyd83JwUzXbNiEi8YuYn4sIVxnZQjCtD2jbjSIqlZnXCopOqZlpIQuBm6N/Epl/H190ZHzdnRS+wBRHnL5vdG5FwmbHBijFhSDu/qvFFPRstWkO/r6xSZ9aH0ullSit1VOr1lFbo8HByQEXlTvkr9pC4KctyR1mWg4By4DGTa9OAA8BUAFmWS4FngS+FgjcwD3ilFo1pwHFgXDX3WMD0U+pUINqg9aQsyx2B4cBFwzN2lGV5A/AHECTLcgcg8Q70zXFyRS4wmXlQmAdObjX7N6HKAKG1hXqOCL82YKUDmFlYgpeLY9VvL+f6ZFUbtGcW3qjyo9VIONnZkl9y+y9Ln+05zqw+HahnY31yTFZBEV6uxg6vp6sTmdUGQpkFRXhW85Nl8BMRewEPV0faeHua35NfxO6YRKb06cTN8kpcHYwdRE8XRzILzRuXzMJiPE3i7+lijH9OcQmNDAOsRs71yb1xE4CAxg3ZHXcRgJgrGVzNLyLT0Gi9t+0wLwzpUdWwK2lnHKR5udQnq8ha+ip+jOlbZuZnV/wl2jZuiK0V4091MvMK8HI32r683FytDtb/LFl5hXi5Gwf7nm7OZFbrhGTmF+LpZurHhaw8xU8rb08izijGiZ3H48jIVZ4xNfM6AsHcZd8zYdHnrNj8h4V2Zm4BXg1M4ujuSlbuHXSGTdh17Axj5r/Lv5Yt59p1y05+Vn4RXiadJ0+XOyyj+SZl1MWJNlaWCd1i45EY+hq+rGTlF+FlMpNCSc9qevnV9Nycq/QA1uw/wdh3vuXVn7ZQUGIor009iYhJpFKnJ+16HmcvXyMjt7AGTacaNE38mMQxJSuHk0mXmfL+Ch5ctorYFONXwPScfMa/+y0PLlvFiYxqeePkhlxkkuZF+Xf8bjNFtOt6Z1Pv61oPEC7u5tOwC3LNBx7VkLoPRH9eWSYiGjVBvnkD6aEX0Tz/IdLIB0HcvhkVzm5g1l7kKm416XXph97ashPvFqDRKgOqO8XZDbmwWlt1G23ROQQ56c4HXIqGq6WG053Z+OXMNMUAUUt7aIFrQ+Rck2UyedlWB6tS6ChslqxCM2kulWu+sLguuvRFvpx0Z4asOkS4NjAvo/nXb19Gew7+f+ydd3hUxfrHP7MbQhLSQ0IglEAIAUIHpQihFxFFqoCKKBdRL/aCRqUpRUUseEWl2EBBSkBA6SVU6S10SEJNQkiHkLI7vz/OsiW7aQqr9/7m8zx5nuw5s/M9M3vOeee85513MB43TYNJOIU8fRT9e9+hf/975ImDtlEH/xBN4Vv0Orxesl7rbhhPHLCvp3kH5IHtJWrB7eveaipS5nWETwnX4T2dMd6OuhAC/QOPY1w9v1Qda5IzsorYfIuddQYpRcYcVfx9SL4D+n/VBgN8uiqWLuNnsWrfcZ7vbTutODe/gG0n4+neLMKi52dr6xzrWZcpYoNj9/Pw1Dm8vWC12QYDHE64zIOTZ9N36hzG97yH1Bu3CPa2PEgHe3mQkmMpD5Cck0uw6WHbRWcaG+baOsTWnLzAAw1qURaSs28S7GU1HvWuREp2rn0Zb2vNCmSYIh4uZ+TQf84qhv+4ln0XNAd21i3teGZuPcyAOat5aelWUk3tSM6+QbB3UT3bqDub8a9Oh5ebq1nv8KUUHvxyGX1nxTD+gXa46HRU8a7Ek20b0fWTRXT8eCGebhW4L6z0qcz/tQjxz/77L+ROJ7XcBtQFEEJ4AvcBIzE5JACklGuAq8Bw4BNggpSy2FcLQogwwBN4B80xYc0FwE0IUUVoLtZeQJHJq/ZIKddJKQtNH3cD9hP5S8TRj12yp9LM+TjkmaPonopGN2A08tJZMNq/XZeO6hNFyzgoUsKJeOJqKheuZ9KtYe1iyzhyuIoiwo7LaEbk63U7eb53B7v9U5dt4NWHOpneqNtXUCaNUq6xUZ1akZmbR79Pf2bBjiM0qBaIXifYciIef093IkMCLfU7rKF8x3AmOY0Z6/5gQl/79jrCcX13/sbhyGtenv59/8n+/LzpDwZO+g83buVRweRsKTQYOXA2kQ9HDWb+m0+zYe9hdh09aVuvo9+2HG3s1LIxG2dOYsWHb9O2cX3emvWDXZmynPfF9XVufgFfr99tNwCy5o8ziSzbfYRXH+5SYl2l6pn6fEhUS9ZO+jfLokcR6O3Jh0s3ANC/bTOC/bwY9MFcpi5ZT7M61dGb3tY47Mcinx3+zqZCBoORrJu3WPjGSF7r351X5ixFSkmgtycbJ7/IsrefZuyAHryx9Tg5+YV29RRRKmV/ESp5Q2AIxMeV73tO0yv7/Vu0iELUCENuNs011+kQtRtgXPkDhk/HQkAVxD2dy69XTBNF0/sQIXWQ22zzPODpi37gsxiWfVNipMhf0m7SFlGtNnJHqSb0T2vYcT4OefYouiffQtf/aeSlc1rOhz8h6UjUuHklBW+NwLBkDvo+j9pWUa0WLgNHUvjDZ3bf+/tx1KfFnKOtOiFq1kVuMuVfqFwVgqtjGPcUhnefRNRrAmGR/0DNclyHLTtq1+Gm5bY7vP0QVWsiTx4sRasYveKuhebtEdXDkFt/1T636aE5J8qZW6Wke7QzKO8Y8U7WW5rdfKlPFJsmPkufVg1ZEGvraNpy7CwtaofgW8m9+LqKHpPDvjbZ4PYtWDv+GZaNHanZ4JhN5jJNQ0NY+fYofnl9BLN3H6fAUPr9p7Tf9fCVVNxc9IQHltEx62hj0fF+MWOMQE93No4ZwLJ/9WFst1a8sXw7OXn5GIxGkrJv0rxGIEv/9QDNqgfy0cb9xdZVlmO6fUhNqwex8rn+/DLqIWZvP0JeYSGZuXlsOnWB9S8OYssrQ8jNL+TXI2dLF1IoTNyxHBJCCBfgfmCNadPDwBop5WkhRJoQooWU8vZd5yVgD3BGSvljKVUPBX5Gc3ZECCGCpJTWr4iWAIOAg8ABIM++ihJ5ClhUdKMQ4mngaYBZD7ZjVCurxG/Z6Qgff8sF6+2nvdkrI3L7KuR2bR6Z6Pc0Mi3ZrkywdyWSrELSkrJuEORVyWGZYB9PCg1GsvPy8XGvWLQqM4cvJBN3JZVu0xdgMEqu38jliTm/0rNVIxbv0t7INa5ZlSQrr3JyRjZBVpEKAMG+tm9rkzOyCfTx4mJqOpevZ9Lvg3nm7QM++o5Frw7nj9OJbDKF60kpmRd7gPCqAXSLDCM5M8c8BcKs4eNpjm4ASM68QaCp/QGeHlzLukGgdyWuZd3A32S0PN1cmTK4m1mj+wffU93fh98On2HzyURiT18gr9DAjbx8Cg0GGwuSlHnDLrws2KcSSZk3HPZvUmYOL/y8nqkDOlPTv/hQxwUbdrJkq5aErlHtGiRZRQskpWeUK0yyJBZs2MnirbsBaBxa3RzVAJCcnkWQr22Yd7CfN8np1mUyzcdSp2ogc159EoCEpFRij54yfceHe+qF4mf6HaKaRXI84SLnrySzZJOW5KhRWC2Srlu1MS2DwHJMzfDzspxrg7rex8c/aQPQBWu3snjdFq19NYNJsor4SM7MJsi7LOeoJxdTM7Rz9MNvzdsHfPQ9i159nEBvT05dTmHcz2vp06ohT36+QNOrVZUkq7dKyelZ9tdEkQiG5PQsAn21MpWtjm1Q++Y8+6V2u3HR63hzYA/zvu7vfsGMmA18sWoLjWtVK6KZ7fg3tO4H03V4e1/35vURQtAkNASdEKTn3MTfq5J5GkdkrWrU8HYnIesmjSqbzsPsdISXn+Xe5uVbrnsbgGjQSsshUJYHS2frATLzuiXUHMDH3zbi7Xa94U3QdRuA4ct3waA5bWTGdbgcr02DAOSxPYha4cgScunJrDTt7extvP1to0Ju64VFouv4EIa5k816gJZIb/hrGDcshkvnytRGM1lpCO+itsqB/79OQ0SHBzF+N9VWu0wa6fYaOeWxh6vNiTZFv1EO7aEd6akIf4uDGb9AZIb9b3gb454tVHjsBcyuf7/KuPx7PIVzP4RrV4v93t+FzEi1PUd9K9tGoZgQ9Zqi6zEIw+fRUKj9bqJJG2TCaXMeDuOJ/YjQCOS5kh12ztaUGUWvwwDH12G9Jui6D8TwxTt256Zodh/y6B8OX+rY6WVet42i8QlAZjm4Dus2Rtelv5bbwqQnatVD1K4PbbpDRTfQu6DLu4Vxzc9231+wcRdLtmo3hEa1qxex+Zl3zOYXx08bd7E4VosWa1xEPzkt086OlKveLaYxxl+wwUV5oGUDnv16qc1Lgt8OnMS3kjv9ps016RWxwRnZBPkUsYe+Re1hllmvstX4clC7pjz79WK74wgLrox7BRfyDQaSsizRAknZNwkqMtUi2MuDJFPEQqHRNDZ0czXv//3EBZvpGqWh1WeJ0E3KumGv6e1BUtZNgr0rmTQL8HF3RQhhjtCNrBpADT8vEq5nE1nVH/cKerpFaEnFezaoxdJDZ011VSIpq4he0fGvt2n8e1vvlv3zRVigL+6uLpxJyeBSejYhvp7m8Xj3BrU4dDGFh5rULXM/KP5/cyciJNyFEIeAfWgRC3NN24cCC03/L8QqukFKeQXYBJQlc9YQYKGU0ggsQ3M+WPOLadttx0WZEUK8DRQCC4ruk1J+I6VsJaVsZeOMAG1A6l8FfCuDTo+IbK0NiMsmCu6mm2NQdUSV6uDAaDcKCSLxeiaX0rLILzTw+9GzdK5ve4PrXL8Wyw9qc+TWxZ2ndZ1qJXq/h7SOZOvYx9nw2qPMH9WX0AAfvv/XQwyLaknM2KeIGfsUXZuEs2LPMaSUHI6/jJdbRTsjEujjSSU3Vw7HazkVVuw5RpfG4dSrFsT2KS+wYcJzbJjwHFV8vVj6+ggCvT3ZNe0ljnzyBkc+eYN76takhr8PXRvW4XBiEl5uruYpGGYN70pUqujKYdPcvBX7T9AlUktI2LlhbZabMjEvt9qelZtnzuWwZE8crWpXw9PNlVfub8fm1x9lw6vD+HhwV1rXDmHW4/dr/Zt+u3/POe7fQ1b9a5oLmZWbx7M/ruHl7vfQopZ9pmdrHu3Wjpj3XibmvZfp2iKSFTsOIKXk0NlEvNzdHc4b/TM82q0dMROeJ2bC83Rt3oAVOw9qv+G5C3h5VLQbBAX6elPJrSKHz13Q+nfnQbo00/JyXDdNnzEajXy1ajODO2rzeu9rFM6pS0nkmhw6e0+cISykKo/27EjMB9HEfBBN11ZNWRH7h9bGM/F4ebiXK1eE9RSWTfuOUCdE699He3Yk5o0RxLwxgq6Nw1mxN05rX8KV4s/Riq4cTtDmPa7YG0eXRnWpVy2Q7ZNhJUFaAAAgAElEQVTHsGH8M2wY/4zpHH2CQG9PrqRl8cK85Ux7/AGe792emOhRxESPomuTCFb8cdR0TVzCy93N/OBv0fPS9OIvaXp/HKVLE+2+YZ37YcOhU4RX0x6kcvMLuJmnhVXuPHGeYD8vVk/4NzFvj6Zr0whW7D6saZ6/hJd7RceabhU5fN6kufswXZpqml2aRvDHqXgAEpKvU2Aw4OfpQVr2DQymB/eL19JJzLpJdS+rgc/VRPAPAp8A7d7W8J5yh/CLBmWfPuF0PYCLZxGVq2q6ehd0zdsj4/bZlgmpjW7gaAzzpkGOVYjzxXPg4alFZYCWuK+0cPjL5xEBweAXCHo9usZttDnv1lStha7vUxgWzIAbVnp6PbphL2E8uA0ZV/IKAg65Eg8BJlul1yMatdaSWloTXBNdnxEYf/4MbmQ7rqc0DRt7eO+ft4dBNRzaw6LI+FOIKiFQOVj7De/tiDy0y7ZQUDWLTJPWyNvJC90r4fLiexQum4c8W/YEhU7lwhlEYDWtX/Uu6Fp00B68raleB92Q5zDMfl/LLXKb9GuIupGg02m/R1gjSLadj/6P0Lx4BhFY9Dosch2H1EY36FkMc6bY6pnQNW+PPLDNbrtDLp2zvQ6btkOeKHLdVwtF1/9fGL770OY6NC6ciWHqvzF88DzG1fORB2IdOiMAHu3alphJLxIz6UXN5u802fxzF/Byd7tjNr84hnVtS8zEF4iZ+AJdmzcsMhZw+9MOkWFd294RGwyQkGJxPG0+dpY6VSwO2+zcPPaeu8g7A7sR8+ZIYt4cSdcm9f70uBSwyTex4fBpwqtqNvhSagaFpoiIy2mZxKdl06FONRLTs7mUkUO+wcDvJy7Qua5tEHXn8BCWH9Ps67qTF2lds4p57G2UkrUnL9C7jNM1ABpVCyAxLZtLGdma5vFEOterUUSzBsuPaA7pdScSaR0ajBCCtBu3LDY9PZvEtCyq+3kihKBTeHX2JCYBsDs+iTBTYs5GIZVN41+TXtx5OkfYrobWuV4Nlh/WXiKuO55A69paPrdL6dkUmvQuZ+QQn5pJiK8nVX0qcfjyNXILCpFSsjv+KnUqly89338Vf/eUjP/BKRt3IkIi15SrwYwQIgDoAjQSQkhAD0ghxBvSEutkNP0VixCiCRAOrDdd7K7AecA8GVRKmSSEKAC6Ay8C7RxU5ajuJ4A+QFerYyob0ojx9/noHn0FhA55aDtcu4Lo9DDySgKcPqQZtsFjwK0Sol4z6Pgwxq/eBZ0e3QhTyoq8XIwxsx1maXfR63i7T3tGff8bRqOkX8sIwqv4M3PDXiJDAunSIJQBLeszdslmes74GV/3ikx/pJv5+92mLyAnr4ACg4GNJxKYPeIBmxUkiiOqYRixcefpNelr3FwrMPnR3uZ9/T6YR8xYbXWBcYN7Er1gNXn5hXRoWMe8ekFZqOLjiV5Ken34g6YxqKtF49OfiXlJ812N69eJ6F82kFdQSIeIWkRFaDf4UZ1a8vKCNSzde5yqvl588tj9AJxPSePNRevR6wRhQf68N7CrvbhN/97HqO9/x2g00q+FqX837iOyWmWtf1tEMHbpZnp+slDr38FafT/9EceFtCxmbTnIrC3a4H7OE70JKCZh0W06Nq1P7JGT9HzjA21JzJEW31q/dz8h5r2XAfho0WpW7z5Ebn4BnV6ezMCoexjTrwdHz1/k+Zk/kHXjJpsPnWBmzHpWTXnVTieqSQSxR0/T660ZWv8+1d+iM2EmMRO0lT3GPf4Q0XOXav3bOJyoxloCz9/+OMJPm7U3Id1bRNK/vZaAz6eSO0/0aM/g92chgKiWTejUwjYBXcfmkcQeiqPnixO0Nj5jWX2k39gpxHwQrbVxQQyrd+zT2vjc2wzs3I4xgx5g/potbNp/BBedHh9PD6Y+a5+5O6phHWKPn6fXe7Nxc3Vh8rD7LRoffkfMGyO09g3uTvSC37X2Naxd6jk6a+0OMm/kMmmxlhvDRa9n8ZsjiWpUl9i4s/Qa/x+tPx9/0KI3ZTYx0aM0vaH3E/3DSvIKCugQWdec3Xt6zEZOXkpGIAgJ8GHCMO2aSsu+waiZP6ETgiBfL6Y90dfSxkbhxB47S69xX2iawx+yaE7+2rxKxrihvYn+foXWxsi65lU9+rdrzjs//spDk2ZRwUXPlOF9EUKw78wFZq7agotOh04nGN82At+KVgn9pBHjukXoHnlBu7cd2QmpVxEdHkReTYSzRyC4Frr+z4Cbh5YPp30fjHNNS9X5BGg5AC5YEoiWiLP1AIxGjMvmoH/6XRA6jHs2QfJFdD2HIC+dRcbt03JDVHRDP1y7vmRGKsZ507TjXfk9+mcmgAB56Txy94bS9VZ9j/6JN0zLfm6FlMvoug5AXo5HnjyArtdQcHVDP+QFk951jAtmIBq1QYRGIDw8oYWWUd2w9GtIulCCYBHt3+aje/w1rX8PbtNsVed+yCvxcOoQuh6PgGtFdINN2fczr2vOibIijRjXLEA37GVN47DJHnbsi7yaAKcPQ9VQrX63SojwptCxL8avxmn28Ik3tXrycjEud2wPHbWrcMEXVHh5Cuh0GLavRV5JRN93OMaE08jDu9F37Yto0BwMBriZjWHuRwDouvZFBIVoUzhM0zgKZ7xV7sgca0b+NI96ndrjWTmAqRdPsHL8FHbOKy34s+T2GZd8jf65Cdo5s3sDJF1E13sY8sJZ5LE96PqOAFd39E+OBUCmX8M4ezLy0E5EvSbo35wJSOSJA8hjZXDYOVvTaMS4dDb60ePNy++SdBFdr6HIi2eRcXvRPfSEdh2OeN2iN3eq9n2/QC2KowwOLLPeinnoR0Zrenu3QPIldN0Hadfxif3oej+mXYePabZYZqRi/P6jstXvgI5NIjSbP/Yj87Kft+k37jNiJr0IwEe//Gax+a9M0Wz+w901m//Fj2TdyGXzoZPMXL6eVZNfKbN+VJMIYo+coteb001jgYEW/fGfEzNRu99M/+V3Vv9xiFv5BXR+dSoDOtzDmIe7FVftX7bBn6yMJT4lDZ0QVPP3ZvxgS6TghiOnuS8iFI+KloiDqMgwYo+fo9ekr3CrUIHJjz1g0Zs2l5g3tdz24x7pRfT8VZpegzrmld+mr9jEyUspCAEh/j5MGKId74HzF5m9fjcueh06IXi3eysCPd15u3srRv2yBaOU9Gtch/BAH2ZuO0JksD9dwqszoEkYY1ftoufXK/F1d2X6Q5alyfddTKGKlwc1fG0dJtM3H2T18URuFRTS+T/LGdA0jDHttYT2Ljodb/e8l1E/b9TG+03rEh7oy8yth4isGkCXejUY0KwuY1dsp+eXy/F1c2V6vw4mvWRmbj2s2XQhGH9/a3xNkQyvdGnBmyt2MG39Pvw83Jjcp51Fr3dbRs1fq7WxWTjhQX7M3HxAG/9G1GRAi3qMjYml5+eLtfHvwE5an11IZvaOI2a9dx9oh5+HG34ebvRoEMrAr1eg1wkaVA1gcMsiL3MVihIQ5X0Wt6tAiBwppWeRbaOBFlLK0VbbtgLvSCm3mT5/B6wyJZksru6pQJaUcqrVtnigE1AbeE1K2UcI0Q4IklIuF0JMAHKklNNN5UNNOo2s6ugFzAA6SilLXkAeMEx66q91UnlpUHqW8TtKCUnO7hq5N0ovcyfJL+9Mnr+GqOH8MDVpcG5iNlHJud5vmZzoVD0quJZe5k5TSoLEO07RZT//x5DXSr2933ncS3ZM3mmEs9d515eewPdOY0gowxv+O8gL83Y7Ve+L5zs5Ve9vwdnnjatz79+6vkNKL3QH+atj9z+Fo+ledxOdk8+ZSwnO1QNtVRqn6v21FYX+DPphY/87X9mXgOGzl/+GC7Ds6F/85L+uz+/WlTAUmFZk21JgGFouiLIyBC0vhTUxpu3mWEIp5c5yHt8XQEUskRe7pZTPlPwVhUKhUCgUCoVCoVD8v0Xn5JdH/w/4yw6JotERpm2dHGz7vMjnEWWo2245CCmldbzaFgf7JxT5nAA0KrJNZVlRKBQKhUKhUCgUCoXib0S5eBQKhUKhUCgUCoVCoVA4HSdPXnKMEKIxUDQDVJ6UsvXfcTwKhUKhUCgUCoVCoVDY8F+6ksU/mX+EQ0JKeRRoVmpBhUKhUCgUCoVCoVAoFP8TqCkbCoVCoVAoFAqFQqFQKJzOPyJCQqFQKBQKhUKhUCgUin80asrGHUdFSCgUCoVCoVAoFAqFQqFwOsohoVAoFAqFQqFQKBQKhcLpqCkbZUAWFDhVTxiMTtUj/TpUDnKupl7vXD0nh1dJo8GpegA4WVPmpDtVTwRUdaqeTL3iVD0A9OqWfCcRgYFO15Spqc7V8/Bwqp74G0JVhd65706+eL6TU/XGzNziVL0vxnR0qh4ABifbxPx8p8o53eaLv+F9orM1DYXO1Qup5Vw9gMuJztdUKP6BqNGvwvnOCIVCoVAoFAqFQqH4b+PvcAj+j6N6VKFQKBQKhUKhUCgUCoXTUQ4JhUKhUCgUCoVCoVAoFE5HTdlQKBQKhUKhUCgUCoWiNHRq2c87jYqQUCgUCoVCoVAoFAqFQuF0lENCoVAoFAqFQqFQKBQKhdNRUzYUCoVCoVAoFAqFQqEoDbXKxh1H9ahCoVAoFAqFQqFQKBQKp6MiJP4kom5jdA88DkKHcf8W5LZVtvvb9ULXshMYDcgb2RhjZkPmdQB0w19HVA9DXjiNcf6MYjW2nbnI1N93YZCSgS0iGNWhmc3+/EIDby7bQtzVVHzdKzJjUFdC/LzYee4SM9bvpcBgoIJez2s97qVNnRCb7/77p7VcTM/m138PtNkupWTK4nXExp3D3bUCUx7vQ8OaVe2OLe7CVaJ/XMmt/EKiIsOIHtQDIQRfrI5lyY6D+Hl6APDSQ53p2KguK/ccY96GXebvn76SwpKXhlG/WiBTVmwl9kS8pvdIDxpWD7LXu5RM9MJ13CooJKpBbaL7dkQIwZrDp/nPut2cT0lj0QtDaVSjivk7p65cY8LSjeTcykcH/PJMP/bEX2HqbzsxGCUDW9ZnVMfm9n26ZBNxV1Lx9XBjxiPdCPHzIuPmLV76eT1HL6fQr3kE7zzY3vyd1YfP8k3sQQQQ5O3BBwO74Ffsr1qkr39aSezRU7i7ujJl5EAa1gqxK/fp0rX8uvMgmTdz2T9rYtnqXfgbsUfPaH36ZD8a1qpm36eJV4j+dpn2GzYOJ3pIb4QQnLhwlYnzV5JXUIiLXse7j/ahSe3qAOw5Fc/Uhb9TaDDg51WJH974l732z6uJPXpa035qgGPthMsm7QKiGtcjeugDCCE4efEqE3/8lZt5+YQE+PLhqEF4uruV3NYfY4g9dAK3ihWY8vRQImvXsO/DX1azYvs+sm7cZP/cD8zbF27cwU/rd6DXCTzcKjJx5GDqhgQ71lm2idgT53Gv4MKUYb1paHWumdt1MYnon343nad1iO7fBSEEn/+2nU1HzyCEIMDLgynDehPk48ncTXtYte84AAaj5HzydbZPfREfDzemLF1vuQ4f60PDGvbHFXfhKtHzV3OroEC7Dgd0RwhLsqV5G/9g+vJN7Jj6In6eHmTezOWdBb9xMTWdii4uvN+iOuF+nraV1m6Irttg0OmQh3cgd6+13V+jLrqugyEoBOOKuXDqgGWftx+6+x8HL+0KMC7+wnzfKxZn6/0NmiKiGbq+T4JOh/GPjcjNy233R/VB17orGIzIG1kYf/kPpKcCoP9wEVy9AIDMSMX47Qd29dvp1W2Mrvdjmn06sNWxfWrRUbNPN7Mxxsyx2KfHXzPZpzMYFxRvn0okrBG6XsO0/j0Qi9zxm61+mx6IFlFgNMKNbIy/zivb72ZdR2RL9I88q/Xp9jUY1/xis18X1Rtd5wfBaETm3cLw42daP1byQv/MO4ha9TDuWo/x5y/LptegBbr+/wKdHuOudcgNS233d+6Lrm137TfMycT40+eQfk07lodGICJbgRDIU4cwLp1drrY64vG5/6Fxn15kp1zjvcZt/nJ9YGrjgFFan+5aj1y/xHZ/577o2vbQzpucLIwLPrNq4xOIyHsAMK5diDywvWx6TuxTUb85un4jtevijw3Ijcts93d8CF2bbpb2LfwC0q8h6jZC9/BTloJBIRh/+Bh5bI+dhsUGnrKygfZ2PS7hMtHzlmr37sYRtjbwhxWaDazsy4ejBptt4KmLSUz4YTk5t/LQCcEv4/5NxQoVim3vXR1jLN1A7HGTfXr0gWLsUxLRC0z2qWEY0QO62dunFZvZMeUF85hxz5lEpi7bSKHBiF8lN354fuhds7+3OXrhKkM/WcDHI/rSs3n9u9a+PWcSGTN7GSEBPgB0b1KP55po45Vt568wdeMBbWzaNIxRbRraaOUXGnhz9W7iktK08X7fdoT4eLIyLoF5e06Yy51OyWDJiF40qOLHp7GH+fVYApm38tn/yiCb+radu8zUdfu054tmdRnVrpG93q87THquzOgXRYivJ5czcujz9a+E+nsD0DSkMhN6tyG3oJCXl8ZyMSMbnRB0Dq/OK11a2PWZQlEcyiHxZxAC3YNPYPjuA8hKQ//MJAwnD8C1K5YyVxMxfDUOCvIR93RF13OINsgE5PbVyAoVEfd0LlbCYDTy/uodzBnemyrelXjkm+V0jqhF3SDLo+7SA6fwdndl7YuP8NvRc3y8fg8zBnfF18ONL4f1IMi7EmeS0xj14+9see1R8/fWH4/Hw9WxEYuNO0fitTTWTHiWIwlXmLhwDYveeNKu3KSFvzNxaG+a1g5h9JcL2Xb8HFGRdQEY3qU1T3WzHSA9eG8jHrxXu+GdvpzCmK8W0SAkiK0n4km8ls6aN0dw5EISE5duZNGLQ+31lm5i4sCuNK1VldFzlrPtZAJRDWoTHlyZz5/ow4QlG23KFxqMjP15LdOG9qR+tUAy0tIRAt5fuYM5Tz6g9elXy+jcINS2T/efxNu9ImtfGcpvR87y8drdzBjSHVcXPc93bcWZlHTOJqfZ6Ez9bQcrXxiMXyV3pq/ZzYLdcYwJb1Dsb2vu66OnSEy+zpqpr3Hk/EUm/rCcRe/+265c52YNeLRrW3q99XGpdQLEHjtDYsp11kx+kSPnLzFxwUoWRY+279P5K5n4+EM0rVOD0Z//yLZjZ4hqXI+Pl67juQc7EdW4HluPnubjJev4/vWnyLqZy6QFq/jmxcepFuDL9eybDtp0WtOe8rKmPf9XFr39jAPtX5k4vK+m/dkPZu1x3y/n9UG9uCeiNku372fe2u288HC34tt6+ASJSddY83E0h88lMum7JSya+LJduU4tIhnWvT33vzbFZnufti0Z0vU+ADbtP8YH81cwe6x9X8XePk/f/hdHEq8ycfF6Fr3ymH27Fq9n4uAeNA2txuivl7LtRDxRDevwVJd7eKG35sT6cet+vly7kwmDezCyy72M7HIvAJuPneWHrQfwreTO1rizJKaks2bcM9p1uGgNi14bYa+3aC0Th/aiaWgIo2f9wrbj54mKDAPganoWu07GU9XP21z+m3W7qB8SxMxRAzifdJ335v3Ct72snHJCoOsxFOPCzyA7Hd2It5BnjsD1q5YyWekYV3+PaN3d7nh0fZ7EuPN3SDgBFSqCNNqVscHZen9LG3Xo+v0LwzeTIDMN/YvTMBzfB8mXLGUux2P4dKxmL9r2QPfA4xjnf6LtK8jH8MnrpbfLun19hmP4/kPNPo2e6Ng+fT3eZJ+6oOsxBONik33a8RuygiuiVZeyaxbV7/04xh+nQ1YaulHjkKcOQapFXyZdQH4zCQrzEa06I7oNRi6dVQ4NHfph/6bwk2hIT8Ul+nOMh3ebHTcAxj1bMMZqjhDRtA36QU9j+PwdKMjHuOIHRLVaEBJaZj3doNEY/jMOMq6jf+1jDMf2QNJFS5lL5zF89IrWp+3vR9d3BMbvPoLa9RF1GmCY9gIA+pemIeo2Qp49Vvb2OmDXdwvY8sU3jPjh679UjxmhQzfoGQz/eVdr4+szMBz9o5g25mltfPhJjN9+qDkGaoRh+OAFcKmA/sWpGI7vh1u5peg5sU+FDt2ApzF8NUHTe/lDTc/mOjyPYcZrml67nugeHK45Hs4ewzD9Fa2Mhyf66C+1c9oBsUdPk5icypopr2h2/cdfWfTOs3blJs1fwcThD9M0rAajP/2ebcdOE9U4gnHfxfD64Ps1G7htH/PWbOOFft0pNBgYO+cXpv1rEPVrVCXjRi4uen3x7eUujjGOn9fs4bujNfv0y1oWvfqEfRt/WcvEIb00e/jVYradOE9UQyv7dCrBxj5l3bzFpF/W8c2zg6nm78P1zCxN7y7ZX9DG2jNWxnJf/dC73j6AlmHVmTXayjlwOVEb76/fz5xHOlPFy51Hvl9H57oh1K3sYy629Mh5vN1cWTv6QX47nsjHWw4zo+99PBgZyoOR2rGfvpbBmKWxNKiijWk7h4XwaIt69PrG1iFtMBp5f80e5gzrRhVvDx6Z9zudw6tTN9DXonforKb33MP8FhfPx5sOMKN/FAA1/DyJGdXHrj+ebNOQ1qHB5BsMPLVgA7FnLxNV194B9j+BUKts3Gn+8pQNIYRBCHFICHFMCLFYCOFhta+fEEIKIeqbPrsJIU4KIRpblXlDCPFVKRovCyFuCSF8rLZ1MtU90mpbc9O214QQ/zEd13EhRK7p/0NCiIFCiPeEEEdMn9cJIexf4ZZE9TDk9WTNU28wYDy6G9GgpU0RGX8CCvK1/y+dRXj7W/adPw55JRhq4Ojla9T096aGvzeuLnrubxTGppOJNmU2nUzg4Wb1AOjRsDa74y8jpaRh1coEeVcCoG6QH3mFBvILDQDcyCvg+11HGR1lGxlgrvPIafq2boIQgqa1Q8jOvcW1zGybMtcys8m5lU+zOtURQtC3dRM2Hj5dWq+ZWb0vjt7NIzS9uHP0bdVA06tVlexb+VzLumGrl3VD0wutpum1asDGuHMAhFXxp3aQv53GjtOJ1KtamfrVAgHw9XDj+JVUagZY9Wnjumw6kWDb/hMJPNzc1KeRddh9/gpSSjxcK9AytCoVXWwHABKJlHAzvxApJTl5+QR5e1AWNh08Qd92zbW2h9Uk++YtrmVk2ZVrGlaTQF9vBzUUU++hk/Rt08xUbw1TvUV+w4xscm7l0SysptanbZqx8dBJAARw41YeADk3bxHk6wXA6j+O0r15A6oFaEYrwLvIm3Vg06ET9G1bTu22zdh4UIsSiE9KpVW9UADaNQxj3f64ktu6/xh929+DEIJmdUPJupFLSnqmXblmdUMJ8vOx2+7pYYm+yM3LL9bGbDp6hr73RGrtCq1mui5ybNuVmaOdp7VDtHbdE8nGo2c0HbeKFp38AhzJ/HbgJL1bNrTo3dvI6jrMK0Yvj2a1TdfhvY3YeNRyHX6wbAOv9u1s88bm3NVU2kSEAlAnOIArObdIzc23VFo1FNJTIDNVe0t4fC8ivIntgWZeh2uXQUrb7QFVtXmVCaa3NQV5UFjguEP/Lr2/Q7NmXeT1JEhLAUMhxkM7zG+SbyPPxVnsReIZhE9A6e0ojuphyLQUW/tU3/ZNlY19ungO4WNxymr26daf1w+po7U145rWv3F7EPWL2JuEk1B42z6eQ3iXJabMgqgdgUy5CqlJWp/u3YquaVvbQresHKauboDpt8zPQ56NQxaU4Vy5Ta1w5LWrcD1Z0zuwDdG4tU0ReeaopU8TTiF8K5t2SKhQAVxctD+9HpmdUa72OuLstp3cTEv/y/WYqRWOTLVq4/7YYtqo2QatjabzNLiG5gwwGrX+vRxvNyZyqOfMPq1ZpH0HtyMa3Wurd/aY1XV42tI+K0TTtsiTB8zlirLpUOl2/VpGFjm5eTSra7KB7Zqz8aB2T7GxgZF1zTZwR9xZ6lUPpn4NLWLV17MSel3JQ/i7NsYol30KsdinI2fM+z9YtpFX+3aysbmr9x+ne9MIqvlrtjrAq5JF7y7Z3wWxB+jeJJwAT8u47W61rziOXk2jpq8nNXw9cdXrub9BTTaduWRTZtOZSzzcqDYAPerXYHdiErKIfVp9PJHeDWuZPzcNqUygp7u93pXr1PT3ooafl6bXsBabTl+0KbPpzEUebqI5V3o0qMXuBHs9a9wruNA6VIsicdXraRjsT7KDl1YKRXHciRwSuVLKZlLKRkA+YP06dCiwHRgCIKW8BbwEfCk0QoDRwFulaAwF9gL9imw/Cjxi9XkIcNik9W8pZTOgN3DOdIzNpJRLgI+klE1M+1cB48rTYOHtB5mWt+RkpiG8ih9Q6Vp0xHjmSHkkSM66QbBVSFmwTyVSsm0f1JOzbxJscjy46HV4VXQl42aeTZl1x+NpUDUAV9OD9MxN+xjRrjHuFRwHx6RkZhNsZZiq+HqTXOSBMjkjmyqmh1StjBcpVk6Ln7bu4+HJs3n7x5Vk3rR3vKw5cJwHmkWY9G4QbF2XjyfJRW78yZk5VPH1tCrjRUqmbV8UJfFaOgIY9c0yBnyygLnbDpGcddO2T70rkVLE+WHd75Y+LX5wXkGvZ9xDHXj4i8V0/GA+51LSGdCyfonHdpuU9EyC/S0e6Sr+PiSn2w8WyktKehbB/paH7yp+3iQXGYQkZ2RRxcpzX8XPmxST9ptDevPRknV0eWM6Hy1Zy0v9tbfECcmpZN28xRMfzWPge7NYsfOgvXZG9p/Q9iHFdI6FhwSxyeQYWbsvjqQ0e+eCTV3pmQQHWPow2N/XoUOiJBas306PV95n+sKVRA/v77BMSmYOwX6253yp56mvFylWZT5dvY0uE75i1f4TPN+7vc13c/ML2HYynu63r4uMbIKt+8jXi+QijsHkzGyqFLlWb/fjpqNnCPLxon5127DWiJAgNhw+BcCRhCtcyckj+YbVPcPLD5lt9ZCTnWGemlAq/kGQdxNdv9HonoxGdO5f+lsEZ+v9DZrCxx8yUi0bMq5r24pB17oLxpNW15aLK/oXP0tRxXsAACAASURBVED//BQ7R4ZDPS8/2+kPWWklPvDrWkaV2z6ViJcfMsvKPmalldi/onkU8uzR8mn4BkDaNfNHmZEKfvYPj7pOD+IyeR76ASMxLCxHBEbRY/QNKPIbppboNNK16Y7x+H7tQ8Ip5Omj6N/7Dv373yNPHLR9K/8PQfgGmKcJAdp56uCB/Da6tlZtvJyArmFLLWKokrfm4POrXLqeE/tU+Ba5DjOvl6zXuhvGEwfstovmHUqcjlJ2+2tdxsdsf8NDqrDpkOacWLv3mNkGJianIhCMmvEtAyZ+wdzft5bQ2tvHcpfGGJnZtuO2Yu2T43HipqNnCPL1pH6IrX1KSEnTxhifL2Dgh9+yYs8xk97dsb/JGdlsOHqGR+6znQ59t9oHcCj+Mv2mzeXpWb9w5qp2D9PG8haHSLCXByk5tmPn5Jxcgr20Mi4609g019YptubkBR5oUIvSSM6+SbDJ2QOmsXB2rn0Zb2u9CmTkamOFyxk59J+ziuE/rmXfhWS7+rNu5bPlzCXahNpPc1EoiuNOJ7XcBtQFEEJ4AvcBIzE5JACklGuAq8Bw4BNggpSyWDe/ECIM8ATeQXNMWHMBcBNCVBHaa8BewO+lHaSU0vqOXAnzq5Oy4mgA6rgK0bQdIqQ2cvvqcimU5YAceSutx8ZnUtKYsX4PEx7sAMCJq9e5kJZFtwa1y1mnbXsdHdvtEkM6tGDtxOdY9ta/CPTx5MOlG2zKHY6/jJtrBcKrVi5TG4otU2wLNAqNkgPxV/jw0fuZ/+/BbDgez6kkB3OUi2o5qKto+60pMBhYuCeOpc8NYOvYx4gIDmD2VsehnEUpr1ZZkQ5qtvsNHYjfLrJwyx7eHNyLTR++xtjB9/Pu99p8d4PRSFziFWa98BizXxrOrFVbSEhKtamjLL9VSWXeH9GfnzfvZuCkL7lxK48KLiWHpJblfC2NR7u3Z92Md3h1SB++Wr7OsY6DbXbnaSn9/tIDHdg04Rn6tGzAgm22A90tx87RonYIvpW0txkOf5+ix1RM23PzC/h67Q6ef6CD3f5R3duSefMW/abNZUHsPhoEeKLXldZfZbw96vRQPRzjpqUYv5uG8K2MaNy29O/97Xp3W9NB/xbzpkm06KDlb9iywrzNMPkZDJ+NxbDgUy0PRYD9ILc0ueKaJ5q0Q1Srjdz+m+MCfwaHp1Mx7W3cFlEtFLmzVLNd5Itl61PjlpUUvv0UhmVz0fe2nwpYDsEy6QGIVp0QNesiN5nyE1SuCsHVMYx7CsO7TyLqNYGwyL9wLHeLcraxRl1zDgZ58iDGuP3oX/kQ3YjXkPEnwWC4s3p/uU/LMW5r2RFRIwy5yTbXC95+iKo1kSftnfGWJjiyb2W3v+8/2Z+fN/3BwEn/sbGBhQYjB84m8uGowcx/82k2HIhj1/GzxR4H3MUxhkP7VIY2ojnfv163k+d729sng9FI3MUkZo0exOznHmHWul0kpKTdNfs7NWYTrz7Y0S7S5G61r2H1YDZMfI6YN0fyaFRLnp9jun4ctK8opY2VD19Jxc1FT7jVtIti63K00W7c7aiIINDTnY1jBrDsX30Y260VbyzfTk6exTFSaDTyWsw2HrunPjWsnEj/c+h0/+y//0LuWA4JIYQLcD+wxrTpYWCNlPK0ECJNCNFCSnl7FP4SsAc4I6X8sZSqhwI/ozk7IoQQQVLKFKv9S4BBwEHgAJBnX4XD452M5hTJBOySOQghngaeBviyd2tGtQg375NZabZvuHz8HYYLijqR6Do+hGHuFDAUluWwzAR7VyLJyrublHmDICuPprmM6Y1+ocFIdl4+Pu4VTeVzeGHheqb270RNU/KZwxeTibuaSrdPfsZglFy/kcsT366i571NWLxDM7CNa1Ujycqbn5yRZZP8ByDY18smaiI5I5tAH+3GU9kqjH/Qfc15dpZtorHf9x8nxN+XfjPma3o1gkmyriszhyBvR3o5VmWyCfSx7Qu7/vPx5J6wEPxMD3hR9WqSmnPTtk+ziunTzByHfeqIk1c1J0dNU5KiXo3qMHtb8Q6JnzbuYnHsXq3ttauTlGY5b5LTMs3TI8rLTxt3sXjrHlO9ITaRBcnpWQT52NYb7Odt86YkOT3LHLK5Ytchoof01trTKpJxP2gPR1X8vPH19MCjoiseFV1pVa8WJy8lsfP4WRZv26dphzrQLhIKGuznU0Q7k0BTu+tUDWTOK1rOkoSkVGKPnLJr64L121myWUuQ2qhOTZKuW/owKS2jXKGn1vRu05yJ31qSuP207QCLd2lvjhvXrEpSuu05b3ee+hQ5TzOyCfS2P08faNmAZ79ZxvP3W6Ikfjt4At9KbvSbNtdKL8umLrvf0Nf2zVtyRhaBPp5cTE3n8vVM+k2bZ94+4MNvWfTaEwR6ezLlMW3up5SS7tGfUt3TKmlodjrCy88yYPHy1SIIykJ2OqRc1KZCAPL0YahWG9hZ4necqvc3aMrM65ZQcwDfAGSWvQ9ehDdG13UAhlnjbO3F7bJpKchzcZqD+7r9WymzXla67Ztfb3/biJDberft07zJ5bZPJZKVjvD2t/Svt7/j/q3dENGhD8bvppVfPz0V/APNH4VvZchIK7a43LsV8ejz8F3Z5sjbfT8jtchvWNk2CuT2cdRriq7HIAyfR0Oh1ibRpA0y4TTka5F2xhP7EaER2jSdfxAyIxXhV+Q8zXTQxoim6HoOxvDZW+Y2Ash1v2BYp9l73ROv2eYsKU7PiX0qM4pchz7FtK9eE3TdB2L44h2781I0uw959A8w2jpbfkpIY8mEmQA0Dq3uwAY6sr/WZTLNdqtO1UDmvGplA4+eMn3Hh3vqheJnGrNENY7geOIV2jasa3ssd3OMsWW3Vm/NqrbjtozsMo8Tzfbpg3nm7QM++o5Frw6niq8XvpXc8ajoyvI/jpJ58xb/mrWYdhGhd8X+xl1M5tXvVwJwLSuHlfuPM33FZtpGhN6V9gVaHXPHyDDeW7yW9Jt5BHt5kJRlmd6QlH2ToCJTLYK9PEgyRS0UGk1jUzdX8/7fT1ywma5RElpdlujgpKwb9nre2jEFe1cy6RXg4+6KEMIccR1ZNYAafl4kXM+mUTXN5oxfvZta/l4Mv7f0PGoKhTV3wo3iLoQ4BOxDi1iYa9o+FFho+n8hVtENUsorwCagLDGUQ4CFUkojsAzN+WDNL6Zttx0XZUJK+baUsgawABjjYP83UspWUspW1s4IAC6fRwQEg28g6PXoGrfR5hRaU7UWur5PYpj/Cdwof4hco2qBJKZlcSk9i/xCA78fO0fn+jVtynSOqMXyQ9qc8XXH42ldW8uxkJWbx7ML1vJyt3tpUdMSMjXk3oZsfe1RNrw8lPlPPUhogA/fP9mHYR1bERM9ipjoUXRtWo8VfxxBSsnh+Mt4uVc0OxtuE+jjRaWKrhw25axY8ccRujTR8i5Y55vYcPgU4dUsg0ajUbL24AneGtSdmFceI+aVx+gaGcaKfSc0vcSreLm52hmRQO9Kml7iVU1v3wm6mBL3Fcd9EbU4dTWV3PwCCg1G9sZf5b661Um8nsmlNFOfHj1L5/q2N/DO9Wux/KCpT+PO07pOtRLfKFTxrsS5lAzSbmjhbjvPXaZOYPHhycO6tiVm4gvETHyBrs0bsmLnQa3t5y7g5eH2px+mh3VtS8z454gZ/xxdm9Vnxe5Dpnov4uXuZn7gv02gr+k3PHdR69Pdh+jSTJtqEuTjxd7TCQDsPnmeWqYcHV2aNWD/mUQKDQZy8/I5cv4SYVUDGdalDTHjxxAzfozWpl3W2hUda7tVtGjvOkSXZprxup6lDSiMRiNfrd7C4E62c3xBi2iImfI6MVNep2vLRqzYvhcpJYfOJuDl4e4wV0RxJCRZwr63HjpOrWDLgHVYhxbEvDGCmDdG0LVxXVbsjdPalXDFdF3YDlACfTy1Pk3Q8o6s2BtHl8bavSPhmuWBcPOxc9SpYnFoZufmsffcJd4Z0I2YN0cS8+ZIujapx4o9xyzXoVsxem5W1+GeY3RpHE69akFsn/oiGyY+x4aJz1HF15ulbzxJoLcnWTdvmfPJLNl5mFZVfPB0tfJLX03UpiX4BIBOj2h4D/JsGcP5ryaAmwe4m46zVoRtokiH33Gy3t+hefEsonJVTVPvgq7Zfci4vbZlqtVGN2A0hm+nQY6VvXCvBHrT7+PhhQitjywt3P/yeYR/FfCtbGWfirzRDa6F7qERGBZ8AjeyHdfzZ7kcDwFBmr5Oj4i8F3mqqH5NdH2ewLjwc7hZfn2ZcAoRVE2LFtG7oLuno5bU0pogS2oo0fheZPLlP9MajQtnEIHVwN+k16KD9mBqTfU66IY8h2H2+5BjNW0s/RqibqTpzZUeEdYIkm3na/8juN3G233aMgp5tMgqEtXroHvk3xi+ec+2jUIHHqb7fLVQLeqlhCgCGz1n9enFM4hAq+uweXv76zCkNrpBz2KYM8VWz4SueXvkgW1224eF+hMz4XliJjxP1+YNitj1inZ2PdDX22QDL2j37p0HHdvAVZsZ3FGzgfc1CufUpSRy8/IpNBjYeyqeutXsVyS7q2OMsU8RM/YpujYJ/2v2acoLbJjwHBsmPEcVXy+Wvj6CQG9PujQOZ//5SxQajPRr04RAb09mPd3/rtnf9eOeZsP40WwYP5oHWjRg+hN9WT/h2bvWvmtZOeZIhyOJVzBK8HV3pVFVfxLTs7mUkUO+wcDvJy7QuW51G73O4SEsPxYPwLqTF2lds4p5bGqUkrUnL9C7DNM1ABpVCyAxLZtLGdma3vFEOtezXZ2sc3gNlh/RcrWtO5FI69BghBCk3biFwaglcr6Ynk1iWhbVTSt1fbblIDl5BbzVo/SphQpFUe5EhESuKReDGSFEANAFaCSEkIAekEKIN6Ql7sho+isWIUQTIBxYb7rwXIHzwH9ul5FSJgkhCoDuwItAu3Ie/0/AamB8mb9hNGJc9QP6J17Xlsc6EAspl9F16Y+8Eo88eRBdzyHg6oZ+yPPacWZex7hAy5quH/kOBFbV9r/2Gcblc+zm0Lrodbzdux2jfvwdo1HSr3kE4UH+zNy0j8hqgXSpX4sBLSIYu2wLPT9bhK97RaYP1LKi/7QnjgtpWczaeoBZWzVHyZzHexPgILlNUaIi6xIbd45eE77EzbUCkx+zZNLtN2U2MdGjABg3pBfRP64ir6CADg3DzJn9p8ds4uTlZASCkAAfJgy93/z9fWcvUMXXixqV/SBX885GNQgl9mQ8vaZ9h1sFFyY/0sOiN2M+MaZMyuMGdCF64TryCgvpEBFKlCkj8oajZ5m8fAtpObk8O3cF9atVZvbT/fHxcOOJqBYM/uxnBIKo8Op0aVAbvU7PqO9/0/q0ZQThVfyZuWEvkSGBdGkQyoCW9Rm7ZDM9Z/ys9ekjlhUeuk1fQE5eAQUGAxtPJDB7xAPUDfLjuS4tGT7nV1x0Oqr5ejJlQPGrp9j0dZMIYo+coteb07W+fsqyBGu/8Z8TM1HLID79l99Z/cchbuUX0PnVqQzocA9jSlh5IqpxPWKPnqHX259q9Y6wpF7pN/FLYsY/p/XpYw8S/W2M9hs2CieqkWa8Jw7vy9SFv2EwGnGt4MLE4X0BCKsaSPtG4Tw88Ut0QjCwQyvCi8yR1LRP0yt6Bm6urkx+sr+V9hfEjB9j0n6I6HlLTdr1iGqsObR+23OEnzZrg9LuzRvS/76Sl43q2KwhsYdP0PPVybi5ujLlafPsMPpFf0TMFG11go9+/pXVOw+Qm19Ap+cnMLBTG8YM6MVP67axM+40FfR6vCt5MHX0MMd92rAOsSfO0+v92VqfWp3X/T78jpg3RmjtGtSd6J9+19rVoA5RpulRn6zcSnxKOjoB1fx9GD/IsnrDhiNnuC8iFI+KlrcdUZFhxB4/R69JX+FWoQKTH3vAojdtLjFvarl8xz3Si+j5q8grKNT0GpbsqDufnMqbP65CLwRhwZV5r3URZ6s0Yly3CN0jL4DQIY/shNSriA4PIq8mwtkj2sNs/2fAzQNRtzG074Nx7iSQEuOmpeiGvgQIZPIF5KFSlv5ztt7foWk0YoyZg37UO9pyg3s3QfIldD0fQV48hzy+D12fx6GiG/rHX9UO8fbynkHV0Q98WoufFQLj5pjS8w8YjRhX/4B++BugE5p9umayT5fjkaes7NMj2vUoM69j/OlTAPQj39ZC4l3d0L/6KcYVc8uX40EaMf62AN1jr2r9e2gbXLuC6PQw8koCnD6ErvtgcK2IbpB2LyLzuuacKCtGI4afv8TlpcmaDd6xDq4monvocWTiGeTh3eg6P4SuQXMwFCJv5mD41hId4TLle3D3MDmI2lL46ds2K3Q47NMlX6N/boKmt3sDJF1E13sY8sJZ5LE96PqOAFd39E+O1boh/RrG2ZORh3Yi6jVB/+ZMQCJPHEAe21u8VhkZ+dM86nVqj2flAKZePMHK8VPYOa+0gNMSMBoxLv4K/XMTtfN09wZIuoCu96PIC2e0Nj78pHaePvWmpY3fvA96PfqXpmn13LqJ4YePtQSXpek5s0+NRoxLZ6MfPd68/C5JF9H1Goq8eBYZtxfdQ09o7RvxukVv7lTt+36BWhRHKZEtUU0iNBv41gyTXbeygRNmEjNBGxOOe/whoucu1e7djcMtNvCPI/y0WXOudW8RSf/2WnJQn0ruPNGjPYPfn4UAoprUp2PTkvNV3bUxRsMwYuPO02vS11q9j/a21PvBPGLGakukjhvck+gFq8nLL6RDwzpENaxT4vGGBVemfYM6PDxtLjqdYGCbxoRXDaRucOW7Zn+d2b51h06xcPtBXHSCihUq8PETDyGEARcheLt7K0b9sgWjlPRrXIfwQB9mbjtCZLA/XcKrM6BJGGNX7aLn1yvxdXdl+kP3mevddzGFKl4e1PC1dZpM33yQ1ccTuVVQSOf/LGdA0zDGdGqOi07H2z3vZdTPG7WxcNO6hAf6MnPrISKrBtClXg0GNKvL2BXb6fnlcnzdXJner4NJK5mZWw/jotOhE4Lx97fG170iSVk3+HrHMeoEeDNgjjZF/dFWEQxsXmSMoVAUgygpa2qZKhAiR0rpWWTbaKCFlHK01batwDtSym2mz98Bq0xJJoureyqQJaWcarUtHugE1AZek1L2EUK0A4KklMuFEBOAHCnldFP5UJNOI6s6wqWUZ0z/Pw90lFJa7tRFKHz38b/WSeVERDZ1phxUtvey33VyS05Keccpafmxu0G1UOfqARjKkTX+TiCcO09NuJU8TedOI1NLDjm+K+jv2Cy6snFgd+llFOVCpqaWXuhO4lG2VX3uFKKUpQbvBsbLZYh8uYMIq1BoZzBm5han6n0xpqNT9QDnL5Pn5PNUDHi09EJ3VPBvmCfuIGrkrmIsLQ/JHUbn/HsblxNLL3MncXHyGAPQD3/nf26NTMPccU59Liwv+pGT/uv6/G6dmUOBaUW2LQWGoeWCKCtD0PJSWBNj2m6O7ZNSljJp2I5pQogItAiNRGxXBlEoFAqFQqFQKBQKhUJxl/nLDomi0RGmbZ0cbPu8yOcRZajbbjkIKeUrVh+3ONg/ocjnBKBRkW0DStNWKBQKhUKhUCgUCoVCcfdwfuyOQqFQKBQKhUKhUCgU/238HVOm/sf5RzgkhBCNgaLZmPKklK3/juNRKBQKhUKhUCgUCoVCcXf5RzgkpJRHgWalFlQoFAqFQqFQKBQKhULxP8E/wiGhUCgUCoVCoVAoFArFPxpnrxr0/wA1CUahUCgUCoVCoVAoFAqF01EOCYVCoVAoFAqFQqFQKBROR03ZUCgUCoVCoVAoFAqFojR06n3+nUY5JMqAcHFyN+mcPDcp7Rr4BzpXs7DQuXpSOlnP6Fw9AKOz21jgXLkbWU7Vw9vfuXoAqUnO1TMYnKvnbP6G9glvb6fqyevXnavn4eFUPQDh5up0TWfyxZiOTtUb88VWp+oBfPFsB+cKOnvc5uwxRmG+c/UACpys6ez7t8G5YxoAAoKcq5d82bl6CkUZUS4exf+xd95xVRzr/3/PHkCQjgooIAjYEOyJGhWjxhpr7KabaHr3mgRjoomJ96Z4701MuTHJTTHGGiyxdzT2jogo9gooXannzO+PPcI5h0PJVYj5/ub9evHSszs7n31md+eZeXZmtuaDEQqFQqFQKBQKhUKh+P8eNUJCoVAoFAqFQqFQKBSKylBf2bjtqBESCoVCoVAoFAqFQqFQKGocFZBQKBQKhUKhUCgUCoVCUeOoKRsKhUKhUCgUCoVCoVBUhlDv8283qkQVCoVCoVAoFAqFQqFQ1DgqIKFQKBQKhUKhUCgUCoWixlEBCYVCoVAoFAqFQqFQKBQ1jlpD4nYQFonWdyxoGnJ/HPL3lVa7RcfeiLbRYDLB9RxMy76DrGuVZrv1xHlmrNiOUUqGt2vG+OjWVvsLi428sXgTCZeu4lW7FjNH3keAtzvbky8wc91uioqNODoYmNinAx1DAwD417rdLDt4gqz8AvZNGVdGU0rJBwvXEpdwEhcnRz54eAARDeuXSZdw7jIxPy0nv7CY6BZhxIzojRCCWSviWPT7AbzdagPw8qDudIsMLznuUnoWA979iuYN6nI1Nw8XRwc+GNGTiADfshoXUolZuJ78YiPRTYOJGdgVIQSZN/J5be4aLmZkE+DtwcyxffCs7cyp1AwmL1rP0YtpvNSnI+Oi2wJwOi2DV39eXZLvhYxsXujRnkb1vG57+dotz19WEBefpJfnuGFEBJdNm3DmIjHfLSa/qIjoqKbEjLkfIQTHzl9m2o9LuVFQSEBdLz4cPxI3F2cOnzrPOz8uMYvAc4N7cF/LpqWa81cRd+SErvnYECIaNiirefYSMd/Hkl9UTHRkY2JG9UMIwatfL+B0in5/5uTl4+7iTOyUZ1i+6zDfrf295PjjF6+wKOYpmgX68cGC1aV6jw6xf8+cvUTMD0t1GyMbEzOyr643exGnU67qejfyca/tTOxbT5OZe4OXv15I/NmLDO3YmrfGDqhW+4qMRt7+cSlHz13GaDIxqHM7Jtx/r93ranV95y43X18nPnhiuN3r+6/Fa1i2/QBZN/LY9+W0CvMsyXfZFuKOndGfkZG9iQi094ykELNgnW5jsxBiBnVDCMHqwyf4fN1OTqWmM//50UQG+Vkddykjm4GfzOG5qEDGtQiyzjS0BVrv0SA05MGtyB2rrfcHNUbrPQp8AzHFfg3H9pfsEj2GIcJbghDI00eRa+dVamuN64FeZ/cZC5pAHthaps6mYRO0PmPALxDT4q8gcV+pZs/hiMYtAZBxy5FH99xxeqJZG7QHxoOmYdq5Drl+sfX+ewehdeoNJiMyNwvT3M8gIw0RHoU21MIv+AVi+uFjZPyuyjWbtEIb8IiuuWcTcssy6/1d+qO17w4mE/J6NqbF/4HMq+BVF8NDr+hzcg0OmLavQe5eX7le87ZoDzwJmgHTjrVlbew+GK1TLzCazDZ+ChlpAGiDHkO0aK/fN0kHMS2efWfqDTNfwx3rkOsW2dG7eQ2zMf38bwu9RxEt7gLAtGYecv+2SvUq4+FvPydqQF9yUtN4L6rjLedni4hohzZiAggN0/a1yLULrfd37YcWPUBvRxXkYZz7GVw5/8c0mrVBGzLO/FysR26Mtd7fbSBah/tKy3T+5yVlilddtFHPIrzqgpQYZ08v3WdBqc8/buHz7finMxeJ+e+v5BcWER3VpMTnJ567zLQ5SykoKsZB05jy4CBahgaSdT2Pt77/lfOp6dRydGD6I4NpHOBXqnkLPjHx/GWm/fxbqebY+2nZKJBTV9KY/P0Sjp6/zEuDezLunshSvSWbiEs8reuN7kNEoF9ZvfMpxMxbres1b0TMkO4IIfho+RY2J5zC0cFAUB1P3h/dBw8XZwCSLqUxddF6cvML0QQseGE0Tg6GavGH24+fZeaq7RQZjRQUFVNQXIyTwcDwuyIYf29bq7wLi428sWA9CRfT8KrtzMyxvQnw9gDg6837WLwnEYOmETOwC12aNORyZg5vLtzA1ZwbCCEYeXcED3duBUDipatMW7KFguJiHDQDU4Z2JyrIr1psvJiezYCPfySknjcArep5MLVXW7aevsKMTQf19m9kI8Z3aFbW3lV7SEjNwMvZiZkDOhLg6UqR0cTba/dxNDUDo0kyKCKYCeZjf9h3nEXxZxBAk7qevN+3PbUcDGVs+D+Bpj77ebsxTJ069c8+hzseuWXp1HJ3CoH24GuY5sxEbluB1u9B5NnjcCOnNI2DI3LTEr2R5VQL0bYbJO4tX7CeL0aTiad+XMXsR/szPro1M1Zsp31IfXxcXUqSLdiTSG5BEd8+dj+uTo78vCuBPpGh5OQXMqJ9M56Mbk2bhn689Ms6HuusN2hdnBwY16UVc3Ye4alubfSMXFxL8oxLOMnWoyeZ/7fHiQjyZ/qCNYzo3KbMKb7wn4VMGdWXiUN7MGfLHrxcXQj29WH3ibO0bxzMPx4dzOiu7Qjx9bE67q05v+HqXIuM6zdY9dpDRATUY/qyOEbc3aKsxo8rmDKkGxP73cOc7Yd1jbpezFq3i3A/H/75YF9Ss6+zI/kC9zQOwiQlrRvWx7O2M7UcHWgTrHeKvV1dGN2uKaPvjmBE+2bM2ZnAa33u5m8LNtz28i3B3Usvz/jjbI0/zvzJzxDRsAHTf/6NEdF3lbV11hymPDiIiSP6MmfDDrzcahPsV5dnP/2JiSP68tqIvuQXFhF3OIkOzcPwqO3C6O4dGNujIz3bNOeZf//EIz07omkacUdOsDXhBPPfGE9Ew/pM/2UlI7q2K6v55S9MGTOAicN6MWfTLl3Ttw592rVgdLe7GN3tLi6nZ9E8qD53NQmhaaBfyfa24Q3ZlnCCV4feR9yRZLYmJDP/9SeJCKrP9HmrGNGlrR29eUwZ25+JD/Rizqbd5numDn3aRTA6uj2jo9tzOaNUzyQlcUOUkgAAIABJREFUofXr0SzIn/Sc60RHNdHLtJrsW7X3CCcvp/HfVx9jaOc2vPV9LD3btsCjtkuZvG8SF5+kX9+3niUiuAHT5yxjRLe7y6RzqeXEuL5dmbNhB08N6F5uftzI1fM9doatSWeZ//woIgJ8mb50MyM6RJa18YffmDK0OxP7d2HO74fwqq0/IwJB/1ZNOX7lKp2bBOPr6WZ13FsL1xFc1ws/g6SNr2fpDiHQxryE6Zd/IbevQuszBnnueMl53UwjTx4BJxdIvwJXL+vbA8LQWnfB9N/3kfs2o3UZgMxIrTj4Wt16UtrXHPsqpp/NdXZfO3U2IJPjdc1rFpqNW6I1a4vpv39HHtqGNvBx5JFdYCyu2Mbq1MvLs9HTMDzzDsavpiLXL8YwbDwyOQGuZ5cmcXDCtGouMm4FONVC69QHeWg7pKcit6/R/w7+jta5H6ZF/wGTsTR/R0e7NhoefwPjd39HblmKYeBjyNOJcD3HQtMR0/pFyB1rdM27e+i2FBUg925G7lyH3B+HYfQLyMM7oTC/NP+iIvs2fjkVuW4RhuETkCcTINfCRkcnTCvnIuN+g1rOaPf0Rh7cDo2aoXXqhfGTichtq9D6jNLLOz21gmv4Z+hNxfjFO6V6yUfK13Oqhda5D/Lg74gW7RGtOmH61+vIXesxjHkBuT8OikvvmZW7z5avXQ43MjLZ/t0cWg8dSNyX3/zh4/vfFVz+TqFheP5djJ+9jVy7EMPIp5AnrO0l9SJy01LktlWQm43WbzRyz6by83Swed8mNAwTpmD8z3vIDb9ieOBJ/RpetynT1fOQ21bqZdqxF/LwDgAM495AbozFtOx75J7N+v1p8VyI5lGA2ecfOcH8yU/rPn/ub4yIbl/m9F6Y9TNTHhrIxOF9mLNxp9nn1+HNbxfxZL9uvD6yH37eHnyxbCNDO7fls6UbqO/jxcynR9OucQjv/7KCIZ30lyi36hPf/G8sT/btyusj+uiav21m6D1tMJkkrcMa4unqQi1HR9oE6R3kuGOn2XrsDPNfGktEoC/TYzcxomNUWb3/LmXKsJ5MHBDNnG0Hdf9k7hhPHNiNsV1ak3gxlf2nL3JPk2CKjSYmzP6V6aP68GLfe+gXFY6LkyNbk85Wiz/MyS9kxN2RPN6tLd/H7afYaGL1xIeYsXwb7Rs1wMfNoj246yi5BYV8+8QgXGs58vP2ePpEhZOcks4XG/by64uj6BHRiNd+WcvYTpEUFBlpE1yfl/p0ZHDbpkxZvJmOYYH4uLnw5oL1PHlvW16/vzN+Xh58sX4XPm61q8fGvAK2Jp1l6asPMbpTS+71c8Nokjz16zZmD+vK+A7NmbHpIO0D6+FTu1apvYdPk1tYzLfDo/X274Fk+jQNZFXSeU5ey+a/I7sxNDKEt9bspWd4A/KKjExdt5/YR3rxcLvGrEq6QKHRRHNfL7R2PSt/C/MXQ8Zvm/pnn0NFaC27/uXK/JanbAghjEKIg0KII0KIhUKI2hb7hgohpBCimfm3sxDimBAiyiLNJCHEV5VovCKEyBdCeFpsu9ec9xMW29qYt00UQnxuPq+jQog88/8PCiGGW6SfaE5f938ugIBQvWGRmaZH1RN2I5rZdODPHIPiQgDkhZMID+9Ks42/kEbDOp4E+Xjg5GCgX1QYGxPPWKXZeOwsQ1rrnbTeLULZeeoiUkoiGtTF10MPMoT7elNQbKSwWHecrYL8qOdem/LYePg4gzu0RAhBq0YB5OTlk5Zl3XBOy8ohN7+Q1qGBCCEY3KElGw4dr9Sm9YeSCKzjTUFRMS0C6ukaDf3JySsgLfu6tUb2dXILCmkdXF/XaNuMDQmn9HM8epohbfWI7BCL7XXcahMV5IeDVv5tvfPUJRr6eJCem18t5VumPA8mMvieNrqtYQ3JuZFPWma2VZq0zGxy8wpoHd5Qt/WeNmw4kAjA6StXad8kBIB7WoSzdl8CoHdwHQx65LmgqBhhEazdeOgYgzu21jVDg8q/hnkFtA4L0jU7tmbDwUSrNFJK1uxLoP9dZRsaK3bH07+97iw3Hj7G4I7meyY0sIJ7poDWoTf1WrLh0DE7ekdL8q1dy4l24Q2pZdOwrC77BIK8gkKKjUYKCotxdDDg6lyLith4oPLrC9AqrCH1vDwqzMsq36OnGNy2uZ5vcP3yn5F8y2ekORsSTgIQ5udDI1/79cz6IycJ9PEk3K9O2Z0NGkF6mv7m2mREHt2DaGI9coisa5B60U5nX4KDIxgcwOAIBoNVY98uNa0Hep2dYVln70I0tad5AaTJarOo2wB5NknfXlSITDmPCC/7fPypesGNkWlX4FoKGIsx7d+KiLIOksnkeCgy+6QzSQivsveCaHUPMnF/SboKCQpHXrui22k0Yjq0A9HcuhMmTx0t1TyXjPAwB6uNxtIAi4MjVpVZhTZetrGxg7XeCVsbzW5eSj2o4uCg/xkMyJzMO0/vqoXevrhy9Aos9MzX0D9ID16YTFBYgLx4GtG8bOf0j5K8dTs30jNuOR+7hDRBpl3Sg3E37W1lMwoj3yLwVssZsBNsrIiG4XqZppvL9MA2RKTtc3Gk9BqePV5apn6BoBmQxw/pvwvzy30uNh5MZHAns38KCzL7BBv/lGn2h2Fmn9+pNRsOHAVACMH1PP265ubl42v2GycvpdKxeSgAofXrcelaJlez9cDtrfpEIbDQLMDX0x2AOh5uRIUElLQ1Smw8cpLB7SLM/qmB2T/lWutl5+r+KaSBrtcugg1HkgHo3DQEB4PeTmsVXJ8rmfqxvx8/Q5P6dWnWoB4AXq4uGDSt2vxhRIAvvp5uxJ9PIdTXh2KjXv/2axXOxsTT1jYnlrY7e0eGsfOk3h7cmHiafq3CcXIwEOjjQcM6nsSfT6WehysRAbodrrWcCPX1JtV8zkIIrhfo909ufiG+Hq7V6vNtib+STkMvN4K83HAyaPRrGsTG5EvW9iZfYkgLPYjYu0kAO8+lIqVEAHlFRopNJgqKjTgaNFyd9CC10STJL9b35RcX4+vmXKXzUSjg9qwhkSelbC2ljAQKgact9o0BtgGjAaSU+cDLwBdCJwB4CnizEo0xwB5gqM32eGCUxe/RwCGz1nNSytZAf+Ck+RxbSykXAQghgoBewLk/arAV7t7I7PTS39np4F5+pSDaROuNwUpIyb6Ov2fpyAV/T1dSc66Xm8bBoOFey4nMGwVWadYmnKZ5/bo4VXHYVGpWDv4WHSc/Lw9SbJxpSmYOfl7uFmncSbVwfnO37GXI+7OZ/NNysm7ojYgbBYV8u24Hz/bvyvX8QtwtOnp+nm6k2DizlOxc/Cze6vp5upFqTnMt9wb1zAGBeh6upOfavCGsgJXxyfSPCqux8k3NyMbfp/QNtJ+3Byk2HdaUzGz8vC3TeJKaoadpHODHRnOjYc2eI1xJzypJd+jUeQZO+TeD3/mMdx4eXNJoSM3Mwd/H5hpm2GhmZOPnbZHG24NUm+u878RZ6ri7EWKn47p67xHuN3fkUzNz8Lc8//LuGW/rcyqjl3yOOu6udvUsqS77ereLwKWWE90mfUzPN2fyeJ9ovNzKD94BpGZk4e/jVarj41nmXP4XUrNy8feyuP+93EjJsnlGsmyeES83Um3S2HKjsIhvN+/l2V4d7Cdw90LmWNZnGSWjfSrl4ink2WNoL32M9tJHyFMJegejImpa76Zmlq1m1RpyJQEBBydwcUOENAMPn4oPqmE94VlHD/DcJPOavq0ctI69MFlMESnJp21X/c16FRAe3tYjU7KvITzLt1G7615MNzt3AJ4+GF78B4bXZ2HasgxyKu74Ci9bG69WbuNRs41nkpDH4zG89z2G6T8gEw9AyoU7Ty/D5hraCRqV6HWy0Lt4Bi2iHTjWAlcPfbqP9//+zqUmKGNvhv3yFdH3Y5j2DdrQxzEt+M8f0/CsA5kW92jmNYRn+c+S1qEnpkR9epio1wCZdx3tsUkYXv0YbeAj5X72T/dPVfH5lv7Js8Q/vTGqPx8tWk2Pv33IRwtX8/KwXgA0DfJn/X49aHH41AUupWeV+Jpb9YlvjOzHR4vX0uONT/ho8RpeHnpfueUCN/2TRRvQsxz/ZNVOtO+fft2dQNfmIQCcTctECMH4/yxm2Mw5fLt5r4Xe7feHlscaTSaaN6iHk4MBfw83UrPstAfN5+Bg0HB3diLzRj6pWdfxt2qrupJiE0i4mJFN4qWrtDRPo3hjQGc+WrmDHn//gY9WbOXlfp2r1caL6Vk88K+5PPLlIvZeSCMlNw9/99LRH/7uLqTatKMt0zhoGu61HMnMK6R3k0BcHA10++o3en69ksfbN8HLxQk/dxcev6sJPWevoNtXv+Hm5EjnEP9Kz+0vi9Du7L+/ILf7rLcC4QBCCDegM/AE5oAEgJRyNXAZeAT4JzBVSllu60MIEQa4AW+hByYsOQc4CyH8hBAC6AusquK5/hOYxB8Os9ueoL2N9rMUUZ0QDUKQ2ys/xXJysE5jfzRyCSdS0pm5dhdTB3etVK80z7KZCps3VvbO7WaK0V3bsmbas/z65pPU83Tjw8X6XOBZK+J4pPvduDo7Ie3kIKpim/3CrjKFxUY2HTtLn8jQGitfu+X5B3SmP/4Av2zcxfB3P+d6fgGOFoGPVqFBLH/vJRa89QyzV26hwDys+Vav4U1W7Imn/91lhwweOn0BZydHGpvX/bB/raypSjms2BNP/7vK6tlSXfbFn76Ipgk2fziRte+/zPdrtnI+NZ2KsKtTlTe8lWA/X9s0lZeDLbPW7uSRrm1wreVUTgo7x1e1hvSuh6hbH9OnkzB9OgkR3AyCGldyUE3rlaNZVdFTCcgT8WjjYtCGPYW8kGw9neFO0PsjPql9N0TDcOQG67n0eHgjGgTrnecqUfXrKFp3QQSEIuOWl27MSsf46esYP34FrW00uHnaP7hCvfJsvFe3ceOv+oa69cE/EOPb4zBOeRzRpCWElZ0y+JfSCwpHbtD15LEDmBL2YXj1Q7THJiJPH9NHodzRVK3OlHErML7zJKbY/6L1G1X5AZVJlHePtovWy3STeZ0mzYAIbY5p2Q8Y/zUJ6vgh7rY/9c6+r6t6mnmbd/PGqP5s/GgSr4/qz5Tv9WdzfL9osq7nMXTaLH7euIPmQf4YzKNBb9UnztuyhzdG9mXj31/j9RF9mfLjUru2VZxXVdo11mm+Wr8LgyYY2LY5AMVGE/tPX+TDB/sz5/lRrD9ykh0nzlWbP7zJ5cwcjlxIYerQbuUKlNeGqezcrhcU8dKcNbw5oDNuzrrfnbczgTcGdGbjG4/y+sBopixcX2021vOozYaYcfz68lheH9iVSSt2k1dUwRTDEi375xN/JR1NCDY/NYC14/vx/d7jnM/MJSu/kI3Jl1j3ZH82PzWAvCIjy47+8alhiv9/uW2LWgohHIB+wM0VyYYAq6WUx4UQ6UKItlLKm6uRvQzsBk5IKX+qJOsxwC/owY6mQghfKaXl5MtFwAjgALAfKCibRZlzHQRclFIeKu9hFkJMACYAfDmgE+PbN7WfWXYGwsOn9OH18AF7wzEbRSC6DsD0/d8rnm9sxt/DlSsWEdorWdfxtZlq4e+pp/H3dKPYaCKnoBBPl1rm9Lm8+Ms6ZgzrTkOfioeKz92yl4W/6w3PqOAGXLGI5qdkZpeZf+7v5W71BjwlM4d65iF+dT1K047o3IZnvlwAwIZDSczZvIfXf1iKAJZm5hDm68OD97QkJSu3ZApEqW3W0eGUrNySURF13GqTln2deh6upGVft5rnVxFbT5wnon5d6rrVrtbynbsrgYWH9AXdokICrUY1pGRk42vx1gDA39uDlAzLNFklw/tD69fjm9ceB+DMlavExSeVsSusgS/Z1/MZ8u6XODs5EhXSgCvpNtfQrqZFmoxs6lmkKTYaWX8gkYWTnyqjt2pPPAF1vBg6XZ9pFRXcgCuW518Vvcxs6lm8DSg2mlh/4BgLYyaU0QM4fjGFoe99qetVk30rdh+ma4vGOBoM1PFwo03jYI6cuUCQzTooczfsYGGcvrBgVKNArqSXPu8p6VllzqWqzN2wg4Ubt+v5BvmVDGPVbczF18PmOfR0t35GMkufkfI4fO4Ka+NP8MnKbeTkFSBMJmoZNB5sZl6IMycD4W5Zn3lDbiXDy82Ipm2QF0+VDiM/eUTveJ4/Uf5BNa13U9PTRrOyIfQWyG2/Ibf9pp/D0AnI9JQ7Sk9mXiudLgDgVcd6hIYZ0aQVWq8RGD+bXMYniTad9XUcKg22mDWz063faHvUQWaXfc8gwiLRug/B+PW79v1gTgYy5QIipCnyyO7y9TKv2thY13qk4k29Jq3Qeo/A+GlMyRoKomVH5JnjJWtUmBL36XonE+4sPe8qXMOmrdD6jMT47zet1oiQaxdgXKv7Xu3RiZB2qcyxdxJl7PWui6xg7Rm5Lw5tzHPo75WqqmEzysSrjv1r2Lgl2n3DMX4+peQelVnX4OJpfboHION3I4KbINkAwNwz11g0bRYAUSEBdny+dRvB39vTxj9llfinpTsOEDPmfgD6to/k7R/0oIibizMfjBum60vJPS9/wOvfLUbTtFv2iUt3HCRmVD9ds10L3v7JekFagAMnzzF0h7mdGOTHFcs2YFYuvp42bTgvN5t2orV/WrIngS1HT/Hd08NLOtX+Xm7cFRqIt5sLc7cd5GJGNm/MW0u35iHV4g8BrmTm8OPWA4T6+tCwjh4IvZKdi6+HbXvQjSuZuaXtwfxCPGvXws/TlStWbdXr+LrrukVGIy//vJoBrRvTKzKsJM3S/UnEDOzC3B3xLNyTyIkr13jgrohqsdHJwQEn87TXFoF+BHm5YjJJruSUjoi4kpOHr0072t/NhSs5efi716bYZCKnoAhPZydWJJ6nayN/HA0adWo706ZBXY6kZCAQBHi6lqxD0atxAAcvXWNQRAVrxygUFtyOERIuQoiDwF70EQvfmrePAW4ueT4Pi9ENUspLwEbgyyrkPxqYJ6U0Ab+iBx8sWWDedjNwUSHmNS4mA29XlE5K+bWUsr2Usn25wQjQnVQdX/Cqq0fRW9yNTLJ5q+TfEG3Ao5jmfVpmIbPyiAyox9lrWVzIyKaw2Miq+JN0b2b9YHdvFsySg/raDWsTTtGhUQBCCLLzCnjmp9W80usu2gZXPmRqbLf2xMaMJzZmPD1bNWHprsNIKTl0+iLuLrVKgg03qefpjmstJw6d1ufQLd11mB4t9bUWLOctrj+URGPzXMA1057j8KdvcvjTN+nVpjl+Xm6M7RTFoXNXcHd2KlOp1vNw1TXOXdE19h+jR0Qj3e6IRizZr68/sMRie2WsPJxM/5bh1V6+Yzu0IHbqC8ROfYGebZqzdPsBvTxPnsO9dq0yawnU8/LA1bkWh06e023dfoAerfU3BtfM01RMJhNf/baJkeYFEy+kpVNsfut18WoGNwoKmfv6E8ROeYaerZuzdOdBXfPUedxdnO1fQ2cnDp06r2vuPEiPVqWrLO9IPEUj/7pWUzFunseafUd5c1Q/Yt96mti3nqZn62Ys3Wm+Z05dwN25nHvGuRaHTl0w6x2mR0sLvWM39ewHz5oE+BE75Zlqta++jyc7j51CSsmNgkIOnTxPaP16Zc5lbM9OxE57kdhpL9KzTYTN9XX+Q2tFlMn3lQeJfeVBerYIY+n+RD3fs5f159DuM+LIobOXzc9IIj0iQivUmPPsCNa/OY71b47j4S5tmBAVVBqMALh0Bnx8wdNcn0XcVTpvujKy0hENm+jDBTUDomET5LXLFR9T03qg19k+fhZ1dgfk8YNV0xSidBFg30CEXyBU0LH8U/TOnUDUq6+Xq8EBrW3Xsp37gEZoo57B+M37kJtVJgutbTRy39aqnSPAhZOIuv7gXQ8MBrRWnZC200Dqh6ANfRLjjx9br/Xh4aOvHQHg7IoIaaqv11CpjQ30cr1po+2XQAJD0UY/q38NwdLGjDREeAvQzPdNWCSkVPK1hj9Lr45Zr100Mt7mGgaGoo16DuPX71nrCQ1qm+vDBiH6qMxjVR3p8idx9jjCN8Da3sM25Vuv9KsRIvIuSP2DQZbzydbPRZsuyCM2X6wJaIQ24mmM386wLtNzyVDbDVz1ul00jkJaXMOxIXWIfed5Yt95XvcJO8z+6eR5ve62CQ7U8zL7w5Nm/7TjYInP9/X0YE+Svn7BzmOnCPbVgyjZN/IoNAedFm3dS7eoJix957nb4hN9vdzZc/yMWfM0wb5lp7K0CWtI7GsPE/vaw/SMDGfpvqNm/3TJ3Iaz7jzX83DT9c5e0vX2HaWHuVO+9dhpvtm0h8/HDcbFqXSR3M5NQ0i6fJW8wiJGdmpJuF8d3h3es9r8YXZeAc/8dxl/G9CFjOt5XEg3twcPJdO9uXW7snvzkJJ259ojJ+kQprcHuzdvxKpDyRQWG7mQns3Zq1lEBfkipWTK4k2E1vPmsa7Wawb5etRmz+lLjO0UxaQBXWnWoF612ZieewOjSV8b4/y1LM5m5hIdWp+zmblcyLpOodHEqqTzdA+z/jJa97D6LEnQRzisPX6RDg19EUJQ38OlZD2JG0XFHLp8jVAfd+p7uHDocjp5RcVIKdl5LpXQSl6G/qUR4s7++wsi7A1D+kMZCJErpXSz2VYHuACkoo/8MZj/DZZmQSHE98BvN9d0KCfvluhrR9xsnTgBp6SUXYQQ9wITpZQDhBDrgSAgApgC5EopPzbnEWLWiTT/jgI2ADfMeQYCl4C7pZR2JyAbpz1ecSGFt0TrO4aSz9Zt/Q1x7xDkpTNw/CDawxPBN7DUwWVd04MT5dFcH0a+5fg5/r5yByaTiaFtm/L0vW35bMNeWjSoS4/mIRQUFfP64k0kXr6Gl0stPh7ZkyAfD77avJ/ZcQdLor0A3zzanzpuLny8ZicrDp8kNUeP4g5r15Tnh/crSSelZPqCNWw7ehJnJ0fef2gAkeZPVg39YDaxMeMBOHL2EjE//UZBURFdI8KYPLIPQghe/34pxy6m6NHSOp5MHdOvjGP87Lct7EhI5lruDZwdHXh/RE8izZ+MGvrvecS+pM/wOXIhhZiFGygoKqZr02AmD4pGCEHm9TxembuGy5k51Pdy558P9sWrtjNpOdcZ+dkCcgsK0YSgtpMjy199EDdnJ/Ku36DHxz+z9tUxuJuHzVVH+ZbQILi0PH9ezrYjJ/TyHPcAkSGBuq1TPyN26gu6rWcuEPPtYt3WqMZMHjsQIQQ/rdvO3E07AejVtgWvDNM/r7ps+wFmr4rDwaChCcEzA3twnzkoJKVk+i8r2JaQrGs+OoTIEL3TOfS9L4md8oxZ8yIxPyyhoLCIrpGNmTy6f8mbipjvY2nZKJDR3ay/CLI76TQzY9cz743xJQvwSSmZPm8l2xLM98yjg0vvmelfEfvW06X3zA9LKCgspmuLcCaP7meht4SWoYGMtlmN/L6Yf5GbX0CR0YiHiwuzX3qYsPr1qsW+6/kFTP5hCScvpyGBoV3a80S/aCpCSsn0OcvYduS4+foOJ7KR+fq+8ymx014E4OMFq1ix6yCpmTn4erkzrOtdPD/Ezjzdq1dK812ymW1JZ3F2cuD9Eb1KPuM19J8/E/vKg7qN5/VPgBUUFdO1WTCTB9+LEIL1R5J5f+kW0nPz8HBxolmDesx+0noJnllrd1I7I7XsZz/DItF6jdY/UXnod+TvKxHRg5CXz8KJQ3rHcviz4Fwbiovgejamr98BIRB9H9SDBFIiTyUg1y+osPyqXa+8oerhUfpnNoWGPLgNuc26zqZBCNrI58HZVdfMzcL01RS9IzNhqp5HQR6mFT9W3rmsZj15reybZBHRDm3oE+bPG25ArluI1m8s8nwy8shutGffRTQIBvNbd5lxFdM37+sH+/hieOnvGKc+YX/cdW3766qIpq31z34KDdPezcjNS9DuG468eBqZuA/tiRiEX8OS9SFk5jVMP32sf2q0/0PoTQSBacca5J6N1plfv15WL6Kd+TOc5k84rl2I1n8s8pzZxufeRdQP0dd1AmRGGqbZ74PQ0EY+jQhrAUhk4n5Msd/ZtanG9OwNB49op3/2U9zUW4DW/0HkuRO63vPvIeoH62uS3NT7ejo4OGKY9C89k/wbGOd/oQfFLHh+1pZK7bXlibnf0eTeLrjVrUN2SirL3/mA7d9VNsC1lFnPVDx1VLRojzZ8QulnTlfPRxvwEPLsCWT8LrQRE/TFYI1GZF4upvlfwuUKlgCzsyCxaN4WbbD5s5+7NyDXL0brOxp5/iQyYQ/a0+/YlOlVTN/N0I9t0gpt0KMgBPL8SUwLv7Ia5SOG6u/cpJRMn/ub2Sc48f7jD5T6p2mziH3necDsn75brLehIpsweewAhBDsO3GGGb+sxGgy4eTowNsPDqRFSAAHT57jjW8XY9AEYfV9ee/hQXiavwp2qz5/X/JZZsxfpWs6OPD22AG0CG5AWlYOIz/4mtz8AnObyoHlkx7FtZYT03/dyLakM3obbnQfIoP0lzNDP/mJ2Nce1vXOXyFm3hqzfwph8tAeCCHo88G3FBUbS86/VXB9pg7X/eGyfUeZvWEPQkB00xAm3t+l2vzhVxt2M3vjHhrW9SI3v5DU7Fzqubsy/O7mPN29PZ+t202LgHr0iGiktwcXbCDxkv7Zz4/H9CLIvFbIV5v2Erv3GAZN440BnYluGsy+M5d5+D+xNPH3KWl3vNy7I92a6ftmLN9Weo2Hdte/rFENNq6NP8Fna3fioOltxefvbkz3sAZsOXWZv28+hMkkGRoZwtMdm/PZ7wm08POmR3gDCoqNvL5qN4mpmXg5O/Hx/R0I8nLjemExk9fs4eS1HKTUj33iLv2l7We/J7A66QIGTdDc14v3erfDycGAYcL7f80ecgUYf/nw1jrP1YxhzKS/XJlXV0DiKaCtlPIpi21bgLeklFvNv7+n8oDEDCBbSjnDYttp4F4YQBLSAAAgAElEQVSgEaUBiXsAXynlEiHEVCoISNjROAO0l1JetbcfqhCQuN00r3we/W3Fp+xb4Gonp+xbuWqlKivF304a/AnD1Iornwp0W7H5IkC1U9ML9TiWt85CNXK1Cosy3k6O3OFvTm+VO37u/K1jLyBRrZQTkKhW7AQk/k9xi+2wP8r/EpC4VSoLSNx2KvlC0u3mZkCixvgz6jY7o6mqlZq20VhUeZrbjcHOZ5Srk5SLNasHKiDxJ/BXDEjctjUkbBgD/N1m22JgLPpaEFVlNPq6FJbEmreXjOeTUm7/H85RoVAoFAqFQqFQKBSKqvEX/ZLFncwtByRsR0eYt91rZ9unNr8fq0LeZRYHkFK+avFzs539U21+nwHKHXIgpQyp7DwUCoVCoVAoFAqFQqFQ3F5UiEehUCgUCoVCoVAoFApFjVNdUzb+EOaFJm1XRyqQUnb4M85HoVAoFAqFQqFQKBQKK7S/3BINdzx3REBCShkPtK40oUKhUCgUCoVCoVAoFIr/E6gpGwqFQqFQKBQKhUKhUChqHBWQUCgUCoVCoVAoFAqFQlHj3BFTNhQKhUKhUCgUCoVCobijEWoNiduNGiGhUCgUCoVCoVAoFAqFosZRIySqgpQ1q1fTkbeMq+Bdt2Y1NUPN6tU0xcU1r2ksqnnNGsVYw3J/QnnW9a9ZvZqua2q6LjUYav5ZrGEbhbd3jerJjIwa1QNq/j7VavhdjbFm67ZZz3StUT2A57/cWqN6f4aNNYrBAMU17KNq+D7FVMN6f0a7rbCGr2Fd35rVUyiqiApIKGo+GKFQKBQ1wZ/RwFQoFIrqpqaDEQqFohShJhjcblSJKhQKhUKhUCgUCoVCoahxVEBCoVAoFAqFQqFQKBQKRY2jAhIKhUKhUCgUCoVCoVBUhibu7L8qIIToK4RIEkIkCyHesLO/lhBivnn/LiFEiMW+N83bk4QQfW5Lkd6OTBQKhUKhUCgUCoVCoVDcuQghDMDnQD8gAhgjhIiwSfYEkCGlDAf+CfzDfGwEMBpoAfQFvjDnd0uogIRCoVAoFAqFQqFQKBT/97kbSJZSnpJSFgLzgME2aQYDP5j/vwjoKYQQ5u3zpJQFUsrTQLI5v1tCfWVDoVAoFAqFQqFQKBSKyrjDv7IhhJgATLDY9LWU8muL3wHAeYvfF4AONtmUpJFSFgshsoA65u07bY4NuNVzVgEJhUKhUCgUCoVCoVAo/uKYgw9fV5DE3kITsoppqnLsH+bODvEoFAqFQqFQKBQKhUKhuB1cAIIsfgcCl8pLI4RwADyB9Coe+4dRIyRuB+FRaH3HgqYh98cht62w2i069UG0jQaTCa7nYFr6LWRdqzTbrcfPMWPldowmyfB2zRjfrY3V/sJiI28s2kjCpat41XZm5qj7CPB2J/NGPi//so74i6kMbdOUtwZ2sTrm/d+2sfv0ZTQheKnXXfTuUrdkv5SSDxauJS7hJC5Ojnzw8AAiGtYvc24J5y4T89Ny8guLiW4RRsyI3gghmLUijkW/H8DbrTYALw/qTrfIcIqMRt7+eQVHz1/BaDQxqE1TJvS4S9dbtoW4Y2dwcXTgg5G9iQj0Lat3IYWYBevILyomulkIMYO6IYRg9eETfL5uJ6dS05n//Ggig/ysjruUkc3AT+bwXPe2jOvSiq0nzjNjxXaM0lym0a3LluniTeYyrcXMkRZlOm8d8RfTGNqmCW8NKC3TFYeT+XrLAYQQ+LrX5h/De+BtWZ7zVxF35IReno8NIaJhg7L2nb1EzPexun2RjYkZ1Q8hBK9+vYDTKfq9kpOXj7uLM7FTnqGwuJipc5aTcPYSmiZ4c2Q/7g4PtLmGybg4OvLBIwPLv4Y/LtM1W4SXXEOAOZv2MHfLHgwGjW4tGjPxgZ4cPnORd+auvGkYz90fzX2tm1WbXsk1TM9i4Htf8Vz/aMb16lRtepm5N3h59mLiz11iaMdWvDWqb7WWZ3l6JZpzlxMXn4SLkxMfPDGciOCyI+ISzlwk5tuF5BcVER3VlJixAxFCcOzcZab9FMuN/EIC6nrz4YRRuLk46/fND0tIOHMBTQjebOHH3f5e1pmGtkDrNQqEhjy0DbljtfX+oMb6ft8ATEtmw7H9JbtE92GI8CgQAnn6KHLd/DLnXIbQFmi9R+t6B7fa1+s9CnwDMcV+ba3XYxgivGWp3tp5levZEhZpXX//vtJqt+jY27r+XvZdlervcqkmf/Fn64lmbdAeGA+ahmnnOuT6xdb77x2E1qk3mIzI3CxMcz+DjDQAtEGPIiLa69cx6RCmX2ffmXpDnwChYdq1HrnhV+v93QahdbzPrJeNad4syEhDhEeiDRlXmtA3ANOPnyCP7K5Yr3lbtAeeBM2AacfasvZ1H4zWqRcYTWb7PrWw7zFEi5v2HcS0uHL7yuhHtEMbMUG3d/ta5NqF1vu79kOLHqDfNwV5GOd+BlfOl5PbH+fhbz8nakBfclLTeC+q423L9ybVZZ+Ukg9+WUFc/HHd548bRkSwHZ9/5iIx//2V/MIioqOaEDPmfoQQJJ67zLQ5SykoKsZB05jy4CBahgaScyOf179ZyOX0LIpNJh6/ryMP3NOmVHPB6tJ2xqND7Puos5eI+WGp7i8iGxMzsq/ezpi9iNMpVwHIuZGPe21nYt96mu1HTzJzyQaKio04OhiY2LcTHcODqrXddt+M73ByMHD2aia+7q5sinncKs9vt+znt4PHATCaTJxKzWDblCfwqu1c6bW5ydeb9rJ4byIGIYgZ1JUuTYJ17U/m4urkSF5RManZN/DzdP1D7cTtyReYuW53aXn16UDH0ADyCot5Zf46zqdnowmN7s0a8mpv69HxW0+cZ8aqHXrbtG1Txne1o/nrZhIuX8XLpRYzR/TUNU9eYOa6PRQZjTgaDEzsfTcdQ63bCs/NXcP5jBz+1rsDM1bvxGgyMfyuCMbf27asxoL1JFxM0/sUY3sT4O2hl9nmfSzek4hB04gZ2IUuTRoCMHnRRrYcO4uPmwvLXh5dkterc9dw+momAElvfnEGyExKSrI26q+MqNqXLO5g9gCNhRCNgIvoi1SOtUmzDHgU2AEMBzZKKaUQYhkwVwgxE2gANAYqdmhV4JYDEkIIIxBvzisReFRKecO8byjwK9BcSnlMCOEMHARGSCnjzWkmAaFSyqcr0HgFmAH4SSmzzNvuBTYBT0opvzVvawPsB/4GNAI6A07m/yeZs5sORALjgTTzthgppXUrtOoFgNb/YUw/fQTZ6Wjj30EmHYC00mCRvHwW+fU0KCpEtO+O6DUSuejLCrM1mkxMX/473zx+P34eroz66le6Nw8h3Ne7JM3ifcfwcKnFmlfHsPJwMp+s2cnM0b1wcjDwQs/2nEjNIDkl3Srf/2zZj4+rC6teGY3JJMnKy7faH5dwkrNp6aye+gyHz1xi2rzVzJ9k7RAA3p23imlj+tOqUQBPfTGPrUdPEt0iHIBHenRg3H3WDYg1+xMpLDaydPIE8gqLGPjul9zfuinJKdc4ezWT1ZMe5fC5K0yL3cj8F0aX1YvdxLRhPWnV0J+nvlvK1qSzRDcLobFfHT59eABTf91gtxz/sTyOrk2DLcp0G988drNMY+neLNh+mb4yWi/TtbuYOeo+c5nexYmUdJJTS8u02GhixsrtLH9hJN6uzny8Zic/7zrC843C9PI8coKzqddY/d6LHD59gWk//8b8Nydgy7tzf2PaQ4NoFRrIU5/NYWtCMtGRjZk5YWSpLQtX4+6iO91FW/cBsPSd57iWnctTn81hwaRxaJrQr2FqOqunPsvhMxeZNm8V8yeNK6v5yyqmjb1fv4afl17DXUln2Hg4iSWTJ+Dk6MC1nOsANG7gy8LXn8DBoJGWlcPQ92dzb1QTfk88VS16JXYvWkfXiPCS39Vln5OjAy8M7MaJS2kkX0770/QA4uKTOJtyjdUzJnL41Hmm/biE+VOeK6v50xKmPTqUVmENeeqf37M1/jjRLZvy9veL+duo/tzVNJTFW/fy3ao4XnygN4u27NHvm/de1u+bd//NggFt0W46VyHQ+ozF9Ms/ITsD7fEY5IlDcPVyqWh2Oqbl/0V07G19MgGhiMAwTN9MA0B7eBKyYRM4d7zMeZcgBFrfsZjmmvXGTS5fr4PNl6UCwhCB4ZhmT9X1Hnm9cj17+v0fxvTTx+b6+21k0kG4alF/XzmH/PpdKDbX3/eNRC6uuP6uXO/2+os/XU9oaCOewvjFO5B5DcNrH2OM3w0pFh24C6cxfvyqrtO5L9qgxzD98BGENEM0ao7xHy8BYHhpBiI8Epl85M7SGzYB41dTdb1XPsR4ZDekXChNc/EUxpkTdb17+qANfEQPPCQf0c8DoLYbhpgv9HusKuX5+du63sRPdD3LDvGFUxg/MtvXpR/a4Mcwff8RNGqGCG2O8e8v6va9/PfK7bOnP+oZjJ++BZlXMbz+T4yHd1rpyz2bMW5dpSeP6oA2bDymz9+uukYl7Pj+ZzbP+prHfvzPbcuzhGq0Ly7+uO7zP3iFw6cuMG3OMuZPLtu8fXfOMqY9MphWoUE89e8f2XrkBNFRTfhk0WqeHdiD6KgmbDmcxCeLVvPDpCeZu2knYQ18+eLFh0nPuU7/yf9kwN0tcXIwEHckWfdR777A4dMXmTZ3BfPfeLKs5twVTHtoAK0aBfLUrLml7Yzxw0vS/GPRmpJ2hpdbbb54dgy+Xu6cuJjK+H//yOa3niTu2JlqbbcF1/GiiX9dWgaVDXI80a0tT3TTO9Kbjp7mx20H/1AwIjklnVWHTrD8lbGkZl/niW+WsHLiQxg0fcD4t4/dz4Ozl7L8xRF/uJ3oVduZLx7sg6+HKydS0hn/w0o2T3oIgMc7t6JDaAMKi42M+34FccfPEW3u1BtNJqav+J1vHumva369hO5NbTT3J+Hh4sSal0axMv4kn6zbzcyRPXXNsb1LNX9axeaJD5Yct+7oaWo7OSKl1DWeHISfhxujPl+k9yn8fEo19iTqdv3tIVYeOsEnq3Ywc2wfc5kls/yVMXqZfbuMla+NxaBpDG3XjAc7RfHGQutrOXNsqb+OePOLxUBWlS+SotoxrwnxPLAGMADfSSkThBDvAnullMuAb4GfhBDJ6CMjRpuPTRBCLACOAsXAc1JK462e0+2YspEnpWwtpYwECgHLmncMsI1SI/KBl9E/ESKEEAHAU8CblWiMQY/mDLXZHg+Msvg9Gjhk1npOStka6A+cNJ9jaynlInPaf1ps+9+CEQABoZCeor+ZMBqRR3YhmlqPZODMMSgqBEBeOInw8LGTkY1hF1JpWMeDIB8PnBwM9IsKZ2PiGas0GxPPMKRNEwB6twhl56lLSCmp7eRIu5D61HIo+xWW2H1JJSMtNE3g7epinefh4wzu0BIhBK0aBZCTl09aVo5VmrSsHHLzC2kdGogQgsEdWrLhUMWdACEEeQWFFBtNFBQW4Wgw4OrsxMajpxjctrmuF1yfnLwC0rKtO6Rp2dd1veD6ul7b5mxIOAlAmJ8PjSwqbUvWHzlJoI8n4X51zGWaRsM6nhZlGla2TI+dZUhryzK9WFqmwf5lylQCUsKNoiKklOQWFOHr7lqa36FjDO7YWrcvNKj88swroHVYkG5fx9ZsOJhorSMla/Yl0P+uKABOXk6jY7NQAOp4uOHu4syRc3onY+PhJAZ3iDJfQ/3Niv1rWGBxDaPYcEiP2c3buo8n+9yDk6Mer6xjtsfFyREHg15lFBQVl7z9ry49gPUHkwis60V4/dJRPNWlV7uWE+3CG1LL0TpOW9N6ABsPJDL4nja6ZlhDXTMz21ozM1u/b8KDdc172rDhwFEATl+5SvsmjQC4p0U4a/clAHDyUiodI/RgWR0PN9ydHDhy1cKWBo0gIxUyr+pveo/uQTRuZX1yWdcg7aJ+49vi4AgGBzA4gsEA17PLprGkQSNIT7PWa2LzEiXrGqTa05N/XM+WgFBIT4XMNF0/YTeimZ36u9iy/rZf31Rd7/b7iz9dL7gxMu0KXEsBYzGm/VsRUdaLbsvk+FKdM0kIrzo394CjIzg46H8GB2RO5p2l17Ax8urlUr0D2xCRtnpHSvXOHrfQK0W06oQ8tr8kXcX2Xbaxz/qNqjxha5+5jpS29hkqt8+WkCbItEtw7Yquvy8O0cpmlEJ+Xun/azlzG6YQW5G8dTs30jNua54lVKN9Gw8mMriT2eeHBZnrbht/kWn2F2EN9bq7U+uSulsIwfW8AgBy8/Lx9fIo3Z5fgJSSG/kFeLq64GDuRG88fIzBHc3tttDACtptBbQOvdnOaMmGQ8es0ujtjKP0bx8JQETD+vh6uQMQ3qAeBcVGCouLq7Xdll9YTANvj5J2W0WsPHSc/ub2GsCyA0mMmrWAof+exzu/bsJoMpU5ZuPRU/Rr1RgnBwOBPh40rONJ/PmUkv2Jl6/9z+3EiAZ18fXQ/Xu4r7e5vIy4ODnQIVQfJePkYCCifl1SLMor/mIaDX0s2vuRYWw8dtZG80ypZkQjdp42a9a3rwlwvaCIH3bE81R0G/KLjWYNT12jVTgbE09baySeZkjbZrpGZBg7T+oaGxNP069VuE2ZpQLQvlEDPGvXKvcaSd1vjwR+KTeR4k9BSrlSStlEShkmpXzfvO1tczACKWW+lHKElDJcSnm3lPKUxbHvm49rKqVcdTvO53avIbEVCAcQQrihj1B4AnNAAkBKuRq4DDyC/l3TqVLKcr2OECIMcAPeQg9MWHIOcBZC+Jk/RdIXuC0FU2U8vJHZFqMQsjOgggaraBuNTD5cabYp2Tfw93Qr+e3v4UqqTYWfkn29JI2DQcO9lhOZN6xHPFiSbXZyn63fw7DPF/PyL+u4mnvDKk1qVg7+ZgcI4OflQYqNM03JzMHP7KT0NO6kWji/uVv2MuT92Uz+aTlZN3Sn3rtNM1xqOdEt5t/0nDKLx6Pb4lXbmdSsXPy93CzyciMlK9daLysXP0/rNKk2aWy5UVjEt5v38myv0kacXl6lnV1/T1dSc+yVqZ6mtEwLytVxNGi8PbALQ2YtotuHcziZmsGwdk1L9qdm5uDvY1OeGdadppSMbPy8LdJ4e5BqU+b7TpyljrsbIWYn3TTQn42HjlFsNHLhagZHz13mijnf1Mwc/G3yq/QaWmieSU1nX/J5Rn34HY/M/JH4M6VvUw+dvsjA975i8Ptf886YfjgYtGrTu1FQyLfrtvNs/2irvKrTPnvUtB5AakYW/j6lUyn8fDwrv298PEnN0F9CNA7wY6M5qLVmTzxX0vUOSdOg+mw8cFS/b9LSOXothyuW97e7l3V9lpMJ7lXsgF88hTybhPbiR2gvfoQ8dVRv8FeEuxcyx6b+dPcqP30ZvWNoL32M9tJHyFMJleuV0betv9MrtFe0idY7uv8r1eQv/mw94VlHDyrdJPOavq0ctI69MCXqo7w4k4Q8EY/h3f9ieO975LED1iMP7gQ9Lx9rvaxK9Drchylxf5ntok1X5P5tFWrperb2Xa3cvqMW9h2Px/De9xim/4BMrNw+u/oZFvoZ9vVF9P0Ypn2DNvRxTAuqYSRDNVGd9uk+37Pkt+4vbOruTFuf71niL94Y1Z+PFq2mx98+5KOFq3l5WC8AHuzRkVOX0+g28R8MnjqLmJF90TRRqultoVleu83bui1Spp2RfI467q4l7QxL1u5PpHmDejg5OFRruy03v5D9Zy6xcFc8h86VX5/nFRax9fg5ekXqAfaTqemsPnSCOc8MI/al0Rg0wW8Hyr4oS82+jr+lr/Z0KwkOCATvLt9KwqU0FuzR/ef/2k5cm3Ca5vXr4mTzEis7r4DNSWetplVYtuXL1cy5gb9HJZpHT9O8fp0Szc827uWxe6JwcXTAaDTZ9CncSM2yY5eXRZ/CWe9TpGZZn5+fp6tVQKUi9p25DJCSlJR0okoHKP6/5bYFJMwLXvRDH7UAMARYLaU8DqQLISwnK70MvA/Uk1L+VEnWY9Aja1uBpkII2zFci4ARwD3o0zXK7z1a87wQ4rAQ4jshxC288rIzj6icQLpo2QnRoBHy98pjJtJeJsI2jZ0kFcxrMppMXMm+TptgfxY/N4zWDf34aNVOqzTSzltP2zzt6pr/Hd21LWumPcuvbz5JPU83Ply8HoD4M5fQhGDzBy+y9t3n+D5uP+evZZVjg/Vve2VRkZ0As9bu5JGubXCt5VThedsWqr2XvhVJFRlNzNtzlMXPDmPLpIdo6u/D7LjS4bi3Wp43WbEnnv53R5b8fqBzG/y9PRjxwdfMWLCK1mFBJUMO7dtQBTvNqkajiewb+cz72+NMfKAnr367uMSOVo0CWD7laRZMeoLZa7ZTUFRcbXqzfovjkR4dcHV2qjyv22SfPWpaD6r2bFf0XEwfN4xfNu5g+LTPuJ5fgKO5gfJA13b4e3sy4t3PmfHLb7T29cRgle8tLJ7sXQ9Rtz6mz17H9NkkRHBTCGpcyUFVrz/L1ft0EqZPJyGCm1VBr3L5cmuKqE6IBiHI7bcS864ef/Gn6/2RcmzfDdEwHLkhVt9Q1x/8gjC+8wTGt8chGkfx/9g77/CqivSPf+bcNEoaIaEESEINSeggWOhdQHpXVBQB62JDIwqogGthdy2rK4gFRRAwINID0qSDhBAChEACBJJAeoC0e+f3x7nkltyQoEl09zef5+Eh95w5873vnHNn3nnPFJqE/LX07uB3ITp0RzRsgty+xvaEhzeiXiM9AFImju5bafb10O3bbl7TonY9qNsA4xuTMb7+KKJ5a2gSWg7NMvQdIHetxzj7cUwRX6INHFv2BX8ZKs8+h23+HaRZvuMgr4y9n+3vvczMsffz+lf6c7vnRBzBDeux8/2Z/PjGU7y9fCO55pdMjtuf8mjaplp/KJr7O4WVSBd3OZWFEZHMGdlLz6ukXIX5ba880I2IGRMZ1C6YY4nJHD6X5DDtjtgE2gfUK56usf/sJWKSUhnz8UqG/2s5+89e4mJ6yVkCtyur76Y8wAv9u3Bv04Z8fyCGwwlX7FLcJg+rJHEp6SzccoA5Q7vapCkymnhx5XYe7BJGQ6sXVeVp8hz7klaaqeks3HqQOUN0zdgraVxIz6ZPy6DSMy3hxzh+Rspzv0tjfVQc/C+OjhDaX/vffyEVsahlNSHErR7YbvQ5J6AHEv5p/nu5+fNRACnlZSHEduDncuQ/DhgupTQJIX5EDz58YnX+B2AFEIz+0N9Tjjw/Bd5CrwfeAj4AbCaFW+/h+ungu5nSobl9HjrZ6QiPWpYfrIc35DgY8NE4BNF1CKavFoCxqMwvWNejBslW0eTk7Os2UwGs09T1rEmR0UROfgGe1UofOuVV3Y1qzk7FFVT/0MasPnKKZTsPs/JX3UlqFVCfZKtofkpmNn5WkVGAul7uNtH3lMwcfD31iHNtD0va0fe2Y/qnPwCw/nAMXUOa4GwwsPloLBnXb/Lo56u5p1kjkjNzrfLKxc/DTs/T3Sb6npKZi6+HbVnYc/xCMlui4/hgwx5ybuYjhGB4u+YkW0WEk7Ou4+de3U6rBsnmaHB5yvTUFf0tSyNz4zIgrAlzf9rN5tP6HOxWgfVJTrcrT6voPEBdb9tREykZ2fhapSkyGon8LZaVr00tPuZkMPDKmIHFn/uG/4OFa7bz8fpdtAqoVzxa4lZ+Je6ht909zMjG1xwZr+vtTt+2LRBC0DrQH00IMnJvUMvq+TtwJoH4K1cZNu9zOjVrVCl6xxOS2PJbLB9EbCMt5zqFRUa++eUg3UKbVLp9Z5JSGT5fXwiuKsoT9JFFK/dG6ZpBDYpHNQCkpGc5eG5sR02kpGfhax7d1LieH4tfeAyAhOSr7DquTx9xMhh4Zfzg4msmzFxAgIfVtK2cDNv6zN1LHyVRDkTzdsikc1BodpLPnUD4N0ZevM2LkZwMhLtd/ZlbTr0Wdnrx5dCzJ9vOXo9aju0NCkF0HYzpq3fKVX+Xrlc57cWfrScz0yxTBgC8fJBZ6SXSieZt0PqOxvjRa8U6ovXdyITTUKCP7jPFHkUEtEDGn/zr6nmWptcare8ojB/PKlGOou29yOgDYCp7qq3MvGZnX23bkS7W9vUbjfHDcCi6ZV8XZMIZK/uOIAJbIONjytS10fe20veujbzNQqfyyC608U+hD3r961PR9i27mMmquR8D0CrQn2SrjnBKRnbxtItblKi7M7KK2/y1+34jfPwgAAZ0DOONr/XAVsSvR3l8YDeEEATU8cHJoDF6wee4uTjrfluGlWZ5/IxMSxsFemc58rdTrAy3XeMqOSObZz9bQa82LXjuG31B3FYN61Sq37Z4x2FybuaTX2Tk6z3H6Ni45ILOG6LiuL+tJQAtpWRoh2CeH2DbBYg8Ec8n2/S1k94a2ZM6njVItm6rs3KLpzz4edSgrkcNMm/k0TskkOOXUikymu7IT0zOyuXZ77eyYGTPYr/wFrN/2kWAjweT7mllm5+9v59Vir+ffRvN5VtZMKJHsWbUxRRirlyjzz++x2iSXMu9wdaT53nrlkZ2Ln4e9nbVJDnTqk+RV4BndVe9zKzvpYPv54gio4nImHOg99EUittSkWtItJVSPiOlLBBC+AC9gMVCiAT0RSbHCtvwqMn8r1SEEK3RV+/cas5nHHbTNqSUyUAh0BdwvEKOHVLKFCmlUUppAhYBdzlI87mUsqOUsmOpwQiAy+fBpw541QaDARHWWV80zJq6jdAGP4Lp+3/B9RzH+dgR5u9HYloWl9KzKSgysjH6LD2DA2zS9AwOYI15SNqWmHN0blz/thFoIQQ9ggM4eF4fMr7/XBJNfL2Z0L0jEeFTiAifQu82zVl74DhSSqLOJ+FezbU42HALX093ari6EGWew7b2wHF6tdbLyHreYmTUaZrV9wWgnrcH+88kIKVkWJfW+LjX4JNHhtA7tAlrj8bqeolXdD27RsvXowY1XJ2JSryi6x2NpVdI49uW3855PhUAACAASURBVLdPjiby1clEvjqZh+5rxxPd2vJCv856mWbcKtN4x2V6zKpMg/xvW6Z1PGoQn5pB+nV9asres5e4r1lDIl6fTsTr0+ndtiVr9x/T7Tt3Efdqbo7L082FqHMXdfv2H6NXm+Di8/tizxFUt7bNkMybBQXcyNfnD+89GU9db0/Wz56u38PWLVh7INp8Dy+VrunqQtT5S+Z7GE2v1vpUk16tW3DgdAIACSlpFBYZ8a5ZnUvXMigy6j/Z7mFNqVHNlWUvPlJpet++8DCRbz9D5NvP8Fjfu3l+WC92zH+u0vSsae7vZ/lNVIEeoP8O5z5LxNxn6d0uhLV7f9M14y/gXt2tONhQrOnloT838Rd0zb2/0atdSwDSsnXnwWQy8dm6XxjTQ5+6dDPf6rmJicMgBE29rH5vlxPA2w88fUAzIEI66YtMlofsdESj5np0XjMgGjXX593fjssJUMsPPGtb9M6UUy/LgV5aGXr2JJ0HHz+9/tYMiNC7Sqm/H8a0/EO4Ub76u1Qqqb340/UuxCF86+n30uCE1r5ryV0k/IP0hQQXz4NcqzeX5p0o0Mz3sWlo2VMMqlrvop1eu/uQMYdK6o2ejnHxfFs9M1q7+5BHd99ex8a++lCrjsW+6AO2aRo0Rhv3JMZFbzuwL9RiX5Mw28U+y0PiGYSfv/7sGJzQOnRDHrfT97XsHCHCOkHqH971reqoYPsmNPQiYvbTRMx+Wq+795nb/PiLuk9jFxzw9XKnhpsrUfHmNn/fMXq11etuP08PDp3W5/bvP3WOAD99+kS9Wl7sj9XXYLiWlcvN/EKWvfwYEbOm0bttMGv3m/22c5dwdyvFb3NzJeqcuY3af5xera38jFO3/AxLO5N9I4/pHy9jxrDezBzdn4gZE4mYMbHS/LbPHx/G2hkPEvnqZMbd3ZraNaszpnPJERs5efkcOp9kk1+Xpg3ZEh1PmnkacuaNPJIysukT1oSI58YR8dw4whrUoWdIEBuj4igoMnIpPZvEtCxaNazDjYJCrucXEObvS8K1TH45dYGg2l535Cdm38xn+tJNzOjbifYBdW2u+VfkIXLzCnh1YMl3pmH1fUlMz7b4pifi6RncyFazhZXmyfN0Dqpv0fxuMzP63EX7RhbNcXeFsPPFiUTOGM+3k4cQ6OOJRzVXS58i6iw97UZP9GwZyJqj+roiW07E07mJblfPlkFsjDprKbNrWbRysOCoPfvOXiLI15vTp0/f2Zwxxf9LRFlDh8vMQIhcKWVNu2NTgfZSyqlWx3YCs6SUu82fvwJ+tlpk0lHeC4BsKeUCq2PngR7oO2e8KKUcLIS4B/CTUq4RQswBcqWU75vTB5p1wqzyqCelvGL+ewbQWUpZcolgM8Y5j9y+kJq11rdVExryt93I3esQPYcjL5+H08fQJr0Efg0sTkNWmu78lUZoawB2nr7AOxv2YjJJhndowbQe7fko8hCh/r70ahlIfmERM1f9Qqx5G6D3x/YpHgbW5/3vyM0vpNBoxMPNlUWPDKKpnzdJGTm8smo7OXkFeNdwY96IHtQPslRKUkre/mEze07G4+bizLwHBxNm3rJq+PxFRIRPAeBE4mXCl/5MfmEhXUOa8NqY/gghmPnVWk4lpSAQ+Pt4Mmf8QHw93bmeV8Br364j/so1JDC8fTCP9eig663ZwZ7Tibi5ODFvdN/iLaCG/+M7ImboqwWfuKhvH5VfWETX4ABeG9oDIQSRJ84yb+1O0nNv4lHNheD6vix63Hbt04+37Ke6ASbf14adZy7wzoZ9mEwmhrc3l+m2w4TWr20p09W/EHslTS/TMb0tZfrBMtsyffh+mvp5s/zgSb7ddwIng0Z9r5rMH9EDr8AmlvL8fj17Ys7q5fnwMMIC9Wj/8Lc+JeL16bp9CUmEf72G/IJCuoY147Vx9xcHQsK/iqB1UAPGde9UbFPStQymfLgUTQj8vDx4a9JQ/M0dSyklb6/YZLmHDw0p/R5+s06/h6FNi+9hQZGRWUvXcepSCs5OBl4a0ZsuLYL46cBxFm3Zi5PBgCYE0wd2pU/bFpWmZ3MPf95JdVeX4m0/K0uvz6yPyM3L1+9xNTcWPTOBJnVrV53eC4/R1L+ObuO3P7HnxBldc/IowoL0bV2Hz/6QiLn6Cvonzl8ifMkq/blp1ZzXJj6AEIKlW39l2fZ9APRtH8aMUfp3SbqWwZQPlqBp5ucmpDb+Ne1WKG8ShtZnrL5NZNSvyL0bEN0eQF5JhLgoqBeANvJJcKsORYVwPVvf6UIIxICJiIbNAImMj0Fus91Kz+FY1yZhaH3HgSZ0vV/t9QLRRtnpfT7boteoOUiJPBeDjPzBNu+icrztb9oabcB4ircd3f0zoscw5OUEOHMM7aEXS9bfyz8sPb+y2tSKbi/KooL1ZIbjJZ9ESAd9W0xNw7R/G3LrSrSBE5AXzyJPHER78k1E/QAwjyyQGdcwLZ7HrR0lhHlagYw9imnNkjLNqlQ9reS7GtGyPdows96BbcjIVWgDxut6MYfQps9B1AvQ1+kAZMZVTF+YXRdvXwzPLsD45hTHz4ex5KgJEdLBvO2nhml/JHLLSrT7JyAvmO176k1EvUB93ZNbeovM9o2ZZrZP6vZF2NlXVPYoDRHaEW3UE7r+vq3ITSvQBj+ITIxDRh9AG/0EokVbfbHUm7mYVnwKVy6Umt/Tn5YzGGPmsWVLaN7jPmrW9iE7JZV1s+ezd0lZM3wtfDy9623PV7R9YvwjgLn9Xfazue52Yd6jIyxt/tyPiZj9NGBu85es1tuLsOa8NmEwQgiOxCWw4PsNGE0mXJydeGPiEEID/UnNzCZ8yWquZuUipeTx/vfyQOfWFs3lG9gTY26jHh5qaaPe/oyIWfpa8ycSL5v9jCK9jRo30MrPWEPrxg0Y161jsU2fbdjFok17aORnXujWaGTxlOHUqlGtUvy2i2lZPPuNPnA6Nfs6of6+fD75AZbv13eIGddFd+MjDsey58wFPrDayQFgY1Qcn+84gpQSJ4PG60O706aRbWAA4LPth4k4fBKDpvHKkK50axFg1tZHgGTdLCCvsBB3N5c78hM/23GURbuO0cjH8vJo8cP3U2g00uv9ZTSu7VU8fXJi51BGdQwGk14f7DxzgXc27dP9/XYtmNa9HR9tP0xofV96BQfomj/uIDbZrDmql6658yiLdkfRyMcSSFr80P341LSMfEzKyGH6ss280Pcu3tm0H5OUDO8YzLSeHflo60G9TxESpGv8sI3Yy/q2n++P70tD83oon/1ymIjDp/QyG3wv3cy71734/RYOnr9M5vU8fGpW4+k+nRjZSZ8CF75yG60b1WHi3xf91++RaY9x3acVu4pvBWMYMv2/rswrKyCxA3jHvIDlrWPPom//Od38+SvKDkicBwZKKU9ZHVsIpAAHMAck7K6ZQ9kBiaVAW/QpGwnA1FsBCkeUGZCoaMwBiSrDeuhiVXH99osbVTj5N8tOU5H4lRxiWOkYC6teU1GxuJU9DLJC2bmp7DQVyR9sb+6Y8gQkKpqqtrGKKS0g8T+Fg4BEpeIgIFGplCMgUdHcaUDij1JWQKKiuRWQqDKK/oT2PvsOd2v5o5RjelOFUtbuN5WBqYrbCxfnqtUDDCOe+6/rHJeFCkhUPH94DQn7YIT5WA8Hxz60+/xIOfIusRqLlPJ5q487HJyfY/c5AQizO/ZQWdoKhUKhUCgUCoVCoVAoKo+KWNRSoVAoFAqFQqFQKBSK/23+S3ey+CvzlwhICCFaAfaTA/OllJ3/jO+jUCgUCoVCoVAoFAqFonL5SwQkpJTR6Gs6KBQKhUKhUCgUCoVCofh/wF8iIKFQKBQKhUKhUCgUCsVfGvFft2bkXx41CUahUCgUCoVCoVAoFApFlaMCEgqFQqFQKBQKhUKhUCiqHDVlQ6FQKBQKhUKhUCgUirLQ1Pv8ikaVqEKhUCgUCoVCoVAoFIoqR42QKAfSZKpSPWGsWj2q2L4/BSf1qFc4ah/miifvetXqVXWUv6r1DIaq1YP/+fpU1KtX5Zry6tWqFazq9qKgoGr1/oT28OPpXatU7+lPd1ep3ifjJlWp3p9St1W1plbFCwfKP6HuNsmq1Ssqqlo9haKcqF6aQqFQKBQKhUKhUCgUZaF22ahw1CtOhUKhUCgUCoVCoVAoFFWOCkgoFAqFQqFQKBQKhUKhqHJUQEKhUCgUCoVCoVAoFApFlaPWkFAoFAqFQqFQKBQKhaIs1KLuFY4qUYVCoVAoFAqFQqFQKBRVjgpIKBQKhUKhUCgUCoVCoahy1JQNhUKhUCgUCoVCoVAoykJt+1nhqIDE70Q0bYV2/4MgNExHdyJ3/2x7/p4BaO27g8mIvJGDKWIxZKUBoD30IqJBE+SFOEzfLSxVY3fcRRZs3IdRSka1b8GUrm1tzhcUGXnlxx3EXLmGVzVXFo7ujb+3O3vjL7Fw6yEKjUacDQZe7HcXXRr7cz2/gAeXrCu+PiX7OkNaN+PViUOKj0kpmb96K7ti4qnm4sz8BwcT0rBuie8Wc+EK4d+uJ6+wkG6hTQgf2RchBB9v2M2qvcfwrlkdgL8N6U730KZkXr/B376IIDrxCsM7t2LWkK4WvZ92sutUAtWcnZg/ph8hDfxK6l1KIfyHreQVFtEtOJDwB7ojhGDT8Tg+2bqfc6nprHh6HGEN6wCw90wiCzfupdBoJL+wiPyiIlwMBkZ1CmFKj/Yly/GHSGKSruJV3Y2FE/rh7+0BwOc7jrD6UCwGTSN8yH3c17wRAH3+vpQars5omsBJ01j59GgAPo48yKpDsXh7uOv2D+tN91bNbct3xUZ2nYjTy/eRYYQ0ql/S3sTLhH8Vodsb1ozwsQMRQhB78Qpzv/uZ/MIinDSN1ycMonVQA5trpZTMX7mFXTFnqebszPxJQwhpVM/xPfzmJ10jtCnho/shzJXst78cYtnOQxgMGt1Dm/HiiN4cT0hi9rINt0R4anAP+rQN1vV+2MyuGLNNk4Y61ku8bNYrpFtoM8LH9LfSO8iyHWa9sKa8OKIvBUVG5iz7mZjEK2hC8OqY/tzVPLDS9ABOX0phzrKfyc0rQBOCH155HBcnQ6XoJaVlMnjuvwms4wNAm6AGzBk/sFLvYWbuDf62aDXRFy4zvEsbZjV2L5EnQSFofcaApiGjfkXu32x7vmFTtN5jwM8f09ov4PRRyzkPb7SBD4G7NwCmlR8X13ulEhSC1ns0CIE8vhd5YIvt+QZN0XqPAl9/TD8tgTO/Wc65e6MNeBA8vEFKTKs+gez02+v9ERsbNde/6y186mJauxjiom6v1zgUre9YEBoyag9y3yY7vWb6eT9/TGsWwSmzXkAL/Xta661ZBGeO/bX0qro8AdGiLdqwyaBpmA5sQ26PsD3fbQha595gMiGvZ2Fa8W/IuKqf9KqNNmY6wqs2SIlx8TzLudL0mrdBe+ARvc0/tB25Y63t+a6D0Dr10tv869mYVn4GmdcsCVyrYXhhITLmIKa1X5ZtX3A7tOGP6XoHIpHbfrQ93/0BtC59dL3cbEzLP4aMq4imYXq53MLPH9M3HyBPHCyf5q0y3R9Zsky7D0HrbKW54hPbMh37pKVMF71dZpna5B3SAW30E7q9e7cgt6y0Pd91IFq3wWAyQf5NjMs+guSL5c6/PDz0xSe0GjyAnNSrvNWqy+/KQ0rJ/OUb2BVtbi8eHU5IQClt/Jc/kldQRLdWzQgfd7/exl+4wtxv1+ltvEHj9YmDbdr46PNJjF/wOR9MHUP/DqFVq/nQIPq3aa7rrfmFXbHndb1x/QlpUKek3sUUwpdv0tunlkGED+uJEIL31u1kR8w5nJ0MNPTxZN64/nhUcyu+7nJGNkPe/Zqn+nVhco8Out7anRa9sbfxE5dvsegNNfuJUWf4ZIvZT3x2fLGfWGg08sYPkZxMSiXnZj75hUXUdHOpdD/xwy0H2B57HiEEPjWqMX9kL04np7Fg/R6MJsmoji2Z0t2B/qptFv1xffH39iDzRh5/W7aZ6KRUhrcLZtYDum99s6CQGd9v4WJ6Npom6BkcyPP9HT/Tu+MusmD9Xr2v0SGYKd0c9DVW/0LM5Wt4VXdl4Zg+el/j7CUWbj1IYZERZycDL/bvTJfG/g41FIqyUAGJ34MQaIMnYfz6XchOxzB1LsZTR+HqZUuaK4kY/zMbCgsQnXqh9RuHaeUnAMhfNyCdXRAde5UqYTSZeHv9ryyedD91PGow9vM19GwRQFM/7+I0q4+exqOaC5ufG8uG6Hg+2HqQhWN641XdjX9P6IefRw3iUtKZsnQjO16cSA1XFyKmjyy+ftRnEfRtGWiju+tkPImpGWx6YxrHEy4zd8UmVrz4SInv9+aKzcwdP4A2gf5M/fQHdp88R7fQJgBM6nkXk3t3tknv4uTEM4O6EXflKmcvW5yUXacSSLyWyaaXH+b4hWTmRmxnxTPjSupF/MLckb1p06guU5esZffpRLoFB9Ksjg8fPjSYOT9us0nvVaMa/35kCD7u1em74EuKjCY2v/ggYz9ZRc+WgTStU8tSjodi8ajmyuaXHmRDVBwfbNzHwgn9OZuSzsaos6ybMZ7U7Os89sVPbHhhAgZNn+n01ZSheNeoVuK7Trq3NZOHDShxHGDXiTgSU9PY9NazHD9/ibnf/cyKV58oae+yn5n74AO0adyAqR99y+6Ys3QLa8YHq7fy5OAedAtrxs7oM3zw41a+fuFRW42YeBJT09k050mOJyQxd/lGVrw8uaTG9xuZO2EQbYL8mfrJcnafjKdbaFMOnE5g+/HTrHntCVycnUjLuQ5As/p+rJz5GE4GjatZOQyft4gerZrza2y8btPcpzl+Pom5369nxczHHehtYO7EQbQJasDUj5cV23Tg9Hm2R51mzaypul62rrdqj95pWfv6NNKyrzP142X88Mrj7D55tlL0iowmZn4VwTuPDCO4QV0yc2/gZNDYFVM5egANa3sT8dpUSybSVKn30MXZiWeGdCfu8lXOXnHQWRACrd94TMv/BTkZaI+8iow7DmlXLGmyMzCt/xrRuW+Jy7XBj2LauxESYsHZtdieUhECrc9YTD98CDmZaJNmIs8eh7RkK710TBuWIjr1Kak36GFM+zZB4qny6f1RGy+cwfTlPP1vt+poU9+C8yfL1us/AdP3/4DsDLRHw5FxUXDNWi8d07ovEV362V6beBrTF29Z9KbPg3N/Qb2qLE8AoaGNmILxP29CVhqGv/0dY8whSLlkSZN0HuM/X9bb4Lv7ow1+CNNS/QWAYfwzmLatxnTmOLi4le85HTZZD1xkpWF4egHGk4chNclKLwHj/ld1vS590e6fiGnZv4pPa/3GIMsqS2v7Rj6B8bM5kJmGYca7GE8ctLPvHMaFL+p69/RHGzJJDzycPYHx/ef1NNVrYgj/N/J0GQGlW5ojpmD8bK5u44x3HZfpP16yaA6ehGnpBwAYJjyLKXI1pjNR5StTe+2x0zF+OAsyr2GY+Q+Mx/fbBBzkoR0Yd2/Uk7fqjDZyCqZP3ii/RjnY99V37Pj4cx755j+/O4/iNn7ecxw/d4m5361jRfjUEune/HYdcx96gDaNGzL1w6XsPhFHt1bN+WD1Fp4c0oNurZrrbfyqLXz9kl73G00mFq7ewr2hTf9UzV2nzut+26uTOX7hCnNXb2PFcxNK6q2OZO7ovrQJqMfUxRHsPpVAt5ZB3NM8gBn3d8XJoPHBz7tYtO0gLwzuVnzd39fuoGtwoJVeAolXM9j0yiO6n7h6GyueG+9AbztzR/U2660p1mtWtzYfPjyYOats/cTNUXEUGI38+PxEBrzzFc4mA/95dBAzvttSqX7i5G7teLaf7iMv3RPFx9sOceBcEosfHaL7+5+u1vX9rPQPx+Lh5srmFyay4XgcH2zez8Jx/XBxMvBMn7uIS0nnbIptMP7Rrm3p3NifgiIjk5f8xK7TiXRrZvsCy2gy8fa6PSx+ZJCu/VkEPYPt+hpHTum2zxjHhuNn+WDLARaO7aP3NSb2t/Q1vt7AjpcfLHFfFIry8IfXkBBCGIUQx4QQJ4QQK4UQ1a3ODRdCSCFEsPmzmxDilBCilVWal4UQn5WhMUMIkSeE8LQ61sOc92NWx9qZj70ohPjE/L1OCiFumv8+JoQYZU77jBDitBAiRgjx7h0Z3aAJMj1Vj/4bjZii9yOCbaOZ8nwsFBbof1+MR3haftzy3EnIz7utRHTSVRrV8qBhLQ9cnAwMDGvC9lOJNmm2n0pgWFv97Xu/kCD2n09CSklIvdr4edQAoKmfN/lFRgqKjDbXJqRlkX79Jh0CbEc/bI+OY+hdYQghaBPkT87NfK5m5dqkuZqVS25ePm2DGiCEYOhdYWyLPnNbe6q7utChSUNcnWxjYNtPnmNo+5a6XkA9Xc+qwwZwNfs6uXkFtA2op+u1b8m2mHgAmtSpRZBVxXmLEH8//DxrEn0xhcZ+tSgy6s7RwDZN2R573vY7xJ5nWPtgvRzDmrA/Xi/H7bHnGdimKS5OBhrU8qCRjyfRF1Nva2dZbI86xdAubXV7Gzck52YeV7NybO3NyiH3Zj5tmzTU7e3Slm3HYgF9lNj1m/kA5N7Mx8+z5Bvu7cdPM7RzK/M9bEDOjVI08vJp29h8Dzu3YlvUaQCW7z7C4/3vwcVZv1c+7vqzVM3FGSeDXmXkFxYVv4nfHnWaoV3amG1qQM6N/Nvo3bKpjUVv1xEe73+vRc/87MZfuUqXFkHFx9yru3LiwuVK0/s1Np7m/nUIbqD/JrxqVsegaZWmdzsq6x5Wd3WhQ9NGuDqXEouuFwgZqZB1TX8LevIQollr2zRZaXA1CaS0Pe5TT195OkF/VinMh6LC2xtaLxAyr+p5mozI2COIpm1s02Snm/XsOjg+dUHT9GBEefX+qI1WiBbtkediytasH6TrZVrr2dlYHr3gDsj4E1BU8NfSq+ryBGjUFJmWDOkpYCzC9NseRGgnmyQy/oSlDb5wBuGpj0SiTgMwGJBnjuufC/KK05VKw6bItBRIT9Xb/Ki9iBA7vXMxVnpxFj0A/yBw99IDNeWhUTPktSuQZmVf2F22emet7Es8g/DyKZGNaHM38tTRsu0DvUyvXbEt0/Jq1mkAmgF5xjyypTxlak1gc+TVy3og0liE6cguRBu7t7l5Ny1/u7oBpT9Lv5ezu/dyIz3jD+Wx/ZhVG9+koV53Z9rV3ZnmurtJI6s2Xq/HBHA9z9zG38jDz8vSxn+3fT99O4QU1+d/lub2E/EM7RBi9tvqm/02Oz8xO1f32wLr63odQth24iwA97YILPYl2gTUIznTcm1k9Fka+HjStK7led4eE8/QjlZ+Yl5B6X7iLb2O9n5iLewRAm7mF3Is4Qr+3h5Uc3HCq3q1SvcTa7q5FP99s7CIjOs3aVTL0+Lvt27K9tgEO/0EhrVvoeuHWvSruzjTIbAers4Gm/TVXJzpbB6t4OJkIKS+Lyl2ZQYQfekqjXystFs1Kal9KtHS1whtzP5z5r5G/bL7Gv+zaNpf+99/IRXxrW9KKdtKKcOAAmCa1bnxwB5gHICUMg/4G/BvoeMPTAVeLUNjPHAIGG53PBoYa/V5HBBl1npKStkWuB+IN3/HtlLKVUKInsBQoLWUMhR4/04MFu7etsOQs9MRHiU7xbfQOnTDVF5HxExK9nXqetYs/lzXswapObaVSUrODeqaKwMng4a7qwuZN/Jt0mw5eZ6W9XxwcbKtrDZExzMgrHFxp/IWqZk51DUPQwOo4+VOil1HKCUrhzpe1mk8SLVq/JbtOsKwBYt57bv1ZN24ye1IzcqlrpfFzjpeNUmxC4CkZOVSx9M2TapdmtJIycrFaDLRsr4vLk4G6nrUJDXLrhyzrxd/ByeDhrubC5k38kjNsr0HdTxrFFfoQsDjS9Yx6qOV/HAwxia/ZftOMOzNf/Pa12vIum5rf2pmDnVr2ZZdSka27ffJyKaO9T3wtpTvK2MG8t7qLfR65QPeW72Zvw0v+da4xD309iDFzjlJycyhjpXTYa2RkJrOkbMXGfvuEiYt/IboBMvIn6jzSQx56zOGzvuc2RMG4WTQHOi5l6Jn+1xZ9NI4cvYCY/++mEkLvyI6QX/r2KJBHbYfP02R0cSlaxmcvHCF5PTsStNLTElDAFM+/JaR8z/niy2/llKeFaMHkJSWyYh5nzNp4VccjrMEHCvzHt4Wd29kjpVDnpNZPP2iTGr5Qf4NtOFT0R4NR/QcUfY8y5pednoZ4O5ZenprvOtA/k20YU+gPfwqosfw8s3r/CM2WiFCOiJPHiqHnhfSehrJ79brhDxZ9rD7qter4vIEhGct2+kQWem2AQA7tLt6YzJPSxG+9ZE3r6M9/BKG599DGzypzC3cdD2rNj8rzeYlQwm9Tj0x3RqVIASGQQ9hWv9t2Ybd0vOyty/t9vZ17oMp9miJ46JdV+TRPeXT9PSxtTEzTbe7VM3exZrFZfrIyxiefx9tSNllaqPt5QMZVvZmXHNor+g2CMPcxWjDH8X0w+8fxVCZpGZkU7eWpQ7T6267Nj7TQRtv9gNeGXc/763aQq+X3+e9VZv5m3k6YUpGNpG/xTK2u20g7M/Q1P02q7bHsxS/zTpNKX7bjwdj6GoeqXsjv5AvfjnEk/3uttO7Xk49a3/NvYSvZ0+/1s2o5urMtC/WcuRcEo92a4tXdbcq8RP/uXk/vd75mp+PnaFHcCB1PS0Bn7oeNRzo5xbrWOuXh+yb+ew4lUCXJg1KnNP7GlbajvoaVmlK7WvEnKdlvdol+hoKRXmp6DDKbqApgBCiJnAv8BjmgASAlHITcAWYBPwDmCOlLDUkLYRoAtQEZqEHJqy5ALgJIeoIvWc9ANhYju85HXhHSplv/k539trbkc9bSrBetL4HUT8IuWfDHUmUJ/YvHbxtsvbH41LTWbj1IHPM6zVYs+FEPINaNXGQZ0kde3Md2ftdqwAAIABJREFU6+qpxt3Xns2zp/HjzMfw9ajJuxHbb2+Dg2P2fQrpIJV9IKU0rmTmcOJSCnOGdy9VwKE9iNt+t++mjWD1M2P4z6OD+H7fCQ6f1zt84zqHsfmlifw4axq+njV5d5XtXOrblV1xGke65v+X7zzEK2MGsP2dF5g5egCvf7O2RFqH97CEzY409DRGo4nsG3ksf+lRXhzRm+e/WF38vdsE+bPu9Wn88PJjLNq0h/zCIsf3p8R3Kv1ZLdZ7+TFeHNGX5xfreiPuaUddLw9Gv7OIBSs307ZxQwwGrdL0ikwmjsZf5N3JI/j2xUeJPHaKfafOVZqer0dNts17jh9fe4KZI/vx8pcR5JpHv1TmPbxzynmdZoAGzTBtX43pq3cQXrURre6+/TV3UJeW1NOgQVNMv6zG9M3fEZ61EWFl6JXKHZZNDQ/w9YfzMWWn/UNG3tLzBD//sqdP/Cl6jqjM8oQ7sVG074Zo2AT5i7mu1DREUEtM677B+M+Z4FMH0annneuV1ua3u09fI2rnT/rnLv304ERZa6mUpVeafR266/ZtX2N7wsMbUa8R8tRvDq/7A5KIDt0QDZsifzFragZE45aYfvpanybjUwdxV1llWpa4g6+zaz3G2Y9jivgSbeDYsi/4EyiPv+K4ftf/X77joN7Gv/siM8cM5PWv9TJesGIjL4zoVzwV4M/UdOyjlEfPNs1nkQcwaIIh7VsC8PHmvUzq1p4ari426crydUtN4+B7WhN9IQVNCOaM6sWAts35ancUF9OzHApUpJ8I8Lf+Xdj+ysMMbtuc3WcuOMjE9qNjnbJ/N0VGEy+u2MqDd7eiodXLsNvlay9+u2cHIC4lnYVbDjBnaMm+hkJRXipsDQkhhBMwELi1etYwYJOU8owQIl0I0V5KeSuE/zfgIBAnpVxaRtbjge/Rgx0thBB+dgGEVcBo4DfgKJBfMosSNAe6CiHmAXnAi1JKm1czQogngCcA/j2oM1PaWy1MmJ1hG733qGX7huhWHo1D0bo/gHHJPDAWleNrWajrUYNkqwhwctZ1/OyG6dX1qEGyeSRFkdFETn4BntVczelzeXb5VhaM6EEju0roVHIaRpOJ0Pq+gD6iYeVe/W1Oq0b1SLZ6Y5+SmVNiWkBdL9voe0pmNr7myG1tq+Hoo+9pw/T/2C5MBXDm8lWG/+M7Xa9hHZvheimZufh51LRJX9fT3SYanpKZi285hr0nZ+bwze7faOxXi0Y++tuD5Oxc/Dyq26Sr61mT5MxcSznmFeBZ3ZU6nrb3IMXqHtwapuZTszq9Q4M4fjGFjkH1qe1uzlvTGH1fB6Z/soxlvxxgpXk9hFaB9UlOty076+GRAHW9bUdNpGRk42tOs3bfMcLH6gsfDugQyhtLdadX1ziiawTY3cOMbPw87crU7i2/rlGz+Fzfti0QQtA60B9NCDJyb1DL6vk7cCaB+OSrDHv7Mzo1C7DTy3FsU6btc+Vrfq7qenvQt12wQ71XRvfX7dtxiPcjIrl0LYMOTRtVil5dLw86NQvAu2Z1lu04RFJaJq98tYbuYc0qzb5bUypCA+rj4uTE6L9/gZuLc5XcQ4fkZCDcvS1OiruX/sa7PORkQOpFfeg+6MO36wcBe29zTaadnjfkZpVfL+VicUdPxpn1osu+7nfbaEa07Ig8c0xfYK8c31N41PpjeiEdkKd/A1M5hsP+GXpVWZ6AzErTF0+8hWctZFbJxUxFs9ZofUZi/PfrxW2wzEyDpPP61ARAnjiICGiGvM1gEF3Pqs339EFmO2jzm7ZC6zVCX/vBrCcCmiOCgqFLX32qgcEJLT8P06bvS9fLtLfPx7F9zVuj9R2F8eNZJXwM0fZeZPSB8t3DYk0rG718bEfa3Mq3WWu0PqMwfmJVpll2ZRp9EBHQHMm2Etc71r6G8Lay17u2nmdp6Y/sQhv/FPo7rT+fS57VGT733wC0CvInOd1Sh+l1d3naeN1PW7vvGOHj7gdgQMdQ3jC/dIhJSOKFRbo/lZF7gy1HT/L+ys3UrOZWJZpXs3JYdyCK99ft4u7mjUi2bnuycvHztPNPvWratk92ftuaQzHsPHmOJdNGFXesj19IZsvxOD74eTdpOTcoNBr5ZtdRurUMKqln7yd6uZNi7Utm5eDrefv2bv1vp+gaHKgvDnn9Ju0C6nLi0tVK9xOtGdS6GSsPnSTQx6v4WHL29eJri/U9apKcZadv9vdvx+w1Owmo7cWke9s4PK/3NSwjIvS+hr3teppS+xrfb2XByJ4l+hr/06hdNiqcihghUU0IcQw4jD5i4Qvz8fHAcvPfy7Ea3SClvAxsBz4tR/7jgOVSShPwI3rwwZofzMduBS7KgxPgDXQBXgJ+EHahRinl51LKjlLKjtbBCACSziFq1QGv2mAwoLXqUvItRN0AtAcewfjdP+C67VDr8hBW35fE9GwuZWRTUGRk44l4egY3sknTs0UAa47pazdsOXmezkH63Lnsm/lM/24zM/rcRftGJXfI2BAdz/1hlgWKJnTrQMQrjxHxymP0bt2ctQdPIKUk6nwS7m6uxcGGW/h61qSGmwtR5jUr1h48Qa9WzQBs1puIjDpDs3q+JfSb1/clYsZEImZMpHdoE9YejdX1Eq/gXs21RLDB16MGNVydiUq8ousdjaVXSOPbll/2zXymf/kTLw2+j4zrN7mUbi7HqLP0bBlkW44tA1lzVJ9LueVEPJ2b+COEoGfLIDZGnaWgyMil9GwSr2XRqqEfNwoKuZ6vz4+9UVDI3riLNDPvlGA9rzHyWCzN6vsxoWdnIl6fTsTr0+ndtiVr9x/T7T13EfdqbsUdV0v5uuvle+6ibu/+Y/Rqo89d9PNy59CZBAD2nzpPgHle5ISenYkIn0JE+BR6t27B2gPR5nt4qXQNVxeizl/SNQ5E06u1Pj+xV+sWHDitaySkpFFYZMS7ZnUuXcsoXouje1hTari5suylyfRu04K1+6PMNl3S76FDm1yJOmfW2x9FrzZmvTYtOHD6vEXPqOvdLCjkhrmcA+vUIrRRPdbPearS9O4NacLppBRuFhQypmsHmtbz482JgytNLz3nOkZzB+zi1QzyCgr5/qVHK/UelsmVRH3qhaeP/tYzpJO+yGR5uJIAbtWhmrm+CGhhu7BhaXreVnotO5RfLznxzvVuaf5eG82IluWfXsDlBFsbQzrpwZM70Qu566+rV9XlCXDxLKJ2PV3X4ITW7j5kzGHbNP5BaKOmYlzyDuRaDV+/GA/Va+qjMgDRNAxpvXCjIy7FI3zqgrev3ua3uQcZa6dXPxBtxOMYv3oXrlv0TMs/wrjgKYx/fwbT+m+RR3fdNhihf8c4hK+9fXbl4x+ENno6xsXzHQbxtHb3IY/uvr2OjebZkponHGlOw/jFAlvNC2dty7RZK2TKHeyAkXgG4ecPPnV07Q7dkMcP2KbxtXTkRFgnSC3nNLQqoEHWDSJmP0nE7Cfp3TbY0sbHm9t4uwC2r5e57o63auPbmtt4T+s2/lxxG7/1neeJNP/r3z6EeY8OZ+s7z1eZ5qBOrXj/wUFsnfU4vcOasvbISbPfdhl3Nxd87QIEvh5mPzHxsq535CS9wvRRubtPnWfxL4f4ZPJQqrk4F1/z7dNjiZz1OJGzHuexXh15ftB97Hhjiu4nHrbyE91cSvETXSx+4uFYeoWWHAVsTT0vd/bHXSS0QR0SrmZyOOEyDWu5V7qfmHDNErD95VQCLevVJjEt0+KnHj9LT6tFPS36+hpRW2Li6dzYv8wREv/aeoDc/Hxevf/eUtOE+fuSmJZl6WtEx9MzOMBWO9iqrxFzjs5B/pa+xtJNzOjbifYBJfsaCsWdIH7/MF5zBkLkSilr2h3zAS4Bqegjggzm/wOkWVAI8RXws5Ry1W3ybo2+dsQtL9MFOCelvE8I0QN9ZMNgIUQk0BAIAV4HcqWU75vzCDTrhFnluwl9ysYO8+d4oIuU0uEeVUVvTCpRSKJZa7SBD4ImMB3dhdy1Dq3XCGTSeeTp39Aenomo06D4TZHMSsO07J8AGB57DWrX01eivpGLae0XyLOW13oiRF8QbOeZC7yzaR8mk2R4uxZM696Oj7YfJrS+L72CA8gvLGLmjzuITU7Dq5or74/qRcNaHny28yiLdkfRyMcSrVz80P341NRX+u33z+V8NnEAjX3NEdlaljcTUkreXrmFPbHncHN2Zt6Dgwgzbzc4/J0viHhFX0P0xIUrhH+rbz/ZtWVjXjNvNzjzm584dSkVIcC/lidzxg0sDmj0mf1vcvPyKSwy4lHNlUWPD6OJXy3eXrODPacTcXNxYt7ovsVbMg3/x3dEzJio613Ut/3MLyyia3AArw3tgRCCyBNnmbd2J+m5N/Go5kJwfV8WPT6cz7YdZNH2QzSq7UVuXgGp2bn4utdg1F0tmdazIx9tPUiovy+9QoL0cvxhG7GX9e2U3h/fl4bm+Zif/XKYiMOnMGgarwy+l24tAriYnsWzS/WBQEUmE4PaNmNaz44AzFwRyakr1xBOzvj7eDHnwSE2HUkpJW9/v549MWdxc3Fm3sPDCAvUFx4a/tanRLw+Xbc3IYnwr9eQX1BI17BmvGbenuvI2UQWrNiI0WTCxcmJNyYMJvTW9l7GQovGik3sORmvazw0hDBzmuHzFxERPkXXSLxM+DfryC8spGtoU14zb1NZUGRk1tJ1nLqUgrOTgZdG9KZLiyB+OnCcRVv24mQwoAnB9Pu7FW/7+fbyjRa9SQ9Y9Ob9p3gXiROJlwn/eq1+D0Ob8trYAVZ6P3HqYrJZry9dgoNISstkyoffoWkCP0933npoCP4+XpWmB+g2bv4VAXQzb89ZWXpbjsby0c87cNI0NE3w9ODu9DQH9irrHgL0mfWR/js0GvEwaCzq34amXlbOXeMwtD6jQWj6Npz7NiK6DkFeSYSzx/Vg64hpejDAWAi52Zi+eFO/NrAlWq+RgECmXEBu/Nb2Da2jxZYah6L1GqXrRe9D7t+EuG8wMjkRzkbresOfAFez3vVsTEve1q8NCEbrORIEyOQLyM3LbPVKe+P+R2z09EF78CVMn7yKwwGvjjSbhKH1GWvZFnPvBkS3B3S9uCioF4A28kldr8hs46I5Fr1JL2P66BXHeo6oTD2H97ASyxOQV0s2zSK4PdqwR0FomA5uR25bjdZ/HPLSWWTMYbSpsxH1GoF5JIPMvIZpyTv6tc1bow15RH9uLp3Tt+i0HmHgVHLwqGjRFm3Iw/qWmId2IH+JQOs7GnnpHDL2CNrjsxB1G1ra/MxrmL5+zzaPDt0RDRqX3PazoOQCkKJle7Rhj1m2NY1chTZgPPLiWWTMIbTpcxD1Aiz2ZVzF9MUC/WJvXwzPLsD45pSyx1vbaw41b/t5cBsycjXagHHIi/G65rTZdprXMC1ZYC7TNmgPPAxCIC/GlyzTvNsPXhWhHdFGPaFr79uK3LQCbfCDyMQ4ZPQBtNFPIFq0BaMReTMX04pP4YqDoe5mnv70DoIxZh5btoTmPe6jZm0fslNSWTd7PnuXlDWIV+eTnYsAc929bD17YuL0uvuR4ZY2fu6/iZj9JGBu47+M0OvusGa8Nn6Q3sbHJbJg+Qa9jXd24o2JQyxtvJnwJT/SvU0Lm20/q0SzWYPibT/f/nE7e04n4ObsxLxx/Qkzbw8//IOlRLzwkK53MZnw5ZvNflsgrw3vhRCC/vO/oLDIiKd594k2AfWYM8p2PayPN++luotz8bafb0f8ovuJzk7MG9vP4icu/JaI5x806+nbfuYXFdG1RSCvDTf7idFnmbdmh9lPdCW4fm0WPTGC6/kFvLZiK/EpaVzPKyje9nN4x+BK9ROf+3YT569logmo7+nO7KHdOJWcxjvrf8UkJcPbBzOtZwc+ijTrtzTrr9pG7OVreFVz4/1xfYunYPR571ty8wv0Nt3NlUWPDqamqwu93l1KY18vnA36ug4Tu4QxqkOLEs/uzjMXeGfDPkwmE8Pbt2Baj/Z8tO0wofVr06tloK69+hdir5j7GmN6632NHUdZtOtY8QhkgMUPW/oatzCMeeF/bjiBcdvSil9VtwIx9H7ov67MKysgMRVoL6WcanVsJzBLSrnb/Pkryg5ILACypZQLrI6dB3oAQVgCEvcAflLKNUKIOZQdkJgG1JdSviGEaA5sAxrJUgrDUUCiMrkVkKgyrAISVcbNG1WrZyzHyu0VSa2Se3JXOlVt4x0sWqYoJ3eyVV5FcOjXqtWr6tWfyzkF4L9esyr5E1bwdhSQqFQcBCQqFQcBiUrlzxhuXEZAoqL5PQGJP8KtgMT/NNl3NhXrD1PV7eGd7AxTUZiquF9bzilcFcn/ZEBi+3d/7YBEr4n/dWVeWZ7FeCDC7thqoORGxbdnnIN8IrBaJBNASrlXSmm3otNtWQI0FkKcQJ9O8nBpwQiFQqFQKBQKhUKhUCgUFc8ffg1gPzrCfKyHg2Mf2n1+pBx5Bzk49rzVxx0Ozs+x+5wAhNkdKwAeLEtfoVAoFAqFQqFQKBQKReWgxlwrFAqFQqFQKBQKhUKhqHKqeKKkY4QQrQD7lYPypZSd/4zvo1AoFAqFQqFQKBQKhQ1q288K5y8RkJBSRgNt/+zvoVAoFAqFQqFQKBQKhaJqUFM2FAqFQqFQKBQKhUKhUFQ5f4kREgqFQqFQKBQKhUKhUPylUdveVziqRBUKhUKhUCgUCoVCoVBUOSogoVAoFAqFQqFQKBQKhaLKUVM2yoE28uEq1ZPJF6pUj7TUqtUD8PCqWr3CgiqVy/vnh1WqB+DsXaNK9bSablWqh1bF8VODoWr1AJyqtkoWPftVqR5GY9XqSVPV6sGfYKOsYr2qL1Ot+8CqFaxiG6Xpf/yZ+RP4ZNykKtV7qvuUKtX7+KUBVaoHIKpXr1pBZ+eq1ft/0Ob/KTb+L6KpXTYqGjVCQqFQKBQKhUKhUCgUCkWVowISCoVCoVAoFAqFQqFQKKocNWVDoVAoFAqFQqFQKBSKslC7bFQ4qkQVCoVCoVAoFAqFQqFQVDkqIKFQKBQKhUKhUCgUCoWiylFTNhQKhUKhUCgUCoVCoSgLoXbZqGjUCAmFQqFQKBQKhUKhUCgUVY4KSCgUCoVCoVAoFAqFQqGoctSUjd+JlJL5X65k128xuLk6M//JSYQ2blQi3T+/X8vaXQfIzr3JkaX/KD4esWMf7y2NoE4tLwAmDOjO6N73ltRYHcmuk/FUc3Fm/sRBhDSsW0Ij5kIy4d+tJ6+wkG4hTQgf2QdhNZxoybYDvL/2F36d/yzeNatzMC6Rpxf9iL+PJwB9mzXgyR7tANgdd4kFm/ZjNJkY1b4FU7q2sdEqKDLySsROYi5fw6u6GwtH9cTf253jl64ye92e4nRP9WhHn5aBXMnK5dWIXVzLvYEQgjEdWvBQlzBbGyN+YVfseaq5ODF//ABCGtQpaePFFMK/30ReYRHdWgYRPrwnQgje+2knO07G42ww0NDHi3nj++NRzY11R2JZ8suh4uvPXLnKqidHcy33BgvW78Fokozq2JIp3duXtG/VNmKSrur2jeuLv7cHmTfy+NuyzUQnpTK8XTCzHugKwM2CQmZ8v4WL6dlomqBncCDP9+9S4vs7wtCuMy6P/w00A0Vb11H441LH6e7uidvMedx8YTKm+FPlyvsWIqwjThOeBE3DuGsjpg0rbM5rPQaj9X4ATCbIu0nR1/+AyxcQQS0wPDKjOJ1x7VLk0V/L1mvZHm3kFNA0TPu2Ireusj3fcyja3f3AZETmZmP67l+QcVX/Lg88jAjtBIBp83Lk0T0l8i+hF9wObYRZb/9WZORq2/M9HrDSy8K07CPIuIpo2gpt+GRLwjoNMH39PjL6QPk0h002a0Yit0fYnu8+BK1zH4uNKz4pthGv2mhjn0R41QYpMS5623KuNL3mbdCGPgpCw3RwG3LHWtvzXQeh3dXborfyU8i8BoDhneWQfAEAmXkN01fvOtSQUjL/x+3sij1HNWcn5k+4n5CGjn6HyYQv22j+HTYmfEQvhBB8uGEP26PjEELg416d+RPux8+zJgfjLvD0FxH41zLXNa2a8mS/u3W9Nbd+987MH9e/9N/9cqvf/TDz737dTnbEnMPZyUBDH0/mjdN/98cvXGH2yshbRvFUvy70adXUYuPanRbNsf0IaeBXUvNSCuHLt1g0h3ZHCMGmqDN8smU/51LTWfHseMLM5ZOUnsXgd78h0M8bKSUmkyS/yKiX45g+hPg70kglfGWkrtEigPAHuiGEIPNGHi98t4mkjGz8vT1YOHEAntXd+GLnUX7+7TQARpOJc6kZ7HnjcTyruTL/p13sOpVAavZ1Qhv48u20UaXrFRl1vSFdLXrLNlv0JvTHs7ob51IzeG1VJCeTrvJc/y5M7qbXk7tPJ/LqD1vJzivAs5ornRv7M29UL1yddVeioMjIKz9EWurPCf3w9/YA4PMdR1h9KBaDphE+5D7ua96I/MIiJn2+hoIiI0UmE/3CmvBM37sAeGn5VmKSruJk0GjdvDFzHh6Js5PB9pn97id2HT+Fm4sz8x8fQ2hggxK2/3PVJtbuPUL29Zsc+c/bxccPnT7HgmU/ceZiMh9Mn0D/Tq1LXGuNlJL5y9ax6/hpXe+x0YQG+pfUW72Ztb8eJfvGTY589qad3s+cuZTMB9PG079TK8ca369nV/Rp/RmdPJKQgJIaMQlJhC9Zrbf5rVoQPn4QQghOXbzC3G/WciO/AP/aXrw7ZQw1q7kBcPpiMnO+WUNuXj6aEPwwaxquzs5WmmesNOs71vzyR/IKCunWqnmxZuyFK8z9di35hUU4aRqvT3yA1o0bkHX9JrO++pGLqem4Ojvx9qMjaOZfp9L0cm7kMXPxSq6kZ1FkMvFov3sYcW97XW/5BnZFx+l6jw53rJd42axXRLdWzQgfd7+V3jpdz6Dx+sTBtA6yPGfR55MYv+BzWtZ0wy83r5SnxzEPffEJrQYPICf1Km+1Kp/fUBZV0V6UStNWaAMmgKYhj+5C7llvq313f0T7brq/cT0H09ovICvtzjQah6L1GwdCQx7bjdy3yfZ8w2Zo/caCXwNMEZ/DqaMW/V4jEU1bgxDI8yeRW5aXrRcUgtZnjG5T1K/I/Zvt9Jqi9R4Dfv66Pactenh4ow18CNy9ATCt/Lh89ga2ROs1Srcxei/y4Fbb8w2aoPUcBb71Mf38JZw5VnxKe/5DuHZZ/5CdgWnNf8qU233hKgt+jcUoYVTLBkxp19jm/OHL6SzYe4ozaTm836cN/ZvY9kNyC4oYvHw3fYLqMKtrSNn2/S+gdtmocP7flajQ2SOEGGh1bIwQYtPtrrNn128xJCansunDOcx9YiJvLnZcsfXo0JoV82c6PDfwng5EvBdOxHvhJYIRALtOniPxagabXp/K3LEDmPvDZge5wJs/bGbuuAFsen0qiVcz2B17rvjclYxs9p1OoJ7ZKbxFhyYNiJg5mYiZk4uDEUaTibc37OU/E/ux7qmRbDhxjrOpGTbXrT56Gg83VzY/N4aHu4TyQaTe6W/m583KJ4YSMX04nz/YnznrfqXIaMJJ03i53138/PQolj8+hGUHY23y3BV7nsRrGWwKn8zc0X2ZuyrSsY2rIpk7pi+bwieTeC2D3acSALinRQBrX3qENS89TKCvN4siDwIwpENLIl6cRMSLk/j7hIH4e7nTvG4t3l63m/88PJh1z41jw/GznE1N/z/2zju8imrrw+/MSe+9EBKS0ENCV0GkF5EOggiIoogFRaQjKAIKqCh67V1UQGmGgEgPVXpPIECAFFp67znnzPfHHE5PwQvR+znv8/BoZtbMb9Y+M3vvWbP23qb+HU+Q/Zs2hqc6teSDbYcBsLNRManX/czo+6DFtT3duTWbp4xi/UsjOJlyi30XU6z6YIIoYvf8dMoWTqN00mhUnXsh1A+1tHNwwnbACDQX42s+pzmCiM3YSVR+OIfKuc8iPtAd6pkGzbSHY1G/8RzqN19As2UNNo+/AIB0Ixn1gomo33wB9bI52Dw1GcQaqgtBRBzxApov5qNZ9BJiuy4QEGxqc/0qmqVT0bzzCtLpPxGHPC0f2qI9BDdE8+4raD6YhthzGDg41kLveTRfLUCz5GXEtp3B31wvCc37U9G8Oxnp9EHEQeNk/y7HoVk6Rf732RtQUY504VT1erc1h01A8/XbaN6drNM0ewm6kYTmwxlo3p+KdPYQ4oAn9btUo19B2h0j+/nRLCjKr0FPQBw6Hs13i9F8MAWxdSfwM3tBuZmM5uPZaD6cgRR3GLH/E4Z9lRVoPpqJ5qOZ1XYu9yUkyXXN3GdZMPJhFqzdYdVu4dodLHisD1vnPqura5IAeKbHfWyY9TTRM8fRNaIhn287qD+mXXh9omeOI3rmOCb26SjrXUgiJSuPra89w4IRvViwfpd1vfU7WTCiN1tfe4aUrDzDc9+kATEznmLD9Cfl536X/Nw3DvBh7atjiJ42lq+fG8b8dbtQa7Q6zWTZx9njWDC8ZzWasSwY3pOts8fJPuo0Gwf48PFTA2gfZvmCGOztQfTUJ5jS7yEC3F3YOmMsC4b1YEH0Husa0btZMKw7W2eMlf3S1Rnf7jlBh0b12TrzSTo0qs+3e04AML5rW6JfHUX0q6OY0vdB7gsPwsPJgX0XU0jJyuPxDpG0Cw0kMS3Hut6GPbLe9CdkvUuppnozxsp6e+XOtLuTPXMGduHpLm3059BotSyI3oOtSsWfc5/Gx8WJ/NJy/jh7WW+z/lgCbo72bJvxBE891IoPthwC4HJ6DlvOXGbTlFF8/fQA3orZh0arxc5GxffPDiZ68kh+e+UxDlxK5UxqGgADWjdh89RRxEweSVlFJev2HTXxad/ZC6SkZ7H13ZksGPcoC38yDQzeplvr5qyeN8liez0vD5Y8O5L+HVpbPc6cfWcvynrvTGfBuGEs/HlDNXovWep5e7Ab9vDaAAAgAElEQVTk2RH079DKylE6jbhLssbiqSx4cggLft5o1W7hihgWPDmErYunkpKexf74SwDMWx7N1OEPE7PwFXq2ieD7rfsBUGs0zPp2DW8+OZhNb03mxxnjsVGpDJoZ2WxdPEXWXFGV5kYWPDmYrYunkJKRzf74RAA+WLeViQN7EP3my7w8uCcfrJO7Ul//sZdmwYFsWDCJJeOHs/iXzfdUb9XuwzSs50f0/Jf5ccZ43luzjQq1mn3xibLeosksGDuIBSs3VaG3iQVjB7F10WRTvfXbmTiwG9FvTuTlwT34YN12/TEarZZl67fTqUUjq+esiUPLV/JJ32F/6Vir1FF7UaV2v7FoVy5D+9kchMgHwNc08CPdSkH79QK0X7yBdP4YQu/H7lyj72i0v/4H7VfzEFrcDz6BpjYFOWg3/YAUb1pfENQQoX4jtN/MR/v1mwiBoRDSpGa9PqPQrvkU7TcLECLuA29zvVy0m39EOn/M4nBxwNNoj+xA++0CtD++A8UFtfOx12No13+O9oe3EZq1A2+zD5EFuWi3/IyUcNzyeHUl2p/ekf/VIhih0Uq8feA8X/Vvz6aRD/HH5VtczikysQl0cWBx9yj6Nw60eo6PjyZyXz2vmn1TUKiGf11AQpIkCXgBWCYIgoMgCM7AIsCyB1ENscfPMrjLAwiCQOsmYRQUl5CRa/ly0bpJGH6e7n/pWmPjEhl8fySCINAqLIjC0nIy800risz8IorKymkdFoQgCAy+P5JdZxP1+9/9bRfTBner1fwrcTcyCfFyI9jLDTsbFY9EhhN7MdX0mi6mMqS13Pj2iQjj8NWbSJKEo50NNir5dipXa/R6vq5ORNTzAcDZ3o5wXw8yCksM54u/wuD2EbKPofVkHwvMfCwooqi8nNah9WQf20ewK07uBHdqGqrXbdUgkLT8Qgu/Np+6QL+WjYm7nkGIl7vBv5aNiE1INvUvIZkhbZvK/rVoyOErN5AkCSc7W9qFBmJvqzKxd7Sz5YFwucG3s1ERUc+X9ILiGstabByB9tZ1pPSboFajObATmwc6W9jZjZlAZfQKqKyo8ZzmCOFNkTJuQmYaaNRoj+5BbGMWUCkz/BaCvQNIkvxHRbn8FQPA1g6kWgg2aIyUdQuy02W9E/sQoh4wMZES46CyXP7/5IsIHt7yjoBgpMvxsmZFOdKNJITm7WrWy0wz6J3cjxB1v6ne5Th92ZnoGSG0ehAp4WTtyjikkexjjk7z1AGESHPNeINmyiWDpn99EFVIl87If1eU1awZ3AgpKw1yMkCjQXvmoD6LRK935ZxBLzURwf3OOwaxcYkMvq+F0XNYVkVdU2Goa+5rwa44ua5xcbDX25VWVFJTdRMbf4XB7XTPfYNqnvuyCsNz3y6CXfFVPPd58rGOdraGeqhSY5IpFnvuCoPbN9dpBlJYVkGm2bOaWVBsqtm+ObvOXQGgob8XYX7Vl23suSsMbndbI0DnlxWN8gpaNwjU+dWcXeeu6o6/ypB2zQEYYrTdmD/OXKJfq8Z6+67NQtl3MZVnu7VHrdXWrNe2mUHvfBJD2jaT9Yy2e7s4ERXsj41REDLuWjr1PF0RBNBIEg9HNeRmbiF+rk4G/xMM5+sTaag/YxOSeKRVI+xsVNT3ciPE2524axkIgoCzvS0Aao0WtVYLuruna7MGCIKAIAhEhQeTnmPavsaeOs/gTm3lNrhRAwpKSsnIs+zwt27UAD8PN4vtQb5eNA0ORKzl5GSxp84z+EGdXsOQqvUahljX86lZL/Z0AoMfbCPfPw1DKCwpI9NMIzOvgKLSclo3CpF/zwfbsOtUAgBJaVm0bxIKwIMtGrH9xDkA/jx3mSb1A2gWLL9QeLg4odL9trGnExjcsbVOM1inWWimWSj3MxrqNDu2Ztep8wAIgkBxqVynF5WW6X2/cjODDs3lL63hgb7czM4lK7/onukJgkBxWTmSJFFSVo67syM2okjs6QsM7nCHeh1as+u0nI0oAMVlOr2SMvw8XPXHrYw9TO92EXi7Olf5m1bH5f0HKcnJrdmwttRRe2GVoHC5XczNBI0GKf4IQtM2pjbJFwza168guN2hdr0wyMmUMzq0Gjmo0cQsoJifDRk3DH0ZPRLY2ILKBlS2oFLVHCAIDIXcDMg30mtslkmVnw2ZVvS8A+Wv6Mnys0llOagra/YxIBRys+TzajVIF04iNDTTLMiRsyAsfLxz4jLyCHFzItjNCTuVyCMNA4hNTjexCXJzoqm3q9UXxnOZ+WSXlvNgfcu+lYLCnfCvHLIhSVK8IAibgFmAM/CTJElX7uQc6Tl5BPh46v8O8PYkIyfvjoIP24+c4nhCIqGB/sx+6lECfUwr54z8QgKMGj9/D1fS8wvxdXcxXEd+If5mNhm6l/LYuET8PFxoFmSZCn066QZD3/kOX3dXZnRvTWM/T9ILSghwMzSsAW5OnL1umk6eXlBMgJusb6MScXWwI6+kHE9nB85cz+D1mP3czCvi3WFd9S8Gt7mRW0jCrWxaBvkafCwosuJjEb5uxj4W4e9u5qPZywvAb0fj6du6qcX2racv8umYh7mamUeAu7F/zpy9lmHmXxEB7ub+leHpXMPXeqCgtJw9F5IZ+2BLqCGjQfDyRcoyVPpSdiZiY9NUNzGsCYKPH5rjB7EdMrpGfQs8fZByjH6/nCyEhs0szMQeg1A9/CjY2FD53kzDNYY3Q/XMNARvf9TfvGsIUFTlk4e33JDeJi8bIbRJlbEMsWNvtOflL8DcSEZ85HE0sTFgZ4/QuCVS2rXq9dy99ammer0G1eh16I024YTledp2tkhrrV7TKOUyLxuhQeOqNR/oiTZB/uos+NZDKi1GHDcTwcsPKfEs2t9XgFR1uQruXqYpnvnZCMHV6N3XA+0FQ/omNraoXlkid073xCCds/yKA5CRX0SAp5Xn0N3sOfRwMbHJMApafLR5PxuPncPFwZ7lL4/Ubz+dfJOh7y3H182FGQM70zjAR9Yzfu7dXaw/9yZ1g4uJ3m1+O3qOvq0NX7rOpNzi9dXbuZlbwLujHtbXQxn5xVVoGuoECx/dXcnIrznAeCMnn2HLVnIrr4Am/oZ63N/dhfQCM42CIvzdjTWcydAFEbKLSvS2vm7O5BSXmuiUVlSy/2IKcwd3lX0qKCY5M5eZ/R+iuLwCexubWui56OtPC70iUz1j0guKaeDtQe8WYfR89ycEBHxdnejUJMTEJsDDsv7MyC+mZYihHfJ3d9YHbjVaLcM/XUtqdj6jO0TRKsS0varUaNh48CRzRg82vZ7cfAJ0Qx4BAjw9yMjNtxoMuBuk5xWY6bmTkVtwV/UycgsI8DL0Ifw93UjPK8DXSCM9rwB/T2Mb+ToAGgf5E3s6gZ5tIth2LJ40XRAnJT0LAYEJy34gp7CYfve1ZPwjcgA8I6+wCk1XM003U03dS/3skf2Y8NGPLF27Ba0ksfK15wBoGhzAzpPnadc4lLNXr3MzO5/03Px7pjemRwde+mQFXae/S3FZBcueG4EoitWUaXV6bvoynf14PyZ89BNL126T9WZPkI/JLWDnqQR+mPY08Uk3rPyadU9dtRdWcfNEKjDK0CrIhfrhVZoLbbsgXT5b+/MDuHogFZppBIXV7tgbV5FSLiBOfh8A6cRuyE6rQc8TqdAoYFSYJwdFaoOXH5SXIA59Hjy8kZIvIO2JrjmI4OpuqlmUKwdGaouNDeITM0GrQXt0B9RQxunF5QS4GPq3AS4OnE2vIXNTh1aSeO/gBd7p2ZLD1+9w6I2Cghn/ugwJIxYAo4FHgDvMTZPHepoj1PJLC0C3dlHs+uwtYt5/nY5RTXnts5+saFgeJ5h9e7RuI3dcv9p+kEn9LL+6R9QPYOeCiUTPHs+YLu2Y9Ks8TMJqNWnmkzWb2yat6vux6aVHWfPcYL7Zf4bySrXepri8kslrdvFa3w64ONgZXb+Vcqylj8Z8ueMwKlFkoO7r4m3OpNzCwdaWxv7e1tsBsxNZ96/m31Wt0TJ99Q6e6BhFsFctOqhWT2mkLgjYjX+Fih8+qflcdyJipRC0sRupnPUUmrXfohpoCHxIVy+gfn0ClQtfRtX/cfnrwl3QAxDad0MIboS06zfZ7MIptOdOoJr6HuK46UhJF0CjuWO5qlI5hPZdEUIaIe0yS+t280So1wApoRbDNarSrKJ/IbTrIvu4W5faLaoQwpuj3fgjmo9mgrc/wv3d75qg0KYzQv1wpL2GFGjNkoloPn4NzS8fIw58Crwsg5NVndH8tpesWBk/G6/270zs/BcY0K45K/fLQZiIYH92vvk80TPHMaZLWyb9sLFqvdo892YX9eXOI6hEgYFtDc99qwaBbJr5FGteHc03scf09ZD1OtvMR6v1UfX4ujmz6/Xx/DZ1DOF+3nyx8xhFZYbMl9r5VYOIjj0JSbQNDcTDSZ4XIKuoBDdHe1oYzYVRu/rzzpcskySo0KiJPZ/MjhljmTPwITRaiY26uS1kG+v1eXX3l0oUiX5lJLtnP0Xc9XQS00w7tm/F7KN9k3DaNzV9EajN/XE3qc39c0807uD+efvpYfwSe4ThCz+juKxcP+eGWqPl5OUU3pvwGCtmP8fOU+c5lHClGs3aXJfMr3uOMntkP2KXzmTWyH68sVyuYyc80oX84lKGLviUlbGHaB4SiEol3jO9A/GJNAsOZO/7s/ht3ku8vWozRaVlNdZbsp6Fib5Mf91zlNmP9SX2venMeuwR3vhRrs+XrN7CtGF99Jkm/wzqpr2otXZVbWPLjgj1wpD+3HIH578zDQs8fRF8AtF+PBPtxzMRGjSD4MZ3qH8HgqIK6jdGG7se7fJ3EDx8EKI61uLA/8JHQPvVPLQr3kO7eTli90fB3adae+v9/tpp/XIulS4hvgS61PzB7v8bt7P3/qn//hf5V2ZIAEiSVCwIwmqgSJKkcvP9giA8BzwH8MXrr/Lc8AGs3LqXdbvkif0iGzYgLcsQxUzLzsX3DrIjPF0NX6xG9HqID1bKjdzKrXtZu203AFEhgaQZpRam5xXiZ/SlCyDAw5V0Mxtfd1euZeVyIzufoe9+r9/+6NLlrJ72pMlXyK4tGvLWr1pyi8sIcHMizSjdN62gxCQdF+SsgrSCIgLcnVFrtBTqJjczpqGvB452tiRm5BIZ5EulRsura3YxIKohvSNCWXX0PGtPy6nXUcEBVnw0TX8M8HAhPd/MRyMfNhw7x97zV/n+xREWD+KWUxfop0shDnB3Ji3f2L9i/NzMtNxcSMuXsySq8s8ab27YSwMfD57sVPX4YGOk7EwEH0NjL3j7IuUYfe13dEIMCcfh7c/k/R5e2M99l/JFs2o/sWVuJoKXIRsFLx+kvKqj2Noje7AdOxkNS0133EqF8jKE+mFIyZeq9ikvC8HTqPHz8EbKtxzTLjRthfjwY2j+8xqoDUErafsaNNvXACA+NR0yb1brnpSXLU8OWZNek1aIvUeg+WQuaNSm+9p0Qjp7GLQ1BD9MNI1SEz28Tb8K3T5v45aIvYbL81PoNKX8bLiRJKe1AlLcUV1Gh/W5DG4fI7gb6bl7IxVYpvgKjaIQewxF8+V8Ux9v2+ZkIF09jxAUiqTTX3U1i3XHlgO6uibX7Dl0M6tr3F1JzysysfF1s0xV7t+uOS9+/RuTHnnIZCjHjew8buYWMGjpj7RuYFa35RdZf+5N6gbTL//65/6F4VYb4Ib+3hSUljPkgxU42NlY1jX5RZY+epj5mF+Ir3v16djrjpxj7ZE4QK7P8ktKSc7KJbK+v07DzC9dZoZBoxhfXcq3t4sTmQXF+Lo5k1lQjJdZZtYfZxLxcHJg6Ee/6LcdS7pJr3d+pFytIb+kjK92H+fjsf2q0TOUo4VeNR3LAHdnLt3KpkV9X7xcHMkqKqFFkA+nU9IY1KapXistz6z+dLLH392ZNDOf/czS3N0c7bkvLIj9l1JpHCDf85/tPEZOcRmfvDgAgJU7D7JurzzxbGRYMGk5efrj03LzTDIJ7gYrdx1i3d6jOr36Znr5d0Vv5a5DrNX5FBVaX5/VAPJXeOMhAgABnm6k5xrbGK4jPNCXb6fJ8/Ikp2WxL+6i7hh37msSiqeuzD2cHZn97Tq83JyJCg2yomnqV4CnO+m5BUY2+foMg5hDp5gzqj8AfdtHMk/3wu7i6MDiZx6VfYw9zJZjccz6dh2twq35+N/rRf95kmcfkSeH/fNcIrlFJYx4+yvuaxpqqedurUwLTGxul2nModPMebyfTq8F836Ss+nOJd9g2jdrAcgtKkHt54YgSfgWW3Qn64x72V7USEEOgpuX4QXXzRMKrQxHCY9A6DwQ7fIlFu1xjRTmIriaaRTlVXeEHqFpG6QbVw1DRq/EIwSFI11LrPqgwlwEV0+DnquHnCVRy2sl45o83APkoZr1woCDNRyXZ6rp4lnzXFPGFOts87Nl3/zr66/BGgHO9qQZZcalFZXh51RznxfgdFoeJ9Jy+eVcKiVqDZUaLU62KqZ2sMxUVlCoiX9SaPfvQKv7Z4EkSV9LktRekqT2zw2XO0Nj+nbVT0LZ8/6WxOw7giRJnL6UhKuT4x0N1zCebyL2+FnC6wcYNHSTTfZs2ZiYo/FIksSZpBu4OtibpFAD+Lq74Oxgx5kkeaxuzNF4ekQ1pkk9Pw4sfoWd8yeyc/5E/D1cWT9jHL5uLmQWFOm/QJxNuYlWkvBwsieyni8p2QVczy2kQq1hS/xVujc1nQSxe9MQNuiCCdvPJ/FAmDzW+npuoX7yuBt5hSRl5RPk4YokSbwRs59wHw/GPSjPKj76/gj9hJM9oxoRc/y87GPyTdlHs5cEXzcXnO3tOJMsz1cRc/w8PSIbArA/IYlvY4/y2fghONqZfsHXaiW2nblEP11nOTLIj5TsPK7nFMj+nb1M92ahpv41D2XDSbkTt/3cFR4ID6ox2vifHUcoKi/ntX6WE5NWhTYxATGwPoJfINjYoHqoF+qjRqtKlBRT8mQ/Sp97lNLnHkV76dydBSMAKekigl8Q+ASAygbx/m5Ipw6ZGvkbJrwSWj6AlK5LPfUJMExi6e2HEBAsj02tjtREBN964O0v67XrghRnNrFU/XDEkS+h+fot00ZWEMFJ10msF4pQL7TmSSZTExF8A+XUSJUNYtvOViayCkMc+SKabxdZbdTFtl2QTuyvXseYa5dNNds8hBRvltYaFCZP7vndElPN1Mvg5ALOujHPjaOQ0qsflsL1Kwg+geDpCyoVYqsHkc6bTWRVLxTx0QlofnzPdEyso7M8XhbAyRUhtClS+nX97tHhPvrJJntGNSLm2DnDc+hYRV1j/BweO0ePKPkLU3KmoeO5O/4K4bphC8Z1TWRIID6uzsRMf5KekY2IOaF77lNu4upgZ/25d7DjTIpO74TRc38hiW93H+OzZwabPPfXs/MN9VBOASUVlayaNJLoqU/Qs0VDYo4n6DRv6TSdzTSdZR9TbunqmgR6tGhY7U/Ut1Vj1r06muipT9AyJIDr2fKKFWdS0mrQSNP5lUCPFnJqc/eIMDackMccbzDaDlBYWs6xqzd4fUg3/QSXrz7cgZbB/uyY9SQv9miPi4OdSTDCRC9Vp3fyAj0iwgx6J+U6ZYPRdmtE1vcnt6SM40k3KSgp448zl6lQawn3MwxdlOtP+Xzb46/wQEO5/uzePIwtZy5TodZwPaeAlKx8ooL9yCkqpUA3H0BZpZpDV64T7iufb92x8/yZmMr7j/dG1NVFY3o9SPRbU4h+awo927Yg5s+Tcht8OQVXR8e7PlxjTM+ORC+cTPTCybLeQZ3elVRcHR3uit6Ynh2Jnj+J6PmT6NmmOTEHT8n36JVUXJ3sLYIevh5uODvYc+ZKqvx7HjxFj9ZyhlC2biiOVqvly99381hXeX6bTpGNuXg9jdLyCtQaDeVqNQufGkL0my/Ts00EMYdO6zSvyc++WRDE18NVp3lN1jx0Wq/p5+7GsYvy5LaHL1ylgZ/8QlxQUkqFLuBsZ6Oi3/0tiVkw6Z7pBXp5cFiX9dGnXSQujvasmv0sPVs3I+awsZ6DdT17O4Pe4dP0aN1Mp+fKsUvJRnpy3bbjnans1P17uG0ETTIK/tZgBHBP24sauZkkt/0ePqBSIUQ+gHTRrA0PCEEcMA7tL/+BYsv5vmrWSJbbXncfOeMw4j7DnEw1kZ+DENJE7muIKoSQJkjZt6o/5laKTs/boFfbYSa3ksHBCRx17VqDplCTHkBaivz73dZs1hbpSi017R0Nv6GjM0JQeI3DUiL93EnJL+F6QQkVGi1brqTRPdRyZShrLO3VitgnurHziW7M6NCUwU2ClGCEwl9GsJYa929BEIT5yBkS71dnpz2zy6KQJEnire9Wc+DMeRzs7Fg8cSyRDRsAMHTGYqKXzgFg6Yrf2HzguDy21dOd4T0e5OXHBrBs1QZij8dhoxJxd3HizWdHER4kByWk28suSRJvr93BgYSrONjZsmhMPyJD5Emphr77PdGz5GUL41NvMWflZsor1HSOCGfu8N4WL9G95n/O2unj8HRxYuW+E/x64BQ2ooC9rS2zeraljW7c7t5L13hn62G0ksTQNk14oUtrPok9QYt6PvRo1oDySjWzoveScCsbD0d73h/enWAvNzaeSeSbA2exEUVEQeDFrq3p1TyUEylpjP1hM038PPXX9GrP9nRtH2Xw8bddHLiQjIOtLYtGPUykbmnToe//RPR0eYWC+GtpzPllK+WVajo3C2OubrnBhxd9R6VGjbuT/GWvVYNA5o/oDcDRy9dY9vt+fn11NJTKkzfuvZjCO5v/lP1r24wXurfjk51HaRHkS4/mYbJ/63aRcDMLD0cH3n+8t34IRq+lKygqr6BSo8HNwZ5vnh6Ai70dPd77mXBfD2x1s5aP6RDJgFqsiqFq1xG7ZyaDSoV65+9UrvsR21HPor18Ac0x0yUvHd7+lIofPq02IGHrafklV2h5PzajXpSX/dy/De3vq1ANeQpt8iWk04dQjZ6IENFGHh5RXIhmxadIN1MQO/ZC7D9S3i5p0cSsQDplGtkXXRws9SLayct+CrolMbevQew3Bik1ESn+KOLLbyEENtB/iZFyM9F+/bY8dnXmR/JJykrQrP5cziYwEbSMnwoR7RCHjtctwbkLacdaxEdGI127LOtNXIhQrwHoMiek3Cy03y6SD/byQzX5HTTzx1vP2VWpLLehW9p0sG7Zz6O7kHauR+z7ONK1K0jnjiG+8KaZj1lov18iH9ukFeKgp+Rlx65dQbv2S9OvRDaWSWtCszZy+qwooj22Gyk2GrHPY0jXryCdP4E44XWEgBD9lxv9cm0NmqAa9pw8R4Ugoj2wGenYbtNzd+8jHyNJvL1+JwcSkuS6ZtQjRIbonsP3lhM9cxwA8anysp/llZV0bh7O3Ed7IggCk7/fQFJGLqIA9bzceXNEb/w9XFm5/yS//nkaG1HE3taGWQO70iasnu65j+XAxWQcbG1Y9LjRc//Bz0RPGyvrXUtjzq/bdM99KHOH6p77xd9Rqdbg7mz03A/vxcbj5/km9hg2Kl091Pt+ekUalv18O3o3By6myJoj++iX7hy6bAXRU5/QacrLfpar1XRuGsrcod0QBIGdcZdZtGEPOUWluDna06yeD988N4ztZxP5ZNshbEQRQQA/V2eSMnPlchzRk0jdcqZDP/qF6FdHyRrX05mzZqfsV9MGzNUtLZpXXMqUlVu5lVdIoIcrHz7xiH54RvTxBA5cTOGDMX31v58kSby9YS8HLqUgSeDn5sSKF+VlP4f+51eiJz9u0Fu7y6B3e5nR4lKmrNpm0BvTFw8nBzILi3nskzUUlVcgCgJOdrZsmjqGE8k3mb16B8Xllbg52tOpcTCBHi60DPanR4Su/lyzi4Sb8rKf74/qTbBu/P6Xu48TffwCKlFk9oBOdGnagIu3snhtbSxaSYtWgr5RDZnYU56EL2ruF9TzcMXJ3hbBxo5e7SN5aXBvE9/f+nkDB+Iu4mBvx+LxI4gMk1fZGfrGh0S/JS9bvHT1ZjYfPk1GnvwlfniX+3h5aB/irl5j0ic/UVBcgp2tLT7urvy+eJru5JbfKSRJ4q0VMRyIu6Rf9jNSt/zj0Hn/IXrhZFlvzR86vUL8PFxlvSG9Zb1Pf6aguFSn58Lvi6bq5DSG33PlJg7EJ8r3zzPDiNQtZTp0/idEz5dXC4lPvs6c79bLv2dUY+aOHoggCPy84yCrdssrQ/Vu24Ipj/bRt7sbD53mmz/2IgBdopowfURfg+aq3zkQfwkHOzsWPT2MSN1ypkMXfEr0my/rNOWlRssrK+kc2YS5owcgCAInEpNZ8ssf8qoptjbMGzOQFqFBnL6Syuzv1qMSBRoG+vHWuKG4OzveM72MvALmfL+ezHw5CPrsI50Z1KGVTm8zB87pynTcUCO9z4l+c6JB74donV5j5uqWGT2RmMKSX830zJYNnfP9bySu333Hy36OX/U9Tbo9hIuPNwXpGWx6czEHv7e+/Lc5n87oa3X7PW0vnJysSRpo3FJe9lMQkU7tR9q/CaH7UKSbSXDxNOKTM8CvviFQn58tByeqwtbKMNGGkYi9HwdRkJfh/PMPhC6DkG6lQOIZCAxFHD5RDgaoK6G4AO3Xb4IgIPQdIwclJAnp6jmknWtMz22tzQ+PROw1Qvbp7EGkQ1sQOg+U9S6fhYAGiMNekPU0lVBUgPY73XK/oc0RezwKCEjpqUhbVlhmY1pp8wmLkJf1FAWkuMNIR7YhdOovvxtciZMDO4Mn6HxUyz4uXwT1whB7j9L/htKJ3UjxZh+hrPi4NyWTdw4myP3ipvV5oV1DPjmWSAtfd3qE+hGXkc8r205SUK7GTiXi42TPppEPmZwj+sJ1zmUWWF32UzXlP/+bYwiqQXso5h/98ix2HPw/V+ZKQOIvBiTuJbcDEnVGTmbNNncbN4+abe4mpSU129xFykUWf+0AACAASURBVFasqdnoLmMtIHEvsRaQuLeCdZzQVUVA4p5irXNyD7kdkKgzapoX5G5TzYSh94w697GO2/C/oUyFO5nU7W5Qxz5KtRwydvcE/wX9vjr+DV/qOqFO9aoKSNxLagxI3G2sBSTuJf+CNv/v8FEJSNQ9/4sBiX/tHBIAkiTN/7uvQUFBQUFBQUFBQUFBQUHh38i/OiChoKCgoKCgoKCgoKCgoFArhH/7FIx3H6VEFRQUFBQUFBQUFBQUFBQU6hwlIKGgoKCgoKCgoKCgoKCgoFDnKEM2FBQUFBQUFBQUFBQUFBRqQvyfmzPyH4+SIaGgoKCgoKCgoKCgoKCgoFDnKAEJBQUFBQUFBQUFBQUFBQWFOkcZsqGgoKCgoKCgoKCgoKCgUBPKKht3HaVEFRQUFBQUFBQUFBQUFBQU6hwlQ6IWCIEN61RPyrxRp3pkZ9St3t+Bb0CdyiWeuFmnegDhzbzrVM8hpG71RHvbOtX7WxDqdqIkaeeWOtVDq61bvTouz38Ff0OZCiPC61RP0mrqVK/Ov7apK+pWD0Cq42dfpapTuU9n9K1TvZeXbq1TPYCJIV51qufqalenena2df+N1raONW1s6t5Hryl1LqnwP4iSIaGgoKCgoKCgoKCgoKCgoFDnKBkSCgoKCgoKCgoKCgoKCgo1oWRf3nWUDAkFBQUFBQUFBQUFBQUFBYU6RwlIKCgoKCgoKCgoKCgoKCgo1DnKkA0FBQUFBQUFBQUFBQUFhZpQlv286yglqqCgoKCgoKCgoKCgoKCgUOcoAQkFBQUFBQUFBQUFBQUFBYU6RxmyoaCgoKCgoKCgoKCgoKBQE8oqG3cdJSDxF5EkiUX/+YJ9h4/iYO/AkjnTaNG0sYXds9PmkJmdg0ajoV2rSOZNeRmVSsXW3fv49PufuZJyjTVff0xUsyZWNRav3c6+c1dwtLNl8dgBRIQEWtidS73FnJ83UVahpkuLhswZ0QdBEPh08z7W/XkKTxcnAF4d1J2ukY2o1GiYt3Iz56+lodFoGRTuy3PtLa99f0oGS/bFo5EkhkeEMMHM5viNbJbsj+dSViHv923Lw43q6fc9F3OYM2m5tK3nxRcDH6iyHPcnXmfJ1sNotFqGt23KhM6tTPZXqDXMjt7LuZtZeDg5sGx4d4I8XTl45QbLdh6jUqPFViUyvff9dAiX9bfEX+WrfafRSBJdGwczvc/997xMAS7eSGf+L1soKi2nwsGWxWWV2Jmd17VbV4Lmz0NQqcj+ZTUZn39hoe0xoD8BU15FkiTKEhJImTQZ26Agwr7+EkGlAhsbspb/SPaKlVWW621U7TviMHE6gqiiYssGKlYvt2pn07knTvPeo+ilJ9BeStBvF3wDcPluLeU/fU3Fup9r1BMi22Mz6kUQRDT7t6Ldstpkv9i1P2KPQaDVQnkp6h8/glupCBFtUT06HmxsQK1Gs/YbpAuna9aLaIc44jkQRLQHtyNtX2u6v8cQxE4Pg1aDVJiPdsVHkJMpX8uQpxEi2wOg3fIr0on9NeoBCM3aIA6bAKKI9vAOpJ3rTfd3G4TYsY+sWZSPdtUnkKvTHPQUQkR7EASki2fQ/vZNzXpNWyMOeUbWO7ILKTbadH+XgYgP9AStFqk4H+3qz/V6ePggPvYigocPSBKabxcZ9v1T9Jq1QRw6Xv4Nj+xE2vWb6f6ugxA79NKVZwHaXz+F3EyERpHydd7GLwjtTx8gxR+tVk+vedvHwzstfew6EPEBI83Vnxk0Bz9tqvnzsho1/6oeIJfpyImGMv3m7dqVaV3q1fE9A7q6fGUM+84k4GBnx+IJI2kRWt/C7qN1W4j58zgFxaWc+HqxfvvyrXtZt/cIKlGFl5szb49/jCAfrxp19dqrNrEv7iKOdnYsHj+ciAZBltrrt7Hx4CnyS0o58cWCWp27rvUkSWLx6i3si0+U28RxQ4gIqWdhdy7lJnOWR1NWqaZLZGPmjHwEQRBIuHaLBSt/p7xSjY0o8sbo/rQMq8/VtEzmLt/A+Wu3mDy4J8/07mjQW7PVoPfUEOttcMpN5vwYQ1llpaz3WF8EQWDqN+tISs8CoLCkDFcnB6Jff4GD56+wbMMuKtUabG1UTB/+MB2ah8t6v/7Bvjid3tNDiWhQhX8//Ca3+VGNmfN4P9m/1FssWLFJ9k8l8saYAbQMM9xncUk3GLXka95vH8LD9TxMzik0aSXXF4KI9ugupD0xpvs790e8v6fhOVz7BeTJvqne+RXSUuUyy8tCu/y9Wv2e1TH2u8+IGtCXwoxM3orq8F+fD8Cla1eC3pwHKhU5v64m8wvLPo17//74T3kVJInShASuvTIZh4gIgha9jcrFBUmjIePTz8j//fca9Rw7dcZr9lwElYrC9WvJ/+5r0+sZPBSvabNQZ6QDUPDLCorWy/2C0DMJVCReAkB96yYZk16sUc/+wYdwnzEXQRQp3rCOoh9M22yngUNxmzIDjU6vePVKSqLXYdukGR5z5yM4O4NGS+F3X1K6fUuNeubYdXgIl2mvgaiiLGYdJT99a7Lfof8QXF6ZjiYzA4DStSspi1lv7VRVYvtAJ5xenQ0qFeWb1lP283fW7br3xnXRh+Q/MxLNhXOgssH5tQWomjZHUNlQvmUjZT9/a/VYBYWaUAISf5F9h4+Rcv0G2375gTPnL7Dgg09Y8/XHFnYfLZyLi7MzkiTxyhtvsXX3fvr36kbjsFA+XjSPN5daHqPXOHeFlMwcts5/kbPJN1nw61ZWz3zawm7hr1tYMKofrcKCeP7zX9l//gpdWsgvyU/2eIBnepk2PNtOJlCh1hAz9zlKKyoZOO8T+jcJIsjNSW+j0Uq8vSeOb4d0wN/FkZGr99M9PIBGXq56m0BXRxb3asMPJ69YXNPTbRtSptawJj6lSv80Wi1v/3GQb8f2xd/NmZHfbKR70xAa+XnqbdafvIibgz3bJj/GH3FX+GDnMZaN6IGHkz2fj+qNn5sziek5TFixjT3TRpFXUsbS7UdZ9/xgvJwdeS16L4eu3qSjb8A9LVO1Rsus5Rt556lBNKvvz8Gn51k+XKJI/bcXcmX0E1TeSqPJ7xvJ37GD8sTLehO70FD8XppI4rBH0eQXYOPtLZ8/I4PEoY8iVVQgOjnRbOd28nfsQJ2eUWX5Ioo4TppN8ayJSFnpOH/6M+pDe9GmJpnaOTphN+Rx1AlxFqdweHEq6mMHq9YwRhCxGfMylR/MhtwsbN74BO3pQ3ArVW+iPbIb7d7NsnmrDtiMfB71R3OhKB/1J29AXg5CUCg2UxZTOX10jXriyBfRfPw65GWhmvUhmrOHIe2aweb6VTTvvAqV5Qid+yEOfQbtd+8iRN4HwQ3RLJ4ENraopryL5txxKCutWXPE82g+fxPyslFNex9N3FFIN9ZMQvP+VKisQOjUF3HQOLQ/LoXQZghhzdG8OxkA1eQlCI0ikS7HV683bAKarxZCfjaqV99Fc+4YpF832NxIQvPRTFmv48OIA8ai/XmZrDFqEtpd69FeOgt2DiBpa/avrvUefQ7Nl/Pl8pzyHpr4o2Z6V9Esmy7rPfgw4sAn5cDD5Xi5nAGcXFDN+RzpYs1BLL2PXy6QfZzynnUfP5xh0BzwJNqfdZofTDPS/Kxmzf9CD0A1+hW0O9ejvXTmzn7Dutarq3tGx76zF0hJy2Tre7M5cyWVhT+uZ/Wbky3surWOYHSvTjwy8x2T7c0bBLF2/qs42tvxy66DvL96Mx++NLZ22nEXSUnPZuuS6Zy9eo0FP21g9RsvWdh1b92cMT070ve1D2p13r9Db198IikZ2Wx96xXOJl1nwcrfWf3acxZ2C1f9zoInBtEqvD7Pf7KC/ecu0yWyMR+s38HEAd3oEtmYvXGX+OC3Hfw47WncnRyZ83g/dp1OMNO7TEpGDlsXTuJs0g0WrNrM6tnPWtHbzIInBtAqrD7Pf7pKr7dswnC9zbvrtuHq6ACAh4sTn08chZ+HK4k3MpjwyQr2LJ1h8G/RZM5evc6ClZtYPed5S70Vm1gwdhCtwoN5/uOf2R+fSJeoJnywfjsTB3ajS1QT2b912/lxhhwI1Wi1LFu/nU4tGgEVpicUBMSh4+WAXn42qklL0Jw/Dhk3DDY3k9F8PFt+Ljr0Ruz/BNqVH8n7KivkZ+Yucmj5SvZ8+jXjfvrq7pxQFAl6ayFJY56gMi2NRhs3UrDTep/myrBH0RQUoNL1abSlpVybMpWK5GRs/PxovPl3CvftQ1tQUK2e9+tvkjbhadRpadRbvZ6S3buovGraBy3e+gfZixdaHC6Vl3Fz+OA78s9j9jyyXnwGTXo6fivXUrY3FrWZXum2LeS/+5apVlkZOW/MQpOagujrh9/KdZQdPIBUVHhH+q4zXyf35WfRZqTj+eNqyvfvRpNkql+2YwtF7y+q/XnNNJymv07h5AloM9Jw+241Fft3o02+amrn5ITDiDGo48/oN9n16AN2dhSMHQb2DriviqFixx9o027+tWtR+Ffz/2oOCUEQ5gqCcE4QhLOCIJwWBMHqp3lBEMIEQTgiCEKiIAirBUEw/5BdI7sOHGJw314IgkDrFs0pKComIyvbws7F2RkAtUZDZaVan+XTMDSE8JDgajViz15i8AMtEQSBVmFBFJaWkZlvWpll5hdSVFZB6/D6CILA4AdasuvMpWrPKwgCpeUVqDVayisqsVWJONuZvj7HpecS4uFMsLszdiqRR5rUI/ZqmolNkJsTTX3cEK1kLnUM9sXZtvp4V9yNTEK83Aj2csPORsUjkeHEXkw1sYm9mMqQ1nIgoE9EGIev3kSSJCICffBzk8u2kZ8n5WoNFWoN13ILCfV2x8vZUb6O8HrsOG94Ab9XZfpnwlWaBPnRrL4/AK5YPlxOrVtTnpxCReo1pMpKcjduwr1PHxMb79GPk/XjT2jy5UZZnS3fU1JlJVKF3OER7OywWuhmqJq2QHvzGlLaDVCrqdyzHZsHu1nY2Y97kYo1P0FFucl2mwe7ob11A22yZcDJGkJ4U6SMm5CVBho12qN7Eds8aGpUVmKwt3fQ/7+UegXycuT/v5EMtnZgY1u9YGgTpMybkK3TO7EPoZVpoEi6dBYqZb+kpAvyV1iAgGCkxDg5U6OiHOlGEkJEu5qdbNAYKTMNstNlzZP7EaLuNzGRLsdBpfxbSckXETy8b+8BW1s5C8TGBlQ2SIV51euFNELKToMcnd6pAwgt7jPVuxJv0Eu9hOCu0/OvDyqVXAYAFWV6u3+OXmOkrFuG8jx1ACHSvDyN9FIuGZWnAaFVR6QLJ2vWu+1j1i1TH/+KZsuOSAmnalemf1XPvz6IKqRLuk5gbX/Dutary3tGR+zJcwzu1F5ugxs1oKCkjIw8y5eZ1o0a4OfhZrH9geaNcLSXm/5WjRqQnpNfK12A2FMJDH6wjdyONAyhsKSMTCvarRqG4GtF+065l3qxZy4wuENr+dzhwVW3iaXltG4YLLeJHVrrAw2CAMWlch1bVFqOn7v80cLbzYWo0CBsVCpTvbMXGNxB1waH16+mDS6ndfhtvZbsOnPBxEaSJLadOE+/9pEARIQE4uchazeq50t5pZqKSjWxp438axisKzszvTydXsMQI/9kPQEoLtP5V1Km1wBYGXuY3u0i8HZ1tizY4EZIWWmQkwEaDdozB608F+eMnotEBPfaZej8VS7vP0hJTu5dO59T69ZUJKdQcU3u0+Rt2oRbb9M+jdeox8n+6Sc0ukCDRtenqUhKoiI5GZA/uKizsrHxqt5/+6iWVKamoL5+DdSVFG/ZjFOPXnfNH3PsIluivpaK5sZ1UFdSsu0PHLr1rNWx6tRkNKnyBzltZgba3BzEGvwzx6ZFFOrrqWhvyvrl27dg36XHHftRrUZEFFq9hpqKnVuw62yp4TRhEmUrftD3Q2UkBAdHUKkQ7O2hshKpuOiuXt8/FkH8Z//7H+R/86qtIAhCR2AA0FaSpJZAL+BaFebvAh9KktQYyAXG36leemYWgX6++r8DfH1ItxKQABg/dQ6dBo7E2cmRh7t1rrVGRn4hAUadC38PN9LNGtL0vEL8jRpIfw9XMowa91V7jzNk0TfM/XkT+SXy198+bZrhaG9H1zn/oecbn/J0m4Z4OJjGZNKLywhwcTT45+JARlFZra+9NqQXlBDgZmjIA9ycyCgoNrMpJsDNBQAblYirgx15JaYvztvPJ9M8wBs7GxUhXm4kZeVxI7cQtUbLrguppBmd816VaUpGDgIw4dNfePSdb4mxMe2EAdgG+FN50xA5rrx1C9sAfxMbh/Bw7MPDaPTbOhrHROParavh+MBAmm7fQoujh8j44svqsyMAwccPbWa6/m8pKx3Rx9fERmzYFNHXH/URs+EKDg7YjXyK8p9N0yGrxcMHKcco1To30+qLnNh9ILZLlqMaMQH1qs8sr7tdZ6TUy6CurFZO8PCG3CwjvSzDi44VxAf7oD13XP7jRhJii/Zgaw/ObghNWoKnb5XH6jXdvfUptQDkZVev2aE32oQT8h/JF5ES41At/AHVW8uRLpwy/YpsVc/LVC8/p3q9+3uivXBSPta3HlJpMeJTM1BNXYo44MkaG6o61/Mw16uhPB/ohTbhpOV52nRGOnmgWi29rbs35BnV1XnZ1b4IiA/0rELzIaRTNQ/z+W/09GU6biaqqe8jDqzNb1jXenV7z9wmPTefAG9DinyAlzsZubUPKhizfu8ROrdsVmv7jNx8ArwM2v5e7qTnVvNl97/kXupl5BUS4GXWJpqdOz23AH9PIxtPNzJ07ebsxx5h6frt9Jj9AUvXb+PVodW/IGbkFRLg6W6qZ60N9jS9pgwzmxOXU/F2dSbU3/Je234ygeYhgdjZ2pCRW0CAl5GepxvpZsGc9Dwr/unKYPbj/Vi6bjs9Zr7P0nXbeHVYb32Z7DyVwMiupkGG2wjuXpBv9BzmZyO4VfMc3tcDrfEwRRtbVK8sQfXS2xaBjH8KtgH+VN6qvk9jHxaOXVgYDdevo2F0NC5du5qfBsdWrRDsbKlIqTqjFkDl548mzfBhTJOeho2fv4WdU+8+BP22Eb9lH6MKCNBvF+zsqbd6PYEr19QqkCH6+aNJv2Wip/K11HPs2Ru/1TF4Lf0PKv8Ai/22LaLAxhbNtVSLfdWh8vVHm27wV5uRhujrZ2Fn36MPXiujcVvyIaKfpX51CL5+aIw1MtMtNFRNmiH6BVB5cK/J9orYHUhlpXhs3I1H9A7KflmOVHjv6kGF/9/8vwlIAIFAliRJ5QCSJGVJkmSRNyQIggD0ANbpNv0IDLljNclyk1DFJCffLVvM/g2/UFFZyeGTtUgpvi0hWYqYa1i5DG5bPN65LdsWTOS3157F192F99bvBCAu+SaiILBn8StsX/gSy09d4Vq+aSDAirThxHcJaxLmE8VYvQwjk8SMXJbtPMb8gZ0AcHe0Z96ATkxdt5uxP/xOPQ8XVKLhNr9XZarWajl59RrvjRvMiqlPcUwlEmeexWDt/jC/HpUK+7AwLj/2OCkvTyL4vXdQuckdpcpbt7jY5xHOd+6K5/BHsfHxsXKld6AnCDi8OJWyrz60MLN/8gUq1q+qeQiDiZ61jZalqd29icrXxqFZ9y2qAWNMT1GvATbDx6P+6T9/VdC65f3dERo01s/3ICWcQnvuOKrp7yM+MxPpagJoNX9R0uqdjNC+K0JII6RduvH0PgHgH4zmzfFo5j2D0DgKGkb8BcEq9Np2QQhuiLRbN05ZFBHCmqPd9BOaj2aBtz/Cfd3/d/XadZX1YjeY7nDzRAgMkQM8taH2kgjtuiAEN0LababpeluzNkNE/gs9UYUQ3hztxh/l9G1vf4T7ayjTutar83vmtoKVuvwvNFIb/zxBfPJ1xvfrVutjrLdL926Ss3up99+2ib/uPcbsx/oS+840Zo3oyxs/xVixNtar+lzVXpOZ1eZjcfS7L9LCLvFmBsuidzL/iUG6a6+Ff1b7c/J/f91zVPbvvenMeuwR3vhRflaWrN7CtGF9TPoX1XslX41VyzadEeqHI+3dqN+mWTIRzcevofnlY8SBT4GX5Yvw30/NfRrBRoV9aBhXRj5O6iuTqP/uO4huhuCPjZ8vIR8u4/r0GVV0PI1PZqlnfq+U7NnNtT7duTFsEKWHD+K76F39vmu9u3Fz5KNkzpqG16w52ARXn6VsHVO9sn27Sevfk4yRgyk7chDPhaZDw0QfXzzffo/c+XNq9s+cWjzj5Qd2kz24FzljhlJx7DBu8xfXeIyZiOUms36i0yuzKPlkqYWZTUQUaDTkDepB3vC+ODz+FGI9y3l8FBRqw/+nOSS2A/MEQbgE7ARWS5K014qdN5AnSZJa9/d1wGJ2KEEQngOeA/hy6SKee3I0K3/byNpN8qQ0Uc2acCvD8DU4LTMLP++qo9/29nb06NSRXQcO0em+qlPDV/62kTVr5Al4ohrUI80okp+eV4Cfu4uJfYCHq8nXhfS8Qnx1KZM+bgbbEZ3a8OIXawDYfPwcnSMaYqtS4e3qTJtAL+Iz8gl2N8pWcHEgrcjwMppWVIafsyHF/m4Q4OZkkr2QVlCCn6uTmY0zaQVFBLg7o9ZoKSyrwN3RXrbPL+aVX3eyZGhXQoy+8HRvGkL3piEArDl+gYtpOQxdLE9EdK/KNMDDlfsaNdBPdtlGoyVJFIkyesmtvJWGbT3DZFq2gYFUmmU5VN5Ko+TUKTl17tp1yq9cxS4slNIzZ/U26vQMyi4l4nz/feT/UfUkSVJmOqJRNF/w8UebbfQl09EZMbQRzu/LWRCClzdOCz+kZN4UVM0ise3cEya8guDiKk9GV1lOZcyaKvXIzULwMsoy8PRF0g3DsIb26B5sn3gFfQl5+mDz0puov3sPMm9VeZzev7wsBE+joIynD1K+ZZaS0LQ1Yt+RaJbNArXacPzW1Wi2ypNuik/PgIyaxz1KedmGYR8AHt5I+ZY+Ck1aIfYegeaTuaCRNYWWHZGSL8pp6YA24SRCg6ZIV85XrZdvpufuZV2vcUvEXo+i+fwNvZ6Ulw03kuRUekCKPyoHZaqZf7HO9czL072q8myJ2Hs4mk9f1+vp97XuhBR3pHYBJb2m0VdVD2+kgqp8HI7mszesaD5Ya83/Rk/KNyvTuKMIDZogseufo1eH98zKnX+ybu8RACLDgknLNgx5SsvJx9fzzoYrHDx3ia827eKnOS9iV8MQw1W7DrF23zEAosLqk5Zj0E7PyTdJ5b8b3Eu9VbuPsPaAnKUSFVqPtByzNtHs3AGeplkT6bkF+OpsYg6dZs7IRwDo264F837eiDmnrqQy9Ig8DCiqQT3SjDJZaqWXV4Cvh6HtVWu07Dx1gbVzTOe6SMst4JUvV9OjVVMmf/GrrBcWRJrRcJz03AL9sJLq/XMz+Pd4P9m/9i2Ypwu4nEu+wbRv5L5ablEJ+yQtNoJAz0A5G0Myz/Zy90YqsBwuITSKQuwxVJ5Hx7ieuW2bk4F09TxCUChSTrrF8X8nlWlp2AbWvk9Tee065VevYh8aSunZs4guLoT98ANp738g29SAJj3NJONB5R+gn8zxNtp8w3NSuG4NXlNmGI7X2aqvX6Ps2FHsmkWgvlZVIjVoM9JR+RsmXK1Jr+S3tbi/Ml3/t+DsjPfHX1Lw2UdUxp3hTtFkpCEaZVyIfgFozfSlfMO9XbZhLS4vT70jDSkz3SSrQ/T1R5tleLcRnJxRhTfC9bMf5P1ePri++wmFsyZh16cflUf+BI0aKTcHddxpbJq1oOJm9Zmf/y+oxbBphTvj/02GhCRJRUA75CBCJrBaEIRxVkxrFbaWJOlrSZLaS5LU/rkn5cn1xgwbxIYfvmDDD1/Qs/ODxGzdiSRJnD6XgKuLE34+pqmDxSWl+nkl1GoN+w4frXHeiDHDBhE9ZwLRcybQs1UTYo6cRZIkziTdwNXRXv9ifBtfd1ec7e04k3QDSZKIOXKWHi3lFTuMx2XuPHORxvXkl8VATzcOX0pGkiRKyis4k5ZLuKfpS3mkvwcpecVczy+hQqNly6WbdA+7s1Swmois50tKdgHXcwupUGvYEn9VH0i4TfemIWw4LU+QtP18Eg+E1UMQBApKy3lx1Xam9GpP2xDTLwfZukBKfmk5vxxLYF7/B+95mXaKCOfizXRKKypRa7ScV4nU15pF7s+cwT40FLvg+gi2tngOGkjBjh0mNvnbt+PSUZ6NXOXpiX14GBUpqdgGBCA4yIEYlbsbzu3bUX7VbNIhMzQXzyMGBSME1AMbG2y79UF9yChGV1JE0fCeFI0dSNHYgWgS4iiZNwXtpQRKpj6r317x2yrKf/mh+mAEICVdRPAPkjMBVDaI93dFOn3I1MjP0HkRWj6AdHuCL0dnbCa/hfq375EuV/2CbkLKJQS/IPD2l/XadUE6e8TUpn444uiX0XyxEIqM0rkFEZx1v3tQqNzZs5KWb0FqIoJvIHj5yZptO1uusBAUJk+2+e0iU03dKg2IovwlulGLGodscO0ygo+RXpuHkG4POzHWG/48mu/fgSKjdMlrV8DJBZzljrXQKBLpH6dnVp5tHkI6d8xSb8SLaL5dbFqeOsQ2DyGdrN0KKXofzTXjrWm+gOa7JdY123ZGOlW7ISL/lV7qZdMybRyFlF51B/pv06uje2ZMr05EvzWV6Lem8n/snXd0FFXfgJ+ZLdmENAiEDknovYuAhCZFBemKIkUQERAFQdHQkWbDAoqooCKggBCQDqGFKkUgEHoLLSEJgRBI2d2Z+f6YNckmGxIU931fv/ucwznszt159nczO3fmN7e0qV+D1XsO6W3w+Rh8PC0u54rIi5Mx15n0/Qq+HPEyAb7539y/2KYJ4ZPfIHzyG7SpV53Ve4/o7ciFK/h4WR7JXBHu8r3YqjHh44cQPn4IbepWY/X+o/q+L17Fx9Piuk20mDl28areJu4/Sus6+hCXQH8fDp69DMD+05coel61aAAAIABJREFUH5j7wUy9CuUIH/ca4eNeo03dqqze72iDL17Dx5JHG2zx4NjFaw5fFK2zDanZd/oiwSWKUiJbAupuajpD5ixhZJc2jOnZnvCJQwmfONThc8R3wRFfjgRIMX9Hm38hW3x1HfH5ZY/vYmZ8W2a+RYTjX/v61RlXp3RmMgKAaxf030XhYmAwINdpinYyx++iVBBy90EoP34I97P9LjwLgcGRIPPyQQqqkv+59D9A6rFjmIODMDmuafw7ub6mKZT9miY4GOuVK0gmE+W/mcftFStJXr++QL6ME8cxlQvCWLoMGE0UeuoZUrc7J0sN2YalerVqg9UxAaXs66vP4QTI/oWx1KuP7cJ5HoQ1+jjGcuUxlCoNRhNe7Z8mfcc2pzLZh8FaWrTG/ueEk0YTRT6ZQ+ra1aRHbCpQfDmxnzyBsWx5ZIffo91TZOza7uwPyEoGm0NboVx68HVhLsepE8hlyiGXLA1GI+Ynn8K2O8uh3b/Hnaebk9y9Pcnd22OPjiJlzHCU09GoN2MxNXDMT2TxxFijNkrMpTxMAsGD+Tf1kEDTNAXYAeyQJOk40A/4IUexRMBfkiSjo5dEGeChp4Rt0eQxIvcfpF2vl7FYPJj+3qjMbV1eHsKq7+eSlp7O0PcmYbXaUFWFxvXr0qtzRwC2RO5h6mdfkXQnmdfeGU/VihWYP8u5q1VojYpERl+gw6SvsJhNTHupY+a2rtO/JTxsEAATenUg7Ke1ZNhsNK9egdAaFQD4OHwbp6/fREKidIAfk17Qn2K8ENqQsYvW8OzUb9CArtXLUqWo88WNUZYZ26Img37bj6pqdK1elkoBPszef5oagf60DinB8Zt3eGPdQe5m2Nh++SZzfj/Dmt56d9uXft3Dpdv3SLXZabVgC++3qcMT5Z3HpRkNMmOfbsKgnzaiahpd61WmUmBhZm87TI1SRWldtTzd61VmTPhO2n++DH9PDz7uoe9/yYGTXEm6y9ydR5m7U+82/V2fDgR4ezJj435Ox+lP6Ia2qEtQ0ayLhH+qTv28POnXujHPfbAASZKopqrUV3PMFq8oXBs/gZBFC5EMBpKWLiP97DlKjBpJatRx7m6JIGXHTnxCm1N16xY0VeHGtBkod+7g2fwJQsaP1VNnEiTM+5b002dcH5x/oiqkz/kQrxlz9GU/N61GjbmIR7/XUM6exL4v8sGff1hUFfviOZhGTgdZRtm9Ce1GDIbOfVEvn0U7th9Dm85I1eqBokBqCsp8vRug3KYzUmBpfQiHYxiHfdZ78KBJH1UVdelcDK+/ry83uG8LxF5B7vgSWsw5tOO/I3cbCB4WDK+8B4B2OwH16ylgMGB4y7GMWnoqyg+f6BNcFiBGdcU3GIZMciypuBXiriI/9SLa1fNoJw7oy7x5eGLo/47DmYj63TS0o3uRKtXCMEZfWUc79Ufum29XvpXfYXh1PPrScdvg5lXk9r3Qrp1Hiz6kj7v3sGDoq5+DtDuJqAtmgqairvkRw2uTQALt2kW0/RH/fb4V32IYPDFzyUjiriJ3eEGvz+iDyM/2033933bUZwLq/Bn65wsX0+cuuRD9YI/LGCfozgNb9Rg79EK7ekF3dnLE2G+0w5mIuiC7M6Dgzr/j01TU337UjzdJ0ssX+G/obp+bjhkHLepUIzLqNO3fnonFw8T0V57P3NZ1/CzC39efFH60dC3r9h0hzWqj5Yj36dHiMV7v2p6PfllLakYGI7/UlzMuWcSfr0YOcOnKSWjtKkRGnaHDux/r7ciArJUfuk78gvDJbwDw8bINrPv9KOlWG61GzaB780a83uXhJ+H7J32hNSsRefwsHcZ9ru+7X9YI1q7vzyV8vL404oQXOxL24yoyrDaa16xEaE19GfDJfZ5lxtINKKqK2WhksmOoREJyCs9N/4Z76RnIksRPW/exZuIw3XfiHB3Gz3b4slY96Dr1a8LHvebwPePw2WleoyKhjuW1ATYcPJFruMaSHQe4kpDE3PWRzF0fCUh8N7IvobUqE3n8HB3Gfqb7+nfN8k3+ivCJQ3XfS50I+z5cb/Ozx9e3MzN+Wa/HZzIyuW8BV2lQVdTVCzC8Mlb/HR7cDjevIbd7Du3aBbSTh5GfeQnMFgwv6cdq5vKegaUxdHtVX3FGklG3r3JeneMvMnDJAiq3fALvogHMuHqKNROns3dB/st554micGPCBEIWLgSDgdvLlpFx7hzF3xpJWtRx7kZEcG+nfk1TOWILmqIQO12/pvHv2gXvxx7D6F+Ywj304/nq6NGkn3zAAwlF4db0KZSYNx8MBlLCf8V24Tz+w97AGn2C1B3b8H2pL14tW6MpCmryHRLHvQuAKaQCRSdMQdM0JEnizvxvcq3O4cp354P3KfrVfJBl7q9egf3ieXyGDMd28gTpO7fj/UIfLC1a6ROXJidze6J+reHZrgMe9Rsi+/vj9ax+zN2Z8B62s6cfZMzlT/loGv5ffIsky6StCUe5eJ5Cr76O7VQ01l3b8Xy+Dx6hrdAUO1pyMnenhBV8/w5H6qzp+Hw6T1/2c204yqULeL4yDPvpaGy7d+T50fQVP+M9diq+i1YhSRIZ61ahXHjwBPACQV5Irsbq/S8iSVIVQNU07Zzj9VTAX9O0112UXQ6s0DTtF0mSvgaiNE37Kq99a/GX3VpJatQjvlHMj9NR+Zd51ATknpjnH6XYo+3dkR/HX57gVh9ASNW8J5L7J7CUc69P9shn1Y1HTT5duP8RjP+qHHFuCpL0eZT8g+P6/9/yH6hTuWd/t/o05cET6v7PU8CVTB4pBVzO9ZFhyD2x9D+JtnaFW32vf7TRrT6AoeX+2VVAcuLj89AL4P0tzCb3dxo3udlpNLo/xiJ7T/zrGmL12Nb/6ptnuU6b/7k6/zdd/XoDsyVJ8gfswHkcc0C4YAzwiyNpcQSY756vKBAIBAKBQCAQCASC/0n+R5fW/G/mX5OQ0DTtMNC0gGUvAo/lW1AgEAgEAoFAIBAIBALBP4JI8QgEAoFAIBAIBAKBQCBwO/+aHhKukCQpHAjO8fYYTdP+2pS3AoFAIBAIBAKBQCD4/4mYn+qR869OSGia1jX/UgKBQCAQCAQCgUAgEAjcjRiyIRAIBAKBQCAQCAQCgcDt/Kt7SAgEAoFAIBAIBAKBQPBIEKtsPHJEjQoEAoFAIBAIBAKBQCBwOyIhIRAIBAKBQCAQCAQCgcDtiCEbBUAye7pXaHDzn6VGfTi0x71OVXGvTza4VVe1ZYhbfQDndl1yqy/Y5N46NQZ4u9Unm91/epTc7LQl3HWrT1M0t/r+I70q3Tz7tiS7ebZvg/sr1XzvtnuFdpt7fe4+UG1W9/oAFDe3+Qb3tk+Sl5dbfUPLFXGrD+CrK0lu9YX6WdzqK2J0f5tvkd372/f6D5y/H3O70Q2IVTYeOaKHhMD9yQiBQCAQCAQCgUAgEPy/RyQkBAKBQCAQCAQCgUAgELgdMWRDIBAIBAKBQCAQCASC/BCrbDxyRI0KBAKBQCAQCAQCgUAgcDsiISEQCAQCgUAgEAgEAoHA7YghGwKBQCAQCAQCgUAgEOSHm1dH+f+AqFGBQCAQCAQCgUAgEAgEbkckJAQCgUAgEAgEAoFAIBC4HTFk4y+iaRrTZn3Bzr37sVg8mDn+PWpUrZKr3MA3R5OQeAtFUWhQtzYT3x6JwWAA4KdlK1i0fCVGg4EWzZrwzvAhuRzTl20k8sQ5PM0mpvfrQvVyJXM5omNuEPbjatJtNkJrViLsuQ5IksRb3/7KpZuJAKSkpuPjZSF83GtcT7xDx8lfElQ8AIA63kYmPVEtd5BB1ZHb9ABJRovag3Zgi/P2MhWRW3eHYqVR13wPZ49kbpJHzYbEG/qLu0mo4fPyrdNd568xY9MBFFWjR71KDHqittN2q13h3VW7iI69hb+nB7N6tKC0v0/m9hvJ9+j01SqGtajLgKY1XTr0Ot1EZLSjTvt2zrtOF/6m12mNSoQ91x5JkgBYtP0AS3YcxGCQaVGzIqO7tcVqV5i0ZC3RMbHIksTbPgqNfD1y7Veu1Qhjn9dBllF2rEdZ+7PTdkPrThie7AyqCulp2BbMQrsRg1S0OOYPfkCLvQqAev4k9h8+y7dOvVuEUmrCRDDI3F66lIS5X+cq4/fMMwSOeBM0jfRTp7j65ggAgn78Aa969bh/8CAxA1/J1wVgaPA4HoNHgSxj27Qa2/KFrss1a43n2JmkvtkP9dwpDPUew9x/GJhMYLNhXTAb5dihfH1/tT4zCQjEY+b32MN/RFm/rEAxSjUbYnxhCEgyyq6NqBuWOn+nFs8gt35Wd2akYf/xM4i9glS9PobuA8FoBLsdZfm3aKePFsiZ6a7eAMNzg0GSUfdsQt283Nnd/GnkFh1BVdAy0lEWfwFxVx/KIddtjPnlN0GWsW9di33VIqftxnadMbbvBqqKlp6Gdd6HaNcug8GAeci7yMGVwWDAvnMj9vBFriXZMNRrjHngCN0XsQbbyhy+9l0wPdUNTVUhPZWMrxw+oxHza+9gqFgVTVWxzv8cNfqIa4mrGAc4nFvX5PqexnZdMHb4M8ZUrF9nj/E95BBHjDs2Yg//qWA+N9apXOcxTA6fsnUt9tWLnbYb2nbG2L5rps827yO065cxPNEW47MvZJaTylUgY8xAtJjzBXP2Ha47t6/D/tsSZ+eTz2Js21U/NtPTsH33Mdr1GAzNnsTYsZezM2yQS6emaUxfvpnI6Av6+btPR9fn7yuxhP20hnSrndAaFQjr2Q5JkpizLpJf9xyhsLcXACOebUWLmhVZc+AECyL2ZX7+7I14fh3dj6qlA5m+ciuRJy/iaTIxvfdTVC9bIrfvahxhi9eTbrMTWj2EsG5tkCSJL9btYtvx80iyRIC3F9N7P0Wgnw/zt/7O2sOnAFAUlYs3b7F7+hv4eVmYviKCyJOO+Ho/49p3JY6wxev09ql6BcK6P5nZPgEs2Po7H6/ezp7pb2TGeuBcDDNWbsWuqBT28mDhsOf1+ly1nchTl3Rfr/ZUL1PcRXw3Cftlox5ftWDCurRCkiQ+WrOTHdEXMRkNlA3wY1qv9vh6WgA4cyOBSb9GcC/diizBsuG98DAZdedvO4k8fRlPk5Hpz7WjepnA3M5rNwlbtkV3Vg0i7NkWSJLExqhzfLllPxfjk1j6ei9qlnX+vjdu36XTJ4sY1q4JA1o1/EdjzPR9+CPDqpdiQLVSufbrRMVayB1eBFlG+yMSbfc6p81Sk/ZI9UP1tuN+Curq+ZB868H7zIF3ixaUnjgBDAaSfllKwty5ucr4PfMMxUeOAE0j7dQprr7xJpbq1Sk9bSoGb280RSF+zpckr137UO6c9Jn/JbU6diAlPoH3az3+t/b1JyXbtKLBzGlIBgMXFi7i5GeznbZ7lSlNk7mzMfn5IRkMHJv0Pje2bKVEyxbUnTQO2WRCtdk4MmEyNyN35+sr2rol1aa/D7LMtUU/c+mLOU7bq06dRJFmzQAweFkwFy3K1gpZ19MGb2+a79vJzXUbOfXu2IeOt0irllScNhnJYCB20c9cmf2l0/YKUyZS+ImmAMienpiLBrC7Uo2Hcvi1akG5KZOQDAYSlvxC7Jyvcn+PTh0pPXokmqaRFn2SC8PeAMBcuhTBn3yIuVRJ0OBM735Yr1176DgFApGQ+ItE7t3P5avX2PzrEo6dOMmkD2exfEHum+7Pp03G27sQmqbxxrvj2bh1B8+0a8P+Q3+wNXI3axZ/j9ls5lbS7dyOE+eJiU9i45ThRF26zuQl61j6bu4bwylL1jH5pY7UCS7D4DlL2BV9ntCalZg1qEdmmQ9+3YRPtka0bLHChI97TX9xaE/uACUJue1zqMtmQ8od5D7voF04DrfissrcTULd8BNSoydzf95uQ/1xxgNq0BlFVZm64Xe+e6kdxX29eP67tbSqUo6Kxfwzy6w4cg5fTzObhndn/YmLfBJxmFk9WmbFuOkgzSuWfqAnMvo8MfG32Dj5db1Of17H0jEu6vTn9Uzu/UyuOv39zCW2HTvDqnGDMZuM3Lp7H4Bfd/8BwOrxr3Hr7n1enfwFS2oUQ852kYgkY+z3JrYP3kZLSsA8ZS7qH3udbpCVvVtRtq0BQK7XFGPvIdg+ehcALf4G1nGvFrhOkWVKTZnCpZf6YI+Lo8Jvq7m7JYKM81kX+uagIIoNHcKF7j1Q797FEBCQuS1h3jfInp4UefEFV3t36fMY+g5pY19HS4zH87Mfse/fhXb1knM5Ty/MnZ9HOX088y0t+Q7pk0ehJSUilw/B8v4XpPbt+GDf36xPAFPvoahRBwoW35/O3q9j++RduJ2Icfxs1KP7IPZKZhH19+2oO/ULTanO4xifH4z9s7FwLxn77PFwJwmpdBDGkdOxjX7xodyGXkOxfzFWd7/7GWrUfqeEg3pwO+qu9Xrx2o0x9BiEMmdCwR2yjPmVt8iYMhItKR7LzO9QDu3Wb44d2Hdtwb55NQCGhs0w9xtOxrRRGJq0BpOJ9FH9wOyB5bNFKLsj0BLi8pA5fK+OIn3SCLRb8Vg+/A77gRy+yM3YN63SfY2ewPzycDLeH4Wx7bMApI3oC37+WMZ/Qvrbr4Cm5R/joFFkTHE4P/gO5WDOGDdj3+xwNnwCc//hZEzNFuNbffUYP1+MsntL/jG6s04lGdPAt7BOHYl2KwGPGd+iHNqDdj3Lp+zegrJF98kNmmHq9zrW6aP193friWepbAjmd2YUKBmBJGN6eQTW6aN057R5KIf3oF3P9lvcE4ES8ZvD2RRTn2FYZ76jv78nIss5alqezsjoC8QkJLFx0hCiLt9g8i8bWfrOy7nKTfllA5NfeJo6waUZ/NUv7Dp5gdAaFQHo27oxA550vjnq9FhNOj2mJ7HPXo/n9XnLqFamODujLxCTcJuN4wYRFRPL5OVbWPpWn9y+ZZuZ/Hx76gSVYvC8X9l16hKh1UMY0OYx3nimOQA/7TzMVxv3Mun59gxs05iBbRoDsP3EeRbuOIR/Ic8s3/jBenzLNrF0VD8Xvk1M7tVB9329nF2nLhJavQIAsbfvsu/MZUoW9s0sfzc1nSnLNvPNkOcoVcSPW0l39Po8fYmYxDtsfG8AUVdimbxiK0vfzH1OmrIigsk921KnfEkGfxfOrtOXCa0WTNPK5Rn5dHOMBplP1kby7dYDjOoYil1RGbNkAzNffIqqpYpx534aRoPscF7Wne/0I+pKHJPDt7F0eK/czvDtTO7ehjrlSjB4wWp2nYkhtGoQlYoH8EWfjkxauTXXZwA+WBNJ8yrlM1//UzFm+lbvoHnVIMDq8vtkIknIT/dB/ekjuJuEPGgi2pkjkHAjs4gWG4P2zWSwWZEatkJq+xzar7kTCnkiy5R+fwqXer+ELS6Oir/9xt2ILWScc27zA4cN5UK37ijZ2nw1LY2rI9/CevkyxsBAKq1bS0pkJOrduwX352DfD4vZMecb+i/M/4FUQZBkmYYff8C2Lj1Ju3GD9ts3c23DJu6eOZtZpubokcSE/8b5BT/gW6UyLZcv4bfaDclIusXOXi+RFncTv2pVabViKauq13mwUJap/sF0DvboRfqNWJpsWU/8xk3cP3sus8jpcZMy/1/ulQH41nJ+GFbpvXdI2rv/rwUsy1T6YCrHer5Ixo1YGmxeR+KmzaRm81+YMDnz/6UHvox3rYdLRiDLlJ8+lTPP98YaG0uNDWu4vXkL6dkcHsFBlBw+lJPPdkNJTsaY7Tox5ItPufH5HO5G7kL28gJN/Wux/o+RPQEseDT8a4ZsSJI0VpKkaEmSoiRJOipJUuM8yi2WJOmMJEknJElaIEmS6a/4tkbupstT+lPzurVqcDflHvGJibnKeXsXAsCuKNjsdv48hn9euZpX+/bGbDYDEFCkcK7Pbos6TefHayNJEnVCypCSlk5CcopTmYTkFO6lZ1A3pCySJNH58dpsPXbaqYymaWw6fJKnG7ruNeCSkkFwO0HPzqsK2unDSBWdeyxwN0lvTPO7ASgAx68nUq6wD2UL+2A2GHiqRjDbzlxxKrPtzBW61NYvKttVD2L/pVg0hzvidAxlCns7JTBcse3YGTo/XierTlMzClCnddh67AwAv0Qe5pX2zTCb9FxegK/+970Qm8DjVYIz3/MxykTftzntV6pQFe3mdbSEWFDsKPu3ITdo6vwF01Oz/u9h+Vt161W3DtaYGGxXr6LZbCSvWYNvu7ZOZYr06sWthT9lXnQot7Kextzfuxf1/r0C++TKNVBvXEOLuwF2O/bIzRibhOYqZ+4zGOuvP4E16wJOvXgWLUn//agxF5HMHmB88E/z79an3KAZWnys041hfkghVdDib0BiHCh21AM7kevl7ZQ8spKA2pULcCdJ///1y2Ay5xujkzuoMlpCNvehSOQ6TXK407L+b7bwsMgVq6HFXdNjtNux74nA0OgJ50Jp2evUE3DUqaYheXiCbACzB9jtaGn3H+yrVA019hraTd2n7N6K8bHmD/BZMn1y2SCU445eNMl34P495IpVCx6jw2nfvRVDowc4LdmPGw3JYskWoy3/GN1dpxWrocVdR4t3/C72bn2gT7K4Ps8YnngyM1GQH7mc+7ZhaPgAp4dnZohOzqZtUPa6vtEE2BZ1ls6NHW1icOkHtIlW6oaU0c/fjWuz9djZPPaYm3WHonm6vv6Ec9uJ83RuVEP3BZVy+JzPiQnJ93RfcGnd16gGW4/rF/PelqxecmlWm8uL2PWHT/F0g+q67/g5Oj9WM1t8GXn4MrJ8j9Vka1TWzcMHK7cyqnNLsqvWHT5J2zpVKFXED4AAHy9HfBfo3KC67itfSvfdzeG764gvqJTua1CdrSf0G9xmVYIyEw11ypck7o7+2T1nL1O5ZFGqlioGgH8hTwyOSeC2nbxI5/rVHM6SDuf9HM77urN8Sd1Zvxpboy8AUKF4EYIDc18vAUScuECZIn5ULJ51w/RPxQgQcfw8ZQL8qFgigHwpHQJJN/XrKkVBO/E7UpV6zmUunwab3i5q1y4g+RbJf7/Z8KpbF+vlGKyONv/OmjX4tm3nVKbIC724tXAhSo4233rpEtbLlwGwx8djT7yFscjD+XNyftdeUl08bPurBDSoz72Ll7gfE4NqsxGzIpwyT3dwKqNpYPLRe86afX1Ji70JwO2oE6TF6f9PPnUag8UD2XH9nRf+9euReukyaTFX0Gw24sJXU/yp9nmWL9mtC7ErV2W+9q1TC4/AYtzavvMvxetbvy5ply6T7vDHh6+maId2eZYP7NaZ+PDVD+XwrleXjMuXybiiO26tXkPh9s6OwN4vEv/DQpTkZADsjmPGUrkSktHI3chdAKipqahp6Q/lFwj+5F+RkJAkqQnQEaivaVpt4Ekgr37Ki4GqQC3AEyhYX/Qc3ExIpETxrG6GJQKLcTMhd0ICYOAbo2ja4VkKeXnRvnVLAC5fucqho1H0HDCYl14bTtTJU7k+F38nhRKF/TJfF/f35eYd54uvm3dSKJ7tSUhxf1/ic5Q5fP4KAT6FModoAFxPvEO3afPo+8kPHIpz0WB4+6OlZHs/5Q54P/hm3wmjEbnPO8i9R0PORIYLbqakUsKvUObrEr6FiE9JzbOMUZbxsZi5k5ZBqtXG/D0nGNqibr4evU6z1VdhH9d16p+9Tn0y6/Ry/C0On7/C8x98R99ZP3D88nUAqpQpzraoM9gVlWuJtzl138pNq+K0X6lwUbSk+MzXWlIiUuFiub6j4cnOmD9ehKnXq9h/yuoeKBUrgfn9eZjHfopUuVa+sRqLl8B2IzbztS02DlNx5+6/5pBgPIKDCfl1ORXCV+LdIncCoaBIAcXQEm9mvtYS45ECnOOTQyojFyuOciDvrpKGZq1RLpwBuy3PMvA369PDgvGZXtjDfyxIaFn4F0VLSsh6fTsByT/3xajcqhOmGT9g6DkI+5Ivc22XGjRHu3I+3xid3QFwO+sco91O1N/L6W7REeOU+Ri6DkBZmnuIzoOQihRDS8xWp7cSkIrkrlNjh25Y5izF1GcI1vn60CFl/3a0jDQ8v12F59crsP32M9xLyfXZB/tyHzMAxqe64Tl3GeZ+Q7F+p/vUS+f15IVsQAosiVyhClJA7q7Y+TqT8nB26Ibly2WY+gzFusAR477taOnpeH63Gs95K/9ijP9snVKkGNqtnL6iuYoZ2nfF44tf9F5D33+ee3uT1gVOSFC4aG5nYRfOtl3w+GwJxhdfw/ajK2erByYk4pNTKOHv3N65Pn/7ZCvjQ3y2pMWSnYfoMu1bxv60huTUNHKy8Y+TPONISMTfyeHz8+FmjgTIzWQXvmzf6bO1kbSeOJe1h04y/GnnJE2a1cau05doW6dKtvic91Ugn6PMtuPnCPT3pmpp59/B5fgk7qam0++LxfT48HtWHzrp8N1z9vl5czNHAuRm8r0cPm/ik3MnqlceiKZ5tSAAYhLuIEkSg+atoPusRczfdjCznO70dtqfS6efcxlXzuykWm3M33GIoW2dn0X9UzGmZtiYv/0gQ9s1yVXOJb6F0e4mZb2+ext8XSdWAKT6oWjnowq2bwemEsWxxWb1uLDFxmIq4XwseASHYA4OpsKKX6kQHo53ixa59uNZpw6S2YQ1JibXtv8kniVLcP/69czXqTdi8SrpPGTr+MwPCX6uO12ij9Jy+RIOvfNerv2UfbYjt6NOoFof3KvFo2QJ0m5k1Wf6jVg8SuYeIgZgKVMaz/JlubXLcW0jSVSdMpEzE98vaHi5/SVKknE96xouIzYuT79HmdJYypXl9i4XPZ4fgKlECTKuZ8VojY3FnOOYsVQIxhISQrXVK6m+dhV+rfRjxhISjJJ8l4rz51Fj83rKjg8Tq08I/jL/liOnJJCoaVoGgKZpiZqm3XBVUNO09ZoD4ABQ5q8INRdPlCRcd+GZ/8Un7F4XjtVqZf8hvWu/oijcTUlh2fyveWf4EEaETcy1T1f1zxICAAAgAElEQVQPx3MaCvI91h08ztONsnpHFPPzZuv0EawcO5gxPdrxzvYT3LPaXX73HLYClNFRvx6P+tOHqGu/R27dA/xzX5w+7J5dlZGAOTuO0vfx6hQy5/+0WXOxlwLVqaOQoqjcTU3nl3cGMrpbW976bgWaptGtaT1K+PvSc+a3zFi+iTreZgw5d+yqi5cLlxKxGuvol7At/QZj55f0YneSyBjxAtbxg7Et/grz0LFg8XpwsC58OWOTDAbMwUFc7PUCV4a/QZmZM5F9fXJ9rkC4jM95u8erI8n4NveNyJ/I5ULwGPA6GbMLMNznb9SnsVt/7Bt/hYyHzOa7/Inndqrb12B7rz/Kr99h6NjbeRelymPsMRD7wrzrwbW7YPGqO9dinzAQZdX3GJ7O3RX6UTjsG1eS/vrz2BZ9jamH3qVcrlgdVJW0V7uQNrQnpk69kALzGVNdUN+GlaQNeQ7rwrmYevbX39u6Di0xAcvH8zEPfBPl9AlQC3Aee5gYhz2H7ae5mLrrzswYB3UmbUgPTJ1eQCr+iGJ8ZHXq4j0XJ09lUzgZb/TCvvhrjN37Ou+iYnWwpucebpWns2DdV5Utq8gY8SL2JfMwds3hrFANMjLQruXtdH1udnbn1U4A9Gpen02Th7LyvVco5ufNhyucEy7HLl3HYjZRyfFk3+W+cvpctdPZyozoGMq2yUPo2LA6iyP/cCq348R56geXxr+QZ977ogA+9OTGvM17Gf5081zbFVUl+moccwf35NuhzzN3y34uJ9zOo64eLj6AryN+xyBLdHIkcuyKyh+XrvNh76dZ9PrzRJw4z75zeo9H13Xq/NplO53PMTZn8376Nq9HIQ/np97/VIxzNu2lb2j9XL68yad9zF6ydhOkUsFoezYUcN8PcuRo840GPIKCufB8L668MZwyH8xE9s1KuhkDi1Hu01lcG/32I+n9+khxdU2ToxKDenTj4s9LWVWjLjt6vkjTeV86fc6vahXqTp7AgRGj/5Ivrzop2bULN39bp8//AZQb0J+EiG2k33B5K1IwXOpd+wO7diZhzfpMf8EdBThmDEY8QoI43f05zg8ZTvDHH2Lw9UUyGPFu3Iirk6cR/VQnPMqXo+jzPR/O/7+KJP93//sf5N8yh8RmYIIkSWeBCGCppmkP7CPlGKrRB3gzj+2vAq8CzPv0I17t34fFy1eybLU+yU+t6lWJu5n1RCguPoHAYnl32/Pw8KB1aDO2Ru6mWeNGFA8sRtuWoUiSRO0a1ZFlmdt3ktkQsY2lv+gT89UqX4q428mZ+7h55y6B/s43iyUK+3Lz9l2nMsWyPX2wKyoRR06zPCxr7gGzyZg55KBG+VKU9fHkcnIqNYtlNUrcu4PkUzjrVO/jD/eyvku+3HeUTb6FdvUcBJaFO657kACU8PEiLjmr22bc3fsE+ni5LFPCtxB2VSUl3YqfpwdR1xPYfOoyn0QcIiXdiiRJeBgN9H5Mv3BYcvAUy4/rDbtep9nq63aK6zq9k71OUyjm55O5rW29qvrfLag0siRx+14qRXwK8W7PrK58L7w9k3IW55+XlpSAVCSrV41UpCjaA+pE3b8dU399gknsNrinP03XLp9Di7+BVLIM2qW8uyLb42IxlcrKpptKlsAef9OpjC0ujtQjR8Bux3btGhkXL+IRFExa1MM9mQFHj4iiWZl1qWigc28CTy/k8hXw/EAfEysVDsAy4WPSp4xGPXcKKSAQy/gPSf9kElrc9Zy7z+37G/UpV6iKoVEo9BoMXt76uEerFSViVZ6fB+B2ovPT7cLF0O4k5VlcPbAD00tvkNlXpnBRjMMmYp//ISTE5vm5vNxke+osFS4KyXm7tUM7kV4Y9lAK7VY8UtFsdRpQTO+JkQfKngjMg0YBYGjeFuXI76AocPcO6pnjyBWqosTnfUGW2xeYOXTHpW93BB6DR+ujtVUF6/dfZG6zzPga9Ub+k2nlchbJx7knAvOro7NiPLo/K8bTUXqMNx8mxn+2TrmVgBTwEL69WzENGkX2vjrGZm1Q9uTdUyEXSQ/p3LcV08CRzs6mrV32jvgl4T7h078FHOfvO87tXWC2J+kAJfyde71lP38X9c0q27NZPYbMdZ7IdsPhk5Qu4k/XD3/QfeVKOPuSUwj0LYjPuQzAMw2qMWTeCqdeEuv/OI1/IU+6frDA4StJXI59FTS+q4m3uX4rOXNfN++k0P2jH1g6qi/F/X3wL+SJl4eZVb8fJzk1nVfmraBp5XLOvuR7BGbrqaj7vHP47lHMN6vMqoPR7Dx5kQWv9ci8iS/h702jkDIU9tYTLf5eFt79ZTNFvD2pVba407CHm3fu5a5TPx+nXgw5na6IuhLH5uPn+GT9bm6lpGJTVBbu+oPQqkH/SIxRV+LYHHWOT9buIiUtA0m142GQ6F3Z9RNs7iYh+RbJuqbyLQwpLnqnhlRHat4J9YcZoBTkQVEWtrg4TCWzEpamkiWxZbtOBb2nZGabf/XPNj+ItKgoZG9vgr//nriPP9HL/JeRdiOWQqWz5gnzKlWStFjn+XRCXnqRHT30RHziwUMYLBY8AgLISEzEs1RJmi/6gX2vvc49x/CUB5FxIxbPUln1aSlVkow41/P3lOzamZNjwjJf+zdqQOHHG1Pu5X4YChVCNptQ7t/n7PvTCxxvRmwsHqWzjiePkiWw5uEP7PIs5/7CpJm22Fg8SmfFaC5ZEmuOY8YaG8u9w3+g2e1Yr14l7cJFLMFBWGNjST0RTcYVPdl4e+NmvOvXI/Fn54m+BYKC8L+ZRsmBpmn3gAboCYQEYKkkSf3z+dhXQKSmabvy2Oc3mqY11DSt4av99UmsevfsxupFC1i9aAFPhjZn1YZNaJrG0ePR+HgXIrCocy+A+6mpmfNK2O12du7dT0hQOQCebNE8s7fEpStXsdlsFPb3o3fPboSPe43wca/Rpm5VVu+PQtM0jl28ho/FI/PC6k+K+flQyOLBsYvX0DSN1fujaF07axz1vtMXCS5R1GmYQlLKfRRHFvVqwm1i7qZRxsfTuQJiY6BwIPgF6F2iqzZAO3+cAuHhCQbHzbhnIaTSIXDrwTdfNUsXJSbpLtdup2BVFDZEX6JV5bJOZVpVKcuqKH1c5+aTl2kcrI8vXfTy00S82ZOIN3vSp3F1Xn2idmYyAuDFRtUIHzuY8LGDaVOnCqv3H8uqU8+C1OkxWju61LauU4Xfz+hP8C7fvIVNUSjs7UWa1UZqht79b++pCxgkqODp3GNDu3gaqURppGIlwGDE8Hhr1D/2OZWRimc1tnLdx7NuzH38MrOeUrGSSMXL6OO1H0DqsSg8goIwlSmDZDLh16kTd7c4PxG8u3kz3k30LqeGwoUxBwdjvXLF1e7yRT17ErlUWf2JsdGIMbQdyv5sP6/U+9x/oR2pL3ch9eUuqKdPZCYjKOSNZfKnZPzwJerJgiVD/k59WqeOIOOtF8l460WUTSuwr1mSfzIC0C6d0fdZVHfKj7VAO+rsJNsTbKl2Y7R4x9/QsxDGN9/HvnIB2vmTBYrRyR1zVn86HlBcdzcM1Se1zE6xbO6ajfR5Cx4C9fxppJJlkQJL6n/DZk+iHHTuAiqVyOpUZqjfFDVOTwJoiTcx1Kyvb/CwIFeqjpp9RRNXvnOnkUuWyfQZnmiD/aDzcB6pZDZfg6aosY6kg9nDMacEyHUa6eOyCzAfiB5jltP4RBuUQwVz6jE2yIqxcg3U6/nE6O46veCIr1hJ/XfR1EV82Xxy/SZosdkSOZKE4fGWBR+u8aezRJms32KT1iiHc8aY7bdYrwlaXA5n45Yo+3InJHoVK0R42CDCwwbRpk5lVv/uaBMvXc/7/O1h5til6/r5+/coWteuDOA030TEsTOZPSEAVFVj05FTvNezLeHv9Cf8nf60qVWJ1Qejdd/lG4422DuHz1v3Xb6h+w5G07qmPtfR5fishOH2E+cJKZ41Jj8lLYODF64yrseThI8ZQPiYAbSpXYnVB05kxZeXz5ItvgMnaF2rEpVLBbJ7+htETBpKxKShFPf3YcXb/Snm603rWpU4fPEadkWl6+O1KeZbiLkDu9CmZkVWHz6p+2Ju4GMxUyxHcqCYr8MX44jv8Ela19Qn0Nx1+hLfbT/IlwM645mth2KzKkGciU0kzWrDrqhk2BWm9GhD+MjetKlRgdV/nHI4Y/W/YY5kQzHfQhTyMHEsRp8navUfp2hdPSTXsZGdRUN7EvHeACLeG8DAlg1565nm7Jjw6j8W46LXnydi3CtEjHuFPqH1eLV6mbyTEQA3Lunnbv+iYDAg1WysT2qZnRLlkDv2R/35c7ifz9AsF6QeO4Y5OAhTWb3N9+/UibtbnFdHS968mULZ2nwPR5svmUyU/2Yet1esJHn9+od2u4NbfxzBp0IIhcqXQzaZKN+9K9c3bHIqk3rtOsVb6L2EfCtXQvbwICMxEZOfLy2XLeHYlGkk/l6wiayTjxzFKyQYz3JlkUwmSnTtTPzGzbnKFapYAZO/H3cOZq0MFvXa6+ys24id9RtzZuIUri/99aGSEQApR47hGRKMxeEP7NqZxE1bcpXzrBCCyc+PuwcPP9T+Ae4dPYZHcDDmsrojoHMn7uRw3N64Cd9m+lxZxiKFsYQEk3HlCvePHsPo54cxQD+v+TZrSlq2yTAFgofh39JDAk3TFGAHsEOSpONAP+AHV2UlSZoIFAMG/1Vfi2aPs3PvPtp2fwFPiwfTx2eNU+v80gBWL1pAWlo6Q0aHYbVZURWVxxvWp1fXzgB07/Q0YVNn0vGFfphMRmZODMvVRTC0ZiUiT5yjw/jZWMwmpvXrnLmt69SvM1fJmPDiM4T9uIoMq53mNSoS6rgYAthw8ITTcA2AQ+dimL1mB0ZZRpYlJjarir8lx3AHTUWNWIbcY5i+RNXxfXArFqnZM2hxV+DCcb3x7PIqeHghVagJzZ5B/X4qBJRAbveC3u1LktB+3+y8OocLjLLM2KceZ9DiLaiaRte6FakUWJjZ249Qo1QArauUo3u9SowJ30X72Svw9/Tg4+65xz7mh16n5+kwYY5ep32fzarTafMIH6sfEhNeeJqwH1eTYXPUqWOG9m5N6zHup994dspcTEYD0/t2RpIkklLuM+iLxciyRKCfD9NCXIwNVVXsC2djevsDkA0okRvQrl/G2K0/6qWzqEf2YmjbBblGA1DsaPdTsH3zAQByldoYu78MqgKqiu2HT/O/YFEUbkyYSPDChfqyn8uWk3HuHIEjR5J2/DgpERHc2xmJT/PmVNqyGRSFuBkzUO7oM7CHLFuGR4UQ5EKFqLpvL9fGvMu9yMi8fapCxtyP8Jz6hb7s5+Y1qFcuYn7pVZRzp1B+d5n7A8DU6TnkUmUw9xoIvQYCkD5uOFryAybE+hv1+ZdRVeyL52AaOV1f3nD3JrQbMRg690W9fBbt2H4MbTojVaunP9VOTUGZ/xEAcpvOSIGl9SEcjmEc9lnv6fOzFNCt/DIX4/CpIMuoezdD7BXkji+hXTmHFvU7cstOyFXr6vGm3kP58ZOHjE/B+t0sPMbN0peo3LYO7dolTM8PRL1wGuXQHoxPdcdQu6E+weL9FKyzp+mxbFyJeVgYlk/1ZTDt29ejxVzI3/ftp1gmzgLZgH3rWrSrlzC98Arq+dMoB3djero7htqN0BQ73Esh44upAEh+hbFM/BQ0Fe1WAhmfT3mIGD/FY7zDuc3h7OVwHtrtiLFRVoxzpjrH+Nmih4vRzXVqW/Ap5rGfZC7BqV27jPE53ace3oOxQzfkWg314+ReCtYvp2V+XK5WB+1WQr4Jz1zOHz7D/N7HmUvwatcuY+wxAPXSadTDezG264Zcq4EjxntY52YNy5Kr1kFLyt8ZWqMikdEX6DDpK/38/VLWSjxdp39LeNggACb06kDYT2vJsNloXr0CoTX0m8uPw7dx+vpNJCRKB/gx6YWnMj9/6PwVivv7ULZo4cy5XUKrhxB58iId3v8Wi9nItBezynf98AfC3+mv+55rS9jiDXp7UT2YUMfN86drIrkUn4QsSZQq4svE57Imi4uIOkuzKkF4ZevyH1q9ApHRF+kwZZ4eX++ns3wfLCB8zACHrz1hi9fpbX71kExfXlQoUZQnqoXQZeZ8ZFmiR+NaVCpZlIolAog8dYkOMxZgMRmZ1iurl1/XT34ifJT+MGZC9zaE/bJJj69qEKFV9Qmcp67chs2uMHDeCkCf9HFSjyfx87LQr0V9nvtsCZIEoVWDaVFN/0xo1SAiT1+mwwc/6nXaM2ui5a6fLiZ8pH5unNC1NWHLtjic5QmtGqTX24nzTFu9k6R7aQz5fjVVSxXj21e65hl7aLXgfyTGh0ZVUdcvQu4zGiQZ7cguSLiB1Kor2o1LcOYocrvnweyB/JyjZ1vyLT05UVAUhRsTJhCycCEYDNxetoyMc+co/tZI0qKOczcigns7d+IT2pzKEVvQFIXY6Xqb79+1C96PPYbRvzCFe+grtF0dPZr0kw+fPP+TgUsWULnlE3gXDWDG1VOsmTidvQvyXyY5LzRF4dDb79JqxVIkg4GLi5aQfPoMtcLGkHTkKNc3bOKPcRNp/Pksqg59DTSN/UP15SkrDxqIT3AQNd9+i5pvvwXAtq7PkeFiMvrsvpPvjqXh8iVIsoFrS37h3pmzVHz3bZKPHiPBkZwo2a0LsQ85mWRB4z337nhqL12MZJCJXbKU1DNnCRozmpSjx7jlSBwU79aF+FW//TWJohATNp6qP/8EBgMJvywl7exZSr/9FvePHefO5i0kb9+JX4tQau3ciqYoXH1/Gvbb+nXLlSnTqLrsZ5Ak7kcdJ2Hxz/kI/yWIVTYeOVJe45H+l5AkqQqgapp2zvF6KuCvadrrLsq+AgwA2mialntGK1fcuenWSlKOPERX2UeBq2U//2lK/aWpO/46Jcu5VWf7fr5bfQDndhVwvPcjIrh67on5/kmMAbm7Qf+TyGb352slNzttCX99Sbe/gqa4t735jwyldPOFiiS7+cLI4P5KNQ8c6F7hw0w2+yhw94Fqy2eJyn8CRcm/zKPEYHCv79C+/Ms8QqK/3+FWH8BXV/IeIvhPEOr38CtF/R2KGN3f5lvcPAmk13/g/P1Y7JV/3d27du7gf/XNs1Sp0f9cnf9bekh4A7MlSfIH7MB5HPM/uOBrIAbY5+iRsFLTtAI+WhMIBAKBQCAQCAQCgUDwKPhXJCQ0TTsMNC1g2X9FzAKBQCAQCAQCgUAgcCP/oytZ/DcjalQgEAgEAoFAIBAIBAKB2/nX9haQJCkcCM7x9hhN0za5Ki8QCAQCgUAgEAgEAoHAffxrExKapuU97bJAIBAIBAKBQCAQCAQPg1hl45EjhmwIBAKBQCAQCAQCgUAgcDsiISEQCAQCgUAgEAgEAoHA7YiEhEAgEAgEAoFAIBAIBAK386+dQ0IgEAgEAoFAIBAIBIJHhiye5z9qREKiIGiqm32ae30NmsLR393rVN0co5sxFvZ2uzOoSoBbfVfOJLrVV666exsAU+FCbvUBbp8oSTIZ3OrT7Db3+hS36nTcPdeVu9uL/8S5+3qMe30ly7rXp9jd7PsP/DBUNztlN/8QTSa36nx8zG71AYT6Wdzqi0xOd6vvCTfHBxBgdO9tmLt/FgJBQREpHoH7kxECgUAgEAgEAoFAIPh/j+ghIRAIBAKBQCAQCAQCQX6IZT8fOaKHhEAgEAgEAoFAIBAIBAK3IxISAoFAIBAIBAKBQCAQCNyOGLIhEAgEAoFAIBAIBAJBfkjief6jRtSoQCAQCAQCgUAgEAgEArcjEhICgUAgEAgEAoFAIBAI3I4YsiEQCAQCgUAgEAgEAkF+iFU2HjkiIfEX0TSNabNms3PffiweFmaOf5caVSvnKjdwxNskJCahKAoN6tZi4ugRGAwGZn/7Pct+W0cRfz8A3hoyiBZNH3+gb/qyTURGn8PTbGJ6385UL1cyV7nomBuELfyNdJuN0BqVCHuuPZIk8dZ3v3Lp5i0AUlLT8fGyED528IODDKqG3LIbyDLa8X1oByOct5euoG8vVgp13Y9w7qjzdrMFuX8Y2vkotG2/ulTsunCdGZsOoGgaPepWYlCzWk7brXaFd3/bTXTsLfw9PZjVrQWl/b0zt99Ivkenr1czLLQOA5rU5NKtZN5auTNz+7Xb9xjeoi59u5XP5f5P1KlUrT5yt1dANqDu24wWscJ5e6vOyE3agqKi3UtGXfIF3E4AQH62P1KNhiBJaGeOoq749oEuAEPDJlheGw0GGduGVViX/eiynPGJNniO/4D7r/dBPXcKuUoNLG+GOb6UhPWnb7Dv3ZGvr1BoKCXGT0AyyNxeuoxb87522u7XvTvFx7yL/eZNAJJ+WsidZcsACHxnDN6tWgKQOGcOd9etyz+++o/jMWgkyDK2Lb9h+/Un1+WatsLzvRmkjuyPev40+PhieXcGhkrVsG1dh3XeJ/m6/kSq1QjjS8OQZBll53qUtb84bZdbdcTwZGdQVchIw77gU7QbMVkFAgIxz1iAEv4jyobl+ftqNMT4wmsgG1B2bUDdsMzZ1+IZ5FadsnwLP4fYK0jV62PoPgAMRlDsKMu/RTt9LF+fXOcxTH2HgyyjbF+H/bclTtsNTz6LsW1XUBW09DRs332Mdl2PTyoXgnngaPDyAlUjY9xgsFnzdTrtv15jzK+MANmAfcsabCvz+Js2aYVlzDTSRg1AvXD6oRzu9hnqNcY8cATIMvaINdhWLnLabmzfBdNT3dBUFdJTyfjqQ7Rrl8FoxPzaOxgqVkVTVazzP0eNPpKvT67bGPMAh2/rGuzhOXztumDs0A1UFS09FevX2XyD30GuUBU0FeuCgvkynS+/6XCuxb4qp7MzxvZ/OtOwznM4DQbMQ95FDq4MBgP2nRtzfd8/2XXhBjO2HNTbizoVGdS0ptN2q13h3TV7iY5ztBddmlPa35vrd+7R8Zs1BBXxBaBO6aJMeqoxABtOXmbenhMomkaLiqUZ3bp+5v40TWP6ii1ERl/Q24iXOlK9bIlc3yv6Sixhi9Y52ogKhHVviyRJzFm/i1/3HqWwtxcAIzq1oEWNikRdvsHEXzY4JDCsQxOerF1Z963cRuSpi3iajEx/8Wmqly2e23c1jrAlG0i32QmtFkJYt9ZIksQX63ez7fg5JEkiwMeL6S8+TaBfVnt5/EosL3y6mE96P0X72pV03287iTx9Wfc9147qZQJz+67dJGzZFt1XNYiwZ1sgSRIbo87x5Zb9XIxPYunrvajp+K57z8Ywa8NezsYmYpAlAv288bWYWT78eaf9zt/5B2uPngVAUVUuxt9m9/iB+HtZXP79XfHN9kOsOHQKgyQR9mxznqist/VPTptPIQ8zkgSJKakU8jDrf8PnHxDjL5sddRpMWGdHjMfO8uVmR4xvvJAZo01RmLAsgpPX41FUlWdL+/FqrXLOOw2pgdyuF0gy2tFdaPs2Om8vWwm53fMQWAY1/Bs4/UfmJql1d6SKtfX2/tJJtM3O7UxeeDZrTpF3xyIZDKSsWE7y/G+ctnt37kqRUWOwx+tt8N2fF3Fvhd4OBR07hfWc/vewx94gfviQfH0l27SiwcxpSAYDFxYu4uRns522e5UpTZO5szH5+SEZDByb9D43tmylRMsW1J00DtlkQrXZODJhMjcjdxcoxgfRZ/6X1OrYgZT4BN6vlfc1dUEp2aYVjRzxnV+4iGgX8TWdOxuzI74j2eKrly2+Px4ivqKtW1Jt+vtIssy1RT9z8Ys5TturTp1EQLNmABi8LJiLFiWiQjUAOty8SspJvX1Ku36dP17q/1DxFm7VkgpTJyMZDMQt/pmrs7902h4yZSL+zZoCIHt6Yi4awN7KNR7KIRC4QiQk/iKR+37n8tVrbF6+mGPRJ5n04acsXzA3V7nPp03Cu1AhNE3jjfcmsnHbDp5p2waA/r16MLB3r4L5os8TE3+LjZNfJ+rSdSb/vI6lY17JVW7Kz+uZ3PsZ6gSXYfCcJeyKPk9ozUrMeqVHZpkPft2Mj6fHg4WShNy6J+qKLyHlDnLv0WgXTkBSXFaZlNuomxYjNWztehdNn0a7dj5PhaKqTN2wn+96t6O4rxfPz19Hq8plqVjMP7PMiqPn8LWY2TSsG+ujL/HJtsPM6tYiK5YtB2lesXTm6+AAP8IHPZu5/5afL6dNlRwXCQ7cX6cycs/BKF9OgDu3MIz+BOXEAYi7mlXm2kWUj94CmxXpiaeQO/dH/eEjCK6KFFINZeYbABhGzESqWBPt/Im8fbKMZdgYUt8bhpZ4E6/ZC7Hvj0S9csm5nKcXpi7Po5w6nvmWevk8qa/3BVVBKhKA19yfse/fBaryQF/JSZOJ6dcXW1wcIeGrSNkagfW88zFwd9064iZPcnrPu2UrLDVqcLFjRySzmaCff+Hezp2o9+490Ofx2mjSxr+Bdisez1nfY/99F9rVy7niM3d6DuV0trqyWrEu/ga5XAhy+ZC8HTmRZEx938D64TuQlIBp8leof+xzSjio+7ahbl+rf8V6TTC++Bq2j9/L3G58cQhq1IEC+4y9h2Gb9R7cTsQ4bjbq0f0QeyXL9/t21J168kaq8zjG5wdj/2wspCRj/2ICJCchlSqPceR0bG/3zj++l0dgnT4K7VYCHtPmoRzek5lwAFD2RKBE/KbH16Appj7DsM58B2QD5mHjsH45De3KBfD2Bbu9YHH+iSxjHjya9Ilvot2Kx/LRfOwHduk3rtmxeGHq2BPlzAOO//8WnyxjfnUU6ZNG6I4Pv8N+YLeTwx65GfumVQAYGj2B+eXhZLw/CmNb/VyWNqIv+PljGf8J6W+/Apr2YN+gUWRMcfg++A7lYA7frs3YNzt8DZ/A3H84GVNHYXxS96W/1Rd8/bGM+4T0Mfn4/nS+8hYZU0aiJcVjmfkdyqGczi3YN692OJth7jecjGmjMDRpzf+xd97xUYjTZSUAACAASURBVFTr/3/P7GbTeychlBB6E0WqIFVAMAQQsCLSlGtXREURvAJiwe5VEEQFpFfpJCC9CgFCgABJSO+9bpnfH7Nkd7ObInLj/fKb9+vFi83OM+czzzkze2ae85wz2NlR/voE0Njj8MUK9If3IWWlW0joDQY+3H2SHx8bIPcXP+2kX1iwZX8RfU3uL54fyY6YBD7bf5ZFEQ8A0NjDhU2TH7YoM7+0gk+i/mT9xGF4OTvw9rajHItPo0dgYwAOXrpOYmYeu2Y/x/mEVOau2cWaN56xcv+DNbuZ+9gQOjUNYtp/1nLo0g36tAsF4Ol+9/PsgG4W9mGNfFk3YyJqlUhWQTERHy3lwXYtOHIlgcSsPHbNmsz5xDTmrtvLmteetNZbt5e5YwfTqWkjpv2wgUOx8fRp25xn+3flpWG9Afj1jzN8t/soc8YOrqq/RdsO0qt106pyDl5OIDE7n11vTuD8zXTmbopizYvW9yQfbNrP3NED6BQSwLRlWzh0JZE+rZsS5u/NV08NZ87GSAt7D2dHvntmBI9/t5aF4wfz+spdrJvxlFW5k/p2YVJfOQC0/1I8vxw+95eCEdcyctkZHce2Vx8ns7CEST9uZscbT6IS5dnIy58fw/mb6aw8fI4fJo+UfdwQyZqXH7P2cUMUc8cMoFOTQKb9uJlDlxPo06YZYQE+fDVhOHPWW/q4OzqOSr2eLW88RVmllhHzfuThZn4EuRiPXxAQhzyOYdXnUJiH+OwspLhoyE4zFVKYi2HbTwjdHrI8mKBQhOAWGJbMAUB8eiZSSEu4ebX2ChFFvN99n/QpE9Glp9NozQZK90eivXHdwqxk1w5y5n9gtbtUUU7qmPDaNcwQRJH7Pl1I1MhHKUtN5aH9e0jeuZvCK6bjbP/GqyRu2sq1Zctxa9WSB9etYmvH+6jIzeGP8U9Slp6Be5vW9Nuwhs1tO9VbuyaOLV/JgW8W88wvP/ztsgRR5P5PFxI58lFKU1MZavSvwMy/Dkb/4pYtx71VS/qtW8Vmo38HzPwbsGENG+vjnyjSbuF8To4ZT3lqGj337iBz126Kr8ZVmVx+d07V5yaTn8Wtgykwqy8r50i/QbfnsCjS4qMPuTD2cSpS07hn93Zydu+h1Ez7xuy5VZ8bTZqISwclGKFwZ7ir1pAQBGGWIAgxgiCcFwThnCAI3WqwWyoIQrTRbr0gCC627Goj8uARRg6TR8o7t29HYXExmdk5VnYuzs4A6PR6tFotAreX5hMVfYXw7p0QBIFOzYMpKq0gq6DIwiaroIji8go6N2+MIAiEd+9EZPQVCxtJktj95yWGdbUcWbIioAnkZ0FBjjwSevlPhFDL7AUKcyE71fbNql9jcHKFhJpHEi+kZhPi5UZjT1c0KhVD2zUj6mqShU3U1SRGdpRv7Aa3acLx+DQko96+KzcJ9nClhY+HVdkAx+PTCPF0tciosCi7oeu0SRhSVhrkZIBeh+HPQwgdLE9RKe5C1YiylHAFwcPnlgjY2YFaLf9TqZCK8muVE1u1w5CahJSeAjodugN7UPfoa2VnP+E5Ktf9glRpNpJdUWEKPtjZ1/1AAjh26kRlYiLapCTQain4/XdcB9avY7QPa0HpyROg1yOVlVEeG4tLnz61+xfWFkNaMlJGquzfwb2ou1nvo3liKpUbV1iO1FeUY7gU/ZdH74XQ1kiZKZCVJrfh8f2IXXpaGpWXmjnmAGZVJ3bphZSVhpSSUD+9Zq2QMlMhO13WO3kAsXOPGvUEe4eqtpKSrkNBrvw5NRHsNKC2q1VPbNEGKT0FKVP2T38sCtV9vS2Nysz1HKv8Ezveh+HmdTkYAVBcCJKhXn5W6VdrU/3hfai7PWBlp3liCtpNK/5y+/0TemJYm2oakajvr6ZRVu2cMVaq2Lgp+gun5e8L8qGkGLFF69r1WrRBSje7Lg5Houpai56D6ZwRgs30CvORSorlbIm6fLylmWnUPLIPVdeazxvsHat8RJLk80hUgcYedDqkshIrjQupOYR4upr6i7ZNiYpLtrCJuprMyA5ygHFwmxCOJ6RX9Re2SMovoqmXG17O8kNkj6YB7L1i6oOiLsQRfn97uY9oFkRRWQVZBZZB0qyCYrmPaBYs9xH3tyfyQu0Pjo4aO9Qq+farQquruiuIuhBHeNd2sl7TRhSVldegV0nnZkGyXtd2RF6QHxhcHExB8bJKrcXdxsqDfzKoYxjexmwNgKhLNwjv0kbWaxIo+1doWfdZhSWyXpNAWa9LGyJj5Gs81N+LZn6eVv61DfKrysxo5utFhU5Ppa6WYDawI/oqwzqbsky3nr3CuG/WEvHlat7fuB+9wfq3JOrSDYZ2CkOjVhHs5UaItzsXkjIsbWKuE36fmY/llTX72LSR7ON91X30stIWBCir0KLTG6jQ6rATRZztVCaDRs0gNwvys+V7qEunEFp2tiykIAcyU2z0r5L8W61Sg8oOVCooKay1/gDsO3REezMRXXIS6LSU7NyOU/+Bde53u3jf24XiG/GUJCZi0GpJ3LCJ4GFDLGwkCexcXQHQuLlRlia3T975i5Sly58LYi+jcrBH1Gj+9jFdO3SU0ty8v10OyP4V3Yin2Ohfgg3/MPPP7g7459HlHkriEyhLvImk1ZK2aQt+Qx+q0T5w1EhSN26+TQ8tce3SmbL4BMqN2lmbt+A9ZHCN9r4R4WRu3HJHtP/vIfyP//u/x10TkBAEoQcwHOgiSVJHYCCQVIP5q5IkdTLa3QRe+Kt6GVlZBPj5Vv0d4OdLRlaWTdtJL8+g59CRODs78VB/0wPhynWbGPHEs7z94UIKCots7nuLzPwiAjzdqv7293QlI99yn4z8Ivw9zGw8XMmsZnPm2k28XZ1p6uddu4MuHpYPvMX54Ope+z5VCIh9RyIdrP2HKqOolAA356q/A1ydyCwqqdFGLYq42tuRX1ZBaaWWpUcvMr1PzRHnHZcSGNauWY3bG7pOBQ9v+ebkFvnZCO417yN2H4Th0hn5j4QrSFcvoPr3clQf/owUexYykmvcF0D09sOQZbo5M2RnIvhYpqqKoa0QfAPQn7BOJRRbtcNp8Rqcf1hNxVcLas+OANT+AWjTTKM/uvQ07Pyt041dhwyh+fYdBH/zLepAeYpMeWwsLn37Ijg4oPL0xLl796ptNSF4+yJlZ1b9LeVkInj7WtiIzVsi+vqjP3Wk1rLqi+Dpg5Rjus6l3CwETx8rO3FAOJpPfkU9biq6FcZ0S40DquHj0W/6pf6Cnt5IeWa/K3nZtvX6jcBu/k+oxkxG99t31sd9b285UKDT1qHng5RjXqe2/VMNGon9F6vk7I+fv5Q1AhuDBJq3PsF+/hLUI6xHIetC8PJFyjads1JOFoJXtTZt1hLBxw/96aN/ufx/Qk/WqP08BVAPHYXjf9aimTCdyh+/AMAQf00OXogqBL9A+Xr1tr6matXLrUFvyCgcvl2L3VPTqVxm1Eu8JgcvzPV8atez7aN1PVZpfrMGu6eep3KprKk/vh+pogzHJZtx/H4D2q2/QbF1fyj3BaaHabm/KK3Rxry/AEgpKGbU0u08/eseTt+UjzXE05X4nEJS8ovRGQxEXk0i3exh1aqP8HAlo1rQOqOgeh/hZtFHrDp4hpELfmTWyu0UlJZVfR+dkMKIeUsIX/Aj748dhFolkllQTICnazU9y4BERkEx/mZBdn8PVzLNbL7Yfoj+c77n9zOxvGjMlsjIL2LfhTjG9bJ8IM4sKCbAoiwX23ruljaZBbVkrpkhIDDu6zXo9Ho2n4mt0a6sUsuhqzcZ1F4efLiemcuu6DhWPD+aTS+PRyUK/H7WOsiTWVhCgIdZfbm7kGFsPwGByYs3svPsVS6lZFna1FWn7q5kFlgHxcwZ3DEMR3s7+n6whAEfLmViu2A87M0Cvq4eSEW5pr8L88DV9uCJFSk3kBIvI778KeLLnyDdiIGc9Dp3U/n5o0832ekz0lH7WV+/ToMGE7RxK36LvkIVYJqCJGjsabRmA4Er19YrkOEYGEBJSkrV36WpaThV67cvfPQxzcaOZmTMOR5ct4rTb75dvRgaPzKcvPMXMVT+vQDzncYpMIDSOvw7b/QvIuYc/dat4pQN/0IeGU5uPf1zCAygPDW16u/y1DQcargXcggOwrFJY3IOme7fRAd7eu7bSY9d2/AbOsTmfjVhHxBIRarpHq4iNR1NgG1t++AgHEIak3/4ztxbKfzvIAiClyAIewVBiDP+bxV1FgShsyAIx8ySAMaZbVsuCEK8MTHgnCAInavvb4u7JiABBALZkiRVAEiSlC1JUqotQ0mSCgEEQRAAs6Ga+mNr0EWoYZGTpV9+wuHfN1BZqeX4aXk+7mOjwtm7YRVbfv0RP29vPvrK+iHCQs/GIVZXszUSVP2Qtp+6WPdIfo0HUb9qEjr3Roq/JAcx/nJxQjUb235/c/AcT3dri7PG9ohvpV7P/qtJPNSmac36DV6nNs6PGupUuO9BhJAWSFEb5S98AiEgGP3sZ9G/NxGhZUcIrSNVztbpaK4nCNhPe42KxZ/b3N1wJYbSqeMoffFpNOMnyiPsf1WvWh0XR0ZyrW8fbjw8jJIjRwj65BMASg4fpvjAAZqtW0/QF19SdvYs6GsPgNhcVEiy3G4/+RUqln5Vezl/G+s2NERuoXLGU+jWLkEVLqdcq0ZNQL9rPVSU/4Wy63fOGPZvQ/vORPTrl6Ia/rhlCY2aoB49Cd2vX9ZDrn6Rdf3ezVS88ji6VT+gjnha3lVUIbbqQOW3H1Ix5wVU9z2A2K5LHSVV17f1peU5q5n0EpU/fW3L8K/TEHo2z1PrNtTt3EjZ82Op/OU/2D36jPxd5Hak7CwcPl2KZtLL8rQjQx3TYOqrt2sj5f8ai/bX/2A3WtbTR25HysnC4eOl2E18GcOVi6Cvx7Sbv6L5wji0K77HbswEAMQWbcFgoGzqSMqmP4rdiPEIfo2si7MtXKeNgICviyOR/xrFxkkPM3Pgvby55TDFFZW4O9oze8j9vLb5EE/9uodG7i5V6f41uFDPPkK2Gt+7C7vff46NMyfh6+bCx5uiqmw6NQ1i26wprJ3xDEv2naBCq7N9/NUEbfZbZkavPPwAUXOeY/i9bVh5SF6TYMGmKF4f0dfCN7msv69XG/9+dCCiILB0SgS/HbvA6RspNu0OxCbQpUlg1XSN49eSiUnJZOw364j4cjXHryWTlFtgtV9t7bPyhbFsePUJ2of4s+/CNU5fNwXwrXys4T6jNi7czEAUBA7Mnsyed55l+aVkkorKzCzq6J9qw9MXwScQw1dvYvjqTYQmraFxWN372WiX6r6VHthP0uB+pIx6hLLjR/Gdt7BqW9KgB0kdN5qsma/jNfMd1I0b/3W9ak42HTOKG7+tYXO7zhx49HF6/vCtxX7urVvRee5sTr7yRt3+NTR/wb9N7Tqzvwb/7pk7mxP19a8ebXiLRhEjSd+6XV4/ysiBzl05OnAo56b9izbz5uLU1Hr9tJq1bX1pW9t3ZDjZv++w0Fa4a3gLiJQkKQyINP5dnVLgaUmS2gFDgC8EQTCPuM6QJKmz8d85G/tbcTetIbEHmC0IwlVgH7BGkqQ/ajIWBOEnYBhwCXjdxvapwFSAHxZ9zNRnnmTl+k2s3SLPDe/QpjXpmaaoe3pmFn4+1iOJt7C3t6f/Az2JPHSYXt3uw8fblAL4aPjDPPeGdVR11YFTrDsi31B0aNKI9DxTyl5GXhF+ZiMDAAGebmTkm9nkF+HrbrLR6Q3sO3eZdW9PqfE4qyjOR3D1MP0UuXjIKdj1IbAZQlBzhE695RRcUQ2VFUiHt1ker5uTxWhUelEpfq5O1WycSS8sIcDNGZ3BQFGFFndHe86nZLMnNpHPIk9TVF6JIAjYq1U80VVe2OfQtRTaBnjh4+JoUd4/WadSfrZpCgaAhw9SYa6VndCyE+LgR9F/9U7VHHyhY3ekhKtQKT/MGmLPIDRthXQ9pkY9Q3Ymdr6m0RHRx89idB9HJ8SmoTh9LM+1FLy8cZy7iLL3X8MQZxrNMiQlIJWXITYNtfi+Orr0dOzMIvnqgEC0GZkWNvp8U5Aqb81q/GbOrPo7+7vvyP5ODswFff4FlQkJNWoBSNUyPgRvP6Tcav41aY7jfLlMwdMLh3c/ofzDGfLClreBlJdtMdosePki5VlP1bqF4fh+1BNeBkAMbYPQtQ+MmwpOLiAZkLSVGPbVkkmUl43gaTbS7OmDlF+L3qkD2D35Inoze/X02eiWfSJPM6mL3CwEb/M69UXKy67RXH8sErtJr6JFzhYxxJ6DIvnBQX/uOGKzlhhi/qxx/+pIOVkWI/KCty9Srpm+oxNiSHMcPpQX2hI8vLCftZCKeTNva2HLhtCTcmydp7XU6eF92E97g0oAg57Kn0wBNYcF32NIrT0zykrPqw69I/vQTDXeLBv0aJd/xa08Gvt532NIq13PpmZd582RfWimyN2u6oFB6M/K07UozMdw5QJiaGv0mZbjCQGuTqQXmjIi5P7C0aaNZX+hQRAENGo5nb5doDeNPV1IyC2ifaA3/cKC6RcWDMDas3Fcycwj4qOlAHQICbTsI/KL8HOv1kd4VO8jCvE1ZhT4mGUAPtqzE8//YL2I7YmriVxPz2Hkx8vpGtqY9Lwis7KK8HOznHIY4O5KRn6xhY2vmc4tHr63Dc8v3siLQ3sTk5TB6z/L/W9WYTHbzlzi0+2H6RHWmHSLsopt6xVY2tjSq056fhFzN0axYPxgOoYEMqBdc84nZ3Bf8yAr2x3RcQzrbHrgliSJ8Htb89oQy+lw+y5e59vIUwD8e3Q//N2dSTfLRskoKMbPzZlVx86z7tQlADo0DsDVXsP5pAzuCw022lTz0aNanRYU4eteu4/bz17mgdZNsVOp8HZ14h5fNy7mFNP41jlZlIfg6mW6h3LzrHOQ5hZCq3uQUm6AVs7uka5fRAhqjpQUV+t++ox0i4wHlX8A+izLPthQYDqGovVr8Xp1hml/o60uOYnyUyfRtG6LLqmmRGMoS03DOcjUnk6NAilLs8zkaP7k4xwYI69Lkn3qNCoHB+y9vanIzsaxUSAPrFjOsedeoLiO/v6foDQ1Dac6/At98nGiavDPqVEgfVcs5+hf8K88NQ2HRqaArEOjQCrSbWfHBEaEEzPzHYvvKozTRMoSb5J75ChuHdpTmpBoa3crKtLSsG9kuoezbxRAZQ3afiMf4dpbs+pV7l3J3f2WjXDgQePnn4EDwExzA0mSrpp9ThUEIRPwBer3I2eDuyZDQpKkYuBe5CBCFrBGEIRnarGfCDQCYoFxNrYvliTpPkmS7pv6jDzC+cSYCLb8upQtvy5lYN/ebN6xG0mSOHcxBlcXZ/x8LNPvS0pLq9aV0Ol0/HH0BM2byAssmq83se+Pw4Q1t55a8PiDXdk0axqbZk1jQKdWbDkejSRJRN9IxtXR3uLBGMDX3RVnB3uibyQjSRJbjkfTv1Orqu3HLt+gWYC3RQpqjaTfBA9fcPOS03dbd0G6caHu/QBp5y8YfpyDYelcpD82I8WetApGALRv5ENibiHJeUVU6vXsjImnX8tgC5t+LRuz+bw8l3NPbCLdmgYgCAIrJgxl34tj2PfiGJ66vy1Te3WoCkYA7IiJtzld4x+t05txCL6NwMsfVGrELg8gXThhaRPcHHH8dPRLPoRisxGhvCyEFu1AFOX2CG0PGTXfKAAYrlxCDGqM4N8I1GrUDw5Gd/ygyaC0hJKxAymZ8AglEx5BH3uxKhgh+DeS53QDgl8AYnATeU56LZSdP4+maVPsgoPBzg734cMpjrR8M4va1/Rw7TpwIBW3FrwURVQecnDVvlVr7Fu3ovjQodr9i4tFbNQYwT9Q9q/PIPQnzfYpLaHkiSGUTo6gdHIEhisxfysYASDduIzgHwQ+AXIbdu+H4axlKr/gb7qBETt1R8qQRwW1816h8vUnqHz9CfR7NqDftqr2YATGdUT8g8DHeM7c/yBS9HFLI7PRZKHj/fIaFwCOzqhf+je6jT8hXbtUL/8M1y8jBAQj+Mr+qXr0R3/GMiVTCDDz754eSOnyA6v+/EnEkFBjEFKF2KYThnqulVGlHxeLGBiM4Ce3qar3QHQnzaYTlZZQ+vQwyqaOpmzqaAxXY247GNFQeoa4y9U0BqA7ZTlFSgg0/e6p7u1pCgJo7I1rSoDYqau8xkr1BTer6127jGCmp+49AP3p29Dr2FWe+16HnkmzsUmz10CraVJCgJlml54YjOeNlJ2Bqr0xk8beQV7XI9X6Brp9I28S84pIzi+W+4tLCVWBhFv0Cwtm84UbAOyJvUm3Jv4IgkBuSXnVGgRJeUUk5hYRbEzRzymRg7wFZRX8duYqsx+6n01vTWLTW5MY0LElW05elPuI+BRcHeyrgg238HV3wdlBQ3R8itxHnLxI/w7yw7X5+g/7oq8SFij//iVn56PTy8fTt30LnB00rHr5cQZ0aMGWUzGyXkKqsU+yoWevITohVdY7FVOll5Blmju//+J1mvvLAx97Z09l3/vT2Pf+NB7u0oZPHx/K3rcnMqBdKFv+jJX1EtNkvWrBBl83Z5zt7YhOlNdv2vJnLP3b1r4QcGFZBdOWbuH5gffTpWkjSiu1HI1LIszfeopiUXkFp+JTLMrs3qIxey5cJ6dYDkDll5aTklfIwPahbHp5PJteHk/7YH/6tW3Gzug4KnV6knMLScwpoENjf0be24YV08ey6bUn6d2yCcevJdPC30v20UFTg48ak4+nY+lvXJS0JgI9XDkel4QkSZRWaInOLqK5u1mALDUBvPzA3Ufus9t2Rbpa91uOAHkh4pCWIBj7+5CWSDl1B5QrLl7ALqQp6qBgUNvhPPRhSvdbLsap8jH1wU79BlBpXPBSdHOT16kCRA9PHO7pgvZ6zYuSA+T8eRbX0OY4NwlBtLOjyegIUnbutrApTU7Bv6+8ho1byzBEe3sqsrOxc3fjwbWriP5gHtkn6rnIcwNT3b+moyNIruZfSXIKAWb+qcz867d2FWc/mEfWX/Cv4Ow5nJs3wzGkMYKdHYER4WTu2mNl59wiFLWHO/mnTld9p3Z3r1qnws7LC89uXSm+UsdCqGYUnY3GsXkzHIzaviPDydm918rOMbQ5and3Ck+fqXfZCv+n8JckKQ3A+L/1a4nMEAThfkADmK+eO884leNzQRDqWPFf5m7KkECSJD1yJOeAIAgXgAnA8trsBUFYA8wAfvorWn17duePoycYNOYJHB3smf+uKXgU/tQktvy6lLKycp6f8Q6VlVoMBgPd772H8RHyKuaffPM9l+OuAQJBgQF88JZVkoYFfdqHcfDiNYbM/gYHjR3znn6kalvEvB+qXjc5+7FhvPPzFiq0Oh5o14I+7VpU2e08HcOw++o5XUMyYNi/HnH0dBBEpIvHISddfnNG+k24cRH8QxAfmQwOjgjN20OPoRh+WVDPGpTn+M4a0o0pv+3DYDAQ0TmMMF9Pvj5wlnaNvOnfMoTRncOYueUQD327EQ9HDZ9GWC/KWJ0yrY6j8WnMGdajVrsGr1ODAcP6H1BNnwOiiOH4PkhPQhz2ONLNa0gXTyKGPwMaR1QT5fNJysvCsGQe0rmjCC07onrra0BCiv0T6eKpOvT0lH/7CU7zvwZRhXbPVgyJN9A8PQ391Vj05sGJaqjad0YzboKcoWGQqPj6I6RC65RZC/R60ufOIWT5zwiiSP76dVTExeH7yiuUXbhAcWQkXhOewWXAANDr0Rfkk/qmPDojqNU0Xb1GLqa4mJTXXqt7yoZBT8X3n+I490v5tZ/7fsdwMx7NE1PQx122DE7YwOnHTQhOTqC2Q929L2WzX7J+Q4eVpgHdL19j9+ZCBEFEf3AnUkoiqlHPIMVfwXD2GOLAkfJUBb0OSorRLV5Ye5l16a36FrtX5suv4TyyByk1EVX40xgSriJFH0fV/xGENka90mL0yz4FQOz/CIJfI3kKh3Eah+7zt6syGGzr6dEu/wLN25/Kegd2ICUnoB7zLIb4yxjOHEU9eBRih3vlxQdLiqn8j/GaLylGt2Mt9vN+AElCf+4EhrPHa9aqQb9yySIc3v9cfgXkvt+RkuKxe2wyhmuX0Z+yXuvkb9EQegY9lUs+x+H9RfKrRSOtNeyGjUbVsSuSXgfFRVR89SEAgrunfGySASkni4ovrVfGt6n34+fYv2fUizLqjTfqnT6MeqisJ7dhEZXfmPTs3zPq5WZR+VU99Ko0F2H/7iL5tZ9R25GS47EbNwnD9cvoTx8xat5n0vx6HiBP49D86x0cPpdft6rbvwMp8bqVhFoUmTW4K1NWR2IwSER0CiXM14Ov/4imXaAX/Vs2ZnTnFszceoSH/rMZDwd7Ph0pr6FwOimTrw9GoxYFREHg/aHd8DC+FWnB3lNczpAHdab37kBTb1NwuU+7UA5eus6QD77Hwc6OeU+a3tIR8dFSNr01CYDZ44bwzorf5T6iTXP6tJUfZj/dEsXl5EwEAYK83JkzfigAf95IYsne46hVIqIg8N6YQXi6ONGnbXMOxt5gyIdL5D7psaEmvY+Xs+nNZ2S9RwfxzqqdVGi1sl4bOfj++bY/iM/MQxSgkZc77z9a+6LCfVo35eDlBIYs/BkHjZp5ZvYRn69k06vyW3lmR/TnnbV7Zf9aN6GP8U0d+y5eY96WP8gtLuP5n7bQupEvSyZHsOpoNDdz8nl/fSTvr49EAib07sQDrZqw+rj8pprx3dsby7hBr7AQnMymX7bw9+Llwd2ZvHQrkiShVom8F96XoGqB/zB/bx7qGMaIRStRiSLvhsvTUnKKinhphfxaVa1eT1M/T+ZtPoCDnZp540yL9EUsWsEm41tMZo/uzzur91Ch0/FAq6YmHy9cY97mA7KPS7fQupEPS6aO4rFenZi1Zi+PfPorkgQRof608jQLHkkGDLtXIT72CogCUvQReXLjBQAAIABJREFUyE5F6PMIUloixEVDYFPEMdPBwQkhrCP0Ccew+H2ky2egaWvEqXNAkuQ1JOLO19qWAOj15Mz/gIAfloJKRdGm9WivX8PjXy9RGXOR0gNRuD35NE4P9kfS6zEU5JP9rpyNbdc8FJ/ZHyBJEoIgkL90sdXbOaoj6fWcnvEW/TasQVCpuLFiFQWXr9DhnZnknj1Hys7d/Pnu+3T7chGtpz8HksTx6fKbwlpOmYRrs6a0n/Ea7We8BkBUxFgqsmvOrKoPk1Yto+WDvXHx8WZBUizb3p/P0WW2X+VcF5Jez6kZbzHA6N91o38djf4lm/nXZvpzSJLEMaN/rYz+dZjxGh2M/kXWwz9Jr+fSW7Poum4VgqgiedVqiq9cJeytGRSci64KTgSOGknaJssBDZeWYbT/bCGSwYAgitz48luLt3PUiV7Ptbffo/3qlQgqkfTf1lB65SpN3nyDouhoco3BCb+IkWRu2Vr/chUaHPMsfyOLJUlabLZ9H2D9Dmv4S2kvgiAEAr8CEySpahXzt4F05CDFYuTsijpvJoTaVqD+v4QgCK0AgyRJcca/PwQ8JEl6oZqdAIRKknTN+PkTAEmSap7glZfWoJWkPxtVt9Gd5NyJum3uNL62roP/IkF/YR7dHUDavKZB9QDKrtSewXCnSbpmPd3kv0lI21qDtHccO8+605LvNIKmYWPE+pK/sp7F38dQVseimncDDZzKKTR0nuM/kKpqP6LmVeb/KwTWMXf+TlOfdTruJP/E4oF1LIp8x1E38Hjb+fpPTbsT3PytxhnJ/zWOJd92NvZtcbCgYfun3u71f+XsncK7gc9TZ1XDJ8b3yUi+6+Y3SEmX/qcfnoXGbW+7zgVBuAI8KElSmjHgcECSpFY27NyQkwAWSJJkPR9RtnkQeEOSpOF16d5NGRIuwNfGRTV0wDUso0O3EICfjRUpANHA8w12lAoKCgoKCgoKCgoKCgr/97i715DYijzD4CPj/1ZziwVB0ACbgF+qByMEQQg0BjMEYCRwsT6id01AQpKkM0DPetgZgF7//SNSUFBQUFBQUFBQUFBQUPg/wUfAWkEQJgE3gUcBBEG4D3hOkqTJwFigD+Bttl7jM8Y3aqwUBMEXedD/HPBcfUTvmoCEgoKCgoKCgoKCgoKCgoLCX0eSpBxggI3vTwOTjZ9XACtq2L//7eje1QEJQRA2AdVftTBTkqTdtuwVFBQUFBQUFBQUFBQUFGxzV0/Z+Ee4qwMSkiRF/NPHoKCgoKCgoKCgoKCgoKCgYE3DL7eqoKCgoKCgoKCgoKCgoKDw/z13dYaEgoKCgoKCgoKCgoKCgsId4e5+y8Y/gpIhoaCgoKCgoKCgoKCgoKCg0OAoAQkFBQUFBQUFBQUFBQUFBYUGR5myoaCgoKCgoKCgoKCgoKBQF8qMjTuOEpCoDwZ9w+pJhobVU+ZC3Xk0mgaXtPN1bVC9EE3D/nzkJOQ1qJ7PP3BdqD2cGlRPUDVwklwD16lUoW1QvX8CqYHrtMHPGQC7Bv49TU5oWL2gJg2rp/8HrgudrmH1Gvo+SqVqUDmNXcNfh17qhu3ze7s7NKje4YLyBtUD6Opq36B6fnbKY5/C/ybKlA0FBQUFBQUFBQUFBQUFBYUGRwmVKSgoKCgoKCgoKCgoKCjUiZJZfqdRMiQUFBQUFBQUFBQUFBQUFBQaHCUgoaCgoKCgoKCgoKCgoKCg0OAoAQkFBQUFBQUFBQUFBQUFBYUGR1lDQkFBQUFBQUFBQUFBQUGhLpS3E95xlAwJBQUFBQUFBQUFBQUFBQWFBkcJSCgoKCgoKCgoKCgoKCgoKDQ4ypSN20SSJOZ9/i1/HDuBg4M9H737Ju1atbSym/TqW2Tl5KDX67m3Uwfef/0lVCoVXyz+ichDRxBFEW8PDxa8+yb+vj5WGvPX7eFgzDUc7eyY//QI2oYEWmnE3EzjnV+2Uq7V0addC955dDCCMZ1oxf5TrPrjFCqVSN92YbwxagBHY2+waHMUWr0eO5WKNzo3pnuQt1W5h25mseBILHoJxrQJZso9zS22n07NZcHRy1zNKeLTgZ14KDTAYntxpY7hqw8xsJk/7z7Q1mY9HrqewoLdJ9FLEmM6hzGlVweL7ZU6PW9tPUxMWg4ejvYsGtWXIA+Xqu2pBcWM+H4L/+rTiWd7tAfg5xMxrD8bhyAItPT1YN4jvbE3r9O1uzkYE4ejxo75T4fbrtPEVGOdaunTLox3xj5kVqcnWXXAWKftW/DGqEFsO3mBZXuPVu1/NTmDdX1a0Mbdseo7oVVnxPCJIIoYTkQi7d9soSn0GY7YbQDoDUglhRjWfgt52QCoPl4DaTdlH/KzMfy00GZ9Vkfs2BX1Uy+AqEJ/YDv6bb9ZbFf1H4Fq0EgwGKC8DO3Sz5BSExF8/NF8/DNSWhIAhmuX0P30eZ16qi7dsZ/yKogi2r1b0a7/1bZdz344vr2A0lefwXDtMri64fDWAlRhbdBGbqfyh8/q5Z9DrwfwmjkLVCLFG9dRuHSJxXbn8Ag8X3sTfWYGAEW/raB44/qq7YKzM4227KQ0ai958/9dL83q/momvwIqFbo9W9FuqNlfh7fmU/baRNnfv4DQoSvqJ/+FIIro/9iB/vfVFtvFfsNRDQyX27CiDN2yz5FSE00G3n5oFixDv+ln9DvX1anX4OfMPd3QTHoFRBHdvm1oN66w2K5+aCR2Q0chGQxQXkrFdx8jJSeAWo3muTdRtWiNZDBQufRLDDFn69QDUN3bHftpr8vn6e4taNf9YtuuV38cZ31E6csTMMTForrnfjTP/Avs7ECrpXLZ1+ijT/9v6k19Tdbbs7V2vXcWyHq3rsN3PpKvw33bqfz+0zq1bJb7X7ouDl1LZsGu4+gNEmO6tGRK704W2yt1et7afJCY1Gw8nOxZNKYfQR6unE/J4v1tR4xWEv/qew8D2zQF4OdjF1l/9ioC0NLfk3nhD1T1F4dupLIg8k9Zr1MoU7q3tdbbfpyY9Fy5fwrvSZC7C9tiElh2MrbK7mpmPuufGUIbf0++OBjN1osJFJRXcua1Ry3KkySJ+Rv2cfDSdbl/euJh2ja27FcBYm6m887K7XL/1DaUd0YPrOqfAJZFnuDTLfs5Mv8lPF2cOBmXyAtLNhLk7U5JeQXFZRW4OmgY07UtUx7sYu3T2n3EpGTh4eTAoscHE+TpBsDiA2fYcCoWlSjyzoje9G4ZQlp+EW+viyS7qBRBEBh7f1ue6iW3S2xqNnM3/0GFTodaEHhvRG8KyipYsP2o3Off25opfTpb62/Yb2rDsQMJ8nTl6LVkFu09iVanx06t4o2HutG9eRBllTpeXbOXpNxCREGkX+sQujZrxIIdx9AbDHfER4CBC3/F2d4OURRQiyLrXpDb7qs9J4iKjUeorMTbwY75D7TBz8l4BjVrizhwLIgiUvQRpOO7LRuycQvEAWPBLwjDlqVw5U/TNjdPxKFPgasnAIZ130BBjtW5UB37nr1xnzELQRQp2bye4p8s+0SnERG4vTqjqk8sWbOS0k3rsWvZGo9ZcxCcnUFvoGjp95Tt2Vmnnk//B2kz/98giiSv+I34r76x2N76wzl49eoFgMrJAY2PD5Ghbaq2q1xceODYH2Rs30XsW7Pq1Asc0I+uH81DUKm49ssKYr742tK/4CB6/udrNO7uCCoVZ+f8m9S9kQQ82Jd75ryLaGeHQavlz9lzyTh4uE69unhq6bd0GD6Eosws/t2h+98uDyBoYH+6L5yHqFJx5ecVnP/8K4vtzsFB9Pn+G+zd3RFUIqfmfEjynn24hDRm9KkjFMRdByDz1GmOvjqjTj3f/v1ov+ADBFHFzRWruPalZRu2+3Au3r17AqBydMTe14ddzVsD4BgURKcvP8MhqBFIEifGPUFZUvKdqIb/bZQpG3ccJSBxmxw8dpKE5GT2rP2F6JhY5nzyJet+/NbK7ssP38PF2RlJknhp1lx2Rf3Bw4P6M/mJsbwydSIAv6zdyLc//coHb75qqRFzncTMXHbNmc75hBTmrt7JmjeftdL44LedzH38YTo1C2Lat6s5dOk6fdq14MSVBKLOX2HzrKlo7NTkFJUA4OHixHfPj8PPw5W41EymLPqZA0/3syhTb5D48PAlfhzeFX9nB8ZtPEa/Jn608DIFAwJdHJjfrwM/RcfbrKOvTsbRtZFXjXWoNxj4cOdxfnxiMP5uToxbup1+LRvTwtejymbDuTjcHDTs/tcodsTE81nUGRaN6lu1feHeUzzQIqjq74zCElacvMy258JxsFPz6oYD7IiJJ6JJc2OdXiMxM4ddc1/gfHwKc3/bzpqZk23U6Q7mPvEwnZoFM+2bVRyKuUaf9mGcuBJPVPQVNr87Ta7TQrlOR9zfgRH3y8GUqykZvLBomUUwAkFEjJiMfvEHUJCL6uWP0F86DRlmP9wp8ei/mAnaSoQegxEffgrDCuMDnbYS/ed1dywWCCLqCS+j/WgGUm4Wmg++x3DmqMXDqv5YJPqobQCIXXqifnI62o9nAiBlpFI5a0r99UQR++feoOy9l5ByMnFc9BO6E4eQkhIs7Ryd0IwYi/7yRdN3lZVUrlyMGNIcsYll4Ks2Pa9Zs8mcOhFdegaBq9dTtj8K7Y3rFmYlu3fUGGzweOEVKs6crL+P1fQ1016nfPbLSDmZOHy2DN1J2/7ajXgU/ZWLNoupFUHE7umXqPz4TcjNwm7udxj+PGbRhoZjURj2/y4f0j09UD/+HNpP367arn78eQzn6+njP3DOaKa+TvmcV+Q6/PhHdCcPywEHI7qDe9DtloN3qq690Ux8kYp/v4560CMAlL3yNLh74PDeZ5TPmAySVKem/fQ3KZv1AlJ2Jo5f/Izu+CGkpGq/Y45OaMLHob98oeorqSCf8rmvI+VmIzZpjsO/v6L06eH/e3rPz6Ds3Rdlvc+X16z3iI3r8NcfEJs0R2wSWrtOLfr/jetCbzDw4Y5j/PjUQ/i7OTNuyVb6tQqhha9nlc2Gs1fl/uKlR9lx8Qaf7TvNojH9CPPzZN3UR1CLIllFpUR8v5kHW4WQU1zGipOX2DZ9lNxfrItix8V4IloHy3p7z/DjuH74uzoy7uc99GsRRAsfd5Pe+Ruy3rQR7LiUyGcHolkU3osR7Zoyol1TAK5m5fPChoO08ZePs19oEE90acmQxb9b+Xjw0g0Ss/LY9d40ziekMnftbta8PsHK7oO1u5k7fgidmjZi2vfrOBR7gz5t5fZKyyvk2JUEAo0P2Le4NzSYb6aMZti/F7PmX6Pxd3Nh3Lfr6demKS38Tf30hlOxuDnas3vGk+yIjuOzncdY9PhDXMvIZWf0Nba9+hiZhSVMWrqVHa8/jloUeXNYL9oG+VJSUcmYr9fRo0VjWvh78dnOo0wfcB99WjXhj0s3+HTXcTIKS/jxmYflNvx+E/1aN6GFn1kbnrks6786nh3nr/HZnhMsGjcQDycHvnviIfzcnInLyGXKzzs48OaTAEzs1YluzRtRqdMz8aff2Xz2KiufH3XHfFSJcjLx8inheDqb9evAs33u4aXB3eBqLL9eSua7cwnM6dkKBAFx8GMYVn8JRXmIz7yNFHcectJMOxfmYdj+M0K3QVZtLA6fiOHoTkiIBTt7kAxWNtY7iXi8NZvs559Fn5GB38p1lP8Rha5an1i2eycFCy37RKm8nNz3ZqK/mYjo64ffyvWUHz2MVFxUq17bhfM5NWY85alp9Ni7g8xduym5GldlcvndOVWfQyY/i1uH9hZFhL39JrlHj9ftGyCIIvd/upDIkY9SmprK0P17SN65m4IrV6tsOrzxKombthK3bDnurVrSb90qNne8j4rcHA6Mf5Ky9Azc27RmwIY1bGzbqRa1+nFs+UoOfLOYZ3754W+XBbKPPT/7iF3hj1KSksojB/Zwc8cu8s187DzjNeI3beHy0uV4tGrJ4PW/sbbDvQAUxSewuXe/moq3RhTp8PF8jo8eR1lqGg/s20n6rj0Um+nFvPt+1eemU57F3awNO3/3FXGff0n2gYOonJ3AUEf/q6BQA3fVlA1BEGYJghAjCMJ5QRDOCYLQrQ77rwVBKL4drchDRxg5RM5E6Ny+LYXFxWRmW0evXZydAdDp9Wi12qpRjFvfA5SVl1uMbtwi6vwVwrt1QBAEOjULpqi0nKwCy84hq6CI4vIKOjcPRhAEwrt1IDL6CgCrD51h8kM90djJcSdvV1mzbeMA/DxcAWgR6EuF3kCl3rKzu5CZT4ibE43dnNCoRIaGBhCVkGFhE+TmRCtvV5snUUxWATllFfQMts68qNJIzSbEy43Gnq5oVCqGtmtG1NUkyzq4msTIjvKN1uA2TTgen4ZkfODYd+UmwR6utPDxsNhHbzBQrtOjMxgo1+rxczHdQERFXyG8eye5TpsHU1RaUUudNpbrtHsnU50ePMPkh3qZ6tTNmepsP3WRYY0sj4mQFkg56ZCbCXodhnNHENp1tTCRrseAtlL+nBiH4F5z3dUHIbQ1UkYqUlYa6HXoj0ch3tvL0qis1PTZ3qHuh7laEMPaYkhLRspIBZ0O3cG9qLv1sbLTPDGVyo0rqnwFoKIcw6Voy+/qQNOhI7qbieiSk0GnpWTndhz7Daj//m3bofL2puzokbqNbVDdX/2hfTX6q92wEirr79sthNDWSJkpYGxDw/H9iF16WhqVV29Ds2Ps0gspKw0pJaH+eg16zrSxrMPDkajvf6B2PaODYuOm6C8YswUK8qGkGLFF67o1W7bDkJqMlH7rPN2DuoeNdntqGpXrf7VoN8ONq0i5ctaSIfEGgsYe1Hb/Y3ptq+ntRd3dht6Tt/QqTF/exnVopf9fui4upNzqL9yM/UVzoi7ftLCJunKTkZ3CABjctinHb6QiSRKOdmrUxofKCp3eYnBLb5As+wtXJ1kvLZcQDxcae7jIem1CiIqzHPmLiktmZPtmsl7rxhxPTK/qn26x/VIiw9o2qfq7U5APvi6WD7VV5V2II/z+9sY+P4iisgqyCixvUbIKiuX+qVmQ3D/d357I86YHwIUbI3k9/EGbA3gXEtMI8fWksZc7GrWKoZ1aEBVrGaiKio1nZBf5OhrcPpTj11OQJImo2HiGdmqBRq0i2MuNEG93LiRl4uvmTNsgXwCc7TU09/Mk0xioFwSBkgq5fYvLK9GoVIR4u9PYy03W7xBKVGyCpf7lREZ2lrNNB7drzvEbsn7bRj74GfvbFn6eVOj0VOr0OGrUdGveCACNWoWfqzPujvZ31MfacHHQVH0u0+mpqvbAppCXCQXZYNAjXTqFENbRcueCHMhKsf4N9Q4EQZSDEQDaCtBpaz0OAE37juiSbqJPkfvE0t07cHiwfn2i7mYC+pty4NmQlYkhLxfRq+YBJQCPLvdQGp9AWeJNJK2W9E1b8B/6UI32gaNGkrbRlBnq1qkD9n6+5Oz/o17H6H1vF4puxFOcmIhBqyVhwyaChw2xNJLAzlW+v7Vzc6MsTb5vzTt/kbJ0+XNB7GVUDvaIGg1/l2uHjlKam/e3y7mF731dKLyRQFGC7OONDZsJeXiopZEkoTH6qHF3ozQ9/bb1PLvcQ0l8AqXGNkzdtIWAWtowaNRIUoxt6NKqJaJaTfaBgwDoS0rRl5Xd9rEo/P/NXROQEAShBzAc6CJJUkdgIJBUi/19gEdN2+siIyubAH/fqr8DfH3JyMq2aTvplZn0fHg0zk5OPNTPdGP2+fdL6TtyPNt2R/Ly5Ges9svMLyLAbJTD39ONjHzLh+eM/CL8jcGFWzaZRpuEzFzOXEti3MfLeHrRL1xISLXS2HP2Mm183NCoLE+FjJIKAsxumgJcHMgsqai+u00MksTHRy/zRo9WtdplFJUSYPZAH+DqRKYxi8OWjVoUcbW3I7+sgtJKLUuPXmR6H8sIt7+bMxN7tGPAV+vp+8VaXOzt6BVqyqCwrlPXGurUzMbD1axOczhz7SbjFv7I04uWcyEhxcqvXWcuMSzI8tQS3L0g3+z8yM+Rv6sBsVt/DJfN0s/VGlQvL0T14nyrQEZNCJ4+SLmmmykpNwvB08fKTjVwJJrPVmA3fhq6X0zpj4JvAJoPF6OZ9QVCqw5W+1npefsiZZvp5WQiePta2IjNWyL6+qM/dXtBAHPUfv7ozDpifUYGKn9/KzungYMJ3LAVn8++ROVvTH8WBDzfmEneZx/ftr6Vv9m2/RV8/NCfvj1/BU8fpJwsk0YNbSgOCEfzya+ox01Ft8KYbqlxQDV8PPpNttP1a9RryHPGq+5zBkA9dBSO/1mLZsJ0Kn/8AgBD/DU5eCGqEPwCEUNbIXhbt7+VprcvUrYpuFpTu4m+/uhP1pzSq+rVH/31K3U+KDS8nl/99e7AdWit/9+5LjKKSiz7CzdnMotKLW0KSwhwN+svHDTkl8n9VnRyJiO+20j4fzbx/sM9UYuisb9oz4DP19D3s9W4OJj6C7nvcTLpuTqRWWx5s51RXEaAMYAh908a8sssAyy7Lt/k4TZNqA+ZBUUEmPfnHq5kVAuYZxRU6/M9XMk02kRdiMPPw4XWQdbXwbn4FN5YvoVradnEZeTKPrm5kFlQrc8tLCHAOC1SrTLWYWk5mQUlBLibMiT93Z3JKLTcNyWvkNjUbDo2lvXfGt6LT3Yco/9HP/PJruMMaNe0qn0AAtydrft88zZUGeu01PLeY09MPG0CfdCoVRbfF5ZVcCohlTCzbIg75aMgwORl2xjz9TrWnoyxKO+L3cfpv+Yov1/P4MUucoAKV0+kIrMH1aL8qukXdeLlBxWliBHTECe+g9BvVL1SxEU/f/QZpgwMfUY6Kl/rc8FxwCD81mzB6xOzPtEMu3YdQG2HPumm1TZz7AMDKEs13VeWp6ZhH2g9BRbAITgIxyaNyTlk/I0TBFp/8D5X3q//VEmnwABKU0z3XKWpaThV0zv/0cc0GzuaiJhz9Fu3ilNvvl29GEIeGU7u+YsYbmOQ4L+NU2AgJcnmPqbi3MjSxz8XfELouDGMj41m8LrfODbD5KNLkxBGHopi2I4t+PeoewqJQ2AAZWZ1Wp6ahkOg9TkB4BgcjFNICNnGqS4uoc3RFhRw389L6bN/D23mvAfiXfNYWQfC//i//3vcTWdOIJAtSVIFgCRJ2ZIkWT+BA4IgqIBPgDdvV8zWoKCtLAeApV8s5PDWdVRqtRw/Y3rIfPW5SfyxeTUjHhrAig2brfarj4ZNG+PJqNcbKCwtZ/WMibwxagCvLd1gMXoTl5rFos2RzOnTzlrbliP1PMd/i7lJnxBfAmsYBart2KuLVB9tumXxzcFzPN2tLc4ay9HCgrIKoq4ksfeF0Rx4eSxlWh1bL5jSFSUbnlV3y6am0aiqTt+cxBujBvHaj5Z1Gh2fjIPGjjA3hzpUqHFkWejyAEJwKNKBLVXf6ec9h/7LmehXfiGvQ1GPBy/bNzDWmvp9m6l8/Um0qxejHvmUbJWfS8Ur46l8dyrald+hmf4uODpZ7VunnmS53X7yK1Qs/cra7nawqWfpX9mB/aQ81J+00Y9QfvwYPvPktTdcxz9O2aGD6DNuf2ShTn1BQDPpZSqX3SF/TSJW3xgit1A54yl0a5egCpfTmFWjJqDftR4qyutf9P/EOWOtp9u5kbLnx1L5y3+we/QZ+bvI7UjZWTh8uhTNpJflqQcGXV0e1u88nfoqFUu+rLEIMaQ59s++QMXXC/4H9Wx9aXle2k95hYofa9b7W/yXrov6JOLYMrl1NJ2C/dg2fRRrpzzCksPnqdDpjP3FTfa+/CgHXhtPWaWOreev1ViW9THV3FcARKdm46BWEeZbv7GP2vrz2m2grFLLD3uO8uKwB6y2tw0OYN/c6bwxsj+hgT68+KvZ2gBW9xS2+kjBdt2a7VpSoeXlFbt5e3ivqqyB1cdjeGt4L6LemsDMoT1YfSLWVil1+2dmEpeRy6I9J5gTbumnTm/gjXVR9AkLwdneruYCuD0fVz43ig0vjuWHiQ/z27GLnI433V6+8lB3osb1ZHioPytjrQcpzJRr2WaGqILgMAxRGzAs/wjBwwehQ4/67VuHZvnB/aQ/PIDMceGUnziK5wcfWUr7+OL54cfkzXmn7ouunr/fAIERI8nYul1eewgIefYZsvZFUZ5q8za93nrV7+majhnFjd/WsKldZ/Y/+jg9f/jWYj/31q24Z+5sTrzyRv11GxJbPlar09AxEcStXM3qNp3Y8+hj9F38HQgCpekZrGl3D5sf6M+Jd97jwaXfY+fqYlVeXXo1tWGjUeGkbfu9qg0FlQqvHt24NHsuhwYOxblpExo/Nq5+fiooVONuCkjsARoLgnBVEITvBEHoW4vtC8BWSZLSajIQBGGqIAinBUE4vfjnlQCs3LCZ8AlTCZ8wFT8fb9IzTCOX6VlZ+PnUnGJvb6+hf+8eRB46arVt+KAB7Nl/qEojYv4SIuYvwc/DhfS8wiq7jLxC/Nwtf1wCqo3wZ+QV4muM/Ad4ujKocysEQaBj0yBEQSCvWB5RSs8r5KXF61gwIZwQd+uHhgBne9LNRoPSi8tNCzXVwbn0fFbG3GTgigN8cvwKW66msOj4FWsNNyfSzUZY0otKq9JlTTbOVTY6g4GiCi3ujvacT8nms8jTDPx6Pb+evMTiIxdYeSqWY/FpBHm44OXsgJ1KZFDrJqz78yoR834gYt4P+Lm7VqvToqrpK6Y6dSMj38wmvwhfd9eqbYPuaW2zTgF2no5h2H02AjwFOeBhNtLs4Y1UaJ3mJ4R1QBwwGv1PH4He7OHqlm1uJtL1GISgZlb7WmnmZiF4+ZnK9vJFyqt5USyDeXq+TgvFch1ICVeRMlMRAoJr18vORPAx0/P2Q8o1XSM4OiE2aY7j/O9w+nETYqt2OLz7Sb3S7G2hy0hHHWCK5Kv8/dFnWqbXGgryQSuPKBdvWIumrdw29p3uwfWxJwjaFYnn6zNxGTESj1dO9gIqAAAgAElEQVRe/0v6Vv76+FWl1wNV/jrM+w7HJRsRW7XDftbHf8lfKS/bYnS57jY0TekQQ9ugHjcVzWcrUQ0ejWrE44gDw2vXa+hzJsfWOWM70wxAf3ifaUqHQU/lT19R/tozVCx4C8HZBUNq3Ytpye1mCujJ7Vb9PA3FceF/cPppM2Lr9jjM/hQxrE3VMTq89zHln81BSq/t4eN/SC+n+nkZiuNH3+G0bJNJ7zavQ9v6d/66MO8LANILS2z3FwVm/UV5Je6Olv1WqK8Hjho1cZn5HLuRauwvHOX+ok0TzhlT9ANcnUgvNP22pxeVWkz/q7IxZmnI/VMl7mYp/Dtjb1pM17DFqj+vErFwGRELl+Hn7kK6eX+eX2Td53tU6/ON/VNSdh4pOQVELFzGwDnfkZFfxOhPlpNVWIyLoz3O9hoCPFwRkB/e80rKSC8sxs+tWh26u5CeL08T0emNdehkj7+7M+lm00cyCkrwM04D1er1vLJyF8M7hzGovWntkS1/XmFQO3lNoCHtm5OUV1DVPgDpBTba0N2sDfXGOjW2YXpBMS/9tpcFo/sR4mW5Rsb7Ww/SxNuNsV3bWGrcIR9vTRfxdnFiQLtmnE+ynMIK8HDz/8feeYdHUbV9+J7ZTbLpgfSEHkILXZEmJYCAKEJABQXEhoq9IQoqyivy2UBBRQVRpCgohoj0XgQEpPeWQnpCSC+bnZnvj1mTbDYNX1xf9dzXxXWxmWfnN6fMnpnnPM85gWyKs97beVeRKkZEeProURJ1Ie8qpF/W0z00Fe3cUQhsVOvX1PQ0DIHls+mGwCCUjOrHxMIfv8epdfnziuTuju+cz8j95ENKjx+tVa8kOQXXkJCyz6aQYEqqSR8Ijhpmk67h0+UGGj30AH0O/UrLN18ndNSdtHhtSo16hckpuIWWR7y6hQRTlGKrFzb2XuKj9cmczAMHMZhMuPj6ltn3WfI1ex57kvy4uFrL91dQmJyMe4OKZQyhsFIZW9w3hlhrGdP3H8Tg4oLJ1xfVbKbEmj5y5cgx8mLj8G5e81pAxckpuFaoU1NIMMWp9n0bIDRqGEkVJk+LUlLIOXZCT/dQFFLXrse7Q+2RkQJBVfxjHBKapuUDNwCPABnAckmS7q9sJ0lSCHAXMLfysUrn+0LTtBs1TbvxkfFjABgzcjgxi74gZtEXDOjdk1XrN6JpGkdOnMLT3d3OIVFQWFS2roTForBj736aNdYHlbgKq9Bu3b2HZo0blmlET5lA9JQJ9G/fkphfj6NpGkdjE/F0NZW9GP+Ov7cn7i7OHI1NRNM0Yn49Tr/2eqpEv/Yt+fVsnK6XdoVSi0I9DzdyC4uZ+Ol3PDcsks5hDassf9sAb+JzCknMLcSsqKy7mEpkk4AqbSvz3oAObB3bl81j+zKpW0uGtQjl+W726RttQ/yIz8ol8WoeZkVh3clYIlvYvsBEtmjIqmN6hMPG0/F0bRKEJEksGX8rm5+6k81P3cm4m9rwSM92jOnSmmBvd44mZVBUakHTNPbFpjCoTROipz5K9NRH6d+hJTH7jup1eikRT1eXquvU5MLRS9Y63XeUfh2sddqhJb+ejS2vU0WvUwBV1dhw6BRDbrRdtAmAyxeQ/IL1UEyDEbljT7STB2xtQpoij3xUd0bklztEcHUHg3X9WTdPpCat0NLq8OJ16QxSUCiSfxAYjBi69UM9ZOsQkwLLByK5Y7fyFx5Pbz2HFZD8g5ECQ9HSq/Xf6eU/fxo5pCFSYDAYjRh734Kyf1e5QWEBBWMGU/hwFIUPR6GePUnxW5OuedeJ3zGfOI6xcROMoQ3A6IT7rbdRtH2rjY3Br/xl3rVvv7IFLzNffpGkgZEkDe7P1Q/eIX/1KrI/rNvOHtWV19BrAJZfbctbOPZWiiaMoGjCCNSzJymZ8dI1lVe7dEZvIz+9DeVukaiHa2jDDt3Q0vQ2LJ3xLOYXxmB+YQzKxpUoq5ehbo6hJhzfZ84gBzdACrDW4c39sRywTVuQgst/Eww39EBNsfZ9ZxfrmhIgd+gCimKzGGa1mudOWdstxNpPB6Lsq9RP7xlI4QPDKXxgOOqZExRPfxH1/Glw98D05mxKvv4E9dSxWrX+Gr3TyKGV7sNfd9rq3TuIwgejKHwwqlzvD96Hdvp/0n3RNtSP+Cs5FcaLS0S2tH1Ji2zRkFVH9fUUNp6Ko2vTYCRJIvFqHhbrrF5Sdj6xmTmE+nhUOV40s65J1Da4PvFX80jMztf1TicQ2bzS+BQeyqoT+niw8cxlujYKLItiVDWNDWcSGFJLusa9nVsQPflBoic/SP/24cTsP2Ed85PwNLngX8kh4e/tgbvJmaOx+roHMftP0K9dOC1CAtj99tNsfuNxNr/xOIE+nqycdD/+Xh5k5OajaRptGwVzPiUDs6Lg5uzEuqMXiGxt69yObN2EVYf0tth44iJdw/S1KiJbN2Xd0QuYLQqJWbnEZ+bQrmEAmqbx2sptNPOvx/29bHfMCPBy44A1kmDfpWSa+vlY2zAXs0Vh3fGLRLayrZ/IVo1ZdURfUG/jyUt0ta6VkVtUwsTF63nuli50bmwbUv7R5gPkF5t55dYetA311zWyrBrXoYyF5tKytTAKzaXsOX+Z8ED9eS8us9zJsC0hk2a/T/CkxOvjvbevnlbWpgvahbrdw6TEgckNXK1t37il7WKY1WA+eRxjo8YYQkLB6ITboCEUVxoT5QpjoqlPPyyx1ghSoxP1P/iYwp9jKN5caTeQasg5fAS3Zk1xbdQQycmJoKhhpK/faGfn3jwMJx9vsg+U7xB07LEn2dGxCzs6d+XstOkkLf+Bc/95u0a9K4cO4xnWDPfGjZCdnGgyMorEdbbXWpCYRFAf3Wnt1SIcg4sLJZmZOHl7EbliGYenzyDj1z+4kLUDyPjtMF7NmuJhLWOzkcNJWLvexiY/MYmQPnr6t3eLcAwmE8WZmZh8fZGsKROeTRrjFdaM3Lh4O42KZB8+gnuFNgyJGkbqOvv219vQh6sV2jD70BGcfLxxtjp8fHv1tFkM8x+NJP1v//sb8o/aZUPTNAXYDmyXJOk4MB74upJZJ6A5cMH64OAmSdIFTdOaX4tWnx5d2bH3V265axyuJhNvTy3fAWHY+EeIWfQFRcVFTHzpNcylZlRVpdsNnRg9fCgAH8xbQGz8ZSRZIjQokDdfetZOo3fb5uw8eYHB0z7B5OzEjHFDy45FvT2f6Cn6avav33MrU75ZTUlpKb0imtM7QveIjujRkVcXr+aO/3yOk9HA2+PvQJIklu04QELGVeat2828dbuhqJAFt9+Ib4WZJKMsM/XmNkxYcxBV04hq2YDw+p7MPXCeCH9v+jUJ4Hh6Dk9vOERuiYVt8Rl8fPACq0fdXOc6NMoyUwd3ZcK3m1FVlaiO4YT712Pu9sNEhPjSr0UjRnYMZ3LMLgZ98iM+rs68H1VT4At0CPVnYOsm3LlgNQZZpnVgfe7uVL4da++24ew8cYHBr3+s1+l9d5TX6YzPiZ76qLVOhzBlUQwlpRZrnTa31mknXl38E3dMn6fX6X3Dyh5AD16IJ9DHi4b+9ewDM1UVNXoBhgmvgiSjHtgKaYnIg0ahXb6Iduog8u3jwMWEYZw+U1+2vWdAAwx3PqKH0UkS6rZo2905qkNVsSyag9NL74Iso+xYh5YUh3HkA6ixZ1EP7cEwMAo54gZQLGgFeZR+rodvyq06YBz5ACgKaAqlX82GghpW2wZQFUo+ex/XNz/Stxvc/DNqQizOYyagnD9j65yoArcF0UhubmB0wtitD0WvP22/Mn9FFIWst6cT8NkCMBjIj15J6cULeD/xNOaTJyjavhXPMeNw7dsPFAU1J4fM1+zzSf8wqoL58w8wvfGhdcvKn9Eux+J07wTUC6drXA+g7hoqlm/m4vTSO0iSjLJzHVpSPIYR96PFnkU9vBd5wHDkiM56RE1BPpYv6rYlbLV6Du4z5vmzMU2bBbIByxZrHd7zMOqFMygHduM0ZCSG9l3QFAvk51Ey5y0AJO96mKbN1mcQr2RQ8tH0OpZRoWTee7i+Nce6LeZq1IRLOI99BOX8aZRfq++nTkPvRg5pgPPoh2D0QwAUv/oUWk4Ni5r9JXrv4/ofq96m1fp9WAc9ALeF0Uhu7vp92L0PRa8+bb9DR038SfeFUZaZOqQ7E5Zs0MekjuGEB9Rj7rZDRIT40a9lI0Z2bsHk6J0MmvM9Pq4uvH9nXwAOJaQx/5djGGUZWZJ47bYe1HMzUc/NpI8Xn8dgkCVaB/ty9w0twWLW9W65kQkrtut67ZoR7u/N3F3HiAiqT7/wBoxsH8bkn/cy6PPV+vh0R/kCsAcvpxPo6UZDH1uHwvvbDrPmVDzFpRYiP1nFyA5hPDlKfynv3SaMnScvMXj65/r4NGZI2fei3llI9GR9l63X7x7ElKVrKDFb6NWmGb3b1Lwz0cYjZ/lu92GMsoSbizPFJSXc8eF3RN3YivDA+szdtJ+IUH/6tWnKyBtbM3nFFga9twQfNxPv36PvABEeWJ9B7cMYOvtbDLLMq8N6YZBlfotL4afD52gRVJ+oOcsBeHZgN/q0asybIyKZuXo3iqribJSZPqw3mQVFTFi0Th/zO7fU9bcc1NuwdRNGdm7J5JXbGDT7O70N79YXZVz260kSsnKZt/0w87brqa8Lxg+hVFH4fMdhmvn5MHLejwD0bdmYCQtX6+12Hcp4JT+PpxfrL4UWVeW2juH0sjrDZq/fR2xmNrK5hBAPE9N+XztLU1E3Lkce9TRIMtqxPZCZgtRrKFpKPFw4BkGNkUc8BiY3pObt4ObbUb+cDpqGunUl8j3PAhJaWgLakTrcN4pC9jv/we/TL0GWKYhZieXSBTwnPkXpqRMU79iGxz3jMPWJLBsTr07Tx0TXgYNx6Xwjso8PbndEAZD9+iuUnqveUagpCqdensqN3y9Dkg0kLvuO/LPnaP7yJHKOHCXD6pwIHjGclOianeF1QVMUDkx6mf4rlyMZDFxcsoycM2dpP2UyWYePkLhuA4denUbXj2bR+vHH0DSNvY8/DUDLCQ/h2bQJ7SY9T7tJzwOwJepuSjKrj8irCw8tW0iLvjfj4efLzMunWT3tbfYsrHqb47qWce+kVxgcvQLJIHNu8bdknzlL56mTyTx0hIR1G9g/5XVunjubiCceBQ12TXwKgKCe3ek8dTKqxYKmqPzy7IuYr9YclaMpCicmT6Hb998iGQxctrZhy5cnkX3kKGnWNgwdMZyk6Eqp5arKqWnT6R69AiSJnKPHiP9m6R8uu+DfjVRVLt3fEUmSWgKqpmnnrZ/fAnw0TXuylu/la5pWc5LVlUSHVpJyZJsj5eBY7fvaX3f86rAGwvUktG6Lil0vtLXRDtUDsKTWvkf5ddXLcexqylfirt9K1nXBr2nNK4z/GRh9allz4TqjqXXYSu46ohQ4dhExraT2len/9jh4NkQyOD6w0jR6uGMFix28UryDxycKcmu3ud5Y6rC+y/XE6OD5tnNVrY/x55G6fLtD9QCOX3Zsv8l0cJ/ZnXMN6y1dJ7p41i0V+noR4OT4eeihV1L+nlP2NaClXfqffnmWApv97er8nxQh4QHMlSTJB7AAF9DTNwQCgUAgEAgEAoFAIPgv+du97//P849xSGia9hvQ4w98r5YlaAUCgUAgEAgEAoFAIBBcb/4xi1oKBAKBQCAQCAQCgUAg+Pvwj4mQqApJkqKByvsjTtY0rW5LCAsEAoFAIBAIBAKBQCD4U/hHOyQ0TYv6q69BIBAIBAKBQCAQCAT/AP6mW2v+LyNSNgQCgUAgEAgEAoFAIBA4HOGQEAgEAoFAIBAIBAKBQOBw/tEpGwKBQCAQCAQCgUAgEFwXRMrGdUdESAgEAoFAIBAIBAKBQCBwOCJCog5oisWxgoriWD35X+CXcrQ302BwrB4gOzn2dnaq5+5QPT8Ht2FucrZD9QC8DI69Fw0eLg7VkwyObUPNwfUJoBaZHSvo4PtC+gvq1OG/30YHPxolxTtWzzfAsXoA5lLH6qmaY/Uc3GecnBx/H5oc/Kzo6+A67eLp2PEQ4EBeiUP1ung6VE4gqDPCISEQCAQCgUAgEAgEAkGtiJSN682/YGpcIBAIBAKBQCAQCAQCwf8awiEhEAgEAoFAIBAIBAKBwOGIlA2BQCAQCAQCgUAgEAhqQRK7bFx3RISEQCAQCAQCgUAgEAgEAocjHBICgUAgEAgEAoFAIBAIHI5I2RAIBAKBQCAQCAQCgaA2RMrGdUdESAgEAoFAIBAIBAKBQCBwOCJC4g+iaRozPprHzn37MbmYmDnlBSJahtvZPfzCFDKuZKEoCjd0aMvrzz2JwWBg/badfLxwMRfjL7Piizm0a9WiSo23V25i58mLuDo78fbY22nTMMjO7mRCClOWrKG4tJTeEWFMGXkLkiTx8dpd/LDnCPU83AB4dmgf+kQ051hcMtO+W2cVgSfaN2BAM/vz7krIYObuUyiqxp1tGjKhc5jN8YPJWczcfYpzV/J4f2BHBoUFA5CUV8Qz639DUTUsqsaYdo0Z3bZxlfW462ISMzfsR9E07uwYzoSe7WyOmy0KL/+0m5MpV/BxdWHWiD6E+niUHU/OyWfoZzE80bsDD3ZvC8Di/af4/vB5NE3jrk4tuK9rG9s6XbGenSfO63U6fjhtGgXb12l8MlMWxeh12jacKXcPRpIknp//A7FpmQDkFRbj6WYi+tXH2HPqIrNWbaHUouBkNPBCQze6+XvanVdq0QH5jvtBklEPbEXbHmN7vNdtyF36gaqgFeSifv8ZZGeWG7i4YnhhFtrJ/agxX1VZpzbna3sjxnsfB1lG2bkOde1ym+Ny39uR+98BqgrFRVgWzYbkBKSmLTHc/1yZnRKzGO3QL7XrteuCcewTSLKMsmMtys/f2epF3o5hwDBdr6QIy8LZaMnx5Qa+ATjPXIgSvQhl3fe16lXG0Lkbzg8/CwYDlo0/UbpycdV2PSIxvfw2Rc8/gHrhzDVpuHS/Ga8Xp4AsU7jqBwoWLbA57nr7cDyfmYSangZAwYplFMX8AEC9OV/g3K4D5iOHuPrcxLqVqVNXnB96FmQZy+bVlP64xOa4cdBwnG4dgaaqUFxIyafvoiXGgdGI82MvYWjeCk1VMX/5EerJw7Xqye26YBz3pN5ntq9F+flb2+vpN7S8DYuLKF04y64NXf7vKyzRi1DWrqhdr2NXnB94Ri/flp+xrKpUvoHDMA4aAaqKVlyE+XNr+QwGnCe+jNy0hd7eO9ZjiV5StUglDJ274TLhOZBlSjf9ROkP1fcT11dmUvjc/Xo/8fTC9PJMDOGtKd2yBvPnH9RNr0sPTI+/iCQbMK+Lxvzd11XaGXv1x23ae+Q/Pgb13Omyv0sBQXh8+QMl33yO+fuqr9VG78buFfRWYV5eg97r75L/xFjUc6eRW0bg+tzU31UpWfwFll+21a2MN3THNPEFvU7Xx2BesahqzZv74frqOxQ8dR/q+dMYOt2Ey4NPgtEJLKWULJiDcvRgld/ddT6Rmev3oagqd3ZuyYReHWyOmy0KL0fv4GRyJj5uJmbdGUloPU+OJWYwbfXuMrsn+nZiQOsmlJRauO+rNZgVFYuqMrBNU56K7FyudzGJmRsPWsen5kzo0dZe76dfOJmahY+rM7OiehPq40FSdj63f/4TTep7AdAh1I83hnTTv6MozFi/n/0JaciSxDN9OzKweaiudymZmVsO6WNuhzAmdGtjr7dmn1XPhVnDehDq7cHqk3Es3F/eX86lZ/PD/YNpHViPD3ce5acTceQUm/nt+btszqdpGm//tIOdZ+JwdTLy9t0DadMgwK7eTyamMWXFJopLLfRu1YQpd/RBkiTWHzvPJ5v2cSk9i+VPjqZtw0AAkrJyuf39b2jiX08vf6Mg3hjS3dqGl5m5bq9ep51bMqFXR/sy/ridkymZehnv6k9oPU/2XExk1qYDlCoKTgYDLw68iW7NQm2++8SyDVy+msdPT9xZ3obnEpi5Zrdepze2ZkKfzvZ6P2zhZFKG3mdG30JoPS+yC4t5dtkGjielE9WpFa/e0QuAInMpz327kctZuciyRGSrJjw/qJtdnQHQpDVyvztBktGO70Hbv8n2eIMw5Mg7wT8E9eev4NyRskPy83MgM1n/kHsVddXnVWvUgHO3m/F44RWQDRTH/EDhN7Zjlem24Xg8/SJKRrpetu+XUhyz8pp1fqd+ZF+az3gTyWAgZcm3JMz9xOZ42PRp1Lu5BwCyqyvOfr7sDo+4Jg2/fn1p/fZ/kGSZxCXfcmnOxzbHW731Br49ewJgcDPh7OfH5rDWAAxOu0zeKX28L0pK4tDY+2vVCx3Qj27vzEA2GDi7aAnHZs+xOe7eIJTen32Mi7c3kkHmwBtvkbhxMx6NGjLywC/knL8IQPqBg+x5btI1lbUqxn35Ce1uH0xeegb/aVdNv7tG/tfKKPh3IhwSf5Cd+w4Qn5jEhm+/4uipM7z5wVxWfDHHzu7D6VPxcHdH0zSefu0/rN+2i9sG9CW8aRPmzHidae/Zf6dM49RF4tOvsv71xzgWl8yby9ez/MX77eymL9/Am/cMpkOTUB6dt4Jdpy7RO0J3HtwXeRMP9u9qYx8e4s/3kx7AaJDJyMknasbn9G0SgFEuD5hRVI23dp5kwdCbCPQwMeqHX4hsEkDz+uUv2cEeJt7u156vjsTanN/fzYVlI7rjbDBQUGph2He76Nc0kAB3k42doqq8tW4fC8YMJNDLjVFfriGyRUOa+/uU2aw8ch4vkzMbnhjB2pOxfLD1N2aN6FN2/J1NB+jVvPyh5Hz6Vb4/fJ7lD96Gk0HmkWWb6R3egCYNrHV64gLx6Vmsn/4Ux2KTeHPZGpa//LB9nS5bw5tjb6dD0wY8+vEydp28QO+24cyaUP6g884PG/B01cvk4+HGp4/fQ4CPJ+eT0pnw3gK2Da400EoS8vAHURbMgJwrGJ6ciXLqIKQnldskxaHsewVKzUjdbkEeMgZ12Udlh+WBd6NdOmV3vVUiyRjHPUXp+5MhKxPj6x+jHtkLyQllJuq+rajbf9bNO3bHOPoxLLOmoCXFYXnzcf2l07s+TtM/o/TIXv1zDXpO9z2N+d2XICsDpzc/RT201+ZlVd27FXWbrid36o7x3scoff+VsuPGeyeiHttft/JVRpZxfvQFil9/Bu1KOqYPFmLZvwvtcpytnasbTkPvQjl74g9peE1+jawnHkJJS8PvmxWU7NyGJfaijVnxpnXkvvuW3dcLFi+k0GTCbcSoupfpkRcofuNZvUzvLsCyf7f+Qm7FsnMjlg2rADB0uRnnB56i5D8vYLzlDgCKnr0PvH0wvfYBxZMeBk2rXk+SMY5/htJ3JqFlZeA8fR7qoT02bajs2YKydbV+eZ16YBwzkdL3Xi477jTm8bq3oSzj/PDzlEx/Di0rHdP/LUA5WKl8uzZh2ag77gw39sR5/FOUzHgBQ/d+4ORE8QvjwdkF04dLUHZvRstIrVXT5bEXKXrtabQr6bjO+grLr1X3E+ehd6OcqdBPzGbMS79AbtQMuXGzOpfR9anJFEx+HC0jDfdPlmDZswM1wfZ3E1c3nKPuwXL6uN0pTBNfwLK/dodgud7Lul5mGu4fL8aytxq94aNt9NS4ixQ8Pg5UBam+H+6ffUv+3p2gKrVqmp54icIpT6JlpuE2ZxGWfTur1HQaNgqlgqaWm03RtOfRsjKRG4fhOmMOBWNvs5NQVJW31u5hwbjBBHq5M2r+T0S2bETzgHplNisPncXL5MKGZ+5m7fGLfLD5ALPu6kd4QD2+f2SYPublFRI1L5q+LRrhbDSwcPwQ3F2cKFVUxi78md7NG9AhyEfXW7+fBfcO0MenheuIDG9QaXy6oI9Pjw+3jk+HmDWiNwAN63kQPeF2u3J8vvsE9d1NrJs4HFXTyCkqKS/fpt9YMCqSQE9XRi3aSGTzUJr7eZfrHbuk6z06lLWn4vlg+1FmDevJ0IgmDI1oAsC5jGyeXLmT1oF6vUSGhTKmcwsGf/Gz3bXsPBNHfGY2618az7GEVN6M3sryp0bb2U2P3sabI/vToVEQjy6MYdfZeHq3akJ4oC9zxt3OGz9usftOQ18fop8bU/6HokK9jGt+YcF9Q/Q2/GIVkS0b27ehqzMbnhmlt+Gm/cy6uz8+biY+vXcgAV7unE/LYsLidWx/sfz8m07F4ubsZN9nVu9iwQNDdb15K4ls3YTmAfXL9Q6e1vvMC2NYe+w8H2zYx6zRA3E2GnhqwE2cT8viQlqWzXkf6NWRrs1CMVsUHlz4EzvPxtPbpVIFSBLygLtRv/8Y8rKRx05Cu3gcrlT4fcq9irpuMVKX/nb1h6UU9Zv/s/97XZFlPF96latPPoyanka9Rcsp2bUNpYqxKv/9GX9cp4Je+DtvcfSueylJTuGGjWvI3LCRwnPny0wuvv5m2f9DH3oAj3bX5oxAlol452323zma4uQUemxaS/r6DeRX0Djz6htl/2/88IN4tSt3IipFxfwSeUud5SRZpscH/8f6YXdRkJTMHds3krB2Pdlnz5XZdJz0PLHRMZz58mt8WrZg4A/fsqLdDQDkxcax6ubIaytjLez9einbP/6C+7+5dgdVVfwvlvHvgUjZuN78Y1I2JEmaKknSSUmSjkmSdESSpK7V2H0tSVKs1eaIJEkdq7KrjS279zJs8AAkSaJjRGty8wtIz7xiZ+fh7g6ARVEoLbWUpR2FNWlEs0YNa9TYevw8w25qiyRJdGgaSl5RCRk5+TY2GTn55BeX0LFpAyRJYthNbdly/Fw1Z9RxdXbCaNCbvqTUUuVtdTw9m0bebjT0dsPZIHNr82C2xqbZ2IR6udHSzwu50gmcDTLOBgMApYqKWs0L0PHkTBrV96JhPU+cDQZujWjK1nOXbevg3GWGt9edKwNbN2ZfbAqa9XybzybQwMeT5p0JOt0AACAASURBVH7lD4gXM3PoEOqPq5MRoyzTpXEgW86Uv4BvPXaGYd3a63XarAF5RcVk5OTZaGbk5Ol12qyhXqfd2rPlqO0suqZpbPjtFENu1Ae7No2CCfDRnTXNQ/wpUVTMSqWX94bN0a6kQVY6KArq0T1IbbrYnvfSSSg16/9POI/k7VuhwpuCpw/a+WNV1mdlpGYt0dKTISMVFAvq/u3InXrYGhUXltu7mMpfVs0l5c4HJ2eo4R227PthrdDSkyAjRdfbtw25c/V6uJhszit37omWkYKWFFen8lVGDm+DmpKIlpYMFgvKrs0Yu/a2s3Me8wilK5eC2XzNGk4R7VEuJ6AkJYKllKKNa3Hp06/O3zcf2IdWWFBnezm8tW2Zdm/BeFMvW6OiSnVqrVS5YROU49aZ5pxsKMhHbt6qRj0prBVaWhKatQ2VfVuRb6itDcsbUb6hJ1p6io1DocbyNW+Nlpqo91OLBcsvmzF0ubmG8rmWlQ9NQ3JxBdkAzi5gsaAV1V63lfuJZeemavuJ+cclZfcjACXFqKeO2v6tFgwt26ImJ6KlJIHFQun2DRh79rWzc7n/cczLF+n3XgWMPfqipiShxl+qo14EavJltNTf9TZi7FGV3kTMK76x1SspLnc+ODtTpxsfkFtGoKaUa1p2bMLYvY+dnct9j2H+fjFahfpTL55Dy9KjwNT4i0jOzuDkZPfd40kZ+nhR3wtno4Fb2zZj69kEG5utZxMY3rE5AAPbNGXfpWQ0TcPV2Vg+5lmUsnFYkiTcXXQti6JiUdSy58zjyVdoVN+zfHxq09h+fDpfaXyKSy0bn6oj+uiFskgLWZKo56Y7tY+nZNHIx4OGPh66XutGbD2fWEkvkeFtm+p6rRqyL95eb82peIa0KY9I7BDqh7+Ha5XXsvXUJYZ1bq2Ph42D9WeMXNt7KCO3gPxiMx0bB+vjYefWbDmpv9SGBdanaQVnQm3Yt2EYW8/E29hsPRPH8I56xOjANk3ZF5uEpmm0CfYjwEt/nmoeUI8Si4LZovfVgpJSFu09zqO9O9nqJabTqL53uV775mw9HWerdzqO4Z1b6noRYey7qOu5OTtxQ5NgXJwMNvauzk50tUZmOBsNtAnxJy23it+doCZwNRNyrugRj2cOIYW1t7XJzdKjIGrpM38EY0Q7LIkJqMn6WFWycR0uves+Vl0rXp07UhQbR3F8AlppKenRMfgNHlitfcCIYaRHx1R7vCp8OneiIDaOIqtGSnQMAbcOqtY+eMRwkn9cdU0aFfG/sTO5l+LIi4tHLS3l0spVNLrtVlsjTcPZU3/2c/b2ojC1Fof4f8mFXXsozLp63c73v1hGwb+Tf4RDQpKk7sDtQGdN09oDA4DLNXxlkqZpHa3/jtRgVy1pGZkEB/iXfQ7y9yOtCocEwEPPT6Hn0FG4u7kyqG+vKm2qIj07j6B6XmWfA308Sav08pyWk0egT0UbL9Kzy22W7fyN4TMXMHXpGnIKi8r+fjQuiaEz5jNs5gKm9WlrEx0BkFZQTJBHeURDkIcr6QW2D8o1kZJXxPDvdtHvm6083KmZXXQEQFpeIUHWBwyAIE830vMKqrUxyjKeLk5kF5VQaC7lyz0neLy3bchueIAPBxPSyC4spqjUws4LSaRUeFjQ67R8xinQx4u07Ep1mp1HYL3q6xTgtwsJ+Hq60yTQl8psPHSa1j6uOBts61Tyrg/ZFfpIzhUk7+of5uQukahnrd1TkjDcNg51Td1C0gGo54eWlVH+OSsTqZ6fvU6/O3B6ZxGGux/GsuzT8utt1grjW/Nx+s8XWL75qOboCECq54d2pVxPy8qoWq//MJzfW4xx1CNYlljDLZ1NGG4fjRL9Td3LV1nf1x8tM71cPzMdydffxkZu1gLJLwDlYB1nmythCAhASSsfjNX0NAwBgXZ2pn4D8ft2FT7vfIgcaJ8OVVek+pXKdMW+TADGW0fgOm8FzuMfx7zgQ/3aYi/ozgvZgBQQjBzWEsnX/lpt9Or5oWVV0MvKRKpnr2cYMAzn95fgNPoRLIutbehiwnjbaCzRVYfq1618GUj1qyjf4BGYPl6O07iJmL/Uy6fs24ZWUoTr/FW4fraS0p++hfw8u+/aaVbuJ1XUqdysBbJ/IMqBP9ZPbPT8/FHTy/uMlpGO7GsbFi83b4kcEIjl1122XzaZcB59PyXXMBsm+QWgZpQ7j7XMNGS/SuULa4nsX4UeYGjVFvf5K/D4YjnFH82sPToCkH39bTTVzDT7Og1rgeQfiLJ/d+Wvl2G8uR/KxXNQWmp3LC230njh5UZ6pRfBtNwCgrz0lD6jQcbT5Ex2oT5uHU1MZ+gnKxn26Y9Mu71nmYNCUVWi5kVz83tL6REWQgdrykJaXiFBnhX13EnPK7LVyyskyEtPiaw4PgEkZeczYsHP3Ld4AwcT9LrJLdYdMXN3HGXkgjU8u3IHmflFducC63iYX0kvv4ggz4p6zmQX2TrH1p9J4LbWVadIViY9J5+gCimQgT4epFWa9EjLySfQ29YmvZJNVSRl5TDiw2XcN+8HDsbqUYBpuQUEVThXkLd7zWO+wVrGQttnj42nYmkd7IuzUXcWzN16kPt7tMPVyTboV9er1IY5lftMftk1lfeZ4lrLB5BbVML2M3F0C2tgf9DTGy2vwotj/lXw9La3qw6jEXnsS8j3vgDN29duXwmDfyCqzViViuxvn47j0m8g9ZdG4zVzNnLAHx+rXIKCKUlKKftckpKKS7B9OiyAS4NQTI0acnXXtf2+moKDKE5OLvtcnJyCqRoNU4NQXBs35Mqu8t8b2eRCj83r6L5+NQG3Dq5Vzy04mILE8gjWwuRk3ENs9Q7NfI+wUXcy+vRRBn7/LXsnlUd8ejRuxPBdWxmyNobA7tcnveJ6828oo+DvwT/CIQEEA5mappUAaJqWqWlaci3f+e+owqEtVbPq6pez3mbXqm8xl5ay71Dd/R9VOc0rK1Q1G/P7dYy+uTMbpj3Gj5Mfwt/Lg3ejt5bZdGgSyuqpE1gx6X7mH7pIicX2obNKh/01RCgFe7qyanQv1o/pS8zZJDIL7Z0ZVU8KSJVsqigf8PHOI9zXtQ3ulUI0w/x8eLh7Wx5atolHlm2iZWA9jBVCOP5wnVayWnPgOEO6tLWzO5+czqzozUzrWFX0SxUVWM3EiNTpZqQGYWg7ftI/dxuoOydyqnZ6VXOWKvTsBdWtP1E6eTzK9wswDL233PTSGSyvTqB0+pMYbhut53hfM1XobYnBPGkclhXzMQwbC4BhxHiU9T/oM7R/lKruv4rllSScH3oG88Lq06TqIFKzBlC8azvpQ/uTec9wzPv34vPGzP9Crm5taFn3I0UT78b8zTyc7rpf/9uWNWiZGZje/xLnh57RUw9Uy3XRUzbHYH5xLKXLv8BobUPjiPuxXGsb1rV863+k+MlRlC75DKc7xwMgN28DqkrRI8MpevwunIaORgoI+YOatsddHn6Wki//m35Six62/dI08QWKP5tlZ+Vy32OYVy6F4iK7Y9ekp1XWe57iz2dX+XXlzAkKJtxNwZPjcBl9vx4hdR00XR59npL5H1Z7CrlxM1wefIriOW9Xebzq4UKq1eZ3kw4NAlj9xEhWPDKM+buOUlKq3wsGWSZ6YhTbnh/N8aRMzlvD8+swPFUznkj4e7iy5cmR/Pjw7UwecCMvrdpNfokZRVVJzSukU0N/Vj58Gx0b+PPelt+q16tE1eN9+f+PJmdiMhoIr5BWUuP5qvhb5abUqrCq7lnnd/y93Ngy5UF+fPZeJg/txUvL1pNfbL4uZTyfnsWsTft5Y6g+uXM65QoJWbkMaN20inNVIWBXvipM6rCCvkVReXH5JsZ2b0fD+l5VWNR9vK8K9fPXUZe8i7rma+TIkeBt79yvkTqUoWT3Nq4MG0DWmCjMB/bh9UbV917d9Oz/VF20UEDUMDJWr611ksNew16kOo2QqOGk/rTGRmN7xy7sGXArRx59gtYz3sStSS2Ouzrohd0Zxfml3/Fd6w5svOse+nzxKUgShalpLI/oxKpe/fh1ymv0/fIznDw97M73l/NvKKPgb8E/xSGxEWgoSdI5SZI+lSTJPlbUlhnW1I7ZkiRVzvwDQJKkRyRJOihJ0sEvvlkGwNIff2L4AxMZ/sBEAvzqk5JePhucmpFJgG/9qk4FgIuLM/16dmfL7r01XtjSH38i6v++JOr/viTA24PUq7llx9Ky8wjwtl0oMcjHi7Tsija5+Fu9/X5e7hhkGVmWuKtHB47H2/towoL8cDUaOJ9lO7MY5GEiNb/8xSI1v4gAtyqrqkYC3E2E1ffgt5Qsu2NBXm6kVpjhSs0rJMDTrZKNe5mNRVXJKynF29WFY0mZfLDlIAPm/sDi/af44pfjLD2gL+o1slM4Kx8eyuLxt+JtciE9r5Cotz4j6q3PrHWaU3b+tOzcslSLMs16XqRdrVSnFWaRLIrK5sNnuPVGW4dE6tVcnv5sOTPvH04jd/u60nKugE+FiApvX7Rc+9A7qXk75H4jUL5+FxT9oVlq3AK5xyAMk+ci3zYWqXNv5MH32H3XhquVZpvr+6FlV+/QUH/djtypp/2BlAQoKUZqYP/AZ1O+q5k2s6JSfX+0qzXoVUjpkMNaYxz1CM4fLMUwcCSGofciDxhWo56dfmY6kl/5DJDkF1AWCg6Aqxty42aYZnyK6/wfkVtG4DL13VrTGCqipKdhqBDxIAcEli0IVnYdOdllM7yF0d/j1Poa82QrnutKpTL5VipT5evbvbk8pUNVMH81h+Ln76dk5stI7h56+G5NelkZSPUr6NX3Q8uuXk/dtw35Br3PyGGtcBr9KC6zlmEYNBLj0HsxDBh+jeXzR7taQ/l+2Yyhi14+Q69bUA7/CooCudmoZ48jh9Xelnb9xDfANpLI2k9c3/4UtwXRyC0jML363jX1Exu9jHSbmUfJPwC1QiQRbu7ITcJw/2A+Hkt+xtC6HW7TP0Ru0RpD63aYJjyDx5KfcR5xLy73PIjTsJrXH9Ey0pD9yyNhJL9A1CsV7wN35CbNcX//CzwWr7bqzUZu0drmPGpCHFpxMXJT28WMq0LNTLfRlP0Cq7j3wnB79zPcF8VgaNUW1zc+QA5vbb3GAFxfe5fi96fpqS1VYDde5FY3Xuiz9xZFJa/YjLer7W9xmL8Prs5OnE+3/e31cnWhS5Mgdl3Q9YM83UjNq6hXQECl1Af9mvSUovLxyRlnowEf63gZEexLw3qexF3Jw8fVBVcnAwNaNgJgUOvGnErNKtfLLU9PSs0rtNfzdCM1r6KeGW9TucNo3ekEm3SNqlh26BxRs5cSNXspAV7upGaXRzukZecT4GX7QhHk7WkTNZGWnY9/hUiVqnA2GvFx1689okEgDX29ibuSo7dPhXOl5hQQ4Gl7LpsxX7GW0dqGqTn5PP3dJmaO6EsjqxPg6OU0TqZkMmD2t4xduJq4KzmM/+pn67W7k5pTqQ29Kut5lF1TdX2mKqat2kFjPx/u69mhaoO8bCTPChGQHvUgP6dq26oosNrmXEG7fB4Cq4jCqAElPdUmOk8OCEK1G6tyysaq4lXfY2z1x8eqkpQUXELLZ9ZdgoMwVxPaHzD8DtKjrz2Vojg5BVNIudPZFBJMSTUawVHDSKmUrlGSqkcqFcUnkPXLHpv1JaqiMDkZ9wbla5S5hYRQmGKr1+K+McRaU0/S9x/E4OKCydcX1WymxJpaceXIMfJi4/BuXvtvqaP5N5TxT0GS/rf//Q35RzgkNE3LB24AHgEygOWSJN1fjfkrQCugC1AfmFzNOb/QNO1GTdNufOQ+fdZ4zIg7WPXVPFZ9NY/+vXoQs34zmqZx5ORpPD3cCPCzDd8vKCwqW1fCYlHYuW9/retGjBlxB9EvP0T0yw/Rv30LYvafQNM0jsYm4WlyKXM2/I6/twfuJmeOWvMsY/afoF87fbePiutNbD56jvBg/WUxMTNbz5VFD6uMzS4gtNKDXdsAb+JzCkjMLcSsqKy7kEJk05rDvX8nNb+IYmvERU5xKYdTrtLUx/4Bpm2IH/FZuSRezcOsKKw7GUtkC9tBN7JFQ1Yd0/NVN56Op2uTICRJYsn4W9n81J1sfupOxt3Uhkd6tmNMF/3h9kqBPpuYnJPP5rPxTBvSnehXHyP61cfo37EVMfuO6XV6KdFap7YOCX9vT9xNLhy9lKjX6b5j9Gtf/jKy98wlmgb52aTT5BYWM/HjZTw3vD+dmzequmISLyL5BkE9fzAYkDv0QDtdaTX5kCbIIx7WnREF5U4R9bu5KDOfQHnnKdQ1S9AO7URd/y01ocWeRQoIBb8gMBiRb+qLdriSQyywfCCS2ndFS7O+DPgFwe9pPL4BSEEN0TJrzhvULp1BCqyg1y0S9fAeGxupgp7coVuZXumMZzG/MAbzC2NQNq5EWb0MdfO15Zeq508jhzRECgwGoxFDrwG2IemFBRSOvZWiCSMomjAC9exJSma8dE27bJSeOo6hYWMMIaFgdMJ14BBKdtruQiBXcMq49O6HJbZuuf9Vl+kMcnADpABrmW7uj+WAbci7FFx+zxhu6IGaYnU6OLtY15QAuUMXUJRa13bQLp1BCgpF8tfb0NCtH+oh2z5j04Ydu+nrBgDmt56l5Pl7KXn+XpQNK7GsXoayueaHTvXCGaTghmXlM/YcYJcmIQVVKF/nHqipevm0zDQMba0r5ruY9LUhKu72UZ1mpX5i7H0Lyn7bflIwZjCFD0dR+HAU6tmTFL816Zp3Y/kd5exJ5NCGSEEhYDTi1HcQlj07yg0K8skf2Z/8sbeTP/Z2lNPHKXz9WdRzpyl87qGyv5t/XEbJtwspjVlevRignD1VSW8glr0V9Arzyb+zP/njhpI/bqhV7znUc6f178h6GLwUEITcsDFaako1SuWoZ08hhzRCCtQ1jX1uwbJvZwXNAgpG3ULB+GEUjB+GcuYERW+8gHr+NLh74Dp9NiVffYJyqvr1cdqG+BN/xTpeWBTWnbhEZEvb39rIlo1YdeQCoIf1d20agiRJJF7NKx/zsvOIzcwh1MeTrIIicq0pFsWlFvZeSqaZdRHJtiG+xGflkZhtHZ9OxRPZwnb8jgyvenzKKihGsc7MXr6aR3xWLg3qeSBJEn3DG7A/Xv8t3RebStjvesH1ib+aR2J2vq53OoHI5pXGw/BQVp3QFwrdeOYyXRsFls3mq5rGhjMJDKklXePezi2Ifm4M0c+NoX9EGDGHTuvjYXwKnq4uds4Gfy933F2cOBqvr98Uc+g0/drUvKBrVn5hefmv5BCfmU2Dep56G2blkng119qGF4lsVbkNG7PqiL4OVsU2zC0qYeLSDTw34CY6Nyp/0R59Uxt2vDiGzc/dw5IHh9LE15tFD+iLibYNDSD+SjaJWVa9YxeIbNXEVq91E1YdOqvrnbxI12ahtUZIfLTpV/JLSnhlSBUO/N9JjdfHem9fPW2uVWe0i3Vb/wkXVzBY009c3ZFCm9kuhlkHLKdOYGzYGNk6VrkMvJWSXZXHqvKoC+fekSj/xViVd/gors2aYmrUEMnJiYCoYWRu2GRn5xrWDCdvb3IP/HbNGjmHj+DerCmuVo3gqGGkr99oZ+fePAyjjzfZB8qfr4ze3sjOuvPOqX596nXtQv7Zmtdby/jtMF7NmuLRuBGykxPNRg4nYe16G5v8xCRC+uhrEHm3CMdgMlGcmYnJ1xfJ+gzl2aQxXmHNyI2rfXxyNP+GMgr+HvxjdtnQNE0BtgPbJUk6DowHvq7C7venqxJJkr4CXvwjen2638TOfQcYOPoBTCYX3n7lhbJjwx+YyKqv5lFUXMzjr7yB2VyKqip07dyR0cP0gXLTzl9468NPycrO4bGXXqNV8zC+nGUbLtc7Ioydpy4yePpnmJycmFFh5fGo//uS6JcfAuD1UYOZsuRnSkot9GrdjN5tdA/l+zFbOZOYjiRBaH1v3hitL1Rz6NJl5m/ah9EgI0sSr/WOoJ6rbViuUZaZ2iuCCav3o2oQ1aoB4fU9mbv/HBH+3vRrGsjxtGyeXn+I3JJStsWl8/H+86y+pzeXrubz7i9nkCQ9ZPKBjs1o4Wsf0miUZaYO7sqEbzejqipRHcMJ96/H3O2HiQjxpV+LRozsGM7kmF0M+uRHfFydeT+qtuAXeOaH7WQXleAky7w6uJvNbEfvtuHsPHGewa/NxeTsxIzx5bPwUW99RvSrj+l1eu9tTFm0ihKzhV4RzendtnmZ3boDJ+zSNZZt309CRhbz1u5k3tqdkJvD/J5h+LpUSHNQVdSYhRge0reMVA9sh7RE5FvuQku8hHb6N+QhY/X1FMbqW25q2Zmoi96rtcxVoqpYln6M0wsz9S0cd21AS47HMHw8atw5tCN7MfQfhtSmkz7LXJCHsuBdAOTwtsi3jdL/rqlYFs+B/Nza9b6Zi9NL7yBJ+jajWlI8hhH3o8WeRT28F3nAcOSIznrkR0E+li/e+WNlq1Jfwfz5B5je+NC6RebPaJdjcbp3AuqF0zXmrtcZRSH3vbeoP3cBGGSKfvoRy6ULeDz6FKWnT1Cycxvuo8fqi4cpFtTcHLLfKM+39J2/GEOTZsiubgSs2Ub2f17FvK+GPFpVwTx/NqZps0A2YNliLdM9D6NeOINyYDdOQ0ZiaN8FTbFAfh4lc/TdPSTvepimzQZNRbuSQclH02sv3+9tOOkdkA3WNozDOOJ+1NhzqIf3YLhlOHLEDaBY0AryKP1v2lBVMC+Yhcurs/Q227oGLTEWp1EPoV48g3LwF4y3jsTQ/kZ90cqCPMxz9RXhLet/xPmJKZhm69tgWratRYu/WJNamWbJZ+/j+uZH+haVm39GTYjFecwElPNnbJ0TVeC2IBrJzQ2MThi79aHo9aftd+iopFc89x3c/u8TJFnGvP4n1PhLuIx/DOXcKSx7d1b/3T+CqlD88bu4zfxY3/ZzQ0yd9YxtO+I86n79/lQ1iuf8H1pudt00P30XtxlzQDZQulEvo/O4R1HOn0bZV72m8x13I4c0xPneh3G+V9/xqGjKk2g5thEMRoPM1CHdmbB4PaqmEdWpBeEB9Zi79TciQvzo16oxIzu1YHL0DgZ9tAIfVxfev1Nf/f1QQirzdx/DKFvHvNu6U8/dxNnULF5ZtQNV1VA1jcERzejbshGUmvXxadBNTPh2C6qqEdWhOeH+PszdcYSIYF/6tWjIyI7NmRyzm0GfrsLH5Mz7UXr0zsHLaczdcbRMb9qtXfGxjkPP9+vMyzG/8H+bDlLPzcSM2/UoMaMsM/WWG5mwYrtevnbNCPf3Zu6uY0QE1adfeANGtg9j8s97GfT5an08vKP8hfjg5XQCPd1o6GM7afH+tsOsORVPcamFyE9WMbJDGE8O03d16N2qCTvPxDH4nUWYnI3MuKt8B4Ko2UvLdsl4PaofU1Zs0p8xWjWmt/WlfvOJC8yI2UFWfhETv4qhVYg/8x+O4mBsEnM37isv/4h++FgX75w6pAcTFq/T67RTS8ID6jN360EiQvz1Nuzcksk/bmfQR8utbagvxLhs/0kSsnKZt+MQ83YcAmDBuCH4VrNgZ1mfGdqLCV//rNdp51aEB9Zn7ub9RIT60691U0be0IrJP2xh0AdL8XE18f7o8joY8N4S8kvMlCoKW07HMv+B2/Fwcebz7Ydo5u/DyE/0ranHdGvLnf6VUps0FXXLCuSRT4AsoR3fB1dSkXrehpaaABePQ1Aj5GETwOSGFNYOetyG+vUM8A1CvuUe0FSQZLRfN12zQwJFIe+9GfjMmY8kyxStjka5dAH3R56k9PRJzLu24TpqHC69I9EUC1pODrnTp1ybRsXiKgrnX36N9suXIhlkUpYtp/DsOZpMfpG8I0e5YnVOBI4YTvqqn/6wxqmXp9Ll+2VIsoHEZd+Rf/Yc4S9PIufI0TLnRPCI4aRUWjDTo0U4bT94B01VkWSZSx99YrM7R3V6eye9wuDoFUgGmXOLvyX7zFk6T51M5qEjJKzbwP4pr3Pz3NlEPPEoaLBr4lMABPXsTuepk1EtFjRF5ZdnX8R8tQ6/pbXw0LKFtOh7Mx5+vsy8fJrV095mz8Lat4L+O5VR8O9Eqm1F6L8DkiS1BFRN085bP78F+Gia9mQVtsGapqVIugt8NlCsadrLle0qoqXHObSS1CPbHSkHp486Vg+gioXr/lQaNHGonLb+2mb3rwdqun1azJ+JVnkXkT8ZS3Zh7UbXkdxkxw+sXg2rT/v6MzB4XHsa1n+DWnTtO5v8V3oltayZ8WdoOriMjg7PlAyOD6x0G3+XYwWvYReV64LFwf3U135xwz+dIsf+fmN08Hxb3AWHyl1ZsbV2o+vMqbhaJiWuM0XXusbEf0my2X4x3T+bA3l1Xyz+etDF07FjPsBDuRl/zxyCmria8r/98lwv+G9X5/+UCAkPYK4kST6ABbiAnr5RFUslSfJHX4LnCPCYYy5RIBAIBAKBQCAQCAQCwe/8IxwSmqb9BvSoo+2ftxGzQCAQCAQCgUAgEAgEgjrxj3BICAQCgUAgEAgEAoFA8Ofyt8uI+J/nH+uQkCQpGqi8T+FkTdM2/BXXIxAIBAKBQCAQCAQCgaCcf6xDQtO0qL/6GgQCgUAgEAgEAoFAIBBUzT/WISEQCAQCgUAgEAgEAsF1w8G7W/0bcPz+XQKBQCAQCAQCgUAgEAj+9QiHhEAgEAgEAoFAIBAIBAKHI1I2BAKBQCAQCAQCgUAgqA2RsXHdEQ6JuqAqjtXTNMfqGf8F3cDRbfgX1KlkcnKsnqI6VM/g7epQPS/Z8SNOdtwVh+r5tQt1qJ7k4DqV/oI8T8ng2MBD1WxxqB4Ovu+Bv2BMdOxvqcNJS3K8pl+AY/UsDr4vDAaHyhmNjg9wdnPwb5ujh+AAJ8c/t3XxdKzegbwSxwoCDzlcUfB3RKRsCAQCgUAgEAgEAoFAIHA4wiEhEAgEOcKxdAAAIABJREFUAoFAIBAIBAKBwOH8C2L1BQKBQCAQCAQCgUAg+G8Ri0hcb0SEhEAgEAgEAoFAIBAIBAKHIxwSAoFAIBAIBAKBQCAQCByOSNkQCAQCgUAgEAgEAoGgNv6CHbz+6YgICYFAIBAIBAKBQCAQCAQORzgkBAKBQCAQCAQCgUAgEDgckbLxB9E0jRlzPmfnrwcwubgw85XniWjR3MamqLiYZ6fNJCE5BYMsE9mjKy88+gAASalpTH3nQ7Kyc/D28uS9qZMICvCz03h75WZ2nrqIq7MTb4+5jTYNg+yu5WRCKlOWrqG4tJTebcKYMnIAUoVwooVbfuX9mG388vbT1PNwY8uxc8xduwtJkjDKMi93DeOGEF+78+6KS2fmzuMomsadEY2ZcGO4zfGDSVeYufME5zJzeX/wDQwKDyk79siqvRxNvUrnEF/m3dG12nrcdTGJmRv26xodw5nQs53NcbNF4eWfdnMy5Qo+ri7MGtGHUB+PsuPJOfkM/SyGJ3p34MHubQHILTbz+s97OJ9xFQmJt4b2oGNIw/I6/X4jO09ewNXJibfvG0qbRsFV1GkKU775ieJSC70jmjPlroFldbpk2wGW7TiAwSDTJyKcF0f0L7+erByG/ucznmgRyAMt7dtKCm+PfNs4kGXUg9vRdq62Pd7zVuQbI0FV0ApyUX+cD9mZ4OOH4d5nQZZBNqDu24i2f0u19VoVUpsbMNz9KEgy6i8bUDd+b3Nc7jUEuc/tunZJMcrSOZB6+do0Im7AMGqiXr7d61HXr7DV6D0EOXIoqKqusfgjSElAat0Jw4gHwWgEiwXlhwVoZ4/Wqie364Jx3JMgyyjb16L8/K3NcUO/oRgGDANVheIiShfOQkuOR/ILxPmdr9FS9PKpF05h+frDayorgKFTV5wffhZkA5ZNqyn9cXHVdt0jMU2eQdELD6JePHNNGi49bsZ70lQkWaZg1Q/kfzXf5rjb0Ci8npuEkp4GQMHypRRG/4BTi1b4TH0Dyd0dFJW8Lz+jaOO6WvWkdl0wjn0CSZZRdqxF+fk7m+Ny5O3ldVpShGXhbLTk+HID3wCcZy5EiV6Esu57akPucBNODzyjt+GWn7HELLU5brhlGMZBUXqfKS6i9PP30JLiwGDA6bHJyE1bgGz4f/bOOz6Kon/Az/fu0isJKRB6r4Iogt0ggg1pKgp2BX/2Lu8rdrEr+opdsStYEBCliYCgSJfee0ulhPTk7ub3x16Su+RS0NscCfN8PvfJ3u7cPju7m53d2e/M4Fg4G/vUr6r1lcfao7dxDK1W7HN+onhyJcfwrGSC//MC+Q/ejHP78R1D6+lnEvx/D4PVQvHMqRR997nXdLZzLiTkiZfJvft6nNs2YWnfmeD7HjMWilD05YfYFy+o1mfreRbBdz9q+H6ZQuHET72nO68vYc+8Rs7tw3Fs3YgkNCbi8x9x7jOOp33jWgreeL5meex5FsF3P4JYLBTNmEpRFc7Qp18l5/9G4Ny6sXS+xCcS/ulkCj9/n6LvvB+DRdv38+KsJTiciit7tGPkOd08lhfZHfxn6kI2HMwkOjSIcVcmkxQdwdoDGTw1/U9XKsVd559K344tADhWUMiTP/3JtvQjiMDYK86le9N403yf/7WeH/7eigDtEhrw/MBzCSrJ344DvDhnhas8bMPIs7pU9P30JxtSDxMdEsi4weeRFB3OgaM5XP7BT7SIiQSgW1JDnr60N/nFdh6YvJB9R7OxiJDctgkP9unhuU93pfLi/NWGs0tLRvbqUNE5czkb0o8QHRzIuMt7kxQVRrHDyZNzVrIx/QgOp+KKTs0Z5frt5yu38sO63UYeG0ZxaYemvPb5XBxOJ1f27MTIC3pUdHw3lw0HMogODWbc8H4kNTDy8uGClUxevgmrxcJjA87hnHbNABjzwzx+37yHmPAQfrr/mtJ1PfjNbHZlHgUF2QWFRAQHMeWuoUZet+3jxV8WG3k9rQMjz+tecTsmzy87nlf3JalBBIu372fcr8sotjsIsFl5uH8verdKojoW7c3gxT834VBwZccmjDy1lcfyFQcP8+LizWw9lM1rfbvRv7XnPUNOkZ3LJy2ib8sEHj+3U7U+gIBeZxN6/3/AaqVw+mQKvpzgPV3yRUQ8/wZZtwzDsXkDWG2E/fcZrO07IlYbhTN/ouDLj6v1RSWfT7Nnn0asVjK+mUTK2+9WSBMz4HKSHn4ApRT5Gzay4657AQhMakzL118hsHEjULBlxI0U7d9fo3wCNEi+gNZjn0GsVlK/nsi+8e94LG/17FNEn30WAJaQEAIbxrK4Xecarx8grk8yXV58FrFY2fvVN2z/39seyzuPfYbYcwyHNSSEoLiGzGpl/B+EJCXR7X+vE5zUGJRi6bAR5O+rPn9JffvQ++XnsVitbPn8K9a+8ZbH8rAmSZz3/tsERUUhVgvLnx7L/jlzCW/WlKHL/yRr2w4A0pevYPEDjxxXfstz/YR36Hr5xWSnZ/Bc197/al31Ct1kw+foCol/yMKlK9iz/wCzv/6YNRu38My4t/nu/YoPNDcPG0LvHt0oKi7m5gceY+GS5ZzXuyevvDuBgf0vZPDFfVmyajXjPvyUVx73vHAs3LiTPRlHmPXE7azdfZBnvpvNtw/dWMHx7Hezeeaai+nWojG3v/89izbt5LxOrQFIOXKMv7bsppGrcAfo3b4Ffbq2RUTYciCdBz/8jl+u7+OxTodTMXbBWj4efCYJ4SEM+3YhyS0TaRMbUZqmUUQIL1zUnU9X7aiY79PaUFDs4Lv1eyosK3M4GTtzCR+P6EdCZCjDJvxCcrumtImLLk0zefU2IoMDmX3XEGZs2MXr81Yybsj5pctf/nU557bxvDF4cfYyzmndmDevvIAih4OCYkfZPt2wgz3ph5n19J2s3X2AZybN5NtHb6m4TyfO5Jnhl9GtZRK3vzOJRRt3cF7nNizdspt5a7cwdcwoAgNsHMrO9fjdyz/8yrmd2oA9u2KGRbAMuAnHpy/CscNY73gOx6ZVkHGgLM3BPTjefRyKi5AzLsTS/1qc346H7CM4PngaHHYIDMJ678s4Nq2E7KOV7l9PtwXrNXdif2sMHMnE9p83ca5d4lHh4Fw+H+eiGUbyU3phvXIkjrefrNn6SxzD78L+xmOG47G3cK5ZAil7yxzLFuBc6HJ06431qlE43noclXMM+9tPQdZhaNwc233PYx99XbU+2433UfzyI6jDGQQ++x7OVYs9Ho4di3/DMc+o9LGceha2EXdQ/Op/AFDpByl6fFTN81cei4XA2x+m4Kn7UIfSCX51AvZli1D7d3umCw4l4PKrcGxZ/48c0f95ksw7bsGRlkb8199T8Ps87Ds9/+fyZ88k6+XnPOapggIOPzEax949WOLiif/6BwoW/4HK8XJuliAWAm64l6JXHoXDGQQ88y7OVX957FPnX/Nwzv/Z2LxTz8Q2/P8ofu2/pcttw+/AuXZZzfInFgJufZCisQ+gDmUQ9OJHOFb8aVQ4uHD88SuOX6cZvtPOJuDGuyl64WGsvZPBFkjhwzdBYBBB477E8edcVEZqzdzgOoYPUfCk6xi+/olxDPft9kwXEkrAgH9+DIPvGk3ef+9CZaYROv4L7EsW4ty7q6Jj0DAcm9aVznLu3k7e3TeA04HExBL63kTsSxaB00GlWCwE3/dfch/5P1RGGuHvf03x4t9x7tlZwRc05FrsG9d6zHYe3E/OyGHHnceQ+/5D7iN3oDLSCHvva+yVOAO9OAGC73wY+7I/K8wvweF0MnbGX3x8fX8SIsMY9tFPJLdvRpu4BqVpJv+91Sgv7r2KGet38vrcFYy7Mpm28Q34ftQV2CwWMrLzGPz+VC5o3wybxcKLs5ZyTpsk3ry6j6u8sJvmO5STz1fLNjL9ziEEB9h44Pt5zFi/i8FdWhi+Wcv4eHhfozz8ZCbJbZuUKw+3G747B7nKw1WMG3IeAE0bhDNl5OUV9tvNvTvRq0UiRQ4Ht3w9l4XbD3BekxhXHhVjf/ubj688l4SIUIZ9/RvJbRrTJrbsfmHy+t2G89ZLmLF5H68vXMe4Ab2ZvXU/RQ4H027sR36xnQGfzeGyDk2xWSx8tWo702/qT3CAlft/+osn56xk4t1XkhAZzrB3fiC5YwvaJMSUOZZvIjIkiNmPXMeMNdt4feZfjBven+1ph5m5ZjvTH7iW9GO53DrhJ2Y8NByrxcLg0zow4syu/Od7z4r5ccP7GxN2Oy/P/IuI4MCy4zn9Dz6+6TLjeL4/heQOzWkT73Y8V242tuOBa5ixdjuvz1nKuGF9iQ4N5t0R/YmPDGNb2mFGfj6DBY9WXT45nIqxf2zk48t7khAWzLAf/yK5eTxtYspeqDQKD+aF5K58umaX13W8tWwbPRvHeF3mFYuF0IcfJ/u+kTjTU4mc8C1Fi+bj3F3u/zA0lOCrRmBfX1bpH9inHwQGcuz6IRAUTNQ30yj6dQbO1INV+pq/MJYtw0ZQlJJC55nTOTLnVwq2bitNEtSyBY3uuZONVwzBkZWFLbbs5Vert97g4P/e5tjCRVhCQ0E5jyuvbV4ay7qrh1N4MIVTZ//CodlzyHNz73zymdLpxrfeTHjX46uMwGKh6ysvsGToMPIPpnDu3JmkzppDzpatpUk2PP5U6XSLkbcQ1bWsErH7u2+x7Y3/kblgIdawUHCqapVisXDW6y8xa+BV5B44yBUL5rB3xiyOujm7P/Igu6ZMY/OEz4hu345+P0zku66nAZC9azdTz0k+vnxWwV+ffc2Ctz/kpi8+8Nk6NRpv1JsmGyIyRkQ2iMhaEVktIl5fy4vB8yKyVUQ2ici9/8T32x9LGNj/QkSE7p07cCwnl/RDhz3ShAQH07uH8UYlMCCATu1ak5pxCIAde/ZyZg+jdr7Xqd347c8lFRzz1m1j4BldEBG6tUwiO7+QjKwcjzQZWTnkFBTSvWUSIsLAM7rw29qyC/LLP/7GQwMv8KjMCwsKLH3bn19U7HU03XVpR2gWHUbTqDACrRYuaZvEvJ2eN/pJkaG0bxiFxUtN4ZlN4wgLrLq+a93BTJrFRNK0QQSBViuXdG7JvK2eb+Tnbd3HoFOMypV+HZuzZFcKShkX9blb9tIkOoI2Dctu2HIKi1ixN42h3Y1ojkCrlUjXDQnAvLVbGNirq2ufNiE7r4CMLM8HtIysbGOftmpi7NNeXfltzRYAJi1ayW39zyIwwMhbbERY6e/mrt5Ck4bRtGnkGelSSpPWqMNpcCQDHA6ca5cgHU/zSKJ2bYTiImN633YkynUz4nAYlREA1oDjrp2VFu1QGQchMxUcdpwrFmLpdqZnooL8sunA4ONaP4C0bI9KTylzLP/diyOvnMNVQO/bYVRGABzcAwGBYAuo2te6AyrtACojBRx2HEvmYTntrMp9QcGgqr8hqCmWtp1wpuxHpR00ojr+mIut17kV0gWOGEnxlK9Kj+vxENjlFOz79uI4sB/sxeTNnkHwBRdW/0PAvnc3jr1GRYIzIx3nkcNYYqq+uZXWHVDpB8C1T51L5mPpUd0+Lftq6XE2KiPFo0KhKixtOqJSDxjnjcOOY/FvWHue45kov8wnwe7HUBnfLVYIDAK7HZXnWUFYrb/8MVw0F1uv8yqkCxwxiuLJX0PR8R9DS/vOOA/uQ6UeALsd+4I52M48v0K6oBv/j6Lvv0C5OwoLyyofAoJqdP5aO3QxfCmGr3jebALOvqBCuuBb7qJw0mf/KE9enQc8nbazKjqDbrmTIi9O29kX4EzZj3N3xcrtEtYdKCkvIl3lRSvmbd7rkWbelr0M6mZc+/t1asGSnQdRShESYMNmMW53Cu2O0stnTmERK/akMvTUdkBJeRFkmg+MB9UCuwO700lBsYP4iFDDd/AQzWIiysrDTs0rlofbypWHu1NLy0NvhATY6NUisTRvnRJjSMsu+39al3qYZtHhNI0ON8r59k2Zt93zAXTe9oMM6tzccLZLYsnedJRSCJBfbOSj0O4gwGohLDCgQh7Tc/NJjAihaUwUgTYrl3Rrw7xNng/g8zbtYlAP461yvy6tWbLjAEop5m3axSXd2hBos9IkJpJmsVGs25cOwOktGxMVGkRlKKWYvX4nl55iRK6u259Bs9gomsZEGtvRtTXzNu323I7NexjU3TgX+nVuxZKdxnZ0atyQ+EijrG8T34BCu4MiexWVgsC69KM0iwylaWSosW9bJzJvd5pHmqTIUNrHRni9Ed+QkcWh/ELOalIxerUybJ264ty/F+fB/WC3UzR3JoHn9qmQLnTkPRR89anntQaFBIeA1YoEBUFxMSo3p8Jv3Qk/tTuFu3dTuHcvqriYQ9Om06B/P4808SOGk/7ZFziysgCwHzLugYPbtUVsNo4tXASAMy8PZ35BjfMa0aM7+bt2U7DHcGdMnUbsxf0qTR83eCDpP06r8foBGvQ4ldxdu8lzOQ5OmUbiJf0rTZ80ZBAHfpwKQHj7dlhsNjIXLATAkZuHIz+/0t+WbufpPTi2czfZu/fgLC5m5+SpNLvsEs9EShEYYbwcDIyKJC/1OCrhj5PtixaTd/iIaevXaEqoFxUSInImcDnQQyl1CtAXqCzW/CagKdBBKdURmFRJuipJy8ykUXxc6ffEuIakZWRWmv5Ydg7zFy/jzNOMCor2rVsyZ+EfAPy6aDG5efkcyTrm8Zv0rGwSo8siEhKiI0gr9/CclpVNQrk06a4089ZtIz46nA5JCRW2Z+6aLVw29kP+74PvGdu3e4XlaTkFJIaHlOUvPJj03OovpsdDWnYeiZFlD/SJEaGkl4s4cE9js1iICArgaH4heUXFTFi8njvP8wyh3Xckh5iwIMZM/5MhH03niZ8Xk1dUXLo8/Wg2iW7RIgkNIkk7Wm6fHi23TxtEku5Kszv9MCu372PYK59ww7gvWLfbuHnLKyxiwq+LufPSig8zJUhkDGQdKptx7DAS1aDS9JbTL8C51a3ZQlQM1ntexProWzgX/lzz6AiA6Fg4UnZ+qiOZxrzyzvMvx/bsBKyDb8Hx7fs1X3+J43BGmeNoJjTw4rhgALbnP8E69FYck96rsFx6nIPatwPsxRWWeaRr0BB1OL3MdzgTaRBXIZ2170ACX/uKgGtGYf+yLNxS4hIJfO4DAse8gbTrWuF31SExcajMshtMdSgDifH0W1q2QxrG41ix+LjXD2CJT8CRllL63ZGWijWu4v9zyIUXEf/tNGJe/R/WhIpNhQI6dwVbAI59eyssc0caNEQdcjuGhzOQBhUr2CwXDiTw1S+xDRuF/SvXPg0Mxnr5NTimfFHT7EFMHOqQ2zE8lIHEVPRZ+w8m6K1JRoTLp/8DwLFkAaqggOAPpxL87g/Yp0+E3CqiP7wgsXGoTDd/ZjoSW+4Ytio5hpW/va8KS2w8zoyy88SZmY40jPdM07o9EpeIY+kfFX/fvjOhH35L2AeTKHzrxaqjIwBpGI9KL7tBdWakVfS1aY8lPsGItijvS0wi/MNJhL35Mdaup9Yoj9IwHme62/9CZhqWuHL7sU17LHGJFZ3BwQReczOFn1f9Bi4tO9ezvIgMI93t4Rog7VguiVFu5UVwIEfzCwFYsz+dAe/+yMD3pvDUZWdhs1jYdySbmNBgxkxbxJAPpvLET3+Ulhdm+BIiw7j5zC5c+Ma3nP/6JMKDAzi7dZLLl0diRHmfZ5lrlIehZT5XeQhw4GgOQz7+mRu+nM2KvZ4PvmA0ZVywbT+9W5RdH9JyjMqCUmdECOk55ZxuacqcRfRr14SQACvnv/8zF344g5tPb0d0SCAJESHc3LMdF370C+e//zMWhI7xZS8NEiPDSc8qV84fyyXR1RTTZnXtx7wC0rNySYwqiyhIiAoj7VjNKh1X7kklNjyEFrFRZY4ot/0bFVbxfsP9eFotRAQFcjSv0CPNnA276NioIYE2a5X+tNxCL/dQhVX8ogynUryyeDMPn9m+RulLkLh4HGme//uWOM//fWu7DljiEyle/LvH/KJ5v6IK8on+aT7RU36lYOJnqGzPe9LyBCQmUnigrAKrKCWFwETP8im4dUuCW7Wi47Qf6fTzVKKSjcrY4FYtcWQdo82ED+g8ZwZNn3jMaJJaQ4ISG1F4sKxsLDyYSmBixea3AEFNkghu1pSjfxzfNTy4USL5B8oiWAsOphDcqGL5ChDSpAmhzZqR6bqvD2/diuKsLE7/fALnzZ9Dx6efqFH+Qhs1Ind/mTPv4EHCGnvma9WLr9J62JVcs2kN/b6fyF+PlEUohjdvxqBF87h0xjQSztRNLMxDTvBP3aNeVEgAjYBMpVQhgFIqUylVWZzZHcCzShmxYUqp9ErSVY2XlxJSyVtru93BQ8++zPVDr6Cp68Ly6J23sXz1egbfejfLV68jIS4Wm9WzgPP24kPKnWje0xiRDx/MWcw9l1Z8YwvQt1t7fnl8FG/fNoS3llRsD+39nYtvT3LvL3bK569iIgHeXriaG3p1Kn0jU4LD6WRjymGGndaeH0cOICTAxseLy8Ksve4vqck+NdI4HE6O5RUw6ZGbeXjIhTw4YTJKKd7+eSE39OlFmFs0RjVZq1wGSLezkcatUIt+LpuZdRjH+P/iGPcglh7nQlik1996X6EXuRe38/efsT95K46pn2K99JqKv/GFY8F07GNuwfHjBKyXXuu5sFFzrENvwfHVWxV+9099jrnTKHr4Ooq//RDbQCPMVh09TOH911L0xO0Uf/0ugXeOgeDQ6p0efm8z3fwiBN56L0Wfjj++9VaLZx4LFs4n9bILSR82kIKli2nw7Eseyy0N42gw9hWOPP3YP4wQ8XIMf5tG0SPXY//uI6yufWodciOOWT9AYc3fcnn/n6g4yzF7CoX3XoP96/exDb0BAEubTuB0UHD7IAruvhrbgGuQeO83pJX7qzmHRAi89T6KPqnB+Vipw8u8co6g2x+k8MM3vP7cuWUDeaOGkXfPDQRec7MRPVSlr/o8hdz1CPnvjquY7HAG2ddcTM6oa8h/93VCH38RQsMqpKvo9DLP/TiKEHznwxS893qFZEE33UHRD195Rmh5W10NTl1vSUo2rVuTeKbfOYTvRl7BR3+spdBux+FUbEw5xLDTO/Dj7YOM8uKPtab5svILmbdlL7/edxULHryG/CI7P63dXum6yu/XysqmuPAQfrt7KD/edjmj+57Oo1P/IKew7O233enk4SmLuK5nB5o2iKhyfTXKoxjRFRYRFtx+OXNGXsJnK7ay72gOWQVFzNt+kF9vu5QFt19OkcPJnqM5FVfgkS9v5bxU6q4Jv6zdXhodUVk+Kt5vVO3blnaYcXOW8vRA7/dVHuuqXlcpEzfs5bxmcTRyq9CoGdX/74feO5q88a9WSGbr1BUcDo5e0YejV15M8DU3YmncpBpd9WWwWG0EtWrB5qFXs/2Oe2j52itYIyMRq43wXj3Z98zzbLhkAEHNm9Fw2FU1yaRrxd5mej/KcYMGkvnzDKPfo+OhhvcYAI2HDCRl+s+lDrFaiTmzFxuffIZFfS8hrEVzml5bg6ZwXpzl/z9aXzmYbV9PYlLHbsy56lrO//BdECEvNY1vO5/K1HP7sPSxJ7hgwvsERIRXWJ9GcyJSX/qQmAM8KSJbgbnAt0qp3ytJ2xoYJiKDgQzgXqXUtvKJRGQUMArg/VfGMur6a/h6ynS+/3k2AF3btyUlvexNYmpGJvENvYfWPfnaWzRvksSNVw0qnZfQMJbxYx8HIDcvnzkL/yQiPIyvp0znu+8nG45mjUh1e3ufdjSb+CjPi0tidITHG/60o9nERUWwL/MIBw5lMfjlT0rnD331M7596AbiIsvWcXqbZuzLyuNIfiENQsrCHxPDg0l1e1OSmlNAfNjxh/FXRWJkKKlubztSs/NKw1fL0oSResx4U2V3OskuLCYqJIi1BzKZs2kPr/+2guyCIkSEIJuVfh1bkBAZSrck4+1cv47NeXbGX8x/wegIsGvzRqQeKav1TztyrOI+bVBunx45Rpzr7U1igwgu6t4eEeGUFklYRDiSk8fa3QeY8/cmXp/yG9n5BYjdTqDVwog2ZW8nVNZhJMrtHImMQR2rGOUgrTtjuWAgjo/HljXTcCf7KCptP9KiA2pDDdvqH8kEtzfd0qBhWRMJL6gVvyPX3lWzdbs73CIEJLohHK3Csfx3ZMQ98JnrISW6IbY7n8DxyWtGk4FqUIczkJiy/SsxDY2ojEpwLplPwE33G1/sxZBjvAlVu7eh0g8ijZqgdm2t9PcV/IcykIZlb4MkNg512M0fEoqlWSuCxxodbUl0DEFjXqbw+dE17tjSmZ6GNaHsIduakIgjw7MO1ZlVdg7l/fg9Ufc+XLZNYWHEvvU+x955k+J11XcSqo5kekQISEwc6sihStM7l8zHduN9AFhad0R6ngfDRkFoOCgnqrgI59wqwmQPZSCxbscwNs6I3qkEx+LfCBj5EMWA9Zy+OFcvM5ozHTuKc8s6LK074Eiv/twpzW+5aAVpGF/xGDZvRfDzRkdt0iCGoDGvUPj8ozXu2NKZmU6AW1SLpWG8RxQKIaFYWrQm9BUjQkBiYgl5Zhz5Tz2Ic9umsvXs240qyMfSorXH/Ap5ykhD4sve4lniEjx9oWFYWrYm/M2PS32hz79J3pj7cWzdiCo2QqudWzfhPLgfa5PmONw6n/TuTMcS7/a/0DABZ2ZFZ9gbbs6xb5L3+P1YO3Qh4Ly+cPv9SHiE0XlpURHFU7/1cJSUBSWkHsv1Xl5kuZUXBUVEhXiG9beOiyYk0Ma29KMkRIaSEBlGtybGOdCvUws+/nOtab79R7JJig4nJsx40LyoY3NW70vnik7NSYwIJTW7nK/cA6lRZuaVKw+NJpglb+w7N4qlaYMIdh/Kpours+qnfllC85gIbjijo+f6IkJIdYvCSM3Or+gMN9IkRoSWOYMD+WXTPs5tmUiA1UJsaDCnNm6xGesmAAAgAElEQVTI+jSjI+mkqDBiXM0pzmmRyNQNu93ylUN8ZLn9GBVO6tEcEqPCsTtc+zE0iISoMFLdmqmmZeUSH1F9BZnd4WTuxt18f8dgt31nHKvS7cjycjyjXMezZDsKy45nalYO9078lReHJtMspvqXAYlhQRXvoapoYuLO6tSjrEw9wsQNe8mzOyh2OAkNsPJg76ojJlRGmkeEnCXO8/9QQsOwtmpDxDtGh7OWmIZEvDye7NH3ENjvUoqX/gkOO+rIYezrVmPr0Jmig5V3wlickkJQUlln5oGNGlGU5lk+FaWkkLNyFcpup2jfPvJ37CS4ZQuKUlLIW7+Bwr1G1N6RWXMI73EqmRM9/+8rozAlhSC3yIGgxokUVdJ0IX7QFWz/z5gardedgoMphCSV9VEW3LgRBakVo48AkgYPZN2jj5V+z09JIWvtevL2GPlLnTGL6NN7sO/riV5/X0LewYOENSlzhjZuTF6KZ77a3TCC2UOMyo30ZSuwBgURHBtLQWYmhYeNishDq9eSvWs3UW1ak/l39WW/RuNv6kWEhFIqBzgNowIhA/hWRG6qJHkQUKCUOh34CPikknV+qJQ6XSl1+qjrjTfFIwYPYOqEt5k64W0uPPdMps3+DaUUqzdsJiIsjPjYiu2z3/z4c7Jzc3nsHs/O844czcLpqkn98OvvGHpJv1LHlNG3MGX0LVx4SlumLVuPUoo1uw4QERxEXLmH57iocMKCA1mzy2jrOG3Zevp0bUu7xvH88cK9zH36TuY+fScJ0RFMfuQm4iLD2ZNxpLTGdeO+VIodTqLLvdnvkhDNnqO57M/KpcjhZOa2AyS3qhgq/m/o0rghew4fY/+RbIocDmZu2EVyO88a+eR2TZm61mhXPGfTHnq1SERE+OrGS5h7z5XMvedKrj+jE6PO7sqInh2JCw8hMTKMXYeMm+olu1I4t00Tpjw2kimPjeTCU9ozbek61z7dT0RIMHFRER7OuKgIwoICWbNrv7FPl66jzynGjUCfU9qzdMtuAHanHaLY7qBBeChfPXQjc8few9yx93B98hmM6tDIozICgAM7kdhEaBAHViuWU3qjNq/0TNOoOZaBt+L46nXIdQuXjIwp61MhOBRp3g6VeRwPXnu2IvGNITYBrDYsp59ndGrpkfGyGwvp0hOVXkVnVt4cu7d4Onqeb3Rq6U68m6PrGag0V2hiSBi2e57FMeVT1I6qH35KfTs3I4lJSFwiWG1Ye/fBueovjzSSUFawW7r3NtrxA0REgRiXP4lrhCQ0MfoxOA6c2zZhadTEeCtvs2E9py/2ZW4h93m55N1wKfmjhpI/aijOrRuOqzICoGjDOmzNmmNtnAS2AEL7X0rBgnkeaSwNyyoQgs/vg32Xqx2+LYCY198m7+dpFMydXSOf2rnZ2GcNjX1q6Z2M82/P5iYe+7Rb79JjWPz8/RQ9NIKih0bgmDMZx/Rvqq6MAJw7NiONmiBxjYxjeNaFOFZ4NluQxLJrgqXHmagU4wZZZaZh6eLqrT8oGEvbzjgPVN0kpYJ/2yYsjZsiCa5jeG5f7EvdmhTk5ZJ33SXkjxxC/sghOLdsOK7KCADnlo1YkpoiCY3BZsN2QT/sSxZ6OHKv7kvujVeQe+MVODatL62MkITGRh8ZgMQnYmnS3OjvogocmzdgTWqGJBq+gD79PcOzc3PIHpRM9rWXkn3tpTg2riutjJCoBqVhxdIoCUtSM5wp1fcK79i8AUs5p/2vBR7OnMF9yBl+GTnDLzOcj9+Pc+tG8u6/tXR+0eSvKfxmQoXKCIAuSQ3ZcyjLrbzYSXL7Zh5pkts1Zeoa4/3CnI276dWyESLC/iPZ2F3l7YGjOezKzCIpOpy48FASo8LYlVlSXhyktatPIjN8jaLCWHMgg/xiO0opluxKoVWJr3Esew5ns/+oy7dxD8ntmnr62novDw/nFuBw+fYdyWbP4WM0aWDcK/xvwd/kFBbz3349K+7TxAbsOZpTVs5v2Udya88oo+TWjZi6weiLZs7WA/RqFo+I0CgypLQ/ibxiO2tSDtEqJoJGkSGsSTlcmscDx3IpsDvYf/gYRXYHM9dsJ7ljS09HxxZMXWX8T81Zv4NerY0+sZI7tmTmmu0UuX6/JzOLrk3Llate+Gv7flrGRXs09+iSFOc6nq7tWLeD5A7NPbejQ3OmrjYqpeds2EkvV99cx/ILuePLWTxwUU96NPcesl9h38ZHsScrj/3H8ox9uyOV5BbVbzvAq327Me+6C5h73QU80rs9A9slVVsZAWDftB5Lk2ZYGiWBzUZg30so/mN+6XKVm8PRS88la2h/sob2x75hLdmj78GxeQPOtBQCTjvDSBgcgq3zKTj2eO9ss4Sc1WsIatmSwKZNkYAAYgcO4OjsXz3SHJk1m0jXSBe2mAYEt2pJ4d695K5egy0qCpvrvjny7LPI31rh3WClZP+9hpBWLQluZrjjBg3kUDk3QEjrVtiioji2YqWXtVTN0b9XE9aqJSEuR+PBA0mdWbEsDWvTmoDoaI4sX1H221WrCYiOItDViWfsuWd7dIZZGRkr/yayVUvCmzfDEhBAq6GD2DtjlkeanP0HaHy+0Tw4ql1brMHBFGRmEhwbi7iu3xEtmhPZuhXHdlfesbzmXyByYn/qIPUlQgKllANYACwQkXXAjcBnXpLuBya7pqcA3scmq4bze/dk4ZLl9Bt+K8FBQbzwnwdKlw269W6mTnib1PRM3v/yW1o1a8qQkUbfmSMGX85Vl1/M0tXreOPDz0CgZ7cuPHl/xbfR53VqzcINO7n42Q8IDgzg+RGXli4b/PInTBltjA7x5NX9eezrXygssnNup1ac16lVhXW58+vqLUxbvh6b1UJwgI3XLzmtQrMFm8XCmAu6MnLaEpxOxeDOzWgbG8n4JZvpHB9Nn1aJrEs7wr0/L+dYYTHzd6Xy9tItTL8uGYDrfviDXYdzyCu2kzxhDs/17c45zeMrOi7uxciJc3E6nQzu3pa2cQ0Yv+BvOjeOpU+7Zgzt3pbR0xbR/50fiQ4J5LXBFTuDK8+Y/r14dOoiih1OmkSH8/yAs8v2aZc2LNywnYufesfYp9cPKNunL3zElMdGGvv02kt47IvpFBYXc27nNpzX2ehIbMhZ3Xn8y+lc8dwHBNisvHDjFZU21amA04lz+mdYbxoNYsG56ndIP4DlwqGoA7tQm1dhuXg4BAVjvdZ466yOZuL8ahwS1xjLpSOMcEERnH/8AmnHMSSn04lj0nvY7hlrDMm5eA6k7MVy+XWovdtQa5diuWAAlg7djTckeTk4Pq8YXl2tY+K72O5/3nD8OQdS9mC54nrUnm2oNUuwJF+BpeOpZY5PDYcl+QqIb4z1suFw2XAA7G8+BtlZVfrsX4wn4JGXXcM+zkQd2I1tyE04d23F+fdirBcNwtL5NMOXm03xhy8bvvanYBt6s9Ee3+mk+LM3jrv/AZwOij4aR/BTbxhDRs79GbVvFwHX3oZz+2Ycyyv2B3DcOBwcffk5Gr47ASwWcqdNxr5zOxF33EPxxvUU/D6f8GuvJ/j8ZKOj1KwsjjxltCcN6XcxQT1OxxIdTegVxpvCo0/+l+KtVTxMl+zTR19GxOLap3uwDrkJtWsLzr//wtJ3EJbOPYzondwc7K59+o9wOij+5A0Cx7xuDPs5/xfU/t3Yrr4V547NOFf+ie3iIVi6nm4cw5xsit4xhqG0z5pC4J3/Jej1L0AEx/wZqL2Vd4pYmb/og9cJfvpNsFjKjuHwkTi3b8KxzAfH0Omg4J1XCX1hPFisFM/5CeeenQTecDuOrZtwuFdOlMPapTuBw24Eux2cisLxL6GOVfE/4fLlv/USYa+8BxYLxTOn4dy9g6Cb78CxZSP2cm3HPXzdehB8853GsXU4yX9jbLXtyEvzOP5lQl9+F7FaKJo5DefunQTddAeOrVU7a4rNYmHMpWcy8qvZOJUyyov4Boyfv4rOjRvSp30zhvZox+gpC+n/1vdEhwTx2pUXALBqbxof/bkWm8WCRYQnLjuLBqFGxN+YS3rz6I8LjPKiQQTPu8LxzfA1CA2mX8cWXPnBNKwWoWOjWK4+rT3gNHz9z2DkxN+MMrdbG9rGRTP+99V0bhRLn3ZNGdq9DaOn/UH/d6cSHRzIa4ONbV2xL43xv68p9T11SS+iQ4JIPZbLB3+up1VsJEM//gWAEae358qOTcry2Kc7IycvMpxdWtC2YRTj/9xA54QG9GnTmKFdWzJ65jL6T5hpOC8z+gu/tnsbxsxezhWf/4pSxm/bu0YE6dc2iSu//M3IY3w0z/U7jZGfTDf24+kdaJsQw/hfl9E5KY4+nVoy9PSOjP7uN/q/+hXRocG8du1FALRNiKH/Ka0Z8MZErBYLjw88F6vrgevhiXNYtusgR3MLSH7xc+7u25OhPY2hMWeu3calXVt7nj9WC2MuP5uRn8807jd6tDe247cVxvHs2IKhPdozevJ8+r8xyTieVxsdCH+zdAN7Dx/jvQV/896CvwH4+MZLia2iSYXNYmHMOZ0Y+csKI9/tm9A2JoLxy7fROS6KPi3iWZeexb2zV3Gs0M78PRm8vWI704edU+k6q8XhIG/cC0S88YEx7OfPU3Ds2kHIbXdh37yB4j8WVPrTgskTCR8zlsivpiIiFP4yFceOah6gHQ72PPYEHSZ+CVYrGZO+JX/rVpIeeZDcNes4OudXsub/TtT559H1999QDgf7nnse+xEjqm/vs8/T4buJIELu2nVkVBM9UN69/b9P0GXS14jVQurEb8nbspXmjz5M9po1HHZVTsQPHkT6tJ9qvl43lMPB+tGP0fv7iYjVyr5vJpGzZSvt//MIR1evIW3WHMDVmeWUqZ4/djrZ+NSznDnlOxAha81a9nzxtRdLRedfj/yXi6d8h1gtbP1yIkc3b6HHmNFkrlrN3pmzWfbYk5wz/g0633U7KFh0xz0AJJ59Jj3GjMZpt6McTv68/2GKjhxHX2NeuPWbT2h3wTmEN4zlxX2bmP7UCyz+xPuwzBrNv0Gq6qG5riAi7QFnSdMLERkLRCul7vaS9iVgq1LqExG5AHhVKVXx1YEbKnVHre4k55rKb1BNYXvN3kj7lMjKO3M0hcZNq0/jQ9S8WdUn8rWztntCdhxne8x/q8v/96MBHJcv+zj6QvARR/dU3sTFDBp2Tao+kQ9xFtTuMXQWeGnyZLazsOrOWH3uK6rdPNa4AtaHhN12nP3Z1DWq6cDX51TTX4cpNKxZdIDPsNfy//5xRmf9W7LKDXdaG2zfVYPKSR9ScLx9PvxLsqoZOcUM0otr9zxdnl2zjlV9yfvqWN18ZV8VOYdP7Ifn8Jg6t8/rS4REODBeRKIBO7AdV/8PXngJ+FpEHgBygNtqZxM1Go1Go9FoNBqNRlNnqaPNIk5k6kWFhFJqJXBWDdMeBS4zd4s0Go1Go9FoNBqNRqPRVEW96NRSo9FoNBqNRqPRaDQaTd2iXkRIeENEpgAty80erZSqWXfzGo1Go9FoNBqNRqPRaEyj3lZIKKUGV59Ko9FoNBqNRqPRaDSamqD7kPA1usmGRqPRaDQajUaj0Wg0mlpHV0hoNBqNRqPRaDQajUajqXXqbZMNjUaj0Wg0Go1Go9FofIYe9tPn6AgJjUaj0Wg0Go1Go9FoNLWOrpDQaDQajUaj0Wg0Go1GU/sopfTHxA8wSvvqru9kyGN9950MedT7VPvqglP76r6zvvtOhjzWd9/JkMf67tOfk++jIyTMZ5T21WmfP5zaV/ed9d3nD6f21X2n9tV9Z333+cOpfXXfqX0azb9AV0hoNBqNRqPRaDQajUajqXV0hYRGo9FoNBqNRqPRaDSaWkdXSJjPh9pXp33+cGpf3XfWd58/nNpX953aV/ed9d3nD6f21X2n9mk0/wJRSvl7GzQajUaj0Wg0Go1Go9GcZOgICY1Go9FoNBqNRqPRaDS1jq6Q0Gg0Go1Go9FoTEREZohIC39vh0aj0Zxo6AqJWkJEGvt7GzRVIyId3KaDyi3rXftbpNFoNBrNiYOIxPt7G8xGRGJNWvVnwBwRGSMiASY5NBqNps6hKyRqjyW+XJmIjBeRCC/zO4jIXF+63NZ9XlUfE3x93KZblls2xNc+4Bu36b/KLXvXBB8AIhIlIsNE5EERecA1HW2Wz+WMFJHWXuafYpIvUUQSXdNxIjJERDqb4arE/0Itulq68teh+tT/2NFMRIJd0yIiN7uuCXeIiM0sbyXb4vPOrkTEKiK3i8hzInJ2uWWP+9rnWm+oiDwqIo+ISLCI3CQiP4nIKyISbobTyzZsNXHdp7hNB4jI4678vSAioSb47haRhq7pNiKyUESOishSEenqa18NtmemCeuMFJEXReRLERlebpnPywzXdfQ9EXlHRGJF5GkRWSci34lIIxN8MeU+scAyEWkgIjG+9rmcF7tNR4nIBBFZKyLfiEiCCb6X3M7T00VkJ7BURPaIyPm+dCmlvgNOBSKBFSLysKvcf1BEHvSlqwQRsbmupbNc+3GNiMwUkf8zo1JEX7tNWXetXrtdnhPq+q2p/+gKidpDfLy+VGB1yU2Q64L8CvAT8I6PXSU84uXzMPAlMN8E32tu05PLLTOjYJNKpr19941Q5AZgFXABEAqEAcnAStcyM5xXA5uBySKyQUR6ui3+zATf7RgVPEtE5A7gZ+By4EcRudUE31vlPuOBO0u+m+Cb6jY9EJgHDACmichNvva5mEHZ9fsl4DJgKdATE3rD9vJg4v6AcqmvfcAHwPnAIeAtERnntsyMykgwzv0EoCXwC3A6xjVIgPd8LRORbBE55vpki0g20Lpkvq99eP5vvwS0AV4HQoD3TfDdoZTKdE3/D3hDKRUNjDbJh4j0qORzGtDdBOWnGOfHZOAaEZksZdF1ZkTVfQZsBPZhlLn5GP/7izBnn2YCK90+K4AkjDJrhQk+APfK49eBFIzr6XKM64KvucztPH0VGKaUagNc5PL7mmIgFwgCIsp9zOBLjHP/aYxr9WXAM0A34CsTfPra7Xs+c5uujWs3+OH6rTnJUUrpTy18gL0mrLPk4rsQ2I5RkIfWYp7OAWZiRH8MMGH9f3ub9vbdR75V3qa9ffehcwsQ7WV+A2CrSc7VQCPX9BkYlRNDTNyv6zAqW2KBHCDRLY+rTfDtx7jRugG40fXJKJk2wed+ni4GWrqmGwJrTDqGG92mVwIWt+8+dwIOYCewy+1T8r3IBN9at2kbRiXLjxg38T4/R12e1a6/glHhK27f15rgGw98ASS4zdtlRt5c63Y/T1cDASbnb4vb9PLKjq+PnQ6MCsH5Xj75Zp0zbt/HAH+6rnU+LzPKHcO9VW2Lj3wPA7OArm7zdplx7NzW714Ol9+/ZuRxM2BzTS8pt2ydj10XY1QovUQt3au5/x96Webzewx97TYlf7V67S5/3tTW9Vt/Tu5PrYb21ndcb2K9jaMqgBkh+CUuG8bb0k1KqTwTPB6IyIXAEy7/C0qpX01SqUqmvX33BU1cb9DFbRrX9yQTfCXr9pYXJyZFZQBWpVQKgFJqmYgkAz+LSJNKtuXfUuw6L/NEZIdSKtXlPiIiZvg6As9h3Pw9opQ6ICJPKaU+N8EFnvvMppTaBaCUyhQRp0nOfSLSRyk1D9gNNAX2iHltn3cCFyql9pZfICL7TPAFlkwopezAKBF5EuNh09QQXKWUEpEZSinl9t3n56lS6h7Xm/uJriibtzHn/6+EKBEZjFFWBCmlil3bYUr+gB9E5DPgWWCKiNyP8WByIVDhPPIRm4DblVLbyi8w6TwNEhGLUsoJoJR6XkT2Y7wkMOM8dY9q/aKKZT5BKfWaiEwC3nDtv6cw9xwFiHc1XxAgUkSk5H8Rc6J63wFmiMhLwCwReZOy83S1j11jgKuUUht8vN6qOCIiVwGTS85TEbEAVwFHTPDpa7fvqe1rN/jn+q05idEVEr6lqhBGn4Y3utri3QSMUUp9KyJJwP9E5DaMUKuNvvS5nJdhFKhZLu+fvnaUo5WI/IRxY1Iyjet7y8p/9o95xG26/PEyKzz1eWCViMzBCMMFaIYRLvqcSc5sEWmtlNoBoJRKEZELgKmAGf06OEUkwFWIXlYyU4w+EMy4ic4G7nfdMHwlIr+Y4XGjmytMUzAeUBKVUqkiEghYTXLeBnwhIk9j/D+uFpG/MaJOzGiL/KZr3d5uRF4xwbdCRC5WSs0qmaGUelZEDmJCCK6bM1wplaOUuqVkphh9rWSbIVRKrRSRvsDdwO9AsBkeF78DV7iml4hIglIqTYy+XTKr+N0/Qik1xtVkaSLQGuMN6SiM68wIX/tcPE3l/+v3mOCbDvQBSvttUkp9LiJpGG9Rfc00t3O0tNmiiLQBTGnDrpTaD1wlIgOAXzGi3czkI8qaL3yOEWmW4TpPfV1BgFJqvIisA+4A2mHcF7fDOE/H+th1ri/XV0OuAV4G3hWRkgqIaIyooWtM8Olrt++p1Ws3+O36rTmJKQlr0piI68FrgFLqex+u83/A466HL/f5lwDjlFIdfeVyW7cTIxx+DV5qg5VSV1T40b/zVdmhlFLqd1/6/IWINAD6Y0RhCMY+nq2UMuPtBSLSDchVSm0vNz8AuFop9bWPfc2Ag663Je7zk4COSilTOmF1OQS4EzhTKXWdWZ5K3NEY+SvfQaovHR0pu4nejxFa6fOoDBHprZTyace8J5KvOme5t7Sm+MTolPBUpdQMX3oq85mNn47hEKXUj9pnjk9EQoDWSqn1teU0m9r2+RNXBJ2osr4B3JddZGK0q7dtqRWf+7XbLGdl1+76uk/97dTUT3SFhEmIiBXoB1yL8bC5SCl1ZS25g5RShSast15XEIjIOUArpdQXru8/ACU9iY91hcf7a9v+UkqdWZ+d2nfiOkVklVKqhy+26UT0+cOpfXXfqX113+mPPJ6InAz7vb7n8WTYp5r6ix5lw8eIMQTm+xjtum/DqJRo6evKCBH5zm365XKLp/vSVYJS6ndXpcNSjB6UM4GlbvN9iogMFJG73L4vFZGdro8ZlTvP4Nk0oz1GM46ngUdN8B0PZoYDnihO7asfTo1Go9HULczqs+pE8fnDWd99/nJq6iG6DwkfIkZnVnsx2sk9opTKFpFdypyOJtu6TV+EMRRPCXEm+BARG8ZIHrcAezAqtJqIyKcYfUoU+1j5KJ5tHIMwhjUMwxhu7Qcf+yLL9b2xTSm1EkBEXvSx63jxRyhTbTu178R1uvfhUlHi4+ZafvD5w6l9vqeDiKz1Ml8MpTpF+05onz+c/sjjiYguD7Wvrjg19RBdIeFbJgODgGGAQ0SmYd4/a1XrNcv5KkZnUy1L+q4QkUiM8Z5fA+7zsS9QKeXeM/ofSqlDwCERCfOxC8qNhKKUch8zO8EEn0ZTV8jAGPe8vvr84dQ+37MLGKB9ddbnD6c/8qjRaDQaN3SFhA9RSt0nxtA4yRh9R7yKMWzV1cAMpVSOD3WhInIqRpRCiIiUtOESIMSHHncuB9q5d+6mlDomIndgjOXt6wqJBu5flFJ3u301Iwpks4hcppT6xX2miFwObDHBdzycDKF42nfiOnNquY+Y2vb5w6l9vqdIKbVH++qszx9Of+Sx1hCRnsA+5RpuW0RuAIZiRLk+rZQ67Eq6uy76/OGs7z5/OTUnN7oPCR+jDOYppUYCLTCGxxmE7/9pUzDePr0GpFIWpfCq67sZKG89zSulHJgTlbFUREaWnykitwPLTPA9AIwTkU9F5B7X5zNgnGuZzxGRd11RJtVxfV11ap9vfX5y7vLRek5Unz+c2ud7zB6KWvvqn9MfeaxNPgCKwOjjDHgJ+AJjuOgPSxKViwitSz5/OOu7z19OzUmMHmWjlhCREKVUvg/XdwZG7WWK6/uNGLWXu/GsvfQZIjIV+LFkFAq3+ddhDBfp62E/4zHGPC4EVrlmn4bRl8QgpVSaL30uZxBGJVJnjEqWDcA24Fql1F1V/fYf+h4FRgJPKaW+8fX6TwSn9tV9p4hUedOhfDxsXm37/OHUPlOO4YPVOMdp34nr84fTH3msTURkjVKqm2v6HSBDKfW06/tqpVT3uuzzh7O++/zl1Jzc6AoJHyIibYExwGGMt+ofAecCO4BblVIrqvj58bpWAX2VUoddtZeTgHuA7kBHZcIQoyKSBPwI5AMrMR7Ye2I0ERmslDrga6fL2wejggBgg6qF4TddzWGuBa7GeNM3WSn1tkmuJIzzpSFGh6jOkmVm3LT7w6l9vqc2nSLiBFa7PuDZFEQppW6pyz5/OLXP1GM4E6Mi26PJklLqGe07cX3+cPojj7WJiKwHuiul7CKyGRillFpYskwp1aUu+/zhrO8+fzk1Jze6Dwnf8ilGSFMkxtCY9wODMSol3gF6+dBldYuCGAZ8qJSaDEwWkdVV/O4f46pw6OVWQSDATKXUb2b43LzzgHkAIhImIiOA4Uqpy3zpEZF2GKN6XIsxrOm3GJV2yb70lEcpdUBEfgGex+hcq+TBUmFUANV5p/b5nlp2DsW4zpwCTAMmKqW2+9jhT58/nNrne05zOS/DqDSfCPzmramh9p2QPn84/ZHH2mQi8LuIZGK8TFoEICJtMMLv67rPH8767vOXU3MSoyMkfIh7GJOIbFdKtfG2zEcuf9SYxlS1XJnQTMTlDQQuBYYDF2OMZvKjUmq6jz1OjIvurSU3ziKyUynVypeecs7OGG+3DwIPKFcTHDOpbaf21Q+nyxsGDMS4gY/FGO7XtI4La9vnD6f2meY9C6NyuS8wWilV6RCk2nfi+fzh9EceawMR6Q00AuYopXJd89oB4UqpVVX+uA74/OGs7z5/OTUnLzpCwrc43aaPVbHMF/ij9rKkmYbg2YllyXefPriLyEUYNwf9gfnAl8AZSqmbfelxYyhGhMR8EZmF0QzG7JERfgDuU0rNMdnjT6f21Q8nQAHG9eUY0AwIrvB1H3cAAA02SURBVGc+fzi1z8eISBxwKtAV2A+ka1/d8fnD6Y881hZKqSVe5m2tLz5/OOu7z19OzcmLjpDwISKSB2zHeIht7ZrG9b2VUirMx77arqFtrmpxeCy3iIWblFK7XPNMjVhwOcIwRka5FugDfA5MMePhT0SClFKFXuafjdEsxYyONGvVqX314hiWDGV8BjAXmKR82CeOv33+cGqfKc6bMSIxgjEq7b5TSpn2YKl9dd/pjzxqNBqNxhNdIeFDRKR5Vctr82HeDERklVKqRy36TsWIWLgS2IkRsfCkUqrK/ezjbYgBrgKGKaX6mOzqjtEspaQjzR+VUuPrk1P76qbTVTm4FvgDIxrKo+BQSt1bl33+cGqfacdwHbC3RFPO6euRoLTPx5wMedRoNBqNJ7rJhg+paYWDiPyllDrT7O0xAbObL3iglPob+BsY7Xrzey0QKCIzMSIWPqxyBb7ZhsMY4zF/YMb6xQ8dada2U/vqhdOsZlInis8fTu3zPaZ2QKx99dLpjzxqNBqNxg0dIeEHRORvpdSp/t6O40VE0jGiFLxixhsvL9tgAS4CrjGxL4law08dadaqU/vqh7OS7QgGBiilvq+PPn84tc8UZ1OMMuNV7at7Pn84/ZFHjUajOVmx+HsDTlLqai1QPkbHlpV9fIqI9Cj/AboDGYCpYfC1yFAgFaMjzY9E5ELMj0Spbaf21Q8nACJiFZFLROQLYA9G++t64/OHU/tMcTYUkTtEZCGwAEjQvrrj84fTH3nUaDQajY6Q8Au13ReDr/BDHxLzq1iszO7ToTaRWuxI019O7avbThE5D6OvisuAZcDZGJ315vna5Q+fP5za53NfBDDY5WwHTMHo/6eJ9p34Pn84/ZFHjUaj0XiiKyT8QB1usrFEKdXb39tR35Fa7EjTX07tq1tOEdmP0enbe8BUpVS2iOxSSrX0pcdfPn84tc8UZz5GxcfjwB9KKWVycy3tq+NOf+RRo9FoNJ7oJhs+RERq+lbyelM3xCS8VUaISGsReVxE1tfWdojIRSLya235zEREgkXkfhF5W0RuFxGbUuqwUuoDsx5ka9upfb7HD87JQBJGqP0AV2SGmbXZte3zh1P7fM9jGMM3vgf8V0Raa1+d8vnD6Y88ajQajcYNHSHhQ+pq5MPxIiKNMG4yhwOnAC9iDDW4zseePsD7QGNgKvAC8AVGW/nnlVI/+tLnD0TkW6AYo4PCS4A9Sqn76pNT++qNUzB6pL8WuBSIBG4FZiilcuq6zx9O7TMHEWnlcl4DtAWewmjKtFX7TnyfP5z+yKNGo9FoDHSFhA8RkZ3Aw5Utr+sP0CIyEqPAbgJ85/pMMzHk92/gAeAvjIeuL4AnlFL/M8PnD0RknVKqq2vaBiwzu5+O2nZqX/1wlvMHABdjXA/6KaUa1iefP5zaZ5q3q8s5TCll+ttv7av7Tn/kUaPRaE5mdIWEDxGRQ8A0vPd2r5RSt9TyJvkUESnCqBx4SCm1wjXPzLadHp1oisiO+nZz4CWPpnccWttO7asfziq2JUQple+anqyUGlqffP5wap+5iMhfSqkzta9u+vzh9EceNRqN5mTB5u8NqGfsqeuVDtXQGKPTvHEikoARIRFgoi9aRIa4fRf373U94sRFNxE55poWIMT1XTAqsSLrgVP7fI8/nF4pebB0YXpHcLXt84dT+0wnWPvqtM8fTn/kUaPRaE4KdIWEb/EWGVFvUEplYnT89J6INMFoa5kuIpsw2lo+5mPl78CASr4roM5XSCilrPXdqX31w1lDajvkzh8hfvU9j/Xd5w+n9tV9pw4n1mg0GpPQFRK+5Tp/b0BtoZTaD7wGvCYi7TDaW/racbOv16nRaDQajUaj0Wg0mhMDPeynb1kiIse8fLLdwqvrLCJynYh4G7L0fGCbSU6riDR0+x4oIqNcURkajebEorajxPwRlVbf81jfff5wal/dd9brCFiNRqPxJ7pCwocopSKUUpFePhG12abbRB7CGH6zPJNcy3yKiFwDHAbWisjvIpIM7MQYcWOEr30ajeb4cQ1BWsLo+ubzh1P7/vF63xWRmpS13irWtc/PPn84/ZFHjUaj0XiiKyR8iIjEVPXx9/b5AKtSKrv8TNc8Mzq3fBw4TSnVGGP4z1nAPUqpwUqpVSb4NBrN8VPa87xSak499PnDqX3/jN3AShEZXlUipdR67Tshff5w1rZPo9FoNOXQw376EBFxAvsBe8kst8XKrOExawtXM4nTlVK55eZHAMuVUh187Cs/tOFmXzs0Gs2/Q0T2KqWa1VefP5za96/WnQSMAxpidMLsLFlmxshM2ud7ToY8ajQajaYM3amlbxkPXAD8CUwE/lD1q8ZnAvCDiNyhlNoNICItgHdcy3xNvIg86PY93P27UmqcCU6NRlMOEelR2SJMiI6qbZ8/nNpnDkqpAyLy/+3dX6hlZRnH8e8zFjNSNmKGNw16IWF/ZtBoLOjKoSCJmsDMUSi6DCKmboSmixql8MIgMvBCCqSLGrqIDGmIKCWItIuiKUYYYbIhhUyjsqLU+XWx9unsfTz7OOfMWus9e5/vBw6svdewfu/LZjPsZ73reR8Gvky3K9PKj8tBdmYyr387YY6SpFUWJHqU5GhVFV1R4uPAfVX1Y+D+JGebDq4HSe6tqheAR6vq9ZO3XwDuSXL/AJEPAJfNeb1MhR5pu/vqBueeWIK8Fpnm9ayq3k53h/tp4MYkzwyRY97yZLaYoyRplo9sDKSqLgeOAHcDx5I80HhIvZoUJGq9nhIj5R9M8qsW2ZJWVdW7kzy2rHktMs3b8nVPA0fH6i1i3uJntpijJGmWBYkeVdXrgMPAbcCb6Jb6nUhyrunAerLm8QnoVin8he7RlMFXgFTV2+iKPLcDf0vyrqEzJW1smfoPbJdM87Z83d1J/rPO++8F7kjyafO2b16LzBZzlCTN8pGNfv0ZOEPXP+JJuh/sB6vqICxFc6TL1nnvGuALVfWlJN/tO7CqrqYrQNxO1yz0arrGmn/oO0vSltSr/5OFzmuRad4WTP+wrKrrgTuAjwFnGab3gHkLntlijpKkWRYk+vU9uiLEdZO/aQvfHCnJ8fXen2xp+hOg14JEVf0C2Du57keTnKmqsxYjpG1l7GV2LZb1LfsclyKvqt7C6iq654ATdCtBbzJv++e1yGwxR0nSLAsSPUryydZjaCHJ85Nmnn17FngzcBXdIzBnsJmlNLqq+iHrf/cKeOOi57XING8QTwA/Bz6U5MnJOD43UJZ5y5HZYo6SpCkWJHq0To+FGcu6TWVVHQL+2vd1kxyuqr3ALcDxqroWuLyqbkzyeN95kua6d4vnFiWvRaZ5/buF7m73z6rqJN3quiEfRzFv8TNbzFGSNMWmlj2qqi9ucDpJ7hptMAOoqlO88o7XFXTbZX0iyVDb8a3kX0XXMPQIsC/JviHzJEmLZ9Jg+iN0y/APAQ8C3x9qJwXzFj+zxRwlSR0LEiOpqs8m+VrrcVyMSYPJaQGeS/LPFmNJ8tTYudJONKcY+X9JDixyXotM8/r/DOeM4wrgVuC2JIfMW6y8Fpkt5ihJO5kFiZG02KpuKFW1n9WmnaeT/G6gnIc2Op/kw0PkSpq1TjFyRt/FwbHzWmSaN8hnuAf4FHAtcAr4ZpKX+s4xbzg7YY6SpFkWJEZSVecW/RGDST+HHwD7gN/SPWe5H/gjcDjJ33vOexY4R7eN6mOsea4zyaN95km6cFV1Jd0KqVH+Exk7r0WmeRd9/RPAi3RNCm8GnkpydIgs85Yjs8UcJUmzLEiMZBlWSFTV14H/AncmOT95bxdwD3Bpks/0nHcJ8H66ZzoPAA8D30ny+z5zJG2sqt5D9z1/Hrgb+DZwJbCLrn/MyUXOa5Fp3iCf4akk+yfHrwEeT/LOvnPMG85OmKMkaZa7bPSoqv7B/G3OLh15OEN4H3BgpRgBkOR8VR2jW+rYqyQvAyeBk1W1m64w8UhV3ZXkvr7zJM31DeAYsBf4KXBzkl9W1XV0K5j6/nE5dl6LTPP69+LKQZKXhtmN2rwly2wxR0nSFFdI6IJV1W+SXL/ZcxeZuRv4IF0x4hrgIeBbSf7Ud5ak9U1/v6vqdJK3Tp37dZIbFjmvRaZ5g3yGLwMrTZZXbgT8a3KcJG8wb/vmtchsMUdJ0ixXSGgz9lTVDbxyj+4CdvcdVlUPAu8AfgQcH6p5pqRXdX7q+N9rzg1R1R47r0WmeT1LcskQ1zVveTNbzFGSNMsVErpgVfUIG2/jdlPPeedZvXMxneudC2lEU3cRp+8gMnm9J8lrFzmvRaZ5/X+GkiRp8ViQkCRJkiRJo9vVegBaHFV159TxrWvOfWX8EUmSJEmSFpUFCW3Gkanjz68594ExByJJkiRJWmwWJLQZNed4vdeSJEmSJM1lQUKbkTnH672WJEmSJGkum1rqgtk1XZIkSZLUFwsSkiRJkiRpdD6yIUmSJEmSRmdBQpIkSZIkjc6ChCRJkiRJGp0FCUmSJEmSNDoLEpIkSZIkaXT/A44eb00XHbZcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Using Pearson Correlation\n", + "plt.figure(figsize= (19, 15))\n", + "cor = df.corr()\n", + "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.20)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0pval
0101.6063301.197484e-06
15.9736131.000000e+00
20.1622381.000000e+00
35084.5856940.000000e+00
44395.1765350.000000e+00
53613.3302830.000000e+00
63478.5125380.000000e+00
73290.5932710.000000e+00
82874.8496550.000000e+00
95.5028001.000000e+00
101.5371501.000000e+00
110.5792141.000000e+00
120.1030431.000000e+00
130.0164281.000000e+00
140.0026861.000000e+00
1542.3464634.995112e-01
1641.3702625.421350e-01
1736.1038637.625720e-01
1834.1910878.290815e-01
1932.2567938.846284e-01
2030.1205169.311403e-01
2129.0637909.485468e-01
2216.8510069.998847e-01
237.4961561.000000e+00
247.9928101.000000e+00
257.7555741.000000e+00
2681.3690093.679256e-04
2760.1369884.297347e-02
28542.5228160.000000e+00
2923.1824469.941736e-01
3082.4618362.768659e-04
31277.0293730.000000e+00
3220.8275869.982732e-01
3395.8796116.629453e-06
34167.0756811.110223e-16
3513.1561669.999968e-01
36100.9293611.470776e-06
37104.7549874.552992e-07
388.5479451.000000e+00
3988.3346485.695510e-05
4075.0100071.798366e-03
415.4384661.000000e+00
4278.1518958.334476e-04
4373.2199982.748840e-03
\n", + "
" + ], + "text/plain": [ + " 0 pval\n", + "0 101.606330 1.197484e-06\n", + "1 5.973613 1.000000e+00\n", + "2 0.162238 1.000000e+00\n", + "3 5084.585694 0.000000e+00\n", + "4 4395.176535 0.000000e+00\n", + "5 3613.330283 0.000000e+00\n", + "6 3478.512538 0.000000e+00\n", + "7 3290.593271 0.000000e+00\n", + "8 2874.849655 0.000000e+00\n", + "9 5.502800 1.000000e+00\n", + "10 1.537150 1.000000e+00\n", + "11 0.579214 1.000000e+00\n", + "12 0.103043 1.000000e+00\n", + "13 0.016428 1.000000e+00\n", + "14 0.002686 1.000000e+00\n", + "15 42.346463 4.995112e-01\n", + "16 41.370262 5.421350e-01\n", + "17 36.103863 7.625720e-01\n", + "18 34.191087 8.290815e-01\n", + "19 32.256793 8.846284e-01\n", + "20 30.120516 9.311403e-01\n", + "21 29.063790 9.485468e-01\n", + "22 16.851006 9.998847e-01\n", + "23 7.496156 1.000000e+00\n", + "24 7.992810 1.000000e+00\n", + "25 7.755574 1.000000e+00\n", + "26 81.369009 3.679256e-04\n", + "27 60.136988 4.297347e-02\n", + "28 542.522816 0.000000e+00\n", + "29 23.182446 9.941736e-01\n", + "30 82.461836 2.768659e-04\n", + "31 277.029373 0.000000e+00\n", + "32 20.827586 9.982732e-01\n", + "33 95.879611 6.629453e-06\n", + "34 167.075681 1.110223e-16\n", + "35 13.156166 9.999968e-01\n", + "36 100.929361 1.470776e-06\n", + "37 104.754987 4.552992e-07\n", + "38 8.547945 1.000000e+00\n", + "39 88.334648 5.695510e-05\n", + "40 75.010007 1.798366e-03\n", + "41 5.438466 1.000000e+00\n", + "42 78.151895 8.334476e-04\n", + "43 73.219998 2.748840e-03" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "\n", + "\n", + "chi2data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 16194\n", + " 1 1.00 1.00 1.00 4802\n", + "\n", + " accuracy 1.00 20996\n", + " macro avg 1.00 1.00 1.00 20996\n", + "weighted avg 1.00 1.00 1.00 20996\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the Decision Tree (GINI) identified 555\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03207803
1684555
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3207 803\n", + "1 684 555" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 4010\n", + " 1 0.41 0.45 0.43 1239\n", + "\n", + " accuracy 0.72 5249\n", + " macro avg 0.62 0.62 0.62 5249\n", + "weighted avg 0.73 0.72 0.72 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 16194\n", + " 1 1.00 1.00 1.00 4802\n", + "\n", + " accuracy 1.00 20996\n", + " macro avg 1.00 1.00 1.00 20996\n", + "weighted avg 1.00 1.00 1.00 20996\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the Decision Tree (Random Forest) identified 493\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03757253
1746493
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3757 253\n", + "1 746 493" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 4010\n", + " 1 0.66 0.40 0.50 1239\n", + "\n", + " accuracy 0.81 5249\n", + " macro avg 0.75 0.67 0.69 5249\n", + "weighted avg 0.79 0.81 0.79 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + "\n", + "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.96 0.90 16194\n", + " 1 0.78 0.45 0.57 4802\n", + "\n", + " accuracy 0.84 20996\n", + " macro avg 0.81 0.70 0.74 20996\n", + "weighted avg 0.84 0.84 0.83 20996\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 488\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03778232
1751488
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3778 232\n", + "1 751 488" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.29919321548002004\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 4010\n", + " 1 0.68 0.39 0.50 1239\n", + "\n", + " accuracy 0.81 5249\n", + " macro avg 0.76 0.67 0.69 5249\n", + "weighted avg 0.80 0.81 0.79 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree0.4479420.624423
1Random Forest0.3979020.772517
2Gradient Boosted0.3938660.778748
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree 0.447942 0.624423\n", + "1 Random Forest 0.397902 0.772517\n", + "2 Gradient Boosted 0.393866 0.778748" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.443332\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 20996
Model: Logit Df Residuals: 20952
Method: MLE Df Model: 43
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1755
Time: 17:58:42 Log-Likelihood: -9308.2
converged: True LL-Null: -11290.
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8585 0.105 -8.157 0.000 -1.065 -0.652
SEX -0.1338 0.038 -3.564 0.000 -0.207 -0.060
AGE 0.0784 0.092 0.856 0.392 -0.101 0.258
PAY_0 0.6364 0.054 11.857 0.000 0.531 0.742
PAY_2 -0.6281 0.089 -7.047 0.000 -0.803 -0.453
PAY_3 -0.0434 0.109 -0.398 0.691 -0.258 0.171
PAY_4 -0.3043 0.148 -2.051 0.040 -0.595 -0.013
PAY_5 -0.1221 0.164 -0.744 0.457 -0.444 0.200
PAY_6 0.4793 0.140 3.412 0.001 0.204 0.755
BILL_AMT1 -1.4159 0.490 -2.891 0.004 -2.376 -0.456
BILL_AMT2 0.7970 0.714 1.117 0.264 -0.602 2.196
BILL_AMT3 1.1717 0.689 1.701 0.089 -0.178 2.522
BILL_AMT4 0.5620 0.670 0.839 0.401 -0.750 1.874
BILL_AMT5 -1.1673 0.813 -1.437 0.151 -2.760 0.425
BILL_AMT6 0.9974 0.744 1.341 0.180 -0.460 2.455
PAY_AMT1 -1.2441 0.283 -4.391 0.000 -1.799 -0.689
PAY_AMT2 -2.1791 0.366 -5.946 0.000 -2.897 -1.461
PAY_AMT3 -0.4265 0.272 -1.571 0.116 -0.959 0.106
PAY_AMT4 -0.4780 0.264 -1.811 0.070 -0.995 0.039
PAY_AMT5 -0.9704 0.268 -3.614 0.000 -1.497 -0.444
PAY_AMT6 -0.5401 0.242 -2.233 0.026 -1.014 -0.066
GRAD 1.3705 0.203 6.740 0.000 0.972 1.769
UNI 1.3583 0.202 6.722 0.000 0.962 1.754
HS 1.2592 0.206 6.127 0.000 0.856 1.662
MARRIED 0.0231 0.147 0.157 0.875 -0.265 0.312
SINGLE -0.1504 0.148 -1.018 0.309 -0.440 0.139
PAY_0_No_Transactions -0.0255 0.114 -0.224 0.823 -0.249 0.198
PAY_0_Pay_Duly 0.1487 0.111 1.346 0.178 -0.068 0.365
PAY_0_Revolving_Credit -0.8102 0.124 -6.515 0.000 -1.054 -0.566
PAY_2_No_Transactions -1.6438 0.215 -7.663 0.000 -2.064 -1.223
PAY_2_Pay_Duly -1.5062 0.205 -7.363 0.000 -1.907 -1.105
PAY_2_Revolving_Credit -1.0120 0.210 -4.827 0.000 -1.423 -0.601
PAY_3_No_Transactions -0.4055 0.262 -1.546 0.122 -0.919 0.108
PAY_3_Pay_Duly -0.3823 0.240 -1.590 0.112 -0.853 0.089
PAY_3_Revolving_Credit -0.4670 0.230 -2.032 0.042 -0.918 -0.017
PAY_4_No_Transactions -1.0351 0.332 -3.117 0.002 -1.686 -0.384
PAY_4_Pay_Duly -1.1078 0.315 -3.514 0.000 -1.726 -0.490
PAY_4_Revolving_Credit -0.9374 0.307 -3.058 0.002 -1.538 -0.337
PAY_5_No_Transactions -0.4004 0.365 -1.096 0.273 -1.117 0.316
PAY_5_Pay_Duly -0.4950 0.352 -1.407 0.159 -1.184 0.195
PAY_5_Revolving_Credit -0.4161 0.342 -1.215 0.224 -1.087 0.255
PAY_6_No_Transactions 0.9055 0.308 2.941 0.003 0.302 1.509
PAY_6_Pay_Duly 0.7502 0.302 2.481 0.013 0.157 1.343
PAY_6_Revolving_Credit 0.5706 0.294 1.941 0.052 -0.006 1.147
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 20996\n", + "Model: Logit Df Residuals: 20952\n", + "Method: MLE Df Model: 43\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1755\n", + "Time: 17:58:42 Log-Likelihood: -9308.2\n", + "converged: True LL-Null: -11290.\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8585 0.105 -8.157 0.000 -1.065 -0.652\n", + "SEX -0.1338 0.038 -3.564 0.000 -0.207 -0.060\n", + "AGE 0.0784 0.092 0.856 0.392 -0.101 0.258\n", + "PAY_0 0.6364 0.054 11.857 0.000 0.531 0.742\n", + "PAY_2 -0.6281 0.089 -7.047 0.000 -0.803 -0.453\n", + "PAY_3 -0.0434 0.109 -0.398 0.691 -0.258 0.171\n", + "PAY_4 -0.3043 0.148 -2.051 0.040 -0.595 -0.013\n", + "PAY_5 -0.1221 0.164 -0.744 0.457 -0.444 0.200\n", + "PAY_6 0.4793 0.140 3.412 0.001 0.204 0.755\n", + "BILL_AMT1 -1.4159 0.490 -2.891 0.004 -2.376 -0.456\n", + "BILL_AMT2 0.7970 0.714 1.117 0.264 -0.602 2.196\n", + "BILL_AMT3 1.1717 0.689 1.701 0.089 -0.178 2.522\n", + "BILL_AMT4 0.5620 0.670 0.839 0.401 -0.750 1.874\n", + "BILL_AMT5 -1.1673 0.813 -1.437 0.151 -2.760 0.425\n", + "BILL_AMT6 0.9974 0.744 1.341 0.180 -0.460 2.455\n", + "PAY_AMT1 -1.2441 0.283 -4.391 0.000 -1.799 -0.689\n", + "PAY_AMT2 -2.1791 0.366 -5.946 0.000 -2.897 -1.461\n", + "PAY_AMT3 -0.4265 0.272 -1.571 0.116 -0.959 0.106\n", + "PAY_AMT4 -0.4780 0.264 -1.811 0.070 -0.995 0.039\n", + "PAY_AMT5 -0.9704 0.268 -3.614 0.000 -1.497 -0.444\n", + "PAY_AMT6 -0.5401 0.242 -2.233 0.026 -1.014 -0.066\n", + "GRAD 1.3705 0.203 6.740 0.000 0.972 1.769\n", + "UNI 1.3583 0.202 6.722 0.000 0.962 1.754\n", + "HS 1.2592 0.206 6.127 0.000 0.856 1.662\n", + "MARRIED 0.0231 0.147 0.157 0.875 -0.265 0.312\n", + "SINGLE -0.1504 0.148 -1.018 0.309 -0.440 0.139\n", + "PAY_0_No_Transactions -0.0255 0.114 -0.224 0.823 -0.249 0.198\n", + "PAY_0_Pay_Duly 0.1487 0.111 1.346 0.178 -0.068 0.365\n", + "PAY_0_Revolving_Credit -0.8102 0.124 -6.515 0.000 -1.054 -0.566\n", + "PAY_2_No_Transactions -1.6438 0.215 -7.663 0.000 -2.064 -1.223\n", + "PAY_2_Pay_Duly -1.5062 0.205 -7.363 0.000 -1.907 -1.105\n", + "PAY_2_Revolving_Credit -1.0120 0.210 -4.827 0.000 -1.423 -0.601\n", + "PAY_3_No_Transactions -0.4055 0.262 -1.546 0.122 -0.919 0.108\n", + "PAY_3_Pay_Duly -0.3823 0.240 -1.590 0.112 -0.853 0.089\n", + "PAY_3_Revolving_Credit -0.4670 0.230 -2.032 0.042 -0.918 -0.017\n", + "PAY_4_No_Transactions -1.0351 0.332 -3.117 0.002 -1.686 -0.384\n", + "PAY_4_Pay_Duly -1.1078 0.315 -3.514 0.000 -1.726 -0.490\n", + "PAY_4_Revolving_Credit -0.9374 0.307 -3.058 0.002 -1.538 -0.337\n", + "PAY_5_No_Transactions -0.4004 0.365 -1.096 0.273 -1.117 0.316\n", + "PAY_5_Pay_Duly -0.4950 0.352 -1.407 0.159 -1.184 0.195\n", + "PAY_5_Revolving_Credit -0.4161 0.342 -1.215 0.224 -1.087 0.255\n", + "PAY_6_No_Transactions 0.9055 0.308 2.941 0.003 0.302 1.509\n", + "PAY_6_Pay_Duly 0.7502 0.302 2.481 0.013 0.157 1.343\n", + "PAY_6_Revolving_Credit 0.5706 0.294 1.941 0.052 -0.006 1.147\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 16194\n", + " 1 0.67 0.35 0.46 4802\n", + "\n", + " accuracy 0.81 20996\n", + " macro avg 0.75 0.65 0.67 20996\n", + "weighted avg 0.79 0.81 0.79 20996\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 4010\n", + " 1 0.69 0.38 0.49 1239\n", + "\n", + " accuracy 0.81 5249\n", + " macro avg 0.76 0.66 0.69 5249\n", + "weighted avg 0.80 0.81 0.79 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445426\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 20996
Model: Logit Df Residuals: 20972
Method: MLE Df Model: 23
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1716
Time: 17:58:42 Log-Likelihood: -9352.2
converged: True LL-Null: -11290.
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8185 0.102 -7.996 0.000 -1.019 -0.618
SEX -0.1216 0.037 -3.286 0.001 -0.194 -0.049
PAY_0 0.6056 0.034 17.614 0.000 0.538 0.673
PAY_2 -0.5868 0.074 -7.895 0.000 -0.732 -0.441
PAY_4 -0.2261 0.069 -3.259 0.001 -0.362 -0.090
PAY_6 0.2449 0.029 8.389 0.000 0.188 0.302
BILL_AMT1 0.2964 0.140 2.121 0.034 0.023 0.570
PAY_AMT1 -1.0360 0.252 -4.117 0.000 -1.529 -0.543
PAY_AMT2 -2.3264 0.330 -7.058 0.000 -2.972 -1.680
PAY_AMT5 -0.8585 0.237 -3.626 0.000 -1.323 -0.394
PAY_AMT6 -0.6965 0.237 -2.934 0.003 -1.162 -0.231
GRAD 1.6137 0.173 9.315 0.000 1.274 1.953
UNI 1.6386 0.172 9.531 0.000 1.302 1.976
HS 1.5765 0.175 8.983 0.000 1.233 1.920
PAY_0_Revolving_Credit -0.9127 0.084 -10.822 0.000 -1.078 -0.747
PAY_2_No_Transactions -1.7245 0.176 -9.789 0.000 -2.070 -1.379
PAY_2_Pay_Duly -1.4835 0.167 -8.874 0.000 -1.811 -1.156
PAY_2_Revolving_Credit -0.9405 0.174 -5.414 0.000 -1.281 -0.600
PAY_3_Revolving_Credit -0.2826 0.067 -4.189 0.000 -0.415 -0.150
PAY_4_No_Transactions -1.1690 0.173 -6.750 0.000 -1.509 -0.830
PAY_4_Pay_Duly -1.2440 0.161 -7.730 0.000 -1.559 -0.929
PAY_4_Revolving_Credit -0.9017 0.151 -5.954 0.000 -1.199 -0.605
PAY_6_No_Transactions 0.3405 0.080 4.232 0.000 0.183 0.498
PAY_6_Pay_Duly 0.1290 0.070 1.837 0.066 -0.009 0.267
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 20996\n", + "Model: Logit Df Residuals: 20972\n", + "Method: MLE Df Model: 23\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1716\n", + "Time: 17:58:42 Log-Likelihood: -9352.2\n", + "converged: True LL-Null: -11290.\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8185 0.102 -7.996 0.000 -1.019 -0.618\n", + "SEX -0.1216 0.037 -3.286 0.001 -0.194 -0.049\n", + "PAY_0 0.6056 0.034 17.614 0.000 0.538 0.673\n", + "PAY_2 -0.5868 0.074 -7.895 0.000 -0.732 -0.441\n", + "PAY_4 -0.2261 0.069 -3.259 0.001 -0.362 -0.090\n", + "PAY_6 0.2449 0.029 8.389 0.000 0.188 0.302\n", + "BILL_AMT1 0.2964 0.140 2.121 0.034 0.023 0.570\n", + "PAY_AMT1 -1.0360 0.252 -4.117 0.000 -1.529 -0.543\n", + "PAY_AMT2 -2.3264 0.330 -7.058 0.000 -2.972 -1.680\n", + "PAY_AMT5 -0.8585 0.237 -3.626 0.000 -1.323 -0.394\n", + "PAY_AMT6 -0.6965 0.237 -2.934 0.003 -1.162 -0.231\n", + "GRAD 1.6137 0.173 9.315 0.000 1.274 1.953\n", + "UNI 1.6386 0.172 9.531 0.000 1.302 1.976\n", + "HS 1.5765 0.175 8.983 0.000 1.233 1.920\n", + "PAY_0_Revolving_Credit -0.9127 0.084 -10.822 0.000 -1.078 -0.747\n", + "PAY_2_No_Transactions -1.7245 0.176 -9.789 0.000 -2.070 -1.379\n", + "PAY_2_Pay_Duly -1.4835 0.167 -8.874 0.000 -1.811 -1.156\n", + "PAY_2_Revolving_Credit -0.9405 0.174 -5.414 0.000 -1.281 -0.600\n", + "PAY_3_Revolving_Credit -0.2826 0.067 -4.189 0.000 -0.415 -0.150\n", + "PAY_4_No_Transactions -1.1690 0.173 -6.750 0.000 -1.509 -0.830\n", + "PAY_4_Pay_Duly -1.2440 0.161 -7.730 0.000 -1.559 -0.929\n", + "PAY_4_Revolving_Credit -0.9017 0.151 -5.954 0.000 -1.199 -0.605\n", + "PAY_6_No_Transactions 0.3405 0.080 4.232 0.000 0.183 0.498\n", + "PAY_6_Pay_Duly 0.1290 0.070 1.837 0.066 -0.009 0.267\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove all insignificant attributes\n", + "sig = glm.pvalues[glm.pvalues < 0.05]\n", + "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n", + "glm_2.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 16194\n", + " 1 0.67 0.35 0.46 4802\n", + "\n", + " accuracy 0.81 20996\n", + " macro avg 0.75 0.65 0.67 20996\n", + "weighted avg 0.79 0.81 0.79 20996\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 4010\n", + " 1 0.68 0.38 0.49 1239\n", + "\n", + " accuracy 0.81 5249\n", + " macro avg 0.76 0.66 0.69 5249\n", + "weighted avg 0.80 0.81 0.79 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2401436742439986\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7697924316455151" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be 0.2697615225249289 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.85 0.86 4010\n", + " 1 0.54 0.56 0.55 1239\n", + "\n", + " accuracy 0.78 5249\n", + " macro avg 0.70 0.71 0.70 5249\n", + "weighted avg 0.79 0.78 0.78 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the Logistic Regression identified 698\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
03408602
1541698
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 3408 602\n", + "1 541 698" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the Logistic Regression identified 471\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
03791219
1768471
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 3791 219\n", + "1 768 471" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.22465942137872466\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gU1dfA8e8hIQkhoSX0AKH3HkAQkCIKCII0RUUFfBVRLCBix45i9weKHUUpAoIICIqAIIL0DtJL6CWVkH7fP2YTNyFlwWw2uzmf5+EhOzM7c6bsnJl7Z+4VYwxKKaVUdoq4OgCllFIFmyYKpZRSOdJEoZRSKkeaKJRSSuVIE4VSSqkcaaJQSimVI00UORCRu0TkV1fH4WoiUlVEYkXEKx+XGSoiRkS882uZziQiu0Sk0zV875qOQREpJiI/i0iUiMy+2u/nFxH5RUTuvYbvdRCRf5wRU0Hmit8igLjLexQicgQoD6QAscAS4BFjTKwr4/JEtm19vzFmmQtjCAUOA0WNMcmuisMWiwFqG2MOOHk5oeTROovIEGAU0C4vtp8tyX1njAn5r/O6xuXnyT4QkZeA54AEIBnYDYwxxqz9z0F6MHe7o+htjAkAmgHNgWdcHM81ceVVsqdcoV+NQrq9qwH7riVJFIJjZJbtPBIMrACccsflUdvRGOMW/4AjwI12nycCi+w++wLvAMeAM8AUoJjd+D7AViAaOAh0tw0vCXwJnAJOAK8BXrZx9wF/2v6eAryTKaafgNG2vysBc4FzWFeFj9pN9xIwB/jOtvz7s1i/ksC3tu8fBZ4HitjFsQb4HxAF7AW6ZvpuTuuwBngfuGgbVxNYDlwAzgPfA6Vs008DUoHLWHduTwGhgAG8bdOsBF61zTcG+BUItovnHts6XABeyLzvMq13MeBd2/RRwJ+2YWnLvNe2T88Dz9l9rzWwFoi0rfckwMduvAEeBvYDh23DPgSO2/bBJqCD3fRewLO2YyPGNr4KsMo2r0u27XG7bfpeWMdTJPAX0CTTsToO2I515eptvw1ssW+0xXEGeM82/JhtWbG2f22xOwZt0zQEfrPtyzPAs1ls05eBRCDJNp/hWBeFz9u281msY62kbfq0bT3cFsOqLObZCQjPZh/mdOx62fbveazfxSNceSzdb/u7FvCH7Tg4j3VCJ6t9kDke27760RbDBWBSNrG+hHVnlPa5gW3eZe2G5bRvWwBbbMfIbGAW8Jr9NrLt+9PANAfmNw7rNxsD/IPtd53DMZK2r9K2XyVgge14OAD8X6Z1/cG2b2KAXUDYNZ1/8/uEf63/yPhDCwF2AB/ajf/AtsHKAIHAz8AEu40eBXTD+sFUBurZxs0HPgWKA+WA9cCDdifZtETREeskk1ZcVxrrZFrJNs9NwIuAD1ADOATcbLfDkoC+tmmLZbF+32IlnkDbwbAPGG4XRzLwBFAU64cSBZRxcB2SsYohvLFOwrVs28IXKIv1Q/wgq22dzcG5EuuEWsc2v5XAm3Y/vFigvW1bvGNb9+wSxWTb9ytjnVTa2eJKW+bntmU0xTrp1rd9ryVwnW2dQoE9wON28zVYJ9QyadsbuBsIsn1nDNaP2c82bizWMVUXENvyguzmVSvTyeIs0MYW8722beZrt/22Yp28imXeplgJbojt7wDguqy2cxbHYCBWUhwD+Nk+t3HwhDgM60RSw7bMH/n3RJa23G+xjqGsjs9OZJ8ocjp2R2AV74Rg/WaWkX2imIFVLFTEtn7tM+3PWlnFY9sH27Auhopn/m522wXr+HwTKymlxZPtvrVNfxR4DOt32A8rIdsnimTgLdv0xXKZX12sc0olu/1Q82qOEazE+rFtnZthJcqudusaD/S0LXsCsO6azr+uTgAOB2pt3FiszGiA3/n3KliwrjZq2k3fln+vJD8F3s9inuWxTj72dx6DgRVZ/EgF62qro+3z/wHLbX+3AY5lmvczwNd2O+yKqzS7ab1scTSwG/YgsNIujpPYkpRt2HpgiIPrcCy7Zdum6QtsybStc0sUz9uNHwkssf39IjDDbpw/1o/pikSBdUK4DDTNYlzaMkMyrfMd2azD48A8u88G6JLLekekLRvraq5PNtNlPkl9AryaaZp/gBvstt+wLI7ftESxCuuqPzjTNBm2cxbH4GD7/ZTLur1ExkTxOzDS7nNdrATubbfcGjnMrxNZJAoHjt3l2C5abJ9vzOJYSksU3wKf2e/zHPZBejxYv/Vz9tstl+2SiHV1n4J199HJkX2LdbF4goy/wz/JmCgSsV18ODC/WlhJ5Easein7aXI9RrAuRFKAQLvxE4Cpduu6zG5cA+CyI8dP5n/uVkfR1xgTiLVD6mGVMYJ1VewPbBKRSBGJxKrsLmsbXwXrCjizalhXBqfsvvcp1lV5Bsba0jOxfqwAd2IV2aTNp1LaPGzzeRbrJJ7meA7rFcy/VytpjmJdZac5YYvBfnwlB9chw7JFpJyIzBSREyISjVUkFszVOW33dxzWVQ+2mNKXZ4yJw/oxZiUY60ooq32T43JEpI6ILBSR07Z1eIMr1yHzeo8RkT22J4EisYpM0r6T3TGSlWrAmEz7uwrWume57EyGY92N7RWRDSLSy8HlXk2MmVXiyuPLG8eP0ezkduxmOB5yWcZTWBdk621PiQ1zMIYqwFHjeH3MD8aYUljrvhPr7jRNTvu2Elf+DjOvzzljTLwj8zNWxfzjWCf0s7bfZNox5MgxUgm4aIyJsRuW+byR+ffjdy11J+6WKAAwxvwBTMUq1gDr1vEy0NAYU8r2r6SxKqzA2pk1s5jVcayroWC775UwxjTMZtEzgAEiUg3rLmKu3XwO282jlDEm0BjT0z7sHFbpPNbVXTW7YVWxrl7SVBYRyTT+pIPrkHnZE2zDmhhjSmAVyUgO01+NU1jFDID1mCZWcU9WzmPdGme1b3LzCVZdTW3bOjxLxnUAu/UQkQ5Y5cGDgNK2E0WU3XeyO0aychx4PdP+9jfGzMhq2ZkZY/YbYwZjJfO3gDkiUjyn71xDjJmd5MrjKxmr/Ds9tGuYb27HbobjAeskmSVjzGljzP8ZYyph3ZV8LCK1HIjhOFD1ak+AxpjztuW8JCIV7eaV3b49xZW/w8zrk3kb5nisGGOmG2PaY20/g3U85HSM2DsJlBGRQLthmc8becItE4XNB0A3EWlmjEnFKst+X0TKAYhIZRG52Tbtl8BQEekqIkVs4+oZY05hVcS+KyIlbONqisgNWS3QGLMF6xb3C2CpMSbSNmo9EC0i42zPr3uJSCMRaeXIihhjUrAqnV4XkUBbIhqNdaWfphzwqIgUFZGBQH1g8dWug00gVjFepIhUxiqft3cGqyz7WswBeotIOxHxwbp9znwCB8C2374C3hORSrbt1lZEfB1YTiBWRV+siNQDHnJg+mRsRRQi8iJQwm78F8CrIlJbLE1EJC3BZd4enwMjRKSNbdriInJLph9stkTkbhEpa1v/tGMoxRZbKtlv+4VABRF5XER8bcdKG0eWiXWR84SIVBeRAKw7sFlXcRWeFruf/T9bvDkduz8Aj9l+c6WwknV28x4oImlJJQLrxJli+5zTMbke6yT+pm1f+InI9Y6sjzFmL7AU624Gct63a23xPCIi3iLSB6v+MyfZzk9E6opIF9vxHo91sZti2xbZHSP2sR/HqhyfYFvnJlh3It+Tx9w2URhjzmGVab5gGzQOq7JunVhFEcuwymExxqwHhmJVdkVhVQClXQHdg3XrvBvr4JwDpF1dZGUGVpnidLtYUoDeWJVJh7Gusr7AKtpw1CisepZDWOWe07FOomn+Bmrb5v06MMAYk1akc7Xr8DJWJVsUsAirYtPeBOB5263yk1exDhhjdtnWZSbWjzcGqxw2IZuvPIlVibwB68mNt3DsuHwSq/gvBuvHOCuX6ZcCv2BVtB7F+mHaFxu8h3VS+xUrAX2JVRkJVtHAN7btMcgYsxGrjmoS1vY+gFWX4KjuwC4RicV6EusOY0y8rZjudWCNbVnX2X/JVsTQDetYO431RFdnB5f5FdYTbauwjtF4rP10NSpjnczs/9Uk52P3c6xtuh3raaHFWAk7w0nPphXwt227LAAeM8Ycto17Cbt9YP8lu99fLax6xHCsBz4c9TbwgIiUy2nfGmMSsSqwh2OdvO/GSt7ZHdvkcqz48m9l+mmsi8FnbeOyPEayWMRgrHqLk8A8YLwx5rerWHeHuM0Ld4WZiNyHVeHX3tWxXC3b1WskVhHR4dymV55NRHoAU4wx1XKd2A2IyN9Y6/O1q2NxJre9o1AFl4j0FhF/W5nqO1h3DEdcG5VyBVtRbE9bUU1lYDzWla9bEpEbRKSCbX3uBZpgPTjj0TRRKGfog3UrfBKruOwOo7euhZVgFXVGYBU97cF6hNpd1cV6ZyMK632WAbZ6Qo+mRU9KKaVypHcUSimlcuR2jVYFBweb0NBQV4ehlFJuZdOmTeeNMWVzn/JKbpcoQkND2bhxo6vDUEoptyIiR3OfKmta9KSUUipHmiiUUkrlSBOFUkqpHGmiUEoplSNNFEoppXKkiUIppVSOnJYoROQrETkrIjuzGS8i8pGIHBCR7SLSwlmxKKWUunbOvKOYitVUbnZ6YLUDVBt4AKsjGqWUUnkkITmF+CTr33/htBfujDGrRCQ0h0n6AN/aGotbJyKlRKRiYWhgSyml8lJCcgrLdp9l1b5zlCpelBMRl1m4Pe9Opa58M7syGTuOCbcNu2LtROQBrLsOqlatmi/BKaVUQZGaariUmMz0v49xNiaBol5FmLb2CAF+3pyJvrLfpCK2PiWLpBgurD5BxYoBV0xzNVyZKLLqHjPLpmyNMZ8BnwGEhYVpc7dKKY+3ZOdpRny3iZDSxQiPuJxhnI9XERJTUrmUmMJtzStzPjaBNtXLcGvTylQN8scYQ1jY5/zzz3leeaUzjz7ahqJF777mWFyZKMLJ2DF5CFb/BUop5dGMMew/G8vuk9EYu+vjI+fjWLTjFAfOxqYPS0xOpU+zSgA0rlySQa2qUMKvaJbz/euv45Ru7EVgoC9ffNGb4GB/qlS5mh6Zs+bKRLEAq5PymUAbIErrJ5RSniol1fD20n+Y+tdh4pNSc52+dWgZ+rWozB2tcy9uv3AhjqefXsYXX2xh/PgbeOmlTjRvXjEvwgacmChEZAbQCQgWkXCsLhCLAhhjpmB1st4Tq7PxOGCos2JRSilXiIpL4oeNx5m35QS7T0VnGNehdjB3tq5KtaDi+Pt4pQ8PDvQlwNexU7Mxhm+/3caTT/5GRMRlxo5tx9ix7fJ0HcC5Tz0NzmW8AR521vKVUio/RFxK5FRUPJFxiaw+cJ7jF+P4+/BFfLyKcCLy37oFv6JF8PfxZvVTnSnuYCLIzbhxy3j77b9o164KU6bcQuPG5fNkvpm5XX8USinlCqmphguXEjkfm0BKqmHZnjN8+schLmfzjkKgnzfdG1agdHEfHuxYg9Dg4nkSx+XLSVy6lERwsD/Dhzendu0yDB/egiJFsno+KG9oolBKqWwYYzgReZn2b63IcbpnetSjWlBxypfwpXnV0k6LZ8mSAzz88GKaNavA3LmDqFs3mLp1g522vDSaKJRShVZcYjJrD14gLjGF/WdiSE41rD10gai4JBJTUq94LPXRLrUo6e9D5VJ+pKRC+9rBlCyW9RNIeenkyRgef3wJs2fvpm7dIB55pJXTl2lPE4VSyqMZY7h4KZFV+88xa8NxSvgVZf/ZWI5fjCM5NevXsvyKFqFokSJ0b1gBgO6NKtCnWSVEnFe8k53ffz/EbbfNIjExhVdf7czYse3wzaM6DkdpolBKub2zMfFExiVxNjqBiLhEvlpzmJRUw9noBE5Hx18xfe1yARQr6kX9SiVoFVqa62sFUy7Ql5DS/vgV9cpiCfkvKSmFokW9aNq0Aj171ua117pQq1YZl8SiiUIpVeAZY/h19xl2nYxm8ooDpGRzJ5BZyWJF6Vy3LAA3N6xAxzplqVSqmDND/c+ioxN44YXl/P33CdasGUZwsD8zZw5waUyaKJRSBVZSSiq1n/sly3EDW4akn/Tjk1OoUtqf0v4++Pt6UT2oONWC/F1SVHStjDHMmbObxx5bwunTsYwc2YqEhBT8/V3fbZAmCqVUgZOQnMIf/5zjgWmb0of1blqJBzrUoHb5gAJTPJRXzp27xL33zueXXw7QvHkFfvrpDlq1quzqsNJpolBKFRibj0Uw8rvNV9QrHJ7Q063uDq5WiRK+nD8fxwcf3MzDD7fG29v1dxH2NFEopVwm6nISk1ccYMb6Y5QN9OXQuUvp4zrUDmZkp1q0rRnkwgidZ9Wqo7z++mrmzh1EQIAP69bd79SX5v4LTRRKqTyX9qLa1uOR7DgRxbmYBE5FxrP/bCxBxX04HR1P1OWkDN9JTjHcWL88vZtWpE+zglPsktfOn49j7NjfmDp1K6GhpThyJJJGjcoV2CQBmiiUUnnoyPlLPDV3O+sPX8xyfJniVmXzdTXKcD42kSYhJQkq7kOfZpWpUsY/n6PNX8YYvv56K2PH/kZ0dALPPNOe55/viL+/81/Y+680USilrllUXBJLd59m/pYT/HXwQoZxgX7evNCrAQ0qlqBehUCKiBToq+b88N1322nQoCxTptxCw4blXB2OwzRRKKWuijGGRTtO8ePmEyzfezbDuDLFfXitbyN6NKrg0ZXPjoqLS+KNN1YzYkQYISElmDt3ECVL+rldwtREoZTKVdTlJL5YfYiz0QnM2ng8w7ih14cyvH11Qkp7dtHR1Vq8eD8PP7yYI0ciqVw5kIceakXp0gX7Zb/saKJQSmVr09GLPDhtE+djEzMML1Pch3kj21GltL/bXR07W3h4NI8/voS5c/dQv34wf/xxHx07VnN1WP+JJgqlVAbR8UnM23yCb9YeyfC46n3tQhl9Ux2K+3jjpckhW6+/vopFi/bzxhtdGDOmHT4+7v9yoFgdzbmPsLAws3HjRleHoZTHWX/4It+uPcLC7f92Xe/rXYQPbm9G53rlPO5t6Ly0fv0JihXzpnHj8ly4EEdUVAI1ajivX4prISKbjDFh1/JdvaNQqpDafCyCfh//hQh4FxGSUv69aBzevjoPdapJcICvCyMs+KKi4nn22d/55JON9OpVhwULBhMU5E9QkGfV12iiUKqQOBsdz9bjkWw8GsFnqw6lDzcGhrWvjjFwY/3yNK9aiqJeBasJiYLGGMOsWbt44omlnD17iVGjWvPqq11cHZbTaKJQykOlpBrWHDjP2ZgEnpy9Lctpvrw3jK71y+dzZO7vu++2c8898wkLq8TChYNp2bKSq0NyKk0USnmAhOQUthyL5HJiCscj4thyLJJ5W05cMd2jXWrRvVFFapYrjq+31jlcjYSEZA4diqB+/bIMGtSQ5ORU7rmnKV6F4O5LE4VSbu7w+Ut0fmdlluPKBfry0eDmlC/hR/Xg4vkbmAdZseIwDz20iLi4JPbvH4WvrzdDhzZ3dVj5RhOFUm4qLjGZvw9dZOjUDQDUr1iCp26uS+niPgQH+FCxZDF9jPU/Onv2Ek8++SvTpm2nRo3SfPZZ73zvr7ogKHxrrJQbi7iUyB/7zvHSz7uIjPu39dU65QP45bEOLozM8xw4cJHWrT8nNjaR557rwHPPdaBYsYLfgJ8zaKJQqoCKjEtkx4kothyL5FxMAusPX+SfMzEZpnm4c01ualCBplVKuShKzxMdnUCJEr7UrFma4cObM2xYc+rXL+vqsFxKE4VSBUBsQjLbjlsV0F4iV7SnlKa0f1HuaF2V/i1CqFUuIJ+j9GyXLiXyyit/8Pnnm9m+/SFCQkrw9ts3uTqsAkEThVIu9uC0jSzddSbDsLKBvkTGJTKuez3qVyxBk5CS+BX10vcbnOTnn//hkUd+4dixKIYPb+4WfUTkJ00USuWj6Pgklu0+w9qDFzhwLpYtxyLTx919XVX6NqtMi6qltaG9fJKcnMqgQbOZN28vDRuWZfXqobRvX9XVYRU4miiUcqKE5BRu/3QdXkWETUcjMowL9LN+fg0qluCTu1tQLUgfX80vxhhEBG/vIlSsGMCbb3bliSfaekQDfs6giUIpJzh2IY4n52zL0CVou5pBxMQn07ZmEHe0qkKNslrH4Arr1oXz8MOL+fzz3rRoUZHJk29xdUgFniYKpa6RMYaftp4kIi6RySsOcj42geI+Xnh7FSHq8r+PrgYH+PLX013w8db6BVeKiLjMs8/+zqefbqJSpUAiIi67OiS34dREISLdgQ8BL+ALY8ybmcZXBb4BStmmedoYs9iZMSn1X52JjmfWhuO899u+K8bdULcs5QL9MMZQroQfD3eu5YIIVWazZu3k0UeXcP58HI8/fh0vv9yJwEBtGddRTksUIuIFTAa6AeHABhFZYIzZbTfZ88APxphPRKQBsBgIdVZMSv0XO8KjeGbednaeiE4fVtzHi1kPtqVKGX9KFtKXsdzB3r3nCQ0txZIld9G8eUVXh+N2nHlH0Ro4YIw5BCAiM4E+gH2iMEAJ298lgZNOjEepq7blWAQ7TkTx3bqj7DsTmz78tuaVea1vI4oXwuYc3EF8fDJvvfUnLVpUpHfvujz7bAeef75joWjAzxmceZRXBuzfGgoH2mSa5iXgVxEZBRQHbsxqRiLyAPAAQNWq+uiacq6LlxKZuGQvMzdc+dLbxP5NuK1FZX2foQBbtuwQI0cuYv/+i4wZ05bevetSVHvn+0+cmSiyehA8c7+rg4Gpxph3RaQtME1EGhljUjN8yZjPgM/A6grVKdGqQm/Z7jPc/23GbnZ9vIvw8Z0taF61FCWLFcVbE0SBdeZMLKNH/8r06TuoVasMv/56N9261XR1WB7BmYkiHKhi9zmEK4uWhgPdAYwxa0XEDwgGzjoxLqWuMOaHbczdHJ7++cEbavBY19r4+2jRkrv47bdDzJmzmxdf7Mgzz3TAz0/3XV5x5pbcANQWkerACeAO4M5M0xwDugJTRaQ+4Aecc2JMSvHXwfP8vO0UB87GsOFIxpfgptzdgu6NtLLTXWzbdpr9+y8yYEAD7rqrMddfX4Xq1Uu7OiyP47REYYxJFpFHgKVYj75+ZYzZJSKvABuNMQuAMcDnIvIEVrHUfcYYLVpSeW7FP2d5as52zsUkpA8rbnsLt37FElQpXYxne9YnVDv3cQuxsYmMH7+CDz/8m9DQUvTtWw9v7yKaJJzEqfdmtnciFmca9qLd37uB650ZgyrcouOTaPLSr+mfA329qV62OI91ra19Rbup+fP3MmrUL4SHR/PAAy2YMOFGvPVlRqfSQjzlUS5eSmR7eCTfrTtK1OWkDEVL/xvcnN5NK7kwOvVf7dhxhttum0XjxuWYNWsA7dpVyf1L6j/TRKHcUkx8EpFxSZyJjufxWVsJDvBl6/HIK6ZrVqUUrUJL83SP+totqJtKSkph9epjdOlSncaNy7No0Z1061ZDH3nNR5oolFs5eC6Wru/+ccXw8IjL1KsQiL+PFzc1rECLqqUJq6bNdbu7v/46zogRC9m16xz//PMItWqVoWfP2q4Oq9DRRKHcxs4TUfT6358AFCvqxZib6lDUqwi1ygVwXY0gvWPwIBcvXubpp5fx+eebqVKlBD/+OIhatcq4OqxCSxOFKvB2n4zmsZlb2H/WakKjWpA/f4zt7OKolLPExyfTrNkUTp6MYcyYtrz0UicCAnxcHVahpolCFVjnYhJ4f9k+pv99LH3Yo11qMfqmui6MSjlLeHg0ISEl8PPz5tVXO9OsWQWaNq3g6rAUmihUARSflMKz83bw4+YT6cOe7lGPETdocwye6PLlJCZM+JO33lrDnDkD6d27Lvfe28zVYSk7DiUKEfEBqhpjDjg5HlUIpaQaTkZeZvnes3y26hAnIv/tUKZn4wo8d0sDKpcq5sIIlbP8+utBRo5cxMGDEdx9dxNat67s6pBUFnJNFCJyC/Ae4ANUF5FmwHhjzG3ODk55vu//Pspz83ZeMbxn4wq8O7AZxbQPY481atRiJk3aQO3aZVi2bAhdu9ZwdUgqG47cUbyC1Tz4CgBjzFYR0W671DU7HRXPmgPn+fLPw+w+ZXUCdF2NMnRrUIE+zSoRHKA9j3mqlBSrYWgvryJcd10IwcH+jBvXXhvwK+Ac2TtJxphIkQyPHmp7TOqqPDl7GxdiE9h7OoZTUfEZxn11Xxhd6mlzGp5u8+ZTjBixkCFDmjBqVBvuuquJq0NSDnIkUewRkUFAEVtLsI8B65wblnJ3xhje/20ffx28wMaj/zaj0bhySUr5+zCwZQjtagVRq2yA9vHg4WJiEnjxxRV89NF6ypb1p2LFQFeHpK6SI4niEeBFIBX4Eas12GecGZRyX7tPRvPyz7v4+/DFDMMrlyrG7BFtqaSV0oXKr78eZNiwnzh5MoYRI8J4442ulCrl5+qw1FVyJFHcbIwZB4xLGyAi/bCShirkouKS+OD3few6Ec36IxmTQwk/b5Y/2UnrHAoxHx8vypUrzty5g2jTJsTV4ahrJLl1/yAim40xLTIN22SMaenUyLIRFhZmNm7cmPuEymmSU1L550wMe0/FMGb2tgzjinoJH97RnJ6NtfOfwigpKYX33ltLdHQCr7/eFYDUVKNtbhUAtvN22LV8N9s7ChG5Gaub0soi8p7dqBJYxVDKwxlj2H82li3HIvht9xn+PnSRy0kpJKdmvLgoX8KXP8Z2xk9b8yzU/vzzWHoDfgMHNkhPEJok3F9ORU9ngZ1APLDLbngM8LQzg1Ku9+y8HRmazgCrnqFU8aJULeNPq9AyNK1SinoVAqlYUusdCrMLF+IYN24ZX365hapVS/Lzz4Pp1auOq8NSeSjbRGGM2QJsEZHvjTHx2U2nPEt8UgqPzdzC0l1nAKueYeKAJjSqXJKQ0v4ujk4VRBcuXGbmzJ089VQ7XnzxBooX1wb8PI0jldmVReR1oAGQ/riCMUYvGTzMou2neHj65vTPq5/qTJUymhzUlfbsOccPP+xi/PhO1KkTxLFjT1CmjN5ZeipHEsVU4DXgHaAHMBSto/Aov+46zQPTNqV/DqtWmilDWurTSuoKcXFJvP76Kt5++y8CAnwYPrwFISElNBxMStEAACAASURBVEl4OEcShb8xZqmIvGOMOQg8LyKrnR2Ycq6klFQ+WXmQn7ae4OC5SwD4+3jx3f1taFG1tIujUwXRkiUHGDlyEYcPR3LvvU15++1ulC1b3NVhqXzgSKJIEKv9joMiMgI4AZRzbljKWS7EJvDJyoN88efhDMOn3N2S7o207X+VtdjYRIYMmUdQUDFWrLiXTp1CXR2SykeOJIongADgUeB1oCQwzJlBqbwVn5TC7Z+u5VJiCgdsvcQBVCrpx/xHrqdcoL4pq66UkpLKjBk7GTy4EQEBPixbNoR69YLx9dUG/AqbXPe4MeZv258xwBAAEdFXLN3AwXOx9J28hpj45PRhPt5F6N8ihNf6NtI+plW2Nm06yYMPLmTTplMUK+ZN//4NtLe5QizHRCEirYDKwJ/GmPMi0hCrKY8ugCaLAigmPol9Z2Lo/8naDMNvaVyRSXc2J1MrwEplEBUVzwsvrGDy5A2UK1ecmTP7069ffVeHpVwspzezJwD9gW1YFdjzsFqOfQsYkT/hKUckp6Tyy87TjJqx5Ypxbw9owsCwKi6ISrmj/v1/YPnywzz8cCtee60LJUtqsaTK+Y6iD9DUGHNZRMoAJ22f/8mf0FRujpy/xODP113Rv8Ow66vTvnYQneuW0zsIlatDhyIoW9afwEBfXn+9C0WKCK1aaZek6l85JYp4Y8xlAGPMRRHZq0miYPhs1UHeWLw3w7A7WlVhYFgVWlbTR1uVYxITU3jnnb949dVVPPpoa956q5u28KqylFOiqCEiaU2JCxBq9xljTD+nRqaucOxCHDM2HOOTlQcBqF0ugHvaVmNI21DXBqbczqpVRxkxYiF79pxnwIAGPPpoG1eHpAqwnBJF/0yfJzkzEJWz5XvPMGzqv82rv3FbY+5sU9WFESl39f77axk9+ldCQ0uxaNGd9OxZ29UhqQIup0YBf8/PQFT2Xvl5N1+tsV6Qu6lBeV67rZG++6CuSmqq4dKlRAIDfbnlljqcOxfH8893xN+/qKtDU25A35wpwE5FXeaxGVvTe477+ZH2NA4p6eKolLvZtessI0YsSu9prk6dIN54o6urw1JuxKm92otIdxH5R0QOiEiWfViIyCAR2S0iu0RkujPjcTeDPl2bniTG926gSUJdlbi4JJ55ZhnNmn3Knj3n6NWrNrn1aKlUVhy+oxARX2NMwlVM7wVMBroB4cAGEVlgjNltN01t4BngemNMhIhoG1JYPcs1fulXYhOSaVS5BAtHdXB1SMrNbNlyin79fuDIkUiGDm3GxIndCA7WJuPVtcn1jkJEWovIDmC/7XNTEfmfA/NuDRwwxhwyxiQCM7HezbD3f8BkY0wEgDHm7FVF76Ee+m4zsQlWsxtzRrRzcTTKnaTdMVStWpKqVUvyxx/38dVXfTRJqP/EkaKnj4BewAUAY8w2oLMD36sMHLf7HG4bZq8OUEdE1ojIOhHp7sB8PZYxhqfmbGPJrtMAbH2xm/ZDrRySnJzKBx+so2vXb0lJSSUoyJ8//riPjh2ruTo05QEcKXoqYow5mukN3xQHvpfVK8GZC0i9gdpAJ6y2o1aLSCNjTGSGGYk8ADwAULWqZz4SmpCcQt3nl6R/fm9QU0r5a5eSKnfr159gxIiFbNlymh49ahEdnUDp0tqRkMo7jiSK4yLSGjC2eodRwD4HvhcO2DcyFILVDEjmadYZY5KAwyLyD1bi2GA/kTHmM+AzgLCwMI+rjXth/k6mrTua/nnV2M5UDdKiApWz2NhExo37jU8+2UjFioHMnj2Q/v3ra7MtKs85UvT0EDAaqAqcAa6zDcvNBqC2iFQXER/gDmBBpmnmYyvGEpFgrKKoQ46F7hnu+Wp9epLo3yKEwxN6apJQDilatAgrVx5l1KjW7NnzMAMGNNAkoZzCkTuKZGPMHVc7Y2NMsog8AiwFvICvjDG7ROQVYKMxZoFt3E0ishurOGusMebC1S7LXb2xeA+r9p0DYNuLN1FSX35SuThw4CKvvPIHkyf3JDDQl02bHsDPT1+HUs7lyBG2wVYkNAv40RgT4+jMjTGLgcWZhr1o97fBulsZ7eg8PUV0fBKfrbJunr4e2kqThMpRQkIyEyeu4fXXV+Pj48X//V8LOnSopklC5Ytci56MMTWB14CWwA4RmS8iV32HoTLq8cFqAO5rF0rnuvr6iMreihWHadp0Ci++uJK+feuxd+8jdOigTzOp/OPQm9nGmL+MMY8CLYBo4HunRuXBzscmMPqHrZyIvAxYb1wrlR1jDK+/vpqkpFSWLLmLmTMHUKlSoKvDUoVMrvetIhKA9aLcHUB94CdA3wK7BskpqYS9tiz98zfDWmvlo7pCaqrhyy830717LapUKcm0abdRqpQfxYpp8aRyDUcKOHcCPwMTjTGrnRyPxxr9w1Z+3HwCgLKBviwbfQMl9YevMtm+/QwjRixk7dpwXnyxIy+/3JmKFfUOQrmWI4mihjEm1emReLCXf96VniQGtgxh4oAmeiehMoiNTeTll1fy/vvrKF26GFOn9uGee5q6OiylgBwShYi8a4wZA8wVkStectMe7hzz1pK9fL3mCAA/PNiW1tXLuDYgVSC99NJK3n13Lfff35w337yRIH2XRhUgOd1RzLL9rz3bXYM/95/n8VlbOR9rNbg784HrNEmoDI4fj+LSpSTq1Qvm6afb07dvPdq398wmapR7y/apJ2PMetuf9Y0xv9v/w6rUVtmYsHgPd3/5N+djE+hYpyzLRnfkuhpBrg5LFRDJyam8995a6tefzIMPLgQgONhfk4QqsBypoxjGlXcVw7MYpoCHv9/Moh2nAJg3sh3Nq5Z2cUSqIFm3LpwRIxaybdsZbrmlNpMm9XR1SErlKqc6ituxHomtLiI/2o0KBCKz/lbhNnXN4fQkse6ZrlQoqf1aq38tWrSP3r1nUKlSID/+OIi+fevpQw3KLeR0R7Eeqw+KEKye6tLEAFucGZQ7+nnbSV762eq876PBzTVJKMB6Ye7kyRgqVy7BjTfW4JVXOvPYY20IDPR1dWhKOSzbRGGMOQwcBpZlN43616gZVu58/Mba3Nq0koujUQXBvn0XGDlyEfv2XWD37ocJCPDh+ec7ujospa5aTkVPfxhjbhCRCDJ2OCRY7fnpIzw2t076E4CGlUrw+I11XByNcrX4+GTefPNPJkz4k2LFvJkwoSvFimnjfcp95XT0pnV3GpwfgbirU1GX2R4eBcD0/7vOxdEoVzt9OpaOHb9m//6LDB7ciPfeu5kKFQJcHZZS/0lORU9pb2NXAU4aYxJFpD3QBPgOq3HAQm3T0Yv0/2QtAOO619MmOQqxpKQUihb1onz54nTsWI3Jk3vSrVtNV4elVJ5wpPXY+VjdoNYEvsV6h2K6U6NyA1GXk9KTRN3ygTzQsYaLI1KukJpqmDJlIzVrfkR4eDQiwhdf3KpJQnkURxJFqq1P637AB8aYUUBl54ZVsC3fe4amL/8KQOVSxVj6REe8iuhjjoXNtm2nadfuSx56aBG1aweRlJTi6pCUcgqHukIVkYHAEKCvbVihLWNJTTUMm7oRgPa1gvlmWGsXR6TymzGGsWN/44MP1lGmTDGmTbuNu+5qrO9EKI/l6JvZI7GaGT8kItWBGc4Nq2AyxlDjWatn1051yzJ1qCaJwkhEiIi4zPDhVgN+pUsXc3VISjmVWN1W5zKRiDdQy/bxgDEm2alR5SAsLMxs3Lgx35drjKHru39w6PwlAPa/3oOiXg51EKg8wNGjkTz22BJefPEGWrSoSGqqoYgWNyo3IiKbjDFh1/LdXM90ItIBOAB8CXwF7BOR669lYe7s89WH0pPErpdv1iRRSCQlpTBx4hoaNPiY3347xD//nAfQJKEKFUeKnt4HehpjdgOISH1gGnBNmcldvbF4LwCrn+pMcV99eaow+Ouv4zz44EJ27jxLnz51+eijHlStWtLVYSmV7xw54/mkJQkAY8weEfFxYkwFTt/Ja9L/rlJGO5QpLJYtO0RUVDzz599Onz71XB2OUi6Tax2FiEwFErDuIgDuAvyNMfc6N7Ss5WcdxbLdZ7j/23+XtfmFbpQpXqhyZKFijGHatO2ULetPjx61SUhIJikplYAA3efK/Tm1jgIYARwEngLGAYeAB69lYe7k7aV7MySJFU920iThwfbuPU+XLt9y773z+frrrQD4+nprklCKXIqeRKQxUBOYZ4yZmD8hud7HKw8wecVBABaOak+jylou7akuX07ijTdW89Zbayhe3IdPP+3F/fe3cHVYShUoObUe+yxWT3abgVYi8oox5qt8i8xFHpy2kaW7zgAw9ua6miQ83M8/7+O111Zz991NeOedbpQvrw34KZVZTncUdwFNjDGXRKQssBjr8ViPdT42IT1J/DG2E9WCirs4IuUMp0/HsnXrabp3r8XAgQ0IDb2f1q0Ldas0SuUopzqKBGPMJQBjzLlcpvUIYa9ZfTR1qltWk4QHSklJ5eOPN1C37iSGDJnH5ctJiIgmCaVykdMdRQ27vrIFqGnfd7Yxpp9TI8tnoU8vSv9bm+bwPJs3n2LEiIVs2HCSG2+swccf96SYNguvlENyShT9M32e5MxAXGnFP2fT/970/I0ujEQ5w+HDEbRu/TnBwf5Mn96PO+5opA34KXUVcuq46Pf8DMRVjDEM/XoDAPNGtiMoQDu99wTGGHbsOEuTJuWpXr00X3/dh96961KqlJ+rQ1PK7Xh8vUNuGo5fCkDZQF+aVSnl4mhUXjh8OIJevWbQvPmnbN9uPZwwZEhTTRJKXSOnJgoR6S4i/4jIARF5OofpBoiIEZF8bz8qLtHqbOaPsZ20OMLNJSam8Oabf9Kw4cf88ccR3nmnGw0alHV1WEq5PYdbtxMRX2NMwlVM7wVMBroB4cAGEVlg326UbbpA4FHgb0fnnVe6vrsSgMGtq+Dvow39ubOUlFTatfuSTZtO0a9ffT744GaqVNF3YJTKC440M95aRHYA+22fm4rI/xyYd2usvisOGWMSgZlAnyymexWYCMQ7HvZ/dzYmnoPnrGbDR3WpnZ+LVnkoOtq6dvHyKsKwYc35+efBzJ07SJOEUnnIkaKnj4BewAUAY8w2oLMD36sMHLf7HE6mvrZFpDlQxRizMKcZicgDIrJRRDaeO3fOgUXn7vt1xwCYOKAJlUppD2XuxhjD1KlbqVHjQ376yWoCfuTIVvTqVcfFkSnleRxJFEWMMUczDXOkF/msCvzTm6oVkSJYfV2MyW1GxpjPjDFhxpiwsmXzpsw5Mi4RgFubVsqT+an8s3v3OTp1+oahQ3+iXr1gatYs4+qQlPJojhTMHxeR1oCx1TuMAvY58L1woIrd5xDgpN3nQKARsNJWiVwBWCAitxpjnN6O+N+HL1Iu0Be/ol7OXpTKQxMnruG555ZTooQvX3zRm6FDm2tvc0o5mSOJ4iGs4qeqwBlgmW1YbjYAtUWkOnACuAO4M22kMSYKCE77LCIrgSfzI0n87/f97D0d4+zFqDxkjEFEqFAhgLvuaszbb3ejbFltZkWp/JBrojDGnMU6yV8VY0yyiDwCLAW8gK+MMbtE5BVgozFmwVVHm0d+32u9iT3zgetcFYJy0MmTMTz22BI6dKjKo4+24Z57mnLPPU1dHZZShUquiUJEPseubiGNMeaB3L5rjFmM1eqs/bAXs5m2U27zywvGGLYejwTguhpB+bFIdQ3SGvB77rnlJCWl0q5diKtDUqrQcqToaZnd337AbWR8msmtLN11GoDbw6rkMqVyla1bT3P//QvYtOkUN91Uk48/7qkV1kq5kCNFT7PsP4vINOA3p0XkRHGJyYz4bjMAQ9pWc3E0KjtRUfGcPBnDrFkDGDiwgb4xr5SLXcvryNUBtzzLfr7qMAD1KgRqz3UFiDGG2bN3s3//BZ57riM33BDKoUOP4eenb8srVRA48mZ2hIhctP2LxLqbeNb5oeW995dZT/V+eV8rF0ei0hw8eJGePadz++1z+Omnf0hKsl7R0SShVMGR469RrHv+pliPtwKkGmOuqNh2BympVthBxX2orG9iu1xCQjLvvPMXr722mqJFi/Dhh90ZObIV3t6FvkFjpQqcHBOFMcaIyDxjTMv8CshZZm2w6t9vb6WV2AXB8ePRvPrqKnr3rssHH9xM5colXB2SUiobjly+rReRFk6PxImMMTw7bwcAQ6+v7uJoCq9z5y4xadJ6AGrVKsPu3Q8ze/ZATRJKFXDZ3lGIiLcxJhloD/yfiBwELmG14WSMMW6TPOZuPpH+d9lA7cEuv6WmGr7+egtPPbWMmJgEunWrQd26wdSoUdrVoSmlHJBT0dN6oAXQN59icZoPf7cqsdc+08XFkRQ+O3ee5aGHFvHnn8fo0KEqU6b0om7d4Ny/qJQqMHJKFAJgjDmYT7E4RVxiMscvXgagYkmtxM5PiYkp3HTTNBITU/jqq1u5775m+k6EUm4op0RRVkRGZzfSGPOeE+LJc7tPRgPwxI3aT0F+Wb78MDfcUA0fHy9++GEg9eoFExzs7+qwlFLXKKfKbC8gAKs58Kz+uYW0N7Hb19Z2nZwtPDya/v1/oGvXb/n2220AtG9fVZOEUm4upzuKU8aYV/ItEic4EXmZ87FWV5ktq2lbQc6SnJzKpEnreeGFFaSkpDJhQlfuuquJq8NSSuWRXOso3NnI7zYB8HSPei6OxLMNGTKPmTN30qNHLSZP7kn16vo0k1KeJKdE0TXfonCSYxfjAHiwYw0XR+J5IiPj8fYuQkCADw8/3Ir+/evTv399raxWygNlW0dhjLmYn4E4Q0RcEsPbV9eTVx4yxjBz5k7q15/MCy8sB6x6iAEDtJVXpTyVxzass3r/OQC8vfTklVcOHLjIzTd/x+DBcwkJKcHdd2s9hFKFgcc20TlnUzgAA1tq2055Yfr0HQwb9hO+vt5MmtSDESPC8PLy2OsMpZQdj00Uaw5cAKB6cHEXR+LekpJSKFrUi7CwSgwY0ICJE7tRqZLbPB2tlMoDHpsozscmULGkH15FtOjpWpw9e4kxY37l0qVEfvzxdurUCeK77/q5OiyllAt4ZNnBzhNRALQK1XcnrlZqquGzzzZRt+4kZs3aScOGZUlJSXV1WEopF/LIO4pBn64FYGBYiIsjcS+HDkVw990/snZtOJ06hfLJJ7dQr5424KdUYeeRiSLJdgXcvpae5K5GyZK+REbG8803fRkypIk+7qqUAjyw6Ck6PomkFMNtzSvric4BCxb8Q79+s0hJSSUoyJ+dO0dyzz1NddsppdJ5XKJ471er74nmVUu5OJKC7dixKPr2nUmfPjPZt+8Cp07FAlBEK/+VUpl4XNHT2oPWY7F3tanm4kgKpuTkVD74YB3jx6/EGMNbb93IE09cR9GiXq4OTSlVQHlcovjnTAwNK5XQx2KzkZKSyhdfbKZLl+r87389CA3VOy+lVM48qujp6IVLAFQs6efiSAqWiIjLjBv3GzExCfj6erNmzTAWLLhDk4RSyiEelSj+PmS1Y9i3eWUXR1IwGGP4/vvt1Ks3mXffXcuKFUcACAry18pqpZTDPKro6eWfdwH6WCzAvn0XGDlyEb//fpjWrSuzdOndNGtWwdVhKaXckMckCmMMlxJTACjl7+PiaFzv8ceXsHHjST7+uCcPPNBSG/BTSl0zj0kUyakGgDHd6rg4Etf57beD1KsXTJUqJfnkk1vw9fWmQoUAV4ellHJzTr3MFJHuIvKPiBwQkaezGD9aRHaLyHYR+V1ErvmZ1uQUK1F4F8Ir59OnY7nzzrncdNN3vPXWGgCqVSulSUIplSecdlYVES9gMtADaAAMFpEGmSbbAoQZY5oAc4CJ17q8RFuzHUULUUdFqamGKVM2Uq/eJObO3cP48Tfwzjs3uTospZSHcebld2vggDHmkDEmEZgJ9LGfwBizwhgTZ/u4DrjmVvzOxSQA/yaMwmDChNU89NAiWrasxPbtI3jppU74+XlMaaJSqoBw5lmlMnDc7nM40CaH6YcDv2Q1QkQeAB4AqFq1apZf3nXSalq8bnnP7lQnJiaB8+fjqF69NCNGhFG9emkGD26kj7sqpZzGmXcUWZ25TJYTitwNhAFvZzXeGPOZMSbMGBNWtmzZLBc2d/MJAOpW8MxEYYxh3rw9NGjwMbffPgdjDEFB/tx5Z2NNEkopp3JmoggH7DusDgFOZp5IRG4EngNuNcYkXOvCVu07Zy2ktP+1zqLAOno0kltvnUm/fj9QpkwxPvqohyYHpVS+cWbR0wagtohUB04AdwB32k8gIs2BT4Huxpiz17qg01HxAJTwwPL5tWuPc+ON0wB4551uPPbYdXh7F74nu5RSruO0M6sxJllEHgGWAl7AV8aYXSLyCrDRGLMAq6gpAJhtu0I+Zoy59WqXNXHpXgBe6JX5oSr3FR2dQIkSvrRoUZFhw5oxduz1VK1a0tVhKaUKIadeghtjFgOLMw170e7vG/NiOb62K+wBLd2/69MLF+J4+ull/PrrIXbtGklAgA//+19PV4ellCrEPKKsJj4plcqlirl1ub0xhmnTtjNmzK9ERFxm9Oi2uPHqKKU8iEckinlbThBSupirw7hmUVHx9O07i5Urj9C2bQhTpvSiSZPyrg5LKaUAD0gU8Ukprg7hmhljEBFKlPAlONifzz7rxfDhLbQ7UqVUgeL2j88s3XUagMGts34Rr6BauvQALVp8Rnh4NCLC7NkD+b//a6lJQilV4Lh9opi/xXrR7vZWVXKZsmA4dSqGO+6YQ/fu3xMXl8TZs5dcHZJSSuXI7Yue0vqgCA7wdXEkuZs8eT3PPruchIRkXn65E+PGXY+vr9vvAqWUh3Prs1RicirrD190m/adNm06RZs2lZk8uSe1awe5OhyllHKIWyeKc7FWix/1KxbMRBEdncCLL65gyJAmtGxZiY8/vgVfXy+3foxXKVX4uHWiWHvwAgCd65VzcSQZGWOYO3cPjz22hFOnYqhatSQtW1bSJsCVUm7Jrc9cfx08D0C7msEujuRfhw9H8Mgjv7B48X6aNavAjz8Ook0b939jXClVeLl1oth7KgaAsoEFpyL7++93sGrVUd5//2YeeaS1NuCnlHJ7bp0oivl4Ua4AJInVq4+SkJDCjTfWYOzYdtx3XzNCQkq4OiyllMoTbn25m5CcQqPKrmtR9fz5OIYN+4mOHafyyit/AODr661JQinlUdz2jiI11bDzRDQ3N8z/Np6MMUydupWxY38jKiqBceOu54UXOuZ7HKrgS0pKIjw8nPj4eFeHogoJPz8/QkJCKFq0aJ7N020TxdzN4QAUccGjposX72fYsAVcf30VpkzpRaNGBeupK1VwhIeHExgYSGhoqD4WrZzOGMOFCxcIDw+nevXqeTZfty16OhtjvUMxoV/jfFleXFwSa9YcA6Bnz9r89NMdrFo1VJOEylF8fDxBQUGaJFS+EBGCgoLy/A7WbRPFrpNRAJTwy7vbq+z88st+GjX6mB49vicyMh4R4dZb62oDfsohmiRUfnLG8ea2iaJYUW98vIo49WR94kQ0AwfOpmfP6fj6evPzz4MpVcrPactTSqmCyG0TxdzN4YSUcV5F9tmzl2jQ4GMWLtzHa691Ztu2EdxwQ6jTlqeUs3h5edGsWTMaNWpE7969iYyMTB+3a9cuunTpQp06dahduzavvvoqxpj08b/88gthYWHUr1+fevXq8eSTT7piFXK0ZcsW7r///gzD+vTpQ9u2bTMMu++++5gzZ06GYQEBAel/79u3j549e1KrVi3q16/PoEGDOHPmzH+K7eLFi3Tr1o3atWvTrVs3IiIirphmxYoVNGvWLP2fn58f8+fPB6BDhw7pwytVqkTfvn0BWLhwIePHj/9PsV0VY4xb/WvZsqVJTU011cYtNG1eX2byWnh4VPrfH364zhw4cCHPl6EKj927d7s6BFO8ePH0v++55x7z2muvGWOMiYuLMzVq1DBLly41xhhz6dIl0717dzNp0iRjjDE7duwwNWrUMHv27DHGGJOUlGQmT56cp7ElJSX953kMGDDAbN26Nf1zRESECQkJMfXq1TOHDh1KH37vvfea2bNnZ/hu2ra5fPmyqVWrllmwYEH6uOXLl5sdO3b8p9jGjh1rJkyYYIwxZsKECeapp57KcfoLFy6Y0qVLm0uXLl0xrl+/fuabb74xxhiTmppqmjVrluV0xmR93AEbzTWed93yqaeLlxIBGNAy75rGiIqK5/nnl/Ppp5tYt+5+WrSoyKOPtsmz+Sv18s+72H0yOk/n2aBSCcb3bujw9G3btmX79u0ATJ8+neuvv56bbroJAH9/fyZNmkSnTp14+OGHmThxIs899xz16tUDwNvbm5EjR14xz9jYWEaNGsXGjRsREcaPH0///v0JCAggNjYWgDlz5rBw4UKmTp3KfffdR5kyZdiyZQvNmjVj3rx5bN26lVKlSgFQq1Yt1qxZQ5EiRRgxYgTHjlkPkXzwwQdcf/31GZYdExPD9u3badq0afqwuXPn0rt3b8qXL8/MmTN55plnct0u06dPp23btvTu3Tt9WOfOnR3ertn56aefWLlyJQD33nsvnTp14q233sp2+jlz5tCjRw/8/f0zDI+JiWH58uV8/fXXgFUP0alTJxYuXMigQYP+c5y5cctEseOEVZEdmAeN7BljmD17N48/voTTp2N55JHW1KxZ+j/PV6mCJiUlhd9//53hw4cDVrFTy5YtM0xTs2ZNYmNjiY6OZufOnYwZMybX+b766quULFmSHTt2AGRZvJLZvn37WLZsGV5eXqSmpjJv3jyGDh3K33//TWhoKOXLl+fOO+/kiSeeoH379hw7doybb76ZPXv2ZJjPxo0badSoUYZhM2bMYPz48ZQvX54BAwY4lCh27tx5xbbISkxMDB06dMhy3PTp02nQoEGGYWfOnKFixYoAVKxYkbNnz+Y4p7W+pAAADlZJREFU/5kzZzJ69Ogrhs+bN4+uXbtSosS/L/OGhYWxevVqTRTZmbXhOAAd65T9T/MxxtCv3w/Mn7+XFi0qsmDBYMLCKuVFiEpd4Wqu/PPS5cuXadasGUeOHKFly5Z069YN+LfP9qxczZMzy5YtY+bMmemfS5fO/UJr4MCBeHl5AXD77bfzyiuvMHToUGbOnMntt9+ePt/du3enfyc6OpqYmBgCA//tVuDUqVOULfvveeDMmTMcOHCA9u3bIyJ4e3uzc+dOGjVqlOU6Xe0TQoGBgWzduvWqvuOoU6dOsWPHDm6++eYrxs2YMeOKephy5cpx8uRJp8SSmVtWZv+y0+onu16Fa+uHIinJ6hVPRGjfvgoffdSd9evv1yShPFKxYsXYunUrR48eJTExkcmTJwPQsGFDNm7cmGHaQ4cOERAQQGBgIA0bNmTTpk25zj+7hGM/LPNz/cWLF0//u23bthw4cIBz584xf/58+vXrB0Bqaipr165l69atbN26lRMnTmRIEmnrZj/vWbNmERERQfXq1QkNDeXIkSPpSSwoKCjD3c7FixcJDg5O3xaOrGtMTEyGimf7f/ZJLU358uU5deoUYCWCcuWyf+/qhx9+4LbbbrvijeoLFy6wfv16brnllgzD4+PjKVYsf1qmcLtEkWp7ImNQWMg1PS+8cuURmjSZwk8/7QVgzJh2jBrVBi8vt9sUSl2VkiVL8tFHH/HOO++QlJTEXXfdxZ9//smyZcsA687j0Ucf5amnngJg7NixvPHGG+zbtw+wTtzvvffeFfO96aabmDRpUvrntJNx+fLl2bNnT3rRUnZEhNtuu43Ro0dTv359goKCspxvVlfy9evX58CBA+mfZ8yYwZIlSzhy5AhHjhxh06ZN6YmiU6dOzJo1i8REq45z6tSp6fUQd955J3/99ReLFi1Kn9eSJUvSi9PSpN1RZPUvc7ETwK233so333wDwDfffEOfPn2y3Q4zZsxg8ODBVwyfPXs2vXr1ws8v46P5+/btu6LYzVnc7uwYb7sbqHOV3Z+eO3eJe++dT+f/b+/uY6uq7ziOvz9ToEWRKYxlCgjEitRaHuuqBpnijDqUzRgBBWXREUCGD9OEBZO5qRF1qAN1gM6g8wGm0UkQwwirYghF61RQfMamdDHKWIeIiCLf/XF+9F77cHtauY/9vpIm957Hb7+59/zu+Z1zvr8zHmbv3n30yIGqs85l2vDhwxk6dCjLli2juLiYZ599lltuuYXBgwdz0kknUVFRwaxZswAoLy/nnnvuYdKkSQwZMoSysrLGX8fJbrzxRhoaGigrK2Po0KFUVVUBMG/ePMaNG8eZZ57Z2E/fmgkTJvDoo482djsBLFiwgJqaGsrLyyktLWXRokXN1jvhhBPYuXMnu3btora2lrq6OiorKxvnDxw4kCOOOIKNGzcybtw4Ro8ezciRIxk2bBjr169vvLBcXFzMypUrWbhwISUlJZSWlrJ06dKUZwBxzJkzhzVr1lBSUsKaNWuYM2cOEF1bSe5Kqq2tZdu2bYwZM6bZNpYtW9ZiA1JVVdXsLCNdZEn3TOeDQUPKbf/423hy+ilUDDgq1jpPPLGZq65axeeff8UNN5zK3Lmn0717+p/odu7tt99myJAh2Q6joN1999306NGjWR9+Ifvkk0+45JJLWLt2bYvzW/rcSXrVzEZ1ZH95d0bx2Z6vARjU+7A2lkzYt28/ZWV9eP316dx661hvJJwrIDNmzKBbt87VQ1BXV8f8+fMztr+8u+tp1959HA70Orz1D8bu3V9x883r6N+/JzNnVjB5cjmTJ5d7zR3nClBRURFTpkzJdhgZVVFRkdH95d0ZBUDZMa0PDLRy5XuceOL93H77et57bwcQXSzzRsJlS75177r8lo7PW96dUQCcX978Ntb6+s+YPft5nnnmHUpLf8C6dVMZPfrYLETnXEJRURE7duzwUuMuIyyMR9H0DqnvKi8bin5HdW82bevWBlav/pDbbhvLddedQteuh2QhMue+rW/fvtTX17N9+/Zsh+I6iQMj3B1MedlQHNm9KwAvv/xvNmzYxtVXV3L66cdSV3cNvXo1b0Scy5YuXboc1JHGnMuGtF6jkHSOpHclfSBpTgvzu0laHuZvlDQgznb7FB3KzJnPUVn5IHfdVc3uUCTQGwnnnDv40tZQSDoEuA84FygFJklq+ujiFUCDmR0H3A20XlYxyWkjH2Dx4leZPfvHbN48g8MO63owQ3fOOZcknV1PJwMfmNlWAEnLgPFAckGU8cBN4fVTwL2SZCku29t+o1//nqxadSkjRqR+2tM559x3l86G4hhgW9L7eqDpAA+Ny5jZPkk7gV7Af5IXkjQNmBbe7q35dNqbMSoCdwa9aZKrTsxzkeC5SPBcJAzu6IrpbChauhew6ZlCnGUwsyXAEgBJNR19DL3QeC4SPBcJnosEz0WCpJq2l2pZOi9m1wP9kt73BZoWT29cRtKhQE/gv2mMyTnnXDuls6F4BSiRNFBSV2AisKLJMiuAy8Pri4B/pro+4ZxzLvPS1vUUrjnMAlYDhwAPmdlbkv5ANMj3CuAvwF8lfUB0JjExxqaXpCvmPOS5SPBcJHguEjwXCR3ORd6VGXfOOZdZeVkU0DnnXOZ4Q+Gccy6lnG0o0lX+Ix/FyMV1krZI2iRpraSCLZvbVi6SlrtIkkkq2Fsj4+RC0sXhs/GWpMczHWOmxPiO9JdUJem18D05LxtxppukhyR9KunNVuZL0oKQp02SRsTasJnl3B/Rxe8PgUFAV+ANoLTJMjOBReH1RGB5tuPOYi7OALqH1zM6cy7Ccj2AdUA1MCrbcWfxc1ECvAYcGd73yXbcWczFEmBGeF0K1GY77jTl4nRgBPBmK/PPA54neoatEtgYZ7u5ekbRWP7DzL4CDpT/SDYeeDi8fgoYq8Is+N9mLsysysy+CG+riZ5ZKURxPhcANwN3AF9mMrgMi5OLXwH3mVkDgJl9muEYMyVOLgw4MOJZT5o/01UQzGwdqZ9FGw88YpFq4PuS2qyFlKsNRUvlP45pbRkz2wccKP9RaOLkItkVRL8YClGbuZA0HOhnZiszGVgWxPlcHA8cL2m9pGpJ52QsusyKk4ubgMmS6oFVwK8zE1rOae/xBMjd8SgOWvmPAhD7/5Q0GRgFjElrRNmTMheSvkdUhXhqpgLKojifi0OJup9+QnSW+ZKkMjP7X5pjy7Q4uZgELDWz+ZJOIXp+q8zM9qc/vJzSoeNmrp5RePmPhDi5QNJZwFzgAjPbm6HYMq2tXPQAyoAXJNUS9cGuKNAL2nG/I8+a2ddm9hHwLlHDUWji5OIK4G8AZrYBKCIqGNjZxDqeNJWrDYWX/0hoMxehu2UxUSNRqP3Q0EYuzGynmfU2swFmNoDoes0FZtbhYmg5LM535O9ENzogqTdRV9TWjEaZGXFyUQeMBZA0hKih6Izj064ALgt3P1UCO83s47ZWysmuJ0tf+Y+8EzMXdwKHA0+G6/l1ZnZB1oJOk5i56BRi5mI1cLakLcA3wA1mtiN7UadHzFz8BnhA0rVEXS1TC/GHpaQniLoae4frMb8DugCY2SKi6zPnAR8AXwC/jLXdAsyVc865gyhXu56cc87lCG8onHPOpeQNhXPOuZS8oXDOOZeSNxTOOedS8obC5RxJ30h6PelvQIplB7RWKbOd+3whVB99I5S8GNyBbUyXdFl4PVXS0UnzHpRUepDjfEXSsBjrXCOp+3fdt+u8vKFwuWiPmQ1L+qvN0H4vNbOhRMUm72zvyma2yMweCW+nAkcnzbvSzLYclCgTcd5PvDivAbyhcB3mDYXLC+HM4SVJ/wp/p7awzImSXg5nIZsklYTpk5OmL5Z0SBu7WwccF9YdG8Yw2Bxq/XcL0+cpMQbIH8O0myRdL+kioppbj4V9FoczgVGSZki6IynmqZIWdjDODSQVdJP0Z0k1isae+H2YNpuowaqSVBWmnS1pQ8jjk5IOb2M/rpPzhsLlouKkbqdnwrRPgZ+a2QhgArCghfWmA38ys2FEB+r6UK5hAnBamP4NcGkb+z8f2CypCFgKTDCzk4gqGcyQdBTwC+BEMysHbkle2cyeAmqIfvkPM7M9SbOfAi5Mej8BWN7BOM8hKtNxwFwzGwWUA2MklZvZAqJaPmeY2RmhlMeNwFkhlzXAdW3sx3VyOVnCw3V6e8LBMlkX4N7QJ/8NUd2ipjYAcyX1BZ42s/cljQVGAq+E8ibFRI1OSx6TtAeoJSpDPRj4yMzeC/MfBq4C7iUa6+JBSc8BsUuam9l2SVtDnZ33wz7Wh+22J87DiMpVJI9QdrGkaUTf6x8RDdCzqcm6lWH6+rCfrkR5c65V3lC4fHEt8AkwlOhMuNmgRGb2uKSNwM+A1ZKuJCqr/LCZ/TbGPi5NLiAoqcXxTUJtoZOJisxNBGYBZ7bjf1kOXAy8AzxjZqboqB07TqJR3OYB9wEXShoIXA9UmFmDpKVEhe+aErDGzCa1I17XyXnXk8sXPYGPw/gBU4h+TX+LpEHA1tDdsoKoC2YtcJGkPmGZoxR/TPF3gAGSjgvvpwAvhj79nma2iuhCcUt3Hu0iKnvekqeBnxONkbA8TGtXnGb2NVEXUmXotjoC2A3slPRD4NxWYqkGTjvwP0nqLqmlszPnGnlD4fLF/cDlkqqJup12t7DMBOBNSa8DJxAN+biF6ID6D0mbgDVE3TJtMrMviaprPilpM7AfWER00F0Ztvci0dlOU0uBRQcuZjfZbgOwBTjWzF4O09odZ7j2MR+43szeIBof+y3gIaLurAOWAM9LqjKz7UR3ZD0R9lNNlCvnWuXVY51zzqXkZxTOOedS8obCOedcSt5QOOecS8kbCueccyl5Q+Gccy4lbyicc86l5A2Fc865lP4P4zil0y63TAQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289), output_dict = True)[\"1\"][\"recall\"],auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PCA\n", + "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2 Component PCA\n", + "First, we will visualize the information retained after performing a 2 component pca." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "principalComponents = pca.fit_transform(X_train)\n", + "principalDf = pd.DataFrame(data = principalComponents\n", + " , columns = ['principal component 1', 'principal component 2'])" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variation per principal component: [0.2955834117 0.1746114375]\n" + ] + } + ], + "source": [ + "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", + "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#visualizing pca\n", + "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", + "fig = plt.figure(figsize = (8,8))\n", + "ax = fig.add_subplot(1,1,1) \n", + "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", + "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", + "ax.set_title('2 component PCA', fontsize = 20)\n", + "targets = [1,0]\n", + "colors = ['r', 'g']\n", + "for target, color in zip(targets,colors):\n", + " indicesToKeep = pca_visualize_df['Y'] == target\n", + " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", + " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", + " , c = color\n", + " , s = 50)\n", + "ax.legend(targets)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform PCA to retain 90% of information\n", + "perform PCA to reduce components so we can run SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "#setting pca threshold to 90%\n", + "pca = PCA(0.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of components before pca: 44\n", + "No. of components after pca: 13\n" + ] + } + ], + "source": [ + "#get number of components after pca\n", + "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", + "print('No. of components after pca: {}'.format(pca.n_components_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the number of components is reduced from 26 components to 13 components" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca on training and test attributes\n", + "X_train_pca = pca.transform(X_train)\n", + "X_test_pca = pca.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "Next, we will used the reduced attributes train set to train our SVM model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we train our SVM model without parameter tuning\n", + "nor pca reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16277415089804265\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7256296305241738" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the SVM original data RBF kernel identified 480\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03810200
1759480
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3810 200\n", + "1 759 480" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 4010\n", + " 1 0.71 0.39 0.50 1239\n", + "\n", + " accuracy 0.82 5249\n", + " macro avg 0.77 0.67 0.69 5249\n", + "weighted avg 0.80 0.82 0.80 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note that the default values of gamma = 1/13 and c= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and no parameter tuning\n", + "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", + "clf_reduced.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1616720894054318\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7038122007330342" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_reduced, y_test, X_test_pca, \n", + " \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the SVM reduced features no tuning RBF kernal identified 485\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03806204
1754485
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3806 204\n", + "1 754 485" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 4010\n", + " 1 0.70 0.39 0.50 1239\n", + "\n", + " accuracy 0.82 5249\n", + " macro avg 0.77 0.67 0.70 5249\n", + "weighted avg 0.80 0.82 0.80 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now try to find best parameters for SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 1, 'gamma': 0.1}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1,10]\n", + " gammas = [0.01, 0.1, 10]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train_pca, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", + "clf_reduced_tuned.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1660719022802057\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", + " \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1239 Defaulters, the SVM reduced features and tuning RBF kernal identified 449\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
03817193
1790449
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 3817 193\n", + "1 790 449" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 4010\n", + " 1 0.70 0.36 0.48 1239\n", + "\n", + " accuracy 0.81 5249\n", + " macro avg 0.76 0.66 0.68 5249\n", + "weighted avg 0.80 0.81 0.79 5249\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"recall\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m auroc])\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 573\u001b[0m \"\"\"\n\u001b[0;32m--> 574\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 575\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \"\"\"\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[1;32m 473\u001b[0m \u001b[0;34m\"the number of features at training time\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[1;32m 475\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" + ] + } + ], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From c8e74ca5d3cb3422706c2d7d2d6cc22088f41a70 Mon Sep 17 00:00:00 2001 From: reon Date: Mon, 18 Nov 2019 19:35:17 +0800 Subject: [PATCH 08/25] gg added --- ...2101_Default - EDIT-Copy1-checkpoint.ipynb | 1950 ++++++++ BT2101_Default - EDIT-Copy1.ipynb | 4362 +---------------- 2 files changed, 2165 insertions(+), 4147 deletions(-) create mode 100644 .ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb new file mode 100644 index 0000000..8cbd088 --- /dev/null +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb @@ -0,0 +1,1950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + "\n", + "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#remove all insignificant attributes\n", + "sig = glm.pvalues[glm.pvalues < 0.05]\n", + "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n", + "glm_2.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be 0.2697615225249289 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289), output_dict = True)[\"1\"][\"recall\"],auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PCA\n", + "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2 Component PCA\n", + "First, we will visualize the information retained after performing a 2 component pca." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "principalComponents = pca.fit_transform(X_train)\n", + "principalDf = pd.DataFrame(data = principalComponents\n", + " , columns = ['principal component 1', 'principal component 2'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", + "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#visualizing pca\n", + "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", + "fig = plt.figure(figsize = (8,8))\n", + "ax = fig.add_subplot(1,1,1) \n", + "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", + "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", + "ax.set_title('2 component PCA', fontsize = 20)\n", + "targets = [1,0]\n", + "colors = ['r', 'g']\n", + "for target, color in zip(targets,colors):\n", + " indicesToKeep = pca_visualize_df['Y'] == target\n", + " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", + " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", + " , c = color\n", + " , s = 50)\n", + "ax.legend(targets)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform PCA to retain 90% of information\n", + "perform PCA to reduce components so we can run SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#setting pca threshold to 90%\n", + "pca = PCA(0.9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pca.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#get number of components after pca\n", + "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", + "print('No. of components after pca: {}'.format(pca.n_components_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the number of components is reduced from 26 components to 13 components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca on training and test attributes\n", + "X_train_pca = pca.transform(X_train)\n", + "X_test_pca = pca.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "Next, we will used the reduced attributes train set to train our SVM model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we train our SVM model without parameter tuning\n", + "nor pca reduction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note that the default values of gamma = 1/13 and c= 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#train svm model with feature reduction and no parameter tuning\n", + "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", + "clf_reduced.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_reduced, y_test, X_test_pca, \n", + " \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now try to find best parameters for SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1,10]\n", + " gammas = [0.01, 0.1, 10]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train_pca, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", + "clf_reduced_tuned.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", + " \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/BT2101_Default - EDIT-Copy1.ipynb index dc02809..8cbd088 100644 --- a/BT2101_Default - EDIT-Copy1.ipynb +++ b/BT2101_Default - EDIT-Copy1.ipynb @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -154,234 +154,7 @@ "id": "FhJ2eAxVQhBm", "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", - "

5 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 20000 2 2 1 24 2 2 -1 -1 \n", - "2 120000 2 2 2 26 -1 2 0 0 \n", - "3 90000 2 2 2 34 0 0 0 0 \n", - "4 50000 2 2 1 37 0 0 0 0 \n", - "5 50000 1 2 1 57 -1 0 -1 0 \n", - "\n", - " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", - "ID ... \n", - "1 -2 ... 0 0 0 0 689 0 \n", - "2 0 ... 3272 3455 3261 0 1000 1000 \n", - "3 0 ... 14331 14948 15549 1518 1500 1000 \n", - "4 0 ... 28314 28959 29547 2000 2019 1200 \n", - "5 0 ... 20940 19146 19131 2000 36681 10000 \n", - "\n", - " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 0 0 0 1 \n", - "2 1000 0 2000 1 \n", - "3 1000 1000 5000 0 \n", - "4 1100 1069 1000 0 \n", - "5 9000 689 679 0 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#rename the target variable to \"Y\" for convenience\n", "df[\"Y\"] = df[\"default payment next month\"] \n", @@ -392,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -403,15 +176,7 @@ "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 24 Columns and 30000 Rows\n" - ] - } - ], + "outputs": [], "source": [ "size = df.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -419,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -429,18 +194,7 @@ "id": "QVaSnvJP3VbO", "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#check for null values\n", "df.isnull().any().sum() " @@ -482,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -492,38 +246,7 @@ "id": "DCSEICWwXWgX", "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "defaults : 22.12 %\n", - "non defaults : 77.88000000000001 %\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Frequency')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "All = df.shape[0]\n", "default = df[df['Y'] == 1]\n", @@ -562,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -573,32 +296,7 @@ "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 60.373333\n", - "1 39.626667\n", - "Name: SEX, dtype: float64\n", - "--------------------------------------------------------\n", - "2 46.766667\n", - "1 35.283333\n", - "3 16.390000\n", - "5 0.933333\n", - "4 0.410000\n", - "6 0.170000\n", - "0 0.046667\n", - "Name: EDUCATION, dtype: float64\n", - "--------------------------------------------------------\n", - "2 53.213333\n", - "1 45.530000\n", - "3 1.076667\n", - "0 0.180000\n", - "Name: MARRIAGE, dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", "print(\"--------------------------------------------------------\")\n", @@ -623,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -632,39 +330,9 @@ "colab_type": "code", "id": "U3IJzhwwe5KK", "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", - "scrolled": true + "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total target attributes:\n", - "non defaults : 77.88000000000001 %\n", - "defaults : 22.12 %\n", - "--------------------------------------------------------\n", - "SEX Male Female\n", - "Y \n", - "non defaults 75.832773 79.223719\n", - "defaults 24.167227 20.776281\n", - "--------------------------------------------------------\n", - "EDUCATION 0 1 2 3 4 5 \\\n", - "Y \n", - "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", - "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", - "\n", - "EDUCATION 6 \n", - "Y \n", - "non defaults 84.313725 \n", - "defaults 15.686275 \n", - "--------------------------------------------------------\n", - "MARRIAGE unknown married single others\n", - "Y \n", - "non defaults 90.740741 76.528296 79.071661 73.993808\n", - "defaults 9.259259 23.471704 20.928339 26.006192\n" - ] - } - ], + "outputs": [], "source": [ "#proportion of target attribute (for reference)\n", "print('Total target attributes:')\n", @@ -695,10 +363,7 @@ "\n", "From the analyses above we conclude that\n", "\n", - "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n", - "2. Categorical attributes EDUCATION and MARRIAGE are likely to be associated with the target variable\n", - "3. SEX is not expected to be an important factor in our models as it appears to be statistically insignificant\n", - " " + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" ] }, { @@ -718,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -726,446 +391,20 @@ }, "colab_type": "code", "id": "HEcCl5Rj-N0T", - "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4" + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0
0LIMIT_BAL
1AGE
2PAY_0
3PAY_2
4PAY_3
5PAY_4
6PAY_5
7PAY_6
8BILL_AMT1
9BILL_AMT2
10BILL_AMT3
11BILL_AMT4
12BILL_AMT5
13BILL_AMT6
14PAY_AMT1
15PAY_AMT2
16PAY_AMT3
17PAY_AMT4
18PAY_AMT5
19PAY_AMT6
\n", - "
" - ], - "text/plain": [ - " 0\n", - "0 LIMIT_BAL\n", - "1 AGE\n", - "2 PAY_0\n", - "3 PAY_2\n", - "4 PAY_3\n", - "5 PAY_4\n", - "6 PAY_5\n", - "7 PAY_6\n", - "8 BILL_AMT1\n", - "9 BILL_AMT2\n", - "10 BILL_AMT3\n", - "11 BILL_AMT4\n", - "12 BILL_AMT5\n", - "13 BILL_AMT6\n", - "14 PAY_AMT1\n", - "15 PAY_AMT2\n", - "16 PAY_AMT3\n", - "17 PAY_AMT4\n", - "18 PAY_AMT5\n", - "19 PAY_AMT6" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#printing numerical attributes\n", - "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns)" + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
count30000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.00000030000.0000003.000000e+0430000.00000030000.00000030000.00000030000.0000003.000000e+0430000.0000030000.00000030000.00000030000.000000
mean167484.32266735.485500-0.016700-0.133767-0.166200-0.220667-0.266200-0.29110051223.33090049179.0751674.701315e+0443262.94896740311.40096738871.7604005663.5805005.921163e+035225.681504826.0768674799.3876335215.502567
std129747.6615679.2179041.1238021.1971861.1968681.1691391.1331871.14998873635.86057671173.7687836.934939e+0464332.85613460797.15577059554.10753716563.2803542.304087e+0417606.9614715666.15974415278.30567917777.465775
min10000.00000021.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-165580.000000-69777.000000-1.572640e+05-170000.000000-81334.000000-339603.0000000.0000000.000000e+000.000000.0000000.0000000.000000
25%50000.00000028.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0000003558.7500002984.7500002.666250e+032326.7500001763.0000001256.0000001000.0000008.330000e+02390.00000296.000000252.500000117.750000
50%140000.00000034.0000000.0000000.0000000.0000000.0000000.0000000.00000022381.50000021200.0000002.008850e+0419052.00000018104.50000017071.0000002100.0000002.009000e+031800.000001500.0000001500.0000001500.000000
75%240000.00000041.0000000.0000000.0000000.0000000.0000000.0000000.00000067091.00000064006.2500006.016475e+0454506.00000050190.50000049198.2500005006.0000005.000000e+034505.000004013.2500004031.5000004000.000000
max1000000.00000079.0000008.0000008.0000008.0000008.0000008.0000008.000000964511.000000983931.0000001.664089e+06891586.000000927171.000000961664.000000873552.0000001.684259e+06896040.00000621000.000000426529.000000528666.000000
\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 \\\n", - "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", - "mean 167484.322667 35.485500 -0.016700 -0.133767 -0.166200 \n", - "std 129747.661567 9.217904 1.123802 1.197186 1.196868 \n", - "min 10000.000000 21.000000 -2.000000 -2.000000 -2.000000 \n", - "25% 50000.000000 28.000000 -1.000000 -1.000000 -1.000000 \n", - "50% 140000.000000 34.000000 0.000000 0.000000 0.000000 \n", - "75% 240000.000000 41.000000 0.000000 0.000000 0.000000 \n", - "max 1000000.000000 79.000000 8.000000 8.000000 8.000000 \n", - "\n", - " PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 \\\n", - "count 30000.000000 30000.000000 30000.000000 30000.000000 30000.000000 \n", - "mean -0.220667 -0.266200 -0.291100 51223.330900 49179.075167 \n", - "std 1.169139 1.133187 1.149988 73635.860576 71173.768783 \n", - "min -2.000000 -2.000000 -2.000000 -165580.000000 -69777.000000 \n", - "25% -1.000000 -1.000000 -1.000000 3558.750000 2984.750000 \n", - "50% 0.000000 0.000000 0.000000 22381.500000 21200.000000 \n", - "75% 0.000000 0.000000 0.000000 67091.000000 64006.250000 \n", - "max 8.000000 8.000000 8.000000 964511.000000 983931.000000 \n", - "\n", - " BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\n", - "count 3.000000e+04 30000.000000 30000.000000 30000.000000 \n", - "mean 4.701315e+04 43262.948967 40311.400967 38871.760400 \n", - "std 6.934939e+04 64332.856134 60797.155770 59554.107537 \n", - "min -1.572640e+05 -170000.000000 -81334.000000 -339603.000000 \n", - "25% 2.666250e+03 2326.750000 1763.000000 1256.000000 \n", - "50% 2.008850e+04 19052.000000 18104.500000 17071.000000 \n", - "75% 6.016475e+04 54506.000000 50190.500000 49198.250000 \n", - "max 1.664089e+06 891586.000000 927171.000000 961664.000000 \n", - "\n", - " PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 \\\n", - "count 30000.000000 3.000000e+04 30000.00000 30000.000000 \n", - "mean 5663.580500 5.921163e+03 5225.68150 4826.076867 \n", - "std 16563.280354 2.304087e+04 17606.96147 15666.159744 \n", - "min 0.000000 0.000000e+00 0.00000 0.000000 \n", - "25% 1000.000000 8.330000e+02 390.00000 296.000000 \n", - "50% 2100.000000 2.009000e+03 1800.00000 1500.000000 \n", - "75% 5006.000000 5.000000e+03 4505.00000 4013.250000 \n", - "max 873552.000000 1.684259e+06 896040.00000 621000.000000 \n", - "\n", - " PAY_AMT5 PAY_AMT6 \n", - "count 30000.000000 30000.000000 \n", - "mean 4799.387633 5215.502567 \n", - "std 15278.305679 17777.465775 \n", - "min 0.000000 0.000000 \n", - "25% 252.500000 117.750000 \n", - "50% 1500.000000 1500.000000 \n", - "75% 4031.500000 4000.000000 \n", - "max 426529.000000 528666.000000 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe()" ] @@ -1190,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1201,44 +440,7 @@ "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", " fig=plt.figure()\n", @@ -1280,9 +482,12 @@ "## Data Preprocessing\n", "\n", "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", - "1. Removing Noise - Inconsistencies and Outliers\n", - "2. One Hot Encoding\n", - "3. Feature selection\n" + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" ] }, { @@ -1290,47 +495,31 @@ "metadata": {}, "source": [ "### Removing Noise\n", - "#### Inconsistency\n", "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 1, 3, 4])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", "df[\"EDUCATION\"].unique()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", "df[\"MARRIAGE\"].unique()" @@ -1354,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1362,154 +551,28 @@ " try:\n", " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", " except:\n", - " pass\n" + " pass" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dummy variables for negative values\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", - "
" - ], - "text/plain": [ - " S_0 S_2 S_3 S_4 S_5 S_6\n", - "ID \n", - "1 1 1 -1 -1 -2 -2\n", - "2 -1 1 0 0 0 1\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 -1 0 -1 0 0 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()\n" + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6\n", - "ID \n", - "1 2 2 0 0 0 0\n", - "2 0 2 0 0 0 2\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 0 0 0 0 0 0\n" - ] - } - ], + "outputs": [], "source": [ "#attributes representing positive values\n", "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", - " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)\n", - "\n", - "print(df[[\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]].head())\n" + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" ] }, { @@ -1522,310 +585,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", - "

8 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", - "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", - "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", - "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", - "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", - "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", - "\n", - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", - "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", - "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", - "\n", - " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", - "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", - "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", - "min ... 0.000000 0.000000 0.000000 0.000000 \n", - "25% ... 150.000000 82.000000 0.000000 0.000000 \n", - "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", - "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", - "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", - "\n", - " S_0 S_2 S_3 S_4 S_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", - "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", - "\n", - " S_6 \n", - "count 26245.000000 \n", - "mean -0.428158 \n", - "std 0.900723 \n", - "min -2.000000 \n", - "25% -1.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 1.000000 \n", - "\n", - "[8 rows x 30 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from scipy import stats\n", "#we are only concerned with the ordinal data\n", @@ -1847,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1861,228 +623,11 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", - "

5 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", - "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", - "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", - "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", - "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", - "\n", - " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", - "ID ... \n", - "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", - "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", - "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", - "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", - "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", - "\n", - "[5 rows x 30 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "df1.head()" ] @@ -2113,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2122,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2131,96 +676,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", - "
" - ], - "text/plain": [ - " GRAD UNI HS MARRIED SINGLE\n", - "0 0.0 1.0 0.0 1.0 0.0\n", - "1 0.0 1.0 0.0 0.0 1.0\n", - "2 0.0 1.0 0.0 0.0 1.0\n", - "3 0.0 1.0 0.0 1.0 0.0\n", - "4 0.0 1.0 0.0 1.0 0.0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", @@ -2232,209 +690,11 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", - "
" - ], - "text/plain": [ - " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "#one hot encoding for S_0 to S_6\n", "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", @@ -2458,223 +718,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.02040820.0789472200000.061163...0.00.01.00.01.00.00.01.00.00.0
10.22449020.1315790200020.056292...1.00.00.01.00.00.01.00.00.00.0
20.16326520.3421050000000.161370...1.00.00.01.00.00.01.00.00.01.0
30.08163320.4210530000000.231605...1.00.00.01.00.00.01.00.00.01.0
40.08163310.9473680000000.079775...0.00.00.01.00.00.01.00.00.01.0
\n", - "

5 rows × 45 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 \\\n", - "0 0.020408 2 0.078947 2 2 0 0 0 0 \n", - "1 0.224490 2 0.131579 0 2 0 0 0 2 \n", - "2 0.163265 2 0.342105 0 0 0 0 0 0 \n", - "3 0.081633 2 0.421053 0 0 0 0 0 0 \n", - "4 0.081633 1 0.947368 0 0 0 0 0 0 \n", - "\n", - " BILL_AMT1 ... PAY_3_Revolving_Credit PAY_4_No_Transactions \\\n", - "0 0.061163 ... 0.0 0.0 \n", - "1 0.056292 ... 1.0 0.0 \n", - "2 0.161370 ... 1.0 0.0 \n", - "3 0.231605 ... 1.0 0.0 \n", - "4 0.079775 ... 0.0 0.0 \n", - "\n", - " PAY_4_Pay_Duly PAY_4_Revolving_Credit PAY_5_No_Transactions \\\n", - "0 1.0 0.0 1.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 1.0 0.0 \n", - "3 0.0 1.0 0.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_5_Pay_Duly PAY_5_Revolving_Credit PAY_6_No_Transactions \\\n", - "0 0.0 0.0 1.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 1.0 0.0 \n", - "3 0.0 1.0 0.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 1.0 \n", - "3 0.0 1.0 \n", - "4 0.0 1.0 \n", - "\n", - "[5 rows x 45 columns]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", @@ -2684,72 +730,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", - "

0 rows × 45 columns

\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", - "Index: []\n", - "\n", - "[0 rows x 45 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#check for perfect collinearity\n", "corr = df1.corr()\n", @@ -2761,17 +744,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 45 Columns and 26245 Rows\n" - ] - } - ], + "outputs": [], "source": [ "size = df1.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -2781,93 +756,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Filter method for feature selection\n", - "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase\n", - "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/Chew/anaconda3/lib/python3.7/site-packages/statsmodels/compat/pandas.py:23: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n", - " data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)\n" - ] - } - ], - "source": [ - "#importing libraries\n", - "from sklearn.datasets import load_boston\n", - "import statsmodels.api as sm\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.feature_selection import RFE\n", - "from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We compute the Pearson Correlation between each input attribute and the target attribute. Those with correlations below a threshold are excluded from the learning phase.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Using Pearson Correlation\n", - "plt.figure(figsize= (19, 15))\n", - "cor = df.corr()\n", - "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mbhlIlQzZz7c" - }, - "source": [ - "## Model Selection\n", - "\n", - "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", - "\n", - "We will be attempting to fit the following models:\n", - "\n", + "### Train Test Split\n", "\n", - "- Decision Tree \n", - "- Logistic Regression\n", - "- Support Vector Machine\n", - "- Neural Network\n" + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2877,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -2888,319 +784,25 @@ "#using holdout sampling for train test split\n", "ft = df1.drop(\"Y\", axis = 1)\n", "target = df1[\"Y\"]\n", - "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.20)\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", "#make the results reproducible (according to instructions)\n", "np.random.seed(123) " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0pval
0101.6063301.197484e-06
15.9736131.000000e+00
20.1622381.000000e+00
35084.5856940.000000e+00
44395.1765350.000000e+00
53613.3302830.000000e+00
63478.5125380.000000e+00
73290.5932710.000000e+00
82874.8496550.000000e+00
95.5028001.000000e+00
101.5371501.000000e+00
110.5792141.000000e+00
120.1030431.000000e+00
130.0164281.000000e+00
140.0026861.000000e+00
1542.3464634.995112e-01
1641.3702625.421350e-01
1736.1038637.625720e-01
1834.1910878.290815e-01
1932.2567938.846284e-01
2030.1205169.311403e-01
2129.0637909.485468e-01
2216.8510069.998847e-01
237.4961561.000000e+00
247.9928101.000000e+00
257.7555741.000000e+00
2681.3690093.679256e-04
2760.1369884.297347e-02
28542.5228160.000000e+00
2923.1824469.941736e-01
3082.4618362.768659e-04
31277.0293730.000000e+00
3220.8275869.982732e-01
3395.8796116.629453e-06
34167.0756811.110223e-16
3513.1561669.999968e-01
36100.9293611.470776e-06
37104.7549874.552992e-07
388.5479451.000000e+00
3988.3346485.695510e-05
4075.0100071.798366e-03
415.4384661.000000e+00
4278.1518958.334476e-04
4373.2199982.748840e-03
\n", - "
" - ], - "text/plain": [ - " 0 pval\n", - "0 101.606330 1.197484e-06\n", - "1 5.973613 1.000000e+00\n", - "2 0.162238 1.000000e+00\n", - "3 5084.585694 0.000000e+00\n", - "4 4395.176535 0.000000e+00\n", - "5 3613.330283 0.000000e+00\n", - "6 3478.512538 0.000000e+00\n", - "7 3290.593271 0.000000e+00\n", - "8 2874.849655 0.000000e+00\n", - "9 5.502800 1.000000e+00\n", - "10 1.537150 1.000000e+00\n", - "11 0.579214 1.000000e+00\n", - "12 0.103043 1.000000e+00\n", - "13 0.016428 1.000000e+00\n", - "14 0.002686 1.000000e+00\n", - "15 42.346463 4.995112e-01\n", - "16 41.370262 5.421350e-01\n", - "17 36.103863 7.625720e-01\n", - "18 34.191087 8.290815e-01\n", - "19 32.256793 8.846284e-01\n", - "20 30.120516 9.311403e-01\n", - "21 29.063790 9.485468e-01\n", - "22 16.851006 9.998847e-01\n", - "23 7.496156 1.000000e+00\n", - "24 7.992810 1.000000e+00\n", - "25 7.755574 1.000000e+00\n", - "26 81.369009 3.679256e-04\n", - "27 60.136988 4.297347e-02\n", - "28 542.522816 0.000000e+00\n", - "29 23.182446 9.941736e-01\n", - "30 82.461836 2.768659e-04\n", - "31 277.029373 0.000000e+00\n", - "32 20.827586 9.982732e-01\n", - "33 95.879611 6.629453e-06\n", - "34 167.075681 1.110223e-16\n", - "35 13.156166 9.999968e-01\n", - "36 100.929361 1.470776e-06\n", - "37 104.754987 4.552992e-07\n", - "38 8.547945 1.000000e+00\n", - "39 88.334648 5.695510e-05\n", - "40 75.010007 1.798366e-03\n", - "41 5.438466 1.000000e+00\n", - "42 78.151895 8.334476e-04\n", - "43 73.219998 2.748840e-03" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.feature_selection import SelectKBest\n", "from sklearn.feature_selection import chi2\n", @@ -3210,21 +812,41 @@ "np.set_printoptions(precision=10)\n", "chi2data = pd.DataFrame(selector.scores_)\n", "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", "\n", - "chi2data" + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, "source": [ - "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function." + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3258,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "scrolled": true }, @@ -3289,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3324,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3333,25 +955,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False,\n", - " random_state=None, splitter='best')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tree = DecisionTreeClassifier()\n", "tree.fit(X_train, y_train)" @@ -3359,25 +965,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 16194\n", - " 1 1.00 1.00 1.00 4802\n", - "\n", - " accuracy 1.00 20996\n", - " macro avg 1.00 1.00 1.00 20996\n", - "weighted avg 1.00 1.00 1.00 20996\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_train, tree.predict(X_train)))" ] @@ -3391,134 +981,34 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the Decision Tree (GINI) identified 555\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03207803
1684555
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3207 803\n", - "1 684 555" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.5\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.80 0.81 4010\n", - " 1 0.41 0.45 0.43 1239\n", - "\n", - " accuracy 0.72 5249\n", - " macro avg 0.62 0.62 0.62 5249\n", - "weighted avg 0.73 0.72 0.72 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, tree.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3545,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3555,51 +1045,18 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_jobs=None, oob_score=False, random_state=None,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "randf.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 16194\n", - " 1 1.00 1.00 1.00 4802\n", - "\n", - " accuracy 1.00 20996\n", - " macro avg 1.00 1.00 1.00 20996\n", - "weighted avg 1.00 1.00 1.00 20996\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_train, randf.predict(X_train)))" ] @@ -3613,134 +1070,34 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the Decision Tree (Random Forest) identified 493\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03757253
1746493
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3757 253\n", - "1 746 493" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.3\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 4010\n", - " 1 0.66 0.40 0.50 1239\n", - "\n", - " accuracy 0.81 5249\n", - " macro avg 0.75 0.67 0.69 5249\n", - "weighted avg 0.79 0.81 0.79 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, randf.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3773,29 +1130,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=4,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_iter_no_change=None, presort='auto',\n", - " random_state=None, subsample=1.0, tol=0.0001,\n", - " validation_fraction=0.1, verbose=0,\n", - " warm_start=False)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", @@ -3804,25 +1141,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.96 0.90 16194\n", - " 1 0.78 0.45 0.57 4802\n", - "\n", - " accuracy 0.84 20996\n", - " macro avg 0.81 0.70 0.74 20996\n", - "weighted avg 0.84 0.84 0.83 20996\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_train, xgb.predict(X_train)))" ] @@ -3836,134 +1157,34 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 488\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03778232
1751488
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3778 232\n", - "1 751 488" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.29919321548002004\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 4010\n", - " 1 0.68 0.39 0.50 1239\n", - "\n", - " accuracy 0.81 5249\n", - " macro avg 0.76 0.67 0.69 5249\n", - "weighted avg 0.80 0.81 0.79 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, xgb.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3981,72 +1202,11 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelRecall-1AUROC
0Decision Tree0.4479420.624423
1Random Forest0.3979020.772517
2Gradient Boosted0.3938660.778748
\n", - "
" - ], - "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree 0.447942 0.624423\n", - "1 Random Forest 0.397902 0.772517\n", - "2 Gradient Boosted 0.393866 0.778748" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evaluation" ] @@ -4083,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4092,251 +1252,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.443332\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 20996
Model: Logit Df Residuals: 20952
Method: MLE Df Model: 43
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1755
Time: 17:58:42 Log-Likelihood: -9308.2
converged: True LL-Null: -11290.
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8585 0.105 -8.157 0.000 -1.065 -0.652
SEX -0.1338 0.038 -3.564 0.000 -0.207 -0.060
AGE 0.0784 0.092 0.856 0.392 -0.101 0.258
PAY_0 0.6364 0.054 11.857 0.000 0.531 0.742
PAY_2 -0.6281 0.089 -7.047 0.000 -0.803 -0.453
PAY_3 -0.0434 0.109 -0.398 0.691 -0.258 0.171
PAY_4 -0.3043 0.148 -2.051 0.040 -0.595 -0.013
PAY_5 -0.1221 0.164 -0.744 0.457 -0.444 0.200
PAY_6 0.4793 0.140 3.412 0.001 0.204 0.755
BILL_AMT1 -1.4159 0.490 -2.891 0.004 -2.376 -0.456
BILL_AMT2 0.7970 0.714 1.117 0.264 -0.602 2.196
BILL_AMT3 1.1717 0.689 1.701 0.089 -0.178 2.522
BILL_AMT4 0.5620 0.670 0.839 0.401 -0.750 1.874
BILL_AMT5 -1.1673 0.813 -1.437 0.151 -2.760 0.425
BILL_AMT6 0.9974 0.744 1.341 0.180 -0.460 2.455
PAY_AMT1 -1.2441 0.283 -4.391 0.000 -1.799 -0.689
PAY_AMT2 -2.1791 0.366 -5.946 0.000 -2.897 -1.461
PAY_AMT3 -0.4265 0.272 -1.571 0.116 -0.959 0.106
PAY_AMT4 -0.4780 0.264 -1.811 0.070 -0.995 0.039
PAY_AMT5 -0.9704 0.268 -3.614 0.000 -1.497 -0.444
PAY_AMT6 -0.5401 0.242 -2.233 0.026 -1.014 -0.066
GRAD 1.3705 0.203 6.740 0.000 0.972 1.769
UNI 1.3583 0.202 6.722 0.000 0.962 1.754
HS 1.2592 0.206 6.127 0.000 0.856 1.662
MARRIED 0.0231 0.147 0.157 0.875 -0.265 0.312
SINGLE -0.1504 0.148 -1.018 0.309 -0.440 0.139
PAY_0_No_Transactions -0.0255 0.114 -0.224 0.823 -0.249 0.198
PAY_0_Pay_Duly 0.1487 0.111 1.346 0.178 -0.068 0.365
PAY_0_Revolving_Credit -0.8102 0.124 -6.515 0.000 -1.054 -0.566
PAY_2_No_Transactions -1.6438 0.215 -7.663 0.000 -2.064 -1.223
PAY_2_Pay_Duly -1.5062 0.205 -7.363 0.000 -1.907 -1.105
PAY_2_Revolving_Credit -1.0120 0.210 -4.827 0.000 -1.423 -0.601
PAY_3_No_Transactions -0.4055 0.262 -1.546 0.122 -0.919 0.108
PAY_3_Pay_Duly -0.3823 0.240 -1.590 0.112 -0.853 0.089
PAY_3_Revolving_Credit -0.4670 0.230 -2.032 0.042 -0.918 -0.017
PAY_4_No_Transactions -1.0351 0.332 -3.117 0.002 -1.686 -0.384
PAY_4_Pay_Duly -1.1078 0.315 -3.514 0.000 -1.726 -0.490
PAY_4_Revolving_Credit -0.9374 0.307 -3.058 0.002 -1.538 -0.337
PAY_5_No_Transactions -0.4004 0.365 -1.096 0.273 -1.117 0.316
PAY_5_Pay_Duly -0.4950 0.352 -1.407 0.159 -1.184 0.195
PAY_5_Revolving_Credit -0.4161 0.342 -1.215 0.224 -1.087 0.255
PAY_6_No_Transactions 0.9055 0.308 2.941 0.003 0.302 1.509
PAY_6_Pay_Duly 0.7502 0.302 2.481 0.013 0.157 1.343
PAY_6_Revolving_Credit 0.5706 0.294 1.941 0.052 -0.006 1.147
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 20996\n", - "Model: Logit Df Residuals: 20952\n", - "Method: MLE Df Model: 43\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1755\n", - "Time: 17:58:42 Log-Likelihood: -9308.2\n", - "converged: True LL-Null: -11290.\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8585 0.105 -8.157 0.000 -1.065 -0.652\n", - "SEX -0.1338 0.038 -3.564 0.000 -0.207 -0.060\n", - "AGE 0.0784 0.092 0.856 0.392 -0.101 0.258\n", - "PAY_0 0.6364 0.054 11.857 0.000 0.531 0.742\n", - "PAY_2 -0.6281 0.089 -7.047 0.000 -0.803 -0.453\n", - "PAY_3 -0.0434 0.109 -0.398 0.691 -0.258 0.171\n", - "PAY_4 -0.3043 0.148 -2.051 0.040 -0.595 -0.013\n", - "PAY_5 -0.1221 0.164 -0.744 0.457 -0.444 0.200\n", - "PAY_6 0.4793 0.140 3.412 0.001 0.204 0.755\n", - "BILL_AMT1 -1.4159 0.490 -2.891 0.004 -2.376 -0.456\n", - "BILL_AMT2 0.7970 0.714 1.117 0.264 -0.602 2.196\n", - "BILL_AMT3 1.1717 0.689 1.701 0.089 -0.178 2.522\n", - "BILL_AMT4 0.5620 0.670 0.839 0.401 -0.750 1.874\n", - "BILL_AMT5 -1.1673 0.813 -1.437 0.151 -2.760 0.425\n", - "BILL_AMT6 0.9974 0.744 1.341 0.180 -0.460 2.455\n", - "PAY_AMT1 -1.2441 0.283 -4.391 0.000 -1.799 -0.689\n", - "PAY_AMT2 -2.1791 0.366 -5.946 0.000 -2.897 -1.461\n", - "PAY_AMT3 -0.4265 0.272 -1.571 0.116 -0.959 0.106\n", - "PAY_AMT4 -0.4780 0.264 -1.811 0.070 -0.995 0.039\n", - "PAY_AMT5 -0.9704 0.268 -3.614 0.000 -1.497 -0.444\n", - "PAY_AMT6 -0.5401 0.242 -2.233 0.026 -1.014 -0.066\n", - "GRAD 1.3705 0.203 6.740 0.000 0.972 1.769\n", - "UNI 1.3583 0.202 6.722 0.000 0.962 1.754\n", - "HS 1.2592 0.206 6.127 0.000 0.856 1.662\n", - "MARRIED 0.0231 0.147 0.157 0.875 -0.265 0.312\n", - "SINGLE -0.1504 0.148 -1.018 0.309 -0.440 0.139\n", - "PAY_0_No_Transactions -0.0255 0.114 -0.224 0.823 -0.249 0.198\n", - "PAY_0_Pay_Duly 0.1487 0.111 1.346 0.178 -0.068 0.365\n", - "PAY_0_Revolving_Credit -0.8102 0.124 -6.515 0.000 -1.054 -0.566\n", - "PAY_2_No_Transactions -1.6438 0.215 -7.663 0.000 -2.064 -1.223\n", - "PAY_2_Pay_Duly -1.5062 0.205 -7.363 0.000 -1.907 -1.105\n", - "PAY_2_Revolving_Credit -1.0120 0.210 -4.827 0.000 -1.423 -0.601\n", - "PAY_3_No_Transactions -0.4055 0.262 -1.546 0.122 -0.919 0.108\n", - "PAY_3_Pay_Duly -0.3823 0.240 -1.590 0.112 -0.853 0.089\n", - "PAY_3_Revolving_Credit -0.4670 0.230 -2.032 0.042 -0.918 -0.017\n", - "PAY_4_No_Transactions -1.0351 0.332 -3.117 0.002 -1.686 -0.384\n", - "PAY_4_Pay_Duly -1.1078 0.315 -3.514 0.000 -1.726 -0.490\n", - "PAY_4_Revolving_Credit -0.9374 0.307 -3.058 0.002 -1.538 -0.337\n", - "PAY_5_No_Transactions -0.4004 0.365 -1.096 0.273 -1.117 0.316\n", - "PAY_5_Pay_Duly -0.4950 0.352 -1.407 0.159 -1.184 0.195\n", - "PAY_5_Revolving_Credit -0.4161 0.342 -1.215 0.224 -1.087 0.255\n", - "PAY_6_No_Transactions 0.9055 0.308 2.941 0.003 0.302 1.509\n", - "PAY_6_Pay_Duly 0.7502 0.302 2.481 0.013 0.157 1.343\n", - "PAY_6_Revolving_Credit 0.5706 0.294 1.941 0.052 -0.006 1.147\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "glm = sm.Logit(y_train,X_train).fit()\n", "glm.summary()" @@ -4344,50 +1262,18 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 16194\n", - " 1 0.67 0.35 0.46 4802\n", - "\n", - " accuracy 0.81 20996\n", - " macro avg 0.75 0.65 0.67 20996\n", - "weighted avg 0.79 0.81 0.79 20996\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 4010\n", - " 1 0.69 0.38 0.49 1239\n", - "\n", - " accuracy 0.81 5249\n", - " macro avg 0.76 0.66 0.69 5249\n", - "weighted avg 0.80 0.81 0.79 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" ] @@ -4401,171 +1287,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.445426\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 20996
Model: Logit Df Residuals: 20972
Method: MLE Df Model: 23
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1716
Time: 17:58:42 Log-Likelihood: -9352.2
converged: True LL-Null: -11290.
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8185 0.102 -7.996 0.000 -1.019 -0.618
SEX -0.1216 0.037 -3.286 0.001 -0.194 -0.049
PAY_0 0.6056 0.034 17.614 0.000 0.538 0.673
PAY_2 -0.5868 0.074 -7.895 0.000 -0.732 -0.441
PAY_4 -0.2261 0.069 -3.259 0.001 -0.362 -0.090
PAY_6 0.2449 0.029 8.389 0.000 0.188 0.302
BILL_AMT1 0.2964 0.140 2.121 0.034 0.023 0.570
PAY_AMT1 -1.0360 0.252 -4.117 0.000 -1.529 -0.543
PAY_AMT2 -2.3264 0.330 -7.058 0.000 -2.972 -1.680
PAY_AMT5 -0.8585 0.237 -3.626 0.000 -1.323 -0.394
PAY_AMT6 -0.6965 0.237 -2.934 0.003 -1.162 -0.231
GRAD 1.6137 0.173 9.315 0.000 1.274 1.953
UNI 1.6386 0.172 9.531 0.000 1.302 1.976
HS 1.5765 0.175 8.983 0.000 1.233 1.920
PAY_0_Revolving_Credit -0.9127 0.084 -10.822 0.000 -1.078 -0.747
PAY_2_No_Transactions -1.7245 0.176 -9.789 0.000 -2.070 -1.379
PAY_2_Pay_Duly -1.4835 0.167 -8.874 0.000 -1.811 -1.156
PAY_2_Revolving_Credit -0.9405 0.174 -5.414 0.000 -1.281 -0.600
PAY_3_Revolving_Credit -0.2826 0.067 -4.189 0.000 -0.415 -0.150
PAY_4_No_Transactions -1.1690 0.173 -6.750 0.000 -1.509 -0.830
PAY_4_Pay_Duly -1.2440 0.161 -7.730 0.000 -1.559 -0.929
PAY_4_Revolving_Credit -0.9017 0.151 -5.954 0.000 -1.199 -0.605
PAY_6_No_Transactions 0.3405 0.080 4.232 0.000 0.183 0.498
PAY_6_Pay_Duly 0.1290 0.070 1.837 0.066 -0.009 0.267
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 20996\n", - "Model: Logit Df Residuals: 20972\n", - "Method: MLE Df Model: 23\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1716\n", - "Time: 17:58:42 Log-Likelihood: -9352.2\n", - "converged: True LL-Null: -11290.\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8185 0.102 -7.996 0.000 -1.019 -0.618\n", - "SEX -0.1216 0.037 -3.286 0.001 -0.194 -0.049\n", - "PAY_0 0.6056 0.034 17.614 0.000 0.538 0.673\n", - "PAY_2 -0.5868 0.074 -7.895 0.000 -0.732 -0.441\n", - "PAY_4 -0.2261 0.069 -3.259 0.001 -0.362 -0.090\n", - "PAY_6 0.2449 0.029 8.389 0.000 0.188 0.302\n", - "BILL_AMT1 0.2964 0.140 2.121 0.034 0.023 0.570\n", - "PAY_AMT1 -1.0360 0.252 -4.117 0.000 -1.529 -0.543\n", - "PAY_AMT2 -2.3264 0.330 -7.058 0.000 -2.972 -1.680\n", - "PAY_AMT5 -0.8585 0.237 -3.626 0.000 -1.323 -0.394\n", - "PAY_AMT6 -0.6965 0.237 -2.934 0.003 -1.162 -0.231\n", - "GRAD 1.6137 0.173 9.315 0.000 1.274 1.953\n", - "UNI 1.6386 0.172 9.531 0.000 1.302 1.976\n", - "HS 1.5765 0.175 8.983 0.000 1.233 1.920\n", - "PAY_0_Revolving_Credit -0.9127 0.084 -10.822 0.000 -1.078 -0.747\n", - "PAY_2_No_Transactions -1.7245 0.176 -9.789 0.000 -2.070 -1.379\n", - "PAY_2_Pay_Duly -1.4835 0.167 -8.874 0.000 -1.811 -1.156\n", - "PAY_2_Revolving_Credit -0.9405 0.174 -5.414 0.000 -1.281 -0.600\n", - "PAY_3_Revolving_Credit -0.2826 0.067 -4.189 0.000 -0.415 -0.150\n", - "PAY_4_No_Transactions -1.1690 0.173 -6.750 0.000 -1.509 -0.830\n", - "PAY_4_Pay_Duly -1.2440 0.161 -7.730 0.000 -1.559 -0.929\n", - "PAY_4_Revolving_Credit -0.9017 0.151 -5.954 0.000 -1.199 -0.605\n", - "PAY_6_No_Transactions 0.3405 0.080 4.232 0.000 0.183 0.498\n", - "PAY_6_Pay_Duly 0.1290 0.070 1.837 0.066 -0.009 0.267\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#remove all insignificant attributes\n", "sig = glm.pvalues[glm.pvalues < 0.05]\n", @@ -4575,50 +1299,18 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 16194\n", - " 1 0.67 0.35 0.46 4802\n", - "\n", - " accuracy 0.81 20996\n", - " macro avg 0.75 0.65 0.67 20996\n", - "weighted avg 0.79 0.81 0.79 20996\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.88 4010\n", - " 1 0.68 0.38 0.49 1239\n", - "\n", - " accuracy 0.81 5249\n", - " macro avg 0.76 0.66 0.69 5249\n", - "weighted avg 0.80 0.81 0.79 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" ] @@ -4634,39 +1326,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.2401436742439986\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7697924316455151" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" ] @@ -4680,27 +1342,11 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.85 0.86 4010\n", - " 1 0.54 0.56 0.55 1239\n", - "\n", - " accuracy 0.78 5249\n", - " macro avg 0.70 0.71 0.70 5249\n", - "weighted avg 0.79 0.78 0.78 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" ] @@ -4719,146 +1365,18 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the Logistic Regression identified 698\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
03408602
1541698
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 3408 602\n", - "1 541 698" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the Logistic Regression identified 471\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
03791219
1768471
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 3791 219\n", - "1 768 471" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" ] @@ -4872,36 +1390,16 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.22465942137872466\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4940,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4954,17 +1452,9 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Explained variation per principal component: [0.2955834117 0.1746114375]\n" - ] - } - ], + "outputs": [], "source": [ "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" @@ -4979,22 +1469,9 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#visualizing pca\n", "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", @@ -5032,7 +1509,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5042,39 +1519,18 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pca.fit(X_train)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No. of components before pca: 44\n", - "No. of components after pca: 13\n" - ] - } - ], + "outputs": [], "source": [ "#get number of components after pca\n", "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", @@ -5090,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5117,23 +1573,9 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn import svm\n", "#train svm model without standardization and no parameter tuning\n", @@ -5143,39 +1585,9 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.16277415089804265\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7256296305241738" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#plot roc for svm\n", "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" @@ -5183,73 +1595,9 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the SVM original data RBF kernel identified 480\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03810200
1759480
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3810 200\n", - "1 759 480" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" @@ -5257,25 +1605,9 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 4010\n", - " 1 0.71 0.39 0.50 1239\n", - "\n", - " accuracy 0.82 5249\n", - " macro avg 0.77 0.67 0.69 5249\n", - "weighted avg 0.80 0.82 0.80 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_original.predict(X_test)))" ] @@ -5296,23 +1628,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", - " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#train svm model with feature reduction and no parameter tuning\n", "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", @@ -5321,39 +1639,9 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.1616720894054318\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7038122007330342" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#plot roc for svm\n", "get_roc(clf_reduced, y_test, X_test_pca, \n", @@ -5362,73 +1650,9 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the SVM reduced features no tuning RBF kernal identified 485\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03806204
1754485
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3806 204\n", - "1 754 485" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" @@ -5436,25 +1660,9 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 4010\n", - " 1 0.70 0.39 0.50 1239\n", - "\n", - " accuracy 0.82 5249\n", - " macro avg 0.77 0.67 0.70 5249\n", - "weighted avg 0.80 0.82 0.80 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" ] @@ -5475,20 +1683,9 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 1, 'gamma': 0.1}" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -5511,23 +1708,9 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", @@ -5536,29 +1719,9 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.1660719022802057\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", " \"SVM reduced features and tuning RBF kernal\")" @@ -5566,73 +1729,9 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 1239 Defaulters, the SVM reduced features and tuning RBF kernal identified 449\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
03817193
1790449
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 3817 193\n", - "1 790 449" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" @@ -5640,25 +1739,9 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 4010\n", - " 1 0.70 0.36 0.48 1239\n", - "\n", - " accuracy 0.81 5249\n", - " macro avg 0.76 0.66 0.68 5249\n", - "weighted avg 0.80 0.81 0.79 5249\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" ] @@ -5679,24 +1762,9 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"recall\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m auroc])\n", - "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 573\u001b[0m \"\"\"\n\u001b[0;32m--> 574\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 575\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \"\"\"\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[1;32m 473\u001b[0m \u001b[0;34m\"the number of features at training time\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[1;32m 475\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" - ] - } - ], + "outputs": [], "source": [ "evaluation.loc[4] = ([\"SVM\" , \n", " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", @@ -5874,7 +1942,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, From ac024cc140d617bf84a3852daaf7f13e012de613 Mon Sep 17 00:00:00 2001 From: reon Date: Tue, 19 Nov 2019 08:38:27 +0800 Subject: [PATCH 09/25] made changes q --- ...2101_Default - EDIT-Copy1-checkpoint.ipynb | 3799 ++++++++++++++++- BT2101_Default - EDIT-Copy1.ipynb | 3799 ++++++++++++++++- 2 files changed, 7288 insertions(+), 310 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb index 8cbd088..fcf3597 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -154,7 +154,234 @@ "id": "FhJ2eAxVQhBm", "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#rename the target variable to \"Y\" for convenience\n", "df[\"Y\"] = df[\"default payment next month\"] \n", @@ -165,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -176,7 +403,15 @@ "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], "source": [ "size = df.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -184,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -194,7 +429,18 @@ "id": "QVaSnvJP3VbO", "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#check for null values\n", "df.isnull().any().sum() " @@ -236,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -246,7 +492,38 @@ "id": "DCSEICWwXWgX", "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "All = df.shape[0]\n", "default = df[df['Y'] == 1]\n", @@ -285,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -296,7 +573,32 @@ "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], "source": [ "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", "print(\"--------------------------------------------------------\")\n", @@ -321,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -332,7 +634,37 @@ "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], "source": [ "#proportion of target attribute (for reference)\n", "print('Total target attributes:')\n", @@ -383,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -394,7 +726,94 @@ "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#printing numerical attributes\n", "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" @@ -402,11 +821,368 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], - "source": [ - "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe()" + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL26245.00.2843370.2378750.00.0816330.2244900.4081631.0
AGE26245.00.3685950.2324220.00.1842110.3421050.5263161.0
PAY_026245.00.3721090.7657300.00.0000000.0000000.0000008.0
PAY_226245.00.3373210.8148780.00.0000000.0000000.0000008.0
PAY_326245.00.3246330.8114910.00.0000000.0000000.0000008.0
PAY_426245.00.2782240.7863140.00.0000000.0000000.0000008.0
PAY_526245.00.2387500.7439230.00.0000000.0000000.0000008.0
PAY_626245.00.2432080.7403450.00.0000000.0000000.0000008.0
BILL_AMT126245.00.2001680.1893800.00.0575900.1229230.2652971.0
BILL_AMT226245.00.2549170.1745860.00.1236710.1857490.3089291.0
BILL_AMT326245.00.2981590.1609770.00.1777390.2370200.3517731.0
BILL_AMT426245.00.3176480.1568050.00.2018360.2611940.3706151.0
BILL_AMT526245.00.3300720.1554220.00.2174530.2741640.3743861.0
BILL_AMT626245.00.4279510.1332430.00.3326990.3777600.4635631.0
PAY_AMT126245.00.0727790.1059660.00.0152700.0413830.0880841.0
PAY_AMT226245.00.0551330.0923160.00.0091910.0306370.0615961.0
PAY_AMT326245.00.0618930.1056440.00.0062510.0302270.0694351.0
PAY_AMT426245.00.0617080.1070400.00.0033210.0265660.0690271.0
PAY_AMT526245.00.0628740.1075020.00.0018550.0275580.0710461.0
PAY_AMT626245.00.0553390.1033570.00.0000000.0224120.0601761.0
S_026245.0-0.1335870.879876-2.0-1.0000000.0000000.0000001.0
S_226245.0-0.3004380.883472-2.0-1.0000000.0000000.0000001.0
S_326245.0-0.3273000.895264-2.0-1.0000000.0000000.0000001.0
S_426245.0-0.3644120.886115-2.0-1.0000000.0000000.0000001.0
S_526245.0-0.3959990.877789-2.0-1.0000000.0000000.0000001.0
S_626245.0-0.4281580.900723-2.0-1.0000000.0000000.0000001.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "LIMIT_BAL 26245.0 0.284337 0.237875 0.0 0.081633 0.224490 0.408163 1.0\n", + "AGE 26245.0 0.368595 0.232422 0.0 0.184211 0.342105 0.526316 1.0\n", + "PAY_0 26245.0 0.372109 0.765730 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_2 26245.0 0.337321 0.814878 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_3 26245.0 0.324633 0.811491 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_4 26245.0 0.278224 0.786314 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_5 26245.0 0.238750 0.743923 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_6 26245.0 0.243208 0.740345 0.0 0.000000 0.000000 0.000000 8.0\n", + "BILL_AMT1 26245.0 0.200168 0.189380 0.0 0.057590 0.122923 0.265297 1.0\n", + "BILL_AMT2 26245.0 0.254917 0.174586 0.0 0.123671 0.185749 0.308929 1.0\n", + "BILL_AMT3 26245.0 0.298159 0.160977 0.0 0.177739 0.237020 0.351773 1.0\n", + "BILL_AMT4 26245.0 0.317648 0.156805 0.0 0.201836 0.261194 0.370615 1.0\n", + "BILL_AMT5 26245.0 0.330072 0.155422 0.0 0.217453 0.274164 0.374386 1.0\n", + "BILL_AMT6 26245.0 0.427951 0.133243 0.0 0.332699 0.377760 0.463563 1.0\n", + "PAY_AMT1 26245.0 0.072779 0.105966 0.0 0.015270 0.041383 0.088084 1.0\n", + "PAY_AMT2 26245.0 0.055133 0.092316 0.0 0.009191 0.030637 0.061596 1.0\n", + "PAY_AMT3 26245.0 0.061893 0.105644 0.0 0.006251 0.030227 0.069435 1.0\n", + "PAY_AMT4 26245.0 0.061708 0.107040 0.0 0.003321 0.026566 0.069027 1.0\n", + "PAY_AMT5 26245.0 0.062874 0.107502 0.0 0.001855 0.027558 0.071046 1.0\n", + "PAY_AMT6 26245.0 0.055339 0.103357 0.0 0.000000 0.022412 0.060176 1.0\n", + "S_0 26245.0 -0.133587 0.879876 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_2 26245.0 -0.300438 0.883472 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_3 26245.0 -0.327300 0.895264 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_4 26245.0 -0.364412 0.886115 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_5 26245.0 -0.395999 0.877789 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_6 26245.0 -0.428158 0.900723 -2.0 -1.000000 0.000000 0.000000 1.0" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" ] }, { @@ -429,7 +1205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -440,7 +1216,44 @@ "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", " fig=plt.figure()\n", @@ -500,9 +1313,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", "df[\"EDUCATION\"].unique()" @@ -517,9 +1341,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", "df[\"MARRIAGE\"].unique()" @@ -543,7 +1378,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -556,17 +1391,190 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
.....................
29995111111
29996000000
29997-1-1-1-100
29998111-100
30000000000
\n", + "

26245 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0\n", + "... ... ... ... ... ... ...\n", + "29995 1 1 1 1 1 1\n", + "29996 0 0 0 0 0 0\n", + "29997 -1 -1 -1 -1 0 0\n", + "29998 1 1 1 -1 0 0\n", + "30000 0 0 0 0 0 0\n", + "\n", + "[26245 rows x 6 columns]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -585,9 +1593,310 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from scipy import stats\n", "#we are only concerned with the ordinal data\n", @@ -609,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -623,11 +1932,230 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.head()" ] @@ -658,7 +2186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +2195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -676,9 +2204,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", @@ -690,11 +2305,211 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#one hot encoding for S_0 to S_6\n", "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", @@ -718,21 +2533,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", - "df1.head()" + "df1.columns" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#check for perfect collinearity\n", "corr = df1.corr()\n", @@ -744,9 +2644,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], "source": [ "size = df1.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -763,7 +2671,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -773,7 +2681,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "colab": {}, "colab_type": "code", @@ -800,9 +2708,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 81.747784 3.335170e-04\n", + "PAY_0 4215.816943 0.000000e+00\n", + "PAY_2 3730.186349 0.000000e+00\n", + "PAY_3 3017.725262 0.000000e+00\n", + "PAY_4 3020.555618 0.000000e+00\n", + "PAY_5 2812.887854 0.000000e+00\n", + "PAY_6 2575.321192 0.000000e+00\n", + "PAY_0_No_Transactions 63.305610 2.349730e-02\n", + "PAY_0_Revolving_Credit 454.630595 0.000000e+00\n", + "PAY_2_Pay_Duly 76.591060 1.225866e-03\n", + "PAY_2_Revolving_Credit 241.568619 0.000000e+00\n", + "PAY_3_Pay_Duly 88.958886 4.790331e-05\n", + "PAY_3_Revolving_Credit 140.257620 3.064993e-12\n", + "PAY_4_Pay_Duly 82.933933 2.446182e-04\n", + "PAY_4_Revolving_Credit 98.729838 2.851222e-06\n", + "PAY_5_Pay_Duly 67.720209 9.456393e-03\n", + "PAY_5_Revolving_Credit 70.202609 5.486203e-03\n", + "PAY_6_Revolving_Credit 73.167377 2.782902e-03\n" + ] + } + ], "source": [ "from sklearn.feature_selection import SelectKBest\n", "from sklearn.feature_selection import chi2\n", @@ -846,7 +2781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -880,7 +2815,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -911,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -946,7 +2881,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -955,9 +2890,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tree = DecisionTreeClassifier()\n", "tree.fit(X_train, y_train)" @@ -965,9 +2916,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13475\n", + " 1 1.00 1.00 1.00 4021\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, tree.predict(X_train)))" ] @@ -981,34 +2948,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (GINI) identified 828\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054151314
11192828
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5415 1314\n", + "1 1192 828" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.6666666666666666\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6729\n", + " 1 0.39 0.41 0.40 2020\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, tree.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1035,7 +3102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1045,18 +3112,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "randf.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13475\n", + " 1 1.00 1.00 1.00 4021\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, randf.predict(X_train)))" ] @@ -1070,34 +3170,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (Random Forest) identified 749\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06310419
11271749
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6310 419\n", + "1 1271 749" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.26\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6729\n", + " 1 0.64 0.37 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, randf.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1130,9 +3330,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", @@ -1141,9 +3361,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13475\n", + " 1 0.79 0.46 0.58 4021\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, xgb.predict(X_train)))" ] @@ -1157,34 +3393,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 750\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06339390
11270750
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6339 390\n", + "1 1270 750" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.26114231320864134\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6729\n", + " 1 0.66 0.37 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, xgb.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -1202,11 +3538,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree0.4099010.606997
1Random Forest0.3707920.768455
2Gradient Boosted0.3712870.778407
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree 0.409901 0.606997\n", + "1 Random Forest 0.370792 0.768455\n", + "2 Gradient Boosted 0.371287 0.778407" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "evaluation" ] @@ -1243,7 +3640,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1252,9 +3649,251 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445436\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737
Time: 19:35:44 Log-Likelihood: -7793.4
converged: True LL-Null: -9431.5
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611
SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039
AGE 0.0615 0.100 0.614 0.539 -0.135 0.258
PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770
PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369
PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115
PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107
PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256
PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616
BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002
BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099
BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978
BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686
BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134
BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007
PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562
PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252
PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019
PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487
PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470
PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116
GRAD 1.3893 0.230 6.030 0.000 0.938 1.841
UNI 1.3690 0.229 5.975 0.000 0.920 1.818
HS 1.3654 0.233 5.863 0.000 0.909 1.822
MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328
SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215
PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274
PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452
PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419
PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989
PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955
PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543
PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027
PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081
PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069
PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007
PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214
PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146
PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334
PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270
PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379
PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117
PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062
PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737\n", + "Time: 19:35:44 Log-Likelihood: -7793.4\n", + "converged: True LL-Null: -9431.5\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", + "SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", + "AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", + "PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", + "PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", + "PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", + "PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", + "PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", + "PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", + "BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", + "BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", + "BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", + "BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", + "BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", + "BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", + "PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", + "PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", + "PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", + "PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", + "PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", + "PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", + "GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", + "UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", + "HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", + "MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", + "SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", + "PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", + "PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", + "PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", + "PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", + "PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", + "PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", + "PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", + "PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", + "PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", + "PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", + "PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", + "PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", + "PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", + "PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", + "PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", + "PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", + "PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", + "PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "glm = sm.Logit(y_train,X_train).fit()\n", "glm.summary()" @@ -1262,18 +3901,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13475\n", + " 1 0.67 0.35 0.46 4021\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.67 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.69 0.35 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" ] @@ -1287,9 +3958,171 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.446914\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17472
Method: MLE Df Model: 23
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709
Time: 19:35:44 Log-Likelihood: -7819.2
converged: True LL-Null: -9431.5
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581
SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041
PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653
PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487
PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260
BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108
BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299
BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293
PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770
PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593
PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428
GRAD 1.2429 0.179 6.961 0.000 0.893 1.593
UNI 1.2478 0.177 7.052 0.000 0.901 1.595
HS 1.2675 0.181 7.014 0.000 0.913 1.622
PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690
PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284
PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147
PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735
PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038
PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115
PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191
PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030
PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356
PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17472\n", + "Method: MLE Df Model: 23\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709\n", + "Time: 19:35:44 Log-Likelihood: -7819.2\n", + "converged: True LL-Null: -9431.5\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", + "SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", + "PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", + "PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", + "PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", + "BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", + "BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", + "BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", + "PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", + "PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", + "PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", + "GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", + "UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", + "HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", + "PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", + "PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", + "PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", + "PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", + "PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", + "PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", + "PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", + "PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", + "PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", + "PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#remove all insignificant attributes\n", "sig = glm.pvalues[glm.pvalues < 0.05]\n", @@ -1299,18 +4132,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13475\n", + " 1 0.66 0.36 0.46 4021\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.67 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.35 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" ] @@ -1326,9 +4191,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.22554888390620112\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7698058845975142" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" ] @@ -1342,11 +4237,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.86 0.86 6729\n", + " 1 0.54 0.55 0.54 2020\n", + "\n", + " accuracy 0.79 8749\n", + " macro avg 0.70 0.70 0.70 8749\n", + "weighted avg 0.79 0.79 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" ] @@ -1365,18 +4276,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Logistic Regression identified 1104\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
05790939
19161104
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5790 939\n", + "1 916 1104" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Logistic Regression identified 716\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06391338
11304716
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6391 338\n", + "1 1304 716" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" ] @@ -1390,16 +4429,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21841387811546378\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1438,7 +4497,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -1452,9 +4511,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variation per principal component: [0.2966725486 0.1736524263]\n" + ] + } + ], "source": [ "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" @@ -1469,9 +4536,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#visualizing pca\n", "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", @@ -1509,7 +4589,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -1519,18 +4599,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pca.fit(X_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of components before pca: 44\n", + "No. of components after pca: 13\n" + ] + } + ], "source": [ "#get number of components after pca\n", "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", @@ -1546,7 +4647,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -1573,9 +4674,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn import svm\n", "#train svm model without standardization and no parameter tuning\n", @@ -1585,9 +4700,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1660749149902864\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7220704605012441" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#plot roc for svm\n", "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" @@ -1595,9 +4740,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM original data RBF kernel identified 733\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06391338
11287733
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6391 338\n", + "1 1287 733" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" @@ -1605,9 +4814,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.36 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_original.predict(X_test)))" ] @@ -1628,9 +4853,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and no parameter tuning\n", "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", @@ -1639,9 +4878,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16374485135727915\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7040433457077317" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#plot roc for svm\n", "get_roc(clf_reduced, y_test, X_test_pca, \n", @@ -1650,9 +4919,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM reduced features no tuning RBF kernal identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06387342
11282738
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6387 342\n", + "1 1282 738" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" @@ -1660,9 +4993,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.37 0.48 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" ] @@ -1683,9 +5032,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 1, 'gamma': 0.1}" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -1708,9 +5068,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", @@ -1719,9 +5093,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16537143473786733\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", " \"SVM reduced features and tuning RBF kernal\")" @@ -1729,9 +5123,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM reduced features and tuning RBF kernal identified 727\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06383346
11293727
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6383 346\n", + "1 1293 727" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" @@ -1739,9 +5197,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.36 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" ] @@ -1762,9 +5236,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"recall\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m auroc])\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 573\u001b[0m \"\"\"\n\u001b[1;32m--> 574\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 575\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \"\"\"\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[0;32m 473\u001b[0m \u001b[1;34m\"the number of features at training time\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" + ] + } + ], "source": [ "evaluation.loc[4] = ([\"SVM\" , \n", " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/BT2101_Default - EDIT-Copy1.ipynb index 8cbd088..fcf3597 100644 --- a/BT2101_Default - EDIT-Copy1.ipynb +++ b/BT2101_Default - EDIT-Copy1.ipynb @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -154,7 +154,234 @@ "id": "FhJ2eAxVQhBm", "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#rename the target variable to \"Y\" for convenience\n", "df[\"Y\"] = df[\"default payment next month\"] \n", @@ -165,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -176,7 +403,15 @@ "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], "source": [ "size = df.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -184,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -194,7 +429,18 @@ "id": "QVaSnvJP3VbO", "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#check for null values\n", "df.isnull().any().sum() " @@ -236,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -246,7 +492,38 @@ "id": "DCSEICWwXWgX", "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "All = df.shape[0]\n", "default = df[df['Y'] == 1]\n", @@ -285,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -296,7 +573,32 @@ "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], "source": [ "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", "print(\"--------------------------------------------------------\")\n", @@ -321,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -332,7 +634,37 @@ "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], "source": [ "#proportion of target attribute (for reference)\n", "print('Total target attributes:')\n", @@ -383,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -394,7 +726,94 @@ "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#printing numerical attributes\n", "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" @@ -402,11 +821,368 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], - "source": [ - "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe()" + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL26245.00.2843370.2378750.00.0816330.2244900.4081631.0
AGE26245.00.3685950.2324220.00.1842110.3421050.5263161.0
PAY_026245.00.3721090.7657300.00.0000000.0000000.0000008.0
PAY_226245.00.3373210.8148780.00.0000000.0000000.0000008.0
PAY_326245.00.3246330.8114910.00.0000000.0000000.0000008.0
PAY_426245.00.2782240.7863140.00.0000000.0000000.0000008.0
PAY_526245.00.2387500.7439230.00.0000000.0000000.0000008.0
PAY_626245.00.2432080.7403450.00.0000000.0000000.0000008.0
BILL_AMT126245.00.2001680.1893800.00.0575900.1229230.2652971.0
BILL_AMT226245.00.2549170.1745860.00.1236710.1857490.3089291.0
BILL_AMT326245.00.2981590.1609770.00.1777390.2370200.3517731.0
BILL_AMT426245.00.3176480.1568050.00.2018360.2611940.3706151.0
BILL_AMT526245.00.3300720.1554220.00.2174530.2741640.3743861.0
BILL_AMT626245.00.4279510.1332430.00.3326990.3777600.4635631.0
PAY_AMT126245.00.0727790.1059660.00.0152700.0413830.0880841.0
PAY_AMT226245.00.0551330.0923160.00.0091910.0306370.0615961.0
PAY_AMT326245.00.0618930.1056440.00.0062510.0302270.0694351.0
PAY_AMT426245.00.0617080.1070400.00.0033210.0265660.0690271.0
PAY_AMT526245.00.0628740.1075020.00.0018550.0275580.0710461.0
PAY_AMT626245.00.0553390.1033570.00.0000000.0224120.0601761.0
S_026245.0-0.1335870.879876-2.0-1.0000000.0000000.0000001.0
S_226245.0-0.3004380.883472-2.0-1.0000000.0000000.0000001.0
S_326245.0-0.3273000.895264-2.0-1.0000000.0000000.0000001.0
S_426245.0-0.3644120.886115-2.0-1.0000000.0000000.0000001.0
S_526245.0-0.3959990.877789-2.0-1.0000000.0000000.0000001.0
S_626245.0-0.4281580.900723-2.0-1.0000000.0000000.0000001.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "LIMIT_BAL 26245.0 0.284337 0.237875 0.0 0.081633 0.224490 0.408163 1.0\n", + "AGE 26245.0 0.368595 0.232422 0.0 0.184211 0.342105 0.526316 1.0\n", + "PAY_0 26245.0 0.372109 0.765730 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_2 26245.0 0.337321 0.814878 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_3 26245.0 0.324633 0.811491 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_4 26245.0 0.278224 0.786314 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_5 26245.0 0.238750 0.743923 0.0 0.000000 0.000000 0.000000 8.0\n", + "PAY_6 26245.0 0.243208 0.740345 0.0 0.000000 0.000000 0.000000 8.0\n", + "BILL_AMT1 26245.0 0.200168 0.189380 0.0 0.057590 0.122923 0.265297 1.0\n", + "BILL_AMT2 26245.0 0.254917 0.174586 0.0 0.123671 0.185749 0.308929 1.0\n", + "BILL_AMT3 26245.0 0.298159 0.160977 0.0 0.177739 0.237020 0.351773 1.0\n", + "BILL_AMT4 26245.0 0.317648 0.156805 0.0 0.201836 0.261194 0.370615 1.0\n", + "BILL_AMT5 26245.0 0.330072 0.155422 0.0 0.217453 0.274164 0.374386 1.0\n", + "BILL_AMT6 26245.0 0.427951 0.133243 0.0 0.332699 0.377760 0.463563 1.0\n", + "PAY_AMT1 26245.0 0.072779 0.105966 0.0 0.015270 0.041383 0.088084 1.0\n", + "PAY_AMT2 26245.0 0.055133 0.092316 0.0 0.009191 0.030637 0.061596 1.0\n", + "PAY_AMT3 26245.0 0.061893 0.105644 0.0 0.006251 0.030227 0.069435 1.0\n", + "PAY_AMT4 26245.0 0.061708 0.107040 0.0 0.003321 0.026566 0.069027 1.0\n", + "PAY_AMT5 26245.0 0.062874 0.107502 0.0 0.001855 0.027558 0.071046 1.0\n", + "PAY_AMT6 26245.0 0.055339 0.103357 0.0 0.000000 0.022412 0.060176 1.0\n", + "S_0 26245.0 -0.133587 0.879876 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_2 26245.0 -0.300438 0.883472 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_3 26245.0 -0.327300 0.895264 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_4 26245.0 -0.364412 0.886115 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_5 26245.0 -0.395999 0.877789 -2.0 -1.000000 0.000000 0.000000 1.0\n", + "S_6 26245.0 -0.428158 0.900723 -2.0 -1.000000 0.000000 0.000000 1.0" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" ] }, { @@ -429,7 +1205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -440,7 +1216,44 @@ "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3df7BcZZ3n8ffHIC4VpEiM3IoEDboZZ/mhCHcIrlUz17EIAZwKlj+WHyXBQWMpKWdXtoowyxYK6Ga2lnXBHzhRY0ItENmpYZNRNGZZe3FnRZMoS4KARLzKJUiEQJaLOBj87h/naWgufW/f7j7d/Zx7P6+qru5++ulznpP+5vme55znnqOIwMzMLDevGHQDzMzMmnGCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5AQ1TZJGJT0raVzSY5K+Lunwhs83SDoo6XUNZaenugsayl4l6T5JH22xvpMk7ZT02/R8Um+2zHqlnzEj6Y8kbZb0G0n7JW2V9ObebZ2Vrc/xskDSP0p6QtJTkn4g6R2927rOOEG15y8i4nDgZOBPgCsAJM0F3gscAC6oV46IbcA3gesalnEF8CiwbrKVSDoU2Az8V2AesBHYnMqtWvoSM8CRwBbgzcAQ8COKGLJq6Ve8jAN/CbyWoo/5G+AfJB1S2paUwAmqAxHxCPBt4IRU9F7gKeAqYOWE6p8E/kzS2ZJOAFYDH4mprzE1AhwC/JeI+KeIuB4Q8OflbYX1U69jJiJ+FBFfi4j9EfF74HPAmyW9puxtsd7rQ7z8LiIeiIg/UPQtz1Mkqvnlbkl3nKA6IOkY4CzgJ6loJXALsAn4Y0kn1+tGxAHgY8CXgfXApyPi5y1WcTxwz4QAuyeVWwX1IWYm+lPg1xHxRLdtt/7rV7xIugf4HcXo+6sRsa+0jSiBE1R7/rukp4D/Dfwv4LOSXg+8E7g5Ih4D7mDCHk5E/ANwF8W/9/XTWM/hFEP5RgeAV3fXfBuAfsXMCyQtAr5IsWdt1dLXeImItwBHAOendWbFCao950TEkRHxhoj4eEQ8C3wQuC8i7k51bgLOl/TKCd+9F7g/DalbGacImkZHAE9303gbiH7FDACSXgt8F/hSRNxSxgZYX/U1XuCFw323AGskvbXrLShRVifEKupC4PWSfp3eHwK8BjiTYtjciXuBSyWp4TDfWyj2iq36ehEzSJpHkZy2RMRnum6l5aIn8dLEK4E3Av+3xGV2xQmqC5LeDrwJeBvwm4aPrqUYgncaPDWKk5afkPRl4COp/H92uDzLRK9iRtIRwFbgHyNiTbfttDz0MF5Oo+j/fwTMAT5BMfvzh920t2xOUN1ZCWyOiF2NhZKuA74vaX5E7G93oRHxnKRzgK8Ca4H7KIb+z5XRaBuonsQM8B6KacnHS7qoofy4iPhVx621QetVvLyK4lzVG4HfA7uAsyNib7cNLpN8R10zM8uRJ0mYmVmWnKAGRNIF6ZImEx/3DrptlifHjLVjJsSLD/GZmVmWWk6SkLQeeDewLyJOSGWfophZVp9V8tcRcXv67HLgYtIstIjYmsqXU1wvag7FXyyvTeXHUvx19Hzgx8AHpzMZYMGCBbF48eJpb2grzzzzDHPnzi1teb0yiHbu3Lnz8Yh4bV9XWrKy4wWqETOOl865j+mfSWMmIqZ8UFwy5WRgd0PZp4B/26TucRRz6F8FHAv8nCIhzUmv3wgcmuocl75zK3Buev1l4GOt2hQRnHLKKVGm733ve6Uur1cG0U5gR0zjN8n5UXa8RFQjZhwv+cRMFeIlIq+YaXkOKiLuBKY7jXEFsCmKC5z+AtgDnJoeeyLioShGR5uAFZLqF0D9u/T9jcA501yXmZnNYN38HdRqSRcCO4BLI+JJ4GiK60HVjaUygIcnlC+l+GvopyLiYJP6LyNpFbAKYGhoiFqt1kXzX2p8fLzU5fVKVdppZtatThPUDcDVQKTnaynuLaImdYPmswVjivpNRcQ60j1OhoeHY2RkpK1GT6VWq1Hm8nqlKu00M+tWRwkqiivqAiDpKxQ3zIJiBHRMQ9VFQP0vk5uVPw4cKemQNIpqrN9Xux45wEVrvjVlndG1Z/epNVYFrWLG8WKNHC/t6+jvoCQtbHj7HmB3er0FODfdcvhYYAnFtZ62A0skHZvuCnsuxQUtA/ge8L70/ZX4LqCVJ2m9pH2SdjeUfUrSI5LuTo+zGj67XNIeSQ9IOqOhfHkq2yNpTUP5sZJ+KOlBSd/wnYarzfFik2mZoCTdAvyA4u6cY5IuBv6jpF3pZlfvBP4NQETcSzEr76fAd4BLIuL5NDpaTXExy/uAW1NdgMuAT0raQ3FO6mulbqENwgZgeZPyz0XESelR/7OE4yh2WI5P3/mSpDmS5lBcvf1Mitmh56W6UNye+nMRsQR4kuLPGqy6NuB4sSZaHuKLiPOaFE+aRKK4zP/LLvWfAuz2JuUPUczysxkiIu6UtHia1V+Y+Qn8Iu2o1ONhT4oPJNVnft5HMfPz/FRnI8WfPdxQTuut3xwvNhlfzdz6qa8zP3s56xNg6DC49MSDk36ew2zLis/6nFEzhasQL5BXzDhBWb/0feZnL2d9Anz+ps1cu2vy/0KjF5S7vk5UeNbnjJspXIV4gbxixgnK+mImzvy03nG8GPhq5tYnnvlp7XC8GHgEZT2QZn6OAAskjQFXAiOSTqI4vDIKfBSKmZ+S6jM/D5Jmfqbl1Gd+zgHWT5j5uUnSNcBP8MzPSnO82GScoKx0nvlp7XC82GR8iM/MzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllqWWCkrRe0j5JuxvK5kvaJunB9DwvlUvS9ZL2SLpH0skN31mZ6j8oaWVD+SmSdqXvXC9JZW+kmZlVz3RGUBuA5RPK1gB3RMQS4I70HuBMYEl6rAJugCKhUdzGeSnFnS2vrCe1VGdVw/cmrsvMzGahlgkqIu4E9k8oXgFsTK83Auc0lN8YhbuAIyUtBM4AtkXE/oh4EtgGLE+fHRERP4iIAG5sWJaZmc1ih3T4vaGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRjLYYGhqiVqt12PyXGzoMLj3x4JR1ylxfp8bHx7Noh5lZr3WaoCbT7PxRdFDeVESsA9YBDA8Px8jISAdNbO7zN23m2l1T/3OMXlDe+jpVq9Uoc7t7QdJ64N3Avog4IZXNB74BLAZGgQ9ExJPpnON1wFnAb4GLIuLH6TsrgSvSYq+JiI2p/BSKQ8+HAbcDf5VG4FZBjhebTKez+B5Lh+dIz/tS+RhwTEO9RcDeFuWLmpRbtW3A5y1t+jbgeLEmOk1QW4D6TLyVwOaG8gvTbL7TgAPpUOBWYJmkeSlolgFb02dPSzot7Rld2LAsqyift7R2OF5sMi0P8Um6BRgBFkgao9hLWQvcKuli4FfA+1P12ymG3nsoht8fAoiI/ZKuBraneldFRD0gP8aLw+9vp4fNPH0/b9nLc5bQ+rxlDucKK3zOctad587ld8opZlomqIg4b5KP3tWkbgCXTLKc9cD6JuU7gBNatcNmrJ6dt+zlOUtofd7S5yx7Ysae584hXiCvmPGVJKxffN7S2uF4MSco6xuft7R2OF6s9GnmZj5vaW1xvNhknKCsdD5vae1wvNhkfIjPzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZWnW3LBw8ZpvTfn5pSf2qSFWGY4Zs8HqKkFJGgWeBp4HDkbEsKT5wDeAxcAo8IGIeFKSgOsobtf8W+CiiPhxWs5K4Iq02GsiYmM37TIz6zfv0JSvjEN874yIkyJiOL1fA9wREUuAO9J7gDOBJemxCrgBICW0K4GlwKnAlZLmldAuMzOrsF6cg1oB1EdAG4FzGspvjMJdwJGSFgJnANsiYn9EPAlsA5b3oF2WAUmjknZJulvSjlQ2X9I2SQ+m53mpXJKul7RH0j2STm5YzspU/8E0ArcZyPEyu3WboAL4rqSdklalsqGIeBQgPR+Vyo8GHm747lgqm6zcZi6Puq0djpdZqttJEu+IiL2SjgK2Sbp/irpqUhZTlL98AUUSXAUwNDRErVabdkMvPfHglJ8PHda6Tjvr65Xx8fEs2lGyFcBIer0RqAGX0TDqBu6SVB91j5BG3QCS6qPuW/rbbBsQx8ss0VWCioi96XmfpNso9k4ek7QwIh5NwbEvVR8Djmn4+iJgbyofmVBem2R964B1AMPDwzEyMtKsWlMXtTyBeZBrd039zzF6wfTX1yu1Wo12tjtD9VF3AH+bftOXjLrTDg941G2Ol1mt4wQlaS7wioh4Or1eBlwFbAFWAmvT8+b0lS3AakmbKIbaB1JwbQU+2zDkXgZc3mm7LHt9G3V3M+KG7kfdOYx0Z8CIe9Ycpcnld8opZroZQQ0BtxWzxzkEuDkiviNpO3CrpIuBXwHvT/Vvp5hivodimvmHACJiv6Srge2p3lX1objNPP0cdXcz4obuR90ecXdvNh2lySFeIK+Y6XiSREQ8FBFvTY/jI+IzqfyJiHhXRCxJz/tTeUTEJRHxpog4MSJ2NCxrfUT88/T4evebZTmSNFfSq+uvKUbLu3lx1A0vH3VfmGZnnUYadQNbgWWS5qWR97JUZjOI48VmzZUkLAsedVs7HC+znBOU9U1EPAS8tUn5E8C7mpQHcMkky1oPrC+7jZYPx4s5QZmZTUOrSxlZ+Zyg2tAqQEfXnt2nlli3cuhsptMGx5Q1mm19kG+3YWZmWXKCMjOzLDlBmZlZlpygzMwsSzNmkkQOJ73NzKw8MyZB5WC2zbAxM+slH+IzM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmlqVsEpSk5ZIekLRH0ppBt8fy5nixdjlmqieLq5lLmgN8ETgdGAO2S9oSET8dbMvKNZ1bgviK563NlngBXyG/LLMpZmaSLBIUcCqwJyIeApC0CVgBOHisGceLtWtWxMxM26HJJUEdDTzc8H4MWDqxkqRVwKr0dlzSA2U14BOwAHi8rOV1Sn/Tssog2vmGPq+vlYHHC7SOmWn8ll1zvEzbwGOmIvECGcVMLglKTcriZQUR64B1PWmAtCMihnux7DJVpZ09NvB4gWr8FlVoY58MPGaq8lvk1M5cJkmMAcc0vF8E7B1QWyx/jhdrl2OmgnJJUNuBJZKOlXQocC6wZcBtsnw5XqxdjpkKyuIQX0QclLQa2ArMAdZHxL19bkbPDgWVrCrt7JlM4gWq8VtUoY09l0nMVOW3yKadinjZYVgzM7OBy+UQn5mZ2Us4QZmZWZacoKjGJVAkjUraJeluSTsG3Z7ZrArxAo6ZnFQhZnKMl1l/DipdAuVnNFwCBTgvt0ugSBoFhiNi4H9MPJtVJV7AMZOLqsRMjvHiEVTDJVAi4jmgfgkUs2YcL9Yux0yHnKCaXwLl6AG1ZSoBfFfSznQ5FhuMqsQLOGZyUZWYyS5esvg7qAGb1iVQMvCOiNgr6Shgm6T7I+LOQTdqFqpKvIBjJhdViZns4sUjqIpcAiUi9qbnfcBtFIcNrP8qES/gmMlIJWImx3hxgqrAJVAkzZX06vprYBmwe7CtmrWyjxdwzGQm+5jJNV5m/SG+TC6B0soQcJskKH6zmyPiO4Nt0uxUkXgBx0w2KhIzWcbLrJ9mbmZmefIhPjMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKCmSdKopGcljUt6TNLXJR3e8PkGSQclva6h7PRUd0FD2ask3Sfpo9Nc70pJIenD5W6R9Vq/YybFyTNpfeOSvtqbLbNeGEC8zJF0jaS9kp6W9BNJR/Zm6zrjBNWev4iIw4GTgT8BroAX7kD5XuAAcEG9ckRsA74JXNewjCuAR4F1rVYmaR5wOZDbzc1s+voaM8BbI+Lw9PBOTfX0M14+DfxL4O3AEcAHgd+VshUlcYLqQEQ8AnwbOCEVvRd4CrgKWDmh+ieBP5N0tqQTgNXAR2J6d4r8D8D1wOOlNNwGpo8xYzNAr+Ml7fz+61Tvl1HYHRFOUFUn6RjgLOAnqWglcAuwCfhjSSfX60bEAeBjwJeB9cCnI+Ln01jHqcBw+p5VXD9iJrlT0q8l/b2kxSU13/qsD/FyInAQeF+Kl59JuqTkzeheRPgxjQcwCoxT7MX8EvgScBjweuAPwEmp3lbguibf/2/ADuAV01jXnFT37el9DfjwoP8N/Mg3ZlL9PwUOBY4EvgDsBg4Z9L+DH/nFC3A+EMDX0jreAvwGOH3Q/w6ND4+g2nNORBwZEW+IiI9HxLMUx23vi4i7U52bgPMlvXLCd+8F7o+IP0xjPR8H7omIH5TXdBuQfsUMEXFnRDwXEU8BfwUcC/yLkrbD+qNf8fJser4qIp6NiHsoRmdnlbERZTlk0A2YAS4EXi/p1+n9IcBrgDOBLR0u810Ux5TrwTIfeJukkyJidVettRz0ImaaCUAlLs8Goxfxck96zvq8phNUFyS9HXgT8DaK4XHdtRTHjDsNnouAf9bw/u+Bv6MYjluF9SpmJB0PvBLYRXHI5hrgEeC+btprg9WreImIn0v6PvDvJH0CeCPwr4DzumtxuZygurMS2BwRuxoLJV0HfF/S/IjY3+5C0yGaxuU9B/y/KE6GWrX1JGaAIeAGYBHwDPB/gHdHxO+7bbANVK/iBYpk9DXgCWAf8O8j4o6uWlsypRNmZmZmWfEkCTMzy5IT1IBIuqDhkjSND181wppyzFg7ZkK8+BCfmZllqbKTJBYsWBCLFy8ubXnPPPMMc+fOLW15vTKIdu7cufPxiHhtX1dasrLjBaoRM46XzrmP6Z/JYqayCWrx4sXs2LGjtOXVajVGRkZKW16vDKKdkn7Z1xX2QNnxAtWIGcdL59zH9M9kMeNzUGZmliUnKDMzy5ITlJmZZamy56DKtuuRA1y05ltT1hlde3afWmNV0CpmHC/WyPHSPo+gzMwsS05QZmaWJScoMxsoSesl7ZO0u6HsU5IekXR3epzV8NnlkvZIekDSGQ3ly1PZHklrGsqPlfRDSQ9K+oakQ/u3ddaNlgnKwWNmPbYBWN6k/HMRcVJ63A4g6TjgXOD49J0vSZojaQ7wRYp7JB0HnJfqAvxNWtYS4Eng4p5ujZVmOiOoDTh4zKxHIuJOYLq3jFgBbIqIf4qIXwB7gFPTY09EPBQRz1HcHXaFJAF/TnE/NYCNwDmlboD1TMsE5eAxswFZLemedBRnXio7Gni4oc5YKpus/DXAUxFxcEK5VUA308xXS7oQ2AFcGhFPUvzwdzXUaQyGicGzlDaDR9IqYBXA0NAQtVqti+a/1NBhcOmJB6esU+b6OjU+Pp5FO6YiaT3wbmBfRJyQyj4FfIQX7wr61w0j78spRs7PA5+IiK2pfDlwHTAH+GpErE3lx1Ls5MwHfgx8MO342MxxA3A1xS3Jr6a4g+xf0vwW9kHzne3Jbnk/6RWyB9nH5PL/Oqc+ptMENZDgiYh1wDqA4eHhKPN6UZ+/aTPX7pr6n2P0gvLW16mKXM9rA/AF4MYJ5Z+LiP/UWDDhsPDrgP8h6Y/Sx18ETqfYcdkuaUtE/JQXDwtvkvRliuR2Q682xvovIh6rv5b0FeCb6e0YcExD1UXA3vS6WfnjwJGSDkk7wo31m613YH1MDv0L5NXHdDSLLyIei4jnI+IPwFcoDuHB5MEzWfkLwTOh3CrMh4WtW5IWNrx9D1CfpLUFOFfSq9JIegnwI2A7sCRNujqUYqdnSxT3E/oe8L70/ZXA5n5sg3WvoxGUpIUR8Wh6OzF4bpb0nyn2huvBI1LwAI9QBM/5ERGS6sGzCQfPTNfXw8K9PFwD1Thkk9PhmslIugUYARZIGgOuBEYknURxRGUU+ChARNwr6Vbgp8BB4JKIeD4tZzWwleKQ8PqIqN+Y7zJgk6RrgJ8AX+vTplmXWiYoB4+VpO+HhXt5uAaqccgmp8M1k4mI85oUT9oPRMRngM80Kb8duL1J+UO8eJTHKqRlgnLwWBkGdU7BzKrLV5KwvvA5BTNrl69mbqXzYWEzK4MTlJXOh4XNrAw+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzAZK0npJ+yTtbiibL2mbpAfT87xULknXS9oj6R5JJzd8Z2Wq/6CklQ3lp0jalb5zvST1dwutUy0TlIPHzHpsA7B8Qtka4I6IWALckd4DnAksSY9VwA1Q9EkUd25eSnEzyyvr/VKqs6rhexPXZZmazghqAw4ea4N3aqwdEXEnsH9C8QpgY3q9ETinofzGKNwFHClpIXAGsC0i9kfEk8A2YHn67IiI+EFEBHBjw7Iscy1v+R4Rd0paPKF4BTCSXm8EasBlNAQPcJekevCMkIIHQFI9eGqk4Enl9eD5djcbZQO3AfgCRWdQV9+pWStpTXp/GS/dqVlKscOytGGnZhgIYKekLanzqe/U3EVxS/jlOGZmmqGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRxBZDQ0PUarXutqLB0GFw6YkHJ/28zHV1Y3x8PJu2tExQk5h1wQN5BFBOwTMZ79RYDzUbLUcH5U1FxDpgHcDw8HCMjIx00MTmPn/TZq7dNXmXO3pBeevqRq1Wo8zt7kanCWoyMzZ4II8Ayil42jSQnRqrrMckLUyxshDYl8rHgGMa6i0C9qbykQnltVS+qEl9q4BOE5SDx8rSs52aXo64oRqHbKow4p7EFmAlsDY9b24oXy1pE8Uh4QOpH9oKfLbh3PYy4PKI2C/paUmnAT8ELgQ+388Nsc51mqAcPNauvu/U9HLEDdU4ZFOFEbekWyh+6wWSxijOPa4FbpV0MfAr4P2p+u3AWcAe4LfAhwBSX3I1sD3Vu6p+eBj4GMV50cMoDgX7cHBFtExQDh4riXdqrKmIOG+Sj97VpG4Al0yynPXA+iblO4ATummjDcZ0ZvE5eKwt3qkxszKUPUnCzDs1ZlYKX+rIzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZck3LDQzK8HiNd+a8vNLT+xTQ2aQrhKUpFHgaeB54GBEDEuaD3wDWAyMAh+IiCclCbiO4vbevwUuiogfp+WsBK5Ii70mIjZ2065mHDxm1VOlPsbKV8YI6p0R8XjD+zXAHRGxVtKa9P4y4ExgSXosBW4AlqZguxIYBgLYKWlLRDxZQtssM1XqcLxTkw33MbNUL85BrQDqncVG4JyG8hujcBdwpKSFwBnAtojYnwJmG7C8B+2yfLwzIk6KiOH0vt7hLAHuSO/hpR3OKooOh4YOZylwKnClpHl9bL8NlvuYWaLbEVQA35UUwN9GxDpgKCIeBYiIRyUdleoeDTzc8N2xVDZZ+ctIWkXRUTE0NEStVpt2Qy898eCUnw8d1rpOO+vrlfHx8SzaUbIVwEh6vRGoUewRv9DhAHdJqnc4I6QOB0BSvcO5pb/Ntj6YNX1MLv+vc+pjuk1Q74iIvSlAtkm6f4q6alIWU5S/vLAIznUAw8PDMTIyMu2GXtTycM1Brt019T/H6AXTX1+v1Go12tnuDPWtw+mms4GZ0eHk1Nl0KJs+ptUh31bdaas+Jof+BfLqY7pKUBGxNz3vk3QbxeGWxyQtTB3NQmBfqj4GHNPw9UXA3lQ+MqG81k27LGt963C62aGB7ndqcuhwcupsOuE+Znbr+ByUpLmSXl1/DSwDdgNbgJWp2kpgc3q9BbhQhdOAA2mveSuwTNK8dB5hWSqzGaixwwFe0uEAtNHhNCu3GcR9jHUzghoCbismWnEIcHNEfEfSduBWSRcDvwLen+rfTjEbaw/FjKwPAUTEfklXA9tTvavq5xba0Xr4bYOWOplXRMTTDR3OVbzY4azl5R3OakmbKCZEHEh7zVuBzzZMjFgGXN5OWxwvlZBVH2P913GCioiHgLc2KX8CeFeT8gAumWRZ64H1nbbFKsMdjk2b+xjzlSSsb9zhmFk7fC0+MzPLkhOUmZllyQnKzMyy5HNQJWo1M2x07dl9aomZWfV5BGVmZlnyCMrMrCJm21Eaj6DMzCxLHkGZDch0rmYx0/aIzdrhEZSZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMspRNgpK0XNIDkvZIWjPo9ljeHC/WLsdM9WSRoCTNAb4InAkcB5wn6bjBtspy5XixdjlmqimXi8WeCuyJiIcAJG0CVgA/HWirSjadi4O2smH53J63oQIXKJ0V8QLdx0y38TKdNlQgXmCWxMwM+a1eoIgYdBuQ9D5geUR8OL3/ILA0IlZPqLcKWJXevhl4oMRmLAAeL3F5vTKIdr4hIl7b53VOKpN4gWrEzKyPF8gmZqoQL5BRzOQyglKTspdlzohYB6zrSQOkHREx3Itll6kq7eyxgccLVOO3qEIb+2TgMVOV3yKndmZxDgoYA45peL8I2Dugtlj+HC/WLsdMBeWSoLYDSyQdK+lQ4Fxgy4DbZPlyvFi7HDMVlMUhvog4KGk1sBWYA6yPiHv73IyeHQoqWVXa2TOZxAtU47eoQht7LpOYqcpvkU07s5gkYWZmNlEuh/jMzMxewgnKzMyy5ARFNS6BImlU0i5Jd0vaMej2zGZViBdwzOSkCjGTY7zM+sWNACwAAAEnSURBVHNQ6RIoPwNOp5iKuh04LyKy+gtzSaPAcERU4Q/9ZqyqxAs4ZnJRlZjJMV48gmq4BEpEPAfUL4Fi1ozjxdrlmOmQExQcDTzc8H4sleUmgO9K2pkux2KDUZV4AcdMLqoSM9nFSxZ/BzVg07oESgbeERF7JR0FbJN0f0TcOehGzUJViRdwzOSiKjGTXbx4BFWRS6BExN70vA+4jeKwgfVfJeIFHDMZqUTM5BgvTlAVuASKpLmSXl1/DSwDdg+2VbNW9vECjpnMZB8zucbLrD/El8klUFoZAm6TBMVvdnNEfGewTZqdKhIv4JjJRkViJst4mfXTzM3MLE8+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZen/A6K07c+5e7/dAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAAEYCAYAAADWNhiqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dfbAcdZ3v8ffH8CAbxCRGTsUka8KaukuEe4XkQly3ds+KhIBVN9xSVijKnKvczRbCqrVqbVzrXlZRL24V7hV0paJkSdisiE+buEtuNmKOSJU8KiSEyOYEoxwTiBgIHHyAsN/7R/9GO8PMmYfzMPOb83lVdU3Pt3/d/euZ7+nvTHefHkUEZmZmOXtZpztgZmY2Vi5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2pkwxk7RP0i8ljUh6QtI/SDqxNP0mSUckvaYUOze1nV2KHS9pt6Q/b2Kd09P6bqvTn+fLy07xBySFpAWStqT5RyS9kNpXnt8g6ThJX03LCkn9bb9ANqoezZ9lkrZJOiTpZ5K+ImlO+6+S1dKjubNY0n2SnkrDtyQtbv9VGgcRMSUGYB/wljQ+F3gIuCY9nw48C/wc+FDVfDcCG0vPrwa+DaiJdQ6kZR4B5tTozyPAX5Rip6dYAAuq2t8EfLwqdhzwfuAPgQNAf6df514dejR/zgcuAk4CfgdYB/y/Tr/WvTb0aO7MABYAAqYB7wV2dPJ1njLfzMoi4qfAFuC0FHob8DTwMYokKPtL4I8lvVXSacCVwJ9FekcbGABuAHYAl9aYfjOwqqr9hha24/mI+L8RcSfwYrPz2dj0UP5siYivRMQzEfEL4LPAm5qd31rXQ7nzdETsS30Rxf7ndc3OPxGmZDGTNB+4APhBCg0AXwJuAX5f0pmVthFxGLicIjHWAR+NiL1NrON3gX5gYxpW1Wh2F3CSpFMlTQPeAfxjm5tlk6SH8+ePgF1jmN8a6LXckfQ08CvgeuCTrc4/nqZaMfvn9OLfCXwH+GR64/8E+KeIeAK4napPSBHxTYo3/2XAdU2uaxXF1+6HKZL19ZLOqNGu8gnpXOCHwE9b3iqbLD2bP5L+M/C/gQ+1M7811JO5ExEzgFdSfGv8QYPmE+qYTq68Ay6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJGr1G34zcAewkBa+5ltH9GT+SHodxaGv90XEd9tZhjXUk7mT1vGcpBuAn0k6NSIOtrussZhq38xqWQWcIulxSY8DnwZmU5wcb4ukPwAWAR8uLfds4BJJR32AiIgfAz+iOPTw9XbXaR2Tdf5Iei3wLeDqiLi53T5bW7LOnSovo7iIaO4Yl9O2qfbN7CiS3gj8HnAG8LPSpGspPslsbnPRA8A2jj5WfQLFydjzgW9Wtb8MmJk+4bT0nkg6nuIELMBxkl5O8SnOP1Q3wXLPH0lzKa6O+1xE3NBmX60NPZA75wJPpuVOBz4OPAXsbrPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAlMx+VNgVUQ8XjXt5rTOoxKqmZO6o3gEeG0a35oeF1JcfmsTK/f8+Z/AKcBVkq4qLe/E+rPYOMk9d2ZQXPQxD/glcC+wIiJ+1ebyxkz+AG9mZrnzOTMzM8uei1mbJF1aur1LefD/6VhDzh9rl3OnNh9mNDOz7GV7Acjs2bNjwYIFADz33HNMnz69sx1qUTf2+f77738yIl7d6X5MtHLuQHe+F83opn47d7pfN/d1XPKn0c0bgZcD9wAPUvzz3kdTfCFwN7AH+DJwXIofn54PpekLSsv6cIo/ApxXiq9IsSFgTTM3lVyyZElUbN++PXLTjX0G7ovxvcFq1+dORHe+F83opn6Pd+5El+ZPzrnTzX0dj/xp5pzZr4E3R8R/Ad4ArJC0DPgU8HcRsYji/wsuS+0vA56KiNcBf5fakX4e4GLg9SmB/l7StHRfsM9R/A/EYop/7uvsTwnYeHHu2Fg4f6xpDYtZKpwj6emxaQjgzcBXU3w9cGEaX5mek6afI0kpfktE/DoifkTxSeisNAxFxKMR8TzFDTdXjnnLrOOcOzYWzh9rRVPnzNInmPspbvH/OWAv8HREHElNhvntbUzmAo8BRMQRSYeBV6X4XaXFlud5rCp+dp1+rAZWA/T19TE4OAjAyMjIb8ZzkWOf29EtuWN56ob8qbffgbz+jnPqazuaKmYR8SLwBkkzgG8Ap9Zqlh5VZ1q9eK1vhzUvsYyItcBagKVLl0Z/fz8A12/cxLV3Ple3//uueWvdaZ0yODhIpf+9rFtyZ7Qd0sFDh7l+46Y6WwCnz31l3Wmd1Os7J+iO/Km334G89j29vs9p6WrGiHha0iCwDJgh6Zj0CWkesD81GwbmA8PpXl+vBA6V4hXleerFrUd0Onca7pB21v9T2Hdpf91pndTrO6eyTuePdb+G58wkvTp9KkLSCcBbKG4muR14e2o2AFQ+2m7mt7/J83bg2+lqlc3AxZKOl7SQ4s7O91Dc02uRpIWSjqM4UdvuTTatizh3bCycP9aKZr6ZzQHWp2PXLwNujYh/kfQwcIukj1P8Ts6Nqf2NwM2Shig+FV0MEBG7JN0KPAwcAa5IhxCQdCXFTXKnAesiYkr/J3sPce7YWDh/rGkNi1lE7KD4mYLq+KMUVwNVx38FXFRnWZ8APlEjfhtwWxP9tYw4d2wsnD/WCt+b0czMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsNi5mk+ZK2S9otaZek96X4LEnbJO1JjzNTXJKukzQkaYekM0vLGkjt90gaKMWXSNqZ5rlOkiZiY80sH973WCua+WZ2BPhARJwKLAOukLQYWAPcHhGLgNvTc4DzgUVpWA18HooEBK4CzgbOAq6qJGFqs7o034qxb5p1mndGNkbe91jTGhaziDgQEd9P488Cu4G5wEpgfWq2Hrgwja8ENkThLmCGpDnAecC2iDgUEU8B24AVadpJEfG9iAhgQ2lZljfvjKxt3vdYK1o6ZyZpAXAGcDfQFxEHoEg64OTUbC7wWGm24RQbLT5cI26Z887Ixov3PdbIMc02lHQi8DXg/RHxzChHc2pNiDbitfqwmuJTOH19fQwODgLQdwJ84PQjdfteaddNRkZGurJfE2W0nZGkCd8Z1csdyDN/YOrkUKf3Pb2SO72eL00VM0nHUiTTxoj4ego/IWlO2hnNAQ6m+DAwvzT7PGB/ivdXxQdTfF6N9i8REWuBtQBLly6N/v5icddv3MS1O+tvyr5L++tO65TBwUEq/e91nd4ZQf3cgTzzB6ZGDnXDvqdXcqfX86WZqxkF3AjsjohPlyZtBion4geATaX4qnQyfxlwOH0K3woslzQzne9YDmxN056VtCyta1VpWZa50XZGaXqzO6N68aY+CFl+vO+xVjRzzuxNwDuBN0t6IA0XANcA50raA5ybngPcBjwKDAFfAN4DEBGHgKuBe9PwsRQDuBz4YppnL7BlHLbNOsw7Ixsj73usaQ0PM0bEndQ+nANwTo32AVxRZ1nrgHU14vcBpzXqi2WnsjPaKemBFPtrip3PrZIuA34CXJSm3QZcQLFj+QXwLih2RpIqOyN46c7oJuAEih2Rd0Y9wvsea0XTF4CYtco7IzObLL6dlZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfYaFjNJ6yQdlPRQKTZL0jZJe9LjzBSXpOskDUnaIenM0jwDqf0eSQOl+BJJO9M810nSeG+kdY7zx9rl3LFWNPPN7CZgRVVsDXB7RCwCbk/PAc4HFqVhNfB5KBIQuAo4GzgLuKqShKnN6tJ81euyvN2E88facxPOHWtSw2IWEXcAh6rCK4H1aXw9cGEpviEKdwEzJM0BzgO2RcShiHgK2AasSNNOiojvRUQAG0rLsh7g/LF2OXesFce0OV9fRBwAiIgDkk5O8bnAY6V2wyk2Wny4RrwmSaspPknR19fH4OBg0ZkT4AOnH6nb2Uq7bjIyMtKV/Zokk54/9XIH8swfmLI55NxpU6/nS7vFrJ5ax5yjjXhNEbEWWAuwdOnS6O/vB+D6jZu4dmf9Tdl3aX/daZ0yODhIpf/2GxOWP/VyB/LMH3AOVXHuNNDr+dLu1YxPpK/ppMeDKT4MzC+1mwfsbxCfVyNuvc35Y+1y7lhN7RazzUDlqqABYFMpvipdWbQMOJwOCWwFlkuamU6+Lge2pmnPSlqWriRaVVqW9S7nj7XLuWM1NTzMKOlLQD8wW9IwxZVB1wC3SroM+AlwUWp+G3ABMAT8AngXQEQcknQ1cG9q97GIqJzYvZziqqUTgC1psB7h/LF2OXesFQ2LWURcUmfSOTXaBnBFneWsA9bViN8HnNaoH5Yn54+1y7ljrfAdQMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLXNcVM0gpJj0gakrSm0/2xfDh3bCycP72hK4qZpGnA54DzgcXAJZIWd7ZXlgPnjo2F86d3dEUxA84ChiLi0Yh4HrgFWNnhPlkenDs2Fs6fHnFMpzuQzAUeKz0fBs6ubiRpNbA6PR2R9Eganw08WW/h+tQ49XJ8jdrnDnltpzvQhrHmDuSZP9BdOZRj7kAT+dNDudNN+VJtzPnTLcVMNWLxkkDEWmDtS2aW7ouIpRPRsYmSY5+71JhyB/J9L3Ltd5dpmD+9kjs59bUd3XKYcRiYX3o+D9jfob5YXpw7NhbOnx7RLcXsXmCRpIWSjgMuBjZ3uE+WB+eOjYXzp0d0xWHGiDgi6UpgKzANWBcRu1pYRM1DAF0uxz53nXHIHcj3vci1311jiu17cupryxTxktMLZmZmWemWw4xmZmZtczEzM7PsZV/MOnErGkn7JO2U9ICk+1JslqRtkvakx5kpLknXpf7tkHRmaTkDqf0eSQOl+JK0/KE0r0Zbh7VnMnNH0jpJByU9VIp1LGdGW4c1Z5Lzx/ucRiIi24HihO1e4BTgOOBBYPEkrHcfMLsq9rfAmjS+BvhUGr8A2ELx/yzLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHR66P3eAPwLOBB7qhpyptw4PXZs/3uc0GHL/ZtZNt6JZCaxP4+uBC0vxDVG4C5ghaQ5wHrAtIg5FxFPANmBFmnZSRHwvigzaULWsWuuw1k1q7kTEHcChqnAnc6beOqw53bDv8T6nJPdiVutWNHMnYb0B/Juk+1Xc6gagLyIOAKTHkxv0cbT4cI34aOuw1nUqd8o6mTPdsP05m+zXz/ucBrri/8zGoKlbGU2AN0XEfkknA9sk/XCUtvX62Grcxlc3v86TkTPdvP05mOzXz/ucBnL/ZtaRW9FExP70eBD4BsUhhycqh2nS48EGfRwtPq9GnFHWYa3rhtsYdTJnumH7czapr5/3OY3lXswm/VY0kqZLekVlHFgOPJTWW7k6aADYlMY3A6vSFUbLgMPp6/pWYLmkmekKoeXA1jTtWUnL0hVFq6qWVWsd1rpuuI1RJ3Om3jqsOZOWP97nNKnTV6CMdaC4cuffKa4s+sgkrO8UiiuXHgR2VdYJvAq4HdiTHmeluCh+/G8vsBNYWlrWu4GhNLyrFF9Kkax7gc/y2zu11FyHh+7PHeBLwAHgBYpPwpd1MmdGW4eH7sof73OaG3w7KzMzy17uhxnNzMxczMzMLH8uZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyyN2WKmaR9kn4paUTSE5L+QdKJpek3SToi6TWl2Lmp7exS7HhJuyX9eRPrnJ7Wd1ud/jxfXnaKPyApJC2QtCXNPyLphdS+8vyGqvmuSvO9pdXXxhrrxfxJbaIUG5H0v9p/layWXsyd1P53JP29pCclHZZ0R7uv0bjo9A+qTeIP6e0D3pLG51L8EN016fl04Fng58CHqua7EdhYen418G3Sj9c1WOdAWuYRYE6N/jwC/EUpdnqKBbCgqv1NwMfrrOf3KH6Eb39lGz04fxrlD7AgtT2m069vLw+9mDsp/o/ALcCrgWnAkk6+zlPmm1lZRPwU2AKclkJvA54GPsZvfyK84i+BP5b0VkmnAVcCfxbp3WxgALgB2AFcWmP6zRQ/UV5uv6HZ7Sj5LPBXwPNtzGst6sH8sUnSK7kj6T8B/w1YHRE/i4gXI+L+ZuefCFOymEmaT/GT5z9IoQGKn7W/Bfh9SWdW2kbEYeByisRYB3w0IvY2sY7fBfqBjWlYVaPZXcBJkk6VNA14B8WnnVa25SLg+Yh4yeEEmxi9lD/JjyUNp8Nfsxs3t3b1UO6cDfwY+Gg6zLhT0ttamH/cTbVi9s+SngbuBL4DfDK98X8C/FNEPAHcTtUnpIj4JsWb/zLguibXtQrYEREPUyTr6yWdUaNd5RPSucAPgZ82uzHpuPsngfc3O4+NSU/lD/Ak8F+B1wJLgFdQ7Pxs/PVa7syj+HZ5GHgNxbfG9ZJObWEZ4+qYTq24Qy6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJOoPqtrdDNwBLKT1Q0QfBW6OiB+1OJ+1p6fyJyJGgPvS0yckXQkckHRSRDzTyrKsoZ7KHeCXwAsU59KOAN+RtB1YDuxucVnjYqp9M6tlFXCKpMclPQ58GpgNnN/uAiX9AbAI+HBpuWcDl0g66gNERPwY+BHFoYevt7iqc4D3ltYxH7hV0l+123drWc75U61yLkZjXI41J+fc2dFuHyfKVPtmdhRJb6S4EvAM4GelSddSfJLZ3OaiB4BtHH2s+gSKBDgf+GZV+8uAmRHxXHXCNXAOcGzp+b0UJ423tNxja1nu+SPpbIqLD/YAMykOYw2mczU2gXLPHYpvdD+hKJr/h6Jg9gMfarPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAiW9HPhTYFVEPF417ea0zqMSqpmTurVExM+rlv8i8FQ6fGQTL+v8AU6hOOd6MvAMxU7wkjaXZa3JOnci4gVJK4EvAmsoLgZZFRE/bGd540HNXeVpZmbWvXzOzMzMsudi1iZJl+ro2wBVhl2d7pt1P+ePtcu5U5sPM5qZWfYaXgCSTireARyf2n81Iq6StJDiv9ZnAd8H3hkRz0s6nuJ/FpZQ3BvsHRGxLy3rwxRXz7wIvDcitqb4CuAzFPf3+mJEXNOoX7Nnz44FCxYA8NxzzzF9+vQWNrt3jOe233///U9GxKvHZWHkkTuQZ/50W5/HO3egO/OnF3KnExq9TuOSP41u3kjxPycnpvFjgbuBZcCtwMUpfgNweRp/D3BDGr8Y+HIaXww8SJGYC4G9FAk0LY2fAhyX2ixu1K8lS5ZExfbt22OqGs9tB+6L8b3Batfnzni/hpOl2/o83rkTXZo/vZA7ndDodRqP/Gl4ziytq3Kp97FpCODNwFdTfD1wYRpfmZ6Tpp8jSSl+S0T8Ooo7VgwBZ6VhKCIejYjnKT5xrWzUL+t+zh0bC+ePtaKp/zNLN6K8H3gd8DmKTzNPR3EbE4Bhip82ID0+BhARRyQdBl6V4neVFlue57Gq+Nktb4l1pW7JHUmrgdUAfX19DA4O/mbayMjIUc9zkGOf29EN+dNrudMJk/E6NVXMIuJF4A2SZgDfAGrdTHK0W+HEKPFa3w5rXpVSL6mmckJ1+7Z3S+5ExFpgLcDSpUujv7//N9MGBwcpP89Bjn1uRzfkT6/lTidMxuvU0h1AIuJpSYMUx61nSDomfUKaR/HDkFB8upkPDKfbo7wSOFSKV5TnqRevXn/NpLp+4yauvfO5uv3ed81bm97G3OTyx9Tp3BnNzp8e5n+s+de603s5f3LRrfnj3OkeDc+ZSXp1+lSEpBOAt1DcFXk78PbUbADYlMY389ufMXg78O10gm8zcLGKn/5eSHEzzHso7ie4SNJCScdRnLht975k1kWcOzYWzh9rRTPfzOZQ/E7NNIrid2tE/Iukh4FbJH2c4qcFbkztbwRuljRE8anoYoCI2CXpVuBhip/yviIdQkDFT09spbi6aF1ETOl//ushzh0bC+ePNa1hMYuIHRR3dq6OP0pxNVB1/FfARXWW9QngEzXitwH+peQe49yxsXD+WCt8OyszM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZjZhJE0X9J2Sbsl7ZL0vhSfJWmbpD3pcWaKS9J1koYk7ZB0ZmlZA6n9HkkDpfgSSTvTPNdJ0uRvqU0E54+1omExc0LZGBwBPhARpwLLgCskLQbWALdHxCLg9vQc4HxgURpWA5+HIteAq4CzgbOAqyr5ltqsLs23YhK2yyaH88ea1sw3MyeUtSUiDkTE99P4s8BuYC6wElifmq0HLkzjK4ENUbgLmCFpDnAesC0iDkXEU8A2YEWadlJEfC8iAthQWpZlzvljrWhYzJxQNh4kLQDOAO4G+iLiABT5BZycms0FHivNNpxio8WHa8Stxzh/rJFjWmk8WkJJmvCEkrSa4hscfX19DA4OAtB3Anzg9CN1+11p14tGRka6fvsknQh8DXh/RDwzylHkWhOijXitPtTMHcgzf3J438dLp/On13KnEyYjX5suZp1OKICIWAusBVi6dGn09/cDcP3GTVy7s/6m7Lu0v+603A0ODlJ5HbqRpGMp8mZjRHw9hZ+QNCd9CJoDHEzxYWB+afZ5wP4U76+KD6b4vBrtX6Je7kCe+dPt7/t46Yb86bXc6YTJyNemrmYcLaHS9GYTql68qR2S5SVdyHMjsDsiPl2atBmoXAA0AGwqxVeli4iWAYfTt/+twHJJM9N51uXA1jTtWUnL0rpWlZZlmXP+WCuauZrRCWXtehPwTuDNkh5IwwXANcC5kvYA56bnALcBjwJDwBeA9wBExCHgauDeNHwsxQAuB76Y5tkLbJmMDbNJ4fyxpjVzmLGSUDslPZBif02RQLdKugz4CXBRmnYbcAFFcvwCeBcUCSWpklDw0oS6CTiBIpmcUD0gIu6k9mFkgHNqtA/gijrLWgesqxG/DzhtDN20LuX8sVY0LGZOKDMz63a+A4iZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2GhYzSeskHZT0UCk2S9I2SXvS48wUl6TrJA1J2iHpzNI8A6n9HkkDpfgSSTvTPNdJ0nhvpHWO88fa5dyxVjTzzewmYEVVbA1we0QsAm5PzwHOBxalYTXweSgSELgKOBs4C7iqkoSpzerSfNXrsrzdhPPH2nMTzh1rUsNiFhF3AIeqwiuB9Wl8PXBhKb4hCncBMyTNAc4DtkXEoYh4CtgGrEjTToqI70VEABtKy7Ie4Pyxdjl3rBXHtDlfX0QcAIiIA5JOTvG5wGOldsMpNlp8uEa8JkmrKT5J0dfXx+DgYNGZE+ADpx+p29lKu140MjKS4/ZNev7Uyx3IM38yfd/Hg3MnQ5ORr+0Ws3pqHXOONuI1RcRaYC3A0qVLo7+/H4DrN27i2p31N2Xfpf11p+VucHCQyuvQAyYsf+rlDuSZPz32vo8H504Xm4x8bfdqxifS13TS48EUHwbml9rNA/Y3iM+rEbfe5vyxdjl3rKZ2i9lmoHJV0ACwqRRfla4sWgYcTocEtgLLJc1MJ1+XA1vTtGclLUtXEq0qLct6l/PH2uXcsZoaHmaU9CWgH5gtaZjiyqBrgFslXQb8BLgoNb8NuAAYAn4BvAsgIg5Juhq4N7X7WERUTuxeTnHV0gnAljRYj3D+WLucO9aKhsUsIi6pM+mcGm0DuKLOctYB62rE7wNOa9QPy5Pzx9rl3LFW+A4gZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7HVNMZO0QtIjkoYkrel0fywfzh0bC+dPb+iKYiZpGvA54HxgMXCJpMWd7ZXlwLljY+H86R3HdLoDyVnAUEQ8CiDpFmAl8PB4LHzBmn8ddfq+a946HquxzpjQ3IHG+TMa51bX876nR3RLMZsLPFZ6PgycXd1I0mpgdXo6IumRND4beLLdletT7c7ZFca07VVeO07LmUxjzR0Y39fw6PVOXG5NWJ/blGPuQBP5M5G5k/m+pxWNXqcx50+3FDPViMVLAhFrgbUvmVm6LyKWTkTHut1U3vZkTLkDeb6GOfa5SzXMn17LnU6YjNepK86ZUXwaml96Pg/Y36G+WF6cOzYWzp8e0S3F7F5gkaSFko4DLgY2d7hPlgfnjo2F86dHdMVhxog4IulKYCswDVgXEbtaWETNQwBTxFTe9vHIHcjzNcyxz13H+55JM+GvkyJecnrBzMwsK91ymNHMzIT8XAIAAAPVSURBVKxtLmZmZpa97ItZzreikbRP0k5JD0i6L8VmSdomaU96nJniknRd2s4dks4sLWcgtd8jaaAUX5KWP5Tm1WjrmGo6kTuS5kvaLmm3pF2S3pfifyPppykXHpB0QWmeD6c+PiLpvEb9Txcz3J3e3y+nCxuQdHx6PpSmL5iMbe5VOe97WpHNfioish0oTtjuBU4BjgMeBBZ3ul8t9H8fMLsq9rfAmjS+BvhUGr8A2ELxfzHLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHVNp6FTuAHOAM9P4K4B/p7iN0t8AH6zRfnHq2/HAwtTnaaP1H7gVuDiN3wBcnsbfA9yQxi8Gvtzp9yHXIfd9T4vbmsV+KvdvZr+5FU1EPA9UbkWTs5XA+jS+HriwFN8QhbuAGZLmAOcB2yLiUEQ8BWwDVqRpJ0XE96LIhg1Vy6q1jqmkI7kTEQci4vtp/FlgN8VdKOpZCdwSEb+OiB8BQxR9r9n/9Kn2zcBX0/zVOVR5378KnFP5FGwt68V9Tyu6bj+VezGrdSua0XYM3SaAf5N0v4pb5gD0RcQBKHZ8wMkpXm9bR4sP14iPto6ppOO5kw7znQHcnUJXpkMz60qHVFp9318FPB0RR6riRy0rTT+c2lvrOp4/kyiL/VRX/J/ZGDR1K6Mu9qaI2C/pZGCbpB+O0rbetrYat0JHXx9JJwJfA94fEc9I+jxwderD1cC1wLtH6WetD6KN3nfnxPiZSq9lFvup3L+ZZX0rmojYnx4PAt+gOHTxRPrqTXo8mJrX29bR4vNqxBllHVNJx3JH0rEUhWxjRHwdICKeiIgXI+I/gC9Q5MJo/awXf5Li0M4xVfGjlpWmvxI4NL5bN2Vkve9pRS77qdyLWba3opE0XdIrKuPAcuAhiv5XrvQZADal8c3AqnS10DLgcPrqvRVYLmlmOjS1HNiapj0raVk6L7Kqalm11jGVdCR30ntxI7A7Ij5dis8pNfvvFLlA6tPF6UrEhcAiihPmNfufzjtsB96e5q/Oocr7/nbg26m9tS7bfU8rstpPdfpKmXG40uYCiivC9gIf6XR/Wuj3KRRXQD0I7Kr0neIcxu3AnvQ4K8VF8SOCe4GdwNLSst5NcWHAEPCuUnxpSry9wGf57R1faq5jqg2dyB3gDykOo+wAHkjDBcDN6X3dkf6I55Tm+Ujq4yOkK71G63/KrXtSPnwFOD7FX56eD6Xpp3T6Pch5yHXf0+I2ZrOf8u2szMwse7kfZjQzM3MxMzOz/LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZll7/8DgrC436upn5EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", " fig=plt.figure()\n", @@ -500,9 +1313,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", "df[\"EDUCATION\"].unique()" @@ -517,9 +1341,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", "df[\"MARRIAGE\"].unique()" @@ -543,7 +1378,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -556,17 +1391,190 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
.....................
29995111111
29996000000
29997-1-1-1-100
29998111-100
30000000000
\n", + "

26245 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0\n", + "... ... ... ... ... ... ...\n", + "29995 1 1 1 1 1 1\n", + "29996 0 0 0 0 0 0\n", + "29997 -1 -1 -1 -1 0 0\n", + "29998 1 1 1 -1 0 0\n", + "30000 0 0 0 0 0 0\n", + "\n", + "[26245 rows x 6 columns]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -585,9 +1593,310 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from scipy import stats\n", "#we are only concerned with the ordinal data\n", @@ -609,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -623,11 +1932,230 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.head()" ] @@ -658,7 +2186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +2195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -676,9 +2204,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", @@ -690,11 +2305,211 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#one hot encoding for S_0 to S_6\n", "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", @@ -718,21 +2533,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", - "df1.head()" + "df1.columns" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#check for perfect collinearity\n", "corr = df1.corr()\n", @@ -744,9 +2644,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], "source": [ "size = df1.shape\n", "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" @@ -763,7 +2671,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -773,7 +2681,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "colab": {}, "colab_type": "code", @@ -800,9 +2708,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 81.747784 3.335170e-04\n", + "PAY_0 4215.816943 0.000000e+00\n", + "PAY_2 3730.186349 0.000000e+00\n", + "PAY_3 3017.725262 0.000000e+00\n", + "PAY_4 3020.555618 0.000000e+00\n", + "PAY_5 2812.887854 0.000000e+00\n", + "PAY_6 2575.321192 0.000000e+00\n", + "PAY_0_No_Transactions 63.305610 2.349730e-02\n", + "PAY_0_Revolving_Credit 454.630595 0.000000e+00\n", + "PAY_2_Pay_Duly 76.591060 1.225866e-03\n", + "PAY_2_Revolving_Credit 241.568619 0.000000e+00\n", + "PAY_3_Pay_Duly 88.958886 4.790331e-05\n", + "PAY_3_Revolving_Credit 140.257620 3.064993e-12\n", + "PAY_4_Pay_Duly 82.933933 2.446182e-04\n", + "PAY_4_Revolving_Credit 98.729838 2.851222e-06\n", + "PAY_5_Pay_Duly 67.720209 9.456393e-03\n", + "PAY_5_Revolving_Credit 70.202609 5.486203e-03\n", + "PAY_6_Revolving_Credit 73.167377 2.782902e-03\n" + ] + } + ], "source": [ "from sklearn.feature_selection import SelectKBest\n", "from sklearn.feature_selection import chi2\n", @@ -846,7 +2781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -880,7 +2815,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -911,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -946,7 +2881,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -955,9 +2890,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tree = DecisionTreeClassifier()\n", "tree.fit(X_train, y_train)" @@ -965,9 +2916,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13475\n", + " 1 1.00 1.00 1.00 4021\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, tree.predict(X_train)))" ] @@ -981,34 +2948,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (GINI) identified 828\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054151314
11192828
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5415 1314\n", + "1 1192 828" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.6666666666666666\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6729\n", + " 1 0.39 0.41 0.40 2020\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, tree.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1035,7 +3102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1045,18 +3112,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "randf.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13475\n", + " 1 1.00 1.00 1.00 4021\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, randf.predict(X_train)))" ] @@ -1070,34 +3170,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (Random Forest) identified 749\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06310419
11271749
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6310 419\n", + "1 1271 749" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.26\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6729\n", + " 1 0.64 0.37 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, randf.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1130,9 +3330,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", @@ -1141,9 +3361,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13475\n", + " 1 0.79 0.46 0.58 4021\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train, xgb.predict(X_train)))" ] @@ -1157,34 +3393,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 750\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06339390
11270750
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6339 390\n", + "1 1270 750" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.26114231320864134\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6729\n", + " 1 0.66 0.37 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, xgb.predict(X_test)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -1202,11 +3538,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree0.4099010.606997
1Random Forest0.3707920.768455
2Gradient Boosted0.3712870.778407
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree 0.409901 0.606997\n", + "1 Random Forest 0.370792 0.768455\n", + "2 Gradient Boosted 0.371287 0.778407" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "evaluation" ] @@ -1243,7 +3640,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1252,9 +3649,251 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445436\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737
Time: 19:35:44 Log-Likelihood: -7793.4
converged: True LL-Null: -9431.5
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611
SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039
AGE 0.0615 0.100 0.614 0.539 -0.135 0.258
PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770
PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369
PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115
PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107
PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256
PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616
BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002
BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099
BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978
BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686
BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134
BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007
PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562
PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252
PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019
PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487
PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470
PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116
GRAD 1.3893 0.230 6.030 0.000 0.938 1.841
UNI 1.3690 0.229 5.975 0.000 0.920 1.818
HS 1.3654 0.233 5.863 0.000 0.909 1.822
MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328
SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215
PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274
PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452
PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419
PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989
PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955
PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543
PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027
PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081
PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069
PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007
PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214
PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146
PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334
PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270
PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379
PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117
PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062
PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737\n", + "Time: 19:35:44 Log-Likelihood: -7793.4\n", + "converged: True LL-Null: -9431.5\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", + "SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", + "AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", + "PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", + "PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", + "PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", + "PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", + "PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", + "PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", + "BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", + "BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", + "BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", + "BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", + "BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", + "BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", + "PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", + "PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", + "PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", + "PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", + "PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", + "PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", + "GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", + "UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", + "HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", + "MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", + "SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", + "PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", + "PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", + "PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", + "PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", + "PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", + "PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", + "PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", + "PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", + "PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", + "PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", + "PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", + "PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", + "PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", + "PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", + "PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", + "PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", + "PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", + "PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "glm = sm.Logit(y_train,X_train).fit()\n", "glm.summary()" @@ -1262,18 +3901,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13475\n", + " 1 0.67 0.35 0.46 4021\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.67 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.69 0.35 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" ] @@ -1287,9 +3958,171 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.446914\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17472
Method: MLE Df Model: 23
Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709
Time: 19:35:44 Log-Likelihood: -7819.2
converged: True LL-Null: -9431.5
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581
SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041
PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653
PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487
PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260
BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108
BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299
BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293
PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770
PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593
PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428
GRAD 1.2429 0.179 6.961 0.000 0.893 1.593
UNI 1.2478 0.177 7.052 0.000 0.901 1.595
HS 1.2675 0.181 7.014 0.000 0.913 1.622
PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690
PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284
PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147
PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735
PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038
PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115
PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191
PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030
PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356
PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17472\n", + "Method: MLE Df Model: 23\n", + "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709\n", + "Time: 19:35:44 Log-Likelihood: -7819.2\n", + "converged: True LL-Null: -9431.5\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", + "SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", + "PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", + "PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", + "PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", + "BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", + "BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", + "BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", + "PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", + "PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", + "PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", + "GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", + "UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", + "HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", + "PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", + "PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", + "PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", + "PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", + "PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", + "PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", + "PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", + "PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", + "PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", + "PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#remove all insignificant attributes\n", "sig = glm.pvalues[glm.pvalues < 0.05]\n", @@ -1299,18 +4132,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13475\n", + " 1 0.66 0.36 0.46 4021\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.67 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.35 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" ] @@ -1326,9 +4191,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.22554888390620112\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7698058845975142" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" ] @@ -1342,11 +4237,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.86 0.86 6729\n", + " 1 0.54 0.55 0.54 2020\n", + "\n", + " accuracy 0.79 8749\n", + " macro avg 0.70 0.70 0.70 8749\n", + "weighted avg 0.79 0.79 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" ] @@ -1365,18 +4276,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Logistic Regression identified 1104\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
05790939
19161104
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5790 939\n", + "1 916 1104" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the Logistic Regression identified 716\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06391338
11304716
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6391 338\n", + "1 1304 716" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" ] @@ -1390,16 +4429,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21841387811546378\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1438,7 +4497,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -1452,9 +4511,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variation per principal component: [0.2966725486 0.1736524263]\n" + ] + } + ], "source": [ "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" @@ -1469,9 +4536,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#visualizing pca\n", "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", @@ -1509,7 +4589,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -1519,18 +4599,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pca.fit(X_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of components before pca: 44\n", + "No. of components after pca: 13\n" + ] + } + ], "source": [ "#get number of components after pca\n", "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", @@ -1546,7 +4647,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -1573,9 +4674,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn import svm\n", "#train svm model without standardization and no parameter tuning\n", @@ -1585,9 +4700,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1660749149902864\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7220704605012441" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#plot roc for svm\n", "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" @@ -1595,9 +4740,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM original data RBF kernel identified 733\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06391338
11287733
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6391 338\n", + "1 1287 733" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" @@ -1605,9 +4814,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.36 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_original.predict(X_test)))" ] @@ -1628,9 +4853,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and no parameter tuning\n", "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", @@ -1639,9 +4878,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16374485135727915\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7040433457077317" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#plot roc for svm\n", "get_roc(clf_reduced, y_test, X_test_pca, \n", @@ -1650,9 +4919,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM reduced features no tuning RBF kernal identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06387342
11282738
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6387 342\n", + "1 1282 738" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" @@ -1660,9 +4993,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.37 0.48 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" ] @@ -1683,9 +5032,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 1, 'gamma': 0.1}" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -1708,9 +5068,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", @@ -1719,9 +5093,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16537143473786733\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", " \"SVM reduced features and tuning RBF kernal\")" @@ -1729,9 +5123,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2020 Defaulters, the SVM reduced features and tuning RBF kernal identified 727\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06383346
11293727
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6383 346\n", + "1 1293 727" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" @@ -1739,9 +5197,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6729\n", + " 1 0.68 0.36 0.47 2020\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" ] @@ -1762,9 +5236,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"recall\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m auroc])\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 573\u001b[0m \"\"\"\n\u001b[1;32m--> 574\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 575\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \"\"\"\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[0;32m 473\u001b[0m \u001b[1;34m\"the number of features at training time\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" + ] + } + ], "source": [ "evaluation.loc[4] = ([\"SVM\" , \n", " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", From c8eb09cf9cc56bae916ddbe06ac390e78a680f3e Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Tue, 19 Nov 2019 22:57:51 +0800 Subject: [PATCH 10/25] edited svm explanation --- ...2101_Default - EDIT-Copy1-checkpoint.ipynb | 1437 ++++++++++++----- BT2101_Default - EDIT-Copy1.ipynb | 1437 ++++++++++++----- 2 files changed, 2028 insertions(+), 846 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb index fcf3597..dbd858a 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAapElEQVR4nO3deZxcZZ3v8c+XgALjAg6LCEhQo4iMsgTEcQNRBBwFRhz16oBeFHVwvDqLouOIG3fQGZfBBQcxF3BUFBFFQTGgiNcrSFBkEZWIKDEBoiyyKYu/+0c9LWWnOl056Uqn6c/79apXn/Oc55zzO51Kffs8p+pUqgpJkrpYZ7oLkCTNXIaIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEpNWU5NAkX27T6yepJFtNd10ASY5OcnybfnSSm6Zw2yckeWOb3ifJ4inc9jOT/HCqtqfRMUTUWZJb+x5/SHJH3/xL1nAtq/3ineTaJE/pm98uyd2TrVdVn6iq53bd77gazk/y0qnY1nhV9dOq2miIGl6d5Owhtveyqnrv6tY16N+uqs6uqies7rY1eutOdwGauarqAWPTSa4GXlFVk774DJJk3aqa9AV7bTNT615dSeZU1T3TXYemn2ciGpkkT05yQZKbkyxN8oEk67ZlY399vibJz4DLWvtzklyZ5KYkHxz/l3mSVyX5SZIbkpyRZMu26Lz28yftTOiAAfVsl+Tctu7yJCcmeWBbdgqwGfD1tv7r2jbn9J1d7dT+Sv9Gko8kuRE4YoK/3A9IcnXbz1FJ0vbzx+GlvprubtPvA3YFjm/7e19r36Ht88YkVww6tr7tPSrJd5LckuSrwMaD9tXmX9lqvCXJVUlekGQn4IPAHq2Ga1vfk5Mck+TrSW4DntTa3jpu/+9ov9+rkrygr338v2P/72yFf7vxw2NJ/iLJt9vz4pIk+/YtO7k9V85qx/KdJNtM9DvSFKsqHz5W+wFcDTxzXNtu9F4U5wCPBBYDr27L1gcKOAPYCNgAeChwK/BXwHrAG4G7gJe2dV4EXAE8ui1/N/DNcdvbaiU1bgc8A7hf29f5wNF9y68FnjKu/93jtvFq4G7gle24NmhtZ4+r46x2XNsCV/Udw9HA8RPto9X00r75BwHLgJe0/e0K3AA8aoJj/D7wb+0Y9wJuH9tf/77ohctNwCPb/JbAY/uO8exx2z257feJ9P74vH9re2tbvk/7vYzt+5lt39tOcFyDfmdb9S3fB1jct/wXwD+2f/dnt+fJtn21XQ/s3JZ/Hjhhuv9PzJaHZyIamar6XlVdWFX3VNXPgOOBp4/rdlRV3VRVdwDPAy6sqq9U1V3AfwA39vV9FfDu6o3t3wW8A3hKks2HrOfHVfWNqrqzqq6l9xf3+HqGcVVVfbwd1x0T9Pm3dlw/Bz4MvLjDfgAOBC6rqk+1/V0IfBl4/viOSR4NbA+8ox3jOcDXJtn+DknWr6pfVdUVk/T9fFVdUFV/qKrfD1h+d9++zwbOBg6a7ACH8NT28/1VdVdVnQUsBF7Y1+dzVfX99rz4NLDjFOxXQzBENDJJtk/y1STXJfkt8DZgk3Hdrumbflj/fFX9AfhV3/JtgI+1IY2bgOX0XriGupie5GFJTknyq1bP8QPqGcY1k3f5kz6/oHdsXWwDPG3smNtxPx/YYkDfhwHLq+p34/a9gqq6kd7ZzeuAa5OcnuRRk9Qy2XEP2nfX4+73MOCXVdV/t9hf0Dt7GnNt3/TtwAPQGmGIaJQ+Tm945ZFV9SDgnUDG9el/YVhGXyAkWYc/faG4BnhZVW3U99igqi4at52J/DtwG7BDq+cV4+oZv42JtjnMvrbum344sLRN3wZs2LfsoZNs+xrg6+OO+QFV9foB+1wGbJJk/XH7HqiqzqiqvWgv0sCxE9QwUW3jDdr3MMc92XaXsuJxPJw//QND08QQ0Sg9ELi5qm5N8jh61xFW5nTgiUn2axfg/4G+C8PAx4C3JnkMQJKNkzwfoA2v3Aw8YpJ6bgV+m+Thbfv9rhu3/vX0LqxP+EK8Em9K8uAkc4HXAp9t7RcDeybZMsnGwJsmqeGLwE5JXphkvST3S7J7G7oa76fAj4F/bf32pHdtYQVt/89JsiHwe3q/l7F3W10HbJ1kvVU85vX69v0M4FnAqX3HfVB6b6jYDnjZ2EpD/Nt9G1gnyeuTrJvkWcDewCmrWJ9GwBDRKL0BeEWSW4GPcO8L6UBVtYzetYNjgF/TOyu5lN6LHFX1GXrXF77QhqMupvdCNeZtwClt2Od5A3bxNuAp9F6wTuPeF7gxRwFHtfVf24Z83gtc1NpWZZz9DOCHwCJ6L3b/3df+FeBH9C42f3Hceh8ADm7vxHpvq+HZwMvpnWkspfeGghVe4NtwzwuBPeldBH9j337HmwO8md4w0G/oXbD/+7bsa/TeKHF9kiWrcMxX0xtevBZYALy8qq5qy95L7yMFy4HjBtQ14b9dGyL7K3rXV34DvB94YbvOpmmWPx1mlNYe7WzkWuC5VfXd6a5H0oo8E9FaJcm+bRhofeBIehdJL5rmsiRNwBDR2uZpwM/pXY/YCziwqu6c3pIkTcThLElSZ56JSJI6m3U3YNxkk01q7ty5012GJM0oF1100a+ratPx7bMuRObOncuiRYumuwxJmlGSDLz7gcNZkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOZt0n1meKuUecMd0l3GdcffRzprsE6T7LMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmcjC5EkWyf5ZpIrklye5H+19ockWZjkyvZz49aeJMckWZzkkiQ7923rkNb/yiSH9LXvkuTSts4xSTKq45EkrWiUZyJ3A/9YVY8FdgcOT7I9cARwTlXNA85p8wD7AvPa4zDgWOiFDnAk8ERgN+DIseBpfQ7rW2+fER6PJGmckYVIVS2rqu+36VuAK4Atgf2BE1u3E4ED2vT+wEnVcz6wUZItgGcDC6vqhqq6EVgI7NOWPaiqvltVBZzUty1J0hqwRq6JJJkL7ARcAGxeVcugFzTAZq3blsA1fastaW0ra18yoF2StIaMPESSPAA4FXh9Vf12ZV0HtFWH9kE1HJZkUZJFy5cvn6xkSdKQRhoiSdajFyCfqqovtObr2lAU7ef1rX0JsHXf6lsBSydp32pA+wqq6riqml9V8zfddNPVOyhJ0h+N8t1ZAT4BXFFV7+9bdDow9g6rQ4Av9bUf3N6ltTtwcxvuOgvYO8nG7YL63sBZbdktSXZv+zq4b1uSpDVg3RFu+8nA3wKXJrm4tb0FOBr4XJJDgV8CL2jLzgT2AxYDtwMvB6iqG5K8C7iw9XtnVd3Qpl8DnABsAHy1PSRJa8jIQqSq/i+Dr1sA7DWgfwGHT7CtBcCCAe2LgB1Wo0xJ0mrwE+uSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJgiTXJ7msr+3tSX6V5OL22K9v2ZuTLE7ykyTP7mvfp7UtTnJEX/u2SS5IcmWSzya536iORZI02CjPRE4A9hnQ/oGq2rE9zgRIsj3wIuBxbZ2PJpmTZA7wEWBfYHvgxa0vwHvatuYBNwKHjvBYJEkDjCxEquo84IYhu+8PnFxVv6+qnwOLgd3aY3FVXVVVdwInA/snCfAM4PNt/ROBA6b0ACRJkxoqRJLsMIX7fG2SS9pw18atbUvgmr4+S1rbRO1/DtxUVXePax8oyWFJFiVZtHz58qk6Dkma9YY9E/lYku8l+bskG63G/o4FHgnsCCwD3tfaM6BvdWgfqKqOq6r5VTV/0003XbWKJUkTGipEquopwEuArYFFST6d5FmrurOquq6q7qmqPwAfpzdcBb0zia37um4FLF1J+6+BjZKsO65dkrQGDX1NpKquBN4KvAl4OnBMkh8n+etht5Fki77ZA4Gxd26dDrwoyf2TbAvMA74HXAjMa+/Euh+9i++nV1UB3wQOausfAnxp2DokSVNj3cm7QJLHAy8HngMsBJ5bVd9P8jDgu8AXBqzzGWAPYJMkS4AjgT2S7Ehv6Olq4FUAVXV5ks8BPwLuBg6vqnvadl4LnAXMARZU1eVtF28CTk7ybuAHwCdW+eglSatlqBABPkxv+OktVXXHWGNVLU3y1kErVNWLBzRP+EJfVUcBRw1oPxM4c0D7Vdw7HCZJmgbDhsh+wB19ZwfrAOtX1e1V9cmRVSdJWqsNe03kbGCDvvkNW5skaRYbNkTWr6pbx2ba9IajKUmSNFMMGyK3Jdl5bCbJLsAdK+kvSZoFhr0m8nrglCRjn8XYAnjhaEqSJM0UQ4VIVV2YZDvgMfQ+Lf7jqrprpJVJktZ6w56JAOwKzG3r7JSEqjppJFVJkmaEYT9s+El697y6GLinNRdgiEjSLDbsmch8YPt2uxFJkoDh3511GfDQURYiSZp5hj0T2QT4UZLvAb8fa6yq542kKknSjDBsiLx9lEVIkmamYd/i+60k2wDzqursJBvSu6uuJGkWG/brcV9J7/vM/6s1bQl8cVRFSZJmhmEvrB8OPBn4LfzxC6o2G1VRkqSZYdgQ+X1V3Tk2076W1rf7StIsN2yIfCvJW4AN2nernwJ8eXRlSZJmgmFD5AhgOXApva+0PZPe961LkmaxYd+d9Qd6X4/78dGWI0maSYa9d9bPGXANpKoeMeUVSZJmjFW5d9aY9YEXAA+Z+nIkSTPJUNdEquo3fY9fVdUHgWeMuDZJ0lpu2OGsnftm16F3ZvLAkVQkSZoxhh3Oel/f9N3A1cDfTHk1kqQZZdh3Z+056kIkSTPPsMNZ/7Cy5VX1/qkpR5I0k6zKu7N2BU5v888FzgOuGUVRkqSZYVW+lGrnqroFIMnbgVOq6hWjKkyStPYb9rYnDwfu7Ju/E5g75dVIkmaUYc9EPgl8L8lp9D65fiBw0siqkiTNCMO+O+uoJF8FntqaXl5VPxhdWZKkmWDY4SyADYHfVtV/AkuSbDuimiRJM8SwX497JPAm4M2taT3gv0dVlCRpZhj2TORA4HnAbQBVtRRveyJJs96wIXJnVRXtdvBJ/mx0JUmSZophQ+RzSf4L2CjJK4GzmeQLqpIsSHJ9ksv62h6SZGGSK9vPjVt7khyTZHGSS/pv+JjkkNb/yiSH9LXvkuTSts4xSbIqBy5JWn3D3gr+P4DPA6cCjwHeVlUfmmS1E4B9xrUdAZxTVfOAc9o8wL7AvPY4DDgWeqEDHAk8EdgNOHIseFqfw/rWG78vSdKITfoW3yRzgLOq6pnAwmE3XFXnJZk7rnl/YI82fSJwLr0L9vsDJ7Uhs/OTbJRki9Z3YVXd0GpZCOyT5FzgQVX13dZ+EnAA8NVh65Mkrb5Jz0Sq6h7g9iQPnoL9bV5Vy9p2lwGbtfYt+dP7cC1pbStrXzKgfaAkhyVZlGTR8uXLV/sgJEk9w35i/XfApe1M4Laxxqp63RTVMeh6RnVoH6iqjgOOA5g/f/6E/SRJq2bYEDmjPVbXdUm2qKplbbjq+ta+BNi6r99WwNLWvse49nNb+1YD+kuS1qCVhkiSh1fVL6vqxCna3+nAIcDR7eeX+tpfm+RkehfRb25Bcxbwv/supu8NvLmqbkhyS5LdgQuAg4HJLvRLkqbYZNdEvjg2keTUVdlwks8A3wUek2RJkkPphcezklwJPKvNA5wJXAUspvfW4b8DaBfU3wVc2B7vHLvIDrwGOL6t8zO8qC5Ja9xkw1n91x4esSobrqoXT7BorwF9Czh8gu0sABYMaF8E7LAqNUmSptZkZyI1wbQkSZOeiTwhyW/pnZFs0KZp81VVDxppdZKktdpKQ6Sq5qypQiRJM8+qfJ+IJEl/whCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM7Wne4CJM0sc484Y7pLuE+5+ujnTHcJq8UzEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKmzaQmRJFcnuTTJxUkWtbaHJFmY5Mr2c+PWniTHJFmc5JIkO/dt55DW/8okh0zHsUjSbDadZyJ7VtWOVTW/zR8BnFNV84Bz2jzAvsC89jgMOBZ6oQMcCTwR2A04cix4JElrxto0nLU/cGKbPhE4oK/9pOo5H9goyRbAs4GFVXVDVd0ILAT2WdNFS9JsNl0hUsDXk1yU5LDWtnlVLQNoPzdr7VsC1/Stu6S1TdS+giSHJVmUZNHy5cun8DAkaXabrlvBP7mqlibZDFiY5Mcr6ZsBbbWS9hUbq44DjgOYP3/+wD6SpFU3LWciVbW0/bweOI3eNY3r2jAV7ef1rfsSYOu+1bcClq6kXZK0hqzxEEnyZ0keODYN7A1cBpwOjL3D6hDgS236dODg9i6t3YGb23DXWcDeSTZuF9T3bm2SpDVkOoazNgdOSzK2/09X1deSXAh8LsmhwC+BF7T+ZwL7AYuB24GXA1TVDUneBVzY+r2zqm5Yc4chSVrjIVJVVwFPGND+G2CvAe0FHD7BthYAC6a6RknScNamt/hKkmYYQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHU240MkyT5JfpJkcZIjprseSZpNZnSIJJkDfATYF9geeHGS7ae3KkmaPWZ0iAC7AYur6qqquhM4Gdh/mmuSpFlj3ekuYDVtCVzTN78EeOL4TkkOAw5rs7cm+ckaqG022AT49XQXMZm8Z7or0DTx+Tm1thnUONNDJAPaaoWGquOA40ZfzuySZFFVzZ/uOqRBfH6uGTN9OGsJsHXf/FbA0mmqRZJmnZkeIhcC85Jsm+R+wIuA06e5JkmaNWb0cFZV3Z3ktcBZwBxgQVVdPs1lzSYOEWpt5vNzDUjVCpcQJEkaykwfzpIkTSNDRJLUmSGiVZbkhCQHTdJnuyQXJ/lBkkd22Mfbk/xTm35Zkod1rVczX//zYYLlmya5oD3fntph+y9L8uE2fYB3vhieIaJROQD4UlXtVFU/W81tvQwwRLQyewE/bs+3b6/mtg6gdxslDcEQuQ9JMjfJFUk+nuTyJF9PskFbtmOS85NckuS0JBu39nOTvCfJ95L8dNBfcen5cJIfJTkD2Kxv2S5JvpXkoiRnJdkiyX7A64FXJPlm6/fF1ufydgeBsfVv7Zs+KMkJ4/Z9EDAf+FQ7s9lgKn9nWnsl+Zd2c9Wzgce0tkcm+Vp7Ln27nfHuCLwX2G/sOZLk2CSL2vPtHX3bvDrJJm16fpJzx+3zL4HnAf/etrXKZ9GzjSFy3zMP+EhVPQ64CXh+az8JeFNVPR64FDiyb511q2o3ei/8/e1jDqT3n/gvgFcCfwmQZD3gQ8BBVbULsAA4qqrOBD4GfKCq9mzb+J+tz3zgdUn+fJiDqarPA4uAl1TVjlV1xzDraWZLsgu9z33tBPw1sGtbdBzw9+259E/AR6vqYuBtwGf7niP/0j6t/njg6UkeP8x+q+r/0fus2T+3ba3uWfR93oz+nIgG+nn7TwVwETA3yYOBjarqW639ROCUvnW+0N9/wDafBnymqu4Blib5Rmt/DLADsDAJ9D6rs2yCul6X5MA2vTW9sPvNqhyYZpWnAqdV1e0ASU4H1qf3B8wp7fkGcP8J1v+bdsa7LrAFveGpS0Za8SxliNz3/L5v+h5gmOGfsXXuYeLnxKAPFAW4vKqetLKNJ9kDeCbwpKq6vQ0hrD9gu+sj3Wv8c24d4Kaq2nFlKyXZlt5Zyq5VdWMbIh17bt3NvSMwPt+mgMNZs0BV3Qzc2He942+Bb61klfHOA16UZE6SLYCxIaqfAJsmeRL0hreSPG7A+g8GbmwBsh2we9+y65I8Nsk69IbNBrkFeOAq1KuZ7zzgwHZ944HAc4HbgZ8neQH88VrdEwas+yDgNuDmJJvT+76hMVcDu7Tp5zOYz7dVYIjMHofQu1h4CbAj8M5VWPc04Ep611KOpQVQ+w6Xg4D3JPkhcDHtesk4XwPWbft+F3B+37IjgK8A32DiobATgI95YX32qKrvA5+l95w6FRh7x9VLgEPb8+1yBnx/UFX9EPhBW74A+E7f4ncA/5nk2/TOvAc5Gfjnrm9Pn2287YkkqTPPRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISKNSJKHJjk5yc/afcfOTPLoJJdNd23SVPET69IIpHdfjtOAE6vqRa1tR2DzaS1MmmKeiUijsSdwV1V9bKyh3dPsmrH5dtflbyf5fnuM3dhyiyTntQ9XXpbkqe1uASe0+UuTvGHNH5K0Is9EpNHYgd4NLVfmeuBZVfW7JPOAz9C7y/H/AM6qqqOSzAE2pHeXgS2rageAJBuNrnRpeIaINH3WAz7chrnuAR7d2i8EFrRb7X+xqi5OchXwiCQfAs4Avj4tFUvjOJwljcbl3Hujv4m8AbgOeAK9M5D7AVTVefRuv/8r4JNJDq6qG1u/c4HDgeNHU7a0agwRaTS+Adw/ySvHGpLsCmzT1+fBwLKq+gO9OyvPaf22Aa6vqo8DnwB2bt/Gt05VnQr8K7DzmjkMaeUczpJGoKqqfQnXB5McAfyO3m3IX9/X7aPAqe3W5t+kd/tygD3o3UX2LuBW4GBgS+D/tFvmA7x55AchDcG7+EqSOnM4S5LUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJn/x+T6PNo2DnX9gAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -821,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -857,326 +857,276 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", - " 26245.0\n", - " 0.284337\n", - " 0.237875\n", - " 0.0\n", - " 0.081633\n", - " 0.224490\n", - " 0.408163\n", - " 1.0\n", + " LIMIT_BAL\n", + " 30000.0\n", + " 167484.322667\n", + " 129747.661567\n", + " 10000.0\n", + " 50000.00\n", + " 140000.0\n", + " 240000.00\n", + " 1000000.0\n", " \n", " \n", - " AGE\n", - " 26245.0\n", - " 0.368595\n", - " 0.232422\n", - " 0.0\n", - " 0.184211\n", - " 0.342105\n", - " 0.526316\n", - " 1.0\n", + " AGE\n", + " 30000.0\n", + " 35.485500\n", + " 9.217904\n", + " 21.0\n", + " 28.00\n", + " 34.0\n", + " 41.00\n", + " 79.0\n", " \n", " \n", - " PAY_0\n", - " 26245.0\n", - " 0.372109\n", - " 0.765730\n", + " PAY_0\n", + " 30000.0\n", + " -0.016700\n", + " 1.123802\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_2\n", - " 26245.0\n", - " 0.337321\n", - " 0.814878\n", + " PAY_2\n", + " 30000.0\n", + " -0.133767\n", + " 1.197186\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_3\n", - " 26245.0\n", - " 0.324633\n", - " 0.811491\n", + " PAY_3\n", + " 30000.0\n", + " -0.166200\n", + " 1.196868\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_4\n", - " 26245.0\n", - " 0.278224\n", - " 0.786314\n", + " PAY_4\n", + " 30000.0\n", + " -0.220667\n", + " 1.169139\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_5\n", - " 26245.0\n", - " 0.238750\n", - " 0.743923\n", + " PAY_5\n", + " 30000.0\n", + " -0.266200\n", + " 1.133187\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_6\n", - " 26245.0\n", - " 0.243208\n", - " 0.740345\n", + " PAY_6\n", + " 30000.0\n", + " -0.291100\n", + " 1.149988\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " BILL_AMT1\n", - " 26245.0\n", - " 0.200168\n", - " 0.189380\n", - " 0.0\n", - " 0.057590\n", - " 0.122923\n", - " 0.265297\n", - " 1.0\n", + " BILL_AMT1\n", + " 30000.0\n", + " 51223.330900\n", + " 73635.860576\n", + " -165580.0\n", + " 3558.75\n", + " 22381.5\n", + " 67091.00\n", + " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", - " 26245.0\n", - " 0.254917\n", - " 0.174586\n", - " 0.0\n", - " 0.123671\n", - " 0.185749\n", - " 0.308929\n", - " 1.0\n", + " BILL_AMT2\n", + " 30000.0\n", + " 49179.075167\n", + " 71173.768783\n", + " -69777.0\n", + " 2984.75\n", + " 21200.0\n", + " 64006.25\n", + " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", - " 26245.0\n", - " 0.298159\n", - " 0.160977\n", - " 0.0\n", - " 0.177739\n", - " 0.237020\n", - " 0.351773\n", - " 1.0\n", + " BILL_AMT3\n", + " 30000.0\n", + " 47013.154800\n", + " 69349.387427\n", + " -157264.0\n", + " 2666.25\n", + " 20088.5\n", + " 60164.75\n", + " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", - " 26245.0\n", - " 0.317648\n", - " 0.156805\n", - " 0.0\n", - " 0.201836\n", - " 0.261194\n", - " 0.370615\n", - " 1.0\n", + " BILL_AMT4\n", + " 30000.0\n", + " 43262.948967\n", + " 64332.856134\n", + " -170000.0\n", + " 2326.75\n", + " 19052.0\n", + " 54506.00\n", + " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", - " 26245.0\n", - " 0.330072\n", - " 0.155422\n", - " 0.0\n", - " 0.217453\n", - " 0.274164\n", - " 0.374386\n", - " 1.0\n", + " BILL_AMT5\n", + " 30000.0\n", + " 40311.400967\n", + " 60797.155770\n", + " -81334.0\n", + " 1763.00\n", + " 18104.5\n", + " 50190.50\n", + " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", - " 26245.0\n", - " 0.427951\n", - " 0.133243\n", - " 0.0\n", - " 0.332699\n", - " 0.377760\n", - " 0.463563\n", - " 1.0\n", + " BILL_AMT6\n", + " 30000.0\n", + " 38871.760400\n", + " 59554.107537\n", + " -339603.0\n", + " 1256.00\n", + " 17071.0\n", + " 49198.25\n", + " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", - " 26245.0\n", - " 0.072779\n", - " 0.105966\n", + " PAY_AMT1\n", + " 30000.0\n", + " 5663.580500\n", + " 16563.280354\n", " 0.0\n", - " 0.015270\n", - " 0.041383\n", - " 0.088084\n", - " 1.0\n", + " 1000.00\n", + " 2100.0\n", + " 5006.00\n", + " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", - " 26245.0\n", - " 0.055133\n", - " 0.092316\n", + " PAY_AMT2\n", + " 30000.0\n", + " 5921.163500\n", + " 23040.870402\n", " 0.0\n", - " 0.009191\n", - " 0.030637\n", - " 0.061596\n", - " 1.0\n", + " 833.00\n", + " 2009.0\n", + " 5000.00\n", + " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", - " 26245.0\n", - " 0.061893\n", - " 0.105644\n", + " PAY_AMT3\n", + " 30000.0\n", + " 5225.681500\n", + " 17606.961470\n", " 0.0\n", - " 0.006251\n", - " 0.030227\n", - " 0.069435\n", - " 1.0\n", + " 390.00\n", + " 1800.0\n", + " 4505.00\n", + " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", - " 26245.0\n", - " 0.061708\n", - " 0.107040\n", + " PAY_AMT4\n", + " 30000.0\n", + " 4826.076867\n", + " 15666.159744\n", " 0.0\n", - " 0.003321\n", - " 0.026566\n", - " 0.069027\n", - " 1.0\n", + " 296.00\n", + " 1500.0\n", + " 4013.25\n", + " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", - " 26245.0\n", - " 0.062874\n", - " 0.107502\n", + " PAY_AMT5\n", + " 30000.0\n", + " 4799.387633\n", + " 15278.305679\n", " 0.0\n", - " 0.001855\n", - " 0.027558\n", - " 0.071046\n", - " 1.0\n", + " 252.50\n", + " 1500.0\n", + " 4031.50\n", + " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", - " 26245.0\n", - " 0.055339\n", - " 0.103357\n", + " PAY_AMT6\n", + " 30000.0\n", + " 5215.502567\n", + " 17777.465775\n", " 0.0\n", - " 0.000000\n", - " 0.022412\n", - " 0.060176\n", - " 1.0\n", - " \n", - " \n", - " S_0\n", - " 26245.0\n", - " -0.133587\n", - " 0.879876\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_2\n", - " 26245.0\n", - " -0.300438\n", - " 0.883472\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_3\n", - " 26245.0\n", - " -0.327300\n", - " 0.895264\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_4\n", - " 26245.0\n", - " -0.364412\n", - " 0.886115\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_5\n", - " 26245.0\n", - " -0.395999\n", - " 0.877789\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_6\n", - " 26245.0\n", - " -0.428158\n", - " 0.900723\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", + " 117.75\n", + " 1500.0\n", + " 4000.00\n", + " 528666.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count mean std min 25% 50% 75% max\n", - "LIMIT_BAL 26245.0 0.284337 0.237875 0.0 0.081633 0.224490 0.408163 1.0\n", - "AGE 26245.0 0.368595 0.232422 0.0 0.184211 0.342105 0.526316 1.0\n", - "PAY_0 26245.0 0.372109 0.765730 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_2 26245.0 0.337321 0.814878 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_3 26245.0 0.324633 0.811491 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_4 26245.0 0.278224 0.786314 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_5 26245.0 0.238750 0.743923 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_6 26245.0 0.243208 0.740345 0.0 0.000000 0.000000 0.000000 8.0\n", - "BILL_AMT1 26245.0 0.200168 0.189380 0.0 0.057590 0.122923 0.265297 1.0\n", - "BILL_AMT2 26245.0 0.254917 0.174586 0.0 0.123671 0.185749 0.308929 1.0\n", - "BILL_AMT3 26245.0 0.298159 0.160977 0.0 0.177739 0.237020 0.351773 1.0\n", - "BILL_AMT4 26245.0 0.317648 0.156805 0.0 0.201836 0.261194 0.370615 1.0\n", - "BILL_AMT5 26245.0 0.330072 0.155422 0.0 0.217453 0.274164 0.374386 1.0\n", - "BILL_AMT6 26245.0 0.427951 0.133243 0.0 0.332699 0.377760 0.463563 1.0\n", - "PAY_AMT1 26245.0 0.072779 0.105966 0.0 0.015270 0.041383 0.088084 1.0\n", - "PAY_AMT2 26245.0 0.055133 0.092316 0.0 0.009191 0.030637 0.061596 1.0\n", - "PAY_AMT3 26245.0 0.061893 0.105644 0.0 0.006251 0.030227 0.069435 1.0\n", - "PAY_AMT4 26245.0 0.061708 0.107040 0.0 0.003321 0.026566 0.069027 1.0\n", - "PAY_AMT5 26245.0 0.062874 0.107502 0.0 0.001855 0.027558 0.071046 1.0\n", - "PAY_AMT6 26245.0 0.055339 0.103357 0.0 0.000000 0.022412 0.060176 1.0\n", - "S_0 26245.0 -0.133587 0.879876 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_2 26245.0 -0.300438 0.883472 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_3 26245.0 -0.327300 0.895264 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_4 26245.0 -0.364412 0.886115 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_5 26245.0 -0.395999 0.877789 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_6 26245.0 -0.428158 0.900723 -2.0 -1.000000 0.000000 0.000000 1.0" + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " ] }, - "execution_count": 90, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1219,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1231,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1243,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1319,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1347,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1430,36 +1380,459 @@ " S_6\n", " \n", " \n", - " ID\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " ID\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 1\n", + " 1\n", + " 1\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 2\n", + " -1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 4\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 5\n", + " -1\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 6\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 7\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 8\n", + " 0\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 9\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 10\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 11\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 12\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " 1\n", + " \n", + " \n", + " 13\n", + " -1\n", + " 0\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 14\n", + " 1\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 15\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 16\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 17\n", + " 0\n", + " 0\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 18\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 19\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 20\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 21\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 22\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 23\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 24\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 25\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 26\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 27\n", + " 1\n", + " -2\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 28\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 30\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 29971\n", + " -1\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 29972\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29973\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 29974\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29975\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29976\n", + " 0\n", + " 0\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29977\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 29978\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29979\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29980\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29981\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " \n", - " \n", - " \n", " \n", + " 29982\n", + " 1\n", " 1\n", " 1\n", " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29983\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29984\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29985\n", + " -1\n", + " -1\n", + " -2\n", + " -1\n", " -1\n", " -1\n", + " \n", + " \n", + " 29986\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", " -2\n", " -2\n", " \n", " \n", - " 2\n", + " 29987\n", " -1\n", - " 1\n", + " -1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29988\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", - " 1\n", " \n", " \n", - " 3\n", + " 29989\n", " 0\n", " 0\n", " 0\n", @@ -1468,7 +1841,16 @@ " 0\n", " \n", " \n", - " 4\n", + " 29990\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -2\n", + " \n", + " \n", + " 29991\n", " 0\n", " 0\n", " 0\n", @@ -1477,25 +1859,34 @@ " 0\n", " \n", " \n", - " 5\n", - " -1\n", - " 0\n", - " -1\n", + " 29992\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 29993\n", " 0\n", " 0\n", " 0\n", + " -2\n", + " -2\n", + " -2\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 29994\n", + " 0\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", - " 29995\n", + " 29995\n", " 1\n", " 1\n", " 1\n", @@ -1504,7 +1895,7 @@ " 1\n", " \n", " \n", - " 29996\n", + " 29996\n", " 0\n", " 0\n", " 0\n", @@ -1513,7 +1904,7 @@ " 0\n", " \n", " \n", - " 29997\n", + " 29997\n", " -1\n", " -1\n", " -1\n", @@ -1522,7 +1913,7 @@ " 0\n", " \n", " \n", - " 29998\n", + " 29998\n", " 1\n", " 1\n", " 1\n", @@ -1531,7 +1922,16 @@ " 0\n", " \n", " \n", - " 30000\n", + " 29999\n", + " 1\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 30000\n", " 0\n", " 0\n", " 0\n", @@ -1541,7 +1941,7 @@ " \n", " \n", "\n", - "

26245 rows × 6 columns

\n", + "

30000 rows × 6 columns

\n", "" ], "text/plain": [ @@ -1552,17 +1952,67 @@ "3 0 0 0 0 0 0\n", "4 0 0 0 0 0 0\n", "5 -1 0 -1 0 0 0\n", + "6 0 0 0 0 0 0\n", + "7 0 0 0 0 0 0\n", + "8 0 -1 -1 0 0 -1\n", + "9 0 0 1 0 0 0\n", + "10 -2 -2 -2 -2 -1 -1\n", + "11 0 0 1 0 0 -1\n", + "12 -1 -1 -1 -1 -1 1\n", + "13 -1 0 -1 -1 -1 -1\n", + "14 1 1 1 0 0 1\n", + "15 0 0 0 0 0 0\n", + "16 1 1 0 0 0 0\n", + "17 0 0 1 1 1 1\n", + "18 0 0 0 -1 -1 -1\n", + "19 1 -2 -2 -2 -2 -2\n", + "20 1 -2 -2 -2 -2 -2\n", + "21 0 0 0 0 0 -1\n", + "22 -1 -1 -1 -1 -1 -1\n", + "23 1 0 0 1 1 1\n", + "24 -2 -2 -2 -2 -2 -2\n", + "25 0 0 0 -1 0 0\n", + "26 0 0 0 0 0 0\n", + "27 1 -2 -1 -1 -1 -1\n", + "28 0 0 0 0 0 0\n", + "29 -1 -1 -1 -1 -1 -1\n", + "30 0 0 0 0 0 0\n", "... ... ... ... ... ... ...\n", + "29971 -1 -1 -1 0 0 -1\n", + "29972 0 0 0 0 0 0\n", + "29973 0 0 0 0 0 -1\n", + "29974 1 -2 -2 -2 -2 -2\n", + "29975 1 1 1 1 0 0\n", + "29976 0 0 -1 -1 -2 -2\n", + "29977 1 1 1 1 1 1\n", + "29978 0 0 0 0 0 0\n", + "29979 0 0 0 0 0 0\n", + "29980 -2 -2 -2 -2 -2 -2\n", + "29981 0 0 0 0 0 0\n", + "29982 1 1 1 1 0 0\n", + "29983 0 0 0 0 0 0\n", + "29984 -2 -2 -2 -2 -2 -2\n", + "29985 -1 -1 -2 -1 -1 -1\n", + "29986 -2 -2 -2 -2 -2 -2\n", + "29987 -1 -1 -2 -2 -2 -2\n", + "29988 0 0 0 0 0 0\n", + "29989 0 0 0 0 0 0\n", + "29990 -1 -1 -1 -1 -1 -2\n", + "29991 0 0 0 0 0 0\n", + "29992 1 1 1 1 1 1\n", + "29993 0 0 0 -2 -2 -2\n", + "29994 0 -1 -1 0 0 0\n", "29995 1 1 1 1 1 1\n", "29996 0 0 0 0 0 0\n", "29997 -1 -1 -1 -1 0 0\n", "29998 1 1 1 -1 0 0\n", + "29999 1 -1 0 0 0 -1\n", "30000 0 0 0 0 0 0\n", "\n", - "[26245 rows x 6 columns]" + "[30000 rows x 6 columns]" ] }, - "execution_count": 94, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1642,7 +2092,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1666,7 +2116,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1690,7 +2140,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1714,7 +2164,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1738,7 +2188,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1762,7 +2212,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1786,7 +2236,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1810,7 +2260,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -2007,7 +2457,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -2031,7 +2481,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -2055,7 +2505,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -2079,7 +2529,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -2103,7 +2553,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2237,7 +2687,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2245,7 +2695,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2253,7 +2703,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2261,7 +2711,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2269,7 +2719,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2353,7 +2803,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2374,7 +2824,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2395,7 +2845,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2416,7 +2866,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2437,7 +2887,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2533,7 +2983,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2553,7 +3003,7 @@ " dtype='object')" ] }, - "execution_count": 97, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2717,24 +3167,25 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 81.747784 3.335170e-04\n", - "PAY_0 4215.816943 0.000000e+00\n", - "PAY_2 3730.186349 0.000000e+00\n", - "PAY_3 3017.725262 0.000000e+00\n", - "PAY_4 3020.555618 0.000000e+00\n", - "PAY_5 2812.887854 0.000000e+00\n", - "PAY_6 2575.321192 0.000000e+00\n", - "PAY_0_No_Transactions 63.305610 2.349730e-02\n", - "PAY_0_Revolving_Credit 454.630595 0.000000e+00\n", - "PAY_2_Pay_Duly 76.591060 1.225866e-03\n", - "PAY_2_Revolving_Credit 241.568619 0.000000e+00\n", - "PAY_3_Pay_Duly 88.958886 4.790331e-05\n", - "PAY_3_Revolving_Credit 140.257620 3.064993e-12\n", - "PAY_4_Pay_Duly 82.933933 2.446182e-04\n", - "PAY_4_Revolving_Credit 98.729838 2.851222e-06\n", - "PAY_5_Pay_Duly 67.720209 9.456393e-03\n", - "PAY_5_Revolving_Credit 70.202609 5.486203e-03\n", - "PAY_6_Revolving_Credit 73.167377 2.782902e-03\n" + "LIMIT_BAL 82.388112 2.822569e-04\n", + "PAY_0 4424.481682 0.000000e+00\n", + "PAY_2 3847.416138 0.000000e+00\n", + "PAY_3 3007.408864 0.000000e+00\n", + "PAY_4 3034.730536 0.000000e+00\n", + "PAY_5 2869.196497 0.000000e+00\n", + "PAY_6 2526.952024 0.000000e+00\n", + "PAY_0_No_Transactions 71.907192 3.727224e-03\n", + "PAY_0_Revolving_Credit 498.806543 0.000000e+00\n", + "PAY_2_Pay_Duly 75.936267 1.438075e-03\n", + "PAY_2_Revolving_Credit 247.861963 0.000000e+00\n", + "PAY_3_Pay_Duly 78.497278 7.644927e-04\n", + "PAY_3_Revolving_Credit 142.164275 1.549982e-12\n", + "PAY_4_Pay_Duly 79.625464 5.751909e-04\n", + "PAY_4_Revolving_Credit 99.566680 2.218832e-06\n", + "PAY_5_Pay_Duly 73.322648 2.683519e-03\n", + "PAY_5_Revolving_Credit 66.651236 1.187101e-02\n", + "PAY_6_Pay_Duly 63.464171 2.277242e-02\n", + "PAY_6_Revolving_Credit 62.888951 2.550124e-02\n" ] } ], @@ -4476,6 +4927,146 @@ "#### Theory\n", "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." ] }, @@ -4497,7 +5088,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -4511,14 +5102,14 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.2966725486 0.1736524263]\n" + "Explained variation per principal component: [0.2920039216 0.1749065908]\n" ] } ], @@ -4536,12 +5127,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4589,7 +5180,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -4599,7 +5190,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -4609,7 +5200,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 73, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -4620,7 +5211,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -4647,7 +5238,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -4674,7 +5265,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -4686,7 +5277,7 @@ " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -4700,19 +5291,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.1660749149902864\n" + "Optimal Threshold: 0.15940300945944538\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4725,10 +5316,10 @@ { "data": { "text/plain": [ - "0.7220704605012441" + "0.7282872884609857" ] }, - "execution_count": 77, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -4740,14 +5331,14 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the SVM original data RBF kernel identified 733\n" + "Of 2015 Defaulters, the SVM original data RBF kernel identified 707\n" ] }, { @@ -4782,14 +5373,14 @@ " \n", " \n", " \n", - " 0\n", - " 6391\n", - " 338\n", + " 0\n", + " 6390\n", + " 344\n", " \n", " \n", - " 1\n", - " 1287\n", - " 733\n", + " 1\n", + " 1308\n", + " 707\n", " \n", " \n", "\n", @@ -4798,11 +5389,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6391 338\n", - "1 1287 733" + "0 6390 344\n", + "1 1308 707" ] }, - "execution_count": 78, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4814,7 +5405,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -4823,12 +5414,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.36 0.47 2020\n", + " 0 0.83 0.95 0.89 6734\n", + " 1 0.67 0.35 0.46 2015\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -4853,7 +5444,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -4865,7 +5456,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 80, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -4878,19 +5469,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16374485135727915\n" + "Optimal Threshold: 0.15995506541340332\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAEWCAYAAACUr7U+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gV1dbA4d8iCSSEEDqhhRCK9GYAAQHpoKCIDREV1E9BudiwexWwcxUFRcCKvTdUFAFpKr1K7yV0QhohCUnO/v6YSTiEVEgyOSfrfR4eMn3NnJlZM3v27BFjDEoppZTyDqWcDkAppZRSBUcTu1JKKeVFNLErpZRSXkQTu1JKKeVFNLErpZRSXkQTu1JKKeVFimViF5FbROQPp+NwmoiEisgpEfEpwmWGiYgREd+iWmZhEpFNInLFBUx3QfugiASIyM8iEisi3+R3em8iIgtF5K4iXuZMEXk+h+HPi8gJETlSlHGVVCLypIi853QcF0tE9opIr2IQx3AR+Su38XJN7PYKJdoJ5oh94JQrmDCzZoz5zBjTpzCXURxl3nmMMfuNMeWMMWlOxuUU+wKjwcXMwxjTzBizMJflnHcxcxH74PVAdaCyMeaGC5g+q/ieFJE99jEYKSJf2f1niMjHWYzfUkSSRaSSiIyz121MpnEesPuPK4gYPYGI1AEeBpoaY0Iucl5XiEhkwURWfNj7y6cFNT9jzIvGmEK5uLP33wT7uDgoIpPcb4LsC8ske3isiCwWkRZuw8eJSIo9PP3fo4URa1HL6x37QGNMOaA10AZ4ovBCKjxO3oV6yx1wfpTQ7V0X2G6MSc3vhFnFLCK3A7cCvexjMAKYbw+eCQwWkcBMk90G/GKMOWl3bwduz2Kc7RcTmweqC0QZY445HYiXbM/ioJV9XHQDbgLuyDR8tD28MrAQ+CTT8K/sm6f0fxMLM9ii+t3zVRRvjDkCzMFK8ACISBkReVVE9ovIURGZLiIBbsOvEZF1IhInIrtEpJ/dP1hE3heRw/bV1vPpV1vuxQ32/F51j0NEfhKRh+y/a4rIdyJy3L6rGeM23jgR+VZEPhWROGB45nWy4/jYnn6fiDwtIqXc4vhbRN60r/i2ikjPTNPmtA5/i8jrInISGCci9UXkTxGJsosDPxORCvb4nwChwM/pV46Z7yTtK9Dn7PnGi8gfIlLFLZ7b7HWIEpH/Sg7FR2IVGb9mjx8rIn+5/27ALfZvekJEnnKbrr2ILBWRGHu93xKR0m7DjYjcJyI7gB12v8kicsDeB1aLSBe38X3EuiPdZa/TahGpIyKL7VHW29vjJnv8Afb+FCMi/4hIS7d57RWRx0RkA5AgIr7u28COfZUdx1ERmWRPmr6sGHtZHSVTkZeINBORuSJy0p72ySy26XjgGeAmez53ikgpe5/aJyLH7H0t2B4//fe9U0T2A39m8VO1A+YYY3aBdQwaY96x/14KHASuc9+ewFDgI7d5rATKikiz9HUBAuz+Wcpq/7X73yEiW0QkWkTmiEhdt2l628dIrIi8BYjbsHPuBLPYtyuJyIcicsie949u4+b0m7cRkTX2vvMV4J/N+vQC5gI17d9mpt3/MnueMSKyXtwe24jICHtd40Vkt4jcY/cPBH5zm9cpsc5D5zwGkEx39dnsnzmdv7LbXzOv2xVileQ8bO9jh0VkhNvwbM9xmebTD3iSs/vvere4e7mNl/Fbuv2Ot0vW54v8jBsgIh/Zv/8Wsc6BeSoVMcbsBP7GLTdlGp4KfAk0zcv8ciIije3faojdna8cZPf72v5N4sV6XBjhNs3jcvZ8uFlErs13kMaYHP8Be7HuFgBqA/8Ck92GvwHMAioBQcDPwEv2sPZALNAb6yKiFtDYHvYjMAMIBKoBK4B77GHDgb/sv7sCBwCxuysCiUBNe56rsU6mpYFwYDfQ1x53HJACDLLHDchi/T4GfrJjD8O6i7nTLY5U4EHAD+uKMBaolMd1SAX+A/hinUgb2NuiDFAVK6G8kdW2trvDAAP42t0LgV1AI3t+C4GX7WFNgVPA5fa2eNVe917Z/K5T7elrAT5AJzuu9GW+ay+jFZAMNLGnuxS4zF6nMGAL8IDbfA3WCbRS+vYGhmFdMftiFYUeAfztYY9g7VOXYCWCVljF2OnzauA277bAMaCDHfPt9jYr47b91gF13JadsU2BpcCt9t/lgMuy2s5Z7INBwGE7dn+7u0M223Uc8Klb9x3ATqx9sxzwPfBJpuV+jLUPZbV/DgNO2tspAvDJNPwpYJ5bd1/gOODnHg/WCfsVu99ErFK3T4Fx2azHcM7ffwfZ69LE7vc08I89fhUgDutRhB/WMZMK3JXNdjlnmwO/Al9hHd9+QLfcfnOs/XwfZ4/P67H2+eezWacrgEi37lpAFHAl1vmht91d1R5+FVAfa7/sBpwG2mY1L7vfTPdlZ7G8vbjtn+R+/spyf81mvVKBCfZ2uNKOtWJu57jc9t9szksZ45D7+SI/474MLLL3gdrAhszbOFNcGecHoDHWMfqg2/CFnN3/SgMvAItzWtcclrUX6IW1P+4HBtj9852D7H5J9u/kA7wELHNb1g2czW83AQlAjcznpRzjzeMKnQLi7Q05H6hgDxN7ofXdxu8I7LH/ngG8nsU8q9s/aIBbv5uBBVmcVMXekF3t7v8D/rT/7gDszzTvJ4AP3Tbq4hzWzceOo6lbv3uAhW5xHMK+qLD7rcAqGs3LOuzPbtn2OIOAtTkcQGGcn9ifdht+L/C7/fczwBduw8oCZ8gisds7TCJWMVbmYenLrJ1pnYdksw4PAD9kOth65LLe0enLBrYB1+R24Nrd04DnMo2zjbNJYC9wR1YHpP33YmA8UCWbdc4usd/s/jvlsm7jODeBzQfudeu+BOtA93Vbbngu87wFmId1rEUBj7sNC7XnV9vu/oxzL7zHYSXwUKzjyM/+vw65J/bMx9ZvuCUEez86jVXEfRvnnpwEiCQPiR2oAbiwE1GmZWb7m2Nd9Gc+Pv8h74n9MeyLLLd+c4Dbs5n+R+D+rOZl95tJ7on9Drfu3M5fWe6v2axXIufuv8ewLsBzPMfltv9mPoYyj0Mu54t8jpuREO3uuzJv40xxGayLyQT77y+wL/Lt4Qux9s8YrHNhLNAz03qcsYen/6uZzbL22r9FJNA9H7/hODLlILuf+8V4UyAxh/Vch32OJI+JPa9F8YOMMUFYO1BjrKtzsO46ywKr7aKsGOB3uz9YJ49dWcyvLtYJ5rDbdDOw7nrPYay1+RLr5ApWMeNnbvOpmT4Pez5PYiXddAdyWK8qnL3qT7cP60o+3UE7BvfhNfO4DucsW0SqiciXYhXbx2GdWKuQP+61eU9jXcljx5SxPGPMaawkkJUqWHeeWf02OS5HRBqJyC9iVaSMA17k/HXIvN4P20VrsfZ2CnabJrt9JCt1gYcz/d51sNY9y2VncidWacdWEVkpIgPyuNz8xJhZTc7fv3zJ+z6KsSry9QIqACOBCSLS1x62HysBDBOrUusgzi2Gx228nVi/1w5jTI7LzCauusBkt21/EiuB1+L8/c/ktl5u6gAnjTHRWQzL6TevSdbHZ17VBW7INO/LsS40EJH+IrJMrMcvMVh3WPk9XjNz3ya5nb/ys79GmXPrdaQfs3k5xxWE7M5L+Rn3nH2IvO0/be3pb8JKspnrm4wxxlTAOt8NAL4Vt0c5wNfGmApu/w7lsKyRWCVUC9z6XWgOyrwN/OXsY6nb5OyjpxigOfnc7/L7jH0R1lVp+jPvE1hXis3cNkywsSorgLVC9bOY1QGsq8gqbtOVN8Y0y2bRXwDXi/U8rwPwndt89mT6YYKMMVe6h53DKp3Autup69YvFOu5ZbpaIiKZhh/K4zpkXvZLdr+WxpjyWMWsksP4+XEYq/gKsJ5XYRV/Z+UEVlFQVr9NbqYBW4GG9jo8ybnrAG7rIdbz9MeAG7HuyCpgXTmnT5PdPpKVA8ALmX7vssaYL7JadmbGmB3GmJuxLr5ewTrIA3Oa5gJizOwQ5+9fqcBR99DyMiNjTIox5husIsrmboM+wrpjvg7reFiTzSw+xnqccF5N+uwWman7ANajJvftH2CM+Qdr/6uTPqJ9zNRxmzYB6yYgnXut9ANAJbHrm2SxzOx+88NkfXzm1QGsO3b3eQcaY14WkTJY55lXger2fjubs/ttVr9ZTuuYzn26HM9fOeyv+ZGXc1x28aXLy3oVhHPOYZy7/2TLWL7GenTxTDbjuIwxS7Aubi/0jauRQKiIvO7W72Jz0DnsHPcuMBrrkWQFYCPnn2NzdCHvsb8B9BaR1sYYlx3E6yJSzQ6sVvrdBPA+MEJEeopViaiWiDQ2xhwG/gBeE5Hy9rD6ItItqwUaY9ZiPTd8D6siUYw9aAUQJ1aFlACxKmI1F5F2eVkRY71G9jXwgogE2Rv1Iaw76XTVgDEi4iciN2A9X5yd33WwBWE91ogRkVpYz03dHcV6RnMhvgUGikgnsSqzjSebncH+3T4AJtkVP3zEqjBWJg/LCcIq/jolIo2BUXkYPxXr9/MVkWeA8m7D3wOeE5GGYmkpIukXJJm3x7vASBHpYI8bKCJXiUhQHuJGRIaJSFV7/dP3oTQ7NhfZb/tfgBCxXhErY+8rHfKyTKyL0gdFpJ59R/0iVk3cPNWaF6sS21X2MkuJSH+gGbDcbbTvsE6C48nibt3NV1gnta/zGHtm04En5GwlvGD7mADrGXkzERls33mM4dwEsA7oKlbbDMG4vVljH0u/AW+LSEX7WOtqD87pN1+KtW+NEasi2mCsej159SnWMdPXPgb8xaqIVhvrLrcM1r6Ram9394RwFKhsr4v7Ol4pVkXAEKzHVDnJ8fyVw/6aZ3k8x7k7CoTJuZXr1gFD7N8lAqsuQ2H4Gmv/qmifH0fnc/qXgbvtbX8eEemIVey96QLjiwf6Ye3HL9v9LioHZSH9RuO4HfMIzr2Iz5N8J3ZjzHGsK/7/2r0ew7oKWiZW0ew8rOeIGGNWACOA17Hu0hZx9srxNqyDZzPWM9dvsYvAsvEFVuWFz91iSQMGYtWE3IN1dfoeVlFvXv0H64p0N/CXPf8P3IYvBxra834BuN4Yk17End91GI9VdBSLdSL8PtPwl4CnxSqCGZuPdcAYs8lely+xrnzjsZ6zJWczyVisSmsrsYpUXyFv+8NYrMch8Vgn3a9yGX8O1kl7O1YRYBLnFk1Nwjqg/8C6YHgfq4IJWM+iPrK3x43GmFVYdSzewtreO8niTYcc9AM2icgpYDLWs70k+7HFC8Df9rIuc5/IGBOPVbFqIFYR2g6gex6X+QHWKzaLsfbRJKzfKa/isEpF9mOd3CcCo4wxGTX2jTEJnE3un2U1E3u8RGPMPGNMYj6W7z79D1j7yZf2sb4R6G8PO4FV6edlrEdADbFqKadPOxdrX9mAVdnol0yzvxXrznIr1n77gD1dtr+5MeYMMNjujsYqjs18TOW0PgeAa7C273Gs/fIRoJT9m4/B2jejsfb5WW7TbsU6J+2295maWL/zeqznsX+Qy7GRh/NXlvtrXtfPTW7nOHfpjSpFiUh6yc9/sUqsorHOYZ9nNWEBmID1DHsPVh75luzPX+cxxvyLlWPcb5jeEvvNBazf52ljzG8XGqB9U9kb6C8izxVQDnKf/2bgNayL1qNAC9yOo7xKr2musiAiw7Eq/1zudCz5Zd8dxmAVme9xOh6llMoPERmFdTGTUymoykKxbFJWXRgRGSgiZcV6Dvcq1h35XmejUkqp3IlIDRHpbD9yugSrPsgPTsfliTSxe5drsCprHcIqCh1itEhGKeUZSmO9WRSP1VjTT8DbjkbkobQoXimllPIieseulFJKeRH9EEEBq1KligkLC3M6DKWU8iirV68+YYypmvuYKjea2AtYWFgYq1atcjoMpZTyKCKSn1YDVQ60KF4ppZTyIprYlVJKKS+iiV0ppZTyIprYlVJKKS+iiV0ppZTyIprYlVJKKS9SYhO7iHwgIsdEZGM2w0VEpojIThHZICJtizpGpZRSKr9KbGIHZmJ9FjE7/bHaW28I3A1MK4KYlFKqxElJczkdglcpsQ3UGGMWi0hYDqNcA3xsf0RlmYhUEJEaxpjDRRKgUkp5udOnUxg8YT5bSXM6FK9SYhN7HtQCDrh1R9r9zkvsInI31l09oaGhRRKcUkp5qn1RCUz65l9++vcwBJV2Ohyvo4k9e5JFvyw/hWeMeQd4ByAiIkI/l6eUUm5OJaey90QCi7Yf54/NR1l/IMYaEFSa2uXK8PbwCFq94myM3kQTe/YigTpu3bWxvnOulFIqB1GnkvluTSS/bzzChshYUl3n3u9UL1+GG+pWYeS1zShX1s+hKL2XJvbszQJGi8iXQAcgVp+vK6VU9iKjT/Pz+sO88vvWjH4h5f0JC/Jnw/y97F5zlOW/DqVVixAHo/R+JTaxi8gXwBVAFRGJBJ4F/ACMMdOB2cCVwE7gNDDCmUiVUqp4e3fxbl75fes5d+bdL6nKywOb8dy4Rbz99l/UqBHEp5P70bJ5dQcjLRlKbGI3xtycy3AD3FdE4SillEeaumAn/5uzDYBujapyc/tQOtavTFnfUjRpMpU9e2IYM6YDEyZ0p3z5Mg5HWzKU2MSulFLqwny2fB+fLtvPlsNxAFQpV5rFj3anbGlfIiPjKO/vi4gwYUJ3LrmkMpdeWtPhiEsWTexKKaVyZYzhk2X7eOanTRn96lYuS5+m1bnz8nB8DEyYsIgXX1zCxx9fy403NmPo0BYORlxyaWJXSimVowVbjzHu503sizoNQJeGVZgypA0VA6130P/8cw+jRv3K9u1R3HRTMy6/XNvzcJImdqWUUuc5mXCGeZuPMnHOVk6cOgPAze3r8MyAZgSU9skYb+zYP3jttaWEh1fkt99uoV+/Bk6FrGya2JVSSgGw90QCnyzbx5bDcfyzKyqjf93KZfn8/y6jVoUAAFwug8tl8PUtRceOtXnqqS489VQXAgL0nfTiQBO7UkqVcAdjEun88p/n9OvdtDpNQoK4vVMYlcudrc3+779HGTnyVwYObMTjj1/Oddc15brrmhZ1yCoHmtiVUqqEOhKbxKz1B3lx9tkGZT4c3o7ujaudN25CwhnGj1/EpElLqVgxgNDQ4KIMVeWDJnallCphXC7DzH/2MuGXzRn9Hu13CaO61Ufk/M9kLFiwh+HDf2L//ljuuqsNL7/ci8qVyxZlyCofNLErpVQJcjQuiQ4vzgegtE8p7uvegLu7hp9TIS6zsmX9CA4uw5IlI7TGuwfQxK6UUiXEFyv288T3/wJQKdBqVKZcmfPTQGqqiylTlnPwYByvvdaXDh1qs27dSEqVyuqjl6q40cSulFJeLCkljVnrDzFl/g4ioxMB6NusOjNujchy/GXLIhk58hfWrz/KwIGNSE114etbSpO6B9HErpRSXmrdgRgGTf07o7thtXK8e1sEYVUCzxs3JiaJJ56Yx4wZq6lZM4jvv7+RQYMaZ/nMXRVvmtiVUsqLGGP4beMRJv6+lb12S3GDWtdk/NXNCc7h2+cxMUl89tm/PPjgZYwbdwVBQfrBFk+liV0ppbxAUkoa0xft4o15OzL6XVI9iPt6NODqVll/hGX79ig++WQ9EyZ0JyysAnv3PkClSgFFFbIqJJrYlVLKQx2NS+J4fDLfro5k5j97M/rf1rEu93VvQPXy/llOl5SUyssv/8VLL/1FQIAvI0a0ITy8oiZ1L6GJXSmlPMSZVBePf7eB+ORU5m4+et7wOzrX4+6u4YQEZ53QAebN28299/7Kjh0nGTq0Ba+91oeQkHKFGbYqYprYlVKqGEtNc/HGvB38tfME6w7EZPRvWTuYWhUC6Nc8hJDy/kSEVcInl5rriYkp3HbbDwQGluaPP4bRu3f9wg5fOUATu1JKFUOxp1OYsXgXby/cldGvXpVABrWuxZieDfJcW93lMnz++b/cdFMzAgL8mDNnGA0bVsbfX0//3kp/WaWUKiYSz6Sxdn80v/57mM+W78/o/2i/SxjZtX6+3yVft+4II0f+wvLlBwEYNqwlLVpUL9CYVfGjiV0ppRzmchnemL+DKfPP1mj38xGe6N+EGyJqE+Sfv8+hnjp1hmefXcDkycupVCmATz65lltuaVHQYatiShO7Uko55PSZVFbtjeahr9dz4lQyAFe1qMGoK+rTrGb5C24c5uabv+OXX7Zzzz2X8tJLPalYUWu7lyRijHE6Bq8SERFhVq1a5XQYSqliKs1lWLM/muW7o3hrwU6SUlyA1Src7w90zbUCXHb27YshONifChX8WbPmMMnJqXTsWKcgQy9UIrLaGJN1O7cqX/SOXSmlCtnBmET+3HqM//648Zz+Fcv6MaRdKLd1rEt41Qt75SwlJY3XX1/G+PGLuPPONkyZ0p+2bWsURNjKQ2liV0qpQpKUksat7y9n5d5oAESgcmBpbm4fSqf6VWhfL/dX1HLy99/7GTnyVzZuPMbVV1/C2LGdCip05cE0sSulVAFaseckWw7HsXxPFLP/PZLR/82b29C/eQi+PqUKZDnTpq3k3ntnU6dOeX788SauuaZxgcxXeT5N7EopdZG2H41n/M+b+Htn1Dn9fUsJj/S9hLu7hhfIV9KMMcTHn6F8+TL079+QRx/txH//241y5Upf9LyV99DErpRS+ZTmMkxbuJNvVkey/+Rp0usgl/YtxVUtavBYv8YEB/gRUNqnwJa5desJRo36FX9/X2bPHkpYWAVeeaV3gc1feQ9N7EoplQfGGN5dspv1kbH8uuFwRv8mNcrTolZ5rr+0Du3rVSrw5SYmpvDii0t45ZW/CQwszSuv9CrwZSjvooldKaVykJrmYtzPm/h02dmW4BpVL0e7sErc37Mh1bL5glpB2LjxGIMGfcmuXdEMG9aSV1/tTfXq+sEWlTNN7EoplYkxhiU7TvDdmkhm/3uYlDSrrP0/PRowslt9AssU7qnTGIOIUKdOeWrXLs+MGQPo2TO8UJepvIcmdqVUiWeMYe7moyzYdpxZ6w6ScCYtY1h4lUC6NqrKTe3q0KRG+UKNIy3NxfTpq/jii40sWHA7wcH+LFw4vFCXqbyPJnalVIkVn5TCUz9sZNb6Qxn9/P1K0atJdZrXKs9N7epQI7hommNds+YwI0f+wsqVh+jVK5yYmCSqVg0skmUr71KiE7uI9AMmAz7Ae8aYlzMNDwU+AirY4zxujJld5IEqpQrc8fhk2r0wL6O7TWgFpg+7lOqF+Mw8K4mJKTzxxHzefHMFVauW5fPPBzNkSPMCeT1OlUwlNrGLiA8wFegNRAIrRWSWMWaz22hPA18bY6aJSFNgNhBW5MEqpS7azmPxLN0Vxfytx1i2OyqjjfamNcrzw32dKONbcK+m5YevbykWL97HyJGX8sILPalQoWgvLJT3KbGJHWgP7DTG7AYQkS+BawD3xG6A9IdqwcAhlFIeY+Xek0yZv4N9UafZf/J0Rv/wqoF0ql+ZPk1D6NqoapHHtWdPNM8+u5DJk/tRsWIAS5feSZlCrpCnSo6SvCfVAg64dUcCHTKNMw74Q0T+AwQCWb5AKiJ3A3cDhIaGFnigSqmcGWNIOJPGwm3H+HPLMVzG8OO6s9fhDauV447O9eh2SVXahFagfD6/b15QzpxJY9KkpUyYsIhSpYTbb29Fz57hmtRVgSrJe1NWD7Ayf8P2ZmCmMeY1EekIfCIizY0xrnMmMuYd4B2wPttaKNEqpQAriS/afpy3F+4i8Uwa/x6MPW8cPx8hvGogvqWEGbdGUK+K85XQlizZx8iRv7J583EGD27CG2/0pU6dYKfDUl6oJCf2SMD9Y8W1Ob+o/U6gH4AxZqmI+ANVgGNFEqFSJZzLZfj3YCwr955k7uajbIiMJTHl7KtoVcqV5qqWNUhJdXFp3YoElPahX/MQqgUVv+fUEyf+Q0LCGX7++WYGDGjkdDjKi5XkxL4SaCgi9YCDwBBgaKZx9gM9gZki0gTwB44XaZRKlTBpLsOzszay5XA8q/dFnzMsqIwvQzuEUr9qOfo1D6FWhaJ5Fe1CGGP46KP1dO1al/Dwirz33kDKlStNYKB+sEUVrhKb2I0xqSIyGpiD9SrbB8aYTSIyAVhljJkFPAy8KyIPYhXTDzfGaFG7UoUgNc3F5Pk7ePPPnRn9ejSuRkiwP1e3qknL2sGULe0Zp6zNm48zatSvLF68j0ce6cTEidoUrCo6nnGUFBL7nfTZmfo94/b3ZqBzUcelVEmy89gpXvtjG79ttL5d7ltKGNE5jMf6NS6wb5cXldOnU3j++cX873//UL58Gd57byAjRrRxOixVwpToxK6UKloJyakciUtiX1QC/0bG8dHSvZxMOANYH1bp37wGY3o2xKeUZzbO8tJLS3jppb+4/fZW/O9/vbXlOOUITexKqUKTlJLG2v0x/LzhED+vO0R8cup549QI9ueFa5vTo3F1ByK8eIcOxXPyZCLNm1dj7NhO9OoVTrduYU6HpUowTexKqQKzaPtxNhyIYd2BGPZGJbDreMI5w/s1C6F1aAXqVy1HtaAyNK4R5FiLbxcrLc3F1KkrefrpP2ncuArLl99FcLC/JnXlOE3sSqkLlpLm4quVB/hq5QGOxydzJC4JsJ6TG6wW3vo2C6F30+q0qBWMn4c9M8/OqlWHuOeeX1iz5jB9+tTn7bev1LbdVbGhiV0plS9Ld0Wx6VAs24/G8/WqyIz+zWuV57pLa3Fd29qEV/XeGuDz5++md+9PqF69HF99dT033NBUk7oqVjSxK6XOkZxqPRePTjjD5sNxpLkM6yNjiIxOZF/U6fPGvyy8Eh/f0YHSvt5xN54VYwwHD8ZTu3Z5unaty3PPdWf06PYEBxe/hnCU0sSuVAl3PD6ZN+Zt50hsEhsPxXI0Lvm8ccqV8SXNZWhVpwKtagdzY0QdGocEedzraBdi9+5o7rtvNmvXHmbr1tFUqODPU091dTospbLlFYldREoDocaYnbmOrJRi86E4vl8TyYq9J9kQebat9dZ1rA+k9Ghcjd5Nq1MtyJ+aFfxLRALP7MyZNF599R+ee24xfn6leP75HgQFaatxqvjz+MQuIlcBk4DSQD0RaQ08a2QsuR4AACAASURBVIy51tnIlCo+UtNcfLR0H0t2HGfhtnNbRQ6rXJa7uoQz7LK6DkVX/ERFnaZLlw/ZsuUE11/flDfe6EutWuVzn1CpYsDjEzswAetzqwsAjDHrRKSBsyEp5SxjDEt2nOCz5ftYvS+aE6fOZAyrUNaPfs1CuP7S2lxat6JW/HKTkpKGn58PlSoF0K1bXV59tQ9XXtnQ6bCUyhdvSOwpxpiYTCcnbc9dlRhJKWnsOZHAruOn+H3jERLPpDF/69kPENapFECXhlVoG1qRWzvWpUq5Mg5GWzy5XIYPP1zLuHGLWLRoOOHhFZk2bYDTYSl1QbwhsW8RkRuBUvaX2u4Hljkck1KFYvW+k3y8dB9pLsOZVBcHYxLZdCjuvPHahVWkXpVAbr0sjBa19ZvfOdm48RijRv3KX3/tp0uXUFwuvS9Qns0bEvto4BnABXyP9bW2JxyNSKkCkuYyHIpJZPa/h/ly5QH2nDjbklvjkCDK+JaiZe1g2oZWpGeTakTUrURAac9sya2oGWN4+uk/mTjxH4KDy/DBB1czfHhrfTShPJ43JPa+xpjHgMfSe4jIYKwkr5THMcbwzuLdrNhz8pwidYBaFQJ4c2gb2oZWdCg67yEixMYmc9ttLXnlld5UqVLW6ZCUKhDi6Z8XF5E1xpi2mfqtNsZc6kQ8ERERZtWqVU4sWnm4uKQUZizaxdQFuzL6dWlYhSrlytC3WQidG1QmyN/PwQg9X2RkHA888DsPPdSRTp3q4HIZSnnol+S8jX3ejnA6Dm/gsXfsItIX6AfUEpFJboPKYxXLK1VsrT8QwyfL9rF0VxRRCckkpZy7yw5qXZMXrm1BYBmPPUSLldRUF2++uZxnnllIWpqLgQMb0alTHU3qyit58lnjGLARSAI2ufWPBx53JCKlcpCa5uKRbzfww9qD5/RvUK0cPZtUo1xpX4LL+nF1q5pUKKsNoRSUFSsOcs89v7Bu3RH692/A1KlXUq+ePspQ3stjE7sxZi2wVkQ+M8YkOR2PUumMMWw7Gs+8zUfZfTyB5XtOkpyads675P2ahfB/XcNpG1pBK2sVssWL93HsWALffHMD113XRLe38nre8Iy9PvAC0BTI+CKDMaaRE/HoM/aSbdOhWK5+62/SMr0y1TgkiM4NqlAhwI+7uoRrzfVCZIzhyy83EhDgx6BBjUlJSSMxMZXy5fX9/eJMn7EXHI+9Y3czE3geeBXoD4xAn7GrIrLxYCx/7TzB6n3RHItLYr3d7nqfptUZ3jmMjuGV9Q6xCO3ceZJ77/2VuXN3M3BgIwYNaoyfnw9+fnohpUoOb0jsZY0xc0TkVWPMLuBpEVnidFDKey3Yeowf1h5k7YFoDpxMzOhfoawfIeX9eaz/JVzbpraDEZY8ycmpvPLK37z44hLKlPHlrbf6M3Kk3vypkskbEnuyWLdEu0RkJHAQqOZwTMqLGGPYEBnLi7O3sHzPyYz+InBVyxoMbR9Kx/DKWsPaQX/8sYtnn13IkCHNmTSpDzVqBDkdklKO8YbE/iBQDhiD9aw9GLjD0YiU19h1/BR3zFzJvqjTGf26NKzClCFtqBioNdeddOxYAitWHGTAgEYMGNCIFSvuol27Wk6HpZTjPD6xG2OW23/GA7cCiIiWg6qLEnP6DLd/sCLjmXnDauV47cZWtKxdweHIlMtleO+9NTz22DyMMRw48CBBQWU0qStl8+jELiLtgFrAX8aYEyLSDKtp2R6AJneVL0dik/h8xX4WbjvGBjuhVyjrx9ShbelUXyvBFQcbNhxl5MhfWLo0km7d6jJt2lUEBWltd6XceWxiF5GXgOuA9VgV5n7A+rLbK8BIJ2NTnuWjf/bywd97zilub1U7mFFX1Kdf8xoORqbcHToUT7t271K+fBk++mgQt97aUi+2lMqCxyZ24BqglTEmUUQqAYfs7m0Ox6U8QGqai3lbjvLcL1s4GGPVbO/brDqDWteiZ5PqlPYt5XCEKt2GDUdp2bI6NWsG8eGH19C3b30qV9YPtiiVHU9O7EnGmEQAY8xJEdmqSV3l5mhcEu//tYd3Fu8+p/+m8X21XfZiZv/+WMaM+Y2fftqWUTFu6NAWToelVLHnyWeycBFJ/zSrAGFu3RhjBjsTlipOYk+n8NWq/Ww8GMes9YfOGXZz+zo81PsSquoz2mIlJSWNKVOW8+yzC3G5DK+80ovWrUOcDkspj+HJif26TN1vORKFKrb2nkjgilcXntOvQ71KXNmiBje3D9Xi9mLI5TJ06zaTpUsjGTCgEW++2Z+wMH0TQan88NjEboyZ73QMqvgxxvDPrije+nMnS3dHATC4TS0mXt8SXx9N5MVVXFwyQUGlKVVKGDGiNY880olBgxpr5TilLoDHJnal3J1JdXHPJ6tYsO14Rr/aFQN4on8TrmqpNduLK2MMn332Lw8//AdTpvTjppua83//d6nTYSnl0UpsYheRfsBkwAd4zxjzchbj3AiMAwyw3hgztEiDVLk6HJvILe8tZ/fxhIx+d3Sux83t69CwujYrWpxt23aCe++dzZ9/7qF9+1pcckkVp0NSyit4TWIXkTLGmOQ8jusDTAV6A5HAShGZZYzZ7DZOQ+AJoLMxJlpEtP35YmTL4Ti+XR3J+3/tAaBKudKM7t6A2zuFafGtB5gyZTmPPDKXgABfpk27iv/7v7b46KMSpQqExyd2EWkPvI/VRnyoiLQC7jLG/CeHydoDO40xu+15fIn1Xvxmt3H+D5hqjIkGMMYcK4z4Vd65XIafNxzis2X7WbHX+hhLaKWyPNynEde01uZEPYExBhGhRo1yXH99U157rQ8hIeWcDkspr+LxiR2YAgwAfgQwxqwXke65TFMLOODWHQl0yDROIwAR+RuruH6cMeb3AolY5dv+qNMMn7kio8g9vGogE65uzuUNtfjWExw5coqHH/6DFi2q8fjjl3PDDc244YZmToellFfyhsReyhizL1Pxa1ou02RVVmsydfsCDYErsNqdXyIizY0xMefNTORu4G6A0NDQPIat8mL38VPc/uGKc757vuiRK6hbOdDBqFReuVyGGTNW8cQT80lMTKVFC32ipVRh84bEfsAujjf2s/P/ANtzmSYSqOPWXRurSdrM4ywzxqQAe0RkG1aiX5l5ZsaYd4B3ACIiIjJfIKgLsO1IPAu3HeONeTtITEmjS8MqDO8URs8m1Z0OTeXRxo3HuOuuWSxffpAePerx9ttXagU5pYqANyT2UVjF8aHAUWCe3S8nK4GGIlIPOAgMATLXeP8RuBmYKSJVsIrmd6MK3dcrD/Dodxsyuu/pGs4TVzZxMCJ1IRISzrBvXyyffHItt9zSQis1KlVEvCGxpxpjhuRnAmNMqoiMBuZgPT//wBizSUQmAKuMMbPsYX1EZDNW0f4jxpiogg5eWaJOJTNvy1Ee++7fjH7v3x5B90uqUaqUJgRPYIzhxx+3sm7dEcaP706HDrXZs+d+/P294TSjlOcQYzy75FhEdgHbgK+A740x8U7GExERYVatWuVkCB5j6a4oJs/fzup90aSknd0P61Yuy/u3t6NBNa0t7Sn27Yth9Ojf+OWX7bRuHcLSpXdqQlf5IiKrjTERTsfhDTz+yDPG1BeRTljF6eNFZB3wpTHmS4dDU9n4ZOleJv6+jfjk1Ix+A1vVpEfjqvRqUp0gfz/nglP5kpKSxuuvL2P8+EWIwKuv9ub++y/DV9vhV8oxHn/H7s7+LvsbwC3GGB8nYtA79qzFnD7DC79uYfX+6IxX1oZ3CqNXk+p0ql9Zi9s91P79sTRpMpU+feozeXI/QkODnQ5JeSi9Yy84Hn/HLiLlsBqXGQI0AX4COjkalMqw89gp/tl1gmd+2pTRr37VQD676zJCgv0djExdqKio03zyyQbuv78DoaHBbNw4inr1KjodllLK5vGJHdgI/AxMNMYscToYBUkpaWw8GMuj3204pw33q1rUYOotbR2MTF0MYwwff7yesWPnEh2dSI8e9WjZsromdaWKGW9I7OHGGJfTQSgroV/91l/sOZGQURmubWgF/tOzIR3DK+Pv58jTEVUAtmw5zqhRv7Jo0T46dqzN9OkDaNlS2xRQqjjy2MQuIq8ZYx4GvhOR8yoKGGMGOxBWiZPmMny7+gB7Tpxm+qJdGf2fH9SchtXK0SG8soPRqYKQmuriyis/JyYmiRkzBnDXXW21ToRSxZjHJnas19sA3nI0ihIq6lQyr83dzufL92f0K+Nbivb1KvHJnZmb3VeeaOHCvXTuXAc/Px8+/3ww9etXolo1bcpXqeLOYxO7MWaF/WcTY8w5yd1ufGZ+0Ufl/eKTUhjx4UpW7YvO6NerSXWeG9SMGsEBDkamCsrhw/E8+OAcvvpqE1OnXsm997ajY8c6uU+olCoWPDaxu7mD8+/a78yin7oI0QlneGfJbqYttIrba1UI4P6eDbmxnZ7wvUVamotp01bx1FN/kpycyoQJV3DnnW2cDksplU8em9hF5CasV9zqicj3boOCgPO+wKYuTEqai76vL2b3ibO12x/r15hRV9R3MCpVGO6662dmzlxHr17hvP32lTRsqPUjlPJEHpvYgRVAFNaX2aa69Y8H1joSkZcwxrA+MpZnZ21i/YGz10jjr27G0A6h+Ploq2LeIi4uGWMMwcH+jBoVQZ8+4QwZ0lw/2KKUB/PYxG6M2QPswfqamyoAy3ZHMXfzUb5edYD4JKu51+rly9CvWQjPDGyGj9aE9hrGGL77bgv33/87Awc2Yvr0AbRvX4v27Ws5HZpS6iJ5bGIXkUXGmG4iEg24v+4mgDHGVHIoNI/z27+HGfXZmozuupXL0qtJdQa0rKHfP/dCe/ZEM3r0b8yevYPWrUMYMaK10yEppQqQxyZ2oLv9fxVHo/Bgu46fYsg7yzgenwxA0xrlmTasLXUr6ytN3ur777cwbNj3+PiU4vXX+zJ6dHv9YItSXsZjE7tba3N1gEPGmDMicjnQEvgUiHMsOA9w4lQyPV9bBECfptUZd3UzalbQ19W8VUpKGn5+PrRtW4NBgxozcWJvatcu73RYSqlC4A2X6j8CRkTqAx9jfQjmc2dDKt6OxScR8bxVNWFAyxq8c1uEJnUvdeLEae688yeuvvpLjDGEhVXg88+v06SulBfzhsTuMsakAIOBN4wx/wG0BlA24pNSaP+C1XbPmB4NeGuofpTFGxlj+PDDtTRu/BYff7yBVq2qk5bmPZ9oVkplz2OL4t2kisgNwK3AILufn4PxFFtnUl088s0GADqGV+ahPpc4HJEqDPv3xzJs2PcsWbKfzp3rMH36AJo3r+Z0WEqpIuINif0O4F6sz7buFpF6wBcOx1TsLN0Vxc3vLsvonj7sUgejUYUpOLgMJ08m8t57Axkxoo1+sEWpEkaM8fziORHxBRrYnTuNMalOxRIREWFWrVrl1OLPs/5ADCNmruRkwhnAeqb+3wFNqV7e3+HIVEGaPXsH06at4vvvb8TPzweXy2hCVx5FRFYbYyKcjsMbePwdu4h0AT4BDmK9wx4iIrcaY/52NjLn/RsZyzVTrc0Q4OfDNyM70rxWsMNRqYJ08GAcDzwwh2+/3UzjxlU4eDCesLAKmtSVKsE8PrEDrwNXGmM2A4hIE6xEX2Kv/JJT0xj/8+aMT6q+eG0LhnYIdTgqVZDS0lxMnbqSp5/+k5QUFy+80IOxYztRurSP06EppRzmDYm9dHpSBzDGbBGR0k4G5KSNB2MZ8OZfGd3PDWquSd0LuVyG999fS+fOoUydeiXh4RWdDkkpVUx4Q2JfIyIzsO7SAW6hhH4EZs3+aAa//Q9g1XqfeUc7yvjqHZy3iI1N4sUXl/D445dTsWIACxbcTsWK/vrBFqXUObwhsY8ExgCPYj1jXwy86WhERczlMoz8dDV/bD4KwMhu9Xm8f2OHo1IFxRjD119v4oEH5nD06CkuvbQmN97YjEqVtFEhpdT5PDqxi0gLoD7wgzFmotPxOOXat/9mfWQsADNHtOOKS/SdZW+xa9dJ7rtvNnPm7KJt2xr8/PPNRETUdDospVQx5rGJXUSeBO4E1gDtRGSCMeYDh8MqUtuPxnPl5CWkugzlyviy4dk+Whvayzz22Dz++ecAkyf347772uHj4w2NRSqlCpPHJnasZ+ktjTEJIlIVmA2UmMR+PD6ZPq8vBiCibkXevS1Ck7qXWLRoL3XqBBMeXpE33uiHCNSqpW27K6XyxpMv/5ONMQkAxpjjePa65Isxhmvesmq+Tx7Smm9HdaJiYIl9EcBrHD+ewPDhP3LFFR/xwgvWRVvt2uU1qSul8sWT79jDReR7+28B6rt1Y4wZ7ExYhe+ndYc4FJtEv2YhXNNav3fj6VwuwwcfrOXRR+dy6tQZnnzycp56qqvTYSmlPJQnJ/brMnW/5UgUReydxbt4cfZWAF4c3MLhaFRBeOONZTz88B906RLK9OkDaNq0qtMhKaU8mMcmdmPMfKdjKGqnz6RmJPUnr2xMJS1+91gJCWc4fPgUDRpU4s4721C1almGDWup76QrpS5aiXku7Q2mL9wFwIRrmnF31/oOR6Mu1C+/bKdZs7cZPPgrXC5DcLA/t97aSpO6UqpAlOjELiL9RGSbiOwUkcdzGO96ETEi4mj7879vOgLALR3qOhmGukCRkXEMHvwVAwd+QWBgaaZOvVLfZFBKFTiPLYrPTETKGGOS8zG+DzAV6A1EAitFZJZ7u/P2eEFYLdstL8h482vawl1sP3qKe7qG46PJwOOsWXOYbt1mkpbm4qWXevLQQx31gy1KqULh8XfsItJeRP4FdtjdrUQkL03Ktsf6dvtuY8wZ4EvgmizGew6YCCQVVMz5tedEAq/8bj1bv7truFNhqAsQF2dda7ZoUY077mjNpk338vjjl2tSV0oVGo9P7MAUYAAQBWCMWQ90z8N0tYADbt2Rdr8MItIGqGOM+SWnGYnI3SKySkRWHT9+PD+x5yoy+jQ3TLc+7PLy4BZULlemQOevCkdMTBL33vsrjRu/RUxMEn5+Pkye3J969fQrbEqpwuUNRfGljDH7MlU8SsvDdFmVZ5uMgSKlsL71Pjy3GRlj3gHeAYiIiDC5jJ5n247E0/cNq6GSB3o1ZEh7/fxqcWeM4csvN/Lgg3M4fvw0Y8a0x8dHH50opYqONyT2AyLSHjD2c/P/ANvzMF0kUMetuzZwyK07CGgOLLQvGkKAWSJytTFmVYFEnospf+4AYGyfRozu0bAoFqkuwqlTZxg8+Cvmzt1NRERNZs++hbZtazgdllKqhPGGxD4Kqzg+FDgKzLP75WYl0FBE6gEHgSHA0PSBxphYoEp6t4gsBMYWVVIfN2sTv244zICWNTSpF3PGGESEwEA/qlQpy1tv9WfkyAj9YItSyhEen9iNMcewknJ+p0sVkdHAHMAH+MAYs0lEJgCrjDGzCjjUfJn5z14AnrqqiZNhqFz8+eceHnpoDt9/fxPh4RX5/PPMDSIqpVTR8vjELiLv4vZsPJ0x5u7cpjXGzMb6Kpx7v2eyGfeKCwwx375ZZdXpG9S6JjWCA4pqsSofjh49xdixc/n00w2Eh1fkxInThIdrxTillPM8PrFjFb2n8weu5dza7h7njXnWs/XbOoU5G4jK0nvvreGRR+aSkHCG//63K088cTkBAX5Oh6WUUoAXJHZjzFfu3SLyCTDXoXAuWprLcDAmkSB/X9qG6h1gcbR27WFatw5h2rSraNy4Su4TKKVUEfL4xJ6FeoDHtrm6eIf1Hvy9VzRwOBKV7tSpM4wfv5Brr21Cp051mDSpL6VL+2jb7kqpYsnjE7uIRHP2GXsp4CSQbbvvxd1XK6ynCLd38thrE6/y009b+c9/fuPAgTgqVgygU6c6lCnj8YeNUsqLefQZSqxbplZYr6sBuIwxBdZATFFLSE7l901HCPDzoWxpj/5pPN6+fTGMGfM7s2Zto3nzanzxxXV07qwNBCmlij+Pzh7GGCMiPxhjLnU6loKw/+RpAG5qVyeXMVVh+/bbzcybt5uJE3vxwAOX4eenbbsrpTyDRyd22woRaWuMWeN0IBfrw7/34FtK9EMvDlm2LJKYmCT69WvAmDEduOGGZoSGBjsdllJK5YvHNo0lIukXJZdjJfdtIrJGRNaKiEcm+a9XRVK/ajlqVtB314tSdHQiI0f+QqdO7/PMMwswxuDn56NJXSnlkTz5jn0F0BYY5HQgBSH2dAoAbetWcDiSksMYw2ef/ctDD83h5MlEHnzwMsaP76613ZVSHs2TE7sAGGN2OR1IQXj4m/UA9Gka4nAkJceCBXu59dYf6NChFn/8cSutW+u2V0p5Pk9O7FVF5KHsBhpjJhVlMBdj57FTzNtylAA/H664pKrT4Xi1pKRUVq48SJcudenePYyffhrCVVc11A+2KKW8hiefzXyAclifV83qn8fYcjgOgNdvaqXFwIVo7txdtGgxjb59P+X48QREhKuvvkSTulLKq3jyHfthY8wEp4O4WMYY3vtrDyJwxSXVnA7HKx05coqHHprDF19spGHDSsyadTNVqwY6HZZSShUKT07sXnFr+8myfaw/EMNdl9fDX9+VLnDR0Yk0a/Y2p06d4dlnu/H445fj7+/Ju71SSuXMk89wPZ0OoCBMXbCTyoGlebx/Y6dD8SoHD8ZRq1Z5KlYM4LnnutOrVziNGlV2OiyllCp0Hvtw0Rhz0ukYLtYPayM5GpfMiM5h+Opz3gIRH5/MQw/NISxsMv/8Y7W7f++97TSpK6VKDE++Y/doaS7Dg19Zr7gNalPL4Wg8nzGGH37Yypgxv3HoUDz33HMpTZroJ1WVUiWPJnaHzN18FIAxPRtSu2JZh6PxfEOHfs+XX26kZcvqfPvtjVx2WW2nQ1JKKUdoYnfI0z9uBOCWDvrFsAuVkpKGr28pRITOnesQEVGD+++/DF9ffayhlCq59AzokJMJyQBUL+/vcCSe6a+/9tOmzQy+/noTAKNHt+fhhztpUldKlXh6FnTAsbgkXAYe66c14fMrKuo0d901iy5dPiQuLpngYL0wUkopd1oU74Clu6MAqK+NpOTLd99tZuTIX4mOTuSRRzrxzDPdKFeutNNhKaVUsaKJ3QH7o04D0Ca0osOReBZjoGHDSkyfPoCWLas7HY5SShVLmtgd8Nny/VQOLE3VoDJOh1KsJSam8MILS6hUKYCHHurIddc1YfDgJpQq5RWNDiqlVKHQZ+xFzOUyHIlLom5lfcUtJ3Pm7KR582m88MIStm+3Hl2IiCZ1pZTKhd6xF7EVe60G867VRmmydPhwPA88MIevv95Eo0aVmT//Nnr0qOd0WEop5TE0sRex9//aA0DfZiEOR1I87d8fy88/b2PChCt49NHOlCmju6hSSuWHnjWLUJrLMHfzUcIql6Wavr+eYc2aw/z55x7Gju1Ehw61OXDgQSrrowqllLog+oy9CE2evwOAHo21RjdAXFwyDzzwO+3avcukSUuJjU0C0KSulFIXQRN7EVq7PxqAx/pf4nAkzjLG8O23m2nSZCpTpixn5MhL2bz5Pm1sRimlCoAWxRcRYwxLdpygX7MQyvj6OB2Oo44eTeD223+kUaPK/PDDTbRvrxUJlVKqoGhiLyKR0YkAVC9fMt9dP3MmjW++2cTQoS0ICSnHokXDad06RNt2V0qpAlZiz6oi0k9EtonIThF5PIvhD4nIZhHZICLzRaTuxSzvwEmrtbkeTUre8/UlS/bRps0Mhg37gSVL9gMQEVFTk7pSShWCEnlmFREfYCrQH2gK3CwiTTONthaIMMa0BL4FJl7MMifN3Q5A1XIl5479xInT3HHHT3TtOpOEhDP8/PPNdO16UddHSimlclFSi+LbAzuNMbsBRORL4Bpgc/oIxpgFbuMvA4ZdzAJX7bMqzjWpEXQxs/EYxhh69vyYzZuP8/jjnXn66a4EBuoHW5RSqrCV1MReCzjg1h0JdMhh/DuB37IbKCJ3A3cDhIaGnjf8942HARjUuiYi3t0k6tatJwgPr0jp0j68/npfqlULpHnzak6HpZRSJUaJLIoHssquJssRRYYBEcD/spuZMeYdY0yEMSaiatWq5w3/ce0hAB7u472vuZ0+ncKTT86nRYtpTJmyHIAePeppUldKqSJWUu/YI4E6bt21gUOZRxKRXsBTQDdjTPKFLiw5NQ2AOpW8s+GV2bN3cN99s9m7N4bhw1szfHhrp0NSSqkSq6Tesa8EGopIPREpDQwBZrmPICJtgBnA1caYYxezsEMxSTSqXu5iZlFsPf30n1x11ef4+/uycOHtfPjhNVSp4p0XMEop5QlK5B27MSZVREYDcwAf4ANjzCYRmQCsMsbMwip6Lwd8Yz8X32+MufpClrfjWDxNa5YvoOidl5rqIjk5lcDA0gwc2IiAAF8eeaQzpUuX7IZ3lFKqOCiRiR3AGDMbmJ2p3zNuf/cqoOXgMhBWObAgZue4lSsPMnLkr7RrV5Pp0wfQoUNtOnSo7XRYSimlbCW1KL7I7DmRAECTGp59xx4bm8To0bPp0OE9Dh+Op2dP/Ua6UkoVRyX2jr2oLN9zEoB6VTz3jn3x4n0MGfItR48mMHp0e55/vgflS2jTuEopVdxpYi9kkdFWU7L9m4c4HEn+GWMQEerWDaZhw8rMmnUzERE1nQ5LKaVUDjSxF7Lo0ykE+Pl4VMM0ycmp/O9//7B69WG+//5G6tatwKJFw50OSymlVB7oM/ZCtmZfND6lPCepL1y4l9atZ/Df/y7Az68UiYmpToeklFIqHzSxF7KtR+KpWcHf6TBydfJkIrff/iPdu39EcnIqs2cP5euvb6BsWT+nQ1NKKZUPWhRfiFLSXADUqhDgcCS58/ERFi3ay5NPXs5TT3XVhK6UUh5KE3sh2nnsFACdG1RxOJKsbdx4jEmTljJjxgCCg/3ZunU0/v66SyillCfTovhCdDA6EYDwqsXr7FL+3AAAFMBJREFUVbeEhDM89thc2rSZwaxZ29i69QSAJnWllPICeiYvRHujrMZpQovRx19++WU7o0fPZt++WO64ozUTJ/amcuXiE59SSqmLo4m9EEXad+whwcXjGXtamounn/6TwMDSLF48nC5d6jodklJKqQKmib0QHT9lfek10MGPo6Smupg+fRW33NKCihUDmDXrZkJCyukHW5RSyktpYi9EK/ecpEO9So41TrN8eST33PML69cfRQTuu689oaHBjsSilFKqaGhiL0SJKWk4kdNjYpJ48sn5TJ++iho1gvj22xsYPLhJ0Qeiir2UlBQiIyNJSkpyOhRVQvj7+1O7dm38/PSV2sKiib2QGGOIT0qlcUjRf9Xt/vt/59NPN3D//R2YMKE7QUH6wRaVtcjISIKCgggLC/OoZo+VZzLGEBUVRWRkJPXq6RciC4sm9kKyel80AEFF9ArZjh1R+Pn5EBZWgQkTruD++zvQtm2NIlm28lxJSUma1FWREREqV67M8ePHnQ7Fq+l77IVk65F4AHo2qV6oy0lOTmXChEW0aDGNsWP/AKBu3Qqa1FWeaVJXRUn3t8Knd+yFZOqCnQA0DgkqtGXMn7+be++dzfbtUQwZ0pxJk/oU2rKUUkp5Br1jLwTJqWkcjk1CBPz9Cue1so8/Xk+vXp+QluZizpxhfPHFddSoUXgXEUoVFh8fH1q3bk3z5s0ZOHAgMTExGcM2bdpEjx49aNSoEQ0bNuS5557DGJMx/LfffiMiIoImTZrQuHFjxo4d68Qq5Gjt2v9v796jo6ruBY5/fxA1QJBnyYWmNYRgEwgQCD4i9PIqoFyRgkgUKo+LLSDVVVHstdy7SMXWR31dDV7k9lpAm4BAeYhWWwSrRVBCiQkSxAgphUYeIUqAYELyu3+ck8kkJGQCkxmY/D5rnbXmnNnnnN/8Mpk9Z589e+/knnvuqbZtzJgxJCcnV9s2depUVq1aVW1bRESE5/HevXsZNWoUsbGxxMfHM2HCBA4fPnxRsR0/fpzhw4fTvXt3hg8fTlFR0TllNm/eTGJiomcJDw9n7dq1AOzfv58bbriB7t27k5KSQmlpKQBpaWn87ne/u6jYzEVQVVv8uCQlJenmPYf1mp9v0GUf7ld/Ki+v0EOHTqiq6ldfleivfvW+nj5d6tdzmKZl9+7dwQ5BW7Vq5Xk8efJkfeyxx1RV9fTp0xoTE6PvvPOOqqqeOnVKb775Zk1LS1NV1ZycHI2JidHc3FxVVS0rK9OFCxf6NbaysrKLPsb48eM1KyvLs15UVKRRUVEaFxen+/bt82yfMmWKrly5stq+lbkpKSnR2NhYXb9+vee5TZs2aU5OzkXFNnfuXH388cdVVfXxxx/Xhx9++LzlCwsLtV27dnrq1ClVVb3jjjs0IyNDVVVnzJihL730kqo6f6vExMQ6j1Pb+w7I1EvgMzwUFmuKbwSVI871/W47vx3zk0++ZObMNyku/oadO2fQpk04v/jF9/12fGN++can7P7nCb8es0eXq5k/uqfP5ZOTk8nOzgYgPT2dAQMGMGKEc4upZcuWpKWlMXjwYGbPns1TTz3FvHnziIuLAyAsLIx77733nGOePHmS++67j8zMTESE+fPnc/vttxMREcHJk85ETatWrWLDhg0sWbKEqVOn0r59e3bu3EliYiJr1qwhKyuLtm3bAhAbG8uWLVto1qwZM2fO5MCBAwA8//zzDBgwoNq5i4uLyc7Opk+fPp5tq1evZvTo0URGRrJ8+XIeeeSRevOSnp5OcnIyo0eP9mwbMmSIz3mty7p163jvvfcAmDJlCoMHD+bJJ5+ss/yqVau45ZZbaNmyJarKpk2bSE9P9+yfmprKrFmzaNmyJdHR0Xz88cdcf/31Fx2naRir2BvBp//8GoBr/DAG+8mTpaSmvsfzz2+jffsWPPPMCMLC7A6KCT3l5eW8++67TJ8+HXCa4ZOSkqqV6datGydPnuTEiRPs2rWLBx98sN7jLliwgDZt2pCTkwNQa3NzTXv37mXjxo00b96ciooK1qxZw7Rp0/joo4+Ijo4mMjKSiRMn8sADDzBw4EAOHDjAyJEjyc3NrXaczMxMEhISqm3LyMhg/vz5REZGMn78eJ8q9l27dp2Ti9oUFxfz/e/X/oU/PT2dHj16VNt2+PBhOnd2Otp27tyZI0eOnPf4y5cvZ86cOQAUFhbStm1bwsKcaiQqKopDhw55yvbv358PPvjAKvYgsIq9ERw4fhqAiKsuLr2ff17IsGHL+Mc/TvDjH/fjiSd+QPv2l8a48yb0NOTK2p9KSkpITEwkPz+fpKQkhg8fDji3CevqQd2QntUbN25k+fLlnvV27epvSbvjjjto3tzpH5OSksKjjz7KtGnTWL58OSkpKZ7j7t6927PPiRMnKC4upnXrqr4uBQUFfOtb3/KsHz58mLy8PAYOHIiIEBYWxq5du0hISKj1NTW0B3nr1q3Jyspq0D6+KigoICcnh5EjRwLO36cm73g7derEnj17GiUWc3526dcIcguK+XbbFhf8s46ysnIAoqPbMnDgd9my5d9ZvHi0VeomJLVo0YKsrCz+/ve/U1paysKFCwHo2bMnmZmZ1cru27ePiIgIWrduTc+ePdmxY0e9x6/rC4L3tpoj77VqVTXVcnJyMnl5eRw9epS1a9cybtw4ACoqKti6dStZWVlkZWVx6NChapV65WvzPvaKFSsoKiqia9euREdHk5+f7/nS0aFDh2qtCcePH6djx46eXPjyWouLi6t1dPNevL+EVIqMjKSgoABwKu5OnTrVeezXX3+dsWPHekaM69ixI1999RVnz54FnMGOunTp4il/5swZWrSwz6xgsIq9kcR2iqi/UA1lZeU8/fSHxMUtpKiohCuuaE56+u3cdNN3GiFCYy4tbdq04YUXXuDpp5+mrKyMSZMm8de//pWNGzcCzpX9/fffz8MPPwzA3Llz+fWvf83evXsBp6J99tlnzznuiBEjSEtL86xXVp6RkZHk5uZ6mtrrIiKMHTuWOXPmEB8fT4cOHWo9bm1XyvHx8eTl5XnWMzIyePvtt8nPzyc/P58dO3Z4KvbBgwezYsUKT8/yJUuWeO6jT5w4kQ8//JA333zTc6y3337bc3uhUuUVe21LzWZ4gNtuu42lS5cCsHTpUsaMGVNnHjIyMrjrrruq5WXIkCGenvw199+7d+85tyFMgAS7916oLUlJSXrNzzfoYxs+1YbYsuWA9ur1kkKqjh6drgUFxQ3a35gLcan1ildVvfXWW3XZsmWqqpqdna2DBg3Sa6+9Vrt166apqalaUVHhKfvGG29ov379NC4uTuPj4/Whhx465/jFxcU6efJk7dmzp/bu3VtXr16tqqorV67UmJgYHTRokM6ePVunTJmiqrX3Tt++fbsCumTJEs+2o0eP6oQJE7RXr14aHx+vM2bMqPX1JSQk6IkTJ3T//v3apUuXavGrqvbt21e3bdumqqqpqamakJCgffr00XHjxumRI0c85XJzc3XkyJEaGxur8fHxmpKSol9++eV5c1ufY8eO6dChQzU2NlaHDh2qhYWFntc7ffp0T7nK2MvLy6vt/8UXX+h1112n3bp10/Hjx+uZM2eqva6jR4/Wel7rFd+4izj5NP7St1+SFo14lOkDu/Jft577Dbmm0tJy7rvvLRYv/htRUVfz4ou38MMfxgUgUmMgNzeX+HibIKgxPffcc7Ru3fqc37KHsp07d/Lss8/y6quv1vp8be87Edmhqv0DEV+os6Z4PztTeX+8Y6t6SjquuKIZR46cZs6cG8nNnW2VujEhZtasWVx1VdOaiOnYsWMsWLAg2GE0WdYr3s9OfnOW5sCAbh3qLPPZZ8eYM+dPvPjiLcTEtGP16gk0a2bjJxsTisLDw7n77ruDHUZAVf6ywQSHXbH72dkK59ZG11qu2M+cOcv8+Zvp3XsRW7YcYM+eYwBWqZugsttxJpDs/db47Irdz0pKy4ltf+5P3TZu3MesWW+Sl3ecSZN68cwzI4iMbHjPeWP8KTw8nMLCQjp06GCzbplGp+rMxx4eHh7sUEKaVex+VqFKSWnFOdvXrduDCGzceDfDhsUEITJjzhUVFcXBgwdtfmwTMOHh4URFRQU7jJBmveL97KrO3XXe4nXMGxXHyy/voG/ffyE5+TucPFlKWFgzwsPtu5QxxtRkveL9p0nfYxeRm0XkMxHJE5H/qOX5q0Rkhfv8RyIS7ctxI8oquOmmV5g9+y1ee82Z0CIi4kqr1I0xxjS6JlvTiEhzYCEwHDgIbBeR9arqPe7idKBIVWNF5E7gSSClvmP/fOp62re4ktdeG8vEib0aI3xjjDGmVk35iv16IE9V96lqKbAcqDme4hhgqft4FTBM6ulhpGcruGdSb/bsmc2kSb2tQ5IxxpiAarJX7MC3gX94rR8EbqirjKqeFZGvgQ7AMe9CIvIT4Cfu6jeLFo3etWhRo8R8uelIjVw1YZaLKpaLKpaLKt8LdgChoilX7LVdStfsSehLGVR1MbAYQEQyrQOIw3JRxXJRxXJRxXJRRUQy6y9lfNGUm+IPAt7TpkUB/6yrjIiEAW2A4wGJzhhjjLkATbli3w50F5GuInIlcCewvkaZ9cAU9/F4YJPa7wONMcZcwppsU7x7z/ynwDtAc+AVVf1URB7FmT5wPfB/wKsikodzpX6nD4de3GhBX34sF1UsF1UsF1UsF1UsF35iA9QYY4wxIaQpN8UbY4wxIccqdmOMMSaEWMV+ARprKNrLkQ+5mCMiu0UkW0TeFZFrghFnINSXC69y40VERSRkf+bkSy5EZIL73vhURNIDHWOg+PA/8l0R2SwiO93/k1HBiDMQROQVETkiIrvqeF5E5AU3V9ki0i/QMYYEVbWlAQtOR7svgBjgSuAToEeNMvcCi9zHdwIrgh13EHMxBGjpPp7VlHPhlmsNvA9sA/oHO+4gvi+6AzuBdu56p2DHHcRcLAZmuY97APnBjrsR8/GvQD9gVx3PjwL+iDOGyI3AR8GO+XJc7Iq94RplKNrLVL25UNXNqnraXd2GM15AKPLlfQGwAHgKOBPI4ALMl1z8GFioqkUAqnokwDEGii+5UOBq93Ebzh1PI2So6vucfyyQMcAydWwD2opI58BEFzqsYm+42oai/XZdZVT1LFA5FG2o8SUX3qbjfBsPRfXmQkT6At9R1Q2BDCwIfHlfXAtcKyJbRGSbiNwcsOgCy5dcpAI/EpGDwFvAfYEJ7ZLU0M8UU4sm+zv2i+C3oWhDgM+vU0R+BPQHBjVqRMFz3lyISDPgOWBqoAIKIl/eF2E4zfGDcVpxPhCRBFX9qpFjCzRfcnEXsERVnxGRZJyxMxJUtaLxw7vkNJXPzkZlV+wNZ0PRVvElF4jID4B5wG2q+k2AYgu0+nLRGkgA3hORfJz7h+tDtAOdr/8j61S1TFX3A5/hVPShxpdcTAdeB1DVrUA4zuQwTZFPnynm/KxibzgbirZKvblwm59fxqnUQ/U+KtSTC1X9WlU7qmq0qkbj9De4TVVDceILX/5H1uJ0rEREOuI0ze8LaJSB4UsuDgDDAEQkHqdiPxrQKC8d64HJbu/4G4GvVbUg2EFdbqwpvoG08Yaivez4mIvfABHASrf/4AFVvS1oQTcSH3PRJPiYi3eAESKyGygH5qpqYfCibhw+5uJB4H9F5AGcZuepIXohgIhk4Nx+6ej2KZgPXAGgqotw+hiMAvKA08C04ER6ebMhZY0xxpgQYk3xxhhjTAixit0YY4wJIVaxG2OMMSHEKnZjjDEmhFjFbowxxoQQq9iNaSARKReRLK8l+jxlo+uayaqB53zPnSHsE3cY1u9dwDFmishk9/FUEeni9dxvRaSHn+PcLiKJPuzzMxFpebHnNsY4rGI3puFKVDXRa8kP0HknqWofnAmGftPQnVV1kaouc1enAl28nrtHVXf7JcqqOF/Ctzh/BljFboyfWMVujB+4V+YfiMjf3OWmWsr0FJGP3av8bBHp7m7/kdf2l0WkeT2nex+Idfcd5s7jnePOdX2Vu/0Jd67zbBF52t2WKiIPich4nHH7f++es4V7pd1fRGaJyFNeMU8VkRcvMM6teE3gISL/IyKZ4sy//kt32/04XzA2i8hmd9sIEdnq5nGliETUcx5jjBer2I1puBZezfBr3G1HgOGq2g9IAV6oZb+ZwH+raiJOxXrQHUI0BRjgbi8HJtVz/tFAjoiEA0uAFFXthTOS5CwRaQ+MBXqqam/gMe+dVXUVkIlzZZ2oqiVeT68CxnmtpwArLjDOm3GGjq00T1X7A72BQSLSW1VfwBkLfIiqDnGHl/1P4AduLjOBOfWcxxjjxYaUNabhStzKzdsVQJp7T7kcZ+zzmrYC80QkCviDqn4uIsOAJGC7O+RuC5wvCbX5vYiUAPk4U3t+D9ivqnvd55cCs4E0nPnefysibwI+TxOrqkdFZJ87Tvfn7jm2uMdtSJytcIZQ7ee1fYKI/ATnc6cz0APIrrHvje72Le55rsTJmzHGR1axG+MfDwCHgT44LWFnahZQ1XQR+Qj4N+AdEbkHZ5rKpar6iA/nmOQ9aYyIdKitkDs++fU4E4vcCfwUGNqA17ICmADsAdaoqopTy/ocJ/AJ8ASwEBgnIl2Bh4DrVLVIRJbgTHZSkwB/VtW7GhCvMcaLNcUb4x9tgAJ3Du27ca5WqxGRGGCf2/y8HqdJ+l1gvIh0csu0F5FrfDznHiBaRGLd9buBv7j3pNuo6ls4HdNq65lejDOVbG3+APwQZ57wFe62BsWpqmU4Teo3us34VwOngK9FJBK4pY5YtgEDKl+TiLQUkdpaP4wxdbCK3Rj/eAmYIiLbcJrhT9VSJgXYJSJZQBywzO2J/p/An0QkG/gzTjN1vVT1DM7sVytFJAeoABbhVJIb3OP9Bac1oaYlwKLKznM1jlsE7AauUdWP3W0NjtO9d/8M8JCqfgLsBD4FXsFp3q+0GPijiGxW1aM4PfYz3PNsw8mVMcZHNrubMcYYE0Lsit0YY4wJIVaxG2OMMSHEKnZjjDEmhFjFbowxxoQQq9iNMcaYEGIVuzHGGBNCrGI3xhhjQsj/Axo4dpXX9IXpAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -4903,10 +5494,10 @@ { "data": { "text/plain": [ - "0.7040433457077317" + "0.6995913850752561" ] }, - "execution_count": 81, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -5032,16 +5623,16 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'C': 1, 'gamma': 0.1}" + "{'C': 1, 'gamma': 0.001}" ] }, - "execution_count": 84, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -5049,14 +5640,14 @@ "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.01, 0.1, 1,10]\n", - " gammas = [0.01, 0.1, 10]\n", + " Cs = [0.001, 0.01, 0.1, 1,10]\n", + " gammas = [0.001, 0.01, 0.1, 10]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,2)\n" + "svc_param_selection(X_train_pca, y_train,5)\n" ] }, { @@ -5068,44 +5659,44 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 85, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", "clf_reduced_tuned.fit(X_train_pca, y_train)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16537143473786733\n" + "Optimal Threshold: 0.153632964235413\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5123,14 +5714,14 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the SVM reduced features and tuning RBF kernal identified 727\n" + "Of 2015 Defaulters, the SVM reduced features and tuning RBF kernal identified 895\n" ] }, { @@ -5165,14 +5756,14 @@ " \n", " \n", " \n", - " 0\n", - " 6383\n", - " 346\n", + " 0\n", + " 6115\n", + " 619\n", " \n", " \n", - " 1\n", - " 1293\n", - " 727\n", + " 1\n", + " 1120\n", + " 895\n", " \n", " \n", "\n", @@ -5181,11 +5772,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6383 346\n", - "1 1293 727" + "0 6115 619\n", + "1 1120 895" ] }, - "execution_count": 87, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -5197,7 +5788,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -5206,12 +5797,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.36 0.47 2020\n", + " 0 0.85 0.91 0.88 6734\n", + " 1 0.59 0.44 0.51 2015\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.72 0.68 0.69 8749\n", + "weighted avg 0.79 0.80 0.79 8749\n", "\n" ] } @@ -5431,7 +6022,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/BT2101_Default - EDIT-Copy1.ipynb index fcf3597..dbd858a 100644 --- a/BT2101_Default - EDIT-Copy1.ipynb +++ b/BT2101_Default - EDIT-Copy1.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -821,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -857,326 +857,276 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", - " 26245.0\n", - " 0.284337\n", - " 0.237875\n", - " 0.0\n", - " 0.081633\n", - " 0.224490\n", - " 0.408163\n", - " 1.0\n", + " LIMIT_BAL\n", + " 30000.0\n", + " 167484.322667\n", + " 129747.661567\n", + " 10000.0\n", + " 50000.00\n", + " 140000.0\n", + " 240000.00\n", + " 1000000.0\n", " \n", " \n", - " AGE\n", - " 26245.0\n", - " 0.368595\n", - " 0.232422\n", - " 0.0\n", - " 0.184211\n", - " 0.342105\n", - " 0.526316\n", - " 1.0\n", + " AGE\n", + " 30000.0\n", + " 35.485500\n", + " 9.217904\n", + " 21.0\n", + " 28.00\n", + " 34.0\n", + " 41.00\n", + " 79.0\n", " \n", " \n", - " PAY_0\n", - " 26245.0\n", - " 0.372109\n", - " 0.765730\n", + " PAY_0\n", + " 30000.0\n", + " -0.016700\n", + " 1.123802\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_2\n", - " 26245.0\n", - " 0.337321\n", - " 0.814878\n", + " PAY_2\n", + " 30000.0\n", + " -0.133767\n", + " 1.197186\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_3\n", - " 26245.0\n", - " 0.324633\n", - " 0.811491\n", + " PAY_3\n", + " 30000.0\n", + " -0.166200\n", + " 1.196868\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_4\n", - " 26245.0\n", - " 0.278224\n", - " 0.786314\n", + " PAY_4\n", + " 30000.0\n", + " -0.220667\n", + " 1.169139\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_5\n", - " 26245.0\n", - " 0.238750\n", - " 0.743923\n", + " PAY_5\n", + " 30000.0\n", + " -0.266200\n", + " 1.133187\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " PAY_6\n", - " 26245.0\n", - " 0.243208\n", - " 0.740345\n", + " PAY_6\n", + " 30000.0\n", + " -0.291100\n", + " 1.149988\n", + " -2.0\n", + " -1.00\n", " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.00\n", " 8.0\n", " \n", " \n", - " BILL_AMT1\n", - " 26245.0\n", - " 0.200168\n", - " 0.189380\n", - " 0.0\n", - " 0.057590\n", - " 0.122923\n", - " 0.265297\n", - " 1.0\n", + " BILL_AMT1\n", + " 30000.0\n", + " 51223.330900\n", + " 73635.860576\n", + " -165580.0\n", + " 3558.75\n", + " 22381.5\n", + " 67091.00\n", + " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", - " 26245.0\n", - " 0.254917\n", - " 0.174586\n", - " 0.0\n", - " 0.123671\n", - " 0.185749\n", - " 0.308929\n", - " 1.0\n", + " BILL_AMT2\n", + " 30000.0\n", + " 49179.075167\n", + " 71173.768783\n", + " -69777.0\n", + " 2984.75\n", + " 21200.0\n", + " 64006.25\n", + " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", - " 26245.0\n", - " 0.298159\n", - " 0.160977\n", - " 0.0\n", - " 0.177739\n", - " 0.237020\n", - " 0.351773\n", - " 1.0\n", + " BILL_AMT3\n", + " 30000.0\n", + " 47013.154800\n", + " 69349.387427\n", + " -157264.0\n", + " 2666.25\n", + " 20088.5\n", + " 60164.75\n", + " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", - " 26245.0\n", - " 0.317648\n", - " 0.156805\n", - " 0.0\n", - " 0.201836\n", - " 0.261194\n", - " 0.370615\n", - " 1.0\n", + " BILL_AMT4\n", + " 30000.0\n", + " 43262.948967\n", + " 64332.856134\n", + " -170000.0\n", + " 2326.75\n", + " 19052.0\n", + " 54506.00\n", + " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", - " 26245.0\n", - " 0.330072\n", - " 0.155422\n", - " 0.0\n", - " 0.217453\n", - " 0.274164\n", - " 0.374386\n", - " 1.0\n", + " BILL_AMT5\n", + " 30000.0\n", + " 40311.400967\n", + " 60797.155770\n", + " -81334.0\n", + " 1763.00\n", + " 18104.5\n", + " 50190.50\n", + " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", - " 26245.0\n", - " 0.427951\n", - " 0.133243\n", - " 0.0\n", - " 0.332699\n", - " 0.377760\n", - " 0.463563\n", - " 1.0\n", + " BILL_AMT6\n", + " 30000.0\n", + " 38871.760400\n", + " 59554.107537\n", + " -339603.0\n", + " 1256.00\n", + " 17071.0\n", + " 49198.25\n", + " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", - " 26245.0\n", - " 0.072779\n", - " 0.105966\n", + " PAY_AMT1\n", + " 30000.0\n", + " 5663.580500\n", + " 16563.280354\n", " 0.0\n", - " 0.015270\n", - " 0.041383\n", - " 0.088084\n", - " 1.0\n", + " 1000.00\n", + " 2100.0\n", + " 5006.00\n", + " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", - " 26245.0\n", - " 0.055133\n", - " 0.092316\n", + " PAY_AMT2\n", + " 30000.0\n", + " 5921.163500\n", + " 23040.870402\n", " 0.0\n", - " 0.009191\n", - " 0.030637\n", - " 0.061596\n", - " 1.0\n", + " 833.00\n", + " 2009.0\n", + " 5000.00\n", + " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", - " 26245.0\n", - " 0.061893\n", - " 0.105644\n", + " PAY_AMT3\n", + " 30000.0\n", + " 5225.681500\n", + " 17606.961470\n", " 0.0\n", - " 0.006251\n", - " 0.030227\n", - " 0.069435\n", - " 1.0\n", + " 390.00\n", + " 1800.0\n", + " 4505.00\n", + " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", - " 26245.0\n", - " 0.061708\n", - " 0.107040\n", + " PAY_AMT4\n", + " 30000.0\n", + " 4826.076867\n", + " 15666.159744\n", " 0.0\n", - " 0.003321\n", - " 0.026566\n", - " 0.069027\n", - " 1.0\n", + " 296.00\n", + " 1500.0\n", + " 4013.25\n", + " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", - " 26245.0\n", - " 0.062874\n", - " 0.107502\n", + " PAY_AMT5\n", + " 30000.0\n", + " 4799.387633\n", + " 15278.305679\n", " 0.0\n", - " 0.001855\n", - " 0.027558\n", - " 0.071046\n", - " 1.0\n", + " 252.50\n", + " 1500.0\n", + " 4031.50\n", + " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", - " 26245.0\n", - " 0.055339\n", - " 0.103357\n", + " PAY_AMT6\n", + " 30000.0\n", + " 5215.502567\n", + " 17777.465775\n", " 0.0\n", - " 0.000000\n", - " 0.022412\n", - " 0.060176\n", - " 1.0\n", - " \n", - " \n", - " S_0\n", - " 26245.0\n", - " -0.133587\n", - " 0.879876\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_2\n", - " 26245.0\n", - " -0.300438\n", - " 0.883472\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_3\n", - " 26245.0\n", - " -0.327300\n", - " 0.895264\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_4\n", - " 26245.0\n", - " -0.364412\n", - " 0.886115\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_5\n", - " 26245.0\n", - " -0.395999\n", - " 0.877789\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", - " \n", - " \n", - " S_6\n", - " 26245.0\n", - " -0.428158\n", - " 0.900723\n", - " -2.0\n", - " -1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.0\n", + " 117.75\n", + " 1500.0\n", + " 4000.00\n", + " 528666.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " count mean std min 25% 50% 75% max\n", - "LIMIT_BAL 26245.0 0.284337 0.237875 0.0 0.081633 0.224490 0.408163 1.0\n", - "AGE 26245.0 0.368595 0.232422 0.0 0.184211 0.342105 0.526316 1.0\n", - "PAY_0 26245.0 0.372109 0.765730 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_2 26245.0 0.337321 0.814878 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_3 26245.0 0.324633 0.811491 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_4 26245.0 0.278224 0.786314 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_5 26245.0 0.238750 0.743923 0.0 0.000000 0.000000 0.000000 8.0\n", - "PAY_6 26245.0 0.243208 0.740345 0.0 0.000000 0.000000 0.000000 8.0\n", - "BILL_AMT1 26245.0 0.200168 0.189380 0.0 0.057590 0.122923 0.265297 1.0\n", - "BILL_AMT2 26245.0 0.254917 0.174586 0.0 0.123671 0.185749 0.308929 1.0\n", - "BILL_AMT3 26245.0 0.298159 0.160977 0.0 0.177739 0.237020 0.351773 1.0\n", - "BILL_AMT4 26245.0 0.317648 0.156805 0.0 0.201836 0.261194 0.370615 1.0\n", - "BILL_AMT5 26245.0 0.330072 0.155422 0.0 0.217453 0.274164 0.374386 1.0\n", - "BILL_AMT6 26245.0 0.427951 0.133243 0.0 0.332699 0.377760 0.463563 1.0\n", - "PAY_AMT1 26245.0 0.072779 0.105966 0.0 0.015270 0.041383 0.088084 1.0\n", - "PAY_AMT2 26245.0 0.055133 0.092316 0.0 0.009191 0.030637 0.061596 1.0\n", - "PAY_AMT3 26245.0 0.061893 0.105644 0.0 0.006251 0.030227 0.069435 1.0\n", - "PAY_AMT4 26245.0 0.061708 0.107040 0.0 0.003321 0.026566 0.069027 1.0\n", - "PAY_AMT5 26245.0 0.062874 0.107502 0.0 0.001855 0.027558 0.071046 1.0\n", - "PAY_AMT6 26245.0 0.055339 0.103357 0.0 0.000000 0.022412 0.060176 1.0\n", - "S_0 26245.0 -0.133587 0.879876 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_2 26245.0 -0.300438 0.883472 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_3 26245.0 -0.327300 0.895264 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_4 26245.0 -0.364412 0.886115 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_5 26245.0 -0.395999 0.877789 -2.0 -1.000000 0.000000 0.000000 1.0\n", - "S_6 26245.0 -0.428158 0.900723 -2.0 -1.000000 0.000000 0.000000 1.0" + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " ] }, - "execution_count": 90, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1219,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1231,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1243,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1319,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1347,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1430,36 +1380,459 @@ " S_6\n", " \n", " \n", - " ID\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " ID\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 1\n", + " 1\n", + " 1\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 2\n", + " -1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 4\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 5\n", + " -1\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 6\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 7\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 8\n", + " 0\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 9\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 10\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 11\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 12\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " 1\n", + " \n", + " \n", + " 13\n", + " -1\n", + " 0\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 14\n", + " 1\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 15\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 16\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 17\n", + " 0\n", + " 0\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 18\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 19\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 20\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 21\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 22\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 23\n", + " 1\n", + " 0\n", + " 0\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 24\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 25\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 26\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 27\n", + " 1\n", + " -2\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 28\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " \n", + " \n", + " 30\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 29971\n", + " -1\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 29972\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29973\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 29974\n", + " 1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29975\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29976\n", + " 0\n", + " 0\n", + " -1\n", + " -1\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29977\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 29978\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29979\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29980\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29981\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " \n", - " \n", - " \n", " \n", + " 29982\n", + " 1\n", " 1\n", " 1\n", " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29983\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 29984\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29985\n", + " -1\n", + " -1\n", + " -2\n", + " -1\n", " -1\n", " -1\n", + " \n", + " \n", + " 29986\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", " -2\n", " -2\n", " \n", " \n", - " 2\n", + " 29987\n", " -1\n", - " 1\n", + " -1\n", + " -2\n", + " -2\n", + " -2\n", + " -2\n", + " \n", + " \n", + " 29988\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", - " 1\n", " \n", " \n", - " 3\n", + " 29989\n", " 0\n", " 0\n", " 0\n", @@ -1468,7 +1841,16 @@ " 0\n", " \n", " \n", - " 4\n", + " 29990\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -1\n", + " -2\n", + " \n", + " \n", + " 29991\n", " 0\n", " 0\n", " 0\n", @@ -1477,25 +1859,34 @@ " 0\n", " \n", " \n", - " 5\n", - " -1\n", - " 0\n", - " -1\n", + " 29992\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 29993\n", " 0\n", " 0\n", " 0\n", + " -2\n", + " -2\n", + " -2\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 29994\n", + " 0\n", + " -1\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", - " 29995\n", + " 29995\n", " 1\n", " 1\n", " 1\n", @@ -1504,7 +1895,7 @@ " 1\n", " \n", " \n", - " 29996\n", + " 29996\n", " 0\n", " 0\n", " 0\n", @@ -1513,7 +1904,7 @@ " 0\n", " \n", " \n", - " 29997\n", + " 29997\n", " -1\n", " -1\n", " -1\n", @@ -1522,7 +1913,7 @@ " 0\n", " \n", " \n", - " 29998\n", + " 29998\n", " 1\n", " 1\n", " 1\n", @@ -1531,7 +1922,16 @@ " 0\n", " \n", " \n", - " 30000\n", + " 29999\n", + " 1\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " -1\n", + " \n", + " \n", + " 30000\n", " 0\n", " 0\n", " 0\n", @@ -1541,7 +1941,7 @@ " \n", " \n", "\n", - "

26245 rows × 6 columns

\n", + "

30000 rows × 6 columns

\n", "" ], "text/plain": [ @@ -1552,17 +1952,67 @@ "3 0 0 0 0 0 0\n", "4 0 0 0 0 0 0\n", "5 -1 0 -1 0 0 0\n", + "6 0 0 0 0 0 0\n", + "7 0 0 0 0 0 0\n", + "8 0 -1 -1 0 0 -1\n", + "9 0 0 1 0 0 0\n", + "10 -2 -2 -2 -2 -1 -1\n", + "11 0 0 1 0 0 -1\n", + "12 -1 -1 -1 -1 -1 1\n", + "13 -1 0 -1 -1 -1 -1\n", + "14 1 1 1 0 0 1\n", + "15 0 0 0 0 0 0\n", + "16 1 1 0 0 0 0\n", + "17 0 0 1 1 1 1\n", + "18 0 0 0 -1 -1 -1\n", + "19 1 -2 -2 -2 -2 -2\n", + "20 1 -2 -2 -2 -2 -2\n", + "21 0 0 0 0 0 -1\n", + "22 -1 -1 -1 -1 -1 -1\n", + "23 1 0 0 1 1 1\n", + "24 -2 -2 -2 -2 -2 -2\n", + "25 0 0 0 -1 0 0\n", + "26 0 0 0 0 0 0\n", + "27 1 -2 -1 -1 -1 -1\n", + "28 0 0 0 0 0 0\n", + "29 -1 -1 -1 -1 -1 -1\n", + "30 0 0 0 0 0 0\n", "... ... ... ... ... ... ...\n", + "29971 -1 -1 -1 0 0 -1\n", + "29972 0 0 0 0 0 0\n", + "29973 0 0 0 0 0 -1\n", + "29974 1 -2 -2 -2 -2 -2\n", + "29975 1 1 1 1 0 0\n", + "29976 0 0 -1 -1 -2 -2\n", + "29977 1 1 1 1 1 1\n", + "29978 0 0 0 0 0 0\n", + "29979 0 0 0 0 0 0\n", + "29980 -2 -2 -2 -2 -2 -2\n", + "29981 0 0 0 0 0 0\n", + "29982 1 1 1 1 0 0\n", + "29983 0 0 0 0 0 0\n", + "29984 -2 -2 -2 -2 -2 -2\n", + "29985 -1 -1 -2 -1 -1 -1\n", + "29986 -2 -2 -2 -2 -2 -2\n", + "29987 -1 -1 -2 -2 -2 -2\n", + "29988 0 0 0 0 0 0\n", + "29989 0 0 0 0 0 0\n", + "29990 -1 -1 -1 -1 -1 -2\n", + "29991 0 0 0 0 0 0\n", + "29992 1 1 1 1 1 1\n", + "29993 0 0 0 -2 -2 -2\n", + "29994 0 -1 -1 0 0 0\n", "29995 1 1 1 1 1 1\n", "29996 0 0 0 0 0 0\n", "29997 -1 -1 -1 -1 0 0\n", "29998 1 1 1 -1 0 0\n", + "29999 1 -1 0 0 0 -1\n", "30000 0 0 0 0 0 0\n", "\n", - "[26245 rows x 6 columns]" + "[30000 rows x 6 columns]" ] }, - "execution_count": 94, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1642,7 +2092,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1666,7 +2116,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1690,7 +2140,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1714,7 +2164,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1738,7 +2188,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1762,7 +2212,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1786,7 +2236,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1810,7 +2260,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -2007,7 +2457,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -2031,7 +2481,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -2055,7 +2505,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -2079,7 +2529,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -2103,7 +2553,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2237,7 +2687,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2245,7 +2695,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2253,7 +2703,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2261,7 +2711,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2269,7 +2719,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2353,7 +2803,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2374,7 +2824,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2395,7 +2845,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2416,7 +2866,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2437,7 +2887,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2533,7 +2983,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2553,7 +3003,7 @@ " dtype='object')" ] }, - "execution_count": 97, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2717,24 +3167,25 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 81.747784 3.335170e-04\n", - "PAY_0 4215.816943 0.000000e+00\n", - "PAY_2 3730.186349 0.000000e+00\n", - "PAY_3 3017.725262 0.000000e+00\n", - "PAY_4 3020.555618 0.000000e+00\n", - "PAY_5 2812.887854 0.000000e+00\n", - "PAY_6 2575.321192 0.000000e+00\n", - "PAY_0_No_Transactions 63.305610 2.349730e-02\n", - "PAY_0_Revolving_Credit 454.630595 0.000000e+00\n", - "PAY_2_Pay_Duly 76.591060 1.225866e-03\n", - "PAY_2_Revolving_Credit 241.568619 0.000000e+00\n", - "PAY_3_Pay_Duly 88.958886 4.790331e-05\n", - "PAY_3_Revolving_Credit 140.257620 3.064993e-12\n", - "PAY_4_Pay_Duly 82.933933 2.446182e-04\n", - "PAY_4_Revolving_Credit 98.729838 2.851222e-06\n", - "PAY_5_Pay_Duly 67.720209 9.456393e-03\n", - "PAY_5_Revolving_Credit 70.202609 5.486203e-03\n", - "PAY_6_Revolving_Credit 73.167377 2.782902e-03\n" + "LIMIT_BAL 82.388112 2.822569e-04\n", + "PAY_0 4424.481682 0.000000e+00\n", + "PAY_2 3847.416138 0.000000e+00\n", + "PAY_3 3007.408864 0.000000e+00\n", + "PAY_4 3034.730536 0.000000e+00\n", + "PAY_5 2869.196497 0.000000e+00\n", + "PAY_6 2526.952024 0.000000e+00\n", + "PAY_0_No_Transactions 71.907192 3.727224e-03\n", + "PAY_0_Revolving_Credit 498.806543 0.000000e+00\n", + "PAY_2_Pay_Duly 75.936267 1.438075e-03\n", + "PAY_2_Revolving_Credit 247.861963 0.000000e+00\n", + "PAY_3_Pay_Duly 78.497278 7.644927e-04\n", + "PAY_3_Revolving_Credit 142.164275 1.549982e-12\n", + "PAY_4_Pay_Duly 79.625464 5.751909e-04\n", + "PAY_4_Revolving_Credit 99.566680 2.218832e-06\n", + "PAY_5_Pay_Duly 73.322648 2.683519e-03\n", + "PAY_5_Revolving_Credit 66.651236 1.187101e-02\n", + "PAY_6_Pay_Duly 63.464171 2.277242e-02\n", + "PAY_6_Revolving_Credit 62.888951 2.550124e-02\n" ] } ], @@ -4476,6 +4927,146 @@ "#### Theory\n", "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." ] }, @@ -4497,7 +5088,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -4511,14 +5102,14 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.2966725486 0.1736524263]\n" + "Explained variation per principal component: [0.2920039216 0.1749065908]\n" ] } ], @@ -4536,12 +5127,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAH6CAYAAAAeHtXEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOyde5xT1b3ovyuZJMMkQWQQ0AEUnBFlqFh81Tq1WlHq2NqqaNrSFk+xtkdry6PnQnv7sKf3Fg9VW1q0py3tFR9HYlFLKwMoVbSjtr6xgEDwUaCDoANKJjB5rvvHToZMZifZCclMZub35ZPPJnuvvfZvJ5n9W+u3fg+ltUYQBEEQhP6Pra8FEARBEAShNIhSFwRBEIQBgih1QRAEQRggiFIXBEEQhAGCKHVBEARBGCCIUhcEQRCEAYIodUEQBEEYIIhSFwYsSqlapdT1SqlHlFI7lFKHlVIfKKValVKzlVLy+x9gKKUuVEpppdQtRZz7dvLc1CuhlHpfKfWsUuompVRVlvPGKqVuVUq9pJQ6oJSKKqX2KaXWK6W+pZQ6Jsc1Z6Zd79JCZRaETEx/pIIwQLgG+BWwB3gS2AmMAq4ClgGXKaWu0ZKBSejOEuB9wA6Mx/i9nAdcnPx/F0qp64GlgAvYCDwAHABqgSbg58D3gRFZrnUDoAGV/P9jpb0VYbAhSl0YyGwHrgBWa60TqZ1Kqe8CzwNXYzykH+ob8YQK5eda67dTb5RSjcALwJVKqY9rrZ9K7v8C8FsMJX611np1ZkdKqfOBO80uopSaCFwArAeGA1copUZprfeW+H6EQYSYH4UBi9b6Ca31n9MVenL/O8B/J99eWEifSqlTlVK/T5pqw0kz61+VUv9u0vZipdRapdR+pVSnUmp70kzbwxyrlNqQNME6lFI/UEq9kTxnq1Lqq2ntvq6U+kdyKWG3UupHmcsISqmTkn3dnZT3j0kZQsmlB1Mzr1LKpZRaqJR6TSl1SCl1MHlv15q0Tb/GSUqpFUqp95Iyv6iU+lSOz/DzSqknk6bqTqXU60qp7ymlXCZtdfKzGaGU+o1Sak/yc9+slPq3jLZ3Y1hkAH6YYUq/MJs8+dBabwY2JN+ek7yWF/hlct/nzBR68txngHOzdJ36Xv8fcDfgAK4rVk5BAJmpC4OXaHIbs3qCUupy4A8Ypta1GKbWYcAU4H9hmPpTbb+WfB9KnrMPYwCxAPi0Uup8rfX7JpdZgaEEWpIyzgB+o5SKAqcDs4BHgb9gWCF+ABwC/sukr/HAc8Am4NfA8YAPWKOU+oLW2p8mrxNYB3wc2Ioxu6xJXt+vlDpDa/1dk2uciGH1eBO4F2PG6QNWKaWmaa2fTG+slPod8BVgN/Awhpn7I8CPgYuVUpdorTO/k2HAM0AEWAlUJ+X6vVIqobVenmz3x+R2FvAURxQxwNsmsheCSm5TSzUzMO71b1rrnCZzrXW4R2fG5z0LOAg8gvFZ3wZcr5RaLEtCQtForeUlr0H1whjM/gPjAT3d4jkjgA8wFMvHTY6PSfv/iUAY44F9aka7u5LX/U3G/g3J/S8Aw9L2T0he8wDwFlCXdmwY8B7wLlCVtv+kZF8a+GnGdc7CGCwcAIam7f9Osn1LRl8jMRSiBj6a5Ro/zLjG9FRfGfuvS+5/GBiSceyW5LFvZexPXWMZYE/bPwljQLYlo/2Fyfa3FPG7SN3nSRn7GzEGThr4WHLf75Lv/0+Rv8HPJc//ddq+h5L7Lu7rvxF59d9XnwsgL3n19gtjRqQx1tqtnjM/ec4SC23/d7LtT0yOHZtU9ocBV9r+Ddke6MATyWNfMTn2/5LHTkzbl1K47wNek3PuTh6flbYvACTIGIQkj81Otv+9yTXeTle2acf/CbyXse8VjAHFMJP2dowByvMZ+zWGtWOoyTlPJY970/aVQqn/PDnI+DFwX5pCfzitbUty39eL/A2mvtPz0vZ9OrlvRV/+fcirf7/E/C4MKpRS38RQ0FuBLxVw6keS2zUW2k5Nbp/IPKC1PqCUegXDQepUDI/pdF406a8tuX3J5Ni/ktsxGIo0nZe11kGTczZgmH4/DCxPrg/XA//SWm81aZ+6jw+bHHtVax032b8Lw2McAKVUDcYyxXvAHKWUySmEgdNM9ge01gezXAMMi4XZfRbLt5JbDXQAr2Eo9/9Oa5NpjreMUqoeY/CxTWv9XNqhNcBeDIe8EVrr9wrtWxBEqQuDBqXUTRjhSlswZsT7Czh9WHL7r5ytDFKOcHuyHE/tH5Z5QGv9gUn71BpzrmMOk2PZvKjfSW6PydgWLC+GNcCMGN0dcY/FUITHAT/Mck42cl0DjFl+KRmv07zfs5AaaI0pov+vYnwWd6fv1FrHlFL3YQw6r8OwKAlCQYj3uzAoUErNwYgn3gRcpA0P+EJIKZY6C21Tynd0luPHZ7QrF6Oy7E/J9UHGtpzyps59RWutcr2O4hq9SWtye3EhJyml0j3cF2V46GsMhQ5HPOMFoSBEqQsDHqXUAuBnwKsYCn1fEd38Lbm9zELbV5LbC01kGQacAXQCrxchRyFMTZrWM0nJ9QpA0kT/BlCnlGowaX9RcvtysYJorTuAzUCjUmp4sf1YILUUUOrZeyYrgf3AeUqpabkaZoTqfQbD+XAbhrOd2etN4BSl1MfLILcwwBGlLgxolFLfB27FWI+++CjWKZdjOLj9u1LqApPrpJth78NwCLs5uX6azo+BocB92iTUqcQcgxHy1oVS6ixgJsbM+ZG0Q7/HMAn/VCllT2s/AiMjWqrN0XAH4MQIRethyldKHauUmtrztIJoT27HHWU/OUkOhL6ZfOtXSk03a6eU+ghGWGGKG5LbH2itrzd7AT/JaCsIlpE1dWHAopSaBfwnxuztr8A3TRy03tZa352vL631e8rIILYSeFIptQbDgWooRvz4WIy4cLTWbyfN/XcCLyulHsQIO/s4hvPYVox49XLzNEbc87kYcd6pOHUb8LUM57PbMKwQnwE2KqVaMGKnr8GYWS7WWrdyFGitf6+UOhO4EXhDKbUOI3XvcIzP7gIMb/6vH8VltmH4PXxOKRVJ9q+Be7XWmY6ER4XW+n6l1BCMZZ21SqlXgWc5kib2PI44B6KUGg9MS77/o2mnBiswLEtXK6VuLtD3QxjkiFIXBjLjk1s7MCdLm6fIcFjKhtZ6dXKmuwBjLfVSjAf4VmBRRtu7lFI7gG9jpKOtwfDW/ilGqFs2569S8haGgrw1uXVhmND/U2u9LkPeiFLqEmAe8AXgZgxHtI3AHK31A6UQSGt9U3JA9HUMBTcMw4y9E+Ozue8o+48rpa7EuOdrAS+GBaKVntEBR43WellycPIN4BIMK4gbwwdjEzCXIxaO65Oy3Ku1juToM6SUWoGxrj4LQ8ELgiWU1pK4SBAGEkqpkzAU+nKt9XV9KowgCL2KrKkLgiAIwgBBlLogCIIgDBBEqQuCIAjCAEHW1AVBEARhgCAzdUEQBEEYIPT7kLYRI0bok046qa/FKBuhUAi3293XYpSdwXKfMHjudbDcJwyeex0s9wmVfa8vvfTSe1rr48yO9XulftJJJ/Hii2aFrQYGGzZs4MILL+xrMcrOYLlPGDz3OljuEwbPvQ6W+4TKvlelVNacC2J+FwRBEIQBgih1QRAEQRggiFIXBEEQhAFCv19TNyMajbJ79246Ozv7WpSsVFdXM2bMGBwOR1+LIgiCIAwQBqRS3717N16vl5NOOgmTqlx9jtaa9vZ2du/ezfjx4/OfIAiCIAgWGJDm987OTmpraytSoQMopaitra1oS4IgCILQ/xiQSh2oWIWeotLlEwRBEPofA1ap9zVf+cpXGDlyJJMnT+5rUQRBEIRBgih1gGAQli2DBQuMbTB41F1ed911rF27tgTCCYIgCII1BqSjXEG0tkJzMyQSEAqB2w3z5kFLCzQ1Fd3tBRdcwNtvv106OQVBEAQhD4N7ph4MGgo9GDQUOhjb1P6Ojr6VTxAEQRAKYHArdb/fmKGbkUgYxwVBEAShnzC4lXogcGSGnkkoBDt29K48giAIgnAUDG6l3tBgrKGb4XZDfX3vyiMIgiAIR8HgVuo+H9iyfAQ2m3G8SD7/+c9z3nnnsW3bNsaMGcPvfve7ovsSBEEQBCsMbu93r9fwcs/0frfZjP0eT9FdP/DAAyUUVBAEQRDyM7iVOhhha21thlPcjh2Gyd3nOyqFLgiCIAh9gSh1MBT47Nl9LYUgCIJQYQTDQfyb/QTaAzTUNuBr9OF1eftarKyIUhcEQRAEE1p3ttJ8fzMJnSAUDeF2uJm3bh4tM1toGld8crJyMrgd5QRBEATBhIRO0Hx/M8FIkFDUCH0ORUMEI0Ga72+mI1KZyclEqQuCIAhCBvsP7yehzZOTJXQC/6bKTE4mSl0QBEEQMgjHw10z9ExC0RA79ldmcjJR6mVk7dq1TJw4kfr6em699da+FkcQBEGwiMvuwu0wT07mdripH16ZycnEUY7yeDfG43FuuukmHn/8ccaMGcPZZ5/NFVdcwaRJk0oktSAIglAuhg8Zjk2Zz3ttyoZvcvHJycrJoFfq5fJufP7556mvr2fChAkAfO5zn2PVqlWi1AVBEPoBNmWjZWZLD/2Q2u9xVmYuk0Gt1IPhYJd3Y4rUGkrz/c20zW8r+ov717/+xdixY7vejxkzhr///e9HJ7AgCILQazSNa6Jtfhv+TX527N9B/fB6fJN9FavQYZArdf9mf17vxtlTi0tKo7XusU8pVVRfgiAIQt/gcXqK1gN9waB2lAu0B8rm3ThmzBh27drV9X737t2ccMIJRfcnCIIgCPkY1Eq9obahbN6NZ599NoFAgLfeeotIJMKKFSu44ooriu5PEARBEPIxqJW6r9FXNu/Gqqoqli5dyvTp0znttNO49tpraWxsLLo/QRAEQcjHoF5T97q8ZfVubG5uprm5uUTSCoIgCEJuBrVSh/7p3SgIgiCUDrNcJd0bBI3y3IEANDQY5bm9lVmpbdArdeh/3o2CIAhCaciWq+R/zvyfZINWaG6GRAJCIXC7Yd48aGmBpsqr1CZKXRAEQRiU5MpVEtgfoKN9D57mZmOm3tUgGTHV3AxtbeCpLKvugHWUM4sTryQqXT5BEISBTq5cJQD++xYaM3QzEgnDJF9hDMiZenV1Ne3t7dTW1lZkwhetNe3t7VRXV/e1KIIgCIOWXLlKEjrBjne3QShE0An+yRAYDg37wbcJvKEQ7Ki8Sm0DUqmPGTOG3bt38+677/a1KFmprq5mzJgxfS2GIAjCoCWVq8RMsduUjfrjJtJ6ykaar+4kAYRc4A7DvOnQ8lA1TfWVV6ltQCp1h8PB+PHj+1oMQRAEoYLxNfqYt25e1uPN136PiXvvIeg6si+U/H/z1Z20XXk53VbUK8BLfsCuqQuCIAhCLlK5SrxOb1d2UbfDjdfppWF4A6v3PEViiPkyaWJINf63Vx/Z0doKdXUwZw4sXmxs6+qM/b3IgJypC4IgCEI+guEgW9/byvVTr6f9UDu1Q2ppHNmIb7KPF5990VhzT3SanhtKdB6pDxIMGt7wFeAlL0pdEARBGHSYxadnZhNtqG2guqqazlhPxV5dVX2kPojfn99Lfnbv5EIR87sgCIIwqEiPT085yYWiIYIRY39HpAOA5vpmU4UO0Bnr5PJTLjfeBAJdM/OgE5ZNhQXTjG0w2rte8jJTFwRBEAYVueLTEzqBf5OfkzmZlh0tOG1OIolIj3ZOm5PV21cb2UgbGsDtprU2RPNMenrKj9H0Vu45makLgiAIg4pc8emhaKhrrXzTvk2mCh0gkoiw5d0txhufj6BL0TwTgq4jHvIhl/G++f07u2b/5UaUuiAIgjCoSMWnm+F2uLvWyg8cPpCzn/bD7cZ/vF78d91IIkuuswQa/6beyT5XUUpdKTVWKfWkUup1pdRmpdS3+lomQRAEYWDha/RhU+bqz6Zs+CYbVdqGDxmes5/a/UfW2wPDIeQ0b5c++y83FaXUgRgwX2t9GvAR4Cal1KQ+lkkQBEEYQHhdXlo+dCveMLiT1nV3BLxhaPnQrV3e740jG6m2m8epV0dh0rI/QodhVrc6+y83FaXUtdZ7tNYvJ/8fBF4H6vpWKkEQBGFAEQzS9IWFbPsFXL0Fzt1lbLf9Apq+sLBLUfsafdiyhKrZNPhet3cVdbE6+y83qlKrhSmlTgKeBiZrrQ9mHLsBuAFg1KhRZ65YsaLX5estOjo68FRYab9yMFjuEwbPvQ6W+4TBc68D5j7fe4+OvTsJDDP0X8IGtqTubnhf4Rk1jo7qamrcNWx851US9NSTNg1T3gHbqNFG5jigI9JBYH/A6FMnupR8w/CGrtl/Kbjooote0lqfZXasIpW6UsoDPAX8X631w7nannXWWfrFF1/sHcH6gA0bNnDhhRf2tRhlZ7DcJwyeex0s9wmD514Hyn0GF8yhzr6kW073FN4wtOl5vDj90+wYuoM5f/4GIcI92rnDsORJJ7Ovv6tbYpmOSAf+TX527N9B/fB6fJN9JVXoAEqprEq94uLUlVIO4CHg/nwKXRAEQRAKxX/CARL7zI8lFPhHt3MyydA3E4UORrjajmO1UbQlDY/TY8Su9xEVtaaujOLnvwNe11rf0dfyCIIgCAOPQP3w7J7qTtjRUAsYzm81VTWm7dwRqL9uXq/ldLdKpc3Uzwe+BPxDKfVqct93tdYtfSiTIAiCMIBoOL4Rt63atFiL21ZN/ahJcBDGDR3Hodgh0z5sbg++z37vyI5gkOADy/G//SiBWmg471P4zpyF1zWIS69qrVu11kprfbrW+ozkSxS6IAiCUDJ8jT5sVQ7TY7YqB77JPhI6wWf9n83ax0rfQ0fWyltbaT1nFHVv38wc1rG4Yx1zVt9M3U9H07qzd0uvVpRSFwRBEIRyk6uOeqpKW/vhdg7HDmftI9BueLkTDBL8zGU0X3m4e4pYJwTjh2i+77JeSxELlWd+FwRBEISy0zSuibb5bVk91d/vfD/n+asDq7npnJvA78d/SoQshVdJxKL4N/l7zXlOlLogCIIwKDkqT/VUNHggQMAT6ZqhZxLS4V5LEQtifhcEQRCEHgyrHpbz+OUTk7XUGxpo6HDiNo98w61cvZYiFkSpC4IgCEIPaofUUuMwD2ercdQwa8os443Ph2+7M6syTTne9Rai1AVBEAQhA5uy8dNLfmp67KeX/PSI57vXi3fVGloeGWIUiEnO2N0R8NpraPnimpJnlMuFrKkLgiAIQgYJnWDh+oWmxxauX8iXp3z5iLJuaqLphX20PbAc/z9Xs8MD9R+9HN+Zs3pVoYPM1AVBEAShB/sP7yehzX3aEzqBf5O/+06PB/3lL6OvuorE6R9Cu1z0RW0VmakLgiAI/YZgOIh/s59Ae4CG2gZ8jb6yZG0Lx8OEoiHTY6FoqIdHe+vOVprvbyahE4SiIdwON/PWzaNlZgtN45pKLl82RKkLgiAI/YLeVJxOe5bk8EnGHDOm6//BcJDm+5sJRoJd+1IDgub7m2mb39ZrZngxvwuCIAgVT7riTCnMUDREMGLs782sbQDpJdb9m/2FmerLiCh1QRAEoeLpbcUZiUdyHt99cHfX/wPtgYJM9eVElLogCIJQ8fS24nTZXV154TNxO9zdEso01DZYbltuRKkLgiAIJScYDrLs5WUseHwBy15eRjAczH9SDnpbcQ4fMhybMleRNmXrllDG1+iz3LbciFIXBEEQSkrrzlbq7qhjzto5LH52MXPWzqHujrqjKkPa24rTpmx5K7mlsFL1rbcQ73dBEAShZJTLEzylODO931PKtxyKM18lt2LblhNR6oIgCELJ8G/2E01ETY9FE1GWv7ocV5WrqDjzvlCchVRyO6qqbyVClLogCIJQMjbv20xnrNP0WGesk7lr5+KschYdZ14JirOSkTV1QRAEoWTsP7w/5/GojlZGnPkARZS6IAiCUDKOHXJswef0doKWgYyY3wVBEATL5Mu9PnloPc4YRArQLr2doGUgI0pdEARBsERHpIO6O+py5l5vfq2TiD1LBxpQPXf3doKWgYyY3wVBEIS8BMNBAvsDeXOvP/zW6oL77u0ELQMZUeqCIAhCXvybs695p6+JP6q3m87GAVBg1/R5gpaBjJjfBUEQhLwE2gOM1CNNj3VbE3fkViufiI3Dd8UP+jRBy0BGlLogCIKQl4baBoLt5vnb09fEP3Xc+ax7f6f5bF3DZ0Y0SZx5GRHzuyAIgpAXX2P2Ne/0NfGrfLfk7Ofqz/1nKcUSMhClLgiCIOTF6/IyZugY02O3Tru1y4Tesudpqm1O03bVNier2zaUSUIBRKkLgiAIFgiGg+w+uNv02ML1C7u83wPtATp1xLRdp45IPHqZEaUuCIIg5MWq9/vYY8bm7GfMMeazfaE0iFIXBEEQ8hJoD5DQCdNjBWWE0yUUSuiBKHVBEAQhLw21DdiUucpI935/btdzOfvJZsIXSoModUEQBCEvVrzfg+EgD299OGu7mqoaSQdbZkSpC4IgCHnxurw0DG/A4/DgtBve7U67E4/D05URzr/Zj83cQg9AXMclHWyZEaUuCIIgWEeBSmaWUcabLgJ7NnMo0Zn11BkNn5XscWVGlLogCIKQl1RBl45IB+F4GIBwPExHpKOroEvDjv24zaPZcEfgor3VvSjx4ESUuiAIgpAXKyFtvrZjsWXxbrdp8O2pLZN0QgpR6oIgCEJe8oW0/d+//l+WH7+PlatceMPgNibzuMPgDUPLQ9V46if1osSDEynoIgiCIOQlV0EXgLfef4ubeYshn4U/roBdx8CO4VC/H3ybweNygE+c5MqNzNQFQRCEvOQKaUvnsBOu8sHhGicJBdrlRHs80NICHnGSKzcyU89D28E2vvOX77D1va2cOuJUFl28iBOGntDXYgmCIPQqqZA2r9NLJB7pcpYzI+SCb1+SIAy4lWLep6FlHDT1nriDFpmp5+CO5+6g7md13PPaPTzf9jz3vHYPdT+r464X7upr0QRBEHodj9ND2/w2Ljrporxtw8QACOkwwegRD3mhvIhSz8IfX/8j8x+bb3rsppabeKfjnV6WSBAEoe/xOD1cPelqXHZXQeelF30RyocodROC4SBXPnhl9gYa5q/+Zu8JJAiCUEH4Gn047I6Czimo6ItQNKLUTfj+ugW5KwkpeGLrul6TRxAEoZLwurysmbmGGkdNQedJ2dXyI0rdhDtf+XW31Idm7E0c7B1hBEEQKpCmcU3s/fZebrvkNiaPnMz4YeM5c/SZuU+SsqtlR7zfTYjpRF6lrvMcFwRBGOi8+s6r/OipH5HQCULRELvUrpztpexq+RGlLgiCIBRMMByk+f5mgpEjCWliOpa1fXrNdaF8iFLvS4JB8PshEICGBiPbktfb11IJgiDkxb/ZnzVtrBmpmutCeRGl3le0tkJzMyQSEAqB2w3z5hlZl5okRYMgCJVNoD1AKBrKetxhcxBNRHE73NiUravmulBeRKn3BcGgodCDaXmUQ8k/juZmaGuTdIpC3yDWI8EiY48Zm/P4NZOuYdwx46gfXo9vsk8Uei8hSr0v8PuNGboZiYRxfPbs3pUpG/KQHzyI9ahfEQwH8W/2E2gP0FDbgK/Rh9dVOX+bHx33UW46+6a+FmPQIUq9LwgEjszMMwmFYEeFJGiQh/zgQaxH/YrWna0039/c5XXudriZt24eLTNbaBrXO3+buz7I4+n+gXi69wUSp94XNDQYCtIMtxvqK8BDNP0hn3q4h0JH9ndIDucBhRXrkVARpHudp9a0Q9EQwUgwb371YDjIspeXseDxBSx7eRnBcPZSqvloqG3A7TB/jomne98hSr0v8PnAluWjt9kqo+awPOQHF/3FeiTk9DrPlV+9dWcrdXfUMWftHBY/u5g5a+dQd0cdrTtbi5LD1+jDpsyfY+Lp3neIUu8LvF6Cf1rJ0o85+eSXbXxyJiw930lwuIe2h+9h1uM3ce5vz2XWI7OIJqJ9I6M85AcX/cF6JAC5vc6z5Vc/mtl9NrwuLy0zW/A6vV0zdrfDjdfpFU/3PkTW1DM4GnOUVVp3tnLpM5/l8MWRrn3rGiLMm54g+syRQjLPtz3P6aecTuCFADeefWPZ5epG6iFvptjlIT/w8PkMfwkzKsV6JABHzN5mij2b2dvK7H721MKdc5vGNdE2vw3/Jj879u8QT/cKQGbqGSzfuLys/QfDQS677zIOxw73OBZNmGdj6pNSr/1hiUAoHV6v4QDp9R6ZsbvdR/aLk1zFUIzZu5jZvVU8Tg+zp85m0bRFzJ46WxR6HyNKPYNHtz+aN+/70eDf7CcS6yz4vIXrF5ZBmhzIQ37w0dRkeLkvWQILFxrbtjaJdKgwijF7i1Pb4EHM75nE4pabvtPxDqM9owvqPrBnM5Ec+ZGzse29bQWfc9SkHvJ+v7GGXl9vzNBFoQ9cPJ7KyZHQC1R6rHc2CjV7+xp9zFtnvrwiTm0DC1HqGZyzW7NOY2m2fs39V/HXrz1bUP9jA3uN8oOFWAM0TDxmQkHXKRmD7CEvDB5ad7Zy2f2XEY1HCcfDuOwu5q6by5qZa3ot1vtoSJm9rZCa3WfGtkv61oGHKPUMVry3AYZZaKig9Z3nCr/Avn3gLPy0W/fnqVMsCIJlguEgl957aTfflnA8TDgeZvp909n77b0VreiKsTCIU9vgQJR6BoFj4tZn0brw/nfVVkGB0SOjDsLo4L8Kv5ggCKb84MkfmDqrAhyKHmL5xuUVm+L0aLLJFTK7F/on4iiXSRmd5AAazvsUQyL526XjjAPt7WWRRxAGG8FwkF8+/8ucbVZvW91L0hRGOeLNhYGFKPVexnfmLBIuR0Hn7BoGHbWV77zTXyhlqkyh/+HfbCEbYhGD+974XRWbTU4YPIj5vZfxurxMPq6Rl/a9av0kBcvH7acyjYH9i0oohCH0LYH2AHGdO8rl8obLC+qzI9JB3R11Zf9dlTPeXBgYyEy9D+jcv6+wExSsPvbd8ggziBDTpQBGzHaNoybrcYfNwawzZlnuLxgOEtgf6JXflcSbC/kQpd4H1HRaj4VPESvzWv9gQEyXAhgx23Zlz3r80S88WpBHeC5zfup3VSrTvCZc8xIAACAASURBVBRREfIhSr0PeLMqWLDnvMNW2Dq80BMxXQpgnpHNYXNQXVXNui+u49KTL7XUT0pR//al32YdLIaiIX770m854Y4TSlIdTYqoCPmQNfU+4BDRgh1xnv7n0+URZhBRTCEMYWBytDHbmb4Z155ybda2f2/7e7f3qd9f8/3NtM1vK1gRN41rYts3trFw/UK2tW9jYu1Ebp12K8d7jy+oH2FgIko9A1cMwlWUNbStKpHsv4BryHrv0ZMzVWYsju/E5l6WSOgNsiVqKTZmO90342gotjpa5oBi075NPLL1EXH2FABR6uaUUaEHw0GCtiJqpGvgjTfg5JNLLlM6/TUXthW6pcqMRQklOnFHwKah5SHw3DbRKFbTnwuYBINGrv5AwCif6/MZRXgGKeWIdsjlm1EIxSz5mA0ojnbmLwwsRKlnEMnuP1MSLMXImqHgnes/x+gnXziq6+dS2lYfgP1Z8TeNa6Lthm34Pz2eHTVQvx98m8ET6QQ6obnZKGLTH4vWtLYa8icSEAoZVfXmzev/A5UiKZcCzOWbUQjFLPmUqy66MHAQpZ5BlYYi5tGWCbQHij73mye8xoNHce1cSnvKqCmWHoADIc7b88hqZr9WBaFwz4OJhDHT7W9FbIJBQ6EH00zCoaTi6c8DlaPAqgIMvr0d/23XETj4Fg1Dx+P79t14Tzola7+5fDNcdhcaTSSeP21kMd7q4uwp5EO83zPwJMo7zmmobSgqZzwKnhxT/HAjV4z2ZfddxvKNy/M+AAdMnHcgcEThZRIKGWVm+xt+vzEgMSM1UBlkWFGArUvmUfebicwZ+hyLT36HOUOfo+43E2ldYu57AbnDyhx2B05b7opN1VXVRXurS5y6kA9R6hlEKDyGvBCa64t3xuq0FTMaMMg1a+mIdnDfxvvyPgAHTJx3Q4NhmjbD7Tbqxvc3BuJA5SjJpwDHaA/Ne39G0AUhl7E/5IKgC5r3/oyOXW+YnmsWVmZTNrxOL2tmrmHNF9fkVNZaa7bfvL0oy5bEqQv5EKWewSF78YrTCi07Woo7UUNHlgmAlcQW+dYBn297HluWn0NqBjBgTH8+H9iy/PRtNuN4f2MgDlSOknwKUK/6E9EsTrFRBf7/+nLWvlMhcUs+uYSF5y9k7NCxtM1vo2lcE03jmlh08SKcdvM/2CpbFau3F1cwRuLUhXzImnovE2gPFOddn+Wc1Bp3XMc5FD2Ew+bg5jU3s8q3ikvrL+1yatu4dyN2Zc+a81on/5mRmgHc/erdOUUcM3RM/3Ci83oN57FMpzKbzdhfgrXnXv8cfD7DKc6M/jpQOUq6RTuk+YDYlI2WmS2s/P7VdB5nfm6nE7YE38zZf3pI3IYNG7op1F0f7Mq6rn60A2Cpiy7kIqdSV0p9CpgLjAS2AHdqrZ/OaHMu8KzWuiR+40qp3wOfAvZprSeXos9CsEFZDfBda+olCJsz8+6NJqJEE1Gm3z+d2y+9nVs23NL1QCuUGkcNdmW3PAN448AbvVLUoiQ0NRnOY36/YZqurzcUXwkUep84E/bCQKU/kksB/vY4D+h95n+LGtqPS7N8FBgqWO5ER1IXXchGVqWulLoEWAX8DXgKOA94Uin1c+DbWuty2anvBpYC95Sp/5xUazshq2q9CMXsO7GZrxZ+min+zX6iiezOc/Mfm1985xo+NHwSX5w6iz9v+zNb39vKjndez3nK0ueXdpOn4uNnPZ6SebmnZuab9m3i1y/9ms5YZ9exzM+hbJRxoNKfyaYAh5/3CdiUZTauoPb8acb/iwgVzJnoSNa+hTKSa6b+Q+AerfW/pXYopb4C/AKYoJT6vNa6M+vZRaK1flopdVKp+7VKvpKMXWgYVj2s8As89HDh56SzdCnMmgVeL5v3be6mPEqKghf2vsSm9a8bs01bNfFIJ04gYvKrcdgcKGU+yhno8bOZM/NspD6HkyljAqGMgUowHMT/8rLKXg7pIxrHn0v1puV0mgSxVuNg0olnFx0qmM/0X3EDXGHAkEupT8ZQ7F1orX+vlNoIPAo8kTTPDyhiVhsquOq0qwru3//2o5A74iUn7/xgHqO/+11oaWH/4f3Fd5QPDQmlj4SuJTqNX0sW+0wui0G/cqIrkEJShqY+h5OrypsVMMVAyClQTlKz6c5Iz9+uw1ltzKbvWZE/VDCLtUfWvoW+QGWzoiul3gFu0Fr/yeTYBGAdxvLzLcD9pVpTT/Z/EvBotjV1pdQNwA0Ao0aNOnPFihWlujQvtb1k2ax+4jEnMqJmREH9/2tvgHcSBy23H+Maw+7w7q73ww7DyQcAm41dE0aw71CBtdnLhEp+aGbOdjZlY+zQsTk/q46ODjz90Ez83qH32HVwl6W0oanPoTpRXfZ7TegEG/duNJXLpmxMGTUlq2d4qegP3+m7h95l5wc7e+wfd8w4jqs5Dv71L3jnnewdjB4NdXX94l5LwWC5T6jse73ooote0lqfZXYsl1J/DHhDa/3vWY6PBFowZvSO3lTq6Zx11ln6xRdfLNWlsf1Qoa086zQEvxsseNS99OnbuPmJ/7A8cLjtlNv49vZvd733dELwVsDtZtlPruarB/rE9cAUh81hOmP3Or1s+8Y2VgdWZzUDb9iwgQsvvLAXpS0NCx5fwOJnF1tq63V6aZvfxovPvlj2e1328jJuXnOz6fJMdVU1Sy9bWvxyiEWnsVJ/p6WOKAiGg9TdUWdqZUl9V557VsCcOeY5ANxuWLIEZs/ut7/fQhks9wmVfa9KqaxKPZf5/SHgu0qp4VrrHnZerfU+pdTHgUeAaaURte/xRCBYbbFtEWa094/SDSGeGnCEQvj21HJDtcoailYWcnjuX9t4LX/a9ifiiTiHYoeoUlXYbXZuufAWJi6d2Kdm4HKFmOXyck5htpZa7pC3XP4WnbFOtry3pbiO+yi/fG8XZunyA5FQQaGfkVWpa61/Dfw618la6xBwaSkFUko9AFwIjFBK7QZ+qLX+XSmvkYuvvH0sSyYeKFultvs33lt83xoaU9Z2txtvfSP/7v0yd21ZnjUsp+T3kaO/VdtWcd+V9/G5hz7XNWt3KmcPL/xSe8XnU5DlXFv2NfqYu26u6TGX3cUFJ16AXdm5/JTLmTJqCmCU0S136F8+f4v2Q+2Fd9pH+eWtFmYpdKBkKZmShAoK/YyKSz6jtf58X14/cOoI0AfK1v++A7vzN8rBipXJ/yRnCad95zdQm6VxOQYmOWrBJ3SCa/9wLZHEkaQbh2KHsndVAq/4fAq73KUqN+7dSDzRM2KiSlURS8R46p9PEYlH+OvOv/Ldv3yXldeuJLA/UPbSmccOOTbn8doh2X40ObCSX74MhXCszKgnjphY8MDNciy5hAoK/QhJE5vBBt4uaz31rA9FS+fCyVH3kdmDx8MbB/9ZXnkzsZH1eoeih7op9HwcrVe8lQIz5cxXn7r+4djhHsdiOkZcx7uyiqXkuuKBK7L2V8r8+fmSm0wYPqHwTvsov3y+GfUr77xSVKGhgvKop0IFFy0ytqLQhQpFlHoGEW05qK0oQraj6F9hOOa0tXWtX+49bkj2qm+9uNReDO4I1LuN1LLvHXovZ+56M6wo7HLmq891/WzEE/Gs55Qy9C9f/oJw1KTsbD7KkF/eSt2CXIVZAH790q8Jx8zvJxqPZh0oSR51YSBSceb3PkcpyqkNoxzFTB16mDf3NBwP77xt3rY3Z/BFoICxL71B3YY6fjT+Ryzethi3w83ctXO58ZwbQZNzbTSfwt68bzPth9uxYSNh8rlX26uPKl1nviI5ZsR0rCv8L5MaRw1tHW0seHzBUTvPrX9zfe7jb61n3kezlxc1pcROY1Z9HXJlZwOIJbIPlDvjnWx5N7tToMSSCwMNUeoZuKqqieVYB04n2N6Gt/aEgvp32B2E40XMksBUSbdFinB4KjFVMUgoSBQS1Kjh+hdhxnl3ElSRrtlrSkkufsYIE8u1NtpQ24DbVm0kxsmgWjn51Uu/QmttqtDBeOBffsrlBQjdHSue72Zki1Y4FD3Eys0rORQ7VP4IgWLGrSV0GivE1yE1o77k3kuKyqDYfjj334jkURcGEpbM70qpJ5RSp2Y5dopS6onSitV3NB432fIDb+TP63hs1R0F9e9xFD8DcKqeWvP9YryYS4UGewJiVVClKUxRKNg2ykbCltuc0LU2evcldLTv6Xas+fgLiEWyhG0lInTGOvMOoB56/SHT/VbMwrnWZK1QU1VjbB01XftSjoVW1oRz8alTcid7vHxikYOZlNPYkiWwcGGP5SCr5KpbEE30NJk3jWvi62d+vSiRt7y7paBlHUHoz1h9Il0IDM1ybChwQUmkqQDuvepeaw0VdFbB9Ffm89g/Vlnu32UrMkeshlPVqB67O4PvF9dfKVBH4uYjVRRm7tcQtUNIW7NaRKKdXDNvLMv8CwiGg7TubKXhtx8inDnO0eCIQbVF14XV23rWtW7d2UrdHXXMWTuHxc8uZs7aOdTdUUfrztZu7czWZK3idri5pvEaFp6/kBmTZjDEPsS0XbHOc6fUnpL1WI2jhllTZhXcZxclcBorJo6+cWRjwZ8zwAttL2T9DgVhoFHINKPHPEwp5QQ+AeTIo9i/OGXEKQXPOK946GrLsyn74c7iTJ8KPhLtXvy5dWcrB6vKWSg2C4XOyrMwcsxEyw/pcBWsnRBnzmuLOeG247n03ks5pCM9BxLKGCx0OiwKkXG+FY/6dFJrsks+uYSF5y9kwUcX4HF68t5XKBrieM/xLJq2iFg8xuF4Tw/6VLtCneeC4SAzHpyR9fgjvkf6fM24mDh6X6Mva9GgfByt5SOTTEtOoQ6TglAucpVe/SHwg+RbDfwtxx/UT0ssV99S4HMjnojjf2k5s8+7KW/bYDxUXMyBhvDQI4oipXz6xBmuFNdUEIvHCjZfh5xALM8adsrX0YKclzd0N0NbyjKWsf6auSb7vY9/D/8mPyu3rOSJt5/oCmtLJxUHHQwHeXhr9sp9NVU1BTvz5bqHmqoadn2wq6D+ykG+OPrN+zaz7OVl3ZwFs+UEcNldaLTp55xJ5ndYTGY/Mwe/H0/4Mc6dTimUI/Q5uRzlWoD3MB6NvwBuB97OaBMBtmqt/1oW6foIpUEXoLhidtjx7GqwoNTDiVhxSl2B95QPdb0tJpyqIPIpxWzHC8hit8oW4E8n/4QZW/8TWx+MTuzKzqwzupuhSxECl1Ly1zZeS90ddabKJhUHvWLTipwDm7iOF1x7O9c9HIodqoiKeZNHTqbaXk1n3NwE/+KeF3l97etdzoJTRk3JmhOgEMfT9O+wdWcrl91/GdF4lHA8jMvuYu66uayZuSarcs7m4JfQiZImDxKEYsmVJvYF4AUApVQQWK21fq+3BOtL6sIOdldHLSsnWwLqLSahy+aJnRcNwbS88cWEU5WUbJ9NAbpZK9h163dpe3MIaxdpFv6tCq0Ud54DOho1ZuWlli+NKe6TezyALWcZI/8sz0pN7UB7gEPR7NEWMybNKFhJFHIPfUVX2dMsSh2OeMNPu2caHxv3seKjRtJI3X/bwTYuvufibgOucDxMOB5m+n3T2fvtvaafezGWHEHoTSzNGbXWyweLQgdYNXpOQe0V4DvRmjexo9gZqeqe2rOhtgGX3VVcXxavV24iVbBjOHg+OMyIQ7BobYxb10TZ8wsn33jFgT1O0Yl1qlT+aM2Xg9u56Y83dPOMtpplzKozXeaa+5JPLmHKqCldM8FciVXcDjcXnXRR3vvIpKBMaX2E1+Vl5TUr8zfEULbr31pvybyeD5uyMfaYsUz4xYSs/R2KHmL5xuWmx8qZzEgQSoHVkDaHUurbSqlnlVI7lVL7Ml/lFrQ3mTr7+1z7us2aQ5iGb75ox/N5a97EIxzHFudkpmHScZO63voafWQrm9tfsCXg1dGwbKoR555Ca81d5yjidrIOLoZEjVe2z9Jus3eFjOXiro2/7aaQrWQZy+VMN+2eaewJdg+9S5njF01bxOyps7sp3HIo4P6SKW3nwZ2WvqNS4LQ78Tq9rLx2JTMenJF31m8WFQH5B2GVYAURBjdWk8/8DPga8CjwJMZa+sDF68V/81Oc+qML+c+PxvPOWj/5pvVF8mmNn2bZJvNZQD7SE6V4XV7+/ex/Z8nfllR85rhsJBSsbYC/joMfjwLnOGjaCf4Jh0gk7GCWzEaDKwY3Pw8RG2waCU+cDEop4mjcOFEOJ7OnzuZXL/wqtwDJzy0z6Um+LGO5TLDheJgJv5jA41963JLTlBUTfTGUJFOaxbrplrtLLlds3reZ/Yf3s3nf5pwFf0qF0+7kjkvvYNYZs1ixaYU1X5Qsf1O5sttVihVEGNxYVerXAAu11reXU5iKoqmJzrnfhBd+lrudgk/NiLL/geV4vprfUe7c8U38v38sJ16EIl69fXW39bqyzgrKUbY1k5RSdUHCBs0zYdsvYOVpELJnCdVTxmDgzrON84ZEQCXAE9E4EjD53QjPnxDhzr8vJUa8oHtJXxPNlWUsnz9DZ6yzIKepcqUqPapMaUXWTU/3Mzg7ejbBcBCvy9vlMR5NRIvKCmdGTVUNdpudW6fdysL1C4nGo93W6KurqnHYHN2y8ln1RcmMikhRrkGYIJQKq0pdAa+VU5BKpGH0pPyNMOKif/lPP98hv1L3NfqY9+jN3ZzeLKHosV73xv43yqZ47XGw221EVO/F30ZtMH4OxHIZPjREq4wXwOGkM90HydwtG2pIfiZpgwKLn1EoGmLHkyvBvz3nzNRKethCnaYqKlVpMEjwM5fhb+ggMBwa9oNvUwhvhB5104PhIMs3LufR7Y9y4PABNu7diF3ZORQ7xB0T76Dujrouk3e6x/jRYk/ANcddwNLr/oDH6eHLU76Mf5OfLe9uof1wO7U1tUwaManH4MjKdzekakiPqIh0zAZh4z8YL+FsQkVgVan/Fvg88HgZZak4fI0+vvqnr+ZXCgqWOl/jOxb69Lq8tFy7io+tmF5wBrbMmfne0N7yzKg1fG6bnVWnKSIkes28bzlhTC6OQlZ7HNr+9jjBP63F68g+M22ub+YbsRtz9tWfnaZa7/k/NH+tgwSGNcQdhnnToeV+aGo/Uje9dWcrl957qWmYGRgDm1S52SpbnkdNgb/juA2O/+Nf8HwBcFofFOUrDuOucrP2S2vzzrgzr7dhwwarogtCWbGq1PcCM5VST2Io9szcpFprnWcBs//hdXkZFob3q/O3LWTm3XTqpUUp48z1ul0f7CqPwlXwh4lxIr1dmNfKZ1LGAUbcDg+dEufh+XDjCyHCdjjwi4sYfuBrNNadga/Rx8a9G7n0nksJJ3KHPPZXp6lgOEjzuz8nmBZYEUr+v3kmtN0ewrNjB8FwkMvuuyyrQk8nnojnD0cr8Ht1h6H+gOoaYFjFzHyeSl4z99y5fO/j3xMTutCvsarUf57cjgM+bnJcAwNOqQMom51uplwzNDgKdER32uxE8vWbxlnHTenxsHnzwJuFXdQizmiyAm1vUwEOf6nY+MXnk8xMF4OX76T6tWrmrptLLBajM9GZV9b+6jTl3+wnkeXLjwP+qU5m19fj3+wnkrDmLxvTMRw2R9YCLmA4P2oNEYvWGhvgezkCOwq3hki5VWEgY0mpa617e85WMSinC8jjoauMIiKFdWwHbV2pX3fOV3vsq7KXp3KuVskCLb1FiXLJlxTVfWvVucsZBVeVo986TQXaA4Qwn1UfcsGTY+PM9vkIPPdjy3HjNVU1JEjkVOqO5J+CaY8a3FFjwOUOGwq95X7wONxQX5w1pKJ8GAShhEg99Tx0qKglhXPAXliUX7WjmkjE2jkuu8u0qtb5Y89n5/s7SzvD1XDNFlg18YjZtdwcfxCO7aS8HvfaUBzRMv/iT2mHx/6gOH4ahl0rSTav8EqjobaBIfYhWQvMPPQhO//tNNo57U5Lij2mY/z5839mxoMzjni/J7/r6ohh5Wq532jbPJNua/k2YKUfdh1jJCqq3w++zeCJAF6b4cwoCEIXlh9xSqmRwHzgLGAscKXWerNS6lvA81rr58okY58SybN22tVOFTbVPH3U6bTuslYG0m4zC9iGWz5+Cw/844GCrpsXBX+a2IuWcG3EpldNojwXTX0tKtl9InmdMt3g9lqYeEOElq9eStML+8Dj6VEAJOUVnh5qVSrSvdHBqKs+a8osywOIcUPHZVXoYHid+2+5Bt9JlzNXOYhYSFkRiUc4fdTpXSbvLf96lfZ7/pvaD2JMei9NSQNttxsm/h1DY9S/b8P3aqzrWDeqqw0nxiLKvgrCQMZqRrlzgABwNUZRl5OB1DzueAxlPzApk1m4kPVWhTKtqf2n1x4spUhdaAXXv2RkfCu7WVzB2IMYddHLca00BR6pwvjFl3HEEnFA0AXNVx6m44HlptnnUl7hpSoDmqJ1Zyujbx/NzWtuZt0b61j3xjpuXnMzo24bZamOeDAcZMYfspdsBQgRYcfza/HOW8ia+xIMsVkz59y4+kZWbFrB9vbtnFY3hV/etJrb/+Zl9na3obRrasDlwnPNTGZffxeLGr/F7OezKHSAG2/MGS8vCIOVQjLKPQlchfFY/Le0Y88DXyixXJVDAQqgI9JheR31qlOv4uY1N1tqmy086vfPLC2Lggo54a5zwRWHw6UIM8uFhhOChiKsBEe5UhEH/P9cjd7s6pUCIMFwkMvuv8y0OMzh2GEuu+8ytt+8ndWB1VkL0Fip/OeKJVP7Tgzh2wT7fu7hNyt/wH88/f2cxYoe2foIj73xWFeylnnKRsvzK2l6Zpfh7FZfb5jSUzPvZcuMhDchk3hytxsmWcshIQiDDatKfSrwGa11QvUsqt4OjCytWBVEAeu8VmuqA7TsaKG6qtqSA1a28Kh94XYok9KN2DFP01oGXjoBzuydSxVNdRQ6q7D8Wzjkgh0eSPRSARD/Zj/ReHZHtM5YJxN+MQG7sh9RrMmypoVkWwvbj6T2nTcdbn06wi1P/chQ6Hk+m/Q8+QDNj8zInnXP5zPyBJhhk7V0QciGVa/2D4DjshybgBHHPrhRsOWZVZabB9oDlj2qs4ZHJSrNZbwIFDxxEhU7S//omI8yb+o3WPqEi3X3gjdsOHDloyoO9R+9vNcKgATaAzljwWM6Rmess0cBmvQlgFyypvsmgOHIFnTBTdMiBFWkqO8vEo9wzYPXdKuS14XXa6yZe73GzByMbWq/rKULgilWlfoq4EdKqQlp+7RSagTwbeDhkktWKVh9WGlo/2BP/nZJcj5Ak+TLKd3RS97pZUXD3qF9LUR2HvI9xO2f/iWzF6/n0ne9tP2qhiVr4ZI3FdnS0wPYqxwctms27dtEPEvoYilj2YstxZtaAoDcFePKQTgeZu0ba7OWraWpyUhJu2QJLFxobNvaZC1dEHJg9S94IXAQ2AI8ndz338A24DDwg9KL1ve0HWyz3lhB7THHW26e6wHqsruYd968HrW3M0n04gO4bFToDB0MB8XV25MlOJMKxnPbL5j4yZk8d7LTKA2bBZu9ioXrF7Lk70u6ZrnVVUZqQpuylbwMqq/Rh8Ne+FpMKBpiy7tbgOwlW502R/bvycr3l8egZGY16MLjMTLGLVpkbGWGLgg5sZp85oBS6iPAl4CLgRCwH1gG3KO1tmCQ7H985y9Wsrkn0TDp/M9Ybp6v2lNKkT/x5BMse3mZqXPTCdrNP/X7Fa0YS0Kp4tc12JIKJmFhPKTR3de8PR6CX7yW5jvm0hEx/8k7bU7sNnu39KmpymGJRIJLJlyCx+nhJxf/hCmjphR9K9A99r2htoGHrnmIKx+8soeznMPmwG6zZ13uufOFO7nytCtpGtdkmm3tcOwwC9f9B6GEyfklTO1bSsdBQRisWI5T11pHgN8lX4OCze9uLkiZ+M7MXtnJjHzpKlt3trJx70a+//R3CBHGjYt5a+fS8sU1NI1rwrdJsbixoEuak3wwpwYVqVKWHeEguhIGDCWUIWHDcuic0+7sseadz0P8gpMu4Lld5ikbIokIT771JNMbpvPD9T/ku3/5btGx6pmx76nv7hHfIwT2B1i9bTUoo4ToVaddxcSlE+nEXKmH42E+sfwT3DH9jq6Y9nTFGgwH+V9rs0StWpypVyXAVeUilGP835+L4AhCpVBwfi2lVBXgzNyvtc6TS7X/4bQ5rc8SNUWZUrOlqwyGgzQvv5Qf1v+4K21niDBEwzTfM522/7WX5z0HSqLwqhLwzXO+waQTzugaVHx5ype54KeTeCVapqIxhVCqmXpG6lcrZK555/MQX//m+pz9xbSRTzjVxyX3XsKb33yT473Wl27SY99TpPqb8aDhUX7T2d2jMFpmtnDJvZdkna1HE1Hmrp3LvHXzmHveXL73se8dCXd74i+ocMTkrx6csWSkRK7PVMHXn4czzvoEK0/VPPH2E6aZ6PprERxBqCSsJp8ZqpRaqpRqAzqBoMlrwFHvPanPFJr/wR8QCZtn9op2HsL/0nK21ubpJM+MVCXAHYEnz7mT2z/1S2ZPnd01MDnYeZBXYhWg0KHPZJh77tweA7Wxx4wt6TU6Y52MXzK+m5NYMBxk2cvLWPD4AlPP8FzWgnTHt3SaxjXx9TO/nlOWmI4RTURZ/MxiTrjjBP74+h+Z9eAXOH3dlYbiNkEBKk8JA3sMDroV24/VXH7K5TjtJqMD+m8RHEGoJKzO1H8NfApjDX0LWeouDDR2vPF367PEo1Q83dJ7xuLs/dt6wlkmb50OI3wunO/byyGTQ9v4gucjLJ39EJ7ho3scL8ifoJykir30sk9gFTa+9/Hv9cq1wvEwzfc30za/jVffebWbWd1pd3Lj6hu7zZ5zWQtymbBPHn6yZZk6Ih1c+eCVxmd/bA7ZLTxB4nZYearmUMda3Ov/SkInqHHUoFA9fEn6YxEcQagkrCr16cBcrfWycgpTaUQ6Q9YSsGgYUkQ4UYrWna1MYD8oTgAAIABJREFUv296dwennnq22/XaP9iDyp38K+eApNrlZum31mV9iG59b2ueznuPqgTEelOpa5gWP9H0s9n1wS7L3diw5cyylk5CJ1j+6nK+85fvdDOrp8zUi59ZzF0v3MWamWu6wiHNFHvJTdj5BqsWHeUOJSfnKZk9Dg+3XnIruz/YLaVPBaGEWH1UhoDd5RSkEmn0TLDmVKWgOlFc+rWs6T3zrFF6jxnZ9aDM1W5qfCTVVdU4bEa4k9vhthROdeoxJ1dEOVRnrJcVOoCCSceYK8aG2gbD18ICk46b1PW55yMUDfHo9kdzOuF1RDpovr+Zyxsuz9ouHAtzOHa4ZzIXyOrA1xdoNNX2ahZNW9Rt2UcQhKPD6uPyduBGpQZCYLR1Fn3pbsttPyjSTzBfek9TNARPHE3cwrdRO2QE7/7Hu/zq8l+x8PyFLPnkEtrmt+X1uF60f2phMhWDBpeZ91UaEQe9vqbuimYPT/Q1+tDa2mjnug9f1xWbno9UXHjeNK2xMA+//jA9szUbxHSMeevmUbu4lgXrF3Qp92B7Gw9vKl0BIHfYGHAV+92Ip7sglAerSroOmAJsU0r9Rim1OOP1X2WUsc84wXM8NWEszViLzcSVL72nKQpqh46mrmp4btk0HDdhcpeHfSGzohPeepfb11HW2brS8Lkpn+eiEy/K07B8MpgRc9jYfPANUyc1r8vLh0edbulzqbZX0zKzhSpb/lWueCLOtAnT8raLJCI89PpDqBwfSjQR7ebw1vroXfivmIAtEssvtAVGdMCS9XZuP/GrDCnSu0Y83QWhPFjVRDMwKlFXAZcA15i8Bh7LlxsmbgvrivXkc0U3p6G2AZdFc246k46bxD3X/E/uRgpGDxuTu00waFTEWrDA2AaTSqyhgaE4cebxbD4atA2Od9Uy2pvLgaD3iZPgZ3/7mWn60mA4yMvvvGxpoLH74G6axjXx6VM+nbdtggQfdH5gSb6t727NO6NP0RHpoPnZm9jsCedfrkmRZ6D4nWdh9utDmFU/g4Sr8N8uiKe7IJQLS0pdaz0+z2tC/l76H8E/r7Q8S3QVqfx8B8dR1Vn4dMc32cfGYCCnfHZlZ9JxOUpUtrZCXR3MmQOLFxvbujpjv89H4JiYUYO8TNREoD7Qzvb27eW7yFFglr7Uv9lP3KL5fcwxYwiGg0dSzeYgkUjwu1es5XWKJCJ56wZ061vBXg/WrC552tjjcMOLQEcH3itmcPXEz1qSoUoZPySrPh2CIBTHoFojL5Rf17xuraGCt+xFhOoHg3g/fTVN/6QoM/eqrXmqwumeyVPSr01zs7FN1awOhY7sV4qG8WdZqkhWLHYNvj21lh3P+or02O9N+zZZzrIXjobxb+4ZM25GTMdMndvMGOkeiY5bN6WHnPCO1TGAIudA8aw94EmNQRMJLtpbbakw0czTZxbk0yEIQnFYVupKqQlKqV8ppf6hlPpXcntXRuW2AcVdJ1vP2KacRYS0+f0QiXCgmoLXjZe/upw9Hbmrwo0bNi77bMjvh0SCoBOWTYUF04xt0AkkEuD34/vwl8o26rPFoeWhajz1kzjj+DPKdJXSkO7UdeDwAcvnrX9rPYH2AJFEfktMTVUNNq3yD+40fOmUa9Cd1kdb7gjsHkpJfBOGp+dDCoXw7anN609iUzaWNi8VT3dB6AWsZpQ7E3gVuBp4Abgnub0aeEUp1Quu0r3PzhrrXulTRucuzmGaJSwQgEiEkw9Q8Ex91bZVjKgZkbPN+GHjsx8MBHhsVIiR/wE3Xg6Lm+Bbn4S6+dBaG4IdO/B+fhYtjwzBURr/qiNomLoHmvY4wOdj6rGTej18zqpXeooxQw3fhIIUkk6GwGXJoJaO3WYnFOvIq3irlA3v5kBBg60EsCP3T+UIqWQ/WY5NezPtvduNt76Rlun34E1UUR3rLrxSSkztgtDLWF0xvQ14BbgsPce7UqoGaEke/0TpxetbrISMAaDh2snXZj3curOV5vsuIxGNdi/MMu5GmpxOJu0rfE19T8cePnTch3K2Ge3J7oD22KgOpn+Jbkok5Ug17Utw47AXmRTw0/zL/+Gzv5/BHxripVusUZCwK2hpAY+H5tc6eeCY3nNx/7cp/8b/bMrjZJiOhvChgwA9S4Pm4PKJl+Nr9DF3zRwi2ZIwavA43LTMbOGC338sr1L3nXYNu157g1AhKxbVLnTC2szeoRVRlX2E5Uof4Nls8MEHNJ15PW1O8DfClhHQXgO1H/0E44aOo21+myh0QehFrD6mzwEWZxZtSb6/DTi31IJVAjZrycBQwKwp5hXaguEgzfdMJxjt6FaYJRjtoHn/L+lwO2g9kYJNo8d7jmeUZ1TONtm8yoMbHuOK/XdlvWa4Cn4WWs/NLTdT98yV/GFiCRU6gIbqqecYNcqBlo1/6NWZ+ro31hUWRqhg/d+MQcCxQ3LkTE1jSBRmnXw1XpeXNcNuMkK/0u9RG05nC/5exZ6R/0XTuCbs+cIiNUw9bgoNx52K2+I40KltJCz8tlQCPGH4eLs3Z+303ccAbjd4vXDPPTDfqN7micDsV+D2x+HuVXD7gicY4RwmCl0Qehmrj+rDkDVmazhkqenYzxkdxNIa51mR47I+vPwvLSfaaZ6YJho+jH/JDWArfJb6mYmfoXFkY1YzcnVVNZNGmHi+B4P4//cVxPNkrIMjdcBLjoKpY87ukmfTP1/o1Vj0to62wk86ZHyHk0dOpspCet5vvlSF5xHD671pJ/yfJ44cSxG3wbj2GJ43jWSNDguphp/5+x/wfXFRV134fERUIv8ARsOMLfDyMhuttdktETURqK+bDEuWQFsbPPJI7n53D7oklILQ51hV6quBW5VS3VxWk+8XAX8utWCVwMXv5pi1pFDwuutg1sObnn6IziyZQjsdsKXtVT51/eKCZqp2ZWfWGbPwNfqypiF12Bzmnu9+P4GhcWJlDFXLh9Pm5IzRZ3TJs2lknhNKPIvPlbgl2/VPHWr4g/oafdjyna8gGo/BDsO5rq3Oy/zpdPcsT/7/pmZ454ShBMNBDsfNq/Kl97uh83W8tSfw6w//wNLn4rQ5cWUbLKTWzxW0NMCHbkiAI3ta27gC388eh9mzweOBrXnqA3QOyLG+IFQ0VpX6POBN4Cml1DtKqY1KqT3AU8n988slYF+iTj3F0oMzSuxI0pYMtra9ltPxqP2fW5l17tcKmqlOHT0Vj9OD12U4IXmd3q6worxxwIEAQRXr07zuripX14AjGNjEhnH5asSW9vq60JtX8Nvwc3REOvC6vMw5cGreBC3tQ+1Qb2RM+3bojzm7n9/xsOXQt/2qk7teuIsvv/4Tq9LjsGdR1GmDjJALwg7ozDGrnzHm0u4V/U49NfeFqwtzRhQE4eixmnymXWvdBFwO3Ak8A9yF4Tj3Ma11exll7DOGn36upWxytnD8SNKWNILhIE8M259zjbL2/Qh0BC0Vg0sxwn3ElblpXBNt89tY8sklXXHA276xja3vbTWtx/3Y2Ai/OodeMXdX26sZUjUk54DDf8IBy3HfR0VyVjptwjRqHDWFnx6PdcWqTxzVmLuxgtpOG/iMgctznTmSBCn4W2eAQHvAsiw3tdxETMcsfYdTj5/Kmplrug38XDFyDjTNcEfgoilXdN+5aFHui4/Jk81QEISSU5ARVmu9FlhbJlkqjsYxHzZ8/nOhjIxd/5+9M4+Pqrz3//uZNcnMgCQoSwJFTIASFPeljb0gihq8XQSdurS0pZugLUt7icu9rW2tXOtSboX+WrmtWK2MYi0VAijWLeoVl6IFBBJcgAZEEoXJJJn1+f1xZiYzmXPOnAmJBnjevvJKMufMOc8Mcb7Pd/t806Itzc1aaBJNfUwbvGHwSSk1LyrwxdGIC7D8rzGtalrW7yltd9Aq7cfeOzY9j9vj9DB//Xzqr6ln4pCJfOnj3/apQbcJGw6bg3nnzEvPIw9sDtDU2qQ7YrOxspTEa323nkxePm4Bhz43lYsfvLjg54ZkmKbWJoLhIN+J5lEalOCccWX676DIWQJx4xRNEXZGDBxhbYxpgQwqHpTe+AU2B2h6ZiWb3nySdScZFAYY3N8mwb8ZOC/jweHDYckSmDMn9wlLloDjU8zxKBTHKAX9XyeEmIpWCT8M2Au8IqV8qi8W1h+oray1dJ4z9fmYFG1hlmZgG1saiZhVNAm4d8Q+ajuxnON22pzMPNWk0v6h2qx53CmN8NqHavnllF8SNxntebjYsPG1U77GvbX3Zhnu1IZDj6ph1ZSIns+it0pRDF4/Yxg3PjKjR8934aCytJLAlkD+anIBS5pXcUukDa/Ly4TR57Btxyp9gymhevvHMKVv8s/jyrQQeXrjF9jBsi3reKFCC7l3xx0BIcCe0I57wlo4r/4h8F6tU/g2ezZcfjnU1cH27QTHjSbw9dNpjL3PWe0nEAwH8bl9ffLaFApFLlbFZ4YLIV5B89KvB85Pfl8vhNgohCjvwzV+atQ31Vuqfr/g3eTPoVC6OAqSwiN5AutROzyeJzWZdX4iatgrHdgSMJyznZAJ1uxYQzRR4JhXtBGbwsJewOPy5Bj0fPir/didfS8T2+mE1e9vMJ1X7jAR2YkQY9KoSZbD5BKZDtefMLDcNPw+JCTY/erTfRJBue+N+7L/Xqqq8L9TYvg/ftgJf3wcpm+Fc3Zr37f/BmpaPOkagRyGDoX776ch8CvKxzzB3Jd/wh0v3cHuQ7tzBuIoFIq+xWqh3O/RvPMaKeVQKeUpUsqhaMZ9KPC7vlrgp0lji/nAlBT/9n7yB0/2B5+/2p/XM444IFFgD3jdhjrdxxtbGg2nd4WiIQSiR/lkewLWrXTjE/o63x6nB6/Ty3VnXcfPn/u57shSI3xuH4suXFTwmrrjsDmwmfw5pyrAzaabHec+znQT9+Mnf0xVWRVFtvyRhSxp2c6PTHPYq06K4/vI2tS1QonGI+nNBQB+P764nZUB9Nck4Kor4LFqeGUEPDYext4ADSMS6RoBPTKjRKn3OCETOQNxFApF32LVnFwA/IeU8qXMB6WULwJ1QJ6B2Ecm2gd4ngpeAR+m7JzNlvXB53P7qLDnESuRhRv17Qe26z4+YuAI0+dNOWkKdlFISZ5GhxPoDNPcMTtdkHdv7b3ce+m91H2+juvPul4LOW9cwh0v3aE7stSIYDhouEkphDOGnkEC4w2URDJtzDTD4SMep4dDcXOZ1id3PqlFFmL5dXMz54Xv3r3F1FPffRyssG0trCPB4rnhRCS9uQA00Zj6enYNdeMxCtoI0op1ITcE3VB7jaDNJKCSL0qUtbFQKBR9htWc+gdoAjR6dAAHemc5/Qt/tZ/56+fTGTHOd7qiUBGygc+Tlj3NxOE7Dg62Gj5fAM4YBY04HTt4rO7jnTHzvKxAUH9NPZPun0RcFjArVsAXr4IDnxmdkx8PhoOU312e5Yll5vG3X7+dNY1raGxppKqsCn+1tukJbAnQ2NLI7oO7CccOfxTcoOJBeJweQ0983jnzmDlxJjc9fZPucRuCeDyGWbYkkojw5gdvEpHxvEVt6XnhwSA79rwFedLKW0VLYeF3qyOBY6Q3F2lqami8eTahV++xfLuETRDYHND99w9sCXDf6/eZRomyNhYKhaLPsGpKfgn8TAjxupQyXS0jhKgAfgLc1heL+7RJ9YFf+qdLaIuGdD9IIw6ou8TJxD+upGZc7jjJUExfTS7FQOkmEg8XZNT/8wv/qfv4hnc2mD5vXeM6BrgHUDOyhufef876DdHUzwIToHvJm5mHFo1HOXGxNlQmHA/jtrv5wdofAJrRMwuFF4LH6eGyMZfx4u4XdY97nV5u+bdb8Lq81F9TT+1DtVndATZhY6X3W1wcXmx6n9LiUmofqiWaJ9jhlKKrbe+BZRzM166dGs7WB9XvTmy6IkRVQ8ebboK6o2eYG3Y1ZL2XRmRGLRQKRd9iNfA7FU0mdqcQ4mUhxCohxMvAzuTjFwohHkl+HVVxtpqRNez98T4WjroGZ5zcsKeANsLUrvyKbt7QbaD4lqLIM5CYXee6Btiw8ex7z+oey+epb3h3A3PXzS3YoAPE7NAUyq1+Nsvjd8Y7CcfDaZnScDxMR6yDjlhHrxl00KaBzTx1pqEQz9pr16aL9/T6+psXNLNr77auLgY9JHxuxOeIxM1F110xuOf829LzwoONmwlb3bD1skG3x2Ft7Z91Cxf91f68I1Mz6W6Y9XLoRqSjFgqFos+x+nEzGGhMfgEMQNN7T+XYj+/ldfUrvC4vi77xIBUbxjPv+Zs1I9yNzkg7y1/5HXPOzxbX+1xsGO/L3YbtTAc6DhQk2ZogYRjK7Iiay4xKZI+Nqcvm0vW2qsqqCvL4eh0Jc86cg9flze7HNuiLh+y+/hSNZRA1qeUSQhuik09HPe6wMfP8G9K/B4Z/BFZHsBfiqec7NwGL28+n5jx9Y5qKQmV62iWOEtoNIkvdDbNZhCbzOWr0qkLxyWLJnEgpj8pCuEJ5/tVHdQ06aK1p857+MRM/c07aSwP4acvJPFy80fCaTmkjZqVfLIkNm2Eos6A8eaEIdL2tVN3Bp4YgKfCjoWewLTHus/DqekND6bQ5tSr2PMRJ0JbsTwdNXAcr4jqS3p2EJ+C50Q7cbyzDX+3X7RXX2wQdDB9kwZO5qs+LLlyULRpkEqEBOLf8XEYMGKFGryoUnzC9+TFyVBMMB/lLeJPpOVEhqX3w0qww/PPDo7gMbK1NQofIX0md9RyTUGb18XnkSw+DeefM0/1wNtKf70mVfU9wYz/sfG0wHGTpW8tMPV+n3cWHoQ8tXS+zmr9qWDUOqzq4BVS/l4uB5ucLePTDZ5i79oemnQipTdDtF97OldVX8tNnf6p7Xt2Guqy/61SERg+P08O3T/82g0sGK4OuUHzCWDbqSQGa7wkhfiaEuKP7V18usj+w/M3lWPGDo9HOrPadxspSwyK4hA3DKWtGLDhvgeEH5c3n31zQtaySKjQzQi9P/dNJP+2TtXTH6Sy2nK8NhoMse2NZjiZ+YEsAKc0taigawmlzWspDZ7Ycjhw4kpgVa22joJx6yUCjScjZhGLtlnvFC2lLM8vJqxy6QvHpYSn8LoT4KrAc7WPnQ6B7tZAE/qN3l9a/WL1jtaUP3U5ibP1wa/r3qmHVeGxFhBK5RWwlwk3CJiyrvOUzrk++86Sl62SRmZtN2p6iKHS6tCEeNncR9RmFZoaXkRKJJCETSKThnPfUfVzxwtr49K5RYndnFcGZ0b1SO1MTP18oGTTvc9qYaax/Z33ee6VaDoPhIDMemdF7efIMmg6+W9AmIGWUzVIT+cSLMms59HLyqU4ClUNXKD49rH6s3gY8BnxfSmk8mUKhjd08tC/9azrnrNPrbne6ePzKlcx4ZEbWB6NEIqVMe0JWPyxXb12Vd3mporYSZwntkfZsw5Ax63v+izD+APgvno13ZG6rHnT1KD/z7jP8ZdtfsAkb7dF2PE4P0bjJRkXAv70Hz55kI1pAPUEKOwKHw8XjV/0tq37BCCua+PmK/WzCxuWfvZwb1t5geE6KlEKe5vlarHMosJ2t0PGxVnrFzYoe9drSrBYmKhSKTw6rRr0M+N9j2aBfNuYy1jcZF1KlEVD2flfuNZ9HY/TBCNp0s6J/FbH4ksXWPiz37TU9fJpjBHMu+QlNrU00tzWz8s0VtOcEXTRZ2PEHYNYWF8wZr3utlOcbT8RzKqbzer1hGNZpx2VzEJWFC8/EkcTjYWY8MsNSIVa+sLJAmBrJVH/7msY1CCFMQ/XjysYx1KvNHNc8X3Odgiz6oE89hZVecbOiR6OQeo8LExUKRZ9g1aj/BZgEPN13S+nfzJw4kxvq83tpSHB+nL33yefRGH0wzjp9Fs8eepZJp0+ytMbL3nOxfiiG7XNbo82MHTyWWafPYuFTC3UNOmjSoE2lgMulq/et5/kWgg1t3nioBwY9EyshZcgfVt7ZutPQUDuFk8YbGhnqHcoT25/I28Z1/TnXp3+uKqvCE7cTsvduV4Jd2LFLiFiq8tCwkudWIXWF4sjHaqHc9UClEGKZEOJqIURt96++XGR/wPL4SAFL7K9nFSUFw0FWbF7BjpYdnFR6EldWX9n7H5DBIDP/9BYlJnreYRFPF0yZ6sRLqOhwwNq1ObK3YK1HOYUTB57k3sETBl8Y6h8vZvy18wyrp1O47W5OPO5Ew+NW5UfzVWq3dLQYFn25wlHW1P86fR2zgTgu7MwcfXn6d3+1H5GnAK9gJKy8ciUuab1xJRVpsPI3ZyTOYyXNoVAoPn2sfjKMQZuj/i3gQWB1t68n+mR1RygyGiPw+nJAC1OX313O3HVzCx52UhCBAL5OyfoHwR3FsN0p5d12Rk3U5wSEb/sZ1Oh/kFspLEtx6wU/Y/qpV3OO80Sm2yew/TN3UvPqfmov/SGxhHk7n5SScyvONTSkVuVH81Vql5WUGXvyLmi6/25oa9OGuRi16kl44hEH3hPHQoP2b+tz+5jtvcBaq5rV0LuEL4/7Mo+Vzc57XYdwsPDzC9m7YG9BRjmzzW3W6bOUh65QHEFYNep/BA4B04CxwIndvkb3yer6GxadrpAbml5aoyulGYqG+mYcZWMjJBLU7ILrXsXQQISiIbbs38IfNv3B9HIb9rxgeGzEwBHpUab5uPW5W3mscRWvRN/lseJ3Gdt6K0vffoCqe6vyqrNFEhFWbVtFu0Fe2mrrlFEvfUrtbPzx4/FIg9ZCqW0uCASyr+PQNhrOuNYtsP5PMHVrGIJBqK2FtuS/7YQJloy14cS07iSvteuMSuPnSLhk1EV8VPdRjmiMQqE4urGaUx8DXC6lzN/PczSTwNo2SMLxH3Va6vs1ygenKsuLg8UsM1EFS1NVBS4XQSK8fTyGnl8RDpa8tiSvhrmRIWrY1cCNG27Ma5BTZGq/pzY2c+rnWHoukFWEl6rM7kme16yuYaJnNPMiUdDbpwhYclqMW5q24s28zm/n0LTuz1R+EMO/BbyZb2ciAYEAzJpF1bBqrX3PTItHwtToCB53GcgJd1sPQGNod3o8qt45p5afoYy5QnEMYtWobwRG9uVC+jvBcNB0LGd3Xh/QTkUBfb+ZZPZU33rirfxk3U/SPdWGYVS/n4Y7f8Cll6PNvTYwDp0yhpX6qmlV03IeS0Ue2sxE0vuIEkcJM8bPYJh3WI9bp4wKEn2P1zP7DTt3nBvXfd+idlheto/UVsTr8jKreSisM0gfhELQpP3b+qv9zP7rd/KubcJpl/D4jvvynuckv1iRmoqmUBy7WA2/zweuF0Jcm1SWK+n+1ZeL7A8sf3O59ZMFNA6I5i3Q0vvg7WnIPuiC2msFbW6MvT1pbV9S7Chm5qkzcx4vpECut2mPtTPMO6xv8ryNjRDXN+gAYQfMO/Rodh1EVRV4DAr9PB6o1P5tfW4fX953XN7UzeP/DFgK0w8sGajJ2r621PAcgVCKbgrFMYpVo/46cDKaqtxuIKjzdVSzeuuqgpTBnM6iHklp5gvZL9+03FDqNGHLs0ABcQuv4c6pd+oazUIK5AActsORjMump96nkTRsFlVVVLW58JhkFKIymr2p8vvBZvC/j82WbgUMtjTz2NCPzf92BAQ7D1mq2ShxlOSVtZ1z9hwVelcojlGsfup+i4LGTRx9xPbmzhI3RMDJQ07uUd9vvp7q+U/Ox2lz9kjq1BWDhNNOLI/KWd2GOr4+8es56ytkzGqxoxi7sNOW6J1QfU/0xM2kYbPSGH4//oXzmD/ZvM4gqw7C54P6eq0oLpHQQu4ej2bQ6+vTrYC/W/5DEvm2zhKGtgveH5j/f7FhvmF5/61FXynYKBSKfo/V0av39/E6+j2DQnHt3bLYdtQZ01rGCpXSzGc4I/FIusitUKlTl6sY7Pa8OXGjIj4rY1bddjdOu5O116xNr637hmbRhYv48VM/zqlqL7YXc+fFd1K3oe6wxU/yScNmKdH5fPhWraX+O1OZckWHYVFbTh1ETQ00NxN8eDmB99fQOEhSdd40/GdMJFXS+PuPn86f8xAwsc3HK+QXbDyr/CzTv0FPBCpbj+n9t0JxTFNQfFQIMRw4DygFWoGXpZTNfbGw/saBEgEFtB35RFcpdSFSmv5qP/PWzi1obSmp03z57seu/islzhIufvBiwzYxMC7iM4o8CATfOeM7vP3h2wBMGzONiUMm4nP7DDc0laWV/PvD/040Hk1LtNpsNk4Zckqv6IkX3HlQU0PNq/u563+/y4KPHiEicqMZeimAhtZN1B64kURJglBbCM+zLzD/uZvS0YA9dguZqQR0jq+C6Ot5Tx1XNi5rtGt3hAT/dUug9hZd4SCFQnF0Y3VKmx34DfAdsv2OuBDi98ANUn5KFVSfEB/67No2xgoSWre/2aP7vPnUn0h0hLKjAnmESULREO989A5xk7B6sb2Y3Qd3M+v0WXzwow/43hPfY8XmFSTI/Wczy1/rRR5GDByRNZTmhV0vcNPTXYat+4YmGA7y5RVfzmmrC0VDTP3TVPb/eP9h64kXMnEsjdfLzO//jpvuXk1ERwK3ewrASjQgJrCUuCo75Vx4I79Rl8n/DA4yZyN4wzLdUqdQKI4trBbK3YqWV78JGAUUJ7/flHz8p72/tP7FoKJB1k8W8GHbBwXfI9jSTO1Lc2h3oj89zQCP08OBjgOmxVMd8Y60IfO6vPy/y/4fHpd+9Xa+/HXmmNWOWAeXBy4vqFp/+ZvL6Yh16K8z1sHyTQV0Ghhg1nkAxlPO8gnVZEYMAlsChmNzo4kogc0B8qrEShgeL2Z8+anmxl/CmbET2H1wt3GKRST/VDJa6hQKxbGFVaP+deAWKeWvpJS7pJTh5PdfAf8JfKPPVthPaNu/u6BSQUdJ4aHPwIM3ErUu6Z3GFotTWlRqOpfdIRxZ3nchxiuT7rK389fPNzQycRknsDmQ8/jqHatNX8+axjWmx63gr/YjhPEyba2wAAAgAElEQVRuaMnGJYbtgVb1z7fs35KunehOZ6yTrQe2IkWePxoBQ4uOT3ZKmO/ehnuGJjcrBh2kMvknWlKSbqlTKBTHFlZNyAnAWwbH3koeP6p5r7O5oJa2aZ//ZsH32PzhFjrza4tk3QcJi8Lnc1LpSaan2m32HO+70OEdej30ZhuJ9mg7z7z3TAEvKEkv1Hn53D5mnznb5BZSd8ORaoH7+XM/RyK56fybDPviWzvM8zEt7S04RJ4Ml4STx9bgc/vwOE02ggJeFHu0zUrUIM0iYMnZ0GaL6U7XUygURz9WjfoO4KsGx74KbO+d5fRTgkHa8rSBZSFg5jnfLfg2Hw1wFWbQhPZV536BcMxctnX2mbN1DVMhwzt6Ij6zcvOjOR7xZWMuM33OtLG5ana9jV5evdDhO4OKzVMyZcVlTDhhQt61LBqnjfTVq2/IJByPaJuV6ETDvxMJBL52miqSUyiOUawa9V8A3xBCbBBCfF8I8RUhxPeEEBuAmcnjvYIQ4hIhxHYhRJMQwrjM95MkEKCASZeUCk+PxD9Kx51mPRqQQcJhZ8M7G0zPcTusDWAxo1DxGQB7OJKeWJdi5sSZhpPXSpwlzJyYq2bXEwpR9CtEyS/lzb+8+2XDexcJF6MHjebSykuNN2oSrt0EQ7+/AIBh3mGmr2e4b7j2w6BBxgN73NB0UqnpdRQKxdGLJVMlpXwEuATwAIuBx4D/AUqAS6SUj/bGYpJV9kuAS4HxwFVCiPG9ce3DorERu1UHVcINkYk9uk11xWk9el7KCBXZi3SPF9ndhtXsRopreo/nKz7To92lTazLxOf2sf7a9Xid3nR4WiDwOr3a472khlaIop+VFjjo8uZ/uPaHbGzeaHzzcIS6DXX86uVfGW/UBAROhrY97wBw6uBq0w3AqWWa11913mXpGfXd8USg8nN9H+lQKBT9E8v+p5TySSnleWiV70OBYinl56SUT/Xies4GmqSU70gpI8AK4Eu9eP2eUVWF12RGeXfcpUN6dBt/dc/yoB7h5sLRF9IZNyraCjPto8E5jxuFm5e+ulT38ZEDRxoaSSNKItD88W5DmVabkdRqL1BIMaCVFrhMbz5zelwOEhI2aIu25Z1mF7XD8vOKAThuT4vpBmDQvw4A4D9jJrZi/UiHrbgE/xm9E+lQKBRHHqafqEKIk4UQFZmPSSkTUsr9UsqEEKJcCHFyL66nHE1bPsWe5GOfLn4/w4JYC40L+LOrZyUGvgic/AEFF4rZ4nHcDrexpx6DNf/p75rxjXm4eU79HN3HZwSms/LKlVlGMt9a252w0rEja3Pw17f/ykV/uoi2aFu6V10iaYu29eqc+WA4yLYD2/j26d9m+menM++ceYbFgFZC9ZZrCgR5suPZ566Zog1A3BJ8x9RT/2dQqwHwuX3Uf309PqcXT1LkyCPc+Jxe6r/ee5EOhUJx5CGMepuFEFOBVcBZUsrNBudMQBvLepWUctVhL0aIK4CLpZTfTv7+NeBsKeUN3c77LvBdgCFDhpyxYsWKw711XrZ9sJlQwtoMcZfdxcknFL7XSRz4kE2RXciMzUOFu4I9YXPd+ZEHBZHhJ7AvZNwbP7RNUD5oJAzWPPYD7QfYfWh3QYVvNgkjiodSetwwWttbCO/djZSS/Z6kLcpYt5BkvY58pF6nTdgYMWAEg0tyIwuF0BZpo7G1EdDC56kIQ1Vpla7RS8gEb37wpu77YRM2Jg6ZyN62vexr25f33vlee/d/04HugVSWVvL2vn/SLo315z3CxbihXX9XCZmgtaOVcDyM2+6mtLi04EhKX9LW1ob3GCnYO1Ze67HyOqF/v9bJkye/LqU8U++YWb/NXOCPRgYdQEq5WQjxv8D30TYAh8seYETG7xVAjgytlPL3wO8BzjzzTDlp0qReuLU5P7zxK7xV9LGlc88Zfg7/d+X/FXyPZTddwn841hPP0Oy7c8yd/GjHj0yf5wvDLzuv5icfP0pI5LaYecKweB1MurgObr8dgIVPLeSO7XcUvMa6V5zc/kgrrFhBw69upnZ6JwkBIRfYkvbw9GYY87Hgr2Mk7S5r1818nXWfr+P2SbcXvLYUwXCQ8rvLs5TeUvhcvmzd9wxcu1yGw3dqRtaw7I1l/GTdT/IWCxZFtfei3aA2sfu/6b219zLprEn88ZHf88DWh/UjQhJmVl/N9yfdoHOwf/Lss8/ySfy/2R84Vl7rsfI64ch9rWbb+nMBKyog69By4b3Bq0CVEOJEIYQLrV3ub7107R4TDAf5p8uaQQeYODR/oZxeIdrm0liWQbdKAhD79mKz6T/ZBvi3QKa8WU+K3jxhqGwVMGcOwT/8P2qndxJ0awYdtDxywgavlcOj460b9Kx7WBmxGgzCsmWwcKH2PZhtvK0WvXUnX9++WeEdaEVqvjCsetja3HrQBuCkqv1vv+RO05z6okvvsnhVhUJxrGLmqZeAhbFR2jkGEleFIaWMCSGuB9ajfS7+QUq5pTeufTgEtgQKCiXnK45q2NVA7YOXkohGCRHGg5v56+bxueHnQhsFt7WF3LDn4w+p/8Yqah+4mETyMU9YM+j1D4E3AixZArdogz5MJ64ZaM3bAP8/IvD6nwmcEjPOGwuI9nD6Z94Rqw0NueNO58/Xxp3WaMa3R7rvScyG7+gNtCkJQ9wGM7bC5Pe0zZM3or3ntd90kXA5TT37zE3C8AHDWVK7hDn1c3LOW1K7hKHeoYbXUSgUCjA36nuAzwIv5LnGeOBfvbUgKWU9UN9b1+sNGlsaCzK0ZSVlhseC4SC1D1xMMN5VPR0iDNEwf9/1bI/61D1hqHQPo2bcVJrjcwk89WuaSqGytcvIAJqnnhz0oWeg3HY3UkpmuE/lbwc3kkBrSXPGwZ6AlYHUtWI0lmobh96kyFFkPmI1GNQMeqZnHkoazNpaaG4Gr9d0fK2lSIAJWQNtnllJ5bq/438j0vUep85r8dBc/t8Ezihi5daV/P29v+cMsAHNqGdOjJt91mwu/+zl1G2oY/uB7YwdPJZFFy5SBl2hUFjCzKivBhYIIR6SUuq6GkIILzAPeKIvFtdfqCqryjspLY2E0ceNNjy8/JXf0Rlp14/PxmI4HA5iMlbQ+gTgP0nr/PPiYtY/DE7sNugjZaB+8fwvuPvlu5FIIokIq2KbSQiICXDEIOoAZwxm+DUPtGYXVLVqmwkrhr3EUUJ7rJ0SZ4nhyFeB4N0fvmtuvAIBzUPXI5FIb1jMohB5IwEWSHvzVVfCreUQ0Slus9nwXjWTWV4vO1p2sG7nOt1r6UUOhnqHcv+X7z+sNSoUimMTs5z6LwEv8JIQolaIrgHhQgiXEOJSNC/eC/S8qukIoLaytiAP2ij83rCrgfnPLCRqkHCN2kEUOsFWwpxNTrxXJXuTq6q0kLQeHk/OoA8pJUtfXUo0EU17kqFoOx1OzZjHktu+djcE3VB7DbS5wL/ZmsiBy+7iiuorqPt8Hf9zyf+w/tr1un3jY8rG5PdGGxu7PPPuZGxYejqsRg8jcR7tRj6or6d5iIeZM+ycMwtmzrDTPMSjpQOSlbOFKNspFArF4WDoqUsp9wshLgAeQvPaY0KID9F81uMBJ/A6cIGUcv8nsdhPi/qmemzY8mpzAyBgwzsbmH9etqeY6guPml1DwpT4Z3iuaJ/haFK9+4lJk7u0vv1+LccMBF0QmACNpZpn7X9P4Os26KNQPfcEEKiGWf/QvPZLk0beaNMTiUcY5h3G7Rd27fu6z2P3T/Dz2kuv5b95asOiZ9i7bVj05r77J/gLMugNuxpyquHnr5+froYHWOp+iznXhdKRnI0VcR6YEGKJ+y1m01Vg15eRA4VCoUhhOkJKSrkdOFMI8QXgC3QJwfwLeFZKqT/p4iijsaXRmkFP0tqeO71LM555hsIIGOmrwB7/0PriJFQ88SzMbtMMe9J7bPjOVGq/0tFVNBeB+cUJ6ls3UePtEl4pVM895IampLR4zS7Yexdc8VU760brvzY9T9SsGM2UjA1LDjZbzmSyQu7TfKiZG5++kW0HtjFu8DhuPv/mtDhPitT7VPtQLc0LmjnUeairqC21qUl+n1M/h8s/ezlDvUNz6heArHY5JRajUCh6izxzITWklM8Dz/fxWvotZoVXemz6YBNtkTYdGVITaVEACW/4gkRajAVI9AiLrnwyQPCsidR+zU4wo2U95ALi7WmDlF5bgYV5njBUHrQBCfB48NpsPPLNlQx/ebquElyveqLJDUtO9bvNlhXuLpSlry7Nqjjf2LyRB956AJddvycv1Rb3fNPTxrUWEurWLuD+Kx4CsiMHRf8qYvEliwuOHCgUCkU++o/8VD8mX39yd4QQOb3QVWVVeGSeYekCXtu/SbdK2uw5q0fFsgrgAlsCRBP6xXbReDS9tmA4yNKNS63fi2Rb24SvQl0dLF4Mzc34Jk1l7TVreyWHnZeaGq3KffHirDWk2tkKpflQs24LGWD475Aqbnt1xzOmfeXbG7MFiFKRg3Jfed4xtwqFQtETLHnqxzqp8OmFD1yozS3P4912xjrZemBr1mP+aj/zVl9fsK67FbYPJiufvHn/ZuPhLvFOtn6orS2wJYDMt6CkJ5rueX+8GO+rv8vximsGTaS57JcE3ltDUxlUnncZ/jNn9o3h8nrTUYnD5canbyz4OR6nh4oBFeyIfWD8tyBhbEevyDcoFAqFZZRRt0jNyBr+ed0/GfObMZbOb2lvyfrd5/Yx+4zruOPVX/eoF90MV4KsfPJHh/abhoVb3ngRPhe0lE93x+GCd2D6Oy78O1zIxx7j3q33s3rHagAuG3MZM9vH4PviDLyJBLPSIfEXoX5ijz1oQ4JBLdXQ2KgVzvn9Wli+h2w7sK3g59iEjY5YB3FhviFaVPHNni5LoVAoeoQy6gXw3PvPYRc24haK5jqiOtXrLlevG3QkfK5qcpbnXLrrQ9OwcPPbGwmOGk7V0tl5awXCDthWOZCOs6bx0pQr+MoTX8nqNV+/cz3/EYEnB2mFc4CuIEyvYEFNrlDGDR5nPhddh5VXruTul+82PWf8ARj6o+/2aE0KhULRU1ROvQC27N9iyaADPL71LzmFY1VlVYbFV4fDz666L+v36lY7RblzXdI8/xlJ+ffaGPnz32CzsMt413aQGw7+mYv/8hVd8ZgOV0ZrWyYpQZjeIFNNLrVpCIW6Hm/r2bjW26cUJrFQ4ihh98Hdec/7zNhzem8zo1AoFBYxNOpJwRnLX5/koj8tWjtyW9WMiMoYy1/5fdZj/mo/LlvvGnWHsPHse89m32fUZThN9h5hhyYkM+OLHawc+B2K7cWHvY6ITetfz6Kbgt1hYUVNrgek9NazMImqt8faaWpt4rIxl5led9o5X+vRehQKheJwMPPUV6PJv6628HVUy8SmGFQ8yPrJAh5+Pruy3Of2sfaU/6Y4QrbhkFBic3PX1MKncMVI5MiM+q6aSf3jxfjCYDNRnE0A/9y7qaAefCMizq7+9TQ6CnY9xqKaXE84ZcgpeBwe7EKT+hMJDA27RzqpLK1kTKlxbUWxo5iZp87s8XoUCoWip5jl1E/8xFZxhDDhhAkU2YsMK8u782J0Jw27GtLqYwSD1Fxdx/4wLD8F1iTtwrRGmLnTyf3/1jPjWjGwIvsBn4+a+55k/s8mc+vnjK16yA0LeZZ4/PBL8l1RbYBMFjqCMD2mADU5XQwK7FJKf6FY13WlydxUm81ObVUtY+8da3jOX7/6V9WuplAoPhXMZGLf/yQXciSQkvu0atQRZIu9JEPIEnAn4OT9SfnWzeB1Sla/dH+P1hWO5WrNN58ymls/n2cwjCRvBbdVXAnwv1sCtPeKIEwOBarJZWFSYBco2WYskyu16v+wI6Ol7+urWNO4xvA5HqfHUs5doVAo+oKCqt+FEA5gJFDU/ZiUcmvuM44uMuU+O6IdlqappdTHZp0+CxobaSgLUXsNWTPP518M9Q+FoN1gelseNuzM1Zr/0VM/yv/EXqrEL3YUs9b/V7wn7dbC4JWVmpHtBYMeDAcJbAnQ2NJI1X3X4Z+9FF9YWleTyzOutXHFt42r/4XWzjex1UHlQTv+X/4NeeJ5/OLRu3s8r12hUCj6EktGXQjhBP4HmAkYDdvsgTk68kjJfU763/N5/YNNeQ1j5od88KQRWpV4xjuYGl1aew3816AzWX/o3YLXpLe5eHH3iwVfxwpFjiI6Y53p/PMPzv4BP7vgZ1okYlzv3kt3oMoCQf1x11OzW1jbPOQpsKtqajGevS7cTD/xAmZdMB38fhpaN1F7d7mp4p+auqZQKD5NrLa0/RdwGTALzYxdD3wTeBp4D/j3vlhcf8Xr8vLx/j2WPd2KAVrO+xflO3PbvpIkBBSfeQ7FjsIr0fcc2pMzFjQWL2wme16SUfrOmJZ6iMs4cRln2T+W9e59kqRy3cFIMG1wQ9EQwWgbF7b+mvmTwiw7TRJ05kkf5Cmw8+8tM5QAtjld+P/rEZg1i6BTptdjNFoX1NQ1hULx6WLVqF8J/BR4JPn7RinlA1LKqUAD8KU+WFu/Zl/iY+snC81I/fqNpYYbgZAL9oQP8OTXnqTInpPdMOXtA28zd91cyu8up2GXNjjvBM8JBV2jp6TSC72N2UjYcDzMPf93T85r1iXPfHlfZbWl2ev5RtS67K6+0bpXKBSKArBq1EcAO6SUcaATyOztegiY3tsL6+/YbdazDXsO7tF01hPGo1ddMaj0VFAzsoZfTf1VwSI1oWiIYETzbvcG9/L2gbfzP0mCA1vamJnKwBttRvooh2xFwjbzNetNiAO08LzN4M88WWCXSqksvmQxdZ+vY/Eli2le0NzVtWBhPVNGTcl5jkKhUHzSWC2U2wscl/z5XbTZ6huSv5/U24s6EojGIloVQZ4QvAM7laWVbN7zBlGMjboUWhU858HO1p2FTWrLICET1G2o0zYdeca3I+D7sVM59d9n89jjt/GUeJeY0V7FQEs+lUPOKmgrq8Jf7cfn7rkmeyHjbrOKEbtjcVxrvtnrZuvxOD1MHz9deegKheJTx6pRfxY4H01k5j7gTiFEJRAG/MDDfbK6fkxYSEs59ZiMM234JJ7f8L+mQ1ZObwZvdA8Nuxr47Wu/7fG6QtEQ21u268q5dkck4NTjT2bW6bPY8Zffs9ZReJGeTdgYMXAE5XeXZxe0rZ9P/TX1PfZcU+2DVjCLFgTDQQIl22hc8W2qmlrw7y3DV1ldcHW+2XpUHl2hUPQXrBr1m4HBAFLKXwshBDADKAZ+A/ysb5bXf3FIsOpLP7biv/Ac6gCjceoCXh8GeysGU/tQrWkhFhJIaPeP6fzreZwexpaNZdO+TebXAWwJ8F+7CIJBqv7+Jp5JXdX43e9pFzbcziIEIm24bcLGyitWMuORGQQjXUV6KW82q0e/QDLbB1ObBSOMKs51q+cH2Ki/qJ4aE4NuFHXovp7Ue6Dy6AqFor9gyahLKfcB+zJ+vwe4p68WdSQw+pCDbYNi+b11AWta/4/jBwyBdkynp9WVvkGixVxVzomN2wdN59aOtQSjuXlkIQQnDzmZB//5YN7XMNF3Et7SoQR/fy+dtgRRo9C7gDgJbNhYdNEi9hzcQ2VpJf4JflZsXmFYQNYR7WDOmjncW3tvj0LxqVx3YHOALfu38NvXf5uuvs9Ez1POrJ5Pkd5s3H8RzXPewVs2LOdauhuBjKhDaj1NrU3p90AZdIVC0V8oaEqbEOI4IUSNEOIKIcTnhRDH5X/W0UmnE+viLSUllI47zfT8mB22H9yZN4ccFQke9e3iurNn43V6syq2S5wlxBNxfvr0f2qGNk+31xsdO1m1bRXlzQuo+0KUiAPtOQbPk0iK7EXcfuHtzDp9Fl6X17SALCZjPLz54dwK9WAQli2DhQu175nCMN3wurxcWX0l408Yz+XjLqfIUUSJsyT9mo0qzk2r56OdXDG/gmWBhek2wGA4yL0b72XK8im5bXQZxXip3Hvme6BQKBT9BaviMw7gNmAOUJJxqF0IsRS4WUppMuzz6ONfxRb7wCWcfdq/U3H8aBz/sBEzGJ7isrsYWzaWzfs35zXsr/zrFTbv34wQguvPvB4hBBUDKqjbUEdbLMN7t7DpmPHoDGIi1iUpZPIcvdx1voK2aCJKNBLtCsVv3KQv2frnP+s+v7vnXOLQNi7XnHwNk0dNNvSUG/duMVxTxAHrRid44a07mL9zKYsu+m/qNtQRjoeJJPSTKqbFeAqFQtFPsOqp3w38EPglMB4tvz4euB34AVD4eLEjnJjVd07AmvefYuSAkYYGHcBlc7HowkWGQijdCUVDtEXaWPraUm7+ws24HW5kPtdch7hJm1139HLX/mq/pTUnZILAa8uNZ6I3NubMRNcToGmPtROOh/nb9r8Zh74bGqj65W9NZ8qDpg0QjLYxp34OwUjQtONAyb8qFIojAaum6WvATVLKX0opt0kpW5Pfb0Mrojv2hkcXoJu+o2UHMx6dYXi8yF7E2mvXMsw3LC2EYlWAJuVBWunr1qOQjYBe7jpVQOZz+XAI48BPKBqi6eXVxpKtkDMT3SyEbih6k9R6r30rTGdBkw3MUfKvCoXiSMCqUU8AWwyObSZv9vboIhgOFvyCjYyTy+7izovvTLd+pYqx7q29l4tGX4TIs3tIeZCpMHihlDhKDI+l9N1ddhdep9ewyju15qtPvhqnTb/E3+P0UNmCsWRrIpEzE91so2LoOSe13uvHgMt6ECIvqm1NoVAcCVg16n8Cvm1w7DtA/lLro4jAloD1bYwEp81pnN+NR9hzcE/WY6lirNOGnpbXk3bb3Wzat4nOWGfeDUB37pp6l6kyXuqYQOSNTHhdXu6tvZcih36EwSZs+EdNM5ZstdlyZqKbbVQMPeek1vuWwVru/HBx291K/lWhUBwxWDXq7wPnCiG2CCFuF0LMS37fCpwDvCOEmJ38uq7vlts/aGxptP7OCTgUOVS4cQJGDByR9/LheJh1O9excMNCEiQMPeVMLjrxIvYu2Mv88+bn6J5neu6pHHM4HqYt0mYux0pXKN7r8uK2a5V3brsbryvp5V8101iyFXJmopvl6w0956TW+wdeDjt+5LQ5ueviu5T8q0KhOGKw6sukCuHKgc/qHL8742cJ9FwS7QigqqzKWB1Oh3gijs1RoHHKR7f7pxTkrBj1l/e8nPY6u/deNwebeeztx3QjC5YrwGVXrl5qv2iYSbZWVeUovFkRfMkRivlKLW/e9QMeHU/efx9PGGxFbhZdejd1G+py7rHyipXsOrSLnz/3816RvlUoFIq+xqr4TEH97Ec7tcO+UND5bpu7R2pkuw/u5gQMpq2ZGCy7sBOXxgnlzlhnlnHO1D1f+NTCwvPYSVLV6m0ZojiReIRIPMKlD13K7VNuZ3fHbqrW/RL/FoHvnT1dM9Ffe033mmaCL3pCMfOYR+LqBDGTejx7HKbuhOlvg//iOXjPms3XJ3496x4jBo5gxiMzelX6VqFQKPqaXqwPPnb4S+Cn1k+WMKh4UI/UyKrKqjjUcqigtUUT0by59ZiMsfXDrVmPpTzeNz94E7fdrSsxm68C3KxavS3SxoInFxCJRzQDKWzUX2fNQGZuOoLhICs2r9AU5l77bdY6rVb/x23wh1UwFA/MGa97j/K7y3td+lahUCj6GkOjLoQYD+yUUoaTP5sipdya75yjhVUtL1nfDgnojGvSpvkmgXWntrKWh7cXPitn7OCxbDuwzfSclo6W9M/dPV4j8qUK8rXVpXL0OQYyLOHAAU1hrqpK89x9uWFuq+vMhz0BCy+Ee18Q+Py5r8dKK50SoVEoFP0Rs7D6ZmBixs//NPhKHTtm+JfbfFBKd2xmxWEm1G/+S4+KvYZ7h+Owme86ykrKAH2Bl+6YybFmUmhbXUImCPz1F1BeDrt3wx13wNy52u8NDVnnWlmnVeJ2ePgUKJ+XoKF1U87xHrXSKRQKRT/AzNpMBrZm/HyBwVfq2DGDLGBkJxK+MMI8B998qJmZj8/knPvOYebjM2k+1AzA5pf+WpDITQq7zZ43BO8UWkGdmVfqsru49KRLWXzJYksV4FbV5VKEoiGa7r9bE4xJidKkFOZqa7MU5szWmYOFjVDUDsF4u25Ff49a6RQKhaIfYPgJLKV8TkrZlvGz6dcnt+RPn6HthXneZw4+2fDY0leXUn5POQ+89QAbmzfywFsPUH5POUtfXcpHH+8zfJ4Zx5ccn7cKfslrS2iLtJl6pZF4hIlDJ1oeXJKpLpcyiqnWNj08uKn8yGDzkUhkKcwVophXHAWvML5v1m10lOl61EqnUCgU/QBL1kkIMUUI8Q2DY98QQkzu1VX1c0rao9bD4gJeeO0x3UPNh5qZUz9H99ic+jm4BwwqeG0CwQmeE2iPtZueJ6UksDnQ615pqiBw8SWLqft8HXdNvQuvU39DYJMS/xsGeuuhUJbCnGloP/lv4QmDLwxPPgh7O+aw8PMLcdqc2Ez+zPXC6XqbE6spCIVCofg0sepy3gYMMTg2GG3QyzHDK879hYXF2/QFW258+kbTp20+LoooMKee6g/Pl9tOGTMzr1Qg6Ih1sPCphSx7Y1l6TGk+MseTzjl7DmuvXatvII+fi9donR5PlsKc2TqLYjD/RVi8DprvgpoWD97K8Sy6cBGtC1u59pRr05K3Oc+1F+luXLpvTqymIBQKheLTxGoNdzXa4BY9/gH8Z+8s58igFXMvOAsJ0w7q74fyVajHRAIhCk+q//a13+YNv6e8cCOBl4RMkCBB3Ya6w+7TNmznC0u40UCnyGbLUpjTXWcEbBLqH4KaXRnPdXc91+vycvuU23ngrQd0b9MZ72TamGm6xwrtVlAoFIpPG6tGPQaUGhwr66W1HDGIAmabCWCm6yzdY+MSg9hookxXNWAUMt6zcarXnXkdv37l18brEiKdG+5udCsGVFD3dF1WAdnh9mnrGkgXXQpzqRb/yq4AACAASURBVA6BlMJcfX2WwlwwHGTbgW3MOm0WrZ2t+Fw+grt3Urp6A9uG25i4L4LPqf/c+qZ6ihxFdMY6c9ZV5ChizY41yngrFIqjAqtGvQH4sRBilZQynQQVQriABcALfbG4/oonZifosDACTEJlC3hPPTX3WDDI7T9p4IHvGz/99GZpnPQwISZjbD+wHY/TY1hcNufMOVmGOdPoLntjGfKT6tOuqYHmZli3DurquhTmMoxy9/70InsRnfFOzVCfGcOTcDD/Qhv1H9RQc+sfYdiwrFs0tjTqGnTQ1PVUi5pCoThasJpTvxkYBzQJIX4lhJgvhPgV0AiMBer6aoH9keqQxV5sobVOdR9UAkAgwPCDcZbUoxV6pRzy5M9LjruG/S27kD1pacOORJpWi5uF9RvffIZQVD/F0Cd92l4vDB4Mt98Os2bleOjd+9NTYj4pQx2yxQg6EtQOXk/bhDE5Pe6qRU2hUBwrWDLqUsq3gLOAF4GvAf+d/N4AnC2l3NxnK+yHVAwebbn63WsvzhlUAsDmzdDZyexXYe+dMHMTnLtL+773Tpi9awhVx48ruFAOwGF3MG3MtJ4ZsmCQqmWP4THQ1/mkjWAh/ekJIPCZtqwe92A4SGesk2hcv8petagpFIqjCcva71LK7cBVfbiWI4aPhx4Huy2cKOGbrSP1j330UfrHoSG4f1W34y0t1P7oZh5+9omC1xeYEeCCEy/gpqdv0j1uasgCAfxb7cz/N/3Dtlj8EzWChfSnh9zQVEq6x73horFa2D4WJZLIaEMU4IyD3eli5RUrVYuaQqE4alDT13qA3WVN2ATg6uqv6h8o7ao7DLpg2emaHvmy07XfKSujfu/zeZXh9NhzaE/Pe60bG/F93E79Q1rPd8pjT/WA13dO/0SNYCHSs54wVLYCoRDBpi1dYftEMp8ukl9SS4vYOiPMeGQ6DbsaTK6qUCgURw6WPXUhxAzgcqACKOp+XEp5di+uq19z2ZjLWL9zvaVzvxl5hOlvVOTO4q6uhqIiGk7opPYaLXQccmuGaf7FUF/hpLGl0Xj0qglrGtcw5+w5PZoMR1UVeDzU7ArRfBcEqjXvt+IQSJeTJ77SwrY3ln1is8X91X7mr59v6Vwb4N8CeDwEhrWQaDMI2yf3Se0uINqmJq8pFIqjBquKcj8FHgE+ixZ43qLzdcwwc+JMax60gHXRt5m7bi7ld5dne4R+P8ESB7XXQNCtGXTQvgfdUPvxEo73HN+zBWbk4TOFYCzJvfr96fYybwRm/QOmNcKNF0Ldv0W5o22d/uvpI3xuH/VfWYlPFOGRWu+9K9WD3624cNEGbc3YbDRWllkO2+tJxSoUCsWRiNXw+yxgkZTyNCnlNVLKb3b/6stF9jd8bh+1lbWWzw9FQwQjwezhIT4fgaWzSRhJnyN5Y+8bPVrftLH6YiqW8Pm0Pm+fDzwegi66Nh4u7RTd19NXNDRQc/YMmu+xsfhvUeZttGOLRLVjqfcuGVafPxXmX+Zk2X3XMWLwaDzOEku3UJPXFArF0YJVo+4Dnu7LhRxplDkHFDwWtbtH2FjaZSi7E4qGeOejdwpeV7GjmJkTZ5qeEwwHWfbGMmP511Tv+OLFBH58KYki/RqCPvdwU9PagkG8H7cz6x8wfl8cu4FEQNgJ95wZZe7OJdy44UZiUQNd+W6otjaFQnG0YDWnvgK4BGXY05Tu+rDgsajdPcKqsipKHCW6w1c8Tg+jB40Ga3YpzQ/O/oFpiL27kIuh/KvXC7Nm0fjUDkIvrbX0enqLYDhIYEuAxmdWUlUdxv8G+CJaAeHKz3alKoxIh91N1PoyUW1tCoXiaMGqUX8a+G8hxGDgKeDj7idIKet7c2H9nepWO3YnxAvoH+juEY4cMNJwmloiHuOJ7U9wxolnFLQuM1GZTCGXFEbyr8FwkOVvLueJ7eYtdRUDKgpaXz5yNh2TYf5kLV9edyFE9Oey6GP0ViSNvUe4sTldavKaQqE4arBq1FMx1lGAXmxXAoV83B7x+Eddxuw967EgFpsm0yMMhoN8ccUX9U+UQDhMm9NAAcYAFw7TMLKZkEum/GvDrgYufvBi2g1U5bLogeJd2hNvaaSqrAp/ddd7krPpSHrlc2p7di9dBJz7L8G3v3kX/jNmKoOuUCiOGqwa9RP7dBVHIL6rZjLvq/O546xofmMjwRWD+jMWpQ3I7177HeG4sdGO9MCAuRxuwzByMBxk5daVhhXhqVB6MBzk0ocutWbQgT0H9xS0xu6euMvuYvaa2fzx1D+y+c3NltXjDgdPBL594X8w6zz9WfYKhUJxpGJVJvb9fF99vdB+h8/HLT9ejSdq4VwBEQe8cPfctHzpb179jen5ccsKAkkk3HrBz3S9zoZdDZTfXc6z7z1r+PRUaiCwJUA4Zj1CIKX1akE9HfdIPEI0EWVf2z7mr59v3IbWW146YPN48X/5lt67oEKhUPQTDI26EKIk8+d8X5/McvsXvklTWfe19ZQIV/5KeAE3fSHKqvvrCIaD7Dq4y/z8QjXfBdz09E05LWaZhtQsMpBKDWzZv4VowspORWPJa0sst7Xl03Ev5L5muOwufC4fS2qX6CvqXbtWhdwVCsVRiZk/GBRCnCel3Ai0kd/MHFM59RQ146byQV0L5beVcUjkKVUXMOPAUu55c1z+C/fAM40lYjljUfMZUpfdhdvu1orFwpLWt16xXDUOmqce2Bzgyuorc/Lk3RXnCtFxPxymjJrCI1c+gtfl5esTv16Yop5CoVAcwZgZ9W8BO5M/H1PiMoXidXm5ovhM/rfzpbzGMC4ka3as6ZN1xGU8p8UsnyFNG8CNm2BCOYO+0K7N47NIKBrimfeeYd76ebptchOHTEwb+31t+yhxlljO1/cEj9PD9PFd+vSZc+IVCoXiaMfQqEsplwMIIZxAE/CulLL5k1rYkcZjFqfPak6wwIaNBL1bFOayu3Kq36vKqihyFKVnj2dS5CjSDGBYpkVeJuyFogh0GojidKfEWcLKrSuzQvupTcSUB6aQSCSw2WxE4hHDnvzeRPWcKxSKYxkrhXJx4O9ouu8KAz4OH7IUsi62FzNtzDRkwUnz/AhEjkGrrazVNegAnbFOpo2ZBoGANq4U8G8GZwFLS8gEdqGfeYnEIsRkjEhylnlfGnRLE+gUCoXiKCevUZdSJoBGYEjfL+fIJNhiPYBhEzYOhg/2iVGfMX5GjkGrb6qnyJ4zVC/Nr1/+NTQ2Qkjzrn0RdMeuFke0tjxnsjG/JAI+p5fLx11ubKwNNjkljhIuOekSLZ9vsxgSMGH0caNZfMlimhc0Z6viKRQKxTGGVT20m4H/EkKc3JeLOVIJPHij5XPPLj+bm/9+c97z7AXWHZY4Spg8anLO440tjXTG9T11gHteuYe2k0aAp2tmec0uaL4LFq+Duhfg+o1gl+CIa3PIHTGIC1g58DtMPnGy5XnnKdpj7Zw69FRa/qOF27xfKrzSvxsneE6wNoFOoVAojnKsGvVbgDJgkxBilxDiVSHExsyvPlxjv6fxw22Wq8Vfb37NkhETQuCyW/di7Ta7bi65qqzK9DoCQWCCSI9bDbpg2enw8y+AFHDDK7D0bGhzQ3tS3S3m0IanzPhwCdOqpmETBWjlonn5lZ4KpJQ81vpiQc/V4/Xm1/t+WpxCoVAcAVj9NN4CrAYeQNOB38wxPE+9OyMGn2TZ2zwUDVraAFz+2cstCbvkyyX7q/2m1wnHwzS174H6ehrGFVO+AOZeDHfUwNxLYPQPIWrwV5KwCeob66m/pl7rB5fd5pwbEBcw4vWdlN9dziuOvebvRwLceYQPozLK8k3LzW+qUCgUxwCWdMuklN/o43Uc2Zx+Ovz9YWvnWuwBn3rSVEYNGgUf6h932V1MGTWF6eOnm/Ze+9w+5p03jztevMPwOpWllQSrJ1L7NTvBDP0Xo7Gw6eMyTFNrE7NOn0XzgmYCv53D1if/zD1nxpAmr3HaDviS43/oFHmU8yWM/xC2DomZnwesaVzDnLOV7KtCoTi2MfXUhRDFQojpQogFQoirhRCqWE6H3eECxrAK8nqyNgn+CX5uOf8Ww9C22+7mkSsfsZRLnnWqcZ92JB5h0qhJBLYEiCYMjKfBejOnznldXmZ9615++n9FOE069dxR+NtY8hv0JN/a4sAj8sxaBdi2DZYt02awKxQKxTGKmUzsaLSw+qPAr4AHge1CiKmf0NqOGHwun/ViLwvnpbxcn9tHVWmVvtRpKtweDGrGbOFCQ6N22wu3md7v58//nM37NxsX1BlsWBIy0ZXHDwYhECBw5XgcJkY97NBy8qZI7WtJPXx3cxE2hzPv+VM2vAtz50J5OTQ05LmBQqFQHJ2YfbzeASSA84HX0Sa1LQV+h5ralsUTb/+1oPNPKTmRtzreNTwuEGm5V6/Lq4W29aROGxo00ZhEQmtJ83hg/nyor4eartaubR+Ylzxs37+VMccfhgxBxjoazwvRbtZVlieiYUvAubvhsdXFDO10QH09i9xvMafePLT+08lwTnOIml1oa2luBq+qhlcoFMcWZkb9PGCBlDJVnvy2EOJ7ye/DpJR7+355RwbvfGC9+t0pbDgHloKJUU8gs+RedaVOg8G0ClyaZK95d6M27pCLjUa5fAljDzoZNLLU2gvIXKdMEHhtObNqb0yvo6pV62sPWYiY61Fkd7F+yPV4bx8Pfj9Bp6Tu7lrzJwmtOr/2Gq0Vz5tIaII6s5Q8rEKhOLYwy6kPA97p9thONNMwtM9WdATSmYhYC79L8OLiww6D6rcMKgZWmJ+QoQKXQ8qoJbm95VTTSy1qPZ3qE6opchiL1OgRjodpenl11jr8m623VOgh7Xa49VbNIHu9eQfSZJIAAtVom5umpnynKxQKxVFHvs/f3pc9Owpx2ZzWPHUBsrOTEmeeSbUS6Mwz0zxDBS6HbkZteNXpLHnKmc5Vp++RzFsPlR781X4corAh7g5sVLaQtY6UIp2th7L2NmEjsLlrQ1LIZLeQG5pK0dIQlZV5z1coFIqjjXxGfb0QYn/qC0iF3J/OfDx57JhlgCiyvP2JCJhw/ATzkwTs2bjB/JyqqrQKXEowZuGF2vfgcSXZRm3kSGa/FGXvnTBzE5y7S/u+906Y/SqwZAlvvvsy8ajFiENqmRL8o6ZlqdEBTNwHdmvF7TmEoiEe2/oYwXAynF9WZVmxzhOGylY0IR2/GuqiUCiOPcxcs1s/sVUc4ZwQdfGeRSfXF7dTMSBPaF3C8R8ZS7sCmtGaP5+GkVouOYHmqXrCMJ926j8/ghrQct0zZgAwNAT3r8q9VNCRoPaRL9FBxHprnoT5A6bivWom/OimrEOBCZj2qefjqXeeYvhdw1l77VpqK2u5Qd5g6Xk2wP++VysUVEVyCoXiGMRs9Koy6hbZ42y37OGOLS6n+oRq7AjiJk96fUCeiWY+H8G/raT2yYsJZhSlpQrUah+fQfOCZrxmufckgdHtJBIWUwhJXDH44dW/AZ9PM6IZVfhbhjmIOfILxhgRkzHaom1MeWCKJnHb7W1y2V1E4hGK7EV0xjvxCDe2hKS+Yh7e929RBl2hUByzFJZEVegiEwlrBlHC0KAm3fq9J75rfJ6AZ2zvs+yNZYyWow1PCwzYRcLrAZ2cc0ImtLa4ZO496NI86MZSrULdv1nLfwM0DnEQEtGca3Rfe1o4R4DN7WLsH0+n/pp6ampqtGr7QACammgtfQ7aX877duQjEo+kx7ZmEk/EuW3ybQwsGsieQ3uy2/wUCoXiGEYZ9V6gvbMNishv2AXsDbfic/u46KSprN+53vDUvaF9zF03l5+P/jmuXa6skaLBcJDAlgD3vX6fYRFZKBrS2uKqqmgYU0Tt9M7sEP3FWkFbzS6oOmjH43TrXssdg8/vETz/GUGMrs1LZyJCZyRC7UO1WkTA6023kA1aNxdeOXyjbkRcxrn1+Vtx293apkKNW1UoFArg8LqPFEkOubEcut5xnOYR/+GLf8h7bigaIiET1D5Um55C1rCrgfK7y5m7bi4bm82H41UMqCD4lVpqp3cSdHeF5kNuCLrh0mugbZAH/21/M5605nBgO38SNpv+8UQsmlWtDjDhhAnGM9wzK/APg0g8QjASzHpvFAqF4lhHGfVeoJCisLakMPrwAcNZUrvE0nNSofRgWDNiwUjQUptXOB4m8H49iWJ9A9vmgl88+F18k6Z2TVpLVpqnjLJwONiw+xkiUj9HHkp00vTB1qzH/NV+HDb9IFBJbjT9sEi9NwqFQqHoR0ZdCHGFEGKLECIhhDjz015PIRTSvlUU6XJTZ581m70L9jJz4kyGeYcZPicVSi9EiAVgw84NWp93wljT/Z43ltAWaaNmZA3NC5pZfMli5p0zryvMHjOvwrfHYfD23V0PBIO8uewXJCKd2R65hOIIrH9I643vLY89nWZQKBQKRf8x6mgz2i8Hnv+0F1IoJ3QIy4pyXzg4KOuhod6h3P/l+/nZ5J8Z9mOnpqEVIsQCWhV5VVmVVkFuQEpnHrrkaMefMB67sFu6R9wGP/r4UZa+uhQaGgiOGk7tv+6gXcSyUxIC7BJO3af1xu9dUsRnIwMs3cNMFCdzUpxCoVAc6/Qboy6lfFtKuf3TXkdPEALLOfWaUefrPu6v9mNL6O8MbAmJf4K/ICEWJBzn8OKv9iOl8Y4jHA/neLoFbR6Sr33Omjns++IFBEa1YRS4kCRlXIGhBzo5qzFkaTN02rDTcNr0J7XZhK1rUpxCoVAc4/Qbo34kc8hpMY4s4MPqUbqHfBFYtD6WK+WK9rg3kjT8RgVtOvfasfNVfG4f886bZ3ianqdrunkweal1X4jyzChoNxjmEnJD0/FdEYBBwbilzdBbH7zF6qtXm4+gVSgUCgXCzIvr9ZsJsQH9YTA3SylXJc95FviRlPI1k+t8F/guwJAhQ85YsWJFH6zWOq83v27ZU68YUMEQz5CcxxMHPuTN8C4S3Wx2hbuC5o49THSPxDb4eNoibTS2NiJlwlLE/7ShpwHw5gdv6ubjbcLGxCETszYLCZkwPN8MZxxiNuPCQVsCRhyCwZ02SCQ4UAK7BmrnV7gr2BPeo/88YWPEgBGUFpfS2tFKOB7GbXdTWlxqfZPTj2hra9NaAI9yjpXXCcfOaz1WXif079c6efLk16WUurVnn2ifupTywl66zu+B3wOceeaZctKkSb1x2R4z+SeTrcU8JFx98tU8NO2hnEP/v707j4+qvvc//vpMNsiCGpAdpZKIAi0uXNyoWxEwemvrlrZaqZdqW6kF0Vuxdr0/Wy2CV+pS2/Jr1SvVqVpXUBSEtthqtV6rgEuiIigICBYmCWSb7/3jnEAYksmZMJOZTN7PxyOPZM58z5nPmQQ+890XfG8K37cl1MV0f889fC4/Wn0N80NnMu2GxQBsjGzkmkUz+P2bD3b4YWLByAVMO2Ya+evyqVhYQdRFqW2spSiviJCF2p3nnb8un4r7ziTa2Egt9RSQQ4Pz18BrZwvX1ovTtMWiMGeZMfklR0mDt2b9kKu96XVzD5/LNW9f0+59zD5pNjeedmP8m+0mVqxYQbr/ZrtCT7lP6Dn32lPuE7rvvWrxmWQIOqXN4N1PYnez9Sw/8BPqdrZ9Wm0BVPsfGFeuW0nFwgoamxs6TKLA7v7yltHt4VVhqrdVd7gK24R13t7kN4xv5pZx4GjGxftrsZjvbXAh+M+Jjh+dAkvu815j8UJv7XqL0+ygwXAiIsFkTFI3sy8CtwEHA4vM7FXn3OQ0h5VcDkb2G7nP4Uh9hIcb/tnuaUUNUHbiWXvNU98tThLNceyVDFtGt3coEoGKClx9DXceDY3J/CsxqMv3Fr7ZOG/Ph4en5nvryTe08VoaDCciEkzGdEg65x5xzg11zhU45wZ0l4S+YePbCZW/aeJN+xwLrw4TipOdm0NQOfK8hOep51hO55KhvwlMeIy3+1sqNOZA+Bivr6G4AQ6uhWX3Qkm99yEGNBhORCRRGVNT766u+5+vBSvovJrowAee9LZNLSnZ/dTy95azs7n9RV4++75R/Mgiqg5JYKqZg2EHHtK5ZOhvAlNVumdp2WSrz4Xq8z8Hp46FoUOhf38mfGU2Gz41lPAYqK7VRi0iIolSUt9Pb9a8B0HWaTFoyAFmzoRZs7ztSidMIFIf4Q+r/hD31BWHOmrWvEr50WMpyisKltgN3t+xvuNybSkvh6IioLbDPvvOKsgpoOy086ClO2DFCrjxRoqBAB0EIiLShoxpfu+ujij+VGLLndbW7u6zpqaG8OowTcTfe7wxBOH6V6gcXZlQ83uiU9J2q6wkUmDc+W+kJKED5OXkqZ9cRCTJlNT3041fvbtzJ0ajEA5TtbWq47IhqO7j7e5mFjzLHlx08F6PI/URFryygGufvZYFrywgUh9p+8SSEsJ3XpHQRjW7dbSmu//8zyf+XM3qIiJJpub3/VRSOogQwQaU7bVDWW0tVFdTfuSwjpu4HZQNHEV4dRhLoOo8Y/yM3T+3TIVrPU991pJZ7c5TryqF2vaXjG+TReFzvY9kaf0bcQp532Yvnc0lYy9RYhcRSSLV1PdTeHWYUChAp7qDqMG1E2HBMRA5sBDKyqhcBbkd7PKW1wyVF9+U8IYulx5zKUCbW7bWNtbG3Y+8vG85vWKXt+vg/r77Yg6VJ19BUV5hh8W1ZaqISPIpqe+nqq1VNLkAe68a7MqHORNg5mQY8q06Vp40jJJ31jP9JeI2WX/bjaO4dCDlfcspDJAwAfIsl0VvLwKIOxWuOdrcZnKt2NaPXZZYn/xlAyqoPHYqoQA7vGnLVBGR5FNS30/lfcspyAk478tveq4t8JZGrXjkfGpGDOtwl7eWfvTK0ZU0ReMPqmvR6JpYs2UNEH/XtbqmOn7zyA+I/Pp2bwAfQCTC4h99iV7BXmq3H47eTEmBN6+8JL+EglD77fdaJU5EJPmU1PdT5ehK8nLa3ha0I1EXJTzG2FQcfz/2j0r23mwlEAdbd24FYNgBw+IWfTFnI4PXXsnK8QNg5UpvAN8BzexK5LYMXsjbBOxZknbe5Fu0ZaqISBdSUt9PJQUlPHzBw506t7axluq6D1h//Ki4NfX1pd54xvDqMDkBmrYBMOhb2Nf7edeu+CPSDWoKoOKLO6k550xYs4byTU3kBOhVaK2w155Bb8X5xUwfP53npj6nLVNFRLqIRr8nwbod6yjMLaSuqS6h81qaoO/lk7jl3tnxHuA1o9c31we6tgGj+o0CYP1LSwPNN28MQfjwBqZt3UrF+t40h9rZYaYdl469dJ9jiW4kIyIinaekngRVW6sSTugATdEmKsdUMnvx1XHLNTZ7c9TL+5YHXlHOOTj5kJO987ZCfggaOmhO35UHa0oaoG9f/nhEYoPkQg4uH3d5m88F3khGRET2i5rfk6Al2SbqqIFH4SI72Nq4PW65LXVbqGmooaKsIvBAOQwOv+Nw7nzpTiqHn01ekFXvHGwtCcHo0Tx5wdiEVpM7Nm+4at8iImmmpJ4ElaMrCVnib2Vpr1Lu+Z9rOl65zcEPnvsBZbeVBW5+bzF98XRq/30KX38tt+PlbA36RqJw1llQ0ieh19l5QDtT7SIRWLAArr3W+x5pZxU7ERHZb0rqSdB6GlfrAWEdTXU7a+RZPLb1+Y5rxAa3vngrO5sS6+NucemyK/llgHXcezXAqO25sGgRR35MQmvaDzpgyL4HV66EIUO8TWzmzPG+DxniHRcRkaRTn3qStDUgrKK8ghG/GNFmMi7MK2Tq2KnMfeJ7KY/tmXefCTQVLs9B5atNRKpXs6DgTwk1v08ZMWXvAy2b1rSumdf6YwEqKmDDBihWc72ISDIpqSdR7ICwO/9yi5fQY9Z2L8gpYMnFS3CRHay3HSmPK0SIaLzV6R0UN8DihVCcV8SCQVuJfpLYbi4FeTGtEuGwt2lNW/zNbJimwXMiIsmk5vcU2bD0UaYv80e1x+TH+uZ6ykrLCN93HTmd3B01EU0uzuA6BxOr4e3bYMI6IBRi+YBd1NHQ/jlt+Nv6v+19oKpqT808lr+ZjYiIJJeSeipEIlx31/ntP+9g9lNXU7XlTRpS3FZywagL4q8Xb7C0DA6ZCc8cBpHwvTxc9VjCr/PwGw/vvTFMeTkUtTMjoKgIyrRErIhIsimpp8I99/DEYc3t90kbvFX1AsP6jUhoMFqici2XUw49peP+dIOmXJj8VfjhmjuCL0XbSshCTF88fc9e7V+sgFA7f16hEFRqiVgRkWRTUk+BV5bexyfxNlNz4HbWwahRga6X66xTU+aaXBMf7PiAc484N9gJBrdFltEYbUz4teoa6/j9a79nzl/nMPPpmQz59RGs/P1NUFKyp8ZeVOQ9XrxYg+RERFJAA+VS4LyRr8YvYPBizkeM++dzgUaY//yAC5hvL7Bu+7qE4mhZhnbE1iiPNkBd+5um7daMwzBcJ5oQWvruW1a8q3h9NhvWvk3xI4u8PvSyMq+GroQuIpISSuop8EGv+kDJ+qUdb0AOHZa9fOp8Xl4+K+GkHrIQlYdW4M6YyVXfCHiS0amE3paoixJeu4hpGuUuItIl1PyeZJH6CE1BZoMZfJQXYHU4B+G1i5h7xtyE4ijIKfB2QntkESX1jiteIqX9922pbayleptGuYuIdBUl9SQLrw4HLttU2CvQanLV26oZ3GcwVzWM8xJzS3KOk6R/eMoPmXDIhL2nliU29Xy/tTT/i4hI11Dze5JVba0KnDxHHnwEWyIf0RhqPzsbUFZaRqQ+woKCVXsn8jiv84PlP2D7ru18f8QwSoqKKN9WS2HAfvVkCVmIyjEa5S4i0lVUU0+y8r7lwZq5HXx22GfJyYufZR3wzUe/zuCfltIQb1W4GFEXZc5f5zB4y2xWDotSuQqaOvptd7Z53kEBuXute1+S762Hr53bRES6jpJ6klWODl4zXfLWkzz2pcfbL+AnG+gL7AAAF0pJREFU2aYcqAk1Ue8SW+UNoKaploqLjI/6F3bc19/J5vnCRli765vMnzKf2SfNZv6U+Wy4eoPX/C8iIl1Gze9JVlJQss9a720yeP+dV5j06UKWXLyEz9//eZqb6r3E6/aUaV2+sxotyqjLmtpdir1FXjOEcnIhJyfQFq8WhaJGeOrhXgz87lF7rXsvIiJdTzX1FMgP0kruoKARqKhg0oAT+fhb73HXQZcwu/E48puTG8+upl3eHPIOPhgUNMPab7zJvEnzCHXwpxGKwiX/hI3zYMLGPK0QJyKSAZTUU6B3c4BqtcH6A2HloEa44QaKPzWSadc/zPdufpGGjuau+zX5Xrm9khHu7mtOH34hAweOYPr46Tx18VNxixc2wu1/KqS4QCvEiYhkCiX1FMhvDDbiLBqCivN2UXPbPG/f8dpaLp/S8XkAs0+azdwz5lKU286mKYkysKFDdz+cNGISSy5eQl4ob5+ihZbPU1xE8dxfePuiT1DfuYhIJlCfegok0v0dNQiPdkx7yXv8wFEdXyDk4MaJN3L732+nIRpg8FyQPn4H619/Hs7cc2jSiElsu3Yb97x6D4uqFoGDs0aexdSxUzWqXUQkAympp0BjTvCytflQ3adVJ3qATwQHRQtYuW4lVz19Vfy90hO4JgYL615kbs1HDCweCHir44VXh1m3fR3nHnkulaMrvYGAIiKSkZTUU6Cpdz4QbPpZoculrDYUuDxAA02ced+ZwRI6gAMzKKSAWurbr7kbzF46m7u/cDcr162kYmEFUReltrGWorwiZi2ZxeKLFmuqmohIhlKfepJt2LGBSAIJus6aGFaT2K8hEmpmV/Ou4CcYDOFA5p8xj0ER4tbc3/r4LSL1ESoWVhBpiOzeca22sZZIg3e8pqEmoXhFRKRrKKkn2YynZyS8MtsXLnTUlBZ7+40HOdegKRqwlu5rijYy7cTpnFE+Oe5rjOw3kvDqMFHX9ry8qIsSXhV8fXsREek6SupJ9nTVUwkvFLMzWs89j/4E5s9PTVAAOV5H/40jvhm32E0Tb6Jqa9XuGnos7bwmIpK5lNSTKFIfoaadZNiRx9Y+DSncdzy/qBgiEQafewl3LGbf3d4c3HH6PAYWD6S8b/nuddxjaec1EZHMpaSeRIlsuxrrw8iH3g8Ba/l5JDDE3kH/uhwIhyEa5YqXYONcmPoqHL/O+77xzt5c8fYBgLd+fcja/tPQzmsiIplLo9+TKJFtV/eRYD98o2sO/loGvbfVQM2evdUH1sLdj7UutBOqvWb1kgJvh7XY0e8hC2nnNRGRDKaknkS7t13tRGIvzT8gofLmwCXwOp/kNUJ5uTcYr7aNLoKiIijb06w+4ZAJbLh6A+FVYaq3VVNWWkblmEoldBGRDKaknkSVoyu57PHLEj/RwdsbViV2SoIdJwf3GeRtujJrVtsFQqF9NmUpzi/WzmsiIt2I+tSTqKSghILEZpp5DDZHI9767wGFguwE18LBoPKjocTffKWkxKuZg/e9RJuyiIhkA9XUk+yUuv4802dz4k3whjeQLaC8KNQH/UhmUNqnv/fzhAneJizhsNeHXlbm1dCV0EVEuj0l9SRrOKCw8ydXVUHvYEUf33gak4cuD/zhYVPNpj0PiotTOn1ORETSQ83vSbapbnOnz33tkGD7oxeE8pl0/FeY+VJO4FHzr296vdNxiYhI96CknmTNHRdp16TauwKVK8wvgh072FwQfFrbx3Uf70dkIiLSHSipJ1moqJN90wabdgar5Q/s3Y/IdVfz4Ojgl+9f1L9zcYmISLehpJ5kQ4eO6tyJLcu2dlTzdvD1t4r44akJ7NvuYNrR6kMXEcl2SupJds6Y8xJeHS5RX162mV8cT0Iryl0+7vJUhiQiIhlAST3Jpo6dSk6n14oNZsb4rUQTeIkccrQSnIhID6CknmQlBSWc25DCXcwcPDiiPqF58L1yg42qFxGR7k1JPQUmDTiB/MYUXbwTjQA7m2q56umrWPDKAiL1wVetExGR7kVJPQUqL76RgkSWcU1Ugok96uDWF29l5tMzGXLLEFauW5mauEREJK2U1FOgpO9gFp94ByX1UFif7mjY/SGgtrGWSEOEioUV1DTUpDcmERFJOiX1FJlw9hVsuGYjv/jkuOSPht/P60VdlPCq4OvMi4hI96CknkLFpQOZ1ueU5F94PwfX1zbWUr2tOjmxiIhIxlBST7Xy8qRfMi+R+WxtKMoroqw0hSP0RUQkLZTUUywy8eTghQPm6gIXdCm5toUsROWYyv26hoiIZB4l9RS79g8JrOQWpK/cwa/eOjx4v7qDHH+XmaK8IkryS1h80WItRiMikoW0n3qK3VX7p+AfnQLU1POicMuw9QnFMOczV7PlwDzKSsuoHFOphC4ikqWU1FPMJdL9HWBDl0PqclndZ2fg6/1s0xhm/XhuAkGIiEh3peb3TBJgh7Zj60vJyc3vsBwOfjbqSq775evJik5ERDKcknp3YjB02GhOGX5qh+VOP+x0rrvwF10SloiIZAYl9UyQwKC3USefx2nDT+uwaEGoYP9iEhGRbkdJPVMETOyVx07lw8iHHZY7a+RZ+xmQiIh0NxoolwkCDqbLtxDF+cVs27ktbrkcy2Hq2KlJCExERLoT1dQzRYCaupn3Geyg3gfFLXf+kedr2pqISA+kpJ4pAtTWm5y3SfuY/mPoldOrzTIFoQLOGHFGMiMTEZFuQkm9G2l2XnW+cnQleTl5bZbJz83XErAiIj2UknqGMH9ueRAlBd5SryX5JRTlFQFaAlZERDRQLjM4OCCay79ym+KWKXR7PoNNOGQCG67eQHhVmOpt1VoCVkRElNQzxRnNw3kwp7r9vnWD83oft9eh4vxiph0zLfXBiYhIt6Dm9wzxvcFfjvu8ReGUky/qomhERKQ7UlLPELcOeId5K/Lb7Vcvzu1N5bGaey4iIu1TUs8EBm9tf5dZNyzj0Ud6kdvs1cwBQg5KQr1ZPPUZ9ZeLiEhc6lPPBA5G9hsJEyZwzvNb+OT+ewi/v4jqPjDsgEPYcO1mJXQREemQknqGuGniTd4PxcUUXzadaUwHYMWKFUroIiISSMY0v5vZzWb2ppm9ZmaPmNmB6Y5pf0W2bghcdmDxwBRGIiIiPUHGJHXgWWCMc+4zwNvAdWmOZ7/NufuydIcgIiI9SMYkdefcM865ltVXXgCGpjOeZLhtx9LAO7CJiIjsL3Mu4NqkXcjMngDCzrn72nn+cuBygAEDBhz7wAMPdGV4gb2y8R9BV37l2EHHtnm8pqaG4uLs71PvKfcJPedee8p9Qs+5155yn5DZ93raaaf9wzk3rq3nujSpm9lSoK3O4+udc4/5Za4HxgHnugDBjRs3zr388svJDTRJ7Efm1dQ7qq07cD9u+1ZXrFjBqaeemuzQMk5PuU/oOffaU+4Tes699pT7hMy+VzNrN6l36eh359zEeM+b2VTgbOBzQRJ6xguY0ANX50VEROLImCltZjYFuBY4xTlXl+54uoz63EVEJEkyZqAccDtQAjxrZq+a2V3pDkhERKQ7yZiaunOuLN0xiIiIdGeZVFMXERGR/aCknkpBBsA56G0Z02AiIiLdmJJ6CvWtIVBif/K8R1Mei4iIZD8l9RSavD430Oj208eclfpgREQk6ympp9B7/XM0B11ERLqMknoKNRT11jx0ERHpMkrqKTQ6Z1DHNXUlfRERSRIl9RS68ZD/6LBMr5xeXRCJiIj0BErqKTT4om9w6Hbi1tbHDhjbZfGIiEh2U1JPpZISnpn8+7hFFp63sIuCERGRbKeknmKHT/kyVx073XsQU2O/6virGFE6ouuDEhGRrKSk3gVu+ffbqb6ymhOHncjg4sGcOPREqq+s5pbJt6Q7NBERySJan7SLjCgdwfPTnk93GCIiksVUUxcREckSSuoiIiJZQkldREQkSyipi4iIZAkldRERkSyhpC4iIpIllNRFRESyhJK6iIhIllBSFxERyRJK6iIiIllCSV1ERCRLKKmLiIhkCSV1ERGRLKGkLiIikiWU1EVERLKEkrqIiEiWUFIXERHJEuacS3cM+8XMtgDvpzuOFOoHfJzuILpAT7lP6Dn32lPuE3rOvfaU+4TMvtdDnXMHt/VEt0/q2c7MXnbOjUt3HKnWU+4Tes699pT7hJ5zrz3lPqH73qua30VERLKEkrqIiEiWUFLPfL9OdwBdpKfcJ/Sce+0p9wk95157yn1CN71X9amLiIhkCdXURUREsoSSeoYysylm9paZVZvZ7HTHkypmNszMlpvZG2a22sxmpDumVDKzHDP7XzN7Mt2xpJKZHWhmD5nZm/7v9oR0x5QKZnaV/3e7yszuN7Ne6Y4pWczst2a22cxWtTpWambPmlmV//2gdMaYLO3c683+3+9rZvaImR2YzhiDUlLPQGaWA9wBnAmMAr5sZqPSG1XKNAFXO+eOBI4HpmfxvQLMAN5IdxBdYD7wtHPuCGAsWXjPZjYE+A4wzjk3BsgBvpTeqJLqbmBKzLHZwDLnXDmwzH+cDe5m33t9FhjjnPsM8DZwXVcH1RlK6plpPFDtnHvXOdcAPACck+aYUsI5t9E594r/cwTvP/8h6Y0qNcxsKHAWsCDdsaSSmfUBTgb+P4BzrsE596/0RpUyuUBvM8sFCoENaY4naZxzfwa2xRw+B7jH//ke4AtdGlSKtHWvzrlnnHNN/sMXgKFdHlgnKKlnpiHA+laPPyBLE11rZjYcOBp4Mb2RpMytwHeBaLoDSbHDgC3A7/yuhgVmVpTuoJLNOfchMBdYB2wEtjvnnklvVCk3wDm3EbwP5ED/NMfTVf4DeCrdQQShpJ6ZrI1jWT1NwcyKgYeBmc65HemOJ9nM7Gxgs3PuH+mOpQvkAscAv3TOHQ3Ukj3NtLv5/cnnAJ8CBgNFZnZxeqOSZDOz6/G6CRemO5YglNQz0wfAsFaPh5JFzXqxzCwPL6EvdM79Md3xpMhJwOfNbC1ed8rpZnZfekNKmQ+AD5xzLS0uD+El+WwzEXjPObfFOdcI/BE4Mc0xpdomMxsE4H/fnOZ4UsrMpgJnAxe5bjL/W0k9M70ElJvZp8wsH2/wzeNpjiklzMzw+l7fcM7dku54UsU5d51zbqhzbjje7/M551xW1uqccx8B681spH/oc8CaNIaUKuuA482s0P87/hxZOCAwxuPAVP/nqcBjaYwlpcxsCnAt8HnnXF264wlKST0D+YMzvg0swftP4g/OudXpjSplTgK+ildzfdX/qkh3ULLfrgQWmtlrwFHAz9IcT9L5LREPAa8Ar+P9f9otVyFri5ndD/wNGGlmH5jZNOAm4AwzqwLO8B93e+3c6+1ACfCs///SXWkNMiCtKCciIpIlVFMXERHJEkrqIiIiWUJJXUREJEsoqYuIiGQJJXUREZEsoaQuPYKZ/djMXKuvDWb2sJmNCHDu1/xzipMc06n+dcck87r+tYf71z47QNkBZnarmb1jZvVm9omZPWVmk5MdVzYys/Fm9uOAZceZ2d3+DoxRM7s7tdFJT6OkLj3JduAE/+savPnTywKsS77IPyfZC1C84l/3nSRfNzB/gZj/xdtoZi4wCbgEWAs8bmZj0xVbNzIe+FHAsicBE/AWmPooZRFJj5Wb7gBEulCTc+4F/+cXzGwd8BegAngwtrC/BW6Oc24L3gYlSeWvcf9ChwVTayHe7lQnxqy5/4SZ/RLI1t3V0uU259x8ADN7Od3BSPZRTV16spbNVYYD+M2iL5vZF8xsNbALOC62+b1V0/aFZvYrM9vur0L1EzPb69+UmX3GzJ4ws3+ZWY2Z/d3MzvCf26f53X88y8zmm9k2/7zb/OWCW8oMMrPfmtm7ZrbTzN42sxtalwnCzE4GjgWua2sTHefca865da3KX2hmr/tN9OvN7Kf+lqMtz7e8T8eY2Qozq/NX4jrGzIrM7Hf+e/WumX05JpYVZvaQmV1uZmv9+1pk3p7lrcv1M7N7zGyrf/0VZjYupsxaM5trZlf5v5dPzOwBMzswplyp//vbZGa7zOyvZnZcTBlnZjPM7GdmtsXMNpvZHWZW0HLPwG2tyjozW9Hee+6cy/Yd+iTNlNSlJxvuf/8o5tgc4Ea8Gvx7cc6fA9QA5wP3AT/0fwbAzI4AngcGAd8Evgg8wt6b9bTlarxNfC4CbgAuB37a6vl+eLXrWcAU4GbgUvzkkoBTgGZgaUcFzWwSEMbrMjjHf61r8JbSjHUPcD9wHt6Ogw/hre+/Ae/9eRG417z95Vs7AW952VnANOAzwKMxZR4FJvuvXYn3f9hyMyuLKXch3lrsl+Ot3302rZaq9ZPyUrylTv8Tb1/wLcBSMxsYc62r8XZhuxjvvf4GMMN/bhEwr1X8JwBXtPGeiHQN55y+9JX1X8CPgY/xupxygcOB5cAOYJBf5m68LW6Pijn3a/7xYv/xcP/xvTHlXgUeaPX4frwdy3q3E9Op/nXGtDrmgDeBUKtj1+P155e2c51c4Ct4LQv5MTGeHec9uQvYGPD9ewFYHnPsu3gfCobGvE9TW5Wp8I/9ttWxA4BG4Futjq3wjx3a6thJ/rlT/MdT/MentCpThJeMf9Xq2Fq8cQq5rY7dCnzU6vE0oAEoj3kf3wFujvl9/Dnmvh8FXmj1+Nvef6UJ/02+DNyd7n8b+squL9XUpSfpi5c4GoG3gMOASufcxlZlPnTOvRrwes/EPF6DV8NucToQds7tTDDOx9zezbR/BHoDY8Db2c7MZprZGjPbiXc/C4EC4JAEX6vDzR/8sQXHsO+4gzBeTfmEmOPLWv1c7X9/bvcLOrcdLxHv1bQOvOKce79VuefxtvYc7x8aD2xxzv2pVZla4Em8wWetLXfexkgt1gD9W3VRTMTrfnnPzHJbdSP8CdirOZ+Of88iGUMD5aQn2Y73n7nDa3Lf4JyLTWqbErhe7CCyBqBXq8d9gY0kLnaP6pbHg/zvM/FGqt+El4Q+Af4NuCPm9TvyIXCwmfVyzu2KU64fkMe+703L49KY463fl4Y2jrUcj421rb25N7Pnvge1EUNLHPFiaHk9A/L9n/sBx+N9IIoVOxshSOwiGUFJXXqSJudcRyOOk7lt4Vb2JKRE9G/nccsHhAuAB51z17cUMLNRnXidFcB/4fU9L4pT7mO85Bcb1wD/+7ZOvHZbYq/fcqzlvje2U2ZAJ2LYhtf8/a02nqtP8FoiGUPN7yKpswy40MwSrdWdEzOK/lxgJ7DKf9ybfRPPRYkG55z7C14T9M/MrCT2eTP7tJkNc841++UuiClyIRDF24c6GY4xs93dB2Z2El4S/7t/6EW8JvSTW5UpxJtjvzLB11oGlAHrnHMvx3y9nuC1GvxYVHuXtFNNXSR1foK3yMifzWweXs39aGCrc+63cc4rAR40s98Ao/FG1d/unGupjT4LfMfMXsRrKr4IL0F1xkV4AwZfNrP/xusv7oM3wvwy4DhgPd7iKkvM7HfAA8Cngf8H/MY590EnXzvWZuBJ81Zn6wX8HK+f/WkA59wSM3seCJvZbLz38xq8Dzk3J/ha9+LNSFhhZnOBd/G6S8bjDaj77wSu9ab/fYaZPQfscM691VZBMzsYb9YBwEHAoWZ2PoBz7qEE70FkH0rqIininHvLzCbg9X0v8A+vAb7Xwanz8Abx3Y/XmrYg5pz/Ag7Gm+4G3kC67wBPdDLGY4Dr8EazD8Ebaf934CvOuX/65Z4xsy8B38f7ILDZjzPoSmpB/A1vmtmtePe3Am9KWmtf9F/3VrzE/3fgdOdcNQlwzu0ys9Pw3suf4DXhb/av93iCcf8F70PFDLypkH/Gm9nQltHsPeDwsFZlLcHXFdmH7TtOSETSxcwccKVzrq3531nLX7DlY+fc+R2VFZH2qU9dREQkSyipi4iIZAk1v4uIiGQJ1dRFRESyhJK6iIhIllBSFxERyRJK6iIiIllCSV1ERCRLKKmLiIhkif8DxCjHnwm2fxIAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4589,7 +5180,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -4599,7 +5190,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -4609,7 +5200,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 73, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -4620,7 +5211,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -4647,7 +5238,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -4674,7 +5265,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -4686,7 +5277,7 @@ " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -4700,19 +5291,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.1660749149902864\n" + "Optimal Threshold: 0.15940300945944538\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4725,10 +5316,10 @@ { "data": { "text/plain": [ - "0.7220704605012441" + "0.7282872884609857" ] }, - "execution_count": 77, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -4740,14 +5331,14 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the SVM original data RBF kernel identified 733\n" + "Of 2015 Defaulters, the SVM original data RBF kernel identified 707\n" ] }, { @@ -4782,14 +5373,14 @@ " \n", " \n", " \n", - " 0\n", - " 6391\n", - " 338\n", + " 0\n", + " 6390\n", + " 344\n", " \n", " \n", - " 1\n", - " 1287\n", - " 733\n", + " 1\n", + " 1308\n", + " 707\n", " \n", " \n", "\n", @@ -4798,11 +5389,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6391 338\n", - "1 1287 733" + "0 6390 344\n", + "1 1308 707" ] }, - "execution_count": 78, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -4814,7 +5405,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -4823,12 +5414,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.36 0.47 2020\n", + " 0 0.83 0.95 0.89 6734\n", + " 1 0.67 0.35 0.46 2015\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -4853,7 +5444,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -4865,7 +5456,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 80, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -4878,19 +5469,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16374485135727915\n" + "Optimal Threshold: 0.15995506541340332\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4903,10 +5494,10 @@ { "data": { "text/plain": [ - "0.7040433457077317" + "0.6995913850752561" ] }, - "execution_count": 81, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -5032,16 +5623,16 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'C': 1, 'gamma': 0.1}" + "{'C': 1, 'gamma': 0.001}" ] }, - "execution_count": 84, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -5049,14 +5640,14 @@ "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.01, 0.1, 1,10]\n", - " gammas = [0.01, 0.1, 10]\n", + " Cs = [0.001, 0.01, 0.1, 1,10]\n", + " gammas = [0.001, 0.01, 0.1, 10]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,2)\n" + "svc_param_selection(X_train_pca, y_train,5)\n" ] }, { @@ -5068,44 +5659,44 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 85, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C= 10, gamma = 0.1)\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", "clf_reduced_tuned.fit(X_train_pca, y_train)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16537143473786733\n" + "Optimal Threshold: 0.153632964235413\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5123,14 +5714,14 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the SVM reduced features and tuning RBF kernal identified 727\n" + "Of 2015 Defaulters, the SVM reduced features and tuning RBF kernal identified 895\n" ] }, { @@ -5165,14 +5756,14 @@ " \n", " \n", " \n", - " 0\n", - " 6383\n", - " 346\n", + " 0\n", + " 6115\n", + " 619\n", " \n", " \n", - " 1\n", - " 1293\n", - " 727\n", + " 1\n", + " 1120\n", + " 895\n", " \n", " \n", "\n", @@ -5181,11 +5772,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6383 346\n", - "1 1293 727" + "0 6115 619\n", + "1 1120 895" ] }, - "execution_count": 87, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -5197,7 +5788,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -5206,12 +5797,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.36 0.47 2020\n", + " 0 0.85 0.91 0.88 6734\n", + " 1 0.59 0.44 0.51 2015\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.72 0.68 0.69 8749\n", + "weighted avg 0.79 0.80 0.79 8749\n", "\n" ] } @@ -5431,7 +6022,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 601992d7fb4ea3897c5fb62b18cf3ffec3ac21a5 Mon Sep 17 00:00:00 2001 From: reon Date: Wed, 20 Nov 2019 00:36:41 +0800 Subject: [PATCH 11/25] TUNE LOGISTIC --- ...2101_Default - EDIT-Copy1-checkpoint.ipynb | 2278 ++++++----------- BT2101_Default - EDIT-Copy1.ipynb | 2278 ++++++----------- 2 files changed, 1646 insertions(+), 2910 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb index dbd858a..4ae5047 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAapElEQVR4nO3deZxcZZ3v8c+XgALjAg6LCEhQo4iMsgTEcQNRBBwFRhz16oBeFHVwvDqLouOIG3fQGZfBBQcxF3BUFBFFQTGgiNcrSFBkEZWIKDEBoiyyKYu/+0c9LWWnOl056Uqn6c/79apXn/Oc55zzO51Kffs8p+pUqgpJkrpYZ7oLkCTNXIaIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEpNWU5NAkX27T6yepJFtNd10ASY5OcnybfnSSm6Zw2yckeWOb3ifJ4inc9jOT/HCqtqfRMUTUWZJb+x5/SHJH3/xL1nAtq/3ineTaJE/pm98uyd2TrVdVn6iq53bd77gazk/y0qnY1nhV9dOq2miIGl6d5Owhtveyqnrv6tY16N+uqs6uqies7rY1eutOdwGauarqAWPTSa4GXlFVk774DJJk3aqa9AV7bTNT615dSeZU1T3TXYemn2ciGpkkT05yQZKbkyxN8oEk67ZlY399vibJz4DLWvtzklyZ5KYkHxz/l3mSVyX5SZIbkpyRZMu26Lz28yftTOiAAfVsl+Tctu7yJCcmeWBbdgqwGfD1tv7r2jbn9J1d7dT+Sv9Gko8kuRE4YoK/3A9IcnXbz1FJ0vbzx+GlvprubtPvA3YFjm/7e19r36Ht88YkVww6tr7tPSrJd5LckuSrwMaD9tXmX9lqvCXJVUlekGQn4IPAHq2Ga1vfk5Mck+TrSW4DntTa3jpu/+9ov9+rkrygr338v2P/72yFf7vxw2NJ/iLJt9vz4pIk+/YtO7k9V85qx/KdJNtM9DvSFKsqHz5W+wFcDTxzXNtu9F4U5wCPBBYDr27L1gcKOAPYCNgAeChwK/BXwHrAG4G7gJe2dV4EXAE8ui1/N/DNcdvbaiU1bgc8A7hf29f5wNF9y68FnjKu/93jtvFq4G7gle24NmhtZ4+r46x2XNsCV/Udw9HA8RPto9X00r75BwHLgJe0/e0K3AA8aoJj/D7wb+0Y9wJuH9tf/77ohctNwCPb/JbAY/uO8exx2z257feJ9P74vH9re2tbvk/7vYzt+5lt39tOcFyDfmdb9S3fB1jct/wXwD+2f/dnt+fJtn21XQ/s3JZ/Hjhhuv9PzJaHZyIamar6XlVdWFX3VNXPgOOBp4/rdlRV3VRVdwDPAy6sqq9U1V3AfwA39vV9FfDu6o3t3wW8A3hKks2HrOfHVfWNqrqzqq6l9xf3+HqGcVVVfbwd1x0T9Pm3dlw/Bz4MvLjDfgAOBC6rqk+1/V0IfBl4/viOSR4NbA+8ox3jOcDXJtn+DknWr6pfVdUVk/T9fFVdUFV/qKrfD1h+d9++zwbOBg6a7ACH8NT28/1VdVdVnQUsBF7Y1+dzVfX99rz4NLDjFOxXQzBENDJJtk/y1STXJfkt8DZgk3Hdrumbflj/fFX9AfhV3/JtgI+1IY2bgOX0XriGupie5GFJTknyq1bP8QPqGcY1k3f5kz6/oHdsXWwDPG3smNtxPx/YYkDfhwHLq+p34/a9gqq6kd7ZzeuAa5OcnuRRk9Qy2XEP2nfX4+73MOCXVdV/t9hf0Dt7GnNt3/TtwAPQGmGIaJQ+Tm945ZFV9SDgnUDG9el/YVhGXyAkWYc/faG4BnhZVW3U99igqi4at52J/DtwG7BDq+cV4+oZv42JtjnMvrbum344sLRN3wZs2LfsoZNs+xrg6+OO+QFV9foB+1wGbJJk/XH7HqiqzqiqvWgv0sCxE9QwUW3jDdr3MMc92XaXsuJxPJw//QND08QQ0Sg9ELi5qm5N8jh61xFW5nTgiUn2axfg/4G+C8PAx4C3JnkMQJKNkzwfoA2v3Aw8YpJ6bgV+m+Thbfv9rhu3/vX0LqxP+EK8Em9K8uAkc4HXAp9t7RcDeybZMsnGwJsmqeGLwE5JXphkvST3S7J7G7oa76fAj4F/bf32pHdtYQVt/89JsiHwe3q/l7F3W10HbJ1kvVU85vX69v0M4FnAqX3HfVB6b6jYDnjZ2EpD/Nt9G1gnyeuTrJvkWcDewCmrWJ9GwBDRKL0BeEWSW4GPcO8L6UBVtYzetYNjgF/TOyu5lN6LHFX1GXrXF77QhqMupvdCNeZtwClt2Od5A3bxNuAp9F6wTuPeF7gxRwFHtfVf24Z83gtc1NpWZZz9DOCHwCJ6L3b/3df+FeBH9C42f3Hceh8ADm7vxHpvq+HZwMvpnWkspfeGghVe4NtwzwuBPeldBH9j337HmwO8md4w0G/oXbD/+7bsa/TeKHF9kiWrcMxX0xtevBZYALy8qq5qy95L7yMFy4HjBtQ14b9dGyL7K3rXV34DvB94YbvOpmmWPx1mlNYe7WzkWuC5VfXd6a5H0oo8E9FaJcm+bRhofeBIehdJL5rmsiRNwBDR2uZpwM/pXY/YCziwqu6c3pIkTcThLElSZ56JSJI6m3U3YNxkk01q7ty5012GJM0oF1100a+ratPx7bMuRObOncuiRYumuwxJmlGSDLz7gcNZkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOZt0n1meKuUecMd0l3GdcffRzprsE6T7LMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmcjC5EkWyf5ZpIrklye5H+19ockWZjkyvZz49aeJMckWZzkkiQ7923rkNb/yiSH9LXvkuTSts4xSTKq45EkrWiUZyJ3A/9YVY8FdgcOT7I9cARwTlXNA85p8wD7AvPa4zDgWOiFDnAk8ERgN+DIseBpfQ7rW2+fER6PJGmckYVIVS2rqu+36VuAK4Atgf2BE1u3E4ED2vT+wEnVcz6wUZItgGcDC6vqhqq6EVgI7NOWPaiqvltVBZzUty1J0hqwRq6JJJkL7ARcAGxeVcugFzTAZq3blsA1fastaW0ra18yoF2StIaMPESSPAA4FXh9Vf12ZV0HtFWH9kE1HJZkUZJFy5cvn6xkSdKQRhoiSdajFyCfqqovtObr2lAU7ef1rX0JsHXf6lsBSydp32pA+wqq6riqml9V8zfddNPVOyhJ0h+N8t1ZAT4BXFFV7+9bdDow9g6rQ4Av9bUf3N6ltTtwcxvuOgvYO8nG7YL63sBZbdktSXZv+zq4b1uSpDVg3RFu+8nA3wKXJrm4tb0FOBr4XJJDgV8CL2jLzgT2AxYDtwMvB6iqG5K8C7iw9XtnVd3Qpl8DnABsAHy1PSRJa8jIQqSq/i+Dr1sA7DWgfwGHT7CtBcCCAe2LgB1Wo0xJ0mrwE+uSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJgiTXJ7msr+3tSX6V5OL22K9v2ZuTLE7ykyTP7mvfp7UtTnJEX/u2SS5IcmWSzya536iORZI02CjPRE4A9hnQ/oGq2rE9zgRIsj3wIuBxbZ2PJpmTZA7wEWBfYHvgxa0vwHvatuYBNwKHjvBYJEkDjCxEquo84IYhu+8PnFxVv6+qnwOLgd3aY3FVXVVVdwInA/snCfAM4PNt/ROBA6b0ACRJkxoqRJLsMIX7fG2SS9pw18atbUvgmr4+S1rbRO1/DtxUVXePax8oyWFJFiVZtHz58qk6Dkma9YY9E/lYku8l+bskG63G/o4FHgnsCCwD3tfaM6BvdWgfqKqOq6r5VTV/0003XbWKJUkTGipEquopwEuArYFFST6d5FmrurOquq6q7qmqPwAfpzdcBb0zia37um4FLF1J+6+BjZKsO65dkrQGDX1NpKquBN4KvAl4OnBMkh8n+etht5Fki77ZA4Gxd26dDrwoyf2TbAvMA74HXAjMa+/Euh+9i++nV1UB3wQOausfAnxp2DokSVNj3cm7QJLHAy8HngMsBJ5bVd9P8jDgu8AXBqzzGWAPYJMkS4AjgT2S7Ehv6Olq4FUAVXV5ks8BPwLuBg6vqnvadl4LnAXMARZU1eVtF28CTk7ybuAHwCdW+eglSatlqBABPkxv+OktVXXHWGNVLU3y1kErVNWLBzRP+EJfVUcBRw1oPxM4c0D7Vdw7HCZJmgbDhsh+wB19ZwfrAOtX1e1V9cmRVSdJWqsNe03kbGCDvvkNW5skaRYbNkTWr6pbx2ba9IajKUmSNFMMGyK3Jdl5bCbJLsAdK+kvSZoFhr0m8nrglCRjn8XYAnjhaEqSJM0UQ4VIVV2YZDvgMfQ+Lf7jqrprpJVJktZ6w56JAOwKzG3r7JSEqjppJFVJkmaEYT9s+El697y6GLinNRdgiEjSLDbsmch8YPt2uxFJkoDh3511GfDQURYiSZp5hj0T2QT4UZLvAb8fa6yq542kKknSjDBsiLx9lEVIkmamYd/i+60k2wDzqursJBvSu6uuJGkWG/brcV9J7/vM/6s1bQl8cVRFSZJmhmEvrB8OPBn4LfzxC6o2G1VRkqSZYdgQ+X1V3Tk2076W1rf7StIsN2yIfCvJW4AN2nernwJ8eXRlSZJmgmFD5AhgOXApva+0PZPe961LkmaxYd+d9Qd6X4/78dGWI0maSYa9d9bPGXANpKoeMeUVSZJmjFW5d9aY9YEXAA+Z+nIkSTPJUNdEquo3fY9fVdUHgWeMuDZJ0lpu2OGsnftm16F3ZvLAkVQkSZoxhh3Oel/f9N3A1cDfTHk1kqQZZdh3Z+056kIkSTPPsMNZ/7Cy5VX1/qkpR5I0k6zKu7N2BU5v888FzgOuGUVRkqSZYVW+lGrnqroFIMnbgVOq6hWjKkyStPYb9rYnDwfu7Ju/E5g75dVIkmaUYc9EPgl8L8lp9D65fiBw0siqkiTNCMO+O+uoJF8FntqaXl5VPxhdWZKkmWDY4SyADYHfVtV/AkuSbDuimiRJM8SwX497JPAm4M2taT3gv0dVlCRpZhj2TORA4HnAbQBVtRRveyJJs96wIXJnVRXtdvBJ/mx0JUmSZophQ+RzSf4L2CjJK4GzmeQLqpIsSHJ9ksv62h6SZGGSK9vPjVt7khyTZHGSS/pv+JjkkNb/yiSH9LXvkuTSts4xSbIqBy5JWn3D3gr+P4DPA6cCjwHeVlUfmmS1E4B9xrUdAZxTVfOAc9o8wL7AvPY4DDgWeqEDHAk8EdgNOHIseFqfw/rWG78vSdKITfoW3yRzgLOq6pnAwmE3XFXnJZk7rnl/YI82fSJwLr0L9vsDJ7Uhs/OTbJRki9Z3YVXd0GpZCOyT5FzgQVX13dZ+EnAA8NVh65Mkrb5Jz0Sq6h7g9iQPnoL9bV5Vy9p2lwGbtfYt+dP7cC1pbStrXzKgfaAkhyVZlGTR8uXLV/sgJEk9w35i/XfApe1M4Laxxqp63RTVMeh6RnVoH6iqjgOOA5g/f/6E/SRJq2bYEDmjPVbXdUm2qKplbbjq+ta+BNi6r99WwNLWvse49nNb+1YD+kuS1qCVhkiSh1fVL6vqxCna3+nAIcDR7eeX+tpfm+RkehfRb25Bcxbwv/supu8NvLmqbkhyS5LdgQuAg4HJLvRLkqbYZNdEvjg2keTUVdlwks8A3wUek2RJkkPphcezklwJPKvNA5wJXAUspvfW4b8DaBfU3wVc2B7vHLvIDrwGOL6t8zO8qC5Ja9xkw1n91x4esSobrqoXT7BorwF9Czh8gu0sABYMaF8E7LAqNUmSptZkZyI1wbQkSZOeiTwhyW/pnZFs0KZp81VVDxppdZKktdpKQ6Sq5qypQiRJM8+qfJ+IJEl/whCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM7Wne4CJM0sc484Y7pLuE+5+ujnTHcJq8UzEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKmzaQmRJFcnuTTJxUkWtbaHJFmY5Mr2c+PWniTHJFmc5JIkO/dt55DW/8okh0zHsUjSbDadZyJ7VtWOVTW/zR8BnFNV84Bz2jzAvsC89jgMOBZ6oQMcCTwR2A04cix4JElrxto0nLU/cGKbPhE4oK/9pOo5H9goyRbAs4GFVXVDVd0ILAT2WdNFS9JsNl0hUsDXk1yU5LDWtnlVLQNoPzdr7VsC1/Stu6S1TdS+giSHJVmUZNHy5cun8DAkaXabrlvBP7mqlibZDFiY5Mcr6ZsBbbWS9hUbq44DjgOYP3/+wD6SpFU3LWciVbW0/bweOI3eNY3r2jAV7ef1rfsSYOu+1bcClq6kXZK0hqzxEEnyZ0keODYN7A1cBpwOjL3D6hDgS236dODg9i6t3YGb23DXWcDeSTZuF9T3bm2SpDVkOoazNgdOSzK2/09X1deSXAh8LsmhwC+BF7T+ZwL7AYuB24GXA1TVDUneBVzY+r2zqm5Yc4chSVrjIVJVVwFPGND+G2CvAe0FHD7BthYAC6a6RknScNamt/hKkmYYQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHU240MkyT5JfpJkcZIjprseSZpNZnSIJJkDfATYF9geeHGS7ae3KkmaPWZ0iAC7AYur6qqquhM4Gdh/mmuSpFlj3ekuYDVtCVzTN78EeOL4TkkOAw5rs7cm+ckaqG022AT49XQXMZm8Z7or0DTx+Tm1thnUONNDJAPaaoWGquOA40ZfzuySZFFVzZ/uOqRBfH6uGTN9OGsJsHXf/FbA0mmqRZJmnZkeIhcC85Jsm+R+wIuA06e5JkmaNWb0cFZV3Z3ktcBZwBxgQVVdPs1lzSYOEWpt5vNzDUjVCpcQJEkaykwfzpIkTSNDRJLUmSGiVZbkhCQHTdJnuyQXJ/lBkkd22Mfbk/xTm35Zkod1rVczX//zYYLlmya5oD3fntph+y9L8uE2fYB3vhieIaJROQD4UlXtVFU/W81tvQwwRLQyewE/bs+3b6/mtg6gdxslDcEQuQ9JMjfJFUk+nuTyJF9PskFbtmOS85NckuS0JBu39nOTvCfJ95L8dNBfcen5cJIfJTkD2Kxv2S5JvpXkoiRnJdkiyX7A64FXJPlm6/fF1ufydgeBsfVv7Zs+KMkJ4/Z9EDAf+FQ7s9lgKn9nWnsl+Zd2c9Wzgce0tkcm+Vp7Ln27nfHuCLwX2G/sOZLk2CSL2vPtHX3bvDrJJm16fpJzx+3zL4HnAf/etrXKZ9GzjSFy3zMP+EhVPQ64CXh+az8JeFNVPR64FDiyb511q2o3ei/8/e1jDqT3n/gvgFcCfwmQZD3gQ8BBVbULsAA4qqrOBD4GfKCq9mzb+J+tz3zgdUn+fJiDqarPA4uAl1TVjlV1xzDraWZLsgu9z33tBPw1sGtbdBzw9+259E/AR6vqYuBtwGf7niP/0j6t/njg6UkeP8x+q+r/0fus2T+3ba3uWfR93oz+nIgG+nn7TwVwETA3yYOBjarqW639ROCUvnW+0N9/wDafBnymqu4Blib5Rmt/DLADsDAJ9D6rs2yCul6X5MA2vTW9sPvNqhyYZpWnAqdV1e0ASU4H1qf3B8wp7fkGcP8J1v+bdsa7LrAFveGpS0Za8SxliNz3/L5v+h5gmOGfsXXuYeLnxKAPFAW4vKqetLKNJ9kDeCbwpKq6vQ0hrD9gu+sj3Wv8c24d4Kaq2nFlKyXZlt5Zyq5VdWMbIh17bt3NvSMwPt+mgMNZs0BV3Qzc2He942+Bb61klfHOA16UZE6SLYCxIaqfAJsmeRL0hreSPG7A+g8GbmwBsh2we9+y65I8Nsk69IbNBrkFeOAq1KuZ7zzgwHZ944HAc4HbgZ8neQH88VrdEwas+yDgNuDmJJvT+76hMVcDu7Tp5zOYz7dVYIjMHofQu1h4CbAj8M5VWPc04Ep611KOpQVQ+w6Xg4D3JPkhcDHtesk4XwPWbft+F3B+37IjgK8A32DiobATgI95YX32qKrvA5+l95w6FRh7x9VLgEPb8+1yBnx/UFX9EPhBW74A+E7f4ncA/5nk2/TOvAc5Gfjnrm9Pn2287YkkqTPPRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISKNSJKHJjk5yc/afcfOTPLoJJdNd23SVPET69IIpHdfjtOAE6vqRa1tR2DzaS1MmmKeiUijsSdwV1V9bKyh3dPsmrH5dtflbyf5fnuM3dhyiyTntQ9XXpbkqe1uASe0+UuTvGHNH5K0Is9EpNHYgd4NLVfmeuBZVfW7JPOAz9C7y/H/AM6qqqOSzAE2pHeXgS2rageAJBuNrnRpeIaINH3WAz7chrnuAR7d2i8EFrRb7X+xqi5OchXwiCQfAs4Avj4tFUvjOJwljcbl3Hujv4m8AbgOeAK9M5D7AVTVefRuv/8r4JNJDq6qG1u/c4HDgeNHU7a0agwRaTS+Adw/ySvHGpLsCmzT1+fBwLKq+gO9OyvPaf22Aa6vqo8DnwB2bt/Gt05VnQr8K7DzmjkMaeUczpJGoKqqfQnXB5McAfyO3m3IX9/X7aPAqe3W5t+kd/tygD3o3UX2LuBW4GBgS+D/tFvmA7x55AchDcG7+EqSOnM4S5LUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJn/x+T6PNo2DnX9gAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4])" + "array([2, 1, 3, 4], dtype=int64)" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3])" + "array([1, 2, 3], dtype=int64)" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1435,760 +1435,197 @@ " 0\n", " 0\n", " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
6000000count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
7000000mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
80-1-100-1std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
9001000min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
10-2-2-2-2-1-1
1100100-1
12-1-1-1-1-11
13-10-1-1-1-1
14111001
15000000
16110000
17001111
18000-1-1-1
191-2-2-2-2-2
201-2-2-2-2-2
2100000-1
22-1-1-1-1-1-1
23100111
24-2-2-2-2-2-2
25000-100
26000000
271-2-1-1-1-1
28000000
29-1-1-1-1-1-1
30000000
.....................
29971-1-1-100-1
29972000000
2997300000-1
299741-2-2-2-2-2
29975111100
2997600-1-1-2-2
29977111111
29978000000
29979000000
29980-2-2-2-2-2-2
29981000000
29982111100
29983000000
29984-2-2-2-2-2-2
29985-1-1-2-1-1-1
29986-2-2-2-2-2-2
29987-1-1-2-2-2-2
29988000000
29989000000
29990-1-1-1-1-1-2
29991000000
29992111111
29993000-2-2-2
299940-1-1000
29995111111
29996000000
29997-1-1-1-100
29998111-100
299991-1000-1
30000000000
\n", - "

30000 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " S_0 S_2 S_3 S_4 S_5 S_6\n", - "ID \n", - "1 1 1 -1 -1 -2 -2\n", - "2 -1 1 0 0 0 1\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 -1 0 -1 0 0 0\n", - "6 0 0 0 0 0 0\n", - "7 0 0 0 0 0 0\n", - "8 0 -1 -1 0 0 -1\n", - "9 0 0 1 0 0 0\n", - "10 -2 -2 -2 -2 -1 -1\n", - "11 0 0 1 0 0 -1\n", - "12 -1 -1 -1 -1 -1 1\n", - "13 -1 0 -1 -1 -1 -1\n", - "14 1 1 1 0 0 1\n", - "15 0 0 0 0 0 0\n", - "16 1 1 0 0 0 0\n", - "17 0 0 1 1 1 1\n", - "18 0 0 0 -1 -1 -1\n", - "19 1 -2 -2 -2 -2 -2\n", - "20 1 -2 -2 -2 -2 -2\n", - "21 0 0 0 0 0 -1\n", - "22 -1 -1 -1 -1 -1 -1\n", - "23 1 0 0 1 1 1\n", - "24 -2 -2 -2 -2 -2 -2\n", - "25 0 0 0 -1 0 0\n", - "26 0 0 0 0 0 0\n", - "27 1 -2 -1 -1 -1 -1\n", - "28 0 0 0 0 0 0\n", - "29 -1 -1 -1 -1 -1 -1\n", - "30 0 0 0 0 0 0\n", - "... ... ... ... ... ... ...\n", - "29971 -1 -1 -1 0 0 -1\n", - "29972 0 0 0 0 0 0\n", - "29973 0 0 0 0 0 -1\n", - "29974 1 -2 -2 -2 -2 -2\n", - "29975 1 1 1 1 0 0\n", - "29976 0 0 -1 -1 -2 -2\n", - "29977 1 1 1 1 1 1\n", - "29978 0 0 0 0 0 0\n", - "29979 0 0 0 0 0 0\n", - "29980 -2 -2 -2 -2 -2 -2\n", - "29981 0 0 0 0 0 0\n", - "29982 1 1 1 1 0 0\n", - "29983 0 0 0 0 0 0\n", - "29984 -2 -2 -2 -2 -2 -2\n", - "29985 -1 -1 -2 -1 -1 -1\n", - "29986 -2 -2 -2 -2 -2 -2\n", - "29987 -1 -1 -2 -2 -2 -2\n", - "29988 0 0 0 0 0 0\n", - "29989 0 0 0 0 0 0\n", - "29990 -1 -1 -1 -1 -1 -2\n", - "29991 0 0 0 0 0 0\n", - "29992 1 1 1 1 1 1\n", - "29993 0 0 0 -2 -2 -2\n", - "29994 0 -1 -1 0 0 0\n", - "29995 1 1 1 1 1 1\n", - "29996 0 0 0 0 0 0\n", - "29997 -1 -1 -1 -1 0 0\n", - "29998 1 1 1 -1 0 0\n", - "29999 1 -1 0 0 0 -1\n", - "30000 0 0 0 0 0 0\n", - "\n", - "[30000 rows x 6 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#attributes representing positive values\n", - "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", - " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outliers\n", - "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2212,7 +1649,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2236,7 +1673,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2260,7 +1697,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2457,7 +1894,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2481,7 +1918,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2505,7 +1942,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2529,7 +1966,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2553,7 +1990,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2687,7 +2124,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2695,7 +2132,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2703,7 +2140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2711,7 +2148,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2719,7 +2156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2803,7 +2240,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2824,7 +2261,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2845,7 +2282,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2866,7 +2303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2887,7 +2324,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3167,25 +2604,23 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 82.388112 2.822569e-04\n", - "PAY_0 4424.481682 0.000000e+00\n", - "PAY_2 3847.416138 0.000000e+00\n", - "PAY_3 3007.408864 0.000000e+00\n", - "PAY_4 3034.730536 0.000000e+00\n", - "PAY_5 2869.196497 0.000000e+00\n", - "PAY_6 2526.952024 0.000000e+00\n", - "PAY_0_No_Transactions 71.907192 3.727224e-03\n", - "PAY_0_Revolving_Credit 498.806543 0.000000e+00\n", - "PAY_2_Pay_Duly 75.936267 1.438075e-03\n", - "PAY_2_Revolving_Credit 247.861963 0.000000e+00\n", - "PAY_3_Pay_Duly 78.497278 7.644927e-04\n", - "PAY_3_Revolving_Credit 142.164275 1.549982e-12\n", - "PAY_4_Pay_Duly 79.625464 5.751909e-04\n", - "PAY_4_Revolving_Credit 99.566680 2.218832e-06\n", - "PAY_5_Pay_Duly 73.322648 2.683519e-03\n", - "PAY_5_Revolving_Credit 66.651236 1.187101e-02\n", - "PAY_6_Pay_Duly 63.464171 2.277242e-02\n", - "PAY_6_Revolving_Credit 62.888951 2.550124e-02\n" + "LIMIT_BAL 74.581893 1.992315e-03\n", + "PAY_0 4250.691969 0.000000e+00\n", + "PAY_2 3793.454513 0.000000e+00\n", + "PAY_3 2943.420461 0.000000e+00\n", + "PAY_4 2997.799899 0.000000e+00\n", + "PAY_5 2809.793752 0.000000e+00\n", + "PAY_6 2384.699487 0.000000e+00\n", + "PAY_0_No_Transactions 75.255770 1.695234e-03\n", + "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", + "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", + "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", + "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", + "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", + "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", + "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", + "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", + "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" ] } ], @@ -3376,8 +2811,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13475\n", - " 1 1.00 1.00 1.00 4021\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3406,7 +2841,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (GINI) identified 828\n" + "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" ] }, { @@ -3442,13 +2877,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%25%50000.0000001.0000001.000000-1.000000
50%50%120000.0000002.0000002.0000000.000000
75%75%210000.0000002.0000002.0000000.000000
maxmax500000.0000002.0000004.000000
110.02040822-2
220.224490221
330.163265220
440.081633220
550.08163312
000.01.00.00.0
110.01.00.01.0
220.01.00.01.0
330.01.00.00.0
440.01.00.0
000.00.00.00.0
110.01.00.00.0
220.00.01.01.0
330.00.01.01.0
440.01.00.0
05415131454031256
111928281233857
\n", @@ -3457,8 +2892,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5415 1314\n", - "1 1192 828" + "0 5403 1256\n", + "1 1233 857" ] }, "execution_count": 38, @@ -3479,12 +2914,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.6666666666666666\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gUVdvA4d9JQgIhCZBCDSEJBELvICWIAooIojRBRBFQEcGuvDZQUbHwYnnBBip+KIKABQEFC0po0kLvJBBCqCE9pO75/piJLjFlA9lsynNfFxfZ2SnPlJ1n5pw5Z5TWGiGEEKIgTo4OQAghRNkmiUIIIUShJFEIIYQolCQKIYQQhZJEIYQQolCSKIQQQhRKEkU+lFKjlVJrHR2HoymlApRSKUop51JcZqBSSiulXEprmfaklNqvlOp9FdNd1TGolKqmlPpRKZWolFpa3OlLk63HV0X9PSqlqiqlDimlapfiMp9QSr1a7Am11mX6H3ACuAykAGeBBYCHo+OqiP/Mbd3XwTEEAhpwKQPbQwNNytM6A2OArSW1/YDegMX8/aUAMcA3QGdH759rXK/RVut0Oc86ppRSDI8Dc/IM6wKsBhLMf/uBGUBN8/sJwB9W48cAZwB3q2ETgV/Nv13MYyvQ/OwOnAZ8ihNrebmjGKS19gDaAe2BZx0cz1Vx5FVyRblCL45Kur0bAUe01tnFnbCQmGPN358ncB1wCAhXSvW5+jAdS2v9ldbaw1yvWzDX0WrYFey0Px8EFlotIwz4HfgDaKq1rgkMML9uVch8XIHJtixQa50GrMW4oLCdozO7DVn3BFZXucBbwCqrz27ALCAaOAd8BFSz+n4wsAtIAo4D/c3hNYBPMbLxaeBVwNn8biywwfz7I2BWnph+AJ4w/64PLAcuAFHAI1bjvQQsA740lz8hn/WrAfyfOf1J4AXAySqOjcD/gESMH2ifPNMWtg4bgXeAS+Z3jTEOxDjgIvAV/1ypLMS4qsq9e3uGPFe6GAfwDHO+yRgHnK9VPPeY6xAHvJh33+VZ72rAf83xE4EN5rDcZd5r7tOLwPN5rrg2Y1xtnQHmAK5W32vgYeAoEGUOew84Ze6DHUCY1fjOwHPmsZFsft8QWG/OK9XcHnea4w/EOJ4SgE1AmzzH6lRgD5CBcTX39zYwY99uxnEOmG0OjzaXlXtF2w2rY9AcpyXwi7kvzwHP5bNNXwYygSxzPuMxipdfMLfzeYxjrYY5fu62Hm/GsD6fefYGYvIZPgfYbvU51Cq+w8CIYuzr3ONrLBBp7ocoYHTe36P5uTuwzZzXNqC71Xd/UMgxWsCxWNA6xgBPA3uBTHOYP/Ad//zeH7Ya34l/jqWLwGKgVgHLDMY4tpythm0B3iki1vzuKKaay/MyhxV4R2EOuxf4pVjn4as5eZfmP678ofmbO+09q+/fBVYA3hhXPD8CM61+mIlAP3MnNgBCze++Bz4GqgO1MW7XH8x7YAK9ME4yyvxcC+NkWt+c5w5gGkZWDzYP9JvNcV/C+NHebo5bLZ/1+z+MxOOJ8cM5Aoy3iiMb4xa1CnCnuT7eNq5DNjDFPFiqAU3MbeEG+GGcDN/Nb1vnOZFYJ4rjQFNzfn8Ab5jftcA4OfU0t8Usc90LShRzzekbYJysu5tx5S5znrmMthgn3ebmdB0xrmpdzHEPAo9ZzVdjnLC8c7c3cDfgY07zJEYRZlXzu9wTQTNAmcvzsZpXE6t5d8A42XY1Y77X3GZuVttvF0aiqZZ3m2IkuDHm3x7Adflt53yOQU+MpPgkUNX83LWA7foS8KXV53HAMYxj0wP4FliYZ7n/h3EM5Xd89ib/k+iNGBcW1c1/p4D7zG3cAePE1dLGfe1iziMJaGZOU89qeutt4Q3EY1wRuwCjzM+5++wPCjhGCznHFLSOMRi/b39zXs7m/n0O4xhvYu7fPub4T2EkqAbmfvo0d1vnM+/BwG6rz17m9uxZRKz5JYreGOfAl8xhRSWKLsD5Yp2H7XFyL8l/5o5Iwbg60MBv/HMVrDCycmOr8bvxz5Xkx+SToYE6GCcf6zuPUcC6fA5MhXG11cv8fD/wu/l3VyA6z7yfBT63+tH+6yrNalxnM44WVsMezD0QzDhiMZOUOWyr+SOxZR2iC1q2Oc7tQESebV1UonjB6vtJwM/m39OAr62+c8e4uv1XosBImpeBtvl8l7tM/zzrPLKAdXgM+M7qswZuLGK943OXjXH1O7iA8fImig+BGXnGOQxcb7X9xuVz/OYmivUYV/2+eca5YjvncwyOst5PRazbS1yZKH4DJll9boaRwF2slhtcyPx6k/9JNNSctgHGBUx4nu8/BqbbuK9zE0UCMJQ8CSvPthgDbM3z/WZgbFHH6FWsYwxwj9XnHkBknnFeBOaZfx/NPRbMzw0xfqNO+cz7Xq68S8rdFtbH22xzm6QC/zGHFZQo2prj+lB0omiOeYdk67/yUkdxu9baE2ODhAK+5nA/jBPSDqVUglIqAfjZHA7Gjjqez/waYVyhn7Ga7mOMq/IraGPLLsb4sQLchVFkkzuf+rnzMOfzHMZJPNepQtbLF+PK5KTVsJMYP75cp80YrL+vb+M6XLFspVRtpdRipdRppVQSRpGYL8Vz1urvNIyrVMyY/l6eNspC4wqYhy/GFVd++6bQ5SilmiqlViqlzprr8Dr/Xoe86/2kUuqg+SRQAkaRXe40BR0j+WkEPJlnfzfEWPd8l53HeIwr3UNKqW1KqYE2Lrc4MeZVn38fXy7YfowWpAHGCSgBY7t0zbNdRgN1sW1fo7VOxUg4EzGO6VVKqVAb1gf+/Zsp6Bi9GtbbphEQkGc9n8FYT4AA4Eer7/ZibKP8nmqKx7gzzHXJHLde7gCt9RPaqKf4EWOfFUhrvRtYY8ZTFE+M/Waz8pIoANBa/4nx1NMsc9BFjKuVllrrmua/GvqfyqhTGOXyeZ3CyPS+VtN5aa1bFrDor4FhSqlGGHcRy63mE2U1j5paa0+t9QCraXXemVm5iHF118hqWABGfUOuBkoplef7WBvXIe+yZ5rD2mitvTCKZFQh4xfHGYxbdMB4TBPj6iY/F4F08t83RfkQo64mxFyH57hyHcBqPcwKwqnACIzy4poYxXe50xR0jOTnFPBanv3trrX+Or9l56W1Pqq1HoVx4ngTWKaUql7YNFcRY16x/Pv4ysao5/g7tKuY7x3ATvMEfwr4M8928dBaP0Qx9rXWeo3Wuh/GyfIQRvFjUesD//7NlCTrbXMKOJrP732Q+X0M0C/P91W11mf/PVv2AI1zHw3WWidh1F8NuYZYpwEP8U/iKkhzYHdxZlyuEoXpXaCfUqqd1tqCcTC9k/ssslKqgVLqZnPcT4H7lFJ9lFJO5nehWuszGJVc/1VKeZnfNVZKXZ/fArXWERiVV/OBNVrr3Gy8FUhSSk01n193Vkq1Ukp1tmVFtNY5GI8avqaU8jQT0RMYV/q5agOPKKWqKKWGY+zk1cVdB5MnRjFeglKqAUb5vLVzGGXZV2MZMEgp1V0p5YpRxJL3BA6Aud8+A2Yrpeqb262bUsrNhuV4YpRlp5hXnA/ZMH42xv5zUUpNwygPzjUfmKGUClGGNkqp3ASXd3vMAyYqpbqa41ZXSt2qlLK+MiyQUupupZSfuf65x1COGZuFgrf9SqCuUuoxpZSbeax0tWWZGBc5jyulgpRSHhh3YEv01T0Vpczf0HSMIpDnrOJrqpQaYx6nVZRSnZVSzW3d10qpOkqp28zEmYFxnObkE8Zqc1l3KaVclFJ3YtSPrSzu+lyFzUCmeYda1VyX1kqpjub3HwGvK6UC4O87+Nvym5HW+gRGkXZHq8FPAw8opZ5RSvmZ82jIvxNjvrTWhzEuYqcUMer1wE+2zDNXuUsUWusLGJVvL5qDpmJU1m0xiyJ+xSiHRWu9FaOC7R2Mq8g/+Wej34NR7HMA4zZwGVa3ffn4GugLLLKKJQcYhPHYbhTG1dN8jKINW03BKIOMxHgaZBHGDyvXX0CIOe/XgGFa69wineKuw8sYFY2JwCqMik1rM4EXzFvnp4qxDmit95vrshjj7iIZo+I3o4BJnsK4Nd+Gcdv9JrYdj09hFP8lY5y4lxQx/hqMH8URjCKKdK4sTpiNkazXYiSgTzEqLsEo7//C3B4jtNbbMeqo5mBs72MY5ee26g/sV0qlYDyJNVJrnW4W070GbDSXdZ31RFrrZIyHEAZhFKscBW6wcZmfYTzRth7jGE2n6BNJXvXNmFMw9ldroLfWeq1VfDcBIzGu+M9i7M/cZGDLvnbCqKyPNce5HqN+4QrmsT/QHDcOo6hloNb6YjHXqdjM5DoAozL4BMZv8mP+ufCYjVH0/ZtSKhnjqbjCLho/xuoxVbPEpC/Gvj1mFl/9hHFO+8DGMF+mkKI2806/P8Y51Ga5T/KIMkgpNRbjkdqejo6luMyr1wSMIqIoR8cjRFmjlKoKRGBUgJ8vpWU+DvhprZ8rcmQrla4RlrAfpdQgjKdsFEY90l6MKy8hRB5a63SMouTSXOY7VzNduSt6EmXaYIyig1iM4rKRWm5ZhSj3pOhJCCFEoeSOQgghRKHKXR2Fr6+vDgwMdHQYQghRruzYseOi1tqv6DH/rdwlisDAQLZv3+7oMIQQolxRSuVt0W4zKXoSQghRKEkUQgghCiWJQgghRKEkUQghhCiUJAohhBCFkkQhhBCiUHZLFEqpz5RS55VS+wr4Ximl3ldKHVNK7VFKdbBXLEIIIa6ePe8oFmB0Z1uQWzD6AwoBHsB4IY0QQogSlp6V36s9bGe3Bnda6/VKqcBCRhkM/J/ZadwWpVRNpVQ984U8QgghrtHF5HSeX7CT36MvXdN8HNkyuwFXvkAmxhz2r0ShlHoA466DgICAUglOCCHKq+MXUnh39SFW7j+LdlK4xKVf0/wcWZmd32sy8+3KVmv9ida6k9a6k5/fVXVVIoQQFZrWmi2RcUz4Yht9/vsnP+47y+WDl7jbuwaHPrmWV3E79o4iBmho9dkf4z0GQgghbJSVY2H13jPMD49i7+lEarlX4dE+IbTzqEoT/xo0bFicNzPnz5GJYgUwWSm1GOgKJEr9hBBC2CYpPYslW0/x+cYoYhPTcc/RxP1ykrsHt+Dxfk1LdFl2SxRKqa+B3oCvUioGmA5UAdBafwSsxnhR+TEgDbjPXrEIIURFcTrhMp9viGLxtlOkZGQT6O5K+tpoYvZc4IknuvHsMz1KfJn2fOppVBHfa+Bhey1fCCEqkj0xCcwLj2L1XqPg5dbW9UiLOM+n0zfRvXtDfol4kNat69hl2eXufRRCCFFZWCya3w6dZ154JFujLuHh5sI9XQMY2ro+rYK9OdzhIl1DfBk/vgNOTvk9H1QyJFEIIUQZczkzh+U7Y/hsQxSRF1NpULMaL9zaHJ/ELJ56dA172tVl+fIRNGvmS7NmvnaPRxKFEEKUEReSM1i4+QQLt5wkPi2LNv41eH9Ue9r5ePDUE2tYuvQAzZr5MHly51KNSxKFEEI42NFzyXy6IYpvI06TlWOhT2gd7g8LokuQN7//HkWrsC/IzMxhxowbePrp7ri5le6pWxKFEEI4gNaazcfjmBceybrDF3BzcWJ4R3/G9wwi2M+DrKwclFK0bVuXAQNCePXVG2nSxNshsUqiEEKIUpSVY2HlnljmrY/iwJkkfD1ceaJfU+6+rhHe1V1JSsrg0Ud/4q+/TrNx4zh8fd1ZvHiYQ2OWRCGEEKUg8XIWX2+NZsHGE5xNSqdJbQ/eGNKa29s3oGoVZ7TWLF26n0cf/ZmzZ1OYNKkzGRk5uLs7/rVBkiiEEMKOTl1K47ONUXyz7RSpmTl0b+zDzCGtub6p39+PtF64kMq9937PTz8do337uvzww0g6d27g4Mj/IYlCCCHsICI6nvnhUfy07wxOSjGobX0mhAXRsv6/+17y8nLj4sU03n33Zh5+uAsuLo6/i7AmiUIIIUpIjkXzy4FzzA+PZPvJeDyrunB/r2DGdg+kXo1qV4y7fv1JXnstnOXLR+Dh4cqWLRPs2mjuWkiiEEKIa5SWmc2yHUYDuRNxafjXqsa0gS0Y0bkhHnkeZb14MY2nn/6FBQt2ERhYkxMnEmjVqnaZTRIgiUIIIa7a+aR0vth8gq/+iiYhLYt2DWsy9+ZQbm5ZBxfnK4uPtNZ8/vkunn76F5KSMnj22Z688EIv3N2rOCb4YpBEIYQQxXT4bDLzwyP5YVcsWRYLN7Wow/1hwXRsVAulCr4z+PLLPbRo4cdHH91Ky5a1SzHiayOJQgghbKC1ZsOxi8wLj2L9kQtUreLEyC4NGdcjiEDf6vlOk5aWxeuvhzNxYif8/b1YvnwENWpULdPFTPmRRCGEEIXIzLawYncs88MjOXQ2GT9PN566qSmjuzaiVnXXAqdbvfooDz+8mhMnEmjQwJOHHupMrVrVChy/LJNEIYQQ+UhIy+Srv6L5YtMJzidn0KyOJ28Na8PgdvVxc3EucLqYmCQee+xnli8/SPPmvvz551h69WpUipGXPEkUQghh5WRcKp9tiOKb7TFczsohLMSXt4e3pVeIb6H1D7lee209q1Yd5fXXb+TJJ7vj6lpwUikvlPGiufKjU6dOevv27Y4OQwhRwew4eYl566NYc+AsLk6K29o2YEJYEM3reRU57datp6lWzYXWresQF5dGYmIGwcG1SiFq2ymldmitO13NtHJHIYSotHIsmjX7zzIvPJKI6ARqVKvCQ9c35t7ugdTxqlrk9ImJ6Tz33G98+OF2Bg5syooVo/DxccfHx70Uoi89kiiEEJVOakY232w/xWcbozh16TIB3u68fFtLhnX0p7oN73rQWrNkyX4ef3wN58+nMmVKF2bMuLEUIncMSRRCiErjXFI6Czad4KstJ0lKz6Zjo1o8P6A5/VrUxbkYj6x++eUe7rnnezp1qs/KlaPo2LG+HaN2PEkUQogK70BsEvM3RPLj7lhyLJr+reoyISyYDgG21yNkZGQTGRlP8+Z+jBjRkuxsC/fc0xZn57LVgZ89SKIQQlRIWmv+PHKB+eFRbDh2EXdXZ0Z3bcS4HkEEFLMOYd26KB56aBVpaVkcPToFNzcX7ruvvZ0iL3skUQghKpSM7Bx+iIhl/oZIjpxLoY6XG1P7h3JXlwBqFLNfpfPnU3nqqbUsXLiH4OBafPLJoFJ/X3VZUPnWWAhRIcWnZvLllpN8sfkkF1MyCK3ryewRbRnYpj6uV/F+h2PHLtGlyzxSUjJ5/vkwnn8+jGrVyn4HfvYgiUIIUa5FXUzl0w2RLNsRQ3qWheub+nF/WDA9mvjY1EAur6SkDLy83GjcuBbjx7dn3Lj2NG/uZ4fIyw9JFEKIckdrzbYT8cwLj+TXg+eo4uTE7e3rMyEsmKZ1PK9qnqmpmbzyyp/Mm7eTPXsewt/fi7ffvqmEIy+fJFEIIcqN7BwLP+07y/zwSHbHJFLTvQqTb2jCmG6NqO1ZdAO5gvz442EmT/6J6OhExo9vXy7eEVGaJFEIIcq8lIxslmw7xWcbojidcJkg3+rMuL0Vwzr4U+0a+lLKzrYwYsRSvvvuEC1b+hEefh89ewaUYOQVgyQKIUSZdSbxMgs2nmDR1miS07PpEujN9EEt6Nu8zjW900FrjVIKFxcn6tXz4I03+vD4490qRAd+9iCJQghR5uw7ncj88EhW7jmDBm4xG8i1a1jzmue9ZUsMDz+8mnnzBtGhQz3mzr312gOu4CRRCCHKBItF88eR88xbH8XmyDiquzpzb/dAxnYPpKH3tXeyFx9/meee+42PP95B/fqexMdfLoGoKwe7JgqlVH/gPcAZmK+1fiPP9wHAF0BNc5z/aK1X2zMmIUTZkp6Vw3cRp5kfHsnxC6nUq1GV5waEMrJLAF5VS6ZSecmSfTzyyM9cvJjGY49dx8sv98bT061E5l0Z2C1RKKWcgblAPyAG2KaUWqG1PmA12gvAN1rrD5VSLYDVQKC9YhJClB1xKRks3HKShZtPEpeaScv6Xrx7ZztubVOPKiXcf9KhQxcJDKzJzz+Ppn37eiU678rAnncUXYBjWutIAKXUYmAwYJ0oNJD7VpAaQKwd4xFClAHHL6QwPzyKb3fGkJFt4cbQ2kwIC6Jb8NU1kMtPeno2b765gQ4d6jFoUDOeey6MF17oVSk68LMHeyaKBsApq88xQNc847wErFVKTQGqA33zm5FS6gHgAYCAAHl0TYjyRmvNlshLzA+P5LdD53F1cWJohwaM7xlEk9pX10CuIL/+GsmkSas4evQSTz7ZjUGDmlGlijzNdC3smSjyuzTI+97VUcACrfV/lVLdgIVKqVZaa8sVE2n9CfAJGK9CtUu0QogSl5VjYfXeM8wLj2Tf6SS8q7vyaJ8QxnRrhK9HydYRnDuXwhNPrGXRor00aeLN2rV3069f4xJdRmVlz0QRAzS0+uzPv4uWxgP9AbTWm5VSVQFf4Lwd4xJC2FlSehaLt0azYOMJYhPTCfarzut3tGZIhwZUtdPV/S+/RLJs2QGmTevFs8+GUbWqPNRZUuy5JbcBIUqpIOA0MBK4K8840UAfYIFSqjlQFbhgx5iEEHZ0OuEyn2+IYvG2U6RkZHNdsDczbm/FDc1qX1MDuYLs3n2Wo0cvMWxYC0aPbk2PHg0JCrL9ZUTCNnZLFFrrbKXUZGANxqOvn2mt9yulXgG2a61XAE8C85RSj2MUS43VWkvRkhDlzJ6YBOaFR7F67xkABrapx4SewbT2r2GX5aWkZDJ9+jree+8vAgNrcvvtobi4OEmSsBO73puZbSJW5xk2zervA0APe8YghLAPi0Xz26HzzAuPZGvUJTzdXBjfM4ix3QOpX7Oa3Zb7/feHmDLlJ2JiknjggQ7MnNkXl6t434SwnRTiCSGK5XJmDst3xvDZhigiL6bSoGY1Xri1OXd2bohnCTWQK8jevee4444ltG5dmyVLhtG9e8OiJxLXTBKFEMImF5IzWLj5BAu3nCQ+LYs2/jV4f1R7BrSqi4sd2ydkZeUQHh7NjTcG0bp1HVatuot+/YLlkddSJIlCCFGoo+eSmR8exXe7TpOVY6FPaB3uDwuiS5B3iTWQK8imTaeYOHEl+/df4PDhyTRp4s2AASF2Xab4N0kUQoh/0Vqz6Xgc88Ij+ePwBdxcnBje0Z/xPYMI9vOw+/IvXbrMf/7zK/Pm7aRhQy++/XYETZp42325In+SKIQQf8vMtrByTyzzw6M4cCYJXw9XnujXlLuva4R3dddSiSE9PZt27T4iNjaZJ5/sxksv9cbDo3SWLfIniUIIQeLlLL42G8idTUonpLYHbw5tzeB29msgl1dMTBL+/l5UrerCjBk30K5dXdq2rVsqyxaFk0QhRCV26lIan22M4pttp0jNzKFHEx9mDm3N9SF+dmkgl5/Ll7OYOXMDb765kWXLhjNoUDPuvbddqSxb2MamRKGUcgUCtNbH7ByPEKIURETHMz88ip/2ncFJKW5rW5/xYUG0rG+fBnIFWbv2OJMmreL48XjuvrsNXbo0KNXlC9sUmSiUUrcCswFXIEgp1Q6YrrW+w97BCSFKTo5F88uBc8wPj2T7yXg8q7pwf69gxnYPpF4N+zWQK8iUKauZM2cbISHe/PrrGPr0CS71GIRtbLmjeAWje/B1AFrrXUqpJnaNSghRYtIys1m2w2ggdyIujYbe1Zg+qAUjOjWkulvplj7n5BgdQzs7O3Hddf74+rozdWpP6cCvjLNl72RprRPyPC8t/TEJUcadT0rni80n+OqvaBLSsmjXsCYf9A/lphZ17NpAriA7d55h4sSVjBnThilTujJ6dJtSj0FcHVsSxUGl1AjAyewJ9lFgi33DEkJcrcNnk5kXHsmKXbFkWSzc1KIO94cF07FRLbs3kMtPcnIG06at4/33t+Ln5069eiX7oiJhf7YkisnANMACfIvRG+yz9gxKCFE8WmvCj15kXngk4UcvUq2KMyO7NGRcjyACfas7LK61a48zbtwPxMYmM3FiJ15/vQ81a1Z1WDzi6tiSKG7WWk8FpuYOUEoNwUgaQggHysy2sGJ3LPPDIzl0Nhk/TzeevrkZo7sGUNPd8Y3UXF2dqV27OsuXj6BrV39HhyOukirq9Q9KqZ1a6w55hu3QWne0a2QF6NSpk96+fbsjFi1EmZGQlslXf0XzxaYTnE/OoFkdTyaEBXFbu/q4uTius7ysrBxmz95MUlIGr73WBzC6Iy+tNhmiYOZ5u9PVTFvgHYVS6maM15Q2UErNtvrKC6MYSghRyk7GpfLZhii+2R7D5awcwkJ8mTW8LWEhvg6pf7C2YUP03x34DR/e4u8EIUmi/Cus6Ok8sA9IB/ZbDU8G/mPPoIQQV9px8hLz1kex5sBZXJwUg9s1YEJYEKF1vRwdGnFxaUyd+iuffhpBQEANfvxxFAMHNnV0WKIEFZgotNYRQIRS6iutdXopxiSEwGggt2b/WeaFRxIRnUCNalWY1Lsx93YLpLZX2akQjou7zOLF+3jmme5Mm3Y91Uup80BRemypzG6glHoNaAH8fXRqreWSQQg7SM3I5pvtp/hsYxSnLl0mwNudVwa3ZFhHf9xdy0bDtIMHL/DNN/uZPr03TZv6EB39ON7epd+6W5QOW466BcCrwCzgFuA+pI5CiBJ3NjGdBZtOsOivkySlZ9OxUS2eH9Ccfi3q4lxGyvnT0rJ47bX1vP32Jjw8XBk/vgP+/l6SJCo4WxKFu9Z6jVJqltb6OPCCUirc3oEJUVkciE1ifngkP+6JJcei6d+qLhPCgukQUMvRoV3h55+PMWnSKqKiErj33ra8/XY//Pwc10ZDlB5bEkWGMh6nOK6UmgicBmrbNywhKjatNX8eucC88Eg2HovD3dWZ0V0bMa5HEAE+7o4O719SUjIZM+Y7fHyqsW7dvfTuHejokEQpsiVRPA54AI8ArwE1gHH2DEqIiiojO4cfImKZvyGSI+dSqOPlxtT+odzVJYAa7lUcHd4VcnIsfP31PkaNaoWHhyu//jqG0FBf3Eq5I0HheEXuca31X+afycAYAKWUNLEUohjiUzP5cstJvth8kospGTSv58XsEW0Z2KY+ri6l30FfUXbsiOXBB4Td8d0AACAASURBVFeyY8cZqlVzYejQFvK2uUqs0EShlOoMNAA2aK0vKqVaYnTlcSMgyUKIIkRdTOXTDZEs2xFDepaF3s38uD8smO6NfRzeQC4/iYnpvPjiOubO3Ubt2tVZvHgoQ4Y0d3RYwsEKa5k9ExgK7MaowP4Oo+fYN4GJpROeEOWP1pptJ+KZFx7JrwfPUcXJiTvaN2B8WBBN65TtnlOHDv2G33+P4uGHO/PqqzdSo0bZaa8hHKewO4rBQFut9WWllDcQa34+XDqhCVG+ZOdY+GnfWeaHR7I7JpFa7lWYckMTxnQLxM/TzdHhFSgyMh4/P3c8Pd147bUbcXJSdO4sryQV/ygsUaRrrS8DaK0vKaUOSZIQ4t9SMrJZvDWazzee4HTCZYJ8qzPj9lYM6+BPNVfHddBXlMzMHGbN2sSMGet55JEuvPlmP+nhVeSrsEQRrJTK7UpcAYFWn9FaD7FrZEKUcbEJl1mw6QRf/xVNckY2XQK9mT6oBX2b1ynzHeGtX3+SiRNXcvDgRYYNa8Ejj3R1dEiiDCssUQzN83mOPQMRorzYdzqR+eGRrNxzBg3c0qou94cF07ZhTUeHZpN33tnME0+sJTCwJqtW3cWAASGODkmUcYV1CvhbaQYiRFlmsWjWHT7PvPBItkRewsPNhXu7B3Jfj0D8a5W9BnJ5WSya1NRMPD3duPXWply4kMYLL/TCvYy13RBlU5EvLipr5MVFojSlZ+XwXcRp5odHcvxCKvVqVOW+HoGM7BKAV9XycZLdv/88Eyeu+vtNc6JyssuLi0qCUqo/8B7gDMzXWr+RzzgjgJcADezWWt9lz5iEsEVcSgYLt5xk4eaTxKVm0qqBF++NbMeA1vWo4lz2GsjlJy0tixkz/mTWrM3UqOHGuHHt0FqXyfYbomyzOVEopdy01hnFGN8ZmAv0A2KAbUqpFVrrA1bjhADPAj201vFKKelDSjjUsfMpfLohim93xpCRbaFPaG0mhAVzXbB3uTrBRkScYciQbzhxIoH77mvHW2/1w9e37BeRibKpyEShlOoCfIrRx1OAUqotMEFrPaWISbsAx7TWkeZ8FmO0zThgNc79wFytdTyA1vp88VdBiGujtWZL5CXmh0fy26HzuLo4MbSDP+N7BtGktoejwyuW3DuGgIAaBATU4IsvbqdXr0aODkuUc7bcUbwPDAS+B9Ba71ZK3WDDdA2AU1afY4C8z+A1BVBKbcQonnpJa/2zDfMW4ppl5VhYvfcM88Ij2Xc6Ce/qrjzaJ4Qx3Rrh61F2G8jlJzvbwpw5W1mx4jC//DIGHx93/vxzrKPDEhWELYnCSWt9Ms9td44N0+V3n5635twFCAF6Y/QdFa6UaqW1TrhiRko9ADwAEBAQYMOihShYUnoWi7dGs2DjCWIT02nsV52ZQ1pzR/sGVK1SdhvIFWTr1tNMnLiSiIiz3HJLE5KSMqhVS14kJEqOLYnilFn8pM16hynAERumiwEaWn32x+gGJO84W7TWWUCUUuowRuLYZj2S1voT4BMwnnqyYdlC/EtMfBqfbzzBkm2nSMnI5rpgb169oxW9m9Yu8w3k8pOSksnUqb/w4YfbqVfPk6VLhzN0aPNyVZciygdbEsVDGMVPAcA54FdzWFG2ASFKqSCMlx2NBPI+0fQ9MApYoJTyxSiKirQtdCFss/tUAvPCI/lp31kABrapx/1hwbRqUMPBkV2bKlWc+OOPk0yZ0oUZM27Ey6t8FZeJ8sOWRJGttR5Z3BlrrbOVUpOBNRj1D59prfcrpV4BtmutV5jf3aSUOoBRnPW01jquuMsSIi+LRfPbIaOB3NaoS3i6uTC+ZxBjuwdSv2b5LZY5duwSr7zyJ3PnDsDT040dOx6galV5kZCwryIb3CmljgOHgSXAt1rr5NIIrCDS4E4U5nJmDst3xvDZhigiL6bSoGY17usRyJ2dG+JZThrI5ScjI5u33trIa6+F4+rqzKpVdxEWJk8zCdvZtcGd1rqxUqo7RtHRy0qpXcBirfXiq1mgEPZwITmDhZtPsHDLSeLTsmjrX4P/jWrPLa3q4lJOGsgVZN26KB56aBWHD8dx550tmT37ZurXL9vvtRAVi033rFrrTcAmpdRLwLvAV4AkCuFwR88lMz88iu92nSYrx0Lf5nW4PyyYzoG1KkSlrtaa114LJyvLws8/j+bmm5s4OiRRCdnS4M4Do6HcSKA58APQ3c5xCVEgrTWbjscxLzySPw5foGoVJ0Z08mdcjyCC/cpXA7n8WCyaTz/dSf/+TWjYsAYLF95BzZpVqVat/BadifLNljuKfcCPwFta63A7xyNEgTKzLazcE8v88CgOnEnC18ONJ/s1ZfR1jfCu7uro8ErEnj3nmDhxJZs3xzBtWi9efvkG6tWTYibhWLYkimCttcXukQhRgMTLWSz6K5ovNp3gbFI6IbU9eGtoG25rV79cNpDLT0pKJi+//AfvvLOFWrWqsWDBYO65p62jwxICKCRRKKX+q7V+EliulPrXo1Hyhjthb6cupfHphii+2X6KtMwcejTxYebQ1vRu6lch6h+svfTSH/z3v5uZMKE9b7zRFx8f6cBPlB2F3VEsMf+XN9uJUhURHc+88Eh+3ncWJ6W4rW19xocF0bJ++W4gl9epU4mkpmYRGurLf/7Tk9tvD6VnT+miRpQ9hb3hbqv5Z3Ot9RXJwmxIJ2/AEyUmx6L55cA55odHsv1kPF5VXXigV2PGdg+kbo2qjg6vRGVnW3j//b+YNm0dHTvW588/x+Lr6y5JQpRZttRRjOPfdxXj8xkmRLGlZWazbEcMn26I4mRcGg29qzF9UAtGdGpIdbeK1+J4y5YYJk5cye7d57j11hDmzBng6JCEKFJhdRR3YjwSG6SU+tbqK08gIf+phLDN+aR0vth8gq/+iiYhLYv2ATWZ2j+Um1vWxbkcdtBni1WrjjBo0NfUr+/Jt9+O4PbbQytcXYuomAq7ZNsKxGH0+jrXangyEGHPoETFdehsEvPDo1ixK5Ysi4WbW9Tl/l5BdGzk7ejQ7EJrTWxsMg0aeNG3bzCvvHIDjz7aFU9P6cBPlB9F9vVU1khfT+WP1prwoxeZFx5J+NGLVKvibDSQ6xlEI5/qjg7Pbo4ciWPSpFUcORLHgQMP4+FRMdp6iPLJLn09KaX+1Fpfr5SK58oXDilAa60r5iWgKDEZ2Tms2BXLpxuiOHQ2GT9PN56+uRmjuwZQ073injTT07N5440NzJy5gWrVXJg5sw/VqlW8+hZReRR29Oa+7tS3NAIRFUdCWiZfmQ3kzidnEFrXk7eHGQ3k3FwqRgO5gpw9m0KvXp9z9OglRo1qxezZN1O3bvnvVkRUboU9HpvbGrshEKu1zlRK9QTaAF8CSaUQnyhHTsal8umGKJZuj+FyVg5hIb7MGt6WsBDfCl9pm5WVQ5UqztSpU51evRoxd+4A+vVr7OiwhCgRttwPfw90Vko1Bv4PWAUsAgbaMzBRfuw4eYl566NYc+AsLk6Kwe0aMCEsiNC6Xo4Oze4sFs0nn+zg9dfD2bRpPP7+Xsyff5ujwxKiRNmSKCxa6yyl1BDgXa31+0opeeqpksuxaNbsP8u88EgiohOoUa0Kk3o35t5ugdT2qlgN5Aqye/dZHnxwJX/9dZobbwwiKyvH0SEJYRc2vQpVKTUcGAPcbg6T/o4rqdSMbL7ZforPNkZx6tJlGvm488rglgzr6I+7a+WosNVa8/TTv/Duu1vw9q7GwoV3MHp06wpfvCYqL1tbZk/C6GY8UikVBHxt37BEWXM2MZ0Fm06w6K+TJKVn06lRLZ4f0IJ+LepU2AZyBVFKER9/mfHjjQ78atUqv+/gFsIWNrWjUEq5ALmv1jqmtc62a1SFkHYUpetAbBLzwyNZsTsWi9bc0qoeE8KCaB9Qy9GhlaqTJxN49NGfmTbtejp0qIfFonGqZAlSlG92fWe2UioMWAicxmhDUVcpNUZrvfFqFijKPq01fxy5wPzwSDYei8Pd1Zkx3RoxrkcQDb0rV/fXWVk5vPPOFl5++U8A7ryzJR061JMkISoVW4qe3gEGaK0PACilmmMkjqvKTKLsSs/K4Yddp5kfHsXR8ynU8XJjav9Q7uoSQA33ylcttWnTKR58cCX79p1n8OBmvP/+LQQEVKyuzoWwhS2JwjU3SQBorQ8qpSpus9pK6FJqJl9tOckXm09yMSWD5vW8mD2iLQPb1MfVxcnR4TnMr79GkpiYzvff38ngwaGODkcIhymyjkIptQDIwLiLABgNuGut77VvaPmTOoqSE3khhU83RLF8ZwzpWRZ6N/Pj/rBgujf2qZRP8GitWbhwD35+7txySwgZGdlkZVmkjyZRIdi1jgKYCDwCPINRR7Ee+N/VLEw4ntaabSeMN8j9evAcVZycuKO90UAupI6no8NzmEOHLvLQQ6v4448TDB/egltuCcHNzQU36eRViMIThVKqNdAY+E5r/VbphCTsITvHwk/7zjI/PJLdMYnUcq/ClBuaMKZbIH6VuMvry5ezeP31cN58cyPVq7vy8ccDmTChg6PDEqJMKaz32Ocw3mS3E6MLj1e01p+VWmSiRCSnZ7Fk2yk+33iC0wmXCfKtzqu3t2JoB3+quVbsDvps8eOPR3j11XDuvrsNs2b1o04d6cBPiLwKu6MYDbTRWqcqpfyA1YAkinIiNuEyCzad4Ou/oknOyKZLkDcv3daSPqG1K/2jnWfPprBr11n692/C8OEtCAycQJcuDRwdlhBlVmGJIkNrnQqgtb6glKq8j7+UI/tOJzIvPJJVe86ggQGt63F/WBBt/Gs6OjSHy8mx8PHHO3j22d9wdXUmOvoxqlWrIklCiCIUliiCrd6VrYDG1u/O1loPsWtkwmYWi2bd4fPMC49kS+QlPNxcGNs9kLE9AvGvVbkayBVk584zTJy4km3bYunbN5gPPhhAtWqVr22IEFejsEQxNM/nOfYMRBRfelYO3+48zacbIjl+IZV6Nary3IBQRnYJwKuqnARzRUXF06XLPHx93Vm0aAgjR7aqlI//CnG1Cntx0W+lGYiw3cWUDBZuPsmXW04Sl5pJqwZevDeyHQNa16OKs5QQgvEY8N6952nTpg5BQbX4/PPBDBrUjJo1K0cX6EKUpMrRL3QFcez8Pw3kMrMt9AmtzYSwYK4L9pYrZCtRUfFMnvwTP/98jIiIB2nTpg5jxrR1dFhClFt2TRRKqf7Ae4AzMF9r/UYB4w0DlgKdtdbS7DofX/11khe+34ersxNDO/gzvmcQTWrLo5zWMjNzmD17M6+88idOTopZs/rRooWfo8MSotyzOVEopdy01hnFGN8ZmAv0A2KAbUqpFdb9RpnjeWK0/P7L1nlXNntiEnhpxX7CQvx4Z0RbfDwqbwO5guTkWOje/VN27DjDkCHNeffdm2nYUDrwE6IkFFmgrZTqopTaCxw1P7dVStnShUcXjHdXRGqtM4HFwOB8xpsBvAWk2x525ZGUnsXkRRH4erjx3p3tJEnkkZRkXLs4Ozsxblx7fvxxFMuXj5AkIUQJsqXm831gIBAHoLXeDdxgw3QNgFNWn2PMYX9TSrUHGmqtVxY2I6XUA0qp7Uqp7RcuXLBh0RWD1ppnv93L6YTL/G9Ue2pVl87pcmmtWbBgF8HB7/HDD4cAmDSpMwMHNnVwZEJUPLYkCiet9ck8w2x5i3x+tat/d1VrNuB7B3iyqBlprT/RWnfSWnfy86s8Zc6Ltkazas8ZnrypKZ0CvR0dTplx4MAFevf+gvvu+4HQUF8aN5ZtI4Q92VJHcUop1QXQZr3DFOCIDdPFAA2tPvsDsVafPYFWwB/mEzt1gRVKqdukQhsOnkni5R8P0KupHxN7NXZ0OGXGW29t5Pnnf8fLy4358wdx333tK32XJELYmy2J4iGM4qcA4BzwqzmsKNuAEKVUEMZrVEcCd+V+qbVOBHxzPyul/gCekiQBqRnZPLxoJzWrVWH2iLZyIsQoalJKUbeuB6NHt+btt/vh51fd0WEJUSkUmSi01ucxTvLForXOVkpNBtZgPB77mdZ6v1LqFWC71npFsaOtBLTWvPD9Pk5cTOWrCdfhW8krr2Njk3n00Z8JCwvgkUe6cs89bbnnHmkTIURpKjJRKKXmYVW3kEtr/UBR02qtV2P0Oms9bFoB4/Yuan6VwdIdMXwXcZrH+obQrbGPo8NxmJwcCx98sI3nn/+drCwL3bv7OzokISotW4qefrX6uypwB1c+zSRKyNFzyUz7YR/dgn2YcmOIo8NxmF27zjJhwgp27DjDTTc15oMPBkiFtRAOZEvR0xLrz0qphcAvdouokrqcmcPDi3bi4ebCeyPb4VyJ6yUSE9OJjU1myZJhDB/eQronEcLBrqYLjyCgUUkHUtm9tGI/R8+n8H/julDbq3J1XKe1ZunSAxw9Gsfzz/fi+usDiYx8lKpVpSsyIcoCW1pmxyulLpn/EjDuJp6zf2iVx/cRp1my/RSTejcmLKTytBMBOH78EgMGLOLOO5fxww+HycoymuhIkhCi7Cj016iMe/62GI+3Ali01v+q2BZXL/JCCs9/t5fOgbV4vG/laVWckZHNrFmbePXVcKpUceK99/ozaVJnXFykm3QhyppCE4XWWiulvtNadyytgCqT9KwcHl4UgauLE++Pao9LJXqXxKlTScyYsZ5Bg5rx7rs306CBl6NDEkIUwJYz01alVAe7R1IJvbbqIAfPJPHfEW2pV6Oao8OxuwsXUpkzZysATZp4c+DAwyxdOlyShBBlXIF3FEopF611NtATuF8pdRxIxejDSWutJXlcg9V7z7Bwy0nuDwvixtA6jg7HriwWzeefR/DMM7+SnJxBv37BNGvmS3BwLUeHJoSwQWFFT1uBDsDtpRRLpREdl8bUZXto17AmT98c6uhw7GrfvvM89NAqNmyIJiwsgI8+GkizZr5FTyiEKDMKSxQKQGt9vJRiqRQysy1M/nonSsH/RrXHtQJX3mZm5nDTTQvJzMzhs89uY+zYdtImQohyqLBE4aeUeqKgL7XWs+0QT4X3xk+H2BOTyEd3d6Sht7ujw7GL33+P4vrrG+Hq6sw33wwnNNQXX9+Kua5CVAaFXc46Ax4Y3YHn908U0y8HzvHZxijGdg+kf6u6jg6nxMXEJDF06Df06fN//N//7QagZ88ASRJClHOF3VGc0Vq/UmqRVHCnEy7z1NLdtGrgxbMDKla9RHa2hTlztvLii+vIybEwc2YfRo9u4+iwhBAlpMg6CnHtsnIsTFm0kxyLZs6oDri5ODs6pBI1Zsx3LF68j1tuacLcuQMICpKnmYSoSApLFH1KLYoK7r9rj7AzOoH/jWpPoG/FeNlOQkI6Li5OeHi48vDDnRk6tDlDhzaXymohKqAC6yi01pdKM5CKat3h83z053Hu6hrAoLb1HR3ONdNas3jxPpo3n8uLL/4OGPUQw4ZJL69CVFQV99nMMuBsYjpPfrOb0LqeTBvYwtHhXLNjxy5x881fMmrUcvz9vbj7bqmHEKIykC467SQ7x8IjiyNIz8phzl0dqFqlfNdLLFq0l3HjfsDNzYU5c25h4sROOFeivqmEqMwkUdjJ+78dZWvUJWaPaEuT2h6ODueqZWXlUKWKM5061WfYsBa89VY/6teXp6OFqEwkUdjBxmMX+d+6Ywzr6M+QDuXzXc/nz6fy5JNrSU3N5Ntv76RpUx++/HKIo8MSQjiAlB2UsAvJGTy6eBeN/Tx4ZXBLR4dTbBaL5pNPdtCs2RyWLNlHy5Z+5ORYHB2WEMKB5I6iBOVYNI8v2UVyehZfTeiKu2v52ryRkfHcffe3bN4cQ+/egXz44a2EhkoHfkJUduXrTFbGffjHMTYcu8gbQ1rTrG75K8evUcONhIR0vvjidsaMaSOPuwohACl6KjF/RcYx+5cjDG5Xnzs7N3R0ODZbseIwQ4YsISfHgo+PO/v2TeKee9pKkhBC/E0SRQmIS8ngkcURBHi789odrcvFSTY6OpHbb1/M4MGLOXIkjjNnUgBwcir7sQshSpcUPV0ji0Xz5NLdxKdm8emkzni4le1Nmp1t4d13tzB9+h9orXnzzb48/vh1VCnn7TyEEPZTts9q5cC88Ej+OHyBGYNb0qpBDUeHU6ScHAvz5+/kxhuD+N//biEwsKajQxJClHFS9HQNdpyM5+01hxnQui53X9fI0eEUKD7+MlOn/kJycgZubi5s3DiOFStGSpIQQthEEsVVSkzL4pGvI6hXsyozh5TNJ4S01nz11R5CQ+fy3/9uZt26EwD4+LiXyXiFEGWTFD1dBa01Ty3bzfnkdJZN7E6NalUcHdK/HDkSx6RJq/jttyi6dGnAmjV3065dxXurnhDC/iRRXIUFm07wy4FzvHBrc9o2LJvFN4899jPbt8fywQcDeOCBjtKBnxDiqkmiKKY9MQm8vvogfZvXYXzPIEeHc4VffjlOaKgvDRvW4MMPb8XNzYW6dctvh4RCiLLBrpeZSqn+SqnDSqljSqn/5PP9E0qpA0qpPUqp35RSZbdGGEhKz2Lyogj8PNyYNbzs1EucPZvCXXct56abvuTNNzcC0KhRTUkSQogSYbdEoZRyBuYCtwAtgFFKqbxv74kAOmmt2wDLgLfsFc+10lrz7Ld7OZ1wmf/d1Z6a7q6ODgmLRfPRR9sJDZ3D8uUHmT79embNusnRYQkhKhh73lF0AY5prSO11pnAYmCw9Qha63Va6zTz4xagzPbJ/dVf0azac4anbmpGx0bejg4HgJkzw3nooVV07FifPXsm8tJLvalaVUoThRAly55nlQbAKavPMUDXQsYfD/yU3xdKqQeABwACAgJKKj6bHYhN4pWVB7i+qR8P9gou9eVbS07O4OLFNIKCajFxYieCgmoxalSrMlMMJoSoeOx5R5HfmUvnO6JSdwOdgLfz+15r/YnWupPWupOfn18Jhli0lIxsJi/aSS33Kswe0dZhfSFprfnuu4O0aPEBd965DK01Pj7u3HVX+ehbSghRftkzUcQA1t2o+gOxeUdSSvUFngdu01pn2DGeYtNa88J3ezkRl8p7I9vj4+HmkDhOnkzgttsWM2TIN3h7V+P992+R5CCEKDX2LHraBoQopYKA08BI4C7rEZRS7YGPgf5a6/N2jOWqLN0Rw/e7Ynm8b1OuC/ZxSAybN5+ib9+FAMya1Y9HH70OFxdpEyGEKD12SxRa62yl1GRgDeAMfKa13q+UegXYrrVegVHU5AEsNa+Qo7XWt9krpuI4ci6ZaT/so3tjHybf2KTUl5+UlIGXlxsdOtRj3Lh2PP10DwICyn6ng0KIikdpnW+1QZnVqVMnvX37drsu43JmDrfN2UB8WiarHw2jtmdVuy7PWlxcGv/5z6+sXRvJ/v2T8PBw/GO4QojyTym1Q2vd6WqmlWcp8/HSiv0cu5DCwnFdSy1JaK1ZuHAPTz65lvj4yzzxRDekGkIIURZIosjj+4jTLNl+isk3NKFniG+pLDMxMZ3bb1/CH3+coFs3fz76aCBt2tQplWULIURRJFFYibyQwnPf7aVLoDeP9Q2x+/K01iil8PJyw9fXnU8+Gcj48R3kdaRCiDJFHp8xpWfl8PCiCNxcnHhvVDtc7Nzb6po1x+jQ4RNiYpJQSrF06XDuv7+jJAkhRJkjicL06qoDHDyTxOwR7ahXo5rdlnPmTDIjRy6jf/+vSEvL4vz5VLstSwghSoIUPQGr9pzhyy3RPNgrmBtCa9ttOXPnbuW5534nIyObl1/uzdSpPXBzk10ghCjbKv1Z6mRcKv9Zvof2ATV56uZmdl3Wjh1n6Nq1AXPnDiAkxDEN+IQQorgqdaLIyM5h8qIIlIL3R7anSgnXSyQlZTBt2jrGjGlDx471+eCDW3Fzc5buN4QQ5UqlThRv/nSYvacT+XhMRxp6u5fYfLXWLF9+kEcf/ZkzZ5IJCKhBx471pQtwIUS5VGnPXGv3n+WzjVGM7R7IzS3rlth8o6LimTz5J1avPkq7dnX59tsRdO1aZl+zIYQQRaqUiSImPo2nlu6mdYMaPDsgtETn/dVXe1m//iTvvHMzkyd3kQ78hBDlXqVLFFk5FqZ8HYFFw5y72uPm4nzN8wwPP0lGRg59+wbz9NPdGTu2Hf7+XiUQrRBCOF6lu9ydtfYwEdEJvDG0NY18ql/TvC5eTGPcuB/o1WsBr7zyJwBubi6SJIQQFUqluqNYd/g8H/8ZyeiuAQxsU/+q56O1ZsGCXTz99C8kJmYwdWoPXnyxVwlGKiqKrKwsYmJiSE9Pd3QoopKoWrUq/v7+VKlSpcTmWWkSxdnEdJ78ZjehdT15cWCLa5rX6tVHGTduBT16NOSjjwbSqpX9GumJ8i0mJgZPT08CAwPlsWhhd1pr4uLiiImJISgoqMTmWymKnrJzLDzydQTpWTnMHd2BqlWKXy+RlpbFxo3RAAwYEMIPP4xk/fr7JEmIQqWnp+Pj4yNJQpQKpRQ+Pj4lfgdbKRLF+78dZeuJS7x2Rysa+3kUe/qffjpKq1YfcMstX5GQkI5SittuayYd+AmbSJIQpckex1uFTxQbjl7kf+uOMbyjP3e0L157htOnkxg+fCkDBizCzc2FH38cRc2apfe2OyGEKAsqdKI4n5zOY0t20cTPg5cHtyzetOdTadHiA1auPMKrr97A7t0Tuf76QPsEKoQdOTs7065dO1q1asWgQYNISEj4+7v9+/dz44030rRpU0JCQpgxYwbWr0f+6aef6NSpE82bNyc0NJSnnnrKEatQqIiICCZMmHDFsMGDB9OtW7crho0dO5Zly5ZdMczD458ShiNHjjBgwACaNGlC8+bNGTFiBOfOnbum2C5dukS/fv0ICQmhX79+xMfH5ztedHQ0N910E82bN6dFixacOHECgDlz5tCkSROUUly8ePHv8VeuXMn06dOvKbZi0VqXq38dO3bUtsjOsei7JBkJkgAAEiNJREFU5m3WzV5YrQ+fTbJpGq21jolJ/Pvv997boo8di7N5WiHyOnDggKND0NWrV//773vuuUe/+uqrWmut09LSdHBwsF6zZo3WWuvU1FTdv39/PWfOHK211nv37tXBwcH64MGDWmuts7Ky9Ny5c0s0tqysrGuex7Bhw/SuXbv+/hwfH6/9/f11aGiojoyM/Hv4vffeq5cuXXrFtLnb5vLly7pJkyZ6xYoVf3/3+++/6717915TbE8//bSeOXOm1lrrmTNn6meeeSbf8a6//nq9du1arbXWycnJOjU1VWut9c6dO3VUVJRu1KiRvnDhwt/jWywW3a5du7/Hyyu/4w7Yrq/yvFthn3r6YN0xNh6L482hrWlax7PI8RMT03nhhd/5+OMdbNkygQ4d6vHII11LIVJRWbz8434OxCaV6Dxb1Pdi+iDb75a7devGnj17AFi0aBE9evTgpptuAsDd3Z05c+bQu3dvHn74Yd566y2ef/55QkON3gtcXFyYNGnSv+aZkpLClClT2L59O0oppk+fztChQ/Hw8CAlJQWAZcuWsXLlShYsWMDYsWPx9vYmIiLi/9s71+gqqiwBfxsQwyMNNkhWEDFAfIQECBBsEZCnYquAZiFRQB6DoyDKKA9Xu5xeMtqt0u2TBgftHic4Iw+RDiAo2qA0iIAEiSEmmE5DzICQREQJyDt7flRxc5Pc5N7E3JsH+1vrrlV16tQ5u/aqW7vOPqf2Jj4+npSUFNLS0mjdujUA0dHRbN26lUaNGjF16lTy8pxFJK+88gr9+vUr1XdRURHp6en06NHDU7Zy5UpGjBhBREQEy5Yt44knnvCrlyVLltC3b19GjBjhKRs8eHDAeq2I1atXs2nTJgAmTpzIoEGDmDdvXqk6mZmZnDt3jptvvhkoPcrp2bOnz3ZFhEGDBrF27VrGjBnzs+X0R4M0FDv2HeHlDdncGd+eMQlXVlpXVVmxIpNHH13P4cPHefjh6+nS5bIQSWoYoeP8+fNs3LiRKVOmAI7bqXfv3qXqdOnShePHj3Ps2DEyMjKYNWuW33afeeYZWrVqxZ49ewAqdK94k52dzYYNG2jcuDHFxcWkpKQwefJkduzYQVRUFBEREYwdO5bHHnuM/v37k5eXx/Dhw8nKyirVTmpqKnFxcaXKli5dylNPPUVERASjR48OyFBkZGSU04UvioqKGDBggM9jS5YsoWvX0kvv8/PziYyMBCAyMpKCgoJy52VnZ9O6dWsSExPZv38/w4YN4/nnn6dx48pXZyYkJLBlyxYzFNXhyPHTzFi2m6vatOB3d3WrdAWAqpKY+A6rVu2lV69I1qy5l4SE6n+IZxiVUZU3/5rk5MmTxMfHk5ubS+/evT1vrurmbPdFVVbObNiwgWXLlnn2L7vM/4vW3Xff7XkQJiUl8fTTTzN58mSWLVtGUlKSp93MzEzPOceOHaOoqIjw8BIPwaFDh7j88ss9+/n5+eTk5NC/f39EhCZNmpCRkUFcXJzPa6rqCqHw8HDS0tKqdI4/zp07x5YtW9i9ezcdO3YkKSmJ5ORkj0GviHbt2vHtt9/WqCwV0aAms4uLlVkrvuToT2dZMLYnLSvIHnf27HnAuUn697+S+fNv5fPP7zcjYTRImjVrRlpaGt988w1nzpxh4cKFAMTGxpKamlqq7r59+2jZsiXh4eHExsaya9cuv+1XZHC8y8qu62/RoiR8Tt++fcnJyaGwsJBVq1aRmJgIQHFxMdu2bSMtLY20tDQOHjxYykhcuDbvtpcvX87Ro0fp1KkTUVFR5ObmeoxYmzZtSo12vv/+e9q2bevRRSDXWlRURHx8vM+ft1G7QEREBIcOHQIco9auXfnvrjp06EDPnj3p3LkzTZo04c477+SLL77wK8upU6do1ix4aZu9aVCG4s9b9rHp60J+e0dXYtu38lln06ZcundfxOrVewGYNetGHnnkVzSu4aRFhlHXaNWqFfPnz+eFF17g7NmzjBs3jk8//ZQNGzYAzshjxowZPP744wDMmTOHZ599luzsbMB5cL/00kvl2r3llltYsGCBZ//CwzgiIoKsrCyPa6kiRIS77rqLmTNnEhMTQ5s2bXy26+tNPiYmhpycHM/+0qVLWb9+Pbm5ueTm5rJr1y6PoRg0aBDLly/nzJkzACQnJ3vmIcaOHctnn33GunXrPG2tX7/e4067wIURha9fWbcTwMiRI1m8eDEAixcvZtSoUeXq9OnTh6NHj1JYWAjAxx9/7LOtsmRnZ5dzuwWN6s6C19avolVPqbnfa+cn1um0/03V4uLicscLCo7rhAkpCnO1U6dXdOPGfT5aMYyapa6telJVveOOO/Stt95SVdX09HQdOHCgXnPNNdqlSxedO3duqf/Pe++9p7169dLrrrtOY2JidPbs2eXaLyoq0gkTJmhsbKx2795dV65cqaqqK1as0M6dO+vAgQN1+vTpOnHiRFX1vfpo586dCmhycrKnrLCwUMeMGaPdunXTmJgYffDBB31eX1xcnB47dkz379+v7du3L/f/79mzp27fvl1VVefOnatxcXHao0cPTUxM1IKCAk+9rKwsHT58uEZHR2tMTIwmJSXp4cOHK9WtP7777jsdMmSIRkdH65AhQ/TIkSOe650yZYqn3kcffaTdunXTuLg4nThxop4+fVpVVV999VW94oortHHjxhoZGVnqnNtvv13T09N99lvTq55EvdZM1wcSEhK07HD5h5/OcPv8T2nUCNbNGMAvwkoHw1q6dA/Tp7/P8eNnmDPnRp588iaaN6+5gFmGURFZWVnExMTUthgNmpdffpnw8PBy31I0ZPLz8xk7diwbN270edzXfSciu1Q1oTr91Xt/i6oye0U6BUWnWHBvr3JGAuDcuWLi4tqRljaV3/9+qBkJw2hATJs2jUsvvbS2xQgpeXl5vPjiiyHrr96vevrvrblsyMrnt3d0pceVzjrsEyfO8Mwzm+nYsRUPPdSH8eO7M358d4u5YxgNkLCwMO67777aFiOk9OnTJ6T91esRRfqBH3jugyyGxUTwL/2iAFi7NpvY2NeYN28r2dlHAGeyzIyEUVvUN/euUb8Jxv1Wb0cUx06d5eElu2kXHsYLd3fn4MEiZsz4gJSUvXTtejmbN09iwICraltM4yInLCyMI0eOWKhxIySom48iLKxmg5fWS0OhqvxmZToHfzjJOw/2pXXzpqSnHuLDD//Jc88NZebMvjRt+vNzYRvGz6VDhw4cOHDAs/TRMILNhQx3NUm9NBRv78jj/T2HGRcXyaervqb3v93ATTddRV7eo7Rp07y2xTMMD5dcckmNZhozjNogqHMUInKriHwtIjki8hsfxy8VkeXu8R0iEuWvzZNnz/P0e5lcfhaem/AeL720nRMnnA9ozEgYhmHUPEEzFCLSGFgI/BroCtwrImU/N5wCHFXVaOBlYB5+yC08weljp0l7/UtmzPgVe/ZMo0WLpjUtvmEYhuESTNfT9UCOqu4DEJFlwCjAOyDKKGCuu/0usEBERCuZtj9XrLTe+yMpmyfTq1dkcCQ3DMMwPATTUFwB/J/X/gGgbIIHTx1VPSciPwJtgO+8K4nIA8AD7u7p9PxJGQFEBL4YaEsZXV3EmC5KMF2UYLoo4drqnhhMQ+FrLWDZkUIgdVDVN4A3AEQktbqfoTc0TBclmC5KMF2UYLooQURS/dfyTTAnsw8A3lmDOgBlg6d76ohIE6AV8H0QZTIMwzCqSDANxU7gahHpJCJNgXuANWXqrAEmutujgY8rm58wDMMwQk/QXE/unMPDwIdAY+BNVf1KRJ7GCXe7Bvgv4H9EJAdnJHFPAE2/ESyZ6yGmixJMFyWYLkowXZRQbV3UuzDjhmEYRmip10EBDcMwjOBjhsIwDMOolDprKIIR/qO+EoAuZopIpoiki8hGEWmwYXP96cKr3mgRURFpsEsjA9GFiIxx742vRGRJqGUMFQH8RzqKyCcistv9n9xWG3IGGxF5U0QKRCSjguMiIvNdPaWLSK+AGq5uDtVg/nAmv/8JdAaaAl8CXcvUeQhY5G7fAyyvbblrUReDgebu9rSLWRduvXBgM7AdSKhtuWvxvrga2A1c5u63q225a1EXbwDT3O2uQG5tyx0kXdwE9AIyKjh+G/ABzjdsNwA7Amm3ro4oPOE/VPUMcCH8hzejgMXu9rvAUGmYAf/96kJVP1HVn9zd7TjfrDREArkvAJ4B/gCcCqVwISYQXfwrsFBVjwKoakGIZQwVgehCgV+4260o/01Xg0BVN1P5t2ijgLfUYTvQWkT8xkKqq4bCV/iPKyqqo6rngAvhPxoagejCmyk4bwwNEb+6EJGewJWqujaUgtUCgdwX1wDXiMhWEdkuIreGTLrQEogu5gLjReQA8D7wSGhEq3NU9XkC1N18FDUW/qMBEPB1ish4IAEYGFSJao9KdSEijXCiEE8KlUC1SCD3RRMc99MgnFHmFhGJU9UfgixbqAlEF/cCyar6ooj0xfl+K05Vi4MvXp2iWs/NujqisPAfJQSiC0RkGPAkMFJVT4dItlDjTxfhQBywSURycXywaxrohHag/5HVqnpWVfcDX+MYjoZGILqYArwDoKrbgDCcgIEXGwE9T8pSVw2Fhf8owa8uXHfL6zhGoqH6ocGPLlT1R1Vtq6pRqhqFM18zUlWrHQytDhPIf2QVzkIHRKQtjitqX0ilDA2B6CIPGAogIjE4huJizE+7Bpjgrn66AfhRVQ/5O6lOup40eOE/6h0B6uKPQEtghTufn6eqI2tN6CARoC4uCgLUxYfALSKSCZwH5qjqkdqTOjgEqItZwJ9F5DEcV8ukhvhiKSJLcVyNbd35mKeASwBUdRHO/MxtQA7wEzA5oHYboK4MwzCMGqSuup4MwzCMOoIZCsMwDKNSzFAYhmEYlWKGwjAMw6gUMxSGYRhGpZihMOocInJeRNK8flGV1I2qKFJmFfvc5EYf/dINeXFtNdqYKiIT3O1JItLe69hfRKRrDcu5U0TiAzjnURFp/nP7Ni5ezFAYdZGTqhrv9csNUb/jVLUHTrDJP1b1ZFVdpKpvubuTgPZex+5X1cwakbJEztcITM5HATMURrUxQ2HUC9yRwxYR+cL93eijTqyIfO6OQtJF5Gq3fLxX+esi0thPd5uBaPfcoW4Ogz1urP9L3fLnpSQHyAtu2VwRmS0io3Fibr3t9tnMHQkkiMg0EfmDl8yTRORP1ZRzG14B3UTkP0UkVZzcE//hls3AMVifiMgnbtktIrLN1eMKEWnppx/jIscMhVEXaebldkpxywqAm1W1F5AEzPdx3lTgVVWNx3lQH3DDNSQB/dzy88A4P/2PAPaISBiQDCSpajecSAbTROSXwF1ArKp2B37nfbKqvguk4rz5x6vqSa/D7wKJXvtJwPJqynkrTpiOCzypqglAd2CgiHRX1fk4sXwGq+pgN5THvwPDXF2mAjP99GNc5NTJEB7GRc9J92HpzSXAAtcnfx4nblFZtgFPikgH4K+q+g8RGQr0Bna64U2a4RgdX7wtIieBXJww1NcC+1U12z2+GJgOLMDJdfEXEVkHBBzSXFULRWSfG2fnH24fW912qyJnC5xwFd4ZysaIyAM4/+tInAQ96WXOvcEt3+r20xRHb4ZRIWYojPrCY0A+0ANnJFwuKZGqLhGRHcDtwIcicj9OWOXFqvpEAH2M8w4gKCI+85u4sYWuxwkydw/wMDCkCteyHBgD7AVSVFXFeWoHLCdOFrfngYVAooh0AmYDfVT1qIgk4wS+K4sAf1PVe6sgr3GRY64no77QCjjk5g+4D+dtuhQi0hnY57pb1uC4YDYCo0WknVvnlxJ4TvG9QJSIRLv79wF/d336rVT1fZyJYl8rj4pwwp774q/AnTg5Epa7ZVWSU1XP4riQbnDdVr8ATgA/ikgE8OsKZNkO9LtwTSLSXER8jc4Mw4MZCqO+8BowUUS247idTviokwRkiEgacB1OysdMnAfqRyKSDvwNxy3jF1U9hRNdc4WI7AGKgUU4D921bnt/xxntlCUZWHRhMrtMu0eBTOAqVf3cLauynO7cx4vAbFX9Eic/9lfAmzjurAu8AXwgIp+oaiHOiqylbj/bcXRlGBVi0WMNwzCMSrERhWEYhlEpZigMwzCMSjFDYRiGYVSKGQrDMAyjUsxQGIZhGJVihsIwDMOoFDMUhmEYRqX8P3HirsL4SxyMAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -3510,12 +2945,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6729\n", - " 1 0.39 0.41 0.40 2020\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.41 0.41 2090\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] } @@ -3524,13 +2959,158 @@ "print(classification_report(y_test, tree.predict(X_test)))" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054221237
11244846
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5422 1237\n", + "1 1244 846" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.40 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[0] = ([\"Decision Tree\" , \n", + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", " auroc])" ] @@ -3553,17 +3133,17 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", - "randf = RandomForestClassifier(n_estimators=300)" + "randf = RandomForestClassifier(n_estimators=200)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -3573,12 +3153,12 @@ " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", " n_jobs=None, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False)" ] }, - "execution_count": 43, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -3589,7 +3169,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -3598,8 +3178,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13475\n", - " 1 1.00 1.00 1.00 4021\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3621,14 +3201,14 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (Random Forest) identified 749\n" + "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" ] }, { @@ -3664,13 +3244,13 @@ " \n", " \n", " 0\n", - " 6310\n", - " 419\n", + " 6273\n", + " 386\n", " \n", " \n", " 1\n", - " 1271\n", - " 749\n", + " 1338\n", + " 752\n", " \n", " \n", "\n", @@ -3679,11 +3259,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6310 419\n", - "1 1271 749" + "0 6273 386\n", + "1 1338 752" ] }, - "execution_count": 45, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -3694,19 +3274,19 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 143, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.26\n" + "Optimal Threshold: 0.25839285714285715\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3723,7 +3303,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -3732,12 +3312,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6729\n", - " 1 0.64 0.37 0.47 2020\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.36 0.47 2090\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.80 0.78 8749\n", "\n" ] } @@ -3771,9 +3351,7 @@ "### Gradient Boosted Trees Classifier\n", "\n", "#### Theory\n", - "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", - "\n", - "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", " \n", "#### Training\n", "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." @@ -3821,12 +3399,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13475\n", - " 1 0.79 0.46 0.58 4021\n", + " 0 0.86 0.96 0.91 13545\n", + " 1 0.79 0.47 0.59 3951\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.71 0.74 17496\n", - "weighted avg 0.84 0.85 0.83 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", "\n" ] } @@ -3839,7 +3417,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," ] }, { @@ -3851,7 +3429,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 750\n" + "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" ] }, { @@ -3887,13 +3465,13 @@ " \n", " \n", " 0\n", - " 6339\n", - " 390\n", + " 6290\n", + " 369\n", " \n", " \n", " 1\n", - " 1270\n", - " 750\n", + " 1360\n", + " 730\n", " \n", " \n", "\n", @@ -3902,8 +3480,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6339 390\n", - "1 1270 750" + "0 6290 369\n", + "1 1360 730" ] }, "execution_count": 51, @@ -3924,12 +3502,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.26114231320864134\n" + "Optimal Threshold: 0.23302683158657422\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3955,12 +3533,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6729\n", - " 1 0.66 0.37 0.47 2020\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.35 0.46 2090\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.78 0.80 0.78 8749\n", "\n" ] } @@ -3984,7 +3562,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same." + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " ] }, { @@ -4024,20 +3602,20 @@ " \n", " 0\n", " Decision Tree\n", - " 0.409901\n", - " 0.606997\n", + " 0.410048\n", + " 0.610882\n", " \n", " \n", " 1\n", " Random Forest\n", - " 0.370792\n", - " 0.768455\n", + " 0.363158\n", + " 0.767925\n", " \n", " \n", " 2\n", " Gradient Boosted\n", - " 0.371287\n", - " 0.778407\n", + " 0.349282\n", + " 0.775518\n", " \n", " \n", "\n", @@ -4045,9 +3623,9 @@ ], "text/plain": [ " Model Recall-1 AUROC\n", - "0 Decision Tree 0.409901 0.606997\n", - "1 Random Forest 0.370792 0.768455\n", - "2 Gradient Boosted 0.371287 0.778407" + "0 Decision Tree 0.410048 0.610882\n", + "1 Random Forest 0.363158 0.767925\n", + "2 Gradient Boosted 0.349282 0.775518" ] }, "execution_count": 55, @@ -4108,7 +3686,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.445436\n", + " Current function value: 0.438684\n", " Iterations 7\n" ] }, @@ -4127,13 +3705,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737 \n", + " Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788 \n", "\n", "\n", - " Time: 19:35:44 Log-Likelihood: -7793.4\n", + " Time: 23:40:29 Log-Likelihood: -7675.2\n", "\n", "\n", - " converged: True LL-Null: -9431.5\n", + " converged: True LL-Null: -9346.0\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4144,136 +3722,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", + " LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", "\n", "\n", - " SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", + " SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", "\n", "\n", - " AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", + " AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", "\n", "\n", - " PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", + " PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", "\n", "\n", - " PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", + " PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", "\n", "\n", - " PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", + " PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", "\n", "\n", - " PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", + " PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", "\n", "\n", - " PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", + " PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", "\n", "\n", - " PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", + " PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", "\n", "\n", - " BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", + " BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", "\n", "\n", - " BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", + " BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", "\n", "\n", - " BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", + " BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", "\n", "\n", - " BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", + " BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", "\n", "\n", - " BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", + " BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", "\n", "\n", - " BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", + " BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", "\n", "\n", - " PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", + " PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", "\n", "\n", - " PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", + " PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", "\n", "\n", - " PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", + " PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", "\n", "\n", - " PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", + " PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", "\n", "\n", - " PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", + " PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", "\n", "\n", - " PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", + " PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", "\n", "\n", - " GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", + " GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", "\n", "\n", - " UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", + " UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", "\n", "\n", - " HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", + " HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", "\n", "\n", - " MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", + " MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", "\n", "\n", - " SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", + " SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", "\n", "\n", - " PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", + " PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", "\n", "\n", - " PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", + " PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", + " PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", "\n", "\n", - " PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", + " PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", + " PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", + " PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", "\n", "\n", - " PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", + " PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", "\n", "\n", - " PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", + " PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", + " PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", "\n", "\n", - " PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", + " PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", "\n", "\n", - " PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", + " PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", + " PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", "\n", "\n", - " PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", + " PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", "\n", "\n", - " PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", + " PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", + " PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", "\n", "\n", - " PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", + " PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", "\n", "\n", - " PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", + " PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + " PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", "\n", "" ], @@ -4285,57 +3863,57 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737\n", - "Time: 19:35:44 Log-Likelihood: -7793.4\n", - "converged: True LL-Null: -9431.5\n", + "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:40:29 Log-Likelihood: -7675.2\n", + "converged: True LL-Null: -9346.0\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", - "SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", - "AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", - "PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", - "PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", - "PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", - "PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", - "PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", - "PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", - "BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", - "BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", - "BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", - "BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", - "BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", - "BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", - "PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", - "PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", - "PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", - "PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", - "PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", - "PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", - "GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", - "UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", - "HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", - "MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", - "SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", - "PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", - "PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", - "PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", - "PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", - "PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", - "PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", - "PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", - "PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", - "PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", - "PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", - "PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", - "PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", - "PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", - "PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", - "PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", - "PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", - "PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", - "PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", "==========================================================================================\n", "\"\"\"" ] @@ -4361,12 +3939,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13475\n", - " 1 0.67 0.35 0.46 4021\n", + " 0 0.84 0.95 0.89 13545\n", + " 1 0.67 0.37 0.47 3951\n", "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.67 17496\n", - "weighted avg 0.79 0.81 0.79 17496\n", + " accuracy 0.82 17496\n", + " macro avg 0.75 0.66 0.68 17496\n", + "weighted avg 0.80 0.82 0.80 17496\n", "\n" ] } @@ -4375,31 +3953,6 @@ "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" ] }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.69 0.35 0.47 2020\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -4409,7 +3962,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -4417,7 +3970,67 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.446914\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438685\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438686\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438689\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438692\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438733\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438766\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438815\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438878\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438953\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439049\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439148\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439242\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439370\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439484\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439636\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439750\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439934\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.440120\n", " Iterations 7\n" ] }, @@ -4436,13 +4049,13 @@ " Method: MLE Df Model: 23 \n", "\n", "\n", - " Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761 \n", "\n", "\n", - " Time: 19:35:44 Log-Likelihood: -7819.2\n", + " Time: 00:03:36 Log-Likelihood: -7700.3\n", "\n", "\n", - " converged: True LL-Null: -9431.5\n", + " converged: True LL-Null: -9346.0\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4453,76 +4066,76 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", + " LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", "\n", "\n", - " SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", + " PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", "\n", "\n", - " PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", + " PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", "\n", "\n", - " PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", + " PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", "\n", "\n", - " PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", + " PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", "\n", "\n", - " BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", + " BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", "\n", "\n", - " BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", + " PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", "\n", "\n", - " BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", + " PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", "\n", "\n", - " PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", + " PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", "\n", "\n", - " PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", + " PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", "\n", "\n", - " PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", + " PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", "\n", "\n", - " GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", + " GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", "\n", "\n", - " UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", + " UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", "\n", "\n", - " HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", + " HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", + " MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", "\n", "\n", - " PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", + " PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", + " PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", "\n", "\n", - " PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", + " PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", "\n", "\n", - " PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", + " PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", "\n", "\n", - " PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", + " PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", + " PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", "\n", "\n", - " PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", + " PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", "\n", "\n", - " PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", + " PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + " PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", "\n", "" ], @@ -4534,81 +4147,86 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17472\n", "Method: MLE Df Model: 23\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709\n", - "Time: 19:35:44 Log-Likelihood: -7819.2\n", - "converged: True LL-Null: -9431.5\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", + "Time: 00:03:36 Log-Likelihood: -7700.3\n", + "converged: True LL-Null: -9346.0\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", - "SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", - "PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", - "PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", - "PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", - "BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", - "BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", - "BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", - "PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", - "PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", - "PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", - "GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", - "UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", - "HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", - "PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", - "PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", - "PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", - "PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", - "PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", - "PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", - "PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", - "PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", - "PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", - "PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 60, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#remove all insignificant attributes\n", - "sig = glm.pvalues[glm.pvalues < 0.05]\n", - "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n", - "glm_2.summary()" + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 13475\n", - " 1 0.66 0.36 0.46 4021\n", - "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.67 17496\n", - "weighted avg 0.79 0.81 0.79 17496\n", - "\n" + "24 Columns left:\n", + "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " dtype='object')\n" ] } ], "source": [ - "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -4617,18 +4235,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.35 0.47 2020\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.34 0.46 2090\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" ] }, { @@ -4642,19 +4260,19 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22554888390620112\n" + "Optimal Threshold: 0.21432968760597085\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4667,28 +4285,28 @@ { "data": { "text/plain": [ - "0.7698058845975142" + "0.7705999826781731" ] }, - "execution_count": 63, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Since the optimal cut off was found to be 0.2697615225249289 using the training data, we will use that as our cut off for our evaluation of the test set." + "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 112, "metadata": { "scrolled": true }, @@ -4699,18 +4317,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.86 0.86 6729\n", - " 1 0.54 0.55 0.54 2020\n", + " 0 0.87 0.81 0.84 6659\n", + " 1 0.50 0.61 0.55 2090\n", "\n", - " accuracy 0.79 8749\n", - " macro avg 0.70 0.70 0.70 8749\n", - "weighted avg 0.79 0.79 0.79 8749\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" ] }, { @@ -4727,14 +4345,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Logistic Regression identified 1104\n" + "Of 2090 Defaulters, the Logistic Regression identified 1273\n" ] }, { @@ -4770,13 +4388,13 @@ " \n", " \n", " 0\n", - " 5790\n", - " 939\n", + " 5361\n", + " 1298\n", " \n", " \n", " 1\n", - " 916\n", - " 1104\n", + " 817\n", + " 1273\n", " \n", " \n", "\n", @@ -4785,29 +4403,29 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5790 939\n", - "1 916 1104" + "0 5361 1298\n", + "1 817 1273" ] }, - "execution_count": 65, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Logistic Regression identified 716\n" + "Of 2090 Defaulters, the Logistic Regression identified 714\n" ] }, { @@ -4843,13 +4461,13 @@ " \n", " \n", " 0\n", - " 6391\n", - " 338\n", + " 6329\n", + " 330\n", " \n", " \n", " 1\n", - " 1304\n", - " 716\n", + " 1376\n", + " 714\n", " \n", " \n", "\n", @@ -4858,17 +4476,17 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6391 338\n", - "1 1304 716" + "0 6329 330\n", + "1 1376 714" ] }, - "execution_count": 66, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" ] }, { @@ -4880,19 +4498,19 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.21841387811546378\n" + "Optimal Threshold: 0.2096696069036731\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4904,16 +4522,16 @@ } ], "source": [ - "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289), output_dict = True)[\"1\"][\"recall\"],auroc]" + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" ] }, { @@ -5088,7 +4706,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -5102,14 +4720,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.2920039216 0.1749065908]\n" + "Explained variation per principal component: [0.295060096 0.1735291836]\n" ] } ], @@ -5127,12 +4745,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAH6CAYAAAAeHtXEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydfXxT5dn4v3eapKVJQCkCWnCAVBywuenc5tZn6kQZddNtvkTHNvfYTafohOIe2J49j3vxGY4pm/uJm1vdxKmYDd+mVFAnbuuYbyAqIFhUBlgQKIhpS5M0uX9/nKSk6UlykiZNml7fzyef05xzn/tcJ0nPdd/Xfb0orTWCIAiCIAx+bIUWQBAEQRCE3CBKXRAEQRBKBFHqgiAIglAiiFIXBEEQhBJBlLogCIIglAii1AVBEAShRBClLgiCIAglgih1oWRRSlUppb6plHpYKbVNKXVYKXVIKdWslKpXSsnvv8RQSp2plNJKqR9mce726LmxV0Qp9Z5Saq1Sao5Syp7kvPFKqZuVUuuUUgeVUiGl1F6l1NNKqeuVUiNSXHN23PXOzVRmQUjE9EcqCCXCxcCvgd3AGmAHMAb4MtAIzFJKXawlA5PQm9uA94AyYCLG7+V04Ozo3z0opb4J3A6UA68Ay4GDQBVQC/wS+B9gVJJrXQloQEX/fjK3tyIMNUSpC6XMG8D5wEqtdSS2Uyn1feAF4EKMh/SDhRFPKFJ+qbXeHnujlJoGvAh8SSl1htb6b9H9XwF+h6HEL9Rar0zsSCn1aWCp2UWUUlOAzwBPAyOB85VSY7TW7+b4foQhhJgfhZJFa/2M1vqxeIUe3b8H+E307ZmZ9KmUOkkp9fuoqTYQNbP+Qyl1tUnbs5VSq5RSB5RSXUqpN6Jm2j7mWKXUs1ETrEMp9b9KqTej52xRSn0rrt23lVKvRZcSdimlfpS4jKCUmhDt6+6ovI9EZeiILj2YmnmVUuVKqYVKqVeVUp1Kqfej93aJSdv4a0xQSj2glNoflfklpdTnU3yGlyml1kRN1V1KqdeVUj9QSpWbtNXRz2aUUuq3Sqnd0c99k1LqPxPa3o1hkQG4McGUfmYyedKhtd4EPBt9+/HotTzA/4vuu9RMoUfP/SfwiSRdx77XPwB3Aw7gG9nKKQggM3Vh6BKKbrutnqCUOg/4M4apdRWGqfUo4GTgvzBM/bG2V0Xfd0TP2YsxgFgAfEEp9Wmt9Xsml3kAQwk0RWW8CPitUioEfBi4HHgc+CuGFeJ/gU7gZyZ9TQT+BWwE7gSOBbzAE0qpr2itfXHyOoHVwBnAFozZZWX0+j6l1Ee01t83ucYHMKwebwF/xJhxeoFHlVIztNZr4hsrpe4CrgB2AQ9hmLk/CfwEOFspdY7WOvE7OQr4JxAEVgAVUbl+r5SKaK2XRds9Et1eDvyNI4oYYLuJ7JmgotvYUs1FGPf6nNY6pclcax3o05nxeV8OvA88jPFZ3wJ8Uym1WJaEhKzRWstLXkPqhTGYfQ3jAT3T4jmjgEMYiuUMk+Pj4v7+ABDAeGCflNDujuh1f5uw/9no/heBo+L2T4pe8yDwNlAdd+woYD+wD7DH7Z8Q7UsDP0+4zscwBgsHgeFx+78Xbd+U0NdoDIWogU8lucaNCdeYGesrYf83ovsfAoYlHPth9Nj1Cftj12gEyuL2T8UYkG1OaH9mtP0Ps/hdxO5zQsL+aRgDJw38R3TfXdH3N2X5G7w0ev6dcfsejO47u9D/I/IavK+CCyAveQ30C2NGpDHW2q2eMz96zm0W2v53tO1PTY4dHVX2h4HyuP3PJnugA89Ej11hcuwP0WMfiNsXU7jvAR6Tc+6OHr88bl8LECFhEBI9Vh9t/3uTa2yPV7Zxx/8N7E/Y9zLGgOIok/ZlGAOUFxL2awxrx3CTc/4WPe6J25cLpf7L6CDjJ8C9cQr9obi2TdF9387yNxj7Tk+P2/eF6L4HCvn/Ia/B/RLzuzCkUEp9B0NBbwG+lsGpn4xun7DQ9pTo9pnEA1rrg0qplzEcpE7C8JiO5yWT/lqj23Umx96JbsdhKNJ41mut/SbnPIth+v0osCy6PjwZeEdrvcWkfew+PmpybIPWOmyyfyeGxzgASqlKjGWK/cBcpZTJKQSAD5rsb9Fav5/kGmBYLMzuM1uuj2410A68iqHcfxPXJtEcbxml1GSMwcdWrfW/4g49AbyL4ZA3Smu9P9O+BUGUujBkUErNwQhX2owxIz6QwelHRbfvpGxlEHOE253keGz/UYkHtNaHTNrH1phTHXOYHEvmRb0nuh2RsM1YXgxrgBnd9HbEPRpDER4D3JjknGSkugYYs/xcMlHHeb8nITbQGpdF/9/C+Czujt+pte5WSt2LMej8BoZFSRAyQrzfhSGBUmouRjzxRuAsbXjAZ0JMsVRbaBtTvmOTHD82oV2+GJNkf0yuQwnbfMobO/dlrbVK9erHNQaS5uj27ExOUkrFe7gvSvDQ1xgKHY54xgtCRohSF0oepdQC4BfABgyFvjeLbp6LbmdZaPtydHumiSxHAR8BuoDXs5AjE06JmtYTicn1MkDURP8mUK2UqjFpf1Z0uz5bQbTW7cAmYJpSamS2/VggthSQ69l7IiuAA8DpSqkZqRomhOpdgOF8uBXD2c7s9RZwolLqjDzILZQ4otSFkkYp9T/AzRjr0Wf3Y51yGYaD29VKqc+YXCfeDHsvhkPYddH103h+AgwH7tUmoU45ZgRGyFsPSqmPAbMxZs4Pxx36PYZJ+OdKqbK49qMwMqLF2vSHJYATIxStjylfKXW0UuqUvqdlRFt0e3w/+0lJdCD0nehbn1Jqplk7pdQnMcIKY1wZ3f6v1vqbZi/gpwltBcEysqYulCxKqcuBH2PM3v4BfMfEQWu71vrudH1prfcrI4PYCmCNUuoJDAeq4Rjx4+Mx4sLRWm+PmvuXAuuVUn/CCDs7A8N5bAtGvHq++TtG3PMnMOK8Y3HqNuCqBOezWzCsEBcAryilmjBipy/GmFku1lo30w+01r9XSp0KXAO8qZRajZG6dyTGZ/cZDG/+b/fjMlsx/B4uVUoFo/1r4I9a60RHwn6htb5PKTUMY1lnlVJqA7CWI2liT+eIcyBKqYnAjOj7R0w7NXgAw7J0oVLqugx9P4Qhjih1oZSZGN2WAXOTtPkbCQ5LydBar4zOdBdgrKWei/EA3wIsSmh7h1JqG3ADRjraSgxv7Z9jhLolc/7KJW9jKMibo9tyDBP6j7XWqxPkDSqlzgEagK8A12E4or0CzNVaL8+FQFrrOdEB0bcxFNxRGGbsHRifzb397D+slPoSxj1fAngwLBDN9I0O6Dda68bo4ORa4BwMK4gLwwdjIzCPIxaOb0Zl+aPWOpiizw6l1AMY6+qXYyh4QbCE0loSFwlCKaGUmoCh0Jdprb9RUGEEQRhQZE1dEARBEEoEUeqCIAiCUCKIUhcEQRCEEkHW1AVBEAShRJCZuiAIgiCUCIM+pG3UqFF6woQJhRYjp3R0dOByuQotRs4p1fuC0r03ua/BhdzX4CLb+1q3bt1+rfUxZscGvVKfMGECL71kVthq8PLss89y5plnFlqMnFOq9wWle29yX4MLua/BRbb3pZRKmnNBzO+CIAiCUCKIUhcEQRCEEkGUuiAIgiCUCIN+Td2MUCjErl276OrqKrQoSamoqGDcuHE4HI5CiyIIgiCUCCWp1Hft2oXH42HChAmYVOUqOFpr2tra2LVrFxMnTkx/giAIgiBYoCTN711dXVRVVRWlQgdQSlFVVVXUlgRBEARh8FGSSh0oWoUeo9jlEwRBEAYfJavUC80VV1zB6NGjmT59eqFFEQRBEIYIotQB/H5obIQFC4yt39/vLr/xjW+watWqHAgnCIIgCNYoSUe5jGhuhro6iESgowNcLmhogKYmqK3NutvPfOYzbN++PXdyCoIgCEIahvZM3e83FLrfbyh0MLax/e3thZVPEARBEDJgaCt1n8+YoZsRiRjHBUEQBGGQMLSVekvLkRl6Ih0dsG3bwMojCIIgCP1gaCv1mhpjDd0MlwsmTx5YeQRBEAShHwxtpe71gi3JR2CzGcez5LLLLuP0009n69atjBs3jrvuuivrvgRBEATBCkPb+93jMbzcE73fbTZjv9udddfLly/PoaCCIAiCkJ6hrdTBCFtrbTWc4rZtM0zuXm+/FLogCIIgFAJR6mAo8Pr6QkshCIIgDHL8AT++TT5a2lqoqarBO82Lp9wzYNcXpS4IgiAIOaB5RzN199UR0RE6Qh24HC4aVjfQNLuJ2uOzT2aWCUPbUU4QBEEQcoA/4Kfuvjr8QT8dISNUuiPUgT9o7G8PDkwyM1HqgiAIgtBPfJt8RLR5MrOIjuDbODDJzMT8LgiCIJQWfr/h/NzSYuQj8XqNaKc80tLW0jNDT6Qj1MG2AwOTzExm6nlk1apVTJkyhcmTJ3PzzTcXWhxBEITSp7kZqqth7lxYvNjYVlcb+/NITVUNLod5MjOXw8XkkQOTzEyUOsZaSOP6RhY8tYDG9Y34A/0vvRoOh5kzZw5PPPEEmzdvZvny5WzevDkH0gqCIAimFLBIl3eaF5syV6k2ZcM7PftkZpkw5JV6845mqpdUM3fVXBavXczcVXOpXlJN847+jepeeOEFJk+ezKRJk3A6nVx66aU8+uijOZJaEARB6EMBi3R5yj00zW7C4/T0zNhdDhcep7Hf7RyY3CdDek093lsxRmxNpO6+Olrnt2b9RbzzzjuMHz++5/24ceN4/vnn+yewIAiCkJwCF+mqPb6W1vmt+Db62HZgG5NHTsY73TtgCh2GuFK34q1Yf0p2SWm01n32KaWy6ksQBEGwQKxIl5liH6AiXW6nO2u9kQuGtPk9n96K48aNY+fOnT3vd+3axXHHHZd1f4IgCEIa8lika7AwpJV6Pr0VTzvtNFpaWnj77bcJBoM88MADnH/++Vn3JwiCIKQhVqTL4zlSVtvlOrJ/CNT0GNLmd+80Lw2rG0yP9ddb0W63c/vttzNz5kzC4TBXXHEF06ZNy7o/QRAEwQLRIl3+5cvw/XslLUdrak4/D++pJzNwGdgLx5BW6jFvxcRcvTZly4m3Yl1dHXV1dTmSVhAEQbBC84EN1O3/HpHKCB3tHbie/QcNf/v+gOZgLxRDWqlDcXgrCoIgCOlpfb+V7/31e2zZv4WTRp3EorMXcdzw3r5K+YxqGgwMeaUOhfdWFARBEFJzx4t3MKdpTs/7F1pf4J5X72Fp3VKuOe2anv35jGoaDAxpRzlBEASh+AlFQr0Uejxzmuawp31Pz/tiycFeKEpWqZvFiRcTxS6fIAhCsfDO+++kPL7w6YU9fxdLDvZCUZJKvaKigra2tqJVnFpr2traqKioKLQogiAIRU9Xd1fK41v3b+35u1hysBeKklxTHzduHLt27WLfvn2FFiUpFRUVjBs3rtBiCIIgFJ40pVIr7KknQFNGTen5O99RTcVOSSp1h8PBxIkTCy2GIAiCkI7mZqOCWiRipHd1uaChwUgWU2uEn1UPr07Zxc0zepe2HspRTSWp1AVBEIRBQHyp1BixvO11ddDaCm43DpuDpXVLTZ3lltYtZax7bJ/9QzWqqSTX1AVBEIRBQAalUq857Rp2z9/N5SdfzierP8nlJ1/O7vm7e4WzCUU2U1dKjQfuAcYCEeC3WuvbCiuVIAiCkBcyLJU61j2Wu794d/7lGsQUlVIHuoH5Wuv1SikPsE4p9ZTWenOhBRMEQRByTCalUtM40wkGRaXUtda7gd3Rv/1KqdeBakCUuiAIQqnh9RpOcWbEl0ptb4fq6pTOdIJB0a6pK6UmAB8Fni+sJIIgCEJesFIq1e83Zud+/5EZfUfHESe79vbCyV+EqGJM0KKUcgN/A/5Pa/2QyfErgSsBxowZc+oDDzwwwBLml/b2dtwlWPe3VO8LSvfe5L4GF4P2viIROHAAAgEoL4eRI42ZOsD+/bRHIrh37ux7ns0G48fDqFEDK2+OyPb7Ouuss9ZprT9melBrXVQvwAGsBhqstD/11FN1qbFmzZpCi5AXSvW+tC7de5P7GlyU5H3913/pNbfcojWYvxYuLLSEWZPt9wW8pJPoxKJaU1dKKeAu4HWt9ZJCyyMIgiAUmJoaIu1+Gk+BlpFQcwC8G8ETpK8znVBcSh34NPA14DWl1Ibovu9rrZsKKJMgCIJQIJprj+eVda/xPzOhoxxcAWiYCU33Qe3BOGe6BPwBP75NPlraWqipqsE7zYunvPS95YtKqWutmwFVaDkEQRCEwuMP+Kl7+CJunHAjHeXGvti2bja0nrvCdE26eUdzn9zvDasbaJrdRO3xpe0tX7Te74IgCEIR4/dDYyMsWGBs41O95gjfJh8RbZ5xLuJ24Rve13nOH/BTd18d/qC/p656R6gDf9DY3x4sbW95UeqCIAhCZjQ3G3Hjc+fC4sXGtrra2J9DWtpaehRzIh2hDrYd2NZnf8qBgI7g2+gzPVYqiFIXBEEQrBNfhCXPceM1VTW4HC7TYy6Hi8kj+zrJZTMQKCVEqQuCIAjWyaAIS3/xTvNiU+ZqyqZseKf3dZLLZiBQSohSFwRBEKyTYRGW/uAp99A0uwmbslFeZnjIlZeV43a6aZrdZFofPZuBQCkhSl0QBEGwTqwIixlZxI37A34a1zey4KkFNK5vxB8wd7jT6CPbFIlQYwMBt8ONs8wJgLPMiduRfCBQSohSFwRBEKzj9R5J4ZqILXncuBnNO5qpXlLN3FVzWbx2MXNXzaV6STXNOwyHu5gne0RHCIaDAATDQdpD7ek92RWoaIS0Mt4MCUSpC4IgCNaxUoTFAlZCz7LxZI/12x5sJxAOABAIB2gPWhgIlABFlXxGEARBGATU1kJrq+EUt22bYXL3ei0rdLAWepavkLb6U+otyznYEKUuCIIgZI7bDfX1R9Kx/usnGaVjtaKwx48Yn7KPccPHZdVvKSNKXRAEQciK/qRjjYWemSngWOjZ4e7DqQUwWSe30m8pI2vqgiAIQsb0Nx2rldCznYdMaqjHsevQrqz6LWVEqQuCIAgZ0990rLHQM4/T05MsxuVw4XF6ekLPskkkY6XfUkbM74IgCEMEf1srvnu/R8u+LdQccxLery7CU3VcVn3lYu269vhaWue34tvoY9uBbUweORnvdG+P4vVO89KwusH03FSz7nT9ljKi1AVBEIYAzY/fQd3aOUQUdDjBtfcFGm69h6ZPLaX289dk3F+u1q7dTndSb/TYrPvFtS/2XMvlcGFTtrSz7lT9ljKi1AVBEEocf1srdWvn4C8/sq/DSLZG3do5tH7qy7hHjs2oz2xn0ZlSe3wtwTeD3HbibRnNunu88ttaMvLKH+yIUheEQc5QfXgJ1vHd+z0iSTKqRRT47l1I/XfuzqjP2Cw60fvdyiw6U2zKltGsuz9e+YMdUeqCMIgZyg8vIT2xAd/v2p7smZkn0uGEbfu2ZtV/Ma5dx3vlx4gtEdTdV0fr/NaSXlsXpS4Ig5Sh/vASUtNrwFfWYRRBMZmtu4IwecyUrK9TbGvXQz2jnIS0CcIgpb8hRULpYhZDnqygiVbg/erNAydcnhnqGeVEqQvCIGWoP7yE5KQa8CWiy8szytle7GQT215KiFIXhEHKUH94CQn4/bB/PyxYQMuaFUkHfIlodElZdSSjnCAIg5Kh/vAS4mhuhupq2LkTFi+mZsUaXEFrp3ZFgmxu3ZBf+QYQySgnCMKgZCBDioQixu+HujpjGzFM7t71QRrOsni+hraNL8Ln8yfiQFOMXvkDhSh1QRjEDOWHlxDF5+tR5jE8QWi6D+pmQ6SinA4dSH6+gqq3382zkANPsXnlDxSi1AVhkDNUH15ClJYW6Oi7fl67A1pvBd93P8uK9hd52rOfbpMnfkUQpnbJILBUkDV1QRCEwUxNDbjMHSbdDhf1Ey/kTycswKbNT48o8J72n3kUUBhIRKkLgiAMZrxesCV5lNts4PXiv+h8gknsskE7tF90Qf7kEwYUUeqCIAiDGY8HmpqMbUy5u1xH9rvdfO+5/0uafAYFC5/7yYCJK+QXWVMXBEEY7NTWQmsrrFoFCxfC5MnGDD6aVGbL/i0pT9+6P7vc70LxIUpdEAShFHC7YdQoWLSoz6GTRp3EC60vJD11yqjsc78LxYWY3wVB6IvfD42N8M47xtbvT3+OULQsOruvoo/n5hmlk/t9qCNKXRCE3sSyk82dC3v2GNvqamO/MCh56723cJaZ115dWreUse6xAyyRkC9EqQuCcIT47GSx2OeOjiP729sLK5+QMbGKbcFw37yxLruLr5/89QJIJeQLUeqCIBzBJDtZD5GIcVxIiT/gp3F9IwueWkDj+kb8gcIuXaSs2KYoqWIugjjKCYIQT5LsZICxf5uUc01F847mPrn4G1Y30DS7idrjawsik5ToHVrITF0QhCOkyE6Gy2WESgmmxMzc/qC/R4l2hDrwB4397cGBX7rwB/zsad+DXZnP36REb+khSj0FxWZGE4S8YyE7mWBOKjN3REcG3MzdvKOZ6iXVrHh9Bd2627SNlOgtPcT8noRiNKMJQt6JZSGrqzuytu5yGQo9mp1MMKeYzNzxVgMzKu2VlNnKpERvCSIzdROK0YwmCANGLDvZbbfB2LHGtrXV2C8kpaaqBpfDfOlioM3cqawGDpuDi6ddTOv8VpmglCCi1E0oNjOaIAw4bjfU1xvx6fX1MkO3gHeaF5syf6QOtJk7ldUgFAnxbvu7aJ2kbJswqBGlbkIxmdEEQRgceMo9NM1uwuP09MzYXQ4XHqdnwM3cqawGAM9sf4bqJdU075CEQqWGrKmbML78GNCYVzXSMK78mIEWSRCEQUDt8bW0zm/Ft9HHtgPbmDxyMt7p3gFft/ZO89KwuiHp8WA4SDAcpO6+Olrnt8q6egkhSt2ErpeeT3k88NLz8B8DJIwgCIMKt9NN/Sn1BZUhZjWIZZILhAOm7WLLiYWWV8gdYn434YH9a1LWHvbtWzOg8giCIGRKzGpw1oSzkraR5cTSQ5S6CS872lIef8mxb4AkEQRByB630815J55HeVm56XFJPlN6iPndhEiyWbrF44IgDC38AT++TT5a2lqoqarBO82Lp9xTaLFo3tHM957+XlLzuySfKT1EqZuRzEkujj3te6RcoSAIRZuoKpZvoz1knlfD7XRL8pkSRMzvCfgD/rQKHWD+6vn5F0YQhKKmmBNVpcq34SxzcvPZN0vymRJElHoCvk0WEssoWLtzbf6FEQShqCnmRFWp8m0Ew0F2vb9rgCUSBgJR6gm0tLVYmql3BjvzL4wgCEVNMSeqKqa0tcLAIUo9gZqqGmNNPQ0dh9/LvzCCIBQ1xaw4iyltrTBwiFJPwDvN2g/9cCSYZ0kEQSh2illxFlPaWmHgEO/3BKyGoZivoqWnWENfBEHInPjMbfHe7zZlKwrFWSxpa4WBQ5T6AFKsoS+CIGRPsSvOYkhbKwwcotQT8Af81hpmmIAmPvQlRszBZta9s9h9w+6ieQgIgpAZojiFYkGUegKWQtqy7DfSHTI91h5q539/9SWW7DsFampg0qS8yCAIgiCUNuIol8Cmt5/PeBZuhZbdm+iIdCU9/ov2p3lyxWKYOxdeeQWapc6xIAiCkBmi1BM48K9nLIW0Zcr4lndT96vg/MugPdQBkQjMmgXthctGJQiCIAw+RKkn4Nr/fl5m6uzdm7ZJsAzm1EULxrS3w0035UEQQRAEoVQRpZ5A60hnXmbqO6vsaQcL2gb3T4dXxkDz8cCSJTJbF4RE/H78v72dxu9/jgW3fo7Gtbdbd3AVhBJHlHoCzSMO5mWmPv60GZYGC912iNigbja02yPgK1zuaEEoOpqbaf74GI7993VcbV/N4vbVXL3qOo5dPJrmHeKHIgii1BNo04fz03FFRUaDhQjgOykM2wqXO9of8NO4vpEFTy2gcX2jzIaEwuL3479gFjMuPEyHE7rLjN3dZdAR6WLGPWcXtCqaIBQDEtKWiIVa6tmw89DOjNp3lMO2UTaYXJjc0ZIoRyg6fD7unB4gkOSpFQgH+e2639JwesPAyiUIRYTM1BPJh5McUOMajz1svX1lEFo9mgXHbhrwWXIx14gWhjAtLdw1PZTyf7RxfePAySMIRYgo9QHCuxFUBg54nQ5YMRUWv/gL5q6aS/WS6pyuGaYyrRdzjWhhCFNTwz7zgmg97OvYNzCyCEKRIkp9oNi8ObP2Cjrtxigg17Pk5h3NVC+pZu6quSxeu7jPoKGYa0QLQxivl9FpXF5Gu0YPjCyCUKSIUh8gfN0bCPfTtJ+LWbIV03ox14gWhjAeD1d84pqUUSS5yr8uTqLCYEWU+gCx0XOYSFn/+sjFLNmKab2Ya0QLQ5urLr6Zcnu56bHysnKuPPXKfl8jnSVLEIoZUeoDxN6j+5/UJtNZstlsw4ppPVYj2uP09MzYXQ4XHqenKGpEJ0NmV6WPp9zD019/GrfDjV0ZbvB2ZcftcBv7+/nbFCdRYbAjIW1x5FMJ7BtVCZlFtfUhk1lyspC0q0+7GpfDZarY4wcNxV4jOhEJwRs61B5fy+4bduflt2nFkmXFxO8P+PFt8tHS1kJNVQ3eaV485Z5+yycI6RClHke+yq4C7D98IOtzXQ4XNmWzPEtOVbv9jhfuQCnzxf3EQcNgqRGd6n7r7qujdX5r0Q5GhOzI128zF06iMsAUComY3+NoaWvJS5y6P+Dnlf2vZneyhts+dxut81stPxBSzTY0mjkfmzPoTOupkBA8oRd+PzQ2woIFxtZv3QLXXydRMd8LhUZm6nHUVNXkJaOcb5MPnW2/OkOPXr+fljUrUs42lFKDyrSeDgnBG5qYmrhffAX/BbPwnRikxR2k5u9OvAvm4Xn0CahNPyj2TvPSsNo8I52V5a9cme8FIVtEqcfhneblW3/5Vs777ZcFQEF7sN1U4fZ5qL1/PJ7zL6JmWgDXWUaq2URcOJn8zAbc2x+g3usFz+Bf54vNrtL5CQilw/LXlvPVh75KBEOBVtgqaFg1j5ufCLLwqiARjN+/KxCk4awgTd86l9oX94I79cA15iSaaD63uvwlA0yh0IhSjyNfjiw1VTVGhULAZn0AACAASURBVJYsFzuueuwqfvP53/SSr++6XSUN7Z00HQ3e9dBwlnlfKhDEe+sqcPwDGhqgqcnSDKaY6e/sShhcXLvyWpa+tLTXvq5IF10RmHM2vQbQsYFt3ZcO07p8Ge5vzUnbf3+cRGWAKRQaWVMfAOom1/Vrpn7/xvt7xcmar9t14i83SrYqoOk+qAjRO4xOQ1jBhrFAR4ex1lhXl7pmez/WJweKfIfgSahcceAP+Lnp7zf1UehWiAC+f6+03D7miLdoxiLqT6m3/BuSHA9CoZGZ+gDQtK2p32v1MUeb1vmt+Db5CEfMq8NEAN80OFQOXfaEayo47IQZX4NrXoSp+8H7VhiPzwf1Jut8zc2G0o9EjEGAy1W0s/t8heCJJ3NxEPse0jqaJfkf6yiHbQPgMtJf870g9BdR6gNAS1tLTmwiMUebNW+vobO707RNRzlsGAO3f4KkD7iAHX7xKXAFoIFOmt5cQy0JSj02i4+fmXdETYp1dfi3b8W3fWVRxeHmOsxJQuWKA7PvISlJBs+uIEz+1Hk5l82MwZbjQSgtik6pK6V+D3we2Ku1nl5oeXLB+BHjc9JPR6iDTXs38dCWh5K2KQ/BuuPSdBR96PWsN6oHaQ3+pvdDx+czZugmNB8bYtbtEwnZIBAOUF5WzrzV83hi9hMlNXsVT+biINX30IckA1nbsEq8p16e8tRcJowZLDkehNKj6JQ6cDdwO3BPgeXILTkIlasoq6DtcFvSNTswZuFdZZldK2QD30vLqN9cDi0tUFMDGzcemZnH4XfCzIu66NRAdAUgEA4QCAeYee9M3r3h3ZKZkRSjJ/NQzFSW6ntI5MbP3MiSf91KJNBFh60bV8SOrbyCpq8+kfJ3KcssQqlQdEpda/13pdSEQsuRS3Ye2pmT2PeucBeecg+dIXPTewxHhIwGEV3hLjb8bC78tfzI2nk4DBUV0NXVq+2yk41a72Z0hjpZ9soy5pyW3sN4MFBsnsxDVfGk+h7iOWvCWfzQMYMbbr0V34k2trlgcocN7xvg/gxwvPl5sswilBLi/T4A9CS16ScV9gr8AT8qlbZWsH1Ehh1r+PWHu1lwegcLZkDjlA78ka4+Ch3g8RNJOVhYudW6h3GxU0yezEM5U1mq7yGGy+7iL5+/D+rqcB9op/65IIv+CvXPBXEfaE8Z5SEZCYVSQmmdXNsopT4PzANGA5uBpVrrvye0+QSwVmvdz8KivfqcADyebE1dKXUlcCXAmDFjTn3ggQdydWnW7V5nue2px55qqV1ER3h5z8uW+x1XPo5dgV2mx0ZXjmZv517LfWWD0sar5qDCHQSUMtbXbTZer4oknakDeJweTqw60fRYe3s77jTJP/pLREc4cPhAz1r/yGEj0yqEVLQH22k50NLTd6yvmpE1vWZv+b63/Z372fn+TlPlY1M2xg8fz6jKUTm/7kB8Z5bkiH4PWmt03AhZAQpFzdGTcbcHYedOc18Qmw3Gj4dRxmcUf1/v+N9hT/uepNce6x5Ltac6p/eTL4rl+8o1cl+9Oeuss9ZprT9mdiypUldKnQOsAp4DXgZOBz4C/BK4QUdPLIRSj+djH/uYfumll3J1adQPlTWztQb9Q2vTb3/Az4ibR/R6GKXilhNv4YY3buiz36XKufBDXu599Y9EcjH1T0NlEN7tuhb31I/Atm34J41j5O65dOvupOcoFJdNv4yzJp7VZ7332Wef5cwzz8ybvGbm6VgoUX/M0+3B9rSezPm+t9kPzub+jfcnPb7w0wtZNGNRzq+by/vqrz9A7HvYvOFJ2p54iKpOmLq7G+/blbjDZfCFL8D9yT8jFi6ERcZnFH9fjesbmbtqbtJllts+d9ugcXrL9++wUMh99UYplVSpp1pTvxG4R2v9n3EdXQH8CpiklLpMa93XPjsU0OBUKaarCSx7ZZllhZ4KW0RTVVk1IAodjLXzZcccYE40ht23vhFWknIpQaO5f+P9PLLlkQFd783numihPZn9AX/KiIdKe2XRZyrLhT+A2+mmvuYSOHMe+OMHllEfkwcfNPxBTBw8cblgsvlnJBkJhVIilV1yOnBv/A6t9e+BM4BPAs8opUbmWiCl1HLgX8AUpdQupVRRDpEfuvRhy20f3/xo/y6mobwbmkbPY+oxUykbKFcIBSuP3tfztqWthe5I8ll6PJ3dnQO23usP+Lm26Vq6us3HmPleF/UH/Ozv3J+3jHO+Tb6USwihSKioFY8VfwDLWftShFpSVmY4eJphs4HX/DPKd0ZCQRhIUs3Uu4A+NQi11uuUUp8GVgNrgR/mUiCt9WW57C9fbN+7FaZYTGaxZ3f/LqYgUAaTv/wtTj56NFc+dmX/+ssE+5GfSE1VDZWOyrTe9/FEukMs27CMcns5w/zDaFzfmNMwrNgM8HDocNJlgUzCz2Im4o17N3Lw8EFGDhvJtNHTqJtcR9O2pj6m49j1fzTxRyzeujgvHuktbS0pP3OdJLtgsZDOEe2mv93EHS/dYW0W39JiPhMH6OyE2bPhL3/pnQXRZjOyIKZYu5SEMUKpkEqpvwrMAv6SeEBr/VZUsTdhxJUPLRTcvuZnfP3j37KknGa8P4rVTvoX1qZg4XM/4e4v3k3N0SfwxoFtean93gsNk1zjaFzfSEtbC+NHjM/Y6awj0kXD6nk4ypz8aOKPuHHVjTlTelYzjTnLnGzYsyHtgCKmoEPhEF3hrl7nB8NBKsoq6Ap39SidFZes4KI/XYQ/6O9RWvkIhaqpqqHc5iQQCZo36I7gW7eM+tOLM5QwXbz/L9beQpBwr32Q5DOsqUltYj/rLPjNb4wZ/bZtMHky/i/VGdkPn3qsZ0BmRqGXWQQhF6RS6g8C31dKjdRaH0g8qLXeq5Q6A3gYmJEvAYuVLZG9VC+ptqacjjkGDvX/mlv3bwUgcuj9/Ct0AAVLX/kd9lf+QDfd2JUdpWFYyCgME7SS5UBDMBIiGAkBuVV6VjONBcNBVr25in/s+EfSAUWqAUIwbCjTmKKP3cP5y8/HbjP/EGIm/0umXdLvZDHeaV6ueezbSY9322HzPx+FAVbqVh3fUsWZO7tBETZ9Eplm7fN68S+Yh28KtIyEmgPg3QieIEdM7G53Ty2D5h3N1N05pY8V4P5TUzjUCcIgJuljWWt9J3BnqpO11h3AubkWalCgehdZSaWcVo3YD+/3/5JTRk0BYG/3ewOaYaAbw6wdM2+H7PDRVni5H1E+wXCQi/90MRdOvTClokulODLJNAapBxQZpSKNEo6ECYQDSa+1Zvsa5q2e1+9kMZ5yD5/tHs/qsu3mgzkNbYf6ucSTIZk4vnmnebl+1fWm/UQUdCeJmzFbNmk++Ap18yJEDkOHM1q/YCY0PTyM2t/1NrGncp5sOdBCe7BdzOtCySHJZ/qJFSesPV37Uh63ys0zbsYf8PO+SmKGHSgUvHwc1hLqJCsqEw6w6s1VzF01t1dZ2XiefPNJjvn5MVz9+NUsXruY65+4vlfbGtd4nNb89noRDAdZtmFZr32ZDhDAGOQ4bOZREJWOSlZsXpGzZDEnjTghuXVGQdWIYzPqrz9kkwgnaeisNoqtmJGYta/nuuFOOpzGvo5yjJLDXyuj/eMf6XV+uoGaJJURShFR6v3EihPWsRXH9O8iGpbWLWWseyy+TT5ylhCgPyhysgSQTBk8ue1JZt47k0A40GMhSPSo927MLlFfIBxg3up5vQYSMRNxJlTaKymzmX8bER2hTCU/lqlCmf6pL1IRMj9WEYKpn74go/76QyYZ2GKRCaGwufCOsLGUY0ZiOFnK66L7fKapBmoRHSlI7n5ByDei1BNI9iBORoW9Im2M8AXvH5t9mlgNXy4/mWtOuwYwHlTJHoJFicX7jlcG/oCfC3zJlVQwHMS30YfnzZ3Mey7FNVJcOxQJ9RpIWElFmkiZrYxHL30Uj9PTc24sFOrLJ305eXlck4FgupAu76mX46ioNO3PUZG+ApmVa1jFaqGb5h3NVC+pZvnG5UkjEzrL4cLN4AkYpnQwZu4eVdEnnCzTAjupBmo2ZSv62H5ByAZR6gmcYsusTGpXdxfnnZg6tO3y3aP7lft96X8+2PN3TVVN9h0VAosDkPiHsm+Tj3CKMK1AOMDm/ZuhpobrXxmW9bXjBxLxscoVZRW92jnLDFtvbH98DPO5J5xL6/xWxg8fz8JPL+S2z91G6/xWzpp4VlKFkmhWjim/uavmsnjtYtMlCU+5h6avr8bjcONSRs1clyrH43DT9PXVadeGrVzDKulKCY8bPq6XiT4USWJiwFDkZ22H1lvhtlWw8B9w25pyWq99u8/afColbVZgJ91ArZhj+wUhW4quSluhueTwB3jRud26aVnDgy/fz5z/MM9IBeCpmQ7mqdzTMiwEY8ee0PO+bnJddh0VOS7bEYtHS1tLSkUAsMe/B7w/pOkP11LRDV3WE/z1kDi7i49V3rxvM22H26iqrGLqqKmcd+J5rHxjZe8Y5oCGxkbcLS2MOu00Fv3H98FjOPFZzVKWSSa82uNrab1hd8ax1ANehUxZdzy0Ad4WJ+5g0Cj963DAE0/AyLF92tZNruM6fZ15PyaZ32IDNbPUwYm5+wWhVLCk1JVSzwDXaK23mBw7EfiN1vqzuRauEFSMPg7ey+AEBY8235VSqeP1wi3fymoNWtl6n9S0rQkilJyNxXa4C+8Ew+JRU1WDTdlSKoV9nfvA46HlmxfStSO78CSz2V1PKtL1viO15U+/BNye3qFVzc1G5a9YkpMlS6C62khyUlubUqHEm5WtrE/HXzebWOpMr5GOnYd2pjy+69CunntOhl3ZGWZz0vSQxh2MWmVSFJeKedsnWrwq7BU4bI6kmd+SJZV5aW3u6kUIQjFhdaZ+JjA8ybHhwGdyIk0RsLNmDGT4/767K03VNI8nayVstzt7vd+4d+PgV+gayiIQLgNnCJwRaHqwAvcJK6G+Hu80L99+PHlcNhhKAWD8tNMhS6Vumtc7UVm7XNDQ0KOsAfD7jTb+uDXpSOTI/tZWcLstZSnLdJ04G3J9jVSZBWMDJY1OGpvusDn4ykkXc/u3/2KURY0RDBqvuM8QUucQ0FrzxnVvMNbdd2YfQ5LKCEOJTNRDn2G0UsoJfBZIXrdwkFFz7DTsmYUrc2zF6PwIA5Q5e68ZHzx8MCe12YuFiC16O11dRgYwDLPpVadelfI8d7mbBU8tYO3OtVld1+VwcfVpV/OTv/3kiNNYvLKOZSzr6DiyP1aPO1X+8UjEOB6TM6pQFs1YRP0p9X1mk5muE2dDrq9x/Ijjk6atjQ2UvNO8KGVumqqwV3D7wdON5QszEj7DVJYGu83OyjdWZiS/IJQySZW6UupGpVRYKRXGeO4+F3sft/8wsIiEwi+DGe80LxVl5dZP0HBBbepZQH8KfFQkKPWRw0YOTDa5fKEBZczSwUg80lEO534V2ieN62k28eiJKbt5ZMsjLF67mBWbV6S9ZMzBzVnmxGFzMPtDs1FKsfSFpb2dxu65CUIh/E5oPAXmzoTLvwjzzoXGDx7Gvzwa254q/3hHR8/gxAqpnLlyVSEsV9fwB/zc/sLtfOH+LyRts+KSFbidbl559xVTZ8dKR6VhKn9zp+XPcCCsGYJQKqQyvzcB+zFUyK+AW4HtCW2CwBat9T/yIl0B8JR7eOKSvzDz3pl02rGkQC+sSR0j7Nvk61FmmfLBUR/s9X7a6GlZ95UMRwRCA2XSTyL3YQcsOylALNHp0289nbKbmCNdOoc6MNaNGz7ZwNRjplJXU8eU26f0ionvcRrb9wtWHBfiIi8EbRBw0PNZV4S6aXhnHk07TqY2Xf7xJCU+zbC69t4fcnGN2Jp2IBwgmCQH/bCyYdz36n00tTRx57o7TavmhSNhTjj6hPQ53OM+w1RpZnNlzRCEUiFVmtgXgRcBlFJ+YKXWev9ACVZIak86l3dnrObjD36O10eltnXbNKz800+o/87dSdu0tLVkp4Q1fHlS73C5umNz674wzD6McHcQKHClLwUrdzzN1wPfwrfJx+v7X89Z18FIkEkjJ1F/Sj2N6xuTmnLDOsL5l0WVeZxcYHjXd2HEtm/9xjpW/qGblsojucd7SFHiMxn5rhDmD/jZsn8L9R+t50DXAUYNG8XUY6bine5Fa91TsKemqqZPNbpJepLlwjmHw4e599V7iZB8/SoQDjDxtok8fdFfqLUlGUkmfIZS71wQrGPJUU5rvSx9q9LCfea5HP/Pk3i9O7Vyidhgw54NKdv0J7b8ws3Afxx5/5Dvh1n3Zcbh7sOUFUeOOg50HuC4JccRCoeS5lTPlv/+63/zwq4XcDvdSU25nbYw9jT+CqFIiIl3fQj75xQdESNRSsNMuL9CGQ6RaUp8JiNfzlxmOdpjM/QNezb0OmZXdrp1N3abne5INy6Hi59M+gmr/77acl78VAo9RiAcoO7hi2j9ywrc51+UtkzqQFgzBKFUsBrS5gCuB74MjAMqEttorfPnLVYgZnSMZrXz9dSzbA0v6ndS9uOd5mVO05yeal+WicDKnU9Tz5FZyuNt/8ppdoHybtDlZYTDBZ6pa3ip9SXCebIYHAoc4p5X7wEM72szs72DMkJlqa8fMynHhhyxHOQtI6F9+xu4TeKr0+L3G45hsRA6r7cn3r0/pIpPn3XfLNDQHjqyDBHL+tYd6e5pG9ERfvncL5Oa3LMloiP4hu+kvrW1V5nUniprCUi9c0GwhlX18AvgKuBxYA3GWnrpM+qY9NXVFOy29107jMdT7uGxSx9j5n0zM7t+GWwe3ju9ZtcwB6RfRraMVmQ+2MgT+VLoiSRbh7cpG0TC2S2VKIVv+0rqR2Y427YSQpclqbzGQ+EQ2mIYhVKqp6a8KVn4eHSEOnhw84NcMu0SPPXWPjMJTROE9Fh1j7oYWKi1/pLW+r+11j9KfOVTyELx9Mj3LD2s2sPm4T3xnDv5XFZ/dXVm4Wga2j7QuxhM+9Gu3IW0abhoE7jCRWB+LwKP/vp1GcYyxpFVgRCrIXRZksprPBAOWB7MBcKB5FXWImT93T2z/ZmsU9UKgmCOVaWugFfzKUgx0m2zpj0POCKWSmmee8K5lCWJ3TVFQdWI3ubcvV1tuVOACh7+IKhiCHwvsAiVOPnb+Ejaz9Zpc5ruz6pASAbx7tmQKj69vKy8J6d9OlwOFw2nN+Bxenr6czlceLST766FyizdHwLhQNalaAVBMMeqUv8dcFk+BSlGKuwVlmuGWyml6Q/4CWegvSrsFUwdNbXXPishXJlw2A7fqvyMMeMqJAWeqXcS5PWR6b+bVGvLGXth5zDe3Yy6yXU96+OJOMocSQcoiYRCAX7wVIDWqp9y25k/O1K45rhb+cFLlSndLK0MHLIpRSsIgjlW19TfBWYrpdYAT9E3O7rWWv86p5IVAeta11lTNgpLplffpsweXA6bo2+RCqeHPblM4Kdgy7EOxm2AnUflrluruIJGPW1NQihZAYhYWIWosFegtcZus/e/QEgO490TiXm9J2Z1qyirwFFm5EoHmHXvrF7OcmYEdTftS3/JWFzUx7zTT6mFGj/c8H2a7oO62UZQZGc52LuhTIPvwuXs1x1s3r+ZJ7c9ycZ9G037lwQygpA7rCr1X0a3xwNnmBzXQMkp9VQFKXqhsWR6bWlrsdSdTdl6SnsmKopqTzUtB6z1Yxm7nVrXSSzXWwZuxqzBHoFrn4frn4PJ1w/QdftJV3cXDac3MHXU1B4v7LqaOv75j3+y4KkF1FTV4J3mxVNuwXvd6zWc4szIIt49Rspc6WhuPONGHtv6GDVVNbxx3Rvc9txtLF67OKXj3Pxz4b6Ho/8Ps2bB7t09IXy1dXW0/jqMb2In28bYmXyoDO9P/4L7o+ca/gHrfTTu2sTc8hY66GurlwQygpA7rMapD/YSIlkxonyEkeLVgqKzYnqtqapJ7UUMnDPpHMYPH5+0HOYox4icZ5SrHV9LZFM6N/8co4wUsbd9EurXGbP1nBLTT2n6LVNlhLV1r3uXw8XUUVN7vLCbdzQz5fYp/Gjij1i8dbGx/ry6gabZTX3qgfchFtee6P1uEqudiD/gx7fJ15MkJn4gkcrrPRAO8P1nvk8wHOyRdcUlK/jtujs5GEhSnlDBc+Pi3re3w003wc03Gx76ra24fT7qE8PS4jz7vaEOGuYDJhmYJYGMIOQOqaeeAmeZ07LybA+2pzW/eqd5mbdqXlKlrrphlPMourq7eGDjA6Yzvne2v5bz2fSP//5jdLn52mu+6bLDSdcZSXxyQmzAY+EzUtoYjD2y5RE6u9NHMEDyWugxJZpxnfKoUrQSqx3DLKFM/EAildc7HAlhjLWZee/MtAPFygRXDv/tt+I7fxwtHTuNQcVXE36rCZXsPNBjpo8oI75fEsgIQu6x/ChVSo1WSv1MKfVXpdQbSqlp0f3XK6VOz5+IheNQ4JDltgufXpi2jafcwxMjrjEekPGWTm28tB2Wb/kz73a8y3VPXGca7rMpmLqWdTYEwgGChUoTq6Jr2Tn06LeEho87JzLaNdraTF2D2+HOuBa6JdxuqK+HRYuMbZoZet29s/AH/T1KuSPU0cuLPJXXe1LSJFj6z7ikic3HQ/V3upm7ep5REOexa6m+9bjev1UTz/7aHdB6K9z2jJOF7lmGs9381vQWDUEQLGNJqSulPg60ABdiFHU5gSOGtGOB+fkQrtAcU1FlraGCzXs3W2pauwPe/Tnc3gTnbIOyMKYzy67uLtNwn0Chc7SXCgqeD73NL5//ZY8zWUVsNhodZAE4u8EZhgXDP8fuG3b3UkCFqB7me+QmIh3mjm2xgUSqqmzZUN4NV643/vY7jdm2vxw6bNHMcwTwh9qpu2fmkd9qEs9+dxDqnwuyaP/JpqVoBUHoH1b/83+BkUnuRIzMcvEq6AXg4zmWqyiYfXiytZA2bYQIWaKmBrfDxZwX4ZLNUJHG6h0IB1i24Ujq/aMjGZSFFSwRS/0aUeCIKnEUOMJGwZ7HHhrGzVf+Ofta6H4/NDbCggXG1p++FK8/4KdxfSMLnlrQq957yz2/6ElNm0hsIBHLle5xuHFFx95260a5Pix50lDGAL7pJM3uHgx0suz5O403Mc9+M/rp2S8IQnKs/qefAtyhtY7QV821ASWX9x3g32+/Yq2hgg+N+ZC1tl6v4QiFkS+8I42ODoaDzFs9r8e0+bX3ji94opZSJWiHkN3YAoTKjOpsF12qaDdRpJbqlDc3Q3U1zJ0Lixcb2+pqY38Smnc0U72kmrmr5vap915zQOFKkuzFpcp7BhI9pu5VmoX/gDPfzuij6MXCGfTcf6rfbMAO855ZYPxW437nfeiHZ78gCKmxqtQPAcckOTYJI469pPAH/Nw19h1ra7Qa09rRpsQ8nj0eatqdSR/Q8YQiIWbcM4P2YDtv6wPWriPkjADdpuvjnnIPKy5eQYW9AhX9oVQ6Ko+EIwZ0xmlg453v+qyZ7/sl570WTPpP2xUJsnzjcm7/+y34L5iF+0C7Yer+K3hfixxZXsiQiALfKYZWrzlUlvI3GyJsLBmVq57fec+M3eXqVyU7QRDSY1WpPwr8SCk1KW6fVkqNAm4AHsq5ZAXGt8mHtup0paBqmMX1d+jxePZesQSb01pWr0A4wG/X/ZZ/uQ/l1Pu90lGJ2zGAD9hBaGUIhoNseLdved3mHc1c+KcLCUfCPTHege4Af/ziH42196izmN8JjafAghnG1u8kaRpYw/nO3G8iohRN0+w03QeeAEeUa9QHIKw0f337r1y35ruMvbqd5uOPnOvdCI5kdvM4HwIzOpyw7aKzYeFCvFMvSfvQ6HESrK3Fv30rjT+9kAU/+ASNP70Q//at/S5UIwhCcqwq9YUY9co2A3+P7vsNsBU4DPxv7kUrLJv2brLeWMPUY6ambxeP2w1f/zpX187FYXNgV+mjC/+w7i4OOC2GnqVRnhVlFXicHlZ/dTXrY15QA0G+k9vkadCwvrX3Z+QP+Dn3j+fSHmrvlbo3rMN88U9f5Mk3n4SWFpqrOjhuPlxbB4trje1x86G5yjwNbMsra+gImYfXdRBg84hutowyYvsv2GpkbzNztOx0wKzZ0HI0XP5FmPE1+NQOYyAQGwy4AsbgYPUf4eKNJE0VXOmopPWEY1jw2Qi+Kz/Fir8MI9XPMLa237yjmeo7pzC3/UEW259nbvuDVN85RQq4CEIesZp85qBS6pPA14CzgQ7gANAI3KO1zrKkQ/Gy6/1dGbXPNHlG845m6u6dRSQUIkSIcuykU9ed7+2lLAIpk23HSKE8FQrvdC+3192O2+nm8ocvz0DyIsfCoMFhi+Y9V/TEeafLHhgK97ZdL3tlGYe7Dydt/4X7v8D2if/HzK8ZCjZGwG68Zn4N3v3AOHrZSPx+ahofxHWG+bp1BQ6WfjyEPWIcL+82Evgko9MOJ34n+kbRM+CZ/SqMfx8mHwDvJtgwFp44kaSWqc5QJys2raCzu9OILb9Ucc3zdu74SHeP/0E8LoeLccPHJa3lbjmGXxCEjLGcfEZrHQTuir5KnlffzawoXSYPKH/AT909M/HHlWwNxFR6spmmhpMDI9l2uJPX7J39mvFqNMe6j+2Recv+Ldl3Ngg54wNn8PClD+Pb6OtJ9frY1sd49I1Hk54z4egJvd4/suWRlNcIRULccPQLdCZJ099ph2XlrzMnfqfPh3dzGQ1miZiBLh0CBz2JVgNp/nv75LKP/mbu+zDsvgXGdoB/mI262RHa0zhsxpLzxBRz4yddOMNlBE3G8zZlQ6PTxvBLbXRByD0Zx7kopexKqcrEVz6EKyRth9sst026VpkE37plhLqsZTDrdR338IxSmiYjMdf2SaNO6nefg4m3Dr6F2+mm/pR6Fs1YRP0p9WlDElXCKGpPe+qiOhrNv/a8lHzwpeDef97R21mupQXPe5191sxdASOGviLTtHspliIWzjC2vg/ZE7XSPgAAIABJREFUkoaopexawZzT5/Ytxxp1Etx5aOeAx/ALgmA9+cxwpdTtSqlWoAvwm7xKimMqkzn792V4yIo9/Aib1j5KVzIdkkIJvG17n7cqA/1el07Mtb3o7EX963CQUensOwbdcWhHynMSjw/rSr1YYsNGpSP1WPfFMZr25UYOAn/AT+Nxe1gwy86GMbDg7zDhPZh4wFg7v+LVMrrKMlS/KX5Lm8co8Hho+eaX04ZVmtER6kApRev8Vm773G1HyrFGM8RZjuEXBCGnWDW/3wl8HmMNfTOQvCJJiVD/0Xq++/R3LbU9bM/sYXvgvd3GJ5+JctbgeKOFsjFxi6MWqbRXHlkPNcm1fdzw4zhn0jk89dZTGfWbEUXk9f6V6V/ps++EkSfwQusL5idomNR9JK+5v62V1w5uTfnfY1M2vjTxPMPhMtn3HAHfy39kyo6To7ncw3R8ortPMZq3jwZ7OExFWQVdYYuhk6nQsL/6aPzbN1KzfSUVux+hS2f2Lx1TzDGLRyLeaV4aVptXoJMCLoKQP6wq9ZnAPK11Yz6FKSau+thVfPep71pSvIGyzDTW0UeNhY4MvOsBFJyyS3OMawyPHvVuWrliCnzFJSvYeWhnz9qxd7rXdP3/kmmXsGb7GrojOS7souHXuz/GdWNeym2//eC1va/hD/h7FSA5uuLolOdMfvAZ+Go7uN3cdPcVBNIYZxxlDra8/GTKNmEHbHr7BebdOwt/fE3zxO9WQbcdunOh0KP9va0PMObXJ3D/hfdnrNAhvWKOZbVLLDwjBVwEIb9YVeodQGbu4IMcT7mH0Ydt7K20MAvP0Bw+/VNfxP7EX+nOoEZeWQQ+8i5cZ5/Eo6cmyfWj4QuT6ph23IdTKnAz6ibX5V6hY9RMd7y9nT8/C2/+MOfdZ8WfN/2Zx994vKeqmT/g53frf5f8BAU//nSYMXd9m699+9f8wv9U2u/cpmy86H8dKlJ0G4E2l41Id5ZZYfrJ4e7DXLriUirsFUmTJykUw+zDsClbxoq59vhaWue39nJIzOQ3KQhC5lhVK7cC1yilnoymih0SHHBau9VKrCWQieE99XKue3o+3RnMkMIKznsDHv+QH6fNTlD3VcAOVcYF07+clVfxQ1vykz+ouww2j4rwwTaj1Gkx0K27e4rltM5vTVltrQcFcw7ex+F1H0UpG8kzoBt0hDpwK0fKkqZaQ5U/TEcOnB+zJRQOEUlxL6Ndo9n73b1ZK+Zk5nlBEPKDVaVeDZwMbFVKrQHeSziutdYLcipZEZAqBjie0ZHMnf9tdjuErCt1exhWnggt08YSPLzRtE2IcMZexf6AH98mH7f+69aMzrOMhrZpk9h44ADj05T3zHtimgTCkTC+jT5a2lqsRRUouHvD3QTTZhSA8rJyguU2IPksfEw7TH3fiQvoKJCbSoQIDpujVwKdGC6Hiwp7hShmQRhEWFXqF2FMTezAOSbHNVBSSt3f1mq5rd2RwsZqgm+Tr0+IVDq67bCtCmpqL8D17L9Mw4XSehX7/UZq0pYWqKmhufZ46h6+qGfNMy8oqPrwJ9n9ynrGJ2tToBl8Z3cn2w5so6aqhjJVZkmxW01Wo9G8T/LkNAD73OB9w8m8z+XB/G5xkOSwOSizlZkqdZuyMXLYyNzLJghC3rAU0qa1npjmNSl9L4OLZX+8wXJbNXx46gYJpTdbdm/KWIlWBmHyKefgPfXy9JXBzEioFub/7vVGApy4wiH5oIwyplZ/hH21H03eyCTNadZocGSQfkFrjXeaF7vN2vj29HGnW6pVPu8T89JZ6InYQD34INcET879wMbi51leVs7yC5dTYa/AYTPiLOOL0uSyLrsgCPlH/mOT8EjbWssPRltZcoXgf/ZJbjl/FNM3fIuJXYv5ylNXccwtd+CyZTa7D9rAO/5zR2plJ0n6YbrWGasKFlctzDepk/AAmLttNmOgUTZyVP4vFiWUQTqVpS8tRSlF/UetmZd/fNaPjc85RREct8PND874AcPDZWmVtW/4Thh5dO4GNWbFkZNQUVbBz875GV9/+OvYsBGKhLArO+FImBWXrDCK0giCMKiw7H8drdD2XaAWGImR+/0fwC1a67fyI17h2FneBRb9l6qHV5vub97yJDP+OpPAGfQ8tLcfHWH5B4OUB8ng04fhAXC/sw/Iwqs4rlrYspPh8RPhlTHQmZl/X1acPfFs3E63kbXuYP6v15Pj3KKS7AoexrfRx5sH37TUfumLS1kycwm7R/yIK9bM58/T4w5Gr/uzc36G2+nmOEZwUKUolaswzP+nf57ylasJ9Pf70HCsH3aPSN1GaZi75wN895Z/MeX2Kb3ys3frbrrD3Vz0p4tonW99CUoQhOLAaka5U4ENwIXAi8A90e2FwMtKqVPyJmGBeN9qli0NF0y5oM9uf8DPub7PG/m54xVM1NQcKCOjWdWBSnhy3JE824lpTlN6I0erhY29Aa6rg9U1sCfNikGumDp6qlGbfn1xlgzoJswL29dysMvaiONXz/+K9n+3sHZJVKHHLx1Etw2rG2hv283WVAodQIPetZO66V8mkDpLbbR7ldoXQ8H0A7aU0Rj2MLTeCkt2TmVly8q0+dkFQRhcWJ0r3gK8DMzSWvckLY/mfG+KHv9s7sUrHE6H9dyZl3+kb5WzZQ/9L4d1KOWM8agueM+q47yC8w/ewf7gjzOO8/WfMJ5ZXx2YmXk8FfYKpo6amrJGeM7J1Itew+pNj7BHWfMr0Fqz7OdfZf5lya8T6A7w23uuTx89oeD2lvsIPDvCkswajQMHoWQe9RrO3FnGcyc6zSMrtJE3YFsVjD3vPFraWtLmZz/BfkJ6wQRBKBqsrql/HFgcr9ABou9vAT6Ra8EKzafHf9ryLPqBjQ/gD8Slv/f7efTJX6V+UCuwaTJykIqFYCXiD/hpXN/IgqcW0Li+sZcs/oCfa49aS2cGpv6MSCG/w+bAO91LS1sLnSnKlOYSG1CZSSFgBe+G37PsEBYhwuORLakrpCn4w8E1lr7bgA3+38u/tiiqYuLIiSnbbLr4TJo+/DPcQfpeX0GXE+pmQ/tlF0p+dkEoQawq9cNAVZJjIzGKvJQUt3zqh9YaKpi7ai7VS6pp3tFs7PP52J0uE52Gg+bP06R06+4+cejNO5qpXlLN3FVzWbx2cS9ZYseWb11BpgW+rFIRhkfOX57Sca+mqgb7AIWtXf/eBy2Vm49HK5WyNno8lfZKujzD0s6s2x3a0uy7227M/q2g0bxx4I3kDRS89dY6ai9qYNFT4EwSTh8ZVoFv+0q807zZRVIIglC0WJ2/rQRuVkq9pbVuju1UStUCi4DH8iFcITlu5d/xdIF/WPq2MRNmLEOZu6WFse/DptEpTlLRiVQGpuJKe2Wv2ZM/YGRFi3d0isky695ZoKA92N6nn1zhCMNTn1hK7UcvpXXa55M67nk/UMec3Geg7YMNxY85ky/f9zp1s6HLDlYK6B1/1AS2v7/DNFY7kTJbGf7xo2F/6vz7nuHHwEEL5Xs1RHIYzjfljQMQgJ0jIJhknb4j0sW2A9skP7sglCBWlXoD8CjwN6XUPuBdYPT/Z+/M46Mq7/3/fmZPZgY1AQkGqGIGNLFiqXtjrwsFGbRWReZatfQ29XoV22LwlrS1/XWxBa2oWLCL3NcVK62jWOqSKIjW1lSvWhdsQoFBUaBhR2UyYfbn98eZSTLJOWfOhFBZnvfrNYY555kzTyYx3/PdPt/c42Vg9oHZ3idIJFJyw1++uKghEOBLrXaePzEzqCppdpu9wHsykzdNZpOlCdzknEVHNqekVyQ37cDOPV+4k/rzbgLM5UD9y1u46W071FnfTslIuDAzGt8pE6j/tYeO+XFmBuF3p1BUY//qU6/hpy/91NLbLJu+jK8/8fWiP9eOjzZZb4mkaEu7Zeat0r4mivwMRw4ZCSh9doXicMOSUZdS7gbqhRAXA2cAI4CtwKtSSvNRVIcqgQCVf4eoG8t/nPPFRYS+y4xvz+K/z48RH4ziNAnuNLSElhX8sTUrdEpmSpQdFeBLwjwm8p78mN1DnPjHnsLitod0h32UucqZcdZ/Wrt2JIJMH+BCOQGnHjUWQiFobMQXjbOwBZafDFGz33IJL7//Eke5j2JPvEi1OhDZY01Sdo/ssvx7k80yKIoR9gz4UhB1wa9Op2hNRx4lA6tQHD6UVD4lpXwWePYA7eXgIhRiYvMNLK6w7kN1Fxf5/fiffJYr7juf39Xuv7duk/D+rz1UnbAZTuo5HqgMGM7YdggHaZ2hL2akBXheeIn5K3eBLxc6/8x1+x2ebR0Nv3bB7SXtpjQ8Kaj93GVEXRB+4EYiD91DYI9gWTjJ5GsxNpoCVm36E36X32BBIc3rmglUBujoNO/hlvn/FPvZC3BKM4V46ziAcB1IAekiNwlbPj6ihi4qFEcMJRl1IcQktEr43p76cwdiY584fj9n/futLF5zp2UvqndxUfSM8Tx2iiipul0XCVf/Hap2xWFDYZFcsCbI9ZnrdV9WqkEHiDthzTEZTaymQfPc9jc8G01ECX58P/EDVX2fw+kpZ9SxAarvrtZuQM5M4RVubFkXQ9IZ9gqTmysJFZ6KgtoEQwRMG3MJf37/z5YMthWEzQ6yyM2fBFHk1ynhgA0VELdDpkgtwcijRlrbnEKhOKSw9KdWCHEcsBwt9L4j9zgW+LEQ4m/A5VLKfx6wXX5ChL50Gzeuv9vSnHGfy1fgvYbbw2QHQ9BbwBMnQWeZHV9NYYtRy4YWw1nYVgeUFCBhtzPd7+Zhf8Kz4fYwqQHcYBTDjYMEac1wO5wsm/440x6dVlg0KBOaoSwm7CLgU0d/ig/2flD0facGpvKVtyXfKHbJDEg7lgx7EgvRHAGB7DG874wZpla8CajZA3/5FMWjBLlfzfyUvsjuCIHKAKG6EH63taiFQqE4+LCayfsNmndeL6WsklKeKqWsAs4DqoBfH6gNfpL43X4em/ZY0XU2YWPeRfMKtLItj/O0QKcLbv88Wr64F5HdEV2DDpCRGeylJmoFVKbsUFNaf7JZn3z7jnbDPQ4Un9PH/OC9NH2uiQWXLKLj1q1s2rvJsGjQhrmLa0PginYZL8jhtDk1oaF3i4+3laV89FYyPBLO8Z3M+996H7ddXxjJBoTa4Zg4RW8StuzdYtoOqVAoDk2sBkUvBL4mpXy590Ep5V+FEE3AA4O+s4OEL538JeZPms/slcYF/lmZ5Z0d7xQcC1QGcNldpRes6SHg7nPgNhf0DnoHKgOUO8rpSusbpIzMlpTPd6Sh9kNHv5sHM1o3tRJcGiSTzdCV7sIhHNzccjNPXv0kk06cxPbO7dY3YESukKx3Pr/vsBGzosEs5j3jHruHYS+/pdUrmKybXjcdn8vH4uM+LK5jb3XynMTyrfW86x6iyj+CVV9ZRfChyWT3dRFzaR66DWh5zIXvqqs45ej3cKRfMaz6d+Ng5JCRhu2Q3a2ZqgJeoTjksOpPbAfD4dD7gF2Ds52Dk8ZzGqn26Q9tATSp0bYnCg6F6kLYTD7eMpx4E1jOuQu7vZ+aXKguRDZtctOQG27izS1xCvNEqwBCP3uyu0iuGL375PM3FmmZJpFJMPnhyax8dyU7unZYupYZQ/dB06euYcHFC+iY3aE7PcxMHc1ld5m2932j7PMc24l5C59wcMHxFwAQqakY1FZFK576BZ3DqKrSJFvrR9fT8e3tLLhkIU2+KSywTaHjhIXUv7UbHn6Y0I+W4TYJEjmdHiRS6b4rFIchVj31n6Hlz9+QUnaXzQohRgL/D7DW5HsIY+pxCtga3Q6dnQUG0WazGU56+2P8S5x792Pcfh78/HM5ARITQ5HMJPupyfndfurT1axyGOeC7RmY1g4jOqGjQrBsvLFn33jOLfjOn2S8iT6E28NkssbW47LfXUr9pz5v+Xq6SLjwtCuY++WHTZeF6kI0rmjUPZfJZJAmd0+pD3czcnfGNA/dWyMgMKKudI15Iyx686OHBwoO+Vw+Gs6ZCefM7Ld8dew9ZJkbMonC60sot7t55tpneGrdU0V13xUKxaGHVU99EppM7LtCiFeEEE8IIV4B3s0dnyiEeDT3OOxu8aOJKGnMi72STmh9qKdpK9weNvQOyx3lbK4qw+f0Mu95+Ob/UfSPu54WdzQRpdVhXp+YcWgGfe7zsPA5F7aMvhH2uXzcNvHH5pvog6bpbpyLjqeT+5+fFXDWCZ8rusxozrzP5Sv62e4e4mTpqSYLJJxrP75HIa8utP9dDb2u7U8XKWsXUHmytUGI+ehJVzbR7/t2O9y8e8v71I+uV7rvCsVhilWjPhSIoKnHxYEhua8vAxuAYb0eZuKohyTh9nDxP+ICpmy/q1uW1SzH25XuYkNgKNi0j79ul5YXNUNPizvcHsbmMFe3Kc9VRAOsHtKl61mXO8t55ppnLOVQexfFbevcht3MYtrQ7aEvlZ2xnZbWjR8+np9d9DPOG30eU06cwh0T72DuRXOL/uxsY07k7REYG38BH8S39TtWFKuGX9iLVqpLh4XZrJirDDpsDprXNwMo3XeF4jDFqqLcBQd6Iwczkd0Ra61J2QzhN5bQcM5MRh01ynTtyKFjoKUFgkFC72VoxNjj7dsu13tfZp4yaHdtwQgsPANmXwxJ0T/iYMPGaVWnmV4Heori8kI05c5yMtLa4JKB4sZhyWvsuzev00vr5lYuHXspGaMcSI4/b/lr0et7nD0zcsPtgxiMEtBVbNqNgPv/dj8/vrD42F0r41QBpfuuUBymHGBJkMODQGXAUg416YANLzfr5jn7IYD6eujowB8O0/LunwiKx8k6tGI2l92FlJLGcxq57fO36f6RDVQGcNvdJDIGbr6Ey9bCuG9oIz6TBnVyEqlp1pv0ousNj+lKdXUX4x0ow+50eop6jWaDbf7wj8exZSBrUiMY/XhXUU+57sSzu59avcmz+pnYbXbT2gSAVCbFkreXMPNM89+tfFhdz7D3Dasr3XeF4vDDslHPCdBcClQDnr7npZTfHsR9HVSE6kJc/6S+clsBEmpyrU6bP95surRbptPng4YG6mmgI/krwm1hPP/0cH/w/qJ/YEN1IWY+faPxmwh4rE672TDDSmGUWVjXk9YkZoWwNhWtL7XlxxPZ+7722rwh7FXUVczImO3NlpHYpHGBuQcH3oxgpw3T8Puoyp455qOOGlX8RsbqzY4En/CwRyZN12bJcsuKWxhfNV63+j+PWcGgXlhd6b4rFIcXlnLqQoh/BzYCvwAagKv6PKYdqA0eDPjdfsv50aFZ7X5nIIVI+T+w1f5qGiY0FDVm/iTMekUa7s2FHWEvbmWtFEaZhXXjTvjmW05++ZyL4z6mpCIybxIaPxVizy1bWXjMl5mSGcMU9yksvGg+2+fsMjVgVvbWRZIr/2G8J2dGUu0+tqjxrR1aW3QfBVjOp8MxO/ZaWprKpgguDZqO0zUqGOw9316hUBy+WPXUfwo8DvyXlNLaX6DDDYvh1pB9Obv2bCNUF+KWZ2/RXTZohUhLlnDR+gx3nqV/WiJJiuJN0Olsuuh+ApUBQ0laj8ND7ffuomGNh/M2vMo4YV2LyCYhdOMifB/cxsxvLcVC4kJ3b4Yh54ydL7yX4WtvwWVXQ8amRRO8Se29Wzaczu1BP3xgHKmwi8KRt5t3vjt46QYJHxyF9Ylu+fG+Jt61CqsrFEcuVqvfK4H/OWINegkk7BB+uInV21frysSWOcoGzWOKtvyRaSH0DYKEmf+sNpQU7c1piQp8Dz0CUeOBJsGaoKHcazwdZ+qnr4SGBsbO/Q23nN3nZkb2PFy5Oj1vAvwJaFkKvoTUhsgMENNKbruDUDtMeg92/hx++TQ0vQQLnoGO+VD/b9dxSe1lxheX8M3XbPhee7v70KjI9sFraQOcJagJW+0hz0d95k6caynqo1AoDg+sGvU/AOcfwH0cPghYs3MNkx+ezL60vgiflUpzQDOyixfDnDna1z5GNzx0m2Gu2JuCmtWbyWaKD1OpeG8rzJoF1dXQ2quvvNf7t/zmVjwG80E9KWj+++Pdz390/o/46QU/ZWi2rMf45cR1knawp+Hm13JGdRMQi/UbIlMKpiHn0JPIMg+LJ8BtF2jDTuJ2bTypLPPAjBnMGD+D8l7V7b0pS8GPn0tBMKiJCwHssKiSZ8H79goX+4rfd/WsVz3kCoXCBKvh95uB/xFCLAZeAD7qu0BK2TKYGzvosFrhLWGzT9IV128125fex5LVS5h5RpFAc2urZkiyWc3oeb3Q2Ki1wdVreeZI3Qhi8Xbdl8dcsMUPF26EFSfoLune79T1aO8B2nt2dMDbbxe8f2Syjfg5+peIO3uq/gu04G06NzVCE8RZdCbc9lLumNdb8hCZvhiFnN/e9jbVt9pIxbV95n+OnhQ0emy07Hmb+tH1rLh2BVMenEgqlSDhAHda86CfWQq+JODMdo+k3VzpAOO0NkhwZ7RRqEVxOCjvStJlLjfQjeohVygUZlg16mPR5qifAHxN57wEBlD3fAhRQg717+VRTZrHgOZ1zeZGPZvVDGrOM4+6IDwuRqQCAnMmEnpqI/6KEQTOuwxv8ypiOgYhP4Yz7hGmRt2ehRm9Z9Fks7BkCXznOwWRgcDOLN4ExHS8Sm8CalL6rWVGJG0QroOGt9BEeEoYImNE30ru7v1kunrGr+Z+jnEnxDNd3cNL6kfXszV2I+Hn7mVDhfbZhdpzBh0KogmjzpgIL6wwrZY/di9sPoaivzdd6X1IE4OerxVQPeQKhcIKVo36/wJ7galoCnKDMHrsEMOqpy4gkS3y8RS7zp49mnEFWkdD8BqtJSvmBm8yQePCMbR89TlCn51B4wtzINM/KpAfwylFGo90ENcRnUHCN1/tZbiAaCpG+NVfEqnfR2A7hNq0KvtQGzRO1t+uDQh9aiqPmLSW9SXphA3DHeAv06IPFofI6BKNal50JAKBgHaD4Pebtrrl6V145gvU0XCfB+I6d2Qej/VogoQ95Vj6femtSV+Oiy6SPTPiQ4+z+ePNqthNoVBYphRP/Qop5YoDuZmDlWgiat1Tl/DZYaex8aONhkumBqaaXyORgFiMqEsz6NFe3nHMBch4t4e57OrlfPHhqWQyadJ2cKYAATe+rt2HhDa4abQJ4lI/t/7AZ+GKtVpuu/sGwrGW2IkZvAnNkLcs1c63LO1zg5Ef+bm8DN/rM4i88hPD1rK+uKSdminXwGML98+gm6QpIvuMW93yFBSeBYNwvYEeQTwOU7Wf2z/+8njRHnW9iIYZXqeXabXTGOEboQy4QqEYMFYL5V4DRh/IjRzMlCoLOmFLmjJHme65MkcZM06bYX4Btxu8XpaMN1aBy8ost//5dqY9Og2Hw0naDkhIObTHojOgejasPjpBy6WPUGZz9a/YFtDp1gz1Vl/PDUTMrpVjx9za8+A10OnSDHvHfFiwyq5VkP/JRcevfdQ/sBJ8PtPe/L64XGWE/ms/DXo02pOmyNcExGLdxwPeUUX3U1B41tKieeR6eDzQrOmmv7zzLfPq9wFUxsdSMUb4RqhqdYVCsV9YNeqNwM1CiGuFEMcJIcr7Pg7kJj9p2ne0W/fUBbzU8Qorr1uJz+nDZdcSpi67C5/Tpx0v9ge7ooKVx2f51sXGxVaxVIx7Xr2HaDJKLF9l32t8a2+DfGJrO9mksWJZBmi62K6Nf9Uhi5b/Bi1vPf26Ozjx7Cmsn3Yhjzw1l+gZ4wHz1rK+3PGFO/bfcIXD3WmK/pvOEmoXRfdTUHgWieiH3kE7nsupr/X1n4BWQF5NrhQRHlXVrlAoBgGr4fc3cl+XmKw5bAvltn+0pWR98/rR9Wy9deuABED2pjq57Cr9drg8LrvLcLRrb7JA09pFZCuN13S5Yd1nRhNL6acMYu6e/Hfr7+YR/HsT2fIssc4Y3hdfovHP36XlmhbqR9f3GxJiRNOqJr4y/iv7Z9gjkR4Pvd+mY/jf20LLjdp+UplUwcQ4j8OD0+YsLDwLBLTwfS71ET4FrThxD4TeK8efy6nHi/1fI8GLnZiw3oBuEzaCgSCL31xMZHeEQGWAUF1IUzNUKBQKi1g16l9jUOU2Di12bGwrKac+8Vit96skXe1csVc00saG08wnvAFIKUkWK8hDM8jrymKmmuwOKRh38nm0/WOHviqbdFIz5ctEl84l+OtxuoNTeleR51vLHl/zOKs2riKV7d/gnsqmiiqjFaWXEe6/aa1Nrvd+1uxcw+59u6ksr6R2aG3/m6xQCBob+xcnJqCRLlo+N4p6sPS7UOEYQiz7YdF1+ar2eRPnMW7huIKJaY0rGrtvlhQKhcIKVkevPniA93FQ4+iKlzTPzu3Sz6cb0qvYKzwuhhx/V9GXzDxzJg+88UDRQjB3GsZVjuPvna8Z9kLbhZ15E+exfO1y3fM2t4fQfy3kkbZHDKvJC6rIczczbTvaeObdZ3TXx9Nx1uxaY7r3ouSMsP6me9rkLN9c+f1En1xGcOXkwuLE3L+Dy6ex7uZ15tfIhd3PGn0Omze2FL0BWHDxAoKBIOMWmt8sqRy7QqGwgtWcOqBNahNCXCmEuD739bgDtbGDiUuGnF7SgI4tH2+yfvE+xV6RCix5ggKBLVt8U9LpYN5XH8ZutFTCk1csY4R/RNFBIMVmdS9bs4w5z81h4WsLWfjaQp577znTve3u2l10/6b4/dDSQrTCx+KzXcyZCIvPdhGt8A24TS48ZBOJMv27n0QmQdPzTeYXyNU1nLWneJlJmaOMhgkNNEeayepICkPPzZJCoVBYwZL/KYSwo01ou57C3HlGCPEb4BtSWmxQPgS5wj2eb8SWWVssoWbYOOsXzxV7RV2wZDz8cRz8p4WX/eLV+2h+1MUXv5QrpjO4EZh55jcYMSJAy7mLCL48k6zQ2uKcGbBL+P1nfsam1E7mPDeHQGWAdTevoyXSolsHYDY4BeCF91/g2Xexsz0xAAAgAElEQVSftfytV5aZJPot0joagrMhmxbEJHiFoPFSaBkNpQSto4ko4fYwi15bZJjWSGaStG23kIoRsHP3ph7BGwPycsGR1X8iltJXILSq9a5QKBRgPaj8I7S8+neBMLAdGA6EgB8Du4EfHIgNHgz8YcsqsFrfLyF07TzrF49EaK2MMela2OfEcu4+nc0QOTrDT5+HWw1EYQBOrDwRgPpLbqLj3CsIP9zEhp3rqBk2jlEXXM60p68juzZDLNWFVzpptNlpmf6EbrjabFY3aEbPKh67h9phJY4z7UO3YlyqR7M1JhOQSpQUts5L2xYr7gP4KNFPIbkf/jgEKk9E7H0NaVTLIKHKWwXRKIHFj+P9NwO1PlUVr1AoSsBq+P0rwG1Syp9LKTdJKRO5rz8Hvg989YDt8CAgzBrLxnZkzIavosrytaMnjmLKNbDPRWnjPAU0fyrJ5iJjO7d8vKX7376KKhq++SBzf/IK02/4BdOevk5rict5iTGRIirjBB+aTOeLK3v2mIiy+M3F3P6X27nx9BvxuXzdIXorU+D0cNqd+61hbqYYZzVs3Vva1opwztGuo3GaueASpq2zEXJ/tmjb27qd/4BwmNAau+H/iLZ0Rmm9KxQKy1j11I8F3jE4907u/KAghLgYWIAW5l8spSzB7T0w/L3sY8trHfYSKurQ2qaSm0vdkcaeMvj16Ri22/Xz8nrJqYaP20Y2a5DHBcLf/SLTn97I7W/cy73/dy9CCBKZBF6nF4Hg5jNvRiB4e9vbJYXcB1PDvFiO30rY2oqUbG8+XfVpvn/+97n8kcv1jbaA39dmua+jg+GucrahH1YHiH+0C7ZE8H/UZazWx5WqSE6hUFjGqgVaD/w7sFLn3L8DRUqCrZHL3S8CvgBsAV4XQjwppdzPMun9I2M1niHB6yztD3AktplkafcB3e/1dhUkTJxGW1b2eHl95FQjUxzEztKXjo254U8j08z6xQl0kig8lzOi9/7fvWz81kaaI828tOkvhjnh3rjtbu6YeAczTpsxKIbKLMdvNWxtdmOgx7yJ81i2ZpmpFx53wJKh26jfNZJlYr3hvPvTk5XdbXn1m2J0zNdEfroHymwsx3fXBZb3plAoFFbNye3AI0KI0cAytJz6scBVwAVohn0wOBPYIKV8D0AI8QhwGfCJGnUXdmviMwI2O4sYtz7DRwK1o3DZXSXlo/PYHQ5Af1CLOw0tj0h8s4BEtGDqG0Bge9pw6lp5Ah4fmyGOsXhKIpNgzH1jeCL0BLa0NU83kUnww+d/yPiq8YPSe22W4zcbUZoviovsjrCtcxvlznK6LN6UbNizgafXP22+UEDzMTv5bLoGOtcbLjv56LEFbXm+ZG5qXR6/fVCm1ykUiiMHIaW1Xi0hxCS0grkJaHW9KTSluf8npTTvXbK6GSGmARdLKb+ee34dcJaU8uY+6/6TXJH48OHDP/vII48MxtsbsmbLW+yzWzNcAsGEERP0T3Z2asYcNI/ZZiNrg9XHQpae6490j2RLYov+NSwyvBNGdtpgVE7IZvPmAknVrIDVwyGrE4UQuV8JaSHHbxM2TswcxbtoQit618uT/75swsb44eMtS8qa0ZnsJLJH+0yzMtt9zUBFQDcaoLe+lPC7TdjwuXzsTewtON73Z3aU+yiS6QT7MsYzeMscHmqH1en+XmjfRGD/tPEHgc7OTnyf8B4OBOr7OrRQ31chF1xwwRtSytP1zlkO/EopVwIrhRA2YCiw6wC0sRkEKvvt5TfAbwBOP/10ef755w/yNgpZ9I0Qyyp3WCpkG1o2lJ1X7+x/IhqF6uoCbzmP66QyJl0D+zKaNOxdY+/i1vW3Fn0vG4KsTgO9NwELnoXz3wKamjRDceed/d+3j3KaJ6nNGbdnIWNR9Ndj97DQP53rf7iM8AldPDABXjUQxMt/X16nlwVjF+yfmhw9Hvem7Cb2xPcwtGwotcN0lOJ6ra++u9pw3rtZu16eckc5l427jN+v/33B8b4/s4VTFnLXK3fx/kfvG15rzNFjePeqd7UnnZ1aBGfDBm3Eayj0iRt0gBdffJED/f/XJ4H6vg4t1PdlHVOjLoT4NPChlLLbBckZ8h2589VAhZTy74O0ny1Ab5MwEugYpGsPmH0jhkFyh6W1l467VP+EyfCR+s02dnT+J0v+ch9PjMngOQHN0hZxZKWBIk5+lnpeKhUpwevVZqX31jNvg46FbsInZWgfJvnlZzIgrBt0gHgmzpoxQ/Bl7DS8pXn3bceajx4djN7rvm1oVgrwzIriyh3lTKudxvbO7Tz33nNkDMRgutJdVPmqKHOUsS+tr89fZtcm8f11819NjfrZI8/ueeLzQcP+3eQoFAqFodnIhdtfA442ef0xwKtCiMsGaT+vAwEhxAlCCBdarv7JQbr2gPk/W4e1djMJpww7Rf9ckeEjvnsWMfOVDCuXwvAYOCxkRUZnh+B3+vDm0vHeBPgT2txzX5IeqdRQiNZRWapnw6zJcGe99rV6NrxdbaPhyc3UTr4Gu6OIWooBuzNRTcHN7ycUcRXtkyxaxBaNwuLFMGeO9rVPdEOvDS2WihFNasc7k516VzUtiutKdzHCN4JTh59qaNABHMJB3bF1rLxuJWWizzhbqUU7Vp52Fz6Xj7u+YC73O3/yfNPzCoVCUSpmf39nAf8rpWwzWpA79z/Afw3GZqSUaeBmYAXwD+BRKWX7YFx7v0haL2J7Y9sb+idGjSLqgsUT0ORMJ0DUQIs94UCbj16EsZ6RdNy6lQXj59D0qlObbz4f6nd7uyVU8fmIuiB4jdBmpec86Pxo1olXZ4g4oyyr2E5M9B+8YoXK8kqor4eODvyfu1C7qUhgKK1rVsRGa6uWppg1S0sZzJqlPW9t7V4y0P50s3nv+RuNQGWAcqex0pDdZid0Soj6Y8azY4GLhS0wZT0cFYeFzbDzLqj/chN0dnLckONYFFyke51FwUVU+azrGSgUCoUVzMLvZ6O1lxXjWeChwdkOSClbgJbBut5gUJ+s4gnHu5aq39/b857uqdbUu5qcKb0mf03WvOr6TXTfOERdkLRRPPwu4bL63PCU6fMgeJthTjbcHtba8nQc0IRMMnbhWJw2Yy/dLrQ7DD0P1uPwUDs0pwzn88GVV1L/0ktsnR/j9vPgnnO0jy3hAJsEv/AYh8ij/av0u6MbwSB0dBB1SpatWTag/vRQXYjGlm/pnsu3/0kpTVXznrz6SW3vDy3Gl5DMfB1mvg4vToDz/5Zb5MxqP4uGBm464yauOPkKmlY1sW7XOsYNHce8ifOUQVcoFAcEM6NeDuw1OZ9nL9ZFVA9J6is+wxN73y2+UMK4of1136OJKMGP79ed/DXlGtg6H3zSSeuIFJOuhZ+UUzSf7hR2ZpzVSyXeJCf7p/f/VLRlS288ap4yRxkIdMPaTlsfZbhci5YvCfOeh9te6um9HjVc0HHzRmPFPZO6A7JZWh+6neDH95u2/5mF9v1JmLcizcyLcgcE3dGEeSvSWvufTxtgM+XhKSSzSZKZJHZhx2Fz8OTVTzLpxEnaC3LplPzc9bIhWvQl1Ab+WEy7ucpR5aviwS89aLhnhUKhGCzMTMcW4GQL16gF/jk42zk42TlupGUJ13lnf7/fsXB7mKzQv0CnC26/0EG0zMbFeblYC1xeq680lpd0nfPcHBa/uZiOvR08vuZxaxftg8vuwu/y88y1z/DMNc+YTnDrJh/29/vB69V6r9d7mfuan6Ejx5pL6JrUHURTMYI77iGajJLIJHTXgEFoP5ejj4Yup+m8ZPckNaD733M+n6Tz90t6XiO09kQAh82B0+4sDMsHArSO9XTXKWzz9dQptI71EB0zsuDnEE3oV9wrFArFYGLmqT8NzBZCLJVS6v6lFUL4gFuApw7E5g4WAiPqsEmtt9sMewaqml+EhhMLjkd2R4hhYIgEzD8jTeJLlxLbqj/PXI8/rP0DncnOAoOqVxF+c/Zmk6uYc9HxF/Ho9Ee736NjdgfhtrDuBLcCcvn1fumAv/2t/9re5NTV9Ax7eIKLrE0Y5ulddhduu7v/TcbKlXDZZZDJEP50CqMezE4n3P7Bb/le4iv9iu0SmQSJTOGQmOjlQYLvXa8bfZl8VRzbjjnIZ+n+OTSuaKTlmpZBEd1RKBQKI8w89Z8BPuBlIURQCNH950sI4RJCTAFeyq2Ze2C3+ckSqrOm6pWxQeu7f+p3PFAZwGU3dsHTdrhv2xMlDXRJZ9MsWd3jWRpVhOcNUql4nV6u7BMN8Ll8NExo4LvnfReJ5Cd//omxF5pPB8ydq3210nMdCvUIr/QhUiG1CWwGXHT8RXTM7ig0mitXwuTJEI9DKkWkwqTVTsDdzr+xZPUSsmn9VEQ2neouwlvy7h9IuvWrGbuc0JmOlVSZr1AoFIOBoVGXUu4ALkRTjnsaiAoh/imE2AJEgWY0jdILc2sPW/xuP6fuM+vsy2GDSe7H+v3hDtWFyBgMT8mTNfQhjWle19z973B7mFRmYNXrekikboV666ZWqucfx6ynbubOl+9k1lM3Uz3/OFo3tepcpUT6hO4B7avfT+Art5hWrve9ASEahS9+sWBdYI8mn2uEsNtpXvMEsay+ClwsG2fD9jW0bmql8dlGEiYyunpYnRynUCgUA8W0HEtKuS4nRXc+2tz0J9FC7T8BPi+lPENKaSxufRhxsv1Yw9Bvb/aRZMnbSwqO+d1+auQxg7+pXp5924424iaSpKWiJx8cTUQJPjSZaKqzO50QI0E01amNax0MLzQful+wQFPDW7AAOjoIfek2Q1lZ3Tx6OAyZQqMbaoOMSTQkmUnyYccGw5+zNwkj120juDRISpZ+AzUYojsKhUJhhiXxbSnlX6SUt0spb8w9bpdSDoJrdugwvGK05fB4c6S54Hk0EWVDZteg72lqYGr3vz/c9+GgXtsmbP28yvAbS0jv06+iz+7rIvzGEt1zJaMTuve7taI8S8V6oBXdpfu75TaTG7MyexlvJjcZ/pyFBLlrZ3GteKObAouT4xQKhWKgDGTo5xHJiWdOhhdWWVaW6024PVxKutwSTpuTGafN6H5eUVYxqNfX8yrDf77fsDo/5oINLzfDOTP1F0SjsGsX0TmzCB/3IZGaCgIj6gjVhfC7/f3X9ppkRygEfj/1o+utF+sFAlBeDl09NyHhU8CRBaOGuFQ2RVoYGGwJ17/jYPMFdmKdxvrwzrT2Hnqfk6nojkKhUAwCyqhbJO7Asqd+3qfOK3ge2R2xpBBXCtPrphcYs7pj64xHuFoZG9uHvl5lx94OViX+YXid8iTUGAULcrPcO+f+kGr7ArI7IPYReG2eflXh0RdXEv7eF4kclSGwPU3oN+X4Gxu1XHt9fXexXlF6jTTNE6mALpOWwWHeYWzt3Kp/UsDayixXnDMV74sv6YrfuFJw9woY31lG8Do7WaRlXXqFQqEYDPZ/9uURwqr3Vlle+8LGFwqeByoDlNvLBm0vDuHgguMvKDgWrAkai7IMIEzQ16v8zvPfMb1O0g6hT03tfyKnEhdNRIkcLTWp2pxhjWXjBVXhrWtXUr1yMrP+LcGdZ6W1vu8bu2g9Jqc011lCzr6XTC5OTS0vsEdT8tPDm4QKd5FiyNPPIPTZGYa5fXcWZrzno/6BlZp878ULaPpcEwsuXtC/Ml+hUCgOAMqoHwBWbVxVUDQWqgtZqbGzTDqbZurYQgP6h7V/GJRrO21O3Tz12l1rTV83dg/4rp7R/0ROJS5sMOcGtKrwJW8vIfjoZbr69MFroNOe0a41EHJtcqG1dsNfeJuEr6brTC8z9azr9HP7wo0NQcvIOfg+2FoQUZg7cS4NExqUh65QKP4lKKNukUvGXmJ9sYRfhAtDv5kS25/MsGWh+e+FKnF/XPvHQbn2xDETdb3Kk4aeZPwiCWecerF+L3pOJS5SAVmD37ZYKkbz+mayBm1/GQHhE7oKpFeLEu3l3Sc099zflaFlqTbJLu+x955s9+Xtw00veWXtlQDduf1uT/ySRYyvOo36m+YdFDPQFQrFkYthTl0IESzlQrlBLIctM8bP4NvPfdtwhnYBAu595wG+03k3+HyE28O6LWIFlJD3ztr7F6Vt27v/Y+dddhdTA1N1vcq5F83loXcM5vYImPfl/9U/FwgQPbqcbb4uhptUhUuk4ZS4Lhf8qcZOQ00JleMGOvL1m6Bjfo8efc0ebfa8z+ll8Ygonr0e3dZAj91D8/rm7nx+39z+iy++aH1vCoVCcYAoJhNr1dRIYJBLwQ4u/G4/K69byZSHpxBLdiKLfCo7yume1BXZHTEdmGJD4MhIklbLFmX/ojTXRxbyzUV+mslMkqbnmxhfNb6fp54fIzqzpX91u9kY0db60QRv7CINnGrw3jZhY+rYqfzlg7/QldZvmVs2NsOvLp+KZT/YREfel4SGt/ocdNuI1FQSf12/1z+eiasec4VCcdBjFn4/ARiT+1rsMebAbvPgoH50PVtv3cpJDotjM3Ph4lFHjTJdJiTWDXqOqaMmdv87mojyTnpLaRcwoDPZaShnetMZN7F19lZmjJ/B2dVnM2P8DLbO3spNZ9yke61oIkpw+TSibtinI8/qEa7u/P2M8TNMVfXsLjfh95sNz/cjryNvhDu3IW/P7PlAVW3ReesKhUJxMGMmE/tBKY9/5aY/SXwuH8cdW1NcXU5A55iRlq55vmscXuNpov2wZ6D51B4rGW4PYzAErt+erJDKpAzlTKuklwd31fNK5PM8uKueKmlsOMPtYVN53LTIsv4b66kfXY/f7eeKk64wXNslE6V5yiY68vh8MH9+gWId9fWE6kKlqdYpFArFQUZJhXJCCIcQYowQorbv40Bt8GCkdesrlgzkD6r+AcDm9ldM143/9EWmSmd9yTjggfbfdg9TieyOkC52lyHBZTFDEs/EWbNzTf8Tra1QXQ2zZsGdd2pfq6u14zpEdkcMw+mgSdE2r+/xvi844QLKHeW6a0v2lE105HnmGZg5s9+wmZJV6xQKheIgw1LQVwjhBO4DZgBGc64O65x6bxIyU9yoC7jvrV/x43O/R+CBx/FcAHEd4RNPCmqHnkzLuYsIvjyzZ7xrkfz3q/98lbYdbTSuaOTG02+kXDroEibTSoBsOmNZbmj33m2FB/LV5NFeE9nyOetgUPN2+1R+ByoD2LAZhtUzMlNw8xCqC9G4olF37YA85T4jYKNjRhKuk0RiTxF4c62uml1JqnUKhUJxkGE1k/sD4BKgAVgKzARiwLXAicA3DsjuDlZKqFQPP9xEcF2W6yfpn487YOo7CapuaKTj3CsIP9xESjotXT+vanb/3+7HJqX5a4TWUubEhvFU8R4qlzwGI27UDCMYVpMD2vFcUWBvgjXBotPndu/b3f3vvKfcdyb8fqmx5XTku2fNv5gtOuPcsmqdQqFQHGRYNerTgR8Cj6IZ9deklG8ADwkhlgCXAYd1S1sBFg16RmbYsHMdLcen8KQh7uy/xpOG5rVP0EAjvooqGr75IMtaluF1enWlSPWQUvKlVA1LXeuK7k1YuCGxZ2HMjlShB56rJo+6NA31SIWm0BZcDy1jY0Tef4DAm7LA+23Z0GLqqQNUllcWPC/JUzbQiO+3rNes+Tz5zza4NEjH7A7liSsUisMCq0Z9FLBeSpkRQsSB3nNElwK/A24Y7M0d6pQnocY/mvUVr+oadNAM/YbN7xQcqyirMCzY0iOWirHUva7ouqwNS1PbMwKaJsL4j1PU5z3wQIDWsR6CV8bJoqm9eVJw/aXajUnc8SreZ9sKvN/I7oipQXcIB7VD+5djWPKUc3ryZLNaGsDr1bTecxrxvQm3hw0nq+VnnCvPXKFQHA5YtRxbgbww9kbg873OnTioOzqMSNu0Gd6jPsa4Wl7CyA8LK8Tz4ebeBVsuu8kkEovYspC2YtYFdLoheGWczg1azjt6eZDglfECGde4U1ubv2GJpWIFWu6ByoBh4RuAw+4YWEV57/x+Pq8fi2nPJ06ErYVDWSK7I4ZRDzXjXKFQHE5YNeovAvnRYw8A3xVC/E4I8b/AfOCJA7C3Q54MwPvvw4gife3DhvY71FeK9O5Jd+9/iNgmTI1sX1I2CI/Qct7hD1rIlnksvS7v/Wqa98ZV+U/8+xMD+5508vtRFyyeAHPOS7D4S6OJvriy+1ygMqD6zxUKxRGB1fD794ChAFLKe4UQApgGlAG/AH58YLZ3aJMRED7NyebPXwL/WKy/SMCWafpVdH3D0OOHBAg+ehnZbMZQUtWMi06YyCtbzNvrehN3wpoxWo46sjtCLKuvttaXWCrGmp1rWLJ6iaGSXpmjjHNHnWt5LwX0UYtrHa0NfcmnBbyJNI0rJ9NStYLxJ5xDPB0nldHfh+o/VygUhxOWjLqUchuwrdfze4B7DtSmDnqsVr/bYcO5JxGoOQtv+4PEbP1bzrzSSc2YM4pfq7WV+uA0Ouw2wifEeeB0wavVpc1+qyivKFqN3pfdaa24LO/tWine8zg8LHp9ERJJOqvfZmcTtoHnsvNqcbnCveA12jS3PPn0wORHL8XmcCGRJLOF6j5qxrlCoTgcKVV85mghRL0Q4iohxOeEEEUGUB/hSBj5cjuhvaOxpQyMm9tT3FOMRoleNoXFgSg/Ob0LKeDat6XhbHA9PA4Pf1z7R7pSxmIweuSr083U1voST8dJZBLG893Zz1x2L7W48CnGxX9d2SSdqc5+NyJOm5M7vnCHmnGuUCgOO6yKzziAn6L1p/dOynYJIe4HvielLD0efKhSioO8bSv+L06j5Zi+IWLtjqoltKyop9j60O0Eb+gseK2AokNl+mIXpekDeRye7up0vR5yPVw2FzZh05101pv9ymXn1eImTiRSkej2zPth8Pm47C48do/y0BUKxWGHVU/9buBbwM+AWrT8ei0wF/gmWrHcEUE0EbX+qQl4x9cF2Wz3yM8Fz0LTS9rXjl+WU//XzabvtfC1hVy4866CqvOYW6tOl4AvoYXwQTOUZY4yXHYXTpt2rNxZjt+l6aqbSbbq4bQ5C6II+eK9Oybe0X39vkgpixp0GIRcdn09bNxIYK+jpIgFqIp3hUJx+GK1UO464LtSyrt7HdsD/DTXt34bmnE/7Am36w860UXCc8OjENOsTv+Rn13dk9z6kldAS2aSpIR+gNkmYd5fnHimf5kNtSOQSBa9tgiBoCvThUM4yGQzLL96OZs+3sQT657Q9bA9dg8IzdB2pbpw2pzYbXaWXdU/iuBz+XA73LjsLt0iOInELuxkpP4gF7dde+1Ac9nRRJRwe5jI7giBygDBnz7Gt168vKRrqIp3hUJxuGLVqGeBdoNzbZQWkD6kieyOWFaUQ8BuV7q7qKsfXi/U9DcuWZllytIpuuNPexNzwxZflrlfW0jUKam+u5rOVM9r0jJNOpNm2qPTWHfzOkNddafdyW8v/y2hZSEcwkEqm8JpczLtsWm6Mqpmfd9paaw/LxDMnzyfGeNnDMigd0u95sL/5Y5ybpYZUk4b1mR1NFTFu0KhOFyxatR/C3wdWKFz7nrg4UHb0UFOoDJQ0nqv04vhCDabTSv66sPWzq1FDTrkFOs+yhWMtYcN28dS2RQtkRZDXfVl05cx7dFpJDI9cex8qF5PRnWUe1hJ+vf59xlbOZZLzrjEdG1fTzwvO6sn9VosnZCPQNiFfXB05BUKheIgx6pR/wC4UgjRDjwJ7ACORdN89wPzhRA35dZKKeUvB32nBwmhuhDXP3m9NYMm4fTRZ0HLD/pLmtpsWrFXn8lm0USU7Z3bLe2lywmjdqbg9ttpPz9BPK2fy46n46zZtYaGCQ26uuqPtD1Smozqm29a2h9oUrDTaqexMLiQv738N9O1fT3x3kNX1u5aa7hHI+KZOI1nN1I7rFZNXFMoFEcEVo16vhCuGjhZ53zvXLsEDlujHk1Eiy/KI2BPx3vwH4UjQKmp0Tx0X3/jUlLOXkDwy/DR/LvYM2G66dLdXZoyXF7QJu8R/+TPP2H19tUlyahu2LUeDLTs+5KWaUb4RhQ1pMWGrnx9wtctD7jJ43V6qR1Wq3TdFQrFEYNV8ZmS+tkPZ7719E3FF/WiLblF+0duBGgxIrsjHMuxlq+fscMvPpPhmI1bTddVlvVMQ+vrEZvpyusVlX04xAVdWIpWeJ1eRg4ZyeI3F1MWLWPxm4t155gvWb2kIPzfm2wyye6XVuJ1uolhvdRd5c4VCsWRhjLWJfJ8ZKX1QjlA2owXRxNRFr+5mDnPzWHxm4uJJqIEKgOIUt5AwH1nwSlrduGy6Rtnp81J7bDa7vfMe8R5z9dMJEbPMFac9BnLn0FWZml6volZz85iW+c2Zj07i+q7q2nd1Nq9pnVTK7NXzDbcR4wEla+3Y0tYM+guuwu/y69y5wqF4ojD0FMXQtQC70opE7l/myKlXDOoOztIiWUT1m+FJJw84hTdU0b542VXLaONv5e0p4/KIPjhMJLZNt3zqWyKoeXa0BizMaSgtZwlMgnTorK6kZ/Bs9pFPGt8M+B1ehEIsmQLiv76zjGXUmqte2bXSkDdTmhZqgn4pAXsMwouSJjqqGXRjEcZMaK0okaFQqE41DELv7cBZwOvYd62JnLnSpMrO0Qps7lJss/aYgG/uex/+h02yx9f+eiV/Oj4H5a0J7uEln8bgSfuMSyWCy0Lsevbu0zb0QAuPOFCxg8fb1pUFqoL0biikXiyvyH2ODzcdPpN1A6rZV96H02rmnTfJ1+AJ5FFC+DSNmgfpinorbsPmqbY+d0pUn+MrIDl8bdZsWgsK85eRP0lpaVLFAqF4lDGzKhfAKzp9W8FcLR/KB/v3Wwp/GzDxusdr3Nq1akFx8285VQqXnLTf8wJa8YeQ/xNYyW3TDZDuC1sOpjF6/Ry5clXFi0s05OM7e3Z5/va5zw3p2gBnpnkLABS+6jvORecGbBPgS+uzWDcDQ8IrTNgypPKKO8AACAASURBVCsz2XruFfgqioy+VSgUisMEQ6Mupfyz3r+PdPK9z1bIktWVIzXzlhOkS8rZA2CDbVvW4cxAyiBekpZpNuzZwHfP+66hCE06myYYCFp6y7xkbN/2uN6efaAyQLmjXLefPF+AJ5HG099yvfDxXKV9yq49Hj0FPDiJYz5uIGGH8MNNNHzzQUvfk0KhUBzqWMoOCyEuEkJ81eDcV4UQR4wnv/2jLZb188rsZbpypHlvWQ83DsQA9Pk2vbEKu0kUu9xRTk1FTbeX7Xf5tRuUXgghGLdwXEERmxn59ri5E+fSMKGhX6h+9JDRhgIx+QK8Uqa/9WyUogYdtBuANTuPiFIPhUKhAKyXfP0UGG5wbijaoJcjgo/lPsuedCqb0m2pCtWFEEL/Ik6nZ0BGvX0oPPF7DG847DZ7917qR9ez7uZ1/b6PeDpONBkl+HBxidpiRBNRpj02zfD8sumarnzvm4z8jY7X6dUq+U0+Zzt2HLYiHZkSdg+x2FCvUCgUhwFWjXodYCQH9hbaxLYjglLGnY4dOla30Gz19tWkM/2zwuXOcp659hlOLK8uWU0/C0x6D1b8FtwpcOQuX54Av+w/QKU50mw4ijUb6yT8x9tL20AfzOoGvE4vmz/umU6XD+UvuHgBTZ9rYsHFC5hfcxNO/ZkwAGTIcMOEG7CbWX4BlSdPGOi3oFAoFIccVhXl0kCFwblKg+OHJ1ks3wqdcdwZ/Y5FE1Em/XaS7njSbCbLaVWn8WxqA94UxIw1YQqRcPIerQlh0nuw6+cQroMNFVATcxH62t34ShjKEnPBhgfvhuBtuqp3VjC9vo5KXT6UD0A0SvQLs7j1Rgx7Ksod5XzmmJOZ/ZqDO89I6Xr1HuGi9rjTBrR/hUKhOBSx6qm3Av8thCgwM7nns4GXBntjByulhMbnTZzX79iS1UvYl9ZviYtn4/zghR+QSMetG3QAAUtbyrqf5ke8zn0eGtrd+K6e0e8lgcoAXty6l/MmoOZDocnaDhCzuoGio0/DYVYPTWu/nGbphDbJt1534DYohXeiFOUUCsWRhVWj/j3gJGCDEOLnQohGIcTPgQgwDtBvRj4McRnkwvWo8vVvpXp6/dOmr1nw2gKkzVZy+H34w8vB79eGxYD21e/XHRoDWl7fJvXfJGODtiFJFm98vDSt+77XNyiAKybf2hF5k4lXxdnnpL8HLsEnnbRc08LbG1/hpK/v67nRyn31pMCfgJb4lUpRTqFQHFFY1X5/RwhxBvBD4Dq0kPtu4HngR1LK9QdshwcZ/rJjSCT2FF84wAnzWZllh4yW1NbmSkOYNhosDo2BXK/5sFlM6biTlA0SDq0PPGXT9n7vueAVL9B4dzUtly+jvnUTRCIQCGjX9ft1r1tw/T697DZh05dvjUa1fUcitI6GL5T9xlDh3Z2BeZVXMX74eKrL/kC09+ec+8wksH5xGVVzj5imDIVCoQCs59SRUq4Drj6AezkkKPf4wIpRB2559hbqjq0rGGByydhLWPGu3lj6Hkq9H0g6YMMT/wvnN1oaGtPNRRfBQ3eSd9hTNrR2sVzoPyYTkEwQfGgyHb8sx/dRlxYBaGzUIgD19YaXhv697KOSo/rNZqe1tXssbTQVIzgb4vpZAUC7+dgyrkorxHPY0Otsc2ShuSZLg86seoVCoTicUQNdSuTM4860vPbeV+/tN8BkbOXYQd+TNwE1m/X7wY2IJqIEl0+j0w3JfNeXQXQgC4RPyF0/FtM862AQOou3vfXuZR9aPrS/hx4MaoNtxsW46ipIFhEb9konNcNrc4V4+t9zzA0b/nPagIv8FAqF4lDFslEXQkwTQvxOCPEXIcRrfR8HcpMHE2eNPMvawpyBjKViWu/30iBbo1uZ9qhx7/ZAsQEh/9klvabYYJfexNxaJX0B2ex+FdJpmwjTOiJF9WyYNRlWBDRP3AxbJkPob/sI2IcZF/o5vdScqkLvCoXiyMNS+F0I8UPgB8BqND1445Fahzk7YzsH9LqszNK0qqm4IS019i7ht4+D78/zS3pZscEuvfEmoKZvxiEW03L3+0E00kbwyjhRk3B7NxLcaWj5bRbfjv8mlI3TOBv07Lqao65QKI5UrObUG4B5UsrvHsjNHArk553LEq1vLBVj3e51lg1pKSz/j7O4rKq0oSVmg136YgNC7X0Oer1aMd4AiSai3Fz5GnGLwnWeNGy8F6piAHH89IxizaJFE7xJsLnctFy+TFW9KxSKIxKr4Xc/WqX7EU+oLlSyQQctJDyucpxh73Y3AkQppe8C1g0tdQKMectZHq+zXGsNW6r1vhdgs2lV8AOgdVMr1XdX87v464YDaLr3kNDa0577bd6g91C/CTrmw4JnoeklWPAMdNwlqT9zmlaAp1AoFEcYVj31R4CLUYYdv9vP8Ucfz/sfvV/S62zCxryJ81i+dnnRtaXeNIwbOq6k9dDTcjb54cl09Sk4c9qcTK+bzgXHX0Bo7yh8C6eBN6uF3L1ezaAb9L93k2tTi0baCB/3IZGaCs4Qn6Njb0e/WfJ6uISDi1wnceXfY4Se2tj/piJHXminh6T2CAaho0MVyykUiiMKq0b9eeAOIcRQ4Dngo74LpJQtg7mxg5mZZ8zkv5/775Je88Pzf8gI/whaLl/G538/GWniJBtpsusiYd7Z3y9pL3nGDx+P1BGgSWVTLF+7nF9d8istjF1C/zvQ3abWOiJF8Mo42R0Q+wjuHjuKE39xnaW6AbezjEcbX8H30CPw7CxIlpi2yBfyldLip1AoFIc4VsPvYeB4YAbwMPB0n8dTB2JzBys3fPYGXPZSdFzhO89/h85kJ+P/sr5ocD0jTSaZ9EbC6VsFVc0vlrSXPGaStV2pLpasXqI98fk04zh3rva1mIeea1PLF8HlJW+zSOLpuK7ufW/K7GW0DLkR3/d/AvE4lKDi180gFPIpFArFoYZVT/2EA7qLQwy/28/zX3meiUsmksgaaZ8VkswkWbJ6Ce73n8Zmh+xgKAQI8HfJARuvP679o+n5J578OTNXuy0pyHUTDkM2S/gUrYBtINj37eO0+QshL3YjJZSVaWH/WAw8Hs3YOxyQNhB+93j2q5BPoVAoDkUsmRYp5QfFHgd6owcb9aPruWvyXSV57M3rmll51C7SlnX8irNziBiw8drWuc30/NZdH8CsWVBdbb3wLBKBWIxIhVaRPhCk7CN209UFdjvccQc0NcHChbB1K8zrPzCnm3gcpk4d2AYUCoXiEMXQqAshynv/u9jjX7Pdg4vNH28mmbHesp/OpvlD/K3iC0ugLCUHXIU+wjfC9HxGQDRVmoIcgQB4vQT2aJXrehSrGdAVu5FS877zKYCqKjjqKO2YHh4PNDcX369CoVAcRph56lEhRF4TtROIFnkccQQqA5ThLL4wx1Huo8gMOCitz+pjoTOpb2w79nYwY/kMznrgLGYsn0HH3o6C85eddJnptSOVUD0bWkdjXUEuFAKbjVCb8S9XsZoBd9qi2E0konnkesTjKqeuUCiOOMyM+teAd3P//o/cc7PHEUeoLkRKGuR0+yJh+96tg74HKSD8cP/Jt/e/fj/V91Tz0DsP8VrHazz0zkNU31PN/a/f371mxvgZlDnK+r02T9oBUbcm8NKZslh4lhv36nf7aXncgz8B5SXqD0osit3kogK67Kc4jkKhUByKGGZ3pZRLAIQQTmADsFFK2WG0/kjE7/Yzxj6M9dkdlta/u63NfMEAxrWmHLBh57qCYx17O5jZMlN3/cyWmVxx8hVU+arwu/1848xvcOfLd5q+RxYIT3AxfcxIwm8uJrI7QqAyQLAmSMuGFiK7I4w6ahSgpSQClQFC76+jfnkLHRvWMG3IClYk+lppAyRc8J7OR6EndhMKaRPj9NgPcRyFQqE4VLFSspUBXgCCgDLqfTi64jjYuaP4/HMB8XTcPDYi4FLXqSW9vysNNcMKxWe+8+ytmlXU25OEpmdm8+BVSwHYEt1S9D1ibvjTqAy37Gwi+6wklorhcXi4Pn09HrunX4ua1+mlUTTSck0L46+dzp/uXGj9GxLQerwW9m9ZCvW7TcRuclGB/OjWksRxFAqF4jCkqFGXUmaFEBFg+L9gP4cc9o/3FjfoABL22Mzb39wpOKb2FGwZ6/1uSTuMuvDygmNrN7xivCcB6yL/B2ge/aPtjxZ/EwmPnSJIpnpy9/G0Zsj1es7zevLBpUF+8G8/sN53n399rqEg+BUbHZu/iG/ufBhhUNRXX1+6OI5CoVAcpli1Ht8DfiCE+PSB3MyhSCQxeHlym4RKzzGWR6ICIGDaU9cVFMud1OU1DuVLGLevnNZNrYy5bwzprIWaAAFJq7UDvUhlUjStairZqOfJZrKE1z0O48aZt9SVIo6jUCgUhzFWjfptQCXwthBikxDi9SN1nnpf9tj0FdkGghQw5t0Piw5a6UtWZgm39VSmzx35VdP1tx33ZYJLgyQy1oRzBko8Ex+wQYdca5s3WVpLnUKhUBzBWLUe7WhysA+h6cC35Y71fhyRDGaDWsIGYlvpnn8sFWPDnp7K9OOuuYFFz7s0bz3vsef+veh5Fy+O95cWDfiEKJjjbrWlTqFQKI5gLGmbSSm/eoD3cchieba6wLh4LYe0wzuJLUwcMlL3vMvmIpnt3x/mdXqpqejVvuX3c9Pc57niysk01e9j3dGScR8J5rWWUfX4CubEnjogc90Hm4wN2obB4gkQaovhV33nCoVCYYqpURdClKFVvR8PbAWel1Ju/xfs65BBSmmtUA6Kr5PwjH8rJ+3Vr0jXM+igjXUNndK/fasqJnjwSbumj+6wg1vbQKAygNfpLcmwlznKsNvsSCkPyA2B2+7GkYEYCTwpiDsACfeeq3nsjZOhZaSkftDfWaFQKA4fzGRix6CF1R8Dfo42nW2dEGLSv2hvhx/FHHoBW+1dhqc9Dg9uuxuvUxNc8Tq9+F3aXHSfq1dxWDQKkyZpLV75gSfptPZ80iRCx081ztv33aOEMpubldetZOvsrSy4eAFnVZ9V5BspnZlnzGTBF+bT+LpT24KAeK4KPubOieB8tMhQPU+hUCgU5p76nWgp4/OAN9Amtd0P/Bo1ta0bkdXC5tYWW1ljM8x3x9NxGs9ppHZoLRv2bKCmoobQKaFCgw6wZAnsMyjg27cP/+//QMs1LQSXBsnKbI/nrZceEGCPJzjNV4PP5aNhQgMSSduOtkHz2L1OL7XDammY0MDi6zbheOdO9Er4skjCbWEaJqgZ6QqFQqGHWaHcOcBtUsq/SinjUsp/ADcAo4UQ5pNAjiCGWO0pl/AF18nabZJJu9lx/uMMvWiv00vt0Fqm103nxIoTWb97PY+0PUI00Ud6/+mnzffS3Ez96Ho6Zndwx8Q7eibNGdx0SAqlaEN1oZIr9M0QiO70QaSip0+9L30LAhUKhUJRiJmnPgJ4r8+xd9H+9Feh5diPeKIOi1XkAipjEruAjNGnLuDimovBwAG2CRujjhpF9d3V3R621+mlcYWm3lY/urSMsy8hcb/yGs60JGkSRYi5CqVo/W4t5H/+g+fvV8tanplnzuyONgQqA3htHmLZ/qI2XpunsCBQoVAoFAUUc7cGoEZ+5BBNRMlaLZKT8LRzI8Ui9adWnUqgIoDf5e+XO1921TKmPTqNaDLaHfqOpWJEk1GCS4M9+eZLLjF/k6lTNTGX6moiK35HTKRMl3uThVK00USUtbvWUj2kush3Yw3RK0QQ+lQQ2z79yWu2fXFCx6sZ6QqFQmFEsZa2FUIIPSmx5/sel1IeO3jbOjQIt5fQNy1gH2ljLz1HIpXA5/LRMbuDcFu4IHf+SNsjpDL6BjiVSfXkm2fMgDlziKa7CJ+ihbQDeyDUBn5HOVxxhabSFo0S2K5Vl8fcxnuySQhdOw+A1k2t/XPx+4EzAzXenhY+//IWWh73ELwyThZtX96EdvfZ8rgH34nNmmqcQqFQKPphZmJ+9C/bxSFKZHfEejubBIfDgcxkyZoEQFZtXMWE6gndRWmgecaPtD3Cr/72K12tddDU29bsXKM98ftp/dkNBLffU2AYGydDy/AbqG9u1sRc0Ax942TjPTuzcOPYLyO9XqIJLSIQTUYNXlA6dqntgXO059H2t1jri/P1N2B3GVTug7qd2ihWX1LNSFcoFAozzEavKqNehEBloKigTDcCKjwVbI9txzSr0edUKZ7xts5tAPz/9u49Pqr6zv/465M7ZAYtdwREJYii9QJ4z3a1tqjAar3UVLFN21i2YqtbpSuu/n7b7XZXd72ylXZb3VZaWU291VZQUStVW6y3ekHlEm+IAbmKSSCZTPLdP84JDiGTOTM5k0mG9/PxyCPMme+c8/lOQj7z/Z7vpWFLPdM/uoWGhNZ3R0t8+ke3UL9zDpEm71zRmLcb2vRZ7PoAUNQOcYNiK6C1sJ0F793LT//jAS6Z8u20V6KzdnDJbvI4eOh/IfIlb+jGsw//hOkDfkL7aZ1a6IsgEkN7pIuIpBDeEOa9UNVh6e3XPaR8CJcdd1m3ZWZM/PSecWLLOEhX96YdmwCovevqpPf62w1qW//qJUhf5VqovwnmPwrfe76QwoJCMGj1F8FtslYaXDO3/uXWtLvc92uAaXXs/mHFX7L2psdg2jvAli3eB5E/X0pD6acfQHbNT58FjSVoj3QRkRSU1HsgWhoNXtjBhYddyA9P+SEDigZ0WWRg8UCqj6ze9bj2jdq0WsZrPnzd+75pZfJpYSVQNyjuJcgEkRjU/BWKKaCFrke0m/P2b0/HxM3wzP7s3pth3te1n/eT9ZAh3X8QAWqPKtIe6SIiKSip90RDeveWBw0YRLQ0ytKvLiVSEqG00GuSlhaWEimJ8NhFj+22kMyaLWvSahm/17yBtz94jQnDDqEsyYD2slaoGDnJS5DR6Kct9vJyGgZHuPXY5B8iWorSnA7hYPBO2Fnc9dM7i2HhlEKYNKn7DyKlUHfq0d7e6SIiklSgDV16g5l9GfgBcChwrHPuxdxGFEBtbVr31N/5aCUAlftXsv7K9btGt48ZNAaH4/erfs/KzSs5yB0EZLZG+6xff4kHvvEo3/rZr7p8vrkIZpz//2DkeKiv9+pQVwcVFdQetA37w/eT/laUtML3noOfnFhIe1kpTa3Jl7QF7wPEiuEkf38MFk+AS6uqmPA/T1O+8fkuE3t5zP8gIiIi3eozSR1vO9dz8Jah7R/WrIGue9L35GDLihfAn0LeMbq9YyBcm2tjR+sOiguKuX7C9cTr4lQdVsUVj10RPB6Dt2IfsuTdpZTFobmLFnJZHBa/+yg1Iy/1urITpoetuel0Wrr5jXAG1z4D1y43fnZMnLmndF/ftgJYPSRFzBMnQiRC1UXXccVNXX8QSZxSJyIiyfWZ7nfn3FvOuVWpS/Yd9QcOC17YYMi7u29wlzgQboff6m1tb8U5x2mLTmP5uuUsOfs+oi3eSHAgZf93QUEha5Y/3GVCBy/R1/15cZfPTfgo/ul1OnPQWgjnnA//emKcf6qMdR+LQWsRtHe32o6DGY+8DY2NRIfsx5ITF3h19TejK49BtAWWnLiAyOCR3ZxIRESgDyX1/mjuvn8JXLagDSY17z7Iq/aN2m6XWT3z7jM56unV1P90IPMfhSPqSdnVf8R+U5iwhaTJubwFKrZ1/VzVxuHdbNvnfT1eAf95EsSKU8eSysBWqF5R6N0CACpnzqF+7nrmj6hmXvx45o+opn7ueipnzunZhURE9hLmXO+tBGtmT+CtG9/ZNc65h/wyy4C53d1TN7PZwGyAESNGTLnnnnuyEG1qr298nVhb13uc78HB0cVjKBg2YtehDxs+3DW3PNGY0jGsa/H2VB/XPojBH33C+ihsKCdlIt1/n/0ZsgNebVlLexcZuqAdjizdn4Khn/YytLt2tu7cSsu2TbgdO/gowHUysatezutSn7DVn38+ciSMDmfJ2VxpbGwkkocj81Wv/kX16l8yrdcpp5zyknNualfP9WpSDyJIUk80depU9+KLuRlTd+iCQ1m5eWWgsuXN0HhNw25Tsu54+Q4uefjbxDu11m88+Ebmrp4LQFV8IovbVnlTvwIk2vVXrmekK+fZY0cw/eydey61+uAAKl/YuCuOPRa36fh1yEJSv/HgG5n35lxmvQ63PZKwoMz8+f1+6ddly5Zx8skn5zqM0Kle/Yvq1b9kWi8zS5rU1f3eA4cPOzzwHK+mEvaYY101bjrEu9/l7D5bRWMpgZKstcHi1++HaJTK25dS/7MI858qYd4zMP+pEup/FqHy9qW74uhycRsLdq1MDWhLSOigBWVERELUZ0a/m9nZwI+BYcBiM3vFOZdsVfI+YWDxwOAJsIty0QeXMKoBPtg3+ctcGgnWFULdMw/BCZdCZSWR99dTkzBljaqq3T5YpLu4TU+UF5RR0O5vyhJr9lroBQVaUEZEJER9Jqk75x4EHsx1HOl4+t0/Bp6nbl206OvXvMwH+3T/uq7uiyfloOKNhG3uO01Z6yzdxW0ydfzo47l48sUcuG0clf94W9IPGSIi0jN9Jqn3R1saPgrcUh/YZjS0NOy2tOyczyyHrjdd82Rwf3vGtuDT7DJZ3CZd5cXlXDz5Ymom17Bs2bJ+f+9cRKQv0z31Hoi2BX/7mooco28ezbNrn9117PH4qu4Tdgb3t//3hODr0VcdVkWBZfdXwOGoOlz3zEVEeoOSeg9csmV88MXQDRpi3sC0xlgjAC3tqafDFdHd6i17XuP21uWBi0dLoyyZtYRoSRob06Spr82uEBHJZ0rqPXD520PS3OHEmxNeu6KWhpaGbhee6WBpXmBd+8dpla/cv5JVX38J0hwvN26fcYzbZ1zKcgVWQO2K2vROLiIiGVFS74EopVS+T1qJvam1ibqtdSx8dWGg8t/5cGxay8SWWpKtzrqx+Dc/ojjNpD4qMoq5J85NWa6jviIikn1K6j0xcyZlbaR133tg0UAqBlfw8JsPBSo/vmUA9TfB/Edh3jMwtInkid3BqRPSnwX41OYXaE1nyKSDifscRPWR1Un3hu9QXlxOxeCKtGMSEZH0Kan3RHU1hZbeSLYd8R2M3WcsbFifujCwbtJYIjGo+SsMa4LN3S3hajD/zAWAt7DMHS/fwVWPX8UdL99BQ0uDt//7HXfAVVd53xsaaGhp4P6iNWnVAeD6rVN27Q1fXlyetFyBFWignIhIL9GUtp6IRpl5xuU8turWtFrr5/3mPP65YTKPpVj6tQCo+JuzoPRp6otbuPK07suffcjZjIyM3GPp1/Licq5YcjlLFjkqPyiApiZv8ZcrrqD29ksoLCqGeDxw/ANjMHL1h4B3T37D3A386OkfcfPymzEzYm0xyovLKbAClsxaQqREc9FFRHqDknoPVU/7PpetvDWtld/aXTtlw0dTsA3au0vqVkjV6GnQ3s7caanP++bal3Zb+rVDxzz0L5wLc16ASZuhakUT0Ris+dUt7Di2NXjwwAEfAzu27HrsnKNicAWXHnMpW5u3MnTAUCYNm0TV4VVK6CIivUhJvYeiDy5hcDNsGRj8NU2tTbwz/jPYC92XO3PoSUQOOQJaW1k+hpS9Aeu2raX2pYVJl35tKYJbToTSOHzvNHhkEUzYagykhB0E322u5q/AiUMAb0OYM+46g1h7jFhbjJLCEkoKSnjkokeU0EVEepnuqffUmjUcvJm0p7ZtiG3DFXafpcv++Gdo9pacGxCgd7ypBJ569q7kK8T5l2spgsZSOO0imPF6jLgL3vUOcOGbBTBpEg0tDUz79TQaWxt3bUEba4vR2NroHffn44uISO9QUu+pCRO489GytF+2dvta2lN8ElifkMkP20jqDw4G9+54oduBa4l2FMMDR5VwVEnq+eYdylph8SHezmoLX13IzvjOLsvtjO9k4SvBpu2JiEg4lNR7qqqKgxuK+d5yvKQbsMX+zta3U5bdkNClP7qBQIPxWmknHg/YlW6weHycQaOCJ/XmEqi7+FyIRHh49cPdll28ZnHg84qISM8pqfdUNApLlnDz8ih1t5dx3DoCJfbCHc0pk7TzV4itj8Dzo4OdF4Oj1sUpLSwNUBiYegzDBo0KVhZv2dqKKV8MVlgrxIqI9Col9TBUVkJ9PeN/eBvPDZvHM8OvSplU920tDJT0fnIMjL4Slo8l8LS5wc3GnLK/CVR2xnFfZXj58GAnBuK0MePgGQDMPHhm9+eeOCPweUVEpOeU1MPSsXf5dddROed6Xv/qcgqTvL0DiweyoSh1S71t3324dDrp7dbmYMbKdiZ9XEy5K+626ABXSPX4czh81bbgrWoHi1d73erVR1Yn/fBSWlhK9ZHVAU8qIiJhUFLPgmcf/gmTfz4Z4v7UMj9hFlFIpCTCDV+8gU2kHhm+vS2zfc7PfW8A08eeSty6ydQOwHilcjxVP7g3rd3m3tz85q6HhQVd7yKX7LiIiGSPknrIGrbUc9pzl9JYAm0dqwD4rey4a+OKo7/LvCfmpT6Rg3gBae+nDjD3lBiHbPkBVli061x7MNhpcaafvZPG+E4K02ipb9m+AYDaN2qxJAEapt3ZRER6mZJ6yBb+ei47ki3pY/DD564j3h5gXrjBsNL0t3bFYNGkNhpaG2lua951LJl24GtfgragvwkGQ97fBMCaLWuSzonX7mwiIr1PST1k9295NmXrOtnc7t04OPTAYzNqqaejqRSeGE/w67TDpI+9Ty0ThkxIOideu7OJiPQ+JfWQvVK0qfsCQZOnQVN7M1+e9OUex9Sd4vQWkwODqnHeqPaqw6oosK5/hbQ7m4hI71NSD1FDSwMf05y6YMAu9aLCIg7c98CeBZVCuvftS+IQucAb1R4tjbJk1hKiJdFdLfby4nKiJVHtziYikgPa0CVEC19d6CXskLrMZ0yYQWlRKWVFZTTHA3xYyMCgFtg+IHj5Y6IHe9P3fJX7V1J/ZT21K2qpAsWVQgAAFy9JREFU21pHxeAK7c4mIpIjSuoh+u2K+0O9B37upHNpb2/nW/FvhXfSTkY2wPYygsXt4IIPPrPH4UhJhJrJNaHHJiIi6VFSD9F761akLuTA2j9dAjYZw1i8enGwQXWZcnDERlg1LFjxAa1Q/UrX27qKiEjuKamHaH3bx6lHKRg4I2U3vcNx35v3BZv+1gPDG7uPww+GkjgsvQsip0zKajwiIpI5JfUQ7SgImIADdtE/uubR9IcypnFPv7ANGkqhKA7xbn4TCtvh/VthZBNw/fVpBiQiIr1Fo99DlGx1tS4KBku8HT+ddBag6egFCPCatiIYspPuV5NzMPfPfkJfsABGjkwjGBER6U1K6iHab+DIwAk48AeADJTE4cLXYGiK5eUL2uGwT0r43d0kjTsSg2s3Hwrr18OcOaHHKiIi4VFSD9GFfDZYQefdMw/Mb3137IhWUlBCoSUfaRcr9uaf//Mf6fZDRnFBIVXfvJlpE8/gsXuKKGuF4jbvufIYRFvgkfvLiHz3SrXQRUT6Ad1TD9HLn6wM/I5OGTmFlza8lNb5F0xfQN3WOlraWrjluVu6LfvAoXDrYzB3GrR0tQOrg9+dez+Rz54FX/ka00aPZtMNDdQeBnWDoWIrVL0BkdJiqNLKcCIi/YGSeojqS2PQFqCgwcotK9M7uYOayTX89q3fcvZvzk5ZPF7gJfaCJC31Aa6QEyee6j2IRmHJEiLTp1Ozuh2amqC8HEoLYMmS3RabERGRvkvd72EaNCit4rM+Oyt4YYOldUsDJXQACuChidCe5CdcEG+j9qWFnx6orIT6epg/H+bN877X13vHRUSkX1BLPUQjBu3HW9tWByrb1NrE2EFjWX/leva/aTStrj3liPizas8KHoyDPxwIbUluvTeVQN2fF8MJl356MBKBGq0MJyLSX6mlHqKhA4YGLlvuiqkYXMHIyMhACR2gta01rXiSJXSAshhUbEvrdCIi0seppR6iDU0bApdtN/t0a9KAs9vaXJAb9gQ6Z3MxzBj1heDnExGRPk8t9RCt2Bhg7XefFaVY/D3LyuKw+IjSnMYgIiLhUlIPSUNLAx+3fJzGK4zaFbXeP7OxR0qKafDNxVDXtC4LFxYRkVxRUg/JwlcXpi6UYEd8B0+995T3IEc/hTH7jMnNhUVEJCuU1EPy0MqH0n7NvW/eS2MsxVqumeq4p56sxe6A5pbsXFtERHJCST0k6z5Jvys71hbzWvhZ2qJ8cGtR8gFzBuuefyI7FxYRkZxQUg9Le2aZ+Vcv/jLw6Pd0jYiVUp6kMV7eoiltIiL5Rkk9JCObMnvdCxvTW/89MAcXD52W9AdcAFSNm5Gda4uISE4oqYdk34Z4evue+1zAvc8zMbtmAUseHEC0hV0t9vIWb/e1JQ8OIHJBdXYuLCIiOaHFZ0Kysn1T1rrRM1FqRUSGjKLy9qXUn3UGtQfHqCuPUdFUQtXqEiIPPaKNWkRE8oySekia4zuzfg1/W/VAWojTGGskUllJ5P311NTWQl0dVFR4W6kqoYuI5B0l9ZBM3VTMe5F4Rq11awcX4EZIoTPiFryvvnZFLTWTa7RRi4jIXkL31ENyXGRixq8NktABrM2ldf+9bmtdZgGJiEi/pKQekk1fPDGze+pGsNcZtBYFLAvgoGJwRQYBiYhIf6WkHpIJ+x+dtUVkMmKwM76ThpaGXEciIiK9REk9JFWHVWX2bgbpTs9wytu8J+Yx+ubRPLv22cxOICIi/YqSekiipVHmHPGt7Mw5z3CqXFNrEw2xBqYvmp69NeZFRKTPUFIP0fXTb+pTc9U7tLv2T7d5FRGRvKWkHqJoaZRFZy9K/4VZ/iDQ1NqkkfAiInsBJfWQXXjEhVx6zKXegywt/5qu8uJyjYQXEdkLKKlnwW3Tb2P25NlpTT/LpgIroOrwquxeREREck5JPUuOGX1M8GSdKvlnuOlLeXE50ZIoS2YtIVKiZWFFRPKdknqWVB2WRss4VcIOukBNwvmGt5Yy//T51F9ZT+X+lWm8WERE+iut/Z4l0dJo8MJBWurpJHUDiw7y1n0XEZG9hlrqearZxXIdgoiI9DIl9Syp/6Q+1PMNLB4YvLCDQ4YcEur1RUSk71NSz5Krn7w61PMVpvOjMlh0bgbz5UVEpF9TUs+S1za8FuqiMveVfS3wOvEXHVLF+MHjw7u4iIj0C0rqWfLutnfDm3/uYO0rf6S0tfsyOJg+9Dh+XXVPSBcWEZH+RKPfs2R76/bwWuoGKwY00FKSvMigGDxz8XKOOOj4kC4qIiL9jVrquRS0Je9g4wHDkpd3MDM6VQldRGQvp6SeLUESdsCWfLQV1g0tSV7eYN3w0qCRiYhInlJS7+scRAvKWLP93W6Lrfn4nV4KSERE+iol9b7OYGtxnFhrS7fFYvHunxcRkfynpN4PxNrjDI93370+vFXd7yIiezsl9b4gxf33QqBm58RuB8pd3Hxo2FGJiEg/o6TeF6QYMDe4tZi/P+A8Stu6fr60DWaPOyf8uEREpF9RUu8HJu2MEr2gmifuG0CkBYr85F7UBpEWvOMXVOc2SBERyTktPtPXOTh39KkQjVJ5+1LWn3UGtQfHqCuPUdFUQtXqEiIPPQKRSK4jFRGRHFNSzyUHxXFoLSJpF7y1Q/U3/8t7UFlJ5P311NTWQl0dVFRAVZUSuoiIAErq2RNwYZkhO2HDoOTPf/mdUiKDR356IBKBmpqexSYiInlJ99SzJeCKchuiUBbr+unSGEw7f16oYYmISP5SUu8Digu77jApKSyi6vS5vRyNiIj0V0rquWaw5OKniBYNpLzV67MvcBAtGsiSi58iUqL75SIiEkyfuaduZjcAfwfEgLeBbzjnPs5tVNl30KADqdy/kvrvf0TtilrqttYxNjaO+u9/pIQuIiJp6Ust9ceBw51zRwCrgatzHE/GGloaghV0cOnx3wEgUhKhZnIN133hOoYOHKqELiIiaeszSd05t9Q5F/cfPgeMyWU8PbHw1YWBy86eMjuLkYiIyN6kzyT1Tr4JPJLrIDL18OqHA09pU4tcRETCYs4FmXsV0sXMngBGdvHUNc65h/wy1wBTgXNckuDMbDYwG2DEiBFT7rnnnixFnJk1W9fwScsngcpOGTVlj2ONjY1E8nBBmXytF+Rv3VSv/kX16l8yrdcpp5zyknNualfP9epAOefcF7p73syqgZnAqckSun+enwM/B5g6dao7+eSTwwyzx/709J+49g/Xpm6tO3AX7FnNZcuW0dfqFIZ8rRfkb91Ur/5F9epfslGvvjT6/XTgKuBvnXM7ch1PT7yy7oVchyAiInuhvnRP/TYgCjxuZq+Y2X/nOqBMPbtmWeB76iIiImHpMy1151xFrmMIy+b2T/rWxyUREdkrKPVkQbv13uBDERGRDkrqWVDiCnMdgoiI7IWU1LPgqOJxuQ5BRET2QkrqWTDrc5cE23pVREQkRErqWVB93N8HK6jELyIiIVJSz4JoaTR1IQfTxn4u+8GIiMheQ0k9hxZ+pTbXIYiISB5RUs+WVF3rDkZGuloGX0REJDNK6lly6PbSbp8/fPuAXopERET2FkrqWTJn9N8lb607+PaYmb0aj4iI5D8l9Syp/uZ8BrR2/dyAVqj+5n/1bkAiIpL3lNSzJDpkP5aesIBIC5TEvWMlcYi04B0frPvpIiISrj6zoUs+qpw5h/UnnkPtXfOo27SKimETqbroeiV0ERHJCiX1LIsMHknNZXfmOgwREdkLqPtdREQkTyipi4iI5AkldRERkTyhpC4iIpInlNRFRETyhJK6iIhInlBSFxERyRNK6iIiInlCSV1ERCRPKKmLiIjkCSV1ERGRPKGkLiIikieU1EVERPKEkrqIiEieUFIXERHJE0rqIiIieUJJXUREJE+Ycy7XMfSImW0C3s91HCEbCmzOdRBZkK/1gvytm+rVv6he/Uum9RrnnBvW1RP9PqnnIzN70Tk3NddxhC1f6wX5WzfVq39RvfqXbNRL3e8iIiJ5QkldREQkTyip900/z3UAWZKv9YL8rZvq1b+oXv1L6PXSPXUREZE8oZa6iIhInlBS72PM7HQzW2VmdWY2L9fxhMHMxprZU2b2lpm9YWaX5zqmMJlZoZn91cweznUsYTGzfc3sPjNb6f/cTsh1TGEws+/5v4MrzOxuMyvLdUyZMrNfmNlGM1uRcGywmT1uZmv875/JZYyZSFKvG/zfxdfM7EEz2zeXMWaiq3olPDfXzJyZDe3pdZTU+xAzKwQWAGcAk4ALzGxSbqMKRRy40jl3KHA8cGme1KvD5cBbuQ4iZPOBR51zhwBHkgf1M7PRwGXAVOfc4UAh8JXcRtUjdwKndzo2D3jSOTcBeNJ/3N/cyZ71ehw43Dl3BLAauLq3gwrBnexZL8xsLPBFYG0YF1FS71uOBeqcc+8452LAPcBZOY6px5xz651zL/v/bsBLEKNzG1U4zGwMMAO4I9exhMXMBgGfA/4HwDkXc859nNuoQlMEDDCzImAgUJ/jeDLmnHsa2Nrp8FnAQv/fC4Ev9WpQIeiqXs65pc65uP/wOWBMrwfWQ0l+XgC3AP8IhDLATUm9bxkNfJDweB15kvw6mNkBwNHAX3IbSWhuxfsP2Z7rQEJ0ELAJ+KV/W+EOMyvPdVA95Zz7ELgRr0W0HtjunFua26hCN8I5tx68D9PA8BzHkw3fBB7JdRBhMLMzgQ+dc6+GdU4l9b7FujiWN9MTzCwC3A/8g3Puk1zH01NmNhPY6Jx7KdexhKwImAz81Dl3NNBE/+zG3Y1/f/ks4EBgP6DczC7KbVSSDjO7Bu923qJcx9JTZjYQuAb4/2GeV0m9b1kHjE14PIZ+3D2YyMyK8RL6IufcA7mOJyQnAWea2Xt4t0o+b2Z35TakUKwD1jnnOnpT7sNL8v3dF4B3nXObnHOtwAPAiTmOKWwfmdkoAP/7xhzHExozqwZmArNcfszFHo/3AfNV/2/IGOBlMxvZk5MqqfctLwATzOxAMyvBG8TzuxzH1GNmZnj3Z99yzt2c63jC4py72jk3xjl3AN7P6g/OuX7f8nPObQA+MLOJ/qFTgTdzGFJY1gLHm9lA/3fyVPJgAGAnvwOq/X9XAw/lMJbQmNnpwFXAmc65HbmOJwzOudedc8Odcwf4f0PWAZP9/38ZU1LvQ/yBIN8BHsP7Y/Mb59wbuY0qFCcBX8Vryb7if03PdVDSre8Ci8zsNeAo4N9zHE+P+T0P9wEvA6/j/f3rtyuVmdndwHJgopmtM7Ma4Hrgi2a2Bm9E9fW5jDETSep1GxAFHvf/fvx3ToPMQJJ6hX+d/OjFEBEREbXURURE8oSSuoiISJ5QUhcREckTSuoiIiJ5QkldREQkTyipy17BzH7g74LU8VVvZveb2fgAr/26/5pIyDGd7J/38DDP65/7AP/cMwOUHWFmt5rZ22bWYmbbzOwRMzst7LjykZkda2Y/CFh2qpnd6e/E2G5md2Y3OtnbKKnL3mQ7cIL/NRdv/vWTAdY1X+y/JuxFL172z/t2yOcNzF9g5q94m9LcCEwDvga8B/zOzI7MVWz9yLHAPwcsexJQibfQVI8WGRHpSlGuAxDpRXHn3HP+v58zs7XAM8B04N7Ohf2tcAudc5vwNjgJlb/+/XMpC2bXIrydo07stB7/783sp0C+7M7WV/zYOTcfwMxezHUwkn/UUpe9WcdGLAcA+N2iL5rZl8zsDaAZOK5z93tC1/b5ZvYzM9vurxD1L2a22/8pMzvCzH5vZh+bWaOZPW9mX/Sf26P73X98hZnNN7Ot/ut+7C8b3FFmlJn9wszeMbOdZrbazH6UWCYIM/scMAW4uqsNdpxzrznn1iaUP9/MXve76D8ws3/ztzDteL7jfZpsZsvMbIe/+tdkMys3s1/679U7ZnZBp1iWmdl9ZjbbzN7z67XYvD3QE8sNNbOFZrbFP/8yM5vaqcx7ZnajmX3P/7lsM7N7zGzfTuUG+z+/j8ys2cz+bGbHdSrjzOxyM/t3M9tkZhvNbIGZlXbUGfhxQllnZsuSvefOuXzazU/6ICV12Zsd4H/f0OnYfwLX4bXg3+3m9f8JNALnAXfh7bZ0XseTZnYI8CdgFPBt4GzgQXbftKcrV+Jt7jAL+BEwG/i3hOeH4rWurwBOB24AvoGfXNLwt0Ab8ESqgmY2DajFu2Vwln+tuXjLd3a2ELgbOBdv58H78Nb+r8d7f/4C/Mq8vegTnYC3PO0VQA1wBPDbTmV+C5zmX7sK72/YU2ZW0anc+Xhru8/GWzN8JglL3fpJ+Qm8pVS/j7fv+CbgCdtzQ40r8XZ1uwjvvf574HL/ucXATQnxnwDM6eI9Eekdzjl96Svvv4AfAJvxbjkVAQcDTwGfAKP8MnfibXV7VKfXft0/HvEfH+A//lWncq8A9yQ8vhtvk4YBSWI62T/P4QnHHLASKEg4dg3e/fzBSc5TBFyI17NQ0inGmd28J/8NrA/4/j0HPNXp2D/ifSgY0+l9qk4oM90/9ouEY/sArcAlCceW+cfGJRw7yX/t6f7j0/3Hf5tQphwvGf8s4dh7eOMUihKO3QpsSHhcA8SACZ3ex7eBGzr9PJ7uVO/fAs8lPP6O96c07d/JF4E7c/1/Q1/59aWWuuxNhuAljlZgFXAQUOWcW59Q5kPn3CsBz7e00+M38VrYHT4P1DrndqYZ50Nu927aB4ABwOHg7XpnZv9gZm+a2U68+iwCSoH907xWys0f/LEFk9lz3EEtXkv5hE7Hn0z4d53//Q+7LujcdrxEvFvXOvCyc+79hHJ/wts69Fj/0LHAJufcHxPKNAEP4w0+S/SU8zZI6vAmMDzhFsUX8G6/vGtmRQm3Ef4I7NadT+qfs0ifoYFysjfZjvfH3OF1udc75zontY/SOF/nQWQxoCzh8RBgPenrvAd2x+NR/vd/wBupfj1eEtoGHAMs6HT9VD4EhplZmXOuuZtyQ4Fi9nxvOh4P7nQ88X2JdXGs43jnWLva+3sjn9Z7VBcxdMTRXQwd1zOgxP/3UOB4vA9EnXWejRAkdpE+QUld9iZx51yqEcdhblu4hU8TUjqGJ3nc8QHhy8C9zrlrOgqY2aQMrrMM+CHevefF3ZTbjJf8Osc1wv++NYNrd6Xz+TuOddR7fZIyIzKIYSte9/clXTzXkua5RPoMdb+LZM+TwPlmlm6r7qxOo+jPAXYCK/zHA9gz8cxKNzjn3DN4XdD/bmbRzs+b2WfNbKxzrs0v9+VORc4H2vH2iA7DZDPbdfvAzE7CS+LP+4f+gteF/rmEMgPx5tg/m+a1ngQqgLXOuRc7fb2e5rlifixqvUvOqaUukj3/grfIyNNmdhNey/1oYItz7hfdvC4K3GtmtwOH4Y2qv80519EafRy4zMz+gtdVPAsvQWViFt6AwRfN7Ba8+8WD8EaYfws4DvgAb3GVx8zsl8A9wGeBfwVud86ty/DanW0EHjZvdbYy4D/w7rM/CuCce8zM/gTUmtk8vPdzLt6HnBvSvNav8GYkLDOzG4F38G6XHIs3oO6WNM610v9+uZn9AfjEObeqq4JmNgxv1gHAZ4BxZnYegHPuvjTrILIHJXWRLHHOrTKzSrx733f4h98E/inFS2/CG8R3N15v2h2dXvNDYBjedDfwBtJdBvw+wxgnA1fjjWYfjTfS/nngQufcq365pWb2FeBavA8CG/04g66kFsRyvGlmt+LVbxnelLREZ/vXvRUv8T8PfN45V0canHPNZnYK3nv5L3hd+Bv98/0uzbifwftQcTneVMin8WY2dOUwdh9weFBCWUvzuiJ7sD3HCYlIrpiZA77rnOtq/nfe8hds2eycOy9VWRFJTvfURURE8oSSuoiISJ5Q97uIiEieUEtdREQkTyipi4iI5AkldRERkTyhpC4iIpInlNRFRETyhJK6iIhInvg/6OLQIfrBhCgAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -5180,7 +4798,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -5190,7 +4808,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -5200,7 +4818,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 39, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -5211,7 +4829,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -5238,7 +4856,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -5265,7 +4883,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -5277,7 +4895,7 @@ " verbose=False)" ] }, - "execution_count": 42, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -5291,19 +4909,19 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15940300945944538\n" + "Optimal Threshold: 0.15585444961401423\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5316,10 +4934,10 @@ { "data": { "text/plain": [ - "0.7282872884609857" + "0.7192717558206292" ] }, - "execution_count": 43, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -5331,14 +4949,14 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2015 Defaulters, the SVM original data RBF kernel identified 707\n" + "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" ] }, { @@ -5373,14 +4991,14 @@ " \n", " \n", " \n", - " 0\n", - " 6390\n", - " 344\n", + " 0\n", + " 6304\n", + " 355\n", " \n", " \n", - " 1\n", - " 1308\n", - " 707\n", + " 1\n", + " 1341\n", + " 749\n", " \n", " \n", "\n", @@ -5389,11 +5007,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6390 344\n", - "1 1308 707" + "0 6304 355\n", + "1 1341 749" ] }, - "execution_count": 44, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -5405,7 +5023,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5414,12 +5032,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6734\n", - " 1 0.67 0.35 0.46 2015\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.36 0.47 2090\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", "\n" ] } @@ -5444,21 +5062,21 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 80, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", - " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -5469,39 +5087,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.15995506541340332\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.6995913850752561" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#plot roc for svm\n", "get_roc(clf_reduced, y_test, X_test_pca, \n", @@ -5510,73 +5098,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2020 Defaulters, the SVM reduced features no tuning RBF kernal identified 738\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06387342
11282738
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6387 342\n", - "1 1282 738" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" @@ -5584,25 +5108,9 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.37 0.48 2020\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" ] @@ -5623,20 +5131,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 1, 'gamma': 0.001}" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -5659,23 +5156,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", - " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", @@ -5684,103 +5167,19 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.153632964235413\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", - " \"SVM reduced features and tuning RBF kernal\")" + " \"SVM reduced features and tuning RBF kernel\")" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2015 Defaulters, the SVM reduced features and tuning RBF kernal identified 895\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06115619
11120895
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6115 619\n", - "1 1120 895" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" @@ -5788,25 +5187,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.91 0.88 6734\n", - " 1 0.59 0.44 0.51 2015\n", - "\n", - " accuracy 0.80 8749\n", - " macro avg 0.72 0.68 0.69 8749\n", - "weighted avg 0.79 0.80 0.79 8749\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" ] @@ -5827,24 +5210,9 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"recall\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m auroc])\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 573\u001b[0m \"\"\"\n\u001b[1;32m--> 574\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 575\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \"\"\"\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[0;32m 473\u001b[0m \u001b[1;34m\"the number of features at training time\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" - ] - } - ], + "outputs": [], "source": [ "evaluation.loc[4] = ([\"SVM\" , \n", " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", @@ -6022,7 +5390,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/BT2101_Default - EDIT-Copy1.ipynb index dbd858a..4ae5047 100644 --- a/BT2101_Default - EDIT-Copy1.ipynb +++ b/BT2101_Default - EDIT-Copy1.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAapElEQVR4nO3deZxcZZ3v8c+XgALjAg6LCEhQo4iMsgTEcQNRBBwFRhz16oBeFHVwvDqLouOIG3fQGZfBBQcxF3BUFBFFQTGgiNcrSFBkEZWIKDEBoiyyKYu/+0c9LWWnOl056Uqn6c/79apXn/Oc55zzO51Kffs8p+pUqgpJkrpYZ7oLkCTNXIaIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEpNWU5NAkX27T6yepJFtNd10ASY5OcnybfnSSm6Zw2yckeWOb3ifJ4inc9jOT/HCqtqfRMUTUWZJb+x5/SHJH3/xL1nAtq/3ineTaJE/pm98uyd2TrVdVn6iq53bd77gazk/y0qnY1nhV9dOq2miIGl6d5Owhtveyqnrv6tY16N+uqs6uqies7rY1eutOdwGauarqAWPTSa4GXlFVk774DJJk3aqa9AV7bTNT615dSeZU1T3TXYemn2ciGpkkT05yQZKbkyxN8oEk67ZlY399vibJz4DLWvtzklyZ5KYkHxz/l3mSVyX5SZIbkpyRZMu26Lz28yftTOiAAfVsl+Tctu7yJCcmeWBbdgqwGfD1tv7r2jbn9J1d7dT+Sv9Gko8kuRE4YoK/3A9IcnXbz1FJ0vbzx+GlvprubtPvA3YFjm/7e19r36Ht88YkVww6tr7tPSrJd5LckuSrwMaD9tXmX9lqvCXJVUlekGQn4IPAHq2Ga1vfk5Mck+TrSW4DntTa3jpu/+9ov9+rkrygr338v2P/72yFf7vxw2NJ/iLJt9vz4pIk+/YtO7k9V85qx/KdJNtM9DvSFKsqHz5W+wFcDTxzXNtu9F4U5wCPBBYDr27L1gcKOAPYCNgAeChwK/BXwHrAG4G7gJe2dV4EXAE8ui1/N/DNcdvbaiU1bgc8A7hf29f5wNF9y68FnjKu/93jtvFq4G7gle24NmhtZ4+r46x2XNsCV/Udw9HA8RPto9X00r75BwHLgJe0/e0K3AA8aoJj/D7wb+0Y9wJuH9tf/77ohctNwCPb/JbAY/uO8exx2z257feJ9P74vH9re2tbvk/7vYzt+5lt39tOcFyDfmdb9S3fB1jct/wXwD+2f/dnt+fJtn21XQ/s3JZ/Hjhhuv9PzJaHZyIamar6XlVdWFX3VNXPgOOBp4/rdlRV3VRVdwDPAy6sqq9U1V3AfwA39vV9FfDu6o3t3wW8A3hKks2HrOfHVfWNqrqzqq6l9xf3+HqGcVVVfbwd1x0T9Pm3dlw/Bz4MvLjDfgAOBC6rqk+1/V0IfBl4/viOSR4NbA+8ox3jOcDXJtn+DknWr6pfVdUVk/T9fFVdUFV/qKrfD1h+d9++zwbOBg6a7ACH8NT28/1VdVdVnQUsBF7Y1+dzVfX99rz4NLDjFOxXQzBENDJJtk/y1STXJfkt8DZgk3Hdrumbflj/fFX9AfhV3/JtgI+1IY2bgOX0XriGupie5GFJTknyq1bP8QPqGcY1k3f5kz6/oHdsXWwDPG3smNtxPx/YYkDfhwHLq+p34/a9gqq6kd7ZzeuAa5OcnuRRk9Qy2XEP2nfX4+73MOCXVdV/t9hf0Dt7GnNt3/TtwAPQGmGIaJQ+Tm945ZFV9SDgnUDG9el/YVhGXyAkWYc/faG4BnhZVW3U99igqi4at52J/DtwG7BDq+cV4+oZv42JtjnMvrbum344sLRN3wZs2LfsoZNs+xrg6+OO+QFV9foB+1wGbJJk/XH7HqiqzqiqvWgv0sCxE9QwUW3jDdr3MMc92XaXsuJxPJw//QND08QQ0Sg9ELi5qm5N8jh61xFW5nTgiUn2axfg/4G+C8PAx4C3JnkMQJKNkzwfoA2v3Aw8YpJ6bgV+m+Thbfv9rhu3/vX0LqxP+EK8Em9K8uAkc4HXAp9t7RcDeybZMsnGwJsmqeGLwE5JXphkvST3S7J7G7oa76fAj4F/bf32pHdtYQVt/89JsiHwe3q/l7F3W10HbJ1kvVU85vX69v0M4FnAqX3HfVB6b6jYDnjZ2EpD/Nt9G1gnyeuTrJvkWcDewCmrWJ9GwBDRKL0BeEWSW4GPcO8L6UBVtYzetYNjgF/TOyu5lN6LHFX1GXrXF77QhqMupvdCNeZtwClt2Od5A3bxNuAp9F6wTuPeF7gxRwFHtfVf24Z83gtc1NpWZZz9DOCHwCJ6L3b/3df+FeBH9C42f3Hceh8ADm7vxHpvq+HZwMvpnWkspfeGghVe4NtwzwuBPeldBH9j337HmwO8md4w0G/oXbD/+7bsa/TeKHF9kiWrcMxX0xtevBZYALy8qq5qy95L7yMFy4HjBtQ14b9dGyL7K3rXV34DvB94YbvOpmmWPx1mlNYe7WzkWuC5VfXd6a5H0oo8E9FaJcm+bRhofeBIehdJL5rmsiRNwBDR2uZpwM/pXY/YCziwqu6c3pIkTcThLElSZ56JSJI6m3U3YNxkk01q7ty5012GJM0oF1100a+ratPx7bMuRObOncuiRYumuwxJmlGSDLz7gcNZkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOZt0n1meKuUecMd0l3GdcffRzprsE6T7LMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmcjC5EkWyf5ZpIrklye5H+19ockWZjkyvZz49aeJMckWZzkkiQ7923rkNb/yiSH9LXvkuTSts4xSTKq45EkrWiUZyJ3A/9YVY8FdgcOT7I9cARwTlXNA85p8wD7AvPa4zDgWOiFDnAk8ERgN+DIseBpfQ7rW2+fER6PJGmckYVIVS2rqu+36VuAK4Atgf2BE1u3E4ED2vT+wEnVcz6wUZItgGcDC6vqhqq6EVgI7NOWPaiqvltVBZzUty1J0hqwRq6JJJkL7ARcAGxeVcugFzTAZq3blsA1fastaW0ra18yoF2StIaMPESSPAA4FXh9Vf12ZV0HtFWH9kE1HJZkUZJFy5cvn6xkSdKQRhoiSdajFyCfqqovtObr2lAU7ef1rX0JsHXf6lsBSydp32pA+wqq6riqml9V8zfddNPVOyhJ0h+N8t1ZAT4BXFFV7+9bdDow9g6rQ4Av9bUf3N6ltTtwcxvuOgvYO8nG7YL63sBZbdktSXZv+zq4b1uSpDVg3RFu+8nA3wKXJrm4tb0FOBr4XJJDgV8CL2jLzgT2AxYDtwMvB6iqG5K8C7iw9XtnVd3Qpl8DnABsAHy1PSRJa8jIQqSq/i+Dr1sA7DWgfwGHT7CtBcCCAe2LgB1Wo0xJ0mrwE+uSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJgiTXJ7msr+3tSX6V5OL22K9v2ZuTLE7ykyTP7mvfp7UtTnJEX/u2SS5IcmWSzya536iORZI02CjPRE4A9hnQ/oGq2rE9zgRIsj3wIuBxbZ2PJpmTZA7wEWBfYHvgxa0vwHvatuYBNwKHjvBYJEkDjCxEquo84IYhu+8PnFxVv6+qnwOLgd3aY3FVXVVVdwInA/snCfAM4PNt/ROBA6b0ACRJkxoqRJLsMIX7fG2SS9pw18atbUvgmr4+S1rbRO1/DtxUVXePax8oyWFJFiVZtHz58qk6Dkma9YY9E/lYku8l+bskG63G/o4FHgnsCCwD3tfaM6BvdWgfqKqOq6r5VTV/0003XbWKJUkTGipEquopwEuArYFFST6d5FmrurOquq6q7qmqPwAfpzdcBb0zia37um4FLF1J+6+BjZKsO65dkrQGDX1NpKquBN4KvAl4OnBMkh8n+etht5Fki77ZA4Gxd26dDrwoyf2TbAvMA74HXAjMa+/Euh+9i++nV1UB3wQOausfAnxp2DokSVNj3cm7QJLHAy8HngMsBJ5bVd9P8jDgu8AXBqzzGWAPYJMkS4AjgT2S7Ehv6Olq4FUAVXV5ks8BPwLuBg6vqnvadl4LnAXMARZU1eVtF28CTk7ybuAHwCdW+eglSatlqBABPkxv+OktVXXHWGNVLU3y1kErVNWLBzRP+EJfVUcBRw1oPxM4c0D7Vdw7HCZJmgbDhsh+wB19ZwfrAOtX1e1V9cmRVSdJWqsNe03kbGCDvvkNW5skaRYbNkTWr6pbx2ba9IajKUmSNFMMGyK3Jdl5bCbJLsAdK+kvSZoFhr0m8nrglCRjn8XYAnjhaEqSJM0UQ4VIVV2YZDvgMfQ+Lf7jqrprpJVJktZ6w56JAOwKzG3r7JSEqjppJFVJkmaEYT9s+El697y6GLinNRdgiEjSLDbsmch8YPt2uxFJkoDh3511GfDQURYiSZp5hj0T2QT4UZLvAb8fa6yq542kKknSjDBsiLx9lEVIkmamYd/i+60k2wDzqursJBvSu6uuJGkWG/brcV9J7/vM/6s1bQl8cVRFSZJmhmEvrB8OPBn4LfzxC6o2G1VRkqSZYdgQ+X1V3Tk2076W1rf7StIsN2yIfCvJW4AN2nernwJ8eXRlSZJmgmFD5AhgOXApva+0PZPe961LkmaxYd+d9Qd6X4/78dGWI0maSYa9d9bPGXANpKoeMeUVSZJmjFW5d9aY9YEXAA+Z+nIkSTPJUNdEquo3fY9fVdUHgWeMuDZJ0lpu2OGsnftm16F3ZvLAkVQkSZoxhh3Oel/f9N3A1cDfTHk1kqQZZdh3Z+056kIkSTPPsMNZ/7Cy5VX1/qkpR5I0k6zKu7N2BU5v888FzgOuGUVRkqSZYVW+lGrnqroFIMnbgVOq6hWjKkyStPYb9rYnDwfu7Ju/E5g75dVIkmaUYc9EPgl8L8lp9D65fiBw0siqkiTNCMO+O+uoJF8FntqaXl5VPxhdWZKkmWDY4SyADYHfVtV/AkuSbDuimiRJM8SwX497JPAm4M2taT3gv0dVlCRpZhj2TORA4HnAbQBVtRRveyJJs96wIXJnVRXtdvBJ/mx0JUmSZophQ+RzSf4L2CjJK4GzmeQLqpIsSHJ9ksv62h6SZGGSK9vPjVt7khyTZHGSS/pv+JjkkNb/yiSH9LXvkuTSts4xSbIqBy5JWn3D3gr+P4DPA6cCjwHeVlUfmmS1E4B9xrUdAZxTVfOAc9o8wL7AvPY4DDgWeqEDHAk8EdgNOHIseFqfw/rWG78vSdKITfoW3yRzgLOq6pnAwmE3XFXnJZk7rnl/YI82fSJwLr0L9vsDJ7Uhs/OTbJRki9Z3YVXd0GpZCOyT5FzgQVX13dZ+EnAA8NVh65Mkrb5Jz0Sq6h7g9iQPnoL9bV5Vy9p2lwGbtfYt+dP7cC1pbStrXzKgfaAkhyVZlGTR8uXLV/sgJEk9w35i/XfApe1M4Laxxqp63RTVMeh6RnVoH6iqjgOOA5g/f/6E/SRJq2bYEDmjPVbXdUm2qKplbbjq+ta+BNi6r99WwNLWvse49nNb+1YD+kuS1qCVhkiSh1fVL6vqxCna3+nAIcDR7eeX+tpfm+RkehfRb25Bcxbwv/supu8NvLmqbkhyS5LdgQuAg4HJLvRLkqbYZNdEvjg2keTUVdlwks8A3wUek2RJkkPphcezklwJPKvNA5wJXAUspvfW4b8DaBfU3wVc2B7vHLvIDrwGOL6t8zO8qC5Ja9xkw1n91x4esSobrqoXT7BorwF9Czh8gu0sABYMaF8E7LAqNUmSptZkZyI1wbQkSZOeiTwhyW/pnZFs0KZp81VVDxppdZKktdpKQ6Sq5qypQiRJM8+qfJ+IJEl/whCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM7Wne4CJM0sc484Y7pLuE+5+ujnTHcJq8UzEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKmzaQmRJFcnuTTJxUkWtbaHJFmY5Mr2c+PWniTHJFmc5JIkO/dt55DW/8okh0zHsUjSbDadZyJ7VtWOVTW/zR8BnFNV84Bz2jzAvsC89jgMOBZ6oQMcCTwR2A04cix4JElrxto0nLU/cGKbPhE4oK/9pOo5H9goyRbAs4GFVXVDVd0ILAT2WdNFS9JsNl0hUsDXk1yU5LDWtnlVLQNoPzdr7VsC1/Stu6S1TdS+giSHJVmUZNHy5cun8DAkaXabrlvBP7mqlibZDFiY5Mcr6ZsBbbWS9hUbq44DjgOYP3/+wD6SpFU3LWciVbW0/bweOI3eNY3r2jAV7ef1rfsSYOu+1bcClq6kXZK0hqzxEEnyZ0keODYN7A1cBpwOjL3D6hDgS236dODg9i6t3YGb23DXWcDeSTZuF9T3bm2SpDVkOoazNgdOSzK2/09X1deSXAh8LsmhwC+BF7T+ZwL7AYuB24GXA1TVDUneBVzY+r2zqm5Yc4chSVrjIVJVVwFPGND+G2CvAe0FHD7BthYAC6a6RknScNamt/hKkmYYQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHU240MkyT5JfpJkcZIjprseSZpNZnSIJJkDfATYF9geeHGS7ae3KkmaPWZ0iAC7AYur6qqquhM4Gdh/mmuSpFlj3ekuYDVtCVzTN78EeOL4TkkOAw5rs7cm+ckaqG022AT49XQXMZm8Z7or0DTx+Tm1thnUONNDJAPaaoWGquOA40ZfzuySZFFVzZ/uOqRBfH6uGTN9OGsJsHXf/FbA0mmqRZJmnZkeIhcC85Jsm+R+wIuA06e5JkmaNWb0cFZV3Z3ktcBZwBxgQVVdPs1lzSYOEWpt5vNzDUjVCpcQJEkaykwfzpIkTSNDRJLUmSGiVZbkhCQHTdJnuyQXJ/lBkkd22Mfbk/xTm35Zkod1rVczX//zYYLlmya5oD3fntph+y9L8uE2fYB3vhieIaJROQD4UlXtVFU/W81tvQwwRLQyewE/bs+3b6/mtg6gdxslDcEQuQ9JMjfJFUk+nuTyJF9PskFbtmOS85NckuS0JBu39nOTvCfJ95L8dNBfcen5cJIfJTkD2Kxv2S5JvpXkoiRnJdkiyX7A64FXJPlm6/fF1ufydgeBsfVv7Zs+KMkJ4/Z9EDAf+FQ7s9lgKn9nWnsl+Zd2c9Wzgce0tkcm+Vp7Ln27nfHuCLwX2G/sOZLk2CSL2vPtHX3bvDrJJm16fpJzx+3zL4HnAf/etrXKZ9GzjSFy3zMP+EhVPQ64CXh+az8JeFNVPR64FDiyb511q2o3ei/8/e1jDqT3n/gvgFcCfwmQZD3gQ8BBVbULsAA4qqrOBD4GfKCq9mzb+J+tz3zgdUn+fJiDqarPA4uAl1TVjlV1xzDraWZLsgu9z33tBPw1sGtbdBzw9+259E/AR6vqYuBtwGf7niP/0j6t/njg6UkeP8x+q+r/0fus2T+3ba3uWfR93oz+nIgG+nn7TwVwETA3yYOBjarqW639ROCUvnW+0N9/wDafBnymqu4Blib5Rmt/DLADsDAJ9D6rs2yCul6X5MA2vTW9sPvNqhyYZpWnAqdV1e0ASU4H1qf3B8wp7fkGcP8J1v+bdsa7LrAFveGpS0Za8SxliNz3/L5v+h5gmOGfsXXuYeLnxKAPFAW4vKqetLKNJ9kDeCbwpKq6vQ0hrD9gu+sj3Wv8c24d4Kaq2nFlKyXZlt5Zyq5VdWMbIh17bt3NvSMwPt+mgMNZs0BV3Qzc2He942+Bb61klfHOA16UZE6SLYCxIaqfAJsmeRL0hreSPG7A+g8GbmwBsh2we9+y65I8Nsk69IbNBrkFeOAq1KuZ7zzgwHZ944HAc4HbgZ8neQH88VrdEwas+yDgNuDmJJvT+76hMVcDu7Tp5zOYz7dVYIjMHofQu1h4CbAj8M5VWPc04Ep611KOpQVQ+w6Xg4D3JPkhcDHtesk4XwPWbft+F3B+37IjgK8A32DiobATgI95YX32qKrvA5+l95w6FRh7x9VLgEPb8+1yBnx/UFX9EPhBW74A+E7f4ncA/5nk2/TOvAc5Gfjnrm9Pn2287YkkqTPPRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISKNSJKHJjk5yc/afcfOTPLoJJdNd23SVPET69IIpHdfjtOAE6vqRa1tR2DzaS1MmmKeiUijsSdwV1V9bKyh3dPsmrH5dtflbyf5fnuM3dhyiyTntQ9XXpbkqe1uASe0+UuTvGHNH5K0Is9EpNHYgd4NLVfmeuBZVfW7JPOAz9C7y/H/AM6qqqOSzAE2pHeXgS2rageAJBuNrnRpeIaINH3WAz7chrnuAR7d2i8EFrRb7X+xqi5OchXwiCQfAs4Avj4tFUvjOJwljcbl3Hujv4m8AbgOeAK9M5D7AVTVefRuv/8r4JNJDq6qG1u/c4HDgeNHU7a0agwRaTS+Adw/ySvHGpLsCmzT1+fBwLKq+gO9OyvPaf22Aa6vqo8DnwB2bt/Gt05VnQr8K7DzmjkMaeUczpJGoKqqfQnXB5McAfyO3m3IX9/X7aPAqe3W5t+kd/tygD3o3UX2LuBW4GBgS+D/tFvmA7x55AchDcG7+EqSOnM4S5LUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJn/x+T6PNo2DnX9gAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "
" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3df7BcZZ3n8ffHIC4VpEiM3IoEDboZZ/mhCHcIrlUz17EIAZwKlj+WHyXBQWMpKWdXtoowyxYK6Ga2lnXBHzhRY0ItENmpYZNRNGZZe3FnRZMoS4KARLzKJUiEQJaLOBj87h/naWgufW/f7j7d/Zx7P6+qru5++ulznpP+5vme55znnqOIwMzMLDevGHQDzMzMmnGCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5AQ1TZJGJT0raVzSY5K+Lunwhs83SDoo6XUNZaenugsayl4l6T5JH22xvpMk7ZT02/R8Um+2zHqlnzEj6Y8kbZb0G0n7JW2V9ObebZ2Vrc/xskDSP0p6QtJTkn4g6R2927rOOEG15y8i4nDgZOBPgCsAJM0F3gscAC6oV46IbcA3gesalnEF8CiwbrKVSDoU2Az8V2AesBHYnMqtWvoSM8CRwBbgzcAQ8COKGLJq6Ve8jAN/CbyWoo/5G+AfJB1S2paUwAmqAxHxCPBt4IRU9F7gKeAqYOWE6p8E/kzS2ZJOAFYDH4mprzE1AhwC/JeI+KeIuB4Q8OflbYX1U69jJiJ+FBFfi4j9EfF74HPAmyW9puxtsd7rQ7z8LiIeiIg/UPQtz1Mkqvnlbkl3nKA6IOkY4CzgJ6loJXALsAn4Y0kn1+tGxAHgY8CXgfXApyPi5y1WcTxwz4QAuyeVWwX1IWYm+lPg1xHxRLdtt/7rV7xIugf4HcXo+6sRsa+0jSiBE1R7/rukp4D/Dfwv4LOSXg+8E7g5Ih4D7mDCHk5E/ANwF8W/9/XTWM/hFEP5RgeAV3fXfBuAfsXMCyQtAr5IsWdt1dLXeImItwBHAOendWbFCao950TEkRHxhoj4eEQ8C3wQuC8i7k51bgLOl/TKCd+9F7g/DalbGacImkZHAE9303gbiH7FDACSXgt8F/hSRNxSxgZYX/U1XuCFw323AGskvbXrLShRVifEKupC4PWSfp3eHwK8BjiTYtjciXuBSyWp4TDfWyj2iq36ehEzSJpHkZy2RMRnum6l5aIn8dLEK4E3Av+3xGV2xQmqC5LeDrwJeBvwm4aPrqUYgncaPDWKk5afkPRl4COp/H92uDzLRK9iRtIRwFbgHyNiTbfttDz0MF5Oo+j/fwTMAT5BMfvzh920t2xOUN1ZCWyOiF2NhZKuA74vaX5E7G93oRHxnKRzgK8Ca4H7KIb+z5XRaBuonsQM8B6KacnHS7qoofy4iPhVx621QetVvLyK4lzVG4HfA7uAsyNib7cNLpN8R10zM8uRJ0mYmVmWnKAGRNIF6ZImEx/3DrptlifHjLVjJsSLD/GZmVmWWk6SkLQeeDewLyJOSGWfophZVp9V8tcRcXv67HLgYtIstIjYmsqXU1wvag7FXyyvTeXHUvx19Hzgx8AHpzMZYMGCBbF48eJpb2grzzzzDHPnzi1teb0yiHbu3Lnz8Yh4bV9XWrKy4wWqETOOl865j+mfSWMmIqZ8UFwy5WRgd0PZp4B/26TucRRz6F8FHAv8nCIhzUmv3wgcmuocl75zK3Buev1l4GOt2hQRnHLKKVGm733ve6Uur1cG0U5gR0zjN8n5UXa8RFQjZhwv+cRMFeIlIq+YaXkOKiLuBKY7jXEFsCmKC5z+AtgDnJoeeyLioShGR5uAFZLqF0D9u/T9jcA501yXmZnNYN38HdRqSRcCO4BLI+JJ4GiK60HVjaUygIcnlC+l+GvopyLiYJP6LyNpFbAKYGhoiFqt1kXzX2p8fLzU5fVKVdppZtatThPUDcDVQKTnaynuLaImdYPmswVjivpNRcQ60j1OhoeHY2RkpK1GT6VWq1Hm8nqlKu00M+tWRwkqiivqAiDpKxQ3zIJiBHRMQ9VFQP0vk5uVPw4cKemQNIpqrN9Xux45wEVrvjVlndG1Z/epNVYFrWLG8WKNHC/t6+jvoCQtbHj7HmB3er0FODfdcvhYYAnFtZ62A0skHZvuCnsuxQUtA/ge8L70/ZX4LqCVJ2m9pH2SdjeUfUrSI5LuTo+zGj67XNIeSQ9IOqOhfHkq2yNpTUP5sZJ+KOlBSd/wnYarzfFik2mZoCTdAvyA4u6cY5IuBv6jpF3pZlfvBP4NQETcSzEr76fAd4BLIuL5NDpaTXExy/uAW1NdgMuAT0raQ3FO6mulbqENwgZgeZPyz0XESelR/7OE4yh2WI5P3/mSpDmS5lBcvf1Mitmh56W6UNye+nMRsQR4kuLPGqy6NuB4sSZaHuKLiPOaFE+aRKK4zP/LLvWfAuz2JuUPUczysxkiIu6UtHia1V+Y+Qn8Iu2o1ONhT4oPJNVnft5HMfPz/FRnI8WfPdxQTuut3xwvNhlfzdz6qa8zP3s56xNg6DC49MSDk36ew2zLis/6nFEzhasQL5BXzDhBWb/0feZnL2d9Anz+ps1cu2vy/0KjF5S7vk5UeNbnjJspXIV4gbxixgnK+mImzvy03nG8GPhq5tYnnvlp7XC8GHgEZT2QZn6OAAskjQFXAiOSTqI4vDIKfBSKmZ+S6jM/D5Jmfqbl1Gd+zgHWT5j5uUnSNcBP8MzPSnO82GScoKx0nvlp7XC82GR8iM/MzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllqWWCkrRe0j5JuxvK5kvaJunB9DwvlUvS9ZL2SLpH0skN31mZ6j8oaWVD+SmSdqXvXC9JZW+kmZlVz3RGUBuA5RPK1gB3RMQS4I70HuBMYEl6rAJugCKhUdzGeSnFnS2vrCe1VGdVw/cmrsvMzGahlgkqIu4E9k8oXgFsTK83Auc0lN8YhbuAIyUtBM4AtkXE/oh4EtgGLE+fHRERP4iIAG5sWJaZmc1ih3T4vaGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRjLYYGhqiVqt12PyXGzoMLj3x4JR1ylxfp8bHx7Noh5lZr3WaoCbT7PxRdFDeVESsA9YBDA8Px8jISAdNbO7zN23m2l1T/3OMXlDe+jpVq9Uoc7t7QdJ64N3Avog4IZXNB74BLAZGgQ9ExJPpnON1wFnAb4GLIuLH6TsrgSvSYq+JiI2p/BSKQ8+HAbcDf5VG4FZBjhebTKez+B5Lh+dIz/tS+RhwTEO9RcDeFuWLmpRbtW3A5y1t+jbgeLEmOk1QW4D6TLyVwOaG8gvTbL7TgAPpUOBWYJmkeSlolgFb02dPSzot7Rld2LAsqyift7R2OF5sMi0P8Um6BRgBFkgao9hLWQvcKuli4FfA+1P12ymG3nsoht8fAoiI/ZKuBraneldFRD0gP8aLw+9vp4fNPH0/b9nLc5bQ+rxlDucKK3zOctad587ld8opZlomqIg4b5KP3tWkbgCXTLKc9cD6JuU7gBNatcNmrJ6dt+zlOUtofd7S5yx7Ysae584hXiCvmPGVJKxffN7S2uF4MSco6xuft7R2OF6s9GnmZj5vaW1xvNhknKCsdD5vae1wvNhkfIjPzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZWnW3LBw8ZpvTfn5pSf2qSFWGY4Zs8HqKkFJGgWeBp4HDkbEsKT5wDeAxcAo8IGIeFKSgOsobtf8W+CiiPhxWs5K4Iq02GsiYmM37TIz6zfv0JSvjEN874yIkyJiOL1fA9wREUuAO9J7gDOBJemxCrgBICW0K4GlwKnAlZLmldAuMzOrsF6cg1oB1EdAG4FzGspvjMJdwJGSFgJnANsiYn9EPAlsA5b3oF2WAUmjknZJulvSjlQ2X9I2SQ+m53mpXJKul7RH0j2STm5YzspU/8E0ArcZyPEyu3WboAL4rqSdklalsqGIeBQgPR+Vyo8GHm747lgqm6zcZi6Puq0djpdZqttJEu+IiL2SjgK2Sbp/irpqUhZTlL98AUUSXAUwNDRErVabdkMvPfHglJ8PHda6Tjvr65Xx8fEs2lGyFcBIer0RqAGX0TDqBu6SVB91j5BG3QCS6qPuW/rbbBsQx8ss0VWCioi96XmfpNso9k4ek7QwIh5NwbEvVR8Djmn4+iJgbyofmVBem2R964B1AMPDwzEyMtKsWlMXtTyBeZBrd039zzF6wfTX1yu1Wo12tjtD9VF3AH+bftOXjLrTDg941G2Ol1mt4wQlaS7wioh4Or1eBlwFbAFWAmvT8+b0lS3AakmbKIbaB1JwbQU+2zDkXgZc3mm7LHt9G3V3M+KG7kfdOYx0Z8CIe9Ycpcnld8opZroZQQ0BtxWzxzkEuDkiviNpO3CrpIuBXwHvT/Vvp5hivodimvmHACJiv6Srge2p3lX1objNPP0cdXcz4obuR90ecXdvNh2lySFeIK+Y6XiSREQ8FBFvTY/jI+IzqfyJiHhXRCxJz/tTeUTEJRHxpog4MSJ2NCxrfUT88/T4evebZTmSNFfSq+uvKUbLu3lx1A0vH3VfmGZnnUYadQNbgWWS5qWR97JUZjOI48VmzZUkLAsedVs7HC+znBOU9U1EPAS8tUn5E8C7mpQHcMkky1oPrC+7jZYPx4s5QZmZTUOrSxlZ+Zyg2tAqQEfXnt2nlli3cuhsptMGx5Q1mm19kG+3YWZmWXKCMjOzLDlBmZlZlpygzMwsSzNmkkQOJ73NzKw8MyZB5WC2zbAxM+slH+IzM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmlqVsEpSk5ZIekLRH0ppBt8fy5nixdjlmqieLq5lLmgN8ETgdGAO2S9oSET8dbMvKNZ1bgviK563NlngBXyG/LLMpZmaSLBIUcCqwJyIeApC0CVgBOHisGceLtWtWxMxM26HJJUEdDTzc8H4MWDqxkqRVwKr0dlzSA2U14BOwAHi8rOV1Sn/Tssog2vmGPq+vlYHHC7SOmWn8ll1zvEzbwGOmIvECGcVMLglKTcriZQUR64B1PWmAtCMihnux7DJVpZ09NvB4gWr8FlVoY58MPGaq8lvk1M5cJkmMAcc0vF8E7B1QWyx/jhdrl2OmgnJJUNuBJZKOlXQocC6wZcBtsnw5XqxdjpkKyuIQX0QclLQa2ArMAdZHxL19bkbPDgWVrCrt7JlM4gWq8VtUoY09l0nMVOW3yKadinjZYVgzM7OBy+UQn5mZ2Us4QZmZWZacoKjGJVAkjUraJeluSTsG3Z7ZrArxAo6ZnFQhZnKMl1l/DipdAuVnNFwCBTgvt0ugSBoFhiNi4H9MPJtVJV7AMZOLqsRMjvHiEVTDJVAi4jmgfgkUs2YcL9Yux0yHnKCaXwLl6AG1ZSoBfFfSznQ5FhuMqsQLOGZyUZWYyS5esvg7qAGb1iVQMvCOiNgr6Shgm6T7I+LOQTdqFqpKvIBjJhdViZns4sUjqIpcAiUi9qbnfcBtFIcNrP8qES/gmMlIJWImx3hxgqrAJVAkzZX06vprYBmwe7CtmrWyjxdwzGQm+5jJNV5m/SG+TC6B0soQcJskKH6zmyPiO4Nt0uxUkXgBx0w2KhIzWcbLrJ9mbmZmefIhPjMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKCmSdKopGcljUt6TNLXJR3e8PkGSQclva6h7PRUd0FD2ask3Sfpo9Nc70pJIenD5W6R9Vq/YybFyTNpfeOSvtqbLbNeGEC8zJF0jaS9kp6W9BNJR/Zm6zrjBNWev4iIw4GTgT8BroAX7kD5XuAAcEG9ckRsA74JXNewjCuAR4F1rVYmaR5wOZDbzc1s+voaM8BbI+Lw9PBOTfX0M14+DfxL4O3AEcAHgd+VshUlcYLqQEQ8AnwbOCEVvRd4CrgKWDmh+ieBP5N0tqQTgNXAR2J6d4r8D8D1wOOlNNwGpo8xYzNAr+Ml7fz+61Tvl1HYHRFOUFUn6RjgLOAnqWglcAuwCfhjSSfX60bEAeBjwJeB9cCnI+Ln01jHqcBw+p5VXD9iJrlT0q8l/b2kxSU13/qsD/FyInAQeF+Kl59JuqTkzeheRPgxjQcwCoxT7MX8EvgScBjweuAPwEmp3lbguibf/2/ADuAV01jXnFT37el9DfjwoP8N/Mg3ZlL9PwUOBY4EvgDsBg4Z9L+DH/nFC3A+EMDX0jreAvwGOH3Q/w6ND4+g2nNORBwZEW+IiI9HxLMUx23vi4i7U52bgPMlvXLCd+8F7o+IP0xjPR8H7omIH5TXdBuQfsUMEXFnRDwXEU8BfwUcC/yLkrbD+qNf8fJser4qIp6NiHsoRmdnlbERZTlk0A2YAS4EXi/p1+n9IcBrgDOBLR0u810Ux5TrwTIfeJukkyJidVettRz0ImaaCUAlLs8Goxfxck96zvq8phNUFyS9HXgT8DaK4XHdtRTHjDsNnouAf9bw/u+Bv6MYjluF9SpmJB0PvBLYRXHI5hrgEeC+btprg9WreImIn0v6PvDvJH0CeCPwr4DzumtxuZygurMS2BwRuxoLJV0HfF/S/IjY3+5C0yGaxuU9B/y/KE6GWrX1JGaAIeAGYBHwDPB/gHdHxO+7bbANVK/iBYpk9DXgCWAf8O8j4o6uWlsypRNmZmZmWfEkCTMzy5IT1IBIuqDhkjSND181wppyzFg7ZkK8+BCfmZllqbKTJBYsWBCLFy8ubXnPPPMMc+fOLW15vTKIdu7cufPxiHhtX1dasrLjBaoRM46XzrmP6Z/JYqayCWrx4sXs2LGjtOXVajVGRkZKW16vDKKdkn7Z1xX2QNnxAtWIGcdL59zH9M9kMeNzUGZmliUnKDMzy5ITlJmZZamy56DKtuuRA1y05ltT1hlde3afWmNV0CpmHC/WyPHSPo+gzMwsS05QZmaWJScoMxsoSesl7ZO0u6HsU5IekXR3epzV8NnlkvZIekDSGQ3ly1PZHklrGsqPlfRDSQ9K+oakQ/u3ddaNlgnKwWNmPbYBWN6k/HMRcVJ63A4g6TjgXOD49J0vSZojaQ7wRYp7JB0HnJfqAvxNWtYS4Eng4p5ujZVmOiOoDTh4zKxHIuJOYLq3jFgBbIqIf4qIXwB7gFPTY09EPBQRz1HcHXaFJAF/TnE/NYCNwDmlboD1TMsE5eAxswFZLemedBRnXio7Gni4oc5YKpus/DXAUxFxcEK5VUA308xXS7oQ2AFcGhFPUvzwdzXUaQyGicGzlDaDR9IqYBXA0NAQtVqti+a/1NBhcOmJB6esU+b6OjU+Pp5FO6YiaT3wbmBfRJyQyj4FfIQX7wr61w0j78spRs7PA5+IiK2pfDlwHTAH+GpErE3lx1Ls5MwHfgx8MO342MxxA3A1xS3Jr6a4g+xf0vwW9kHzne3Jbnk/6RWyB9nH5PL/Oqc+ptMENZDgiYh1wDqA4eHhKPN6UZ+/aTPX7pr6n2P0gvLW16mKXM9rA/AF4MYJ5Z+LiP/UWDDhsPDrgP8h6Y/Sx18ETqfYcdkuaUtE/JQXDwtvkvRliuR2Q682xvovIh6rv5b0FeCb6e0YcExD1UXA3vS6WfnjwJGSDkk7wo31m613YH1MDv0L5NXHdDSLLyIei4jnI+IPwFcoDuHB5MEzWfkLwTOh3CrMh4WtW5IWNrx9D1CfpLUFOFfSq9JIegnwI2A7sCRNujqUYqdnSxT3E/oe8L70/ZXA5n5sg3WvoxGUpIUR8Wh6OzF4bpb0nyn2huvBI1LwAI9QBM/5ERGS6sGzCQfPTNfXw8K9PFwD1Thkk9PhmslIugUYARZIGgOuBEYknURxRGUU+ChARNwr6Vbgp8BB4JKIeD4tZzWwleKQ8PqIqN+Y7zJgk6RrgJ8AX+vTplmXWiYoB4+VpO+HhXt5uAaqccgmp8M1k4mI85oUT9oPRMRngM80Kb8duL1J+UO8eJTHKqRlgnLwWBkGdU7BzKrLV5KwvvA5BTNrl69mbqXzYWEzK4MTlJXOh4XNrAw+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzAZK0npJ+yTtbiibL2mbpAfT87xULknXS9oj6R5JJzd8Z2Wq/6CklQ3lp0jalb5zvST1dwutUy0TlIPHzHpsA7B8Qtka4I6IWALckd4DnAksSY9VwA1Q9EkUd25eSnEzyyvr/VKqs6rhexPXZZmazghqAw4ea4N3aqwdEXEnsH9C8QpgY3q9ETinofzGKNwFHClpIXAGsC0i9kfEk8A2YHn67IiI+EFEBHBjw7Iscy1v+R4Rd0paPKF4BTCSXm8EasBlNAQPcJekevCMkIIHQFI9eGqk4Enl9eD5djcbZQO3AfgCRWdQV9+pWStpTXp/GS/dqVlKscOytGGnZhgIYKekLanzqe/U3EVxS/jlOGZmmqGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRxBZDQ0PUarXutqLB0GFw6YkHJ/28zHV1Y3x8PJu2tExQk5h1wQN5BFBOwTMZ79RYDzUbLUcH5U1FxDpgHcDw8HCMjIx00MTmPn/TZq7dNXmXO3pBeevqRq1Wo8zt7kanCWoyMzZ4II8Ayil42jSQnRqrrMckLUyxshDYl8rHgGMa6i0C9qbykQnltVS+qEl9q4BOE5SDx8rSs52aXo64oRqHbKow4p7EFmAlsDY9b24oXy1pE8Uh4QOpH9oKfLbh3PYy4PKI2C/paUmnAT8ELgQ+388Nsc51mqAcPNauvu/U9HLEDdU4ZFOFEbekWyh+6wWSxijOPa4FbpV0MfAr4P2p+u3AWcAe4LfAhwBSX3I1sD3Vu6p+eBj4GMV50cMoDgX7cHBFtExQDh4riXdqrKmIOG+Sj97VpG4Al0yynPXA+iblO4ATummjDcZ0ZvE5eKwt3qkxszKUPUnCzDs1ZlYKX+rIzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZck3LDQzK8HiNd+a8vNLT+xTQ2aQrhKUpFHgaeB54GBEDEuaD3wDWAyMAh+IiCclCbiO4vbevwUuiogfp+WsBK5Ii70mIjZ2065mHDxm1VOlPsbKV8YI6p0R8XjD+zXAHRGxVtKa9P4y4ExgSXosBW4AlqZguxIYBgLYKWlLRDxZQtssM1XqcLxTkw33MbNUL85BrQDqncVG4JyG8hujcBdwpKSFwBnAtojYnwJmG7C8B+2yfLwzIk6KiOH0vt7hLAHuSO/hpR3OKooOh4YOZylwKnClpHl9bL8NlvuYWaLbEVQA35UUwN9GxDpgKCIeBYiIRyUdleoeDTzc8N2xVDZZ+ctIWkXRUTE0NEStVpt2Qy898eCUnw8d1rpOO+vrlfHx8SzaUbIVwEh6vRGoUewRv9DhAHdJqnc4I6QOB0BSvcO5pb/Ntj6YNX1MLv+vc+pjuk1Q74iIvSlAtkm6f4q6alIWU5S/vLAIznUAw8PDMTIyMu2GXtTycM1Brt019T/H6AXTX1+v1Go12tnuDPWtw+mms4GZ0eHk1Nl0KJs+ptUh31bdaas+Jof+BfLqY7pKUBGxNz3vk3QbxeGWxyQtTB3NQmBfqj4GHNPw9UXA3lQ+MqG81k27LGt963C62aGB7ndqcuhwcupsOuE+Znbr+ByUpLmSXl1/DSwDdgNbgJWp2kpgc3q9BbhQhdOAA2mveSuwTNK8dB5hWSqzGaixwwFe0uEAtNHhNCu3GcR9jHUzghoCbismWnEIcHNEfEfSduBWSRcDvwLen+rfTjEbaw/FjKwPAUTEfklXA9tTvavq5xba0Xr4bYOWOplXRMTTDR3OVbzY4azl5R3OakmbKCZEHEh7zVuBzzZMjFgGXN5OWxwvlZBVH2P913GCioiHgLc2KX8CeFeT8gAumWRZ64H1nbbFKsMdjk2b+xjzlSSsb9zhmFk7fC0+MzPLkhOUmZllyQnKzMyy5HNQJWo1M2x07dl9aomZWfV5BGVmZlnyCMrMrCJm21Eaj6DMzCxLHkGZDch0rmYx0/aIzdrhEZSZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMspRNgpK0XNIDkvZIWjPo9ljeHC/WLsdM9WSRoCTNAb4InAkcB5wn6bjBtspy5XixdjlmqimXi8WeCuyJiIcAJG0CVgA/HWirSjadi4O2smH53J63oQIXKJ0V8QLdx0y38TKdNlQgXmCWxMwM+a1eoIgYdBuQ9D5geUR8OL3/ILA0IlZPqLcKWJXevhl4oMRmLAAeL3F5vTKIdr4hIl7b53VOKpN4gWrEzKyPF8gmZqoQL5BRzOQyglKTspdlzohYB6zrSQOkHREx3Itll6kq7eyxgccLVOO3qEIb+2TgMVOV3yKndmZxDgoYA45peL8I2Dugtlj+HC/WLsdMBeWSoLYDSyQdK+lQ4Fxgy4DbZPlyvFi7HDMVlMUhvog4KGk1sBWYA6yPiHv73IyeHQoqWVXa2TOZxAtU47eoQht7LpOYqcpvkU07s5gkYWZmNlEuh/jMzMxewgnKzMyy5ARFNS6BImlU0i5Jd0vaMej2zGZViBdwzOSkCjGTY7zM+sWNACwAAAEnSURBVHNQ6RIoPwNOp5iKuh04LyKy+gtzSaPAcERU4Q/9ZqyqxAs4ZnJRlZjJMV48gmq4BEpEPAfUL4Fi1ozjxdrlmOmQExQcDTzc8H4sleUmgO9K2pkux2KDUZV4AcdMLqoSM9nFSxZ/BzVg07oESgbeERF7JR0FbJN0f0TcOehGzUJViRdwzOSiKjGTXbx4BFWRS6BExN70vA+4jeKwgfVfJeIFHDMZqUTM5BgvTlAVuASKpLmSXl1/DSwDdg+2VbNW9vECjpnMZB8zucbLrD/El8klUFoZAm6TBMVvdnNEfGewTZqdKhIv4JjJRkViJst4mfXTzM3MLE8+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZen/A6K07c+5e7/dAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4])" + "array([2, 1, 3, 4], dtype=int64)" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3])" + "array([1, 2, 3], dtype=int64)" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1435,760 +1435,197 @@ " 0\n", " 0\n", " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
6000000count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
7000000mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
80-1-100-1std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
9001000min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
10-2-2-2-2-1-1
1100100-1
12-1-1-1-1-11
13-10-1-1-1-1
14111001
15000000
16110000
17001111
18000-1-1-1
191-2-2-2-2-2
201-2-2-2-2-2
2100000-1
22-1-1-1-1-1-1
23100111
24-2-2-2-2-2-2
25000-100
26000000
271-2-1-1-1-1
28000000
29-1-1-1-1-1-1
30000000
.....................
29971-1-1-100-1
29972000000
2997300000-1
299741-2-2-2-2-2
29975111100
2997600-1-1-2-2
29977111111
29978000000
29979000000
29980-2-2-2-2-2-2
29981000000
29982111100
29983000000
29984-2-2-2-2-2-2
29985-1-1-2-1-1-1
29986-2-2-2-2-2-2
29987-1-1-2-2-2-2
29988000000
29989000000
29990-1-1-1-1-1-2
29991000000
29992111111
29993000-2-2-2
299940-1-1000
29995111111
29996000000
29997-1-1-1-100
29998111-100
299991-1000-1
30000000000
\n", - "

30000 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " S_0 S_2 S_3 S_4 S_5 S_6\n", - "ID \n", - "1 1 1 -1 -1 -2 -2\n", - "2 -1 1 0 0 0 1\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 -1 0 -1 0 0 0\n", - "6 0 0 0 0 0 0\n", - "7 0 0 0 0 0 0\n", - "8 0 -1 -1 0 0 -1\n", - "9 0 0 1 0 0 0\n", - "10 -2 -2 -2 -2 -1 -1\n", - "11 0 0 1 0 0 -1\n", - "12 -1 -1 -1 -1 -1 1\n", - "13 -1 0 -1 -1 -1 -1\n", - "14 1 1 1 0 0 1\n", - "15 0 0 0 0 0 0\n", - "16 1 1 0 0 0 0\n", - "17 0 0 1 1 1 1\n", - "18 0 0 0 -1 -1 -1\n", - "19 1 -2 -2 -2 -2 -2\n", - "20 1 -2 -2 -2 -2 -2\n", - "21 0 0 0 0 0 -1\n", - "22 -1 -1 -1 -1 -1 -1\n", - "23 1 0 0 1 1 1\n", - "24 -2 -2 -2 -2 -2 -2\n", - "25 0 0 0 -1 0 0\n", - "26 0 0 0 0 0 0\n", - "27 1 -2 -1 -1 -1 -1\n", - "28 0 0 0 0 0 0\n", - "29 -1 -1 -1 -1 -1 -1\n", - "30 0 0 0 0 0 0\n", - "... ... ... ... ... ... ...\n", - "29971 -1 -1 -1 0 0 -1\n", - "29972 0 0 0 0 0 0\n", - "29973 0 0 0 0 0 -1\n", - "29974 1 -2 -2 -2 -2 -2\n", - "29975 1 1 1 1 0 0\n", - "29976 0 0 -1 -1 -2 -2\n", - "29977 1 1 1 1 1 1\n", - "29978 0 0 0 0 0 0\n", - "29979 0 0 0 0 0 0\n", - "29980 -2 -2 -2 -2 -2 -2\n", - "29981 0 0 0 0 0 0\n", - "29982 1 1 1 1 0 0\n", - "29983 0 0 0 0 0 0\n", - "29984 -2 -2 -2 -2 -2 -2\n", - "29985 -1 -1 -2 -1 -1 -1\n", - "29986 -2 -2 -2 -2 -2 -2\n", - "29987 -1 -1 -2 -2 -2 -2\n", - "29988 0 0 0 0 0 0\n", - "29989 0 0 0 0 0 0\n", - "29990 -1 -1 -1 -1 -1 -2\n", - "29991 0 0 0 0 0 0\n", - "29992 1 1 1 1 1 1\n", - "29993 0 0 0 -2 -2 -2\n", - "29994 0 -1 -1 0 0 0\n", - "29995 1 1 1 1 1 1\n", - "29996 0 0 0 0 0 0\n", - "29997 -1 -1 -1 -1 0 0\n", - "29998 1 1 1 -1 0 0\n", - "29999 1 -1 0 0 0 -1\n", - "30000 0 0 0 0 0 0\n", - "\n", - "[30000 rows x 6 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#attributes representing positive values\n", - "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", - " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outliers\n", - "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2212,7 +1649,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2236,7 +1673,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2260,7 +1697,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2457,7 +1894,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2481,7 +1918,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2505,7 +1942,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2529,7 +1966,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2553,7 +1990,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2687,7 +2124,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2695,7 +2132,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2703,7 +2140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2711,7 +2148,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2719,7 +2156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2803,7 +2240,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2824,7 +2261,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2845,7 +2282,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2866,7 +2303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2887,7 +2324,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3167,25 +2604,23 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 82.388112 2.822569e-04\n", - "PAY_0 4424.481682 0.000000e+00\n", - "PAY_2 3847.416138 0.000000e+00\n", - "PAY_3 3007.408864 0.000000e+00\n", - "PAY_4 3034.730536 0.000000e+00\n", - "PAY_5 2869.196497 0.000000e+00\n", - "PAY_6 2526.952024 0.000000e+00\n", - "PAY_0_No_Transactions 71.907192 3.727224e-03\n", - "PAY_0_Revolving_Credit 498.806543 0.000000e+00\n", - "PAY_2_Pay_Duly 75.936267 1.438075e-03\n", - "PAY_2_Revolving_Credit 247.861963 0.000000e+00\n", - "PAY_3_Pay_Duly 78.497278 7.644927e-04\n", - "PAY_3_Revolving_Credit 142.164275 1.549982e-12\n", - "PAY_4_Pay_Duly 79.625464 5.751909e-04\n", - "PAY_4_Revolving_Credit 99.566680 2.218832e-06\n", - "PAY_5_Pay_Duly 73.322648 2.683519e-03\n", - "PAY_5_Revolving_Credit 66.651236 1.187101e-02\n", - "PAY_6_Pay_Duly 63.464171 2.277242e-02\n", - "PAY_6_Revolving_Credit 62.888951 2.550124e-02\n" + "LIMIT_BAL 74.581893 1.992315e-03\n", + "PAY_0 4250.691969 0.000000e+00\n", + "PAY_2 3793.454513 0.000000e+00\n", + "PAY_3 2943.420461 0.000000e+00\n", + "PAY_4 2997.799899 0.000000e+00\n", + "PAY_5 2809.793752 0.000000e+00\n", + "PAY_6 2384.699487 0.000000e+00\n", + "PAY_0_No_Transactions 75.255770 1.695234e-03\n", + "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", + "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", + "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", + "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", + "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", + "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", + "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", + "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", + "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" ] } ], @@ -3376,8 +2811,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13475\n", - " 1 1.00 1.00 1.00 4021\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3406,7 +2841,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (GINI) identified 828\n" + "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" ] }, { @@ -3442,13 +2877,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%25%50000.0000001.0000001.000000-1.000000
50%50%120000.0000002.0000002.0000000.000000
75%75%210000.0000002.0000002.0000000.000000
maxmax500000.0000002.0000004.000000
110.02040822-2
220.224490221
330.163265220
440.081633220
550.08163312
000.01.00.00.0
110.01.00.01.0
220.01.00.01.0
330.01.00.00.0
440.01.00.0
000.00.00.00.0
110.01.00.00.0
220.00.01.01.0
330.00.01.01.0
440.01.00.0
05415131454031256
111928281233857
\n", @@ -3457,8 +2892,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5415 1314\n", - "1 1192 828" + "0 5403 1256\n", + "1 1233 857" ] }, "execution_count": 38, @@ -3479,12 +2914,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.6666666666666666\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3510,12 +2945,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6729\n", - " 1 0.39 0.41 0.40 2020\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.41 0.41 2090\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] } @@ -3524,13 +2959,158 @@ "print(classification_report(y_test, tree.predict(X_test)))" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054221237
11244846
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5422 1237\n", + "1 1244 846" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.40 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[0] = ([\"Decision Tree\" , \n", + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", " auroc])" ] @@ -3553,17 +3133,17 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", - "randf = RandomForestClassifier(n_estimators=300)" + "randf = RandomForestClassifier(n_estimators=200)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -3573,12 +3153,12 @@ " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", " n_jobs=None, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False)" ] }, - "execution_count": 43, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -3589,7 +3169,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -3598,8 +3178,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13475\n", - " 1 1.00 1.00 1.00 4021\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3621,14 +3201,14 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (Random Forest) identified 749\n" + "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" ] }, { @@ -3664,13 +3244,13 @@ " \n", " \n", " 0\n", - " 6310\n", - " 419\n", + " 6273\n", + " 386\n", " \n", " \n", " 1\n", - " 1271\n", - " 749\n", + " 1338\n", + " 752\n", " \n", " \n", "\n", @@ -3679,11 +3259,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6310 419\n", - "1 1271 749" + "0 6273 386\n", + "1 1338 752" ] }, - "execution_count": 45, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -3694,19 +3274,19 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 143, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.26\n" + "Optimal Threshold: 0.25839285714285715\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3723,7 +3303,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -3732,12 +3312,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6729\n", - " 1 0.64 0.37 0.47 2020\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.36 0.47 2090\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.80 0.78 8749\n", "\n" ] } @@ -3771,9 +3351,7 @@ "### Gradient Boosted Trees Classifier\n", "\n", "#### Theory\n", - "In this part we train a gradient boosted trees classifier using xgBoost. xgBoost is short for \"Extreme Gradient Boosting\". It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", - "\n", - "xgBoost uses the gradient descent algorithm that we learnt in BT2101 at each iteration to maximise the reduction in the error term. (More details? math?)\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", " \n", "#### Training\n", "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." @@ -3821,12 +3399,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13475\n", - " 1 0.79 0.46 0.58 4021\n", + " 0 0.86 0.96 0.91 13545\n", + " 1 0.79 0.47 0.59 3951\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.71 0.74 17496\n", - "weighted avg 0.84 0.85 0.83 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", "\n" ] } @@ -3839,7 +3417,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We observe that the xgBoost ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," ] }, { @@ -3851,7 +3429,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 750\n" + "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" ] }, { @@ -3887,13 +3465,13 @@ " \n", " \n", " 0\n", - " 6339\n", - " 390\n", + " 6290\n", + " 369\n", " \n", " \n", " 1\n", - " 1270\n", - " 750\n", + " 1360\n", + " 730\n", " \n", " \n", "\n", @@ -3902,8 +3480,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6339 390\n", - "1 1270 750" + "0 6290 369\n", + "1 1360 730" ] }, "execution_count": 51, @@ -3924,12 +3502,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.26114231320864134\n" + "Optimal Threshold: 0.23302683158657422\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3955,12 +3533,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6729\n", - " 1 0.66 0.37 0.47 2020\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.35 0.46 2090\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.78 0.80 0.78 8749\n", "\n" ] } @@ -3984,7 +3562,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From the accuracy and AUROC, we observe that the XGBoost performs similarly to the random forest ensemble. It has a slight bump in AUROC at 0.76, but the accuracy is the same." + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " ] }, { @@ -4024,20 +3602,20 @@ " \n", " 0\n", " Decision Tree\n", - " 0.409901\n", - " 0.606997\n", + " 0.410048\n", + " 0.610882\n", " \n", " \n", " 1\n", " Random Forest\n", - " 0.370792\n", - " 0.768455\n", + " 0.363158\n", + " 0.767925\n", " \n", " \n", " 2\n", " Gradient Boosted\n", - " 0.371287\n", - " 0.778407\n", + " 0.349282\n", + " 0.775518\n", " \n", " \n", "\n", @@ -4045,9 +3623,9 @@ ], "text/plain": [ " Model Recall-1 AUROC\n", - "0 Decision Tree 0.409901 0.606997\n", - "1 Random Forest 0.370792 0.768455\n", - "2 Gradient Boosted 0.371287 0.778407" + "0 Decision Tree 0.410048 0.610882\n", + "1 Random Forest 0.363158 0.767925\n", + "2 Gradient Boosted 0.349282 0.775518" ] }, "execution_count": 55, @@ -4108,7 +3686,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.445436\n", + " Current function value: 0.438684\n", " Iterations 7\n" ] }, @@ -4127,13 +3705,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737 \n", + " Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788 \n", "\n", "\n", - " Time: 19:35:44 Log-Likelihood: -7793.4\n", + " Time: 23:40:29 Log-Likelihood: -7675.2\n", "\n", "\n", - " converged: True LL-Null: -9431.5\n", + " converged: True LL-Null: -9346.0\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4144,136 +3722,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", + " LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", "\n", "\n", - " SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", + " SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", "\n", "\n", - " AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", + " AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", "\n", "\n", - " PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", + " PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", "\n", "\n", - " PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", + " PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", "\n", "\n", - " PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", + " PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", "\n", "\n", - " PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", + " PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", "\n", "\n", - " PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", + " PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", "\n", "\n", - " PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", + " PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", "\n", "\n", - " BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", + " BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", "\n", "\n", - " BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", + " BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", "\n", "\n", - " BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", + " BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", "\n", "\n", - " BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", + " BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", "\n", "\n", - " BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", + " BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", "\n", "\n", - " BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", + " BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", "\n", "\n", - " PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", + " PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", "\n", "\n", - " PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", + " PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", "\n", "\n", - " PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", + " PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", "\n", "\n", - " PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", + " PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", "\n", "\n", - " PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", + " PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", "\n", "\n", - " PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", + " PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", "\n", "\n", - " GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", + " GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", "\n", "\n", - " UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", + " UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", "\n", "\n", - " HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", + " HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", "\n", "\n", - " MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", + " MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", "\n", "\n", - " SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", + " SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", "\n", "\n", - " PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", + " PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", "\n", "\n", - " PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", + " PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", + " PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", "\n", "\n", - " PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", + " PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", + " PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", + " PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", "\n", "\n", - " PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", + " PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", "\n", "\n", - " PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", + " PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", + " PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", "\n", "\n", - " PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", + " PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", "\n", "\n", - " PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", + " PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", + " PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", "\n", "\n", - " PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", + " PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", "\n", "\n", - " PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", + " PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", + " PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", "\n", "\n", - " PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", + " PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", "\n", "\n", - " PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", + " PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + " PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", "\n", "" ], @@ -4285,57 +3863,57 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1737\n", - "Time: 19:35:44 Log-Likelihood: -7793.4\n", - "converged: True LL-Null: -9431.5\n", + "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:40:29 Log-Likelihood: -7675.2\n", + "converged: True LL-Null: -9346.0\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8354 0.114 -7.302 0.000 -1.060 -0.611\n", - "SEX -0.1196 0.041 -2.920 0.004 -0.200 -0.039\n", - "AGE 0.0615 0.100 0.614 0.539 -0.135 0.258\n", - "PAY_0 0.6546 0.059 11.081 0.000 0.539 0.770\n", - "PAY_2 -0.5622 0.098 -5.715 0.000 -0.755 -0.369\n", - "PAY_3 -0.1059 0.113 -0.941 0.347 -0.327 0.115\n", - "PAY_4 -0.2041 0.159 -1.287 0.198 -0.515 0.107\n", - "PAY_5 -0.0925 0.178 -0.521 0.603 -0.441 0.256\n", - "PAY_6 0.3289 0.147 2.244 0.025 0.042 0.616\n", - "BILL_AMT1 -1.0372 0.528 -1.964 0.049 -2.072 -0.002\n", - "BILL_AMT2 0.6000 0.765 0.785 0.433 -0.899 2.099\n", - "BILL_AMT3 1.5425 0.732 2.107 0.035 0.107 2.978\n", - "BILL_AMT4 0.2846 0.715 0.398 0.690 -1.116 1.686\n", - "BILL_AMT5 -1.9241 0.913 -2.107 0.035 -3.714 -0.134\n", - "BILL_AMT6 1.3621 0.839 1.623 0.105 -0.283 3.007\n", - "PAY_AMT1 -1.1622 0.306 -3.794 0.000 -1.763 -0.562\n", - "PAY_AMT2 -2.0159 0.390 -5.172 0.000 -2.780 -1.252\n", - "PAY_AMT3 -0.5874 0.310 -1.898 0.058 -1.194 0.019\n", - "PAY_AMT4 -0.0638 0.281 -0.227 0.820 -0.614 0.487\n", - "PAY_AMT5 -1.0410 0.291 -3.576 0.000 -1.612 -0.470\n", - "PAY_AMT6 -0.3858 0.256 -1.507 0.132 -0.887 0.116\n", - "GRAD 1.3893 0.230 6.030 0.000 0.938 1.841\n", - "UNI 1.3690 0.229 5.975 0.000 0.920 1.818\n", - "HS 1.3654 0.233 5.863 0.000 0.909 1.822\n", - "MARRIED 0.0176 0.158 0.111 0.911 -0.292 0.328\n", - "SINGLE -0.0968 0.159 -0.609 0.543 -0.408 0.215\n", - "PAY_0_No_Transactions 0.0316 0.124 0.255 0.799 -0.211 0.274\n", - "PAY_0_Pay_Duly 0.2120 0.122 1.734 0.083 -0.028 0.452\n", - "PAY_0_Revolving_Credit -0.6866 0.137 -5.022 0.000 -0.955 -0.419\n", - "PAY_2_No_Transactions -1.4519 0.236 -6.151 0.000 -1.914 -0.989\n", - "PAY_2_Pay_Duly -1.3960 0.225 -6.201 0.000 -1.837 -0.955\n", - "PAY_2_Revolving_Credit -0.9936 0.230 -4.318 0.000 -1.445 -0.543\n", - "PAY_3_No_Transactions -0.5672 0.275 -2.059 0.039 -1.107 -0.027\n", - "PAY_3_Pay_Duly -0.5717 0.250 -2.284 0.022 -1.062 -0.081\n", - "PAY_3_Revolving_Credit -0.5379 0.239 -2.249 0.025 -1.007 -0.069\n", - "PAY_4_No_Transactions -0.7044 0.356 -1.980 0.048 -1.402 -0.007\n", - "PAY_4_Pay_Duly -0.8759 0.338 -2.593 0.010 -1.538 -0.214\n", - "PAY_4_Revolving_Credit -0.7888 0.328 -2.404 0.016 -1.432 -0.146\n", - "PAY_5_No_Transactions -0.4367 0.393 -1.110 0.267 -1.208 0.334\n", - "PAY_5_Pay_Duly -0.4715 0.378 -1.247 0.212 -1.213 0.270\n", - "PAY_5_Revolving_Credit -0.3428 0.369 -0.930 0.352 -1.065 0.379\n", - "PAY_6_No_Transactions 0.4826 0.323 1.492 0.136 -0.151 1.117\n", - "PAY_6_Pay_Duly 0.4427 0.316 1.401 0.161 -0.177 1.062\n", - "PAY_6_Revolving_Credit 0.2143 0.308 0.695 0.487 -0.390 0.818\n", + "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", "==========================================================================================\n", "\"\"\"" ] @@ -4361,12 +3939,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13475\n", - " 1 0.67 0.35 0.46 4021\n", + " 0 0.84 0.95 0.89 13545\n", + " 1 0.67 0.37 0.47 3951\n", "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.67 17496\n", - "weighted avg 0.79 0.81 0.79 17496\n", + " accuracy 0.82 17496\n", + " macro avg 0.75 0.66 0.68 17496\n", + "weighted avg 0.80 0.82 0.80 17496\n", "\n" ] } @@ -4375,31 +3953,6 @@ "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" ] }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.69 0.35 0.47 2020\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test)>0.5)))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -4409,7 +3962,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -4417,7 +3970,67 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.446914\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438685\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438686\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438689\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438692\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438733\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438766\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438815\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438878\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438953\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439049\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439148\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439242\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439370\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439484\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439636\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439750\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439934\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.440120\n", " Iterations 7\n" ] }, @@ -4436,13 +4049,13 @@ " Method: MLE Df Model: 23 \n", "\n", "\n", - " Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761 \n", "\n", "\n", - " Time: 19:35:44 Log-Likelihood: -7819.2\n", + " Time: 00:03:36 Log-Likelihood: -7700.3\n", "\n", "\n", - " converged: True LL-Null: -9431.5\n", + " converged: True LL-Null: -9346.0\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4453,76 +4066,76 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", + " LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", "\n", "\n", - " SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", + " PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", "\n", "\n", - " PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", + " PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", "\n", "\n", - " PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", + " PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", "\n", "\n", - " PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", + " PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", "\n", "\n", - " BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", + " BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", "\n", "\n", - " BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", + " PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", "\n", "\n", - " BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", + " PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", "\n", "\n", - " PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", + " PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", "\n", "\n", - " PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", + " PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", "\n", "\n", - " PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", + " PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", "\n", "\n", - " GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", + " GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", "\n", "\n", - " UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", + " UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", "\n", "\n", - " HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", + " HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", + " MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", "\n", "\n", - " PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", + " PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", + " PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", "\n", "\n", - " PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", + " PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", "\n", "\n", - " PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", + " PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", "\n", "\n", - " PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", + " PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", + " PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", "\n", "\n", - " PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", + " PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", "\n", "\n", - " PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", + " PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + " PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", "\n", "" ], @@ -4534,81 +4147,86 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17472\n", "Method: MLE Df Model: 23\n", - "Date: Mon, 18 Nov 2019 Pseudo R-squ.: 0.1709\n", - "Time: 19:35:44 Log-Likelihood: -7819.2\n", - "converged: True LL-Null: -9431.5\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", + "Time: 00:03:36 Log-Likelihood: -7700.3\n", + "converged: True LL-Null: -9346.0\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.7989 0.111 -7.183 0.000 -1.017 -0.581\n", - "SEX -0.1204 0.040 -2.986 0.003 -0.199 -0.041\n", - "PAY_0 0.5793 0.037 15.471 0.000 0.506 0.653\n", - "PAY_2 -0.6376 0.077 -8.284 0.000 -0.788 -0.487\n", - "PAY_6 0.2012 0.030 6.656 0.000 0.142 0.260\n", - "BILL_AMT1 -0.8123 0.359 -2.261 0.024 -1.516 -0.108\n", - "BILL_AMT3 2.2505 0.535 4.206 0.000 1.202 3.299\n", - "BILL_AMT5 -0.9943 0.358 -2.779 0.005 -1.696 -0.293\n", - "PAY_AMT1 -1.3333 0.287 -4.642 0.000 -1.896 -0.770\n", - "PAY_AMT2 -2.3238 0.373 -6.232 0.000 -3.055 -1.593\n", - "PAY_AMT5 -0.9218 0.252 -3.658 0.000 -1.416 -0.428\n", - "GRAD 1.2429 0.179 6.961 0.000 0.893 1.593\n", - "UNI 1.2478 0.177 7.052 0.000 0.901 1.595\n", - "HS 1.2675 0.181 7.014 0.000 0.913 1.622\n", - "PAY_0_Revolving_Credit -0.8716 0.092 -9.431 0.000 -1.053 -0.690\n", - "PAY_2_No_Transactions -1.6757 0.200 -8.387 0.000 -2.067 -1.284\n", - "PAY_2_Pay_Duly -1.4967 0.179 -8.379 0.000 -1.847 -1.147\n", - "PAY_2_Revolving_Credit -1.0909 0.182 -6.007 0.000 -1.447 -0.735\n", - "PAY_3_No_Transactions -0.3503 0.159 -2.199 0.028 -0.663 -0.038\n", - "PAY_3_Pay_Duly -0.3319 0.111 -3.003 0.003 -0.548 -0.115\n", - "PAY_3_Revolving_Credit -0.3494 0.081 -4.332 0.000 -0.507 -0.191\n", - "PAY_4_No_Transactions -0.2852 0.130 -2.193 0.028 -0.540 -0.030\n", - "PAY_4_Pay_Duly -0.5573 0.103 -5.433 0.000 -0.758 -0.356\n", - "PAY_4_Revolving_Credit -0.4761 0.075 -6.332 0.000 -0.623 -0.329\n", + "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 60, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#remove all insignificant attributes\n", - "sig = glm.pvalues[glm.pvalues < 0.05]\n", - "glm_2 = sm.Logit(y_train,X_train[sig.index]).fit()\n", - "glm_2.summary()" + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 105, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 13475\n", - " 1 0.66 0.36 0.46 4021\n", - "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.67 17496\n", - "weighted avg 0.79 0.81 0.79 17496\n", - "\n" + "24 Columns left:\n", + "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " dtype='object')\n" ] } ], "source": [ - "print(classification_report(y_train,list(glm_2.predict(X_train[sig.index])>0.5)))" + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -4617,18 +4235,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.35 0.47 2020\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.34 0.46 2090\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.5)))" + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" ] }, { @@ -4642,19 +4260,19 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22554888390620112\n" + "Optimal Threshold: 0.21432968760597085\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU1dfA8e8lvUEgCTUJSWihtwhSRRARFEGaomIBfopgR8SOioi9vWDB3kFABAEREBBEWhCklxBaSGjpve19/5hNXEIIC2Sz2c35PA8P2ZnZmTNl58zcO3Ov0lojhBBCXEg1ewcghBCicpNEIYQQokySKIQQQpRJEoUQQogySaIQQghRJkkUQgghyiSJogxKqTuUUsvtHYe9KaVClVIZSimXClxmmFJKK6VcK2qZtqSU2q2U6nUZ37usY1Ap5aWU+lUplaqUmnup368oSqnflFJ3X8b3eiil9tsipsrMHr9FAOUo71EopY4AdYBCIANYBjyotc6wZ1zOyLytx2qtV9oxhjDgMOCmtS6wVxzmWDTQRGsdY+PlhFFO66yUGgU8BHQtj+1nTnLfaa2Dr3Rel7n8ctkHSqkXgWeBXKAA2ANM1FpvuOIgnZij3VEM1Fr7Au2A9sDTdo7nstjzKtlZrtAvRRXd3g2BA5eTJKrAMTLHfB4JBFYDNrnjcqrtqLV2iH/AEeA6i89vAEssPnsAbwHHgFPAx4CXxfhBwHYgDTgE3GAeXgP4HEgATgCvAC7mcfcAf5n//hh4q0RMC4HHzX/XB+YDZzCuCh+2mO5FYB7wnXn5Y0tZvxrAN+bvHwWeA6pZxLEe+D8gFdgH9Cnx3bLWYT3wLpBkHtcIWAUkAmeB7wF/8/TfAiYgG+PO7UkgDNCAq3maNcBU83zTgeVAoEU8d5nXIRF4vuS+K7HeXsDb5ulTgb/Mw4qWebd5n54FnrX4XidgA5BiXu8ZgLvFeA1MAA4Ch83D3geOm/fBVqCHxfQuwDPmYyPdPD4EWGueV6Z5e9xqnv4mjOMpBfgbaFPiWJ0M7MC4cnW13Abm2KPNcZwC3jEPP2ZeVob5XxcsjkHzNC2BFeZ9eQp4ppRt+hKQB+Sb5zMG46LwOfN2Po1xrNUwT1+0rceYY1hbyjx7AXEX2IdlHbsu5v17FuN38SDnH0tjzX83Bv40HwdnMU7olLYPSsZj3lc/m2NIBGZcINYXMe6Mij63MM87yGJYWfu2A7DNfIzMBeYAr1huI/O+Pwl8a8X8JmP8ZtOB/Zh/12UcI0X7qmj71QcWmY+HGOB/Jdb1J/O+SQd2A1GXdf6t6BP+5f7j3B9aMLATeN9i/HvmDVYL8AN+BaZbbPRUoC/GD6YBEGke9wvwCeAD1AY2A/dbnGSLEkVPjJNMUXFdTYyTaX3zPLcCLwDuQAQQC/Sz2GH5wGDztF6lrN83GInHz3wwHADGWMRRADwGuGH8UFKBWlauQwFGMYQrxkm4sXlbeABBGD/E90rb1hc4ONdgnFCbmue3BnjN4oeXAXQ3b4u3zOt+oUQx0/z9Bhgnla7muIqW+al5GW0xTrrNzd/rCFxtXqcwYC/wqMV8NcYJtVbR9gbuBALM35mI8WP2NI+bhHFMNQOUeXkBFvNqXOJkcRrobI75bvM287DYftsxTl5eJbcpRoIbZf7bF7i6tO1cyjHoh5EUJwKe5s+drTwhjsY4kUSYl/kz/53Iipb7DcYxVNrx2YsLJ4qyjt1xGMU7wRi/mZVcOFH8iFEsVM28ft1L7M/GpcVj3gf/YlwM+ZT87oW2C8bx+RpGUiqK54L71jz9UeARjN/hEIyEbJkoCoDXzdN7XWR+zTDOKfUt9kOjSzlGMBLrh+Z1boeRKPtYrGsOMMC87OnAxss6/9o7AVgdqLFxMzAyowb+4L+rYIVxtdHIYvou/Hcl+QnwbinzrINx8rG88xgJrC7lR6owrrZ6mj//D1hl/rszcKzEvJ8GvrTYYeddpVlM62KOo4XFsPuBNRZxxGNOUuZhm4FRVq7DsQst2zzNYGBbiW19sUTxnMX48cAy898vAD9ajPPG+DGdlygwTgjZQNtSxhUtM7jEOt92gXV4FFhg8VkDvS+y3slFy8a4mht0gelKnqQ+AqaWmGY/cI3F9htdyvFblCjWYlz1B5aY5pztXMoxONJyP11k3V7k3ETxBzDe4nMzjATuarHciDLm14tSEoUVx+4qzBct5s/XlXIsFSWKb4BZlvu8jH1QHA/Gb/2M5Xa7yHbJw7i6L8S4++hlzb7FuFg8wbm/w784N1HkYb74sGJ+jTGSyHUY9VKW01z0GMG4ECkE/CzGTwe+sljXlRbjWgDZ1hw/Jf85Wh3FYK21H8YOicQoYwTjqtgb2KqUSlFKpWBUdgeZx4dgXAGX1BDjyiDB4nufYFyVn0MbW3o2xo8V4HaMIpui+dQvmod5Ps9gnMSLHC9jvQL572qlyFGMq+wiJ8wxWI6vb+U6nLNspVRtpdRspdQJpVQaRpFYIJfmpMXfWRhXPZhjKl6e1joL48dYmkCMK6HS9k2Zy1FKNVVKLVZKnTSvw6ucvw4l13uiUmqv+UmgFIwik6LvXOgYKU1DYGKJ/R2Cse6lLruEMRh3Y/uUUluUUjdZudxLibGk+px/fLli/TF6IRc7ds85Hi6yjCcxLsg2m58SG21lDCHAUW19fcxPWmt/jHXfhXF3WqSsfVuf83+HJdfnjNY6x5r5aaNi/lGME/pp82+y6Biy5hipDyRprdMthpU8b5T8/XheTt2JoyUKALTWfwJfYRRrgHHrmA201Fr7m//V0EaFFRg7s1EpszqOcTUUaPG96lrrlhdY9I/AMKVUQ4y7iPkW8zlsMQ9/rbWf1nqAZdhlrNJZjKu7hhbDQjGuXoo0UEqpEuPjrVyHksuebh7WRmtdHaNIRpUx/aVIwChmAIzHNDGKe0pzFuPWuLR9czEfYdTVNDGvwzOcuw5gsR5KqR4Y5cEjgJrmE0WqxXcudIyU5jgwrcT+9tZa/1jaskvSWh/UWo/ESOavA/OUUj5lfecyYiwpnvOPrwKM8u/i0C5jvhc7ds85HjBOkqXSWp/UWv9Pa10f467kQ6VUYytiOA6EXuoJUGt91rycF5VS9SzmdaF9m8D5v8OS61NyG5Z5rGitf9Bad8fYfhrjeCjrGLEUD9RSSvlZDCt53igXDpkozN4D+iql2mmtTRhl2e8qpWoDKKUaKKX6maf9HLhXKdVHKVXNPC5Sa52AURH7tlKqunlcI6XUNaUtUGu9DeMW9zPgd611innUZiBNKTXZ/Py6i1KqlVLqKmtWRGtdiFHpNE0p5WdORI9jXOkXqQ08rJRyU0oNB5oDSy91Hcz8MIrxUpRSDTDK5y2dwijLvhzzgIFKqa5KKXeM2+eSJ3AAzPvtC+AdpVR983bropTysGI5fhgVfRlKqUjgASumL8BcRKGUegGobjH+M2CqUqqJMrRRShUluJLb41NgnFKqs3laH6XUjSV+sBeklLpTKRVkXv+iY6jQHJuJC2/7xUBdpdSjSikP87HS2ZplYlzkPKaUCldK+WLcgc25hKvwotg9Lf+Z4y3r2P0JeMT8m/PHSNYXmvdwpVRRUknGOHEWmj+XdUxuxjiJv2beF55KqW7WrI/Weh/wO8bdDJS9bzeY43lQKeWqlBqEUf9ZlgvOTynVTCnV23y852Bc7Baat8WFjhHL2I9jVI5PN69zG4w7ke8pZw6bKLTWZzDKNJ83D5qMUVm3URlFESsxymHRWm8G7sWo7ErFqAAqugK6C+PWeQ/GwTkPKLq6KM2PGGWKP1jEUggMxKhMOoxxlfUZRtGGtR7CqGeJxSj3/AHjJFpkE9DEPO9pwDCtdVGRzqWuw0sYlWypwBKMik1L04HnzLfKT1zCOqC13m1el9kYP950jHLY3At85QmMSuQtGE9uvI51x+UTGMV/6Rg/xjkXmf534DeMitajGD9My2KDdzBOassxEtDnGJWRYBQNfG3eHiO01tEYdVQzMLZ3DEZdgrVuAHYrpTIwnsS6TWudYy6mmwasNy/rassvmYsY+mIcaycxnui61splfoHxRNtajGM0B2M/XYoGGCczy3+NKPvY/RRjm+7AeFpoKUbCPuekZ3YVsMm8XRYBj2itD5vHvYjFPrD8ksXvrzFGPWIcxgMf1noTuE8pVbusfau1zsOowB6DcfK+EyN5X+jY5iLHigf/VaafxLgYfMY8rtRjpJRFjMSot4gHFgBTtNYrLmHdreIwL9xVZUqpezAq/LrbO5ZLZb56TcEoIjp8semFc1NK9Qc+1lo3vOjEDkAptQljfb60dyy25LB3FKLyUkoNVEp5m8tU38K4Yzhi36iEPZiLYgeYi2oaAFMwrnwdklLqGqVUXfP63A20wXhwxqlJohC2MAjjVjgeo7jsNi23rlWVwijqTMYoetqL8Qi1o2qG8c5GKsb7LMPM9YROTYqehBBClEnuKIQQQpTJ4RqtCgwM1GFhYfYOQwghHMrWrVvPaq2DLj7l+RwuUYSFhREdHW3vMIQQwqEopY5efKrSSdGTEEKIMkmiEEIIUSZJFEIIIcokiUIIIUSZJFEIIYQokyQKIYQQZbJZolBKfaGUOq2U2nWB8Uop9YFSKkYptUMp1cFWsQghhLh8tryj+AqjqdwL6Y/RDlAT4D6MjmiEEEKUo0KTJie/tFbdrWezF+601muVUmFlTDII+MbcWNxGpZS/UqpeVWhgSwghykNOfiFn0nPZHZ/KkcQs9iWkYdKQnV/IgVPpHE3MKpfl2PPN7Aac23FMnHnYeYlCKXUfxl0HoaGhFRKcEEJUBlpr4pKzOZ6cReyZTJbuTCD2TCYn00rrx8gQWdcPP1cX/NLyidt1htoB3lcUgz0TRWndY5balK3WehYwCyAqKkqauxVCOB2tNUcSs9h8OJHTabnsik8l5nQGp9NzSc85t8faWj7uDG5XHy93F1rUq46fpxttQ/ypV8MTTzcXtNZERX3K4f1nefnla3n44c64ud152bHZM1HEcW7H5MEY/RcIIYTTybEoDkrKzCM+NZvEjDyOnM0k5kwGKVn5533H3bUaLetX57rmdahXw5OGAd40CvLF39u91GX8/fdxWreujZ+fB599NpDAQG9CQi6lR+bS2TNRLMLopHw20BlIlfoJIYQzyMkvZMuRJDYcSmTLkSS2HEkudTpPt2o0reNH0zp+BPl6ULeGJ9e3qEOj2r5U93TD3dW6540SE7N46qmVfPbZNqZMuYYXX+xF+/b1ym19bJYolFI/Ar2AQKVUHEYXiG4AWuuPMTpZH4DR2XgWcK+tYhFCiPJUaNIcPJ3O7hNp7IpPZV9COh5u1UjOzGPfyXRyC0znTB9Sy4smtf0Y2LYedat7EejrTn1/L3w8ruwUrLXmm2/+5YknVpCcnM2kSV2ZNKnrFc2zNLZ86mnkRcZrYIKtli+EEFfCZNLsjk/jwKl04pKzSck2iom2Hk0mJ99EXuG5ySDQ152IIF/ah/rTKMiXhgHe9G1Rl/BAH5vFOHnySt5882+6dg3h449vpHXrOjZZjsP1RyGEEOVBa01SZh6n0nKJS85i14lU1h48S0pWHqfTc8nKO//dg9Ba3rQN8celmqJX0yAaBvrQqn4Ngvw8Kizu7Ox8MjPzCQz0ZsyY9jRpUosxYzpQrVppzweVD0kUQginlpSZx9ajyUQfSSL6aDKHz2biWk1xOj231Okj6/oxsE19CrUmPNCHDqE1aRTkg7+3u9V1BraybFkMEyYspV27usyfP4JmzQJp1izQ5suVRCGEcBp5BSb2JqSxIy6F33efYtPhRPIL/3ui3tfDleCaXjTw9yKkljduLorgmsaTRHVreNK4tq8do7+w+Ph0Hn10GXPn7qFZswAefPCqCl2+JAohhEMqNGkOn83g138T2B2fyoFTGRxLOvdNZKXguua16deyLldHBBBS68pePLOHP/6I5ZZb5pCXV8jUqdcyaVJXPK6wEvxSSaIQQlR6h85k8FP0cRIz8th1IpW45Gwycs99CS0iyIc7OocSWsubNsH+tA2pgbe7457i8vMLcXNzoW3bugwY0IRXXulN48a17BKL425FIYRTOZ2ew+bDSWw7lkJWXiEr957CRanzmqpoUtsoJmpmfv/gqrCadI4IwMWGlbkVKS0tl+efX8WmTSdYv340gYHezJ49zK4xSaIQQlQIrTX7T6Wz5UgyKZl57D2Zxl8Hz1KtmiItOx9TicZ5wgN9yM4r5N5uYXi6udC3RR06hNa0T/AVQGvNvHl7eOSRZZw8mcH48VeRm1uIt7f9uw2SRCGEsImCQhMbY5P4ZsMRdsSlltqInadbNer7enHrVSH4e7kTEeRDZF0/Qmt5o5Rz3CFY48yZTO6++xd++y2G9u3rsnDhbVx1VQN7h1VMEoUQ4ooVmjQbYxOZtzWOo4mZHDiVcV4dQo8mgbRqUIPrW9ShVYMauLnY/0q5sqhe3YOzZ7N4771+TJjQCVc7P4ZbkiQKIcRlMZk0W44kMXPNIdYeOHPOuIhAH66NrE1ITS+GdgymUVDlfOzUntauPcq0aeuYP38Evr7ubNw41qYvzV0JSRRCCKvk5BeyYs8pVu49xYnkbKKP/tfQXXigD/1a1uWOzqEE1/SqUsVGl+rs2SwmTVrBV19tJyzMnyNHUmjVqnalTRIgiUIIUYZTaTl8/tdh/j50ll0n0s4Z1ym8Ft0bB3LrVSHUqe5ppwgdh9aaL7/czqRJK0hLy+Xpp7vz3HM98fZ2s3doFyWJQgiB1poz6bnEnM5gT0Ia/xxLZuvRZE6l/dfMRddGAfRtUYcb29QjyNdD7houw3ff7aBFiyA+/vhGWrasbe9wrCaJQogq6GRqDvP/iWPfyXQ2H048JyEU8XSrRo8mgYzuFs61kY5zUqtMsrLyefXVdYwbF0VwcHXmzx9BjRqelbqYqTSSKISoIkwmzdcbjjBzdQxnM/KKh/t6uNIh1J9WDWrQukENmtTxI7KuH55uLvYL1gksXXqQCROWcuRICg0a+PHAA1dRs6aXvcO6LJIohHBChSZNXHIWexPS2XnCaCAv5nRG8fgbW9djSIcG9GpW22neaK4s4uLSePTRZcyfv5fmzQP588976Nmzob3DuiKSKIRwEkmZeXy74SibDify96HEc8YF1/Sia6MAosJqMe6aCIduA6mymzZtLUuWHOTVV3szcWJX3N0d/85MGR3NOY6oqCgdHR1t7zCEsLtdJ1KZG32cmDMZbIpNosCiDYz+repSr4YXV0fUom2IvzyVZGObN5/Ay8uV1q3rkJiYRWpqLhERlau5EaXUVq111OV8Vy4rhKjktNYcT8pm78k0ElKyWb7nFNFHks/pirNvizqE1vKmZ9MgejQOdLjKUkeVmprDM8/8wUcfRXPTTU1ZtGgkAQHeBAQ4XnPmZZFEIUQllJVXwJIdCfy6I+G8t5693Fxo1aA63RoH0q9lXVo1qGGnKKsurTVz5uzmscd+5/TpTB56qBNTp/a2d1g2I4lCiEqg0Nwcxj/HktlwKJF1B88Wj2tWx48ODWvSo0kgobW8aVbXT9pJsrPvvtvBXXf9QlRUfRYvHknHjvXtHZJNSaIQwo72nUxj6Y4EPlgVUzzMy82FyLp+DO0QzO2dQ/Gp4N7MROlycwuIjU2mefMgRoxoSUGBibvuaotLFUjacgQKYQfxKdk8s2Ana/YbxUrhgT70bBLIhN6Nqe0nFc+VzerVh3nggSVkZeVz8OBDeHi4cu+97e0dVoWRRCFEBUpIzebdFQeY/88JCk2a8EAfPr6zI83q+tk7NFGK06czeeKJ5Xz77Q4iImoya9bACu+vujKoemssRAUzmTSztxznu41H2ZNgNKzXKMiH929rLxXRlVhMTBKdOn1KRkYezz7bg2ef7YGXV+VvwM8WJFEIYQO5BYX8dfAsmw4nMWttbPHwPpG1ebhPE9qG+NsxOlGWtLRcqlf3oFGjmowZ057Ro9vTvHmQvcOyK0kUQpST3IJCth5NZunOBL7beKx4eGRdP3o1q83YHuEE+nrYMUJRlszMPF5++U8+/fQfdux4gODg6rz55vX2DqtSkEQhxBXKKzCxYFscr/22j+Ss/OLh93QNY3S3cEKd7OUrZ/Trr/t58MHfOHYslTFj2jtEHxEVSRKFEJdpb0Iav+1MYMbqGIpaz3i4TxOGdwyWXt4cREGBiREj5rJgwT5atgxi3bp76d491N5hVTqSKIS4BIUmzbytx3l92X6SMo2mumt4uXF3l4bcd00jfKvgEzGOSGuNUgpX12rUq+fLa6/14bHHujhFA362IEe1EFaIOZ3O138fZfmek5xKy8XDtRpD2jfg9s6hdAitKW0rOZCNG+OYMGEpn346kA4d6jFz5o32DqnSk0QhRBlSsvJ4/Kd/WbXvdPGwx/s2Zdw1jXB3df43cp1JcnI2zzzzB598spX69f1ITs62d0gOw6aJQil1A/A+4AJ8prV+rcT4UOBrwN88zVNa66W2jEkIa/xzLJn/++Mgaw6cQWsY0r4Bo7uH07J+dal7cEBz5uzi4YeXcfZsFo8+ejUvvdQLPz95As1aNksUSikXYCbQF4gDtiilFmmt91hM9hzwk9b6I6VUC2ApEGarmIQoy9mMXD7/6zCfro0t7tuhgb8XT97QjEHtGtg5OnEl9u07S1iYP8uW3UH79vXsHY7DseUdRScgRmsdC6CUmg0MAiwThQaqm/+uAcTbMB4hzrMzLpVftp8gITWbpTtPFg+/tlkQr9zSmgb+jtnHcVWXk1PA66//RYcO9Rg4sBnPPNOD557rWSUa8LMFWyaKBsBxi89xQOcS07wILFdKPQT4ANeVNiOl1H3AfQChofLomrgyqVn5fLDqIMv3nOR40n/l1L0jazO2ezhXRwRI5bQDW7kylvHjl3DwYBITJ3Zh4MBmuLnJ00xXwpaJorRfWsl+V0cCX2mt31ZKdQG+VUq10lqbzvmS1rOAWWB0hWqTaIXTO5maw6tL97Lo3/9uXO+8OpT7ezYipJa8FOfoTp3K4PHHl/PDDztp3LgWy5ffSd++jewdllOwZaKIA0IsPgdzftHSGOAGAK31BqWUJxAInEaIcpCQms3M1TEs3B5Pek4BAO4u1Xj31nbc0KouLnLn4DRWrIhl3rw9vPBCT55+ugeenvJQZ3mx5ZbcAjRRSoUDJ4DbgNtLTHMM6AN8pZRqDngCZxDiCu06kcqHa2KK6x183F24q0tDekfWpkeTIEkQTuLff09y8GASw4a14I47WtOtWwjh4TXtHZbTsVmi0FoXKKUeBH7HePT1C631bqXUy0C01noRMBH4VCn1GEax1D1aaylaEpdFa82fB87w7Yaj/GF+7yGyrh8P92nCgNbypIszycjIY8qU1bz//ibCwvwZPDgSV9dqkiRsxKb3ZuZ3IpaWGPaCxd97gG62jEE4N601u+PTeGXJHjbGJhUPb1bHj2m3tCIqrJYdoxO28Msv+3jood+Ii0vjvvs6MH36dbjKy482JYV4wiElZebx/C+7WLIzoXhYSC0vhrQPZsRVIfJYq5PaufMUt9wyh9atazNnzjC6dg25+JfEFZNEIRzKvpNpzFgVw5r9Z8jILSAiyIdrm9Vm1NUNCQv0sXd4wgby8wtZt+4YvXuH07p1HZYsuZ2+fSPkkdcKJIlCOISkzDx+2XaClxcb72tGNazJpH7N6BwRYOfIhC39/fdxxo1bzO7dZ9i//0EaN67FgAFN7B1WlSOJQlRqJpNm0rwdzP8nDgA/D1e+vPcqqXtwcklJ2Tz11Eo+/fQfQkKq8/PPI2jcWPa5vUiiEJXWyj2neGfFAfYkpNEoyIexPSK4qU09/Dyl9zFnlpNTQLt2HxMfn87EiV148cVe+Pq62zusKk0ShaiUvtlwhBcW7gbggV6NeLJfM2m11cnFxaURHFwdT09Xpk69lnbt6tK2bV17hyWQRCEqmdX7TvPG7/vZm5BG49q+zL2/CzV95GrSmWVn5zN9+l+8/vp65s0bzsCBzbj77nb2DktYsCpRKKXcgVCtdYyN4xFVkNaa33ad5OM/D7EjLhUwKqs/GdVRkoSTW778EOPHL+HQoWTuvLMNnTpJc+6V0UUThVLqRuAdwB0IV0q1A6ZorW+xdXDCuZ1Ky2HR9nim/7YXc/cPdG0UwKu3tJZHXauAhx5ayowZW2jSpBYrV46iT58Ie4ckLsCaO4qXMZoHXw2gtd6ulGps06iEU9t1IpVP18WycLvRRqSPuwt3dmnI7Z1CaRggCcKZFRYaDUO7uFTj6quDCQz0ZvLk7tKAXyVnzd7J11qnlKhIlPaYxCVbe+AMby3fX1y81DbEn4d7N6ZXs9rSSF8V8M8/CYwbt5hRo9rw0EOdueOONvYOSVjJmkSxVyk1Aqhmbgn2EWCjbcMSzuazdbG8smQvHq7VGNK+AQ/1aUK4FC9VCenpubzwwmo++GAzQUHe1KvnZ++QxCWyJlE8CLwAmICfMVqDfdqWQQnnUNSa64uLdnMkMYu2If58c28nanjLexBVxfLlhxg9eiHx8emMGxfFq6/2wd/f095hiUtkTaLop7WeDEwuGqCUGoKRNIQ4R2ZuAV/8dZgF204QezazeHin8Fp8OipKkkQV4+7uQu3aPsyfP4LOnYPtHY64TOpi3T8opf7RWncoMWyr1rqjTSO7gKioKB0dHW2PRYsypOfk8/byA3y38SgF5keYmterTqewmoztESFdjVYR+fmFvPPOBtLScpk2rQ9gNMMifZDbn/m8HXU5373gHYVSqh9GN6UNlFLvWIyqjlEMJQRZeQV8tOYQ/7fKeMUmItCHx/o2pX+ruri6SB8BVclffx0rbsBv+PAWxQlCkoTjK6vo6TSwC8gBdlsMTweesmVQonIzmTQbDyfy1u/7+edYSvHwt4a3ZWiHBtLURhWTmJjF5Mkr+fzzbYSG1uDXX0dy001N7R2WKEcXTBRa623ANqXU91rrnAqMSVRS62PO8tm6WFbv/69bc9dqionXN2Nsj3Dc5A6iSkpMzGb27F08+WRXXnjhGnzkbXqnY01ldgOl1DSgBVD8uILWWi4ZqohCk6DMJ6IAACAASURBVOb+b7eycu8pANoG16Bb40CGdQwmIsjXztEJe9i79ww//bSbKVN60bRpAMeOPUatWtKroLOyJlF8BbwCvAX0B+5F6iiqhNTsfN5beYAfNx8jJ9/Y5euevFYqpquwrKx8pk1by5tv/o2vrztjxnQgOLi6JAknZ02i8NZa/66UektrfQh4Tim1ztaBCfvacCiRkZ8a71X6uLvw0s0tGXV1Q6mYrMKWLYth/PglHD6cwt13t+XNN/sSFCQvTVYF1iSKXGXUTh5SSo0DTgC1bRuWsIe8AhMv/bqb7zcdKx42vlcjJklfEFVeRkYeo0YtICDAi9Wr76ZXrzB7hyQqkDWJ4jHAF3gYmAbUAEbbMihR8fbEpzH++60cScwCYEiHBgzrGEzXRoF2jkzYS2GhiR9/3MXIka3w9XVn5cpRREYG4uEhDfhVNRfd41rrTeY/04FRAEopecXSSeQWFDJjVUzxexATrm3EQ72b4OnmYufIhD1t3RrP/fcvZuvWBLy8XBk6tIX0NleFlZkolFJXAQ2Av7TWZ5VSLTGa8ugNSLJwcOtjzvK/b6LJyisEYN64LkSFSQf2VVlqag7PP7+amTO3ULu2D7NnD2XIkOb2DkvYWVlvZk8HhgL/YlRgL8BoOfZ1YFzFhCfKm8mkWbIzgQ/XHGJvQhoA9/WM4PG+TeUuQjB06E+sWnWYCROu4pVXelOjhjTgJ8q+oxgEtNVaZyulagHx5s/7KyY0UZ4KTZr3/zjI4h3xxJ4xGusb3jGYR/s2pYG/PNpYlcXGJhMU5I2fnwfTpvWmWjXFVVdJl6TiP2UlihytdTaA1jpJKbVPkoRjWrXvFM/8vIuTacYL9vd0DeOp/pFyB1HF5eUV8tZbfzN16loefrgTr7/eV1p4FaUqK1FEKKWKmhJXQJjFZ7TWQ2wambhi6Tn5jP/+H9YdPAvAyE4hTBvcWt6FEKxde5Rx4xazd+9Zhg1rwcMPd7Z3SKISKytRDC3xeYYtAxHlp6DQxEu/7mH2lmPkF2qubRbEW8PbEuDrYe/QRCXw7rsbePzx5YSF+bNkye0MGNDE3iGJSq6sRgH/qMhAxJXLLzTx/sqDzFoXS16BCZdqindGtGVIBylOqOpMJk1mZh5+fh7ceGNTzpzJ4rnneuItHUkJK8ibM05i7YEzPPvLTo4nZRPg485zQ5tzS3tJEAJ27z7NuHFLinuaa9o0gFdf7WPvsIQDsWmiUErdALwPuACfaa1fK2WaEcCLgAb+1VrfbsuYnE1SZh5PzP2XVftOAzC2ezjP3dTCzlGJyiArK5+pU//krbc2UKOGB6NHt0NrLc2xiEtmdaJQSnlorXMvYXoXYCbQF4gDtiilFmmt91hM0wR4GuimtU5WSkkbUlZKycpj2pK9zN0aVzzslwndaBfib8eoRGWxbVsCQ4b8xJEjKdx7bzveeKMvgYHS6q+4PBdNFEqpTsDnGG08hSql2gJjtdYPXeSrnYAYrXWseT6zMd7N2GMxzf+AmVrrZACt9elLX4WqJTO3gGcW7GTh9vjiYW8Oa8PwqBA7RiUqi6I7htDQGoSG1uDrrwfTs2dDe4clHJw1dxQfADcBvwBorf9VSl1rxfcaAMctPscBJZ/BawqglFqPUTz1otZ6mRXzrpJOpubQ7721pGbnA/DqLa257aoQedxVUFBgYsaMzSxatJ8VK0YREODNn3/eY++whJOwJlFU01ofLVGuWWjF90o7e+lSlt8E6IXRdtQ6pVQrrXWK5URKqfuA+wBCQ0OtWLRz0VqzYNsJJs/fQX6hZsrAFtzdJUwShABg8+YTjBu3mG3bTtK/f2PS0nKpWVPethflx5pEcdxc/KTN9Q4PAQes+F4cYFkeEozRDEjJaTZqrfOBw0qp/RiJY4vlRFrrWcAsgKioqJLJxqnlFhQy9uvo4pfmZo3qyPUtpRVPYfQRMXnyCj76KJp69fyYO3c4Q4c2l8pqUe6sSRQPYBQ/hQKngJXmYRezBWiilArH6OzoNqDkE02/ACOBr5RSgRhFUbHWhe78Fm4/waR5O8grMNG9cSDv3NqW2n7SSJswuLlVY82aozz0UCemTu1N9eryQqWwDWsSRYHW+rZLnbHWukAp9SDwO0b9wxda691KqZeBaK31IvO465VSezCKsyZprRMvdVnOptCkmTx/B/O2xhHo686rt7SWuwgBQExMEi+//CczZw7Az8+DrVvvw9NTXocStqW0LrskRyl1CNgPzAF+1lqnV0RgFxIVFaWjo6PtGYJNbYxN5PE524lPzaFzeC3eHtGW4JryWGNVl5tbwBtvrGfatHW4u7uwZMnt9OghTzMJ6ymltmqtoy7nu9b0cNdIKdUVo+joJaXUdmC21nr25SxQXNgnfx5i+m/7AHiqfyT394yQ8mbB6tWHeeCBJezfn8itt7bknXf6Ub++n73DElWIVfesWuu/gb+VUi8C7wHfA5IoyklBoYmnft7JvK1xhAV48+EdHWlRv7q9wxKVgNaaadPWkZ9vYtmyO+jXr7G9QxJVkDUv3PlivCh3G9AcWAh0tXFcVYbWmjs/38TG2CRa1KvOoge74epSzd5hCTsymTSff/4PN9zQmJCQGnz77S34+3vi5SUN+An7sOaMtAu4GnhDa91Yaz1Ra73JxnFVGe+uPMjG2CQGtq3Pkoe7S5Ko4nbsOEX37l9w332L+eyzfwCoV89PkoSwK2uKniK01iabR1IFTf9tL5/8GUuXiAA+uK2d1EdUYRkZebz00hrefXcjNWt68dVXg7jrrrb2DksIoIxEoZR6W2s9EZivlDrv0Sjp4e7yJWfmMWnev6zce5rwQB8+urODJIkq7sUX1/D22xsYO7Y9r712HQEB8qSbqDzKuqOYY/5ferYrRwu2xfHYnH8BaNWgOvPGdZW+q6uo48dTyczMJzIykKee6s7gwZF07171mqgRlV9ZPdxtNv/ZXGt9TrIwv0gnPeBdonHfbmXZ7pMAvH9bOwa1a2DniIQ9FBSY+OCDTbzwwmo6dqzPn3/eQ2CgtyQJUWlZU3M6upRhY8o7EGf37IKdLNt9kjrVPdj54vWSJKqojRvjiIqaxcSJy+nVK4yvvx5s75CEuKiy6ihuxXgkNlwp9bPFKD8gpfRviZIycwt48Id/WL3/DEF+Hvw1uTdu8mRTlbRkyQEGDvyR+vX9+PnnEQweHCl1U8IhlFVHsRlIxGj1dabF8HRgmy2DchapWflc+/YakjLzGNSuPm8PbyuPv1YxWmvi49Np0KA6110XwcsvX8sjj3TGz08a8BOO46JtPVU2jtLW0yd/HmLW2liSsvJ4fUgbRlwlPdBVNQcOJDJ+/BIOHEhkz54J+Pq62zskUYXZpK0npdSfWutrlFLJnNvhkAK01rrW5SywKnhx0W6++vsI9Wt48vndUfSOrGPvkEQFyskp4LXX/mL69L/w8nJl+vQ+eHlJC6/CcZV19BZ1dxpYEYE4g0KTZsQnG9h6NBmANZOuxd1VipqqkpMnM+jZ80sOHkxi5MhWvPNOP+rW9bV3WEJckbIejy16GzsEiNda5ymlugNtgO+AtAqIz2Hk5Bcy4P11xJ7NBGD9U70lSVQh+fmFuLm5UKeODz17NmTmzAH07dvI3mEJUS6sOZP9gtENaiPgG4yGAX+waVQOZlNsIm1eWk7s2Ux6Ng3i8PQBNPCXPourApNJ8/HH0TRq9AFxcWkopfjss5slSQinYk3BqUlrna+UGgK8p7X+QCklTz2ZnUzN4c7PN5FfqJnYtykP9Wli75BEBfn335Pcf/9iNm06Qe/e4eTnF9o7JCFswqquUJVSw4FRQNHbQdKUJZBfaOL+b6MpMGkWTuhG2xB/e4ckKoDWmkmTVvDeexupVcuLb7+9hTvuaC3vRAinZU2iGA2Mx2hmPFYpFQ78aNuwKjetNRtiE5mycDcHT2fw3I3NJUlUIUopkpOzGTPGaMCvZk0pZhTOzar3KJRSrkBR11oxWusCm0ZVBnu/R5GdV0jzF5YB4OfhyvM3tZB3JKqAo0dTeOSRZbzwwjV06FAPk0lTrZrcQQjHcSXvUVy0Mlsp1QOIAT4HvgAOKKW6Xc7CHF2hSXPtW2sAqKZg/dO9JUk4ufz8Qt54Yz0tWnzIihWx7N9/FkCShKhSrCl6ehcYoLXeA6CUag58C1xWZnJkH62J4WRaDgPb1peOhqqAv/8+zv33L2bXrtMMGtSMDz7oT2hoDXuHJUSFsyZRuBclCQCt9V6lVJVriyA1O5/3/zgIwJvD2kiSqAJWrowlNTWHX365lUGDIu0djhB2Y02i+Ecp9QnGXQTAHVSxRgGz8wq5atpK8gs1s0Z1lI6GnJTWmm+/3UFQkDf9+zdh8uRuPP54F2mjSVR51rxwNw44BDwJTAZigfttGVRlorXRLEdegYmb2tTj+pZ17R2SsIF9+87Su/c33H33L3z55XYAPDxcJUkIwUXuKJRSrYFGwAKt9RsVE1LlobWm//vr2HcynTuvDuWVwa3tHZIoZ9nZ+bz66jpef309Pj7ufPLJTYwd28HeYQlRqVzwjkIp9QxG8x13ACuUUqX1dOe0tNa8unQv+06mE+Djzgs3tbR3SMIGfv31AK+8so5bb23Fvn0TuO++jvJEkxAllHVHcQfQRmudqZQKApZiPB5bJUz/bR+frjuMm4uSBv6czMmTGWzffpIbbmjM8OEtCAsbS6dO0jWtEBdS1tkvV2udCaC1PnORaZ3K34fOMmttLF0iAtg/tb9UXjuJwkITH364hWbNZjBq1AKys/NRSkmSEOIiyrqjiLDoK1sBjSz7ztZaD7FpZHYSn5LN6K+2UKe6B28ObyPFEE7in38SGDduMVu2xHPddRF8+OEAvLykyTIhrFFWohha4vMMWwZSWUz/bR85+SY+u6sdwTW97R2OKAeHDyfTqdOnBAZ688MPQ7jttlbyHowQl6Csjov+qMhAKoN9J9P49d94+reqS/cm0rGfI9Nas3Pnadq0qUN4eE2+/HIQAwc2w9/f096hCeFwqky9gzXGfm00Nvh0/+Z2jkRcicOHk7npph9p3/4Tduw4BcCoUW0lSQhxmWyaKJRSNyil9iulYpRST5Ux3TCllFZK2a39qJmrY4hLzkYpCA2QIidHlJdXyGuv/UXLlh/y559HeOutvrRoEWTvsIRweNY04QGAUspDa517CdO7ADOBvkAcsEUptciy3SjzdH7Aw8Ama+dd3uJTspmxKoaa3m6smtjLXmGIK1BYaKJr18/ZujWBIUOa8957/QgJkQb8hCgP1jQz3kkptRM4aP7cVin1f1bMuxNG3xWxWus8YDYwqJTppgJvADnWh12+3l5+gOz8Qj4ZFUVNH2mywZGkpRnXLi4u1Rg9uj2//jqS+fNHSJIQohxZU/T0AXATkAigtf4XuNaK7zUAjlt8jjMPK6aUag+EaK0XlzUjpdR9SqlopVT0mTNnrFi09fafTGf+P3H0aBJIp/Ba5TpvYTtaa776ajsREe+zcOE+AMaPv4qbbmpq58iEcD7WJIpqWuujJYZZ04t8ac8fFnenp5SqhtHXxcSLzUhrPUtrHaW1jgoKKr8y54JCE+O/34q7SzWmDGxRbvMVtrVnzxl69fqae+9dSGRkII0aSYIXwpasqaM4rpTqBGhzvcNDwAErvhcHWHb/FgzEW3z2A1oBa8zPtNcFFimlbtZaV0hfp7/vPsWhM5k8d2NzGtf2q4hFiiv0xhvrefbZVVSv7sFnnw3k3nvby0uRQtiYNYniAYzip1DgFLDSPOxitgBNlFLhwAngNuD2opFa61Sg+GUFpdQa4ImKShIArywx6tXv6NywohYpLpPWGqUUdev6cscdrXnzzb4EBfnYOywhqoSLJgqt9WmMk/wl0VoXKKUeBH4HXIAvtNa7lVIvA9Fa60WXHG05WnvgDAmpOdzYuh5e7tKWU2UVH5/OI48so0ePUB5+uDN33dWWu+5qa++whKhSLpoolFKfYlG3UERrfd/Fvqu1XorR6qzlsBcuMG2vi82vPP22KwGAqYNbVeRihZWKGvB79tlV5Oeb6No12N4hCVFlWVP0tNLib0/gFs59mskhrdp3mqiGNaklj8NWOtu3n2Ts2EVs3ZrA9dc34sMPB0iFtRB2ZE3R0xzLz0qpb4EVNouoAmw/nsKptFzulLqJSik1NYf4+HTmzBnG8OEtpAE/IezM6jezLYQDDn2G/Wq90SHRyM6h9g5FYFRUz527h4MHE3n22Z5cc00YsbGP4Ol5OYenEKK8WfNmdrJSKsn8LwXjbuIZ24dmG2fSc/llezzXNqtNoK+HvcOp8g4dSmLAgB+49dZ5LFy4n/x84xUdSRJCVB5l/hqVcc/fFuPxVgCT1vq8im1H8uGaGAAe6NXIzpFUbbm5Bbz11t+88so63Nyq8f77NzB+/FW4SpezQlQ6ZSYKrbVWSi3QWnesqIBsSWvNl+uP0DbEn/ahNe0dTpV2/HgaU6euZeDAZrz3Xj8aNKhu75CEEBdgzeXbZqVUB5tHUgG2H08B4Gpp08kuzpzJZMaMzQA0blyLPXsmMHfucEkSQlRyF7yjUEq5aq0LgO7A/5RSh4BMjDactNba4ZLHy4v34O5aTYqdKpjJpPnyy208+eRK0tNz6ds3gmbNAomIkLs6IRxBWUVPm4EOwOAKisWmUrLy2HYshQ6h/vh7y7sTFWXXrtM88MAS/vrrGD16hPLxxzfRrJl0MyuEIykrUSgArfWhCorFpuZGxwHwWF9phrqi5OUVcv3135KXV8gXX9zMPfe0k3cihHBAZSWKIKXU4xcaqbV+xwbx2Mymw4n4e7vRo4l0jWlrq1Yd5pprGuLu7sJPPw0nMjKQwEDpXlYIR1VWZbYL4IvRHHhp/xxGRm4BK/eepnez2vYOxanFxaUxdOhP9OnzDd988y8A3buHSpIQwsGVdUeRoLV+ucIisaHZm48B0KOplI3bQkGBiRkzNvP886spLDQxfXof7rijjb3DEkKUk4vWUTiDFXtO4VJNcXPbBhefWFyyUaMWMHv2Lvr3b8zMmQMID5enmYRwJmUlij4VFoUNbT+ewqbDSYzpHo6L9IRWblJScnB1rYavrzsTJlzF0KHNGTq0uVRWC+GELlhHobVOqshAbGXOFqNF9Hu6htk3ECehtWb27F00bz6T559fBRj1EMOGSSuvQjgrp29YZ/7WOLo3DiSkllSoXqmYmCT69fuOkSPnExxcnTvvlHoIIaoCp26i89/jKeQVmmgYIEniSv3ww05Gj16Ih4crM2b0Z9y4KFxcnP46QwiBkyeKFXtOATCme7idI3Fc+fmFuLm5EBVVn2HDWvDGG32pX9+hno4WQlwhp04UX6w/TEgtLyKCfO0disM5fTqTiROXk5mZx88/30rTpgF8990Qe4clhLADpy07yC80kZVXiLebU+fCcmcyaWbN2kqzZjOYM2cXLVsGUVhosndYQgg7ctqzaFGx04O9G9s5EscRG5vMnXf+zIYNcfTqFcZHH91IZKS8pChEVee0iWLLEePp3v6t6to5EsdRo4YHKSk5fP31YEaNaiOPuwohACcuevo7JpHIun64ypM5ZVq0aD9DhsyhsNBEQIA3u3aN56672kqSEEIUc8qzaE5+IftPpVOnuqe9Q6m0jh1LZfDg2QwaNJsDBxJJSMgAoJq8vS6EKMEpi55mrY0FpNipNAUFJt57byNTpqxBa83rr1/HY49djZubi71DE0JUUk6ZKDYfNuonhnQItnMklU9hoYnPPvuH3r3D+b//609YmL+9QxJCVHJOV/SkteavmLN0Dq+Fu6vTrd5lSU7OZvLkFaSn5+Lh4cr69aNZtOg2SRJCCKs43Zl09f7TAPSOlE6KtNZ8//0OIiNn8vbbG1i9+ggAAQHeUlkthLCa0xU9fbbuMAB3dQmzbyB2duBAIuPHL+GPPw7TqVMDfv/9Ttq1kzobIcSlc6pEkZSZx9+HEukcXgsv96pdOfvoo8uIjo7nww8HcN99HaUBPyHEZXOqRLFw+wkAxvaIsHMk9rFixSEiIwMJCanBRx/diIeHK3XrSjtXQogrY9PLTKXUDUqp/UqpGKXUU6WMf1wptUcptUMp9YdSquGVLG/5bqPZjqpWP3HyZAa33z6f66//jtdfXw9Aw4b+kiSEEOXCZolCKeUCzAT6Ay2AkUqpFiUm2wZEaa3bAPOANy53eVprNsQm0sDfq8p0eWoyaT7+OJrIyBnMn7+XKVOu4a23rrd3WEIIJ2PLO4pOQIzWOlZrnQfMBgZZTqC1Xq21zjJ/3Ahc9osP+06mA3Bjm3qXOwuHM336Oh54YAkdO9Znx45xvPhiLzw9nao0UQhRCdjyrNIAOG7xOQ7oXMb0Y4DfShuhlLoPuA8gNDS01C8fTcwE4JqmQZceqQNJT8/l7NkswsNrMm5cFOHhNRk5spU87iqEsBlb3lGUdubSpU6o1J1AFPBmaeO11rO01lFa66igoNITwa4TaSgF7UKc8yUyrTULFuylRYsPufXWeWitCQjw5vbbW0uSEELYlC0TRRwQYvE5GIgvOZFS6jrgWeBmrXXu5S5s85EkGgX54uPhfEUvR4+mcPPNsxky5Cdq1fLigw/6S3IQQlQYW55VtwBNlFLhwAngNuB2ywmUUu2BT4AbtNanL3dB+YUmNh9O4ua29a8k3kppw4bjXHfdtwC89VZfHnnkalylaRIhRAWyWaLQWhcopR4EfgdcgC+01ruVUi8D0VrrRRhFTb7AXPMV8jGt9c2XuqyY00YT2c5U7JSWlkv16h506FCP0aPbMWlSN0JDa9g7LCFEFWTTchqt9VJgaYlhL1j8fV15LGffyTQAOoXXKo/Z2VViYhZPPbWS5ctj2b17PL6+7vzf/w2wd1hCiCrMKQr0txxJxsO1Gk3r+Nk7lMumtebbb3cwceJykpOzefzxLkg1hBCiMnCKRLE3IY02wTUctlnx1NQcBg+ew5o1R+jSJZiPP76JNm3q2DssIYQAnKCZ8YJCE3sT0qjv72XvUC6Z1sbTwtWrexAY6M2sWTfx11+jJUkIISoVh08Uh85kkpNvIqphTXuHckl+/z2GDh1mEReXhlKKuXOH87//dZQ+q4UQlY7DJ4ptx5IBCK7pbedIrJOQkM5tt83jhhu+Jysrn9OnM+0dkhBClMnh6yg2HzH6x27rAI/Gzpy5mWeeWUVubgEvvdSLyZO74eGELwgKIZyLw5+l4pKyAajl427nSC5u69YEOnduwMyZA2jSJMDe4QghhFUcPlEcScystC/apaXl8sILqxk1qg0dO9bnww9vxMPDRZrfEEI4FIdOFNFHkjidnsv/KlmPdlpr5s/fyyOPLCMhIZ3Q0Bp07FhfmgAXQjgkhz5zLd6RAMANreraOZL/HD6czIMP/sbSpQdp164uP/88gs6dL7ubDSGEsDuHThRFTXcE16w871B8//1O1q49yrvv9uPBBztJA35CCIfn0IniREo2kXX97F7mv27dUXJzC7nuuggmTerKPfe0Izi4ul1jEkKI8uKwl7vJmXkcT8rm2sjadovh7NksRo9eSM+eX/Hyy38C4OHhKklCCOFUHPaOYntcCgBNavtW+LK11nz11XYmTVpBamoukyd34/nne1Z4HKLyy8/PJy4ujpycHHuHIqoIT09PgoODcXNzK7d5Omyi2BNv1E90axxY4cteuvQgo0cvolu3ED7++CZatbLfXY2o3OLi4vDz8yMsLMzuRaTC+WmtSUxMJC4ujvDw8HKbr8MWPc3ZchwvNxfqVPeskOVlZeWzfv0xAAYMaMLChbexdu29kiREmXJycggICJAkISqEUoqAgIByv4N12ERxLCmL7PzCClnWb78dpFWrD+nf/3tSUnJQSnHzzc2kAT9hFUkSoiLZ4nhz2ETh4+5Cfxu/P3HiRBrDh89lwIAf8PBw5ddfR+LvXzF3MEIIUVk4ZKJIy8knM6+QlvVt93TR6dOZtGjxIYsXH+CVV67l33/Hcc01YTZbnhC24uLiQrt27WjVqhUDBw4kJSWleNzu3bvp3bs3TZs2pUmTJkydOrW4nxSA3377jaioKJo3b05kZCRPPPGEPVahTNu2bWPs2LHnDBs0aBBdunQ5Z9g999zDvHnzzhnm6/vfwzAHDhxgwIABNG7cmObNmzNixAhOnTp1RbElJSXRt29fmjRpQt++fUlOTj5vmtWrV9OuXbvif56envzyyy8A9OjRo3h4/fr1GTx4MACLFy9mypQpVxTbJdFaO9S/jh076mOJmbrh5MV6zpZjurzFxaUW//3++xt1TExiuS9DVB179uyxdwjax8en+O+77rpLv/LKK1prrbOysnRERIT+/ffftdZaZ2Zm6htuuEHPmDFDa631zp07dUREhN67d6/WWuv8/Hw9c+bMco0tPz//iucxbNgwvX379uLPycnJOjg4WEdGRurY2Nji4XfffbeeO3fuOd8t2jbZ2dm6cePGetGiRcXjVq1apXfu3HlFsU2aNElPnz5da6319OnT9ZNPPlnm9ImJibpmzZo6MzPzvHFDhgzRX3/9tdZaa5PJpNu1a1fqdFqXftwB0foyz7sO+dTTbvMTT55uLuU2z9TUHJ57bhWffLKVjRvH0qFDPR5+uHO5zV+Il37dXfy0XnlpUb86Uwa2tHr6Ll26sGPHDgB++OEHunXrxvXXXw+At7c3M2bMoFevXkyYMIE33niDZ599lsjISABcXV0ZP378efPMyMjgoYceIjo6GqUUU6ZMYejQofj6+pKRkQHAvHnzWLx4MV999RX33HMPtWrVYtu2bbRr144FCxawfft2/P2Nxj0bN27M+vXrqVatGuPGjePYMeMhkvfee49u3bqds+z09HR27NhB27Zti4fNnz+fgQMHUqdOHWbPns3TTz990e3yww8/0KVLFwYOHFg87Nprr7V6F8/i4QAAD45JREFUu17IwoULWbNmDQB33303vXr14vXXX7/g9PPmzaN///54e5/bv056ejqrVq3iyy+/BIx6iF69erF48WJGjBhxxXFejEMmiuNJWQC0C77yVmO11sydu4dHH13GyZMZPPhgJxo1cqze8oSwRmFhIX/88QdjxowBjGKnjh07njNNo0aNyMjIIC0tjV27djFx4sSLznfq1KnUqFGDnTt3ApRavFLSgQMHWLlyJS4uLphMJhYsWMC9997Lpk2bCAsLo06dOtx+++089thjdO/enWPHjtGvXz/27t17znyio6Np1arVOcN+/PFHpkyZQp06dRg2bJhViWLXrl3nbYvSpKen06NHj1LH/fDDD7Ro0eKcYadOnaJevXoA1KtXj9OnT5c5/9mzZ/P444+fN3zBggX06dOH6tX/K26Piopi3bp1kiguZOvRZNxdqhEacGW92mmtGTLkJ375ZR8dOtRj0aKRREXVL6cohTjXpVz5l6fs7GzatWvHkSNH6NixI3379gWM4/9CT8hcypMzK1euZPbs2cWfa9a8+IXW8OHDcXExSgRuvfVWXn75Ze69915mz57NrbfeWjzfPXv2FH8nLS2N9PR0/Pz8ioclJCQQFBRU/PnUqVPExMTQvXt3lFK4urqya9cuWrVqVeo6XeoTQn5+fmzfvv2SvmOthIQEdu7cSb9+/c4b9+OPP55XD1O7dm3i4+NtEktJDlmZnV9oIq/QdPnfNz9Wq5Sie/cQPvjgBjZvHitJQjglLy8vtm/fztGjR8nLy2PmzJkAtGzZkujo6HOmjY2NxdfXFz8/P1q2bMnWrVsvOv8LJRzLYSWf6/fx8Sn+u0uX/2/v7oOrqu88jr8/S4UEi9k1LIwt2sQxhcQQkIcW7VBEWsYKhYVxRJ6UHbuOINIWxWEHZ9ZdH0pbQRfRReo6SWsbIk4FBhAW2SgVeRC2ERRsmtIM4DCaxixLUR4C3/3jnCSXPNwcIvcmN/m+Zu7Mveeeh+/9zr3nd8/vnPP93UhFRQVVVVWsWbOGyZMnA3D+/Hl27NhBWVkZZWVlfPTRRxc0EnWfLXbdJSUl1NTUkJ2dTVZWFpWVlfWNWGZm5gVHO59++im9e/euz0WUz3rixIkLTjzHPmIbtTp9+/bl2LGgyvWxY8fo06fl+65eeeUVJk2a1OSO6urqanbv3s24ceMumH7q1CnS05NTEDUlG4q3yqsYmdO2O7LffLOSgoIVrF37IQAPPngTDzzwTbp1S8lUOBdZRkYGy5Yt46mnnuLs2bNMnz6dt99+mzfeeAMIjjzmzZvHww8/DMCCBQt48sknKS8vB4Id99KlS5usd+zYsSxfvrz+dd3OuG/fvhw8eLC+a6klkpg0aRLz588nNzeXzMzMZtfb3D/53NxcKioq6l8XFxezadMmKisrqaysZO/evfUNxc0330xJSQlnzpwBoLCwsP48xLRp03jnnXfYsGFD/bo2bdpU351Wp+6IorlH424ngAkTJlBUVARAUVEREydObDEPxcXFTJ06tcn01atXM378eNLSLrw0v7y8vEm3W6Kk3N6x9rxRe97Iyry89ZljVFWd5O671zB6dBGnT9fSq1ePBEXoXMd1ww03MGjQIFatWkV6ejpr167l8ccfp3///gwcOJDhw4czd+5cAAoKCnjmmWeYOnUqubm55Ofn1/87jvXII49QU1NDfn4+gwYNorS0FIDFixczfvx4brnllvp++pZMmTKFl19+ub7bCWDZsmXs2bOHgoIC8vLyWLFiRZPlBgwYwPHjxzlx4gSVlZUcPnyYESNG1L+fnZ3NFVdcwa5duxg/fjwjR45k6NChDB48mO3bt9efWE5PT2f9+vU8++yz5OTkkJeXR2FhYdwjgCgWLlzIli1byMnJYcuWLSxcuBAIzq3EdiVVVlZy5MgRRo0a1WQdq1atarYBKS0tbXKUkSiymGumU8GAgYPt1Lgn+MVdw/huXt9IyxQX7+f++zfy17+eYcGCm1i06Nv07HnpCmY515KDBw+Sm5vb3mF0ak8//TS9evVq0offmX388cdMmzaNrVu3Nvt+c987SXvNbFhbtpdyRxRnaoNzE1dlRL9Durb2PPn5fSgru48nnhjjjYRzncjs2bPp0aNr9RAcPnyYJUuWJG17KXfV04lTtWT36hH3ruyTJ8/w2GPbuOaaDObMGc6MGQXMmFHgNXec64TS0tKYOXNme4eRVMOHD0/q9lLuiOLk6VqGZ1/Z4k5//fpyrr/+eX760+2Ul1cDwckybyRce0m17l2X2hLxfUu5I4pzZlxzZdP7J44e/T/mzXud1177kLy8v2fbtlmMHPm1dojQuQZpaWlUV1d7qXGXFBaOR9H4CqkvKuUaCoBeaU3DPnSohs2b/8RPfjKG+fNvpHv3S1few7m26tevH0ePHqWqqqq9Q3FdRN0Id5dSyl311OOqHHtr+05GXJvJ7t0fsWPHEX74w+ByuOrqz8j8gndrO+dcZ9Rhr3qSdKukP0iqkLSwmfd7SCoJ398lKSvKevv0uIw5czYwYsSLLF26k5MngxtovJFwzrlLL2ENhaRuwHPA94A8YKqkxrcu3gPUmNl1wNNAy2UVY9w0dCUvvLCXefO+yf79s7n88u6XMnTnnHMxEnmO4htAhZkdApC0CpgIxBZEmQg8Gj5/FVguSRanP8xqz3N1VgYbN05nyJD4d3s655z74hLZUHwVOBLz+ijQeICH+nnMrFbScSAT+EvsTJLuBe4NX57e85d7349QEbgr6E2jXHVhnosGnosGnosG/du6YCIbiuauBWx8pBBlHsxsJbASQNKetp6Q6Ww8Fw08Fw08Fw08Fw0k7Wl9ruYl8mT2UeDqmNf9gMbF0+vnkfQlIAP4NIExOeecu0iJbCjeBXIkZUvqDtwJrGs0zzrg7vD57cB/xzs/4ZxzLvkS1vUUnnOYC2wGugEvmdkHkv6NYJDvdcB/Ar+SVEFwJHFnhFWvTFTMKchz0cBz0cBz0cBz0aDNuUi5G+6cc84lV8oVBXTOOZdc3lA455yLq8M2FIkq/5GKIuRivqQDkvZJ2iqp05bNbS0XMfPdLskkddpLI6PkQtId4XfjA0m/SXaMyRLhN3KNpFJJvw9/J7e1R5yJJuklSZ9Ier+F9yVpWZinfZKGRFqxmXW4B8HJ7z8B1wLdgfeAvEbzzAFWhM/vBEraO+52zMVooGf4fHZXzkU4Xy9gG7ATGNbecbfj9yIH+D3wd+HrPu0ddzvmYiUwO3yeB1S2d9wJysW3gSHA+y28fxvwOsE9bCOAXVHW21GPKOrLf5jZGaCu/EesiUBR+PxVYIw6Z8H/VnNhZqVm9ln4cifBPSudUZTvBcBjwM+AU8kMLsmi5OKfgOfMrAbAzD5JcozJEiUXBtQNi5lB03u6OgUz20b8e9EmAr+0wE7gbyW1WgupozYUzZX/+GpL85hZLVBX/qOziZKLWPcQ/GPojFrNhaQbgKvNbH0yA2sHUb4XXwe+Lmm7pJ2Sbk1adMkVJRePAjMkHQU2Ag8kJ7QO52L3J0DHHbjokpX/6AQif05JM4BhwKiERtR+4uZC0t8QVCGelayA2lGU78WXCLqfbiY4yvydpHwz+98Ex5ZsUXIxFSg0syWSbiS4fyvfzM4nPrwOpU37zY56ROHlPxpEyQWSvgMsAiaY2ekkxZZsreWiF5APvCmpkqAPdl0nPaEd9Tey1szOmtmfgT8QNBydTZRc3AO8AmBmO4A0goKBXU2k/UljHbWh8PIfDVrNRdjd8gJBI9FZ+6GhlVyY2XEz621mWWaWRXC+ZoKZtbkYWgcW5TeyhuBCByT1JuiKOpTUKJMjSi4OA2MAJOUSNBRdcXzadcBd4dVPI4DjZnastYU6ZNeTJa78R8qJmIufA18GVofn8w+b2YR2CzpBIuaiS4iYi83AWEkHgHPAAjOrbr+oEyNiLh4EfiHpxwRdLbM64x9LScUEXY29w/Mx/wJcBmBmKwjOz9wGVACfAf8Yab2dMFfOOecuoY7a9eScc66D8IbCOedcXN5QOOeci8sbCuecc3F5Q+Gccy4ubyhchyPpnKSymEdWnHmzWqqUeZHbfDOsPvpeWPKifxvWcZ+ku8LnsyR9Jea9FyXlXeI435U0OMIyP5LU84tu23Vd3lC4juhzMxsc86hM0nanm9kggmKTP7/Yhc1shZn9Mnw5C/hKzHs/MLMDlyTKhjifJ1qcPwK8oXBt5g2FSwnhkcPvJP1P+LipmXmul7Q7PArZJyknnD4jZvoLkrq1srltwHXhsmPCMQz2h7X+e4TTF6thDJCnwmmPSnpI0u0ENbd+HW4zPTwSGCZptqSfxcQ8S9KzbYxzBzEF3ST9h6Q9Csae+Ndw2jyCBqtUUmk4baykHWEeV0v6civbcV2cNxSuI0qP6XZ6LZz2CfBdMxsCTAGWNbPcfcC/m9lggh310bBcwxTgW+H0c8D0Vrb/fWC/pDSgEJhiZgMJKhnMlnQlMAm43swKgMdjFzazV4E9BP/8B5vZ5zFvvwpMjnk9BShpY5y3EpTpqLPIzIYBBcAoSQVmtoygls9oMxsdlvJ4BPhOmMs9wPxWtuO6uA5ZwsN1eZ+HO8tYlwHLwz75cwR1ixrbASyS1A/4rZn9UdIYYCjwbljeJJ2g0WnOryV9DlQSlKHuD/zZzMrD94uA+4HlBGNdvChpAxC5pLmZVUk6FNbZ+WO4je3hei8mzssJylXEjlB2h6R7CX7XVxEM0LOv0bIjwunbw+10J8ibcy3yhsKlih8DHwODCI6EmwxKZGa/kbQLGAdslvQDgrLKRWb2zxG2MT22gKCkZsc3CWsLfYOgyNydwFzglov4LCXAHcCHwGtmZgr22pHjJBjFbTHwHDBZUjbwEDDczGokFRIUvmtMwBYzm3oR8bouzrueXKrIAI6F4wfMJPg3fQFJ1wKHwu6WdQRdMFuB2yX1Cee5UtHHFP8QyJJ0Xfh6JvBW2KefYWYbCU4UN3fl0QmCsufN+S3wDwRjJJSE0y4qTjM7S9CFNCLstroCOAkcl9QX+F4LsewEvlX3mST1lNTc0Zlz9byhcKnieeBuSTsJup1ONjPPFOB9SWXAAIIhHw8Q7FD/S9I+YAtBt0yrzOwUQXXN1ZL2A+eBFQQ73fXh+t4iONpprBBYUXcyu9F6a4ADwNfMbHc47aLjDM99LAEeMrP3CMbH/gB4iaA7q85K4HVJpWZWRXBFVnG4nZ0EuXKuRV491jnnXFx+ROGccy4ubyicc87F5Q2Fc865uLyhcM45F5c3FM455+LyhsI551xc3lA455yL6/8BCJegqS+KdRoAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4667,28 +4285,28 @@ { "data": { "text/plain": [ - "0.7698058845975142" + "0.7705999826781731" ] }, - "execution_count": 63, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "get_roc(glm_2, y_train, X_train[sig.index], \"Logistic Regression\")" + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Since the optimal cut off was found to be 0.2697615225249289 using the training data, we will use that as our cut off for our evaluation of the test set." + "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 112, "metadata": { "scrolled": true }, @@ -4699,18 +4317,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.86 0.86 6729\n", - " 1 0.54 0.55 0.54 2020\n", + " 0 0.87 0.81 0.84 6659\n", + " 1 0.50 0.61 0.55 2090\n", "\n", - " accuracy 0.79 8749\n", - " macro avg 0.70 0.70 0.70 8749\n", - "weighted avg 0.79 0.79 0.79 8749\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289)))" + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" ] }, { @@ -4727,14 +4345,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Logistic Regression identified 1104\n" + "Of 2090 Defaulters, the Logistic Regression identified 1273\n" ] }, { @@ -4770,13 +4388,13 @@ " \n", " \n", " 0\n", - " 5790\n", - " 939\n", + " 5361\n", + " 1298\n", " \n", " \n", " 1\n", - " 916\n", - " 1104\n", + " 817\n", + " 1273\n", " \n", " \n", "\n", @@ -4785,29 +4403,29 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5790 939\n", - "1 916 1104" + "0 5361 1298\n", + "1 817 1273" ] }, - "execution_count": 65, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.2697615225249289, \"Logistic Regression\")" + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2020 Defaulters, the Logistic Regression identified 716\n" + "Of 2090 Defaulters, the Logistic Regression identified 714\n" ] }, { @@ -4843,13 +4461,13 @@ " \n", " \n", " 0\n", - " 6391\n", - " 338\n", + " 6329\n", + " 330\n", " \n", " \n", " 1\n", - " 1304\n", - " 716\n", + " 1376\n", + " 714\n", " \n", " \n", "\n", @@ -4858,17 +4476,17 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6391 338\n", - "1 1304 716" + "0 6329 330\n", + "1 1376 714" ] }, - "execution_count": 66, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,glm_2.predict(X_test[sig.index])>0.50, \"Logistic Regression\")" + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" ] }, { @@ -4880,19 +4498,19 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.21841387811546378\n" + "Optimal Threshold: 0.2096696069036731\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4904,16 +4522,16 @@ } ], "source": [ - "auroc = get_roc(glm_2, y_test, X_test[sig.index], \"Logistic Regression\")" + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm_2.predict(X_test[sig.index])>0.2697615225249289), output_dict = True)[\"1\"][\"recall\"],auroc]" + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" ] }, { @@ -5088,7 +4706,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -5102,14 +4720,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.2920039216 0.1749065908]\n" + "Explained variation per principal component: [0.295060096 0.1735291836]\n" ] } ], @@ -5127,12 +4745,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5180,7 +4798,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -5190,7 +4808,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -5200,7 +4818,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 39, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -5211,7 +4829,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -5238,7 +4856,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -5265,7 +4883,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -5277,7 +4895,7 @@ " verbose=False)" ] }, - "execution_count": 42, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -5291,19 +4909,19 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15940300945944538\n" + "Optimal Threshold: 0.15585444961401423\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5316,10 +4934,10 @@ { "data": { "text/plain": [ - "0.7282872884609857" + "0.7192717558206292" ] }, - "execution_count": 43, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -5331,14 +4949,14 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2015 Defaulters, the SVM original data RBF kernel identified 707\n" + "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" ] }, { @@ -5373,14 +4991,14 @@ " \n", " \n", " \n", - " 0\n", - " 6390\n", - " 344\n", + " 0\n", + " 6304\n", + " 355\n", " \n", " \n", - " 1\n", - " 1308\n", - " 707\n", + " 1\n", + " 1341\n", + " 749\n", " \n", " \n", "\n", @@ -5389,11 +5007,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6390 344\n", - "1 1308 707" + "0 6304 355\n", + "1 1341 749" ] }, - "execution_count": 44, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -5405,7 +5023,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5414,12 +5032,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6734\n", - " 1 0.67 0.35 0.46 2015\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.36 0.47 2090\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", "\n" ] } @@ -5444,21 +5062,21 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 80, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.07692307692307693,\n", - " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -5469,39 +5087,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.15995506541340332\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.6995913850752561" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#plot roc for svm\n", "get_roc(clf_reduced, y_test, X_test_pca, \n", @@ -5510,73 +5098,9 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2020 Defaulters, the SVM reduced features no tuning RBF kernal identified 738\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06387342
11282738
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6387 342\n", - "1 1282 738" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" @@ -5584,25 +5108,9 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6729\n", - " 1 0.68 0.37 0.48 2020\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" ] @@ -5623,20 +5131,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 1, 'gamma': 0.001}" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -5659,23 +5156,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", - " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", - " shrinking=True, tol=0.001, verbose=False)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", @@ -5684,103 +5167,19 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.153632964235413\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", - " \"SVM reduced features and tuning RBF kernal\")" + " \"SVM reduced features and tuning RBF kernel\")" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2015 Defaulters, the SVM reduced features and tuning RBF kernal identified 895\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06115619
11120895
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6115 619\n", - "1 1120 895" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#confusion matrix\n", "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" @@ -5788,25 +5187,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.91 0.88 6734\n", - " 1 0.59 0.44 0.51 2015\n", - "\n", - " accuracy 0.80 8749\n", - " macro avg 0.72 0.68 0.69 8749\n", - "weighted avg 0.79 0.80 0.79 8749\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" ] @@ -5827,24 +5210,9 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "X.shape[1] = 44 should be equal to 13, the number of features at training time", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m evaluation.loc[4] = ([\"SVM\" , \n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclf_reduced_tuned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"recall\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m auroc])\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 572\u001b[0m \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 573\u001b[0m \"\"\"\n\u001b[1;32m--> 574\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 575\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \"\"\"\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 472\u001b[0m raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[0;32m 473\u001b[0m \u001b[1;34m\"the number of features at training time\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: X.shape[1] = 44 should be equal to 13, the number of features at training time" - ] - } - ], + "outputs": [], "source": [ "evaluation.loc[4] = ([\"SVM\" , \n", " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", @@ -6022,7 +5390,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, From a5cf36ba80c8f1ad13d3c5bbb15f67ae3bc13754 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Wed, 20 Nov 2019 08:19:36 +0800 Subject: [PATCH 12/25] included a copy for pca in case need --- ...t - EDIT - INCLUDES - PCA-checkpoint.ipynb | 5397 +++++++++++++++++ BT2101_Default - EDIT - INCLUDES - PCA.ipynb | 5397 +++++++++++++++++ BT2101_Default - EDIT-Copy1.ipynb | 3 +- 3 files changed, 10795 insertions(+), 2 deletions(-) create mode 100644 .ipynb_checkpoints/BT2101_Default - EDIT - INCLUDES - PCA-checkpoint.ipynb create mode 100644 BT2101_Default - EDIT - INCLUDES - PCA.ipynb diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT - INCLUDES - PCA-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT - INCLUDES - PCA-checkpoint.ipynb new file mode 100644 index 0000000..27df175 --- /dev/null +++ b/.ipynb_checkpoints/BT2101_Default - EDIT - INCLUDES - PCA-checkpoint.ipynb @@ -0,0 +1,5397 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 74.581893 1.992315e-03\n", + "PAY_0 4250.691969 0.000000e+00\n", + "PAY_2 3793.454513 0.000000e+00\n", + "PAY_3 2943.420461 0.000000e+00\n", + "PAY_4 2997.799899 0.000000e+00\n", + "PAY_5 2809.793752 0.000000e+00\n", + "PAY_6 2384.699487 0.000000e+00\n", + "PAY_0_No_Transactions 75.255770 1.695234e-03\n", + "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", + "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", + "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", + "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", + "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", + "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", + "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", + "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", + "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054031256
11233857
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5403 1256\n", + "1 1233 857" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.41 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054221237
11244846
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5422 1237\n", + "1 1244 846" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd1xV9f/A8ddHUBzgBCci4MK9NWdWWmaa5czMhqNs2N67bGfzpw3RsmxoZn01NbNhgSv3zhWo4EYEEWTe9++Pc6grIVyVy2W8n4+HDzn7fcY973M+n3M+x4gISiml1IUq4+kAlFJKFW+aSJRSSl0UTSRKKaUuiiYSpZRSF0UTiVJKqYuiiUQppdRFKTWJxBgzyhiz1NNxeJoxJsgYc9oY41WIyww2xogxxruwlulOxpjtxpjeFzDdBR2DxpgKxpgfjDGJxpi55zt9YXL1+Cqpv0djTHljzE5jTE1Px3KxjDELjDF9XRrXE++RGGP2AbWALOA0sAS4R0ROF3owJZy9rceJyC8ejCEYiAbKikimp+KwYxGgsYjsdfNygimgdTbGjAYmAt0KYvvZSfA3IMXulQCsBN4UkbUXO39PMcaMAj62O70AH/5dR0TEtxBieABoKCL32N1fAMOBdKfRdolIBxfm9RIQKCK3uiNWF5bfDXhHRLrkN64n70gG2ju2LdAOeMKDsVwwT15ll5Qr/PNRSrd3A2D3hSSRPGI+ZP/+/IBLgJ1ApDHmigsP07NE5EsR8bXX62rsdXTqdxY37c87gFk5+r3iHIcrScQVxpgyxhi3ncNFZCUQYIxp58rIhf4P2Af0cep+A1jk1O0DTAYOAEeBj4AKTsMHAZuAU8DfQD+7fxVgBnAYOAi8BHjZw24Fltt/fwRMzhHTfOBB+++6wDzgONZV5b1O4z0PfAt8YS9/XC7rVwX43J5+P/A0UMYpjhXA/wGJWD/gK3JMm9c6rADeAeLtYQ2xri5PAHHAl0BVe/xZgAM4g3Xn9ygQDAjgbY/zOzDJnm8SsBTwd4rnZnsdTgDP5Nx3Oda7AvCWPX4isNzul73MW+x9Ggc85TRdZ2AV1pXxYWAKUM5puAB3A3uAaLvfe0CMvQ/WAz2dxvcCnrSPjSR7eH0gwp5Xsr09RtjjD8A6nrKvzFvnOFYfA7YAaYC38zawY19nx3EUeNvuf8Be1mn7X1ecjkF7nBbAz/a+PAo8mcs2fQHrajbDns9YrAvAp+3tfAzrWKtij5+9rcfaMUTkMs/eQGwu/acA65y6w5zi2wUMP499nX183QpE2fshGhiV8/dod3cD1trzWot195U97HfyOEbPcSyeax1jgUeArUC63S8Q+J5/f+93O41fhn+PpThgNlDtHMsMxTq2vJz6fQE8f47xG9nb6mY7ruPA407HpPN+X2/3X25vi1VYv+tgO/6F9n7aA4xxWsZLwBxgrr3t1gGt7GFPAHNyxPQhTudG4FOcfqvn3N4Xmgwu5h9n/xAD7Z36ntPwd4EFQHWsK6YfgFedfriJQF97J9cDwuxh/8O6ta0E1ATWAHfkPHCBXlgnoeyivWr2Tqlrz3M98CxQzj44ooCr7HGft3fudfa4FXJZv8+xEpOfvaN3A2Od4sgEHgDKAiPs9anu4jpkYhVzeGP9cBvZ28IHCMA6Wb6b27bOcaJxTiR/A03s+f0OvGYPa451EPewt8Vke93PlUim2tPXwzqZd7Pjyl5muL2MNlgn5Wb2dB2wroq97XH/Au53mq9gndCqZ29v4Caghj3NQ8ARoLw9LPtE0RQw9vJqOM2rkdO822OdjLvYMd9ibzMfp+23CSsRVci5TbF+0KPtv32BS3Lbzrkcg35YSfMhoLzd3eUc2/V54Aun7jHAXqxj0xf4DpiVY7mfYx1DuR2fvcn9JHs51oVHJftfDHCbvY3bY51IW7i4r73teZwCmtrT1HGa3nlbVAdOAqPt6Uba3dn77HfOcYzmcY451zrGYv2+A+15edn790msY7yRvX+vsMd/GCuB1bP304zsbZ3LvAcBm3P0cyWRfGTPuz3W76KxPfwlYGaOaZbb8TXDOn948++FafY84oBLneaRAVxvj/+4fex429vgNFDZHrecPW0bp+U9CnyT7zndXckin528z16BJHtD/sq/V9EGK6s3dBq/K/9eiX6MVW6Xc5617J3gfOcyEliWy4FrsK7Wetnd44Hf7L+7AAdyzPsJ4FOnH/V/rvKcxvWy42ju1O8O4HenOA5hJzG73xqsH5Er63DgXMu2x7kO2JhjW+eXSJ52Gn4XsMT++1nga6dhFbGukv6TSLCS6hnngzCXZQbmWOcbzrEO9wPfO3ULcHk+630ye9lYV8+DzjFezkTyITApxzi7+PeHuA+nK7yc2xQrcb9AjivknNs5l2NwpPN+ymfdnufsRPIrcJdTd1Osk4W303JD85hfb3I/yYbZ09bDusCJzDH8Y+A5F/d1diJJAIaQI6Hl2BajgTU5hq8Cbs3vGL2AdYwFbnbq7g5E5RjnGSDc/ntP9rFgd9fH+o2WyWXet+B0l2X3+wJItbdD9r8Z9rDsRFLbafwNwFD773MlkmedukPsfV/Jqd+bwHSneTjf+XlhXTh1tbt/Bm6z/74O2JJjeXcCS/M7Rj1Zxn6diPxijLkU+Arwx9rIAVgnrPXGmOxxDdYGAGtHLs5lfg2wMu5hp+nKYF1VnUVExBgzG+vHHAHciLXDs+dT1xiT4DSJFxDp1P2feTrxx8rs+5367cf6cWY7KPZechpe18V1OGvZ9tMh7wM9sa5qy2CdVM/HEae/U7CucrFj+md5IpJijDlxjnn4Y10R/X2+yzHGNAHeBjpi7XtvrKtGZznX+yFgnB2jAJXtGMA6RvKKw1kD4BZjzESnfuXs+ea67BzGAi8CO40x0cALIrLQheWeT4w51eW/x5c31oVItrxiPpd6WNsyAWu7dMnxO/DGKi51ZV8jIsnGmBFYV/UzjDErgIdEZGeOUXOuD/z3N3OuY/RCOG+bBkBQLr/33+2/g4AfjDEOp+GCVVrgHBNYvzu/XJb3mog8f65gROR81805/rpAnIgkO/XbD7TMbXwRyTLGHOTf4/szrLvOT7Hu8nPW7/hhHQ958vjjvyLyBzATq9gErFurM1i3wFXtf1Xk38qyGKx6gZxisK4U/J2mqywiLc6x6K+BocaYBlh3IfOc5hPtNI+qIuInIv2dw85jleKwrhAaOPULwqrvyFbPOGUKe/ghF9ch57Jftfu1FpHKWAeDyWP883EY6/YXsB5DxSpOyk0c1pVXbvsmPx9i1RU1ttfhSc5eB3BaD2NMT6x6i+FY5dVVsYoHs6c51zGSmxjg5Rz7u6KIfJ3bsnMSkT0iMhLrxPI68K0xplJe01xAjDkd4r/HVyZWPcs/oV3AfK8HNtgnpRjgjxzbxVdE7uQ89rWI/CQifbGKtXZiFW/mtz7w399MQXLeNjHAnlx+7wPt4bFA3xzDy+c4+WfbAjQswEfrz7UPnfsfAvztYy5bzm1XP/sPu3K+nj0dWMWiHYwxLbAeUPgqx7KaAZvzC9TjicT2LtDXGNNWRBxYB9s72c9iG2PqGWOussedAdxmjLnCfmqhnjEmTEQOY1XCvWWMqWwPa2jf8fyHiGzEqtyaDvwkItlZdw1wyhjzmP38vpcxpqUxppMrKyIiWcA3wMvGGD87UT3Iv3c8YJ107jXGlDXGDMPaWYvPdx1sfljFhAnGmHpY9QPOjmKVpV+Ib4GBxphuxphyWEU4OU/wANj77RPgbWNMXXu7dTXG+LiwHD+ssvTTxpgwrNvp/MbPxNp/3saYZ7HuSLJNByYZYxobS2tjTHYCzLk9woEJxpgu9riVjDHXGGNyu7L8D2PMTcaYAHv9s4+hLDs2B+fe9guB2saY+40xPvaxku9jlravgQeMMSHGGF/gFaxK0wt5qsvYv6HnsO7wnnSKr4kxZrR9nJY1xnQyxjRzdV8bY2oZY661T3JpWMdpVi5hLLaXdaMxxtu+i2lux+Buq4B0Y8xDxnoHxMsY08oYk/1k1UfAK8aYILBKAIwx1+Y2IxHZh1VkXiBPZWEdq8E5LjpzLjMaqwL9Ffs4aot1h/Gl02idjTGDjDFlse4Ok7AeaEBEUrAeNPgaWCEiOZN3L+DH/AItEolERI5jVQ4+Y/d6DKtCaLUx5hTwC1Y5MCKyBmtDvYN1FfoH/17N3IxVLLED6zbzW6wroXP5GuiDUxa2E8FArMeSo7GuvqZjPU3lqolY9TxRWGWaX2H98LL9CTS25/0yVplodpHR+a7DC1gVbInAIqwrDGevAk8bYxKMMQ+fxzogItvtdZmNdXeShFW+mnaOSR7GquRei/UEyeu4dow9jFW8mIR1Yp+Tz/g/YR3cu7Fu41M5+3b/baxkvhQrQc3AqlgFq77hM3t7DBeRdVh1ZFOwtvderPJ7V/UDthtjTmM9SXaDiKTaP9CXgRX2si5xnkhEkrAekhiIVUSyB7jMxWV+glUEEYF1jKZi7afzUdeO+TTW/moF9BaRpU7xXQncgHX1egRrf2YnC1f2dRmshwkO2eNcilW/cRb72B9gj3sCq4J3gIjEnec6nTc7+fbHeohnH9Zv8mP+vTB5G+s9t1+NMUlYT/XldVH5MVadj7MnjfWSZva/3O5mcjMH61wQb4xZk8d4I7DOJ0ewzhdPisgyp+HfY5VUxNvjDs5x0fEZ1v4/q1jLGNMViBeRDfkF6pEXEkszY8ytWI8M9/B0LOfLvvpNwCqCivZ0PEoVNcaY8sBGrAr6Y0UgnnxfajTGhGIVy9UWp5fCjTHzganZFxd5KXUvtKnzY4wZiPWUkMGqx9qKdeWmlMpBRFKxiqqLBbvO5EHgK8nRsoiIDHJ1PppIVH4GYd3yGqyy2BtEb2OVKvaMMVWwKuX3AVflPXY+89JzglJKqYtRJCrblVJKFV/FrmjL399fgoODPR2GUkoVK+vXr48TkQB3zLvYJZLg4GDWrVvn6TCUUqpYMcbkbD2gwGjRllJKqYuiiUQppdRF0USilFLqomgiUUopdVE0kSillLoomkiUUkpdFLclEmPMJ8aYY8aYbecYbowx7xtj9hpjthhj2rsrFqWUUu7jzjuSmVhNbJ/L1VhNHzcGbsf6uJFSSqkClpqR22dgCo7bXkgUkQhjTHAeowwCPrcbAFxtjKlqjKljf9xJKaXURTpxOo2nPl3Prwfi3bocT77ZXo+zP0YUa/f7TyIxxtyOdddCUFBQoQSnlFLFVXRcMu8u3smCbYeRMgbvE6luXZ4nK9tz+3xkrk0Ri8g0EekoIh0DAtzSVIxSShVrIsK6ffHc/vk6Ln/rd+ZvO8yZv+IZVb0KO6cNduuyPXlHEovTR+mBQP79IL1SSikXZDmEn7YfITwyio0HEqhSoSz3XNaIdr4VaDyxCvXrn89Xwi+MJxPJAuAeY8xsoAuQqPUjSinlmpT0TOaui2XG8mgOxKdQIUs48esBRg1sxkNXNi3UWNyWSIwxXwO9AX9jTCzwHFAWQEQ+AhYD/YG9QApwm7tiUUqpkuLYqVQ+W7WPL1YfIPFMBvUrlCP1lxhiNx/jwQe68uSj3Qs9Jnc+tTUyn+EC3O2u5SulVEmy+2gS4RFRzN90iAyHg6ua1yZ9axyfPr+Sbt3q8/OGO2jVqpZHYit23yNRSqnSQkRY+fcJwiOj+H3XccqXLcPQ9vUY1rou7Rr7s2tXHF0b+zN2bHvKlMnt+aXCoYlEKaWKmIwsB4u2HGZaRBQ7Dp/C39eHh69sQu1kB4/d/xO72tZm3rzhNG3qT9Om/p4OVxOJUkoVFadSM5i95gCfrtjH4cRUGtX05fUhrehcqwqPPbyUuXN30LRpDe65p5OnQz2LJhKllPKwgwln+HR5NLPXxnA6LZOuoTV45fpWXNokgGXLomlzxQekp2cxadJlPPJIN3x8itapu2hFo5RSpci2g4mER0axcIv15sOA1nUY3zOUlvWqkJGRRZkyhjZtatO/f2NeeulyGjWq7uGIc6eJRCmlCpHDIfyx+zjTIqJYFXUCXx9vbusWzG09QqhXtQKnTqVx330/8uefB1mxYgz+/hWZPXuop8POkyYSpZQqBKkZWczfdJDwyGj2HjtNnSrlebJ/GDd0DqJy+bKICHPnbue++5Zw5Mhp7rqrE2lpWVSsWPQ/G6WJRCml3OhkcjpfrN7PZ6v2E3c6jeZ1KvPuiLZc07oOZb2sJHH8eDK33PI/fvxxL+3a1Wb+/Bvo1KmehyN3nSYSpZRyg/0nkpmxPJpv1sWQmuGgd9MAxvcMpVvDGhhz9jsflSv7EBeXwrvvXsXdd3fG27vo34U400SilFIFaP3+k4RHRPHTjiOULVOG69rVZVzPUJrU8jtrvIiI/bz8ciTz5g3H17ccq1eP8+hLhRdDE4lSSl2kLIfw844jTIuIYoPdAu9dvRtyS9dgalYuf9a4cXEpPPLIz8ycuYng4Krs25dAy5Y1i20SAU0kSil1wc6kZ/Ht+himL49m/4kU6levwAvXtmBYx0Aqljv79CoifPrpJh555GdOnUrjiSd68PTTvahYsayHoi84mkiUUuo8HU9K4/NV+5i1ej8JKRm0rV+Vx/qFcVWL2njlcWfxxRdbaN48gI8+uoYWLWoWXsBupolEKaVctOdoEtMjo/l+40EyHA6ubF6L8T1D6dCg2n8q0AFSUjJ45ZVIJkzoSGBgZebNG06VKuWLdTFWbjSRKKVUHkSEVVEnmB4ZzW87j+HjXYbhnQIZ2yOUEP9K55xu8eI93H33YvbtS6BePT/uvLMT1apVKMTIC48mEqWUykVGloPFWw8THhnFtoOnqFGpHA/2bcJNlzSgeqVy55wuNvYU99+/hHnz/qJZM3/++ONWevVqUIiRFz5NJEop5SQpNYM5a2P4ZHk0hxJTaRhQidcGt+K6dvUoX9Yr3+lffjmCRYv28Morl/PQQ90oVy7/aYo7Y32osPjo2LGjrFu3ztNhKKVKmEMJZ5i5ch9f/3mApLRMuoRU5/ZeoVzWNP9Hc9esOUiFCt60alWLEydSSExMIzS0WiFF7hpjzHoR6eiOeesdiVKqVNt+KJHpkdH8sPkQAvRvVYfxPUNoHVg132kTE1N58slf+fDDdQwY0IQFC0ZSo0ZFatSo6P7AixBNJEqpUkfEaoE3PDKKFXtPUKmcF7d0C+a27sEEVss/CYgIc+Zs54EHfuLYsWQmTuzMpEmXF0LkRZMmEqVUqZGWmcX8TYeYHhnF7qOnqV25PI9fHcbIzkFUqeD6i4FffLGFm2/+Hx071mXhwpF06FDXjVEXfZpIlFIlXkJKOl/+eYCZK/dxPCmNsNp+vD28DQNa16Wciw0kpqVlEhV1kmbNAhg+vAWZmQ5uvrkNXl7Fq4FFd9BEopQqsQ6cSGHG8ii+WRfLmYwsejUJ4J3hoXRv9N8WePOybFk0d965iJSUDPbsmYiPjze33dbOjZEXL5pIlFIlzsYDJwmPjGLJtiN4lTEMaluPcT1DCKtd+bzmc+xYMg8/vJRZs7YQGlqNadMGFrnvpRcFukWUUiVClkP45a+jhEdEsW7/SSqX9+aOSxtya7dgauVogdcVe/fG07lzOKdPp/PUUz156qmeVDiPepTSRBOJUqpYO5OexbcbYvlkeTTRcckEVqvAcwObM7xjfSpdwN3DqVNpVK7sQ8OG1Rg7th1jxrSjWbMAN0RecmgiUUoVS3Gn0/h81X5mrdrHyZQM2gRWYeqN7bmqRS28L6ACPDk5nRdf/IPw8A1s2XIngYGVefPNKws+8BJIE4lSqljZe+w0M5ZHMW/DQTKyHPRpZrXA2yk49xZ4XfHDD7u4554fOXAgkbFj25WIb4QUJk0kSqkiT0T4Mzqe8IgofrVb4B3aIZCxPUJoGOB7wfPNzHQwfPhcvv9+Jy1aBBAZeRs9egQVYOSlgyYSpVSRlZnl4MdtRwiPjGJLbCLVK5Xj/j6NGX1JA2r4+lzwfEUEYwze3mWoU8eX1167ggce6FoqGlh0B00kSqki53Ra5j8t8B5MOEOofyVeub4Vg9u71gJvXlavjuXuuxcTHj6Q9u3rMHXqNQUUdemliUQpVWQcSUzl05XRfPXnAZJSM+kcUp3nr23BFWH5t8Cbn5Mnz/Dkk7/y8cfrqVvXj5MnzxRQ1MqticQY0w94D/ACpovIazmGBwGfAVXtcR4XkcXujEkpVfTsOHSK6ZFRLNh8CIcIV7eqw/ieobStn38LvK6YM2cb9967hLi4FO6//xJeeKE3fn4XXjSmzua2RGKM8QKmAn2BWGCtMWaBiOxwGu1p4BsR+dAY0xxYDAS7KyalVNEhIkTsiWN6ZBSRe+KoWM6L0V0bMKZ7CPWrF2wz7Dt3xhEcXJUlS0bRrl2dAp23cu8dSWdgr4hEARhjZgODAOdEIkB2mwVVgENujEcpVQSkZzpYsNlqgXfnkSRq+vnwWL8wbuwcRJUCeuw2NTWT119fTvv2dRg4sClPPtmTp5/upQ0suok7E0k9IMapOxbokmOc54GlxpiJQCWgT24zMsbcDtwOEBSkj+YpVRwlpmTw5Zr9fLZyH0dPpdG0lh+Th7Xh2jaut8Dril9+ieKuuxaxZ088Dz3UlYEDm1L2IivoVd7cmUhyqxnL+V3fkcBMEXnLGNMVmGWMaSkijrMmEpkGTAPrU7tuiVYp5RYx8Sl8siKaOWtjSEnPomdjf94c2oaejf0v+AXC3Bw9epoHH1zKV19tpVGj6ixdehN9+zYssPmrc3NnIokF6jt1B/LfoquxQD8AEVlljCkP+APH3BiXUqoQbIpJIDwyih+3HqaMMVzbti7jeoTSvO75tcDrqp9/juLbb3fw7LO9eOKJnpQvrw+lFhZ3bum1QGNjTAhwELgBuDHHOAeAK4CZxphmQHnguBtjUkq5kcMh/LrzGOERUazZF49feW/G9wrl1m7B1KlSocCXt3nzEfbsiWfo0OaMGtWK7t3rExJSrcCXo/LmtkQiIpnGmHuAn7Ae7f1ERLYbY14E1onIAuAhINwY8wBWsdetIqJFV0oVM6kZWczbEMuMyGii4pKpV7UCzwxozohO9fF1w/c7Tp9O57nnlvHee38SHFyV664Lw9u7jCYRD3HrvZ/9TsjiHP2edfp7B9DdnTEopdznRHYLvKv3E5+cTuvAKvzfyHZc3bL2BbXA64r//W8nEyf+SGzsKW6/vT2vvtoH7wKsrFfnTwsRlVLn7e/jp5mxPJp562NJy3TQp1lNxvcMpXNI9QKtQM9p69ajXH/9HFq1qsmcOUPp1q1+/hMpt9NEopRyiYiwdt9JpkVE8evOo5T1KsOQ9vUY2yOURjUvvAXe/GRkZBEZeYDLLw+hVataLFp0I337huojvUWIJhKlVJ4ysxws2X6E8MhoNsckUK1iWSZe3pibuzbA/yJa4HXFypUxTJiwkO3bj7Nr1z00alSd/v0bu3WZ6vxpIlFK5So5LZNv1sUwY3k0sSfPEOJfiZeua8mQ9oFUcHNz6/HxZ3j88V8ID99A/fqV+e674TRqVN2ty1QXThOJUuosR0+lMnPlPr5cvZ9TqZl0Cq7GMwOa06dZLbwusgVeV6SmZtK27UccOpTEQw915fnne+PrW87ty1UXThOJUgqAnUdOER4RzYLNB8lyCP1a1mZcz1DaBxXOI7WxsacIDKxM+fLeTJp0GW3b1qZNm9qFsmx1cTSRKFWKiQjL98YRHhlNxO7jVCjrxaguVgu8QTUKtgXeczlzJoNXX13O66+v4NtvhzFwYFNuuaVtoSxbFQyXEokxphwQJCJ73RyPUqoQpGc6WLjlENMirBZ4A/x8eOSqpozqEkTVioVXjLR06d/cddci/v77JDfd1JrOnesV2rJVwck3kRhjrgHeBsoBIcaYtsBzInK9u4NTShWsxDMZfL3mAJ+uiOboqTSa1PLljaGtGdS2Lj7ehfs47cSJi5kyZS2NG1fnl19Gc8UVoYW6fFVwXLkjeRGr+fdlACKyyRjTyK1RKaUKVOzJFD5Zvo85aw+QnJ5F90Y1eH1Iay5tEuDWFwhzysqyGvb28irDJZcE4u9fkcce66ENLBZzruy9DBFJyHGwaXtYShUDW2ITCI+MZvHWwxhgYJu6jOsZQou6VQo9lg0bDjNhwkJGj27NxIldGDWqdaHHoNzDlUTylzFmOFDGbsn3PmC1e8NSSl0oh0P4becxwiOj+DM6Hj8fb8b1COHW7u5pgTc/SUlpPPvsMt5/fw0BARWpU8ev0GNQ7uVKIrkHeBZwAN9hteb7hDuDUkqdv9SMLL7feJDwyCiijidTt0p5nr6mGSM61cevfMF8wvZ8LV36N2PGzOfQoSQmTOjIK69cQdWq5T0Si3IfVxLJVSLyGPBYdg9jzGCspKKU8rD45HRmrdrPrNX7iDudTst6lXnvhrb0b1WHsh7+Rnm5cl7UrFmJefOG06VLoEdjUe5j8vv8hzFmg4i0z9FvvYh0cGtk59CxY0dZt26dJxatVJESHZfMjOVRfLs+ltQMB5eHWS3wXhLq3hZ485KRkcXbb6/i1Kk0Xn75CsAqaitTCG/Eq7zZ5+2O7pj3Oe9IjDFXYX0Gt54x5m2nQZWxirmUUoVMRFi/32qB9+e/jlK2TBkGt6/H2B4hNK7l2bqH5csP/NPA4rBhzf9JIJpESr68iraOAduAVGC7U/8k4HF3BqWUOluWQ/hp+xGmRUSxKSaBqhXLcs9ljRjdtQE1/Txb53DiRAqPPfYLM2ZsJCioCj/8MJIBA5p4NCZVuM6ZSERkI7DRGPOliKQWYkxKKVtyWiZz18XwyYp9HIhPoUGNikwa1IIhHQKpWK5ovHtx4sQZZs/exqOPduPZZy+lUiVtYLG0ceVIrGeMeRloDvxz6SMiesmhlJscO5XKZ6v28cXqAySeyaBDg2o82b8ZfZsXTgu8+fnrr+N88812nnuuN02a1ODAgQeoXr3wHy1WRYMriWQm8BIwGbgauA2tI1HKLXYfTSI8Ior5mw6R4XBwVfPajO8VQocGReNbHCkpGbz8cgRvvrkSX99yjB3bnsDAyppESjlXEklFEfnJGDNZRP4GnjbGRLo7MLaL1PsAACAASURBVKVKCxFh5d8nmBYRxR92C7w3dK7PmO4hBPtX8nR4/1iyZC933bWI6OgEbrmlDW++2ZeAgKITn/IcVxJJmrGeJfzbGDMBOAjUdG9YSpV8GVlWC7zhEdHsOHwKf18fHr6yCaO6NKBaEatnOH06ndGjv6dGjQosW3YLvXsHezokVYS4kkgeAHyBe4GXgSrAGHcGpVRJdio1g9lrDvDpin0cTkylUU1fXh/SikFt61G+bOG2wJuXrCwHX3+9jZEjW+LrW45ffhlNWJg/Pj5Fo5JfFR35HhEi8qf9ZxIwGsAYo6+oKnWeDiac4dPl0cxeG8PptEy6htbgletbcWmTgCL3rsX69Ye4446FrF9/mAoVvBkypLl+rVCdU56JxBjTCagHLBeROGNMC6ymUi4HNJko5YJtBxOZFhHFoq2HARjQug7je4bSsl7ht8Cbn8TEVJ55ZhlTp66lZs1KzJ49hMGDm3k6LFXE5fVm+6vAEGAzVgX791gt/74OTCic8JQqnhwO4ffdxwiPiGZV1Al8fbwZ0z2YW7uHUK9q0X3CaciQb/jtt2juvrsTL710OVWqaAOLKn953ZEMAtqIyBljTHXgkN29q3BCU6r4Sc3IYv6mg4RHRrP32GnqVCnPk/3DuKFzEJU91AJvfqKiThIQUBE/Px9efvlyypQxdOqkn7xVrssrkaSKyBkAEYk3xuzUJKJU7k4mp/PF6v18tspqgbd5ncq8O6It17T2fAu855KensXkySuZNCmCe+/tzOuv99UWetUFySuRhBpjspuKN0CwUzciMtitkSlVDOyLS2bG8mjmro8hNcNB76YBjO8ZSreGNTzWAq8rIiL2M2HCQv76K46hQ5tz771dPB2SKsbySiRDcnRPcWcgShUn6/fHEx4RzU87jlC2TBmua1eXcT1DaeLhFnhd8c47q3jwwaUEB1dl0aIb6d+/sadDUsVcXo02/lqYgShV1GU5hJ93WC3wbjiQQJUKZbmrd0Nu6RpMzcpFu1La4RCSk9Px8/PhmmuacPx4Ck8/3YuKFYtmvY0qXvL9sFVRox+2UoUtJT2Tb9fHMmN5NPtPpFC/egXG9QhlWMei0wJvXrZvP8aECYv++VKhKp088mGrgmCM6Qe8B3gB00XktVzGGQ48DwiwWURudGdMSrnqWFIqn6/czxd/7ichJYO29avyeL8wrmxRu0i0wJuflJQMJk36g8mTV1Glig9jxrRFRIp03Y0qnlxOJMYYHxFJO4/xvYCpQF8gFlhrjFkgIjucxmkMPAF0F5GTxhhtw0t53J6jSUyPjOb7jQfJcDi4snktxvcMpUODasXmJLxx42EGD/6GffsSuO22trzxRl/8/St6OixVQuWbSIwxnYEZWG1sBRlj2gDjRGRiPpN2BvaKSJQ9n9lY76bscBpnPDBVRE4CiMix818FpS6eiLAq6gThEVEs23UcH+8yDO8UyNgeoYQUoRZ485N9xxEUVIWgoCp89tl19OrVwNNhqRLOlTuS94EBwP8ARGSzMeYyF6arB8Q4dccCOZ8xbAJgjFmBVfz1vIgscWHeShWIjCwHi7ceJjwyim0HT1GjUjke7NuEmy5pQPUi1gJvXjIzHUyZsoYFC3bx88+jqVGjIn/8caunw1KlhCuJpIyI7M9xS5/lwnS5lQHkrNn3BhoDvbHa7oo0xrQUkYSzZmTM7cDtAEFBQS4sWqm8JaVmMGdtDJ8sj+ZQYioNAyrx2uBWXNeuaLXA64o1aw4yYcJCNm48wtVXN+LUqTSqVSu6zbCokseVRBJjF2+JXe8xEdjtwnSxQH2n7kCsZlZyjrNaRDKAaGPMLqzEstZ5JBGZBkwD66ktF5atVK4OJZxh5sp9fP3nAZLSMrkktDqTrmvJZU1rFrkWePNz+nQ6jz32Mx9+uI46dfyYO3cYQ4Y0Kzb1OKrkcCWR3IlVvBUEHAV+sfvlZy3Q2BgTgvUxrBuAnE9k/Q8YCcw0xvhjFXVFuRa6Uq7bdjCR6ZFRLNxyGAH6t6rD+J4htA6s6unQLljZsmX4/ff9TJzYmUmTLqdyZR9Ph6RKKVcSSaaI3HC+MxaRTGPMPcBPWPUfn4jIdmPMi8A6EVlgD7vSGLMDq7jsERE5cb7LUio3IsLvu48zPTKKFXtPUKmcF7d0C+a27sEEViueTzDt3RvPiy/+wdSp/fHz82H9+tspX77ov8uiSrZ8X0g0xvwN7ALmAN+JSFJhBHYu+kKiyk9aZhbzNx1iemQUu4+epnbl8tzaPZiRnYOoUqF4vsmdlpbJG2+s4OWXIylXzotFi26kZ099Gku5zqMvJIpIQ2NMN6yiqReMMZuA2SIy2x0BKXWhElLS+fLPA8xcuY/jSWmE1fbj7eFtGNC6LuW8i2YLvK5YtiyaO+9cxK5dJxgxogVvv30VdesW/Ta9VOnh0j2xiKwEVhpjngfeBb4ENJGoIuHAiRRmLI/im3WxnMnIoleTAN4ZHkr3RkW7BV5XiAgvvxxJRoaDJUtGcdVVjTwdklL/4coLib5YLxLeADQD5gPd3ByXUvnacOAk0yOjWLLtCF5lDIPa1mNczxDCalf2dGgXxeEQZszYQL9+jahfvwqzZl1P1arlqVBMi+VUyefKHck24AfgDRGJdHM8SuUpyyH88tdRwiOiWLf/JJXLe3PHpQ25tVswtYp4C7yu2LLlKBMmLGTVqliefbYXL7xwGXXqaDGWKtpcSSShIuJweyRK5eFMehbfbohlRmQU+06kEFitAs8NbM7wjvWp5FP8n1o6fTqdF174nXfeWU21ahWYOXMQN9/cxtNhKeWSc/4CjTFvichDwDxjzH8e7dIvJKrCcDwpjVmr9jFr9X5OpmTQJrAKU29sz1UtauFdRD9heyGef/533nprFePGteO11/pQo0bxfDxZlU55XcrNsf/XLyOqQrf32GlmLI9i3oaDZGQ56NPMaoG3U3DxaYE3PzExiSQnZxAW5s/jj/fguuvC6NFDmwBSxU9eX0hcY//ZTETOSib2i4b6BUVVoESEP6PjCY+I4tedx/DxLsPQDoGM7RFCwwBfT4dXYDIzHbz//p88++wyOnSoyx9/3Iq/f0VNIqrYcqVweQz/vSsZm0s/pS5IZpaDxduOEB4RxdaDiVSvVI77+zRm9CUNqOFbspr9WL06lgkTFrJ581GuuaYxU6b093RISl20vOpIRmA98htijPnOaZAfkJD7VEq57nRaJrPXHODTFfs4mHCGUP9KvHJ9Kwa3L34t8Lpi0aLdDBz4NXXr+vHdd8O57rqwElNMp0q3vO5I1gAnsFrtnerUPwnY6M6gVMl2JDGVT1dG89WfB0hKzaRzSHWev7YFV4QVvxZ48yMiHDqURL16lenTJ5QXX7yM++7rgp9fybrTUqVbvm1tFTXa1lbxtePQKaZHRrFg8yEcIlzdqg7je4bStn7xbYE3L7t3n+Cuuxaxe/cJduy4G1/f4vOhLFXyeKStLWPMHyJyqTHmJGd/kMoAIiLV3RGQKllEhIg9cUyPjCJyTxwVy3kxumsDxnQPoX71kvmIa2pqJq+9tpxXX11OhQrevPrqFVSoUPzfdVHqXPI6urM/p+tfGIGokiUtM4sFmw4xY3k0O48kUdPPh8f6hXFj5yCqVCy5TX0cOXKaXr0+Zc+eeEaObMnbb19F7dol54kzpXKT1+O/2W+z1wcOiUi6MaYH0Br4AjhVCPGpYiYxJYMv1+xn5op9HLNb4J08rA3XtineLfDmJyMji7JlvahVqxK9ejVg6tT+9O3b0NNhKVUoXPkeySagE9YXEn8GFgEhIjLA/eH9l9aRFE0x8SnMWB7NN+tiSEnPomdjf8b3DKVnY/8S/WSSwyFMm7aeV16JZOXKsQQGFu8GI1XJ5dHvkQAOEckwxgwG3hWR940x+tSWAmBTTALhEVH8uO0wZYzh2rZ1GdcjlOZ1S/4JdfPmI9xxx0L+/PMgl18eQkZGlqdDUsojXPrUrjFmGDAauM7uV3ILuVW+HA7h153HCI+IYs2+ePzKezO+Vyi3dgumTpUKng7P7USERx75mXffXU316hWYNet6Ro1qVaLvvJTKi6tvtt+F1Yx8lDEmBPjavWGpoig1I4t5G2KZERlNVFwy9apW4JkBzRnRqT6+JaAFXlcZYzh58gxjx1oNLFarVvKTp1J5cek9EmOMN5D9aba9IpLp1qjyoHUkhe/E6TQ+X7WfWav3E5+cTuvAKozvGcrVLWuXqBZ487J/fwL33beEZ5+9lPbt6+BwSIl7eVKVbB6tIzHG9ARmAQex3iGpbYwZLSIr3BGQKjr+Pn6aGcujmbc+lrRMB32a1WR8z1A6h1QvNcU4GRlZvPPOal544Q8ARoxoQfv2dTSJKOXElfKId4D+IrIDwBjTDCuxuCWzKc8SEdZExxMeGc0vfx2lnHcZhrS3WuBtVLN0vQ+xcmUMd9yxkG3bjjFoUFPef/9qgoKqeDospYocVxJJuewkAiAifxljtK2HEiYzy8GS7VYLvJtjE6lWsSz3XtGYm7s2wL+EtcDrql9+iSIxMZX//W8EgwaFeTocpYosV94jmQmkYd2FAIwCKorILe4NLXdaR1KwTqdl8s3aGD5ZEU3syTOE+FdibI8QhrQPpEK5ktcCb15EhFmzthAQUJGrr25MWlomGRkObSNLlQiefo9kAnAv8ChWHUkE8H/uCEYVnqOnUpm5ch9frt7PqdRMOgVX45kBzenTrBZepbD8f+fOOO68cxG//76PYcOac/XVjfHx8candN6MKXVe8kwkxphWQEPgexF5o3BCUu6088gpwiOiWbD5IFkOoV/L2ozrGUr7oGqeDs0jzpzJ4JVXInn99RVUqlSOjz8ewLhx7T0dllLFSl6t/z6J9SXEDUAnY8yLIvJJoUWmCoyIsHxvHNMirBZ4K5T1YlQXqwXeoBolswVeV/3ww25eeimSm25qzeTJfalVq3Q9UKBUQcjrjmQU0FpEko0xAcBiQBNJMZKe6eCHzYcIj4xi55EkAvx8eOSqpozqEkTViqW33P/IkdNs2nSEfv0aMWxYc4KDx9G5cz1Ph6VUsZVXIkkTkWQAETlujCkdb56VAIlnMvjqzwPMXBnN0VNpNKnly5tDW3Nt27r4eJeuCnRnWVkOPv54PU888Svlynlx4MD9VKhQVpOIUhcpr0QS6vStdgM0dP52u4gMdmtk6rzFxKfw6Yp9zFl7gOT0LLo3qsHrQ1pzaZOAUvMC4bls2HCYCRMWsnbtIfr0CeWDD/pToYI2GadUQcgrkQzJ0T3FnYGoC7clNoFpEVH8uO0IBhjYpi7jeobQoq6+PAcQHX2Szp3D8fevyFdfDeaGG1qW+sSqVEHK68NWvxZmIOr8OBzCbzuPER4ZxZ/R8fj5eDOuRwi3di8dLfDmR0TYuvUYrVvXIiSkGp9+OoiBA5tStWp5T4emVIlTeppsLSFSM7L4fuNBwiOjiDqeTN0q5Xn6mmaM6FQfv/JaVAPWHcg99/zIkiV72bjxDlq3rsXo0W08HZZSJZZbE4kxph/wHuAFTBeR184x3lBgLtBJRPS19VzEJ6cza9V+Pl+1jxPJ6bSsV5n3bmhL/1Z1KFtKWuDNT3p6Fm+/vYoXX/yDMmUMkyf3pXnzAE+HpVSJ53IiMcb4iEjaeYzvBUwF+gKxwFpjzALndrvs8fyw3pz/09V5lza/7TzK3V9u5ExGFpeHWS3wXhJaelrgdUVWloNu3Wawfv1hBg9uxrvvXkX9+lpHpFRhcKUZ+c7ADKAKEGSMaQOME5GJ+UzaGevbJVH2fGYDg4AdOcabBLwBPHyesZcKMfEp3D97E6EBlXh3RFsa1/LzdEhFyqlTaVSu7IOXVxnGjGnH88/3ZsCAJp4OS6lSxZUykfeBAcAJABHZDFzmwnT1gBin7li73z+MMe2A+iKyMK8ZGWNuN8asM8asO378uAuLLhkyshxM/HojIvDhqA6aRJyICDNnbiI09D3mz98JwF13ddIkopQHuJJIyojI/hz9slyYLrdyl3+aGrZfcHwHeCi/GYnINBHpKCIdAwJKT5n35J92sSkmgdeGtC71TZk427HjOL17f8Ztt80nLMyfhg2rezokpUo1V+pIYuziLbHrPSYCu12YLhao79QdCBxy6vYDWgK/22X9tYEFxphrtcIdlu08xscRUdx0SRDXtK7j6XCKjDfeWMFTT/1G5co+TJ8+kNtua6dfK1TKw1xJJHdiFW8FAUeBX+x++VkLNDbGhGB9pvcG4MbsgSKSCPhndxtjfgce1iQChxPP8OA3mwir7cfT1zT3dDhFgohgjKF2bV9GjWrFm2/2JSCgkqfDUkrhQiIRkWNYSeC8iEimMeYe4Cesx38/EZHtxpgXgXUisuC8oy0FMrMc3Pf1JtIyHUwd1Z7yZUtv21gAhw4lcd99S+jZM4h77+3CzTe34eab9Z0QpYoSV57aCsepbiObiNye37Qishir1WDnfs+eY9ze+c2vNHj/1z2s2RfP28Pb0DCg9DZpnpXl4IMP1vLUU7+RkeGgW7dAT4eklDoHV4q2fnH6uzxwPWc/jaUKyIq9cfzfsr0M7RDI4Pal98S5adMRxo1bwPr1h7nyyoZ88EF/rVBXqghzpWhrjnO3MWYW8LPbIiqljielcd/sTYT6V+LFQS08HY5HJSamcuhQEnPmDGXYsOb64qVSRdyFNJESAjQo6EBKM4dDeGDOJpJSM/hiXGcqlitdTaCJCHPn7mDPnhM89VQvLr00mKio+yhfvnRtB6WKq3zfIzHGnDTGxNv/ErDuRp50f2ilx4d//M3yvXE8f20LwmpX9nQ4hervv+Pp3/8rRoz4lvnzd5GRYb2ipElEqeIjz1+rscoU2mA9vgvgEJH/VLyrC7cmOp63lu5iYJu63NCpfv4TlBBpaZlMnrySl16KpGzZMrz3Xj/uuqsT3t7aAKVSxU2eiURExBjzvYh0KKyASpP45HTu/Xoj9atX5JXrS9fHlmJiTjFpUgQDBzbl3Xevol690nUnplRJ4srl3xpjTHu3R1LKiAgPz91MfHI6U29sXyq+JXL8eDJTpqwBoFGj6uzYcTdz5w7TJKJUMXfOOxJjjLeIZAI9gPHGmL+BZKw2tERENLlchBnLo/lt5zGeH9iclvVKdnPnDofw6acbefTRX0hKSqNv31CaNvUnNLSap0NTShWAvIq21gDtgesKKZZSY1NMAq8v2cmVzWtxS7dgT4fjVtu2HePOOxexfPkBevYM4qOPBtC0qX/+Eyqlio28EokBEJG/CymWUiHxTAYTv95ATb/yvDm0TYmuF0lPz+LKK2eRnp7FJ59cy623ti3R66tUaZVXIgkwxjx4roEi8rYb4inRRIQnvtvC4YRUvpnQlSoVS2a9yG+/RXPppQ0oV86Lb74ZRliYP/7+2gy+UiVVXpXtXoAvVnPvuf1T5+mLPw+weOsRHrmqKe2DSl79QGzsKYYM+YYrrviczz/fDECPHkGaRJQq4fK6IzksIi8WWiQl3PZDiUxauIPeTQMY3zPU0+EUqMxMB1OmrOGZZ5aRleXg1VevYNSo1p4OSylVSPKtI1EX73RaJhO/2ki1imV5a1ibEvchptGjv2f27G1cfXUjpk7tT0hIybvbUkqdW16J5IpCi6IEExGe/n4r+04k89X4S6jh6+PpkApEQkIq3t5l8PUtx913d2LIkGYMGdJMK9OVKoXOWUciIvGFGUhJNXd9LP/bdIj7+zThktAang7nookIs2dvo1mzqTzzzG+AVQ8ydKi20qtUaaUNG7nRnqNJPDt/G90a1uDuyxp5OpyLtndvPFdd9QUjR84jMLAyN92k9SBKqQtrRl654Ex6Fnd/tQFfH2/eHdEWr2JeL/LVV1sZM2Y+Pj7eTJlyNRMmdMTLS69DlFKaSNzmhR+2s/voaT4f05malct7OpwLlpGRRdmyXnTsWJehQ5vzxht9qVtXn/5WSv1LE4kbzN90kNlrY7ird0N6NQnwdDgX5NixZB56aCnJyel8990ImjSpwRdfDPZ0WEqpIkjLJgpYdFwyT363lY4NqvFg3yaeDue8ORzCtGnradp0CnPmbKNFiwCyshyeDkspVYTpHUkBSs3I4u4vN1DWuwzvj2yHdzGrQ4iKOslNN33HqlWx9O4dzIcfXkNYmDawqJTKmyaSAvTq4r/YcfgU02/uSN2qFTwdznmrUsWHhIRUPvvsOkaPbq2P8yqlXFK8LpmLsCXbDvPZqv2M7RFCn+a1PB2OyxYs2MXgwXPIynJQo0ZFtm27i5tvLtmtEiulCpYmkgIQE5/CI99uoXVgFR7rF+bpcFxy4EAi1103m0GDZrN79wkOHz4NUOKab1FKuZ8WbV2kjCwHE7/eCAJTRrannHfRzs2ZmQ7efXc1zz33OyLC66/34YEHLqFsWS9Ph6aUKqY0kVykyT/tYlNMAlNvbE9QjaLfXHpWloPp0zdw+eUh/N//XU1wcFVPh6SUKuaK9uVzEbds5zE+jojipkuCuKZ1HU+Hc04nT57hscd+JikpDR8fb1asGMOCBTdoElFKFQhNJBfocOIZHvxmE2G1/Xj6muaeDidXIsKXX24hLGwqb721imXL9gFQo0ZFrUxXShUYLdq6AJlZDu77ehNpmQ6mjmpP+SJYv7B79wnuumsRv/4aTefO9fjpp5to27a2p8NSSpVAmkguwPu/7mHNvnjeGdGGhgG+ng4nV/ffv4R16w7xwQf9uf32DtrAolLKbTSRnKcVe+P4v2V7GdYhkOvbBXo6nLP8/PPfhIX5U79+FT788Bp8fLypXbtoJjqlVMnh1stUY0w/Y8wuY8xeY8zjuQx/0BizwxizxRjzqzGmgTvjuVjHk9K4b/YmGgb48sKgFp4O5x9HjpzmxhvnceWVX/D66ysAaNCgqiYRpVShcFsiMcZ4AVOBq4HmwEhjTM5a6Y1ARxFpDXwLvOGueC6WwyE8MGcTSakZTL2xPRXLef5mzuEQPvpoHWFhU5g37y+ee+5SJk++0tNhKaVKGXeeDTsDe0UkCsAYMxsYBOzIHkFEljmNvxq4yY3xXJQP//ib5XvjeG1wK5rWLhrf43j11UiefnoZl18ewgcf9KdpU21gUSlV+NyZSOoBMU7dsUCXPMYfC/yY2wBjzO3A7QBBQUEFFZ/L1kTH89bSXVzbpi4jOtUv9OU7S0pKIy4uhZCQakyY0JGQkGqMHNlSH+dVSnmMO+tIcjuzSa4jGnMT0BF4M7fhIjJNRDqKSMeAgML9UFR8cjr3fr2RoOoVefl6z52wRYTvv/+L5s0/YMSIbxERatSoyI03ttIkopTyKHcmkljA+fI9EDiUcyRjTB/gKeBaEUlzYzznTUR4eO5m4pPTmXJje/zKl/VIHPv3J3DttbMZPPgbqlevwPvvX63JQylVZLizaGst0NgYEwIcBG4AbnQewRjTDvgY6Ccix9wYywWZsTya33Ye4/mBzWlZr4pHYli1KoY+fWYBMHlyX+677xK8i3jDkEqp0sVtiUREMo0x9wA/AV7AJyKy3RjzIrBORBZgFWX5AnPtK+wDInKtu2I6H5tiEnjtx51c2bwWt3QLLvTlnzqVRuXKPrRvX4cxY9ryyCPdCQryTDJTSqm8GJFcqy2KrI4dO8q6devcuozEMxlc834kIrD43p5UqVh4RVonTqTw+OO/sHRpFNu334Wvb7lCW7ZSquQyxqwXkY7umLfnX4YoYkSEx+dt4UhiKt9M6FpoSUREmDVrCw89tJSTJ8/w4INd0WoQpVRxoIkkhy9W7+fHbUd44uow2gdVK5RlJiamct11c/j993107RrIRx8NoHXr4vO5XqVU6aaJxMn2Q4lMWvgXvZsGML5nqNuXJyIYY6hc2Qd//4pMmzaAsWPb6+dulVLFij7+Yzudlsk9X22kWqWyvDWsjdtP5j/9tJf27acRG3sKYwxz5w5j/PgOmkSUUsWOJhKsO4Onv9/K/hPJvHdDO2r4+rhtWYcPJ3HDDd/Sr9+XpKRkcOxYstuWpZRShUGLtoC562P536ZDPNi3CZeE1nDbcqZOXcOTT/5GWlomL7zQm8ce646Pj+4CpVTxVurPYnuOJvHs/G10a1iDuy9r5NZlrV9/mC5d6jF1an8aN3ZfwlJKqcJUqhPJmfQs7v5qA74+3rw7oi1eBVw/cepUGs8+u4zRo1vToUNdPvjgGnx8vLR5E6VUiVKqE8kLP2xnz7HTfD6mMzUrly+w+YoI8+b9xX33LeHw4SSCgqrQoUNdypcv1ZtbKVVCldoz2/xNB5m9Noa7L2tIz8YF16JwdPRJ7rnnRxYv3kPbtrX57rvhdOlStD7Jq5RSBalUJpLouGSe/G4rHRtU44E+TQp03l9+uZWIiP28885V3HNPZ21gUSlV4pW6RJKakcXdX26grHcZ3h/ZDm+viz/RR0buJy0tiz59QnnkkW7cemtbAgMrF0C0SilV9JW6y+VXF//FjsOnmDy0DXWrVrioecXFpTBmzHx69ZrJiy/+AYCPj7cmEaVUqVKq7kiWbDvMZ6v2M65HCH2aX3hbViLCzJmbeOSRn0lMTOOxx7rzzDO9CjBSVVJkZGQQGxtLamqqp0NRpUT58uUJDAykbNnCa7W81CSSmPgUHvl2C20Cq/Bov7CLmtfixXsYM2YB3bvX56OPBtCyZc0CilKVNLGxsfj5+REcHKyPfSu3ExFOnDhBbGwsISEhhbbcUlG0lZ7p4J6vNwIw5cb2lLuACvCUlAxWrDgAQP/+jZk//wYiIm7TJKLylJqaSo0aNTSJqEJhjKFGjRqFfgdcKhLJ5KW72ByTwOtDWlO/esXznv7HH/fQsuUHXH31lyQkpGKM4dprm2oDi8olmkRUYfLE8VbiE8lvO48yLSKKmy4Jon+rOuc17cGDpxg2bC79+3+Fj483P/wwkqpVC+7FRaWUKglKBvGxoQAAEyNJREFUdCI5nHiGh77ZTFhtP56+pvl5TXvsWDLNm3/AwoW7eemly9i8eQKXXhrsnkCVciMvLy/atm1Ly5YtGThwIAkJCf8M2759O5dffjlNmjShcePGTJo0CefPb//444907NiRZs2aERYWxsMPP+yJVcjTxo0bGTdu3Fn9Bg0aRNeuXc/qd+utt/Ltt9+e1c/X1/efv3fv3k3//v1p1KgRzZo1Y/jw4Rw9evSiYouPj6dv3740btyYvn37cvLkyVzHO3DgAFdeeSXNmjWjefPm7Nu3D4ApU6bQqFEjjDHExcX9M/7ChQt57rnnLiq2AiUixepfhw4dxBUZmVky7MOV0uyZH2XvsSSXphERiY1N/Ofv995bLXv3nnB5WqVy2rFjh6dDkEqVKv3z98033ywvvfSSiIikpKRIaGio/PTTTyIikpycLP369ZMpU6aIiMjWrVslNDRU/vrrLxERycjIkKlTpxZobBkZGRc9j6FDh8qmTZv+6T558qQEBgZKWFiYREVF/dP/lltukblz5541bfa2OXPmjDRq1EgWLFjwz7DffvtNtm7delGxPfLII/Lqq6+KiMirr74qjz76aK7jXXrppbJ06VIREUlKSpLk5GQREdmwYYNER0f/f3tnH11FdS3w3zaAAYmhguSBVD5VQgKEEFpTUCIqWBXQiEQB+Xj4WhDhKWJXXXQtUF8tWD950KqvdQXfayACDSJWtIAUpAQNEkMMNKYQKcpHBAoJBQNkvz9muLlJbpIbkntvEvZvrVlr5syZc/bsNXf2Pfuc2Vu7du2qRUVFnvplZWUaFxfnqVcZX88dkKUBei8321Vbr274kk8Kj/FySn96Xt221vonTpzhF7/YyOuv7yAz82Hi4zsxa9YPgyCpcanw9LtfkPfNyQZts0/nK5k3Msbv+omJieTk5ACQlpbG4MGDGT58OABt2rRh8eLFJCUlMWPGDJ5//nnmzp1L797OKscWLVrwyCOPVGmzpKSEmTNnkpWVhYgwb9487rvvPtq2bUtJSQkAK1euZO3ataSmpjJ58mSuuuoqdu7cSVxcHBkZGWRnZ9OuXTsAevXqxdatW7nsssuYNm0a+/c7i1xeeeUVBg8eXKHv4uJicnJy6N+/v6ds1apVjBw5kqioKJYvX85TTz1Vq17S0tJITExk5MiRnrJbbrnFb71WxzvvvMOmTZsAmDRpEklJSSxcuLBCnby8PM6dO8ftt98OVBwlDRgwwGe7IkJSUhJr165l7Nix9ZazvjRLQ/Lxl9+y+KMC7h/YhXsH1BznSlVZsSKPxx5bx6FDJTz66A/o2TM4udoNI5icP3+eDRs2MHXqVMBxaw0cOLBCnZ49e1JSUsLJkyfJzc3liSeeqLXdZ599lsjISHbt2gVQrfvGm/z8fNavX09YWBhlZWVkZGQwZcoUtm/fTrdu3YiKimLcuHE8/vjjDBkyhP379zNixAh2795doZ2srCxiY2MrlC1btox58+YRFRXFmDFj/DIkubm5VXThi+LiYm666Saf59LS0ujTp6IL/fDhw3Tq5MzNdurUiSNHjlS5Lj8/n3bt2pGcnMy+ffu47bbbWLBgAWFhYTXKkpCQwJYtW8yQBIKi4u94LD2bnle35enRNf9TU1WSk99m9eo9xMd3Ys2aB0lI6BwkSY1LjbqMHBqS06dPExcXR2FhIQMHDvT881XValf41GXlz/r161m+fLnn+Hvfq/2P2P333+95UaakpPDMM88wZcoUli9fTkpKiqfdvLw8zzUnT56kuLiYiIgIT9nBgwe5+uryoKuHDx+moKCAIUOGICK0aNGC3NxcYmNjfd5TXVc4RUREkJ2dXadrauPcuXNs2bKFnTt3cu2115KSkkJqaqrH4FdHx44d+eabbxpUloulWU22l5Upj6dnU3zmLEvGxdOmlW87efbsecB5iIYM+T6LFt3BJ588bEbEaJa0bt2a7OxsvvrqK0pLS1myZAkAMTExZGVlVai7d+9e2rZtS0REBDExMezYsaPW9qszSN5llb9ruOKKKzz7iYmJFBQUUFRUxOrVq0lOTgagrKyMbdu2kZ2dTXZ2Nl9//XUFI3Lh3rzbTk9P5/jx43Tv3p1u3bpRWFjoMXLt27evMFo6duwYHTp08OjCn3stLi4mLi7O5+Zt9C4QFRXFwYMHAcfodexY9buzLl26MGDAAHr06EGLFi245557+Oyzz2qV5cyZM7RuXb8wTw1FszIkv/3L3/m44FueHhXDDf8W4bPOpk2F9Ov3Gu+8sweAJ574ETNn/pCwBgjeaBiNmcjISBYtWsQLL7zA2bNnGT9+PB9//DHr168HnJHLrFmz+NnPfgbAk08+yXPPPUd+fj7gvNhfeumlKu0OHz6cxYsXe44vvKyjoqLYvXu3x3VVHSLCvffey+zZs4mOjqZ9+/Y+2/U1EoiOjqagoMBzvGzZMtatW0dhYSGFhYXs2LHDY0iSkpJIT0+ntLQUgNTUVM88yLhx4/jrX//Ke++952lr3bp1HnfdBS6MSHxtld1aAKNGjWLp0qUALF26lNGjR1epM2jQII4fP05RUREAGzdu9NlWZfLz86u49UJGoGbxA7VVt2pr+96j2v3na3Vm2mdaVlZW5fyRIyU6cWKGwnzt3v0V3bBhr49WDKNhaWyrtlRV7777bn3rrbdUVTUnJ0eHDh2q119/vfbs2VPnz59f4ffz7rvvanx8vPbu3Vujo6N1zpw5VdovLi7WiRMnakxMjPbr109XrVqlqqorVqzQHj166NChQ3XGjBk6adIkVfW9eurTTz9VQFNTUz1lRUVFOnbsWO3bt69GR0frT3/6U5/3FxsbqydPntR9+/Zp586dq/z+BwwYoJmZmaqqOn/+fI2NjdX+/ftrcnKyHjlyxFNv9+7dOmLECO3Vq5dGR0drSkqKHjp0qEbd1sa3336rw4YN0169eumwYcP06NGjnvudOnWqp96HH36offv21djYWJ00aZJ+9913qqr66quv6jXXXKNhYWHaqVOnCtfcddddmpOT47PfYK/aEqf9pkNCQoJWHo4fO1XKna9uIbzlZbw7cwgR4RWDlS1btosZM/5ESUkpTz75I+bOvZk2bYIX0My4dNm9ezfR0dGhFqNZ8/LLLxMREVHlW5LmzOHDhxk3bhwbNmzwed7XcyciO1Q1IRDyNHl/jqoyZ8XnHDtVyuJx8VWMCMC5c2XExnYkO3sav/zlrWZEDKMZMX36dC6//PJQixFU9u/fz4svvhhqMTw0+VVbv/94Hxv3HOHpUTHEXhMJwKlTpTz77GauvTaSRx4ZxIQJ/ZgwoZ/FPDKMZkh4eDgPPfRQqMUIKoMGDQq1CBVo0iOS7H/8kwXv72FETBQTE7sCsHZtPjExv2Hhwq3k5x8FnMk8MyJGqGhq7mOjaROK563JjkhOnD7Lo2mfEXVlOM/f15+vvy5m1qz3ycjYQ58+V7N582RuuqlrqMU0LnHCw8M5evSohZI3goKqk48kPDy4wWWbpCFRVX6+KodDJ87w9rREItu05POsb/jgg7/zq1/dyuzZibRqVfNXoYYRDLp06cKBAwc8SzsNI9BcyJAYTJqkIfm/zK94P/cQE2I7syXjb8T/543cfHNX9u9/jPbt655vxDACRcuWLYOaqc4wQkFA50hE5A4R+ZuIFIjIz32cv1xE0t3z20WkW21tnj57nmfW5nH1WXhu4hpeeimTU6ecD4zMiBiGYQSfgBkSEQkDlgA/BvoAD4pI5c81pwLHVbUX8DKwkFooLDpF6clSsl/PZtasH7Jr13SuuKJVQ4tvGIZh+EkgXVs/AApUdS+AiCwHRgPeAWlGA/Pd/ZXAYhERrWHZwbkypd2eE2Rs/nfi4+uW8dAwDMNoeAJpSK4B/uF1fAConODDU0dVz4nICaA98K13JRH5CfAT9/C7nMOTc/2I+Hwp0IFKurqEMV2UY7oox3RRzg2BajiQhsTXWsfKIw1/6qCqbwBvAIhIVqA+829qmC7KMV2UY7oox3RRjohk1V7r4gjkZPsB4Ptex12AysHzPXVEpAUQCRwLoEyGYRhGAxNIQ/IpcJ2IdBeRVsADwJpKddYAk9z9McDGmuZHDMMwjMZHwFxb7pzHo8AHQBjwpqp+ISLP4IQzXgP8HvhfESnAGYk84EfTbwRK5iaI6aIc00U5potyTBflBEwXTS6MvGEYhtG4aNJBGw3DMIzQY4bEMAzDqBeN1pAEIrxKU8UPXcwWkTwRyRGRDSLSbMMe16YLr3pjRERFpNku/fRHFyIy1n02vhCRtGDLGCz8+I1cKyIfichO93dyZyjkDDQi8qaIHBGR3GrOi4gscvWUIyLxDdJxoHL41mfDmZz/O9ADaAV8DvSpVOcR4DV3/wEgPdRyh1AXtwBt3P3pl7Iu3HoRwGYgE0gItdwhfC6uA3YC33OPO4Za7hDq4g1gurvfBygMtdwB0sXNQDyQW835O4H3cb7huxHY3hD9NtYRiSe8iqqWAhfCq3gzGljq7q8EbpXmmfChVl2o6keq+i/3MBPnm53miD/PBcCzwPPAmWAKF2T80cV/AEtU9TiAqh4JsozBwh9dKHClux9J1W/amgWqupmav8UbDbylDplAOxGpd6ypxmpIfIVXuaa6Oqp6DrgQXqW54Y8uvJmK84+jOVKrLkRkAPB9VV0bTMFCgD/PxfXA9SKyVUQyReSOoEkXXPzRxXxggogcAP4EzAyOaI2Our5P/KKx5iNpsPAqzQC/71NEJgAJwNCAShQ6atSFiFyGE0V6crAECiH+PBctcNxbSTij1C0iEquq/wywbMHGH108CKSq6osikojz/VqsqpYFXrxGRUDem411RGLhVcrxRxeIyG3AXGCUqn4XJNmCTW26iABigU0iUojjA17TTCfc/f2NvKOqZ1V1H/A3HMPS3PBHF1OBtwFUdRsQjhPQ8VLDr/dJXWmshsTCq5RTqy5cd87rOEakufrBoRZdqOoJVe2gqt1UtRvOfNEoVQ1YsLoQ4s9vZDXOQgxEpAOOq2tvUKUMDv7oYj9wK4CIROMYkksx//EaYKK7eutG4ISqHqxvo43StaWBC6/S5PBTF78G2gIr3PUG+1V1VMiEDhB+6uKSwE9dfAAMF5E84DzwpKoeDZ3UgcFPXTwB/I+IPI7jypncHP94isgyHFdmB3c+aB7QEkBVX8OZH7oTKAD+BUxpkH6boS4NwzCMINJYXVuGYRhGE8EMiWEYhlEvzJAYhmEY9cIMiWEYhlEvzJAYhmEY9cIMidHoEJHzIpLttXWroW636iKd1rHPTW702M/dkCI3XEQb00Rkors/WUQ6e537nYj0aWA5PxWROD+ueUxE2tS3b8OoDjMkRmPktKrGeW2FQep3vKr2xwkG+uu6Xqyqr6nqW+7hZKCz17mHVTWvQaQsl/M3+CfnY4AZEiNgmCExmgTuyGOLiHzmbj/yUSdGRD5xRzE5InKdWz7Bq/x1EQmrpbvNQC/32lvdHBa73FwPl7vlC6Q8B8wLbtl8EZkjImNwYp79we2ztTuSSBCR6SLyvJfMk0Xkvy9Szm14BdwTkd+KSJY4uUeedstm4Ri0j0TkI7dsuIhsc/W4QkTa1tKPYdSIGRKjMdLay62V4ZYdAW5X1XggBVjk47ppwKuqGofzIj/ghsNIAQa75eeB8bX0PxLYJSLhQCqQoqp9cSJBTBeRq4B7gRhV7Qf8l/fFqroSyMIZOcSp6mmv0yuBZK/jFCD9IuW8AycMygXmqmoC0A8YKiL9VHURTiylW1T1FjdUyi+A21xdZgGza+nHMGqkUYZIMS55TrsvU29aAovdOYHzOHGjKrMNmCsiXYA/quqXInIrMBD41A0f0xrHKPniDyJyGijECTN+A7BPVfPd80uBGcBinFwnvxOR9wC/Q9arapGI7HXjHH3p9rHVbbcucl6BEw7EO8PdWBH5Cc7vuhNOAqecStfe6JZvdftphaM3w7hozJAYTYXHgcNAf5yRdJWkVaqaJiLbgbuAD0TkYZyw2UtV9Sk/+hjvHeBRRHzmt3FjO/0AJwjgA8CjwLA63Es6MBbYA2SoqorzVvdbTpwsgAuAJUCyiHQH5gCDVPW4iKTiBCasjAB/VtUH6yCvYdSIubaMpkIkcNDNH/EQzr/xCohID2Cv685Zg+Pi2QCMEZGObp2rxP+c9nuAbiLSyz1+CPiLO6cQqap/wpnI9rVyqhgnrL0v/gjcg5MjI90tq5OcqnoWx0V1o+sWuxI4BZwQkSjgx9XIkgkMvnBPItJGRHyN7gzDb8yQGE2F3wCTRCQTx611ykedFCBXRLKB3jgpRfNwXrgfikgO8Gcct0+tqOoZnOioK0RkF1AGvIbzUl7rtvcXnNFSZVKB1y5Mtldq9ziQB3RV1U/csjrL6c69vAjMUdXPcfKzfwG8ieMuu8AbwPsi8pGqFuGsKFvm9pOJoyvDuGgs+q9hGIZRL2xEYhiGYdQLMySGYRhGvTBDYhiGYdQLMySGYRhGvTBDYhiGYdQLMySGYRhGvTBDYhiGYdSL/wcrmjPwL/D/CgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.40 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06273386
11338752
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6273 386\n", + "1 1338 752" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25839285714285715\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.36 0.47 2090\n", + "\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.80 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13545\n", + " 1 0.79 0.47 0.59 3951\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06290369
11360730
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6290 369\n", + "1 1360 730" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23302683158657422\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.35 0.46 2090\n", + "\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.78 0.80 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree0.4100480.610882
1Random Forest0.3631580.767925
2Gradient Boosted0.3492820.775518
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree 0.410048 0.610882\n", + "1 Random Forest 0.363158 0.767925\n", + "2 Gradient Boosted 0.349282 0.775518" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788
Time: 23:40:29 Log-Likelihood: -7675.2
converged: True LL-Null: -9346.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441
SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019
AGE 0.1518 0.101 1.503 0.133 -0.046 0.350
PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727
PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361
PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078
PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030
PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137
PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841
BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197
BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455
BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018
BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517
BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731
BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350
PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665
PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238
PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048
PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027
PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242
PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162
GRAD 1.4579 0.228 6.385 0.000 1.010 1.905
UNI 1.4595 0.227 6.428 0.000 1.015 1.904
HS 1.4009 0.231 6.076 0.000 0.949 1.853
MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450
SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291
PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201
PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396
PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640
PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040
PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918
PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398
PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123
PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157
PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161
PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332
PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361
PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291
PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173
PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079
PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070
PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679
PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591
PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:40:29 Log-Likelihood: -7675.2\n", + "converged: True LL-Null: -9346.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 13545\n", + " 1 0.67 0.37 0.47 3951\n", + "\n", + " accuracy 0.82 17496\n", + " macro avg 0.75 0.66 0.68 17496\n", + "weighted avg 0.80 0.82 0.80 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438685\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438686\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438689\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438692\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438733\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438766\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438815\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438878\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438953\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439049\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439148\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439242\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439370\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439484\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439636\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439750\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439934\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.440120\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17472
Method: MLE Df Model: 23
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761
Time: 00:03:36 Log-Likelihood: -7700.3
converged: True LL-Null: -9346.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477
PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642
PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426
PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149
PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290
BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026
PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659
PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499
PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327
PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231
PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276
GRAD 1.4314 0.179 7.995 0.000 1.081 1.782
UNI 1.4280 0.178 8.036 0.000 1.080 1.776
HS 1.3923 0.182 7.637 0.000 1.035 1.750
MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273
PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839
PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406
PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138
PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548
PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104
PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927
PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009
PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780
PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17472\n", + "Method: MLE Df Model: 23\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", + "Time: 00:03:36 Log-Likelihood: -7700.3\n", + "converged: True LL-Null: -9346.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24 Columns left:\n", + "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.34 0.46 2090\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21432968760597085\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7705999826781731" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.81 0.84 6659\n", + " 1 0.50 0.61 0.55 2090\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Logistic Regression identified 1273\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053611298
18171273
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5361 1298\n", + "1 817 1273" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Logistic Regression identified 714\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06329330
11376714
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6329 330\n", + "1 1376 714" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2096696069036731\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PCA\n", + "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2 Component PCA\n", + "First, we will visualize the information retained after performing a 2 component pca." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "principalComponents = pca.fit_transform(X_train)\n", + "principalDf = pd.DataFrame(data = principalComponents\n", + " , columns = ['principal component 1', 'principal component 2'])" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variation per principal component: [0.295060096 0.1735291836]\n" + ] + } + ], + "source": [ + "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", + "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#visualizing pca\n", + "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", + "fig = plt.figure(figsize = (8,8))\n", + "ax = fig.add_subplot(1,1,1) \n", + "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", + "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", + "ax.set_title('2 component PCA', fontsize = 20)\n", + "targets = [1,0]\n", + "colors = ['r', 'g']\n", + "for target, color in zip(targets,colors):\n", + " indicesToKeep = pca_visualize_df['Y'] == target\n", + " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", + " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", + " , c = color\n", + " , s = 50)\n", + "ax.legend(targets)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform PCA to retain 90% of information\n", + "perform PCA to reduce components so we can run SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "#setting pca threshold to 90%\n", + "pca = PCA(0.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of components before pca: 44\n", + "No. of components after pca: 13\n" + ] + } + ], + "source": [ + "#get number of components after pca\n", + "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", + "print('No. of components after pca: {}'.format(pca.n_components_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the number of components is reduced from 26 components to 13 components" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca on training and test attributes\n", + "X_train_pca = pca.transform(X_train)\n", + "X_test_pca = pca.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "Next, we will used the reduced attributes train set to train our SVM model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we train our SVM model without parameter tuning\n", + "nor pca reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15585444961401423\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7192717558206292" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06304355
11341749
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6304 355\n", + "1 1341 749" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.36 0.47 2090\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note that the default values of gamma = 1/13 and c= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "#train svm model with feature reduction and no parameter tuning\n", + "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", + "clf_reduced.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_reduced, y_test, X_test_pca, \n", + " \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now try to find best parameters for SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001, 0.01, 0.1, 1,10]\n", + " gammas = [0.001, 0.01, 0.1, 10]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train_pca, y_train,5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", + "clf_reduced_tuned.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", + " \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT - INCLUDES - PCA.ipynb b/BT2101_Default - EDIT - INCLUDES - PCA.ipynb new file mode 100644 index 0000000..27df175 --- /dev/null +++ b/BT2101_Default - EDIT - INCLUDES - PCA.ipynb @@ -0,0 +1,5397 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 74.581893 1.992315e-03\n", + "PAY_0 4250.691969 0.000000e+00\n", + "PAY_2 3793.454513 0.000000e+00\n", + "PAY_3 2943.420461 0.000000e+00\n", + "PAY_4 2997.799899 0.000000e+00\n", + "PAY_5 2809.793752 0.000000e+00\n", + "PAY_6 2384.699487 0.000000e+00\n", + "PAY_0_No_Transactions 75.255770 1.695234e-03\n", + "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", + "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", + "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", + "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", + "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", + "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", + "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", + "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", + "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054031256
11233857
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5403 1256\n", + "1 1233 857" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.41 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054221237
11244846
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5422 1237\n", + "1 1244 846" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.81 0.81 6659\n", + " 1 0.41 0.40 0.41 2090\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13545\n", + " 1 1.00 1.00 1.00 3951\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06273386
11338752
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6273 386\n", + "1 1338 752" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25839285714285715\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.36 0.47 2090\n", + "\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.80 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13545\n", + " 1 0.79 0.47 0.59 3951\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06290369
11360730
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6290 369\n", + "1 1360 730" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23302683158657422\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.94 0.88 6659\n", + " 1 0.66 0.35 0.46 2090\n", + "\n", + " accuracy 0.80 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.78 0.80 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree0.4100480.610882
1Random Forest0.3631580.767925
2Gradient Boosted0.3492820.775518
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree 0.410048 0.610882\n", + "1 Random Forest 0.363158 0.767925\n", + "2 Gradient Boosted 0.349282 0.775518" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788
Time: 23:40:29 Log-Likelihood: -7675.2
converged: True LL-Null: -9346.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441
SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019
AGE 0.1518 0.101 1.503 0.133 -0.046 0.350
PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727
PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361
PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078
PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030
PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137
PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841
BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197
BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455
BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018
BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517
BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731
BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350
PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665
PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238
PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048
PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027
PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242
PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162
GRAD 1.4579 0.228 6.385 0.000 1.010 1.905
UNI 1.4595 0.227 6.428 0.000 1.015 1.904
HS 1.4009 0.231 6.076 0.000 0.949 1.853
MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450
SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291
PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201
PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396
PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640
PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040
PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918
PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398
PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123
PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157
PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161
PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332
PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361
PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291
PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173
PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079
PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070
PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679
PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591
PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:40:29 Log-Likelihood: -7675.2\n", + "converged: True LL-Null: -9346.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 13545\n", + " 1 0.67 0.37 0.47 3951\n", + "\n", + " accuracy 0.82 17496\n", + " macro avg 0.75 0.66 0.68 17496\n", + "weighted avg 0.80 0.82 0.80 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438685\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438686\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438689\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438692\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438733\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438766\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438815\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438878\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.438953\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439049\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439148\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439242\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439370\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439484\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439636\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439750\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.439934\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.440120\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17472
Method: MLE Df Model: 23
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761
Time: 00:03:36 Log-Likelihood: -7700.3
converged: True LL-Null: -9346.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477
PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642
PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426
PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149
PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290
BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026
PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659
PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499
PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327
PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231
PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276
GRAD 1.4314 0.179 7.995 0.000 1.081 1.782
UNI 1.4280 0.178 8.036 0.000 1.080 1.776
HS 1.3923 0.182 7.637 0.000 1.035 1.750
MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273
PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839
PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406
PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138
PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548
PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104
PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927
PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009
PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780
PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17472\n", + "Method: MLE Df Model: 23\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", + "Time: 00:03:36 Log-Likelihood: -7700.3\n", + "converged: True LL-Null: -9346.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24 Columns left:\n", + "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.34 0.46 2090\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21432968760597085\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7705999826781731" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.81 0.84 6659\n", + " 1 0.50 0.61 0.55 2090\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Logistic Regression identified 1273\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053611298
18171273
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5361 1298\n", + "1 817 1273" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the Logistic Regression identified 714\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06329330
11376714
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6329 330\n", + "1 1376 714" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2096696069036731\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PCA\n", + "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2 Component PCA\n", + "First, we will visualize the information retained after performing a 2 component pca." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca\n", + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2)\n", + "principalComponents = pca.fit_transform(X_train)\n", + "principalDf = pd.DataFrame(data = principalComponents\n", + " , columns = ['principal component 1', 'principal component 2'])" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explained variation per principal component: [0.295060096 0.1735291836]\n" + ] + } + ], + "source": [ + "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", + "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#visualizing pca\n", + "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", + "fig = plt.figure(figsize = (8,8))\n", + "ax = fig.add_subplot(1,1,1) \n", + "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", + "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", + "ax.set_title('2 component PCA', fontsize = 20)\n", + "targets = [1,0]\n", + "colors = ['r', 'g']\n", + "for target, color in zip(targets,colors):\n", + " indicesToKeep = pca_visualize_df['Y'] == target\n", + " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", + " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", + " , c = color\n", + " , s = 50)\n", + "ax.legend(targets)\n", + "ax.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform PCA to retain 90% of information\n", + "perform PCA to reduce components so we can run SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "#setting pca threshold to 90%\n", + "pca = PCA(0.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. of components before pca: 44\n", + "No. of components after pca: 13\n" + ] + } + ], + "source": [ + "#get number of components after pca\n", + "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", + "print('No. of components after pca: {}'.format(pca.n_components_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the number of components is reduced from 26 components to 13 components" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "#perform pca on training and test attributes\n", + "X_train_pca = pca.transform(X_train)\n", + "X_test_pca = pca.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "Next, we will used the reduced attributes train set to train our SVM model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we train our SVM model without parameter tuning\n", + "nor pca reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15585444961401423\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7192717558206292" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06304355
11341749
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6304 355\n", + "1 1341 749" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.95 0.88 6659\n", + " 1 0.68 0.36 0.47 2090\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note that the default values of gamma = 1/13 and c= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "#train svm model with feature reduction and no parameter tuning\n", + "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", + "clf_reduced.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_reduced, y_test, X_test_pca, \n", + " \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now try to find best parameters for SVM model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001, 0.01, 0.1, 1,10]\n", + " gammas = [0.001, 0.01, 0.1, 10]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train_pca, y_train,5)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", + "clf_reduced_tuned.fit(X_train_pca, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", + " \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/BT2101_Default - EDIT-Copy1.ipynb index 4ae5047..27df175 100644 --- a/BT2101_Default - EDIT-Copy1.ipynb +++ b/BT2101_Default - EDIT-Copy1.ipynb @@ -2960,7 +2960,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -5390,7 +5389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 683e64eef8205e0547bc9245753ebd21bfce0016 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Wed, 20 Nov 2019 08:20:57 +0800 Subject: [PATCH 13/25] Master copy to be submitted --- .../BT2101_Default - EDIT-MASTER-checkpoint.ipynb | 0 ...opy1-checkpoint.ipynb => BT2101_Default - EDIT-MASTER.ipynb | 3 +-- 2 files changed, 1 insertion(+), 2 deletions(-) rename BT2101_Default - EDIT-Copy1.ipynb => .ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb (100%) rename .ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb => BT2101_Default - EDIT-MASTER.ipynb (99%) diff --git a/BT2101_Default - EDIT-Copy1.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb similarity index 100% rename from BT2101_Default - EDIT-Copy1.ipynb rename to .ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb b/BT2101_Default - EDIT-MASTER.ipynb similarity index 99% rename from .ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb rename to BT2101_Default - EDIT-MASTER.ipynb index 4ae5047..27df175 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-Copy1-checkpoint.ipynb +++ b/BT2101_Default - EDIT-MASTER.ipynb @@ -2960,7 +2960,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -5390,7 +5389,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 9f106dafbd444706c1e0db2ce38cef4f8fd9641e Mon Sep 17 00:00:00 2001 From: Janson-Chew Date: Wed, 20 Nov 2019 16:01:32 +0800 Subject: [PATCH 14/25] hi can see? --- .DS_Store | Bin 8196 -> 8196 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index d6901fc33840474cf27bd83ef26c6925ea54fe2a..6caac8e3468289edd39fc6b83a8dace75e2dee45 100644 GIT binary patch literal 8196 zcmeHM-D(p-6h6~d-BiRB3M~k-3W67DNvhb23NcMC0#=J#^+F}x&Bk_Nvm264LyeK# z_zv|E1n+$XA3zZC);I9RJN@RTHQ6*J5wt?*z|1#0Gv}O{Z_n)JY>0^Enr?<@o`?(- zj>)sA4iP@i%YaO%o*5v6K2e%7WKy0g^kQJP?$8Wq1~dbj0nLDB;D2BM?`$r{koUeE z)wO0oGjJjq;LisWg=1D>SE6`ypb{wna0=Z*&_*60bWDX=gi&)w%KM;o+++`Tt{x2Z`! zML35K=pJ(VMe?XdyA+U9^uw~_hYoc4al3_b^`{WDh*Ev_O6q&SZ+i={SwL?QUQOax zMY>O`@bU<~1+rA{I{NO8;rG%QeivXn6#K~x_b_3{c6NCz4{KPxg*_JN;cy@9P0YuI zF?=J<{q>J8Uktx})us@ZHBl)KsVui;;IRI~QZ?aiQ0sI%3X6jFqU2yW^8h z4%)osj=`V-Z5y_@P8n8r`q#p~$JR0vi_ise9bp$`#J>msLB9nN@q0ZHKY|6P)u^d6 z^UwH>ys$c~zgoLzs)uh#8o+fnU}F!T@Fw)5Dl zv`Wd@)i8)`FR0XovgcMX<=GQ2aKlzEYzA&a*shBn@*{csD}PRn;Hw(Cf1p*{?pfi7O}@|(eC=5Ezz>5h%;pRwaLNlf@p)PU zE>e};Sk&+{&>Jkl}N{dA{_@F{9%Z;iK$#CtFS8(J&=F?A>fc-cl!M=ZiTK+5Cgve De<*8O delta 556 zcmZp1XmOa}FDSymz`)4BAi$85ZWx@LpIfl8a2or>2Eonj94s95AXyd$J%)6KOokGe z3ZNJRLu5DyvRrPyi%UvrNiqY&%^w0bcQ&HRrQnn+$bi@iGLj)V07MD_?aO2+V5nrs zt7k}Jh-XM<$Y4kXlG#APe4tbwLkUAMievbgl~6U})`#lQt1Ni+1tVFQn~&@Z0veI@ zq4pyK36A#MRA-+ccq_;MMN1l+YK68tF6VcIacUdVrPnW*68`$7tn&kG4m b4i#0L{7;B~^J1P9=FRL9-&i(QurmVy5j~Sq From 9aeaedcd673f831c48de3fc12548e22a58acd226 Mon Sep 17 00:00:00 2001 From: Janson-Chew Date: Wed, 20 Nov 2019 16:02:58 +0800 Subject: [PATCH 15/25] . . --- .DS_Store | Bin 8196 -> 8196 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index 6caac8e3468289edd39fc6b83a8dace75e2dee45..4cf38fe0b70040a32baa7bdab07852bfcb0c93f2 100644 GIT binary patch delta 31 mcmZp1XmQxUEFxrLVyvTJYF4YGP;G8*prc@6xLH)>JvRV?>IfkK delta 31 mcmZp1XmQxUEFxrVVWOj8VN|Q5P;G8*prc@Jxmi@?JvRV@TL>!v From dac59f76f7e952eb6e5ffceedc9d5fa748bc3ff1 Mon Sep 17 00:00:00 2001 From: Janson-Chew Date: Wed, 20 Nov 2019 16:37:46 +0800 Subject: [PATCH 16/25] why is my PR not working --- .DS_Store | Bin 8196 -> 8196 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index 4cf38fe0b70040a32baa7bdab07852bfcb0c93f2..037ade346878442ce6fb5546fa3c8b788279e7c9 100644 GIT binary patch delta 30 kcmZp1XmQxUA|hyEtfOFVSgWH@ZEkJ=WLj<(6?xAM0DLY7^#A|> delta 30 lcmZp1XmQxUA|hyFtfOFRR;!~>ZEkL$qhMjUSybdbHvoIy2kig= From 6d5b7316620550d34d958f7dad1d89bc2816f232 Mon Sep 17 00:00:00 2001 From: Janson-Chew Date: Wed, 20 Nov 2019 16:50:23 +0800 Subject: [PATCH 17/25] hi --- .DS_Store | Bin 8196 -> 8196 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index 037ade346878442ce6fb5546fa3c8b788279e7c9..7db16079758be136d45eec6582c64cbdcf23e038 100644 GIT binary patch delta 19 acmZp1XmQxUBEn{7tfOFRwwX)h7dHSlYz0jK delta 19 acmZp1XmQxUBEn{2tfOFVxS31j7dHSlas^BP From e7da22f68405339ebe28c5945a00d3c811ca03c2 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Wed, 20 Nov 2019 23:16:06 +0800 Subject: [PATCH 18/25] updated --- ...101_Default - EDIT-MASTER-checkpoint.ipynb | 1341 ++++++++--------- BT2101_Default - EDIT-MASTER.ipynb | 1341 ++++++++--------- 2 files changed, 1248 insertions(+), 1434 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb index 27df175..4b6137d 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "
" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3df7BcZZ3n8ffHIC4VpEiM3IoEDboZZ/mhCHcIrlUz17EIAZwKlj+WHyXBQWMpKWdXtoowyxYK6Ga2lnXBHzhRY0ItENmpYZNRNGZZe3FnRZMoS4KARLzKJUiEQJaLOBj87h/naWgufW/f7j7d/Zx7P6+qru5++ulznpP+5vme55znnqOIwMzMLDevGHQDzMzMmnGCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5AQ1TZJGJT0raVzSY5K+Lunwhs83SDoo6XUNZaenugsayl4l6T5JH22xvpMk7ZT02/R8Um+2zHqlnzEj6Y8kbZb0G0n7JW2V9ObebZ2Vrc/xskDSP0p6QtJTkn4g6R2927rOOEG15y8i4nDgZOBPgCsAJM0F3gscAC6oV46IbcA3gesalnEF8CiwbrKVSDoU2Az8V2AesBHYnMqtWvoSM8CRwBbgzcAQ8COKGLJq6Ve8jAN/CbyWoo/5G+AfJB1S2paUwAmqAxHxCPBt4IRU9F7gKeAqYOWE6p8E/kzS2ZJOAFYDH4mprzE1AhwC/JeI+KeIuB4Q8OflbYX1U69jJiJ+FBFfi4j9EfF74HPAmyW9puxtsd7rQ7z8LiIeiIg/UPQtz1Mkqvnlbkl3nKA6IOkY4CzgJ6loJXALsAn4Y0kn1+tGxAHgY8CXgfXApyPi5y1WcTxwz4QAuyeVWwX1IWYm+lPg1xHxRLdtt/7rV7xIugf4HcXo+6sRsa+0jSiBE1R7/rukp4D/Dfwv4LOSXg+8E7g5Ih4D7mDCHk5E/ANwF8W/9/XTWM/hFEP5RgeAV3fXfBuAfsXMCyQtAr5IsWdt1dLXeImItwBHAOendWbFCao950TEkRHxhoj4eEQ8C3wQuC8i7k51bgLOl/TKCd+9F7g/DalbGacImkZHAE9303gbiH7FDACSXgt8F/hSRNxSxgZYX/U1XuCFw323AGskvbXrLShRVifEKupC4PWSfp3eHwK8BjiTYtjciXuBSyWp4TDfWyj2iq36ehEzSJpHkZy2RMRnum6l5aIn8dLEK4E3Av+3xGV2xQmqC5LeDrwJeBvwm4aPrqUYgncaPDWKk5afkPRl4COp/H92uDzLRK9iRtIRwFbgHyNiTbfttDz0MF5Oo+j/fwTMAT5BMfvzh920t2xOUN1ZCWyOiF2NhZKuA74vaX5E7G93oRHxnKRzgK8Ca4H7KIb+z5XRaBuonsQM8B6KacnHS7qoofy4iPhVx621QetVvLyK4lzVG4HfA7uAsyNib7cNLpN8R10zM8uRJ0mYmVmWnKAGRNIF6ZImEx/3DrptlifHjLVjJsSLD/GZmVmWWk6SkLQeeDewLyJOSGWfophZVp9V8tcRcXv67HLgYtIstIjYmsqXU1wvag7FXyyvTeXHUvx19Hzgx8AHpzMZYMGCBbF48eJpb2grzzzzDHPnzi1teb0yiHbu3Lnz8Yh4bV9XWrKy4wWqETOOl865j+mfSWMmIqZ8UFwy5WRgd0PZp4B/26TucRRz6F8FHAv8nCIhzUmv3wgcmuocl75zK3Buev1l4GOt2hQRnHLKKVGm733ve6Uur1cG0U5gR0zjN8n5UXa8RFQjZhwv+cRMFeIlIq+YaXkOKiLuBKY7jXEFsCmKC5z+AtgDnJoeeyLioShGR5uAFZLqF0D9u/T9jcA501yXmZnNYN38HdRqSRcCO4BLI+JJ4GiK60HVjaUygIcnlC+l+GvopyLiYJP6LyNpFbAKYGhoiFqt1kXzX2p8fLzU5fVKVdppZtatThPUDcDVQKTnaynuLaImdYPmswVjivpNRcQ60j1OhoeHY2RkpK1GT6VWq1Hm8nqlKu00M+tWRwkqiivqAiDpKxQ3zIJiBHRMQ9VFQP0vk5uVPw4cKemQNIpqrN9Xux45wEVrvjVlndG1Z/epNVYFrWLG8WKNHC/t6+jvoCQtbHj7HmB3er0FODfdcvhYYAnFtZ62A0skHZvuCnsuxQUtA/ge8L70/ZX4LqCVJ2m9pH2SdjeUfUrSI5LuTo+zGj67XNIeSQ9IOqOhfHkq2yNpTUP5sZJ+KOlBSd/wnYarzfFik2mZoCTdAvyA4u6cY5IuBv6jpF3pZlfvBP4NQETcSzEr76fAd4BLIuL5NDpaTXExy/uAW1NdgMuAT0raQ3FO6mulbqENwgZgeZPyz0XESelR/7OE4yh2WI5P3/mSpDmS5lBcvf1Mitmh56W6UNye+nMRsQR4kuLPGqy6NuB4sSZaHuKLiPOaFE+aRKK4zP/LLvWfAuz2JuUPUczysxkiIu6UtHia1V+Y+Qn8Iu2o1ONhT4oPJNVnft5HMfPz/FRnI8WfPdxQTuut3xwvNhlfzdz6qa8zP3s56xNg6DC49MSDk36ew2zLis/6nFEzhasQL5BXzDhBWb/0feZnL2d9Anz+ps1cu2vy/0KjF5S7vk5UeNbnjJspXIV4gbxixgnK+mImzvy03nG8GPhq5tYnnvlp7XC8GHgEZT2QZn6OAAskjQFXAiOSTqI4vDIKfBSKmZ+S6jM/D5Jmfqbl1Gd+zgHWT5j5uUnSNcBP8MzPSnO82GScoKx0nvlp7XC82GR8iM/MzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllqWWCkrRe0j5JuxvK5kvaJunB9DwvlUvS9ZL2SLpH0skN31mZ6j8oaWVD+SmSdqXvXC9JZW+kmZlVz3RGUBuA5RPK1gB3RMQS4I70HuBMYEl6rAJugCKhUdzGeSnFnS2vrCe1VGdVw/cmrsvMzGahlgkqIu4E9k8oXgFsTK83Auc0lN8YhbuAIyUtBM4AtkXE/oh4EtgGLE+fHRERP4iIAG5sWJaZmc1ih3T4vaGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRjLYYGhqiVqt12PyXGzoMLj3x4JR1ylxfp8bHx7Noh5lZr3WaoCbT7PxRdFDeVESsA9YBDA8Px8jISAdNbO7zN23m2l1T/3OMXlDe+jpVq9Uoc7t7QdJ64N3Avog4IZXNB74BLAZGgQ9ExJPpnON1wFnAb4GLIuLH6TsrgSvSYq+JiI2p/BSKQ8+HAbcDf5VG4FZBjhebTKez+B5Lh+dIz/tS+RhwTEO9RcDeFuWLmpRbtW3A5y1t+jbgeLEmOk1QW4D6TLyVwOaG8gvTbL7TgAPpUOBWYJmkeSlolgFb02dPSzot7Rld2LAsqyift7R2OF5sMi0P8Um6BRgBFkgao9hLWQvcKuli4FfA+1P12ymG3nsoht8fAoiI/ZKuBraneldFRD0gP8aLw+9vp4fNPH0/b9nLc5bQ+rxlDucKK3zOctad587ld8opZlomqIg4b5KP3tWkbgCXTLKc9cD6JuU7gBNatcNmrJ6dt+zlOUtofd7S5yx7Ysae584hXiCvmPGVJKxffN7S2uF4MSco6xuft7R2OF6s9GnmZj5vaW1xvNhknKCsdD5vae1wvNhkfIjPzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZWnW3LBw8ZpvTfn5pSf2qSFWGY4Zs8HqKkFJGgWeBp4HDkbEsKT5wDeAxcAo8IGIeFKSgOsobtf8W+CiiPhxWs5K4Iq02GsiYmM37TIz6zfv0JSvjEN874yIkyJiOL1fA9wREUuAO9J7gDOBJemxCrgBICW0K4GlwKnAlZLmldAuMzOrsF6cg1oB1EdAG4FzGspvjMJdwJGSFgJnANsiYn9EPAlsA5b3oF2WAUmjknZJulvSjlQ2X9I2SQ+m53mpXJKul7RH0j2STm5YzspU/8E0ArcZyPEyu3WboAL4rqSdklalsqGIeBQgPR+Vyo8GHm747lgqm6zcZi6Puq0djpdZqttJEu+IiL2SjgK2Sbp/irpqUhZTlL98AUUSXAUwNDRErVabdkMvPfHglJ8PHda6Tjvr65Xx8fEs2lGyFcBIer0RqAGX0TDqBu6SVB91j5BG3QCS6qPuW/rbbBsQx8ss0VWCioi96XmfpNso9k4ek7QwIh5NwbEvVR8Djmn4+iJgbyofmVBem2R964B1AMPDwzEyMtKsWlMXtTyBeZBrd039zzF6wfTX1yu1Wo12tjtD9VF3AH+bftOXjLrTDg941G2Ol1mt4wQlaS7wioh4Or1eBlwFbAFWAmvT8+b0lS3AakmbKIbaB1JwbQU+2zDkXgZc3mm7LHt9G3V3M+KG7kfdOYx0Z8CIe9Ycpcnld8opZroZQQ0BtxWzxzkEuDkiviNpO3CrpIuBXwHvT/Vvp5hivodimvmHACJiv6Srge2p3lX1objNPP0cdXcz4obuR90ecXdvNh2lySFeIK+Y6XiSREQ8FBFvTY/jI+IzqfyJiHhXRCxJz/tTeUTEJRHxpog4MSJ2NCxrfUT88/T4evebZTmSNFfSq+uvKUbLu3lx1A0vH3VfmGZnnUYadQNbgWWS5qWR97JUZjOI48VmzZUkLAsedVs7HC+znBOU9U1EPAS8tUn5E8C7mpQHcMkky1oPrC+7jZYPx4s5QZmZTUOrSxlZ+Zyg2tAqQEfXnt2nlli3cuhsptMGx5Q1mm19kG+3YWZmWXKCMjOzLDlBmZlZlpygzMwsSzNmkkQOJ73NzKw8MyZB5WC2zbAxM+slH+IzM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmlqVsEpSk5ZIekLRH0ppBt8fy5nixdjlmqieLq5lLmgN8ETgdGAO2S9oSET8dbMvKNZ1bgviK563NlngBXyG/LLMpZmaSLBIUcCqwJyIeApC0CVgBOHisGceLtWtWxMxM26HJJUEdDTzc8H4MWDqxkqRVwKr0dlzSA2U14BOwAHi8rOV1Sn/Tssog2vmGPq+vlYHHC7SOmWn8ll1zvEzbwGOmIvECGcVMLglKTcriZQUR64B1PWmAtCMihnux7DJVpZ09NvB4gWr8FlVoY58MPGaq8lvk1M5cJkmMAcc0vF8E7B1QWyx/jhdrl2OmgnJJUNuBJZKOlXQocC6wZcBtsnw5XqxdjpkKyuIQX0QclLQa2ArMAdZHxL19bkbPDgWVrCrt7JlM4gWq8VtUoY09l0nMVOW3yKadinjZYVgzM7OBy+UQn5mZ2Us4QZmZWZacoKjGJVAkjUraJeluSTsG3Z7ZrArxAo6ZnFQhZnKMl1l/DipdAuVnNFwCBTgvt0ugSBoFhiNi4H9MPJtVJV7AMZOLqsRMjvHiEVTDJVAi4jmgfgkUs2YcL9Yux0yHnKCaXwLl6AG1ZSoBfFfSznQ5FhuMqsQLOGZyUZWYyS5esvg7qAGb1iVQMvCOiNgr6Shgm6T7I+LOQTdqFqpKvIBjJhdViZns4sUjqIpcAiUi9qbnfcBtFIcNrP8qES/gmMlIJWImx3hxgqrAJVAkzZX06vprYBmwe7CtmrWyjxdwzGQm+5jJNV5m/SG+TC6B0soQcJskKH6zmyPiO4Nt0uxUkXgBx0w2KhIzWcbLrJ9mbmZmefIhPjMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKCmSdKopGcljUt6TNLXJR3e8PkGSQclva6h7PRUd0FD2ask3Sfpo9Nc70pJIenD5W6R9Vq/YybFyTNpfeOSvtqbLbNeGEC8zJF0jaS9kp6W9BNJR/Zm6zrjBNWev4iIw4GTgT8BroAX7kD5XuAAcEG9ckRsA74JXNewjCuAR4F1rVYmaR5wOZDbzc1s+voaM8BbI+Lw9PBOTfX0M14+DfxL4O3AEcAHgd+VshUlcYLqQEQ8AnwbOCEVvRd4CrgKWDmh+ieBP5N0tqQTgNXAR2J6d4r8D8D1wOOlNNwGpo8xYzNAr+Ml7fz+61Tvl1HYHRFOUFUn6RjgLOAnqWglcAuwCfhjSSfX60bEAeBjwJeB9cCnI+Ln01jHqcBw+p5VXD9iJrlT0q8l/b2kxSU13/qsD/FyInAQeF+Kl59JuqTkzeheRPgxjQcwCoxT7MX8EvgScBjweuAPwEmp3lbguibf/2/ADuAV01jXnFT37el9DfjwoP8N/Mg3ZlL9PwUOBY4EvgDsBg4Z9L+DH/nFC3A+EMDX0jreAvwGOH3Q/w6ND4+g2nNORBwZEW+IiI9HxLMUx23vi4i7U52bgPMlvXLCd+8F7o+IP0xjPR8H7omIH5TXdBuQfsUMEXFnRDwXEU8BfwUcC/yLkrbD+qNf8fJser4qIp6NiHsoRmdnlbERZTlk0A2YAS4EXi/p1+n9IcBrgDOBLR0u810Ux5TrwTIfeJukkyJidVettRz0ImaaCUAlLs8Goxfxck96zvq8phNUFyS9HXgT8DaK4XHdtRTHjDsNnouAf9bw/u+Bv6MYjluF9SpmJB0PvBLYRXHI5hrgEeC+btprg9WreImIn0v6PvDvJH0CeCPwr4DzumtxuZygurMS2BwRuxoLJV0HfF/S/IjY3+5C0yGaxuU9B/y/KE6GWrX1JGaAIeAGYBHwDPB/gHdHxO+7bbANVK/iBYpk9DXgCWAf8O8j4o6uWlsypRNmZmZmWfEkCTMzy5IT1IBIuqDhkjSND181wppyzFg7ZkK8+BCfmZllqbKTJBYsWBCLFy8ubXnPPPMMc+fOLW15vTKIdu7cufPxiHhtX1dasrLjBaoRM46XzrmP6Z/JYqayCWrx4sXs2LGjtOXVajVGRkZKW16vDKKdkn7Z1xX2QNnxAtWIGcdL59zH9M9kMeNzUGZmliUnKDMzy5ITlJmZZamy56DKtuuRA1y05ltT1hlde3afWmNV0CpmHC/WyPHSPo+gzMwsS05QZmaWJScoMxsoSesl7ZO0u6HsU5IekXR3epzV8NnlkvZIekDSGQ3ly1PZHklrGsqPlfRDSQ9K+oakQ/u3ddaNlgnKwWNmPbYBWN6k/HMRcVJ63A4g6TjgXOD49J0vSZojaQ7wRYp7JB0HnJfqAvxNWtYS4Eng4p5ujZVmOiOoDTh4zKxHIuJOYLq3jFgBbIqIf4qIXwB7gFPTY09EPBQRz1HcHXaFJAF/TnE/NYCNwDmlboD1TMsE5eAxswFZLemedBRnXio7Gni4oc5YKpus/DXAUxFxcEK5VUA308xXS7oQ2AFcGhFPUvzwdzXUaQyGicGzlDaDR9IqYBXA0NAQtVqti+a/1NBhcOmJB6esU+b6OjU+Pp5FO6YiaT3wbmBfRJyQyj4FfIQX7wr61w0j78spRs7PA5+IiK2pfDlwHTAH+GpErE3lx1Ls5MwHfgx8MO342MxxA3A1xS3Jr6a4g+xf0vwW9kHzne3Jbnk/6RWyB9nH5PL/Oqc+ptMENZDgiYh1wDqA4eHhKPN6UZ+/aTPX7pr6n2P0gvLW16mKXM9rA/AF4MYJ5Z+LiP/UWDDhsPDrgP8h6Y/Sx18ETqfYcdkuaUtE/JQXDwtvkvRliuR2Q682xvovIh6rv5b0FeCb6e0YcExD1UXA3vS6WfnjwJGSDkk7wo31m613YH1MDv0L5NXHdDSLLyIei4jnI+IPwFcoDuHB5MEzWfkLwTOh3CrMh4WtW5IWNrx9D1CfpLUFOFfSq9JIegnwI2A7sCRNujqUYqdnSxT3E/oe8L70/ZXA5n5sg3WvoxGUpIUR8Wh6OzF4bpb0nyn2huvBI1LwAI9QBM/5ERGS6sGzCQfPTNfXw8K9PFwD1Thkk9PhmslIugUYARZIGgOuBEYknURxRGUU+ChARNwr6Vbgp8BB4JKIeD4tZzWwleKQ8PqIqN+Y7zJgk6RrgJ8AX+vTplmXWiYoB4+VpO+HhXt5uAaqccgmp8M1k4mI85oUT9oPRMRngM80Kb8duL1J+UO8eJTHKqRlgnLwWBkGdU7BzKrLV5KwvvA5BTNrl69mbqXzYWEzK4MTlJXOh4XNrAw+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzAZK0npJ+yTtbiibL2mbpAfT87xULknXS9oj6R5JJzd8Z2Wq/6CklQ3lp0jalb5zvST1dwutUy0TlIPHzHpsA7B8Qtka4I6IWALckd4DnAksSY9VwA1Q9EkUd25eSnEzyyvr/VKqs6rhexPXZZmazghqAw4ea4N3aqwdEXEnsH9C8QpgY3q9ETinofzGKNwFHClpIXAGsC0i9kfEk8A2YHn67IiI+EFEBHBjw7Iscy1v+R4Rd0paPKF4BTCSXm8EasBlNAQPcJekevCMkIIHQFI9eGqk4Enl9eD5djcbZQO3AfgCRWdQV9+pWStpTXp/GS/dqVlKscOytGGnZhgIYKekLanzqe/U3EVxS/jlOGZmmqGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRxBZDQ0PUarXutqLB0GFw6YkHJ/28zHV1Y3x8PJu2tExQk5h1wQN5BFBOwTMZ79RYDzUbLUcH5U1FxDpgHcDw8HCMjIx00MTmPn/TZq7dNXmXO3pBeevqRq1Wo8zt7kanCWoyMzZ4II8Ayil42jSQnRqrrMckLUyxshDYl8rHgGMa6i0C9qbykQnltVS+qEl9q4BOE5SDx8rSs52aXo64oRqHbKow4p7EFmAlsDY9b24oXy1pE8Uh4QOpH9oKfLbh3PYy4PKI2C/paUmnAT8ELgQ+388Nsc51mqAcPNauvu/U9HLEDdU4ZFOFEbekWyh+6wWSxijOPa4FbpV0MfAr4P2p+u3AWcAe4LfAhwBSX3I1sD3Vu6p+eBj4GMV50cMoDgX7cHBFtExQDh4riXdqrKmIOG+Sj97VpG4Al0yynPXA+iblO4ATummjDcZ0ZvE5eKwt3qkxszKUPUnCzDs1ZlYKX+rIzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZck3LDQzK8HiNd+a8vNLT+xTQ2aQrhKUpFHgaeB54GBEDEuaD3wDWAyMAh+IiCclCbiO4vbevwUuiogfp+WsBK5Ii70mIjZ2065mHDxm1VOlPsbKV8YI6p0R8XjD+zXAHRGxVtKa9P4y4ExgSXosBW4AlqZguxIYBgLYKWlLRDxZQtssM1XqcLxTkw33MbNUL85BrQDqncVG4JyG8hujcBdwpKSFwBnAtojYnwJmG7C8B+2yfLwzIk6KiOH0vt7hLAHuSO/hpR3OKooOh4YOZylwKnClpHl9bL8NlvuYWaLbEVQA35UUwN9GxDpgKCIeBYiIRyUdleoeDTzc8N2xVDZZ+ctIWkXRUTE0NEStVpt2Qy898eCUnw8d1rpOO+vrlfHx8SzaUbIVwEh6vRGoUewRv9DhAHdJqnc4I6QOB0BSvcO5pb/Ntj6YNX1MLv+vc+pjuk1Q74iIvSlAtkm6f4q6alIWU5S/vLAIznUAw8PDMTIyMu2GXtTycM1Brt019T/H6AXTX1+v1Go12tnuDPWtw+mms4GZ0eHk1Nl0KJs+ptUh31bdaas+Jof+BfLqY7pKUBGxNz3vk3QbxeGWxyQtTB3NQmBfqj4GHNPw9UXA3lQ+MqG81k27LGt963C62aGB7ndqcuhwcupsOuE+Znbr+ByUpLmSXl1/DSwDdgNbgJWp2kpgc3q9BbhQhdOAA2mveSuwTNK8dB5hWSqzGaixwwFe0uEAtNHhNCu3GcR9jHUzghoCbismWnEIcHNEfEfSduBWSRcDvwLen+rfTjEbaw/FjKwPAUTEfklXA9tTvavq5xba0Xr4bYOWOplXRMTTDR3OVbzY4azl5R3OakmbKCZEHEh7zVuBzzZMjFgGXN5OWxwvlZBVH2P913GCioiHgLc2KX8CeFeT8gAumWRZ64H1nbbFKsMdjk2b+xjzlSSsb9zhmFk7fC0+MzPLkhOUmZllyQnKzMyy5HNQJWo1M2x07dl9aomZWfV5BGVmZlnyCMrMrCJm21Eaj6DMzCxLHkGZDch0rmYx0/aIzdrhEZSZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMspRNgpK0XNIDkvZIWjPo9ljeHC/WLsdM9WSRoCTNAb4InAkcB5wn6bjBtspy5XixdjlmqimXi8WeCuyJiIcAJG0CVgA/HWirSjadi4O2smH53J63oQIXKJ0V8QLdx0y38TKdNlQgXmCWxMwM+a1eoIgYdBuQ9D5geUR8OL3/ILA0IlZPqLcKWJXevhl4oMRmLAAeL3F5vTKIdr4hIl7b53VOKpN4gWrEzKyPF8gmZqoQL5BRzOQyglKTspdlzohYB6zrSQOkHREx3Itll6kq7eyxgccLVOO3qEIb+2TgMVOV3yKndmZxDgoYA45peL8I2Dugtlj+HC/WLsdMBeWSoLYDSyQdK+lQ4Fxgy4DbZPlyvFi7HDMVlMUhvog4KGk1sBWYA6yPiHv73IyeHQoqWVXa2TOZxAtU47eoQht7LpOYqcpvkU07s5gkYWZmNlEuh/jMzMxewgnKzMyy5ARFNS6BImlU0i5Jd0vaMej2zGZViBdwzOSkCjGTY7zM+sWNACwAAAEnSURBVHNQ6RIoPwNOp5iKuh04LyKy+gtzSaPAcERU4Q/9ZqyqxAs4ZnJRlZjJMV48gmq4BEpEPAfUL4Fi1ozjxdrlmOmQExQcDTzc8H4sleUmgO9K2pkux2KDUZV4AcdMLqoSM9nFSxZ/BzVg07oESgbeERF7JR0FbJN0f0TcOehGzUJViRdwzOSiKjGTXbx4BFWRS6BExN70vA+4jeKwgfVfJeIFHDMZqUTM5BgvTlAVuASKpLmSXl1/DSwDdg+2VbNW9vECjpnMZB8zucbLrD/El8klUFoZAm6TBMVvdnNEfGewTZqdKhIv4JjJRkViJst4mfXTzM3MLE8+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZen/A6K07c+5e7/dAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1529,7 +1529,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,7 +1553,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1577,7 +1577,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1601,7 +1601,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1625,7 +1625,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1649,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1673,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1697,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1894,7 +1894,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1918,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1942,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1966,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1990,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2124,7 +2124,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2132,7 +2132,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2140,7 +2140,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2148,7 +2148,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2156,7 +2156,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2240,7 +2240,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2261,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2282,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2303,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2324,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2604,23 +2604,24 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 74.581893 1.992315e-03\n", - "PAY_0 4250.691969 0.000000e+00\n", - "PAY_2 3793.454513 0.000000e+00\n", - "PAY_3 2943.420461 0.000000e+00\n", - "PAY_4 2997.799899 0.000000e+00\n", - "PAY_5 2809.793752 0.000000e+00\n", - "PAY_6 2384.699487 0.000000e+00\n", - "PAY_0_No_Transactions 75.255770 1.695234e-03\n", - "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", - "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", - "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", - "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", - "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", - "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", - "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", - "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", - "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" + "LIMIT_BAL 78.806194 7.074564e-04\n", + "PAY_0 4532.900073 0.000000e+00\n", + "PAY_2 3833.396843 0.000000e+00\n", + "PAY_3 2921.087799 0.000000e+00\n", + "PAY_4 2778.451579 0.000000e+00\n", + "PAY_5 2791.382213 0.000000e+00\n", + "PAY_6 2434.267474 0.000000e+00\n", + "PAY_0_No_Transactions 78.446517 7.742745e-04\n", + "PAY_0_Pay_Duly 59.486746 4.837585e-02\n", + "PAY_0_Revolving_Credit 483.224042 0.000000e+00\n", + "PAY_2_Pay_Duly 73.909964 2.336982e-03\n", + "PAY_2_Revolving_Credit 238.193174 0.000000e+00\n", + "PAY_3_Pay_Duly 74.057499 2.256805e-03\n", + "PAY_3_Revolving_Credit 128.025091 2.222005e-10\n", + "PAY_4_Pay_Duly 72.030496 3.623036e-03\n", + "PAY_4_Revolving_Credit 82.321461 2.872173e-04\n", + "PAY_5_Pay_Duly 69.393208 6.568060e-03\n", + "PAY_5_Revolving_Credit 61.028427 3.642149e-02\n" ] } ], @@ -2811,8 +2812,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13545\n", - " 1 1.00 1.00 1.00 3951\n", + " 0 1.00 1.00 1.00 13476\n", + " 1 1.00 1.00 1.00 4020\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2841,7 +2842,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" + "Of 2021 Defaulters, the Decision Tree (GINI) identified 852\n" ] }, { @@ -2876,14 +2877,14 @@ " \n", " \n", " \n", - " 0\n", - " 5403\n", - " 1256\n", + " 0\n", + " 5395\n", + " 1333\n", " \n", " \n", - " 1\n", - " 1233\n", - " 857\n", + " 1\n", + " 1169\n", + " 852\n", " \n", " \n", "\n", @@ -2892,8 +2893,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5403 1256\n", - "1 1233 857" + "0 5395 1333\n", + "1 1169 852" ] }, "execution_count": 38, @@ -2919,7 +2920,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2945,12 +2946,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.81 0.81 0.81 6659\n", - " 1 0.41 0.41 0.41 2090\n", + " 0 0.82 0.80 0.81 6728\n", + " 1 0.39 0.42 0.41 2021\n", "\n", - " accuracy 0.72 8749\n", + " accuracy 0.71 8749\n", " macro avg 0.61 0.61 0.61 8749\n", - "weighted avg 0.72 0.72 0.72 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", "\n" ] } @@ -2969,14 +2970,14 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + "Of 2021 Defaulters, the Decision Tree (Entropy) identified 810\n" ] }, { @@ -3011,14 +3012,14 @@ " \n", " \n", " \n", - " 0\n", - " 5422\n", - " 1237\n", + " 0\n", + " 5447\n", + " 1281\n", " \n", " \n", - " 1\n", - " 1244\n", - " 846\n", + " 1\n", + " 1211\n", + " 810\n", " \n", " \n", "\n", @@ -3027,11 +3028,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5422 1237\n", - "1 1244 846" + "0 5447 1281\n", + "1 1211 810" ] }, - "execution_count": 125, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -3044,7 +3045,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3056,7 +3057,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3065,6 +3066,16 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6062870404745417" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -3073,7 +3084,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3082,11 +3093,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.81 0.81 0.81 6659\n", - " 1 0.41 0.40 0.41 2090\n", + " 0 0.82 0.81 0.81 6728\n", + " 1 0.39 0.40 0.39 2021\n", "\n", " accuracy 0.72 8749\n", - " macro avg 0.61 0.61 0.61 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] @@ -3105,7 +3116,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3132,7 +3143,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3142,7 +3153,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3157,7 +3168,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 140, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -3168,7 +3179,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3177,8 +3188,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13545\n", - " 1 1.00 1.00 1.00 3951\n", + " 0 1.00 1.00 1.00 13476\n", + " 1 1.00 1.00 1.00 4020\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3200,14 +3211,14 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" + "Of 2021 Defaulters, the Decision Tree (Random Forest) identified 763\n" ] }, { @@ -3242,14 +3253,14 @@ " \n", " \n", " \n", - " 0\n", - " 6273\n", - " 386\n", + " 0\n", + " 6307\n", + " 421\n", " \n", " \n", - " 1\n", - " 1338\n", - " 752\n", + " 1\n", + " 1258\n", + " 763\n", " \n", " \n", "\n", @@ -3258,11 +3269,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6273 386\n", - "1 1338 752" + "0 6307 421\n", + "1 1258 763" ] }, - "execution_count": 142, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -3273,19 +3284,19 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.25839285714285715\n" + "Optimal Threshold: 0.31\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3302,7 +3313,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -3311,12 +3322,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.94 0.88 6659\n", - " 1 0.66 0.36 0.47 2090\n", + " 0 0.83 0.94 0.88 6728\n", + " 1 0.64 0.38 0.48 2021\n", "\n", - " accuracy 0.80 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", - "weighted avg 0.79 0.80 0.78 8749\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -3327,7 +3338,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -3358,7 +3369,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -3376,7 +3387,7 @@ " warm_start=False)" ] }, - "execution_count": 49, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -3389,7 +3400,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -3398,11 +3409,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13545\n", - " 1 0.79 0.47 0.59 3951\n", + " 0 0.86 0.96 0.91 13476\n", + " 1 0.80 0.48 0.60 4020\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.72 0.75 17496\n", + " macro avg 0.83 0.72 0.75 17496\n", "weighted avg 0.85 0.85 0.84 17496\n", "\n" ] @@ -3421,14 +3432,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" + "Of 2021 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 764\n" ] }, { @@ -3463,14 +3474,14 @@ " \n", " \n", " \n", - " 0\n", - " 6290\n", - " 369\n", + " 0\n", + " 6335\n", + " 393\n", " \n", " \n", - " 1\n", - " 1360\n", - " 730\n", + " 1\n", + " 1257\n", + " 764\n", " \n", " \n", "\n", @@ -3479,11 +3490,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6290 369\n", - "1 1360 730" + "0 6335 393\n", + "1 1257 764" ] }, - "execution_count": 51, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3494,19 +3505,19 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.23302683158657422\n" + "Optimal Threshold: 0.22217593115970874\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3523,7 +3534,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -3532,12 +3543,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.94 0.88 6659\n", - " 1 0.66 0.35 0.46 2090\n", + " 0 0.83 0.94 0.88 6728\n", + " 1 0.66 0.38 0.48 2021\n", "\n", - " accuracy 0.80 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", - "weighted avg 0.78 0.80 0.78 8749\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -3548,7 +3559,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3566,7 +3577,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 58, "metadata": { "scrolled": true }, @@ -3599,35 +3610,35 @@ " \n", " \n", " \n", - " 0\n", - " Decision Tree\n", - " 0.410048\n", - " 0.610882\n", + " 0\n", + " Decision Tree (GINI)\n", + " 0.421573\n", + " 0.612897\n", " \n", " \n", - " 1\n", + " 1\n", " Random Forest\n", - " 0.363158\n", - " 0.767925\n", + " 0.377536\n", + " 0.762511\n", " \n", " \n", - " 2\n", + " 2\n", " Gradient Boosted\n", - " 0.349282\n", - " 0.775518\n", + " 0.378031\n", + " 0.774845\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree 0.410048 0.610882\n", - "1 Random Forest 0.363158 0.767925\n", - "2 Gradient Boosted 0.349282 0.775518" + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.421573 0.612897\n", + "1 Random Forest 0.377536 0.762511\n", + "2 Gradient Boosted 0.378031 0.774845" ] }, - "execution_count": 55, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -3668,7 +3679,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -3677,7 +3688,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -3685,7 +3696,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", + " Current function value: 0.443375\n", " Iterations 7\n" ] }, @@ -3704,13 +3715,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774 \n", "\n", "\n", - " Time: 23:40:29 Log-Likelihood: -7675.2\n", + " Time: 08:26:11 Log-Likelihood: -7757.3\n", "\n", "\n", - " converged: True LL-Null: -9346.0\n", + " converged: True LL-Null: -9430.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3721,136 +3732,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + " LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", "\n", "\n", - " SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + " SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", "\n", "\n", - " AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + " AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", "\n", "\n", - " PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + " PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", "\n", "\n", - " PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + " PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", "\n", "\n", - " PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + " PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", "\n", "\n", - " PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + " PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", "\n", "\n", - " PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + " PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", "\n", "\n", - " PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + " PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", "\n", "\n", - " BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + " BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", "\n", "\n", - " BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + " BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", "\n", "\n", - " BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + " BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", "\n", "\n", - " BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + " BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", "\n", "\n", - " BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + " BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", "\n", "\n", - " BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + " BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", "\n", "\n", - " PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + " PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", "\n", "\n", - " PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + " PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", "\n", "\n", - " PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + " PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", "\n", "\n", - " PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + " PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", "\n", "\n", - " PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + " PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", "\n", "\n", - " PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + " PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", "\n", "\n", - " GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + " GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", "\n", "\n", - " UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + " UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", "\n", "\n", - " HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + " HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", "\n", "\n", - " MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + " MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", "\n", "\n", - " SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + " SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", "\n", "\n", - " PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + " PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", "\n", "\n", - " PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + " PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + " PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", "\n", "\n", - " PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + " PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + " PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + " PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", "\n", "\n", - " PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + " PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", "\n", "\n", - " PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + " PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + " PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", "\n", "\n", - " PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + " PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", "\n", "\n", - " PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + " PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + " PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", "\n", "\n", - " PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + " PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", "\n", "\n", - " PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + " PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + " PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", "\n", "\n", - " PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + " PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", "\n", "\n", - " PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + " PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + " PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", "\n", "" ], @@ -3862,62 +3873,62 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", - "Time: 23:40:29 Log-Likelihood: -7675.2\n", - "converged: True LL-Null: -9346.0\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774\n", + "Time: 08:26:11 Log-Likelihood: -7757.3\n", + "converged: True LL-Null: -9430.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", - "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", - "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", - "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", - "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", - "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", - "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", - "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", - "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", - "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", - "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", - "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", - "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", - "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", - "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", - "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", - "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", - "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", - "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", - "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", - "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", - "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", - "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", - "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", - "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", - "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", - "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", - "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", - "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", - "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", - "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", - "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", - "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", - "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", - "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", - "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", - "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", - "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", - "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", - "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", - "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", - "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", - "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", - "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + "LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", + "SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", + "AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", + "PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", + "PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", + "PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", + "PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", + "PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", + "PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", + "BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", + "BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", + "BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", + "BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", + "BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", + "BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", + "PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", + "PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", + "PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", + "PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", + "PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", + "PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", + "GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", + "UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", + "HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", + "MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", + "SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", + "PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", + "PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", + "PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", + "PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", + "PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", + "PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", + "PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", + "PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", + "PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", + "PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", + "PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", + "PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", + "PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", + "PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", + "PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", + "PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", + "PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", + "PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 57, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -3929,7 +3940,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3938,12 +3949,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.95 0.89 13545\n", - " 1 0.67 0.37 0.47 3951\n", + " 0 0.83 0.95 0.89 13476\n", + " 1 0.67 0.36 0.47 4020\n", "\n", - " accuracy 0.82 17496\n", - " macro avg 0.75 0.66 0.68 17496\n", - "weighted avg 0.80 0.82 0.80 17496\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", "\n" ] } @@ -3961,7 +3972,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -3969,67 +3980,58 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438685\n", + " Current function value: 0.443375\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438686\n", + " Current function value: 0.443376\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438689\n", + " Current function value: 0.443378\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438692\n", + " Current function value: 0.443381\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438733\n", + " Current function value: 0.443383\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438766\n", + " Current function value: 0.443392\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438815\n", + " Current function value: 0.443403\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438878\n", + " Current function value: 0.443417\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438953\n", + " Current function value: 0.443435\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439049\n", + " Current function value: 0.443453\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439148\n", + " Current function value: 0.443472\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439242\n", + " Current function value: 0.443503\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439370\n", + " Current function value: 0.443533\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439484\n", + " Current function value: 0.443589\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439636\n", + " Current function value: 0.443651\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439750\n", + " Current function value: 0.443736\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439934\n", + " Current function value: 0.443875\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440120\n", + " Current function value: 0.444048\n", " Iterations 7\n" ] }, @@ -4042,19 +4044,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17472 \n", + " Model: Logit Df Residuals: 17469 \n", "\n", "\n", - " Method: MLE Df Model: 23 \n", + " Method: MLE Df Model: 26 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762 \n", "\n", "\n", - " Time: 00:03:36 Log-Likelihood: -7700.3\n", + " Time: 08:26:12 Log-Likelihood: -7769.1\n", "\n", "\n", - " converged: True LL-Null: -9346.0\n", + " converged: True LL-Null: -9430.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4065,76 +4067,85 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + " LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", + "\n", + "\n", + " SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", + "\n", + "\n", + " PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", + "\n", + "\n", + " PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", "\n", "\n", - " PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + " PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", "\n", "\n", - " PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + " PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", "\n", "\n", - " PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + " BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", "\n", "\n", - " PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + " BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", "\n", "\n", - " BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + " PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", "\n", "\n", - " PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + " PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", "\n", "\n", - " PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + " PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", "\n", "\n", - " PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + " PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", "\n", "\n", - " PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + " GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", "\n", "\n", - " PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + " UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", "\n", "\n", - " GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + " HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", "\n", "\n", - " UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + " MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", "\n", "\n", - " HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + " PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", "\n", "\n", - " MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + " PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", "\n", "\n", - " PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + " PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", "\n", "\n", - " PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + " PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + " PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + " PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + " PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", "\n", "\n", - " PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + " PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", "\n", "\n", - " PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + " PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + " PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", "\n", "\n", - " PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + " PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", "\n", "" ], @@ -4144,44 +4155,47 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17472\n", - "Method: MLE Df Model: 23\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", - "Time: 00:03:36 Log-Likelihood: -7700.3\n", - "converged: True LL-Null: -9346.0\n", + "Model: Logit Df Residuals: 17469\n", + "Method: MLE Df Model: 26\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762\n", + "Time: 08:26:12 Log-Likelihood: -7769.1\n", + "converged: True LL-Null: -9430.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", - "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", - "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", - "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", - "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", - "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", - "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", - "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", - "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", - "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", - "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", - "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", - "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", - "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", - "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", - "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", - "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", - "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", - "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", - "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", - "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", - "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", - "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", - "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + "LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", + "SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", + "PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", + "PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", + "PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", + "PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", + "BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", + "BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", + "PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", + "PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", + "PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", + "PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", + "GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", + "UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", + "HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", + "MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", + "PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", + "PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", + "PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", + "PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", + "PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", + "PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", + "PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", + "PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", + "PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", + "PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", + "PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 109, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -4199,20 +4213,21 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "24 Columns left:\n", - "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", - " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + "27 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions'],\n", " dtype='object')\n" ] } @@ -4225,7 +4240,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -4234,12 +4249,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.95 0.88 6659\n", - " 1 0.68 0.34 0.46 2090\n", + " 0 0.83 0.95 0.89 6728\n", + " 1 0.67 0.36 0.47 2021\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.78 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -4259,19 +4274,19 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.21432968760597085\n" + "Optimal Threshold: 0.1993195782749319\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4284,10 +4299,10 @@ { "data": { "text/plain": [ - "0.7705999826781731" + "0.7718794071347034" ] }, - "execution_count": 108, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -4305,7 +4320,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 66, "metadata": { "scrolled": true }, @@ -4316,12 +4331,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.81 0.84 6659\n", - " 1 0.50 0.61 0.55 2090\n", + " 0 0.88 0.78 0.82 6728\n", + " 1 0.46 0.63 0.53 2021\n", "\n", - " accuracy 0.76 8749\n", - " macro avg 0.68 0.71 0.69 8749\n", - "weighted avg 0.78 0.76 0.77 8749\n", + " accuracy 0.74 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", + "weighted avg 0.78 0.74 0.76 8749\n", "\n" ] } @@ -4344,14 +4359,14 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Logistic Regression identified 1273\n" + "Of 2021 Defaulters, the Logistic Regression identified 1273\n" ] }, { @@ -4386,13 +4401,13 @@ " \n", " \n", " \n", - " 0\n", - " 5361\n", - " 1298\n", + " 0\n", + " 5240\n", + " 1488\n", " \n", " \n", - " 1\n", - " 817\n", + " 1\n", + " 748\n", " 1273\n", " \n", " \n", @@ -4402,11 +4417,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5361 1298\n", - "1 817 1273" + "0 5240 1488\n", + "1 748 1273" ] }, - "execution_count": 115, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -4417,14 +4432,14 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Logistic Regression identified 714\n" + "Of 2021 Defaulters, the Logistic Regression identified 720\n" ] }, { @@ -4459,14 +4474,14 @@ " \n", " \n", " \n", - " 0\n", - " 6329\n", - " 330\n", + " 0\n", + " 6377\n", + " 351\n", " \n", " \n", - " 1\n", - " 1376\n", - " 714\n", + " 1\n", + " 1301\n", + " 720\n", " \n", " \n", "\n", @@ -4475,11 +4490,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6329 330\n", - "1 1376 714" + "0 6377 351\n", + "1 1301 720" ] }, - "execution_count": 116, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4497,19 +4512,19 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2096696069036731\n" + "Optimal Threshold: 0.2091273384286262\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4526,7 +4541,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -4684,243 +4699,278 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### PCA\n", - "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2 Component PCA\n", - "First, we will visualize the information retained after performing a 2 component pca." + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#perform pca\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "principalComponents = pca.fit_transform(X_train)\n", - "principalDf = pd.DataFrame(data = principalComponents\n", - " , columns = ['principal component 1', 'principal component 2'])" + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.295060096 0.1735291836]\n" + "Optimal Threshold: 0.16372570606114417\n" ] - } - ], - "source": [ - "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", - "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ + }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7264392577614385" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "#visualizing pca\n", - "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", - "fig = plt.figure(figsize = (8,8))\n", - "ax = fig.add_subplot(1,1,1) \n", - "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", - "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", - "ax.set_title('2 component PCA', fontsize = 20)\n", - "targets = [1,0]\n", - "colors = ['r', 'g']\n", - "for target, color in zip(targets,colors):\n", - " indicesToKeep = pca_visualize_df['Y'] == target\n", - " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", - " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", - " , c = color\n", - " , s = 50)\n", - "ax.legend(targets)\n", - "ax.grid()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform PCA to retain 90% of information\n", - "perform PCA to reduce components so we can run SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "#setting pca threshold to 90%\n", - "pca = PCA(0.9)" + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 37, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2009 Defaulters, the SVM default parameters identified 739\n" + ] + }, { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06382358
11270739
\n", + "
" + ], "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" + "Predicted 0 1\n", + "Actual \n", + "0 6382 358\n", + "1 1270 739" ] }, - "execution_count": 73, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pca.fit(X_train)" + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No. of components before pca: 44\n", - "No. of components after pca: 13\n" + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6740\n", + " 1 0.67 0.37 0.48 2009\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" ] } ], "source": [ - "#get number of components after pca\n", - "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", - "print('No. of components after pca: {}'.format(pca.n_components_))" + "print(classification_report(y_test, clf_original.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the number of components is reduced from 26 components to 13 components" + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." ] }, { - "cell_type": "code", - "execution_count": 75, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#perform pca on training and test attributes\n", - "X_train_pca = pca.transform(X_train)\n", - "X_test_pca = pca.transform(X_test)" + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 48, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.001}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Apply SVM model\n", - "Next, we will used the reduced attributes train set to train our SVM model" + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001,0.01, 0.1, 1]\n", + " gammas = [0.001, 0.01, 0.1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, we train our SVM model without parameter tuning\n", - "nor pca reduction" + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn import svm\n", - "#train svm model without standardization and no parameter tuning\n", - "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", - "clf_original.fit(X_train, y_train)" + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15585444961401423\n" + "Optimal Threshold: 0.15927524092941694\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4929,33 +4979,23 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7192717558206292" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "#plot roc for svm\n", - "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning RBF kernel\")" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" + "Of 2009 Defaulters, the SVM reduced features and tuning RBF kernal identified 671\n" ] }, { @@ -4990,14 +5030,14 @@ " \n", " \n", " \n", - " 0\n", - " 6304\n", - " 355\n", + " 0\n", + " 6437\n", + " 303\n", " \n", " \n", - " 1\n", - " 1341\n", - " 749\n", + " 1\n", + " 1338\n", + " 671\n", " \n", " \n", "\n", @@ -5006,23 +5046,23 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6304 355\n", - "1 1341 749" + "0 6437 303\n", + "1 1338 671" ] }, - "execution_count": 78, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#confusion matrix\n", - "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernal\")" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -5031,166 +5071,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.95 0.88 6659\n", - " 1 0.68 0.36 0.47 2090\n", + " 0 0.83 0.96 0.89 6740\n", + " 1 0.69 0.33 0.45 2009\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.78 8749\n", + " macro avg 0.76 0.64 0.67 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test, clf_original.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "note that the default values of gamma = 1/13 and c= 1" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "#train svm model with feature reduction and no parameter tuning\n", - "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", - "clf_reduced.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_reduced, y_test, X_test_pca, \n", - " \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now try to find best parameters for SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001, 0.01, 0.1, 1,10]\n", - " gammas = [0.001, 0.01, 0.1, 10]\n", - " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", - " grid_search.fit(X, y)\n", - " grid_search.best_params_\n", - " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,5)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", - "clf_reduced_tuned.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", - " \"SVM reduced features and tuning RBF kernel\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" ] }, { @@ -5243,6 +5135,21 @@ "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parameter tuning\n", + "##### Learning rate\n", + "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", + "\n", + "##### Momentum\n", + "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", + "\n", + "##### Loss function\n", + "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/BT2101_Default - EDIT-MASTER.ipynb b/BT2101_Default - EDIT-MASTER.ipynb index 27df175..4b6137d 100644 --- a/BT2101_Default - EDIT-MASTER.ipynb +++ b/BT2101_Default - EDIT-MASTER.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1529,7 +1529,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,7 +1553,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1577,7 +1577,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1601,7 +1601,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1625,7 +1625,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1649,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1673,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1697,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1894,7 +1894,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1918,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1942,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1966,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1990,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2124,7 +2124,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2132,7 +2132,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2140,7 +2140,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2148,7 +2148,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2156,7 +2156,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2240,7 +2240,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2261,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2282,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2303,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2324,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2604,23 +2604,24 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 74.581893 1.992315e-03\n", - "PAY_0 4250.691969 0.000000e+00\n", - "PAY_2 3793.454513 0.000000e+00\n", - "PAY_3 2943.420461 0.000000e+00\n", - "PAY_4 2997.799899 0.000000e+00\n", - "PAY_5 2809.793752 0.000000e+00\n", - "PAY_6 2384.699487 0.000000e+00\n", - "PAY_0_No_Transactions 75.255770 1.695234e-03\n", - "PAY_0_Revolving_Credit 481.798565 0.000000e+00\n", - "PAY_2_Pay_Duly 70.057810 5.666696e-03\n", - "PAY_2_Revolving_Credit 242.800821 0.000000e+00\n", - "PAY_3_Pay_Duly 83.050209 2.372483e-04\n", - "PAY_3_Revolving_Credit 133.546708 3.279310e-11\n", - "PAY_4_Pay_Duly 80.991396 4.056023e-04\n", - "PAY_4_Revolving_Credit 95.721673 6.943457e-06\n", - "PAY_5_Pay_Duly 81.026163 4.019845e-04\n", - "PAY_5_Revolving_Credit 63.051153 2.470371e-02\n" + "LIMIT_BAL 78.806194 7.074564e-04\n", + "PAY_0 4532.900073 0.000000e+00\n", + "PAY_2 3833.396843 0.000000e+00\n", + "PAY_3 2921.087799 0.000000e+00\n", + "PAY_4 2778.451579 0.000000e+00\n", + "PAY_5 2791.382213 0.000000e+00\n", + "PAY_6 2434.267474 0.000000e+00\n", + "PAY_0_No_Transactions 78.446517 7.742745e-04\n", + "PAY_0_Pay_Duly 59.486746 4.837585e-02\n", + "PAY_0_Revolving_Credit 483.224042 0.000000e+00\n", + "PAY_2_Pay_Duly 73.909964 2.336982e-03\n", + "PAY_2_Revolving_Credit 238.193174 0.000000e+00\n", + "PAY_3_Pay_Duly 74.057499 2.256805e-03\n", + "PAY_3_Revolving_Credit 128.025091 2.222005e-10\n", + "PAY_4_Pay_Duly 72.030496 3.623036e-03\n", + "PAY_4_Revolving_Credit 82.321461 2.872173e-04\n", + "PAY_5_Pay_Duly 69.393208 6.568060e-03\n", + "PAY_5_Revolving_Credit 61.028427 3.642149e-02\n" ] } ], @@ -2811,8 +2812,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13545\n", - " 1 1.00 1.00 1.00 3951\n", + " 0 1.00 1.00 1.00 13476\n", + " 1 1.00 1.00 1.00 4020\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2841,7 +2842,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (GINI) identified 857\n" + "Of 2021 Defaulters, the Decision Tree (GINI) identified 852\n" ] }, { @@ -2876,14 +2877,14 @@ " \n", " \n", " \n", - " 0\n", - " 5403\n", - " 1256\n", + " 0\n", + " 5395\n", + " 1333\n", " \n", " \n", - " 1\n", - " 1233\n", - " 857\n", + " 1\n", + " 1169\n", + " 852\n", " \n", " \n", "\n", @@ -2892,8 +2893,8 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5403 1256\n", - "1 1233 857" + "0 5395 1333\n", + "1 1169 852" ] }, "execution_count": 38, @@ -2919,7 +2920,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2945,12 +2946,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.81 0.81 0.81 6659\n", - " 1 0.41 0.41 0.41 2090\n", + " 0 0.82 0.80 0.81 6728\n", + " 1 0.39 0.42 0.41 2021\n", "\n", - " accuracy 0.72 8749\n", + " accuracy 0.71 8749\n", " macro avg 0.61 0.61 0.61 8749\n", - "weighted avg 0.72 0.72 0.72 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", "\n" ] } @@ -2969,14 +2970,14 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Entropy) identified 846\n" + "Of 2021 Defaulters, the Decision Tree (Entropy) identified 810\n" ] }, { @@ -3011,14 +3012,14 @@ " \n", " \n", " \n", - " 0\n", - " 5422\n", - " 1237\n", + " 0\n", + " 5447\n", + " 1281\n", " \n", " \n", - " 1\n", - " 1244\n", - " 846\n", + " 1\n", + " 1211\n", + " 810\n", " \n", " \n", "\n", @@ -3027,11 +3028,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5422 1237\n", - "1 1244 846" + "0 5447 1281\n", + "1 1211 810" ] }, - "execution_count": 125, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -3044,7 +3045,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3056,7 +3057,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3065,6 +3066,16 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6062870404745417" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -3073,7 +3084,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3082,11 +3093,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.81 0.81 0.81 6659\n", - " 1 0.41 0.40 0.41 2090\n", + " 0 0.82 0.81 0.81 6728\n", + " 1 0.39 0.40 0.39 2021\n", "\n", " accuracy 0.72 8749\n", - " macro avg 0.61 0.61 0.61 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] @@ -3105,7 +3116,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3132,7 +3143,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3142,7 +3153,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3157,7 +3168,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 140, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -3168,7 +3179,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3177,8 +3188,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13545\n", - " 1 1.00 1.00 1.00 3951\n", + " 0 1.00 1.00 1.00 13476\n", + " 1 1.00 1.00 1.00 4020\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3200,14 +3211,14 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Random Forest) identified 752\n" + "Of 2021 Defaulters, the Decision Tree (Random Forest) identified 763\n" ] }, { @@ -3242,14 +3253,14 @@ " \n", " \n", " \n", - " 0\n", - " 6273\n", - " 386\n", + " 0\n", + " 6307\n", + " 421\n", " \n", " \n", - " 1\n", - " 1338\n", - " 752\n", + " 1\n", + " 1258\n", + " 763\n", " \n", " \n", "\n", @@ -3258,11 +3269,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6273 386\n", - "1 1338 752" + "0 6307 421\n", + "1 1258 763" ] }, - "execution_count": 142, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -3273,19 +3284,19 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.25839285714285715\n" + "Optimal Threshold: 0.31\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3302,7 +3313,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -3311,12 +3322,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.94 0.88 6659\n", - " 1 0.66 0.36 0.47 2090\n", + " 0 0.83 0.94 0.88 6728\n", + " 1 0.64 0.38 0.48 2021\n", "\n", - " accuracy 0.80 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", - "weighted avg 0.79 0.80 0.78 8749\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -3327,7 +3338,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -3358,7 +3369,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -3376,7 +3387,7 @@ " warm_start=False)" ] }, - "execution_count": 49, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -3389,7 +3400,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -3398,11 +3409,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13545\n", - " 1 0.79 0.47 0.59 3951\n", + " 0 0.86 0.96 0.91 13476\n", + " 1 0.80 0.48 0.60 4020\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.72 0.75 17496\n", + " macro avg 0.83 0.72 0.75 17496\n", "weighted avg 0.85 0.85 0.84 17496\n", "\n" ] @@ -3421,14 +3432,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 730\n" + "Of 2021 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 764\n" ] }, { @@ -3463,14 +3474,14 @@ " \n", " \n", " \n", - " 0\n", - " 6290\n", - " 369\n", + " 0\n", + " 6335\n", + " 393\n", " \n", " \n", - " 1\n", - " 1360\n", - " 730\n", + " 1\n", + " 1257\n", + " 764\n", " \n", " \n", "\n", @@ -3479,11 +3490,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6290 369\n", - "1 1360 730" + "0 6335 393\n", + "1 1257 764" ] }, - "execution_count": 51, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3494,19 +3505,19 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.23302683158657422\n" + "Optimal Threshold: 0.22217593115970874\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3523,7 +3534,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -3532,12 +3543,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.94 0.88 6659\n", - " 1 0.66 0.35 0.46 2090\n", + " 0 0.83 0.94 0.88 6728\n", + " 1 0.66 0.38 0.48 2021\n", "\n", - " accuracy 0.80 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", - "weighted avg 0.78 0.80 0.78 8749\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -3548,7 +3559,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3566,7 +3577,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 58, "metadata": { "scrolled": true }, @@ -3599,35 +3610,35 @@ " \n", " \n", " \n", - " 0\n", - " Decision Tree\n", - " 0.410048\n", - " 0.610882\n", + " 0\n", + " Decision Tree (GINI)\n", + " 0.421573\n", + " 0.612897\n", " \n", " \n", - " 1\n", + " 1\n", " Random Forest\n", - " 0.363158\n", - " 0.767925\n", + " 0.377536\n", + " 0.762511\n", " \n", " \n", - " 2\n", + " 2\n", " Gradient Boosted\n", - " 0.349282\n", - " 0.775518\n", + " 0.378031\n", + " 0.774845\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree 0.410048 0.610882\n", - "1 Random Forest 0.363158 0.767925\n", - "2 Gradient Boosted 0.349282 0.775518" + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.421573 0.612897\n", + "1 Random Forest 0.377536 0.762511\n", + "2 Gradient Boosted 0.378031 0.774845" ] }, - "execution_count": 55, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -3668,7 +3679,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -3677,7 +3688,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -3685,7 +3696,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", + " Current function value: 0.443375\n", " Iterations 7\n" ] }, @@ -3704,13 +3715,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774 \n", "\n", "\n", - " Time: 23:40:29 Log-Likelihood: -7675.2\n", + " Time: 08:26:11 Log-Likelihood: -7757.3\n", "\n", "\n", - " converged: True LL-Null: -9346.0\n", + " converged: True LL-Null: -9430.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3721,136 +3732,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", + " LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", "\n", "\n", - " SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", + " SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", "\n", "\n", - " AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", + " AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", "\n", "\n", - " PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", + " PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", "\n", "\n", - " PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", + " PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", "\n", "\n", - " PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", + " PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", "\n", "\n", - " PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", + " PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", "\n", "\n", - " PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", + " PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", "\n", "\n", - " PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", + " PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", "\n", "\n", - " BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", + " BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", "\n", "\n", - " BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", + " BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", "\n", "\n", - " BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", + " BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", "\n", "\n", - " BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", + " BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", "\n", "\n", - " BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", + " BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", "\n", "\n", - " BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", + " BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", "\n", "\n", - " PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", + " PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", "\n", "\n", - " PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", + " PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", "\n", "\n", - " PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", + " PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", "\n", "\n", - " PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", + " PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", "\n", "\n", - " PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", + " PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", "\n", "\n", - " PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", + " PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", "\n", "\n", - " GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", + " GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", "\n", "\n", - " UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", + " UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", "\n", "\n", - " HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", + " HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", "\n", "\n", - " MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", + " MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", "\n", "\n", - " SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", + " SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", "\n", "\n", - " PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", + " PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", "\n", "\n", - " PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", + " PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", + " PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", "\n", "\n", - " PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", + " PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", + " PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", + " PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", "\n", "\n", - " PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", + " PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", "\n", "\n", - " PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", + " PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", + " PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", "\n", "\n", - " PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", + " PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", "\n", "\n", - " PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", + " PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", + " PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", "\n", "\n", - " PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", + " PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", "\n", "\n", - " PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", + " PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", + " PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", "\n", "\n", - " PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", + " PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", "\n", "\n", - " PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", + " PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + " PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", "\n", "" ], @@ -3862,62 +3873,62 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Tue, 19 Nov 2019 Pseudo R-squ.: 0.1788\n", - "Time: 23:40:29 Log-Likelihood: -7675.2\n", - "converged: True LL-Null: -9346.0\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774\n", + "Time: 08:26:11 Log-Likelihood: -7757.3\n", + "converged: True LL-Null: -9430.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.6669 0.115 -5.794 0.000 -0.893 -0.441\n", - "SEX -0.0998 0.041 -2.406 0.016 -0.181 -0.019\n", - "AGE 0.1518 0.101 1.503 0.133 -0.046 0.350\n", - "PAY_0 0.6132 0.058 10.606 0.000 0.500 0.727\n", - "PAY_2 -0.5521 0.097 -5.672 0.000 -0.743 -0.361\n", - "PAY_3 -0.1450 0.114 -1.272 0.203 -0.368 0.078\n", - "PAY_4 -0.2759 0.156 -1.766 0.077 -0.582 0.030\n", - "PAY_5 -0.2080 0.176 -1.183 0.237 -0.553 0.137\n", - "PAY_6 0.5380 0.155 3.481 0.000 0.235 0.841\n", - "BILL_AMT1 -0.7825 0.500 -1.566 0.117 -1.762 0.197\n", - "BILL_AMT2 -0.0258 0.755 -0.034 0.973 -1.507 1.455\n", - "BILL_AMT3 1.5494 0.749 2.068 0.039 0.081 3.018\n", - "BILL_AMT4 0.0813 0.733 0.111 0.912 -1.355 1.517\n", - "BILL_AMT5 0.0417 0.862 0.048 0.961 -1.648 1.731\n", - "BILL_AMT6 -0.1684 0.775 -0.217 0.828 -1.687 1.350\n", - "PAY_AMT1 -1.2954 0.322 -4.028 0.000 -1.926 -0.665\n", - "PAY_AMT2 -2.0115 0.395 -5.095 0.000 -2.785 -1.238\n", - "PAY_AMT3 -0.5552 0.308 -1.803 0.071 -1.159 0.048\n", - "PAY_AMT4 -0.5722 0.306 -1.872 0.061 -1.171 0.027\n", - "PAY_AMT5 -0.8231 0.297 -2.776 0.006 -1.404 -0.242\n", - "PAY_AMT6 -0.6911 0.270 -2.559 0.011 -1.220 -0.162\n", - "GRAD 1.4579 0.228 6.385 0.000 1.010 1.905\n", - "UNI 1.4595 0.227 6.428 0.000 1.015 1.904\n", - "HS 1.4009 0.231 6.076 0.000 0.949 1.853\n", - "MARRIED 0.1156 0.171 0.678 0.498 -0.219 0.450\n", - "SINGLE -0.0443 0.171 -0.259 0.796 -0.380 0.291\n", - "PAY_0_No_Transactions -0.0445 0.125 -0.356 0.722 -0.290 0.201\n", - "PAY_0_Pay_Duly 0.1583 0.121 1.303 0.193 -0.080 0.396\n", - "PAY_0_Revolving_Credit -0.9060 0.136 -6.684 0.000 -1.172 -0.640\n", - "PAY_2_No_Transactions -1.4969 0.233 -6.428 0.000 -1.953 -1.040\n", - "PAY_2_Pay_Duly -1.3561 0.224 -6.062 0.000 -1.795 -0.918\n", - "PAY_2_Revolving_Credit -0.8463 0.229 -3.700 0.000 -1.295 -0.398\n", - "PAY_3_No_Transactions -0.6681 0.278 -2.402 0.016 -1.213 -0.123\n", - "PAY_3_Pay_Duly -0.6530 0.253 -2.581 0.010 -1.149 -0.157\n", - "PAY_3_Revolving_Credit -0.6335 0.241 -2.627 0.009 -1.106 -0.161\n", - "PAY_4_No_Transactions -1.0250 0.354 -2.899 0.004 -1.718 -0.332\n", - "PAY_4_Pay_Duly -1.0151 0.334 -3.041 0.002 -1.670 -0.361\n", - "PAY_4_Revolving_Credit -0.9244 0.323 -2.859 0.004 -1.558 -0.291\n", - "PAY_5_No_Transactions -0.5952 0.392 -1.518 0.129 -1.364 0.173\n", - "PAY_5_Pay_Duly -0.8164 0.376 -2.171 0.030 -1.553 -0.079\n", - "PAY_5_Revolving_Credit -0.6476 0.366 -1.770 0.077 -1.365 0.070\n", - "PAY_6_No_Transactions 1.0133 0.340 2.984 0.003 0.348 1.679\n", - "PAY_6_Pay_Duly 0.9398 0.332 2.829 0.005 0.289 1.591\n", - "PAY_6_Revolving_Credit 0.7534 0.324 2.326 0.020 0.119 1.388\n", + "LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", + "SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", + "AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", + "PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", + "PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", + "PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", + "PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", + "PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", + "PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", + "BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", + "BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", + "BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", + "BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", + "BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", + "BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", + "PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", + "PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", + "PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", + "PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", + "PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", + "PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", + "GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", + "UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", + "HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", + "MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", + "SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", + "PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", + "PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", + "PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", + "PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", + "PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", + "PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", + "PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", + "PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", + "PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", + "PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", + "PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", + "PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", + "PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", + "PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", + "PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", + "PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", + "PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", + "PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 57, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -3929,7 +3940,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3938,12 +3949,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.95 0.89 13545\n", - " 1 0.67 0.37 0.47 3951\n", + " 0 0.83 0.95 0.89 13476\n", + " 1 0.67 0.36 0.47 4020\n", "\n", - " accuracy 0.82 17496\n", - " macro avg 0.75 0.66 0.68 17496\n", - "weighted avg 0.80 0.82 0.80 17496\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", "\n" ] } @@ -3961,7 +3972,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -3969,67 +3980,58 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438684\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.438685\n", + " Current function value: 0.443375\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438686\n", + " Current function value: 0.443376\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438689\n", + " Current function value: 0.443378\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438692\n", + " Current function value: 0.443381\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438733\n", + " Current function value: 0.443383\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438766\n", + " Current function value: 0.443392\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438815\n", + " Current function value: 0.443403\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438878\n", + " Current function value: 0.443417\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.438953\n", + " Current function value: 0.443435\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439049\n", + " Current function value: 0.443453\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439148\n", + " Current function value: 0.443472\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439242\n", + " Current function value: 0.443503\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439370\n", + " Current function value: 0.443533\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439484\n", + " Current function value: 0.443589\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439636\n", + " Current function value: 0.443651\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439750\n", + " Current function value: 0.443736\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.439934\n", + " Current function value: 0.443875\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440120\n", + " Current function value: 0.444048\n", " Iterations 7\n" ] }, @@ -4042,19 +4044,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17472 \n", + " Model: Logit Df Residuals: 17469 \n", "\n", "\n", - " Method: MLE Df Model: 23 \n", + " Method: MLE Df Model: 26 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761 \n", + " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762 \n", "\n", "\n", - " Time: 00:03:36 Log-Likelihood: -7700.3\n", + " Time: 08:26:12 Log-Likelihood: -7769.1\n", "\n", "\n", - " converged: True LL-Null: -9346.0\n", + " converged: True LL-Null: -9430.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4065,76 +4067,85 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", + " LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", + "\n", + "\n", + " SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", + "\n", + "\n", + " PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", + "\n", + "\n", + " PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", "\n", "\n", - " PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", + " PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", "\n", "\n", - " PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", + " PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", "\n", "\n", - " PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", + " BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", "\n", "\n", - " PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", + " BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", "\n", "\n", - " BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", + " PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", "\n", "\n", - " PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", + " PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", "\n", "\n", - " PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", + " PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", "\n", "\n", - " PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", + " PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", "\n", "\n", - " PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", + " GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", "\n", "\n", - " PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", + " UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", "\n", "\n", - " GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", + " HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", "\n", "\n", - " UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", + " MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", "\n", "\n", - " HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", + " PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", "\n", "\n", - " MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", + " PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", "\n", "\n", - " PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", + " PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", "\n", "\n", - " PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", + " PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", + " PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", + " PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", + " PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", "\n", "\n", - " PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", + " PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", "\n", "\n", - " PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", + " PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", + " PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", "\n", "\n", - " PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + " PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", "\n", "" ], @@ -4144,44 +4155,47 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17472\n", - "Method: MLE Df Model: 23\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1761\n", - "Time: 00:03:36 Log-Likelihood: -7700.3\n", - "converged: True LL-Null: -9346.0\n", + "Model: Logit Df Residuals: 17469\n", + "Method: MLE Df Model: 26\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762\n", + "Time: 08:26:12 Log-Likelihood: -7769.1\n", + "converged: True LL-Null: -9430.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.6990 0.113 -6.170 0.000 -0.921 -0.477\n", - "PAY_0 0.5690 0.037 15.303 0.000 0.496 0.642\n", - "PAY_2 -0.5810 0.079 -7.338 0.000 -0.736 -0.426\n", - "PAY_4 -0.2896 0.072 -4.034 0.000 -0.430 -0.149\n", - "PAY_6 0.2279 0.032 7.188 0.000 0.166 0.290\n", - "BILL_AMT3 0.6680 0.183 3.660 0.000 0.310 1.026\n", - "PAY_AMT1 -1.2364 0.295 -4.195 0.000 -1.814 -0.659\n", - "PAY_AMT2 -2.2214 0.369 -6.027 0.000 -2.944 -1.499\n", - "PAY_AMT4 -0.8717 0.278 -3.138 0.002 -1.416 -0.327\n", - "PAY_AMT5 -0.7532 0.266 -2.827 0.005 -1.275 -0.231\n", - "PAY_AMT6 -0.8021 0.268 -2.990 0.003 -1.328 -0.276\n", - "GRAD 1.4314 0.179 7.995 0.000 1.081 1.782\n", - "UNI 1.4280 0.178 8.036 0.000 1.080 1.776\n", - "HS 1.3923 0.182 7.637 0.000 1.035 1.750\n", - "MARRIED 0.1903 0.042 4.524 0.000 0.108 0.273\n", - "PAY_0_Revolving_Credit -1.0207 0.093 -10.994 0.000 -1.203 -0.839\n", - "PAY_2_No_Transactions -1.7729 0.187 -9.475 0.000 -2.140 -1.406\n", - "PAY_2_Pay_Duly -1.4833 0.176 -8.409 0.000 -1.829 -1.138\n", - "PAY_2_Revolving_Credit -0.9115 0.186 -4.911 0.000 -1.275 -0.548\n", - "PAY_3_Revolving_Credit -0.2508 0.075 -3.350 0.001 -0.398 -0.104\n", - "PAY_4_No_Transactions -1.2810 0.180 -7.101 0.000 -1.635 -0.927\n", - "PAY_4_Pay_Duly -1.3305 0.164 -8.104 0.000 -1.652 -1.009\n", - "PAY_4_Revolving_Credit -1.0886 0.157 -6.918 0.000 -1.397 -0.780\n", - "PAY_6_No_Transactions 0.2156 0.081 2.650 0.008 0.056 0.375\n", + "LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", + "SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", + "PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", + "PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", + "PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", + "PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", + "BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", + "BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", + "PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", + "PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", + "PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", + "PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", + "GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", + "UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", + "HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", + "MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", + "PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", + "PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", + "PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", + "PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", + "PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", + "PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", + "PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", + "PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", + "PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", + "PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", + "PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 109, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -4199,20 +4213,21 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "24 Columns left:\n", - "Index(['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT3',\n", - " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + "27 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions'],\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions'],\n", " dtype='object')\n" ] } @@ -4225,7 +4240,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -4234,12 +4249,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.95 0.88 6659\n", - " 1 0.68 0.34 0.46 2090\n", + " 0 0.83 0.95 0.89 6728\n", + " 1 0.67 0.36 0.47 2021\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.78 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -4259,19 +4274,19 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.21432968760597085\n" + "Optimal Threshold: 0.1993195782749319\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4284,10 +4299,10 @@ { "data": { "text/plain": [ - "0.7705999826781731" + "0.7718794071347034" ] }, - "execution_count": 108, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -4305,7 +4320,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 66, "metadata": { "scrolled": true }, @@ -4316,12 +4331,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.81 0.84 6659\n", - " 1 0.50 0.61 0.55 2090\n", + " 0 0.88 0.78 0.82 6728\n", + " 1 0.46 0.63 0.53 2021\n", "\n", - " accuracy 0.76 8749\n", - " macro avg 0.68 0.71 0.69 8749\n", - "weighted avg 0.78 0.76 0.77 8749\n", + " accuracy 0.74 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", + "weighted avg 0.78 0.74 0.76 8749\n", "\n" ] } @@ -4344,14 +4359,14 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Logistic Regression identified 1273\n" + "Of 2021 Defaulters, the Logistic Regression identified 1273\n" ] }, { @@ -4386,13 +4401,13 @@ " \n", " \n", " \n", - " 0\n", - " 5361\n", - " 1298\n", + " 0\n", + " 5240\n", + " 1488\n", " \n", " \n", - " 1\n", - " 817\n", + " 1\n", + " 748\n", " 1273\n", " \n", " \n", @@ -4402,11 +4417,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5361 1298\n", - "1 817 1273" + "0 5240 1488\n", + "1 748 1273" ] }, - "execution_count": 115, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -4417,14 +4432,14 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the Logistic Regression identified 714\n" + "Of 2021 Defaulters, the Logistic Regression identified 720\n" ] }, { @@ -4459,14 +4474,14 @@ " \n", " \n", " \n", - " 0\n", - " 6329\n", - " 330\n", + " 0\n", + " 6377\n", + " 351\n", " \n", " \n", - " 1\n", - " 1376\n", - " 714\n", + " 1\n", + " 1301\n", + " 720\n", " \n", " \n", "\n", @@ -4475,11 +4490,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6329 330\n", - "1 1376 714" + "0 6377 351\n", + "1 1301 720" ] }, - "execution_count": 116, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4497,19 +4512,19 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2096696069036731\n" + "Optimal Threshold: 0.2091273384286262\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4526,7 +4541,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -4684,243 +4699,278 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SVM is computationally expensive for a dataset with a lot of features. Therefore, it is neccessary at this stage to do some data reduction." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### PCA\n", - "We would like to reduce the dimensionality of our dataset before training an SVM on it. This can be done through Principle Component Analysis (PCA). The idea would be to reduce the number of features without loss of information." + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2 Component PCA\n", - "First, we will visualize the information retained after performing a 2 component pca." + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#perform pca\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=2)\n", - "principalComponents = pca.fit_transform(X_train)\n", - "principalDf = pd.DataFrame(data = principalComponents\n", - " , columns = ['principal component 1', 'principal component 2'])" + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Explained variation per principal component: [0.295060096 0.1735291836]\n" + "Optimal Threshold: 0.16372570606114417\n" ] - } - ], - "source": [ - "#amount of information each principal component holds after projecting the data to a lower dimensional subspace.\n", - "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This shows that the information of principal component 1 retained is 28.4% and principal component 2 retained is 17.8%, both of which is quite low" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ + }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7264392577614385" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "#visualizing pca\n", - "pca_visualize_df = pd.concat([principalDf, y_train], axis = 1)\n", - "fig = plt.figure(figsize = (8,8))\n", - "ax = fig.add_subplot(1,1,1) \n", - "ax.set_xlabel('Principal Component 1', fontsize = 15)\n", - "ax.set_ylabel('Principal Component 2', fontsize = 15)\n", - "ax.set_title('2 component PCA', fontsize = 20)\n", - "targets = [1,0]\n", - "colors = ['r', 'g']\n", - "for target, color in zip(targets,colors):\n", - " indicesToKeep = pca_visualize_df['Y'] == target\n", - " ax.scatter(pca_visualize_df.loc[indicesToKeep, 'principal component 1']\n", - " , pca_visualize_df.loc[indicesToKeep, 'principal component 2']\n", - " , c = color\n", - " , s = 50)\n", - "ax.legend(targets)\n", - "ax.grid()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, there is no clear linear separation for the target attributes for 2 pca components, justifying the above percentages. Nonetherless, we will continue to use PCA by finding the optimmum number of PC components which retains 90% of information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform PCA to retain 90% of information\n", - "perform PCA to reduce components so we can run SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "#setting pca threshold to 90%\n", - "pca = PCA(0.9)" + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 37, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2009 Defaulters, the SVM default parameters identified 739\n" + ] + }, { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06382358
11270739
\n", + "
" + ], "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=0.9, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" + "Predicted 0 1\n", + "Actual \n", + "0 6382 358\n", + "1 1270 739" ] }, - "execution_count": 73, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pca.fit(X_train)" + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No. of components before pca: 44\n", - "No. of components after pca: 13\n" + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6740\n", + " 1 0.67 0.37 0.48 2009\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" ] } ], "source": [ - "#get number of components after pca\n", - "print('No. of components before pca: {}'.format(len(X_train.columns)))\n", - "print('No. of components after pca: {}'.format(pca.n_components_))" + "print(classification_report(y_test, clf_original.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the number of components is reduced from 26 components to 13 components" + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." ] }, { - "cell_type": "code", - "execution_count": 75, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#perform pca on training and test attributes\n", - "X_train_pca = pca.transform(X_train)\n", - "X_test_pca = pca.transform(X_test)" + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 48, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.001}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Apply SVM model\n", - "Next, we will used the reduced attributes train set to train our SVM model" + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001,0.01, 0.1, 1]\n", + " gammas = [0.001, 0.01, 0.1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, we train our SVM model without parameter tuning\n", - "nor pca reduction" + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn import svm\n", - "#train svm model without standardization and no parameter tuning\n", - "clf_original = svm.SVC(kernel = 'rbf', probability = True, gamma = 'scale')\n", - "clf_original.fit(X_train, y_train)" + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15585444961401423\n" + "Optimal Threshold: 0.15927524092941694\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4929,33 +4979,23 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7192717558206292" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "#plot roc for svm\n", - "get_roc(clf_original, y_test, X_test, \"SVM original data RBF kernel\")" + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning RBF kernel\")" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2090 Defaulters, the SVM original data RBF kernel identified 749\n" + "Of 2009 Defaulters, the SVM reduced features and tuning RBF kernal identified 671\n" ] }, { @@ -4990,14 +5030,14 @@ " \n", " \n", " \n", - " 0\n", - " 6304\n", - " 355\n", + " 0\n", + " 6437\n", + " 303\n", " \n", " \n", - " 1\n", - " 1341\n", - " 749\n", + " 1\n", + " 1338\n", + " 671\n", " \n", " \n", "\n", @@ -5006,23 +5046,23 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6304 355\n", - "1 1341 749" + "0 6437 303\n", + "1 1338 671" ] }, - "execution_count": 78, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#confusion matrix\n", - "confusion(y_test,clf_original.predict(X_test), \"SVM original data RBF kernel\")" + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernal\")" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -5031,166 +5071,18 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.95 0.88 6659\n", - " 1 0.68 0.36 0.47 2090\n", + " 0 0.83 0.96 0.89 6740\n", + " 1 0.69 0.33 0.45 2009\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.78 8749\n", + " macro avg 0.76 0.64 0.67 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test, clf_original.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Evidently, SVM model fit with no tuning or feature reduction with RBF kernal shows low performance. Now, we will fit SVM model with reduced standardized features and access accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "note that the default values of gamma = 1/13 and c= 1" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#train svm model with feature reduction and no parameter tuning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mclf_reduced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'rbf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobability\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclf_reduced\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_pca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 210\u001b[0m \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 267\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m 269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "#train svm model with feature reduction and no parameter tuning\n", - "clf_reduced = svm.SVC(kernel = 'rbf', probability = True, gamma = 1/13, C = 1)\n", - "clf_reduced.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_reduced, y_test, X_test_pca, \n", - " \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced.predict(X_test_pca), \"SVM reduced features no tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test, clf_reduced.predict(X_test_pca)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, by reducing features through pca, Although the AUROC did not change much (0.001 increase), the number of correctly idendtified defaulters increased from 297 to 307, suggesting a better recall." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now try to find best parameters for SVM model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001, 0.01, 0.1, 1,10]\n", - " gammas = [0.001, 0.01, 0.1, 10]\n", - " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)\n", - " grid_search.fit(X, y)\n", - " grid_search.best_params_\n", - " return grid_search.best_params_\n", - "svc_param_selection(X_train_pca, y_train,5)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, )\n", - "clf_reduced_tuned.fit(X_train_pca, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(clf_reduced_tuned, y_test, X_test_pca, \n", - " \"SVM reduced features and tuning RBF kernel\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test_pca), \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test, clf_reduced_tuned.predict(X_test_pca)))" + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" ] }, { @@ -5243,6 +5135,21 @@ "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parameter tuning\n", + "##### Learning rate\n", + "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", + "\n", + "##### Momentum\n", + "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", + "\n", + "##### Loss function\n", + "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." + ] + }, { "cell_type": "code", "execution_count": null, From dee5f761697509fe6c4d007bcb0d5d2f1f5f32e7 Mon Sep 17 00:00:00 2001 From: reon Date: Thu, 21 Nov 2019 00:13:46 +0800 Subject: [PATCH 19/25] check svm --- .../BT2101 - FINAL--checkpoint.ipynb | 5584 ++++++++++++++++ ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 5588 +++++++++++++++++ ...101_Default - EDIT-MASTER-checkpoint.ipynb | 5304 ---------------- BT2101 - FINAL-.ipynb | 5584 ++++++++++++++++ BT2101_Default - EDIT-MASTER- Reon.ipynb | 5588 +++++++++++++++++ BT2101_Default - EDIT-MASTER.ipynb | 5304 ---------------- 6 files changed, 22344 insertions(+), 10608 deletions(-) create mode 100644 .ipynb_checkpoints/BT2101 - FINAL--checkpoint.ipynb create mode 100644 .ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb delete mode 100644 .ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb create mode 100644 BT2101 - FINAL-.ipynb create mode 100644 BT2101_Default - EDIT-MASTER- Reon.ipynb delete mode 100644 BT2101_Default - EDIT-MASTER.ipynb diff --git a/.ipynb_checkpoints/BT2101 - FINAL--checkpoint.ipynb b/.ipynb_checkpoints/BT2101 - FINAL--checkpoint.ipynb new file mode 100644 index 0000000..312a64d --- /dev/null +++ b/.ipynb_checkpoints/BT2101 - FINAL--checkpoint.ipynb @@ -0,0 +1,5584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAAEYCAYAAADWNhiqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbAcdZ3v8ffH8CAbxCRGTsUka8KaukuEe4XkQly3ds+KhIBVN9xSVijKnKvczRbCqrVqbVzrXlZRL24V7hV0paJkSdisiE+buEtuNmKOSJU8KiSEyOYEoxwTiBgIHHyAsN/7R/9GO8PMmYfzMPOb83lVdU3Pt3/d/euZ7+nvTHefHkUEZmZmOXtZpztgZmY2Vi5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2pkwxk7RP0i8ljUh6QtI/SDqxNP0mSUckvaYUOze1nV2KHS9pt6Q/b2Kd09P6bqvTn+fLy07xBySFpAWStqT5RyS9kNpXnt8g6ThJX03LCkn9bb9ANqoezZ9lkrZJOiTpZ5K+ImlO+6+S1dKjubNY0n2SnkrDtyQtbv9VGgcRMSUGYB/wljQ+F3gIuCY9nw48C/wc+FDVfDcCG0vPrwa+DaiJdQ6kZR4B5tTozyPAX5Rip6dYAAuq2t8EfLwqdhzwfuAPgQNAf6df514dejR/zgcuAk4CfgdYB/y/Tr/WvTb0aO7MABYAAqYB7wV2dPJ1njLfzMoi4qfAFuC0FHob8DTwMYokKPtL4I8lvVXSacCVwJ9FekcbGABuAHYAl9aYfjOwqqr9hha24/mI+L8RcSfwYrPz2dj0UP5siYivRMQzEfEL4LPAm5qd31rXQ7nzdETsS30Rxf7ndc3OPxGmZDGTNB+4APhBCg0AXwJuAX5f0pmVthFxGLicIjHWAR+NiL1NrON3gX5gYxpW1Wh2F3CSpFMlTQPeAfxjm5tlk6SH8+ePgF1jmN8a6LXckfQ08CvgeuCTrc4/nqZaMfvn9OLfCXwH+GR64/8E+KeIeAK4napPSBHxTYo3/2XAdU2uaxXF1+6HKZL19ZLOqNGu8gnpXOCHwE9b3iqbLD2bP5L+M/C/gQ+1M7811JO5ExEzgFdSfGv8QYPmE+qYTq68Ay6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJGr1G34zcAewkBa+5ltH9GT+SHodxaGv90XEd9tZhjXUk7mT1vGcpBuAn0k6NSIOtrussZhq38xqWQWcIulxSY8DnwZmU5wcb4ukPwAWAR8uLfds4BJJR32AiIgfAz+iOPTw9XbXaR2Tdf5Iei3wLeDqiLi53T5bW7LOnSovo7iIaO4Yl9O2qfbN7CiS3gj8HnAG8LPSpGspPslsbnPRA8A2jj5WfQLFydjzgW9Wtb8MmJk+4bT0nkg6nuIELMBxkl5O8SnOP1Q3wXLPH0lzKa6O+1xE3NBmX60NPZA75wJPpuVOBz4OPAXsbrPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAlMx+VNgVUQ8XjXt5rTOoxKqmZO6o3gEeG0a35oeF1JcfmsTK/f8+Z/AKcBVkq4qLe/E+rPYOMk9d2ZQXPQxD/glcC+wIiJ+1ebyxkz+AG9mZrnzOTMzM8uei1mbJF1aur1LefD/6VhDzh9rl3OnNh9mNDOz7GV7Acjs2bNjwYIFADz33HNMnz69sx1qUTf2+f77738yIl7d6X5MtHLuQHe+F83opn47d7pfN/d1XPKn0c0bgZcD9wAPUvzz3kdTfCFwN7AH+DJwXIofn54PpekLSsv6cIo/ApxXiq9IsSFgTTM3lVyyZElUbN++PXLTjX0G7ovxvcFq1+dORHe+F83opn6Pd+5El+ZPzrnTzX0dj/xp5pzZr4E3R8R/Ad4ArJC0DPgU8HcRsYji/wsuS+0vA56KiNcBf5fakX4e4GLg9SmB/l7StHRfsM9R/A/EYop/7uvsTwnYeHHu2Fg4f6xpDYtZKpwj6emxaQjgzcBXU3w9cGEaX5mek6afI0kpfktE/DoifkTxSeisNAxFxKMR8TzFDTdXjnnLrOOcOzYWzh9rRVPnzNInmPspbvH/OWAv8HREHElNhvntbUzmAo8BRMQRSYeBV6X4XaXFlud5rCp+dp1+rAZWA/T19TE4OAjAyMjIb8ZzkWOf29EtuWN56ob8qbffgbz+jnPqazuaKmYR8SLwBkkzgG8Ap9Zqlh5VZ1q9eK1vhzUvsYyItcBagKVLl0Z/fz8A12/cxLV3Ple3//uueWvdaZ0yODhIpf+9rFtyZ7Qd0sFDh7l+46Y6WwCnz31l3Wmd1Os7J+iO/Km334G89j29vs9p6WrGiHha0iCwDJgh6Zj0CWkesD81GwbmA8PpXl+vBA6V4hXleerFrUd0Onca7pB21v9T2Hdpf91pndTrO6eyTuePdb+G58wkvTp9KkLSCcBbKG4muR14e2o2AFQ+2m7mt7/J83bg2+lqlc3AxZKOl7SQ4s7O91Dc02uRpIWSjqM4UdvuTTatizh3bCycP9aKZr6ZzQHWp2PXLwNujYh/kfQwcIukj1P8Ts6Nqf2NwM2Shig+FV0MEBG7JN0KPAwcAa5IhxCQdCXFTXKnAesiYkr/J3sPce7YWDh/rGkNi1lE7KD4mYLq+KMUVwNVx38FXFRnWZ8APlEjfhtwWxP9tYw4d2wsnD/WCt+b0czMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsNi5mk+ZK2S9otaZek96X4LEnbJO1JjzNTXJKukzQkaYekM0vLGkjt90gaKMWXSNqZ5rlOkiZiY80sH973WCua+WZ2BPhARJwKLAOukLQYWAPcHhGLgNvTc4DzgUVpWA18HooEBK4CzgbOAq6qJGFqs7o034qxb5p1mndGNkbe91jTGhaziDgQEd9P488Cu4G5wEpgfWq2Hrgwja8ENkThLmCGpDnAecC2iDgUEU8B24AVadpJEfG9iAhgQ2lZljfvjKxt3vdYK1o6ZyZpAXAGcDfQFxEHoEg64OTUbC7wWGm24RQbLT5cI26Z887Ixov3PdbIMc02lHQi8DXg/RHxzChHc2pNiDbitfqwmuJTOH19fQwODgLQdwJ84PQjdfteaddNRkZGurJfE2W0nZGkCd8Z1csdyDN/YOrkUKf3Pb2SO72eL00VM0nHUiTTxoj4ego/IWlO2hnNAQ6m+DAwvzT7PGB/ivdXxQdTfF6N9i8REWuBtQBLly6N/v5icddv3MS1O+tvyr5L++tO65TBwUEq/e91nd4ZQf3cgTzzB6ZGDnXDvqdXcqfX86WZqxkF3AjsjohPlyZtBion4geATaX4qnQyfxlwOH0K3woslzQzne9YDmxN056VtCyta1VpWZa50XZGaXqzO6N68aY+CFl+vO+xVjRzzuxNwDuBN0t6IA0XANcA50raA5ybngPcBjwKDAFfAN4DEBGHgKuBe9PwsRQDuBz4YppnL7BlHLbNOsw7Ixsj73usaQ0PM0bEndQ+nANwTo32AVxRZ1nrgHU14vcBpzXqi2WnsjPaKemBFPtrip3PrZIuA34CXJSm3QZcQLFj+QXwLih2RpIqOyN46c7oJuAEih2Rd0Y9wvsea0XTF4CYtco7IzObLL6dlZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfYaFjNJ6yQdlPRQKTZL0jZJe9LjzBSXpOskDUnaIenM0jwDqf0eSQOl+BJJO9M810nSeG+kdY7zx9rl3LFWNPPN7CZgRVVsDXB7RCwCbk/PAc4HFqVhNfB5KBIQuAo4GzgLuKqShKnN6tJ81euyvN2E88facxPOHWtSw2IWEXcAh6rCK4H1aXw9cGEpviEKdwEzJM0BzgO2RcShiHgK2AasSNNOiojvRUQAG0rLsh7g/LF2OXesFce0OV9fRBwAiIgDkk5O8bnAY6V2wyk2Wny4RrwmSaspPknR19fH4OBg0ZkT4AOnH6nb2Uq7bjIyMtKV/Zokk54/9XIH8swfmLI55NxpU6/nS7vFrJ5ax5yjjXhNEbEWWAuwdOnS6O/vB+D6jZu4dmf9Tdl3aX/daZ0yODhIpf/2GxOWP/VyB/LMH3AOVXHuNNDr+dLu1YxPpK/ppMeDKT4MzC+1mwfsbxCfVyNuvc35Y+1y7lhN7RazzUDlqqABYFMpvipdWbQMOJwOCWwFlkuamU6+Lge2pmnPSlqWriRaVVqW9S7nj7XLuWM1NTzMKOlLQD8wW9IwxZVB1wC3SroM+AlwUWp+G3ABMAT8AngXQEQcknQ1cG9q97GIqJzYvZziqqUTgC1psB7h/LF2OXesFQ2LWURcUmfSOTXaBnBFneWsA9bViN8HnNaoH5Yn54+1y7ljrfAdQMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLXNcVM0gpJj0gakrSm0/2xfDh3bCycP72hK4qZpGnA54DzgcXAJZIWd7ZXlgPnjo2F86d3dEUxA84ChiLi0Yh4HrgFWNnhPlkenDs2Fs6fHnFMpzuQzAUeKz0fBs6ubiRpNbA6PR2R9Eganw08WW/h+tQ49XJ8jdrnDnltpzvQhrHmDuSZP9BdOZRj7kAT+dNDudNN+VJtzPnTLcVMNWLxkkDEWmDtS2aW7ouIpRPRsYmSY5+71JhyB/J9L3Ltd5dpmD+9kjs59bUd3XKYcRiYX3o+D9jfob5YXpw7NhbOnx7RLcXsXmCRpIWSjgMuBjZ3uE+WB+eOjYXzp0d0xWHGiDgi6UpgKzANWBcRu1pYRM1DAF0uxz53nXHIHcj3vci1311jiu17cupryxTxktMLZmZmWemWw4xmZmZtczEzM7PsZV/MOnErGkn7JO2U9ICk+1JslqRtkvakx5kpLknXpf7tkHRmaTkDqf0eSQOl+JK0/KE0r0Zbh7VnMnNH0jpJByU9VIp1LGdGW4c1Z5Lzx/ucRiIi24HihO1e4BTgOOBBYPEkrHcfMLsq9rfAmjS+BvhUGr8A2ELx/yzLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHR66P3eAPwLOBB7qhpyptw4PXZs/3uc0GHL/ZtZNt6JZCaxP4+uBC0vxDVG4C5ghaQ5wHrAtIg5FxFPANmBFmnZSRHwvigzaULWsWuuw1k1q7kTEHcChqnAnc6beOqw53bDv8T6nJPdiVutWNHMnYb0B/Juk+1Xc6gagLyIOAKTHkxv0cbT4cI34aOuw1nUqd8o6mTPdsP05m+zXz/ucBrri/8zGoKlbGU2AN0XEfkknA9sk/XCUtvX62Grcxlc3v86TkTPdvP05mOzXz/ucBnL/ZtaRW9FExP70eBD4BsUhhycqh2nS48EGfRwtPq9GnFHWYa3rhtsYdTJnumH7czapr5/3OY3lXswm/VY0kqZLekVlHFgOPJTWW7k6aADYlMY3A6vSFUbLgMPp6/pWYLmkmekKoeXA1jTtWUnL0hVFq6qWVWsd1rpuuI1RJ3Om3jqsOZOWP97nNKnTV6CMdaC4cuffKa4s+sgkrO8UiiuXHgR2VdYJvAq4HdiTHmeluCh+/G8vsBNYWlrWu4GhNLyrFF9Kkax7gc/y2zu11FyHh+7PHeBLwAHgBYpPwpd1MmdGW4eH7sof73OaG3w7KzMzy17uhxnNzMxczMzMLH8uZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyyN2WKmaR9kn4paUTSE5L+QdKJpek3SToi6TWl2Lmp7exS7HhJuyX9eRPrnJ7Wd1ud/jxfXnaKPyApJC2QtCXNPyLphdS+8vyGqvmuSvO9pdXXxhrrxfxJbaIUG5H0v9p/layWXsyd1P53JP29pCclHZZ0R7uv0bjo9A+qTeIP6e0D3pLG51L8EN016fl04Fng58CHqua7EdhYen418G3Sj9c1WOdAWuYRYE6N/jwC/EUpdnqKBbCgqv1NwMfrrOf3KH6Eb39lGz04fxrlD7AgtT2m069vLw+9mDsp/o/ALcCrgWnAkk6+zlPmm1lZRPwU2AKclkJvA54GPsZvfyK84i+BP5b0VkmnAVcCfxbp3WxgALgB2AFcWmP6zRQ/UV5uv6HZ7Sj5LPBXwPNtzGst6sH8sUnSK7kj6T8B/w1YHRE/i4gXI+L+ZuefCFOymEmaT/GT5z9IoQGKn7W/Bfh9SWdW2kbEYeByisRYB3w0IvY2sY7fBfqBjWlYVaPZXcBJkk6VNA14B8WnnVa25SLg+Yh4yeEEmxi9lD/JjyUNp8Nfsxs3t3b1UO6cDfwY+Gg6zLhT0ttamH/cTbVi9s+SngbuBL4DfDK98X8C/FNEPAHcTtUnpIj4JsWb/zLguibXtQrYEREPUyTr6yWdUaNd5RPSucAPgZ82uzHpuPsngfc3O4+NSU/lD/Ak8F+B1wJLgFdQ7Pxs/PVa7syj+HZ5GHgNxbfG9ZJObWEZ4+qYTq24Qy6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJOoPqtrdDNwBLKT1Q0QfBW6OiB+1OJ+1p6fyJyJGgPvS0yckXQkckHRSRDzTyrKsoZ7KHeCXwAsU59KOAN+RtB1YDuxucVnjYqp9M6tlFXCKpMclPQ58GpgNnN/uAiX9AbAI+HBpuWcDl0g66gNERPwY+BHFoYevt7iqc4D3ltYxH7hV0l+123drWc75U61yLkZjXI41J+fc2dFuHyfKVPtmdhRJb6S4EvAM4GelSddSfJLZ3OaiB4BtHH2s+gSKBDgf+GZV+8uAmRHxXHXCNXAOcGzp+b0UJ423tNxja1nu+SPpbIqLD/YAMykOYw2mczU2gXLPHYpvdD+hKJr/h6Jg9gMfarPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAiW9HPhTYFVEPF417ea0zqMSqpmTurVExM+rlv8i8FQ6fGQTL+v8AU6hOOd6MvAMxU7wkjaXZa3JOnci4gVJK4EvAmsoLgZZFRE/bGd540HNXeVpZmbWvXzOzMzMsudi1iZJl+ro2wBVhl2d7pt1P+ePtcu5U5sPM5qZWfYaXgCSTireARyf2n81Iq6StJDiv9ZnAd8H3hkRz0s6nuJ/FpZQ3BvsHRGxLy3rwxRXz7wIvDcitqb4CuAzFPf3+mJEXNOoX7Nnz44FCxYA8NxzzzF9+vQWNrt3jOe233///U9GxKvHZWHkkTuQZ/50W5/HO3egO/OnF3KnExq9TuOSP41u3kjxPycnpvFjgbuBZcCtwMUpfgNweRp/D3BDGr8Y+HIaXww8SJGYC4G9FAk0LY2fAhyX2ixu1K8lS5ZExfbt22OqGs9tB+6L8b3Batfnzni/hpOl2/o83rkTXZo/vZA7ndDodRqP/Gl4ziytq3Kp97FpCODNwFdTfD1wYRpfmZ6Tpp8jSSl+S0T8Ooo7VgwBZ6VhKCIejYjnKT5xrWzUL+t+zh0bC+ePtaKp/zNLN6K8H3gd8DmKTzNPR3EbE4Bhip82ID0+BhARRyQdBl6V4neVFlue57Gq+Nktb4l1pW7JHUmrgdUAfX19DA4O/mbayMjIUc9zkGOf29EN+dNrudMJk/E6NVXMIuJF4A2SZgDfAGrdTHK0W+HEKPFa3w5rXpVSL6mmckJ1+7Z3S+5ExFpgLcDSpUujv7//N9MGBwcpP89Bjn1uRzfkT6/lTidMxuvU0h1AIuJpSYMUx61nSDomfUKaR/HDkFB8upkPDKfbo7wSOFSKV5TnqRevXn/NpLp+4yauvfO5uv3ed81bm97G3OTyx9Tp3BnNzp8e5n+s+de603s5f3LRrfnj3OkeDc+ZSXp1+lSEpBOAt1DcFXk78PbUbADYlMY389ufMXg78O10gm8zcLGKn/5eSHEzzHso7ie4SNJCScdRnLht975k1kWcOzYWzh9rRTPfzOZQ/E7NNIrid2tE/Iukh4FbJH2c4qcFbkztbwRuljRE8anoYoCI2CXpVuBhip/yviIdQkDFT09spbi6aF1ETOl//ushzh0bC+ePNa1hMYuIHRR3dq6OP0pxNVB1/FfARXWW9QngEzXitwH+peQe49yxsXD+WCt8OyszM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZjZhJE0X9J2Sbsl7ZL0vhSfJWmbpD3pcWaKS9J1koYk7ZB0ZmlZA6n9HkkDpfgSSTvTPNdJ0uRvqU0E54+1omExc0LZGBwBPhARpwLLgCskLQbWALdHxCLg9vQc4HxgURpWA5+HIteAq4CzgbOAqyr5ltqsLs23YhK2yyaH88ea1sw3MyeUtSUiDkTE99P4s8BuYC6wElifmq0HLkzjK4ENUbgLmCFpDnAesC0iDkXEU8A2YEWadlJEfC8iAthQWpZlzvljrWhYzJxQNh4kLQDOAO4G+iLiABT5BZycms0FHivNNpxio8WHa8Stxzh/rJFjWmk8WkJJmvCEkrSa4hscfX19DA4OAtB3Anzg9CN1+11p14tGRka6fvsknQh8DXh/RDwzylHkWhOijXitPtTMHcgzf3J438dLp/On13KnEyYjX5suZp1OKICIWAusBVi6dGn09/cDcP3GTVy7s/6m7Lu0v+603A0ODlJ5HbqRpGMp8mZjRHw9hZ+QNCd9CJoDHEzxYWB+afZ5wP4U76+KD6b4vBrtX6Je7kCe+dPt7/t46Yb86bXc6YTJyNemrmYcLaHS9GYTql68qR2S5SVdyHMjsDsiPl2atBmoXAA0AGwqxVeli4iWAYfTt/+twHJJM9N51uXA1jTtWUnL0rpWlZZlmXP+WCuauZrRCWXtehPwTuDNkh5IwwXANcC5kvYA56bnALcBjwJDwBeA9wBExCHgauDeNHwsxQAuB76Y5tkLbJmMDbNJ4fyxpjVzmLGSUDslPZBif02RQLdKugz4CXBRmnYbcAFFcvwCeBcUCSWpklDw0oS6CTiBIpmcUD0gIu6k9mFkgHNqtA/gijrLWgesqxG/DzhtDN20LuX8sVY0LGZOKDMz63a+A4iZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2GhYzSeskHZT0UCk2S9I2SXvS48wUl6TrJA1J2iHpzNI8A6n9HkkDpfgSSTvTPNdJ0nhvpHWO88fa5dyxVjTzzewmYEVVbA1we0QsAm5PzwHOBxalYTXweSgSELgKOBs4C7iqkoSpzerSfNXrsrzdhPPH2nMTzh1rUsNiFhF3AIeqwiuB9Wl8PXBhKb4hCncBMyTNAc4DtkXEoYh4CtgGrEjTToqI70VEABtKy7Ie4Pyxdjl3rBXHtDlfX0QcAIiIA5JOTvG5wGOldsMpNlp8uEa8JkmrKT5J0dfXx+DgYNGZE+ADpx+p29lKu140MjKS4/ZNev7Uyx3IM38yfd/Hg3MnQ5ORr+0Ws3pqHXOONuI1RcRaYC3A0qVLo7+/H4DrN27i2p31N2Xfpf11p+VucHCQyuvQAyYsf+rlDuSZPz32vo8H504Xm4x8bfdqxifS13TS48EUHwbml9rNA/Y3iM+rEbfe5vyxdjl3rKZ2i9lmoHJV0ACwqRRfla4sWgYcTocEtgLLJc1MJ1+XA1vTtGclLUtXEq0qLct6l/PH2uXcsZoaHmaU9CWgH5gtaZjiyqBrgFslXQb8BLgoNb8NuAAYAn4BvAsgIg5Juhq4N7X7WERUTuxeTnHV0gnAljRYj3D+WLucO9aKhsUsIi6pM+mcGm0DuKLOctYB62rE7wNOa9QPy5Pzx9rl3LFW+A4gZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7HVNMZO0QtIjkoYkrel0fywfzh0bC+dPb+iKYiZpGvA54HxgMXCJpMWd7ZXlwLljY+H86R3HdLoDyVnAUEQ8CiDpFmAl8PB4LHzBmn8ddfq+a946HquxzpjQ3IHG+TMa51bX876nR3RLMZsLPFZ6PgycXd1I0mpgdXo6IumRND4beLLdletT7c7ZFca07VVeO07LmUxjzR0Y39fw6PVOXG5NWJ/blGPuQBP5M5G5k/m+pxWNXqcx50+3FDPViMVLAhFrgbUvmVm6LyKWTkTHut1U3vZkTLkDeb6GOfa5SzXMn17LnU6YjNepK86ZUXwaml96Pg/Y36G+WF6cOzYWzp8e0S3F7F5gkaSFko4DLgY2d7hPlgfnjo2F86dHdMVhxog4IulKYCswDVgXEbtaWETNQwBTxFTe9vHIHcjzNcyxz13H+55JM+GvkyJecnrBzMwsK91ymNHMzIT8XAIAAAPVSURBVKxtLmZmZpa97ItZzreikbRP0k5JD0i6L8VmSdomaU96nJniknRd2s4dks4sLWcgtd8jaaAUX5KWP5Tm1WjrmGo6kTuS5kvaLmm3pF2S3pfifyPppykXHpB0QWmeD6c+PiLpvEb9Txcz3J3e3y+nCxuQdHx6PpSmL5iMbe5VOe97WpHNfioish0oTtjuBU4BjgMeBBZ3ul8t9H8fMLsq9rfAmjS+BvhUGr8A2ELxfzHLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHVNp6FTuAHOAM9P4K4B/p7iN0t8AH6zRfnHq2/HAwtTnaaP1H7gVuDiN3wBcnsbfA9yQxi8Gvtzp9yHXIfd9T4vbmsV+KvdvZr+5FU1EPA9UbkWTs5XA+jS+HriwFN8QhbuAGZLmAOcB2yLiUEQ8BWwDVqRpJ0XE96LIhg1Vy6q1jqmkI7kTEQci4vtp/FlgN8VdKOpZCdwSEb+OiB8BQxR9r9n/9Kn2zcBX0/zVOVR5378KnFP5FGwt68V9Tyu6bj+VezGrdSua0XYM3SaAf5N0v4pb5gD0RcQBKHZ8wMkpXm9bR4sP14iPto6ppOO5kw7znQHcnUJXpkMz60qHVFp9318FPB0RR6riRy0rTT+c2lvrOp4/kyiL/VRX/J/ZGDR1K6Mu9qaI2C/pZGCbpB+O0rbetrYat0JHXx9JJwJfA94fEc9I+jxwderD1cC1wLtH6WetD6KN3nfnxPiZSq9lFvup3L+ZZX0rmojYnx4PAt+gOHTxRPrqTXo8mJrX29bR4vNqxBllHVNJx3JH0rEUhWxjRHwdICKeiIgXI+I/gC9Q5MJo/awXf5Li0M4xVfGjlpWmvxI4NL5bN2Vkve9pRS77qdyLWba3opE0XdIrKuPAcuAhiv5XrvQZADal8c3AqnS10DLgcPrqvRVYLmlmOjS1HNiapj0raVk6L7Kqalm11jGVdCR30ntxI7A7Ij5dis8pNfvvFLlA6tPF6UrEhcAiihPmNfufzjtsB96e5q/Oocr7/nbg26m9tS7bfU8rstpPdfpKmXG40uYCiivC9gIf6XR/Wuj3KRRXQD0I7Kr0neIcxu3AnvQ4K8VF8SOCe4GdwNLSst5NcWHAEPCuUnxpSry9wGf57R1faq5jqg2dyB3gDykOo+wAHkjDBcDN6X3dkf6I55Tm+Ujq4yOkK71G63/KrXtSPnwFOD7FX56eD6Xpp3T6Pch5yHXf0+I2ZrOf8u2szMwse7kfZjQzM3MxMzOz/LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZll7/8DgrC436upn5EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 87.607833 6.958961e-05\n", + "PAY_0 4290.860246 0.000000e+00\n", + "PAY_2 3694.483464 0.000000e+00\n", + "PAY_3 2997.074680 0.000000e+00\n", + "PAY_4 2974.745645 0.000000e+00\n", + "PAY_5 2892.367773 0.000000e+00\n", + "PAY_6 2604.494518 0.000000e+00\n", + "PAY_0_No_Transactions 66.414350 1.247718e-02\n", + "PAY_0_Revolving_Credit 453.062761 0.000000e+00\n", + "PAY_2_Pay_Duly 79.291751 6.259323e-04\n", + "PAY_2_Revolving_Credit 218.897534 0.000000e+00\n", + "PAY_3_Pay_Duly 93.542174 1.308043e-05\n", + "PAY_3_Revolving_Credit 129.158774 1.504441e-10\n", + "PAY_4_Pay_Duly 83.376851 2.176696e-04\n", + "PAY_4_Revolving_Credit 87.804736 6.592136e-05\n", + "PAY_5_Pay_Duly 80.166291 5.012063e-04\n", + "PAY_5_Revolving_Credit 62.035334 3.008744e-02\n", + "PAY_6_Pay_Duly 60.981544 3.674297e-02\n", + "PAY_6_Revolving_Credit 67.326788 1.028709e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (GINI) identified 867\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054101317
11155867
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5410 1317\n", + "1 1155 867" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.40 0.43 0.41 2022\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Entropy) identified 849\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
055201207
11173849
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5520 1207\n", + "1 1173 849" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6203316219666027" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.82 0.82 6727\n", + " 1 0.41 0.42 0.42 2022\n", + "\n", + " accuracy 0.73 8749\n", + " macro avg 0.62 0.62 0.62 8749\n", + "weighted avg 0.73 0.73 0.73 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 745\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06312415
11277745
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6312 415\n", + "1 1277 745" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3258333333333333\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.64 0.37 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13477\n", + " 1 0.80 0.47 0.59 4019\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.83 0.72 0.75 17496\n", + "weighted avg 0.84 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06327400
11284738
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6327 400\n", + "1 1284 738" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2395015270917305\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.65 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.428783 0.616773\n", + "1 Random Forest 0.368447 0.761964\n", + "2 Gradient Boosted 0.364985 0.773571" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1788
Time: 23:04:29 Log-Likelihood: -7743.5
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8836 0.115 -7.704 0.000 -1.108 -0.659
SEX -0.1289 0.041 -3.126 0.002 -0.210 -0.048
AGE 0.0683 0.101 0.678 0.498 -0.129 0.266
PAY_0 0.6745 0.060 11.220 0.000 0.557 0.792
PAY_2 -0.5470 0.096 -5.678 0.000 -0.736 -0.358
PAY_3 -0.0739 0.108 -0.683 0.495 -0.286 0.138
PAY_4 -0.4037 0.154 -2.618 0.009 -0.706 -0.101
PAY_5 0.1256 0.171 0.735 0.462 -0.209 0.460
PAY_6 0.2827 0.138 2.043 0.041 0.011 0.554
BILL_AMT1 -1.4104 0.529 -2.667 0.008 -2.447 -0.374
BILL_AMT2 1.3442 0.771 1.743 0.081 -0.167 2.856
BILL_AMT3 0.9654 0.740 1.305 0.192 -0.484 2.415
BILL_AMT4 0.2228 0.721 0.309 0.757 -1.189 1.635
BILL_AMT5 -1.7122 0.919 -1.863 0.063 -3.514 0.090
BILL_AMT6 1.5981 0.850 1.879 0.060 -0.069 3.265
PAY_AMT1 -1.3296 0.306 -4.345 0.000 -1.929 -0.730
PAY_AMT2 -1.8619 0.393 -4.742 0.000 -2.631 -1.092
PAY_AMT3 -0.4035 0.300 -1.347 0.178 -0.991 0.184
PAY_AMT4 -0.5948 0.300 -1.982 0.048 -1.183 -0.007
PAY_AMT5 -0.9541 0.292 -3.267 0.001 -1.527 -0.382
PAY_AMT6 -0.5320 0.258 -2.065 0.039 -1.037 -0.027
GRAD 1.1287 0.210 5.370 0.000 0.717 1.541
UNI 1.1123 0.209 5.325 0.000 0.703 1.522
HS 1.0960 0.213 5.149 0.000 0.679 1.513
MARRIED 0.1835 0.162 1.134 0.257 -0.134 0.501
SINGLE 0.0426 0.162 0.262 0.793 -0.276 0.361
PAY_0_No_Transactions 0.0631 0.126 0.501 0.616 -0.184 0.310
PAY_0_Pay_Duly 0.2038 0.123 1.654 0.098 -0.038 0.445
PAY_0_Revolving_Credit -0.7870 0.138 -5.684 0.000 -1.058 -0.516
PAY_2_No_Transactions -1.3878 0.235 -5.907 0.000 -1.848 -0.927
PAY_2_Pay_Duly -1.3056 0.223 -5.868 0.000 -1.742 -0.870
PAY_2_Revolving_Credit -0.7845 0.227 -3.461 0.001 -1.229 -0.340
PAY_3_No_Transactions -0.5573 0.270 -2.061 0.039 -1.087 -0.027
PAY_3_Pay_Duly -0.5669 0.242 -2.339 0.019 -1.042 -0.092
PAY_3_Revolving_Credit -0.5462 0.231 -2.368 0.018 -0.998 -0.094
PAY_4_No_Transactions -1.1499 0.351 -3.276 0.001 -1.838 -0.462
PAY_4_Pay_Duly -1.2105 0.331 -3.655 0.000 -1.860 -0.561
PAY_4_Revolving_Credit -1.1121 0.321 -3.466 0.001 -1.741 -0.483
PAY_5_No_Transactions 0.0540 0.383 0.141 0.888 -0.697 0.805
PAY_5_Pay_Duly -0.0559 0.367 -0.152 0.879 -0.775 0.663
PAY_5_Revolving_Credit 0.0811 0.357 0.227 0.820 -0.618 0.780
PAY_6_No_Transactions 0.3831 0.308 1.243 0.214 -0.221 0.987
PAY_6_Pay_Duly 0.3360 0.301 1.116 0.264 -0.254 0.926
PAY_6_Revolving_Credit 0.0761 0.292 0.261 0.794 -0.496 0.648
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:04:29 Log-Likelihood: -7743.5\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8836 0.115 -7.704 0.000 -1.108 -0.659\n", + "SEX -0.1289 0.041 -3.126 0.002 -0.210 -0.048\n", + "AGE 0.0683 0.101 0.678 0.498 -0.129 0.266\n", + "PAY_0 0.6745 0.060 11.220 0.000 0.557 0.792\n", + "PAY_2 -0.5470 0.096 -5.678 0.000 -0.736 -0.358\n", + "PAY_3 -0.0739 0.108 -0.683 0.495 -0.286 0.138\n", + "PAY_4 -0.4037 0.154 -2.618 0.009 -0.706 -0.101\n", + "PAY_5 0.1256 0.171 0.735 0.462 -0.209 0.460\n", + "PAY_6 0.2827 0.138 2.043 0.041 0.011 0.554\n", + "BILL_AMT1 -1.4104 0.529 -2.667 0.008 -2.447 -0.374\n", + "BILL_AMT2 1.3442 0.771 1.743 0.081 -0.167 2.856\n", + "BILL_AMT3 0.9654 0.740 1.305 0.192 -0.484 2.415\n", + "BILL_AMT4 0.2228 0.721 0.309 0.757 -1.189 1.635\n", + "BILL_AMT5 -1.7122 0.919 -1.863 0.063 -3.514 0.090\n", + "BILL_AMT6 1.5981 0.850 1.879 0.060 -0.069 3.265\n", + "PAY_AMT1 -1.3296 0.306 -4.345 0.000 -1.929 -0.730\n", + "PAY_AMT2 -1.8619 0.393 -4.742 0.000 -2.631 -1.092\n", + "PAY_AMT3 -0.4035 0.300 -1.347 0.178 -0.991 0.184\n", + "PAY_AMT4 -0.5948 0.300 -1.982 0.048 -1.183 -0.007\n", + "PAY_AMT5 -0.9541 0.292 -3.267 0.001 -1.527 -0.382\n", + "PAY_AMT6 -0.5320 0.258 -2.065 0.039 -1.037 -0.027\n", + "GRAD 1.1287 0.210 5.370 0.000 0.717 1.541\n", + "UNI 1.1123 0.209 5.325 0.000 0.703 1.522\n", + "HS 1.0960 0.213 5.149 0.000 0.679 1.513\n", + "MARRIED 0.1835 0.162 1.134 0.257 -0.134 0.501\n", + "SINGLE 0.0426 0.162 0.262 0.793 -0.276 0.361\n", + "PAY_0_No_Transactions 0.0631 0.126 0.501 0.616 -0.184 0.310\n", + "PAY_0_Pay_Duly 0.2038 0.123 1.654 0.098 -0.038 0.445\n", + "PAY_0_Revolving_Credit -0.7870 0.138 -5.684 0.000 -1.058 -0.516\n", + "PAY_2_No_Transactions -1.3878 0.235 -5.907 0.000 -1.848 -0.927\n", + "PAY_2_Pay_Duly -1.3056 0.223 -5.868 0.000 -1.742 -0.870\n", + "PAY_2_Revolving_Credit -0.7845 0.227 -3.461 0.001 -1.229 -0.340\n", + "PAY_3_No_Transactions -0.5573 0.270 -2.061 0.039 -1.087 -0.027\n", + "PAY_3_Pay_Duly -0.5669 0.242 -2.339 0.019 -1.042 -0.092\n", + "PAY_3_Revolving_Credit -0.5462 0.231 -2.368 0.018 -0.998 -0.094\n", + "PAY_4_No_Transactions -1.1499 0.351 -3.276 0.001 -1.838 -0.462\n", + "PAY_4_Pay_Duly -1.2105 0.331 -3.655 0.000 -1.860 -0.561\n", + "PAY_4_Revolving_Credit -1.1121 0.321 -3.466 0.001 -1.741 -0.483\n", + "PAY_5_No_Transactions 0.0540 0.383 0.141 0.888 -0.697 0.805\n", + "PAY_5_Pay_Duly -0.0559 0.367 -0.152 0.879 -0.775 0.663\n", + "PAY_5_Revolving_Credit 0.0811 0.357 0.227 0.820 -0.618 0.780\n", + "PAY_6_No_Transactions 0.3831 0.308 1.243 0.214 -0.221 0.987\n", + "PAY_6_Pay_Duly 0.3360 0.301 1.116 0.264 -0.254 0.926\n", + "PAY_6_Revolving_Credit 0.0761 0.292 0.261 0.794 -0.496 0.648\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13477\n", + " 1 0.68 0.36 0.47 4019\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.76 0.66 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442592\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442594\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442597\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442605\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442612\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442624\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442637\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442737\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442811\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442889\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442974\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443019\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443123\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443275\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443429\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17469
Method: MLE Df Model: 26
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1772
Time: 23:04:30 Log-Likelihood: -7758.2
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.9104 0.113 -8.043 0.000 -1.132 -0.689
SEX -0.1269 0.041 -3.125 0.002 -0.206 -0.047
PAY_0 0.6108 0.038 16.108 0.000 0.536 0.685
PAY_2 -0.4983 0.080 -6.204 0.000 -0.656 -0.341
PAY_4 -0.3056 0.071 -4.288 0.000 -0.445 -0.166
PAY_6 0.2928 0.032 9.175 0.000 0.230 0.355
BILL_AMT1 -1.6993 0.530 -3.204 0.001 -2.739 -0.660
BILL_AMT2 2.3783 0.580 4.104 0.000 1.243 3.514
PAY_AMT1 -1.4384 0.295 -4.876 0.000 -2.017 -0.860
PAY_AMT2 -1.8153 0.366 -4.964 0.000 -2.532 -1.099
PAY_AMT4 -1.0161 0.271 -3.749 0.000 -1.547 -0.485
PAY_AMT5 -0.7785 0.260 -2.997 0.003 -1.288 -0.269
GRAD 1.2983 0.184 7.065 0.000 0.938 1.659
UNI 1.2842 0.183 7.026 0.000 0.926 1.642
HS 1.2736 0.187 6.805 0.000 0.907 1.640
MARRIED 0.1585 0.042 3.770 0.000 0.076 0.241
PAY_0_Revolving_Credit -0.9345 0.094 -9.959 0.000 -1.118 -0.751
PAY_2_No_Transactions -1.5378 0.194 -7.920 0.000 -1.918 -1.157
PAY_2_Pay_Duly -1.2152 0.186 -6.551 0.000 -1.579 -0.852
PAY_2_Revolving_Credit -0.6567 0.188 -3.484 0.000 -1.026 -0.287
PAY_3_Pay_Duly -0.2770 0.094 -2.957 0.003 -0.461 -0.093
PAY_3_Revolving_Credit -0.3492 0.078 -4.499 0.000 -0.501 -0.197
PAY_4_No_Transactions -1.2304 0.183 -6.729 0.000 -1.589 -0.872
PAY_4_Pay_Duly -1.2137 0.172 -7.053 0.000 -1.551 -0.876
PAY_4_Revolving_Credit -1.0025 0.157 -6.388 0.000 -1.310 -0.695
PAY_6_No_Transactions 0.3215 0.090 3.574 0.000 0.145 0.498
PAY_6_Pay_Duly 0.2205 0.078 2.812 0.005 0.067 0.374
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17469\n", + "Method: MLE Df Model: 26\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1772\n", + "Time: 23:04:30 Log-Likelihood: -7758.2\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.9104 0.113 -8.043 0.000 -1.132 -0.689\n", + "SEX -0.1269 0.041 -3.125 0.002 -0.206 -0.047\n", + "PAY_0 0.6108 0.038 16.108 0.000 0.536 0.685\n", + "PAY_2 -0.4983 0.080 -6.204 0.000 -0.656 -0.341\n", + "PAY_4 -0.3056 0.071 -4.288 0.000 -0.445 -0.166\n", + "PAY_6 0.2928 0.032 9.175 0.000 0.230 0.355\n", + "BILL_AMT1 -1.6993 0.530 -3.204 0.001 -2.739 -0.660\n", + "BILL_AMT2 2.3783 0.580 4.104 0.000 1.243 3.514\n", + "PAY_AMT1 -1.4384 0.295 -4.876 0.000 -2.017 -0.860\n", + "PAY_AMT2 -1.8153 0.366 -4.964 0.000 -2.532 -1.099\n", + "PAY_AMT4 -1.0161 0.271 -3.749 0.000 -1.547 -0.485\n", + "PAY_AMT5 -0.7785 0.260 -2.997 0.003 -1.288 -0.269\n", + "GRAD 1.2983 0.184 7.065 0.000 0.938 1.659\n", + "UNI 1.2842 0.183 7.026 0.000 0.926 1.642\n", + "HS 1.2736 0.187 6.805 0.000 0.907 1.640\n", + "MARRIED 0.1585 0.042 3.770 0.000 0.076 0.241\n", + "PAY_0_Revolving_Credit -0.9345 0.094 -9.959 0.000 -1.118 -0.751\n", + "PAY_2_No_Transactions -1.5378 0.194 -7.920 0.000 -1.918 -1.157\n", + "PAY_2_Pay_Duly -1.2152 0.186 -6.551 0.000 -1.579 -0.852\n", + "PAY_2_Revolving_Credit -0.6567 0.188 -3.484 0.000 -1.026 -0.287\n", + "PAY_3_Pay_Duly -0.2770 0.094 -2.957 0.003 -0.461 -0.093\n", + "PAY_3_Revolving_Credit -0.3492 0.078 -4.499 0.000 -0.501 -0.197\n", + "PAY_4_No_Transactions -1.2304 0.183 -6.729 0.000 -1.589 -0.872\n", + "PAY_4_Pay_Duly -1.2137 0.172 -7.053 0.000 -1.551 -0.876\n", + "PAY_4_Revolving_Credit -1.0025 0.157 -6.388 0.000 -1.310 -0.695\n", + "PAY_6_No_Transactions 0.3215 0.090 3.574 0.000 0.145 0.498\n", + "PAY_6_Pay_Duly 0.2205 0.078 2.812 0.005 0.067 0.374\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT2', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 6727\n", + " 1 0.67 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21826195076750313\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7727873645667978" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.78 0.82 6727\n", + " 1 0.46 0.63 0.53 2022\n", + "\n", + " accuracy 0.74 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.74 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 1282\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
052351492
17401282
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5235 1492\n", + "1 740 1282" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 731\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06363364
11291731
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6363 364\n", + "1 1291 731" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2308092391259058\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1619910386617494\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7250888729990617" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM default parameters identified 741\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06365362
11281741
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6365 362\n", + "1 1281 741" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6727\n", + " 1 0.67 0.37 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1607879330309063\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM reduced features and tuning linear kernal identified 672\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06433294
11350672
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6433 294\n", + "1 1350 672" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.96 0.89 6727\n", + " 1 0.70 0.33 0.45 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.64 0.67 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15824600205442427\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.88 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Neural Network (5x26) identified 761\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06341386
11261761
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6341 386\n", + "1 1261 761" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24385364385235758\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.89 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
3Logistic Regressionprecision recall f1-score ...0.768110
5Neural Network0.376360.771884
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 \\\n", + "0 Decision Tree (GINI) 0.428783 \n", + "1 Random Forest 0.368447 \n", + "2 Gradient Boosted 0.364985 \n", + "3 Logistic Regression precision recall f1-score ... \n", + "5 Neural Network 0.37636 \n", + "\n", + " AUROC \n", + "0 0.616773 \n", + "1 0.761964 \n", + "2 0.773571 \n", + "3 0.768110 \n", + "5 0.771884 " + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'keras'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mloadtxt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# define the keras model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'" + ] + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb new file mode 100644 index 0000000..ff9f477 --- /dev/null +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -0,0 +1,5588 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 88.108919 6.061985e-05\n", + "PAY_0 4383.304779 0.000000e+00\n", + "PAY_2 3808.897634 0.000000e+00\n", + "PAY_3 2929.844986 0.000000e+00\n", + "PAY_4 3128.967849 0.000000e+00\n", + "PAY_5 2916.099105 0.000000e+00\n", + "PAY_6 2553.774724 0.000000e+00\n", + "PAY_0_No_Transactions 75.763632 1.499570e-03\n", + "PAY_0_Pay_Duly 70.548603 5.076335e-03\n", + "PAY_0_Revolving_Credit 432.083997 0.000000e+00\n", + "PAY_2_Pay_Duly 91.371021 2.433442e-05\n", + "PAY_2_Revolving_Credit 208.148061 0.000000e+00\n", + "PAY_3_Pay_Duly 96.953056 4.833864e-06\n", + "PAY_3_Revolving_Credit 111.611983 5.228360e-08\n", + "PAY_4_Pay_Duly 88.985156 4.755470e-05\n", + "PAY_4_Revolving_Credit 87.590881 6.991450e-05\n", + "PAY_5_Pay_Duly 79.941208 5.308188e-04\n", + "PAY_5_Revolving_Credit 62.597352 2.699279e-02\n", + "PAY_6_Pay_Duly 66.360306 1.261937e-02\n", + "PAY_6_Revolving_Credit 63.991063 2.050515e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (GINI) identified 847\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
053681359
11175847
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5368 1359\n", + "1 1175 847" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.38 0.42 0.40 2022\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.71 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Entropy) identified 823\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054131314
11199823
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5413 1314\n", + "1 1199 823" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6064648683126901" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.39 0.41 0.40 2022\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 782\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06264463
11240782
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6264 463\n", + "1 1240 782" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2805714285714286\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.93 0.88 6727\n", + " 1 0.63 0.39 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.73 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.97 0.91 13477\n", + " 1 0.81 0.48 0.60 4019\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.84 0.72 0.76 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 770\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06289438
11252770
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6289 438\n", + "1 1252 770" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2100144956690176\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.93 0.88 6727\n", + " 1 0.64 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.418892 0.609249\n", + "1 Random Forest 0.386746 0.761906\n", + "2 Gradient Boosted 0.380811 0.775534" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805
Time: 23:33:29 Log-Likelihood: -7727.2
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656
SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038
AGE 0.1478 0.101 1.470 0.141 -0.049 0.345
PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769
PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331
PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169
PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007
PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205
PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782
BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144
BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139
BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594
BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607
BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764
BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090
PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351
PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987
PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089
PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122
PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184
PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060
GRAD 1.2777 0.220 5.807 0.000 0.846 1.709
UNI 1.2382 0.219 5.667 0.000 0.810 1.667
HS 1.1544 0.222 5.196 0.000 0.719 1.590
MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564
SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363
PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197
PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312
PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549
PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004
PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925
PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376
PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109
PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064
PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107
PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445
PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476
PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409
PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388
PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217
PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285
PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505
PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383
PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805\n", + "Time: 23:33:29 Log-Likelihood: -7727.2\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", + "SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", + "AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", + "PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", + "PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", + "PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", + "PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", + "PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", + "PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", + "BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", + "BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", + "BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", + "BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", + "BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", + "BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", + "PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", + "PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", + "PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", + "PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", + "PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", + "PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", + "GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", + "UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", + "HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", + "MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", + "SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", + "PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", + "PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", + "PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", + "PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", + "PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", + "PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", + "PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", + "PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", + "PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", + "PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", + "PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", + "PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", + "PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", + "PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", + "PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", + "PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", + "PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", + "PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13477\n", + " 1 0.68 0.36 0.47 4019\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.76 0.66 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441658\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441662\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441670\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441678\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441690\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441709\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441720\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441740\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441771\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441820\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441865\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441905\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441966\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442051\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442158\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442265\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442384\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442495\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17471
Method: MLE Df Model: 24
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789
Time: 23:38:59 Log-Likelihood: -7741.9
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687
SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052
PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713
PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360
PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135
PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340
BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520
BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657
PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396
PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337
PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524
PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257
GRAD 1.3015 0.188 6.913 0.000 0.933 1.671
UNI 1.2630 0.187 6.755 0.000 0.897 1.629
HS 1.1983 0.191 6.273 0.000 0.824 1.573
MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316
PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653
PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277
PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103
PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548
PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925
PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005
PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870
PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589
PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17471\n", + "Method: MLE Df Model: 24\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789\n", + "Time: 23:38:59 Log-Likelihood: -7741.9\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", + "SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", + "PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", + "PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", + "PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", + "PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", + "BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", + "BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", + "PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", + "PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", + "PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", + "PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", + "GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", + "UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", + "HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", + "MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", + "PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", + "PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", + "PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", + "PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", + "PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", + "PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", + "PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", + "PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", + "PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.64 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2233371077226231\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.771318789360392" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.79 0.83 6727\n", + " 1 0.47 0.62 0.54 2022\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 1252\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053411386
17701252
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5341 1386\n", + "1 770 1252" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06319408
11284738
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6319 408\n", + "1 1284 738" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.22122047856901356\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "optimal = \n", + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15978089375876542\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU1frA8e8hIYGQAinUEEIJhN4iSBURpCjCpYoFBbyKKBaUa0Gxi3DtFyzY+KkgIEgREAWpIkiR0HtooRPSezm/P2YSNyFlA9lsdvN+noeH7MzszDuzZ+adOTNzjtJaI4QQQtyoCvYOQAghhHOQhCKEEKJESEIRQghRIiShCCGEKBGSUIQQQpQISShCCCFKhNMnFKXUvUqp3+wdh70ppYKUUglKKZdSXGawUkorpVxLa5m2pJTar5TqcR3fu64yqJSqrJT6WSkVq5T6sbjfL6uKWy6UUm8qpa4opS6UwLK1UqrRjc5H5K9UE4pS6qRSKtk8sF1QSs1WSnnacpla6zla69ttuYyyyNzWvbI/a61Pa609tdaZ9ozLXkriQKK1bq61Xl/Ecq45WN5AGRwK1AD8tNbDruP7+cX3olLqhLkPRiql5pvDP1dKfZvP9K2UUqlKKV+l1Kvmuj2RZ5qnzOGvlkSMeeZdF3gGaKa1rlnC856tlHqzJOdZViileiilIkt7ufa4QhmgtfYE2gBtgRfsEMMNs+dZt7Oc8RdHOd3e9YAjWuuM4n4xv5iVUg8A9wO9zH0wDPjdHD0bGKyUqpLna6OA5Vrrq+bnI8AD+UxzpLgxWqkeEKW1vmSj+ZcKR9tnrzterXWp/QNOYhTm7M/TgRUWn92Bd4HTwEXgM6CyxfiBQDgQBxwH+prDfYCvgPPAWeBNwMUc9yDwh/n3Z8C7eWJaCkw0/64NLAIuAyeAJyymexVYCHxvLv+hfNbPB/jW/P4p4CWggkUcm4H/AbHAIeC2PN8tbB02Ax8AV81xDYG1QBRwBZgDVDWn/w7IApKBBOA/QDCgAVdzmvXAG+Z844HfAH+LeEaZ6xAFvJz3t8uz3pWB98zpY4E/zGHZy3zA/E2vAJMtvtcB2ALEmOs9A3CzGK+Bx4CjwAlz2EfAGfM32Al0s5jeBXjRLBvx5vi6wEZzXonm9hhhTn8nRnmKAf4EWuUpq88Be4BUwNVyG5ix7zDjuAi8bw4/bS4rwfzXCYsyaE7THFht/pYXgRfz2aavAWlAujmfsRgngC+Z2/kSRlnzMafP3tZjzRg25jPPGcCHheyfh4FRebbnOeAui33ge+Ag0NxiXQ6aw18tYL4uGPv1FSDC/E0ty2K+ZR/ohVGGs8xtMNuc/kfgAkZZ25gdi0W5fsjic95tr4FGwMPmtk0z5/1zAbFr4Akz7ivAf/lnny5wHyykDD3PP+XzAPCvPLFm7+cx5jI7m8PPmL/5A0UdL4EqebZbAsaxrYLF8qOABYBvQeUHqGT+rlFmPNuBGoUe4+2VUIBAYC/wkcX4D4FlgC/gBfwMTLXYgWOB3uaGqQOEmuOWAJ+bG7I6sA14JJ+E0t38YZT5uZq54bM39k5gCuAGNDB/0D4WO1M6MMictnI+6/ctRoLyMn+gI8BYizgygKeBisAIc318rVyHDGACRqGsjLFT9DYLVYBZAD7Mb1vnKTCWCeU40Nic33rgHXNcM4xC2NXcFu+a615QQplpfr8OxoGgsxlX9jK/MJfRGmPHamp+rz1ws7lOwRgHpqfy7MyrMcpDZXPYfYCf+Z1nMA4slcxxkzDKVBNAmcvzszyQWMy7HcYO2tGM+QFzm7lbbL9wjIRUOe82xUiE95t/ewI357ed8ymDXhgHzmcwdlgvoGMB2/VV4HuLz2OAYxhl0xP4Cfguz3K/xShD+ZXP+zCS2CSMqxOXPOMnA2ssPvfBODmqmCehvAhMM4dNx6hlKCyhjMM4gapr/pbryF0WCyv7PYDIPPMbY243d4xjRnhxE4r592zgzSKOWdqM1xcIwtinHzLHWbMP5i1Dw/jneDMC4ySnVp79fDRGmXwT4+A+01zG7RiJyNOK42V+2+0pYCvGsdfd3OY/FFR+gEfMeXqY8bQHvAvdXrZOInlW6CTGgSreDP53/jmrVubGbWgxfSf+OTP9HPggn3nWwDhIWV7JjATW5bMzK/MH6m5+/jew1vy7I3A6z7xfAL6x2JmuOeuzmNbFjKOZxbBHgPUWcZzDTGbmsG0YVRDWrMPpgpZtTjMI2JVnWxeVUF6yGD8eWGX+PSW7oJmfPTDO5K5JKBg7RjLQOp9x2csMzLPOdxewDk8Bi/PszD2LWO/o7GVjnGEPLOTAYJlQPgXeyDPNYeAWi+03Jp/ym51QNmJcRfjnmSbXds6nDI60/J2KWLdXyZ1QfgfGW3xugpHoXS2W26CIed4LrMHY16KA5y3GBZnzCzQ/zyH3Cd+rGIkjCGM/qmj+X5fCE8paYJzF59uztxFFl/0e5Dkw5pl3VXNe2Vdq6yn5hNI3z37yezH2wTFFzD88u8yasR61GNfSXH4Ni2FRGLcLijpeXrPdME7YLGtFahVWfjASd64r96L+2aNeb5DWeo1S6hZgLuCPcTkVgHHg2qmUyp5WYRyowSi0K/OZXz2Mgn3e4nsVMK5EctFaa6XUPIwCuxG4B2NHyJ5PbaVUjMVXXIBNFp+vmacFf4yz+VMWw05hnLVnO6vNX8pifG0r1yHXspVS1YGPgW4YZycVMA6uxWH51EwSxlkvZkw5y9NaJymlogqYhz/Gmfbx4i5HKdUYeB/jbNkDo2DvzPPdvOv9DPCQGaMGvM0YwCgjhcVhqR7wgFJqgsUwN3O++S47j7HA68AhpdQJ4DWt9XIrllucGPOqzbXlK/ugnK2wmNFazwHmKKUqYhwA5yildmmtf9Van1ZKbQTuU0rNMMd3y2cep5VSx4C3MQ6AZyzKbUFxW8ZluQ5W778A5lOKb2Gc6QdgVOuAUQZiCwviBuSNvbYZizX7YN7yOwqYiHEAB2Nf8LeY5KLF38kAWuu8wzwp+niZn3rAYqVUlsWwTAouP99hlNd5SqmqGMfKyVrr9IIWYLfHhrXWGzDOEN41B13B2FjNtdZVzX8+2rh5CMaKNsxnVmcwznD8Lb7nrbVuXsCifwCGKqXqYVyVLLKYzwmLeVTVWntprftbhl3IKl3ByPb1LIYFYdQJZ6ujcu95QRhXLdasQ95lTzWHtdJae2NUZ6hCpi+O8xiXxYDx+CpGNVN+rgAp5P/bFOVTjKqQEHMdXiT3OoDFeiilumHUSQ8Hqmmtq2IcRLK/U1AZyc8Z4K08v7eH1vqH/Jadl9b6qNZ6JEYVzTRgoXlDu6jtXpwY8zrHteUrg9wHIat+d611utb6R4z6/RYWo/4P4/7ZEIz94e8CZvEtRrXdNU+G5eM8xoHJMu5sxd1/78G4l9oL495LsDk8uwwkYhxosxX2ZJi1+0je2M+Zfxe1D+ZahnnM+QJ4HKMqtiqwL5/vWKOo42V+63YG6JenzFfSWlseo3K+Z5aR17TWzTCqse/EKBsFsvd7KB8CvZVSbbTWWRgb+wMz86OUqqOU6mNO+xUwWil1m1KqgjkuVGt9HuOG8ntKKW9zXEPzCugaWutdGPXCXwK/aq2zr0i2AXFKqefM5/9dlFItlFI3WbMi2ngcdwHwllLKyyw8E/nnCgiMg88TSqmKSqlhQFNgZXHXweSFUX0Yo5Sqg1EvbukiRl379VgIDFBKdVZKuWFU7eRb6M3f7WvgfaVUbXO7dVJKuVuxHC+Mm9oJSqlQ4FErps/A+P1clVJTMK5Qsn0JvKGUClGGVkqp7ESYd3t8AYxTSnU0p62ilLpDKeVlRdwope5TSgWY659dhjLN2LIoeNsvB2qaj9q6m2WlozXLxDgZelopVd983P5tYL628ikwpdSD2etolrF+GDfV/7KYbBHGAfQ1jORSkPkYVVcLrFj0AoxyH6iUqoZxYxiA6yj7XhgJKAojcbydZ3w4xtNqHsp4THxsIXFZu49MUkpVMx9hfhJj3bNjKWwfzCv7hOMygFJqNLmTudWsOF5eBPyUUj4WX/sM4/hUz5w+QCk1sKBlKKVuVUq1NK8K4zBOmAt97cCuCUVrfRnjDOdlc9BzGDcdtyql4jDqepuY027DuFn1AcZZ6Qb+OVsbhVFdcQDjknMhRv1gQX7AOMOZaxFLJjAAo37yBMYZwJcYZ0HWmoBxhhSB8aTTXIyDbba/gBBz3m8BQ7XW2VVJxV2H1zBuLMcCKzBu0FqaCryklIpRSj1bjHVAa73fXJd5GGeX8Rg3sFML+MqzGDfDt2Pc9J2GdWXrWYwzzniMnWN+4ZPzK/ALxo3RUxhXRpaX6O9jHLx+w9gBvsK4uQhG/f//mdtjuNZ6B8Y9tBkY2/sYRh22tfoC+5VSCRhPnt2ttU7RWidh/LabzWXdbPklrXU8xo3cARhVgUeBW61c5tcY1RAbMcpoCsbvZK04jKvA0xhJcDrwqNb6D4v4EvknqcwpaEZa62St9RqtdbIVy/0C47fbDfzNtWW1OGX/W4zf/qw5/dY84z/AuN93ESMhFrgOGOWjmfk7LSlkuqUYVbHhGPvaV+bwovbBXLTWBzCehtxixtcS46mu61XY8fIQxnEuwly/2hjldBnwm1IqHmPbFXYyUxPjt4jDuP+ygdwnyNfIftpJ2JhS6kGMm4Vd7R1LcZlnwzEYVVMn7B2PEKVFKaUxyv0xe8fiCOxd5SXKKKXUALPaoArGfa69GE+tCCFEviShiIIMxLj5eA6jmu5uLZezQohCSJWXEEKIEiFXKEIIIUqEQzVYBuDv76+Dg4PtHYYQQjiUnTt3XtFaB9hyGQ6XUIKDg9mxY4e9wxBCCIeilDpV9FQ3Rqq8hBBClAhJKEIIIUqEJBQhhBAlQhKKEEKIEiEJRQghRImQhCKEEKJE2CyhKKW+VkpdUkrtK2C8Ukp9rJQ6ppTao5RqZ6tYhBBC2J4tr1BmYzTxXZB+GG1EhQAPY3S2JIQQooRlZGYVPVEJsNmLjVrrjUqp4EImGQh8azY4uFUpVVUpVcvscEcIIcQN0loz4r1NbLsSXyrLs+c9lDrk7hwpktz9r+dQSj2slNqhlNpx+fLlUglOCCEc2c79l+j8wKJSSyZg36ZX8utSNt+mj7XWs4BZAGFhYdI8shBC5ONyfCo/bDvN4l1nOXElEWobHZZO7NWYJ6fZfvn2TCiRGN2MZgvE6HtDCCFEMWitmb/9DM//tDdnWF3vSkzqGcItrWvhU7kiT5ZCHPZMKMuAx5VS8zD6NY6V+ydCCFE801Yd4tP1x3M+d63oxpdTelKpokupx2KzhKKU+gHoAfgrpSKBV4CKAFrrz4CVQH/gGJAEjLZVLEII4UxS0jP5ZN0xPl77T1f3SQevcneTGkx9/ha7JBOw7VNeI4sYr4HHbLV8IYRwNmdjklkafpbpqw7nDEuOiKXh+RQ+/+QOWrasYcfoHLA/FCGEKG92nLzK91tPsST8n9vMtzYJYGLHYLZuPsPYse2oUCG/55xKlyQUIYQog45fTmD1gYt8tuE4MUnpAHi4ViB583na+HryzTt3ANCyWXV7hpmLJBQhhChDtNa8tGQfc/46nTPM082FuicTWfXdPpo08eOJKbfaMcKCSUIRQgg7S0nPZMbaY4SfieGPY1dyhr8/vDXeV1MZPHgBx9IyeeONW5k0qTPu7mXz0F02oxJCCCcXGZ3E36dj2HD4Mov+jswZ3ry2NxVdKjDrvnZU96nMlStJ9O8fwptv9qRRI187Rlw0SShCCGFDKemZ7Dodw46TV9HA0vCzpKRncTYmOWea+v5VuKNlLZ7qFUJSYjovv7yWu/rMYfPmMfj7ezBv3lD7rUAxSEIRQogSdCkuhaXh57ickMrGI5c5dCF3W1oVFLhWqMCQdoGE1vRiYJvaVPeuhNaahQsP8OSTq7hwIYHx428iNTUTDw/H6bZKEooQQtyA6MQ0pv96mJ93nyMhNSPXOH9PN5rV8mZwuzp0bxxAff8qVHS5NkFcvpzIAw8s4ZdfjtG2bU2WLr2bm27Kt63cMk0SihBCFEN0YhovLt7LmegkzkYnE20+0gvQo0kAgdUq0yqwKne2qoWHm3WHWG9vd65cSeLDD/vw2GMdcHV1nKsSS5JQhBCiAFlZmpNRiaw9dIndkbHsPHmVc7EpOeN7Na2Ou6sL3UL8GdwuELdiJIKNG0/x1lubWLRoOJ6ebmzd+lCZeDnxRkhCEUIIC+djk9kTGcvPu8+xfE/u9mqVgvb1qjHiproMD6tbwBwKd+VKEpMmrWb27HCCg6ty8mQMLVpUd/hkApJQhBDl3LmYZNYeusTJK4ksCT/HlYTUnHE1vN25o2VtOjf0o3vjgGJdgeSlteabb8KZNGk1cXGpvPBCV156qTseHhVLYjXKBEkoQohy58zVJN5ffYQ1By4Sn+dG+sgOdenVtAYdG/jhWcIvEH7//R6aNQvgs8/uoHnzstNkSkmRhCKEcGpnriaxNSKKIxfj2Rpxlb1nY3PGVfOoSIdgX4a0r8NtTWvg6+FWolVPSUnpvP32JsaNCyMw0JtFi4bj41PJKaq38iMJRQjhFBJTM4i4nMixy/GsPXSZXaejOReTTFaeTsNbB/rQsLonQ9sF0rmRv83iWbnyKI89tpKTJ2OoU8eLRx+9iWrVKttseWWBJBQhhMO5HJ/KmegkLsSmMGPtMU5GJZKUlnnNdM1qedOyjg+3Na1Om7pVqVbFLd/3QEpSZGQcTz21ikWLDtK0qT8bNjxI9+71bLrMskISihCiTMvIzOJKQhrbT15l24mr/LLvPFcS0q6ZrnezGnSs70tIDS+a1vQiwMsdpUq/aumttzayYsVR3n67J8880xk3N/v0nmgPyug40XGEhYXpHTt22DsMIYSNZWZp3vnlIF9sOpFreIOAKrSs40PP0OpU9XAjpLontXwq2SV5ZNu27SyVK7vSsmUNoqKSiI1NpUGDanaLJz9KqZ1a6zBbLkOuUIQQZYLWmuOXE/h0fQTL95wjNSMrZ9zD3RvQso4PbepWpa6vhx2jzC02NoUXX/ydTz/dwZ13NmbZspH4+Xng51d2YixNklCEEKUuJT2TTUev8PfpaI5ejGfL8SgS89wD6du8Jm2CqjIirC7VqrjZKdL8aa2ZP38/Tz/9K5cuJTJhQgfeeKOnvcOyO0koQgiby8jMIjEtk1NRiaw5cJGP1x7LGedVyZUKFRTdQvxpU7cqnRv6c3MDX7tWYRXl++/3MGrUEsLCarN8+Ujat69t75DKBEkoQogSdexSAmeuJhGVmMbCnWfYfy6O+JSMa6br1bQGrwxoVqaqsAqTmppBREQ0TZsGMHx4czIyshg1qjUuNn5qzJFIQhFCXJeU9Ewio5M4fTWJ6MR0tkRE8eu+C9e8ee5XxY2neoVQxc0VpaBFHR9a1vGhShntxjY/69ad4NFHV5CUlM7RoxNwd3dl9Oi29g6rzHGcX1QIYTcxSWms2neBqMQ0dp6K5tD5OC7Fp5KR961BjHc/nusXSg1vd3yruFHdq5IdIi4Zly4l8uyzv/Hdd3to0KAas2YNKLP9uZcFsmWEELlkZmn2RMZw9FICG45cZsPhy/l2HNUq0IeBbepQ17cyvlXcqe9XBR8naujw2LGrdOjwBQkJaUye3I3Jk7tRubLzrJ8tSEIRohxLy8jiQmwKy/eeY82Bi6RlZrHvbFyuaXwqV6R1oA8jbgqif8ua+FSuWKZvmN+ouLhUvL3dadiwGmPHtmXMmLY0bRpg77AcgiQUIcqRpLQM5mw9zV8nrnLwfBxnY5Jzja/u5c6gNrW5qb4v7etVI8DTHT9PdztFW7oSE9N4/fUNfPHF3+zZ8yiBgd7897+32zsshyIJRQgndj42mYSUDHacimbBjjPsOh2Ta/wdLWvRIKAKzWv70KNJAJUqlp9mQiz9/PNhHn/8F06fjmXs2LZO1UdJaZKEIoQTycoy3jZ/duEedp+JuWZ8Xd/K/KttIONuaWB1f+fOLCMji+HDf2Tx4kM0bx7Apk2j6do1yN5hOSwpUUI4uKuJaTz87Q7Ox6ZcU4XVq2kNBrWtTSVXF9rXq1bm3ji3F601SilcXStQq5Yn77xzG08/3alcNeRoC5JQhHAwyWmZHDgfx+ZjV/hiU0SulwbvaFWL2j6VuLmBHz2aVMfFSTtyuhFbt0by2GMr+eKLAbRrV4uZM++wd0hOQxKKEA5ga0QULy/Zx4kride8+9Eh2JceoQGM79HITtE5hujoZF588Xc+/3wntWt7ER2dXPSXRLHYNKEopfoCHwEuwJda63fyjA8C/g+oak7zvNZ6pS1jEsIRpGdmset0DB+uOcKpqKScqiyfyhUZ06U+Vdxd6BlanQYBnnaO1DHMn7+PJ55YxZUrSTz11M289loPvLzKx9NrpclmCUUp5QLMBHoDkcB2pdQyrfUBi8leAhZorT9VSjUDVgLBtopJiLJIa01UYhrbT1zltwMXOXIxnqOXEkizaL795ga+TOgZQhcbdlnrzA4dukJwcFVWrbqXtm1r2Tscp2XLK5QOwDGtdQSAUmoeMBCwTCga8Db/9gHO2TAeIewuM0tz9FI8y8LPseNUNBGXE7mSkHrNdHe0qkVYvWq0r1eNlnV8nPpFQltISclg2rQ/aNeuFgMGNOHFF7vx0kvdpSFHG7NlQqkDnLH4HAl0zDPNq8BvSqkJQBWgV34zUko9DDwMEBQkj/QJx5OQmsEzC8L5df/FXMN9q7jRLcSfjvV9qeVTma4h/tTwdty2r8qCNWsiGD9+BUePXuWZZzoxYEATKpbT92tKmy0TSn6nVHlbkhsJzNZav6eU6gR8p5RqobXOyvUlrWcBs8DoAtgm0QpRwrKyNK8vP8Cmo5c5fjkRADeXCvyrbR0GtK5N54Z+VJCnsErMxYsJTJz4G3Pn7qVRI19+++0+evduaO+wyhVbJpRIoK7F50CurdIaC/QF0FpvUUpVAvyBSzaMSwibSk7L5NMNx/n496M5w24LrU5dXw9eGdBMqq9sZPXqCBYuPMCUKd154YVuVKokD7GWNltu8e1AiFKqPnAWuBu4J880p4HbgNlKqaZAJeCyDWMSokQlp2UScSWBiMuJXI5PZUtEFKsP/FOtNbpLMJP7N8VV6u5tYvfuCxw9epWhQ5tx770t6dKlLvXrV7N3WOWWzRKK1jpDKfU48CvGI8Ffa633K6VeB3ZorZcBzwBfKKWexqgOe1BrLVVaokyKSkjlwPk4riSksu1ENPvOxrL3bGyuaSq6KJrU8OLmBr48368pleXNa5tISEjjlVfW8dFHfxEcXJVBg0Jxda0gycTObHpNaL5TsjLPsCkWfx8AutgyBiGu18W4FP46cZVFOyPZEhGV6zHebMPDAmkbVI3AapWp5VOJur4euLtKErGlJUsOMWHCL0RGxvHww+2YOrUXrq5yBVgWSCWjEHlcTUxj2i+HmL/jTK7hfZrXoFfTGjQIqEI9vyr4VXGT+yGlbO/ei/zrX/Np2bI68+cPpXPnukV/SZQaSSii3Ft94CJf/RFBQmoGB8/Hk2k2bVJBwdO9GnN3hyAC5K1qu0lPz2TTptP07Fmfli1rsGLFPfTu3UAeBS6DJKGIcmn3mRjm/HWKBTsic4Z5VXKlV9PquFaowJiu9WlfT+rj7e3PP88wbtxy9u+/zOHDj9OokS/9+4fYOyxRAEkootw4dimeiQt2E5WQltM2VmhNL9rXq8awsLq0qVvVzhGKbFevJvP882v44ou/qVvXm59+Gk6jRr72DksUQRKKcHoZmVms2HueJ+eFA1CnamUe6d6AO1vVpmWgj52jE3mlpGTQps1nnDsXzzPPdOLVV3vg6Sn9uDgCSSjCKaWkZ3LkYjx3zdica/ibg1pw38317BSVKExkZByBgd5UquTKG2/cSps2NWnduqa9wxLFIAlFOI2YpDTzBvsJDl2IzzVucv+mtK5blQ71pdqkrElOTmfq1D+YNm0zCxcOY8CAJjzwQBt7hyWug1UJRSnlBgRprY/ZOB4hrJaVpfl68wk+2xBBTFJaro6nWgf6cHvzmnSo78tNwZJEyqrffjvO+PErOH48mvvua0WHDnXsHZK4AUUmFKXUHcD7gBtQXynVBnhFa/0vWwcnRF6xSemcuprIvO1nWLD9TE4SGdkhiDpVK+Hn6U6/FjWp6iF17mXdhAkrmTFjOyEhvqxZcz+33dbA3iGJG2TNFcrrGM3OrwPQWocrpaSvUVFqMjKz+GzDcVbsvcDB83E5w70ruTKyYxAPdW0g74k4iMxMo7UBF5cK3HxzIP7+Hjz3XFdpyNFJWPMrpmutY/K8ESztbYlS8ffpaO7+fCtp5oEotKYXD3QOJqS6J2FSleVQ/v77POPGLef++1sxYUJH7r23lb1DEiXMmoRyUCk1HKhgthz8JLDVtmGJ8m5vZCz3fLmV+JQMwLgn8tP4LrhI/yEOJz4+lSlT1vHxx9sICPCgVi0ve4ckbMSahPI4MAXIAn7CaD34BVsGJcqvU1GJvLRkH5uOXgHg1iYBPHN7E1rUkfdFHNFvvx1nzJilnDsXz7hxYbz99m1UrSo9UjoraxJKH631c8Bz2QOUUoMxkosQJeJUVCJz/jrNrI0RADSu4cmTtzXmjla17ByZuBFubi5Ur16FRYuG07FjoL3DETamiup+RCn1t9a6XZ5hO7XW7W0aWQHCwsL0jh077LFoYQPbT15l4oJwzlxNzhn2YOdgXr2ruR2jEtcrPT2T99/fQlxcKm+9dRtgPN4tXR3bn3ncDrPlMgq8QlFK9cHonreOUup9i1HeGNVfQly3uJR0hnzyJ0cvJQBGx1SzRoVxS0iAHHwc1B9/nM5pyHHYsGY5iUR+z/KjsCqvS8A+IAXYbzE8HnjelkEJ57b6wEX+/a1xlenuWoHF47vQrLa3naMS1ysqKonnnlvDV1/tIijIh59/Hsmddza2d1jCDgpMKFrrXcAupdQcrXVKKcYknNC5mGR+P3SJ5bvP8deJqwBMHdySEWF15QzWwUVFJTNv3j7+85/OTJlyC1WqyEul5ZU1N+XrKKXeApoBOY9naDQJ3T0AACAASURBVK3lFEQU6WxMMv9ddYgl4ecAcK2gCPL1YNao9oTWlKsSR3Xw4GUWLNjPK6/0oHFjP06ffhpf38r2DkvYmTUJZTbwJvAu0A8YjdxDEQXQWnP6ahITF+zmfEwy52KNi9vQml6M7BDEyA5BuEn/3w4rKSmdt97ayH//+yeenm6MHduOwEBvSSYCsC6heGitf1VKvau1Pg68pJTaZOvAhOM5FZVIv482kZSWmTNseFggw8LqSgONTmDVqmOMH7+CEydieOCB1vz3v70JCKhi77BEGWJNQklVRrsrx5VS44CzQHXbhiUczaELcfT90DjP6NzQj393a0CPJgHkabJHOKiEhDTuv38xfn6VWbfuAXr0CLZ3SKIMsiahPA14Ak8AbwE+wBhbBiUcx6X4FHq+u4GEVKOJlG4h/nw3tqOdoxIlITMzix9+2MfIkS3w9HRjzZr7CQ31x91dGnIU+SuyZGit/zL/jAfuB1BKySuv5VxWlmb07O1sOHIZgHp+Hkz9V0s6NfSzc2SiJOzceY5HHlnOzp3nqVzZlSFDmknviaJIhSYUpdRNQB3gD631FaVUc4wmWHoCklTKoRNXEvn3tzs4Zr6QCPC/kW0Z0Lq2HaMSJSU2NoWXX17HzJnbqV69CvPmDWHw4Kb2Dks4iMLelJ8KDAF2Y9yIX4zR0vA0YFzphCfKikMX4nj2x93sO2v0R1KnamXuvTmIh7s1wNVFntpyFkOGLGDt2hM89thNvPlmT3x8pCFHYb3CrlAGAq211slKKV/gnPn5cOmEJuwtMTWD2X+eZO5fpzkbY7S15e/pxox72nFzA6nachYREdEEBHjg5eXOW2/1pEIFxU03SVe8ovgKSygpWutkAK31VaXUIUkm5cPaQxeZvHgf52P/aSChW4g/T9wWIo//OpG0tEzeffdP3nhjI0880YFp03pLi8DihhSWUBoopbKbqFdAsMVntNaDbRqZKFVaa3aciua1n/fnVGu1qONNWD1fJt/RlIpSreVUNm48xbhxyzl48ApDhzbjiSfkyTxx4wpLKEPyfJ5hy0CE/Ry9GM8j3+0k4koiAH2b1+SJ20KkwUYn9cEHW5g48TeCg6uyYsU99O8fYu+QhJMorHHI30szEGEfW45HMfILo0fn20Kr85++oTSpKV20OpusLE1iYhpeXu7ccUdjLl9O4qWXuuPhUdHeoQknIm8olWNrDlzkIbMZ+Q9HtGFQW7kR64z277/EuHErcnpObNzYj7ffvs3eYQknZNOKcaVUX6XUYaXUMaVUvn2oKKWGK6UOKKX2K6Xm2jIeAZlZmm82n6Db9LU5yWT60FaSTJxQUlI6L7ywhjZtPufgwcvceWcIRfXQKsSNsPoKRSnlrrVOLcb0LsBMoDcQCWxXSi3TWh+wmCYEeAHoorWOVkpJG2E2tDcylgEz/sg1bMUTXWle28dOEQlb2bXrPIMHL+DkyRhGj27D9Om98ff3sHdYwskVmVCUUh2ArzDa8ApSSrUGHtJaTyjiqx2AY1rrCHM+8zDebTlgMc2/gZla62gArfWl4q+CKEpcSjqjvtpG+JkYAEaE1WXi7Y2p4S0vrTkbrTVKKYKCfAgK8uH//m8Q3bvXs3dYopyw5grlY+BOYAmA1nq3UupWK75XBzhj8TkSyPtsYmMApdRmwAV4VWu9yop5Cyst3hXJ0/N3A+DmWoHX7mrOyA5Bdo5KlLSMjCxmzNjGsmWHWb36fvz8PNiw4UF7hyXKGWsSSgWt9ak8zZBnFjSxhfzaLc9bgesKhAA9MNoG26SUaqG1jsk1I6UeBh4GCAqSg6G1pizdx7dbTgHw+sDmjOoUbN+AhE1s23aWceOWs2vXBfr1a0RcXCrVqkmHV6L0WZNQzpjVXtq8LzIBOGLF9yKBuhafAzGab8k7zVatdTpwQil1GCPBbLecSGs9C5gFEBYWJncVi3AhNoVRX//FkYtGA45z/92Rzg397RyVKGkJCWk899xqPv10B7VqefHjj8MYMqSp9EEj7MaahPIoRrVXEHARWGMOK8p2IEQpVR+jU667gXvyTLMEGAnMVkr5Y1SBRVgXushr3rbTzP7zJIcuxAPQpIYX8x6+mWpV3OwcmbCFihUrsH79KSZM6MAbb/TE29vd3iGJcs6ahJKhtb67uDPWWmcopR4HfsW4P/K11nq/Uup1YIfWepk57nal1AGMarRJWuuo4i6rPNNaszT8HP9ZuIe0zCzA6L99TNf6DA+rW8S3haM5duwqr7++gZkz++Pl5c7OnQ9TqZK8TibKBlXUc+lKqePAYWA+8JPWOr40AitIWFiY3rFjhz1DKBM2HLnMzHXH2Hbias4wvypurJ/UA69K8vazs0lNzWD69M289dYm3NxcWLHiHrp1k6e3hPWUUju11mG2XIY1PTY2VEp1xqiyek0pFQ7M01rPs2VgIn/bT15lztZTLAk3bke5VlDc0zGIp3s1lqotJ7Vu3QkefXQFhw9HMWJEc95/vw+1a0vzOKLssepaWWv9J/CnUupV4ENgDiAJpRR9t+Ukn6w/ntOk/M0NfHn29iaESXPyTk1rzVtvbSI9PYtVq+6lT59G9g5JiAJZ82KjJ8YLiXcDTYGlQGcbxyVMWmsGzPgjp0n5Xk1r8EL/UBoGeNo5MmErWVmar776m759G1G3rg/fffcvqlatROXKUpUpyjZrrlD2AT8D07XWm2wcj7Dw5aYIZv95ksjoZHyruLHgkU40qi6JxJnt2XORceOWs2VLJFOmdOe1126lVi2p3hKOwZqE0kBrnWXzSEQukxfvZc5fpwGjf5JP7m1HhQryfoGzSkhI47XX1vPBB1upVq0ys2cPZNSo1vYOS4hiKTChKKXe01o/AyxSSl3zKJj02GgbWmsGzdzM7shYAHZPuR0f6bPC6b366nree28LDz3Ulnfe6YWfnzTkKBxPYVco883/pafGUrL+8CUe/OafRgLCp/SWZOLEzpyJJTExndBQf55/viuDBoXStas0LSQcV4H9oWitt5l/NtVa/275D+PmvCghaRlZTFt1KCeZNPCvQsTb/anqIY8BO6OMjCzef38LTZvO5JFHlgPg7+8hyUQ4PGvuoYzh2quUsfkME9dh31mjjxKtwaWCYuljXWhRR/oncVZbt0Yybtxydu++yB13hDBjRn97hyREiSnsHsoIjEeF6yulfrIY5QXE5P8tURw//R3JxAVG0/IjOwQxoWcjaleVVmKd1YoVRxgw4Adq1/bip5+GM2hQqDTkKJxKYVco24AojFaCZ1oMjwd22TKo8mDWxuO8vfIQAM/e3pjHe4bYOSJhC1przp2Lp04db3r1asDrr9/Kk092xMtLGnIUzqfItrzKGkdvyyv8TAz//nYHl+ON3pQ/u68dfVvUsnNUwhaOHIli/PgVHDkSxYEDj+HpKffEhP3YtS0vpdQGrfUtSqlocneMpQCttZY2P4ohIzOLF37ay487IwHwcHNh1ZPdCZLHQ51OSkoG77zzB1On/kHlyq5MnXoblStLi8DC+RVWyrO7+ZWemW7Qn8eucM+Xf+V8/ujuNgxsU8eOEQlbuXAhge7dv+Ho0auMHNmC99/vQ82a0rqBKB8KTCgWb8fXBc5prdOUUl2BVsD3QFwpxOewMrM0qw9c5NstJ/nzuNHFS9/mNZlxT1tcXQp8Wls4qPT0TCpWdKFGjSp0716PmTP707t3Q3uHJUSpsuY6fAlwk1KqIfAtsAKYC9xpy8AcVUJqBuPn/M3GI5dzhnlXcuWF/k0Z2UHeM3A2WVmaWbN28vbbm/jzz7EEBnrz5Zd32TssIezCmoSSpbVOV0oNBj7UWn+slJKnvPIx569TTF68D4A6VStz781B9Gpag8Y1pHE/Z7R79wUeeWQ5f/11lp4965OenmnvkISwK6u6AFZKDQPuBwaZw6Q9EAtZWZrXlx9g9p8nARjcrg7vDWst7xg4Ka01kyat5sMPt+LrW5nvvvsX997bUn5vUe5Z+6b8eIzm6yOUUvWBH2wbluNISc/kzv/9wbFLCQB8O6YD3RsH2DkqYUtKKaKjkxk71mjIsVo1eRlVCLDyPRSllCuQ3VXcMa11hk2jKkRZeg9Fa83Qz7aw81Q0vZvV4H8j21Kpoou9wxI2cOpUDE8+uYopU26hXbtaZGVp6U5AOJTSeA+lyMeNlFLdgGPAV8DXwBGlVBdbBuUIloafpeu0dew8FQ3ArPvbSzJxQunpmUyfvplmzT5h9eoIDh++AiDJRIh8WFPl9QHQX2t9AEAp1RT4DrBppiurjl9O4JkFuwk/YzRn1i6oKl+MCpP6cyf0559neOSR5ezbd4mBA5vw8cf9CAqShjuFKIg1CcUtO5kAaK0PKqXKbRsSd3y8iZT0LKq4ubD08S40qi5PcDmrNWsiiI1NYcmSEQwcGGrvcIQo86xJKH8rpT7HuCoBuJdy2DhkVpZm4oJwUtKzGNY+kP8Ok+5ZnY3Wmu++20NAgAf9+oXw3HNdmDixk7TBJYSVrHllexxwHPgP8BwQATxiy6DKoum/HmZJ+DkaBlTh5QHN7B2OKGGHDl2hZ89veeCBJXzzTTgA7u6ukkyEKIZCr1CUUi2BhsBirfX00gmp7PlyUwSfbThOkxperHqqm9wvcSLJyem8/fYmpk3bTJUqbnz++Z089FA7e4clhEMqrLXhFzF6Zvwbo+mV17XWX5daZGVARmYWo2dvZ9NR48meeQ/fLMnEyfz88xHefHMT993Xinff7U2NGtKQoxDXq7ArlHuBVlrrRKVUALAS47HhcuHAuTj6f7wJgEoVK7DksS5UqyLVH87gwoUEwsMv0LdvI4YNa0Zw8EN06CCtPwtxowpLKKla60QArfVlpVS5aiL3+Z/2ADC4bR2mDW1FRWkh2OFlZmbx+ec7eeGF33Fzc+H06aeoXLmiJBMhSkhhCaWBRV/yCmho2be81nqwTSOzow1HLrMnMpZWgT68P6KNvcMRJeDvv88zbtxytm8/R69eDfjkk/5UrixN0glRkgpLKEPyfJ5hy0DKknd+Mfp6/0CSiVM4cSKaDh2+wN/fg7lzB3P33S3kXpgQNlBYB1u/l2YgZcULP+3l4Pk4Qmt60TBAbtA6Kq01e/deolWrGtSvX41vvhnIgAFNqFq1kr1DE8JpyY0BCxfjUvhh22kAXruruZ2jEdfrxIlo7rzzB9q2/Zw9ey4CcP/9rSWZCGFjNk0oSqm+SqnDSqljSqnnC5luqFJKK6Xs2j7Y+Dl/AzBtSEs6NvCzZyjiOqSlZfLOO3/QvPknbNhwknff7U2zZtKVgBClxZqmVwBQSrlrrVOLMb0LMBPoDUQC25VSyyzbBTOn8wKeAP6ydt62kJyWmdNy8IibpKteR5OZmUXnzl+xc+d5Bg9uyocf9qFuXWnIUYjSZE3z9R2UUnuBo+bn1kqp/1kx7w4YfadEaK3TgHnAwHymewOYDqRYH3bJGz17GwCT+ze1ZxiimOLijHMcF5cKjBnTlp9/HsmiRcMlmQhhB9ZUeX0M3AlEAWitdwO3WvG9OsAZi8+R5rAcSqm2QF2t9fLCZqSUelgptUMptePy5ctWLLp4PlpzlK0RV6ni5sK/uzco8fmLkqe1ZvbscBo0+IilS42n8saPv4k772xs58iEKL+sSSgVtNan8gzLtOJ7+T2XmdM9pPmi5AfAM0XNSGs9S2sdprUOCwgo2TrxczHJfLDmCC4VFOsnWZMnhb0dOHCZHj3+j9GjlxIa6k/Dhr72DkkIgXX3UM4opToA2rwvMgE4YsX3IoG6Fp8DgXMWn72AFsB6852AmsAypdRdWutS6eNXa82gmZsBWDiuEwFe7qWxWHEDpk/fzOTJa/H2dufLLwcwenRb6T1RiDLCmoTyKEa1VxBwEVhjDivKdiBEKVUfOAvcDdyTPVJrHQv4Z39WSq0Hni2tZAKwNPwcl+JT8arkStugaqW1WHEdtNYopahZ05N7723Jf//bm4CAKvYOSwhhociEorW+hJEMikVrnaGUehz4FXABvtZa71dKvQ7s0FovK3a0JSg1I5On5hv9XqyZeIs9QxGFOHcuniefXEW3bkE88URHRo1qzahR0rmZEGVRkQlFKfUFFvc+smmtHy7qu1rrlRitFFsOm1LAtD2Kml9JevIHI5nc0bIWNbzlhbeyJjMzi08+2c7kyWtJT8+ic+dAe4ckhCiCNVVeayz+rgT8i9xPbzmc3w9eZNX+CwC8K135ljnh4Rd46KFl7Nx5nttvb8gnn/SXG+9COABrqrzmW35WSn0HrLZZRKVg7P8Zt2neHNSCym4udo5G5BUbm8K5c/HMnz+UYcOaSUOOQjgIq9+Ut1AfqFfSgZSWfWdjAejUwI/7bnbY1XAqWmt+/PEAR49GMXlyd265JZiIiCepVOl6iqcQwl6seVM+Wil11fwXg3F18qLtQyt56ZlZ3D1rKwBP9QqxczQC4Pjxq/TvP5cRIxaydOlh0tONV5wkmQjheArda5VR19Aa47FfgCyt9TU36B3FrI0RJKRmcN/NQdL4o52lpmbw7rt/8uabm6hYsQIffdSX8eNvwtVVGsAWwlEVmlC01loptVhr3b60ArKlLzZFADCpT6idIxFnzsTxxhsbGTCgCR9+2Ic6dbztHZIQ4gZZczq4TSnVzuaR2FhsUjoxSen0DK2Oj3T9aheXLycyY4bRCGejRr4cOPAYP/44TJKJEE6iwCsUpZSr1joD6Ar8Wyl1HEjEaKNLa60dKsl8u+UkAKO7BNszjHIpK0vzzTe7+M9/1hAfn0rv3g1o0sSfBg2kdQIhnElhVV7bgHbAoFKKxWYuxqXw3uojVPdyp0tD/6K/IErMvn2XePTRFfzxx2m6dQvis8/upEkT+Q2EcEaFJRQFoLU+Xkqx2MyX5r2T94a3loYES1FaWia33/4daWmZfP31XTz4YBt5p0QIJ1ZYQglQSk0saKTW+n0bxGMTuyONd0+6NpIz49Kwdu0JbrmlHm5uLixYMIzQUH/8/T3sHZYQwsYKuynvAnhiNDOf3z+HEJuUzrYTV+nayF/Ojm0sMjKOIUMWcNtt3/Ltt7sB6No1SJKJEOVEYVco57XWr5daJDay8O9IAIaFSeOCtpKRkcWMGdt4+eV1ZGZmMXXqbdx7byt7hyWEKGVF3kNxZJlZmnd+OUh1L3f6t6xl73Cc1v33L2bevH3069eImTP7U7++PL0lRHlUWEK5rdSisJHtJ6+Snqm5u0MQFV3kDeySFBOTgqtrBTw93XjssZsYMqQpQ4Y0lWpFIcqxAo+yWuurpRmILfx5PAqAsV3r2zkS56G1Zt68fTRtOpOXX14LGPdJhg6VVoGFKO+c+rR97aGLeLi5yJvxJeTYsav06fM9I0cuIjDQm/vuk/skQoh/OHWTrplZkJHlsG1Zlilz5+5lzJiluLu7MmNGP8aNC8NFqhGFEBacNqFciE3h4Pk4xnSR6q4bkZ6eScWKLoSF1Wbo0GZMn96b2rUd5qlxIUQpctqEMvevUwD0bVHTzpE4pkuXEnnmmd9ITEzjp59G0LixH99/P9jeYQkhyjCnrbNISDU6ampfTx5hLY6sLM2sWTtp0mQG8+fvo3nzADIzs+wdlhDCATjtFUr4mWgCvNxxkba7rBYREc199/3Eli2R9OgRzKef3kFoqDRXI4SwjtMmlOT0LDzdnXb1bMLHx52YmBT+7/8Gcf/9reQxYCFEsThllVdKeiYHz8fRuaF081uUZcsOM3jwfDIzs/Dz82DfvvGMGtVakokQoticMqEcvhAPQE3vSnaOpOw6fTqWQYPmMXDgPI4cieL8+QQAad5fCHHdnLJOaJHZIKQ84XWtjIwsPvxwK6+8sh6tNdOm9eLpp2+mYkUXe4cmhHBwTplQ0s2nkhpV97RzJGVPZmYWX375Nz171ud//+tHcHBVe4ckhHASTlnldfB8PCHVPeU+gCk6OpnnnltNfHwq7u6ubN48hmXL7pZkIoQoUU6XUKISUgk/E0OH+r72DsXutNbMmbOH0NCZvPfeFtatOwmAn5+HJFshRIlzuiqvT9cfB+Cm4PKdUI4ciWL8+BX8/vsJOnSow6+/3kebNnJPSQhhO06XUC4npALQr2X5Png+9dQqduw4xyef9Ofhh9tLQ45CCJtzuoRyLiaZBv5VcHctf08trV59nNBQf+rW9eHTT+/A3d2VmjXlwQQhROmw6WmrUqqvUuqwUuqYUur5fMZPVEodUErtUUr9rpSqd6PLPHwhnsY1yldruBcuJHDPPYu4/fbvmTZtMwD16lWVZCKEKFU2SyhKKRdgJtAPaAaMVEo1yzPZLiBMa90KWAhMv5FlpmVkEZeSQUiN8nEgzcrSfPbZDkJDZ7Bo0UFeeeUW3n33dnuHJYQop2x5hdIBOKa1jtBapwHzgIGWE2it12mtk8yPW4HAG1ngjpNGr8XBflVuZDYOY+rUTTz66Arat6/Nnj3jePXVHlSq5HS1mEIIB2HLo08d4IzF50igYyHTjwV+yW+EUuph4GGAoKCgAmfw0pJ9ALQM9ClepA4kPj6VK1eSqF+/GuPGhVG/fjVGjmwhjwELIezOllco+R3h8u2PVyl1HxAG/De/8VrrWVrrMK11WEBAQL4LS8/MIuJKIiHVPZ3yHorWmsWLD9Ks2SeMGLEQrTV+fh7cc09LSSZCiDLBlgklEqhr8TkQOJd3IqVUL2AycJfWOvV6F3bmqlFzdkerWtc7izLr1KkY7rprHoMHL8DXtzIff9xPkogQosyxZZXXdiBEKVUfOAvcDdxjOYFSqi3wOdBXa33pRha27YRx/yS0pnNdnWzZcoZevb4D4N13e/Pkkzfj6irvlAghyh6bJRStdYZS6nHgV8AF+FprvV8p9TqwQ2u9DKOKyxP40TzjPq21vut6lnfiSiIAYU7yhnxcXCre3u60a1eLMWPaMGlSF4KCnPfekBDC8dn0kSCt9UpgZZ5hUyz+7lVSy/p8YwSVK7rg7+leUrO0i6ioJJ5/fg2//RbB/v3j8fR043//62/vsIQQokhO8YxpQmoGAI58W0FrzXff7eGZZ34jOjqZiRM7OfT6CCHKH6dIKGsPGbdfXugXaudIrk9sbAqDBs1n/fqTdOoUyGef3UmrVjXsHZYQQhSLUySURTuNHhp7NKlu50iKR2uNUgpvb3f8/T2YNetOxo5tJ93wCiEcklM8LuRVyZUqbi7U9fWwdyhW+/XXY7RrN4vIyDiUUvz44zD+/e/2kkyEEA7LKRLK8j3nCXGQlxnPn4/n7rsX0rfvHJKS0rl0KdHeIQkhRIlw+Cqv7BcaA6tVtnMkRZs5cxsvvriW1NQMXnutB8891wV3d4f/CYQQAnCChHLkYjwA/VuW/Tfkd+48T8eOdZg5sz8hIX72DkcIIUqUwyeUTUevANC8tredI7lWXFwqU6as4/77W9G+fW0++eQO3N1dpNkUIYRTcviEsv6w8chwUBm6Ia+1ZtGigzz55CrOn48nKMiH9u1rS9PyQgin5vBHuLMxyfhWcSszZ/0nTkTz+OO/sHLlUdq0qclPPw2nY8cb6uZFCCEcgkMnlKS0DNIzNf1b1rR3KDnmzNnLxo2n+OCDPjz+eAdpyFEIUW44dELZfCwKgHZB1ewax6ZNp0hNzaRXrwZMmtSZBx9sQ2Bg2bunI4QQtuTQp88nzRaGWwVWtcvyr1xJYsyYpXTvPpvXX98AgLu7qyQTIUS55NBXKGmZWQAEeJVuC8Naa2bPDmfSpNXExqby3HNdePnl7qUag3Ac6enpREZGkpKSYu9QRDlQqVIlAgMDqVixYqkv26ETyq7T0QB4l/LTUytXHmXMmGV06VKXzz67kxYtHKsNMVG6IiMj8fLyIjg4uMw8PCKck9aaqKgoIiMjqV+/fqkv36GrvNxdXQBKZSdNSkpn8+bTAPTvH8LSpXezceNoSSaiSCkpKfj5+UkyETanlMLPz89uV8MOnVBW7jtPs1q2v1/xyy9HadHiE/r1m0NMTApKKe66q4k05CisJslElBZ7ljWHTShaa7SG1IxMmy3j7Nk4hg37kf795+Lu7srPP4+katVKNlueEEI4ModNKFcT0wDo28I276BcupRIs2afsHz5Ed5881Z27x7HLbcE22RZQtiSi4sLbdq0oUWLFgwYMICYmJiccfv376dnz540btyYkJAQ3njjDbTWOeN/+eUXwsLCaNq0KaGhoTz77LP2WIVC7dq1i4ceeijXsIEDB9KpU6dcwx588EEWLlyYa5inp2fO30eOHKF///40atSIpk2bMnz4cC5evHhDsV29epXevXsTEhJC7969iY6OvmaadevW0aZNm5x/lSpVYsmSJQCMHTuW1q1b06pVK4YOHUpCQgIAM2bM4Jtvvrmh2GzCONN3nH/t27fXWmv9x9HLut5zy/VPf5/RJSkyMjbn748+2qqPHYsq0fmL8ufAgQN2XX6VKlVy/h41apR+8803tdZaJyUl6QYNGuhff/1Va611YmKi7tu3r54xY4bWWuu9e/fqBg0a6IMHD2qttU5PT9czZ84s0djS09NveB5Dhw7V4eHhOZ+jo6N1YGCgDg0N1RERETnDH3jgAf3jjz/m+m72tklOTtaNGjXSy5Ytyxm3du1avXfv3huKbdKkSXrq1Klaa62nTp2q//Of/xQ6fVRUlK5WrZpOTEzUWmsdG/vP8ejpp5/OmVdiYqJu06ZNgfPJr8wBO7SNj88O+5RXdrP1Qb5VSmR+sbEpvPTSWj7/fCdbtz5Eu3a1eOKJjiUybyGyvfbzfg6ciyvReTar7c0rA5pbNW2nTp3Ys2cPAHPnzqVLly7cfvvtAHh4eDBjxgx69OjBY489xvTp05k8eTKhoUbX2q6urowfP/6aeSYkJDBhwgR27NiBUopXXnmFIUOG4OnpmXNGvXDhQpYvX87s2bN58MEH8fX1ZdeuFavFKwAAEEFJREFUXbRp04bFixcTHh5O1arG+2SNGjVi8+bNVKhQgXHjxnH6tPEwzIcffkiXLl1yLTs+Pp49e/bQunXrnGGLFi1iwIAB1KhRg3nz5vHCCy8UuV3mzp1Lp06dGDBgQM6wW2+91aptWpilS5eyfv16AB544AF69OjBtGnTCpx+4cKF9OvXDw8Po21Cb2/jHrHWmuTk5Jz7Ix4eHgQHB7Nt2zY6dOhww3GWFIdNKJfjUwEI9ruxRiG11vz44wGeemoVFy4k8PjjHWjY0L5v3gthC5mZmfz++++MHTsWMKq72rdvn2uahg0bkpCQQFxcHPv27eOZZ54pcr5vvPEGPj4+7N27FyDfap28jhw5wpo1a3BxcSErK4vFixczevRo/vrrL4KDg6lRowb33HMPTz/9NF27duX06dP06dOHgwcP5prPjh07aNGiRa5hP/zwA6+88go1atRg6NChViWUffv2XbMt8hMfH0+3bt3yHTd37lyaNWuWa9jFixepVcvoWqNWrVpcunSp0PnPmzePiRMn5ho2evRoVq5cSbNmzXjvvfdyhoeFhbFp0yZJKCXhb/MdFN8qbtc9D601gwcvYMmSQ7RrV4tly0YSFla7pEIU4hrWXkmUpOTkZNq0acPJkydp3749vXv3BozyX9ATQcV5UmjNmjXMmzcv53O1akWfkA0bNgwXF+Ox/xEjRvD6668zevRo5s2bx4gRI3Lme+DAgZzvxMXFER8fj5fXP72znj9/noCAgJzPFy9e5NixY3Tt2hWlFK6uruzbt48WLVrku07FfSLKy8uL8PDwYn3HWufPn2fv3r306dMn1/BvvvmGzMxMJkyYwPz58xk9ejQA1atX59ChQzaJ5Xo55E35zCzNusOXget7RC49PTPnu1271uXjj/vy/+3dfZRU9X3H8fcnK7IglDQgNITIg4gsu4I8JAE8EYUUjbVQOJaVZ3IwKhDRINj20HO6LY15MthSMECtB/Ak2xUJyoNCo4UQlZWHsgKCIeuyGiyEBSkggsDy7R/37uywzLqzMA87y/d1zpwzc+fOvd/5ntn57v3dO9/fli0PeDFxjVKzZs0oKSnhgw8+4OzZsyxYsACA3Nxctm3bdtG6ZWVltGjRgpYtW5Kbm8v27dvr3H5thSl6Wc3fRVx3XfVQ9YABAygtLaWiooKXXnqJkSNHAnDhwgU2b95MSUkJJSUlfPTRRxcVk6r3Fr3toqIijh07RufOnenUqRPl5eWRYte6deuLjp4+/vhj2rRpE8lFPO/15MmTF51Aj75FF78q7dq14+DBg0BQMNq2rf13ay+88AIjRoyI+Qv3rKws8vPzWbFiRWTZmTNnaNasYc1Um5EF5egnwXBX9z+r/zzyGzeW07PnQl5+Oajsjz8+kEce+QZZWRmZCufi1qpVK+bNm8dTTz3FuXPnGDt2LG+88QavvfYaEBzJTJ8+nSeeeAKAWbNm8eSTT7Jv3z4g+IKfO3fuJdsdOnQo8+fPjzyu+tJu164de/fujQxp1UYSI0aMYMaMGeTk5NC6deuY2411ZJCTk0NpaWnkcWFhIevWraO8vJzy8nK2b98eKSh33HEHRUVFnD0bXCG6ZMmSyHmSMWPG8NZbb7F27drIttatWxcZxqtSdYQS61ZzuAtg2LBhLF26FIClS5cyfPjwWvNQWFjI6NGjI4/NLPLezIzVq1dHzmdBMGxYc7gv7ZJ91j/Rt759+9rCjaXW8W/W2PrdB2u9yqGmw4c/sQkTVhoUWOfO/2Kvv15W94ucS4CGdJWXmdm9995ry5YtMzOznTt32qBBg6xbt2524403WkFBgV24cCGy7urVq61Pnz7WvXt3y8nJsZkzZ16y/ZMnT9qECRMsNzfXevbsaStWrDAzs+XLl1uXLl1s0KBBNm3aNJs4caKZxb7aauvWrQbYkiVLIssqKips1KhRdsstt1hOTo499NBDMd9fXl6enThxwvbv32/t27e/KH4zs969e1txcbGZmRUUFFheXp716tXLRo4caYcPH46st3fvXrvrrrusa9eulpOTY/n5+Xbo0KHPzW1djhw5YoMHD7auXbva4MGD7ejRo5H3O3ny5Mh6VbFXVlZGllVWVtrAgQMtLy/PcnNzbcyYMRdd9dW7d2+rqKiIud90XeWlYD+Zo1+/ftbv0UWse/cQ7z95D1lx/Fq9sHAX06a9wiefnGXWrIHMnn07zZunvnGauzrt3buXnJycdIfRaD399NO0bNnykt+iNGY7duxg7ty5PP/88zGfj/WZk7TdzPolM66MHOcpP3qKptd8Ia5iAnD+/AXy8tpSUvIwP/jBEC8mzjUiU6ZMoWnT1HYcT7cjR44wZ86cdIdxiYy8yuuPJ87QqXXtvz85deosc+Zs4oYbWjF16tcYN64n48b19H5KzjVC2dnZjB8/Pt1hpFTVlXoNTcYdoZw+V8mxT8/x1/1iz9O+Zs0+cnOf4cc/fpN9+4IZHSV5MXFplWlDyy5zpfOzlnFHKKc+Ow9A344XX+t+4MAJpk9/lZUr36NHj+vZtGkS3/xmx3SE6NxFsrOzOXr0qLewd0lnFsyHkp2dnia2GVdQPj1bSXMgp0bb+rKyY6xf/z4//OEQZswYwLXXZqUnQOdq6NChAwcOHKCioiLdobirQNWMjemQcQXl+Olz3HbDF8luksWWLR+xefMfePTR/tx+e0c+/PAxWl9hKxbnEq1JkyZpmT3PuVRL6jkUSXdL+p2kUkl/G+P5ppKKwuffltQpnu12v74FU6eupX//Z5k7t5hTYSt7LybOOZc+SSsokrKABcC3gR7AaEk1f0o6GThmZl2Bp4Ha23BGWfbzbSxatJ3p07/Brl1TuO4K+nk555xLjGQOeX0dKDWzMgBJ/wkMB6Ib3gwHCsL7LwLzJcnquEzherJYvfW79Onz5cRH7Zxz7rIks6B8BfhD1OMDQM0JRiLrmNl5SceB1sCR6JUkPQg8GD787J1D39kdR6fpq0EbauTqKua5qOa5qOa5qHZzsneQzIIS6/rImkce8ayDmS0GFgNI2pbs9gGZwnNRzXNRzXNRzXNRTdK2ute6Msk8KX8A+GrU4w7A/9a2jqRrgFbAx0mMyTnnXJIks6BsBW6S1FnStcD9wKoa66wCJob37wP+u67zJ8455xqmpA15hedEvgesB7KA58zsXUn/RNBGeRXwH8DzkkoJjkzuj2PTi5MVcwbyXFTzXFTzXFTzXFRLei4yrn29c865hinjmkM655xrmLygOOecS4gGW1CS1bYlE8WRixmS9kjaKel1SY22zXJduYha7z5JJqnRXjIaTy4kjQo/G+9K+mWqY0yVOP5GbpC0QdKO8O/knnTEmWySnpN0WNLuWp6XpHlhnnZK6pPQAJI9x/Dl3AhO4r8PdAGuBd4BetRYZyqwMLx/P1CU7rjTmIs7gebh/SlXcy7C9VoCm4BioF+6407j5+ImYAfwp+HjtumOO425WAxMCe/3AMrTHXeScnE70AfYXcvz9wCvEvwGsD/wdiL331CPUCJtW8zsLFDVtiXacGBpeP9FYIga52QTdebCzDaY2afhw2KC3/w0RvF8LgDmAD8BzqQyuBSLJxffBRaY2TEAMzuc4hhTJZ5cGFA150UrLv1NXKNgZpv4/N/yDQeWWaAY+KKkhPWwaqgFJVbblq/Uto6ZnQeq2rY0NvHkItpkgv9AGqM6cyGpN/BVM1uTysDSIJ7PRTegm6Q3JRVLujtl0aVWPLkoAMZJOgC8AjySmtAanPp+n9RLQ50PJWFtWxqBuN+npHFAP2BQUiNKn8/NhaQvEHStnpSqgNIons/FNQTDXncQHLX+VlKemf1fkmNLtXhyMRpYYmY/kzSA4PdveWZ2IfnhNShJ/d5sqEco3ralWjy5QNK3gNnAMDP7LEWxpVpduWgJ5AEbJZUTjBGvaqQn5uP9G3nZzM6Z2X7gdwQFprGJJxeTgRcAzGwzkE3QOPJqE9f3yeVqqAXF27ZUqzMX4TDPIoJi0ljHyaGOXJjZcTNrY2adzKwTwfmkYWaW9KZ4aRDP38hLBBdsIKkNwRBYWUqjTI14cvEhMARAUg5BQbka52ReBUwIr/bqDxw3s4OJ2niDHPKy5LVtyThx5uKnQAtgeXhdwodmNixtQSdJnLm4KsSZi/XAUEl7gEpglpkdTV/UyRFnLh4H/l3S9wmGeCY1xn9AJRUSDHG2Cc8X/QPQBMDMFhKcP7oHKAU+Bb6T0P03wpw655xLg4Y65OWccy7DeEFxzjmXEF5QnHPOJYQXFOeccwnhBcU551xCeEFxDY6kSkklUbdOn7Nup9o6q9ZznxvDbrXvhK1Kbr6MbTwsaUJ4f5Kk9lHPPSupR4Lj3Crp1jhe85ik5le6b+fq4gXFNUSnzezWqFt5ivY71sx6ETQd/Wl9X2xmC81sWfhwEtA+6rkHzGxPQqKsjvMZ4ovzMcALiks6LyguI4RHIr+V9D/hbWCMdXIlbQmPanZKuilcPi5q+SJJWXXsbhPQNXztkHAOjV3hXBNNw+U/UvUcNE+FywokzZR0H0FPtV+E+2wWHln0kzRF0k+iYp4k6d8uM87NRDX2k/RzSdsUzH3yj+Gy6QSFbYOkDeGyoZI2h3lcLqlFHftxLi5eUFxD1CxquGtluOww8Odm1gfIB+bFeN3DwL+a2a0EX+gHwjYb+cBt4fJKYGwd+/9LYJekbGAJkG9mtxB0lpgi6UvACCDXzHoC/xz9YjN7EdhGcCRxq5mdjnr6RWBk1ON8oOgy47yboL1Kldlm1g/oCQyS1NPM5hH0arrTzO4MW7D8PfCtMJfbgBl17Me5uDTI1ivuqnc6/FKN1gSYH54zqCToS1XTZmC2pA7Ar8zs95KGAH2BrWFbmmYExSmWX0g6DZQTtDe/GdhvZvvC55cC04D5BHOtPCtpLRB3q3wzq5BUFvZR+n24jzfD7dYnzusI2oxEz7g3StKDBH/XXyaYSGpnjdf2D5e/Ge7nWoK8OXfFvKC4TPF94I9AL4Ij60smzzKzX0p6G/gLYL2kBwjadS81s7+LYx9joxtJSoo5v07YO+rrBM0G7we+Bwyux3spAkYB7wErzcwUfLvHHSfBrIQ/AhYAIyV1BmYCXzOzY5KWEDRArEnAr81sdD3idS4uPuTlMkUr4GA4f8V4gv/OLyKpC1AWDvOsIhj6eR24T1LbcJ0vSeoY5z7fAzpJ6ho+Hg/8Jjzn0MrMXiE44R3rSquTBO30Y/kV8FcEc3QUhcvqFaeZnSMYuuofDpf9CXAKOC6pHfDtWmIpBm6rek+SmkuKdbTnXL15QXGZ4hlgoqRiguGuUzHWyQd2SyoBuhNMdbqH4Iv3vyTtBH5NMBxUJzM7Q9CNdbmkXcAFYCHBl/OacHu/ITh6qmkJsLDqpHyN7R4D9gAdzWxLuKzecYbnZn4GzDSzdwjmj38XeI5gGK3KYuBVSRvMrILgCrTCcD/FBLly7op5t2HnnHMJ4UcozjnnEsILinPOuYTwguKccy4hvKA455xLCC8ozjnnEsILinPOuYTwguKccy4h/h+DzV6oGiFUawAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7301560712348498" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM default parameters identified 761\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06331396
11261761
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6331 396\n", + "1 1261 761" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.001}" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001,0.01, 0.1, 1]\n", + " gammas = [0.001, 0.01, 0.1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16199599731203465\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM reduced features and tuning RBF kernel identified 732\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06383344
11290732
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6383 344\n", + "1 1290 732" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6727\n", + " 1 0.68 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parameter tuning\n", + "##### Learning rate\n", + "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", + "\n", + "##### Momentum\n", + "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", + "\n", + "##### Loss function\n", + "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Neural Network (5x26) identified 788\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06281446
11234788
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6281 446\n", + "1 1234 788" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.204745415479134\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEWCAYAAACe8xtsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gV1dbA4d9OQhICoYbeQu89goAUEURQBBFRVETBT5FiQ671ioqIIip6wYu9I1xABQERUIqgiPQqvYVOCKTXs74/ZhJPQkhOICcnJ1nv8/CQ6WvmzMya2bNnjxERlFJKqdzw8XQASimlvI8mD6WUUrmmyUMppVSuafJQSimVa5o8lFJK5ZomD6WUUrlWJJOHMeYeY8xST8fhacaYmsaYGGOMbz4uM9QYI8YYv/xapjsZY3YaY7pdwXRXtA8aY4obY340xlw0xszJ7fTewBhz2BjTw9NxXC1jzEvGmK9zMf6Nxpgf3BmTCzFUMsbsNsYE5DSux5OHvaPE2yexU8aYz40xJd25TBH5RkRudOcyCqLMB6WIHBWRkiKS6sm4PMVOYvWuZh4i0lREVuawnEsS5lXsgwOBSkB5EbnjCqbPHFs3O7bpmfqvMcbcf7Xzz2v2+UGMMe2c+tUzxrj0wpox5n5jzBr3RXhVXgNeT+vIdG6McfViwxhzs/37XbDPqR8ZY4IzjdPDGLPJGBNrjDlmjBkEICKngRXAQzktx+PJw9ZXREoCrYDWwLMejueKePJqurBcyedGEd3etYC9IpKS2wmziTkWuM8YE3oVcV1tDLlxHng1D+bjNrldT2PMNUBpEVmXaVBf+wKvZC4uNkpjbZ+qQGOgOvCm07KaADOB5+1xWwEbnab/Bng4x6WIiEf/AYeBHk7dk4FFTt0BwBTgKHAamAEUdxreD9gCRAEHgJvs/qWBT4CTwHF7Y/raw+4H1th/zwCmZIppPvCk/XdVYB5wFjgEPOo03kvAXOBre/kPZrF+pYEv7emPAC8APk5xrAX+A1wE/gZuyDRtduuwFniHfw6musCvQARwzt4JytjjfwU4gHggBvgXEAoI4GePsxKYYM83GlgKhDjFc5+9DhHAvzP/dpnWuzjwlj3+RWCN3S9tmUPt3/Qc8LzTdO2AP4AL9npPA/ydhgswCtgHHLL7vQscs3+DjUBnp/F9gefsfSPaHl4DWG3PK9beHnfa49+CtT9dAH4HWmTaV58GtgGJgJ/zNrBj32DHcRp42+5/1F5WjP2vA077oD1OU2CZ/VueBp7LYpu+DCQByfZ8hmNdAL5gb+czWPtaaXv8tG093I5hdRbz7AaEY+2Dnzn1XwPc79Q9DNgNRAI/A7UyLcPPadyV2McCudxPszonZIr3c+Bt4BTQ1e5XD5CcjhusE2kCkGpvvwtAbfv/tGPyY+CM07y+Bh53OhcssNdjP/B/2Z0L7H5f28OLAd9inUv8s1ivF4GPszs3Zhq2GHjLqXs28Ollxh0AbHfqnglMyOac7AfEpf3Glx0vp5O7u/+R8eCrDmwH3nUaPtX+wcoBwcCPwCSng/Ui0BPrIKoGNLKH/QB8AJQAKgLrgYeddui05NEF68Rj7O6yWCfYqvY8N9o/rD9QBzgI9HLaYZKB/va4xbNYvy+xklEw1oG2FxjuFEcK8IS9c91pr085F9chBRhj/9jFsQ6inlgJtwLWCXLq5XZGsk4eB4AG9vxWAq/bw5pgHXDX2dtiir3ul9u5p9vTV8M6cDvacaUt8yN7GS2xTsSN7enaAtfa6xSKdcJ63Gm+gnWSLZe2vYF7gfL2NGOxTiyB9rBxWPtUQ8DYyyvvNK96TvNug3UCbm/HPNTeZgFO228LVvIpnnmbYiW9IfbfJYFrs9rOWeyDwVgnurFAoN3d/jLb9SXsE5LdPQzrRFbHXuZ3wFeZlvsl1j6U1f7ZDSt5VMY66TW0+6cnD6z9ez/WydcPK1n9ns26rSRj8riq/TRTvJ9jJYNHnbZf5uTh0rHvNP5RoK399x6sY7yx07DW9t+rgPft36gV1gXhDZc7F6T9Vvbfi+zYfS+zXnOAcVmcG0/by1kKtHQaVhlrX+0O3GPHHHyZeU8FZjl1H8S6SNyOtd99jX3OcRpnG3BrtududySE3PyzN1AM1lWhAL/wz9WywboyrOs0fgf+ueL8AHgni3lWwjohOd+hDAZWZHHgGnsH6WJ3/x/wq/13e+Bopnk/i32FZu8cl1zNOY3ra8fRxKnfw8BKpzhOYCcuu996YIiL63D0cst2Oug3X+6gJOvk8YLT8JHAEvvvF4FvnYYFYV0FX3KQYx088c47exbLrJ5pne+6zDo8Dnzv1C1A9xzWOzJt2Vgng36XGS9z8vgvma7I7Om7Om2/YVnsv2nJYzXW3UFIpnEybOcs9sHBzr9TDuv2EhmTxy/ASKfuhlgnMT+n5dbJZn7dgHD778nAbPtv5+TxE/YFj9PvG4dVhJbVuq0kY/K4qv0007ifYyWPAKzjtjdOyYNcHPtOw78CnsQ6Ie+xt8MInO5KsC4YUnE6QQOTgM+dfpfVmeb7EtaF7yrgPZyO8yzWaxkwIlO/TliJJwjrvHOKjHdoA7AufM8B111mvj2xjocGTv2S7G3cAOuCYx7wTabp1gL3Zfe7FZRy8v4istwY0xXrlioE60ergLXhNhpj0sY1WCdlsH7QxVnMrxbWlfxJp+l8sDZ0BiIixphZWDvYauBurEycNp+qxpgLTpP4Ar85dV8yTychWFfpR5z6HcG6Gk9zPG2vdxpe1cV1yLBsY0xFrJ20M9bVqw/WjpMbp5z+jsPaubBjSl+eiMQZYyIuM48QrKuzA7ldjjGmAVaxRBjWb+9HxvJYuHS9x2IVE1TFOpGVsmMAax/JLg5ntYChxpgxTv387flmuexMhgOvAH8bYw4BL4vIQheWm5sYM6vKpfuXH9ZJNE12MTt7AzhgjGmZqX8t4F1jzFtO/QzWfnzChfnm+X4qIonGmAlYV9CDM8Xq0rHvZBVwK9Yd2Gqs5DcEq4jrNxFxGGOqAudFJNppuiNY+2marJZxrR3P4EzHeWaRWNvCeR3XOnVOMsYMxdpmP9r9FmIV6+4RkUsqARhjrsU6nw4Ukb1Og+KxLoD32uO9BizPNHkw1jn4sgrKA3MARGQV1pXFFLvXOawVbSoiZex/pcV6uA7Wj1U3i1kdw7r6CHGarpSINL3Mor8FBhpjamHdbcxzms8hp3mUEZFgEenjHHY2q3QO6yqwllO/mljlsGmqGae93B5+wsV1yLzsSXa/FiJSCqs4x2Qzfm6cxCpWBKwqo1hFRVk5h3XgZfXb5OS/WM9+6tvr8BwZ1wGc1sMY0xnrOcQgoKyIlMEq+kub5nL7SFaOARMz/d5BIvJtVsvOTET2ichgrKKSN4C5xpgS2U1zBTFmdoJL968UrOKO9NBcmZGIRGAVcUzIIr6HM22X4iLyO1bJAFiJPk3lzLPO1J3Tfuqqz7Ceb9yWKdbsjpustsUqrJNyN/vvNVhX/V3tbrC2c7lMtZYyH8tZzXsp1vr+YoyplMXwNNuw7gSyI2TcThOxinWrGGOcEyjGmNZYdz3DROSXLJZ12X3CfthfD9iaXTAFKnnYpgI9jTGtRMSBVTb+jn21gjGmmjGmlz3uJ8ADxpgbjDE+9rBGInIS60d7yxhTyh5W176zuYSIbMYqV/wY+FlE0jLueiDKGPO0Xb/e1xjTzK4ZkSOxqsD+D5hojAm2k9OT/HNnA9aJ5lFjTDFjzB1Y5cqLc7sOtmDsB4HGmGpY5f3OTmOVjV+JuUBfY0xHY4w/VvFMlge8/bt9CrxtjKlqb7cOrtQdt9chCogxxjQCHnFh/BSs38/PGPMi1p1Hmo+BCcaY+sbSwhiTlvQyb4+PgBHGmPb2uCXsao8ZrggvxxhzrzGmgr3+aftQqh2bg8tv+4VAZWPM48aYAHtfae/KMrEufJ4wxtQ2VhX317CKnnJdG8v2NtbzqcZO/WYAzxpjmgIYY0rb+yoichbrBHqv/TsPI+dEmNN+6hJ7HV/CunhI65fTcXMaqG7vw2nT7MO6SL0Xq+gprcLD7djJQ0SOYVWgmGSMCTTGtMC60/zGhTgnY90B/GKMCbnMaIuxkhWQ/g5WJ2OMv728cVh302vt4V2AB7AqsdwH/MfelhhjmgFLgDEi8iOX+gzrvFnHGBNkbz/nO+R2wGEROZLFtOkKXPKwd8YvsWrzgLVi+4F1xpgorNurhva467E24DtYV5ur+Ocq7D6sIoddWLeEc4Eq2Sz6W6AH1o+cFksq0Bfr4dghrCvqj7Gudlw1Buvq7CDWFc1MrBNrmj+B+va8J2LdYqYVB+V2HV7Geuh7EesB3XeZhk8CXjBW/e+ncrEOiMhOe11mYd2FRGM9sEu8zCRPYT2Q+wurdsobuLa/PYVVdBiNdTKfncP4P2OVye/FKkZIIGPxwdtYCXwpVlL6BKscGawTzxf29hgkIhuwnnlNw9re+7HKyF11E7DTGBODVQPsLhFJEJE4rN92rb2sa50nsotCemLta6ewapJd7+IyP8Uqs1+NtY8mYP1OV8Q+cU7GqpCQ1u97rN9vln0M7sB61pDm/7ASQARWrbHfc1hMTvtpbnyLtT86y+64+RXYCZwyxpxzmmYVECEiR526DbDZaZzBWM94TgDfA+NFZJkrQYrIBKwH+cuNMeWyGL4JuOh00RCMdRceiZWcbwJ6i0iEMaYU1jlytIgct4usPgE+s0sxxmIV+X9i/nlHZKfTsj61p/8T65hJxKqAkOYerAuGbKXVMFIeYKyXsB4Ukes8HUtu2Ve5F7CKlw55Oh6lvJ0x5kasyg/9PRhDRazE2VpEErIbt8DdeaiCyxjT1xgTZJfjT8G6szjs2aiUKhxEZKknE4cdwxkRaZxT4gBNHip3+mHdsp/AKmq7K4caJEqpQkqLrZRSSuWa3nkopZTKtYLykqDLQkJCJDQ01NNhKKWUV9m4ceM5EamQV/PzuuQRGhrKhg0bPB2GUkp5FWNMtu9t5JYWWymllMo1TR5KKaVyTZOHUkqpXNPkoZRSKtc0eSillMo1TR5KKaVyzW3JwxjzqTHmjDFmx2WGG2PMe8aY/caYbcaYNu6KRSmlVN5y553H51jNCF9Ob6z2keoDD2E1P6yUUioPiQhJKY48n6/bXhIUkdXGmNBsRukHfGk3rLfOGFPGGFPF/piLUkopF+w5Fc3CbSdISE4lOVVISnWw9dgFohNSOHEhnhSHe9ov9OQb5tXI+NGecLvfJcnDGPMQ1t0JNWvWzJfglFLKk0SExBQHickOjkXGEZeUyt7T0aSkOohOSOGnHafYdTIqffxivoYgfz+K+frg6wOJSQ78o5M5v+scFUoH5nl8nkweWX3CNMsUKSIfAh8ChIWFaTPASimv5XBYdwfnYhKJjE0mLimFnSeimLMxHBHBIcLe0zEuzat4MV9uaVGFe66tRasaZdL7iwhhYR9xdM85Xnnleh59tD3Fit2bp+vhyeQRDtRw6q6O9Z0IpZTyasfOxxGTmEJiioNNRyI5dC6W2RuOufTs4cYmlWhSpRTRCSm0qVWWAD8fUhxC06qlCPL3pWqZ4pQp7k+Anw8+PhmvwX///RjNm1ckODiAjz/uS0hIEDVq5Oar2a7zZPJYAIw2xswC2gMX9XmHUsqbiAjhkfHEJqWwZt85dp2M4rtNx7Mct1wJfypVCKRX00r4+/ngawxlgopRtUxxihfzpWnV0hT3972iOCIi4njmmeV8/PFmxo/vyksvdaN16yo5T3gV3JY8jDHfAt2AEGNMODAeKAYgIjOAxUAfYD8QBzzgrliUUupqpaQ62HUyitjEVA6cjWH2X8fYfvziJeNVLR1IaEgJhlxbi4BiPgT4+dK4SinKlfDP85hEhC+/3MpTTy0jMjKeceM6Mm5cxzxfTlbcWdtqcA7DBRjlruUrpdSVcDisu4nIuCSiEpKZtf4Y52IS+fPQ+UvG9fUx9G9VjRsaV6R4MV/a1CpL6eLF8i3Wp59ezptv/k7HjjWYMeNmmjevlG/L9rrveSilVF5xOIQ/Dkaw6UgkP+04xbHzcUQnplwyXmAxH8JqlSUowI8RXesQWMyX2uVLUNYNdxM5iY9PJjY2mZCQIIYPb039+uUYPrzNJc8/3E2Th1KqyNhzKprf9p1lzoZwwiPjiE1KzTC8WpnihIWWpW2tsoSGlKBckD9VyxQnNKSEhyLOaMmS/YwatZhWrSozb94gGjYMoWHDEI/EoslDKVXopD3I3n0yihV7zrL5aCR/n4rOME5wgB9jutejVGAxujasQK3yQQT4XdkDa3c7cSKaxx9fwpw5u2jYsDyjR1/j6ZA0eSilvFNKqoOI2CQSkx2cjk7g71PRzNsYzqFzsVyMT75k/A51ytOwcjBDOtSiTkgJjMnfYp4r9csvB7ntttkkJaUyYcL1jBvXkYAAz5+6PR+BUkplcvJiPOsPnScx2UFiSirhF+I5dj4OsIqeImKTuBB3aYIAMAYe7loHX2NoXq009SqWpFb5Evj7eVcj4snJqRQr5kvLlpXp06c+r77anXr1ynk6rHSaPJRSHiUifLfpODtOXGTv6WjW7o+47Lj1K5bEGIOvMdzfMRR/Px8aVAomsJgPpQKL0bJGmXyt7eQOUVGJ/Pvfv/Lnn8dZu3YYISFBzJo10NNhXUKTh1IqXx0+F8unaw9x4GwMRyLiCI+MTx9WMTiACsEBtAstR5/mVWhStRTBgX4E+PkQ5O+Hbz7XKMpPIsLcubt47LElnDoVw8iR15CYmEpQUMG8Y9LkoZRyi1SHcPJiPN+uP0qKQ/hxywki45KJT/6nhlOt8kHc0qIKlUoF8nDXOlQMzvsG/LzB2bOxDB36Az/9tJ/WrSszf/5dXHNNNU+HlS1NHkqpPHU6KoHRMzfx1+HIDP3Ll/CnXe1ytKxemna1y9OpXnmveWjtbqVKBXDuXBxTp/Zi1Kh2+HnB8xlNHkqpXElrpmPf6RgESEpxEBmXxO8HzhGTkMLWcKvJjtLFi3FXuxo0qVKKXk0rE1isYFaD9ZTVq48wceJvzJs3iJIl/Vm37sF8f9HvamjyUEplKSXVwd+nopn91zE2H4skwM+XjUcis53GGOjWsAJhtcoyunv9fIrUu5w7F8e4ccv4/PMthIaW4fDhCzRrVtGrEgdo8lBKOUlJdbDt+EW+/uMI323+p3VYPx9DaEgJejapRDFfQ9OqpWlRvTShdhXYYr4+BPn76t1FNkSEzz7bwrhxy4iKSuTZZ6/jhRe6EBTknbXDNHkoVUSdjU7kq3VHCI+M46/D5zl2Pj7DcH9fH4Z2rEXHuiFc36iih6IsXL7+ehtNmlRgxoybadrUu7epJg+lipDkVAcv/7iTmX8exfnT1tXLFqdNzTJUKV2cNrXK0rJ6aVrXLFuoq8bmh7i4ZF577TdGjAijevVSzJs3iNKlA72uiCormjyUKuTORCXwzvK9bDgcyb4z/3zetFvDCtzRtga9mlbCz7fg1+7xNosX72PUqMUcPnyBatWCeeSRayhbtrinw8ozmjyUKkSSUhycjkrg+IV41u4/x8o9ZzN8sKhJlVL0aV6Zkd3qFYqr34IoPDyKxx9fwrx5u2ncOIRVq+6nS5dang4rz2nyUMrLRSUkM3XZPlbsOcOhc7FZjvPgdbV54ZYm+RxZ0TRx4moWLdrHa691Z+zYjvhf4adlCzpjfdDPe4SFhcmGDRs8HYZSHrdkxykmLNzF8Qv/POhuWaMMrWuUoV3tclQqFUibmmX0Rbx8sH79cYoX96N580pERMRx8WIideqU9XRYGRhjNopIWF7NT+88lPIie09H8826IyzbdZoTFxMACPDz4fmbG3Nv+1paFJXPLl5M4LnnfuG//93ALbc0YMGCwZQvH0T58kGeDs3tNHkoVYCdi0lk3JytbDgSSXKqg4RkR/qwrg0qMKZ7PcJCC04z3UWFiDB79k6eeOJnzpyJZcyYdkyY0N3TYeUrTR5KFUAxiSmM+mYTq/aeBSCkZADNqpamTa0yNKxcipubV9FqtB709dfbuO++HwgLq8rChYNp27aqp0PKd5o8lCpAVu45w+Qle9hzOppU+0WMwe1qMmlAcw9HphITUzh4MJLGjSswaFBTUlIc3HdfS3yLaDVnTR5KeVhyqoOpy/cyfcWB9H5NqpRiTPd69G5exYORqTQrVhzikUcWEReXzL59YwgI8OOBB1p7OiyP0uShlIf8tu8sC7acYMmOU0QnpgDQsW55Xrq1KQ0qBXs4OgVw5kwsTz21lK++2kadOmX58MO+BeL74QWBbgWl8omIcDoqkRd+2M7y3WcyDOvbsipT72ylzzEKkP37z9Ou3UfExCTx/POdef75zhT38k/c5iVNHkq5QVKKg8XbT7Jm/zk2HY3Exxj2OzUNAtazjEe61qVmEajW6U2iohIpVSqAunXLMnx4a4YNa03jxhU8HVaBo8lDqTy06Wgk7y7fl15LKk2jysHc0qIKISUD6N6oIl0a6MmooImNTeKVV1bx0Ueb2LbtEapXL8Wbb97o6bAKLE0eSl2li/HJTF+xnw9XH0zvF1IygPs61GLItbUoW8Lfg9EpV/z44x5Gj/6Jo0cvMnx4a6/9xkZ+0uShVC6N+XYze09Fk5TqID4plVNRCenDejSuyENd6tK2ljZn7g1SUhwMGjSH77//m6ZNK/Dbbw9w3XU1PR2WV9DkoZSLdp2IYuCM34lLSgXglhZVrK/o+fjQqEowg8JqUEJr4ngFEcEYg5+fD1WqlOT112/giSc6FNpGDN1B93SlcpDqEF75cSdf/HEEgArBASx7ogtlgrQ4yhutWxfOqFGL+eijvrRpU4Xp02/2dEheSZOHUlkQEXYcj2LuxmPpSQNg6p2t6N+6mgcjU1cqMjKe5577hQ8+2EjVqsFERsbnPJG6LLcmD2PMTcC7gC/wsYi8nml4TeALoIw9zjMistidMSmVmcMh7DoZxd7T0azZf44jEXFsPBKZYZzO9UP4/IF2+hzDS82evYNHH13CuXNxPP74tbz8cjeCgwM8HZZXc1vyMMb4AtOBnkA48JcxZoGI7HIa7QXgfyLyX2NME2AxEOqumJTK7Ot1R3jhhx0Z+tUsF0SdCiVoXaMs93cMpWnVUtrUuZf7++9zhIaWYcmSe2jdWpt8yQvuvPNoB+wXkYMAxphZQD/AOXkIUMr+uzRwwo3xKMW28Ass2HKCwxFxbAu/wJnoRACGdapNr6aVqFqmODXK6Ut73i4hIYU33lhDmzZV6Nu3Ic8915kXXuhSZBsxdAd3Jo9qwDGn7nCgfaZxXgKWGmPGACWAHlnNyBjzEPAQQM2aWo1O5U58Uipjvt3MX4fPczE+Ob1/vYolqVQqkGd6N6JTvRAPRqjy0vLlBxk5chH79p1n7NgO9O3bkGLFtBZVXnNn8sjqPj/zN28HA5+LyFvGmA7AV8aYZiLiyDCRyIfAh2B9htYt0apCJzEllbeW7uXLPw6TkOygmK/hyZ4NCAstS9taZQnw0xNKYXL6dAxPPrmUmTO3U69eOZYuvZeePet6OqxCy53JIxyo4dRdnUuLpYYDNwGIyB/GmEAgBDiDUrn0w+bj/LbvHHtPR7PHfokvzTO9GzGiq55ICrNlyw4yd+4uXnyxC88+25nAQK1M6k7u3Lp/AfWNMbWB48BdwN2ZxjkK3AB8boxpDAQCZ1HKRakOYeKi3Xy69lB6vzJBxagQHEDLGqVpF1qOfq2qaRMhhdTWrafYt+88Awc24Z57mtOpUw1q1y7r6bCKBLclDxFJMcaMBn7Gqob7qYjsNMa8AmwQkQXAWOAjY8wTWEVa94uIFkupbMUnpfLd5nDmbznB+kPn0/sPbleDp29qpC/vFQExMUmMH7+Cd9/9k9DQMvTv3wg/Px9NHPnIrfd19jsbizP1e9Hp711AJ3fGoAqXhdtOMHrm5vRuHwNDO4byr16NKK5NSxQJP/zwN2PG/ER4eBQPPdSGSZN64OentajymxYKqgIvPimVT9ce4s9D51ltN3U+sltdRnevR5C/7sJFyfbtp7ntttk0b16R2bMH0rFjjZwnUm6hR54qsFbuOcPU5fvYcuxCer9yJfyZfHsLejSp5MHIVH5KTk7lt9+O0r17bZo3r8SiRXfTs2cdrX7rYZo8VIFzNjqR295fS7hT20MTb2vG3e1qYoy+6V2U/P77MUaMWMjOnWfZs2c09eqVo0+f+p4OS6HJQxUQSSkOVu09yxe/H2bN/nOA1UzIG7e3oEPd8h6OTuW38+fjeeaZ5Xz00SZq1CjFd98Nol69cp4OSznR5KE8RkTYdDSSqcv38du+c+n9Q0r6c1vrajx/cxMPRqc8JSEhhVatZnDiRDRjx3bgpZe6UbKk1qAraDR5qHx1JiqBn3ac4uM1Bzl2/p9iKX9fH+69thYPdq5N1TLFPRih8pTw8CiqVy9FYKAfEyZcT6tWlWnZsrKnw1KXoclD5YuYxBTu++RPNh395+F382qlaV+7HHdeU4P6lYI9GJ3ypPj4ZCZNWsMbb6xl7tw76Nu3IUOHtvJ0WCoHLiUPY4w/UFNE9rs5HlWIiAjzt5zgyz8OZ0ga/xncmp5NKhGotWWKvKVLDzBy5CIOHIjk3ntb0K6dfmjLW+SYPIwxNwNvA/5AbWNMK2C8iNzm7uCUd0pJdfDc99v534bw9H6Nq5TizrDqDO0YqjWmFABjxixm2rS/qF+/HMuXD+GGG+p4OiSVC67cebyC1ZT6CgAR2WKMqefWqJRXcjiEoZ+tT3/4HeDnw60tq/Jw17rUq1jSw9GpgiDVbqzS19eHa6+tTkhIEE8/fZ02YuiFXPnFkkXkQqarRW1/SmUwecnf/HfVAUSgSulAnr6pEf1aVdW7DJVu06aTjBixkCFDWjBmTHvuuaeFp0NSV8GV5LHbGDMI8LFbyH0MWOfesFRBl+oQtoZf4PC5WH7ZfYZF208CcO+1NXm+TxNtZ0qli45O5MUXV/Dee+upUCGIKlW0ckRh4EryGA28CDiA77BayX3WnUGpgu1MdALtJv6SoV+Qvy8LRneiXkU9Mah/LF16gGHD5nPiRDQjRoTx2n/B9FIAACAASURBVGs3UKZMoKfDUnnAleTRS0SeBp5O62GMGYCVSFQRs+HweQbO+AOA2iEleO225jSuEqzNoKss+fv7UrFiCebNG0T79tU9HY7KQyanz2cYYzaJSJtM/TaKSFu3RnYZYWFhsmHDBk8sushKdQj7zkTz4g87WX/Y+n5G5/ohfDU88yfpVVGXnJzK22//QVRUIhMn3gBYFSl8fPTZl6fZ5+2wvJrfZe88jDG9sD4RW80Y87bToFJYRViqkBMRZv11jPd+2cfJiwnp/T8c0pYbm+qbvyqjNWuOpjdieMcdTdKThiaOwim7YqszwA4gAdjp1D8aeMadQSnPEhG+WneEF+f/87O3Cy3H6O716Fi3PH6++uEd9Y+IiDiefno5n3yymZo1S/Pjj4O55ZYGng5Ludllk4eIbAY2G2O+EZGEy42nCheHQ7h9xu9stt8I79G4Em/f2ZJSgcU8HJkqqCIi4pk1awf/+ldHXnyxKyX0e/FFgisPzKsZYyYCTYD0ahIiopcWhUhiSirzt5zgX3O3AdC+djlm3NuWsnoiUFnYvfss//vfTsaP70aDBuU5evQJypXTBi2LEleSx+fAq8AUoDfwAPrMo1CJTUyh6fif07u7NKjAh0PaattT6hJxcclMnLiaN9/8nZIl/Rk+vA3Vq5fSxFEEuZI8gkTkZ2PMFBE5ALxgjPnN3YEp91uy4xTfbQpn6a7T6f3WPH091csGeTAqVVAtWbKfkSMXcejQBYYObcmbb/akQoUSng5LeYgrySPRWG1MHDDGjACOAxXdG5Zyp4txyfxr3lZ+3mkljevqhdCraSXuvbaWNieishQTk8SQId9TvnxxVqwYSrduoZ4OSXmYK8njCaAk8CgwESgNDHNnUMp9dp+Move71o1jYDEflj3RlRrl9E5DXSo11cG33+5g8OBmlCzpz/LlQ2jUKISAAG3EULmQPETkT/vPaGAIgDFGXxX1QusPnWfQB9bb4WO612NM9/r4+2m1W3WpjRtP8PDDC9m48STFi/tx++1N9Kt+KoNszxzGmGuMMf2NMSF2d1NjzJdow4heJSImkclL/k5PHI/dUJ+xNzbUxKEucfFiAo8++hPt2n3M8ePRzJp1OwMGNPZ0WKoAyu4N80nA7cBWrIfk32O1qPsGMCJ/wlNXIzohmclL9vDVuiPp/abf3YabW1TxYFSqILv99v/x66+HGDXqGl59tTulS2sjhipr2RVb9QNaiki8MaYccMLu3pM/oamrsXj7SUZ+sym9e9T1dXnwujr63oa6xMGDkVSoEERwcAATJ3bHx8dwzTX6OViVveySR4KIxAOIyHljzN+aOAo+EeH1n/7mg9UHAStpjLq+HkH++pBTZZSUlMqUKb8zYcJqHn20HW+80VNbvlUuy+6MUscYk9bsugFCnboRkQFujUzlWkxiCqO+2cSqvWcxBpY81oWGlfX7GupSq1cfYcSIhezefY6BA5vw6KPaQrLKneySx+2Zuqe5MxB1dcIj47jujRWAVQV37dPdKV8ywMNRqYLonXf+4MknlxIaWoZFi+6mT5/6ng5JeaHsGkb85XLDVMERl5TCCz/s4LtNxwEY0bUuz/Ru5OGoVEHjcAixsUkEBwdw880NOHs2jhde6EJQkDZ4qa6MFoR7qaQUBxsOn+f5H3Zw6FwsAK/2b8a919bycGSqoNm58wwjRixK/6Jfgwblee21GzwdlvJybk0expibgHcBX+BjEXk9i3EGAS8BAmwVkbvdGVNhsP9MDD3fWUXaRyA71CnPNw+214/uqAzi4pKZMGEVU6b8QenSAQwb1goR0SZoVJ5wOXkYYwJEJDEX4/sC04GeQDjwlzFmgYjschqnPvAs0ElEIo0x2mZWDn7ceoIx324G4NaWVXmiZwNqh2jjdCqjzZtPMmDA/zh8+AIPPNCKyZN7EhKizdCovJNj8jDGtAM+wWrTqqYxpiXwoIiMyWHSdsB+ETloz2cW1rsju5zG+T9guohEAojImdyvQtHx1JytzN0YDsCANtV4e1ArD0ekCpq0O4uaNUtTs2ZpvviiP126aFGmynuu3Hm8B9wC/AAgIluNMde7MF014JhTdziQuT5gAwBjzFqsoq2XRGSJC/MuUkSEPw5EpCeOv57vQYVgrUml/pGS4mDatPUsWLCHZcuGUL58EKtW3e/psFQh5kry8BGRI5nKSVNdmC6rglXJYvn1gW5AdeA3Y0wzEbmQYUbGPAQ8BFCzZk0XFl14RCck03/6Wg6ctR6KfzeyoyYOlcH69ccZMWIhmzefonfvekRFJVK2rH6cSbmXKy3jHbOLrsQY42uMeRzY68J04UANp+7qWE2cZB5nvogki8ghYA9WMslARD4UkTARCatQoYILiy4cklIcNH9pKQfOxtK2Vlm+H9mRNjXLejosVUDExCQxatQirr32Y06fjmXOnDtYtOhuTRwqX7iSPB4BngRqAqeBa+1+OfkLqG+MqW2M8QfuAhZkGucH4HoAu+XeBsBB10Iv3A6fi6XZS9anYVtWL83cER1orYlDOSlWzIeVK48wZkw7du8excCBTbQmlco3rhRbpYjIXbmdsYikGGNGAz9jPc/4VER2GmNeATaIyAJ72I3GmF1YRWHjRCQit8sqTCJiErnzw3XsPxMDQJ/mlZl+dxs9KSgA9u8/zyuvrGL69D4EBwewceNDBAbq61oq/xmRzI8hMo1gzAGs4qTZwHciEp0fgV1OWFiYbNiwwZMhuM3GI+e5/b9/pHd/Pbw919UP8WBEqqBITExh8uS1TJz4G/7+vixadDedO2stKuU6Y8xGEQnLq/m58iXBusaYjljFTi8bY7YAs0RkVl4FUdQlpzp4fNYWFm0/CVjVcN+6o6XebSgAVqw4xCOPLGLPngjuvLMpb7/di6pVtcFL5Vku3e+KyO/A78aYl4CpwDeAJo+rdDY6kbeW7mHWX//UaH7h5sY82LmOB6NSBYmIMHHibyQnO1iy5B569arn6ZCUAlx7SbAk1st9dwGNgflARzfHVejFJqbQZfIK4pOtWs/aoKFK43AIn3yyiZtuqkeNGqX56qvbKFMmkOLFtRFDVXC4cuexA/gRmCwiv7k5niJh5Z4z3P/ZX4D1saZxvTRpKMu2bacZMWIhf/wRzosvduHll6+nShUtolIFjyvJo46IONweSRFxJjohPXH8X+faPHVjQw9HpAqCmJgkXn55Je+8s46yZYvz+ef9uO++lp4OS6nLumzyMMa8JSJjgXnGmEuqZOmXBHPv5MV4rp+yEoAnezbg0Rv0IzzK8tJLK3nrrT948MHWvP56D8qX10YMVcGW3Z3HbPt//YJgHth67AL9pq8FYEz3epo4FMeOXSQ2NplGjUJ45pnr6N+/EdddV7Sa31He67JvmIvIevvPxiLyi/M/rAfnykXHzselJ45nezdirBZVFWkpKQ7efvsPGjeezsMPLwQgJCRIE4fyKq40TzIsi37D8zqQwmr3ySg6T7a+LT76+no83LWuhyNSnrRuXThhYR8yduxSunUL5Ysv+ns6JKWuSHbPPO7Eqp5b2xjzndOgYOBC1lOpNIkpqTz27RaW7DwFwN3ta/JUL73jKMoWLdpL377fUrVqMN99N4j+/Rvpi6DKa2X3zGM9EIHVGu50p/7RwGZ3BlUYvLt8H0t2niI4wI/JA1vQu3kVT4ekPEBEOHEimmrVStGjRx1eeeV6HnusPcHarL7ycjm2bVXQFPS2rUSEKUv3MH3FAZpUKcXixzp7OiTlIXv3RjBy5CL27o1g165RlCzp7+mQVBGWb21bGWNWiUhXY0wkGT/iZAARkXJ5FURh4XAIgz74gw1HIgGYcofW0y+KEhJSeP31NUyatIbixf2YNOkGihfXlm9V4ZLdHp32qVlt1tUFRyJi6frmSgCC/H1ZNe56/eJfEXTqVAxdunzGvn3nGTy4GW+/3YvKlUt6Oiyl8txlk4fTW+U1gBMikmSMuQ5oAXwNROVDfF4hKiE5PXHc2rIqb97RggA/X88GpfJVcnIqxYr5UqlSCbp0qcX06X3o2VNr1qnCy5Wquj9gfYK2LvAl1jseM90alRdJTnUwaIb1DY7B7Wry3uDWmjiKEIdDmDFjA3Xrvkd4eBTGGD7++FZNHKrQcyV5OEQkGRgATBWRMUA194blPe756E/+PhXNHW2rM2lAc0+Ho/LR1q2n6NjxEx55ZBH165cn2W4hWamiwKXP0Bpj7gCGAGlvNGnb0EBcUgrrD58H4PXbW3g4GpVfRIRx45Yxdeo6ypUrzldf3cY99zTXdzZUkeJK8hgGjMRqkv2gMaY28K17wyr4RIRnv9sOwP0dQ/H10RNHUWGMITIynuHDrUYMy5Yt7umQlMp3ORZbicgO4FFggzGmEXBMRCa6PbIC7qt1R5i/5QQAo7vr190KuyNHLtC//yw2bbI+FfzRR7fywQd9NXGoIivH5GGM6QzsBz4BPgX2GmM6uTuwgiopxcHQT9fz4vydAGx/6UZCSmqV3MIqOTmVyZPX0qTJ+yxbdpA9e84B4KN3mqqIc6XY6h2gj4jsAjDGNAa+AvLsTUVvcSEuiVavLAOgToUSfDgkjOBAffxTWP3++zEefnghO3acoV+/hrz3Xm9q1izt6bCUKhBcSR7+aYkDQER2G2OKXDsL+89E0+Pt1QCElPRn+RNd9eqzkFu+/CAXLybwww930q+ffipYKWeuJI9NxpgPsO42AO6hCDaM+Oqi3QD0a1WVqXe20po1hZCI8NVX26hQIYjevevz9NOdePLJDtomlVJZcOU9jxHAAeBfwNPAQeBhdwZVkDgcQr9pa1i55yyta5bh3btaa+IohP7++xzdu3/J0KE/8NlnWwAICPDTxKHUZWR752GMaQ7UBb4Xkcn5E1LBMnX5XraGXyTAz4fpd7fxdDgqj8XHJ/Paa7/xxhtrKVHCnw8+uIUHH9TfWamcZNeq7nNYXwzcBFxjjHlFRD7Nt8gKAIdDeO/X/QDsfLkXfr6u3Kgpb/Ljj3t59dXfuPfeFkyZ0pNKlbQRQ6Vckd2dxz1ACxGJNcZUABZjVdUtMj5dewiA7o0qauIoRE6dimHLllPcdFM97rijCaGhD9Kunba4o1RuZHdGTBSRWAAROZvDuIVOQnIqExdbD8k/GNLWw9GovJCa6uD99/+iYcNpDBnyPfHxyRhjNHEodQWyu/Oo4/TtcgPUdf6WuYgMcGtkHnbb+78jAiO71aWY3nV4vU2bTjJixEL++usEPXrU4f33+1C8uL6jo9SVyi553J6pe5o7AylIPllziN0nrc+VPNmzgYejUVfr0KFI2rX7iJCQIGbOHMBddzXTGnNKXaXsPgb1S34GUlAcPhfLhIXWO5Hrnr1Bn3V4KRFh+/YztGhRidq1y/LZZ/3o27chZcoEejo0pQoFPTM6SfsGOcCD19Wmcmk90XijQ4ciueWWb2nd+gO2bTsNwJAhLTVxKJWH3Jo8jDE3GWP2GGP2G2OeyWa8gcYYMcZ4tL2s91fu50x0Ivd3DOWFW5p4MhR1BZKSUnn99TU0bfo+q1YdZsqUnjRpUsHTYSlVKLnSPAkAxpgAEUnMxfi+wHSgJxAO/GWMWeDcTpY9XjBWk+9/ujpvd5mydC8Az/Vp7OFIVG6lpjro2PETNm48yYABjZk6tRc1amgjhkq5iytNsrczxmwH9tndLY0x/3Fh3u2A/SJyUESSgFlAvyzGmwBMBhJcDzvvvbHkbwCMAX8/Lc3zFlFR1vWMr68Pw4a15scfBzNv3iBNHEq5mStnyfeAW4AIABHZClzvwnTVgGNO3eFk+va5MaY1UENEFmY3I2PMQ8aYDcaYDWfPnnVh0bmTmJLKJ2usFwI3vtAzz+ev8p6I8PnnW6hT513mz7cS/8iR13DLLVo7Tqn84Ery8BGRI5n6pbowXVZ1ISV9oDE+WN8KGZvTjETkQxEJE5GwChXyvgz7w1UHSUpx8NptzSlXQhvCK+h27TpLt25f8MAD82nUKIS6dct5OiSlihxXnnkcM8a0A8R+jjEG2OvCdOFADafu6sAJp+5goBmw0q5zXxlYYIy5VUQ2uBJ8XkhITuWj3w4CMLBt9fxarLpCkyev5fnnf6VUqQA+/rgvDzzQWr+ropQHuJI8HsEquqoJnAaW2/1y8hdQ3xhTGzgO3AXcnTZQRC4CIWndxpiVwFP5mTgApi7fR1RCCs/3aazPOgowEcEYQ+XKJbnnnua8+WZPKlQo4emwlCqyckweInIG68SfKyKSYowZDfwM+AKfishOY8wrwAYRWZDraPPY+kPnmbHqAAD3dwr1bDAqSydORPPYY0vo3Lkmjz7anvvua8l997X0dFhKFXk5Jg9jzEc4PatIIyIP5TStiCzGao3Xud+Llxm3W07zy0vHL8SnvxA449622n5VAZPWiOHzz/9KcrKDjh21SFGpgsSVYqvlTn8HAreRsRaVV7rTThzv39OGm5pV9nA0ytmWLad48MEFbNx4khtvrMv77/fRh+JKFTCuFFvNdu42xnwFLHNbRPlgy7ELhEfG0y60HH2aV/F0OCqTixcTOHEimtmzB3LHHU20EUOlCiCX3zB3UhuoldeB5BcRof/0tQC80r+ph6NRYP0mc+bsYt++CJ5/vgtdu4Zy8OBjBAZeye6plMoPrrxhHmmMOW//u4B11/Gc+0Nzj112U+sNKpWkUeVSHo5GHThwnj59ZnLnnXOZP38PycnWK0SaOJQq2LI9Qo1VXtASq6otgENELnl47k36/mcNAG/d0crDkRRtiYkpTJnyO6+++hvFivnw7rs3MXLkNfhpdWmlvEK2yUNExBjzvYgUiu+whkfG4RCoWS6I5tW17SNPOnYsigkTVtO3b0OmTu1FtWp6F6iUN3HlMm+9MaaN2yPJB/d9sh6walip/Hf2bCzTplm/Qb165di1axRz5tyhiUMpL3TZOw9jjJ+IpADXAf9njDkAxGK1WSUi4lVn4ItxyRw8F0twgB/NquldR35yOITPPtvMv/61nOjoRHr2rEPDhiHUqVPW06Eppa5QdsVW64E2QP98isWtFmyzmtUa2jHUs4EUMTt2nOGRRxaxZs1ROneuyYwZt9CwYUjOEyqlCrTskocBEJED+RSL26SkOvj3DzsAGHNDPQ9HU3QkJaVy441fkZSUyqef3sr997fSdzaUKiSySx4VjDFPXm6giLzthnjcIq39qhsaVSTAz9fD0RR+v/56iK5da+Hv78v//ncHjRqFEBIS5OmwlFJ5KLsH5r5ASaym07P65zV+3HqSskHFmK4Pyt0qPDyK22//Hzfc8CVffrkVgOuuq6mJQ6lCKLs7j5Mi8kq+ReImRyJi2XM6mjvDahBYTO863CElxcG0aev5979XkJrqYNKkG7jnnhaeDksp5UY5PvPwdm8vs75bdUtLbcPKXYYM+Z5Zs3bQu3c9pk/vQ+3aWotKqcIuu+RxQ75F4UY/7zxF7ZASdK6f95+vLcouXEjAz8+HkiX9GTXqGm6/vTG3395YH4grVURc9pmHiJzPz0DcITYxhYRkBxWDAzwdSqEhIsyatYPGjafz73//CljPNQYO1NZvlSpKCnVDQrP/sj470rWh3nXkhf37z9Or19cMHjyP6tVLce+9+lxDqaKqUDdd+v5Kq4runWE1PByJ95s5czvDhs0nIMCPadN6M2JEGL769UWliqxCmzwuxidzLiaRm5tXoXxJLba6UsnJqRQr5ktYWFUGDmzC5Mk9qVrVq2pqK6XcoNAmj1HfbALg+kYVPRyJdzpzJpaxY5cSG5vEd9/dSYMG5fn66wGeDkspVUAU2nKHwxGxAAxoXc3DkXgXh0P48MONNGw4jdmzd9C0aQVSUx2eDkspVcAUyjuP+KRUwiPj6duyKj4+WgPIVQcPRnLvvd/xxx/hdOsWyn//ezONGmkjhkqpSxXK5LHl2AUAOtUt7+FIvEvp0gFcuJDAF1/0Z8iQFlr1Vil1WYWy2GrBVuuruQ0r64PdnCxYsIcBA2aTmuqgfPkgduwYyX33tdTEoZTKVqFMHt+ut97vaFxFv1B3OUePXqR//1n06zeLvXsjOHkyBkCL+ZRSLil0xVZxSSkA9G1ZVRtCzEJKioOpU9cxfvxKRIQ33ujBE09cSzHdVkqpXCh0yWNb+EUAWlbXT81mJTXVwccfb6J799r85z+9CQ0t4+mQlFJeqNAVW/1xIAKA6+prLaE0kZHxPP30MqKjEwkI8GPt2mEsWHCXJg6l1BUrdMnj+IV4ggP9aFRZn3eICN98s41Gjabz1lt/sGLFYQDKlw/SB+JKqatS6Iqt/j4VRa3y+uW6vXsjGDlyEb/8coh27arx88/30qpVZU+HpZQqJArVncfRiDh2HI+iVrkSng7F4x5/fAkbNpzg/ff78PvvwzRxKKXyVKG68/h4zUEAbmhcNNuzWrbsAI0ahVCjRmn++9+bCQjwo3Llkp4OSylVCLn1zsMYc5MxZo8xZr8x5pkshj9pjNlljNlmjPnFGFPrSpclIqzae5a6FUowoE31qwvcy5w6FcPdd8/jxhu/5o031gJQq1YZTRxKKbdxW/IwxvgC04HeQBNgsDGmSabRNgNhItICmAtMvtLlnY1J5EhEHP1aFZ2GEB0OYcaMDTRqNI1583YzfnxXpky50dNhKaWKAHfeebQD9ovIQRFJAmYB/ZxHEJEVIhJnd64DrviWYf9p6w3pCkXok7OTJv3GI48som3bqmzbNoKXXupGYGChKolUShVQ7jzTVAOOOXWHA+2zGX848FNWA4wxDwEPAdSsWTPLiX/eeQqA1jUL97sL0dGJnDsXR+3aZRkxIozatcsyeHAzrXqrlMpX7rzzyOpsJlmOaMy9QBjwZlbDReRDEQkTkbAKFbL+HvnSXacpG1Ss0L7fISJ8//1umjR5nzvvnIuIUL58EHff3VwTh1Iq37kzeYQDzh8Prw6cyDySMaYH8Dxwq4gkXsmCRISTFxOoHVI4q+geOXKBW2+dxYAB/6NcueK8915vTRhKKY9yZ7HVX0B9Y0xt4DhwF3C38wjGmNbAB8BNInLmShd09Lz12KRtrbJXHGxB9ccfx+jR4ysApkzpyWOPXYufX6F6PUcp5YXcljxEJMUYMxr4GfAFPhWRncaYV4ANIrIAq5iqJDDHvpI+KiK35nZZ52KsG5bC1AR7VFQipUoF0KZNFYYNa8W4cZ2oWVMbe1RKFQxurZojIouBxZn6vej0d4+8WE5CsvWN7eplvb9ZkoiIOJ55ZjlLlx5k586RlCzpz3/+08fTYSmlVAaFol7ngbNWNd0ALy7OERG++mobY8cuJTIynief7IA+1lBKFVSFInms+Nt6XFK7gnc+ML94MYH+/WezcuVhOnSozowZt9CiRSVPh6WUUpdVOJLHnrMAlAos5uFIckdEMMZQqlQAISFBfPjhLQwf3kY/BauUKvC8t5zHtuO49eXAAa29q1mSn3/eT5s2HxIeHoUxhjlz7uD//q+tJg6llFfw+uSx51Q0AA93revhSFxz8mQ0d901l5tu+oa4uGTOnIn1dEhKKZVrXl9sFZ+cCkDZEgW/yGr69PU899yvJCam8PLL3Xj66U4EBHj9T6CUKoK8/sy197R15xHkX/BXZePGk7RvX43p0/tQv355T4ejlFJXrOCfcXMQWMwXgJIF8Ao+KiqRF19cwZAhLWjbtirvv38zAQG+2rSIUsrrFbwzbi6tOxhBcAFrhlxEmDdvN489toSTJ6OpWbM0bdtW1ebSlVKFhtefzfaejqa4ffdREBw6FMno0T+xePE+WrWqzHffDaJ9+6L1ZUOlVOHn1cnjfGwSCckOWtUoON/w+Oab7axefYR33unF6NHttBFDpVSh5NXJY8aqAwAMbFsjhzHd67ffjpCYmEqPHnUYN64j99/fiurVC08jjUoplZlXXxZvC79AkL8vA9t6pljo3Lk4hg2bT5cun/PKK6sACAjw08ShlCr0vPrO489D52lRPf+LrESEzz/fwrhxy7h4MZGnn+7Ev//dJd/jUAVfcnIy4eHhJCQkeDoUVUQEBgZSvXp1ihVz77tvXps8RAQRCCnhn+/LXrx4H8OGLaBTpxrMmHELzZpVzPcYlHcIDw8nODiY0NBQraKt3E5EiIiIIDw8nNq1a7t1WV5bbPXX4UgArq2TPy/bxcUls3btUQD69KnP/Pl3sXr1A5o4VLYSEhIoX768Jg6VL4wxlC9fPl/udL02eRyOsNqE6tHE/U2X//TTPpo1e5/evb/hwoUEjDHcemtDbcRQuUQTh8pP+bW/eW3y2Gs3iFi6uPvK9Y4fj+KOO+bQp89MAgL8+PHHwZQpE+i25SmllLfw2uSR9vXAskHuSR5nzsTSpMn7LFy4l1dfvZ6tW0fQtWuoW5allDv5+vrSqlUrmjVrRt++fblw4UL6sJ07d9K9e3caNGhA/fr1mTBhAiKSPvynn34iLCyMxo0b06hRI5566ilPrEK2Nm/ezIMPPpihX79+/ejQoUOGfvfffz9z587N0K9kyZLpf+/du5c+ffpQr149GjduzKBBgzh9+vRVxXb+/Hl69uxJ/fr16dmzJ5GRkZeMs2LFClq1apX+LzAwkB9++AGAzp07p/evWrUq/fv3B2DhwoWMHz/+qmK7ataDZ+/517ZtWxERaT9xuTR9cYnktfDwi+l/v/vuOtm/PyLPl6GKjl27dnk6BClRokT63/fdd5+8+uqrIiISFxcnderUkZ9//llERGJjY+Wmm26SadOmiYjI9u3bpU6dOrJ7924REUlOTpbp06fnaWzJyclXPY+BAwfKli1b0rsjIyOlevXq0qhRIzl48GB6/6FDh8qcOXMyTJu2beLj46VevXqyYMGC9GG//vqrbN++/apiGzdunEyaNElERCZNmiT/+te/sh0/IiJCypYtK7GxsZcMGzBggHzxknqMHgAAEL1JREFUxRciIuJwOKRVq1ZZjieS9X4HbJA8PBd7ZW0rESE+OZU2tcrm2TwvXkzghRd+5YMPNrJu3YO0aVOFRx9tn2fzV+rlH3ey60RUns6zSdVSjO/b1OXxO3TowLZt2wCYOXMmnTp14sYbbwQgKCiIadOm0a1bN0aNGsXkyZN5/vnnadSoEQB+fn6MHDnyknnGxMQwZswYNmzYgDGG8ePHc/vtt1OyZEliYqwSgrlz57Jw4UI+//xz7r//fsqVK8fmzZtp1aoV33//PVu2bKFMGavafb169Vi7di0+Pj6MGDGCo0etiipTp06lU6dOGZYdHR3Ntm3baNmyZXq/efPm0bdvXypVqsSsWbN49tlnc9wuM2fOpEOHDvTt2ze93/XXX+/ydr2c+fPns3LlSgCGDh1Kt27deOONNy47/ty5c+nduzdBQUEZ+kdHR/Prr7/y2WefAdZzjW7durFw4UIGDRp01XFeCa9MHvvOxHAxPplr65S76nmJCHPm7OLxx5dw6lQMo0e3o27dvEtKShUUqamp/PLLLwwfPhywiqzatm2bYZy6desSExNDVFQUO3bsYOzYsTnOd8KECZQuXZrt27cDZFk0k9nevXtZvnw5vr6+OBwOvv/+ex544AH+/PNPQkNDqVSpEnfffTdPPPEE1113HUePHqVXr17s3r07w3w2bNhAs2bNMvT79ttvGT9+PJUqVWLgwIEuJY8dO3Zcsi2yEh0dTefOnbMcNnPmTJo0aZKh3+nTp6lSpQoAVapU4f/bu/voqOozgePfZ3lLgJC1vFWLCl0R8mKIEFqwIEIKvrGwcNQYXiqu1hVEaqN46MFz1lVraQvoRnCRdT1J1zYJeMrLAYUFGgtiAMM2AgKNKR0CmoUsZJEi7zz7x72ZDHkhd9LMTCZ5PufMOXPv3Ln3l+dM5pnf7977/I4fP37N/efn55OVlVVn/apVq0hPT6dbt5obkNPS0ti2bZslj2B8fsz5NZN4/V93J7eqMnnyClavPsjgwdezdm0maWk3NEcTjakjmB5Cczp79iypqan4fD6GDBnC2LFjAefz39CVOcFcsbN582by8/P9y9dd1/iPrwcffJB27ZyCphkZGbz00ks8+uij5Ofnk5GR4d/v/v37/e/56quvOH36NHFxcf51FRUV9OzZ07987NgxysrKGDFiBCJC+/bt2bdvH8nJyfX+TcFemRQXF0dJSUlQ7/GqoqKCvXv3cvfdd9d5LS8vr855nV69evHll1+GpC1eROUJ8+LDJwEY1MS7yy+6sw+KCCNG3Eh29j3s2vW4JQ7TKsXGxlJSUsLhw4e5cOECS5cuBSApKYni4uKrtj106BBdu3YlLi6OpKQkdu/e3ej+G0pCgetq33fQpUsX//Phw4dTVlZGZWUlq1evZvLkyQBcuXKFoqIiSkpKKCkp4YsvvrgqcVT/bYH7LigooKqqin79+tG3b198Pp8/sXXv3v2qXtHJkyfp0aOHPxZe/tbTp09fdXI78BGY6Kr17t2biooKwEkOvXo1fF/YihUrmDRpUp07w0+cOMGuXbu4//77r1p/7tw5YmNjG21zqERl8vjdQafr15TLdD/80EdKyjLWrDkIwLPP3sHTT3+Xdu2iMhTGeBYfH092djYLFy7k4sWLTJ06lY8++ojNmzcDTg9lzpw5PP/88wDMnTuXV199ldLSUsD5Ml+8eHGd/Y4bN44lS5b4l6u/oHv37s2BAwf8w1INEREmTZpEVlYWCQkJdO/evd791veLPyEhgbKyMv9yXl4eGzZswOfz4fP52L17tz953HXXXRQUFHDhwgUAcnJy/Oc1pkyZwscff8z69ev9+9qwYYN/KK5adc+jvkftISuACRMmkJubC0Bubi4TJ05sMA55eXlkZmbWWb9y5UrGjx9PTMzVtwmUlpbWGbILp6j8xjx84mvSB/YK6ia9ysozPPLIakaPzuX8+UvExXUKYQuNaZluv/12Bg0aRH5+PrGxsaxZs4ZXXnmFAQMGcNtttzF06FBmz54NQEpKCq+//jqZmZkkJCSQnJzs/xUd6IUXXqCqqork5GQGDRpEYWEhAAsWLGD8+PGMGTPGP+7fkIyMDN59913/kBVAdnY2xcXFpKSkkJiYyLJly+q8b+DAgZw6dYrTp0/j8/koLy9n2LBh/tf79etHt27d2LlzJ+PHj2fkyJEMGTKE1NRUtm/f7j95HRsby7p163jjjTfo378/iYmJ5OTkXLOn4MW8efPYtGkT/fv3Z9OmTcybNw9wztUEDkP5fD6OHDnCqFGj6uwjPz+/3qRSWFhYpzcSTqIB13RHg8FDhujJsS/xk3sH8k+j/s7Te/Ly9vLUU+/zl79cYO7cO5g//046h+j+EGMCHThwgISEhEg3o1V77bXXiIuLq3NOoDU7duwYU6ZMYcuWLfW+Xt/nTkR2q2pac7Uh6noeZy845yu+Ge/9Tu9Ll66QnNyLkpIn+elP0y1xGNOKzJw5k06d2tZIQnl5OYsWLYpoG6Luaquqry8SAwz8ZsNXWp05c4GXX97KTTfFM2vWUKZNS2HatBSrMWRMKxQTE8P06dMj3YywGjp0aKSbEH09j/OXnJ7Hrb271vv6unWlJCW9yc9/vp3S0hOAc0LOEoeJlGgbGjbRLVyft6jreVy+osTHdqiTDI4e/Yo5cz5g1aqDJCb2ZOvWGYwceXOEWmmMIyYmhhMnTlhZdhMW6s7nUfvKrFCIuuRx/tIVxgysewXEoUNVbNz4J372s3SysobTsWO7CLTOmKv16dOHo0ePUllZGemmmDaieibBUIu65AFwzr3Jb9euLygqOsKPfjSMO++8mfLyZ+jevXMj7zYmfDp06BDyGd2MiYSQnvMQkXtE5I8iUiYi8+p5vZOIFLiv7xSRvl7227NzR2bNWs+wYW+zePEOzpxxbvqxxGGMMeERsuQhIu2ApcC9QCKQKSK1b8F8DKhS1VuA14CGy00GeHthEW+9tZs5c77L3r0z6RKBecyNMaYtC+Ww1XeAMlU9BCAi+cBEILAAzETgRff5e8ASERFt5HKB3h07sO6THzJ48LXvWjXGGBMaoUwe3wKOBCwfBWpPkOHfRlUvicgpoDvwv4EbicgTwBPu4vmS//nHfR6qJ7cFPagVqzbMYlHDYlHDYlFjQHPuLJTJo77rEmv3KLxsg6ouB5YDiEhxc95iH80sFjUsFjUsFjUsFjVEpLjxrbwL5Qnzo8CNAct9gNrF5/3biEh7IB44GcI2GWOMaQahTB6fAP1FpJ+IdAQeBtbW2mYt8Ij7/AHgd42d7zDGGBN5IRu2cs9hzAY2Au2Ad1T1MxF5CWci9rXAfwD/KSJlOD2Ohz3senmo2hyFLBY1LBY1LBY1LBY1mjUWUVeS3RhjTORFXWFEY4wxkWfJwxhjTNBabPIIVWmTaOQhFlkisl9E9ojIFhFpteWEG4tFwHYPiIiKSKu9TNNLLETkIfez8ZmI/CbcbQwXD/8jN4lIoYj8wf0/uS8S7Qw1EXlHRI6LyL4GXhcRyXbjtEdEBjf5YKra4h44J9j/BHwb6Ah8CiTW2mYWsMx9/jBQEOl2RzAWo4HO7vOZbTkW7nZxwFZgB5AW6XZH8HPRH/gDcJ273CvS7Y5gLJYDM93niYAv0u0OUSzuBAYD+xp4/T7gA5x77IYBO5t6rJba8/CXNlHVC0B1aZNAE4Fc9/l7QLq0zgkTGo2Fqhaq6tfu4g6ce2paIy+fC4CXgV8A58LZuDDzEosfAktVtQpAVY+HuY3h4iUWClRPPxpP3XvOWgVV3cq175WbCPxKHTuAvxWRJtV5aqnJo77SJt9qaBtVvQRUlzZpbbzEItBjOL8sWqNGYyEitwM3quq6cDYsArx8Lm4FbhWR7SKyQ0TuCVvrwstLLF4EponIUeB94OnwNK3FCfb7pEEtdT6PZitt0gp4/jtFZBqQBowKaYsi55qxEJG/wanOPCNcDYogL5+L9jhDV3fh9Ea3iUiyqv5fiNsWbl5ikQnkqOoiERmOc39ZsqpeCX3zWpRm+95sqT0PK21Sw0ssEJHvA/OBCap6PkxtC7fGYhEHJAMfiogPZ0x3bSs9ae71f2SNql5U1T8Df8RJJq2Nl1g8BqwAUNUiIAanaGJb4+n7xIuWmjystEmNRmPhDtW8hZM4Wuu4NjQSC1U9pao9VLWvqvbFOf8zQVWbtSBcC+Hlf2Q1zsUUiEgPnGGsQ2FtZXh4iUU5kA4gIgk4yaMtzg28FviBe9XVMOCUqlY0ZUctcthKQ1faJOp4jMUvga7ASveagXJVnRCxRoeIx1i0CR5jsREYJyL7gcvAXFU9EblWh4bHWDwL/LuI/BhnmGZGa/yxKSJ5OMOUPdzzO/8MdABQ1WU453vuA8qAr4FHm3ysVhg/Y4wxIdZSh62MMca0YJY8jDHGBM2ShzHGmKBZ8jDGGBM0Sx7GGGOCZsnDtDgicllESgIefa+xbd+GKogGecwP3aqsn7rlPAY0YR9PisgP3OczROSGgNfeFpHEZm7nJyKS6uE9z4hI57/22MYEsuRhWqKzqpoa8PCF6bhTVXUQTsHNXwb7ZlVdpqq/chdnADcEvPa4qu5vllbWtPNNvLXzGcCSh2lWljxMVHB7GNtE5L/dxx31bJMkIrvc3soeEenvrp8WsP4tEWnXyOG2Are4701354DY686V0Mldv0Bq5lBZ6K57UUSeE5EHcGqM/do9ZqzbY0gTkZki8ouANs8QkTea2M4iAoraici/iUixOHN3/Iu7bg5OEisUkUJ33TgRKXLjuFJEujZyHGPqsORhWqLYgCGrVe6648BYVR0MZADZ9bzvSeBfVTUV58v7qFuKIgP4nrv+MjC1keP/PbBXRGKAHCBDVW/DqcgwU0S+AUwCklQ1BXgl8M2q+h5QjNNDSFXVswEvvwdMDljOAAqa2M57cEqQVJuvqmlACjBKRFJUNRundtFoVR3tlil5Afi+G8tiIKuR4xhTR4ssT2LavLPuF2igDsASd4z/Mk6dptqKgPki0gf4rap+LiLpwBDgE7d0SyxOIqrPr0XkLODDKdk9APizqpa6r+cCTwFLcOYKeVtE1gOey7+raqWIHHLrCn3uHmO7u99g2tkFpxRH4ExwD4nIEzj/19fjTHq0p9Z7h7nrt7vH6YgTN2OCYsnDRIsfA8eAQTg95joTPanqb0RkJ3A/sFFEHscpQZ2rqj/xcIypgUUURaTe+WHcWkrfwSm09zAwGxgTxN9SADwEHARWqaqK803uuZ04s+UtAJYCk0WkH/AcMFRVq0QkB6f4X20CbFLVzCDaa0wdNmxlokU8UOHOvzAd51f3VUTk28Ahd6hmLc7wzRbgARHp5W7zDfE+x/tBoK+I3OIuTwd+754jiFfV93FORtd3xdNpnBLx9fkt8A84c0wUuOuCaqeqXsQZfhrmDnl1A84Ap0SkN3BvA23ZAXyv+m8Skc4iUl8vzphrsuRhosWbwCMisgNnyOpMPdtkAPtEpAQYiDPd5n6cL9n/EpE9wCacIZ1Gqeo5nKqjK0VkL3AFWIbzRbzO3d/vcXpFteUAy6pPmNfabxWwH7hZVXe564Jup3suZRHwnKp+ijNf+WfAOzhDYdWWAx+ISKGqVuJcCZbnHmcHTqyMCYpV1TXGGBM063kYY4wJmiUPY4wxQbPkYYwxJmiWPIwxxgTNkocxxpigWfIwxhgTNEsexhhjgvb/jiiUtlgstDkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.93 0.88 6727\n", + " 1 0.64 0.39 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
3Logistic Regression0.6191890.766563
4SVM0.3620180.723867
5Neural Network0.3897130.774088
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.418892 0.609249\n", + "1 Random Forest 0.386746 0.761906\n", + "2 Gradient Boosted 0.380811 0.775534\n", + "3 Logistic Regression 0.619189 0.766563\n", + "4 SVM 0.362018 0.723867\n", + "5 Neural Network 0.389713 0.774088" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4586 - accuracy: 0.8061\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 1s 83us/step - loss: 0.4432 - accuracy: 0.8153\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 1s 86us/step - loss: 0.4412 - accuracy: 0.8154 0s - loss: 0.4414 - accuracy: 0.\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 1s 83us/step - loss: 0.4397 - accuracy: 0.8165\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 1s 82us/step - loss: 0.4385 - accuracy: 0.8156\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 1s 80us/step - loss: 0.4377 - accuracy: 0.8170\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 1s 84us/step - loss: 0.4374 - accuracy: 0.8162\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 1s 82us/step - loss: 0.4362 - accuracy: 0.8173\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 91us/step - loss: 0.4350 - accuracy: 0.8172\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4346 - accuracy: 0.8176\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.65 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb deleted file mode 100644 index 4b6137d..0000000 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER-checkpoint.ipynb +++ /dev/null @@ -1,5304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-4Rm0wjQMUHi" - }, - "source": [ - "# BUILDING A DEFUALT DETECTION MODEL\n", - "\n", - "---\n", - "\n", - "\n", - "\n", - "## Table of Contents\n", - "1. Problem Description (Brief Write Up)\n", - "2. Exploratory Data Analysis (EDA)\n", - "3. Data Pre-processing\n", - "4. Model Selection\n", - "5. Evaluation\n", - "6. Discussion and Possible Improvements\n", - "\n", - "## 1. Problem Description\n", - "\n", - "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", - "\n", - "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", - "\n", - "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", - "\n", - "### X1 - X5: Indivual attributes of customer\n", - "\n", - "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", - "\n", - "X2: Gender (1 = male; 2 = female). \n", - "\n", - "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", - "\n", - "X4: Marital status (1 = married; 2 = single; 3 = others). \n", - "\n", - "X5: Age (year). \n", - "\n", - "### X6 - X11: Repayment history from April to Septemeber 2005\n", - "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", - "\n", - "\n", - "X6 = the repayment status in September, 2005\n", - "\n", - "X7 = the repayment status in August, 2005\n", - "\n", - "X8 = the repayment status in July, 2005\n", - "\n", - "X9 = the repayment status in June, 2005\n", - "\n", - "X10 = the repayment status in May, 2005\n", - "\n", - "X11 = the repayment status in April, 2005. \n", - "\n", - "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", - "\n", - "X12 = amount of bill statement in September, 2005; \n", - "\n", - "X13 = amount of bill statement in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X17 = amount of bill statement in April, 2005. \n", - "\n", - "### X18 - X23: Amount of previous payment (NT dollar)\n", - "X18 = amount paid in September, 2005\n", - "\n", - "X19 = amount paid in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X23 = amount paid in April, 2005. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aM_aIU6UPHe4" - }, - "source": [ - "## EDA\n", - "\n", - "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", - "\n", - "### Importing packages and the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Is0wEkk3LJCt" - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "x_Z7u_9vRC5m" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KhmX9KWWyrUW" - }, - "outputs": [], - "source": [ - "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", - "df = pd.read_csv(url, header = 1, index_col = 0)\n", - "# Dataset is now stored in a Pandas Dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "colab_type": "code", - "id": "FhJ2eAxVQhBm", - "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", - "

5 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 20000 2 2 1 24 2 2 -1 -1 \n", - "2 120000 2 2 2 26 -1 2 0 0 \n", - "3 90000 2 2 2 34 0 0 0 0 \n", - "4 50000 2 2 1 37 0 0 0 0 \n", - "5 50000 1 2 1 57 -1 0 -1 0 \n", - "\n", - " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", - "ID ... \n", - "1 -2 ... 0 0 0 0 689 0 \n", - "2 0 ... 3272 3455 3261 0 1000 1000 \n", - "3 0 ... 14331 14948 15549 1518 1500 1000 \n", - "4 0 ... 28314 28959 29547 2000 2019 1200 \n", - "5 0 ... 20940 19146 19131 2000 36681 10000 \n", - "\n", - " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 0 0 0 1 \n", - "2 1000 0 2000 1 \n", - "3 1000 1000 5000 0 \n", - "4 1100 1069 1000 0 \n", - "5 9000 689 679 0 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#rename the target variable to \"Y\" for convenience\n", - "df[\"Y\"] = df[\"default payment next month\"] \n", - "df = df.drop(\"default payment next month\", axis = 1)\n", - "df0 = df #backup of df\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "zcuPyfM86AKj", - "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 24 Columns and 30000 Rows\n" - ] - } - ], - "source": [ - "size = df.shape\n", - "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "QVaSnvJP3VbO", - "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check for null values\n", - "df.isnull().any().sum() " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eVYXnIGH9Zq6" - }, - "source": [ - "From the above analyses, we observe that:\n", - "1. The data indeed has 30000 rows and 24 columns\n", - "2. There are no null values\n", - "\n", - "We will now explore the features more in depth." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "W6hhPNl1Slau" - }, - "source": [ - "### Exploring the features" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1Sp2F3gzXX2F" - }, - "source": [ - "**1) Exploring target attribute:**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 - }, - "colab_type": "code", - "id": "DCSEICWwXWgX", - "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "defaults : 22.12 %\n", - "non defaults : 77.88000000000001 %\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Frequency')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "All = df.shape[0]\n", - "default = df[df['Y'] == 1]\n", - "nondefault = df[df['Y'] == 0]\n", - "\n", - "x = len(default)/All\n", - "y = len(nondefault)/All\n", - "\n", - "print('defaults :',x*100,'%')\n", - "print('non defaults :',y*100,'%')\n", - "\n", - "# plotting target attribute against frequency\n", - "labels = ['non default','default']\n", - "classes = pd.value_counts(df['Y'], sort = True)\n", - "classes.plot(kind = 'bar', rot=0)\n", - "plt.title(\"Target attribute distribution\")\n", - "plt.xticks(range(2), labels)\n", - "plt.xlabel(\"Class\")\n", - "plt.ylabel(\"Frequency\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tysR0WHw4SGU" - }, - "source": [ - "**2) Exploring categorical attributes**\n", - "\n", - "Categorical attributes are:\n", - "- Sex\n", - "- Education\n", - "- Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 323 - }, - "colab_type": "code", - "id": "s61SSRII00UB", - "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 60.373333\n", - "1 39.626667\n", - "Name: SEX, dtype: float64\n", - "--------------------------------------------------------\n", - "2 46.766667\n", - "1 35.283333\n", - "3 16.390000\n", - "5 0.933333\n", - "4 0.410000\n", - "6 0.170000\n", - "0 0.046667\n", - "Name: EDUCATION, dtype: float64\n", - "--------------------------------------------------------\n", - "2 53.213333\n", - "1 45.530000\n", - "3 1.076667\n", - "0 0.180000\n", - "Name: MARRIAGE, dtype: float64\n" - ] - } - ], - "source": [ - "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Uudv5XE828nb" - }, - "source": [ - "**Findings**\n", - "\n", - "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", - "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", - "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 357 - }, - "colab_type": "code", - "id": "U3IJzhwwe5KK", - "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total target attributes:\n", - "non defaults : 77.88000000000001 %\n", - "defaults : 22.12 %\n", - "--------------------------------------------------------\n", - "SEX Male Female\n", - "Y \n", - "non defaults 75.832773 79.223719\n", - "defaults 24.167227 20.776281\n", - "--------------------------------------------------------\n", - "EDUCATION 0 1 2 3 4 5 \\\n", - "Y \n", - "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", - "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", - "\n", - "EDUCATION 6 \n", - "Y \n", - "non defaults 84.313725 \n", - "defaults 15.686275 \n", - "--------------------------------------------------------\n", - "MARRIAGE unknown married single others\n", - "Y \n", - "non defaults 90.740741 76.528296 79.071661 73.993808\n", - "defaults 9.259259 23.471704 20.928339 26.006192\n" - ] - } - ], - "source": [ - "#proportion of target attribute (for reference)\n", - "print('Total target attributes:')\n", - "print('non defaults :',y*100,'%')\n", - "print('defaults :',x*100,'%')\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with Sex\n", - "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(sex_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with education\n", - "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(education_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with marriage\n", - "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(marriage_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kOriUQ0wxbhD" - }, - "source": [ - "**Conclusion**\n", - "\n", - "From the analyses above we conclude that\n", - "\n", - "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "77GAylGWnPJO" - }, - "source": [ - "**3) Analysis of Numerical Attributes**\n", - "\n", - "The numerical attributes are:\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "HEcCl5Rj-N0T", - "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", - "
" - ], - "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", - "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", - "\n", - " 9 10 11 12 13 14 15 \\\n", - "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", - "\n", - " 16 17 18 19 \n", - "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#printing numerical attributes\n", - "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", - "
" - ], - "text/plain": [ - " count mean std min 25% \\\n", - "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", - "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", - "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", - "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", - "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", - "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", - "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", - "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", - "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", - "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", - "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", - "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", - "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", - "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", - "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", - "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", - "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", - "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", - "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", - "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", - "\n", - " 50% 75% max \n", - "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", - "AGE 34.0 41.00 79.0 \n", - "PAY_0 0.0 0.00 8.0 \n", - "PAY_2 0.0 0.00 8.0 \n", - "PAY_3 0.0 0.00 8.0 \n", - "PAY_4 0.0 0.00 8.0 \n", - "PAY_5 0.0 0.00 8.0 \n", - "PAY_6 0.0 0.00 8.0 \n", - "BILL_AMT1 22381.5 67091.00 964511.0 \n", - "BILL_AMT2 21200.0 64006.25 983931.0 \n", - "BILL_AMT3 20088.5 60164.75 1664089.0 \n", - "BILL_AMT4 19052.0 54506.00 891586.0 \n", - "BILL_AMT5 18104.5 50190.50 927171.0 \n", - "BILL_AMT6 17071.0 49198.25 961664.0 \n", - "PAY_AMT1 2100.0 5006.00 873552.0 \n", - "PAY_AMT2 2009.0 5000.00 1684259.0 \n", - "PAY_AMT3 1800.0 4505.00 896040.0 \n", - "PAY_AMT4 1500.0 4013.25 621000.0 \n", - "PAY_AMT5 1500.0 4031.50 426529.0 \n", - "PAY_AMT6 1500.0 4000.00 528666.0 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Analysis of PAY_0 to PAY_6**\n", - "\n", - "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", - "\n", - "However, the presence of -2, -1 in these columns indicates that\n", - "1. There is anomalous data, OR \n", - "2. The numbers do not strictly correspond to the number of months of payment delay. \n", - "\n", - "This means we must conduct some data transformation.\n", - "\n", - "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "awXnqvLOS-wB", - "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", - " fig=plt.figure()\n", - " for i, var_name in enumerate(variables):\n", - " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", - " df[var_name].hist(bins=n_bins,ax=ax)\n", - " ax.set_title(var_name)\n", - " fig.tight_layout() # Improves appearance a bit.\n", - " plt.show()\n", - "\n", - "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", - "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", - "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", - "\n", - "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", - "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", - "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "C6c_Gz6wUrJ8" - }, - "source": [ - "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", - "\n", - "Now that we have an idea of the features, we will move on to feature selection and data preparation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AQBksEyEf4Sf" - }, - "source": [ - "## Data Preprocessing\n", - "\n", - "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", - "1. Removing Noise - Inconsistencies\n", - "2. Dealing with negative values of PAY_0 to PAY_6\n", - "3. Outliers\n", - "4. One Hot Encoding\n", - "5. Train Test Split\n", - "6. Feature selection\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Noise\n", - "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 1, 3, 4])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", - "df[\"EDUCATION\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, for Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", - "df[\"MARRIAGE\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Separating negative and positive values for PAY_0 to PAY_6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", - "\n", - "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(0,7):\n", - " try:\n", - " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dummy variables for negative values\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", - "
" - ], - "text/plain": [ - " S_0 S_2 S_3 S_4 S_5 S_6\n", - "ID \n", - "1 1 1 -1 -1 -2 -2\n", - "2 -1 1 0 0 0 1\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 -1 0 -1 0 0 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#attributes representing positive values\n", - "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", - " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outliers\n", - "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", - "

8 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", - "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", - "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", - "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", - "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", - "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", - "\n", - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", - "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", - "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", - "\n", - " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", - "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", - "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", - "min ... 0.000000 0.000000 0.000000 0.000000 \n", - "25% ... 150.000000 82.000000 0.000000 0.000000 \n", - "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", - "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", - "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", - "\n", - " S_0 S_2 S_3 S_4 S_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", - "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", - "\n", - " S_6 \n", - "count 26245.000000 \n", - "mean -0.428158 \n", - "std 0.900723 \n", - "min -2.000000 \n", - "25% -1.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 1.000000 \n", - "\n", - "[8 rows x 30 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy import stats\n", - "#we are only concerned with the ordinal data\n", - "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", - "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", - "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", - "df = df[rows]\n", - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature Scaling\n", - "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", - "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "scaler = MinMaxScaler()\n", - "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", - "df1 = df.copy()\n", - "df1[cols.columns] = scaler.fit_transform(cols)\n", - "df = df1" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", - "

5 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", - "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", - "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", - "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", - "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", - "\n", - " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", - "ID ... \n", - "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", - "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", - "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", - "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", - "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", - "\n", - "[5 rows x 30 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### One-Hot Encoding for Categorical attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", - "\n", - "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", - "\n", - "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", - "\n", - "The following categorical columns will be one-hot encoded\n", - "\n", - "1. EDUCATION\n", - "2. MARRIAGE\n", - "3. S0 - S6\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import OneHotEncoder" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "onenc = OneHotEncoder(categories='auto')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", - "
" - ], - "text/plain": [ - " GRAD UNI HS MARRIED SINGLE\n", - "0 0.0 1.0 0.0 1.0 0.0\n", - "1 0.0 1.0 0.0 0.0 1.0\n", - "2 0.0 1.0 0.0 0.0 1.0\n", - "3 0.0 1.0 0.0 1.0 0.0\n", - "4 0.0 1.0 0.0 1.0 0.0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#one hot encoding for EDUCATION and MARRIAGE\n", - "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", - "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", - "#drop one of each category to prevent dummy variable trap\n", - "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", - "onehot.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", - "
" - ], - "text/plain": [ - " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#one hot encoding for S_0 to S_6\n", - "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", - "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", - "#drop one of each category to prevent dummy variable trap\n", - "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", - "names = []\n", - "for X in range(0,7):\n", - " if X == 1:\n", - " continue\n", - " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", - " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", - " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", - " try:\n", - " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", - " except:\n", - " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", - "onehot_PAY.columns = names\n", - "onehot_PAY.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", - " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", - " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", - " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", - " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", - " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", - " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", - " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", - " 'PAY_6_Revolving_Credit'],\n", - " dtype='object')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", - "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", - "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", - "df1.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", - "

0 rows × 45 columns

\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", - "Index: []\n", - "\n", - "[0 rows x 45 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check for perfect collinearity\n", - "corr = df1.corr()\n", - "for i in range(len(corr)):\n", - " corr.iloc[i,i] = 0\n", - "#corr[corr == 1] = 0\n", - "corr[corr.eq(1).any(1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 45 Columns and 26245 Rows\n" - ] - } - ], - "source": [ - "size = df1.shape\n", - "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train Test Split\n", - "\n", - "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import *\n", - "from sklearn.model_selection import *" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "VOB68z_hM1jW" - }, - "outputs": [], - "source": [ - "#using holdout sampling for train test split\n", - "ft = df1.drop(\"Y\", axis = 1)\n", - "target = df1[\"Y\"]\n", - "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", - "#make the results reproducible (according to instructions)\n", - "np.random.seed(123) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter method for feature selection\n", - "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", - "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Significant values are:\n", - " 0 pval\n", - "LIMIT_BAL 78.806194 7.074564e-04\n", - "PAY_0 4532.900073 0.000000e+00\n", - "PAY_2 3833.396843 0.000000e+00\n", - "PAY_3 2921.087799 0.000000e+00\n", - "PAY_4 2778.451579 0.000000e+00\n", - "PAY_5 2791.382213 0.000000e+00\n", - "PAY_6 2434.267474 0.000000e+00\n", - "PAY_0_No_Transactions 78.446517 7.742745e-04\n", - "PAY_0_Pay_Duly 59.486746 4.837585e-02\n", - "PAY_0_Revolving_Credit 483.224042 0.000000e+00\n", - "PAY_2_Pay_Duly 73.909964 2.336982e-03\n", - "PAY_2_Revolving_Credit 238.193174 0.000000e+00\n", - "PAY_3_Pay_Duly 74.057499 2.256805e-03\n", - "PAY_3_Revolving_Credit 128.025091 2.222005e-10\n", - "PAY_4_Pay_Duly 72.030496 3.623036e-03\n", - "PAY_4_Revolving_Credit 82.321461 2.872173e-04\n", - "PAY_5_Pay_Duly 69.393208 6.568060e-03\n", - "PAY_5_Revolving_Credit 61.028427 3.642149e-02\n" - ] - } - ], - "source": [ - "from sklearn.feature_selection import SelectKBest\n", - "from sklearn.feature_selection import chi2\n", - "\n", - "selector = SelectKBest( score_func = chi2, k=10)\n", - "selector.fit(X_train, y_train)\n", - "np.set_printoptions(precision=10)\n", - "chi2data = pd.DataFrame(selector.scores_)\n", - "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", - "chi2data.index = X_train.columns\n", - "\n", - "print(\"Significant values are:\")\n", - "print(chi2data[chi2data[\"pval\"] < 0.05])\n", - "\n", - "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", - "X_train_filter = X_train[cols]\n", - "X_test_filter = X_test[cols]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mbhlIlQzZz7c" - }, - "source": [ - "## Model Selection\n", - "\n", - "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", - "\n", - "We will be attempting to fit the following models:\n", - "\n", - "\n", - "- Decision Tree \n", - "- Logistic Regression\n", - "- Support Vector Machine\n", - "- Neural Network\n", - "\n", - "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def get_roc(model, y_test, X_test, name):\n", - " try:\n", - " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", - " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", - " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", - " except:\n", - " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", - " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", - " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", - " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", - " plt.xlim([0.0, 1.0])\n", - " plt.ylim([0.0, 1.05])\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver operating characteristic for ' + name)\n", - " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", - " plt.legend(loc=\"lower right\")\n", - " \n", - " #find- best threshold\n", - " optimal_idx = np.argmax(tpr - fpr)\n", - " optimal_threshold = thresholds[optimal_idx]\n", - " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", - " \n", - " plt.show()\n", - " \n", - " return auc(fpr, tpr)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "def confusion(y_test, predictions, name):\n", - " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", - " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", - " return conf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluation \n", - "We will select the model based on the model evaluation. The key metrics we will compute are:\n", - "\n", - "1. Accuracy\n", - "2. Recall\n", - "3. AUROC\n", - "\n", - "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", - "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", - "\n", - "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "H89tM6NvaN17" - }, - "source": [ - "### Decision Trees\n", - "\n", - "#### Theory:\n", - "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", - "\n", - "Below is a binary decision tree that has been split for a few iterations.\n", - "\n", - "![image.png](https://elf11.github.io/images/decisionTree.png)\n", - "\n", - "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", - "\n", - "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", - "\n", - "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", - "\n", - "#### Training\n", - "We will now construct a simple decision tree using the GINI index." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False,\n", - " random_state=None, splitter='best')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree = DecisionTreeClassifier()\n", - "tree.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 13476\n", - " 1 1.00 1.00 1.00 4020\n", - "\n", - " accuracy 1.00 17496\n", - " macro avg 1.00 1.00 1.00 17496\n", - "weighted avg 1.00 1.00 1.00 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, tree.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (GINI) identified 852\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
053951333
11169852
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 5395 1333\n", - "1 1169 852" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.5\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.80 0.81 6728\n", - " 1 0.39 0.42 0.41 2021\n", - "\n", - " accuracy 0.71 8749\n", - " macro avg 0.61 0.61 0.61 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, tree.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", - "![image.png](http://www.sciencealert.com/images/15_.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Entropy) identified 810\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
054471281
11211810
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 5447 1281\n", - "1 1211 810" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", - "tree2.fit(X_train, y_train)\n", - "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.5\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.6062870404745417" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.81 0.81 6728\n", - " 1 0.39 0.40 0.39 2021\n", - "\n", - " accuracy 0.72 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.72 0.72 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, tree2.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", - " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random Forest Classifier\n", - "\n", - "#### Theory\n", - "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", - "\n", - "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", - "\n", - "#### Training\n", - "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "randf = RandomForestClassifier(n_estimators=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=200,\n", - " n_jobs=None, oob_score=False, random_state=None,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "randf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 13476\n", - " 1 1.00 1.00 1.00 4020\n", - "\n", - " accuracy 1.00 17496\n", - " macro avg 1.00 1.00 1.00 17496\n", - "weighted avg 1.00 1.00 1.00 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, randf.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Random Forest) identified 763\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06307421
11258763
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6307 421\n", - "1 1258 763" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.31\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 6728\n", - " 1 0.64 0.38 0.48 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, randf.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[1] = ([\"Random Forest\" , \n", - " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradient Boosted Trees Classifier\n", - "\n", - "#### Theory\n", - "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", - " \n", - "#### Training\n", - "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=4,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_iter_no_change=None, presort='auto',\n", - " random_state=None, subsample=1.0, tol=0.0001,\n", - " validation_fraction=0.1, verbose=0,\n", - " warm_start=False)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import GradientBoostingClassifier\n", - "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", - "xgb.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.96 0.91 13476\n", - " 1 0.80 0.48 0.60 4020\n", - "\n", - " accuracy 0.85 17496\n", - " macro avg 0.83 0.72 0.75 17496\n", - "weighted avg 0.85 0.85 0.84 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, xgb.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 764\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06335393
11257764
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6335 393\n", - "1 1257 764" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.22217593115970874\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 6728\n", - " 1 0.66 0.38 0.48 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, xgb.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", - " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4215730.612897
1Random Forest0.3775360.762511
2Gradient Boosted0.3780310.774845
\n", - "
" - ], - "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.421573 0.612897\n", - "1 Random Forest 0.377536 0.762511\n", - "2 Gradient Boosted 0.378031 0.774845" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logistic Regression\n", - "\n", - "#### Theory\n", - "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", - "\n", - "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", - "\n", - "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", - "\n", - "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", - "\n", - "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", - "\n", - "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", - "\n", - "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", - "\n", - "\n", - "#### Training\n", - "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.443375\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774
Time: 08:26:11 Log-Likelihood: -7757.3
converged: True LL-Null: -9430.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629
SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073
AGE 0.0807 0.100 0.805 0.421 -0.116 0.277
PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745
PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367
PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179
PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069
PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250
PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741
BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444
BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081
BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579
BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725
BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615
BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187
PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809
PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898
PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148
PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032
PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034
PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295
GRAD 1.2438 0.217 5.736 0.000 0.819 1.669
UNI 1.2506 0.215 5.804 0.000 0.828 1.673
HS 1.1383 0.219 5.195 0.000 0.709 1.568
MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504
SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369
PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203
PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295
PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625
PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873
PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827
PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333
PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005
PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051
PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005
PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451
PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577
PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528
PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376
PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271
PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396
PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466
PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388
PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17452\n", - "Method: MLE Df Model: 43\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774\n", - "Time: 08:26:11 Log-Likelihood: -7757.3\n", - "converged: True LL-Null: -9430.2\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", - "SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", - "AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", - "PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", - "PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", - "PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", - "PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", - "PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", - "PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", - "BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", - "BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", - "BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", - "BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", - "BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", - "BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", - "PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", - "PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", - "PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", - "PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", - "PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", - "PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", - "GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", - "UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", - "HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", - "MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", - "SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", - "PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", - "PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", - "PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", - "PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", - "PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", - "PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", - "PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", - "PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", - "PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", - "PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", - "PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", - "PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", - "PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", - "PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", - "PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", - "PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", - "PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", - "PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "glm = sm.Logit(y_train,X_train).fit()\n", - "glm.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 13476\n", - " 1 0.67 0.36 0.47 4020\n", - "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.68 17496\n", - "weighted avg 0.80 0.81 0.79 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.443375\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443376\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443378\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443381\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443383\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443392\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443403\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443417\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443435\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443453\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443472\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443503\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443533\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443589\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443651\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443736\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443875\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.444048\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17469
Method: MLE Df Model: 26
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762
Time: 08:26:12 Log-Likelihood: -7769.1
converged: True LL-Null: -9430.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639
SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076
PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690
PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388
PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146
PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291
BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571
BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886
PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928
PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109
PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268
PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408
GRAD 1.3777 0.188 7.342 0.000 1.010 1.746
UNI 1.3823 0.187 7.407 0.000 1.017 1.748
HS 1.2816 0.190 6.731 0.000 0.908 1.655
MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243
PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756
PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930
PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839
PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364
PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163
PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103
PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219
PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588
PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753
PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718
PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17469\n", - "Method: MLE Df Model: 26\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762\n", - "Time: 08:26:12 Log-Likelihood: -7769.1\n", - "converged: True LL-Null: -9430.2\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", - "SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", - "PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", - "PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", - "PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", - "PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", - "BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", - "BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", - "PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", - "PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", - "PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", - "PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", - "GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", - "UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", - "HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", - "MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", - "PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", - "PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", - "PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", - "PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", - "PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", - "PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", - "PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", - "PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", - "PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", - "PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", - "PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#remove the most insignificant attribute, and retrain\n", - "train_log = X_train.copy()\n", - "glm = sm.Logit(y_train,train_log).fit()\n", - "while max(glm.pvalues) > 0.01:\n", - " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", - " train_log = train_log.drop(least,axis = 1)\n", - " glm = sm.Logit(y_train,train_log).fit()\n", - "glm.summary() " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", - " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT6', 'GRAD',\n", - " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", - " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", - " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", - " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_6_No_Transactions'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "count = len(glm.pvalues.index)\n", - "print(str(count) + \" Columns left:\")\n", - "print(glm.pvalues.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6728\n", - " 1 0.67 0.36 0.47 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", - "\n", - "We now Calculate the AUROC for the train set." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.1993195782749319\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7718794071347034" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.78 0.82 6728\n", - " 1 0.46 0.63 0.53 2021\n", - "\n", - " accuracy 0.74 8749\n", - " macro avg 0.67 0.70 0.68 8749\n", - "weighted avg 0.78 0.74 0.76 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", - "\n", - "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", - "\n", - "\n", - "Calculate the confusion matrices for both cut offs to better compare their performance." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Logistic Regression identified 1273\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
052401488
17481273
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 5240 1488\n", - "1 748 1273" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Logistic Regression identified 720\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
06377351
11301720
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 6377 351\n", - "1 1301 720" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is evident that the lower cutoff is better able to detect defualts." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.2091273384286262\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "iCxBcin11EI8" - }, - "source": [ - "### Support Vector Machine\n", - "#### Theory\n", - "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", - "\n", - " \n", - "\n", - "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", - "\n", - "Any hyperplane can be written as the set of points X satisfying\n", - "\n", - "\n", - "\n", - "
\n", - " \n", - "
\n", - "\n", - "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", - "\n", - "#### Margin\n", - "A margin is a separation of line to the closest class points.\n", - "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", - "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "\n", - "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", - "\n", - "##### Hard Margin\n", - "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", - "\n", - " Minimize ||W|| subject to:\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "##### Soft Margin\n", - "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", - "\n", - "We then wish to minimize\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", - "\n", - "#### Computing SVM classifier\n", - "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", - "\n", - "##### Primal\n", - "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", - "\n", - "We can rewrite the optimization problem as follows\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "This is called the primal problem.\n", - "\n", - "##### Dual\n", - "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", - " \n", - "\n", - "
\n", - "
\n", - " \n", - "\n", - "
\n", - "
\n", - "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", - "\n", - "Here, the variables C are defined such that\n", - " \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parameter Tuning\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Kernel\n", - "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", - "\n", - "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Regularization (C value)\n", - "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", - "\n", - "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "Left: low regularization value, right: high regularization value\n", - "\n", - "\n", - "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Gamma\n", - "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "\n", - "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply SVM model\n", - "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### SVM without parameter tuning\n", - "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import svm\n", - "#train svm model without standardization and no parameter tuning\n", - "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", - "clf_original.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.16372570606114417\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7264392577614385" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2009 Defaulters, the SVM default parameters identified 739\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06382358
11270739
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6382 358\n", - "1 1270 739" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6740\n", - " 1 0.67 0.37 0.48 2009\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, clf_original.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### SVM with Parameter tuning\n", - "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", - "\n", - "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", - "\n", - "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 0.01, 'gamma': 0.001}" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001,0.01, 0.1, 1]\n", - " gammas = [0.001, 0.01, 0.1]\n", - " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", - " grid_search.fit(X, y)\n", - " grid_search.best_params_\n", - " return grid_search.best_params_\n", - "svc_param_selection(X_train, y_train,3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", - "clf_reduced_tuned.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.15927524092941694\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", - " \"SVM reduced features and tuning RBF kernel\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2009 Defaulters, the SVM reduced features and tuning RBF kernal identified 671\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06437303
11338671
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6437 303\n", - "1 1338 671" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.96 0.89 6740\n", - " 1 0.69 0.33 0.45 2009\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.64 0.67 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[4] = ([\"SVM\" , \n", - " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neural Networks\n", - "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", - "\n", - "#### Theory\n", - "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", - "\n", - ".\n", - "\n", - "\n", - "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", - "\n", - "\n", - "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", - "\n", - "\n", - "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", - "\n", - "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Parameter tuning\n", - "##### Learning rate\n", - "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", - "\n", - "##### Momentum\n", - "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", - "\n", - "##### Loss function\n", - "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test,predictions))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from numpy import loadtxt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "# define the keras model\n", - "model = Sequential()\n", - "model.add(Dense(12, input_dim=26, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# evaluate the keras model\n", - "#recall, accuracy = model.evaluate(df1, target)\n", - "#print('Accuracy: %.2f' % (accuracy*100))\n", - "#print('Recall: %.2f' % (recall*100))\n", - "\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "print(classification_report(y_test,predictions))" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "BT2101 disrudy ", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/BT2101 - FINAL-.ipynb b/BT2101 - FINAL-.ipynb new file mode 100644 index 0000000..312a64d --- /dev/null +++ b/BT2101 - FINAL-.ipynb @@ -0,0 +1,5584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3df7BcZZ3n8ffHIC4VpEiM3IoEDboZZ/mhCHcIrlUz17EIAZwKlj+WHyXBQWMpKWdXtoowyxYK6Ga2lnXBHzhRY0ItENmpYZNRNGZZe3FnRZMoS4KARLzKJUiEQJaLOBj87h/naWgufW/f7j7d/Zx7P6+qru5++ulznpP+5vme55znnqOIwMzMLDevGHQDzMzMmnGCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5AQ1TZJGJT0raVzSY5K+Lunwhs83SDoo6XUNZaenugsayl4l6T5JH22xvpMk7ZT02/R8Um+2zHqlnzEj6Y8kbZb0G0n7JW2V9ObebZ2Vrc/xskDSP0p6QtJTkn4g6R2927rOOEG15y8i4nDgZOBPgCsAJM0F3gscAC6oV46IbcA3gesalnEF8CiwbrKVSDoU2Az8V2AesBHYnMqtWvoSM8CRwBbgzcAQ8COKGLJq6Ve8jAN/CbyWoo/5G+AfJB1S2paUwAmqAxHxCPBt4IRU9F7gKeAqYOWE6p8E/kzS2ZJOAFYDH4mprzE1AhwC/JeI+KeIuB4Q8OflbYX1U69jJiJ+FBFfi4j9EfF74HPAmyW9puxtsd7rQ7z8LiIeiIg/UPQtz1Mkqvnlbkl3nKA6IOkY4CzgJ6loJXALsAn4Y0kn1+tGxAHgY8CXgfXApyPi5y1WcTxwz4QAuyeVWwX1IWYm+lPg1xHxRLdtt/7rV7xIugf4HcXo+6sRsa+0jSiBE1R7/rukp4D/Dfwv4LOSXg+8E7g5Ih4D7mDCHk5E/ANwF8W/9/XTWM/hFEP5RgeAV3fXfBuAfsXMCyQtAr5IsWdt1dLXeImItwBHAOendWbFCao950TEkRHxhoj4eEQ8C3wQuC8i7k51bgLOl/TKCd+9F7g/DalbGacImkZHAE9303gbiH7FDACSXgt8F/hSRNxSxgZYX/U1XuCFw323AGskvbXrLShRVifEKupC4PWSfp3eHwK8BjiTYtjciXuBSyWp4TDfWyj2iq36ehEzSJpHkZy2RMRnum6l5aIn8dLEK4E3Av+3xGV2xQmqC5LeDrwJeBvwm4aPrqUYgncaPDWKk5afkPRl4COp/H92uDzLRK9iRtIRwFbgHyNiTbfttDz0MF5Oo+j/fwTMAT5BMfvzh920t2xOUN1ZCWyOiF2NhZKuA74vaX5E7G93oRHxnKRzgK8Ca4H7KIb+z5XRaBuonsQM8B6KacnHS7qoofy4iPhVx621QetVvLyK4lzVG4HfA7uAsyNib7cNLpN8R10zM8uRJ0mYmVmWnKAGRNIF6ZImEx/3DrptlifHjLVjJsSLD/GZmVmWWk6SkLQeeDewLyJOSGWfophZVp9V8tcRcXv67HLgYtIstIjYmsqXU1wvag7FXyyvTeXHUvx19Hzgx8AHpzMZYMGCBbF48eJpb2grzzzzDHPnzi1teb0yiHbu3Lnz8Yh4bV9XWrKy4wWqETOOl865j+mfSWMmIqZ8UFwy5WRgd0PZp4B/26TucRRz6F8FHAv8nCIhzUmv3wgcmuocl75zK3Buev1l4GOt2hQRnHLKKVGm733ve6Uur1cG0U5gR0zjN8n5UXa8RFQjZhwv+cRMFeIlIq+YaXkOKiLuBKY7jXEFsCmKC5z+AtgDnJoeeyLioShGR5uAFZLqF0D9u/T9jcA501yXmZnNYN38HdRqSRcCO4BLI+JJ4GiK60HVjaUygIcnlC+l+GvopyLiYJP6LyNpFbAKYGhoiFqt1kXzX2p8fLzU5fVKVdppZtatThPUDcDVQKTnaynuLaImdYPmswVjivpNRcQ60j1OhoeHY2RkpK1GT6VWq1Hm8nqlKu00M+tWRwkqiivqAiDpKxQ3zIJiBHRMQ9VFQP0vk5uVPw4cKemQNIpqrN9Xux45wEVrvjVlndG1Z/epNVYFrWLG8WKNHC/t6+jvoCQtbHj7HmB3er0FODfdcvhYYAnFtZ62A0skHZvuCnsuxQUtA/ge8L70/ZX4LqCVJ2m9pH2SdjeUfUrSI5LuTo+zGj67XNIeSQ9IOqOhfHkq2yNpTUP5sZJ+KOlBSd/wnYarzfFik2mZoCTdAvyA4u6cY5IuBv6jpF3pZlfvBP4NQETcSzEr76fAd4BLIuL5NDpaTXExy/uAW1NdgMuAT0raQ3FO6mulbqENwgZgeZPyz0XESelR/7OE4yh2WI5P3/mSpDmS5lBcvf1Mitmh56W6UNye+nMRsQR4kuLPGqy6NuB4sSZaHuKLiPOaFE+aRKK4zP/LLvWfAuz2JuUPUczysxkiIu6UtHia1V+Y+Qn8Iu2o1ONhT4oPJNVnft5HMfPz/FRnI8WfPdxQTuut3xwvNhlfzdz6qa8zP3s56xNg6DC49MSDk36ew2zLis/6nFEzhasQL5BXzDhBWb/0feZnL2d9Anz+ps1cu2vy/0KjF5S7vk5UeNbnjJspXIV4gbxixgnK+mImzvy03nG8GPhq5tYnnvlp7XC8GHgEZT2QZn6OAAskjQFXAiOSTqI4vDIKfBSKmZ+S6jM/D5Jmfqbl1Gd+zgHWT5j5uUnSNcBP8MzPSnO82GScoKx0nvlp7XC82GR8iM/MzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllqWWCkrRe0j5JuxvK5kvaJunB9DwvlUvS9ZL2SLpH0skN31mZ6j8oaWVD+SmSdqXvXC9JZW+kmZlVz3RGUBuA5RPK1gB3RMQS4I70HuBMYEl6rAJugCKhUdzGeSnFnS2vrCe1VGdVw/cmrsvMzGahlgkqIu4E9k8oXgFsTK83Auc0lN8YhbuAIyUtBM4AtkXE/oh4EtgGLE+fHRERP4iIAG5sWJaZmc1ih3T4vaGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRjLYYGhqiVqt12PyXGzoMLj3x4JR1ylxfp8bHx7Noh5lZr3WaoCbT7PxRdFDeVESsA9YBDA8Px8jISAdNbO7zN23m2l1T/3OMXlDe+jpVq9Uoc7t7QdJ64N3Avog4IZXNB74BLAZGgQ9ExJPpnON1wFnAb4GLIuLH6TsrgSvSYq+JiI2p/BSKQ8+HAbcDf5VG4FZBjhebTKez+B5Lh+dIz/tS+RhwTEO9RcDeFuWLmpRbtW3A5y1t+jbgeLEmOk1QW4D6TLyVwOaG8gvTbL7TgAPpUOBWYJmkeSlolgFb02dPSzot7Rld2LAsqyift7R2OF5sMi0P8Um6BRgBFkgao9hLWQvcKuli4FfA+1P12ymG3nsoht8fAoiI/ZKuBraneldFRD0gP8aLw+9vp4fNPH0/b9nLc5bQ+rxlDucKK3zOctad587ld8opZlomqIg4b5KP3tWkbgCXTLKc9cD6JuU7gBNatcNmrJ6dt+zlOUtofd7S5yx7Ysae584hXiCvmPGVJKxffN7S2uF4MSco6xuft7R2OF6s9GnmZj5vaW1xvNhknKCsdD5vae1wvNhkfIjPzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZWnW3LBw8ZpvTfn5pSf2qSFWGY4Zs8HqKkFJGgWeBp4HDkbEsKT5wDeAxcAo8IGIeFKSgOsobtf8W+CiiPhxWs5K4Iq02GsiYmM37TIz6zfv0JSvjEN874yIkyJiOL1fA9wREUuAO9J7gDOBJemxCrgBICW0K4GlwKnAlZLmldAuMzOrsF6cg1oB1EdAG4FzGspvjMJdwJGSFgJnANsiYn9EPAlsA5b3oF2WAUmjknZJulvSjlQ2X9I2SQ+m53mpXJKul7RH0j2STm5YzspU/8E0ArcZyPEyu3WboAL4rqSdklalsqGIeBQgPR+Vyo8GHm747lgqm6zcZi6Puq0djpdZqttJEu+IiL2SjgK2Sbp/irpqUhZTlL98AUUSXAUwNDRErVabdkMvPfHglJ8PHda6Tjvr65Xx8fEs2lGyFcBIer0RqAGX0TDqBu6SVB91j5BG3QCS6qPuW/rbbBsQx8ss0VWCioi96XmfpNso9k4ek7QwIh5NwbEvVR8Djmn4+iJgbyofmVBem2R964B1AMPDwzEyMtKsWlMXtTyBeZBrd039zzF6wfTX1yu1Wo12tjtD9VF3AH+bftOXjLrTDg941G2Ol1mt4wQlaS7wioh4Or1eBlwFbAFWAmvT8+b0lS3AakmbKIbaB1JwbQU+2zDkXgZc3mm7LHt9G3V3M+KG7kfdOYx0Z8CIe9Ycpcnld8opZroZQQ0BtxWzxzkEuDkiviNpO3CrpIuBXwHvT/Vvp5hivodimvmHACJiv6Srge2p3lX1objNPP0cdXcz4obuR90ecXdvNh2lySFeIK+Y6XiSREQ8FBFvTY/jI+IzqfyJiHhXRCxJz/tTeUTEJRHxpog4MSJ2NCxrfUT88/T4evebZTmSNFfSq+uvKUbLu3lx1A0vH3VfmGZnnUYadQNbgWWS5qWR97JUZjOI48VmzZUkLAsedVs7HC+znBOU9U1EPAS8tUn5E8C7mpQHcMkky1oPrC+7jZYPx4s5QZmZTUOrSxlZ+Zyg2tAqQEfXnt2nlli3cuhsptMGx5Q1mm19kG+3YWZmWXKCMjOzLDlBmZlZlpygzMwsSzNmkkQOJ73NzKw8MyZB5WC2zbAxM+slH+IzM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmlqVsEpSk5ZIekLRH0ppBt8fy5nixdjlmqieLq5lLmgN8ETgdGAO2S9oSET8dbMvKNZ1bgviK563NlngBXyG/LLMpZmaSLBIUcCqwJyIeApC0CVgBOHisGceLtWtWxMxM26HJJUEdDTzc8H4MWDqxkqRVwKr0dlzSA2U14BOwAHi8rOV1Sn/Tssog2vmGPq+vlYHHC7SOmWn8ll1zvEzbwGOmIvECGcVMLglKTcriZQUR64B1PWmAtCMihnux7DJVpZ09NvB4gWr8FlVoY58MPGaq8lvk1M5cJkmMAcc0vF8E7B1QWyx/jhdrl2OmgnJJUNuBJZKOlXQocC6wZcBtsnw5XqxdjpkKyuIQX0QclLQa2ArMAdZHxL19bkbPDgWVrCrt7JlM4gWq8VtUoY09l0nMVOW3yKadinjZYVgzM7OBy+UQn5mZ2Us4QZmZWZacoKjGJVAkjUraJeluSTsG3Z7ZrArxAo6ZnFQhZnKMl1l/DipdAuVnNFwCBTgvt0ugSBoFhiNi4H9MPJtVJV7AMZOLqsRMjvHiEVTDJVAi4jmgfgkUs2YcL9Yux0yHnKCaXwLl6AG1ZSoBfFfSznQ5FhuMqsQLOGZyUZWYyS5esvg7qAGb1iVQMvCOiNgr6Shgm6T7I+LOQTdqFqpKvIBjJhdViZns4sUjqIpcAiUi9qbnfcBtFIcNrP8qES/gmMlIJWImx3hxgqrAJVAkzZX06vprYBmwe7CtmrWyjxdwzGQm+5jJNV5m/SG+TC6B0soQcJskKH6zmyPiO4Nt0uxUkXgBx0w2KhIzWcbLrJ9mbmZmefIhPjMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKCmSdKopGcljUt6TNLXJR3e8PkGSQclva6h7PRUd0FD2ask3Sfpo9Nc70pJIenD5W6R9Vq/YybFyTNpfeOSvtqbLbNeGEC8zJF0jaS9kp6W9BNJR/Zm6zrjBNWev4iIw4GTgT8BroAX7kD5XuAAcEG9ckRsA74JXNewjCuAR4F1rVYmaR5wOZDbzc1s+voaM8BbI+Lw9PBOTfX0M14+DfxL4O3AEcAHgd+VshUlcYLqQEQ8AnwbOCEVvRd4CrgKWDmh+ieBP5N0tqQTgNXAR2J6d4r8D8D1wOOlNNwGpo8xYzNAr+Ml7fz+61Tvl1HYHRFOUFUn6RjgLOAnqWglcAuwCfhjSSfX60bEAeBjwJeB9cCnI+Ln01jHqcBw+p5VXD9iJrlT0q8l/b2kxSU13/qsD/FyInAQeF+Kl59JuqTkzeheRPgxjQcwCoxT7MX8EvgScBjweuAPwEmp3lbguibf/2/ADuAV01jXnFT37el9DfjwoP8N/Mg3ZlL9PwUOBY4EvgDsBg4Z9L+DH/nFC3A+EMDX0jreAvwGOH3Q/w6ND4+g2nNORBwZEW+IiI9HxLMUx23vi4i7U52bgPMlvXLCd+8F7o+IP0xjPR8H7omIH5TXdBuQfsUMEXFnRDwXEU8BfwUcC/yLkrbD+qNf8fJser4qIp6NiHsoRmdnlbERZTlk0A2YAS4EXi/p1+n9IcBrgDOBLR0u810Ux5TrwTIfeJukkyJidVettRz0ImaaCUAlLs8Goxfxck96zvq8phNUFyS9HXgT8DaK4XHdtRTHjDsNnouAf9bw/u+Bv6MYjluF9SpmJB0PvBLYRXHI5hrgEeC+btprg9WreImIn0v6PvDvJH0CeCPwr4DzumtxuZygurMS2BwRuxoLJV0HfF/S/IjY3+5C0yGaxuU9B/y/KE6GWrX1JGaAIeAGYBHwDPB/gHdHxO+7bbANVK/iBYpk9DXgCWAf8O8j4o6uWlsypRNmZmZmWfEkCTMzy5IT1IBIuqDhkjSND181wppyzFg7ZkK8+BCfmZllqbKTJBYsWBCLFy8ubXnPPPMMc+fOLW15vTKIdu7cufPxiHhtX1dasrLjBaoRM46XzrmP6Z/JYqayCWrx4sXs2LGjtOXVajVGRkZKW16vDKKdkn7Z1xX2QNnxAtWIGcdL59zH9M9kMeNzUGZmliUnKDMzy5ITlJmZZamy56DKtuuRA1y05ltT1hlde3afWmNV0CpmHC/WyPHSPo+gzMwsS05QZmaWJScoMxsoSesl7ZO0u6HsU5IekXR3epzV8NnlkvZIekDSGQ3ly1PZHklrGsqPlfRDSQ9K+oakQ/u3ddaNlgnKwWNmPbYBWN6k/HMRcVJ63A4g6TjgXOD49J0vSZojaQ7wRYp7JB0HnJfqAvxNWtYS4Eng4p5ujZVmOiOoDTh4zKxHIuJOYLq3jFgBbIqIf4qIXwB7gFPTY09EPBQRz1HcHXaFJAF/TnE/NYCNwDmlboD1TMsE5eAxswFZLemedBRnXio7Gni4oc5YKpus/DXAUxFxcEK5VUA308xXS7oQ2AFcGhFPUvzwdzXUaQyGicGzlDaDR9IqYBXA0NAQtVqti+a/1NBhcOmJB6esU+b6OjU+Pp5FO6YiaT3wbmBfRJyQyj4FfIQX7wr61w0j78spRs7PA5+IiK2pfDlwHTAH+GpErE3lx1Ls5MwHfgx8MO342MxxA3A1xS3Jr6a4g+xf0vwW9kHzne3Jbnk/6RWyB9nH5PL/Oqc+ptMENZDgiYh1wDqA4eHhKPN6UZ+/aTPX7pr6n2P0gvLW16mKXM9rA/AF4MYJ5Z+LiP/UWDDhsPDrgP8h6Y/Sx18ETqfYcdkuaUtE/JQXDwtvkvRliuR2Q682xvovIh6rv5b0FeCb6e0YcExD1UXA3vS6WfnjwJGSDkk7wo31m613YH1MDv0L5NXHdDSLLyIei4jnI+IPwFcoDuHB5MEzWfkLwTOh3CrMh4WtW5IWNrx9D1CfpLUFOFfSq9JIegnwI2A7sCRNujqUYqdnSxT3E/oe8L70/ZXA5n5sg3WvoxGUpIUR8Wh6OzF4bpb0nyn2huvBI1LwAI9QBM/5ERGS6sGzCQfPTNfXw8K9PFwD1Thkk9PhmslIugUYARZIGgOuBEYknURxRGUU+ChARNwr6Vbgp8BB4JKIeD4tZzWwleKQ8PqIqN+Y7zJgk6RrgJ8AX+vTplmXWiYoB4+VpO+HhXt5uAaqccgmp8M1k4mI85oUT9oPRMRngM80Kb8duL1J+UO8eJTHKqRlgnLwWBkGdU7BzKrLV5KwvvA5BTNrl69mbqXzYWEzK4MTlJXOh4XNrAw+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMsuQEZWZmWXKCMjOzLDlBmZlZlpygzMwsS05QZmaWJScoMzPLkhOUmZllyQnKzAZK0npJ+yTtbiibL2mbpAfT87xULknXS9oj6R5JJzd8Z2Wq/6CklQ3lp0jalb5zvST1dwutUy0TlIPHzHpsA7B8Qtka4I6IWALckd4DnAksSY9VwA1Q9EkUd25eSnEzyyvr/VKqs6rhexPXZZmazghqAw4ea4N3aqwdEXEnsH9C8QpgY3q9ETinofzGKNwFHClpIXAGsC0i9kfEk8A2YHn67IiI+EFEBHBjw7Iscy1v+R4Rd0paPKF4BTCSXm8EasBlNAQPcJekevCMkIIHQFI9eGqk4Enl9eD5djcbZQO3AfgCRWdQV9+pWStpTXp/GS/dqVlKscOytGGnZhgIYKekLanzqe/U3EVxS/jlOGZmmqGIeBQgIh6VdFQqPxp4uKHeWCqbqnysSXlTklZRxBZDQ0PUarXutqLB0GFw6YkHJ/28zHV1Y3x8PJu2tExQk5h1wQN5BFBOwTMZ79RYDzUbLUcH5U1FxDpgHcDw8HCMjIx00MTmPn/TZq7dNXmXO3pBeevqRq1Wo8zt7kanCWoyMzZ4II8Ayil42jSQnRqrrMckLUyxshDYl8rHgGMa6i0C9qbykQnltVS+qEl9q4BOE5SDx8rSs52aXo64oRqHbKow4p7EFmAlsDY9b24oXy1pE8Uh4QOpH9oKfLbh3PYy4PKI2C/paUmnAT8ELgQ+388Nsc51mqAcPNauvu/U9HLEDdU4ZFOFEbekWyh+6wWSxijOPa4FbpV0MfAr4P2p+u3AWcAe4LfAhwBSX3I1sD3Vu6p+eBj4GMV50cMoDgX7cHBFtExQDh4riXdqrKmIOG+Sj97VpG4Al0yynPXA+iblO4ATummjDcZ0ZvE5eKwt3qkxszKUPUnCzDs1ZlYKX+rIzMyy5ARlZmZZcoIyM7MsOUGZmVmWnKDMzCxLTlBmZpYlJygzM8uSE5SZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZck3LDQzK8HiNd+a8vNLT+xTQ2aQrhKUpFHgaeB54GBEDEuaD3wDWAyMAh+IiCclCbiO4vbevwUuiogfp+WsBK5Ii70mIjZ2065mHDxm1VOlPsbKV8YI6p0R8XjD+zXAHRGxVtKa9P4y4ExgSXosBW4AlqZguxIYBgLYKWlLRDxZQtssM1XqcLxTkw33MbNUL85BrQDqncVG4JyG8hujcBdwpKSFwBnAtojYnwJmG7C8B+2yfLwzIk6KiOH0vt7hLAHuSO/hpR3OKooOh4YOZylwKnClpHl9bL8NlvuYWaLbEVQA35UUwN9GxDpgKCIeBYiIRyUdleoeDTzc8N2xVDZZ+ctIWkXRUTE0NEStVpt2Qy898eCUnw8d1rpOO+vrlfHx8SzaUbIVwEh6vRGoUewRv9DhAHdJqnc4I6QOB0BSvcO5pb/Ntj6YNX1MLv+vc+pjuk1Q74iIvSlAtkm6f4q6alIWU5S/vLAIznUAw8PDMTIyMu2GXtTycM1Brt019T/H6AXTX1+v1Go12tnuDPWtw+mms4GZ0eHk1Nl0KJs+ptUh31bdaas+Jof+BfLqY7pKUBGxNz3vk3QbxeGWxyQtTB3NQmBfqj4GHNPw9UXA3lQ+MqG81k27LGt963C62aGB7ndqcuhwcupsOuE+Znbr+ByUpLmSXl1/DSwDdgNbgJWp2kpgc3q9BbhQhdOAA2mveSuwTNK8dB5hWSqzGaixwwFe0uEAtNHhNCu3GcR9jHUzghoCbismWnEIcHNEfEfSduBWSRcDvwLen+rfTjEbaw/FjKwPAUTEfklXA9tTvavq5xba0Xr4bYOWOplXRMTTDR3OVbzY4azl5R3OakmbKCZEHEh7zVuBzzZMjFgGXN5OWxwvlZBVH2P913GCioiHgLc2KX8CeFeT8gAumWRZ64H1nbbFKsMdjk2b+xjzlSSsb9zhmFk7fC0+MzPLkhOUmZllyQnKzMyy5HNQJWo1M2x07dl9aomZWfV5BGVmZlnyCMrMrCJm21Eaj6DMzCxLHkGZDch0rmYx0/aIzdrhEZSZmWXJCcrMzLLkBGVmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZckJyszMspRNgpK0XNIDkvZIWjPo9ljeHC/WLsdM9WSRoCTNAb4InAkcB5wn6bjBtspy5XixdjlmqimXi8WeCuyJiIcAJG0CVgA/HWirSjadi4O2smH53J63oQIXKJ0V8QLdx0y38TKdNlQgXmCWxMwM+a1eoIgYdBuQ9D5geUR8OL3/ILA0IlZPqLcKWJXevhl4oMRmLAAeL3F5vTKIdr4hIl7b53VOKpN4gWrEzKyPF8gmZqoQL5BRzOQyglKTspdlzohYB6zrSQOkHREx3Itll6kq7eyxgccLVOO3qEIb+2TgMVOV3yKndmZxDgoYA45peL8I2Dugtlj+HC/WLsdMBeWSoLYDSyQdK+lQ4Fxgy4DbZPlyvFi7HDMVlMUhvog4KGk1sBWYA6yPiHv73IyeHQoqWVXa2TOZxAtU47eoQht7LpOYqcpvkU07s5gkYWZmNlEuh/jMzMxewgnKzMyy5ARFNS6BImlU0i5Jd0vaMej2zGZViBdwzOSkCjGTY7zM+sWNACwAAAEnSURBVHNQ6RIoPwNOp5iKuh04LyKy+gtzSaPAcERU4Q/9ZqyqxAs4ZnJRlZjJMV48gmq4BEpEPAfUL4Fi1ozjxdrlmOmQExQcDTzc8H4sleUmgO9K2pkux2KDUZV4AcdMLqoSM9nFSxZ/BzVg07oESgbeERF7JR0FbJN0f0TcOehGzUJViRdwzOSiKjGTXbx4BFWRS6BExN70vA+4jeKwgfVfJeIFHDMZqUTM5BgvTlAVuASKpLmSXl1/DSwDdg+2VbNW9vECjpnMZB8zucbLrD/El8klUFoZAm6TBMVvdnNEfGewTZqdKhIv4JjJRkViJst4mfXTzM3MLE8+xGdmZllygjIzsyw5QZmZWZacoMzMLEtOUGZmliUnKDMzy5ITlJmZZen/A6K07c+5e7/dAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAAEYCAYAAADWNhiqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbAcdZ3v8ffH8CAbxCRGTsUka8KaukuEe4XkQly3ds+KhIBVN9xSVijKnKvczRbCqrVqbVzrXlZRL24V7hV0paJkSdisiE+buEtuNmKOSJU8KiSEyOYEoxwTiBgIHHyAsN/7R/9GO8PMmYfzMPOb83lVdU3Pt3/d/euZ7+nvTHefHkUEZmZmOXtZpztgZmY2Vi5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2pkwxk7RP0i8ljUh6QtI/SDqxNP0mSUckvaYUOze1nV2KHS9pt6Q/b2Kd09P6bqvTn+fLy07xBySFpAWStqT5RyS9kNpXnt8g6ThJX03LCkn9bb9ANqoezZ9lkrZJOiTpZ5K+ImlO+6+S1dKjubNY0n2SnkrDtyQtbv9VGgcRMSUGYB/wljQ+F3gIuCY9nw48C/wc+FDVfDcCG0vPrwa+DaiJdQ6kZR4B5tTozyPAX5Rip6dYAAuq2t8EfLwqdhzwfuAPgQNAf6df514dejR/zgcuAk4CfgdYB/y/Tr/WvTb0aO7MABYAAqYB7wV2dPJ1njLfzMoi4qfAFuC0FHob8DTwMYokKPtL4I8lvVXSacCVwJ9FekcbGABuAHYAl9aYfjOwqqr9hha24/mI+L8RcSfwYrPz2dj0UP5siYivRMQzEfEL4LPAm5qd31rXQ7nzdETsS30Rxf7ndc3OPxGmZDGTNB+4APhBCg0AXwJuAX5f0pmVthFxGLicIjHWAR+NiL1NrON3gX5gYxpW1Wh2F3CSpFMlTQPeAfxjm5tlk6SH8+ePgF1jmN8a6LXckfQ08CvgeuCTrc4/nqZaMfvn9OLfCXwH+GR64/8E+KeIeAK4napPSBHxTYo3/2XAdU2uaxXF1+6HKZL19ZLOqNGu8gnpXOCHwE9b3iqbLD2bP5L+M/C/gQ+1M7811JO5ExEzgFdSfGv8QYPmE+qYTq68Ay6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJGr1G34zcAewkBa+5ltH9GT+SHodxaGv90XEd9tZhjXUk7mT1vGcpBuAn0k6NSIOtrussZhq38xqWQWcIulxSY8DnwZmU5wcb4ukPwAWAR8uLfds4BJJR32AiIgfAz+iOPTw9XbXaR2Tdf5Iei3wLeDqiLi53T5bW7LOnSovo7iIaO4Yl9O2qfbN7CiS3gj8HnAG8LPSpGspPslsbnPRA8A2jj5WfQLFydjzgW9Wtb8MmJk+4bT0nkg6nuIELMBxkl5O8SnOP1Q3wXLPH0lzKa6O+1xE3NBmX60NPZA75wJPpuVOBz4OPAXsbrPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAlMx+VNgVUQ8XjXt5rTOoxKqmZO6o3gEeG0a35oeF1JcfmsTK/f8+Z/AKcBVkq4qLe/E+rPYOMk9d2ZQXPQxD/glcC+wIiJ+1ebyxkz+AG9mZrnzOTMzM8uei1mbJF1aur1LefD/6VhDzh9rl3OnNh9mNDOz7GV7Acjs2bNjwYIFADz33HNMnz69sx1qUTf2+f77738yIl7d6X5MtHLuQHe+F83opn47d7pfN/d1XPKn0c0bgZcD9wAPUvzz3kdTfCFwN7AH+DJwXIofn54PpekLSsv6cIo/ApxXiq9IsSFgTTM3lVyyZElUbN++PXLTjX0G7ovxvcFq1+dORHe+F83opn6Pd+5El+ZPzrnTzX0dj/xp5pzZr4E3R8R/Ad4ArJC0DPgU8HcRsYji/wsuS+0vA56KiNcBf5fakX4e4GLg9SmB/l7StHRfsM9R/A/EYop/7uvsTwnYeHHu2Fg4f6xpDYtZKpwj6emxaQjgzcBXU3w9cGEaX5mek6afI0kpfktE/DoifkTxSeisNAxFxKMR8TzFDTdXjnnLrOOcOzYWzh9rRVPnzNInmPspbvH/OWAv8HREHElNhvntbUzmAo8BRMQRSYeBV6X4XaXFlud5rCp+dp1+rAZWA/T19TE4OAjAyMjIb8ZzkWOf29EtuWN56ob8qbffgbz+jnPqazuaKmYR8SLwBkkzgG8Ap9Zqlh5VZ1q9eK1vhzUvsYyItcBagKVLl0Z/fz8A12/cxLV3Ple3//uueWvdaZ0yODhIpf+9rFtyZ7Qd0sFDh7l+46Y6WwCnz31l3Wmd1Os7J+iO/Km334G89j29vs9p6WrGiHha0iCwDJgh6Zj0CWkesD81GwbmA8PpXl+vBA6V4hXleerFrUd0Onca7pB21v9T2Hdpf91pndTrO6eyTuePdb+G58wkvTp9KkLSCcBbKG4muR14e2o2AFQ+2m7mt7/J83bg2+lqlc3AxZKOl7SQ4s7O91Dc02uRpIWSjqM4UdvuTTatizh3bCycP9aKZr6ZzQHWp2PXLwNujYh/kfQwcIukj1P8Ts6Nqf2NwM2Shig+FV0MEBG7JN0KPAwcAa5IhxCQdCXFTXKnAesiYkr/J3sPce7YWDh/rGkNi1lE7KD4mYLq+KMUVwNVx38FXFRnWZ8APlEjfhtwWxP9tYw4d2wsnD/WCt+b0czMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsNi5mk+ZK2S9otaZek96X4LEnbJO1JjzNTXJKukzQkaYekM0vLGkjt90gaKMWXSNqZ5rlOkiZiY80sH973WCua+WZ2BPhARJwKLAOukLQYWAPcHhGLgNvTc4DzgUVpWA18HooEBK4CzgbOAq6qJGFqs7o034qxb5p1mndGNkbe91jTGhaziDgQEd9P488Cu4G5wEpgfWq2Hrgwja8ENkThLmCGpDnAecC2iDgUEU8B24AVadpJEfG9iAhgQ2lZljfvjKxt3vdYK1o6ZyZpAXAGcDfQFxEHoEg64OTUbC7wWGm24RQbLT5cI26Z887Ixov3PdbIMc02lHQi8DXg/RHxzChHc2pNiDbitfqwmuJTOH19fQwODgLQdwJ84PQjdfteaddNRkZGurJfE2W0nZGkCd8Z1csdyDN/YOrkUKf3Pb2SO72eL00VM0nHUiTTxoj4ego/IWlO2hnNAQ6m+DAwvzT7PGB/ivdXxQdTfF6N9i8REWuBtQBLly6N/v5icddv3MS1O+tvyr5L++tO65TBwUEq/e91nd4ZQf3cgTzzB6ZGDnXDvqdXcqfX86WZqxkF3AjsjohPlyZtBion4geATaX4qnQyfxlwOH0K3woslzQzne9YDmxN056VtCyta1VpWZa50XZGaXqzO6N68aY+CFl+vO+xVjRzzuxNwDuBN0t6IA0XANcA50raA5ybngPcBjwKDAFfAN4DEBGHgKuBe9PwsRQDuBz4YppnL7BlHLbNOsw7Ixsj73usaQ0PM0bEndQ+nANwTo32AVxRZ1nrgHU14vcBpzXqi2WnsjPaKemBFPtrip3PrZIuA34CXJSm3QZcQLFj+QXwLih2RpIqOyN46c7oJuAEih2Rd0Y9wvsea0XTF4CYtco7IzObLL6dlZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfYaFjNJ6yQdlPRQKTZL0jZJe9LjzBSXpOskDUnaIenM0jwDqf0eSQOl+BJJO9M810nSeG+kdY7zx9rl3LFWNPPN7CZgRVVsDXB7RCwCbk/PAc4HFqVhNfB5KBIQuAo4GzgLuKqShKnN6tJ81euyvN2E88facxPOHWtSw2IWEXcAh6rCK4H1aXw9cGEpviEKdwEzJM0BzgO2RcShiHgK2AasSNNOiojvRUQAG0rLsh7g/LF2OXesFce0OV9fRBwAiIgDkk5O8bnAY6V2wyk2Wny4RrwmSaspPknR19fH4OBg0ZkT4AOnH6nb2Uq7bjIyMtKV/Zokk54/9XIH8swfmLI55NxpU6/nS7vFrJ5ax5yjjXhNEbEWWAuwdOnS6O/vB+D6jZu4dmf9Tdl3aX/daZ0yODhIpf/2GxOWP/VyB/LMH3AOVXHuNNDr+dLu1YxPpK/ppMeDKT4MzC+1mwfsbxCfVyNuvc35Y+1y7lhN7RazzUDlqqABYFMpvipdWbQMOJwOCWwFlkuamU6+Lge2pmnPSlqWriRaVVqW9S7nj7XLuWM1NTzMKOlLQD8wW9IwxZVB1wC3SroM+AlwUWp+G3ABMAT8AngXQEQcknQ1cG9q97GIqJzYvZziqqUTgC1psB7h/LF2OXesFQ2LWURcUmfSOTXaBnBFneWsA9bViN8HnNaoH5Yn54+1y7ljrfAdQMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLXNcVM0gpJj0gakrSm0/2xfDh3bCycP72hK4qZpGnA54DzgcXAJZIWd7ZXlgPnjo2F86d3dEUxA84ChiLi0Yh4HrgFWNnhPlkenDs2Fs6fHnFMpzuQzAUeKz0fBs6ubiRpNbA6PR2R9Eganw08WW/h+tQ49XJ8jdrnDnltpzvQhrHmDuSZP9BdOZRj7kAT+dNDudNN+VJtzPnTLcVMNWLxkkDEWmDtS2aW7ouIpRPRsYmSY5+71JhyB/J9L3Ltd5dpmD+9kjs59bUd3XKYcRiYX3o+D9jfob5YXpw7NhbOnx7RLcXsXmCRpIWSjgMuBjZ3uE+WB+eOjYXzp0d0xWHGiDgi6UpgKzANWBcRu1pYRM1DAF0uxz53nXHIHcj3vci1311jiu17cupryxTxktMLZmZmWemWw4xmZmZtczEzM7PsZV/MOnErGkn7JO2U9ICk+1JslqRtkvakx5kpLknXpf7tkHRmaTkDqf0eSQOl+JK0/KE0r0Zbh7VnMnNH0jpJByU9VIp1LGdGW4c1Z5Lzx/ucRiIi24HihO1e4BTgOOBBYPEkrHcfMLsq9rfAmjS+BvhUGr8A2ELx/yzLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHR66P3eAPwLOBB7qhpyptw4PXZs/3uc0GHL/ZtZNt6JZCaxP4+uBC0vxDVG4C5ghaQ5wHrAtIg5FxFPANmBFmnZSRHwvigzaULWsWuuw1k1q7kTEHcChqnAnc6beOqw53bDv8T6nJPdiVutWNHMnYb0B/Juk+1Xc6gagLyIOAKTHkxv0cbT4cI34aOuw1nUqd8o6mTPdsP05m+zXz/ucBrri/8zGoKlbGU2AN0XEfkknA9sk/XCUtvX62Grcxlc3v86TkTPdvP05mOzXz/ucBnL/ZtaRW9FExP70eBD4BsUhhycqh2nS48EGfRwtPq9GnFHWYa3rhtsYdTJnumH7czapr5/3OY3lXswm/VY0kqZLekVlHFgOPJTWW7k6aADYlMY3A6vSFUbLgMPp6/pWYLmkmekKoeXA1jTtWUnL0hVFq6qWVWsd1rpuuI1RJ3Om3jqsOZOWP97nNKnTV6CMdaC4cuffKa4s+sgkrO8UiiuXHgR2VdYJvAq4HdiTHmeluCh+/G8vsBNYWlrWu4GhNLyrFF9Kkax7gc/y2zu11FyHh+7PHeBLwAHgBYpPwpd1MmdGW4eH7sof73OaG3w7KzMzy17uhxnNzMxczMzMLH8uZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyyN2WKmaR9kn4paUTSE5L+QdKJpek3SToi6TWl2Lmp7exS7HhJuyX9eRPrnJ7Wd1ud/jxfXnaKPyApJC2QtCXNPyLphdS+8vyGqvmuSvO9pdXXxhrrxfxJbaIUG5H0v9p/layWXsyd1P53JP29pCclHZZ0R7uv0bjo9A+qTeIP6e0D3pLG51L8EN016fl04Fng58CHqua7EdhYen418G3Sj9c1WOdAWuYRYE6N/jwC/EUpdnqKBbCgqv1NwMfrrOf3KH6Eb39lGz04fxrlD7AgtT2m069vLw+9mDsp/o/ALcCrgWnAkk6+zlPmm1lZRPwU2AKclkJvA54GPsZvfyK84i+BP5b0VkmnAVcCfxbp3WxgALgB2AFcWmP6zRQ/UV5uv6HZ7Sj5LPBXwPNtzGst6sH8sUnSK7kj6T8B/w1YHRE/i4gXI+L+ZuefCFOymEmaT/GT5z9IoQGKn7W/Bfh9SWdW2kbEYeByisRYB3w0IvY2sY7fBfqBjWlYVaPZXcBJkk6VNA14B8WnnVa25SLg+Yh4yeEEmxi9lD/JjyUNp8Nfsxs3t3b1UO6cDfwY+Gg6zLhT0ttamH/cTbVi9s+SngbuBL4DfDK98X8C/FNEPAHcTtUnpIj4JsWb/zLguibXtQrYEREPUyTr6yWdUaNd5RPSucAPgZ82uzHpuPsngfc3O4+NSU/lD/Ak8F+B1wJLgFdQ7Pxs/PVa7syj+HZ5GHgNxbfG9ZJObWEZ4+qYTq24Qy6MiG+VA5LeCeyOiAdSaCNwraQPRsQLpaa7gF9HxH80ua5VwBcAImK/pO9QJOoPqtrdDNwBLKT1Q0QfBW6OiB+1OJ+1p6fyJyJGgPvS0yckXQkckHRSRDzTyrKsoZ7KHeCXwAsU59KOAN+RtB1YDuxucVnjYqp9M6tlFXCKpMclPQ58GpgNnN/uAiX9AbAI+HBpuWcDl0g66gNERPwY+BHFoYevt7iqc4D3ltYxH7hV0l+123drWc75U61yLkZjXI41J+fc2dFuHyfKVPtmdhRJb6S4EvAM4GelSddSfJLZ3OaiB4BtHH2s+gSKBDgf+GZV+8uAmRHxXHXCNXAOcGzp+b0UJ423tNxja1nu+SPpbIqLD/YAMykOYw2mczU2gXLPHYpvdD+hKJr/h6Jg9gMfarPfYzalixnFG78pInaWg5I+A3xX0qyIONTKAiW9HPhTYFVEPF417ea0zqMSqpmTurVExM+rlv8i8FQ6fGQTL+v8AU6hOOd6MvAMxU7wkjaXZa3JOnci4gVJK4EvAmsoLgZZFRE/bGd540HNXeVpZmbWvXzOzMzMsudi1iZJl+ro2wBVhl2d7pt1P+ePtcu5U5sPM5qZWfYaXgCSTireARyf2n81Iq6StJDiv9ZnAd8H3hkRz0s6nuJ/FpZQ3BvsHRGxLy3rwxRXz7wIvDcitqb4CuAzFPf3+mJEXNOoX7Nnz44FCxYA8NxzzzF9+vQWNrt3jOe233///U9GxKvHZWHkkTuQZ/50W5/HO3egO/OnF3KnExq9TuOSP41u3kjxPycnpvFjgbuBZcCtwMUpfgNweRp/D3BDGr8Y+HIaXww8SJGYC4G9FAk0LY2fAhyX2ixu1K8lS5ZExfbt22OqGs9tB+6L8b3Batfnzni/hpOl2/o83rkTXZo/vZA7ndDodRqP/Gl4ziytq3Kp97FpCODNwFdTfD1wYRpfmZ6Tpp8jSSl+S0T8Ooo7VgwBZ6VhKCIejYjnKT5xrWzUL+t+zh0bC+ePtaKp/zNLN6K8H3gd8DmKTzNPR3EbE4Bhip82ID0+BhARRyQdBl6V4neVFlue57Gq+Nktb4l1pW7JHUmrgdUAfX19DA4O/mbayMjIUc9zkGOf29EN+dNrudMJk/E6NVXMIuJF4A2SZgDfAGrdTHK0W+HEKPFa3w5rXpVSL6mmckJ1+7Z3S+5ExFpgLcDSpUujv7//N9MGBwcpP89Bjn1uRzfkT6/lTidMxuvU0h1AIuJpSYMUx61nSDomfUKaR/HDkFB8upkPDKfbo7wSOFSKV5TnqRevXn/NpLp+4yauvfO5uv3ed81bm97G3OTyx9Tp3BnNzp8e5n+s+de603s5f3LRrfnj3OkeDc+ZSXp1+lSEpBOAt1DcFXk78PbUbADYlMY389ufMXg78O10gm8zcLGKn/5eSHEzzHso7ie4SNJCScdRnLht975k1kWcOzYWzh9rRTPfzOZQ/E7NNIrid2tE/Iukh4FbJH2c4qcFbkztbwRuljRE8anoYoCI2CXpVuBhip/yviIdQkDFT09spbi6aF1ETOl//ushzh0bC+ePNa1hMYuIHRR3dq6OP0pxNVB1/FfARXWW9QngEzXitwH+peQe49yxsXD+WCt8OyszM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZjZhJE0X9J2Sbsl7ZL0vhSfJWmbpD3pcWaKS9J1koYk7ZB0ZmlZA6n9HkkDpfgSSTvTPNdJ0uRvqU0E54+1omExc0LZGBwBPhARpwLLgCskLQbWALdHxCLg9vQc4HxgURpWA5+HIteAq4CzgbOAqyr5ltqsLs23YhK2yyaH88ea1sw3MyeUtSUiDkTE99P4s8BuYC6wElifmq0HLkzjK4ENUbgLmCFpDnAesC0iDkXEU8A2YEWadlJEfC8iAthQWpZlzvljrWhYzJxQNh4kLQDOAO4G+iLiABT5BZycms0FHivNNpxio8WHa8Stxzh/rJFjWmk8WkJJmvCEkrSa4hscfX19DA4OAtB3Anzg9CN1+11p14tGRka6fvsknQh8DXh/RDwzylHkWhOijXitPtTMHcgzf3J438dLp/On13KnEyYjX5suZp1OKICIWAusBVi6dGn09/cDcP3GTVy7s/6m7Lu0v+603A0ODlJ5HbqRpGMp8mZjRHw9hZ+QNCd9CJoDHEzxYWB+afZ5wP4U76+KD6b4vBrtX6Je7kCe+dPt7/t46Yb86bXc6YTJyNemrmYcLaHS9GYTql68qR2S5SVdyHMjsDsiPl2atBmoXAA0AGwqxVeli4iWAYfTt/+twHJJM9N51uXA1jTtWUnL0rpWlZZlmXP+WCuauZrRCWXtehPwTuDNkh5IwwXANcC5kvYA56bnALcBjwJDwBeA9wBExCHgauDeNHwsxQAuB76Y5tkLbJmMDbNJ4fyxpjVzmLGSUDslPZBif02RQLdKugz4CXBRmnYbcAFFcvwCeBcUCSWpklDw0oS6CTiBIpmcUD0gIu6k9mFkgHNqtA/gijrLWgesqxG/DzhtDN20LuX8sVY0LGZOKDMz63a+A4iZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2GhYzSeskHZT0UCk2S9I2SXvS48wUl6TrJA1J2iHpzNI8A6n9HkkDpfgSSTvTPNdJ0nhvpHWO88fa5dyxVjTzzewmYEVVbA1we0QsAm5PzwHOBxalYTXweSgSELgKOBs4C7iqkoSpzerSfNXrsrzdhPPH2nMTzh1rUsNiFhF3AIeqwiuB9Wl8PXBhKb4hCncBMyTNAc4DtkXEoYh4CtgGrEjTToqI70VEABtKy7Ie4Pyxdjl3rBXHtDlfX0QcAIiIA5JOTvG5wGOldsMpNlp8uEa8JkmrKT5J0dfXx+DgYNGZE+ADpx+p29lKu140MjKS4/ZNev7Uyx3IM38yfd/Hg3MnQ5ORr+0Ws3pqHXOONuI1RcRaYC3A0qVLo7+/H4DrN27i2p31N2Xfpf11p+VucHCQyuvQAyYsf+rlDuSZPz32vo8H504Xm4x8bfdqxifS13TS48EUHwbml9rNA/Y3iM+rEbfe5vyxdjl3rKZ2i9lmoHJV0ACwqRRfla4sWgYcTocEtgLLJc1MJ1+XA1vTtGclLUtXEq0qLct6l/PH2uXcsZoaHmaU9CWgH5gtaZjiyqBrgFslXQb8BLgoNb8NuAAYAn4BvAsgIg5Juhq4N7X7WERUTuxeTnHV0gnAljRYj3D+WLucO9aKhsUsIi6pM+mcGm0DuKLOctYB62rE7wNOa9QPy5Pzx9rl3LFW+A4gZmaWPRczMzPLnouZmZllz8XMzMyy52JmZmbZczEzM7PsuZiZmVn2XMzMzCx7LmZmZpY9FzMzM8uei5mZmWXPxczMzLLnYmZmZtlzMTMzs+y5mJmZWfZczMzMLHsuZmZmlj0XMzMzy56LmZmZZc/FzMzMsudiZmZm2XMxMzOz7HVNMZO0QtIjkoYkrel0fywfzh0bC+dPb+iKYiZpGvA54HxgMXCJpMWd7ZXlwLljY+H86R3HdLoDyVnAUEQ8CiDpFmAl8PB4LHzBmn8ddfq+a946HquxzpjQ3IHG+TMa51bX876nR3RLMZsLPFZ6PgycXd1I0mpgdXo6IumRND4beLLdletT7c7ZFca07VVeO07LmUxjzR0Y39fw6PVOXG5NWJ/blGPuQBP5M5G5k/m+pxWNXqcx50+3FDPViMVLAhFrgbUvmVm6LyKWTkTHut1U3vZkTLkDeb6GOfa5SzXMn17LnU6YjNepK86ZUXwaml96Pg/Y36G+WF6cOzYWzp8e0S3F7F5gkaSFko4DLgY2d7hPlgfnjo2F86dHdMVhxog4IulKYCswDVgXEbtaWETNQwBTxFTe9vHIHcjzNcyxz13H+55JM+GvkyJecnrBzMwsK91ymNHMzIT8XAIAAAPVSURBVKxtLmZmZpa97ItZzreikbRP0k5JD0i6L8VmSdomaU96nJniknRd2s4dks4sLWcgtd8jaaAUX5KWP5Tm1WjrmGo6kTuS5kvaLmm3pF2S3pfifyPppykXHpB0QWmeD6c+PiLpvEb9Txcz3J3e3y+nCxuQdHx6PpSmL5iMbe5VOe97WpHNfioish0oTtjuBU4BjgMeBBZ3ul8t9H8fMLsq9rfAmjS+BvhUGr8A2ELxfzHLgLtTfBbwaHqcmcZnpmn3AG9M82wBzh9tHVNp6FTuAHOAM9P4K4B/p7iN0t8AH6zRfnHq2/HAwtTnaaP1H7gVuDiN3wBcnsbfA9yQxi8Gvtzp9yHXIfd9T4vbmsV+KvdvZr+5FU1EPA9UbkWTs5XA+jS+HriwFN8QhbuAGZLmAOcB2yLiUEQ8BWwDVqRpJ0XE96LIhg1Vy6q1jqmkI7kTEQci4vtp/FlgN8VdKOpZCdwSEb+OiB8BQxR9r9n/9Kn2zcBX0/zVOVR5378KnFP5FGwt68V9Tyu6bj+VezGrdSua0XYM3SaAf5N0v4pb5gD0RcQBKHZ8wMkpXm9bR4sP14iPto6ppOO5kw7znQHcnUJXpkMz60qHVFp9318FPB0RR6riRy0rTT+c2lvrOp4/kyiL/VRX/J/ZGDR1K6Mu9qaI2C/pZGCbpB+O0rbetrYat0JHXx9JJwJfA94fEc9I+jxwderD1cC1wLtH6WetD6KN3nfnxPiZSq9lFvup3L+ZZX0rmojYnx4PAt+gOHTxRPrqTXo8mJrX29bR4vNqxBllHVNJx3JH0rEUhWxjRHwdICKeiIgXI+I/gC9Q5MJo/awXf5Li0M4xVfGjlpWmvxI4NL5bN2Vkve9pRS77qdyLWba3opE0XdIrKuPAcuAhiv5XrvQZADal8c3AqnS10DLgcPrqvRVYLmlmOjS1HNiapj0raVk6L7Kqalm11jGVdCR30ntxI7A7Ij5dis8pNfvvFLlA6tPF6UrEhcAiihPmNfufzjtsB96e5q/Oocr7/nbg26m9tS7bfU8rstpPdfpKmXG40uYCiivC9gIf6XR/Wuj3KRRXQD0I7Kr0neIcxu3AnvQ4K8VF8SOCe4GdwNLSst5NcWHAEPCuUnxpSry9wGf57R1faq5jqg2dyB3gDykOo+wAHkjDBcDN6X3dkf6I55Tm+Ujq4yOkK71G63/KrXtSPnwFOD7FX56eD6Xpp3T6Pch5yHXf0+I2ZrOf8u2szMwse7kfZjQzM3MxMzOz/LmYmZlZ9lzMzMwsey5mZmaWPRczMzPLnouZmZll7/8DgrC436upn5EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 87.607833 6.958961e-05\n", + "PAY_0 4290.860246 0.000000e+00\n", + "PAY_2 3694.483464 0.000000e+00\n", + "PAY_3 2997.074680 0.000000e+00\n", + "PAY_4 2974.745645 0.000000e+00\n", + "PAY_5 2892.367773 0.000000e+00\n", + "PAY_6 2604.494518 0.000000e+00\n", + "PAY_0_No_Transactions 66.414350 1.247718e-02\n", + "PAY_0_Revolving_Credit 453.062761 0.000000e+00\n", + "PAY_2_Pay_Duly 79.291751 6.259323e-04\n", + "PAY_2_Revolving_Credit 218.897534 0.000000e+00\n", + "PAY_3_Pay_Duly 93.542174 1.308043e-05\n", + "PAY_3_Revolving_Credit 129.158774 1.504441e-10\n", + "PAY_4_Pay_Duly 83.376851 2.176696e-04\n", + "PAY_4_Revolving_Credit 87.804736 6.592136e-05\n", + "PAY_5_Pay_Duly 80.166291 5.012063e-04\n", + "PAY_5_Revolving_Credit 62.035334 3.008744e-02\n", + "PAY_6_Pay_Duly 60.981544 3.674297e-02\n", + "PAY_6_Revolving_Credit 67.326788 1.028709e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (GINI) identified 867\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054101317
11155867
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5410 1317\n", + "1 1155 867" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.40 0.43 0.41 2022\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Entropy) identified 849\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
055201207
11173849
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5520 1207\n", + "1 1173 849" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6203316219666027" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.82 0.82 6727\n", + " 1 0.41 0.42 0.42 2022\n", + "\n", + " accuracy 0.73 8749\n", + " macro avg 0.62 0.62 0.62 8749\n", + "weighted avg 0.73 0.73 0.73 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 745\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06312415
11277745
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6312 415\n", + "1 1277 745" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3258333333333333\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEWCAYAAADcsGj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gU1frA8e+bEEoaJSEEEkLoobfQlSJNEcGCXNGrYkdF7L1h/Vm4eu2CvaPitSEIKiIISpdeQ0kCpEIq6Xt+f8xElxDIUjabTd7P8/CQnbLzTn3nnDl7RowxKKWUUlWRj6cDUEoppY5Fk5RSSqkqS5OUUkqpKkuTlFJKqSpLk5RSSqkqS5OUUkqpKqtKJikRuUxEFng6Dk8TkSgRyRER30pcZrSIGBGpVVnLdCcR2SQiQ05ivpM6BkWknoh8LyKZIvLlic5fmVw9vqrr+SgidUVkq4iEeWDZtezzLLqyl10ViEhPEVniyrQVJikR2SMiefbBnCQi74tI4KmHeWzGmE+MMSPduYyqyN7Ww0s/G2PijTGBxpgST8blKfZJ3OZUvsMY08kYs6iC5RyVmE/hGBwPNAFCjDEXn8T8ZWMbIiIO+/zLEZFEEflCRHqf6ne7eny543y0E1/pOuWVWcec07ms47gR+NkYk2LH9LGIFNoxHBSRBSLSrpJicRsR+V1E8p237+k4fk5g+UclZGPMGiBPRM6paH5XS1LnGWMCge5AD+D+k4jV4zxZOqguJZMTUUO3dwtguzGm+ERnPE7M++3zLwjoB2wFlojIsJMP07PsxBdor9c52OvoNOwIbtqfNwAflRn2tL38CCAFeMsNy/WEyc7b1xiz8kRmFhEfETndNW+fYO2D4zPGHPcfsAcY7vT5OeAHp891gOlAPJAMvAnUcxo/DvgLyALigLPt4fWBd4ADwD7gScDXHjcJ+N3++01gepmYvgXusP9uBnwFpAK7galO000DZgMf28u/tpz1qw98aM+/F3gI8HGKYynwCpCJdXEYVmbe463DUuBF4KA9rjWwEEgH0uyd1MCe/iPAAeQBOcA9QDRggFr2NIuAJ+zvzQYWAKFO8Vxhr0M68HDZfVdmvesB/7GnzwR+t4eVLvNKe5+mAQ86zdcH+APIsNf7VaC203gD3AzsAHbbw14CEux9sBo402l6X+AB+9jItsc3Bxbb35Vrb49/2dOPwTqeMoBlQNcyx+q9wHqgAKjlvA3s2FfZcSQDL9jD4+1l5dj/+uN0DNrTdAJ+svdlMvBAOdv0MaAQKLK/5xqsG8GH7O2cgnWs1benL93W19gxLC7nO4cAieUMfxVY5fQ5xim+bcCEE9jXpcfXJGCXvR92A5eVPR/tzwOAlfZ3rQQGOI1bxHGO0WMci8dax0TgbmADUGgPiwS+5p/z/Wan6X3451hKA2YBDY+xzFZYx5av07CPgWlOn8cCmU6f2wK/8s/5+1HpvnSK9w473kzgM6CO0/j7gCSsa8U19raPtsc1sJefinXM3g+IPe5a4DfgZazjfifQ1/6OBKzj8d/H2b6/A5OOMe4MrHMiE1gB9C0z3xNY53uefbw0AN7DOvcTgcf553rZDuu8zbS3z6f28GUceS5fZA9vYQ/zO+7xcbyRTid+6Ukeae+Al5zG/xf4DmiEdaf3PfB/TheFTGCEfQBFADH2uG+AGUAAEGZvoBvKnhTAIHtHlO6whvYGa2Z/52rgEaA21oG3CxjllKSKgPPtaeuVs34fYiW9IHsnbAeucYqjGLgd8AP+Za9PIxfXoRi4BetiWQ9oY2+LOkBje4f+t7xtXeYi5pyk4uyDoZ79+Rl7XEf7ADjD3hbT7XU/VpJ6zZ4/AitRDLDjKl3mW/YyumFd8DvY8/XCupuvZU+7BbitTJL6Cet4qGcP+zcQYs9zJ9aJWtceV3oRag+IvbwQp+9q4/TdPbEu9H3tmK+0t1kdp+33F1aSq1d2m2KdbJfbfwcC/crbzuUcg0FYJ+WdQF37c99jbNdpwMdOn6/Guqi0spf5P+CjMsv9EOsYKu/4HEL5F/CzsG5qAux/CcBV9jbuiXWR6OTivq5lf0cW0N6ep6nT/M7bohFwCLjcnm+i/bl0ny3iGMfoSSap1VjXnXp27H9hJaLaWOfTHuwbR+AurOQYYe+nd0q3dTnfPQ5YV2bY30nK3lefAaudxrcDhtnLDrOXNb1MvH8C4VjH+3bsG2Osm6sDWOdpAPAFRyapT+1jI8g+VnYCVzolqWJ7m/sCz2DdcLxs78fRWNcl/xNJUkCoPd9Ee1/+GysBN3Sabw/QAev6VwuYA7wO+NvruZp/rpdfYt0k+tjbf6A9vJbzupaJ4TDQ8XQkqRysuyID/MI/d/+ClQlbO03fn3/uoGcAL5bznU2wLnzOJa6JwK/lnBSCdZc5yP58HbDQ/rsvEF/mu+8H3nO6YBx1d+o0ra8dR0enYTcAi5zi2I+dIO1hK+yDxZV1iD/Wsu1pzgfWltnWFSWph5zG3wT8aP/9CPCZ0zh/rLv6o5KUfRDlAd3KGVe6zMgy63zJMdbhNuBrp88GOKuC9T5Uumysu/5xx5iubJJ6A3iizDTbgMFO2+/qco7f0iS1GKu0E1pmmiO2cznH4ETn/VTBuk3jyCT1C3CT0+f2WDcPtZyW2+o43zeE8i/gMfa8EVg3T0vKjJ8BPOrivi5NUhnARZRJlmW2xeXAijLj/8C+CB7vGD2JdUwErnD6PBDYVWaah4G37L93lB4L9ufmWOeoTznffSVOpUN72MdAvr0dDNYNb+fjxD0eWFkm3kucPr8AvGr//SHwpNO4jvYyorESQDHQzmn8zVjPy8BKUlucxvWw5w1xGpZ5rFixks1he70ySvcf1k3NsjLTrsQuldnzPeI0LsI+lpxLh5cDP9l/f4p1jkaU+c7jJalknEri5f1ztY7xfGNMENbBFIOVgcEqDfgDq0UkQ0QygB/t4WAdJHHlfF8Le8cccJpvBtbdyRGMtSazsC4UAJdiVZOVfk+z0u+wv+cBrARSKuE46xWKdVe012nYXqydUWqfHYPz+GYursMRyxaRMBGZJSL7RCQL66QI5cQkOf19GOuODzumv5dnjDmMdVdUnlCsO53y9s1xlyMi7URkjt2IJgt4mqPXoex63ykiW+wWbxlY1aSl8xzrGClPC+DOMvu7Oda6l7vsMq7BuhveKiIrRWSMi8s9kRjLasbRx1ctXD9GjyUC68TPwNoufctsl8uw7nRd2dcYY3Kxkt1krGP6BxGJcWF94Ohz5ljH6Mlw3jYtgKgy63kP1noCRAHfO43bgLWNymu9dwir1FLWM8aYBkBLrJu8tqUjRCTcbrRSev6+z9HHvkvnJ0duwzCsG+bjXYeSnf7OA0qMMellhh1vO99kjGlg/+vjFFNF+7Ls9q8DJDtt49f451i+E+uauEpENojIlceJp1QQ1jF8TCf0IMwY8xvWjpluD0rD2jidnDZAffPPg88ErOcwZSVg3eGEOs0XbIzpdIxFfwaMF5EWWKWnr5y+Z7fTdzQwxgQZY0Y7h32cVUrDuqtt4TQsCqvOuFSEiEiZ8ftdXIeyy/4/e1hXY0wwVvFajjP9iTiAVS0CWE2hsaocypOGdcdY3r6pyBtYz+ba2uvwAEeuAzith4iciVUFMAGrGqEB1l1f6TzHOkbKkwA8VWZ/+xtjPitv2WUZY3YYYyZiXRSeBWaLSMDx5jmJGMvaz9HHVzFHXnROZr9fAKyxk0sC8FuZ7RJojLmRE9jXxpj5xpgRWFV9Wym/0UDZ9YGjz5nTyXnbJAA7yjnfz7PHJwIjyoyva4xJOvprWQ+0Plbze2PMHqxq/ldEpI49+Fmsc76LfexP4uhj/1gOYN3slIpy+jsFKOH41yF3cGVflt3+h7Eedzhf87oCGGMOGGOuNcY0xSoJzhSRlhzj+Lav52CVgI/pZFpr/BcYISLdjTEOrAP5xdLfGohIhIiMsqd9B7hKRIbZrUMiRCTGGHMA64Hqf0Qk2B7XWkQGl7dAY8xarAeKbwPzjTGlmXcFkCUi94r1+xRfEensavNKYzW9/QJ4SkSC7I12B1YJp1QYMFVE/ETkYqz62bknug62IKyq0wwRicB6HuMsGas++mTMBs4TkQEiUhurWqvcE8jeb+8CL4hIM3u79Xc6GY8nCOvZRY59p32jC9MXY+2/WiLyCBDsNP5t4AkRaSuWriJSmlzLbo+3gMki0teeNkBEzhWR8u6IjyIi/xaRxvb6lx5DJXZsDo697ecA4SJym4jUsY+Vvq4sE+sG63YRaSnWTzeeBj43J9f6T+xz6FGsKqAHnOJrJyKX28epn4j0FpEOru5rEWkiImPtpF2AdZyW1zR9rr2sS8VqWvwvrKqrOSe6PifhD6DQLpnXtdeli4j0sse/CTwtIlHwd83F2PK+yE5C8VjPWMtljJmHdWxcaw8Kwnq8kSkizbGegbnqC+BqEYmxt/GjTsspwjp/nxaRQPvCfjtHXofcYQ7QSUT+Ze/LS7Ge880tb2JjTAJWA47pTte8NiIyCEBEJtjXNfinyrTEvs6mc/T5NRirSrPoeEGecJIyxqRi1a8+bA+6F+sh3592EfhnrHp3jDErsOo9X8S6e/6NfzL3FVhVbZuxit6zse7gjuUzYDhWvWdpLCXAeVhN43dj3TW+jVWd5KpbsA68XVh1sJ9indSllmMV+dOAp4DxTsXsE12Hx7AeamcCP2A9KHX2f8BDdlH6RE4AjDGb7HWZhXXXlo11h1ZwjFnuwqoOWYnVIuxZXDse7sKqcs3GShqfVzD9fGAe1kPkvVh39c5VCC9gncALsJLfO1gPycF6vvOBvT0mGGNWYT2TfBVre+/Eupt11dnAJrF+h/MS1vODfLtq9Clgqb2sfs4zGWOysRq8nIdVnbMDGOriMt/FagW2GOsYzcfaTyeimR1zDtb+6gIMMcYscIpvJHAJ1t1xEtb+LE1EruxrH6zqmv32NIOxnicdwT72x9jTpmNVt40xxqSd4DqdMDuxj8ZqkLUH65ycwT83PS9gPW74RUSysVqVHe+GdQbWM5XjmQ7ca9/4Pco/jcG+458aHVdi/x6rauw3rHPhpzKT3IRVvbjbnuYDrOus29jX8rFY1/B0rMQ4xhhz8Diz/Rvr+WXpNe9L/qlu7QusFJFcrGvbzcaYeHvco8Cn9vl1oT3sMqwbi+MqbTGnyiEik7Ba55zh6VhOlH3XnoFVLbfb0/EoVdWISF1gLVZjixRPx1OTiEgP4BVXrq1VslskdXJE5DwR8berE6Zj3T3v8WxUSlVNdim6gyaoymeMWevqzb8mqeplHFZ1zX6sKspLjBaVlVJeTKv7lFJKVVlaklJKKVVleWWnp6GhoSY6OtrTYSillFdZvXp1mjGmccVTVh1emaSio6NZtWqVp8NQSimvIiJle5io8rS6TymlVJWlSUoppVSVpUlKKaVUlaVJSimlVJWlSUoppVSVpUlKKaVUleX2JCUi74pIiohsPMZ4EZGXRWSniKwXkZ7ujkkppZR3qIyS1PtYr0g4lnOw+plrC1yP9VI9pZRSp4kxhqISh6fDOClu/zGvMWaxiEQfZ5JxwId2R6h/ikgDEWlqv1RQKaVUOQqLHezPyGNXWg7bk3PYkZzDzpRsMvOKKCx2UFjioKDYYf1d7Dil1357UlXocSKCI1+Cl2gPOyJJicj1WCUtoqKc37yslFLVU2ZeEfHph9l7MJf4g4eJTz9M/MHD7E0/zIHMPBxOmadJcB3ahAXSIiSA2rV8qF3LB1/gr9UH+GP5fnw9thanpiokqfJecX5U0jfGzARmAsTGxnrrTYFSSh0hNbuAHSnZJNjJZ+/Bw3//nZl35JvVQwNrE9XIn97RDYkKiSSqkT/RIf60DQuivr/fUd/9/PNLmfPUH/zrX5144YVRRERcW1mrddpUhSSVCDR3+hyJ9T4kpZSqVkochm1J2ayOP8SavYdYvfcQ8QcP/z2+lo8Q2bAezRv5c163prRoFEDzRv60CPEnqpE/AXUqvmSnpOSyb18WPXo05eab+9CjR1OGD2/lztVyq6qQpL4DpojILKAvkKnPo5RS3szhMOzPzGNnSg47U3KIS7X+37w/i9zCEgAaB9WhV1RDLu/Xgo7Ngolq5E/T+nWp5Xty7dkcDsM776zh3nt/Jjw8kI0bb8Lf38+rExRUQpISkc+AIUCoiCQCjwJ+AMaYN4G5wGhgJ3AYuMrdMSml1MkwxpCWU8iBzDz2Z+STlJlHak4BadmFpOYUkJpt/UvPLaCo5J+nEg39/WgTFsiFPSPp1aIhvVo0JLJhPUTKe9px4tavT2by5Dn88UciQ4ZE88Yb5+Ljc3q+29Mqo3XfxArGG+Bmd8ehlFKuMMaQkl1AXEoOO1NziEvJIS41l4RDhzmQmU9h8ZFNuX19hJCA2jQOqkPjoDrEhAfROKgOEQ3r0aZxIG3CAgkJrOO2eJcvT2TgwHdp2LAeH3xwPpdf3vW0Jb+qoCpU9ymllEc4HIa41Bz+Ssjgr4QMNu7LJC41l5yC4r+nCaxTi9aNA+ga2YCzO9WlWYN6NK1v/R9evy6N/Gt7pNSSmJhFZGQwvXtH8MQTQ7nhhlgaNapX6XG4myYppVSNkFdYwq40q1S05UAW6xIyWJ+Y+XdCCqpTi84R9bmoZwStwwJp0ziQ1mGBhAXVqVIlk/j4TKZOncdvv+1l27YphIUFcP/9Z3o6LLfRJKWUqlYcDsOutFzWJWSwaX8WcalWw4V9GXkY+zFRLR+hQ9NgLugRQbfmDejevD6tQgOr9HOcoqISXnppOY8+ughjDNOmDaFhw7qeDsvtNEkppbxaek4Bq/YeYl1CBusSM1ifkEm2XTqq6+dD68aB9IxqyMW9mtM6LIA2YYFEhwRQ1897ft6ak1PIwIHvsn59MmPGtOOVV84hOrqBp8OqFJqklFJeJ+NwIT9uTGLO+gMsi0vDYazSUUzTIMZ2b2aXjhrQunEgvlW4dFSRoqIS/Px8CQyszbBhLZk2bTDnnx9Tpaof3U2TlFLKK2TlF/HTpmTmrN/Pkh1pFDsMLUL8uXFIa86KCaNTs/peVTo6HmMMn366gfvu+4V58y6jc+cwXnhhlKfD8ghNUkqpKim/qIT1iZms3nuIlXsO8vvONAqLHUQ0qMc1Z7RkTNdmdI4Irnalim3b0rjpprksXLibPn0iqGard8I0SSmlPC6/qIS96YfZnpzNGrvLoE37syi2e1CNDvHnsr5RjOnajJ5RDapdYir11FOLefzxxdSrV4vXXx/N9df3wvcke6CoLjRJKaUqzeHCYlbtOcSu1Bx2p+WyKy2XXam57M/8p+VdXT8fukU24LpBregV1ZAeUQ3c+mPYqiQnp5Dx4zvyn/+MJDw80NPhVAmapJRSbrU/I49ftqbwy5ZklsWl/91jQ1CdWrRqHEBsdENahkbSMjSA1o0DaR8ehF8NKT0kJeVw550LuOKKrowa1YannhpWpZvBe4ImKaXUaeVwGDbsy+SXLcn8vCWFzQeyAKvK7vJ+LRjaPoyYpkGEBNSuttV2FXE4DDNmrOL++38hL6+YgQOtF0FogjqaJiml1CnLzi9iyY40Fm5NYdG2VNJyCvAR6NWiIfefE8OwDk1o3TigxiYlZ3/9lcTkyXNYvnwfZ53VktdfH0379qGeDqvK0iSllDphxhh2pOSweHsqC7emsGL3QYodhuC6tRjcPoxhMWEMbteYhgG1PR1qlbN8eSK7d2fw8ccXcOmlXTRxV0CM8b6X3MbGxppVq1Z5OgylapQDmXks3ZnO0p1p/L4zjdTsAgDaNQlkaEwYw2Ka0DOqwUm/D6m6MsbwzTdbyc8vZuLELjgchqysAho0qPwujURktTEmttIXfAq0JKWUKldhsYPlu9P5ZUsKS3akEpeaC0BIQG0GtgnljDahDGgTQmRDfw9HWnXt3ZvBlCnzmDNnO0OGRHPJJZ3x8RGPJChvpUlKKfW3nIJiftuWyoLNSSzcmkJ2fjF1/Xzo2zKES3pHMbBNKDHhQfqAvwJFRSW8+OKfPPbYb4jA9OkjuPXWflq1dxI0SSlVw+UXlTB/UxLfrN3H0p3pFJY4aBRQm3M6hzOyYzhntA2tNt0NVZalSxO4996fOf/8GF566Wyioup7OiSvpUlKqRpqZ0o2n61I4Ks1iWQcLiKiQT0u79+CkR2bEBvdyKs7ZvWE9PTDLF68lwsu6MCQIdGsWHEtvXtHeDosr6dJSqkaJL+ohB83JvHp8nhW7DlILR9hVKdwJvaJYkDrEK3GOwnGGD76aD133rmA3NxC4uNvJzTUXxPUaaJJSqkaYEeyVWr631qr1NQixJ/7zonhop6RNA6qGV0OucPWrWnceOMPLFq0h/79I3nzzTGEhmpDktNJk5RS1VR+UQnzNh7g0+XxrNxzCD9fq9R0aZ8o+rXSUtOpOngwj9jYmfj5+TJjxhiuvbanblM30CSllJczxpCaU8D2pBy2JWezPSnb+j85m8OFJUSH+HP/OTGM7xVZYzpqdad165Lo1i2cRo3q8d574xg8OJqwsABPh1VtaZJSyss4HIatSdksi0tjWVw6a+MPcehw0d/jQwJq0z48iAmxzRnZqQn9W4Vo0+fT4MCBbG6/fT6ff76JhQuvYOjQllx8cSdPh1XtaZJSygtk5hUxf2MSi3ek8kdcOum5hQC0DA1gZMdwYpoG0b5JEO3CgwjV0tJpVVLi4I03VvHggwspKCjmsceGMGBAc0+HVWNoklKqiipxGJbsSOWrNfuYvymJwmIHYUF1GNSuMQNahzCwTSjNGtTzdJjVmjGG0aM/ZcGCOIYPb8Xrr4+mbdsQT4dVo2iSUqqK2ZGczew1iXyzdh/JWQU08Pfjkt7NuahnJF0j62vVXSXIzi4gIKA2Pj7CFVd0ZdKkblxySWfd9h6gSUqpKiApM5+5Gw7w7V/7WJeYia+PMLR9Y6adF8lZHcKoU0t7fKgMxhi++moLt976I9OmDea663px2WVdPR1WjaZJSikPScnOZ96GJH5Yf4CVew9iDHRoGsxD53ZgXPcI/f1SJdu9+xBTpsxj7twddO8eTvfu4Z4OSaFJSqlKYYxhX0YeWw5ks+VAFsvi0li+20pM7ZsEcfvwdozu0pQ2YYGeDrVGevvtNUydOg9fXx9efHEUU6b0oVYtfeVIVaBJSik3KC5x8O1f+1mfmGElpqQssvOLARCBtmGBTD2rLWO6NqVtkyAPR1tzGWMQEZo3D+bss9vw8svnEBkZ7OmwlBN96aFSp9He9FzmbUzi3d93k2K/FLBXi4Z0aBpETHgwHZoGExMeREAdvT/0pLS0w9xzz080axbEk0+e5elwKo2+9FCpGmhnSg7zNhxg7sYkthzIAqBrZH2uHBDNdWe2orZWG1UZxhjef/8v7r77JzIzC7jvvoGeDklVwO1JSkTOBl4CfIG3jTHPlBkfBXwANLCnuc8YM9fdcSl1KowxLN6RxszFcSzdmQ5YJaaHzu3AqE7hNG+knYxWNdu3p3Pttd+xZEk8Awc25803x9C5c5inw1IVcGuSEhFf4DVgBJAIrBSR74wxm50mewj4whjzhoh0BOYC0e6MS6mTVVTi4If1B5ixeBdbDmTRJLgO954dwwU9Igivr68Er8oKC0vYvj2dt98+j6uu6qGdwXoJd5ek+gA7jTG7AERkFjAOcE5SBih9Ulkf2O/mmJQ6YQkHDzN/UxLvLd3Dvow82oYF8tz4rozr3kx/w1SFzZu3g19/3cNzz42gc+cw9u69jTr6PNCruHtvRQAJTp8Tgb5lppkGLBCRW4AAYHh5XyQi1wPXA0RFRZ32QJVylpyVzx9x6fwRl86yXWkkHMwDoE/LRjxxfieGtAvTO/EqbN++LG67bT6zZ28mJiaUhx4aRHBwHU1QXsjde6y8s7hsc8KJwPvGmP+ISH/gIxHpbIxxHDGTMTOBmWC17nNLtKpGy8wrYs76/Xy1OpE18RkABNetRf/WIVwzsCUD24Rqc/EqrrjYweuvr+ShhxZSVOTgySeHcvfdA6ldW0u73srdSSoRcO4uOJKjq/OuAc4GMMb8ISJ1gVAgxc2xKUVxiYMlO9P4anUiCzYnU1jsoF2TQO4e1Z7B7RrToWkwvlpi8hoHD+bxyCO/MmBAc157bTStWzfydEjqFLk7Sa0E2opIS2AfcAlwaZlp4oFhwPsi0gGoC6S6OS5Vw5U4DF+uSuDFn7f/3YnrxN7NuahXJF0itBNXb5KZmc9bb63hjjv6ExYWwNq1NxAd3UD3YTXh1iRljCkWkSnAfKzm5e8aYzaJyOPAKmPMd8CdwFsicjtWVeAk442/MFZeY9Weg0z7fhMb92XRq0VDHhvbiaEx2omrtzHG8MUXm7jttvkkJ+cwYEBzBgxoTsuWDT0dmjqN3P4U0f7N09wywx5x+nszoL+oU26XlJnPM/O28M1f+wkPrstLl3RnbLdmesftheLiDnLzzXOZPz+Onj2b8v33E4mNbebpsJQbaFMXVe3lF5Xwzu+7ee3XnRQ7DFOGtuGmoa3xr62HvzcyxjBu3Czi4zN5+eWzuemm3vj6aq8e1ZWeparaMsawYHMyT/2whfiDhxnVqQkPju5IVIj2BuGNlizZS69ezfD39+P998+nWbMgmjXT1pbVnd5+qGppR3I2V7y7ghs+Wk2dWj58fE1fZlweqwnKC6Wm5jJp0jcMGvQ+L7+8HIDY2GaaoGoILUmpasMYw5YD2cxaGc8ny+MJqO3Lo+d15N/9WuCn1UFex+EwvPvuWu655ydycgp54IEzmDq1bF8AqrrTJKW8WmlimrvhAD9sOMDutFx8fYQJsc25a2Q7QgL17bbe6rbbfuSVV1Zw5plRvPnmGDp2bOzpkJQHaJJSXikzr4hPlu9l9qpEdqXl4iPQv3UI153ZilGdmmhy8lK5uYUUFJTQqFE9rruuJz17NuXKK7tpC8waTJOU8iopWfm8s3Q3n/wZT05BMf1aNeJaTUzVwg8/bOfmm+cycGAUn3xyIV26NKFLlyaeDkt5mMtJSkRqA1HGmJ1ujEepcm1IzOST5Xv535p9FDscnNu1GZMHt6JTs/qeDk2dorO7V4YAACAASURBVMTELG699Uf+978tdOzYmMmTe3k6JFWFuJSkRORc4AWgNtBSRLoDjxpjLnBncKpmy84v4rt1+/lsRTwb92VR18+Hi2MjuX5QK1qEBHg6PHUa/PjjTi6++EtKShz83/8N4447+mtnsOoIrpakHsd6xcavAMaYv0SkjduiUjXW4cJiFm9PZf6mZOZvSuJwYQkx4UE8Pq4T47pHUL+en6dDVKdBUVEJfn6+dO8ezujRbXnmmWHanZEql6tJqsgYk1Hm4aX2r6dOi+z8In7cmMT8Tcks2ZFKQbGDBv5+nNe1GZf0aU735tpZaHWRkZHPAw/8wsaNKSxaNInw8EA+/3y8p8NSVZirSWqLiEwAfOwezW8F/nRfWKomSMnO572le/j4z71k5xfTrH5dJvaJYmSnJvSJbkQt/W1TtWGMYdasjdx++3xSUw9zyy19KCwsoW5dbbuljs/VI2QK8AjgAP6H1av5/e4KSlVvCQcP8/qiOL5ak0hRiYNzOodzzRmt6BmlJabqKCkphyuu+JqfftpFbGwz5s69jJ49m3o6LOUlXE1So4wx9wL3lg4QkQuxEpZSLikoLmHmb7t49dedGAMX9bIaQbQM1UYQ1VlwcB3S0g7z6qvnMHlyrHYGq06Iq0nqIY5OSA+WM0ypcv25K50Hv95AXGou53ZpykNjOtC0fj1Ph6XcZOHC3UyfvoyvvpqAv78fq1Zdj4++4VidhOMmKREZhfVq9wgRecFpVDBW1Z9Sx7UhMZOZS3bx/br9RDasx3uTejM0JszTYSk3SU7O4a67fuLjj9fTunVD4uMzad8+VBOUOmkVlaRSgI1APrDJaXg2cJ+7glLeLb+ohF+2pPDe0t2s2nuIgNq+3Dy0NVOGtqWe/gamWnI4DG+9tZr77vuF3NxCHn54EPfffwb19CcD6hQdN0kZY9YCa0XkE2NMfiXFpLxMcYmD9fsyWbYzjaU701kdf4jCYgdRjfx5eExHLo6NJLiuXqyquw8/XE/37uG88ca5xMSEejocVU24+kwqQkSeAjoCdUsHGmPauSUq5RXyi0r4+M+9vLEojvTcQgA6NA3min4tOLNdY85oE4qvVvNUWzk5hTz99BJuvbUvTZoE8v33E2nYsK620FSnlatJ6n3gSWA6cA5wFfpMqsbKLShmzvr9vPTzDvZn5nNGm1D+1bs5A1qHaCevNcS3327lllvmkZCQRZs2jbj66h40aqQNYdTp52qS8jfGzBeR6caYOOAhEVnizsBU1WGMYU18Br9tT2XZzjT+Ssig2GHo1rwB0y/uxoA2WrVTU8THZzJ16jy+/XYbXbqEMWvWeAYMaO7psFQ15mqSKhCrDB8nIpOBfYA20armdqXm8MWqRL5ft599GXn4CHSJbMB1g1pxZttQ+rcK0aqdGubRRxfx00+7eO654dx2Wz/8/LQhjHIvMabiLvhEpC+wGWgIPAXUB541xix1b3jli42NNatWrfLEomuEvMISXl64g7cW78IAZ7YNZWy3Zgzr0EQ7eK2B/vgjgfr169KxY2OSk3PIzy+mRYsGng5LnQQRWW2MifV0HCfCpZKUMWa5/Wc2cDmAiES6KyhV+YwxxKXm8uvWFD74Yw+Jh/IY3yuSe8+OoXGQPmeqiQ4dyuO++35m5sw1jB/fkS+/vJgmTQI9HZaqYSpMUiLSG4gAfjfGpIlIJ6zukc4CNFF5uUO5hcxYvIu5Gw4Qf/AwAJ0jgpl+cTf6tQrxcHTKE4wxfPLJBu64Yz4HD+Zxxx39mDZtiKfDUjVURT1O/B9wEbAOq7HE11g9oD8LTHZ/eMpd8otKeG/pHl5ftJPcgmIGtWvMdYNaMbR9YyIb+ns6POVBb7+9huuvn0OfPhEsWHA53buHezokVYNVVJIaB3QzxuSJSCNgv/15m/tDU+5Q4jB8s3Yf/1mwjf2Z+QyLCePec2Jo1yTI06EpD8rPL2bv3gzatw/lssu6UquWD1dc0U07g1UeV1GSyjfG5AEYYw6KyFZNUN5rf0Ye13ywii0HsugaWZ/pE7oxoLU2H6/pfvopjptumovDYdi69Wb8/f246qoeng5LKaDiJNVKREp7Ohcg2ukzxpgL3RaZOq1W7D7ITZ+s5mBuIc+N78r4npHa6WcNl5SUwx13zOezzzbStm0jZswYo03KVZVTUZK6qMznV90ViHKPw4XFvPbrTt78bRfNG9bj/av60DmivqfDUh62ZUsq/fu/Q15eMY8+Opj77jtD35KrqqSKOpj9pbICUaeXMYa5G5J46ofN7M/M56KekUwb25Eg7ei1RsvKKiA4uA7t24dy9dU9mDw5lnbttBWnqrr01qkays4v4pFvN/H12n3EhAfx0sQe9I5u5OmwlAdlZxfw6KOL+Oij9WzceCNNmgTywgujPB2WUhVye5ISkbOBlwBf4G1jzDPlTDMBmAYYYJ0x5lJ3x1UdGWOYvTqRZ3/cSnpuIZf2jeKJcZ21J/IazBjD119vZerUeezfn80NN/SiTh29N1Xe44SOVhGpY4wpOIHpfYHXgBFAIrBSRL4zxmx2mqYtcD8w0BhzSES0T8CTcDC3kHu/Ws9Pm5Pp1aIh707qTddI7bqmJissLOGii75gzpztdOvWhNmzJ9Cvn/7+XnkXl5KUiPQB3sHqsy9KRLoB1xpjbqlg1j7ATmPMLvt7ZmH99mqz0zTXAa8ZYw4BGGNSTmwVajZjDN+t289TP2wh43ARD53bgasHttSWezWYMQYRoXZtX8LDA/jPf0YydWpfatXS3zwp7+NqSeplYAzwDYAxZp2IDHVhvgggwelzItC3zDTtAERkKVaV4DRjzI8uxlWjrY0/xONzNrM2PoOOTYN576redGqmLfdqsqVL47nllnl8+OEFdO4cxltvjfV0SEqdEleTlI8xZm+Z1zKUuDBfebfzZbtdrwW0BYZg9QW4REQ6G2MyjvgikeuB6wGioqJcDLt6OpRbyHPzt/LZigTCgurw3PiuXNQzUp891WDp6Ye5776fefvttURF1efgwTxPh6TUaeFqkkqwq/yM/ZzpFmC7C/MlAs5vRIvE6lqp7DR/GmOKgN0isg0raa10nsgYMxOYCdarOlyMu1pxOAyz1yTyzLytZOYVcd2ZLbl1eDsC9UF4jfbJJ+u57bb5HDqUx913D+CRRwYTGFjb02EpdVq4enW7EavKLwpIBn62h1VkJdBWRFpivSjxEqBsy71vgInA+yISilX9t8vFuGqMrUlZPPT1RlbtPURsi4Y8eUFnYsKDPR2WqgI2bUqlbdtGvPnmGLp2beLpcJQ6rVxNUsXGmEtO9MuNMcUiMgWYj/W86V1jzCYReRxYZYz5zh43UkQ2Y1Uh3m2MST/RZVVXuQXFvPTLDt75fTfBdWtpl0aKvLwinnpqCYMGtWDkyNZMmzaEWrV89JhQ1ZKrSWqlXQ33OfA/Y0y2qwswxswF5pYZ9ojT3wa4w/6nbLkFxby/bA/v/L6bg7mFTOzTnHtGxdAwQKtxarL583dy001z2bXrEA6HYeTI1tSurf3tqerL1TfzthaRAVjVdY+JyF/ALGPMLLdGV0PFpx/mmg9WsiMlh6HtG3Pr8HZ0b66/earJDhzI5rbb5vPFF5to3z6EhQuvYOjQlp4OSym3c/mJuzFmGbBMRKYB/wU+ATRJnUbx6YeZuSSO2asTqVPLl4+v6csZbfVVGgrmzNnOt99u5fHHh3DPPQO11whVY7j6Y95ArB/hXgJ0AL4FBrgxrholv6iENxbF8cZvcWDg/B7NmDK0LVEh+obcmmz16v0kJGRx/vkxXHNNT0aMaE10tJaoVc3i6u3YRuB74DljzBI3xlOjZOcXMX9TMq8u3MGe9MOM7daMB8/tQJPgup4OTXlQVlYBDz+8kFdfXUn79iGMHdseHx/RBKVqJFeTVCtjjMOtkdQgfyVk8NqvO1myI5X8IgetGgdo1Z6yOgievZlbb/2RpKQcbrwxlqeeGqat9lSNdtwkJSL/McbcCXwlIkf9gFbfzHviFmxK4qZP1lDsMEzsE8X4XpH0jGpAmd48VA20evUBJkyYTffu4XzzzSX06RPh6ZCU8riKSlKf2//rG3lPkcNhePbHrcxYvIsuEfV5ZWIPokMDPB2W8rDCwhKWLUtgyJBoYmObMWfOREaNaqOdwSplO+6ZYIxZYf/ZwRjzi/M/rAYUygXGGB78ZgMzFu/isr5RzL6xvyYoxeLFe+ne/U1GjPiI+PhMAM49t50mKKWcuHo2XF3OsGtOZyDV2Tu/7+azFQncPLQ1T57fmTq19MeXNVla2mGuvvpbBg9+n7y8Yr755l9ERWnv9UqVp6JnUv/CanbeUkT+5zQqCMgofy7lbMGmJJ6Zt5WRHZtw18j2+uyphsvNLaRLlzdISzvMvfcO5JFHBuPv7+fpsJSqsip6JrUCSMfqvfw1p+HZwFp3BVUdGGN47dedTF+wna6R9Xn+4m6aoGqwxMQsIiODCQiozZNPDqVv30g6d9aXUCtVkeMmKWPMbmA3Vq/nykWFxQ6embeVd5fu5vzuzXjmoq7U9dMqvpro8OEinnxyMdOnL2POnEsZObI111zT09NhKeU1Kqru+80YM1hEDnHkywoFq2/YRm6Nzgtt3p/FlM/WsCs1l0kDonn0vI5agqqh5s7dwc03z2XPngwmTepOjx7hng5JKa9TUXVf6Svi9VemLtiTlssV7y6nlo8P703qzdAYrc6pqa6//nveemsNHTqEsmjRlQweHO3pkJTyShVV95X2MtEc2G+MKRSRM4CuwMdAlpvj8xpJmflc8e4KShyGWdf3pU1YoKdDUpWspMSBiODjI/TvH0l0dAPuumuAvkpDqVPgahP0b7BeHd8a+BDrN1Kfui0qL7NxXybDX/iN1OwC3p3UWxNUDbRy5T769Hmbt99eA8BVV/XggQfO1ASl1ClyNUk5jDFFwIXAf40xtwDaZwtwMLeQu75cR10/X766cQA9ohp6OiRViTIz85kyZS59+77NgQPZhIXpj7SVOp1cfn28iFwMXA6cbw+r8T/uWBt/iGs+WEVWXhEzr+hFx2bBng5JVaIfftjOtdd+T0pKLlOm9OHJJ88iOLiOp8NSqlpxNUldDdyE9aqOXSLSEvjMfWFVfWk5BVz34SoC6vjy2XX9aB8e5OmQVCXz9fUhIiKI77+fSGxsM0+Ho1S1JMYc1bl5+ROK1ALa2B93GmOK3RZVBWJjY82qVas8tXiMMUz5bC0/bUpmztQzaNdEE1RNUFBQzPPPL6OkxMGjjw4BrI6D9VUayluIyGpjTKyn4zgRrr6Z90zgI2Af1m+kwkXkcmPMUncGV1U99v1mflh/gLtHtdcEVUMsWrSHyZPnsG1bOpde2gVjzN8t+ZRS7uNqdd+LwGhjzGYAEemAlbS8KiOfDgkHD/PZinjOaBPKTUNaezoc5WapqbncdddPfPjhOlq2bMDcuZdyzjltPR2WUjWGq0mqdmmCAjDGbBGR2m6KqcoqcRge/nYjtXyE5y/uqj1J1ADJybnMnr2ZBx88kwcfPJN69Wp8eyGlKpWrSWqNiMzAKj0BXEYN62DWGMM9s9ezaFsq087rSNP69TwdknKTDRuS+e67bTz44CA6dw4jIeF2GjXS/a2UJ7j6O6nJQBxwD3AvsAu4wV1BVUXvL9vDV2sSmTqsLZMGtvR0OMoNcnMLuffen+jZcyYvvvgnKSm5AJqglPKgCktSItIFaA18bYx5zv0hVT3bk7N56octjOjYhNuG6fOI6uj777cxZco84uMzufrq7jz33AhCQvw9HZZSNV5FvaA/gPUG3jVAbxF53BjzbqVEVkU4HIZn5m2lnp8vz13UVVtzVUMZGflcccU3NGsWxOLFkzjzzBaeDkkpZauoJHUZ0NUYkysijYG5QI1KUg98vYGFW1N46NwONAyocW1Fqq3iYgezZm3k0ku70KBBXRYuvIJOncK0rz2lqpiKklSBMSYXwBiTKiKuPsOqFg5k5jFrZQKTBkRz7ZmtPB2OOk2WL0/khhvmsG5dMg0b1uXcc9vRo0dTT4ellCpHRUmqlYj8z/5bgNZOnzHGXOi2yKqAr9fuA+DKAdGeDUSdFhkZ+TzwwC+8+eYqmjYNYvbsixk9Wp8xKlWVVZSkLirz+VV3BVLVGGP4dHk8/VuF0DJUe7auDsaM+ZQ//khk6tS+PPHEUIKCtDNYpaq6il56+EtlBVLVbNiXSeKhPKZqaz6vtmNHOhERwfj7+/Hss8OpV8+Pnj21ak8pb1GjnjG5KqegmDu+WEdw3VoM79DE0+Gok1BQUMxjjy2iS5c3ePbZ3wEYODBKE5RSXsbtSUpEzhaRbSKyU0TuO85040XEiIjH+wP89q997EzJ4bXLetJIW/R5nYULd9O165tMm/YbF1zQgcmTPX5IKaVO0gklKRE5oUp8EfEFXgPOAToCE0WkYznTBQFTgeUn8v3uMn9TMi1DAzijTainQ1En6JlnfmfYsA8pKXEwf/6/+eyzi2jaVHuqV8pbuZSkRKSPiGwAdtifu4nIKy7M2gfr3VO7jDGFwCxgXDnTPQE8B+S7Frb77EjOZsXudAa3a6wdyHoJh8OQk1MIwJgx7Xj44UFs2HAjI0dqL/VKeTtXS1IvA2OAdABjzDpgqAvzRQAJTp8T7WF/E5EeQHNjzJzjfZGIXC8iq0RkVWpqqothn5gSh2HKp2sJrOPHFf211wFvsG5dEgMHvssNN1iHT+fOYTz++FDtrVypasLVJOVjjNlbZliJC/OVVxT5+1XA9o+DXwTurOiLjDEzjTGxxpjYxo0bu7DoE/fVmkS2JWfz2NhOtGoc6JZlqNMjJ6eQu+5aQK9eM4mLO8g557SpeCallNdx9VUdCSLSBzD2c6ZbgO0uzJcINHf6HAnsd/ocBHQGFtlVa+HAdyIy1hhTqe+Hzyss4T8LttGteQNGdwmvzEWrE7RixT7Gj/+ChIQsrruuJ888M1x7KleqmnI1Sd2IVeUXBSQDP9vDKrISaCsiLbFePX8JcGnpSGNMJvB36wQRWQTcVdkJCuCHDQdIzirgxQnd9VlUFVX6yvaoqPpERzdg1qzxDBjQvOIZlVJey6UkZYxJwUowJ8QYUywiU4D5gC/wrjFmk4g8Dqwyxnx3ot/pLsvi0mgUUJt+rUI8HYoqo6iohJdeWs6CBXH8+OO/CQ8PZPHiqzwdllKqEriUpETkLZyeJZUyxlxf0bzGmLlYvac7D3vkGNMOcSWe0y2noJjv/trPyE5N9FUcVcyyZQlMnjyHDRtSGDOmHdnZBdSvX9fTYSmlKomr1X0/O/1dF7iAI1vtebWbP1lDscMwuJ17GmSoE5eVVcDddy9g5sw1REYG8/XX/2LcuPZaFatUDeNqdd/nzp9F5CPgJ7dEVMn+3JXOb9tTubxfCybE6vONqqJWLR8WLdrLnXf2Z9q0IQQGas8fStVErpakymoJVIsfEs3bcIA6tXx4YHQHvUv3sG3b0nj66d95441z8ff3Y926ydSte7KHqFKqOnC1x4lDInLQ/peBVYp6wL2huV9OQTHfrtvPmW0bU0/fyOox+fnFPPror3Tt+ibffruVDRuSATRBKaUqLkmJVbzohtWEHMBhjDmqEYU3+uTPvWQcLmLKWfpDUE9ZsCCOm276gbi4Q1x2WRemTx9JeLj+kFopZakwSRljjIh8bYzpVRkBVaav1iTSJ7oR3Zs38HQoNZIxhscf/w0fH+Hnny9n2LBWng5JKVXFuNot0goR6enWSCrZ3vRctifnMKqz9i5RmUpKHMyYsYqkpBxEhM8/H8/69TdqglJKleu4JSkRqWWMKQbOAK4TkTggF6tPPmOM8drE9dNm67nHyI76UsPKsnbtASZP/oEVK/Zx8GAe999/JhERwZ4OSylVhVVU3bcC6AmcXwmxVKr5m5KICQ+ieSN/T4dS7WVnF/DII7/y8ssrCA3155NPLmTixM6eDksp5QUqSlICYIyJq4RYKs38TUms3HOIh87t4OlQaoT77/+F119fyQ039OLpp4fRsKF2BquUck1FSaqxiNxxrJHGmBdOczxuF5eaw91friMmPIgrB0R7Opxqa8+eDIqKSmjbNoSHHhrEv//dlX79Ij0dllLKy1TUcMIXCMR6pUZ5/7zO3V+uw9dHeOuKWPx8XW03olxVVFTCs8/+TseOrzFlyjwAwsMDNUEppU5KRSWpA8aYxyslkkqwMyWbNfEZPHpeR30W5QZLl8Zzww1z2LQplfPPj+Hll8/2dEhKKS/n0jOp6uL7dQcAGN5BW/Sdbl9/vYULL/yCqKj6fPvtJYwd297TISmlqoGKktSwSomiEuQWFPPmb3GM6tRES1GniTGG/fuziYgIZtSoNjz55FBuu60fAQHaGaxS6vQ47kMZY8zBygrEnYpLHEyY8QcFxQ6uOUN/NHo6bNmSytChHzBo0Pvk5RXh7+/Hgw8O0gSllDqtakTLgYVbU9i0P4tnL+pCn5aNPB2OV8vLK+KhhxbSrdubrF+fzP33n0GdOtoRrFLKPar91cXhMMxYvIsmwXW4qKe2MDsVCQmZDBnyAbt2HeLyy7syffpIwsICPB2WUqoaq/ZJ6uPle1m99xDTL+5GLW1yflKKikrw8/MlIiKYM8+M4u23z2Po0JaeDkspVQNU+6v2zMW76NuyERf1jPB0KF6npMTBK68sp02bV0hOzsHHR3j//fM1QSmlKk21TlLFJQ5Sswvo1ryBvnX3BK1evZ++fd9m6tQfad8+hMLCEk+HpJSqgap1dd/8TckUFDuIbdHQ06F4DYfDcPvtP/LqqysJCwtg1qyLmDChkyZ5pZRHVOsk9dv2FBr6+zFMf7zrMh8fIT09jxtvjOXJJ8+iQYO6ng5JKVWDVevqvh0pObQPD8LXR0sBx7Nr1yHGjv2MTZtSAPjwwwt49dXRmqCUUh5XbZOUMYadyTm0DfPKfnArRWFhCU8/vYROnV7n11/3sHVrGmCVppRSqiqottV9SVn5ZBcU065JoKdDqZIWL97L5Mlz2LIljYsu6sB//3s2kZH6llylVNVSbZPU0p3pAHRsVt/DkVRNP/64k7y8YubMmci557bzdDhKKVWualvd98P6/USH+NMzqoGnQ6kSjDG8995afv55FwAPPzyITZtu0gSllKrSqm2S2peRR7smQdp0Gti0KYXBg9/n6qu/44MP1gFQr54f/v5+Ho5MKaWOr1omqdTsAnbaLftqssOHi7j//p/p3n0Gmzal8s47Y/ngg/M9HZZSSrmsWj6T+mH9fhwGxnZr5ulQPOrLLzfxzDNLmTSpO88/P4LQUH2PllLKu1TLJDV3QxJtwwJp26TmlaT27cti8+ZURoxozeWXd6NDh8b06aP9FiqlvJPbq/tE5GwR2SYiO0XkvnLG3yEim0VkvYj8IiItTmV5xhhW7DlIuxqWoIqLHbz00p/ExLzGlVd+Q2FhCT4+oglKKeXV3JqkRMQXeA04B+gITBSRjmUmWwvEGmO6ArOB505lmclZBQB0bFZzfvOzcuU++vZ9m9tum88ZZ0Tx++9XU7u2r6fDUkqpU+bu6r4+wE5jzC4AEZkFjAM2l05gjPnVafo/gX+fygK3JmUB1JhOZbdtS6Nfv3do0iSAL74Yz/jxHbVFo1Kq2nB3kooAEpw+JwJ9jzP9NcC88kaIyPXA9QBRUVHH/IKtSdkAxIRX35KUMYaNG1Po0qUJ7duH8u67Y7nggg4EB9fxdGhKKXVaufuZVHm39KbcCUX+DcQCz5c33hgz0xgTa4yJbdy48TEXuGrPISIa1KN+Nf0N0M6dBzn77E/o2XPm333tXXlld01QSqlqyd0lqUSgudPnSGB/2YlEZDjwIDDYGFNwsgvLKyzht+0pXNk/+mS/osoqKCjm+eeX8eSTi6ld25cXXxxF27aNPB2WUkq5lbuT1EqgrYi0BPYBlwCXOk8gIj2AGcDZxpiUU1lYanYBRSWGmKbVq6qvqKiE3r3fYsOGFC6+uCP//e/ZNGtWs1ovKqVqJrcmKWNMsYhMAeYDvsC7xphNIvI4sMoY8x1W9V4g8KX9wD/eGDP2ZJa3Jv4QAK0bB5yO8D0uK6uA4OA6+Pn5cs01PWjXLoRzzmnr6bCUUqrSuP3HvMaYucDcMsMecfp7+Ola1oZ9mdTz86VbpHd3KutwGN55Zw333vszs2aNZ+TI1tx6az9Ph6WUUpWuWvU4sT8jj8ZBdbz6pX0bNiQzefIPLFuWwKBBLWjevHpVXSql1ImoNh3MFpU4WLQtlQGtQzwdykl76qnF9Ow5k+3b03n//XEsWnQlHTocuyWjUkpVd9WmJLV67yHyikoY3M77LurGGESEsLAArryyG88+O5yQEO0MVimlqk1J6tetKdT29WGQFyWphIRMLrzwc956aw0A113Xi7ffHqsJSimlbNUmSS3ZkUbXyPoE1Kn6hcPiYgcvvPAHHTq8xo8/7qSwsMTTISmlVJVU9a/oLkjJymfzgSweHN3B06FUaNWq/Vx77XesW5fMuee25dVXRxMd7d2tEZVSyl2qRZLak34YgFZe8PuogwfzSEs7zFdfTeCCC2K0M1illDqOapGkPli2h9q+PnSJqO/pUI5ijOGzzzayb18Wd989kJEjW7Nz51Tq1q0Wm14ppdzK659J5RQU88vWZM7r1oyw4LqeDucIO3akM3Lkx1x22f/49tttlJQ4ADRBKaWUi7w+Sc3fmER+kYNL+zaveOJKUlBQzGOPLaJLlzdYsWIfr702mt9+m4Svr9dvbqWUqlRef0u/dGcajYPq0DOq6rzkMC7uEE8+uYTx4zvywgsjadpUO4NVSqmT4fVJau/Bw7QKDfB4A4Tk5By++moLN93Um44dG7N16820bq2v0lBKqVPh1fVPxSUONu3PpIMHX83hcBjefHMV7du/yu23z2f3brsndk1QSil1PvnY9AAAE+JJREFUyry6JLX5QBb5RQ56tfBMVd+6dUlMnvwDf/6ZyNCh0bz++rm0bFl1qh1V1VFUVERiYiL5+fmeDkXVAHXr1iUyMhI/P+9/Q7lXJ6m41BwAj5Sk8vKKGD78I0Tgo48u4LLLuni8ylFVXYmJiQQFBREdHa3HiXIrYwzp6ekkJibSsmVLT4dzyrw6SSUczAOgaf3Ka3q+cOFuhgyJpl49P2bPvpguXZrQqFG9Slu+8k75+fmaoFSlEBFCQkJITU31dCinhdc+kzLG8M1f++jWvEGl9Ne3d28G48bNYtiwD5k1ayMAgwdHa4JSLtMEpSpLdTrWvDZJxaXmsCs1l0t6u/f3UUVFJTz//FI6dnydn3/exfPPj+Diizu6dZlKKaUsXpukEg9ZVX1twwLdupwJE2Zzzz0/M3x4K7ZsuZm77hqAn5+vW5eplDv4+vrSvXt3OnfuzHnnnUdGRsbf4zZt2sRZZ51Fu3btaNu2LU888QTGmL/Hz5s3j9jYWDp06EBMTAx33XWXJ1bhuNauXcu11157xLBx48bRv3//I4ZNmjSJ2bNnHzEsMPCf68j27dsZPXo0bdq0oUOHDkyYMIHk5ORTiu3gwYOMGDGCtm3bMmLECA4dOnTUNL/++iv/3969B1dVXwsc/y4eJUQCRZAUihUwPEKCRMAWLvYSoAJqUXmUCBQCRUSkOhXFex3uTPAxolS0pVAp7XWCt4ZEwCDGVisvRQpKKGkSCWLESNE0YEwlPCohWfePvXPyJieY8wrrM3Nmzt5nn73XWXNOVvZv//bvFxcX53mEhYWxZcsWwGk5Wrp0Kf369SM6OppVq1YBkJGRQVJS0jeKLeipasg9hg4dqi/t+1Sv+a8M/azkrDa34uKzeubMeVVV3bHjqKan5zX7Mczl5dChQ4EOQa+44grP89mzZ+sTTzyhqqpnz57VPn366JtvvqmqqmfOnNEJEybo6tWrVVU1JydH+/Tpo3l5zu+grKxM16xZ06yxlZWVfeN9TJ06VbOysjzLJSUl2rNnTx0wYIAePXrUsz4xMVE3btxY472VuTl37pxGRUXp1q1bPa/t2LFDc3JyvlFsS5Ys0eXLl6uq6vLly/Xhhx++6PbFxcXauXNnPXPmjKqqvvDCCzpr1iwtLy9XVdWioiJVVa2oqNC4uDjPdtXV950DMjUI/oY35RGyHSc+/9c5WrcSukW0a7Z9qip//GM2Dz74F+66awhPPjmW0aNDv3eMCS6PvvYBhz4/1az7HNijI0kTY7zefsSIEWRnZwOQkpLCyJEjGTduHADh4eGsXr2a+Ph4Fi1axIoVK1i6dCkDBgwAoE2bNtx777119nn69Gnuu+8+MjMzERGSkpKYMmUKHTp04PRppyfupk2byMjIIDk5mTlz5nDllVdy8OBB4uLiSE9PJysri29/25m6Jioqij179tCqVSvuuecejh07BsCvfvUrRo4cWePYpaWlZGdnM3jwYM+6zZs3M3HiRCIjI0lNTeWRRx5pNC8pKSmMGDGCiRMnetaNHj3a67w25NVXX2XXrl0AJCYmEh8fz9NPP93g9ps2beLmm28mPNyZAPX5558nJSWFVq2cxq9u3boBzrWn+Ph4MjIymDZt2jeOMxiFdJH6Tscw2jTTeHgffvgFCxe+zs6dBQwf3pOEBO9/8MaEkvLycrZv3868efMAp6lv6NChNba59tprOX36NKdOnSI3N5cHH3yw0f0+/vjjdOrUiZycHIB6m7RqO3LkCNu2baN169ZUVFSQnp7O3Llzee+99+jVqxeRkZHMmDGDBx54gBtvvJFjx44xfvx48vLyauwnMzOT2NjYGus2bNhAUlISkZGRTJ061asilZubWycX9SktLeWHP/xhva+lpKQwcGDN69ZFRUV0794dgO7du3PixImL7j81NZXFixd7lj/++GPS0tJIT0/nqquuYtWqVfTt2xeAYcOGsXv3bitSwWb/p1/S/zvNMyZecnIWCxZkEB7elrVrb2X+/KG0atVyeseY4NKUM57mdO7cOeLi4igoKGDo0KHcdNNNgNOC0FBvsKb0Etu2bRupqame5c6dG7+x/Sc/+QmtWzvXeBMSEnjssceYO3cuqampJCQkePZ76NAhz3tOnTpFaWkpERFVv//CwkKuuuoqz3JRURH5+fnceOONiAht2rQhNzeX2NjYej9TU3vDRUREkJWV1aT3eKuwsJCcnBzGjx/vWff1118TFhZGZmYmr7zyCj/72c/YvXs34JxVff755z6JJRiEZMeJ8grlH1+e+8YjTZSVOdO2DxvWgzvvjOXw4UUsWDDMCpRpkdq3b09WVhaffvop58+fZ82aNQDExMSQmZlZY9ujR4/SoUMHIiIiiImJ4cCBA43uv6FiV31d7RE3rriiaqLSESNGkJ+fz8mTJ9myZQuTJ08GoKKigr1795KVlUVWVhafffZZjQJV+dmq7zstLY2SkhJ69+5Nr169KCgo8BTQLl261DjL+/LLL+natasnF9581tLS0hqdHKo/qhfUSpGRkRQWFgJOEapsrqvPyy+/zKRJk2qMFtGzZ0+mTJkCwKRJkzxNteDktH37lnsrTEgWqTPnLwDw/d6XNj5eYWEp06dvJjHR6TkTG9uN9evvIDLStz0FjQkGnTp1YtWqVTzzzDOUlZUxc+ZM3n33XbZt2wY4Z1z3338/Dz/8MABLlizhySef5MiRI4BTNJ599tk6+x03bhyrV6/2LFcWgsjISPLy8jzNeQ0RESZNmsTixYuJjo6mS5cu9e63vjOY6Oho8vPzPcsbNmzgjTfeoKCggIKCAg4cOOApUvHx8aSlpXH+/HkAkpOTPdedZsyYwV//+ldef/11z77eeOMNTxNmpcozqfoetZv6AG677TbWr18PwPr167n99tsbzMOGDRuYPn16jXV33HEHO3bsAODtt9+mX79+nteOHDlSp6mzRQl0z41LefQeMOiSevZduFCua9a8rx07Ltd27R7XRx/dpRUVFU3ahzGXIth696mq/vjHP9YXX3xRVVWzs7N11KhR2q9fP7322mt12bJlNX4br732mg4ZMkQHDBig0dHR+tBDD9XZf2lpqc6ePVtjYmL0uuuu082bN6uq6saNG7VPnz46atQoXbRokSYmJqpq/b3s9u/fr4AmJyd71p08eVKnTZumgwYN0ujoaF2wYEG9ny82NlZPnTqln3zyifbo0aPOb/v666/Xffv2qarqsmXLNDY2VgcPHqyTJ0/WEydOeLbLy8vT8ePHa1RUlEZHR2tCQoL+85//vGhuG/PFF1/omDFjNCoqSseMGaPFxcWezztv3jzPdpWxV/biq1RSUqK33HKLxsbG6vDhw2v0Yrz11ls1Ozu7zjFbSu8+ceIOLd/rP0hbTXqKw49PIMzLe5Y++qiYmTNfYf/+zxk7tjfPP38rfft28XGkxjjy8vKIjo4OdBgt2nPPPUdERESde6VasqKiImbMmMH27dvrvFbfd05EDqjqMH/F1xxCsrnvQnkFHdq18bpAAXTs2I7S0vO89NJk3nprlhUoY1qYhQsX0q5d892SEgqOHTvGypUrAx2GT4Vk774KhY5hFw9dVUlPP0xqai6pqVOJjOzABx/ca50ijGmhwsLCmDVrVqDD8Ksbbrgh0CH4XEieSVWoEn6RQWULCv7FxIkbmDLlZY4cKebkyTMAVqBMQIVi07oJTS3puxaSRerfZeVc3blul8uysnKefvpdBg5cw65dBTz77DgyM++2Xnsm4MLCwiguLm5RfzxMcFJ15pMKC/PfFEa+FJLNfV9fqOAHfepeU7pwoYJ16/7GhAlR/PrXE7j66k4BiM6Yunr27Mnx48dbzBw/JrhVzszbEoRkkQKI6eHMxltcfJYVK/aQlBRPeHhb3n//Lrp0CQ9wdMbU1LZt2xYxS6ox/ubz5j4RmSAiH4pIvoj8dz2vtxORNPf190Sklzf7DWvTiuTkLPr3X83KlXt5551PAaxAGWNMC+LTIiUirYE1wM3AQGC6iNS+HXseUKKqUcBzQMNDA1dz/8//zNy5r9K/f1cOHlzAhAlRzRm6McaYIODrM6nvA/mqelRVzwOpQO3xQG4H1rvPNwFjxYvRHj86/AW///1Edu+ey6BBkc0atDHGmODg62tS3wX+UW35OPCDhrZR1Qsi8hXQBfii+kYicjdwt7v4dTEP5M6fD/Pn+yTuUNKVWrm6jFkuqlguqlguqvQPdABN5esiVd8ZUe0+uN5sg6quA9YBiEhmqA3t4SuWiyqWiyqWiyqWiyoiktn4VsHF1819x4Grqy33BGpPfOLZRkTaAJ2AL30clzHGmBDg6yK1H+grIr1F5FvAncDWWttsBRLd51OBHWp3PBpjjMHHzX3uNaafA28CrYEXVPUDEXkMZ8j4rcD/Av8nIvk4Z1B3erHrdT4LOvRYLqpYLqpYLqpYLqqEXC5CcqoOY4wxl4eQHLvPGGPM5cGKlDHGmKAV1EXKV0MqhSIvcrFYRA6JSLaIbBeRawIRpz80lotq200VERWRFtv92JtciMg097vxgYik+DtGf/HiN/I9EdkpIgfd38ktgYjT10TkBRE5ISK5DbwuIrLKzVO2iAzxd4xNEuj56xt64HS0+BjoA3wL+DswsNY29wJr3ed3AmmBjjuAuRgNhLvPF17OuXC3iwDeAfYBwwIddwC/F32Bg0Bnd7lboOMOYC7WAQvd5wOBgkDH7aNc/CcwBMht4PVbgD/j3KM6HHgv0DFf7BHMZ1I+G1IpBDWaC1Xdqapn3cV9OPektUTefC8AHgdWAP/2Z3B+5k0u5gNrVLUEQFVP+DlGf/EmFwp0dJ93ou49my2Cqr7Dxe81vR14UR37gG+LSHf/RNd0wVyk6htS6bsNbaOqF4DKIZVaGm9yUd08nP+UWqJGcyEi1wNXq2qGPwMLAG++F/2AfiKyR0T2icgEv0XnX97kYhnwUxE5DvwJuM8/oQWdpv49Cahgnk+q2YZUagG8/pwi8lNgGDDKpxEFzkVzISKtcEbTn+OvgALIm+9FG5wmv3ics+vdIhKrqv/ycWz+5k0upgPJqrpSREbg3J8Zq6oVvg8vqITU381gPpOyIZWqeJMLRORHwFLgNlX92k+x+VtjuYgAYoFdIlKA0+a+tYV2nvD2N/Kqqpap6ifAhzhFq6XxJhfzgJcBVHUvEIYz+Ozlxqu/J8EimIuUDalUpdFcuE1cv8MpUC31ugM0kgtV/UpVu6pqL1XthXN97jZVDbmBNb3gzW9kC06nGkSkK07z31G/Rukf3uTiGDAWQESicYrUSb9GGRy2ArPdXn7Dga9UtTDQQTUkaJv71HdDKoUcL3PxS6ADsNHtO3JMVW8LWNA+4mUuLgte5uJNYJyIHALKgSWqWhy4qH3Dy1w8CPxeRB7Aad6a0xL/qRWRDTjNu13d629JQFsAVV2Lcz3uFiAfOAvMDUyk3rFhkYwxxgStYG7uM8YYc5mzImWMMSZoWZEyxhgTtKxIGWOMCVpWpIwxxgQtK1ImaIlIuYhkVXv0usi2vRoa9bmJx9zljqT9d3coof6XsI97RGS2+3yOiPSo9tofRGRgM8e5X0TivHjPL0Qk/Jse2xh/siJlgtk5VY2r9ijw03FnqupgnMGLf9nUN6vqWlV90V2cA/So9tpdqnqoWaKsivO3eBfnLwArUiakWJEyIcU9Y9otIn9zH/9RzzYxIvK+e/aVLSJ93fU/rbb+dyLSupHDvQNEue8d685DlOPO19POXf+UVM3j9Yy7bpmIPCQiU3HGUXzJPWZ79wxomIgsFJEV1WKeIyK/ucQ491JtgFAReV5EMsWZP+pRd939OMVyp4jsdNeNE5G9bh43ikiHRo5jjN9ZkTLBrH21pr50d90J4CZVHQIkAKvqed89wK9VNQ6nSBx3h8FJAEa668uBmY0cfyKQIyJhQDKQoKqDcEZqWSgiVwKTgBhVvQ54ovqbVXUTkIlzxhOnqueqvbwJmFxtOQFIu8Q4J+AMf1RpqaoOA64DRonIdaq6Cmd8ttGqOtodIul/gB+5ucwEFjdyHGP8LmiHRTIGt7mv1rq2wGr3Gkw5zlh0te0FlopIT+AVVf1IRMYCQ4H97rBR7XEKXn1eEpFzQAHOdA79gU9U9Yj7+npgEbAaZ76qP4jI64DXU4Oo6kkROeqOnfaRe4w97n6bEucVOMMAVZ9ddZqI3I3z++6OM8Ffdq33DnfX73GP8y2cvBkTVKxImVDzAFAEDMZpCagzqaGqpojIe8CtwJsichfO9ATrVfURL44xs/qAtCJS7xxl7nhx38cZtPRO4OfAmCZ8ljRgGnAYSFdVFadieB0nzgy0TwFrgMki0ht4CLhBVUtEJBlnINXaBHhLVac3IV5j/M6a+0yo6QQUunMAzcI5i6hBRPoAR90mrq04zV7bgaki0s3d5koRucbLYx4GeolIlLs8C3jbvYbTSVX/hNMpob4edqU404fU5xXgDpx5jtLcdU2KU1XLcJrthrtNhR2BM8BXIhIJ3NxALPuAkZWfSUTCRaS+s1JjAsqKlAk1vwUSRWQfTlPfmXq2SQByRSQLGIAzVfYhnD/mfxGRbOAtnKawRqnqv3FGit4oIjlABbAW5w9+hru/t3HO8mpLBtZWdpyotd8S4BBwjaq+765rcpzuta6VwEOq+nfgIPAB8AJOE2KldcCfRWSnqp7E6Xm4wT3OPpxcGRNUbBR0Y4wxQcvOpIwxxgQtK1LGGGOClhUpY4wxQcuKlDHGmKBlRcoYY0zQsiJljDEmaFmRMsYYE7T+H+WoRHn+t0a8AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.64 0.37 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13477\n", + " 1 0.80 0.47 0.59 4019\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.83 0.72 0.75 17496\n", + "weighted avg 0.84 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06327400
11284738
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6327 400\n", + "1 1284 738" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2395015270917305\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.65 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.428783 0.616773\n", + "1 Random Forest 0.368447 0.761964\n", + "2 Gradient Boosted 0.364985 0.773571" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1788
Time: 23:04:29 Log-Likelihood: -7743.5
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8836 0.115 -7.704 0.000 -1.108 -0.659
SEX -0.1289 0.041 -3.126 0.002 -0.210 -0.048
AGE 0.0683 0.101 0.678 0.498 -0.129 0.266
PAY_0 0.6745 0.060 11.220 0.000 0.557 0.792
PAY_2 -0.5470 0.096 -5.678 0.000 -0.736 -0.358
PAY_3 -0.0739 0.108 -0.683 0.495 -0.286 0.138
PAY_4 -0.4037 0.154 -2.618 0.009 -0.706 -0.101
PAY_5 0.1256 0.171 0.735 0.462 -0.209 0.460
PAY_6 0.2827 0.138 2.043 0.041 0.011 0.554
BILL_AMT1 -1.4104 0.529 -2.667 0.008 -2.447 -0.374
BILL_AMT2 1.3442 0.771 1.743 0.081 -0.167 2.856
BILL_AMT3 0.9654 0.740 1.305 0.192 -0.484 2.415
BILL_AMT4 0.2228 0.721 0.309 0.757 -1.189 1.635
BILL_AMT5 -1.7122 0.919 -1.863 0.063 -3.514 0.090
BILL_AMT6 1.5981 0.850 1.879 0.060 -0.069 3.265
PAY_AMT1 -1.3296 0.306 -4.345 0.000 -1.929 -0.730
PAY_AMT2 -1.8619 0.393 -4.742 0.000 -2.631 -1.092
PAY_AMT3 -0.4035 0.300 -1.347 0.178 -0.991 0.184
PAY_AMT4 -0.5948 0.300 -1.982 0.048 -1.183 -0.007
PAY_AMT5 -0.9541 0.292 -3.267 0.001 -1.527 -0.382
PAY_AMT6 -0.5320 0.258 -2.065 0.039 -1.037 -0.027
GRAD 1.1287 0.210 5.370 0.000 0.717 1.541
UNI 1.1123 0.209 5.325 0.000 0.703 1.522
HS 1.0960 0.213 5.149 0.000 0.679 1.513
MARRIED 0.1835 0.162 1.134 0.257 -0.134 0.501
SINGLE 0.0426 0.162 0.262 0.793 -0.276 0.361
PAY_0_No_Transactions 0.0631 0.126 0.501 0.616 -0.184 0.310
PAY_0_Pay_Duly 0.2038 0.123 1.654 0.098 -0.038 0.445
PAY_0_Revolving_Credit -0.7870 0.138 -5.684 0.000 -1.058 -0.516
PAY_2_No_Transactions -1.3878 0.235 -5.907 0.000 -1.848 -0.927
PAY_2_Pay_Duly -1.3056 0.223 -5.868 0.000 -1.742 -0.870
PAY_2_Revolving_Credit -0.7845 0.227 -3.461 0.001 -1.229 -0.340
PAY_3_No_Transactions -0.5573 0.270 -2.061 0.039 -1.087 -0.027
PAY_3_Pay_Duly -0.5669 0.242 -2.339 0.019 -1.042 -0.092
PAY_3_Revolving_Credit -0.5462 0.231 -2.368 0.018 -0.998 -0.094
PAY_4_No_Transactions -1.1499 0.351 -3.276 0.001 -1.838 -0.462
PAY_4_Pay_Duly -1.2105 0.331 -3.655 0.000 -1.860 -0.561
PAY_4_Revolving_Credit -1.1121 0.321 -3.466 0.001 -1.741 -0.483
PAY_5_No_Transactions 0.0540 0.383 0.141 0.888 -0.697 0.805
PAY_5_Pay_Duly -0.0559 0.367 -0.152 0.879 -0.775 0.663
PAY_5_Revolving_Credit 0.0811 0.357 0.227 0.820 -0.618 0.780
PAY_6_No_Transactions 0.3831 0.308 1.243 0.214 -0.221 0.987
PAY_6_Pay_Duly 0.3360 0.301 1.116 0.264 -0.254 0.926
PAY_6_Revolving_Credit 0.0761 0.292 0.261 0.794 -0.496 0.648
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1788\n", + "Time: 23:04:29 Log-Likelihood: -7743.5\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8836 0.115 -7.704 0.000 -1.108 -0.659\n", + "SEX -0.1289 0.041 -3.126 0.002 -0.210 -0.048\n", + "AGE 0.0683 0.101 0.678 0.498 -0.129 0.266\n", + "PAY_0 0.6745 0.060 11.220 0.000 0.557 0.792\n", + "PAY_2 -0.5470 0.096 -5.678 0.000 -0.736 -0.358\n", + "PAY_3 -0.0739 0.108 -0.683 0.495 -0.286 0.138\n", + "PAY_4 -0.4037 0.154 -2.618 0.009 -0.706 -0.101\n", + "PAY_5 0.1256 0.171 0.735 0.462 -0.209 0.460\n", + "PAY_6 0.2827 0.138 2.043 0.041 0.011 0.554\n", + "BILL_AMT1 -1.4104 0.529 -2.667 0.008 -2.447 -0.374\n", + "BILL_AMT2 1.3442 0.771 1.743 0.081 -0.167 2.856\n", + "BILL_AMT3 0.9654 0.740 1.305 0.192 -0.484 2.415\n", + "BILL_AMT4 0.2228 0.721 0.309 0.757 -1.189 1.635\n", + "BILL_AMT5 -1.7122 0.919 -1.863 0.063 -3.514 0.090\n", + "BILL_AMT6 1.5981 0.850 1.879 0.060 -0.069 3.265\n", + "PAY_AMT1 -1.3296 0.306 -4.345 0.000 -1.929 -0.730\n", + "PAY_AMT2 -1.8619 0.393 -4.742 0.000 -2.631 -1.092\n", + "PAY_AMT3 -0.4035 0.300 -1.347 0.178 -0.991 0.184\n", + "PAY_AMT4 -0.5948 0.300 -1.982 0.048 -1.183 -0.007\n", + "PAY_AMT5 -0.9541 0.292 -3.267 0.001 -1.527 -0.382\n", + "PAY_AMT6 -0.5320 0.258 -2.065 0.039 -1.037 -0.027\n", + "GRAD 1.1287 0.210 5.370 0.000 0.717 1.541\n", + "UNI 1.1123 0.209 5.325 0.000 0.703 1.522\n", + "HS 1.0960 0.213 5.149 0.000 0.679 1.513\n", + "MARRIED 0.1835 0.162 1.134 0.257 -0.134 0.501\n", + "SINGLE 0.0426 0.162 0.262 0.793 -0.276 0.361\n", + "PAY_0_No_Transactions 0.0631 0.126 0.501 0.616 -0.184 0.310\n", + "PAY_0_Pay_Duly 0.2038 0.123 1.654 0.098 -0.038 0.445\n", + "PAY_0_Revolving_Credit -0.7870 0.138 -5.684 0.000 -1.058 -0.516\n", + "PAY_2_No_Transactions -1.3878 0.235 -5.907 0.000 -1.848 -0.927\n", + "PAY_2_Pay_Duly -1.3056 0.223 -5.868 0.000 -1.742 -0.870\n", + "PAY_2_Revolving_Credit -0.7845 0.227 -3.461 0.001 -1.229 -0.340\n", + "PAY_3_No_Transactions -0.5573 0.270 -2.061 0.039 -1.087 -0.027\n", + "PAY_3_Pay_Duly -0.5669 0.242 -2.339 0.019 -1.042 -0.092\n", + "PAY_3_Revolving_Credit -0.5462 0.231 -2.368 0.018 -0.998 -0.094\n", + "PAY_4_No_Transactions -1.1499 0.351 -3.276 0.001 -1.838 -0.462\n", + "PAY_4_Pay_Duly -1.2105 0.331 -3.655 0.000 -1.860 -0.561\n", + "PAY_4_Revolving_Credit -1.1121 0.321 -3.466 0.001 -1.741 -0.483\n", + "PAY_5_No_Transactions 0.0540 0.383 0.141 0.888 -0.697 0.805\n", + "PAY_5_Pay_Duly -0.0559 0.367 -0.152 0.879 -0.775 0.663\n", + "PAY_5_Revolving_Credit 0.0811 0.357 0.227 0.820 -0.618 0.780\n", + "PAY_6_No_Transactions 0.3831 0.308 1.243 0.214 -0.221 0.987\n", + "PAY_6_Pay_Duly 0.3360 0.301 1.116 0.264 -0.254 0.926\n", + "PAY_6_Revolving_Credit 0.0761 0.292 0.261 0.794 -0.496 0.648\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13477\n", + " 1 0.68 0.36 0.47 4019\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.76 0.66 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442590\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442592\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442594\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442597\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442605\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442612\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442624\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442637\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442684\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442737\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442811\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442889\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442974\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443019\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443123\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443275\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.443429\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17469
Method: MLE Df Model: 26
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1772
Time: 23:04:30 Log-Likelihood: -7758.2
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.9104 0.113 -8.043 0.000 -1.132 -0.689
SEX -0.1269 0.041 -3.125 0.002 -0.206 -0.047
PAY_0 0.6108 0.038 16.108 0.000 0.536 0.685
PAY_2 -0.4983 0.080 -6.204 0.000 -0.656 -0.341
PAY_4 -0.3056 0.071 -4.288 0.000 -0.445 -0.166
PAY_6 0.2928 0.032 9.175 0.000 0.230 0.355
BILL_AMT1 -1.6993 0.530 -3.204 0.001 -2.739 -0.660
BILL_AMT2 2.3783 0.580 4.104 0.000 1.243 3.514
PAY_AMT1 -1.4384 0.295 -4.876 0.000 -2.017 -0.860
PAY_AMT2 -1.8153 0.366 -4.964 0.000 -2.532 -1.099
PAY_AMT4 -1.0161 0.271 -3.749 0.000 -1.547 -0.485
PAY_AMT5 -0.7785 0.260 -2.997 0.003 -1.288 -0.269
GRAD 1.2983 0.184 7.065 0.000 0.938 1.659
UNI 1.2842 0.183 7.026 0.000 0.926 1.642
HS 1.2736 0.187 6.805 0.000 0.907 1.640
MARRIED 0.1585 0.042 3.770 0.000 0.076 0.241
PAY_0_Revolving_Credit -0.9345 0.094 -9.959 0.000 -1.118 -0.751
PAY_2_No_Transactions -1.5378 0.194 -7.920 0.000 -1.918 -1.157
PAY_2_Pay_Duly -1.2152 0.186 -6.551 0.000 -1.579 -0.852
PAY_2_Revolving_Credit -0.6567 0.188 -3.484 0.000 -1.026 -0.287
PAY_3_Pay_Duly -0.2770 0.094 -2.957 0.003 -0.461 -0.093
PAY_3_Revolving_Credit -0.3492 0.078 -4.499 0.000 -0.501 -0.197
PAY_4_No_Transactions -1.2304 0.183 -6.729 0.000 -1.589 -0.872
PAY_4_Pay_Duly -1.2137 0.172 -7.053 0.000 -1.551 -0.876
PAY_4_Revolving_Credit -1.0025 0.157 -6.388 0.000 -1.310 -0.695
PAY_6_No_Transactions 0.3215 0.090 3.574 0.000 0.145 0.498
PAY_6_Pay_Duly 0.2205 0.078 2.812 0.005 0.067 0.374
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17469\n", + "Method: MLE Df Model: 26\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1772\n", + "Time: 23:04:30 Log-Likelihood: -7758.2\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.9104 0.113 -8.043 0.000 -1.132 -0.689\n", + "SEX -0.1269 0.041 -3.125 0.002 -0.206 -0.047\n", + "PAY_0 0.6108 0.038 16.108 0.000 0.536 0.685\n", + "PAY_2 -0.4983 0.080 -6.204 0.000 -0.656 -0.341\n", + "PAY_4 -0.3056 0.071 -4.288 0.000 -0.445 -0.166\n", + "PAY_6 0.2928 0.032 9.175 0.000 0.230 0.355\n", + "BILL_AMT1 -1.6993 0.530 -3.204 0.001 -2.739 -0.660\n", + "BILL_AMT2 2.3783 0.580 4.104 0.000 1.243 3.514\n", + "PAY_AMT1 -1.4384 0.295 -4.876 0.000 -2.017 -0.860\n", + "PAY_AMT2 -1.8153 0.366 -4.964 0.000 -2.532 -1.099\n", + "PAY_AMT4 -1.0161 0.271 -3.749 0.000 -1.547 -0.485\n", + "PAY_AMT5 -0.7785 0.260 -2.997 0.003 -1.288 -0.269\n", + "GRAD 1.2983 0.184 7.065 0.000 0.938 1.659\n", + "UNI 1.2842 0.183 7.026 0.000 0.926 1.642\n", + "HS 1.2736 0.187 6.805 0.000 0.907 1.640\n", + "MARRIED 0.1585 0.042 3.770 0.000 0.076 0.241\n", + "PAY_0_Revolving_Credit -0.9345 0.094 -9.959 0.000 -1.118 -0.751\n", + "PAY_2_No_Transactions -1.5378 0.194 -7.920 0.000 -1.918 -1.157\n", + "PAY_2_Pay_Duly -1.2152 0.186 -6.551 0.000 -1.579 -0.852\n", + "PAY_2_Revolving_Credit -0.6567 0.188 -3.484 0.000 -1.026 -0.287\n", + "PAY_3_Pay_Duly -0.2770 0.094 -2.957 0.003 -0.461 -0.093\n", + "PAY_3_Revolving_Credit -0.3492 0.078 -4.499 0.000 -0.501 -0.197\n", + "PAY_4_No_Transactions -1.2304 0.183 -6.729 0.000 -1.589 -0.872\n", + "PAY_4_Pay_Duly -1.2137 0.172 -7.053 0.000 -1.551 -0.876\n", + "PAY_4_Revolving_Credit -1.0025 0.157 -6.388 0.000 -1.310 -0.695\n", + "PAY_6_No_Transactions 0.3215 0.090 3.574 0.000 0.145 0.498\n", + "PAY_6_Pay_Duly 0.2205 0.078 2.812 0.005 0.067 0.374\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT2', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 6727\n", + " 1 0.67 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.65 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21826195076750313\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7727873645667978" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.78 0.82 6727\n", + " 1 0.46 0.63 0.53 2022\n", + "\n", + " accuracy 0.74 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.74 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 1282\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
052351492
17401282
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5235 1492\n", + "1 740 1282" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 731\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06363364
11291731
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6363 364\n", + "1 1291 731" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2308092391259058\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1619910386617494\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7250888729990617" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM default parameters identified 741\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06365362
11281741
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6365 362\n", + "1 1281 741" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6727\n", + " 1 0.67 0.37 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.1607879330309063\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM reduced features and tuning linear kernal identified 672\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06433294
11350672
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6433 294\n", + "1 1350 672" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.96 0.89 6727\n", + " 1 0.70 0.33 0.45 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.64 0.67 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15824600205442427\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.88 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Neural Network (5x26) identified 761\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06341386
11261761
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6341 386\n", + "1 1261 761" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24385364385235758\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.89 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
3Logistic Regressionprecision recall f1-score ...0.768110
5Neural Network0.376360.771884
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 \\\n", + "0 Decision Tree (GINI) 0.428783 \n", + "1 Random Forest 0.368447 \n", + "2 Gradient Boosted 0.364985 \n", + "3 Logistic Regression precision recall f1-score ... \n", + "5 Neural Network 0.37636 \n", + "\n", + " AUROC \n", + "0 0.616773 \n", + "1 0.761964 \n", + "2 0.773571 \n", + "3 0.768110 \n", + "5 0.771884 " + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'keras'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mloadtxt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# define the keras model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'" + ] + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=26, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb new file mode 100644 index 0000000..ff9f477 --- /dev/null +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -0,0 +1,5588 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", + "df[\"MARRIAGE\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", + "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", + "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " GRAD UNI HS MARRIED SINGLE\n", + "0 0.0 1.0 0.0 1.0 0.0\n", + "1 0.0 1.0 0.0 0.0 1.0\n", + "2 0.0 1.0 0.0 0.0 1.0\n", + "3 0.0 1.0 0.0 1.0 0.0\n", + "4 0.0 1.0 0.0 1.0 0.0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", + " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", + " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", + " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", + " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", + " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", + " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", + " 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 45 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 45 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 45 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split\n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", + "#make the results reproducible (according to instructions)\n", + "np.random.seed(123) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 88.108919 6.061985e-05\n", + "PAY_0 4383.304779 0.000000e+00\n", + "PAY_2 3808.897634 0.000000e+00\n", + "PAY_3 2929.844986 0.000000e+00\n", + "PAY_4 3128.967849 0.000000e+00\n", + "PAY_5 2916.099105 0.000000e+00\n", + "PAY_6 2553.774724 0.000000e+00\n", + "PAY_0_No_Transactions 75.763632 1.499570e-03\n", + "PAY_0_Pay_Duly 70.548603 5.076335e-03\n", + "PAY_0_Revolving_Credit 432.083997 0.000000e+00\n", + "PAY_2_Pay_Duly 91.371021 2.433442e-05\n", + "PAY_2_Revolving_Credit 208.148061 0.000000e+00\n", + "PAY_3_Pay_Duly 96.953056 4.833864e-06\n", + "PAY_3_Revolving_Credit 111.611983 5.228360e-08\n", + "PAY_4_Pay_Duly 88.985156 4.755470e-05\n", + "PAY_4_Revolving_Credit 87.590881 6.991450e-05\n", + "PAY_5_Pay_Duly 79.941208 5.308188e-04\n", + "PAY_5_Revolving_Credit 62.597352 2.699279e-02\n", + "PAY_6_Pay_Duly 66.360306 1.261937e-02\n", + "PAY_6_Revolving_Credit 63.991063 2.050515e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (GINI) identified 847\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
053681359
11175847
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5368 1359\n", + "1 1175 847" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.38 0.42 0.40 2022\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.71 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", + "![image.png](http://www.sciencealert.com/images/15_.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Entropy) identified 823\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054131314
11199823
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5413 1314\n", + "1 1199 823" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6064648683126901" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.80 0.81 6727\n", + " 1 0.39 0.41 0.40 2022\n", + "\n", + " accuracy 0.71 8749\n", + " macro avg 0.60 0.61 0.60 8749\n", + "weighted avg 0.72 0.71 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", + " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13477\n", + " 1 1.00 1.00 1.00 4019\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 782\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06264463
11240782
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6264 463\n", + "1 1240 782" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2805714285714286\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.93 0.88 6727\n", + " 1 0.63 0.39 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.73 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = ([\"Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.97 0.91 13477\n", + " 1 0.81 0.48 0.60 4019\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.84 0.72 0.76 17496\n", + "weighted avg 0.85 0.85 0.84 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 770\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06289438
11252770
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6289 438\n", + "1 1252 770" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2100144956690176\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.93 0.88 6727\n", + " 1 0.64 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", + " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.418892 0.609249\n", + "1 Random Forest 0.386746 0.761906\n", + "2 Gradient Boosted 0.380811 0.775534" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805
Time: 23:33:29 Log-Likelihood: -7727.2
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656
SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038
AGE 0.1478 0.101 1.470 0.141 -0.049 0.345
PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769
PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331
PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169
PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007
PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205
PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782
BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144
BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139
BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594
BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607
BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764
BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090
PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351
PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987
PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089
PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122
PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184
PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060
GRAD 1.2777 0.220 5.807 0.000 0.846 1.709
UNI 1.2382 0.219 5.667 0.000 0.810 1.667
HS 1.1544 0.222 5.196 0.000 0.719 1.590
MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564
SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363
PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197
PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312
PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549
PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004
PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925
PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376
PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109
PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064
PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107
PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445
PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476
PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409
PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388
PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217
PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285
PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505
PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383
PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17452\n", + "Method: MLE Df Model: 43\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805\n", + "Time: 23:33:29 Log-Likelihood: -7727.2\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", + "SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", + "AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", + "PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", + "PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", + "PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", + "PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", + "PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", + "PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", + "BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", + "BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", + "BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", + "BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", + "BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", + "BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", + "PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", + "PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", + "PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", + "PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", + "PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", + "PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", + "GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", + "UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", + "HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", + "MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", + "SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", + "PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", + "PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", + "PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", + "PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", + "PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", + "PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", + "PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", + "PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", + "PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", + "PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", + "PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", + "PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", + "PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", + "PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", + "PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", + "PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", + "PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", + "PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 13477\n", + " 1 0.68 0.36 0.47 4019\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.76 0.66 0.68 17496\n", + "weighted avg 0.80 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441655\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441658\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441662\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441670\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441678\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441690\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441709\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441720\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441740\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441771\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441820\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441865\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441905\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.441966\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442051\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442158\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442265\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442384\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.442495\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17471
Method: MLE Df Model: 24
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789
Time: 23:38:59 Log-Likelihood: -7741.9
converged: True LL-Null: -9429.0
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687
SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052
PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713
PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360
PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135
PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340
BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520
BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657
PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396
PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337
PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524
PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257
GRAD 1.3015 0.188 6.913 0.000 0.933 1.671
UNI 1.2630 0.187 6.755 0.000 0.897 1.629
HS 1.1983 0.191 6.273 0.000 0.824 1.573
MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316
PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653
PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277
PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103
PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548
PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925
PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005
PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870
PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589
PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17471\n", + "Method: MLE Df Model: 24\n", + "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789\n", + "Time: 23:38:59 Log-Likelihood: -7741.9\n", + "converged: True LL-Null: -9429.0\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", + "SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", + "PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", + "PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", + "PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", + "PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", + "BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", + "BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", + "PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", + "PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", + "PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", + "PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", + "GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", + "UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", + "HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", + "MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", + "PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", + "PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", + "PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", + "PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", + "PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", + "PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", + "PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", + "PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", + "PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.64 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2233371077226231\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gUVdvA4d8hgRRSICRAQkuAQOgBAkgVKSIIwosKomIBX0XEirzYURGxfYoK2BUbRUAEAVGqItIl0ksILZSEhPRezvfHbOImhGQJ2Wx289zXxUV2ZnbmmbLzzJwzc47SWiOEEEJcSTVbByCEEKJyk0QhhBCiRJIohBBClEgShRBCiBJJohBCCFEiSRRCCCFKJImiBEqpu5RSv9k6DltTSjVWSqUopZwqcJmBSimtlHKuqGVak1LqgFKqbxm+V6ZjUCnlppT6WSmVqJRafLXfryhKqV+UUveW4Xu9lVJHrBFTZWaL3yKAspf3KJRSJ4F6QC6QAqwBJmmtU2wZlyMybesHtNbrbBhDIHACqK61zrFVHKZYNBCstY6w8nICKad1VkqNBR4FepTH9jMlue+01g2vdV5lXH657AOl1MvA80AmkAMcBCZrrbdec5AOzN7uKIZprT2AUKAj8KyN4ykTW14lO8oV+tWootu7CXC0LEmiChwji0znEV9gI2CVOy6H2o5aa7v4B5wEBph9fgtYZfbZBXgHOA1EAx8DbmbjhwPhQBJwHLjJNNwb+AI4D5wFXgOcTOPuA/40/f0x8E6RmJYDT5n+DgCWAhcxrgofM5vuZWAJ8J1p+Q8Us37ewDem758CXgCqmcWxBfgQSAQOA/2LfLekddgCvAdcMo1rBmwA4oBY4Huglmn6b4E8IB3jzu1/QCCgAWfTNJuA6ab5JgO/Ab5m8dxjWoc44MWi+67IersB/2eaPhH40zQsf5n3mvZpLPC82fe6AluBBNN6zwZqmI3XwCPAMeCEadj7wBnTPtgN9Dab3gl4znRsJJvGNwL+MM0r1bQ9RpumH4pxPCUAfwHtixyrU4G9GFeuzubbwBT7LlMc0cC7puGnTctKMf3rjtkxaJqmDbDWtC+jgeeK2aavAFlAtmk+4zEuCl8wbecYjGPN2zR9/rYeb4rhj2Lm2ReIusI+LOnYdTLt31iM38UkLj+WHjD93Rz43XQcxGKc0CluHxSNx7SvfjTFEAfMvkKsL2PcGeV/bm2at5/ZsJL2bSdgj+kYWQwsAl4z30amfX8B+NaC+U3F+M0mA0cw/a5LOEby91X+9gsAVpiOhwjgv0XW9QfTvkkGDgBhZTr/VvQJv6z/KPxDawjsA943Gz/LtMF8AE/gZ2Cm2UZPBAZi/GAaACGmcT8BnwA1gbrADuAhs5NsfqLog3GSyS+uq41xMg0wzXM38BJQA2gKRAKDzHZYNjDCNK1bMev3DUbi8TQdDEeB8WZx5ABPAtUxfiiJgI+F65CDUQzhjHESbm7aFi6AH8YPcVZx2/oKB+cmjBNqC9P8NgFvmP3wUoBepm3xjmndr5Qo5pi+3wDjpNLDFFf+Mj8zLaMDxkm3lel7nYHrTOsUCBwCnjCbr8Y4ofrkb2/gbqCO6TuTMX7MrqZxUzCOqZaAMi2vjtm8mhc5WcQA3Uwx32vaZi5m2y8c4+TlVnSbYiS4saa/PYDritvOxRyDnhhJcTLgavrczcIT4jiME0lT0zJ/5N8TWf5yv8E4hoo7Pvty5URR0rE7AaN4pyHGb2YdV04UCzCKhaqZ1q9Xkf3ZvLh4TPvgH4yLoZpFv3ul7YJxfL6BkZTy47nivjVNfwp4HON3OBIjIZsnihzgTdP0bqXMryXGOSXAbD80u5pjBCOxzjWtcyhGouxvtq4ZwBDTsmcC28p0/rV1ArA4UGPjpmBkRg2s59+rYIVxtdHMbPru/Hsl+QnwXjHzrIdx8jG/8xgDbCzmR6owrrb6mD7/F9hg+rsbcLrIvJ8FvjLbYZddpZlN62SKo7XZsIeATWZxnMOUpEzDdgBjLVyH01datmmaEcCeItu6tETxgtn4icAa098vAQvMxrlj/JguSxQYJ4R0oEMx4/KX2bDIOt9xhXV4Alhm9lkD/UpZ7/j8ZWNczQ2/wnRFT1IfAdOLTHMEuN5s+40r5vjNTxR/YFz1+xaZptB2LuYYHGO+n0pZt5cpnCjWAxPNPrfESODOZsttWsL8+lJMorDg2N2A6aLF9HlAMcdSfqL4BvjUfJ+XsA8K4sH4rV80326lbJcsjKv7XIy7j76W7FuMi8WzFP4d/knhRJGF6eLDgvk1x0giAzDqpcynKfUYwbgQyQU8zcbPBOaZres6s3GtgXRLjp+i/+ytjmKE1toTY4eEYJQxgnFV7A7sVkolKKUSMCq7/UzjG2FcARfVBOPK4LzZ9z7BuCovRBtbeiHGjxXgTowim/z5BOTPwzSf5zBO4vnOlLBevvx7tZLvFMZVdr6zphjMxwdYuA6Flq2UqquUWqiUOquUSsIoEvPl6lww+zsN46oHU0wFy9Nap2H8GIvji3ElVNy+KXE5SqkWSqmVSqkLpnV4ncvXoeh6T1ZKHTI9CZSAUWSS/50rHSPFaQJMLrK/G2Gse7HLLmI8xt3YYaXUTqXUUAuXezUxFhXA5ceXM5Yfo1dS2rFb6HgoZRn/w7gg22F6SmychTE0Ak5py+tjftBa18JY9/0Yd6f5Stq3AVz+Oyy6Phe11hmWzE8bFfNPYJzQY0y/yfxjyJJjJAC4pLVONhtW9LxR9PfjWpa6E3tLFABorX8H5mEUa4Bx65gOtNFa1zL989ZGhRUYO7NZMbM6g3E15Gv2PS+tdZsrLHoBcJtSqgnGXcRSs/mcMJtHLa21p9Z6iHnYJaxSLMbVXROzYY0xrl7yNVBKqSLjz1m4DkWXPdM0rL3W2gujSEaVMP3VOI9RzAAYj2liFPcUJxbj1ri4fVOajzDqaoJN6/AchdcBzNZDKdUbozx4FFDbdKJINPvOlY6R4pwBZhTZ3+5a6wXFLbsorfUxrfUYjGT+JrBEKVWzpO+UIcaiznH58ZWDUf5dEFoZ5lvasVvoeMA4SRZLa31Ba/1frXUAxl3JXKVUcwtiOAM0vtoToNY61rScl5VS/mbzutK+Pc/lv8Oi61N0G5Z4rGit52ute2FsP41xPJR0jJg7B/gopTzNhhU9b5QLu0wUJrOAgUqpUK11HkZZ9ntKqboASqkGSqlBpmm/AO5XSvVXSlUzjQvRWp/HqIj9P6WUl2lcM6XU9cUtUGu9B+MW93PgV611gmnUDiBJKTXV9Py6k1KqrVKqiyUrorXOxah0mqGU8jQloqcwrvTz1QUeU0pVV0rdDrQCVl/tOph4YhTjJSilGmCUz5uLxijLLoslwDClVA+lVA2M2+eiJ3AATPvtS+BdpVSAabt1V0q5WLAcT4yKvhSlVAjwsAXT52AqolBKvQR4mY3/HJiulApWhvZKqfwEV3R7fAZMUEp1M01bUyl1c5Ef7BUppe5WSvmZ1j//GMo1xZbHlbf9SqC+UuoJpZSL6VjpZskyMS5ynlRKBSmlPDDuwBZdxVV4fuyu5v9M8ZZ07P4APG76zdXCSNZXmvftSqn8pBKPceLMNX0u6ZjcgXESf8O0L1yVUj0tWR+t9WHgV4y7GSh53241xTNJKeWslBqOUf9ZkivOTynVUinVz3S8Z2Bc7OaatsWVjhHz2M9gVI7PNK1ze4w7ke8pZ3abKLTWFzHKNF80DZqKUVm3TRlFEeswymHRWu8A7seo7ErEqADKvwK6B+PW+SDGwbkEyL+6KM4CjDLF+Wax5ALDMCqTTmBcZX2OUbRhqUcx6lkiMco952OcRPNtB4JN854B3Ka1zi/Sudp1eAWjki0RWIVRsWluJvCC6Vb56atYB7TWB0zrshDjx5uMUQ6beYWvPI1RibwT48mNN7HsuHwao/gvGePHuKiU6X8FfsGoaD2F8cM0LzZ4F+Ok9htGAvoCozISjKKBr03bY5TWehdGHdVsjO0dgVGXYKmbgANKqRSMJ7Hu0FpnmIrpZgBbTMu6zvxLpiKGgRjH2gWMJ7pusHCZX2I80fYHxjGagbGfrkYDjJOZ+b9mlHzsfoaxTfdiPC20GiNhFzrpmXQBtpu2ywrgca31CdO4lzHbB+ZfMvv9NceoR4zCeODDUm8DDyql6pa0b7XWWRgV2OMxTt53YyTvKx3blHKsuPBvZfoFjIvB50zjij1GilnEGIx6i3PAMmCa1nrtVay7RezmhbuqTCl1H0aFXy9bx3K1TFevCRhFRCdKm144NqXUYOBjrXWTUie2A0qp7Rjr85WtY7Emu72jEJWXUmqYUsrdVKb6DsYdw0nbRiVswVQUO8RUVNMAmIZx5WuXlFLXK6Xqm9bnXqA9xoMzDk0ShbCG4Ri3wucwisvu0HLrWlUpjKLOeIyip0MYj1Dbq5YY72wkYrzPcpupntChSdGTEEKIEskdhRBCiBLZXaNVvr6+OjAw0NZhCCGEXdm9e3es1tqv9CkvZ3eJIjAwkF27dtk6DCGEsCtKqVOlT1U8KXoSQghRIkkUQgghSiSJQgghRIkkUQghhCiRJAohhBAlkkQhhBCiRFZLFEqpL5VSMUqp/VcYr5RSHyilIpRSe5VSnawVixBCiLKz5h3FPIymcq9kMEY7QMHAgxgd0QghhChnWTl51/R9q71wp7X+QykVWMIkw4FvTI3FbVNK1VJK+VeFBraEEOJaxCRncDoujeTMHGKTM4lLzUJrSEzPJjopg/i0LA6dTyI6KRPFtXVZCbZ9M7sBhTuOiTINuyxRKKUexLjroHHjxhUSnBBC2FJsSibrD0UTfiaRrJw8TsSm8PfphFK/51OzBnU9XfCq4UxyairREfH4utW4plhsmSiK6x6z2MSntf4U+BQgLCxMmrsVQjiMtKwcziVk8Oexixw8n8RvB6NJSMu+bLp2DbwZ1iEArTV1PV3p3KQ27jWcCKjlhoerMz7uNXBxrka1agqtNWFhn3H6SCyvvnoDjz3WjerV7ypzjLZMFFEU7pi8IUb/BUII4ZAS07L5MyKW7SfiWLX3PAnp2eTmFb729alZg5D6nlzXtA43hNQltFEtvN2qWzT/v/46Q7t2dfH0dOHzz4fh6+tOo0ZX0yNz8WyZKFZgdFK+EOgGJEr9hBDCUcSnZrH07yhSM3PZdzaB7ZGXSM7MKRjf1LcmNV2cub6FHy3qe+Lv5UrP5r641XC66mXFxaXxzDPr+PzzPUybdj0vv9yXjh39y21drJYolFILgL6Ar1IqCqMLxOoAWuuPMTpZH4LR2XgacL+1YhFCCGvIy9OcS0znaHQy/5xJ5PCFJCJiUriQmEFqVm7BdM7VFF5u1bm5hT/9W9Wlf0g9vN0tu0soidaab775h6efXkt8fDpTpvRgypQe1zzfoqz51NOYUsZr4BFrLV8IIcpTelYuH22KIC41iz8jYjkVl1bsdD41a9DIx53gep7c3M6fAa3q4uxknTcRpk5dx9tv/0WPHo34+OObadeunlWWY3f9UQghRHlLy8rhRGwqkRdTydOa4xdTibqUhgbOXEoj/EwCOWZ1CQ1qudE1yIcAb1cCfWsS2qgWzet64O/thlO14p7TKT/p6dmkpmbj6+vO+PEdCQ72Yfz4TlSz4nIlUQghqhStNSdiU/knKoHkjBxW7j3PjhOXrjh9y3qehPh7EuTrQb8QP0aENkAp6yaDK1mzJoJHHllNaGh9li4dRcuWvrRs6Wv15UqiEEI4nLSsHPZGJXIpNYtTcWnsP5vIqUuppGXmciEpgzSz+gNPF2cGtq5HtyAfgut50rC2G+41nPD1cKG6lYqMrta5c8k88cQaFi8+SMuWdZg0qUuFLl8ShRDCrp1NSOd4TApHo5NZezCa7cXcHdRwNk74jX3cGdzWn8A67rRr6E2Lep74eVaehFCc9esj+c9/FpGVlcv06TcwZUoPXFwq9tQtiUIIUenl5Wn2nU1kb1QCf59O4HxiOjtPxl/2DkJ1J4VzNUWP5r7c0NKPNgHeBPq6U9fT1UaRl112di7VqzvRoUN9hgwJ5rXX+tG8uY9NYpFEIYSodOJSMvkzIpY1+y8Qk5zJ7lPxhca713CiT7Avtd1r4OfpQligD039atLMz8NGEZefpKRMXnxxA9u3n2XLlnH4+rqzcOFtNo1JEoUQwqZy8zRrD0az50w8J2NTORGbytHoFACcqincqztxXVMfgut6MqJjAG0beOPifPUvpVV2WmuWLDnI44+v4cKFFCZO7EJmZi7u7rYvFpNEIYSoMFprLiZnEhmbyt6oBA6cS2LDoZhCbyzX83Khb0s/RnZqyI2t6+Fa3fGSQlEXL6Zy770/8csvEXTsWJ/ly++gS5cGtg6rgCQKIYRVZGTnsuPEJaKTMtgaGceWiFiikzIvm87XowZPDGzNja3r0cjH3QaR2p6XlwuxsWnMmjWIRx7pirOz7e8izEmiEEKUm7MJ6Xy37RSbj11k/9mky8YH1nGnZ3NfejTzpb63K20CvKrEHUNx/vjjFDNmbGbp0lF4eNRg27YHrPrS3LWQRCGEuCbpWblsOhLD4t1RbDgcUzC8R7M6DGhVj+ua1qFBLbdyadvIEcTGpjFlylrmzQsnMLAWJ08m0LZt3UqbJEAShRDiKmTl5LFkdxTrD0WTnp3LX8fjCo1XCt4d1cGmby9XVlprvvoqnClT1pKUlMmzz/bihRf64G4HCVQShRCiVIfOJ/HKzwfYFln4Zbbewb64VndiWIcArmvqY5fvK1Sk777bS+vWfnz88c20aVPX1uFYTBKFEOIyP+05S/iZBP44epFLaVkFPa55u1Xn0X7NuT2skcWd6VRlaWnZvP76ZiZMCKNhQy+WLh2Ft7drpS5mKo4kCiGqMK01F5IyOJ+YwbHoZA6cS+KbracKxgf51iSkvie9g/24uZ0/gb41bRitfVm9+hiPPLKakycTaNDAk4cf7kLt2m62DqtMJFEIUUUkpmezPTKOwxeSORGbypaIWGKSL39ctU2AF10CfZgyqCU1K7hNIUcQFZXEE0+sYenSQ7Rq5cvvv99Hnz5NbB3WNZGjQAgHlZGdy/YTl9hxIo7wMwlsiShc8ezp4kxwXQ96B/vRqUkt3Ko70bFxbXxq1rBRxI5hxow/WLXqGK+/3o/Jk3tQowxdm1Y2yuhozn6EhYXpXbt22ToMISqtmOQM3lt7jAU7Thca3srfi+GhAdzczp8Gtdzsrpy8Mtux4yxubs60a1ePuLg0EhMzadq0tq3DKkQptVtrHVaW78odhRAO4sC5RBbsOM1324wEUdfThYf7NqNLoA+t/b0kMVhBYmIGzz23no8+2sXQoS1YsWIMdeq4U6eOY71hLolCCDt25lIaM1YdYs2BCwXDugb68Ei/5lzfws+GkTk2rTWLFh3gySd/JSYmlUcf7cr06f1sHZbVSKIQwo6cT0zn53/Ose5QDJEXU4hNySoYF9akNm/e1t4hmtqu7L77bi/33PMTYWEBrFw5hs6dA2wdklVJohCiksrL00TGprBo5xn+OZNI+JkEsnLzCsa39vfiji6N6dbUh17NfeVNaCvLzMwhMjKeVq38GDWqDTk5edxzTwecKnHveOVFEoUQlcyOE5f4cMMxNh+LLTT8hpZ+tKjvSY9mvvRu7it1DhVo48YTPPzwKtLSsjl27FFcXJy5//6Otg6rwkiiEMLG8vI0G4/E8NnmyEJNZFRTMDy0Abd3bkjXIB+cq8CVa2UTE5PK00//xrff7qVp09p8+umwCu+vujKoemssRCWw/2wivx2MZm9UAluPx5GZ82+R0qiwhjzctzlB8ha0TUVEXKJr189IScni+ed78/zzvXGros2WSKIQooIkpmfz/fZTrAg/x+ELyQXDg3xrcnM7f27r3FCayKgEkpIy8fJyoVmz2owf35Fx4zrSqlXVfoJMEoUQVnQiNpV5W06w/1wSu0/FFwwf1iGA//YOom2At9Q1VBKpqVm8+urvfPbZ3+zd+zANG3rx9ts32jqsSkEShRDlLCsnj/nbTzHvr5OcjEsrGD6wdT2GdQhgWHt/eUKpkvn55yNMmvQLp08nMn58R7voI6IiSaIQ4hpprdl5Mp4tEbH8euBCoWKlDo1q8fSNLegdXLWLLiqrnJw8Ro1azLJlh2nTxo/Nm++nV6/Gtg6r0pFEIUQZZOXk8c3Wk/y89zz/nEkoNC7ItyaTbmjOkHb+uDlAg3COSGuNUgpn52r4+3vwxhv9efLJ7g7RgJ81SKIQ4ipk5eTxwfpjfLf9VEFnPs38ajIqrBH9QurSvK6HFCtVctu2RfHII6v57LNhdOrkz5w5N9s6pEpPEoUQFth/NpF31x5lw+GYgmGP9Q/moT5Npc8GOxEfn85zz63nk092ExDgSXx8uq1DshtWPcKVUjcB7wNOwOda6zeKjG8MfA3UMk3zjNZ6tTVjEsIShy8k8cPOKMLPxHMhMYNziRkAuDhX457uTZgyKIQazvICnL1YtGg/jz22htjYNJ544jpeeaUvnp4utg7LblgtUSilnIA5wEAgCtiplFqhtT5oNtkLwA9a64+UUq2B1UCgtWISoiSxKZl8uP4Yvx6I5kJSRsHwkPqe3BLagAGt6hIW6GPDCEVZHT4cS2BgLdasuYuOHf1tHY7dseYdRVcgQmsdCaCUWggMB8wThQa8TH97A+esGI8QV7RsTxRPLvqn4PN1TX144ebWtG3gbcOoRFllZOTw5pt/0qmTP8OGteS553rzwgt9qkQDftZgzUTRADhj9jkK6FZkmpeB35RSjwI1gQHFzUgp9SDwIEDjxvLomrh2aw9Gsy0yjtiUTFbtPU9OntHT45RBLXn4+mbyEpwdW7cukokTV3Hs2CUmT+7OsGEtqV5dnma6FtZMFMX90or2uzoGmKe1/j+lVHfgW6VUW611XqEvaf0p8CkYXaFaJVpRJfx9Op4Hv9lVqB+HrkE++Hm4MPWmEBo7WM9kVUl0dApPPfUb8+fvo3lzH3777W4GDmxm67AcgjUTRRTQyOxzQy4vWhoP3ASgtd6qlHIFfIEYhCgnCWlZLNx5hp//OceBc0kADGlXnxeHtsbf283G0YnysnZtJEuWHOSll/rw7LO9cXWVp9HKizW35E4gWCkVBJwF7gDuLDLNaaA/ME8p1QpwBS5aMSZRRSRnZPPu2qPM3366UMusfVv68eotbeXOwUH8888Fjh27xG23teauu9rRs2cjgoJq2zosh2O1RKG1zlFKTQJ+xXj09Uut9QGl1KvALq31CmAy8JlS6kmMYqn7tNZStCTKbP2haD7adJxdpgb46nu50ryuB3d0bcTgtv44Sd2DQ0hJyWLatI28//52AgNrMWJECM7O1SRJWIlV781M70SsLjLsJbO/DwI9rRmDcHwxSRm89esRImJSCDc1p9ElsDaD2tRnfK8geVPawfz002EeffQXoqKSePDBTsycOQBneafFqqQQT9it8DMJPL9sX0G9A8A93ZswrmeQ9OvgoPbti+Y//1lEu3Z1WbToNnr0aFT6l8Q1k0Qh7Mr5xHTeX3eMv0/HczQ6BYCmfjV589b2dJGX4RxSdnYumzefpl+/INq1q8eqVXcycGBTeeS1AkmiEJVebp7m/XVHWbjzDDHJmYDRn/SAVvV4YkCwvBTnwP766wwTJqzkwIGLHDkyiebNfRgyJNjWYVU5kihEpbZ41xmmrThAWlYuYHT+89/eTekaJHcPjuzSpXSeeWYdn332N40aefHjj6No3lz2ua1IohCVzq6Tl1i48wxLdkcVDHtiQDDjegXh5So9jzm6jIwcQkM/5ty5ZCZP7s7LL/fFw6OGrcOq0iRRiEojJimDGasPsTzceC+ztb8XAbXc+GBMKO415FB1dFFRSTRs6IWrqzPTp99AaGh9OnSob+uwBJIoRCWQmJ7NiDlbOBGbCkAjHzc+HRtGK3+vUr4pHEF6ejYzZ/7Jm29uYcmS2xk2rCX33htq67CEGYsShVKqBtBYax1h5XhEFZKckc03W0/x6R+RJKZn07aBFw/1acbN7fylUb4q4rffjjNx4iqOH4/n7rvb07VrA1uHJIpRaqJQSt0MvAvUAIKUUqHANK31f6wdnHA8WmvWH4rhu+2n2HTEaK0luK4H743uwA0t68rLcVXIo4+uZvbsnQQH+7Bu3Vj6929q65DEFVhyR/EqRvPgGwG01uFKqeZWjUo4nJ0nL/H55kh+PRBdMKxHszrc0iGA0V0aSYKoInJzjXa3nJyqcd11DfH1dWfq1F7SgF8lZ8neydZaJxT5IUt7TKJUWmsW74ri9V8OkZCWDUDD2m4MaefPI32b4+0uTzBVJX//fZ4JE1Yydmx7Hn20G3fd1d7WIQkLWZIoDimlRgHVTC3BPg5ss25Ywt79euACzyzdS7wpQXRqXIt3R4VK0xpVUHJyJi+9tJEPPtiBn587/v6etg5JXCVLEsUk4CUgD/gRozXYZ60ZlLBfWTl5/GfuloL2l0Z2asDLt7SR9x+qqN9+O864ccs5dy6ZCRPCeP31/tSq5WrrsMRVsiRRDNJaTwWm5g9QSo3ESBpCALAtMo5Ve8/zy/7zxKZk4VRNsebx3gTXk6vHqqxGDSfq1q3J0qWj6Natoa3DEWWkSuv+QSn1t9a6U5Fhu7XWna0a2RWEhYXpXbt22WLRoojfj15k0c7TrN53oWBYx8a1GNcziGEdAmwYmbCV7Oxc3n13K0lJmcyY0R+AvDwtjztXAqbzdlhZvnvFOwql1CCMbkobKKXeNRvlhVEMJaqwWeuOMmvdMQAa+7gTFlibx/sH06SO1EFUVX/+ebqgAb/bb29dkCAkSdi/koqeYoD9QAZwwGx4MvCMNYMSlVdenmbOxghmrTtGU9+a/DSpp9Q/VHFxcWlMnbqOL77YQ+PG3vz88xiGDm1h67BEObpiotBa7wH2KKW+11pnVGBMopI6FZfK1KV72RZ5iY6Na/HNuK54SpKo8uLi0lm4cD//+18PXnrpemrWlAb8HI0lldkNlFIzgNZAweMKWmu5ZKgCLiZnsjz8LF9vPcmZS+kADA8N4P07Oto2MGFThw5d5IcfDibiJbQAACAASURBVDBtWl9atKjD6dNP4uPjZuuwhJVYkijmAa8B7wCDgfuROgqHdzI2lUkL/mb/2X+7Gb2jSyMe7NOUpn4eNoxM2FJaWjYzZvzB22//hYdHDcaP70TDhl6SJBycJYnCXWv9q1LqHa31ceAFpdRmawcmbCMmOYN5W04yd9PxgmEf392Zga3r4SSVklXamjURTJy4ihMnErj33g68/fZA/Pzk4YWqwJJEkamM9juOK6UmAGeButYNS9jCd9tO8dqqg2Rk59GugTcTrm/Gze39bR2WqARSUrIYO3YZdeq4sXHjvfTtG2jrkEQFsiRRPAl4AI8BMwBvYJw1gxIV50RsKp/8fpxfD1woaG7j83vC6N9KWnKt6nJz81iwYD9jxrTFw6MG69aNJSTEFxcXacCvqil1j2utt5v+TAbGAiil5BVLO5aYns1nf0Ty9V8nSc7MKRh+X49AHu8fTG15aqXK2737HA89tJLdu8/j5ubMrbe2lt7mqrASE4VSqgvQAPhTax2rlGqD0ZRHP0CShZ05cymNl1cc4PejF8nJ07hWr8bosEZ0CfJhRGgAzk7VbB2isLHExAxefHEjc+bspG7dmixceCsjR7aydVjCxkp6M3smcCvwD0YF9jKMlmPfBCZUTHjiWuXk5vHHsYvMXH2YYzEpAPQPqcuDfZrSNchHipdEIbfe+gMbNpzgkUe68Npr/fD2lgb8RMl3FMOBDlrrdKWUD3DO9PlIxYQmrkV2bh4fbTrO7I0RZOUYTzMPbe/PhOub0baBt42jE5VJZGQ8fn7ueHq6MGNGP6pVU3TpIl2Sin+VlCgytNbpAFrrS0qpw5Ik7MPqfed5YmE4Wbl5+Hu78nj/YAa1qS91D6KQrKxc3nnnL6ZP/4PHHuvKm28OlBZeRbFKShRNlVL5TYkrINDsM1rrkVaNTFy1+NQsJi/+hw2HYwoShHQzKorzxx+nmDBhJYcOxXLbba157LFutg5JVGIlJYpbi3yebc1AxLX563gsd35mPKDW2MedJQ93p66nlC+Ly7333laeeuo3AgNrsWrVnQwZEmzrkEQlV1KjgOsrMhBRdtOW7+frradwca7GrNGhDG4nL8mJwvLyNKmpWXh6unDzzS24eDGNF17og7v0Wy4sIG/O2Kn9ZxNZezCa5eFnORmXhp+nC+snXy9NfovLHDgQw4QJqwp6mmvRog6vv97f1mEJO2LVRKGUugl4H3ACPtdav1HMNKOAlwEN/KO1vtOaMdm7pIxsRs79iwjTo65gPO76+sh2kiREIWlp2Uyf/jvvvLMVb28Xxo0LRWstdVbiqlmcKJRSLlrrzKuY3gmYAwwEooCdSqkVWuuDZtMEA88CPbXW8UopaUOqBBcSMxj64WZiU7JoUsedWaND6dCwlvQgJi6zZ895Ro78gZMnE7j//lDeemsgvr7utg5L2KlSE4VSqivwBUYbT42VUh2AB7TWj5by1a5AhNY60jSfhRjvZhw0m+a/wBytdTyA1jrm6lehatgeGccDX+8iJSuHl4a2ZlyvIFuHJCqh/DuGxo29adzYm6+/HkGfPk1sHZawc5a02fABMBSIA9Ba/wPcYMH3GgBnzD5HmYaZawG0UEptUUptMxVViSI2HYlh9KfbSM7M4ZtxXSVJiMvk5OQxa9Y2+vf/htzcPOrUcef33++TJCHKhSVFT9W01qeKlGvmWvC94spDdDHLDwb6YrQdtVkp1VZrnVBoRko9CDwI0LhxYwsW7RhOx6Xx+upDrDlwAYC5d3Wid7CfjaMSlc2OHWeZMGEle/ZcYPDg5iQlZVK7tnQkJMqPJYnijKn4SZvqHR4FjlrwvSigkdnnhhjNgBSdZpvWOhs4oZQ6gpE4dppPpLX+FPgUICwsrGiycTjRSRnMXH2In8KNzdWynidf3d+FgFry4xf/SknJYurUtXz00S78/T1ZvPh2br21lVRWi3JnSaJ4GKP4qTEQDawzDSvNTiBYKRWE0dnRHUDRJ5p+AsYA85RSvhhFUZGWhe54cvM001bs5/vtp9EaQhvV4r3RoQT5Si9i4nLVq1dj06ZTPPpoV6ZP74eXl4utQxIOypJEkaO1vuNqZ6y1zlFKTQJ+xXg89kut9QGl1KvALq31CtO4G5VSBzGKs6ZoreOudlmOIDkjm3HzdrLzZDw1nKux4L/X0blJbVuHJSqZiIhLvPrq78yZMwRPTxd2734QV1d5HUpYl9K65JIcpdRx4AiwCPhRa51cEYFdSVhYmN61a5ctQyh3n2+OZMbqQ2gNj/UP5skBwVJ8IArJzMzhrbe2MGPGZmrUcGLVqjvp3VsqqoXllFK7tdZhZfmuJT3cNVNK9cAoOnpFKRUOLNRaLyzLAkVhn2+O5LVVhwB4d1QHRnaS1jtFYRs3nuDhh1dx5Egco0e34d13BxEQ4GnrsEQVYtE9q9b6L+AvpdTLwCzge0ASxTU4m5DOk4vC2XHiEm0bePHVfV3x85QyZlGY1poZMzaTnZ3HmjV3MWhQc1uHJKogS16488B4Ue4OoBWwHOhh5bgc2vfbT/HS8gM4VVN0C/Lh83vD8JTmN4RJXp7miy/+5qabmtOokTfffvsfatVyxc1NjhFhG5bcUewHfgbe0lpvtnI8Du/ZH/eyYMcZmvrW5JvxXWlYW5pVEP/auzeaCRNWsnVrFC+91IdXXrkBf38pZhK2ZUmiaKq1zrN6JA4uIzuXB77exZ8RsXi5OrPskZ54yxWiMElJyeKVVzbx3nvbqF3bjXnzhnPPPR1sHZYQQAmJQin1f1rrycBSpdRlj0ZJD3eWOxWXyp2fbedsQjr39wxkyqCWuNeQRxrFv15+eRP/939beeCBjrzxxgDq1JE7TVF5lHS2WmT6X3q2uwZzN0Xw1hqjq/F7uzdh2rA2No5IVBZnziSSmppNSIgvzzzTixEjQujVq+o0USPsxxUbBdRa7zD92Uprvd78H0altijF4QtJBUnis3vCeGV4WxtHJCqDnJw83n13K61azeGhh1YC4OvrLklCVFqWtB47rphh48s7EEfzw84z3DTLqPtf+WgvBrauZ+OIRGWwbVsUYWGfMnnyb/TtG8jXX4+wdUhClKqkOorRGI/EBimlfjQb5QkkFP8tAfDj31H8b+leAN6/I5S2DbxtHJGoDFatOsqwYQsICPDkxx9HMWJEiLyBL+xCSXUUOzD6oGiI0VNdvmRgjzWDsmfrDkbz1A//APDzpF60ayhJoirTWnPuXDINGngxYEBTXn31Bh5/vBue8nKlsCOltvVU2VTmtp6ORScz9MM/qe5UjfWTr6eel6utQxI2dPRoHBMnruLo0TgOHnwED48atg5JVGFWaetJKfW71vp6pVQ8hTscUoDWWvuUZYGOatORGCZ8txutjQ6GJElUXRkZObzxxp/MnPknbm7OzJzZHzc3eRxa2K+Sjt787k59KyIQe3YsOpmJ3/9NnoYfJ/aQOokq7MKFFPr0+Ypjxy4xZkxb3n13EPXre9g6LCGuyRUThdnb2I2Ac1rrLKVUL6A98B2QVAHxVXrLw8/y+MJwAL4d31WSRBWVnZ1L9epO1KtXkz59mjBnzhAGDmxm67CEKBeWPB77E0Y3qM2AbzDeoZhv1ajsQEJaFiPnbilIEtKfddWUl6f5+ONdNGv2AVFRSSil+PzzWyRJCIdiScFpntY6Wyk1Epiltf5AKVWln3rKzdOM/WIH+84m0relH7NGh1LLXSoqq5p//rnAQw+tZPv2s/TrF0R2dq6tQxLCKizqClUpdTswFsh/O6jKtmaXk5vHXZ9vZ9/ZRP53U0sm9pX+AaoarTVTpqxl1qxt+Pi48e23/+Guu9rJOxHCYVmSKMYBEzGaGY9USgUBC6wbVuU1Zcletp+4xIBW9Xj4eileqIqUUsTHpzN+vNGAX+3abrYOSQirsug9CqWUM5B/6Ryhtc6xalQlsOV7FDtOXGLUJ1up6+nC9uf6yxVkFXLqVAKPP76Gl166nk6d/MnL01SrJvtf2I9reY+i1MpspVRvIAL4AvgSOKqU6lmWhdmzfVGJjPpkKwC/PN5bkkQVkZ2dy1tvbaF167msXRvJkSOxAJIkRJViSdHTe8AQrfVBAKVUK+BboEyZyR6lZOYwbPafgPF0Ux0PaX6hKvjrrzM89NBK9u+PYfjwlnzwwWAaN5bHn0XVY0miqJGfJAC01oeUUlXmEZ/0rFzu/nw7AC/c3Ioh7fxtHJGoKOvWRZKYmMFPP41m+PAQW4cjhM2UWkehlJoHZGLcRQDcBbhrre+1bmjFq8g6Cq014+btZOORi4zp2piZI9tVyHKFbWit+fbbvfj5uTN4cDCZmTlkZ+dJG03CIVi1jgKYABwH/gdMBSKBh8qyMHvz1q9H2HjkIndfJ0nC0R0+HEu/ft9w770/8dVXxkuULi7OkiSEoJSiJ6VUO6AZsExr/VbFhFQ5LN51ho82HadhbTdeuUV6pnNU6enZvP76Zt58cws1a9bgk0+G8sADnWwdlhCVyhXvKJRSz2E033EXsFYpVVxPdw7paHQyU5YYHQ/98FB3nOQJF4f1889Hee21zYwe3ZbDhx/hwQc7yxNNQhRR0h3FXUB7rXWqUsoPWI3xeKxDS8nM4d4vje7Cv7wvjIBa8jKVo7lwIYXw8AvcdFNzbr+9NYGBD9C1awNbhyVEpVVSHUWm1joVQGt9sZRpHcZ/v97F+cQMnr6xBf1CpJ9rR5Kbm8fcuTtp2XI2Y8cuIz09G6WUJAkhSlHSHUVTs76yFdDMvO9srfVIq0ZmA/O3n2ZrZBz9Q+oyqV+wrcMR5ejvv88zYcJKdu48x4ABTZk7dwhublW2yTIhrkpJieLWIp9nWzMQW8vIzuWl5ftxrV6NWXeE2jocUY5OnIina9fP8PV1Z/78kdxxR1t5s16Iq1BSx0XrKzIQW1uw4zQ5eZoPx3TE01WuNO2d1pp9+2Jo374eQUG1+eqr4Qwb1pJataSLWiGuVpWodyjNpdQsPv79OE7VFDeE1LV1OOIanTgRz9ChC+jY8RP27o0GYOzYDpIkhCgjqyYKpdRNSqkjSqkIpdQzJUx3m1JKK6Vs0n7UXZ9vJzopk7l3dcK1upMtQhDlICsrlzfe+JM2beby++8neeedgbRuLb0OCnGtLGnrCQCllIvWOvMqpncC5gADgShgp1JqhXm7UabpPIHHgO2Wzrs8/bLvPIfOJ3FzO38GtalvixBEOcjNzaNHjy/Yvfs8I0e2YtasQTRqJA34CVEeLGlmvKtSah9wzPS5g1LqQwvm3RWj74pIrXUWsBAYXsx004G3gAzLwy4fienZPPz93wDMvFWa6LBHSUnGtYuTUzXGjevIzz+PYenSUZIkhChHlhQ9fQAMBeIAtNb/ADdY8L0GwBmzz1GmYQWUUh2BRlrrlSXNSCn1oFJql1Jq18WLFy1YtGUeW2B0/T3jP23xkgpsu6K1Zt68cJo2fZ/lyw8DMHFiF4YObWHjyIRwPJYkimpa61NFhlnSi3xxzx8WNFWrlKqG0dfF5NJmpLX+VGsdprUO8/MrnzLn84np/H70Il0Ca3NXtyblMk9RMQ4evEjfvl9z//3LCQnxpVkzH1uHJIRDs6SO4oxSqiugTfUOjwJHLfheFNDI7HND4JzZZ0+gLbDJ9Ex7fWCFUuoWrbXV2xF/Zuk+AF4c2traixLl6K23tvD88xvw8nLh88+Hcf/9HaVtJiGszJJE8TBG8VNjIBpYZxpWmp1AsFIqCDgL3AHcmT9Sa50I+OZ/VkptAp6uiCSx48Qlfj96keZ1PWjfsJa1FyfKgdYapRT163tw113tePvtgfj51bR1WEJUCaUmCq11DMZJ/qporXOUUpOAXwEn4Eut9QGl1KvALq31iquOtpy8tHw/AEsn9LBVCMJC584l8/jja+jduzGPPdaNe+7pwD33dLB1WEJUKaUmCqXUZ5jVLeTTWj9Y2ne11qsxWp01H/bSFabtW9r8ysPR6GQOX0imW5AP3u5SgV1Z5Tfg9/zzG8jOzqNHj4a2DkmIKsuSoqd1Zn+7Av+h8NNMdmXuxggAnhggT8dUVuHhF3jggRXs3n2eG29sxty5Q6TCWggbsqToaZH5Z6XUt8Baq0VkRfGpWfwUfo5mfjXp3qyOrcMRV5CYmMG5c8ksWnQbt9/eWhrwE8LGLH4z20wQYJfPk05fZbwU/tKwNjaORJjTWrN48UGOHYvj+ef7cP31gURGPo6ra1kOTyFEebPkzex4pdQl078EjLuJ56wfWvnKyc1j3UGjgbjrW0j7P5XF8eOXGDJkPqNHL2H58iNkZxuv6EiSEKLyKPHXqIx7/g4Yj7cC5GmtL6vYtger918gKSOHZweH2DoUAWRm5vDOO3/x2mubqV69Gu+/fxMTJ3bB2VkaNBaisikxUWittVJqmda6c0UFZC3hpxMAuKNrYxtHIgDOnEli+vQ/GDasJbNmDaJBAy9bhySEuAJLLt92KKU6WT0SK1uw4zRdAmvjLd1f2szFi6nMnr0DgObNfTh48BEWL75dkoQQldwV7yiUUs5a6xygF/BfpdRxIBWjDSettbab5BF5MYX07Fx8atawdShVUl6e5quv9vC//60jOTmTgQOb0rKlL02b1rZ1aEIIC5RU9LQD6ASMqKBYrGbx7ihA3p2whf37Y3j44VX8+edpevduzMcfD6VlS9/SvyiEqDRKShQKQGt9vIJisZrFu6KoWcOJVv5SxFGRsrJyufHGb8nKyuXLL2/hvvtC5Z0IIexQSYnCTyn11JVGaq3ftUI85S4jO5fYlEy6BcmbvRVlw4YTXH99E2rUcOKHH24nJMQXX193W4clhCijkiqznQAPjObAi/tnF5b+bRQ7DW3vb+NIHF9UVBK33voD/ft/wzff/ANAr16NJUkIYedKuqM4r7V+tcIisZLfDhgv2d0S2qCUKUVZ5eTkMXv2Dl58cSO5uXnMnNmfu+5qb+uwhBDlpNQ6Cnt38HwSAd6u8lisFY0du4yFC/czeHBz5swZQlCQPM0khCMpKVH0r7AorOTguSQuJmcy9jq7bJqqUktIyMDZuRoeHjV45JEu3HprK269tZVUVgvhgK5YR6G1vlSRgVjDc8v2UcOpGhNvaGbrUByG1pqFC/fTqtUcXnxxA2DUQ9x2m7TyKoSjctiGddKzcgk/k0Boo1r4e7vZOhyHEBFxiUGDvmPMmKU0bOjF3XdLPYQQVYHDNtH56R+RANweJj2jlYf58/cxbtxyXFycmT17MBMmhOHk5LDXGUIIMw6bKP4+HQ/AyE6SKK5FdnYu1as7ERYWwG23teattwYSEGA3T0cLIcqBQyaKC4kZ/H70Ivf1CMSpmpSbl0VMTCqTJ/9GamoWP/44mhYt6vDddyNtHZYQwgYcsuzgww3HABjdpZGNI7E/eXmaTz/dTcuWs1m0aD9t2viRm5tn67CEEDbkkHcUS3ZHEVLfU9p2ukqRkfHcffePbN0aRd++gXz00c2EhEgDfkJUdQ6XKCJiUsjMyaNvy7q2DsXueHu7kJCQwddfj2Ds2PbyuKsQAnDAoqc9pkrsHs3q2DgS+7BixRFGjlxEbm4edeq4s3//RO65p4MkCSFEAYdLFNsiL1HDqZokilKcPp3IiBELGT58IUePxnH+fAoA1aTyXwhRhMMVPe07m0CXoNo4yzP+xcrJyWPWrG1Mm7YJrTVvvjmAJ5+8jurVnWwdmhCiknKoRBGXksnR6BT6t6pn61AqrdzcPD7//G/69Qviww8HExhYy9YhCSEqOYe67F5/KAaAkPryQpi5+Ph0pk5dS3JyJi4uzmzZMo4VK+6QJCGEsIhDJYqdJy9RTcGQdtJJERgN+H3//V5CQubwf/+3lY0bTwJQp467VFYLISzmUEVP209comdzX6pL/QRHj8YxceIq1q8/QdeuDfj117sJDa1v67CEEHbIYRJFYlo2py+lMUoaAQTgiSfWsGvXOebOHcKDD3aWBvyEEGXmMIli50mj+4w2DbxtHIntrF17nJAQXxo18uajj27GxcWZ+vU9bB2WEMLOWfUyUyl1k1LqiFIqQin1TDHjn1JKHVRK7VVKrVdKlbkruhmrDwHQJdDnGiK2TxcupHDnnUu58cbvePPNLQA0aVJLkoQQolxYLVEopZyAOcBgoDUwRinVushke4AwrXV7YAnwVlmWpbXmRGwqLs7V8HBxmJukUuXlaT7+eBchIbNZuvQQ06Zdzzvv3GjrsIQQDsaadxRdgQitdaTWOgtYCAw3n0BrvVFrnWb6uA0oUwVDZGwqAEPbB5Q9Wjs0c+ZmHn54FZ07B7B37wRefrkvrq5VJ1EKISqGNc8qDYAzZp+jgG4lTD8e+KW4EUqpB4EHARo3bnzZ+D2nEwD4T8cGZYvUjiQnZxIbm0ZQUG0mTAgjKKg2Y8a0lcddhRBWY807iuLOXLrYCZW6GwgD3i5uvNb6U611mNY6zM/P77Lxl1IzAWjbwHGbFddas2zZIVq3nsvo0UvQWlOnjjt33tlOkoQQwqqsmSiiAPOegxoC54pOpJQaADwP3KK1zizLgiIvpuLp4oyna/UyBVrZnTqVwC23LGTkyB/w8XHjgw8GS3IQQlQYaxY97QSClVJBwFngDuBO8wmUUh2BT4CbtNYxZV3Q+sMxtKzv6ZDdnm7deoYBA74F4J13BvL449fh7CzvRAghKo7VEoXWOkcpNQn4FXACvtRaH1BKvQrs0lqvwChq8gAWm66QT2utb7ma5eTlaS4mZ9KxkWO1W5SUlImXlwudOvkzblwoU6b0pHHjqvuOiBDCdqz6iIzWejWwusiwl8z+HnCtywiPMiqyHeX9ibi4NJ55Zh2//RbJgQMT8fCowYcfDrF1WEKIKszun6Xce8ZIFANa23fT4lprvv12L5Mn/0Z8fDpPPdUdqYYQQlQGdp8odp0yuj7193a1cSRll5iYwYgRi9i06STduzfk44+H0r69fSc+IYTjsPtEceaS8b6eqx320Ka1RimFl5cLvr7ufPrpUMaP7yTdkQohKhW7fnxGa80/UYnc0sH+3sj+9dcIOnX6lKioJJRSLF58O//9b2dJEkKISseuE8XxiykANPOzn8bvzp9P5o47lnDTTd+TlpZNTEyqrUMSQogS2XXR05/HYgHo36qujSOxzJw5O3juuQ1kZubwyit9mTq1Jy5VqBFDIYR9suuz1N6oRABa2kkf2bt3n6dbtwbMmTOE4OA6tg5HCCEsYteJ4sC5JDxdnStt16dJSZm89NJGxo5tT+fOAcydezMuLk7S/IYQwq7YdaJITM8muG7lq5/QWrN06SEef3wN588n07ixN507B0gT4EIIu2S3Z66snDwuJGVwU9v6tg6lkBMn4pk06RdWrz5GaGh9fvxxFN26ST/eQgj7ZbeJYt9Z443sJnXcbRxJYd9/v48//jjFe+8NYtKkrtKAnxDC7tltooi8aDxW2qESNAa4efMpMjNzGTCgKVOm9OC++0Jp2NBx+8YQQlQtdnu5m//EU7sGtmtRNTY2jXHjltOnzzxeffV3AFxcnCVJCCEcit3eUew5E08zv5o2eeJJa828eeFMmbKWxMRMpk7tyYsv9qnwOETll52dTVRUFBkZGbYORVQRrq6uNGzYkOrVy68jN7tNFPvPJjHYRhXZq1cfY9y4FfTs2YiPPx5K27b28cKfqHhRUVF4enoSGBgoj0ULq9NaExcXR1RUFEFBQeU2X7sseopNMXpMbVKnZoUtMy0tmy1bTgMwZEgwy5ffwR9/3C9JQpQoIyODOnXqSJIQFUIpRZ06dcr9DtYuE0VUfDoATX0rJlH88ssx2rady+DB35OQkIFSiltuaSkN+AmLSJIQFckax5tdJopTccYTT4FWThRnzyZx++2LGTJkPi4uzvz88xhq1bLffi+EEKIs7DJR7DltvEPRzM96iSImJpXWreeycuVRXnvtBv75ZwLXXx9oteUJYS1OTk6EhobStm1bhg0bRkJCQsG4AwcO0K9fP1q0aEFwcDDTp09Ha10w/pdffiEsLIxWrVoREhLC008/bYtVKNGePXt44IEHCg0bPnw43bt3LzTsvvvuY8mSJYWGeXj827LD0aNHGTJkCM2bN6dVq1aMGjWK6Ojoa4rt0qVLDBw4kODgYAYOHEh8fPxl02zcuJHQ0NCCf66urvz0008A9O7du2B4QEAAI0aMAGDlypVMmzbtmmK7Klpru/rXuXNnPfaL7brJ1JXaGqKiEgv+fv/9bToiIs4qyxFVw8GDB20dgq5Zs2bB3/fcc49+7bXXtNZap6Wl6aZNm+pff/1Va611amqqvummm/Ts2bO11lrv27dPN23aVB86dEhrrXV2draeM2dOucaWnZ19zfO47bbbdHh4eMHn+Ph43bBhQx0SEqIjIyMLht9777168eLFhb6bv23S09N18+bN9YoVKwrGbdiwQe/bt++aYpsyZYqeOXOm1lrrmTNn6v/9738lTh8XF6dr166tU1NTLxs3cuRI/fXXX2uttc7Ly9OhoaHFTqd18ccdsEuX8bxrl089nUtIp56XS7nOMzExgxde2MAnn+xm27YH6NTJn8ce61auyxBV2ys/H+DguaRynWfrAC+mDWtj8fTdu3dn7969AMyfP5+ePXty4403AuDu7s7s2bPp27cvjzzyCG+99RbPP/88ISEhADg7OzNx4sTL5pmSksKjjz7Krl27UEoxbdo0br31Vjw8PEhJMfqMWbJkCStXrmTevHncd999+Pj4sGfPHkJDQ1m2bBnh4eHUqmW8PNu8eXO2bNlCtWrVmDBhAqdPGw+RzJo1i549exZadnJyMnv37qVDhw4Fw5YuXcqwYcOoV68eCxcu5Nlnny11u8yfP5/u3bszbNiwgmE33HCDxdv1SpYvX86mTZsAuPfee+nbty9vvvnmFadfsmQJgwcPxt29cIsTycnJN0AkngAAD1BJREFUbNiwga+++gow6iH69u3LypUrGTVq1DXHWRq7TBRn49NpHVA+L7VprVm8+CBPPLGGCxdSmDSpK82a1S6XeQtRmeTm5rJ+/XrGjx8PGMVOnTt3LjRNs2bNSElJISkpif379zN58uRS5zt9+nS8vb3Zt28fQLHFK0UdPXqUdevW4eTkRF5eHsuWLeP+++9n+/btBAYGUq9ePe68806efPJJevXqxenTpxk0aBCHDh0qNJ9du3bRtm3bQsMWLFjAtGnTqFevHrfddptFiWL//v2XbYviJCcn07t372LHzZ8/n9atWxcaFh0djb+/PwD+/v7ExMSUOP+FCxfy1FNPXTZ82bJl9O/fHy+vf897YWFhbN68WRLFlaRn59Kotts1z0drzciRP/DTT4fp1MmfFSvGEBZmf92qCvtwNVf+5Sk9PZ3Q0FBOnjxJ586dGThwIPBvn+3FuZonZ9atW8fChQsLPteuXfqF1u23346Tk9HP/ejRo3n11Ve5//77WbhwIaNHjy6Y78GDBwu+k5SURHJyMp6e//Y/c/78efz8/Ao+R0dHExERQa9evVBK4ezszP79+2nbtm2x63S1Twh5enoSHh5+Vd+x1Pnz59m3bx+DBg26bNyCBQsuq4epW7cu586ds0osRdldZXZaVg4A3ZqWveOf7OxcwDhIevVqxAcf3MSOHQ9IkhAOyc3NjfDwcE6dOkVWVhZz5swBoE2bNuzatavQtJGRkXh4eODp6UmbNm3YvXt3qfO/UsIxH1b0uf6aNf99EKV79+5ERERw8eJFfvrpJ0aOHAlAXl4eW7duJTw8nPDwcM6ePVsoSeSvm/m8Fy1aRHx8PEFBQQQGBnLy5MmCJFanTp1CdzuXLl36//buP7iq+szj+PtTCiRYSFcoTBUUOiImxoAIbWoHEWkZK1lYOo4YflRaW0cQqUVx2MGddVdraStoI1pkXSfp2gbEqYUBhUU2lor8EFYFBZtSmgnsMMiyLEup/H72j3OSXJKbm5OYe2/uzfOauTP3nnvuOU+euTnfe77fc54vffr0qc9FlL/1xIkTFw08xz5iG7U6/fr149ChQ0DQEPTt2/x9Vy+//DKTJk1qckf10aNH2b59O+PHj79o+alTp8jN/fQ/mKPIuIbi3IXgioz8L7at6+nNN2soKlrKqlUfAfDggzdy//1foUsHnfzIufaSl5dHWVkZTz75JGfPnmXq1Km89dZbvPHGG0Bw5jFnzhwefvhhAObNm8cTTzxBdXU1EBy4Fy9e3GS748aNY8mSJfWv6w7G/fr1Y+/evfVdS82RxKRJk5g7dy75+fn07t077nbj/ZLPz89n37599a8rKytZt24dNTU11NTUsHPnzvqG4uabb2bFihWcOXMGgPLy8vpxiClTpvD222+zdu3a+m2tW7euvjutTt0ZRbxH424ngAkTJlBRUQFARUUFEydObDYPlZWVlJaWNlm+cuVKSkpKyMm5+NL86urqJt1uyZJxR8fTZy8A0Ldn6wazjxw5yV13/ZYxYyo4ffocPVv5eeeywfXXX8/QoUNZvnw5ubm5rFq1iscff5whQ4Zw3XXXMXLkSGbPng1AUVERTz/9NKWlpeTn51NYWFj/6zjWI488wrFjxygsLGTo0KFUVVUBsHDhQkpKSrjlllvq++mbM3nyZF566aX6bieAsrIyduzYQVFREQUFBSxdurTJ56655hqOHz/OiRMnqKmpoba2luLi4vr3Bw0aRK9evdi2bRslJSWMGjWKG264gWHDhrF58+b6geXc3FzWrFnDM888w+DBgykoKKC8vDzhGUAU8+fPZ8OGDQwePJgNGzYwf/58IBhbie1Kqqmp4cCBA4wePbrJNpYvXx63AamqqmpylpEssphrpjNB74H5NuC7P+fdf/hG5P7Fysrd3Hffa/zlL2eYN+9GFiy4iR492q9glnPN2bt3L/n5+ekOI6s99dRT9OzZs0kffjY7fPgwU6ZMYePGjXHfj/e9k7TTzEa0ZX8Zd0Zx/oLRrctnWjUIde7cBQoL+/Lee/fyox+N9UbCuSwyc+ZMunfvXD0EtbW1LFq0KGX7y7irns6dtxZntTt58gyPPbaJK67IY9askUybVsS0aUVec8e5LJSTk8P06dPTHUZKjRw5MqX7y7gzigsYl32++ZH+NWuqufba5/jJTzZTXX0UCAbLvJFw6ZJp3bsusyXj+5ZxZxTnLxhX9+vZZPnBg//HnDmv8+qrH1FQ8AU2bZrBqFFXpiFC5xrk5ORw9OhRLzXuUsLC+SgaXyH1aWVcQwFweZwziv37j7F+/Z/48Y/HMnfuV+nWrUsaInPuYv379+fgwYMcOXIk3aG4TqJuhrv2lJENxSXdg7C3b/8vtmw5wA9+UMxNN11Jbe0D9G5h/MK5VOratWu7zjTmXDokdYxC0q2S/iBpn6T5cd7vLmlF+P42SQOjbPfSbl2YNWstxcUvsHjxVk6eDG6g8UbCOefaX9IaCkldgGeBbwIFQKmkxrcu3g0cM7OrgKeA5ssqxhg/qpznn9/JnDlfYffumVxySbf2DN0551yMZHY9fRnYZ2b7ASQtByYCsQVRJgKPhs9fAZZIkiUYtjczBgzI47W1Uxk+PPHdns455z69ZDYUlwMHYl4fBBpP8FC/jpmdk3Qc6A38d+xKku4B7glfnt5x+J4PIlQE7gz60ChXnZjnooHnooHnosGQtn4wmQ1FvGsBG58pRFkHM1sGLAOQtKOtt6FnG89FA89FA89FA89FA0k7Wl4rvmQOZh8EBsS87g80Lp5ev46kzwJ5wP8kMSbnnHOtlMyG4h1gsKRBkroBdwKrG62zGrgrfH478B+Jxiecc86lXtK6nsIxh9nAeqAL8KKZfSjpnwkm+V4N/Cvwb5L2EZxJ3Blh08uSFXMG8lw08Fw08Fw08Fw0aHMuMq7MuHPOudTKuKKAzjnnUssbCueccwl12IYiWeU/MlGEXMyVtEfSLkkbJWVt2dyWchGz3u2STFLWXhoZJReS7gi/Gx9K+nWqY0yVCP8jV0iqkvRu+H9yWzriTDZJL0r6WNIHzbwvSWVhnnZJGh5pw2bW4R4Eg99/Ar4EdAPeBwoarTMLWBo+vxNYke6405iLMUCP8PnMzpyLcL2ewCZgKzAi3XGn8XsxGHgX+Jvwdd90x53GXCwDZobPC4CadMedpFzcBAwHPmjm/duA1wnuYSsGtkXZbkc9o6gv/2FmZ4C68h+xJgIV4fNXgLHKzoL/LebCzKrM7K/hy60E96xkoyjfC4DHgJ8Cp1IZXIpFycX3gWfN7BiAmX2c4hhTJUouDOgVPs+j6T1dWcHMNpH4XrSJwC8tsBX4vKQWayF11IYiXvmPy5tbx8zOAXXlP7JNlFzEupvgF0M2ajEXkq4HBpjZmlQGlgZRvhdXA1dL2ixpq6RbUxZdakXJxaPANEkHgdeA+1MTWofT2uMJ0HHno2i38h9ZIPLfKWkaMAIYndSI0idhLiR9hqAK8YxUBZRGUb4XnyXofrqZ4Czz95IKzex/kxxbqkXJRSlQbmaLJH2V4P6tQjO7kPzwOpQ2HTc76hmFl/9oECUXSPo6sACYYGanUxRbqrWUi55AIfCmpBqCPtjVWTqgHfV/ZJWZnTWzPwN/IGg4sk2UXNwNvAxgZluAHIKCgZ1NpONJYx21ofDyHw1azEXY3fI8QSORrf3Q0EIuzOy4mfUxs4FmNpBgvGaCmbW5GFoHFuV/5LcEFzogqQ9BV9T+lEaZGlFyUQuMBZCUT9BQdMb5aVcD3w6vfioGjpvZoZY+1CG7nix55T8yTsRc/Az4HLAyHM+vNbMJaQs6SSLmolOImIv1wDhJe4DzwDwzO5q+qJMjYi4eBP5F0g8JulpmZOMPS0mVBF2NfcLxmH8EugKY2VKC8ZnbgH3AX4HvRNpuFubKOedcO+qoXU/OOec6CG8onHPOJeQNhXPOuYS8oXDOOZeQNxTOOecS8obCdTiSzkt6L+YxMMG6A5urlNnKfb4ZVh99Pyx5MaQN27hX0rfD5zMkXRbz3guSCto5znckDYvwmQck9fi0+3adlzcUriP6xMyGxTxqUrTfqWY2lKDY5M9a+2EzW2pmvwxfzgAui3nve2a2p12ibIjzOaLF+QDgDYVrM28oXEYIzxx+L+k/w8eNcda5VtL28Cxkl6TB4fJpMcufl9Slhd1tAq4KPzs2nMNgd1jrv3u4fKEa5gB5Mlz2qKSHJN1OUHPrV+E+c8MzgRGSZkr6aUzMMyQ908Y4txBT0E3SLyTtUDD3xD+Fy+YQNFhVkqrCZeMkbQnzuFLS51rYj+vkvKFwHVFuTLfTq+Gyj4FvmNlwYDJQFudz9wI/N7NhBAfqg2G5hsnA18Ll54GpLez/b4HdknKAcmCymV1HUMlgpqRLgUnAtWZWBDwe+2EzewXYQfDLf5iZfRLz9ivAt2JeTwZWtDHOWwnKdNRZYGYjgCJgtKQiMysjqOUzxszGhKU8HgG+HuZyBzC3hf24Tq5DlvBwnd4n4cEyVldgSdgnf56gblFjW4AFkvoDvzGzP0oaC9wAvBOWN8klaHTi+ZWkT4AagjLUQ4A/m1l1+H4FcB+whGCuixckrQUilzQ3syOS9od1dv4Y7mNzuN3WxHkJQbmK2BnK7pB0D8H/9RcJJujZ1eizxeHyzeF+uhHkzblmeUPhMsUPgcPAUIIz4SaTEpnZryVtA8YD6yV9j6CscoWZ/X2EfUyNLSAoKe78JmFtoS8TFJm7E5gN3NKKv2UFcAfwEfCqmZmCo3bkOAlmcVsIPAt8S9Ig4CFgpJkdk1ROUPiuMQEbzKy0FfG6Ts67nlymyAMOhfMHTCf4NX0RSV8C9ofdLasJumA2ArdL6huuc6mizyn+ETBQ0lXh6+nA78I+/Twze41goDjelUcnCMqex/Mb4O8I5khYES5rVZxmdpagC6k47LbqBZwEjkvqB3yzmVi2Al+r+5sk9ZAU7+zMuXreULhM8Rxwl6StBN1OJ+OsMxn4QNJ7wDUEUz7uITig/rukXcAGgm6ZFpnZKYLqmisl7QYuAEsJDrprwu39juBsp7FyYGndYHaj7R4D9gBXmtn2cFmr4wzHPhYBD5nZ+wTzY38IvEjQnVVnGfC6pCozO0JwRVZluJ+tBLlyrllePdY551xCfkbhnHMuIW8onHPOJeQNhXPOuYS8oXDOOZeQNxTOOecS8obCOedcQt5QOOecS+j/AcKgZNFLbXZqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.771318789360392" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we will use that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.79 0.83 6727\n", + " 1 0.47 0.62 0.54 2022\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 1252\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053411386
17701252
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5341 1386\n", + "1 770 1252" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Logistic Regression identified 738\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06319408
11284738
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6319 408\n", + "1 1284 738" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defualts." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.22122047856901356\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3xN9//A8ddHQiQSQRIjiQiCxB5BzapRo5SiWm1V0a8qOlV166C614/uoUvNahW1SmuUEqP2jBVbZO/kfn5/nJv0JjIucnNzb97Px8ND7jnnnvM+4573OZ/POZ+P0lojhBBCFKScvQMQQghRukmiEEIIUShJFEIIIQoliUIIIUShJFEIIYQolCQKIYQQhZJEUQil1L1KqVX2jsPelFJBSqlEpZRLCS4zWCmllVKuJbVMW1JK7VNKdbuO713XMaiUcldK/aaUilNKLbjW75cUpdTvSqmR1/G9LkqpQ7aIqTSzx28RQDnKexRKqRNADSALSARWABO11on2jMsZmbf1g1rrNXaMIRg4DpTXWmfaKw5zLBpooLU+auPlBFNM66yUGgE8AnQsju1nTnI/aK0Db3Re17n8YtkHSqmXgeeBNCAT2A9M0lpvvuEgnZij3VEM0Fp7Ai2BVsCzdo7nutjzKtlZrtCvRRnd3nWAw9eTJMrAMTLPfB7xBdYBNrnjcqrtqLV2iH/ACaCnxee3gGUWn92Ad4BTwAXgU8DdYvxAYBcQDxwD+piHewNfAeeAM8A0wMU87gFgo/nvT4F38sT0K/Ck+W9/YBFwCeOq8FGL6V4GFgI/mJf/YD7r5w18Z/7+SeAFoJxFHJuA/wPigINAjzzfLWwdNgHvA1fM4+oDa4Fo4DLwI1DFPP33gAlIwbhzexoIBjTgap7mT+A183wTgFWAr0U895vXIRp4Me++y7Pe7sC75unjgI3mYdnLHGnep5eB5y2+1w7YDMSa13smUMFivAYmAEeA4+ZhHwKnzftgO9DFYnoX4DnzsZFgHl8bWG+eV5J5e9xlnr4/xvEUC/wNNM9zrE4BdmNcubpabgNz7BHmOC4A75mHnzIvK9H8rwMWx6B5mibAavO+vAA8l882fQVIBzLM8xmDcVH4gnk7X8Q41rzN02dv6zHmGNbnM89uQFQB+7CwY9fFvH8vY/wuJnL1sfSg+e8Q4C/zcXAZ44ROfvsgbzzmffWzOYZoYGYBsb6McWeU/bmxed5+FsMK27etgZ3mY2QBMA+YZrmNzPv+PPC9FfObgvGbTQAOYf5dF3KMZO+r7O3nDywxHw9Hgf/lWdf55n2TAOwDwq/r/FvSJ/zr/UfuH1ogsAf40GL8B+YNVg3wAn4DZlhs9DigF8YPJgAINY/7BfgMqARUB7YCD1mcZLMTRVeMk0x2cV1VjJOpv3me24GXgApAPSAS6G2xwzKAQeZp3fNZv+8wEo+X+WA4DIyxiCMTeAIoj/FDiQOqWbkOmRjFEK4YJ+EQ87ZwA/wwfogf5LetCzg4/8Q4oTY0z+9P4A2LH14i0Nm8Ld4xr3tBiWKW+fsBGCeVjua4spf5hXkZLTBOumHm77UBbjKvUzBwAHjcYr4a44RaLXt7A/cBPubvTML4MVc0j5uMcUw1ApR5eT4W8wrJc7K4CLQ3xzzSvM3cLLbfLoyTl3vebYqR4EaY//YEbspvO+dzDHphJMVJQEXz5/ZWnhBHY5xI6pmX+TP/nciyl/sdxjGU3/HZjYITRWHH7jiM4p1AjN/MGgpOFD9hFAuVM69f5zz7MyS/eMz74F+Mi6FKeb9b0HbBOD7fwEhK2fEUuG/N058EHsP4HQ7GSMiWiSITeNM8vXsR82uEcU7xt9gP9a/lGMFIrB+b17klRqLsYbGuqUA/87JnAFuu6/xr7wRgdaDGxk3EyIwa+IP/roIVxtVGfYvpO/DfleRnwPv5zLMGxsnH8s5jOLAunx+pwrja6mr+/D9grfnv9sCpPPN+FvjGYodddZVmMa2LOY7GFsMeAv60iOMs5iRlHrYVGGHlOpwqaNnmaQYBO/Ns66ISxQsW48cDK8x/vwT8ZDHOA+PHdFWiwDghpAAt8hmXvczAPOt8dwHr8Diw2OKzBroXsd4x2cvGuJobWMB0eU9SnwCv5ZnmEHCzxfYbnc/xm50o1mNc9fvmmSbXds7nGBxuuZ+KWLeXyZ0o/gDGW3xuhJHAXS2WW6+Q+XUjn0RhxbG7FvNFi/lzz3yOpexE8R3wueU+L2Qf5MSD8Vu/ZLnditgu6RhX91kYdx/drNm3GBeLZ8j9O9xI7kSRjvniw4r5hWAkkZ4Y9VKW0xR5jGBciGQBXhbjZwCzLdZ1jcW4xkCKNcdP3n+OVkcxSGvthbFDQjHKGMG4KvYAtiulYpVSsRiV3X7m8bUxroDzqoNxZXDO4nufYVyV56KNLT0X48cKcA9GkU32fPyz52Gez3MYJ/FspwtZL1/+u1rJdhLjKjvbGXMMluP9rVyHXMtWSlVXSs1VSp1RSsVjFIn5cm3OW/ydjHHVgzmmnOVprZMxfoz58cW4Espv3xS6HKVUQ6XUUqXUefM6vM7V65B3vScppQ6YnwSKxSgyyf5OQcdIfuoAk/Ls79oY657vsvMYg3E3dlAptU0p1d/K5V5LjHn5c/Xx5Yr1x2hBijp2cx0PRSzjaYwLsq3mp8RGWxlDbeCktr4+Zr7WugrGuu/FuDvNVti+9efq32He9bmktU61Zn7aqJh/HOOEftH8m8w+hqw5RvyBK1rrBIthec8beX8/Fa+n7sTREgUAWuu/gNkYxRpg3DqmAE201lXM/7y1UWEFxs6sn8+sTmNcDflafK+y1rpJAYv+CRiqlKqDcRexyGI+xy3mUUVr7aW17mcZdiGrdBnj6q6OxbAgjKuXbAFKKZVn/Fkr1yHvsmeYhzXXWlfGKJJRhUx/Lc5hFDMAxmOaGMU9+bmMcWuc374pyicYdTUNzOvwHLnXASzWQynVBaM8eBhQ1XyiiLP4TkHHSH5OA9Pz7G8PrfVP+S07L631Ea31cIxk/iawUClVqbDvXEeMeZ3l6uMrE6P8Oye065hvUcduruMB4ySZL631ea31/7TW/hh3JR8rpUKsiOE0EHStJ0Ct9WXzcl5WStWymFdB+/YcV/8O865P3m1Y6LGitZ6jte6Msf00xvFQ2DFi6SxQTSnlZTEs73mjWDhkojD7AOillGqptTZhlGW/r5SqDqCUClBK9TZP+xUwSinVQylVzjwuVGt9DqMi9l2lVGXzuPpKqZvzW6DWeifGLe6XwEqtdax51FYgXik1xfz8uotSqqlSqq01K6K1zsKodJqulPIyJ6InMa70s1UHHlVKlVdK3QmEAcuvdR3MvDCK8WKVUgEY5fOWLmCUZV+PhcAApVRHpVQFjNvnvCdwAMz77WvgPaWUv3m7dVBKuVmxHC+Mir5EpVQo8LAV02diLqJQSr0EVLYY/yXwmlKqgTI0V0plJ7i82+MLYJxSqr152kpKqdvy/GALpJS6TynlZ17/7GMoyxybiYK3/VKgplLqcaWUm/lYaW/NMjEucp5QStVVSnli3IHNu4ar8OzYK1r+M8db2LE7H3jM/JurgpGsC5r3nUqp7KQSg3HizDJ/LuyY3IpxEn/DvC8qKqU6WbM+WuuDwEqMuxkofN9uNsczUSnlqpQaiFH/WZgC56eUaqSU6m4+3lMxLnazzNuioGPEMvbTGJXjM8zr3BzjTuRHipnDJgqt9SWMMs0XzYOmYFTWbVFGUcQajHJYtNZbgVEYlV1xGBVA2VdA92PcOu/HODgXAtlXF/n5CaNMcY5FLFnAAIzKpOMYV1lfYhRtWOsRjHqWSIxyzzkYJ9Fs/wANzPOeDgzVWmcX6VzrOryCUckWByzDqNi0NAN4wXyr/NQ1rANa633mdZmL8eNNwCiHTSvgK09hVCJvw3hy402sOy6fwij+S8D4Mc4rYvqVwO8YFa0nMX6YlsUG72Gc1FZhJKCvMCojwSga+Na8PYZprSMw6qhmYmzvoxh1CdbqA+xTSiViPIl1t9Y61VxMNx3YZF7WTZZfMhcx9MI41s5jPNF1i5XL/Brjibb1GMdoKsZ+uhYBGCczy3/1KfzY/QJjm+7GeFpoOUbCznXSM2sL/GPeLkuAx7TWx83jXsZiH1h+yeL3F4JRjxiF8cCHtd4Gxiqlqhe2b7XW6RgV2GMwTt73YSTvgo5tijhW3PivMv08xsXgc+Zx+R4j+SxiOEa9xVlgMTBVa736GtbdKg7zwl1ZppR6AKPCr7O9Y7lW5qvXWIwiouNFTS+cm1KqL/Cp1rpOkRM7AKXUPxjr8429Y7Elh72jEKWXUmqAUsrDXKb6DsYdwwn7RiXswVwU289cVBMATMW48nVISqmblVI1zeszEmiO8eCMU5NEIWxhIMat8FmM4rK7tdy6llUKo6gzBqPo6QDGI9SOqhHGOxtxGO+zDDXXEzo1KXoSQghRKLmjEEIIUSiHa7TK19dXBwcH2zsMIYRwKNu3b7+stfYresqrOVyiCA4OJiIiwt5hCCGEQ1FKnSx6qvxJ0ZMQQohCSaIQQghRKEkUQgghCiWJQgghRKEkUQghhCiUJAohhBCFslmiUEp9rZS6qJTaW8B4pZT6SCl1VCm1WynV2laxCCGEuH62vKOYjdFUbkH6YrQD1AAYi9ERjRBCiGKWkWW6oe/b7IU7rfV6pVRwIZMMBL4zNxa3RSlVRSlVqyw0sCWEEMUpNSOLM7EpHL2YyB8HLpCSYSI1I4vV+y+guLEuK8G+b2YHkLvjmCjzsKsShVJqLMZdB0FBQSUSnBBClEZxKRlEnLhCUnoWH687iklrDl9IvGq6+j4euCdncuVCElUybmyZ9kwU+XWPmW/i01p/DnwOEB4eLs3dCiGcitaa+BSjV9qD5+NJSs8kI0uTmaXJNJn46/AlMrI0v/17Nt/vD20TiJ+XG62DqhLs40F9v0q0bfslpw9d5tVXb+HRR9tTvvy91x2fPRNFFLk7Jg/E6L9ACCGcSmaWiT1n4th24gpbIq/gUk6Rkp7FthNXqOxenksJBfammks930rU8fGge1gN2gZXpZpHBapXrpgz/u+/T1OzdhXKlSvHl18OwNfXg9q1r6VH5vzZM1EsweikfC7QHoiT+gkhhKNJzcgiITWTyEuJpGaa2BMVS2JaFkcvJlLeRXEuLpVdp2Nzfce9vAuNanrRxL8yHhVc6d+8FinpWTSs4UVapolWQVXwdHOlvEs5XF0Ubq7lCKjijlL5FcRAdHQyzzyzhi+/3MnUqTfz8svdaNWqVrGto80ShVLqJ6Ab4KuUisLoArE8gNb6U4xO1vthdDaeDIyyVSxCCFGczselsut0DD/+c4oNRy4XOF11LzeqV3ajnm8lWtauwr03BdE0wBs3V5diiUNrzXff/ctTT60mJiaFyZM7Mnlyx2KZtyVbPvU0vIjxGphgq+ULIURxyDJplu85x5GLiWw9Hs25uFRORifnjO9Qz4e+zWoC5Nwh1POrVGzJoDBTpqzh7bf/pmPH2nz66W00a1bDJstxuP4ohBDCVrTWRF5OYk9UHJuOXmbZnnMkp2fljPeo4EI5pehY34c7WgVwUz0falfzKNEYU1IySErKwNfXgzFjWtGgQTXGjGlNuXL5F0sVB0kUQogyKcukiU5KY9vxGLYej+bfqLir6hIqVXChZ1gNOtT3YUCLWlT3qljA3ErGihVHmTBhOS1b1mTRomE0auRLo0a+Nl+uJAohhFMymTSXE9PYHBlNTFI6KRkmtp+MwbWcYuuJK1xJSr/qO9W93OjTtCbdQ6vTsIYX/lXc7RD51c6eTeDxx1ewYMF+GjXyYeLEtiW6fEkUQginoLXmUmIap6KT+Wx9JKv3Xyhw2rBalQmr5UUTf2861Pehqb83vp4VCnyqyJ7++COSO+6YR3p6Fq+9dguTJ3fEza1kT92SKIQQDiUuJYPjl5P4+9hlNh65zL+nY6lY3oXofO4QHuxcl4Y1vehQzwc/LzfcXMuVymSQn4yMLMqXd6FFi5r069eAadO6ExJSzS6xSKIQQpRKmVkm9p6N59jFRM7Hp7L1+BX+Onzpqumqe7nRqKYXDWt4kWXStAqqgn8Vd8LrVHWYpGApPj6NF19cyz//nGHTptH4+nowd+5Qu8YkiUIIUWokp2fy7qrD/LT1VK6njbL5erpxc0M/OtT3wd+7Iu3qVsPVxTm61dFas3Dhfh57bAXnzycyfnxb0tKy8PCw//pJohBClLgrSelsOHKJPVFxxKdmkJ5p4sC5BA5dSMiZpmeYUaHcKqgqzQO98XYvT8Xytn83wR4uXUpi5Mhf+P33o7RqVZNff72btm0D7B1WDkkUQogSsfdMHG+vPESWSbPxaO63mYOqeVCxfDmqVarA8Ha1eaJnQ6e5U7BG5cpuXL6czAcf9GbChHa4upaudZdEIYQodusOXuTnnWc4ciGBmOR0LsTnbvSuSwNf2tSpypDWgQRUcbfpy2Kl1fr1J5k+fQOLFg3D07MCW7Y8WGq3gyQKIUSxSMvMYsPhy7yx4iBHLxr9I3i6udKnaU083VxJTMtkcOsAOtTzcchK5uJy+XIykyevZvbsXQQHV+HEiViaNq1eapMESKIQQlwnk0nzz/Er7Dsbx7RlB3KNa1e3Gu/f1ZKAUvLCWmmgteabb3YxefJq4uPTePbZzrzwQlc8PMrbO7QiSaIQQlhlT1Qc+8/FkZKexe97z/PP8StXTfPqwCa0DqpK04Ab7wPBGf3ww24aN/bj009vo0mT6vYOx2qSKIQQ+TKZNBuOXmbzsWi+33yCJIvHVZWCen6VGNgigOHta+NTyQ2XUlx0Yi/JyRm8/voGxo0LJzCwMosWDcPbu2KpLmbKjyQKIQQmk9Fq6ubIaH7YfJKTV5JIzTDljK/lXZEeYTUY3i6I0JpeuFdwcdpHVYvL8uVHmDBhOSdOxBIQ4MXDD7elalXHLIqTRCFEGZSakcWZ2BT2RMWxaEfUVZ3vNKzhSd+mtXAtpxjcJlDqGq5BVFQ8jz++gkWLDhAW5stffz1A16517B3WDZFEIUQZEBWTzIzlB9l/Lp7oxDTiUzOvmub+DnXoFGI8turr6WaHKJ3D9OnrWbbsCK+/3p1JkzpSoYLj33kpo6M5xxEeHq4jIiLsHYYQpZbWmp2nY9lxMoYdp2LYEpm7Se2mAZW5qa4PVTzKE1Ldi/BgSQw3auvWM7i7u9KsWQ2io5OJi0ujXr2q9g4rF6XUdq11+PV8V+4ohHAC6ZkmtkRGM/vvE+w4FUNsckbOuPIuinZ1qzG2Sz16NrZNV5llVVxcKs899weffBJB//4NWbJkOD4+Hvj4lGyvd7YmiUIIB6S15vjlJPafi+e7zSfZavGoagXXcnQPrc7D3eoTVqsyniXcd0FZoLVm3rx9PPHESi5eTOKRR9rx2mvd7R2WzcgRJIQDSc808X9rjzD77xMkWNQzuJZTPNytPt0a+dGmjn36LChLfvhhN/ff/wvh4f4sXTqcNm387R2STUmiEKIUOnIhgRPRyUScvMLeM3FUdHUhNTOLTUejc6a5o1UAtzWrRaOaXtSu5lxFHaVRWlomkZExhIX5MWxYEzIzTdx/fwtcykDjhZIohCglEtMyWRBxmi2R0azcl7sbzyoe5anv50nnEF9a1PbmqVsblen2kkraunXHefjhZSQnZ3DkyCO4ubkyalQre4dVYiRRCGFn+87G8djcXTkN6QHU863Es/3CCJW7Bbu6eDGJp55axfff76Zevap8/vmAEu+vujQoe2ssRClw8Hw8b684xN6zcTlNcAdUcWdI6wAe79nQ4Zp4cEZHj16hXbsvSExM5/nnu/D8811wdy/9DfjZgiQKIUpIakYWP+84w/Rl+3PaTVIKbm7ox2M9G9A6qHQ9d19WxcenUbmyG/XrV2XMmFaMHt2KsDA/e4dlV5IohLChVfvO88lfx4i8lERcyn/vNrSoXYUXbgujbbA8oVRaJCWl8+qrf/HFFzvYvfthAgMr8/bbt9o7rFJBEoUQxexCfCrfbT7B/IgoLiUYxUpVPMozsKU/NSpXZEK3ELwdoA+CsuS33w4xceLvnDoVx5gxrRyij4iSJIlCiGKQmpHFX4cv8d6qwxy6kJAzvGN9H94c0lwqpEupzEwTw4YtYPHigzRp4seGDaPo3DnI3mGVOpIohLgOm49Fs2hHFC5KkZppdOSTnmk0y13BtRzTBjZlYCt/3Fwdv0E4Z6S1RimFq2s5atXy5I03evDEEx2cogE/W5BEIYSVzsSmsGTXWX7ddYaD5/+7a6jj40HjWpUZ2NKf3k1q4i9NcpdqW7ZEMWHCcr74YgCtW9di1qzb7B1SqSeJQogi/HX4EiO/3pprWItAb96/qyX1/DztFJW4VjExKTz33B989tl2/P29iIlJsXdIDsOmiUIp1Qf4EHABvtRav5FnfBDwLVDFPM0zWuvltoxJCGt8tfE46w5eZOPR/zr08fV04+2hzenWyE/einYw8+bt5dFHV3D5cjKPP34Tr7zSDS8vaVrdWjZLFEopF2AW0AuIArYppZZorfdbTPYCMF9r/YlSqjGwHAi2VUxCFCYhNYO3Vx7iu80nc4b1alwDf++K3Blem6YB3naMTtyIgwcvExxchRUr7qVVq1r2Dsfh2PKOoh1wVGsdCaCUmgsMBCwThQYqm//2Bs7aMB4hcolLzuDXf89wNjaVVfvOE3k5KWdcHR8P5vzvJukC1EGlpmby5psbad26FgMGNOK557rwwgtdy0QDfrZgy0QRAJy2+BwFtM8zzcvAKqXUI0AloGd+M1JKjQXGAgQFyaNr4sakZmQx5tttuVpirVm5IuF1qnJH6wDuaRckRUsObM2aSMaPX8aRI1eYNKkDAwY0onx5eZrpRtgyUeT3S8vb7+pwYLbW+l2lVAfge6VUU621KdeXtP4c+ByMrlBtEq1walpr/j4Wze6oON5ccRCACi7leGNIM/o1q0VFOZE4vAsXEnnyyVXMmbOHkJBqrFp1H7161bd3WE7BlokiCqht8TmQq4uWxgB9ALTWm5VSFQFf4KIN4xJlSGpGFm/8fpDZf5/IGVZOwe0t/Png7rLTTHRZsHp1JAsX7uell7ry7LNdqFhRHuosLrbcktuABkqpusAZ4G7gnjzTnAJ6ALOVUmFAReCSDWMSZciP/5zkjeUHSUgzeoK7rXktnujZkLq+lXCR1lmdwr//nufIkSsMHdqYe+9tRqdOtalbVxpXLG42SxRa60yl1ERgJcajr19rrfcppV4FIrTWS4BJwBdKqScwiqUe0FpL0ZK4IVExyfR6bz0pGUYLrS/1b8w97YOkeMmJJCamM3XqOj788B+Cg6swaFAorq7lJEnYiE3vzczvRCzPM+wli7/3A51sGYMoO1Izsrjtow0cu2Q8veRe3oVdU3tJMxpO5pdfDvLII78TFRXP2LGtmTGjJ66u8jSTLUkhnnB42e0uLdwelTPs+zHt6NKgbPch4Iz27LnAHXfMo1mz6sybN5SOHWsX/SVxwyRRCIe0ev8FfvznJH8e+q9Kq2ENT3qE1WBSr4a4yvPyTiMjI4sNG07RvXtdmjWrwbJl99CrVz155LUESaIQDuXvY5eZOGcnV5LSAfCpVIFmgd5MuCVEOgFyQn//fZpx45ayb98lDh2aSEhINfr1a2DvsMocSRSi1Dsbm8Lcbaf5YcvJnAQRVM2Db0e3o65vJTtHJ2zhypUUnnlmDV98sYPatSvz88/DCAmRCwF7kUQhSq2T0Ul8tj6SOf+cyhnWNKAy7w9rSYMaXnaMTNhSamomLVt+ytmzCUya1IGXX+6Gp2cFe4dVpkmiEKWKyaSZs/UUP2w5mdPng6ebK9PvaMrtLfylaQ0nFhUVT2BgZSpWdOW1126hZcuatGhR095hCSRRCDvTWrNoxxkuxKdy4Fw8S3efyxnXLMCbid1D6N1EThbOLCUlgxkzNvLmm5tYuPBOBgxoxMiRLe0dlrBgVaJQSlUAgrTWR20cjygj3vj9IKv2nyfyUtJV44a3C2Jy70ZUqyTFDc5u1apjjB+/jGPHYrjvvua0axdg75BEPopMFEqp24D3gApAXaVUS2Cq1voOWwcnnEdcSgYno5NYtuccn/0VmTN8eLva+Hm68dDN9fGo4CJFS2XII48sZ+bMbTRoUI01a0bQo0c9e4ckCmDNHcWrGM2DrwPQWu9SSoXYNCrhVLaduMKdn27ONaxZgDfzHroJjwpS+lmWZGUZDUO7uJTjppsC8fX1YMqUztKAXylnzd7J0FrH5rnSk/aYhFVORSfnJInn+oUSWrMy4cFVJUGUQTt2nGPcuKWMGNGcRx5pz733Nrd3SMJK1vxaDyilhgHlzC3BPgZssW1YwpGt3Heen7ae4q/Dl8hu4nHiLSGM7Sp9A5RFCQlpvPTSOj76aCt+fh7UqiWPNjsaaxLFROAlwAT8jNEa7LO2DEo4rqcW/JvT5lJYrcoEVnVnxE116NpQ2l0qi1atOsbo0b9y9mwC48aF8/rrPahSpaK9wxLXyJpE0VtrPQWYkj1AKTUYI2kIAUBiWiaT5u9i5b4LAKx5sish1eXKsayrUMGF6tUrsWjRMNq3D7R3OOI6qaK6f1BK7dBat84zbLvWuo1NIytAeHi4joiIsMeiRT7ikjOYtmw/C8x3EeVdFIvHd6JpgLedIxP2kJGRxXvvbSY+Po3p03sAxkuU5aSjKLszn7fDr+e7Bd5RKKV6Y3RTGqCUes9iVGWMYihRhsWlZPDVxuN89MeRnGHDwgN5Y3BzOSmUURs3nsppwO/OOxvnJAg5HhxfYUVPF4G9QCqwz2J4AvCMLYMSpdu+s3Hc9tHGnM8PdAzmpf6N5YRQRkVHJzNlyhq++monQUHe/PbbcPr3b2jvsEQxKjBRaK13AjuVUj9qrVNLMCZRCkVeSmTmuqNEnIjh1JVkwHhZ7vU7mslLcmVcdHQKc+fu5emnO/LSSzdTSd6odzrWVGYHKKWmA42BnMcVtNZyyVAGrD98ibdXHmLPmTgAKriU46Gu9bi7XZA08V2GHThwifnz9zF1ajcaNvTh1KknqFbN3d5hCRuxJlHMBqpK6LEAACAASURBVKYB7wB9gVFIHYXTO3YpkXu+2MKF+DQAWtSuwtO9G9Gxvo/cQZRhyckZTJ++nrff/htPzwqMGdOawMDKkiScnDWJwkNrvVIp9Y7W+hjwglJqg60DEyUvJT2LlfvOs3B7FBuPXgagWqUK/P5YF2pUlmffy7oVK44yfvwyjh+PZeTIFrz9di/8/OSusiywJlGkKeMS8phSahxwBqhu27BESbkYn8pHa4/w686zJKRl5gxvUN2TSbc2pE/TWnaMTpQWiYnpjBixGB8fd9atG0m3bsH2DkmUIGsSxROAJ/AoMB3wBkbbMihhe7ujYpm17mjOC3IA3Rr50bWBH3e0CqCqVEiWeVlZJn76aS/DhzfF07MCa9aMIDTUFzc3aaerrClyj2ut/zH/mQCMAFBKySuWDur0lWS6vLUu17BXbm/CyI7B9glIlErbt5/loYeWsn37OdzdXRkypLH0NleGFZoolFJtgQBgo9b6slKqCUZTHt0BSRYOZP/ZeCbM2cHxy0ZHQZUquDB3bAeaBcob1OI/cXGpvPjiOmbN2kb16pWYO3cIgweH2TssYWeFvZk9AxgC/ItRgb0Yo+XYN4FxJROeuFHbT8bw+LydnL6SAkCLQG9GdarLoFbSk5i42pAh81m79jgTJrRl2rTueHvLQwyi8DuKgUALrXWKUqoacNb8+VDJhCZu1Lm4FIZ88jcAXm6ufD2qLW2Dq9k5KlHaREbG4OfngZeXG9Ond6dcOUXbtnIhIf5TrpBxqVrrFACt9RXgoCQJx2AyaWb8foAOM9YC8Hy/MPa80luShMglPT2L11/fQJMmHzNt2noA2rcPlCQhrlLYHUU9pVR2U+IKCLb4jNZ6sE0jE9csLiWDaUv/a8kVYNzN9flfV+mLWOS2fv1Jxo1byoEDlxk6tDGPPtre3iGJUqywRDEkz+eZtgxEXL/V+y/w8A/byTQZTcaXU9AssAq/jO8ob1GLq7z//maefHIVwcFVWLbsHvr1a2DvkEQpV1ijgH+UZCDi+hy5kMD/vjP652hRuwqP92zALY3kfUiRm8mkSUpKx8vLjdtua8ilS8m88EJXPDzK2zs04QDkzRkHNmP5AT5bHwnA7FFt6SYJQuRj376LjBu3LKenuYYNfXj99R72Dks4kMIqs2+YUqqPUuqQUuqoUirfPiyUUsOUUvuVUvuUUnNsGY+z0FrT78MNOUniq5HhkiTEVZKTM3j22TW0bPkZBw5con//BhTVo6UQ+bH6jkIp5aa1TruG6V2AWUAvIArYppRaorXebzFNA+BZoJPWOkYpJWe7QphMmvVHLvHInJ057TL981wPabBPXGXnznMMHjyfEydiGTWqJW+91QtfXw97hyUcVJGJQinVDvgKo42nIKVUC+BBrfUjRXy1HXBUax1pns9cjHcz9ltM8z9gltY6BkBrffHaV6FsuBCfSvvX/6s2alTDi/njOuDtLmXM4j9aa5RSBAV5ExTkzbffDqJr1zr2Dks4OGvuKD4C+gO/AGit/1VK3WLF9wKA0xafo4C8z+A1BFBKbQJcgJe11iusmHeZsnr/hZwK6y4NfJk6oAkh1T3tHJUoTTIzTcycuZUlSw6xevUIfHw8+OuvB+wdlnAS1iSKclrrk3kes8yy4nv5PZeZt4DUFWgAdMNoO2qDUqqp1jo214yUGguMBQgKCrJi0c5j09HLOUni6T6NGN8txM4RidJm69YzjBu3lJ07z9O3bwjx8WlUrSodCYniY02iOG0uftLmeodHgMNWfC8KqG3xORCjGZC802zRWmcAx5VShzASxzbLibTWnwOfA4SHh5eJ2rjEtEyGfvI3B88n4OXmypO3NmRUp7r2DkuUIomJ6UyZsppPPomgVi0vFiy4kyFDwuTdGVHsrEkUD2MUPwUBF4A15mFF2QY0UErVxejs6G7gnjzT/AIMB2YrpXwxiqIirQvduWUniWYB3sx/qAPuFVzsHZIoZcqXL8eff57kkUfa8dpr3alc2c3eIQknZU2iyNRa332tM9ZaZyqlJgIrMeofvtZa71NKvQpEaK2XmMfdqpTaj1GcNVlrHX2ty3Imxy8n0fuD9aRnmvD1dGPJxE5yhShyHD16hVdf/YtZs/rh5eXG9u1jqVhRXocStqWKeq5aKXUMOATMA37WWieURGAFCQ8P1xEREfYMwWaOX06i74frSc0w0TOsBp/c15ryLjZ91UU4iLS0TN56axPTp2+gQgUXli27hy5d5GkmYT2l1Hatdfj1fNeaHu7qK6U6YhQdvaKU2gXM1VrPvZ4Fiqudj0vlq42RfLHhOACf3Nuavs2kr2phWLfuOA8/vIxDh6K5664mvPdeb/z9vewdlihDrLpn1Vr/DfytlHoZ+AD4EZBEUQzSMrMY8snfnIlNIaCKO0/3aSRJQuTQWjN9+gYyMkysWHEvvXvLU2+i5Fnzwp0nxotydwNhwK9ARxvHVSaciU2h0xtGnxFjOtflxf6N7RyRKA1MJs1XX+2gT58Qatf25vvv76BKlYq4y8uVwk6sKQDfC9wEvKW1DtFaT9Ja/2PjuMqEoebe557o2ZBn+4baORpRGuzefYHOnb9m7NilfPnlDgBq1fKSJCHsypqip3paa5PNIylDtNY89P12zsWlUq1SBR7rKf0BlHWJiem88sqfvP/+FqpWdWf27IHcf38Le4clBFBIolBKvau1ngQsUkpd9WiU9HB3/V74ZS+r9l/Ay82V9U9b0xqKcHYvv/wn7767mQcfbMUbb/TEx0ca8BOlR2F3FPPM/0vPdsXofFwqP/5zioAq7myccou8I1GGnT4dR1JSBqGhvjzzTGcGDQqlc+ey1USNcAwF1lForbea/wzTWv9h+Q+jUltco2OXErlphtEC7Cu3N5EkUUZlZpp4773NhIXN4qGHlgLg6+shSUKUWtZUZo/OZ9iY4g7E2Z24nESPd/8C4J72QfRsXMPOEQl72LIlivDwz5k0aRXdugXz7beD7B2SEEUqrI7iLoxHYusqpX62GOUFxOb/LZGf2ZuO8/JvRjcc426uzzPyhFOZtGzZYQYM+Al/fy9+/nkYgwaFyl2lcAiF1VFsBaIxWn2dZTE8Adhpy6CcSWxyek6S+Gh4K25v4W/niERJ0lpz9mwCAQGV6dmzHq++eguPPdYeLy9pwE84jgIThdb6OHAco7VYcZ2W7zkPwEv9G0uSKGMOH45m/PhlHD4czf79E/D0rMALL3S1d1hCXLPCip7+0lrfrJSKIXeHQwrQWutqNo/OwS2IOM1zi/cAcHe72kVMLZxFamomb7yxkRkzNuLu7sqMGT1wd5cWXoXjKuzozX7A37ckAnEmWmteX34gp5G/6Xc0xaOCnCjKgvPnE+na9RuOHLnC8OFNee+93tSsKd3WCsdWWNFT9tvYtYGzWut0pVRnoDnwAxBfAvE5HK01768+nJMklkzsRPPAKnaOSthaRkYW5cu7UKNGJbp2rcOsWf3o1au+vcMSolhY83jsLxjdoNYHvsN4h2KOTaNyUDFJ6YS+uIKP1h4FYMPTt0iScHImk+bTTyOoX/8joqLiUUrx5Ze3S5IQTsWaRGEy92k9GPhAa/0IEGDbsBzP5cQ0er3/F2mZxo3Y1ud6ULuaNMPgzP799zwdO37Fww8vo0EDHzIysuwdkhA2YVVXqEqpO4ERQPbbQdKUpYWtx68w7LPNAPQMq84X94fL8/FOTGvN5Mmr+eCDLVSr5s7339/Bvfc2k30unJY1iWI0MB6jmfFIpVRd4CfbhuU4tNY5SeLrB8Lp1rC6nDCcnFKKmJgUxowxGvCrWtXd3iEJYVNFFj1prfcCjwIRSqlQ4LTWerrNI3MAV5LSGT17GwC1q7nTPbQG5cpJknBGJ0/GMmjQXHbsOAfAF1/czmefDZAkIcqEIhOFUqoLcBT4CvgaOKyU6mTrwEq7i/GptH5tNesOXWJw6wDWTupm75CEDWRkZPHWW5to3PhjVq+O5NChywByQSDKFGuKnt4H+mmt9wMopcKA74FwWwZWml1KSGPs99sBGNI6kHeHSQczzujvv0/z0ENL2bv3IgMHNuKjj/oSFORt77CEKHHWJIoK2UkCQGt9QClVwYYxlWoHzsXT98MNAHQPrS5JwomtWRNJXFwqv/xyFwMHSkOOouxSWl/VeV3uCZSaDaRh3EUA3At4aK1H2ja0/IWHh+uIiAh7LJpLCWm0f30NJg1vDG7GXW1rS8W1E9Fa8/33u/Hz86Bv3wakpWWSkWHC07PMXhcJJ6KU2q61vq6SIGveoxgHHAOeBqYAkcBD17MwRxYVk0zb6UaS+GpkOHe3C5Ik4UQOHrxM9+7fMXLkL3zzzS4A3NxcJUkIQRFFT0qpZkB9YLHW+q2SCan0uZKUTuc31wFGcVOPMOl0yFmkpGTw+usbePPNTVSqVIHPPuvPgw+2tndYQpQqBd5RKKWew2i+415gtVIqv57unN7pK8nc9LrRfemDnevy9QNt7RyRKE6//XaYadM2cNddTTl4cAJjx7aRJ5qEyKOwO4p7geZa6ySllB+wHOPx2DIjMS2TQbM2kZ5l4qGb6/FsX+kq3BmcP5/Irl3n6dMnhDvvbExw8IO0ayet0ghRkMLqKNK01kkAWutLRUzrlG77aAPRSenc3ba2JAknkJVl4uOPt9Go0UxGjFhMSkoGSilJEkIUobA7inoWfWUroL5l39la68E2jczOlu4+y8noZEJrevHGkOb2DkfcoB07zjFu3FK2bTtLz571+Pjjfri7S5NlQlijsEQxJM/nmbYMpDTJyDIxcY7RLfiXI8vse4VO4/jxGNq1+wJfXw/mzBnM3Xc3lSfWhLgGhXVc9EdJBlKafLXR6HSoZ1h1AqtKU+GOSGvNnj0Xad68BnXrVuWbbwYyYEAjqlSpaO/QhHA4Za7ewRqr9p0HYNqgZnaORFyP48dj6N//J1q1+ozduy8AMGJEC0kSQlwnmyYKpVQfpdQhpdRRpdQzhUw3VCmllVJ2L+dZtvscO07F0jnEl5recmJxJOnpWbzxxkaaNPmYv/46wTvv9KJxYz97hyWEw7OmrScAlFJuWuu0a5jeBZgF9AKigG1KqSWW7UaZp/PCaMb8H2vnbSsf/3mUt1YcAmBi9xA7RyOuRVaWiY4dv2L79nMMHhzGBx/0pnZtacBPiOJgTTPj7ZRSe4Aj5s8tlFL/Z8W82wFHtdaRWut0YC4wMJ/pXgPeAlKtD7v47Y6KzUkScx5sz031fOwZjrBSfLxx7eLiUo7Ro1vx22/DWbRomCQJIYqRNUVPHwH9gWgArfW/wC1WfC8AOG3xOYo8fW0rpVoBtbXWSwubkVJqrFIqQikVcenSJSsWfW201jz8ww4AfpvYmY4hvsW+DFG8tNbMnr2LevU+5NdfDwIwfnxb+vdvaOfIhHA+1iSKclrrk3mGWdOLfH7PH+Y0VauUKofR18Wkomaktf5cax2utQ738yv+MuevNh7nTGwKozoF0yxQrkRLu/37L9Gt27eMGvUroaG+1K9fzd4hCeHUrKmjOK2Uagdoc73DI8BhK74XBdS2+BwInLX47AU0Bf40P9NeE1iilLpda12i7Yh/9McRAB7t3qAkFyuuw1tvbeL559dSubIbX345gFGjWknbTELYmDWJ4mGM4qcg4AKwxjysKNuABkqpusAZ4G7gnuyRWus4IKeMRyn1J/BUSSeJqJhk4lMzubd9EFUrSZPSpZXWGqUUNWt6cu+9zXj77V74+VWyd1hClAlFJgqt9UWMk/w10VpnKqUmAisBF+BrrfU+pdSrQITWesk1R2sDT87/F4CejaXp8NLo7NkEHntsBV26BPHoo+25//4W3H+/9CooREkqMlEopb7Aom4hm9Z6bFHf1Vovx2h11nLYSwVM262o+RW3jCwTW49fAaBbQ3nevjTJbsDv+efXkpFhomPHQHuHJESZZU3R0xqLvysCd5D7aSaH9e3fJwAY27WetP1TiuzadZ4HH1zC9u3nuPXW+nz8cT+psBbCjqwpeppn+Vkp9T2w2mYRlZDEtEymLTsAwKhOwfYNRuQSF5fK2bMJzJs3lDvvbCxJXAg7s/rNbAt1gTrFHUhJm7/NuCl6/Y5m1PJ2t3M0ZZvWmgUL9nPkSDTPP9+Vm28OJjLyMSpWvJ7DUwhR3Kx5MztGKXXF/C8W427iOduHZlt7zsQBMKSNdFpjT8eOXaFfvzncdddCfv31EBkZxis6kiSEKD0K/TUq456/BcbjrQAmrfVVFduOaOepGNxcy+Hm6mLvUMqktLRM3nnnb6ZN20D58uX48MM+jB/fFldXadBYiNKm0EShtdZKqcVa6zYlFVBJOHg+nhPRydzWvJa9QymzTp+O57XX1jNgQCM++KA3AQGV7R2SEKIA1ly+bVVKtbZ5JCVonrl+YmhreeSyJF26lMTMmVsBCAmpxv79E1iw4E5JEkKUcgXeUSilXLXWmUBn4H9KqWNAEkYbTlpr7bDJ4/vNRtNVN8u7EyXCZNJ8881Onn56DQkJafTqVY9GjXypV6+qvUMTQlihsKKnrUBrYFAJxVIiLsSnkmnS3N7CX9oIKgF7917k4YeXsXHjKbp0CeLTT/vTqJG0ziuEIyksUSgArfWxEoqlRCzaEQVAf6mfsLn09CxuvfV70tOz+Prr23nggZbyToQQDqiwROGnlHqyoJFa6/dsEI/NfbzOyHvdQ6vbORLntXbtcW6+uQ4VKrgwf/6dhIb64uvrYe+whBDXqbDKbBfAE6M58Pz+OZzUjCwS0zIJremFq4s8hlncoqLiGTJkPj16fMd33xmNLXbuHCRJQggHV9gdxTmt9aslFkkJ+H3vOQAe7lbfzpE4l8xMEzNnbuXFF9eRlWVixowe3Htvc3uHJYQoJkXWUTiTrcdjAOgsXZ0WqxEjFjN37l769g1h1qx+1K0rTzMJ4UwKSxQ9SiyKEvLT1lOEVPfEx9PN3qE4vNjYVFxdy+HpWYEJE9oyZEgYQ4aESWW1EE6owIJ6rfWVkgzE1hbvNJ52qia92N0QrTVz5+4lLGwWL764FjDqIYYOlVZehXBWZaZG97WlRpPirw5sYudIHNfRo1fo3fsHhg9fRGBgZe67T+ohhCgLykQTnemZJq4kpdO4VmVCa0pzEddjzpw9jB79K25ursyc2Zdx48JxkSfHhCgTykSiWPLvWQAGtfK3cySOJyMji/LlXQgP92fo0Ma89VYv/P0d8uloIcR1KhOJ4qkFxjP9g6URQKtdvJjEpEmrSEpK5+ef76JhQx9++GGwvcMSQtiB05cdbDxyGYA2dariK087Fclk0nz++XYaNZrJvHl7adLEj6wsk73DEkLYkdPfUSzbY7xk9/6wlnaOpPSLjIzhvvt+ZvPmKLp1C+aTT24jNFTeORGirHPqRKG15o8DF/B2L0+QjzQjURRvbzdiY1P59ttBjBjRXB53FUIATl70tPbgRS4mpNG7SQ17h1JqLVlyiMGD55GVZcLHx4O9e8dz//0tJEkIIXI4baLQWjN1yT4AJtwSYudoSp9Tp+IYNGguAwfO5fDhaM6dSwSQPjqEEFdx2qKnqJgUomJSeKhrPer4VLJ3OKVGZqaJDz7YwtSpf6K15s03e/LEEzdRvryLvUMTQpRSTpsoRs/eBkDb4Gp2jqR0ycoy8eWXO+jevS7/9399CQ6uYu+QhBClnFMWPWmtOXLRKEqRDoogJiaFKVNWk5CQhpubK5s2jWbJkrslSQghrOKUiWJ3VBwAPcOql+kyd601P/64m9DQWbz77mbWrTsBgI+Ph1RWCyGs5pRFT5+vjwRgTOd6do7Efg4fjmb8+GX88cdx2rULYOXK+2jZsqa9wxJCOCCnSxQZWSaW7TmHr6cbN9Uru/UTjz++goiIs3z8cT/Gjm0jDfgJIa6b0yWK38wNAPZvXqvMFa+sXn2M0FBfatf25pNPbsPNzZWaNT3tHZYQwsHZ9DJTKdVHKXVIKXVUKfVMPuOfVErtV0rtVkr9oZSqcyPLyzJpnl64Gyhb706cP5/IPfcs4tZbf+DNNzcBUKdOFUkSQohiYbNEoZRyAWYBfYHGwHClVOM8k+0EwrXWzYGFwFs3ssxLCWlkmjQPdAzGz8v5GwA0mTSffhpBaOhMFi06wNSpN/POO7faOywhhJOx5R1FO+Co1jpSa50OzAUGWk6gtV6ntU42f9wC3FA74FtPGL23NgvwvpHZOIwZMzbw8MPLaNPGn927x/Hyy92oWNHpShOFEHZmy7NKAHDa4nMU0L6Q6ccAv+c3Qik1FhgLEBQUVOAMPlh9GHDul+wSEtK4fDmZunWrMm5cOHXrVmX48KZlrj5GCFFybHlHkd+ZS+c7oVL3AeHA2/mN11p/rrUO11qH+/n55buwjCwTkZeTAJyypVitNYsXH6Bx44+5666FaK3x8fHgnnuaSZIQQtiULRNFFFDb4nMgcDbvREqpnsDzwO1a67TrXdjKfecBGBbufL3YnTwZy+23z2Xw4PlUq+bORx/1leQghCgxtix62gY0UErVBc4AdwP3WE6glGoFfAb00VpfvJGFafO9yt3tCi6ackSbN5+mZ8/vAXjnnV489thNuLrKOxFCiJJjs0Shtc5USk0EVgIuwNda631KqVeBCK31EoyiJk9ggfkK+ZTW+vbrWd7OU7EA1KxcsTjCt7v4+DQqV3ajdetajB7dksmTOxEUVDYq6YUQpYtNH5HRWi8HlucZ9pLF3z2LYzmZWSa+3nQccPxEER2dzDPPrGHVqkj27RuPp2cF/u//+tk7LCFEGeYUz1KuP3IJgJ5hNRy2EUCtNd9/v5tJk1YRE5PCk092QKohhBClgVMkirOxqQBMuKW+nSO5PnFxqQwaNI8//zxBhw6BfPppf5o3l+5bhRClg1Mkig3mO4p6fo7VZIXWGqUUlSu74evrweef92fMmNYOe1ckhHBODv/4jNaalfsuEFjVHW/38vYOx2orVx6ldevPiYqKRynFggV38r//tZEkIYQodRw+UaRkZAFwa2PH6Gvh3LkE7r57IX36/EhycgYXLybZOyQhhCiUwxc9HTOfaGt6l/5GAGfN2spzz60lLS2TV17pxpQpnXBzc/hdIIRwcg5/ltoSGQ1AWK3Kdo6kaNu3n6N9+wBmzepHgwY+9g5HCCGs4vCJYt0h44XuNnWq2jmSq8XHp/HSS+sYMaI5bdr48/HHt+Hm5iLNbwghHIrDJ4qdp2Lp0sAXjwqlZ1W01ixadIDHHlvBuXMJBAV506aNvzQBLoRwSA595jobm0JKRhZB1UpPa7HHj8cwceLvLF9+hJYta/Lzz8No3975GioUQpQdDp0o/jhoFDsNaOFv50j+8+OPe1i//iTvv9+biRPbSQN+QgiH59CJYv6207iXd7F7R0UbNpwkLS2Lnj3rMXlyRx54oCWBgaW/cl0IIazhsJe7V5LS2XMmjkpurrjY6SW1y5eTGT36V7p2nc2rr/4FgJubqyQJIYRTcdg7in9PG82K39u+5Puf0Foze/YuJk9eTVxcGlOmdOLFF7uWeByi9MvIyCAqKorU1FR7hyLKiIoVKxIYGEj58sXXUoXDJoojFxMA6BFWvcSXvXz5EUaPXkKnTrX59NP+NG1a8jEIxxAVFYWXlxfBwcHyWLSwOa010dHRREVFUbdu3WKbr8MWPa3adwGA0JolU8yTnJzBpk2nAOjXrwG//no369ePkiQhCpWamoqPj48kCVEilFL4+PgU+x2swyaK9CwTlSq4UKEEnir6/fcjNG36MX37/khsbCpKKW6/vZE04CesIklClCRbHG8Omyh2R8VxUz3bNoNx5kw8d965gH795uDm5spvvw2nShXH7kFPCCGulUMmisS0TAC8bPim88WLSTRu/DFLlx5m2rRb+Pffcdx8c7DNlieErbi4uNCyZUuaNm3KgAEDiI2NzRm3b98+unfvTsOGDWnQoAGvvfYaWuuc8b///jvh4eGEhYURGhrKU089ZY9VKNTOnTt58MEHcw0bOHAgHTp0yDXsgQceYOHChbmGeXr+14fN4cOH6devHyEhIYSFhTFs2DAuXLhwQ7FduXKFXr160aBBA3r16kVMTMxV06xbt46WLVvm/KtYsSK//PILAF26dMkZ7u/vz6BBgwBYunQpU6dOvaHYronW2qH+tWnTRv8TGa3rTFmq3/j9gC5uUVFxOX9/+OEWffRodLEvQ5Qd+/fvt3cIulKlSjl/33///XratGlaa62Tk5N1vXr19MqVK7XWWiclJek+ffromTNnaq213rNnj65Xr54+cMD4nWVkZOhZs2YVa2wZGRk3PI+hQ4fqXbt25XyOiYnRgYGBOjQ0VEdGRuYMHzlypF6wYEGu72Zvm5SUFB0SEqKXLFmSM27t2rV6z549NxTb5MmT9YwZM7TWWs+YMUM//fTThU4fHR2tq1atqpOSkq4aN3jwYP3tt99qrbU2mUy6ZcuW+U6ndf7HHRChr/O865BPPS2IOA3Abc1qFds84+JSeeGFtXz22Xa2bHmQ1q1r8eij7Ytt/kK88ts+9p+NL9Z5NvavzNQBTayevkOHDuzevRuAOXPm0KlTJ2699VYAPDw8mDlzJt26dWPChAm89dZbPP/884SGhgLg6urK+PHjr5pnYmIijzzyCBERESilmDp1KkOGDMHT05PExEQAFi5cyNKlS5k9ezYPPPAA1apVY+fOnbRs2ZLFixeza9cuqlSpAkBISAibNm2iXLlyjBs3jlOnjIdIPvjgAzp16pRr2QkJCezevZsWLVrkDFu0aBEDBgygRo0azJ07l2effbbI7TJnzhw6dOjAgAEDcobdcsstVm/Xgvz666/8+eefAIwcOZJu3brx5ptvFjj9woUL6du3Lx4euZslSkhIYO3atXzzzTeAUQ/RrVs3li5dyrBhw244zqI4ZKLYa/6xNfG/8SeehThLvwAADzlJREFUtNYsWLCfxx9fwfnziUyc2I769UtfS7RC3KisrCz++OMPxowZAxjFTm3atMk1Tf369UlMTCQ+Pp69e/cyadKkIuf72muv4e3tzZ49ewDyLV7J6/Dhw6xZswYXFxdMJhOLFy9m1KhR/PPPPwQHB1OjRg3uuecennjiCTp37sypU6fo3bs3Bw4cyDWfiIgImjZtmmvYTz/9xNSpU6lRowZDhw61KlHs3bv3qm2Rn4SEBLp06ZLvuDlz5tC4ceNcwy5cuECtWsYFba1atbh48WKh8587dy5PPvnkVcMXL15Mjx49qFz5v3NeeHg4GzZskERRkDMxybQLrnbDtftaawYPns8vvxykdetaLFkynPDw0tNulHAu13LlX5xSUlJo2bIlJ06coE2bNvTq1Qv4r8/2/FzLb2vNmjXMnTs353PVqkVfaN155524uLgAcNddd/Hqq68yatQo5s6dy1133ZUz3/379+d8Jz4+noSEBLy8vHKGnTt3Dj8/v5zPFy5c4OjRo3Tu3BmlFK6uruzdu5emTZvmu07Xeg7x8vJi165d1/Qda507d449e/bQu3fvq8b99NNPV9XDVK9enbNnz9oklrwcsjLbpCHY9/pbjM0wd5+qlKJz59p89FEftm59UJKEcEru7u7s2rWLkydPkp6ezqxZswBo0qQJERERuaaNjIzE09MTLy8vmjRpwvbt24ucf0EJx3JY3uf6K1WqlPN3hw4dOHr0KJcuXeKXX35h8ODBAJhMJjZv3syuXbvYtWsXZ86cyZUkstfNct7z5s0jJiaGunXrEhwczIkTJ3KSmI+PT667nStXruDr65uzLaxZ14SEhFwVz5b/LJNatho1anDu3DnASATVqxf83tX8+fO54447rnqjOjo6mq1bt3LbbbflGp6amoq7u3uRMRcHh0sUWSZNYtr/t3f/wVXVZx7H35+lYAICtTjSrajQMcWEGH6mTe0oIl1G3QjVdUR+VTq2jiBSi+KwgzPrrtTSVpAiushaJ2ltA8VpCwsWFtlYKgIatgoKNqU0E7LTUcqyrKUWCHn2j3OSXJObm5M092ee18yduef3k2duzvee7zn3+TbyycHdS9Arr9RRUrKWTZveBeDBB6/h/vs/R58+WZcK57pk8ODBrF69mieeeIJz584xa9YsXn31VV5++WUguPJYuHAhDz/8MACLFy/m8ccfp7a2FghO3CtXrmy33ylTprBmzZqW6eaT8dChQzl8+HBL11JHJHHrrbeyaNEiCgsLGTJkSNz9xvsmX1hYyJEjR1qmq6qq2LZtG3V1ddTV1bF///6WhuL6669nw4YNnD17FoCKioqW+xAzZ87ktddeY+vWrS372rZtW0t3WrPmK4p4r7bdTgBTp06lsrISgMrKSqZNm9ZhHqqqqpgxY0a7+Rs3bqS8vJy8vI8+ml9bW9uu2y1Zsu7s2Hg+eHRvRBevKI4fP81dd/2cSZMqOXOmkYEDM3+Mbed62tixYxk9ejTr168nPz+fTZs2sWzZMkaOHMnVV19NaWkpCxYsAKCkpIRVq1YxY8YMCgsLKS4ubvl2HOuRRx7h5MmTFBcXM3r0aKqrqwFYvnw55eXl3HDDDS399B2ZPn06L7zwQku3E8Dq1aupqamhpKSEoqIi1q5d2267q666ilOnTvHBBx9QV1dHfX09ZWVlLctHjBjBoEGD2LdvH+Xl5Vx77bWMHz+eMWPGsHv37pYby/n5+WzZsoWnnnqKgoICioqKqKioSHgFEMWSJUvYsWMHBQUF7NixgyVLlgDBvZXYrqS6ujqOHTvGxIkT2+1j/fr1cRuQ6urqdlcZySKLeWY6G4waPdZO37SMdXPGM2XUJyNtU1V1kPvue4k//eksixdfw9Kl19G/f88VzHKuI4cPH6awsDDdYeS0J598koEDB7brw89l7733HjNnzmTnzp1xl8f73Enab2YTunO8rLuiONvYBEDfLpTuaGxsorj4Et58816++c3J3kg4l0PmzZvHBRf0rh6C+vp6VqxYkbLjZd1TT6fPNNIPuPTjHd+jOH36LI89tovLLx/M/PmlzJ5dwuzZJV5zx7kclJeXx5w5c9IdRkqVlpam9HhZd0VxvinoKiu45MK4y7dsqWXUqGf49rd3U1t7Aghulnkj4dIl27p3XXZLxuct6xqKM41NlA6/qN2Jv6Hh/7jttg3ccksVAwb0Y9euuaxadWOaonQukJeXx4kTJ7yxcClh4XgUbZ+Q+mtlXdfTmcYmhl3U/omno0dPsn377/jWtyazaNHn6devTxqic+6jhg0bRkNDA8ePH093KK6XaB7hridlXUPRZMaVYbfT66//N3v2HOPrXy/juuuuoL7+AYYM6f4P8ZzraX379u3RkcacS4ekdj1JulHSbyQdkbQkzvILJG0Il++TNDzKfofm92P+/K2UlT3HypV7OX06+AGNNxLOOdfzktZQSOoDPA3cBBQBMyS1/eni3cBJM7sSeBLouKxijAX/sJFnn93PwoWf4+DBeQwY0K8nQ3fOORcjmV1PnwWOmNlRAEnrgWlAbEGUacCj4fsXgTWSZAnu/FljE5cNH8RL/z6TceN6rsy4c865+JLZUFwKHIuZbgDaDvDQso6ZNUo6BQwB/hi7kqR7gHvCyTM1f7zn7QgVgXuDi2mTq17Mc9HKc9HKc9FqZHc3TGZDEe+HC22vFKKsg5mtA9YBSKrp7s/Qc43nopXnopXnopXnopWkms7Xii+ZN7MbgMtipocBbYunt6wj6WPAYOB/khiTc865LkpmQ/EGUCBphKR+wJ3A5jbrbAbuCt/fDvxnovsTzjnnUi9pXU/hPYcFwHagD/C8mb0j6V8IBvneDHwf+KGkIwRXEndG2PW6ZMWchTwXrTwXrTwXrTwXrbqdi6wrM+6ccy61sq7Wk3POudTyhsI551xCGdtQJKv8RzaKkItFkg5JOiBpp6Qr0hFnKnSWi5j1bpdkknL20cgouZB0R/jZeEfSj1MdY6pE+B+5XFK1pF+H/yc3pyPOZJP0vKT3Jb3dwXJJWh3m6YCkcZF2bGYZ9yK4+f074NNAP+AtoKjNOvOBteH7O4EN6Y47jbmYBPQP38/rzbkI1xsI7AL2AhPSHXcaPxcFwK+Bi8LpS9IddxpzsQ6YF74vAurSHXeScnEdMA54u4PlNwO/IPgNWxmwL8p+M/WKoqX8h5mdBZrLf8SaBlSG718EJis3RyfqNBdmVm1mfw4n9xL8ZiUXRflcADwGfAf4SyqDS7Eoufga8LSZnQQws/dTHGOqRMmFAYPC94Np/5uunGBmu0j8W7RpwA8ssBf4uKROayFlakMRr/zHpR2tY2aNQHP5j1wTJRex7ib4xpCLOs2FpLHAZWa2JZWBpUGUz8VngM9I2i1pr6RcHckrSi4eBWZLagBeAu5PTWgZp6vnEyBzx6PosfIfOSDy3ylpNjABmJjUiNInYS4k/Q1BFeK5qQoojaJ8Lj5G0P10PcFV5q8kFZvZ/yY5tlSLkosZQIWZrZD0eYLfbxWbWVPyw8so3TpvZuoVhZf/aBUlF0j6IrAUmGpmZ1IUW6p1louBQDHwiqQ6gj7YzTl6Qzvq/8gmMztnZr8HfkPQcOSaKLm4G/gJgJntAfIICgb2NpHOJ21lakPh5T9adZqLsLvlWYJGIlf7oaGTXJjZKTO72MyGm9lwgvs1U82s28XQMliU/5GfEzzogKSLCbqijqY0ytSIkot6YDKApEKChqI3jk+7Gfhy+PRTGXDKzP7Q2UYZ2fVkySv/kXUi5uK7wIXAxvB+fr2ZTU1b0EkSMRe9QsRcbAemSDoEnAcWm9mJ9EWdHBFz8SDwb5K+QdDVMjcXv1hKqiLoarw4vB/zT0BfADNbS3B/5mbgCPBn4CuR9puDuXLOOdeDMrXryTnnXIbwhsI551xC3lA455xLyBsK55xzCXlD4ZxzLiFvKFzGkXRe0psxr+EJ1h3eUaXMLh7zlbD66FthyYuR3djHvZK+HL6fK+lTMcuek1TUw3G+IWlMhG0ekNT/rz226728oXCZ6EMzGxPzqkvRcWeZ2WiCYpPf7erGZrbWzH4QTs4FPhWz7KtmdqhHomyN8xmixfkA4A2F6zZvKFxWCK8cfiXpv8LXNXHWGSXp9fAq5ICkgnD+7Jj5z0rq08nhdgFXhttODscwOBjW+r8gnL9crWOAPBHOe1TSQ5JuJ6i59aPwmPnhlcAESfMkfScm5rmSnupmnHuIKegm6V8l1SgYe+Kfw3kLCRqsaknV4bwpkvaEedwo6cJOjuN6OW8oXCbKj+l2+lk4733g78xsHDAdWB1nu3uB75nZGIITdUNYrmE68IVw/nlgVifHvwU4KCkPqACmm9nVBJUM5kn6BHArMMrMSoBlsRub2YtADcE3/zFm9mHM4heB22KmpwMbuhnnjQRlOpotNbMJQAkwUVKJma0mqOUzycwmhaU8HgG+GOayBljUyXFcL5eRJTxcr/dheLKM1RdYE/bJnyeoW9TWHmCppGHAT83st5ImA+OBN8LyJvkEjU48P5L0IVBHUIZ6JPB7M6sNl1cC9wFrCMa6eE7SViBySXMzOy7paFhn57fhMXaH++1KnAMIylXEjlB2h6R7CP6v/5ZggJ4DbbYtC+fvDo/TjyBvznXIGwqXLb4BvAeMJrgSbjcokZn9WNI+4O+B7ZK+SlBWudLM/jHCMWbFFhCUFHd8k7C20GcJiszdCSwAbujC37IBuAN4F/iZmZmCs3bkOAlGcVsOPA3cJmkE8BBQamYnJVUQFL5rS8AOM5vRhXhdL+ddTy5bDAb+EI4fMIfg2/RHSPo0cDTsbtlM0AWzE7hd0iXhOp9Q9DHF3wWGS7oynJ4D/DLs0x9sZi8R3CiO9+TRBwRlz+P5KfAlgjESNoTzuhSnmZ0j6EIqC7utBgGngVOShgI3dRDLXuALzX+TpP6S4l2dOdfCGwqXLZ4B7pK0l6Db6XScdaYDb0t6E7iKYMjHQwQn1P+QdADYQdAt0ykz+wtBdc2Nkg4CTcBagpPulnB/vyS42mmrAljbfDO7zX5PAoeAK8zs9XBel+MM732sAB4ys7cIxsd+B3ieoDur2TrgF5Kqzew4wRNZVeFx9hLkyrkOefVY55xzCfkVhXPOuYS8oXDOOZeQNxTOOecS8obCOedcQt5QOOecS8gbCueccwl5Q+Gccy6h/wdTbjdrZrdphQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "optimal = \n", + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[3] = [\"Logistic Regression\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15978089375876542\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.7301560712348498" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM default parameters identified 761\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06331396
11261761
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6331 396\n", + "1 1261 761" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.66 0.38 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.001}" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.001,0.01, 0.1, 1]\n", + " gammas = [0.001, 0.01, 0.1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", + "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16199599731203465\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf4AAAEWCAYAAACHePXKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gV1dbA4d8iCYSQToBACgm9STM0RSmiUlSwY++IlKti93567V1siFy9KhYUFBtNQVGkCwFEpHcSegIhhJC+vz9mgoeQCkkmOWe9z8NDpq+ZMzNr9p49M2KMQSmllFKeoYbTASillFKq8mjiV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jiV0oppTxIlUz8InKDiMxxOg6niUi0iKSJiFclLjNGRIyIeFfWMiuSiKwVkd6nMd1p7YMiUltEpovIERH5uqzTuxMRmScid1byMieKyHPFDH9ORJJEZF9lxlWdiMgOEelXScuaICJPVMayKpJ9zmxWBeIodv/PV2Lit3eC43YC2mfP2L98wiycMWaSMeaiilxGVVTwgDPG7DLG+Btjcp2MyynlcTAZY9oaY+aVsJxTLnbOYB+8CmgA1DXGXH0a0xcW3+Mist0+BhNFZIrd/78i8mkh47cXkUwRCRWRp+x1+1eBce6z+z9VHjFWByISBTwAtDHGhJ/hvHqLSGL5RFZ9lDaxlJYxZrgx5tnyml8+l2M6zf63Q0QeLTCOa247LCIz7X0kf/hEEclymUeaiFxb3rE6obQl/kuNMf5AR6AT8FjFhVRxnCzFuksJuiw8dHs3BjYZY3LKOmFhMYvILcBNQD/7GIwD5tqDJwJXiEidApPdDMwwxhyyuzcBtxQyzqYzia0aagwkG2MOOB2Im2zP6iDYPm6uAp4QkQsLDM/PbQ2B/cA7BYa/Yhe+8v9NqchgK6t2t0xV/caYfcBsrAsAAESkloi8JiK7RGS/XXVT22X4YBH5U0RSRWSriPS3+weJyIcisldEdttVcF72sFtFZKH99wQRec01DhH5QUTG2H83EpFvROSgXSr6l8t4T4nIVBH5XERSgVsLrpMdx6f29DtF5P9EpIZLHItE5B276naDiFxQYNri1mGRiLwhIoeAp0SkqYj8KiLJYlU3ThKRYHv8z4BoYLp9ZflwwZKoWFWnz9rzPSoic0QkzCWem+11SBaRJ6SYKjuxqqRft8c/IiILXX834Ab7N00SkX+7TNdVRJaISIq93uNEpKbLcCMiI0VkM7DZ7veWiCTY+8AKETnPZXwvsUq0W+11WiEiUSIy3x5lteuVtohcYu9PKSKyWETau8xrh4g8IiJ/AcdExNt1G9ixx9tx7BeRsfak+ctKsZfVw3UftKdtKyI/i8ghe9rHC9mmTwNPAtfa87lDRGrY+9ROETlg72tB9vj5v+8dIrIL+LWQn6oLMNsYsxWsY9AY87799xJgN3Cl6/YErgc+cZnHcsBPRNrmrwtQ2+5fqML2X7v/7SKyXqwS0mwRaewyzYX2MXJERMYB4jLsKRH53KW74L4dKiIfi8gee97fu4xb3G/eSURW2vvOFMC3iPXpB/wMNLJ/m4l2/+72PFNEZLW43BYSkdvsdT0qIttE5G67fx3gR5d5pYl1HjqpNCwFagWK2D+LO38Vtb8WXLcQEZlhz+Ow/Xeky/CSzhs3yT/njX8Xtgx7vGHADcDD9jpPt/ufVDPnuh3yt4GIPGDv/3tF5LbTHLeuWLfRUkVkuVjn2xPHaHGMMfHAWlxyV4HhGcBUoE1p5lccEekp1vmuj93dSv45d2wUkWtcxp0oIu+JyCwROQb0sfu9K1YNxFER+UNEmrpMU+T8Ss0YU+w/YAdWaQMgElgDvOUy/E1gGhAKBADTgRftYV2BI8CFWBcZEUAre9j3wH+BOkB9YBlwtz3sVmCh/ff5QAIgdncIcBxoZM9zBdbJtibQBNgGXGyP+xSQDQyxx61dyPp9Cvxgxx6DVQq6wyWOHOB+wAe41l6f0FKuQw4wGvDGOtE2s7dFLaAeVsJ5s7BtbXfHAAbwtrvnAVuBFvb85gEv2cPaAGlAT3tbvGave78iftd37ekjAC/gHDuu/GV+YC+jA5AJtLanOxvobq9TDLAeuM9lvgbrBBuav72BG4G69jQPAPsAX3vYQ1j7VEusRNEBq5o8f17NXObdGTgAdLNjvsXeZrVctt+fQJTLsk9sU2AJcJP9tz/QvbDtXMg+GADstWP3tbu7FbFdnwI+d+m+HdiCtW/6A98CnxVY7qdY+1Bh++eNwCF7O8UBXgWG/xv4xaX7YuAg4OMaD/A48LLd7xWsWrvPgaeKWI9bOXX/HWKvS2u73/8Bi+3xw4BUrJKVD9YxkwPcWcR2OWmbAzOBKVjHtw/Qq6TfHGs/38k/x+dVWPv8c0WsU28g0aU7AkgGBmKdHy60u+vZwwcBTbH2y15AOtC5sHnZ/Sa6LruQ5e3AZf+k5PNXoftrIetVF+vizw9r3/wa+N5l+DxKPm+cb2/TsfbvVtR546R1LOI4PTGOvQ1ygGfs32igvR1DTmPcyfY/PzvuBOxjtJA4Yzh5/+puz+vyInKbH9bF8qfFrWtR//K3AdbxlwB0tfvXsbtvwzpmOgNJQFuXZRwBzrX3B1+73yGs/OkNTAIml2F+JcZc2sSfBhy1V24uVvUJWAfEMaCpy/g9gO323/8F3ihkng2wkkltl37XAb8VctIVYBdwvt19F/Cr/Xc3YFeBeT8GfOxysplfzLp52XG0cel3NzDPJY492Bcddr9lWFWvpVmHXUUt2x5nCLCqsB2xiJ13HvB/LsNHAD/Zfz8JfOkyzA/IopAD2N7BjgMdijlgIgus89Ai1uE+4LsCB0DfEtb7cP6ygY3A4OIOJpfu94BnC4yzkX+SxA7g9kL23/yDez7wNBBWxDoXlfivc/2dSli3pzg5wc0FRrh0t8RKTt4uy21SwjxvAH7BOtaSgUddhkXb84u0uydx8oX5U1gJPhrrOPKx/4+i5MRf8Nj6Efui2GU/SseqQr8ZWOoyTIBESpH4sapZ87BP8AWWWeRvjpWsCh6fiyl94n8E+yLMpd9s4JYipv8euLewedn9JlJy4r/dpbuk81eh+2sp9sGOwGGX7nkUf96Y7DKsDkWcNwpbxyKO0xPj2NvgOCcfWwf456K7VONinauzgZYuw56j5MSfYs/TYBWGXPeVHVi5LQXrgmMPcFaB9ciwh6cAScVsc2P/djsLzONaYEGBcf8L/MdlGZ8WGD4R+J9L90BgQxnmV2LiL21V/xBjTID9w7TCuroHq9TqB6ywq8pSgJ/s/mCdXLYWMr/GWCegvS7T/Rer1HwSY63NZKyTL1jVmJNc5tMofx72fB7HSsr5EopZrzD+KTXk24lVEsi3247BdXijUq7DScsWkfoiMlms2wKpWCfeMMrGtTVyOlZJADumE8szxqRjJYnChGFdWRb22xS7HBFpYVcl7rPX4QVOXYeC6/2AXWV6xN5OQS7TFLWPFKYx8ECB3zsKa90LXXYBd2CVejbYVYWXlHK5ZYmxoEacun95U/p9FGM1NOwHBAPDgWdE5GJ72C6sBHGjWI1uh3ByNT8u423B+r02G2OKXWYRcTUG3nLZ9oewEnwEp+5/pqT1chEFHDLGHC5kWHG/eSMKPz5LqzFwdYF598S6EEFEBojIUrtKNQXrBFzW47Ug121S0vmrVPuriPiJ1dBzp31MzgeC5eT7xaU9b+RfXJanZHNymxfX5Zd23HpYx43r9ivN/hVmT/8gVv7yKTB8iDEmGKu2YxTwu4i4Nvx8zRgTbP8r6be/D/jKGLPGpV9joFuB3/gGwHUZha1HUb9XaeZXorLe4/8d64oi/557EtbVVFuXjRNkrMYSYK1Q01PnRAJWaTnMZbpAY0zbIhb9JXCVWPcTuwHfuMxnu8s8go0xAcaYga5hF7NKSVhXkY1d+kVj3TfNFyEiUmD4nlKuQ8Flv2j3a2+MCcSqxpVixi+LvVi3YgDrHj5WFWBhkrCuZAv7bUryHrABaG6vw+OcvA7gsh5i3c9/BLgGq0QXjFW1lT9NUftIYRKA5wv83n7GmC8LW3ZBxpjNxpjrsC7OXgaminW/tqTtXpYYC9rDqftXDlZDohOhlWZGxphsY8zXwF9AO5dBn2CVuK/EOh5WFjGLT7FuV5zyJEBRiyzQnYB1K8t1+9c2xizG2v9cW0SLazdWbYWfS3fBE1+o2O1dCllmUb/5Xgo/PksrAavE7zrvOsaYl0SkFtZ55jWggb3fzuKf/baw36y4dcznOl2x569i9teCHsCqSepmH5Pn2/0LHpeFKfi7+VH0eaNg/PnSKXm9z9RBrOMm0qVfVBHjnsQYk2uMeR3rnDeimHG+BXKxLv5Ox9XAEBG5z6VfAvB7gd/Y3xhzj+viy7CM0syvRKfzHP+bwIUi0tEYk4d1L/gNEakPICIR+aUR4EPgNhG5QKxGThEi0soYsxeYA7wuIoH2sKYi0quwBRpjVmH98P/DauiUYg9aBqSK1WCmtlgNxdqJSJfSrIixHpP7CnheRALsC4sxWCXxfPWBf4mIj4hcjXV/c1ZZ18EWgF21JCIRWPdtXe3Hus93OqYCl4rIOWI1tnuaIg58+3f7CBgrVuMiL7EatNUqxXICsO7lpolIK6CkHS4A64A9CHiLyJNAoMvw/wHPikhzsbQXkfwTT8Ht8QEwXES62ePWEZFBIhJQirgRkRtFpJ69/vn7UK4dWx5Fb/sZQLhYj8DVsveVbqVZJtZF6/0iEmuXyF8ApphStvoXq5HdIHuZNURkANAW+MNltG+wToJPU0hp38UU4CKsff50TAAek38aCQbZxwRY9+jbisgVYjXY+xcnJ4A/gfPFejdFEC5PBtnH0o/AeLEaqvmISH7yKu43X4K1b/1LrIZyV2DdFy2tz7GOmYvtY8BXrAZmkVg1gbWwE4693V0f79wP1LXXxXUdB4rVUDEcqwRYnGLPX8XsrwUFYBXAUkQkFPhPGbbBVOASsRqk1cS6v15cXijsHPUncL0df3+s2zDlyj5Xf4vVSNrPPvfcXMbZvITVMPGUBqD2vjUYq43J+tMMcw9wAdb+mH+BMQNoIVYDSh/7XxcRaX2ayyiX+ZU58RtjDmKVGPJfuvAIVhXiUrGqmX7BuvrEGLMMqxHCG1ilvN/5p/RzM9bBtQ7rnu9U7Cq2InwJ9AO+cIklF7gU657WdqyS7P+wqpJLazTWlfo2YKE9/49chv8BNLfn/TxwlTEmvyqsrOvwNFZjjCNYJ8pvCwx/Efg/sapwHizDOmCMWWuvy2Ssq/ijWPfHMouY5EGsRnXLsapsX6Z0+8ODWLdbjmKdlEt6vGU21kl9E1Y1bAYnV22NxUpEc7AuKD7EaoAE1n3hT+ztcY2xWubeBYzD2t5bKORJjWL0B9aKSBrwFla7hQz7tsjzwCJ7Wd1dJzLGHMVq+HUpVhXcZqBPKZf5EfAZVvXrdqz1H12GmFOxalV2YZ38XwHuMcacaM1sV8/mJ/9Jhc3EHu+4MeYXY8zxMizfdfrvsPaTyfax/jcwwB6WhFXieQmrqrg5sMhl2p+x9pW/sBq0zSgw+5uwat82YO2399nTFfmbG2OygCvs7sNY9z8LHlPFrU8CMBhr+x7E2i8fAmrYv/m/sPbNw1j7/DSXaTdgnZO22ftMI6zfeTXWveM5lHBslOL8Vej+Wsis3sQ6ZpKApVi3W0u7DdYCI7HOe3vtdS3u/QQfAm3sdc5/8uJeez3yq52/L2riMzQKa9vsw9rWX1L0+a0wM7HW7y6XftPt7ZuKdQ64xd4mp8W+pXYB8IiI3GnvRxcBQ7EuDPZhHUOlKWQVNv9ymV9+S3lVCBG5Fatx0ulW/TjGLl2mYFXJb3c6HqWUKk8i8jIQboy5xelYqpsq+cpedXpE5FK7GqwO1r3JNVilD6WUqtbEen69vV0t3xWr8eN3TsdVHWnidy+Dsap/9mBVtQ41WqWjlHIPAVi3co5h3YJ5HesdLKqMtKpfKaWU8iBa4ldKKaU8iH4oooKFhYWZmJgYp8NQSqlqY8WKFUnGmHolj6lOhyb+ChYTE0N8fLzTYSilVLUhImV5A6MqI63qV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jit4nIRyJyQET+LmK4iMjbIrJFRP4Skc6VHaNSSil1pjTx/2Mi1mcwizIA6/33zYFhwHuVEJNSSnmUjOxcp0Nwe5r4bcaY+VjfpS/KYOBTY1kKBItIw8qJTiml3N+e/Ue5bMwsp8Nwe/rmvtKLABJcuhPtfnsLjigiw7BqBYiOjq6U4JRSqjoyxrD1QBrXjFvEoexcqKPl0Yqmib/0pJB+hX7a0BjzPvA+QFxcnH7+UCmlXPy9+wh7Uo6zZFsyHy/acaK/5OQxrHsMjzsXmkfQxF96iUCUS3ck1nfvlVJKlWD93lQ+XrSdr1ckUvBr8D2bhdGhVk3uv64D3t41NPFXME38pTcNGCUik4FuwBFjzCnV/EoppSzfr9rN2J83kZSWSXrWP4322oT4seG7zRzclcqmVcNp2MDfwSg9jyZ+m4h8CfQGwkQkEfgP4ANgjJkAzAIGAluAdOA2ZyJVSqmq7eDRTB74ejXzNx0EoG6dmgw7vwltQuvw0StLmPJyPC1b1mXapCs16TtAE7/NGHNdCcMNMLKSwlFKqWpj476jfBWfwNfxCRgDRzNzADgrIogPb4mjfqAvhw4dp2nTtzl+PJtnnunNww+fS61amoKcoFtdKaVUmR08msn3q3bz/Kz1J/Xv3bIerRsG0rphIJe2b8ju3Uch0JfQ0Nq8+OIF9OvXhGbNQh2KWoEmfqWUUmWQm2cY9cVKfvx734l+XWJCGN23OXExIfjVtNJKamom9933E+PHx7NgwW107x7J8OFxToWtXGjiV0opVaLcPMP8TQe5beLyE/1euao9l3VohK+P14l+xhimTl3Hvff+xL59adxzTxytWoU5EbIqgiZ+pZRSRcrIzuWTxTt48ccNJ/p1iQnho1u7EODrc9K4xhiuuWYqU6euo2PHcL7/fihdu0ZUdsiqBJr4lVJKneTd37awdFsyCzYnndR/eK+m3HleLGH+tU7qn52di7d3DUSEXr0ac+65UYwa1RVvb30LX1WkiV8ppRQZ2bn0fPk3UtKzyMmz3rAT1ziE5g38aRLmT/924USF+p0y3YIFOxk+fCZPPHE+Q4e2Y9SorpUduiojTfxKKeXBMrJzeXX2Rj5cuP1Ev2HnN+GqsyNp0SCgyOmSktJ5+OGf+fjjP2ncOIjQ0NqVEa4qB5r4lVLKAw14awH7jhzncHr2iX7924Yz/obO1KhR2KdJ/vHVV2u5556ZpKZm8sgj5/LEE+dTp07Nig5ZlRNN/Eop5eby8gxJxzL5df0BPv9jJ4ePZbM75Thgle4bBPpyXdeoE4/ilUQEWrcOY8KES2jXrn5Fhq4qgJiCX0tQ5SouLs7Ex8c7HYZSysMs2ZrMrxv2k3o8hynxCScNqxdQi3Oa1uWxAa0JD/ItcV7p6dk899x86tatzQMPnIMxBmMosWbgdInICmOMPvRfQbTEr5RSbmTmX3t5aOrqkz6K0y4ikPaRwXSLDaV3i/oE+fkUM4eTzZq1mZEjZ7FjRwr33GPlYhFBKibnq0qgiV8ppaq5oxnZrNuTyquzNxK/8zAAzev7896NnYkN88frNErmu3enct99s5k6dR2tW4cxb94t9OoVU86RKydo4ldKqWooOzeP+ZsOMn7eVlbYyR6gfkAt5tx/PsF+Z9bYLjExlVmzNvP883158MFzqFnTq+SJVLWgiV8ppaqZ9+ZtZdyvmzlmV+c3r+/P9d2iaR8ZRPvIYHy8Tu/FOcuX72bevB089NC5dOsWSULC/fqYnhvSxK+UUtVE4uF0er7824nuBy9qwVVnR5WqgV5xjhzJ4N///pXx45fTqFEAd98dR2BgLU36bkoTv1JKVXE7ko7xxA9/n3iFbnSoHzP+1ZNA39I30iuMMYavvlrLfffN5sCBY4wa1ZXnnutLYGCtkidW1ZYmfqWUqoKMMWw5kMaDU/9idUIKAHVqevHq1R0YeFbDclnGvn1p3H77NFq3DmP69OuIi2tULvNVVZsmfqWUqiKMMWzYd5R7J69i0/60E/0jQ2rzznWd6BQdcsbLyMzM4auv1nLjje1p2DCABQtuo0OHBnidZrsAVf1o4ldKKYcdz8rlrk/jWbjln6/hicC1cVEM6RRBl5jQ03okr6B583YwfPgMNm5MJjY2hJ49o+ncuXxqD1T1oYlfKaUcsiflOB8s2MbHi3ac6Hd3ryb0alGPc5qGldtyDh48xoMP/synn64mNjaYWbOup2fP6HKbv6peNPErpVQl2pF0jEVbk3h9ziYOHcs60f/mHo156tK25f4aXGMMfft+ysaNSTz+eE/+/e/z8SvDm/uU+9HEr5RSFcQYw7xNB1m58zBJaZl8uSzhlHE+vCWOC1o3KPdlr1t3kObNQ/Hx8eLtt/vToIE/bdrUK/flqOpHE79SSpWT9KwcVu5M4c+Ew6zalcLcDQdOGh4RXJtjWTm8PbQTHaKCCfT1Rsr5pffHjmXxzDO/M3bsUl5+uR9jxvSgT5/Ycl2Gqt408SulVBnl5RlW7jrM9NV72HIwjZ3J6SQePn7KeLW8a3BHz1iu6xpNeJDvab9Rr7SmT9/IqFE/smvXEW6/vSO33NKhQpenqidN/EopVQJjDMezc5myPIFJf+xiy4G0k4Y3ruvHwLPCqeXtRefGITQJq0OPJnUr7LO1hXnssV946aVFtGlTj/nzb+W88xpX2rJV9aKJXymlCkjPyuH/vvub1IxsUo/n8PeeIyd95jYqtDYXtg7nso6N6BAZVO7V9aWVk5NHZmYOderUZMiQVgQF+TJmTA/9oI4qliZ+pZTHOnwsi/X7UknPzOWv3UdIPZ7N9NV7SHZpbR/XOIQeTerSLiKI0Do1GXBWOPUDzuzd+OXhjz8SufvuGfToEcl7711Ct26RdOsW6XRYqhrQxK+U8gjGGPYcyWDrgTS+WZnIoi3JJKVlFjpuh8ggLmzTgOG9muJdxd5ol5KSweOPz2XChHgaNQrgwgubOh2SqmY08Sul3IoxhiVbk5m0bBcrdx6mTi1vsnLy2HUo/ZRxO0QFc8lZDekaG0qdWt7E1PWrcone1bx5Oxg6dCoHD6Zz773deOaZPgQE6Ad1VNlo4ldKVWtHjmfz9+4j5BnDtyt3892q3SeGhQf6UtvHi3aNAunRpC41vWtwduMQ2jYKpFl9f8fuzZeVMQYRISYmmFatwpg162J91a46bZr4lVJV3oGjGWzal0ZGdi7zNx9k5a7D1BBhT8pxktKyThn/lh6NGdS+EV1jQx2ItvxkZOTw8ssLWbVqH999dy0xMcHMm3er02Gpak4Tv1Kqypm/6SCLtyaTnpXD8h2HWb839ZRx2jQMpEtMKDVE6N4klOYNAvCuIbRuGEidWtX/1DZ37jZGjJjFpk3JDB3ajoyMHGrX1lftqjNX/Y+OciIi/YG3AC/gf8aYlwoMjwY+AYLtcR41xsyq9ECVckOJh9P57+/biN95cpIP9vOhplcNWjcM5LZzY2gdHkjtmjWIDfMvl6/VVUXJyence+9PTJq0hqZNQ5g9+0Yuukgb8Knyo4kfEBEv4F3gQiARWC4i04wx61xG+z/gK2PMeyLSBpgFxFR6sEq5gYzsXGat2cvyHYeZtWYvR45nnxjWJKwOPZrWZXivpkSF+jkYpTO8vWuwaFECTzxxPo891lNL+arcaeK3dAW2GGO2AYjIZGAw4Jr4DRBo/x0E7KnUCJWqxjbuO8qsNXv5ed1+RGDtnn9K9SF+PgztEsVFbRvQt1X5f6ymOli9eh9vvLGUDz64lKAgXzZsGEktN7hdoaom3bMsEYDrZ7MSgW4FxnkKmCMio4E6QL+iZiYiw4BhANHR+s1r5TmMMexITmfS0p1k5+Yx/a+9J316FqwS/eCOjYipW4c7zovFv6Z3pb7atipJS8viqafm8eabSwkNrc3Gjcm0a1dfk76qULp3WQo765gC3dcBE40xr4tID+AzEWlnjMk7ZUJj3gfeB4iLiys4H6XcxpH0bDbsS2Xx1mQ27T/Kj3/vO2l4eKAvLRr4c07TMC5q24BOUSHU1tfJAvDDDxsYPfpHEhJSueuuzrz0Uj9CQ2s7HZbyAJr4LYlAlEt3JKdW5d8B9AcwxiwREV8gDDiAUm5uf2oGv204wPHsXNYkHuGv3UcKfSlOsJ8PvVvU44rOkZzXPKzaPCdf2XJz8/jPf+YRHOzL5MlXcc45USVPpFQ50cRvWQ40F5FYYDcwFLi+wDi7gAuAiSLSGvAFDlZqlEpVEmMMc9bt56vlCew9ksG6Qh6na9kggJt7NKZjVDCN6/rRKSrEY6vsSyM7O5fx45dz880dCAmpzfTp1xEe7o+Pj9aAqMqliR8wxuSIyChgNtajeh8ZY9aKyDNAvDFmGvAA8IGI3I91G+BWY4xW4yu3kt/a/qGpf5GbZ+3eNb1r0Co8gKvjoriiUwS1a3pRy7uGlubLYPHiBIYPn8GaNQfw8fFixIguREUFOR2W8lCa+G32M/mzCvR70uXvdcC5lR2XUhUpLTOHGav38P78bexOOU5mzj9NVlqFBzDpzm7U9dd3wZ+uQ4eO89hjv/D++yuJigrk+++vZfDgVk6HpTycJn6lPMyKnYd4e+4WFm1JIifvn0qrMP9a9G9Wl3ObhdG/XTiBvvr8+Jm6996f+PLLNTzwQA+eeqo3/v41nQ5JKURrqytWXFyciY+PdzoM5eFycvO4d8qfLNycdOJlORHBtWnTKJALWtWnf7twgv00KZWHjRuT8PX1pnHjYHbuTCElJYMOHcKdDqtaEZEVxpg4p+NwV1riV8qNJaVlMvqLVSzZlnyiX68W9bj/whZ0jAp2MDL3k5GRw4svLuCllxYxeHBLvvrqaho3DqZxY6cjU+pkmviVchNZOXk8P3Md6/cdJTfPsCPpGMkuL8956OKWDDu/CT5V+Hvz1dWcOVsZMWImWyBRaBQAACAASURBVLce5oYbzuL11y9yOiSliqSJX6lqLCM7l3d+3Uz8jsP8sf3Qif4do4LpFB1MmH8tujepy2UdGumjdhXk449Xcfvt02jePJRffrmJCy5o4nRIShVLE79S1cSR49n8vukgO5KOMWvNXjbsO3rS8DYNA+ndsh4P99dW4xUtNzeP/fuP0ahRAFdc0Zr9+49x333d8fXVU6qq+nQvVaoKS83I5v3ftzHuty2nDIsIrk2HqCCa1w/gjvNitRV+JVm1ai/Dh88kPT2blSuHERTky6OP9nQ6LKVKzS0Tv4jUBKKNMaeeLZWqovLyDMt2HGLz/qNs2p/Gz+v2sy8148TwC9s0oG+r+pzfoh71/GtR01vv1Vemo0cz+c9/5vHWW38QFubHG29cjLf+BqoacrvELyKDgLFATSBWRDoC/zHGXO5sZEqdyhjDR4t2MP63LSc1xAOo5V2DpvXqcPf5TRncqRG1vPXVrk7ZtCmZvn0/Yc+eo9x999m88MIFhIToB3VU9eR2iR94BuuTur8BGGP+FJFmzoak1Kl2pxznmglL2J1yHIDGdf24uG04fVrW56zIIPz106yOy87OxcfHi9jYYHr1imH06K507x7pdFhKnRF3PLNkG2NSCrxHXN9SpKqE5LRMnpu5noVbkjh4NBOAHk3q8vmd3fDSVvdVRnZ2LmPHLuH991eyYsUwgoN9mTTpCqfDUqpcuGPiXy8i1wA17K/t3QssdTgm5WGMMazfe5RPl+zgz4QUjmXlkHDo+InhYf41GXRWQ66Ki6RPy/rOBapOsXDhLoYPn8HatQcZMqQVmZk5ToekVLlyx8Q/CngSyAO+xfri3mOORqQ8hjGG1+Zs5N3ftp7UP6auH9d1jSY3L4+L2oTTr00DhyJURcnKymXEiJl8+OEqoqOD+OGHoVx2WUunw1Kq3Llj4r/YGPMI8Eh+DxG5AusiQKlyl5aZw9fxCczfdJBFW5PJysmjto8XF7Suzy3nxNAlJtTpEFUp+PjUICkpnYceOof//KcXderotwuUe3K7j/SIyEpjTOcC/VYYY852Ih79SI97yszJ5ftVu3nrl83sOfLPI3ftIgIZ2iWaoV2i8NZX41Z569cfZMyYObz77kCaNAkhL8/oGw6rAP1IT8VymxK/iFwM9AciRGSsy6BArGp/pU7btoNpTF2RSHpWLhv3HT3pozeNgny5p3dTrjo7ito19ZG76uD48Wyef34Br7yyCH//mmzenEyTJiGa9JVHcJvEDxwA/gYygLUu/Y8CjzoSkaq2jDGkZeaweGsyT01by16XUn2DwFo0r+/P9d2iGdwxglCtEq5WfvppCyNHzmLbtsPcfHMHXn31QurXr+N0WEpVGrdJ/MaYVcAqEZlkjMkocQKlXOw7ksGE37ey9WAa2bl5LN126KThzer7c1+/5lzQqoGW6qu5GTM24eNTg19/vZk+fWKdDkepSuc2id9FhIg8D7QBfPN7GmNaOBeSqoo27T/Km79s4tcNB8jI/uduUKvwAM5rHkZ4oC/N6vvTu2V9WoYHOBipOhO5uXmMH7+cuLhG9OgRxUsv9cPHpwa19AVJykO5454/EXgOeA0YANyG3uNXLhZsPsjzM9ef+LpdRHBtWoUHcEXnSAaeFU6Blz+pamzFij3cffcMVqzYy+jRXenRIwp/f701ozybOyZ+P2PMbBF5zRizFfg/EVngdFDKWcYYPv9jF89OX0dWrnUdGNc4hOcub0er8ECHo1PlLTU1kyee+JVx45ZTv34dJk++kmuuaet0WEpVCe6Y+DPFKrJtFZHhwG5AX43mwRZvTeLxb9ewIzkdgGviIhndtzlRoX4OR6Yqyief/Mk77yxj5MguPPdcX4KCfEueSCkP4Y6J/37AH/gX8DwQBNzuaESq0mXm5PLqTxv538LtJ/p1iQlh/A1nUy+gloORqYqybdthdu5MoU+fWIYPj6Nnz2g6dWrodFhKVTlul/iNMX/Yfx4FbgIQEf2clgfIzMnlk8U7+GXdAZbt+KdVfv+24Tx4cUua1fd3MDpVUbKycnnttcU8++x8IiMD2bBhJD4+Xpr0lSqCWyV+EekCRAALjTFJItIW69W9fQFN/m7oaEY2y3cc4rMlO/lt48ET/ZvUq8PVZ0dx13mx+gY9NzZ//k6GD5/B+vVJXHlla958sz9e+nsrVSy3Sfwi8iJwJbAaq0Hfd1hf5nsZGO5kbKr8Hc3I5vU5m5i4eMeJfj5ewsg+zbi9ZyyBvj7OBacqRXz8Hnr1mkhMTDAzZlzHoEH6xK5SpeE2iR8YDHQwxhwXkVBgj9290eG4VDn6bMkOpsQn8PfuVAACfb0Z0acZF7cNJzZM377m7vLyDGvXHuCssxpw9tkN+fDDyxg6tB1+fnqhp1RpuVPizzDGHAcwxhwSkQ2a9N3H4WNZvL9gG+/Nsz53Gx3qR99W9fn3oNb4aNWuR1i79gD33DOT5cv3sGnTKKKigrj99k5Oh6VUteNOib+JiOR/eleAGJdujDFXOBOWOlO/bTzAbR8vP9G9+NG+NAqu7WBEqjKlp2fz7LO/89prSwgMrMW77w4kIkLfvaDU6XKnxH9lge5xjkShyo0xhqsmLGHFzsMA3H1+E0b0aUZQba3W9RTHjmXRvv0Etm07zK23duTVVy8kLEzfv6DUmXCbxG+Mmet0DKr8HM3Ipt/Y39mfmgnAhBs707+dPp7lKVJTMwkMrEWdOjW5667O9OgRSa9eMU6HpZRb0JujqsrJyzOc9dQc9qdm0i02lM3PD9Ck7yFycvJ4662lREW9weLFCQA8+mhPTfpKlSNN/DYR6S8iG0Vki4g8WsQ414jIOhFZKyJfVHaM7m5PynFu/XgZTR6fdaLf5GHdtfGeh1i2bDddu37AfffN5pxzomjYUF+4pFRFcJuq/oJEpJYxJrOU43oB7wIXAonAchGZZoxZ5zJOc+Ax4FxjzGER0ff/l5MN+1L56e99fDB/G8eycqkhcFGbcN4c2lG/lOchHnnkZ159dTENGwbw9ddXc+WVrfW3V6qCuF3iF5GuwIdY7+iPFpEOwJ3GmNHFTNYV2GKM2WbPYzLWewHWuYxzF/CuMeYwgDHmQEXE70ny8gz9xv7OtqRjAITWqcnr13Skf7twhyNTlcEYA4CI0KCBP6NHd+XZZ/sSGKjfUlCqIrld4gfeBi4BvgcwxqwWkT4lTBMBJLh0JwLdCozTAkBEFgFewFPGmJ/KJWIPdeen8WxLOkYt7xq8fGV7BndspKU8D7FlyyFGjpzFbbd1ZOjQdowZ08PpkJTyGO6Y+GsYY3YWSCC5JUxTWLYxBbq9geZAb6z3/i8QkXbGmJRTZiYyDBgGEB0dXcqwPcOxzBw+Wridaav3sPlAGgAbnu2vCd9DZGbm8Oqri3nuufnUrOnF9de3czokpTyOOyb+BLu639j37kcDm0qYJhGIcumOxHrlb8FxlhpjsoHtIrIR60JgeYHxMMa8D7wPEBcXV/ACwmMdOpZFjxfnkpmTB0Cn6GA+u6ObJn0PsWDBTu66azobNyZzzTVteeONi2nUKMDpsJTyOO6Y+O/Bqu6PBvYDv9j9irMcaC4iscBuYChwfYFxvgeuAyaKSBhW1f+2cozbbe09cpyPFm7ngwXbARjcsRGvXd1BW+t7mP37j5GVlcuPP95A//7NnA5HKY/ljok/xxgztCwTGGNyRGQUMBvr/v1Hxpi1IvIMEG+MmWYPu0hE1mHdOnjIGJNc3sG7k4zsXNbvTeXy8YtP9HvpirMY2lVvf3iCvDzDhx+uJDMzl1GjunLlla255JIW+Pq642lHqepD8lvWugsR2QpsBKYA3xpjjjoZT1xcnImPj3cyhEqXkZ3Ll8t28fT0fx6KuKFbNM8ObkeNGlqt7wnWrNnP8OEzWbw4gf79mzFr1vV6S0eVmoisMMbEOR2Hu3K7S29jTFMROQeruv5pEfkTmGyMmexwaB5hTeIRbvl4GYeOZQEwoF04d/SMpVN0iCZ9D3DsWBZPP/07Y8cuISSkNhMnDubmmzto0leqCnG7xA9gjFkMLBaRp4A3gUmAJv4KNnVFIg9+vRqAMRe24IrOEUSG6AdVPMm6dQd5/fUl3H57R156qR916+rvr1RV43aJX0T8sV6+MxRoDfwAnONoUB7ANel/dXcPusaGOhyRqiwJCUf46act3HXX2XTpEsGWLaOJjQ1xOiylVBHcLvEDfwPTgVeMMQucDsbdHT6WxcgvVrJ4q9XO8ZnBbTXpe4icnDzefvsPnnzyNwAGD25F/fp1NOkrVcW5Y+JvYozJczoITzB52S4e/XYNAD5ewo/3nkez+vpctidYujSR4cNnsHr1fgYNas64cQOpX7+O02EppUrBbRK/iLxujHkA+EZETnlUwRhzhQNhuaU1iUe489Pl7E+1voF09dmRvHp1B4ejUpUlJSWDCy/8jKCgWnzzzTVcfnkrbbynVDXiNokf6/E9gHGORuHGUtKzuH3iclbu+uctxUsfu4DwIF8Ho1KVwRjDL79so1+/JgQH+/LDD0Pp0qURAQH6QR2lqhu3SfzGmGX2n62NMSclf/vlPHMrPyr38c2KRB6wG+8BTLqzG+c2C3MwIlVZNm1KZsSImcydu52ZM69n4MDm9O0b63RYSqnT5I7vTL29kH53VHoUbuSHP3efSPpjLmzBjpcGadL3ABkZOTz99DzOOus94uP3MH78QC6+uKnTYSmlzpDblPhF5FqsR/hiReRbl0EBwClf0FOlsyflOPdO/hOAX8b0oll9f4cjUpVlwIBJzJu3g6FD2/HGGxcTHq6/vVLuwG0SP7AMSMb6st67Lv2PAqsciaiaS0nP4pyXfgVgeK+mmvQ9wP79aYSE1KZmTS8eeugcHnusJxddpKV8pdyJ2yR+Y8x2YDvW1/jUGdqedIx+Y38H4LIOjXh0QCuHI1IVKS/P8MEHK3j00bk88si5PPpoTwYObO50WEqpCuA2iV9EfjfG9BKRw4Dr43wCGGOMvlWmlH76ey/DP18JwMCzwnn7uk4OR6Qq0urV+xg+fCZLlybSp08MQ4boRZ5S7sxtEj/Qx/5fW52dhuzcPEZOWsmcdftP9Hvo4paM7KPfTXdn77zzB/ffP5vQ0Np89tnl3HDDWfpMvlJuzm0Sv8vb+qKAPcaYLBHpCbQHPgdSHQuuGhj41gI2H0gjwNebS9o35LZzY2nRQN/C566ys3Px8fGia9cI7rijEy++2I/Q0NpOh6WUqgRuk/hdfA90EZGmwKfATOAL4BJHo6qiVuw8zJXvLQagto8Xq5+8SD+f68Z27kzhX//6iYYN/Zkw4RK6dYukW7dIp8NSSlUid3yOP88Ykw1cAbxpjBkNRDgcU5U0ZsqfJ5J+bFgdVjzRT5O+m8rOzuXVVxfRps14fvllG82ba5MXpTyVO5b4c0TkauAmYIjdz8fBeKqcPxNSGPPVn2w7eAyAGaN70i4iyOGoVEX566/93Hjjt6xZc4DLLmvJO+8MIDpaf2+lPJU7Jv7bgRFYn+XdJiKxwJcOx1RlTPh9Ky/9uAGA81vU4/2bzsbXx8vhqFRFCgysRVZWLt9/fy2DB2uLfaU8nRhzyofsqj0R8Qbym6NvMcbkOBVLXFyciY+Pd2rxJ3lh1nren78N0HftuzNjDJ9//hdz5mzj00+HICLk5Rm9jaOqDRFZYYyJczoOd+V2JX4ROQ/4DNiN9Qx/uIjcZIxZ5Gxkzvp53f4TSX/Z4xdQP1C/qOeONm5M4p57ZvLbbzvo1i2CI0cyCQ721aSvlDrB7RI/8AYw0BizDkBEWmNdCHjs1eOu5HTu+tSqdVj0aF9N+m4oIyOHF15YwMsvL8LPz4cJEwZx111na8JXSp3CHRN/zfykD2CMWS8iNZ0MyEmHjmVxw4dLAbilR2MigvVZbXeUmZnD//63kmuuactrr11Igwb6XQWlVOHcMfGvFJH/YpXyAW7AQz/Sk5WTR/cX5pKVm8cj/VtxT2/92Io72bv3KG+8sZTnn+9LUJAvf/89Ql/Co5QqkTs+xz8c2Ao8DDwCbAPudjQiB6Rn5XDWU7PJys3j8k4RmvTdSG5uHuPHL6dVq3d5660/WLZsN4AmfaVUqbhViV9EzgKaAt8ZY15xOh4nDXl3EZk5edT28eLlK9s7HY4qJytX7mX48BksX76HCy6IZfz4QbRoUdfpsJRS1YjbJH4ReRy4A1iJ9creZ4wxHzkcliOWbktm0/40usSEMHlYD7y0gZdbMMYwbNh0EhJSmTTpCq67rp1+UEcpVWZuk/ix7uW3N8YcE5F6wCzA4xL/B/O38fys9QA8eFFLTfrVnDGGH37YSK9ejQkJqc0XX1xJvXp+hIRotb5S6vS40z3+TGPMMQBjzEHca91KZej7S04k/X8PbE23JloFXJ3t2JHCpZd+yeWXT2HcuGUAtGhRV5O+UuqMuFOJv4mIfGv/LUBTl26MMVc4E1bFm7/pICMmrSQt03pB4bJ/X0D9AH1Wv7rKzs5l7NglPP3079SoIYwdexGjR3dzOiyllJtwp8R/ZYHucY5EUckysnO5+SOrNHhj92geH9gav5ru9LN6ngcemMM77yxjyJBWvP12f6Ki9IM6Sqny4zYZwhgz1+kYKltenqHVEz8BcHOPxjwzuJ3DEanTlZycTkZGDhERgYwZ04N+/Zpw2WUtnQ5LKeWGPO4+uDuZs24fAJ2ig3nq0rYOR6NOhzGGiRP/pGXLcQwfPhOAmJhgTfpKqQqjid8mIv1FZKOIbBGRR4sZ7yoRMSLi+Lv/X7Q/rzvhRn0ne3W0fv1Bevf+hNtu+4GWLcN44YW+ToeklPIAblPVX5CI1DLGZJZyXC/gXeBCIBFYLiLTXN/5b48XAPwL+KO84y2rkZNWsjM5nQHtwmmgH92pdmbM2MQVV0zB378mH3xwKbff3kkv3pRSlcLtSvwi0lVE1gCb7e4OIvJOCZN1BbYYY7YZY7KAycDgQsZ7FngFyCjPmMvqkal/MXPNXgCeuKSNk6GoMkpNta5FzzsvmuHD49i4cRR33tlZk75SqtK4XeIH3gYuAZIBjDGrgT4lTBMBJLh0J9r9ThCRTkCUMWZGSQGIyDARiReR+IMHD5Yl9hIZY/hprXVvf90zF9NIv7ZXLezZc5Rrr53Kued+RHZ2LkFBvrz99gDq1avjdGhKKQ/jjom/hjFmZ4F+uSVMU1hxy5wYKFIDeAN4oDQBGGPeN8bEGWPi6tWrV5pJSm3B5iSOHM9mVJ9m+theNZCbm8c77/xBq1bjmDZtI0OHtsWYkqdTSqmK4o6ZI0FEugLGvnc/GthUwjSJQJRLdySwx6U7AGgHzLPfjR4OTBORy4wx8eUWeSncP+VPAAZ3bFSZi1WnYc+eo1x22ZesWLGXiy5qyvjxA2naNNTpsJRSHs4dE/89WNX90cB+4Be7X3GWA81FJBbYDQwFrs8faIw5AoTld4vIPODBykz6KelZDPtsBcnHsujeJJTmDQIqa9GqjIwxiAj16vlRr14dJk++kmuuaasf1FFKVQlul/iNMQewEndZpskRkVHAbMAL+MgYs1ZEngHijTHTKiDUUvtuVSL3T1kNWM/sf3FndyfDUUUwxjB16jpefHEhc+feTEhIbX788Qanw1JKqZO4XeIXkQ9wuT+fzxgzrLjpjDGzsL7o59rvySLG7X0GIZZJbp45kfRfvao9V8dFlTCFcsK2bYcZOXIWP/20hU6dwklKSteP6SilqiS3S/xYVfv5fIHLObnFfrXyVbwV+qMDWmnSr4Ly8gwvvbSQZ5+dj49PDd5882JGjuyKt7c7tptVSrkDt0v8xpgprt0i8hnws0PhnJHs3Dwe+3YNAHf2jHU4GlUYEVi0KIFLLmnBm29eTEREoNMhKaVUsTyhWBILNHY6iNOxYe9RAPq1ro+3lyf8VNVDUlI6d901jW3bDiMifPPNNXz99dWa9JVS1YLblfhF5DD/3OOvARwCinz3flWVnZvHpeMWAlY1v3JeXp7h449X8fDDv5Camsl55zWmSZMQfH3d7jBSSrkxtzpjifW8VAesR/IA8oypnq9Lufa/SwBoHxlEs/r66J7T/v77APfcM5OFC3fRs2c0EyYMom3b+k6HpZRSZeZWid8YY0TkO2PM2U7HciY27z/Kyl0pBPv58MPIc50ORwH//W8869cf5KOPLuOWWzrqu/WVUtWWO944XiYinZ0O4kx8vtR64/CUYT30pS8OmjVrM3/8kQjAc8/1ZcOGUdx2m35FTylVvblN4heR/NqLnljJf6OIrBSRVSKy0snYyuqTJVbib17f3+FIPFNiYipXXfUVgwZ9wWuvWbdcgoJ8CQvzczgypZQ6c+5U1b8M6AwMcTqQM5FwKB2AS9o31JJlJcvJyWPcuGU88cRv5OTk8cILfXnggXOcDksppcqVOyV+ATDGbHU6kDOxZvcRAK7rGu1wJJ7n009Xc//9sxkwoBnjxg2kSZMQp0NSSqly506Jv56IjClqoDFmbGUGc7o27rOe3W8Y5OtwJJ7hyJEMNm5MpmvXCG66qT0NGtRh4MDm2rZCKeW23CnxewH+2CX/6uqvxBQAokP1fnJFMsYwZcpa7r9/NjVqCNu330vNml4MGtTC6dCUUqpCuVPi32uMecbpIM6EMYbfNh7k7MYh+qa+CrRlyyFGjpzFnDlbiYtrxIQJg6hZ08vpsJRSqlK4U+Kv1iV9gM0H0gBo3VBf2FNRNm5MokOHCdSs6cU77wzgnnvi8NKLLKWUB3GnxH+B0wGcqRGTrKcOb+xeLT8tUKXt3p1KREQgLVrU5Zln+nDjje1p1EgvsJRSnsdtijrGmENOx3AmcnLz2GKX+FuF68deysuBA8e4+ebvaN78nRMf1Xn44XM16SulPJY7lfirtXkbDwJwd68mDkfiHvLyDB9+uJJHHvmFtLQsHnnkXBo21BciKaWUJv4q4s8EqzX/LT1inA3EDWRn59K376csXLiLXr0a8957g2jdup7TYSmlVJWgib+KWLvHenFPo+DaDkdSfWVn5+Lj44WPjxe9ezfmzjs7cfPNHfSZfKWUcuE29/irs/zH+CJDNOmfrmnTNtK8+TssXpwAwLPP9uWWWzpq0ldKqQI08VcBKenZAPRpqd93L6uEhCMMGTKZwYMn4+9fEx8f3aWVUqo4WtVfBRxMywQgNqyOw5FUL+PHL+fhh38mL8/w8sv9uP/+7vj46It4lFKqOJr4q4B1e1IBiK2nib8s0tOz6d07hnHjBhITE+x0OEopVS1ovWgV8Lf9Rb5W4fpseXEOHz7OPffMYMqUvwEYM6YH06dfp0lfKaXKQEv8VcD0v/bQumEgDYO0cV9hjDF88cUaxoyZQ1JSOhER1guOatTQhntKKVVWmvgdtiPpGPtTM7m8U6TToVRJmzYlM2LETObO3U6XLo346acb6NSpodNhKaVUtaWJ32HvzdsKQKdora4uzF9/7Sc+fg/jxw9k2LCz9YM6Sil1hjTxO2z2un0AXNBKH+XLN3fuNnbsSOGOOzpz5ZWt6dMnhrp1/ZwOSyml3IIWnxy0ctdhUtKzubJzJN5akmX//jRuvPFb+vX7jLfe+oPc3DxERJO+UkqVI802Dlq+3fqg4B09Yx2OxFl5eYYJE+Jp2XIcX3+9jiefPJ9ly+7San2llKoAWtXvoJlr9hLs50NLD3+Mb82a/YwYMZPevWN4771BtGwZ5nRISinltrRI5RBjDH8lHiEiuDZeHvhYWlpaFl9/vRaADh3CWbLkDubOvVmTvlJKVTBN/DYR6S8iG0Vki4g8WsjwMSKyTkT+EpG5ItL4TJY3c81eANpHBp3JbKql77/fQOvW7zJ06Dds334YgG7dIvWDOkopVQk08QMi4gW8CwwA2gDXiUibAqOtAuKMMe2BqcArZ7LMX9cfAOCR/q3OZDbVys6dKQwePJnLL59CSIgvCxbcRmxsiNNhKaWUR9F7/JauwBZjzDYAEZkMDAbW5Y9gjPnNZfylwI1nssDFW5OJDKlNsF/NM5lNtXH8eDZdunzAsWPZvPrqhdx7bzf9oI5SSjlAE78lAkhw6U4EuhUz/h3Aj0UNFJFhwDCA6OjoQsfZl5pBv9bu/+z+mjX7adeuPrVr+/D++5fSuXNDoqM97/aGUkpVFVrVbyns5rIpdESRG4E44NWiZmaMed8YE2eMiatXr94pw9fvtb7G16KB+7bmP3ToOMOGTad9+wl88816AIYMaaVJXymlHKYlfksiEOXSHQnsKTiSiPQD/g30MsZknu7C5q7fD0BfN3xbnzGGzz//iwcemMOhQ8d58MEe9O/fzOmwlFJK2TTxW5YDzUUkFtgNDAWudx1BRDoB/wX6G2MOnMnCvlxm3VXoHO1+Ddtuuuk7Jk1aQ/fukfz88yA6dAh3OiSllFIuNPEDxpgcERkFzAa8gI+MMWtF5Bkg3hgzDatq3x/42n7sbJcx5rKyLisjO5fdKcfxr+XtNp+VPX48G2/vGvj4eHH11W0477xo7rrrbLdZP6WUciea+G3GmFnArAL9nnT5u195LGfFTuu59RF9mpbH7Bw3Z85WRoyYyZ13dubRR3syeLDnPJ6olFLVkTbuq2Q7k9MB6N+2eleB7917lKFDp3LxxZ/j5VWDbt0inA5JKaVUKWiJv5KtTkgBoK5/LYcjOX1Tp67jjjumkZmZw9NP9+aRR86lVi3dlZRSqjrQs3UlO5yeRYifD0G1fZwOpcyMMYgI0dFBdO8eybhxA2jevK7TYSmllCoDTfyVbMuBNGLC6jgdRpkcPZrJk0/+RnZ2HuPGDaRr1whmzz6jFxcqpZRyiN7jr2Tbko6RkZ3ndBilYozhm2/W0br1u7z11h8YYzCm0PcaKaWUqia0xF+J0jJzAGjXKNDhSEqWkHCE4cNnMmvWZjp2DOebb66hW7dIp8NSSil1hjTxm1Ht0wAAFQxJREFUV6LvVu0GoH+7qt+iPy/PsGLFHsaOvYjRo7vh7a2VQ0op5Q408VeivxOPAHB+i1Pf318VLFy4iylT/ubttwfQuHEwO3bch6+v7iJKKeVOtBhXiVYlHKZlgwB8vKrWZk9OTufOO6dx3nkfM336JvbtSwPQpK+UUm6oamUgN7c3JYO8KtQ4zhjDxIl/0rLlOD75ZDUPP3wOa9eOoGFD9/1qoFJKeTot0lWyRsG1nQ7hhLS0LB5/fC4tW4YxYcIgzjqrgdMhKaWUqmCa+CtJZk4uRzNzaBfhbIv+9PRsJkyIZ/TorgQE1GLRottp3DhYP6ijlFIeQhN/JVm+3fo4T/P6zlWj//jjZkaOnMX27Sm0aFGXSy5pQWys+30aWCmlVNE08VeSw+lZAESF+lX6svfsOcp99/3E11+vo2XLuvz22y307h1T6XGoqi87O5vExEQyMjKcDkV5AF9fXyIjI/HxqX6vMK/ONPFXkvV7UwGICq38e/zXXfcNy5bt5rnn+vDgg+foB3VUkRITEwkICCAmJgb5//buPTqq6l7g+PcHqAkQAgZJUVoeEiEPIBKgpCACFkQUuVokAlVAbC8PZQmKF5e4qEDxXRXBUi+3C7SSRFCUG4pcwPhCgYZLTAK0KcU0omngQoCABAL53T/OYUhCQiYmMwOZ32etWWvOPq/f7DXJb84+++wtdvvH+I6qcujQIfbv30/Hjh0DHU5QsQzgJ98eOQnANX6alW/Hju/o1KkVrVqFsnjxbTRtegXXX3+1X85tLl8lJSWW9I1fiAgREREcPHgw0KEEHXucz0+aNHKq2tf/UI8eLWH69PX06bOM3/72MwC6dYu0pG+8Zknf+It91wLDrvj9ZE/BMTq3ae6z46sqq1bt5pFHPuRf/zrOtGm9eeqpAT47nzHGmMuTXfH7wdGTpewuOMapM2d9do4FCz4lKWk1bduGsX37r3jtteGEh4f47HzG+Erjxo2Jj48nLi6OESNGcOTIEc+6Xbt2MXjwYG644QaioqKYP39+hRkj169fT69evYiOjqZr16489thjgfgIF7Vz504efPDBCmUjR44kMTGxQtmECRNYvXp1hbLmzc9fPOTm5jJ8+HA6d+5MdHQ0o0ePprCwsE6xHT58mCFDhhAVFcWQIUMoKiq6YJv09HTi4+M9r5CQEN5//30Axo0bR5cuXYiLi+OBBx6gtLQUgLS0NObOnVun2Ew9OjfVqr1880pISNB3d3yj7f8jTVO352t9OnXqjB44cFxVVb/+ukhffXWrlpaerddzmOCye/fuQIegzZo187y///77dcGCBaqq+v3332unTp10w4YNqqp64sQJHTZsmC5evFhVVbOzs7VTp066Z88eVVUtLS3VJUuW1GtspaWldT7GqFGjNDMz07NcVFSk7dq1065du+q+ffs85ePHj9dVq1ZV2Pdc3Zw8eVI7d+6sa9eu9az76KOPNDs7u06xzZo1S5955hlVVX3mmWf08ccfv+j2hw4d0latWumJEydUVXXdunVaVlamZWVleu+99+rrr7+uqqplZWUaHx/v2a68qr5zQIZeAv+/G+rLmvr9IMudnGdYt/qble+TT/KYPHkd7duHs379ODp0aMn06T+tt+Mb8/R/72L3d8fq9Zgx17Zg7ohYr7dPTEwkKysLgJUrV9KvXz+GDh0KQNOmTVm8eDEDBw5k2rRpPP/88zz55JN07doVgCZNmjB16tQLjnn8+HEefvhhMjIyEBHmzp3LL37xC5o3b87x4848FatXryYtLY3ly5czYcIErr76anbu3El8fDxr1qwhMzOTli1bAtC5c2e2bNlCo0aNmDx5Mvn5+QC88sor9OvXr8K5i4uLycrKokePHp6yd999lxEjRhAZGUlKSgpPPPFEjfWycuVKEhMTGTFihKds0KBBXtdrdT744AM+/vhjAMaPH8/AgQN57rnnqt1+9erV3HbbbTRt6jymPHz4cM+6Pn36sH//fsC5lz9w4EDS0tIYPXp0neM0dWOJ3w+KS84A0CKk7s+qHjx4glmzNrJixVeeZG8dZExDdPbsWTZv3sykSZMAp5k/ISGhwjbXX389x48f59ixY+Tk5PDoo4/WeNz58+cTHh5OdnY2QJXN2ZXl5uayadMmGjduTFlZGWvWrGHixIls27aNDh06EBkZydixY5kxYwb9+/cnPz+fW2+9lT179lQ4TkZGBnFxcRXKkpOTmTt3LpGRkYwaNcqrxJ+Tk3NBXVSluLiYm266qcp1K1euJCYmpkJZYWEhbdu2BaBt27YcOHDgosdPSUlh5syZF5SXlpby1ltv8eqrr3rKevXqxWeffWaJ/xJgid8P/n6gmPgft6zzcb744htGjEjm2LFTPPFEf+bMGUDTpjbwhfGN2lyZ16eTJ08SHx9PXl4eCQkJDBkyBHBuS1b3I7c2P343bdpESkqKZ7lVq5pHr7znnnto3LgxAElJScybN4+JEyeSkpJCUlKS57i7d+/27HPs2DGKi4sJCzs/WmdBQQHXXHN+Wu7CwkL27t1L//79ERGaNGlCTk4OcXFxVX6m2v7IDwsLIzMzs1b7eKugoIDs7GxuvfXWC9ZNnTqVAQMGVPjR0aZNG7777jufxGJqxzr3+cH+opNcWYepeEtLnU6BsbHXMHhwRzIz/52FC2+xpG8apNDQUDIzM/nnP//J6dOnWbJkCQCxsbFkZGRU2Hbfvn00b96csLAwYmNj2bFjR43Hr+4HRPmyyiMXNmvWzPM+MTGRvXv3cvDgQd5//33uvvtuAMrKyvjyyy/JzMwkMzOTb7/9tkLSP/fZyh87NTWVoqIiOnbsSIcOHcjLy/P8KImIiKjQGnH48GFat27tqQtvPmtxcXGFjnjlX+V/pJwTGRlJQUEB4CT2Nm3aVHvsd955h7vuuuuCUfeefvppDh48yO9+97sK5SUlJYSGXjqTlAUzS/x+cPjEaa5rVfsv/IkTp5k9exN9+iyjtPQs4eEhrFp1D7Gx1f8xGtNQhIeHs2jRIl588UVKS0sZN24cn3/+OZs2bQKcloHp06fz+OOPAzBr1iwWLlxIbm4u4CTiyskHYOjQoSxevNizfC65RkZGsmfPHk9TfnVEhLvuuouZM2cSHR1NRERElcet6ko7OjqavXv3epaTk5P58MMPycvLIy8vjx07dngS/8CBA0lNTeX0aWe47+XLl3vu448dO5YvvviCdevWeY714Ycfem5fnHPuir+qV+VmfoA777yTFStWALBixQpGjhxZbT0kJyczZsyYCmXLli1jw4YNJCcn06hRxfSSm5t7wW0OExiW+H3s9JkyANrW8tG6detyiY19neee20LPnj+ixO0nYEwwufHGG+nRowcpKSmEhobywQcfsGDBArp06UK3bt3o3bs3Dz30EADdu3fnlVdeYcyYMURHRxMXF+e5ei1vzpw5FBUVERcXR48ePUhPTwfg2Wef5Y477mDw4MGe+9zVSUpK4k9/+pOnmR9g0aJFZGRk0L17d2JiYli6dOkF+3Xt2pWjR49SXFxMXl4e+fn59O3b17O+Y8eOtGjRgm3btnHHHXdw0003kZCQQHx8PFu2bPF0tAsNDSUtLY3XXnuNqKgoYmJiWL58+UWv0L0xe/ZsNm7cSFRUFBs3bmT27NmA0zeh/COIeXl5fPPNN9x8880V9p88eTKFhYUkJiYSHx/PvHnzPOvS09O5/fbb6xSfqR/iPDlhfOVH18dqyD3P8+6Un5HQvuZ7iUeOlDBp0lree28P0dGtWbr0DgYMaO+HSI2BPXv2EB0dHegwGrSXX36ZsLCwC57lb8gKCwsZO3YsmzdvvmBdVd85Edmhqr38FV+wsSt+Hzt60hnAoudPvOvc17z5lRQUFLNw4WAyMydb0jemgZkyZQpXXeWfOTsuFfn5+bz00kuBDsO4rFe/j5WpMqbPTy7aG3f79m+ZO/djVq68m1atQvn88wdo1Mge0TOmIQoJCeG+++4LdBh+1bt370CHYMqxK34/uKdXuyrLjxwpYdq0dfTtu4ysrEL+8Q+nk5ElfRNIdvvP+It91wLDrvj9oNt14RWWVZXU1F3MmLGBAwdOMH36T5k3bxAtWgRX85+59ISEhHDo0CEiIiJsYCjjU6rKoUOHCAmxOUX8zRK/H1xRxTP8b7+dTbt2LUhLG0NCwrUBiMqYC7Vr1479+/fbHOnGL0JCQmjXruoWUeM71qvfx5pdd4Oe+DaXU6fO8MILXzB2bDc6dWrFkSMlhIVdSeM6DOxjjDENkfXq9y3LOi4RGSYifxORvSIyu4r1V4lIqrt+m4h08Oa4jRsJ6elf0737Up56Kp1333VGy2rZMsSSvjHGGL+zzAOISGNgCXAbEAOMEZHKw1pNAopUtTPwMlD9lFXlnC4tY/DgNzlzpoz168cxa1a/mncyxhhjfMQSv6MPsFdV96nqaSAFqDxW5Uhghft+NXCLeNH76WzJGebMuYmcnCkMG9a5XoM2xhhjass69zmuA74pt7wfqDy5vWcbVT0jIkeBCOD/Kh9MRH4N/NpdPLVgwS05CxbUe8yXo9ZUUV9ByurCYfVwntXFeV0CHUBDZonfUdWVe+Vej95s4xSqvgG8ASAiGdZJxWF1cZ7VhcPq4Tyri/NEJKPmrcwPZU39jv3Aj8sttwMqTxzt2UZEmgDhwGG/RGeMMcbUE0v8jr8AUSLSUUSuBO4F1lbaZi0w3n0/CvhI7VlIY4wxlxlr6sdzz/4hYAPQGPijqu4SkXlAhqquBf4LeEtE9uJc6d/r5eHf8EnQlyeri/OsLhxWD+dZXZxndeFDNoCPMcYYE0Ssqd8YY4wJIpb4jTHGmCBiib8e+Gq438uRF3UxU0R2i0iWiGwWkfaBiNMfaqqLctuNEhEVkQb7KJc3dSEio93vxi4RWenvGP3Fi7+Rn4hIuojsdP9OhgciTn8QkT+KyAERyalmvYjIIreuskSkp79jbJBU1V51eOF0BvwH0Am4EvgKiKm0zVRgqfv+XiA10HEHsC4GAU3d91OCuS7c7cKAT4GtQK9Axx3A70UUsBNo5S63CXTcAayLN4Ap7vsYIC/QcfuwPgYAPYGcatYPB9bjjKPSF9gW6Jgbwsuu+OvOZ8P9XoZqrAtVTVfV793FrThjJjRE3nwvAOYDzwMl/gzOz7ypi18BS1S1CEBVD/g5Rn/xpi4UaOG+D+fCMUUaDFX9lIuPhzISeFMdW4GWItLWP9E1XJb4666q4X6vq24bVT0DnBvut6Hxpi7Km4Tza74hqrEuRORG4MeqmubPwALAm+/FDcANIrJFRLaKyDC/Redf3tTFb4Bfish+4M/Aw/4J7ZJU2/8pxgv2HH/d1etwv5c5rz+niPwS6AXc7NOIAueidSEijXBmeZzgr4ACyJvvRROc5v6BOK1An4lInKoe8XFs/uZNXYwBlqvqSyKSiDN+SJyqlvk+vEtOsPzv9Cu74q87G+73PG/qAhH5OfAkcKeqnvJTbP5WU12EAXHAxyKSh3P/cm0D7eDn7d/IB6paqqpfA3/D+SHQ0HhTF5OAdwBU9UsgBGcCn2Dk1f8UUzuW+OvOhvs9r8a6cJu3/4CT9BvqfVyooS5U9aiqtlbVDqraAae/w52q2hAnJ/Hmb+R9nI6fiEhrnKb/fX6N0j+8qYt84BYAEYnGSfwH/RrlpWMtcL/bu78vcFRVCwId1OXOmvrrSH073O9lxcu6eAFoDqxy+zfmq+qdAQvaR7ysi6DgZV1sAIaKyG7gLDBLVQ8FLmrf8LIuHgX+U0Rm4DRrT2igFwqISDLO7Z3Wbp+GucAVAKq6FKePw3BgL/A9MDEwkTYsNmSvMcYYE0Ssqd8YY4wJIpb4jTHGmCBiid8YY4wJIpb4jTHGmCBiid8YY4wJIpb4jakjETkrIpnlXh0usm2H6mYiq+U5P3ZnePvKHea2yw84xmQRud99P0FEri23bpmIxNRznH8RkXgv9nlERJrW9dzGmKpZ4jem7k6qany5V56fzjtOVXvgTAD1Qm13VtWlqvqmuzgBuLbcugdVdXe9RHk+ztfxLs5HAEv8xviIJX5jfMC9sv9MRP7Xff2sim1iRWS720qQJSJRbvkvy5X/QUQa13C6T4HO7r63uPO4Z7tznV/llj/rznWfJSIvumW/EZHHRGQUzrwJb7vnDHWv1HuJyBQReb5czBNE5LUfGOeXlJtgRUR+LyIZIrJLRJ52y6bj/ABJF5F0t2yoiHzp1uMqEWlew3mMMRdhid+Yugst18y/xi07AAxR1Z5AErCoiv0mA6+qajxO4t3vDtGaBPRzy88C42o4/wggW0RCgOVAkqp2wxmZc4qIXA3cBcSqandgQfmdVXU1kIFzZR6vqifLrV4N3F1uOQlI/YFxDsMZmvecJ1W1F9AduFlEuqvqIpyx2Aep6iB3+N45wM/duswAZtZwHmPMRdiQvcbU3Uk3+ZV3BbDYvad9Fmfs+cq+BJ4UkXbAe6r6dxG5BUgA/uIOaRyK8yOiKm+LyEkgD2fq1i7A16qa665fAUwDFgMlwDIRWQd4PQ2wqh4UkX3uOOl/d8+xxT1ubeJshjNEbc9y5aNF5Nc4/4faAjFAVqV9+7rlW9zzXIlTb8aYH8gSvzG+MQMoBHrgtKyVVN5AVVeKyDbgdmCDiDyIMw3pClV9wotzjCs/qY+IRFS1kTs+fB+ciV/uBR4CBtfis6QCo4G/AmtUVcXJwl7HCXwFPAssAe4WkY7AY0BvVS0SkeU4k9FUJsBGVR1Ti3iNMRdhTf3G+EY4UODOoX4fztVuBSLSCdjnNm+vxWny3gyMEpE27jZXi0h7L8/5V6CDiHR2l+8DPnHviYer6p9xOs5V1bO+GGeq4Kq8B/wbzjzxqW5ZreJU1VKcJvu+7m2CFsAJ4KiIRAK3VRPLVqDfuc8kIk1FpKrWE2OMlyzxG+MbrwPjRWQrTjP/iSq2SQJyRCQT6Aq86faknwP8j4hkARtxmsFrpKolOLOXrRKRbKAMWIqTRNPc432C0xpR2XJg6bnOfZWOWwTsBtqr6na3rNZxun0HXgIeU9WvgJ3ALuCPOLcPznkDWC8i6ap6EOeJg2T3PFtx6soY8wPZ7HzGGGNMELErfmOMMSaIWOI3xhhjgoglfmOMMSaIWOI3xhhjgoglfmOMMSaIWOI3xhhjgoglfmOMMSaI/D82qH1m0UTgigAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the SVM reduced features and tuning RBF kernel identified 732\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06383344
11290732
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6383 344\n", + "1 1290 732" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6727\n", + " 1 0.68 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[4] = ([\"SVM\" , \n", + " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Parameter tuning\n", + "##### Learning rate\n", + "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", + "\n", + "##### Momentum\n", + "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", + "\n", + "##### Loss function\n", + "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 2022 Defaulters, the Neural Network (5x26) identified 788\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06281446
11234788
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6281 446\n", + "1 1234 788" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.204745415479134\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.93 0.88 6727\n", + " 1 0.64 0.39 0.48 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
3Logistic Regression0.6191890.766563
4SVM0.3620180.723867
5Neural Network0.3897130.774088
\n", + "
" + ], + "text/plain": [ + " Model Recall-1 AUROC\n", + "0 Decision Tree (GINI) 0.418892 0.609249\n", + "1 Random Forest 0.386746 0.761906\n", + "2 Gradient Boosted 0.380811 0.775534\n", + "3 Logistic Regression 0.619189 0.766563\n", + "4 SVM 0.362018 0.723867\n", + "5 Neural Network 0.389713 0.774088" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4586 - accuracy: 0.8061\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 1s 83us/step - loss: 0.4432 - accuracy: 0.8153\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 1s 86us/step - loss: 0.4412 - accuracy: 0.8154 0s - loss: 0.4414 - accuracy: 0.\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 1s 83us/step - loss: 0.4397 - accuracy: 0.8165\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 1s 82us/step - loss: 0.4385 - accuracy: 0.8156\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 1s 80us/step - loss: 0.4377 - accuracy: 0.8170\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 1s 84us/step - loss: 0.4374 - accuracy: 0.8162\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 1s 82us/step - loss: 0.4362 - accuracy: 0.8173\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 91us/step - loss: 0.4350 - accuracy: 0.8172\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4346 - accuracy: 0.8176\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6727\n", + " 1 0.65 0.36 0.47 2022\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT-MASTER.ipynb b/BT2101_Default - EDIT-MASTER.ipynb deleted file mode 100644 index 4b6137d..0000000 --- a/BT2101_Default - EDIT-MASTER.ipynb +++ /dev/null @@ -1,5304 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-4Rm0wjQMUHi" - }, - "source": [ - "# BUILDING A DEFUALT DETECTION MODEL\n", - "\n", - "---\n", - "\n", - "\n", - "\n", - "## Table of Contents\n", - "1. Problem Description (Brief Write Up)\n", - "2. Exploratory Data Analysis (EDA)\n", - "3. Data Pre-processing\n", - "4. Model Selection\n", - "5. Evaluation\n", - "6. Discussion and Possible Improvements\n", - "\n", - "## 1. Problem Description\n", - "\n", - "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", - "\n", - "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", - "\n", - "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", - "\n", - "### X1 - X5: Indivual attributes of customer\n", - "\n", - "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", - "\n", - "X2: Gender (1 = male; 2 = female). \n", - "\n", - "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", - "\n", - "X4: Marital status (1 = married; 2 = single; 3 = others). \n", - "\n", - "X5: Age (year). \n", - "\n", - "### X6 - X11: Repayment history from April to Septemeber 2005\n", - "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", - "\n", - "\n", - "X6 = the repayment status in September, 2005\n", - "\n", - "X7 = the repayment status in August, 2005\n", - "\n", - "X8 = the repayment status in July, 2005\n", - "\n", - "X9 = the repayment status in June, 2005\n", - "\n", - "X10 = the repayment status in May, 2005\n", - "\n", - "X11 = the repayment status in April, 2005. \n", - "\n", - "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", - "\n", - "X12 = amount of bill statement in September, 2005; \n", - "\n", - "X13 = amount of bill statement in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X17 = amount of bill statement in April, 2005. \n", - "\n", - "### X18 - X23: Amount of previous payment (NT dollar)\n", - "X18 = amount paid in September, 2005\n", - "\n", - "X19 = amount paid in August, 2005\n", - "\n", - ". . .\n", - "\n", - "X23 = amount paid in April, 2005. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aM_aIU6UPHe4" - }, - "source": [ - "## EDA\n", - "\n", - "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", - "\n", - "### Importing packages and the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Is0wEkk3LJCt" - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "x_Z7u_9vRC5m" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "KhmX9KWWyrUW" - }, - "outputs": [], - "source": [ - "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", - "df = pd.read_csv(url, header = 1, index_col = 0)\n", - "# Dataset is now stored in a Pandas Dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "colab_type": "code", - "id": "FhJ2eAxVQhBm", - "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", - "

5 rows × 24 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 20000 2 2 1 24 2 2 -1 -1 \n", - "2 120000 2 2 2 26 -1 2 0 0 \n", - "3 90000 2 2 2 34 0 0 0 0 \n", - "4 50000 2 2 1 37 0 0 0 0 \n", - "5 50000 1 2 1 57 -1 0 -1 0 \n", - "\n", - " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", - "ID ... \n", - "1 -2 ... 0 0 0 0 689 0 \n", - "2 0 ... 3272 3455 3261 0 1000 1000 \n", - "3 0 ... 14331 14948 15549 1518 1500 1000 \n", - "4 0 ... 28314 28959 29547 2000 2019 1200 \n", - "5 0 ... 20940 19146 19131 2000 36681 10000 \n", - "\n", - " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", - "ID \n", - "1 0 0 0 1 \n", - "2 1000 0 2000 1 \n", - "3 1000 1000 5000 0 \n", - "4 1100 1069 1000 0 \n", - "5 9000 689 679 0 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#rename the target variable to \"Y\" for convenience\n", - "df[\"Y\"] = df[\"default payment next month\"] \n", - "df = df.drop(\"default payment next month\", axis = 1)\n", - "df0 = df #backup of df\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "zcuPyfM86AKj", - "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 24 Columns and 30000 Rows\n" - ] - } - ], - "source": [ - "size = df.shape\n", - "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "colab_type": "code", - "id": "QVaSnvJP3VbO", - "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check for null values\n", - "df.isnull().any().sum() " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eVYXnIGH9Zq6" - }, - "source": [ - "From the above analyses, we observe that:\n", - "1. The data indeed has 30000 rows and 24 columns\n", - "2. There are no null values\n", - "\n", - "We will now explore the features more in depth." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "W6hhPNl1Slau" - }, - "source": [ - "### Exploring the features" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1Sp2F3gzXX2F" - }, - "source": [ - "**1) Exploring target attribute:**\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 - }, - "colab_type": "code", - "id": "DCSEICWwXWgX", - "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "defaults : 22.12 %\n", - "non defaults : 77.88000000000001 %\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Frequency')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "All = df.shape[0]\n", - "default = df[df['Y'] == 1]\n", - "nondefault = df[df['Y'] == 0]\n", - "\n", - "x = len(default)/All\n", - "y = len(nondefault)/All\n", - "\n", - "print('defaults :',x*100,'%')\n", - "print('non defaults :',y*100,'%')\n", - "\n", - "# plotting target attribute against frequency\n", - "labels = ['non default','default']\n", - "classes = pd.value_counts(df['Y'], sort = True)\n", - "classes.plot(kind = 'bar', rot=0)\n", - "plt.title(\"Target attribute distribution\")\n", - "plt.xticks(range(2), labels)\n", - "plt.xlabel(\"Class\")\n", - "plt.ylabel(\"Frequency\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tysR0WHw4SGU" - }, - "source": [ - "**2) Exploring categorical attributes**\n", - "\n", - "Categorical attributes are:\n", - "- Sex\n", - "- Education\n", - "- Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 323 - }, - "colab_type": "code", - "id": "s61SSRII00UB", - "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 60.373333\n", - "1 39.626667\n", - "Name: SEX, dtype: float64\n", - "--------------------------------------------------------\n", - "2 46.766667\n", - "1 35.283333\n", - "3 16.390000\n", - "5 0.933333\n", - "4 0.410000\n", - "6 0.170000\n", - "0 0.046667\n", - "Name: EDUCATION, dtype: float64\n", - "--------------------------------------------------------\n", - "2 53.213333\n", - "1 45.530000\n", - "3 1.076667\n", - "0 0.180000\n", - "Name: MARRIAGE, dtype: float64\n" - ] - } - ], - "source": [ - "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", - "print(\"--------------------------------------------------------\")\n", - "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Uudv5XE828nb" - }, - "source": [ - "**Findings**\n", - "\n", - "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", - "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", - "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 357 - }, - "colab_type": "code", - "id": "U3IJzhwwe5KK", - "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total target attributes:\n", - "non defaults : 77.88000000000001 %\n", - "defaults : 22.12 %\n", - "--------------------------------------------------------\n", - "SEX Male Female\n", - "Y \n", - "non defaults 75.832773 79.223719\n", - "defaults 24.167227 20.776281\n", - "--------------------------------------------------------\n", - "EDUCATION 0 1 2 3 4 5 \\\n", - "Y \n", - "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", - "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", - "\n", - "EDUCATION 6 \n", - "Y \n", - "non defaults 84.313725 \n", - "defaults 15.686275 \n", - "--------------------------------------------------------\n", - "MARRIAGE unknown married single others\n", - "Y \n", - "non defaults 90.740741 76.528296 79.071661 73.993808\n", - "defaults 9.259259 23.471704 20.928339 26.006192\n" - ] - } - ], - "source": [ - "#proportion of target attribute (for reference)\n", - "print('Total target attributes:')\n", - "print('non defaults :',y*100,'%')\n", - "print('defaults :',x*100,'%')\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with Sex\n", - "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(sex_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with education\n", - "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(education_target)\n", - "print(\"--------------------------------------------------------\")\n", - "#analysing default payment with marriage\n", - "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", - "print(marriage_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kOriUQ0wxbhD" - }, - "source": [ - "**Conclusion**\n", - "\n", - "From the analyses above we conclude that\n", - "\n", - "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "77GAylGWnPJO" - }, - "source": [ - "**3) Analysis of Numerical Attributes**\n", - "\n", - "The numerical attributes are:\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "HEcCl5Rj-N0T", - "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", - "
" - ], - "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", - "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", - "\n", - " 9 10 11 12 13 14 15 \\\n", - "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", - "\n", - " 16 17 18 19 \n", - "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#printing numerical attributes\n", - "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", - "
" - ], - "text/plain": [ - " count mean std min 25% \\\n", - "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", - "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", - "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", - "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", - "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", - "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", - "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", - "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", - "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", - "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", - "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", - "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", - "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", - "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", - "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", - "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", - "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", - "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", - "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", - "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", - "\n", - " 50% 75% max \n", - "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", - "AGE 34.0 41.00 79.0 \n", - "PAY_0 0.0 0.00 8.0 \n", - "PAY_2 0.0 0.00 8.0 \n", - "PAY_3 0.0 0.00 8.0 \n", - "PAY_4 0.0 0.00 8.0 \n", - "PAY_5 0.0 0.00 8.0 \n", - "PAY_6 0.0 0.00 8.0 \n", - "BILL_AMT1 22381.5 67091.00 964511.0 \n", - "BILL_AMT2 21200.0 64006.25 983931.0 \n", - "BILL_AMT3 20088.5 60164.75 1664089.0 \n", - "BILL_AMT4 19052.0 54506.00 891586.0 \n", - "BILL_AMT5 18104.5 50190.50 927171.0 \n", - "BILL_AMT6 17071.0 49198.25 961664.0 \n", - "PAY_AMT1 2100.0 5006.00 873552.0 \n", - "PAY_AMT2 2009.0 5000.00 1684259.0 \n", - "PAY_AMT3 1800.0 4505.00 896040.0 \n", - "PAY_AMT4 1500.0 4013.25 621000.0 \n", - "PAY_AMT5 1500.0 4031.50 426529.0 \n", - "PAY_AMT6 1500.0 4000.00 528666.0 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Analysis of PAY_0 to PAY_6**\n", - "\n", - "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", - "\n", - "However, the presence of -2, -1 in these columns indicates that\n", - "1. There is anomalous data, OR \n", - "2. The numbers do not strictly correspond to the number of months of payment delay. \n", - "\n", - "This means we must conduct some data transformation.\n", - "\n", - "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 669 - }, - "colab_type": "code", - "id": "awXnqvLOS-wB", - "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", - " fig=plt.figure()\n", - " for i, var_name in enumerate(variables):\n", - " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", - " df[var_name].hist(bins=n_bins,ax=ax)\n", - " ax.set_title(var_name)\n", - " fig.tight_layout() # Improves appearance a bit.\n", - " plt.show()\n", - "\n", - "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", - "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", - "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", - "\n", - "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", - "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", - "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "C6c_Gz6wUrJ8" - }, - "source": [ - "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", - "\n", - "Now that we have an idea of the features, we will move on to feature selection and data preparation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "AQBksEyEf4Sf" - }, - "source": [ - "## Data Preprocessing\n", - "\n", - "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", - "1. Removing Noise - Inconsistencies\n", - "2. Dealing with negative values of PAY_0 to PAY_6\n", - "3. Outliers\n", - "4. One Hot Encoding\n", - "5. Train Test Split\n", - "6. Feature selection\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Noise\n", - "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 1, 3, 4])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", - "df[\"EDUCATION\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, for Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", - "df[\"MARRIAGE\"].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Separating negative and positive values for PAY_0 to PAY_6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", - "\n", - "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(0,7):\n", - " try:\n", - " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dummy variables for negative values\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", - "
" - ], - "text/plain": [ - " S_0 S_2 S_3 S_4 S_5 S_6\n", - "ID \n", - "1 1 1 -1 -1 -2 -2\n", - "2 -1 1 0 0 0 1\n", - "3 0 0 0 0 0 0\n", - "4 0 0 0 0 0 0\n", - "5 -1 0 -1 0 0 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('Dummy variables for negative values')\n", - "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#attributes representing positive values\n", - "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", - " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outliers\n", - "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8527341.56471735.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000001.0000001.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", - "

8 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", - "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", - "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", - "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", - "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", - "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", - "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", - "\n", - " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", - "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", - "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", - "\n", - " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", - "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", - "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", - "min ... 0.000000 0.000000 0.000000 0.000000 \n", - "25% ... 150.000000 82.000000 0.000000 0.000000 \n", - "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", - "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", - "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", - "\n", - " S_0 S_2 S_3 S_4 S_5 \\\n", - "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", - "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", - "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", - "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", - "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", - "\n", - " S_6 \n", - "count 26245.000000 \n", - "mean -0.428158 \n", - "std 0.900723 \n", - "min -2.000000 \n", - "25% -1.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 1.000000 \n", - "\n", - "[8 rows x 30 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy import stats\n", - "#we are only concerned with the ordinal data\n", - "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", - "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", - "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", - "df = df[rows]\n", - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature Scaling\n", - "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", - "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "scaler = MinMaxScaler()\n", - "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", - "df1 = df.copy()\n", - "df1[cols.columns] = scaler.fit_transform(cols)\n", - "df = df1" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", - "

5 rows × 30 columns

\n", - "
" - ], - "text/plain": [ - " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", - "ID \n", - "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", - "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", - "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", - "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", - "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", - "\n", - " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", - "ID ... \n", - "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", - "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", - "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", - "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", - "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", - "\n", - "[5 rows x 30 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### One-Hot Encoding for Categorical attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", - "\n", - "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", - "\n", - "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", - "\n", - "The following categorical columns will be one-hot encoded\n", - "\n", - "1. EDUCATION\n", - "2. MARRIAGE\n", - "3. S0 - S6\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import OneHotEncoder" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "onenc = OneHotEncoder(categories='auto')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GRADUNIHSMARRIEDSINGLE
00.01.00.01.00.0
10.01.00.00.01.0
20.01.00.00.01.0
30.01.00.01.00.0
40.01.00.01.00.0
\n", - "
" - ], - "text/plain": [ - " GRAD UNI HS MARRIED SINGLE\n", - "0 0.0 1.0 0.0 1.0 0.0\n", - "1 0.0 1.0 0.0 0.0 1.0\n", - "2 0.0 1.0 0.0 0.0 1.0\n", - "3 0.0 1.0 0.0 1.0 0.0\n", - "4 0.0 1.0 0.0 1.0 0.0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#one hot encoding for EDUCATION and MARRIAGE\n", - "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", - "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", - "#drop one of each category to prevent dummy variable trap\n", - "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", - "onehot.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", - "
" - ], - "text/plain": [ - " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 1.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 1.0 0.0 \n", - "\n", - " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", - "0 0.0 1.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 1.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 \n", - "\n", - " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", - "0 1.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 1.0 \n", - "3 0.0 0.0 1.0 \n", - "4 0.0 0.0 1.0 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#one hot encoding for S_0 to S_6\n", - "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", - "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", - "#drop one of each category to prevent dummy variable trap\n", - "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", - "names = []\n", - "for X in range(0,7):\n", - " if X == 1:\n", - " continue\n", - " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", - " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", - " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", - " try:\n", - " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", - " except:\n", - " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", - "onehot_PAY.columns = names\n", - "onehot_PAY.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", - " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", - " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", - " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", - " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", - " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", - " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", - " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", - " 'PAY_6_Revolving_Credit'],\n", - " dtype='object')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", - "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", - "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", - "df1.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", - "

0 rows × 45 columns

\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", - "Index: []\n", - "\n", - "[0 rows x 45 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check for perfect collinearity\n", - "corr = df1.corr()\n", - "for i in range(len(corr)):\n", - " corr.iloc[i,i] = 0\n", - "#corr[corr == 1] = 0\n", - "corr[corr.eq(1).any(1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has 45 Columns and 26245 Rows\n" - ] - } - ], - "source": [ - "size = df1.shape\n", - "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train Test Split\n", - "\n", - "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import *\n", - "from sklearn.model_selection import *" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "VOB68z_hM1jW" - }, - "outputs": [], - "source": [ - "#using holdout sampling for train test split\n", - "ft = df1.drop(\"Y\", axis = 1)\n", - "target = df1[\"Y\"]\n", - "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", - "#make the results reproducible (according to instructions)\n", - "np.random.seed(123) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter method for feature selection\n", - "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", - "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Significant values are:\n", - " 0 pval\n", - "LIMIT_BAL 78.806194 7.074564e-04\n", - "PAY_0 4532.900073 0.000000e+00\n", - "PAY_2 3833.396843 0.000000e+00\n", - "PAY_3 2921.087799 0.000000e+00\n", - "PAY_4 2778.451579 0.000000e+00\n", - "PAY_5 2791.382213 0.000000e+00\n", - "PAY_6 2434.267474 0.000000e+00\n", - "PAY_0_No_Transactions 78.446517 7.742745e-04\n", - "PAY_0_Pay_Duly 59.486746 4.837585e-02\n", - "PAY_0_Revolving_Credit 483.224042 0.000000e+00\n", - "PAY_2_Pay_Duly 73.909964 2.336982e-03\n", - "PAY_2_Revolving_Credit 238.193174 0.000000e+00\n", - "PAY_3_Pay_Duly 74.057499 2.256805e-03\n", - "PAY_3_Revolving_Credit 128.025091 2.222005e-10\n", - "PAY_4_Pay_Duly 72.030496 3.623036e-03\n", - "PAY_4_Revolving_Credit 82.321461 2.872173e-04\n", - "PAY_5_Pay_Duly 69.393208 6.568060e-03\n", - "PAY_5_Revolving_Credit 61.028427 3.642149e-02\n" - ] - } - ], - "source": [ - "from sklearn.feature_selection import SelectKBest\n", - "from sklearn.feature_selection import chi2\n", - "\n", - "selector = SelectKBest( score_func = chi2, k=10)\n", - "selector.fit(X_train, y_train)\n", - "np.set_printoptions(precision=10)\n", - "chi2data = pd.DataFrame(selector.scores_)\n", - "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", - "chi2data.index = X_train.columns\n", - "\n", - "print(\"Significant values are:\")\n", - "print(chi2data[chi2data[\"pval\"] < 0.05])\n", - "\n", - "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", - "X_train_filter = X_train[cols]\n", - "X_test_filter = X_test[cols]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "mbhlIlQzZz7c" - }, - "source": [ - "## Model Selection\n", - "\n", - "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", - "\n", - "We will be attempting to fit the following models:\n", - "\n", - "\n", - "- Decision Tree \n", - "- Logistic Regression\n", - "- Support Vector Machine\n", - "- Neural Network\n", - "\n", - "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def get_roc(model, y_test, X_test, name):\n", - " try:\n", - " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", - " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", - " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", - " except:\n", - " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", - " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", - " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", - " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", - " plt.xlim([0.0, 1.0])\n", - " plt.ylim([0.0, 1.05])\n", - " plt.xlabel('False Positive Rate')\n", - " plt.ylabel('True Positive Rate')\n", - " plt.title('Receiver operating characteristic for ' + name)\n", - " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", - " plt.legend(loc=\"lower right\")\n", - " \n", - " #find- best threshold\n", - " optimal_idx = np.argmax(tpr - fpr)\n", - " optimal_threshold = thresholds[optimal_idx]\n", - " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", - " \n", - " plt.show()\n", - " \n", - " return auc(fpr, tpr)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "def confusion(y_test, predictions, name):\n", - " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", - " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", - " return conf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluation \n", - "We will select the model based on the model evaluation. The key metrics we will compute are:\n", - "\n", - "1. Accuracy\n", - "2. Recall\n", - "3. AUROC\n", - "\n", - "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", - "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", - "\n", - "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "H89tM6NvaN17" - }, - "source": [ - "### Decision Trees\n", - "\n", - "#### Theory:\n", - "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", - "\n", - "Below is a binary decision tree that has been split for a few iterations.\n", - "\n", - "![image.png](https://elf11.github.io/images/decisionTree.png)\n", - "\n", - "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", - "\n", - "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", - "\n", - "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", - "\n", - "#### Training\n", - "We will now construct a simple decision tree using the GINI index." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False,\n", - " random_state=None, splitter='best')" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree = DecisionTreeClassifier()\n", - "tree.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 13476\n", - " 1 1.00 1.00 1.00 4020\n", - "\n", - " accuracy 1.00 17496\n", - " macro avg 1.00 1.00 1.00 17496\n", - "weighted avg 1.00 1.00 1.00 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, tree.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (GINI) identified 852\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
053951333
11169852
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 5395 1333\n", - "1 1169 852" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.5\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.80 0.81 6728\n", - " 1 0.39 0.42 0.41 2021\n", - "\n", - " accuracy 0.71 8749\n", - " macro avg 0.61 0.61 0.61 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, tree.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we can use entropy as a measure of heterogeneity. Entropy as a measure of heterogeneity is given by:\n", - "![image.png](http://www.sciencealert.com/images/15_.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Entropy) identified 810\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
054471281
11211810
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 5447 1281\n", - "1 1211 810" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", - "tree2.fit(X_train, y_train)\n", - "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.5\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.6062870404745417" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.82 0.81 0.81 6728\n", - " 1 0.39 0.40 0.39 2021\n", - "\n", - " accuracy 0.72 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.72 0.72 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, tree2.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", - " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random Forest Classifier\n", - "\n", - "#### Theory\n", - "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", - "\n", - "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", - "\n", - "#### Training\n", - "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "randf = RandomForestClassifier(n_estimators=200)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=200,\n", - " n_jobs=None, oob_score=False, random_state=None,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "randf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 1.00 1.00 1.00 13476\n", - " 1 1.00 1.00 1.00 4020\n", - "\n", - " accuracy 1.00 17496\n", - " macro avg 1.00 1.00 1.00 17496\n", - "weighted avg 1.00 1.00 1.00 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, randf.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Random Forest) identified 763\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06307421
11258763
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6307 421\n", - "1 1258 763" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.31\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 6728\n", - " 1 0.64 0.38 0.48 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, randf.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[1] = ([\"Random Forest\" , \n", - " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradient Boosted Trees Classifier\n", - "\n", - "#### Theory\n", - "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", - " \n", - "#### Training\n", - "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=4,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=300,\n", - " n_iter_no_change=None, presort='auto',\n", - " random_state=None, subsample=1.0, tol=0.0001,\n", - " validation_fraction=0.1, verbose=0,\n", - " warm_start=False)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import GradientBoostingClassifier\n", - "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", - "xgb.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.96 0.91 13476\n", - " 1 0.80 0.48 0.60 4020\n", - "\n", - " accuracy 0.85 17496\n", - " macro avg 0.83 0.72 0.75 17496\n", - "weighted avg 0.85 0.85 0.84 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train, xgb.predict(X_train)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 764\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06335393
11257764
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6335 393\n", - "1 1257 764" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.22217593115970874\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.94 0.88 6728\n", - " 1 0.66 0.38 0.48 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, xgb.predict(X_test)))" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", - " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4215730.612897
1Random Forest0.3775360.762511
2Gradient Boosted0.3780310.774845
\n", - "
" - ], - "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.421573 0.612897\n", - "1 Random Forest 0.377536 0.762511\n", - "2 Gradient Boosted 0.378031 0.774845" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logistic Regression\n", - "\n", - "#### Theory\n", - "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", - "\n", - "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", - "\n", - "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", - "\n", - "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", - "\n", - "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", - "\n", - "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", - "\n", - "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", - "\n", - "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", - "\n", - "\n", - "#### Training\n", - "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.443375\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17452
Method: MLE Df Model: 43
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774
Time: 08:26:11 Log-Likelihood: -7757.3
converged: True LL-Null: -9430.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629
SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073
AGE 0.0807 0.100 0.805 0.421 -0.116 0.277
PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745
PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367
PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179
PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069
PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250
PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741
BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444
BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081
BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579
BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725
BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615
BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187
PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809
PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898
PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148
PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032
PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034
PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295
GRAD 1.2438 0.217 5.736 0.000 0.819 1.669
UNI 1.2506 0.215 5.804 0.000 0.828 1.673
HS 1.1383 0.219 5.195 0.000 0.709 1.568
MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504
SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369
PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203
PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295
PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625
PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873
PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827
PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333
PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005
PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051
PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005
PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451
PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577
PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528
PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376
PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271
PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396
PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466
PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388
PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17452\n", - "Method: MLE Df Model: 43\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1774\n", - "Time: 08:26:11 Log-Likelihood: -7757.3\n", - "converged: True LL-Null: -9430.2\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8552 0.115 -7.424 0.000 -1.081 -0.629\n", - "SEX -0.1537 0.041 -3.748 0.000 -0.234 -0.073\n", - "AGE 0.0807 0.100 0.805 0.421 -0.116 0.277\n", - "PAY_0 0.6293 0.059 10.697 0.000 0.514 0.745\n", - "PAY_2 -0.5549 0.096 -5.778 0.000 -0.743 -0.367\n", - "PAY_3 -0.0316 0.107 -0.295 0.768 -0.242 0.179\n", - "PAY_4 -0.3679 0.153 -2.412 0.016 -0.667 -0.069\n", - "PAY_5 -0.0939 0.176 -0.534 0.593 -0.438 0.250\n", - "PAY_6 0.4463 0.150 2.971 0.003 0.152 0.741\n", - "BILL_AMT1 -1.4964 0.537 -2.787 0.005 -2.549 -0.444\n", - "BILL_AMT2 0.5563 0.778 0.715 0.475 -0.969 2.081\n", - "BILL_AMT3 2.1495 0.730 2.946 0.003 0.720 3.579\n", - "BILL_AMT4 -0.6749 0.714 -0.945 0.345 -2.074 0.725\n", - "BILL_AMT5 -0.1766 0.914 -0.193 0.847 -1.968 1.615\n", - "BILL_AMT6 0.5273 0.847 0.623 0.534 -1.133 2.187\n", - "PAY_AMT1 -1.4248 0.314 -4.536 0.000 -2.040 -0.809\n", - "PAY_AMT2 -1.6287 0.373 -4.368 0.000 -2.360 -0.898\n", - "PAY_AMT3 -0.4426 0.301 -1.469 0.142 -1.033 0.148\n", - "PAY_AMT4 -0.5395 0.291 -1.852 0.064 -1.110 0.032\n", - "PAY_AMT5 -0.6096 0.293 -2.077 0.038 -1.185 -0.034\n", - "PAY_AMT6 -0.8220 0.269 -3.060 0.002 -1.348 -0.295\n", - "GRAD 1.2438 0.217 5.736 0.000 0.819 1.669\n", - "UNI 1.2506 0.215 5.804 0.000 0.828 1.673\n", - "HS 1.1383 0.219 5.195 0.000 0.709 1.568\n", - "MARRIED 0.1828 0.164 1.114 0.265 -0.139 0.504\n", - "SINGLE 0.0458 0.165 0.278 0.781 -0.277 0.369\n", - "PAY_0_No_Transactions -0.0388 0.124 -0.314 0.753 -0.281 0.203\n", - "PAY_0_Pay_Duly 0.0541 0.123 0.441 0.659 -0.186 0.295\n", - "PAY_0_Revolving_Credit -0.8931 0.137 -6.537 0.000 -1.161 -0.625\n", - "PAY_2_No_Transactions -1.3274 0.232 -5.723 0.000 -1.782 -0.873\n", - "PAY_2_Pay_Duly -1.2617 0.222 -5.693 0.000 -1.696 -0.827\n", - "PAY_2_Revolving_Credit -0.7755 0.226 -3.432 0.001 -1.218 -0.333\n", - "PAY_3_No_Transactions -0.5298 0.268 -1.978 0.048 -1.055 -0.005\n", - "PAY_3_Pay_Duly -0.4216 0.241 -1.749 0.080 -0.894 0.051\n", - "PAY_3_Revolving_Credit -0.4431 0.229 -1.939 0.053 -0.891 0.005\n", - "PAY_4_No_Transactions -1.1297 0.346 -3.262 0.001 -1.808 -0.451\n", - "PAY_4_Pay_Duly -1.2158 0.326 -3.730 0.000 -1.855 -0.577\n", - "PAY_4_Revolving_Credit -1.1482 0.317 -3.627 0.000 -1.769 -0.528\n", - "PAY_5_No_Transactions -0.3928 0.392 -1.002 0.316 -1.161 0.376\n", - "PAY_5_Pay_Duly -0.4660 0.376 -1.238 0.216 -1.203 0.271\n", - "PAY_5_Revolving_Credit -0.3218 0.366 -0.879 0.380 -1.040 0.396\n", - "PAY_6_No_Transactions 0.8190 0.330 2.481 0.013 0.172 1.466\n", - "PAY_6_Pay_Duly 0.7542 0.323 2.333 0.020 0.121 1.388\n", - "PAY_6_Revolving_Credit 0.5003 0.314 1.593 0.111 -0.115 1.116\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "glm = sm.Logit(y_train,X_train).fit()\n", - "glm.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 13476\n", - " 1 0.67 0.36 0.47 4020\n", - "\n", - " accuracy 0.81 17496\n", - " macro avg 0.75 0.65 0.68 17496\n", - "weighted avg 0.80 0.81 0.79 17496\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logisitc model with all features performs quite well on both the train and test set with an accuracy of about 0.8. We will now try removing all the insignificant features to see how that affects the model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.443375\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443376\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443378\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443381\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443383\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443392\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443403\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443417\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443435\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443453\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443472\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443503\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443533\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443589\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443651\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443736\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.443875\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.444048\n", - " Iterations 7\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17469
Method: MLE Df Model: 26
Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762
Time: 08:26:12 Log-Likelihood: -7769.1
converged: True LL-Null: -9430.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639
SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076
PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690
PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388
PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146
PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291
BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571
BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886
PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928
PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109
PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268
PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408
GRAD 1.3777 0.188 7.342 0.000 1.010 1.746
UNI 1.3823 0.187 7.407 0.000 1.017 1.748
HS 1.2816 0.190 6.731 0.000 0.908 1.655
MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243
PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756
PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930
PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839
PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364
PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163
PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103
PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219
PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588
PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753
PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718
PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Logit Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17469\n", - "Method: MLE Df Model: 26\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1762\n", - "Time: 08:26:12 Log-Likelihood: -7769.1\n", - "converged: True LL-Null: -9430.2\n", - "Covariance Type: nonrobust LLR p-value: 0.000\n", - "==========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8611 0.114 -7.586 0.000 -1.084 -0.639\n", - "SEX -0.1551 0.041 -3.821 0.000 -0.235 -0.076\n", - "PAY_0 0.6159 0.038 16.259 0.000 0.542 0.690\n", - "PAY_2 -0.5476 0.081 -6.737 0.000 -0.707 -0.388\n", - "PAY_4 -0.2806 0.069 -4.074 0.000 -0.416 -0.146\n", - "PAY_6 0.2284 0.032 7.127 0.000 0.166 0.291\n", - "BILL_AMT1 -1.2965 0.370 -3.501 0.000 -2.022 -0.571\n", - "BILL_AMT3 2.0358 0.434 4.696 0.000 1.186 2.886\n", - "PAY_AMT1 -1.5045 0.294 -5.117 0.000 -2.081 -0.928\n", - "PAY_AMT2 -1.8075 0.357 -5.069 0.000 -2.506 -1.109\n", - "PAY_AMT4 -0.7783 0.260 -2.991 0.003 -1.288 -0.268\n", - "PAY_AMT6 -0.9305 0.267 -3.490 0.000 -1.453 -0.408\n", - "GRAD 1.3777 0.188 7.342 0.000 1.010 1.746\n", - "UNI 1.3823 0.187 7.407 0.000 1.017 1.748\n", - "HS 1.2816 0.190 6.731 0.000 0.908 1.655\n", - "MARRIED 0.1604 0.042 3.814 0.000 0.078 0.243\n", - "PAY_0_Revolving_Credit -0.9381 0.093 -10.083 0.000 -1.120 -0.756\n", - "PAY_2_No_Transactions -1.3368 0.208 -6.442 0.000 -1.744 -0.930\n", - "PAY_2_Pay_Duly -1.2071 0.188 -6.420 0.000 -1.576 -0.839\n", - "PAY_2_Revolving_Credit -0.7352 0.190 -3.877 0.000 -1.107 -0.364\n", - "PAY_3_No_Transactions -0.4779 0.161 -2.975 0.003 -0.793 -0.163\n", - "PAY_3_Pay_Duly -0.3203 0.111 -2.887 0.004 -0.538 -0.103\n", - "PAY_3_Revolving_Credit -0.3769 0.080 -4.682 0.000 -0.535 -0.219\n", - "PAY_4_No_Transactions -0.9679 0.194 -5.000 0.000 -1.347 -0.588\n", - "PAY_4_Pay_Duly -1.0817 0.168 -6.454 0.000 -1.410 -0.753\n", - "PAY_4_Revolving_Credit -1.0178 0.153 -6.661 0.000 -1.317 -0.718\n", - "PAY_6_No_Transactions 0.2160 0.080 2.691 0.007 0.059 0.373\n", - "==========================================================================================\n", - "\"\"\"" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#remove the most insignificant attribute, and retrain\n", - "train_log = X_train.copy()\n", - "glm = sm.Logit(y_train,train_log).fit()\n", - "while max(glm.pvalues) > 0.01:\n", - " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", - " train_log = train_log.drop(least,axis = 1)\n", - " glm = sm.Logit(y_train,train_log).fit()\n", - "glm.summary() " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", - " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT6', 'GRAD',\n", - " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", - " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", - " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", - " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_6_No_Transactions'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "count = len(glm.pvalues.index)\n", - "print(str(count) + \" Columns left:\")\n", - "print(glm.pvalues.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6728\n", - " 1 0.67 0.36 0.47 2021\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", - "\n", - "We now Calculate the AUROC for the train set." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.1993195782749319\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7718794071347034" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the optimal cut off was found to be 0.21432968760597085 using the training data, we will use that as our cut off for our evaluation of the test set." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.78 0.82 6728\n", - " 1 0.46 0.63 0.53 2021\n", - "\n", - " accuracy 0.74 8749\n", - " macro avg 0.67 0.70 0.68 8749\n", - "weighted avg 0.78 0.74 0.76 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", - "\n", - "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", - "\n", - "\n", - "Calculate the confusion matrices for both cut offs to better compare their performance." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Logistic Regression identified 1273\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
052401488
17481273
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 5240 1488\n", - "1 748 1273" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.21432968760597085, \"Logistic Regression\")" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2021 Defaulters, the Logistic Regression identified 720\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PredictedFalseTrue
Actual
06377351
11301720
\n", - "
" - ], - "text/plain": [ - "Predicted False True \n", - "Actual \n", - "0 6377 351\n", - "1 1301 720" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is evident that the lower cutoff is better able to detect defualts." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.2091273384286262\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> 0.21432968760597085)),auroc]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "iCxBcin11EI8" - }, - "source": [ - "### Support Vector Machine\n", - "#### Theory\n", - "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", - "\n", - " \n", - "\n", - "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", - "\n", - "Any hyperplane can be written as the set of points X satisfying\n", - "\n", - "\n", - "\n", - "
\n", - " \n", - "
\n", - "\n", - "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", - "\n", - "#### Margin\n", - "A margin is a separation of line to the closest class points.\n", - "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", - "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "\n", - "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", - "\n", - "##### Hard Margin\n", - "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", - "\n", - " Minimize ||W|| subject to:\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "##### Soft Margin\n", - "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", - "\n", - "We then wish to minimize\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", - "\n", - "#### Computing SVM classifier\n", - "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", - "\n", - "##### Primal\n", - "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", - "\n", - "We can rewrite the optimization problem as follows\n", - " \n", - "\n", - "
\n", - "
\n", - "\n", - "This is called the primal problem.\n", - "\n", - "##### Dual\n", - "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", - " \n", - "\n", - "
\n", - "
\n", - " \n", - "\n", - "
\n", - "
\n", - "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", - "\n", - "Here, the variables C are defined such that\n", - " \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parameter Tuning\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Kernel\n", - "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", - "\n", - "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Regularization (C value)\n", - "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", - "\n", - "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "Left: low regularization value, right: high regularization value\n", - "\n", - "\n", - "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Gamma\n", - "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "\n", - "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Apply SVM model\n", - "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### SVM without parameter tuning\n", - "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import svm\n", - "#train svm model without standardization and no parameter tuning\n", - "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", - "clf_original.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.16372570606114417\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "0.7264392577614385" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#plot roc for svm\n", - "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2009 Defaulters, the SVM default parameters identified 739\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06382358
11270739
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6382 358\n", - "1 1270 739" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.95 0.89 6740\n", - " 1 0.67 0.37 0.48 2009\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, clf_original.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### SVM with Parameter tuning\n", - "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", - "\n", - "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", - "\n", - "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 0.01, 'gamma': 0.001}" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001,0.01, 0.1, 1]\n", - " gammas = [0.001, 0.01, 0.1]\n", - " param_grid = {'C': Cs, 'gamma' : gammas}\n", - " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", - " grid_search.fit(X, y)\n", - " grid_search.best_params_\n", - " return grid_search.best_params_\n", - "svc_param_selection(X_train, y_train,3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", - "clf_reduced_tuned.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.15927524092941694\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", - " \"SVM reduced features and tuning RBF kernel\")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2009 Defaulters, the SVM reduced features and tuning RBF kernal identified 671\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06437303
11338671
\n", - "
" - ], - "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6437 303\n", - "1 1338 671" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernal\")" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.83 0.96 0.89 6740\n", - " 1 0.69 0.33 0.45 2009\n", - "\n", - " accuracy 0.81 8749\n", - " macro avg 0.76 0.64 0.67 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[4] = ([\"SVM\" , \n", - " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neural Networks\n", - "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", - "\n", - "#### Theory\n", - "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", - "\n", - ".\n", - "\n", - "\n", - "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", - "\n", - "\n", - "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", - "\n", - "\n", - "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", - "\n", - "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Parameter tuning\n", - "##### Learning rate\n", - "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", - "\n", - "##### Momentum\n", - "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", - "\n", - "##### Loss function\n", - "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test,predictions))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from numpy import loadtxt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "# define the keras model\n", - "model = Sequential()\n", - "model.add(Dense(12, input_dim=26, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# evaluate the keras model\n", - "#recall, accuracy = model.evaluate(df1, target)\n", - "#print('Accuracy: %.2f' % (accuracy*100))\n", - "#print('Recall: %.2f' % (recall*100))\n", - "\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "print(classification_report(y_test,predictions))" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "BT2101 disrudy ", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} From eacfbcb3a46a4149fc528567ecacc0a653365be3 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Thu, 21 Nov 2019 00:36:35 +0800 Subject: [PATCH 20/25] Updated EVERYTHING --- ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 1820 +++++++++-------- BT2101_Default - EDIT-MASTER- Reon.ipynb | 1820 +++++++++-------- 2 files changed, 1970 insertions(+), 1670 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb index ff9f477..c68730d 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1529,7 +1529,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,7 +1553,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1577,7 +1577,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1601,7 +1601,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1625,7 +1625,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1649,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1673,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1697,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1894,7 +1894,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1918,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1942,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1966,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1990,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2124,7 +2124,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2132,7 +2132,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2140,7 +2140,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2148,7 +2148,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2156,7 +2156,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2240,7 +2240,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2261,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2282,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2303,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2324,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2604,26 +2604,24 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 88.108919 6.061985e-05\n", - "PAY_0 4383.304779 0.000000e+00\n", - "PAY_2 3808.897634 0.000000e+00\n", - "PAY_3 2929.844986 0.000000e+00\n", - "PAY_4 3128.967849 0.000000e+00\n", - "PAY_5 2916.099105 0.000000e+00\n", - "PAY_6 2553.774724 0.000000e+00\n", - "PAY_0_No_Transactions 75.763632 1.499570e-03\n", - "PAY_0_Pay_Duly 70.548603 5.076335e-03\n", - "PAY_0_Revolving_Credit 432.083997 0.000000e+00\n", - "PAY_2_Pay_Duly 91.371021 2.433442e-05\n", - "PAY_2_Revolving_Credit 208.148061 0.000000e+00\n", - "PAY_3_Pay_Duly 96.953056 4.833864e-06\n", - "PAY_3_Revolving_Credit 111.611983 5.228360e-08\n", - "PAY_4_Pay_Duly 88.985156 4.755470e-05\n", - "PAY_4_Revolving_Credit 87.590881 6.991450e-05\n", - "PAY_5_Pay_Duly 79.941208 5.308188e-04\n", - "PAY_5_Revolving_Credit 62.597352 2.699279e-02\n", - "PAY_6_Pay_Duly 66.360306 1.261937e-02\n", - "PAY_6_Revolving_Credit 63.991063 2.050515e-02\n" + "LIMIT_BAL 85.131154 1.364065e-04\n", + "PAY_0 4436.456236 0.000000e+00\n", + "PAY_2 3950.467050 0.000000e+00\n", + "PAY_3 3110.398233 0.000000e+00\n", + "PAY_4 3096.770229 0.000000e+00\n", + "PAY_5 2977.392709 0.000000e+00\n", + "PAY_6 2449.597828 0.000000e+00\n", + "PAY_0_No_Transactions 75.890061 1.454296e-03\n", + "PAY_0_Revolving_Credit 482.349587 0.000000e+00\n", + "PAY_2_Pay_Duly 76.560100 1.235192e-03\n", + "PAY_2_Revolving_Credit 237.062546 0.000000e+00\n", + "PAY_3_Pay_Duly 79.130634 6.519343e-04\n", + "PAY_3_Revolving_Credit 135.381715 1.723299e-11\n", + "PAY_4_Pay_Duly 74.678430 1.946930e-03\n", + "PAY_4_Revolving_Credit 98.449450 3.100197e-06\n", + "PAY_5_Pay_Duly 72.744346 3.071499e-03\n", + "PAY_5_Revolving_Credit 70.267645 5.406887e-03\n", + "PAY_6_Revolving_Credit 60.999670 3.661838e-02\n" ] } ], @@ -2704,7 +2702,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2724,7 +2722,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -2755,7 +2753,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2790,7 +2788,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -2799,7 +2797,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2813,7 +2811,7 @@ " random_state=None, splitter='best')" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2825,7 +2823,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2834,8 +2832,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13477\n", - " 1 1.00 1.00 1.00 4019\n", + " 0 1.00 1.00 1.00 13463\n", + " 1 1.00 1.00 1.00 4033\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2857,14 +2855,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (GINI) identified 847\n" + "Of 2008 Defaulters, the Decision Tree (GINI) identified 902\n" ] }, { @@ -2899,14 +2897,14 @@ " \n", " \n", " \n", - " 0\n", - " 5368\n", - " 1359\n", + " 0\n", + " 5410\n", + " 1331\n", " \n", " \n", - " 1\n", - " 1175\n", - " 847\n", + " 1\n", + " 1106\n", + " 902\n", " \n", " \n", "\n", @@ -2915,11 +2913,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5368 1359\n", - "1 1175 847" + "0 5410 1331\n", + "1 1106 902" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2930,19 +2928,19 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.5\n" + "Optimal Threshold: 1.0\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2959,7 +2957,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2968,12 +2966,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6727\n", - " 1 0.38 0.42 0.40 2022\n", + " 0 0.83 0.80 0.82 6741\n", + " 1 0.40 0.45 0.43 2008\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.71 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.62 0.63 0.62 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", "\n" ] } @@ -2992,14 +2990,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Entropy) identified 823\n" + "Of 2008 Defaulters, the Decision Tree (Entropy) identified 865\n" ] }, { @@ -3034,14 +3032,14 @@ " \n", " \n", " \n", - " 0\n", - " 5413\n", - " 1314\n", + " 0\n", + " 5508\n", + " 1233\n", " \n", " \n", - " 1\n", - " 1199\n", - " 823\n", + " 1\n", + " 1143\n", + " 865\n", " \n", " \n", "\n", @@ -3050,11 +3048,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5413 1314\n", - "1 1199 823" + "0 5508 1233\n", + "1 1143 865" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3067,19 +3065,19 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.5\n" + "Optimal Threshold: 1.0\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU9dbA8e8hgdBCDdIxhBp654I0USwIgqggKEpTKXbsDRUV9SKoL1iwXFREEFFBUERUBBGkSxXE0EIvAUJJSDnvHzPBZUlZIJtNOZ/n4SE7O+VM2TkzvzNFVBVjjDHmQuULdADGGGNyNkskxhhjLoolEmOMMRfFEokxxpiLYonEGGPMRbFEYowx5qLkmUQiIreKyNxAxxFoIlJFRI6LSFAWTjNcRFREgrNqmv4kIutFpMMFDHdB26CIFBKRb0XkqIhMO9/hs5Kv21du/T2KSEER+UtELgl0LBdLRGaKSCef+g3EfSQisg0oCyQBx4E5wD2qejzLg8nl3GU9SFXnBTCGcGArkF9VEwMVhxuLAjVUdYufpxNOJs2ziPQF7gVaZ8byc5Pgz8BJt9MR4Hfgv6q67GLHHygicivwnvsxCAjh33lEVYtmQQwPAtVU9R738ySgJ3Dao7dNqtrUh3G9CFRS1X7+iNWH6bcGxqpqy4z6DeQZSVd3xTYCGgNPBDCWCxbIo+zccoR/PvLo8r4U2HwhSSSdmHe7v79Q4D/AX8BCEbniwsMMLFX9TFWLuvN1Le48enQ7i5/W593Ap17dXvaMw5ck4gsRySciftuHq+rvQBkRaexLz1n+D9gGXOnx+TVgtsfnEGA0sAPYB7wLFPL4vhuwGjgG/ANc43YvDnwI7AF2AS8CQe53/YDf3L/fBUZ7xTQDeMj9uwIwHTiAc1R5n0d/zwFfApPc6Q9KZf6KA5+4w28HngbyecSxCPg/4CjOD/gKr2HTm4dFwFjgsPtdNZyjy0PAQeAzoITb/6dAMnAK58zvUSAcUCDY7Wc+MNIdbywwFwjziOd2dx4OAc94rzuv+S4EvO72fxT4ze2WMs073HV6EHjKY7gWwGKcI+M9wDiggMf3CgwD/ga2ut3eBHa662AF0Naj/yDgSXfbiHW/rwwscMd1wl0evdz+u+BsTylH5g28ttXHgDVAPBDsuQzc2Je7cewDxrjdd7jTOu7+a4XHNuj2Uxf40V2X+4AnU1mmz+MczSa44xmIcwD4tLuc9+Nsa8Xd/lOW9UA3hgWpjLMDEJ1K93HAco/PtT3i2wT0PI91nbJ99QOi3PWwFbjV+/fofm4NLHPHtQzn7Cvlu/mks42msS2mNY/RwCPAWuC0260S8DX//t6HefSfj3+3pYPAFKBkGtOMwNm2gjy6TQKeS6P/6u6yut2N6wDwuMc26bneV7jdf3OXxWKc33W4G/8sdz39DQzwmMaLwFRgmrvslgP13e+eAKZ6xfQOHvtG4H94/FbTXN4Xmgwu5h9n/xAruSv1TY/v3wBmAqVwjpi+BUZ5/HCPAp3clVwRqO1+9w3OqW0R4BJgKXC394YLtMPZCaU07ZV0V0oFd5wrgGeBAu7GEQVc7fb7nLtyu7v9Fkpl/j7BSUyh7oreDAz0iCMReBDID/Ry56eUj/OQiNPMEYzzw63uLosQoAzOzvKN1Ja1147GM5H8A9R0xzcfeMX9rg7ORtzGXRaj3XlPK5GMd4eviLMzb+3GlTLN991pNMTZKUe6wzXFOSoOdvvdCDzgMV7F2aGVSlnewG1AaXeY4cBeoKD7XcqOohYg7vRKe4yruse4m+DsjFu6Md/hLrMQj+W3GicRFfJepjg/6L7u30WB/6S2nFPZBkNxkuZwoKD7uWUay/U5YJLH5wHAFpxtsyjwFfCp13Q/wdmGUts+O5D6TrYjzoFHEfffTqC/u4yb4OxI6/q4roPdcRwDarnDlPcY3nNZlAJigL7ucL3dzynrbD5pbKPp7GPSmsdonN93JXdcQe76fRJnG6/urt8r3P4fxklgFd319GHKsk5l3N2AP726+ZJI3nXH3QTnd1HD/f5FYKLXML+58UXi7D+C+ffANGUcB4H2HuNIAG5w+3/c3XaC3WVwHCjm9lvAHbahx/QeBb7IcJ/ur2SRwUre5s5ArLsgf+Lfo2jByerVPPpvxb9Hou/htNt5j7OsuxI8z1x6A7+ksuEKztFaO/fzncDP7t8tgR1e434C+J/Hj/qcozyPfoPcOOp4dLsbmO8Rx27cJOZ2W4rzI/JlHnakNW23n+7AKq9lnVEiedrj+6HAHPfvZ4HPPb4rjHOUdE4iwUmqpzw3wlSmWclrnm9JYx4eAL72+KxAxwzmOyZl2jhHz93S6M87kbwDjPTqZxP//hC34XGE571McRL383gdIXsv51S2wd6e6ymDeXuOsxPJT8BQj8+1cHYWwR7TjUhnfB1IfSdb2x22Is4BzkKv798DRvi4rlMSyRHgRrwSmtey6Ass9fp+MdAvo230AuYxGrjd4/NlQJRXP88A77t//52yLbifK+P8RvOlMu478DjLcrtNAuLc5ZDy70P3u5REUs6j/5XATe7faSWSZz0+V3XXfRGPbv8FPvAYh+eZXxDOgVMr9/OPQH/37+7AGq/pDQHmZrSNBrKNvbuqzhOR9sBkIAxnIZfB2WGtEJGUfgVnAYCzIr9LZXyX4mTcPR7D5cM5qjqLqqqITMH5MS8A+uCs8JTxVBCRIx6DBAELPT6fM04PYTiZfbtHt+04P84Uu9RdSx7fV/BxHs6atnt1yFtAW5yj2nw4O9Xzsdfj75M4R7m4MZ2ZnqqeFJFDaYwjDOeI6J/znY6I1ATGAM1w1n0wzlGjJ+/5Hg4McmNUoJgbAzjbSHpxeLoUuENE7vXoVsAdb6rT9jIQeAH4S0S2As+r6iwfpns+MXqrwLnbVzDOgUiK9GJOS0WcZXkEZ7m09PodBOM0l/qyrlHVEyLSC+eo/kMRWQQMV9W/vHr1nh849zeT1jZ6ITyXzaVAlVR+7/Pdv6sA34pIssf3itNa4BkTOL+70FSm94qqPpdWMKp6vvPmGX8F4KCqnvDoth2ol1r/qpokIrv4d/v+GOes8384Z/ne9Z1QnO0hXQG//FdVfwUm4jSbgHNqdQrnFLiE+6+4/lss24lTF/C2E+dIIcxjuGKqWjeNSX8O3CQil+KchUz3GM9Wj3GUUNVQVe3sGXY6s3QQ5wjhUo9uVXDqHSkqikemcL/f7eM8eE97lNutgaoWw9kYJJ3+z8cenNNfwLkMFac5KTUHcY68Uls3GXkHp1ZUw52HJzl7HsBjPkSkLU7doidOe3UJnObBlGHS2kZSsxN4yWt9F1bVz1ObtjdV/VtVe+PsWF4FvhSRIukNcwExetvNudtXIk6d5UxoFzDeG4CV7k5pJ/Cr13IpqqpDOI91rao/qGonnGatv3CaNzOaHzj3N5OZPJfNTuDvVH7vXd3vo4FOXt8X9Nr5p1gDVMvES+vTWoee3XcDYe42l8J72VVO+cMtzld0hwOnWbSpiNTFuUBhste0IoE/Mwo04InE9QbQSUQaqWoyzsY2NuVabBGpKCJXu/1+CPQXkSvcqxYqikhtVd2DU4R7XUSKud9Vc894zqGqq3CKWx8AP6hqStZdChwTkcfc6/eDRKSeiDT3ZUZUNQn4AnhJRELdRPUQ/57xgLPTuU9E8ovIzTgr67vznQdXKE4z4RERqYhTH/C0D6ct/UJ8CXQVkdYiUgCnCcd7Bw+Au94+AsaISAV3ubUSkRAfphOK05Z+XERq45xOZ9R/Is76CxaRZ3HOSFJ8AIwUkRriaCAiKQnQe3m8DwwWkZZuv0VE5DoRSe3I8hwicpuIlHHnP2UbSnJjSybtZT8LKCciD4hIiLutZHiZpetz4EERqSoiRYGXcYqmF3JVl7i/oRE4Z3hPesRXU0T6uttpfhFpLiKRvq5rESkrIte7O7l4nO00KZUwvnOn1UdEgt2zmDpuDP62GDgtIsPFuQckSETqi0jKlVXvAi+LSBVwWgBE5PrURqSq23CazDPlqiycbTXc66DTe5pbcQroL7vbUSOcM4zPPHprISLdRCQ/ztlhLM4FDajqSZwLDT4HFqmqd/JuB3yfUaDZIpGo6gGc4uAzbqfHcApCS0TkGDAPpx0YVV2Ks6DG4hyF/sq/RzO34zRLbMA5zfwS50goLZ8DV+KRhd1E0BXnsuStOEdfH+BcTeWre3HqPFE4bZqTcX54Kf4AarjjfgmnTTSlyeh85+F5nALbUWA2zhGGp1HA0yJyREQePo95QFXXu/MyBefsJBanfTU+jUEexilyL8O5guRVfNvGHsZpXozF2bFPzaD/H3A27s04p/FxnH26PwYnmc/FSVAf4hRWwak3fOwuj56quhynRjYOZ3lvwWm/99U1wHoROY5zJdktqhrn/kBfAha50/qP50CqGotzkURXnCaSv4HLfZzmRzhNEAtwttE4nPV0Piq4MR/HWV/1gQ6qOtcjvquAW3COXvfirM+UZOHLus6HczHBbref9jj1jbO4234Xt99DOAXeLqp68Dzn6by5ybczzkU823B+k+/x74HJGJz73H4SkVicq/rSO6h8D6fm4+lJcW7STPmX2tlMaqbi7AsOi8jSdPrrhbM/2Yuzv3hSVX/x+P5rnJaKw26/PbwOOj7GWf9nNWuJSCvgsKquzCjQgNyQmJeJSD+cS4bbBDqW8+Ue/R7BaYLaGuh4jMluRKQgsAqnQL8/G8ST4U2NIhKB0yxXTj1uCheRGcD4lIOL9OS5G9rM+RGRrjhXCQlOHWstzpGbMcaLqsbhNFXnCG7N5CFgsno9WURVu/k6HkskJiPdcE55Bact9ha101hjcjwRKY5TlN8GXJ1+3xmMy/YJxhhjLka2KLYbY4zJuXJc01ZYWJiGh4cHOgxjjMlRVqxYcVBVy/hj3DkukYSHh7N8+fJAh2GMMTmKiHg/PSDTWNOWMcaYi2KJxBhjzEWxRGKMMeaiWCIxxhhzUSyRGGOMuSiWSIwxxlwUvyUSEflIRPaLyLo0vhcReUtEtojIGhFp4q9YjDHG+I8/z0gm4jxiOy3X4jz6uAZwF87LjYwxxmSyuITUXgOTefx2Q6KqLhCR8HR66QZ84j4AcImIlBCR8u7LnYwxxlykQ8fjeep/K/hpx2G/TieQd7ZX5OyXEUW73c5JJCJyF85ZC1WqVMmS4IwxJqfafugEY7/7ixlr96D5hOBDcX6dXiCL7am9PjLVRxGr6gRVbaaqzcqU8cujYowxJsdbtSOGIZNW0GH0fL5Zu4dTfx3m1lLF+GtCD79ON5BnJNF4vJQeqMS/L6Q3xhjjg+Rk5ae/9jNhwT8s2xZDaMFghrSvRtNihalZuTiVK5/PW8IvTCATyUzgHhGZArQEjlp9xBhjfBOXkMRXK3fxwcIoog6eoGCScviXndx6XW0evaZ2lsbit0QiIp8DHYAwEYkGRgD5AVT1XeA7oDOwBTgJ9PdXLMYYk1scPnGaSUu28/Hv2zh04jTlC+Yn7pdoolfu46EHW/Hko5dleUz+vGqrdwbfKzDMX9M3xpjcZPuhE3z421a+WL6TuIRkLq9VBt0Yw8fPL6Z168r8uPJu6tcvG5DYctz7SIwxJi9ZtSOGCQuimLN+L/nz5aNL/fL0alSRlrXLsGnTQS6rEcbAgU3Ily+165eyhiUSY4zJZrwL6MXcAvql8fDkQ3PZ3qgc06f3pFatMGrVCgt0uJZIjDEmu/AuoFcsUYhnu9ShbaUSPPXoPB6btoFatUpzzz3NAx3qWSyRGGNMgMWcOM2nS7bzyeJtHDx+mnoVi/FW78Z0rleOX+dvo2mDdzl9OomRIy/nkUdaExKSvXbd2SsaY4zJQ1IroN/ZLoJWEaVJTEwmOCgfDRuWo3PnGrz4YkeqVy8V6JBTZYnEGGOy2KodMby/MIo56/YSlE/o3qgid7aLoGbZUI4di+eBB+bwxx+7WLRoAGFhhZky5aZAh5wuSyTGGJMFUgro7y+IYum2w4QWDObu9tXo1zqcssUKoqpMm7ae+++fw969xxk6tDnx8UkULpz9XxtlicQYY/woLiGJr1ft4v2FUUQdcAroz3SpQ6/mlSnq1joOHDjBHXd8w/ffb6Fx43LMmHELzZtXDHDkvrNEYowxfpBeAT046OyzjGLFQjh48CRvvHE1w4a1IDg4+5+FeLJEYowxmci7gN6hVhnucgvoIv/eNLhgwXZeemkh06f3pGjRAixZMiigNxVeDEskxhiTCVIroA9qG0GtcqFn9Xfw4EkeeeRHJk5cTXh4CbZtO0K9epfk2CQClkiMMeaCZVRA96Sq/O9/q3nkkR85diyeJ55ow9NPt6Nw4fwBij7zWCIxxpjz5EsBPTWTJq2hTp0yvPvuddSte0kWRuxflkiMMcZHMSmPcF+ccQEd4OTJBF5+eSGDBzejUqViTJ/ek+LFC+boZqzUWCIxxpgM7Dh0kg9+i8qwgO7pu+/+Ztiw79i27QgVK4YyZEhzSpYslMWRZw1LJMYYk4bVO48wYcE/GRbQPUVHH+OBB+YwffpGIiPD+PXXfrRrd2kWRp31LJEYY4yH8ymgp+allxYwe/bfvPxyR4YPb02BAkFZEHVgifOiwpyjWbNmunz58kCHYYzJZVIroA9oUzXDAjrA0qW7KFQomPr1y3Lo0EmOHo0nIqJkFkXuGxFZoarN/DFuOyMxxuRp3gX0uhWK8eYtjbiufvlUC+iejh6N48knf+Kdd5bTpUtNZs7sTenShSldunDWBJ9NWCIxxuRJqRbQ20bQqlraBfQUqsrUqet58MEf2L//BPfe24KRIztmUeTZjyUSY0ye4l1A79aoIndmUED3NmnSGm6//RuaNavArFm9adq0gh8jzv4skRhjcr2LLaADxMcnEhUVQ2RkGXr2rEtiYjK3396QoAyav/ICSyTGmFzrQu9A9/bLL1sZMmQ2J08m8Pff9xISEkz//o39GHnOYonEGJPrXEwB3dP+/Sd4+OG5fPrpGiIiSjJhQtds97707MCWiDEm19hx6CQf/hbFF8ujOZWQdF4FdG9bthymRYv3OX78NE891ZannmpLoUI5/wGL/mCJxBiT463eeYT3F0Tx/bo9F1xAT3HsWDzFioVQrVpJBg5szIABjYmMLOOHqHMPSyTGmBwpOVn5+a/9TFgYxdKtTgH9rnbV6H+Z7wV0TydOnOaFF37l/fdXsmbNECpVKsZ//3uVHyLPfSyRGGNylLiEJL5xC+j/XEQB3dO3327innu+Z8eOowwc2DhXvCMkK1kiMcbkCGkV0DvXL0/+C7wENzExmZ49p/H1139Rt24ZFi7sT5s2VTI38DzAEokxJlvLzAJ6ClVFRAgOzkf58kV55ZUrePDBVnniAYv+YInEGJMtZWYB3dOSJdEMG/Yd77/flSZNyjN+/HWZFHHeZYnEGJNtpFVA79c6nHLFz7+A7ikm5hRPPvkT7723ggoVQomJOZVJURu/JhIRuQZ4EwgCPlDVV7y+rwJ8DJRw+3lcVb/zZ0zGmOzHHwV0T1OnruO+++Zw8OBJHnjgPzz/fAdCQ0MyIXIDfkwkIhIEjAc6AdHAMhGZqaobPHp7GvhCVd8RkTrAd0C4v2IyxmQvMSdO89kf25n4+3YOHo/PlAJ6av766yDh4SWYM+dWGjcun2njNQ5/npG0ALaoahSAiEwBugGeiUSBYu7fxYHdfozHGJNN+KOA7ikuLpFXX/2NJk3K07VrLZ58si1PP93OHrDoJ/5MJBWBnR6fo4GWXv08B8wVkXuBIsCVqY1IRO4C7gKoUsUuzTMmp/pz5xEm+KGA7mnevCiGDp3N338fZvjwVnTtWov8+e1qLH/yZyJJ7bDC+72+vYGJqvq6iLQCPhWReqqafNZAqhOACeC8atcv0Rpj/MKfBXRP+/Yd56GH5jJ58lqqVy/F3Lm30alTtUwbv0mbPxNJNFDZ43Mlzm26GghcA6Cqi0WkIBAG7PdjXMaYLOBdQK9QvCBPXxfJLS2qZEoB3duPP0bx5ZcbePbZdjzxRFsKFrSLUrOKP5f0MqCGiFQFdgG3AH28+tkBXAFMFJFIoCBwwI8xGWP8zLuAXqe8fwroAH/+uZe//z7MTTfV4dZb63PZZZWpWrVkpk7DZMxviURVE0XkHuAHnEt7P1LV9SLyArBcVWcCw4H3ReRBnGavfqpqTVfG5EDeBfT2NctwV7sIWmdSAd3T8eOnGTHiF9588w/Cw0vQvXttgoPzWRIJEL+e+7n3hHzn1e1Zj783AJf5MwZjjH95F9Cvb1iRO9tVpXa5YhkPfAG++eYv7r33e6Kjj3HXXU0YNepKgoPtaqxAskZEY8x5O6eAHhLMne0i6N+6aqYW0L2tXbuPG26YSv36lzB16k20bl0544GM31kiMcb4LK0Ceq/mlQkt6J9HryckJLFw4Q46dqxK/fplmT27D506RdglvdmIJRJjTIaOnHQe4Z4VBXRPv/++k8GDZ7F+/QE2bbqH6tVL0blzDb9Nz1wYSyTGmDTtPHySD3/bytRlO/1eQPd0+PApHn98Hu+/v5LKlYvx1Vc9qV69lN+mZy6OJRJjzDn+3HmECQuj+H5t1hTQPcXFJdKo0bvs3h3L8OGteO65DhQtWsDv0zUXzhKJMQZwCui/bNrPewuytoCeIjr6GJUqFaNgwWBGjrycRo3K0bBhOb9P11w8SyTG5HGBKKB7OnUqgVGjfuPVVxfx5Zc307VrLe64o5Hfp2syj0+JREQKAFVUdYuf4zHGZJFAFdA9zZ37D0OHzuaff2K47bYGtGhRMUumazJXholERK4DxgAFgKoi0ggYoao3+Ds4Y0zmC1QB3du9937HuHHLqFGjFPPm9eWKKyKybNomc/lyRvICzuPffwFQ1dUiUt2vURljMl0gC+gpkpKcB3sHBeXjP/+pRFhYYR57rI09YDGH82XtJajqEa8jFXseljE5QKAL6J5WrtzD4MGz6Nu3Affe25Jbb22QpdM3/uNLItkoIj2BfO6TfO8Hlvg3LGPMxYhLSGLG6l28v3ArW/Yfz/ICuqfY2HieffYX3nprKWXKFKZ8+cx7iZXJHnxJJPcAzwLJwFc4T/N9wp9BGWMuTHYooHuaO/cfBgyYwe7dsQwe3IyXX76CEiWy9kzI+J8vieRqVX0MeCylg4j0wEkqxphsILsU0L0VKBDEJZcUYfr0nrRsWSlgcRj/koxe/yEiK1W1iVe3Fara1K+RpaFZs2a6fPnyQEzamGwnOxTQPSUkJDFmzGKOHYvnpZeuAJw6Tb58gUtmxuHut5v5Y9xpnpGIyNU4r8GtKCJjPL4qhtPMZYwJgJQC+oQFUfwR4AK6p99+23HmAYs331znTAKxJJL7pde0tR9YB8QB6z26xwKP+zMoY8y5slMB3dOhQyd57LF5fPjhKqpUKc633/amS5eaAYvHZL00E4mqrgJWichnqhqXhTEZYzykVkB/o1cjrmsQmAK6t0OHTjFlyjoefbQ1zz7bniJF7AGLeY0vxfaKIvISUAc4c96sqnbIYYwfeRfQ29Usw93ZoIAOsHHjAb74Yj0jRnSgZs3S7NjxIKVKFQpoTCZwfEkkE4EXgdHAtUB/rEZijN94FtDziXB9owrc2TaCyPKBKaB7OnkygZdeWsB///s7RYsWYODAJlSqVMySSB7nSyIprKo/iMhoVf0HeFpEFvo7MGPykrQK6P1ah1O+ePbYSc+Zs4WhQ2ezdesR7rijIf/9byfKlCkS6LBMNuBLIokX5zz6HxEZDOwCLvFvWMbkDdm1gO7t+PHT9O37NaVLF+KXX+6gQ4fwQIdkshFfEsmDQFHgPuAloDgwwJ9BGZPbHTl5ms/+2MH/Fm3LlgV0cB6w+Pnn6+jdux5FixZg3ry+1K4dRkiIPWDRnC3DLUJV/3D/jAX6AoiI3aJqzAVIKaB/sXwnJ09nrwK6pxUrdnP33bNYsWIPhQoFc+ONdexthSZN6SYSEWkOVAR+U9WDIlIX51EpHQFLJsb4aE30ESYsiOK7bFhA93T0aBzPPPML48cv45JLijBlyo306BEZ6LBMNpfene2jgBuBP3EK7F/jPPn3VWBw1oRnTM6VnKzM37yf937NvgV0bzfe+AU//7yVYcOa8+KLHSkewDvlTc6R3hlJN6Chqp4SkVLAbvfzpqwJzZicKT4xiRmrdjNhYVS2LqCniIqKoUyZwoSGhvDSSx3Jl09o3txeeWt8l14iiVPVUwCqelhE/rIkYkzaUgroE3/fxoHYeCKzYQHd0+nTSYwe/TsjRy7gvvta8OqrnewJveaCpJdIIkQk5VHxAoR7fEZVe/g1MmNyiNQK6GN7RnBZ9exVQPe0YMF2Bg+excaNB7nppjrcd1/LQIdkcrD0EsmNXp/H+TMQY3KanFJA9zZ27GIeemgu4eElmD27D5071wh0SCaHS++hjT9lZSDG5ASpFtDbRtDvsuxbQAcn7hMnThMaGsJ119XkwIGTPP10OwoXzn41G5PzZPhiq+zGXmxlAsG7gF6+eEEGXFaVW1pkzwK6p/Xr9zN48Owzbyo0eVNAXmyVGUTkGuBNIAj4QFVfSaWfnsBzgAJ/qmoff8ZkzPk4ejKBSX9sP6uAPrZXQ7o0qJAtC+ieTp5MYOTIXxk9ejHFi4cwYEAjVDXb1m1MzuVzIhGREFWNP4/+g4DxQCcgGlgmIjNVdYNHPzWAJ4DLVDVGROwZXiZbyIkFdE+rVu2hR48v2LbtCP37N+K11zoRFlY40GGZXCrDRCIiLYAPcZ6xVUVEGgKDVPXeDAZtAWxR1Sh3PFNw7k3Z4NHPncB4VY0BUNX95z8LxmSenFpAT5FyxlGlSnGqVCnOxx93p127SwMdlsnlfDkjeQvoAnwDoKp/isjlPgxXEdjp8Tka8L7GsCaAiCzCaf56TlXn+DBuYzJNSgF9woIolkTlnAK6p8TEZMaNW8rMmZv48ce+lC5dmF9/7RfosEwe4Usiyaeq271O55N8GC6183/vyn4wUAPogPPsroUiUk9Vj5w1IpG7gLsAqlSp4sOkjclYSgH9/YVR/O0W0J/qHJkjCuieli7dxeDBs1i1ai/XXm65fS4AACAASURBVFudY8fiKVkyZyRAkzv4kkh2us1b6tY97gU2+zBcNFDZ43MlnMesePezRFUTgK0isgknsSzz7ElVJwATwLlqy4dpG5OmnFxA93T8+Gkee+xH3nlnOeXLhzJt2s3ceGNkjqjhmNzFl0QyBKd5qwqwD5jndsvIMqCGiFTFeRnWLYD3FVnfAL2BiSIShtPUFeVb6MacH+8CetsaYYzt2SjHFNC95c+fj/nzt3PvvS0YObIjxYqFBDokk0f5kkgSVfWW8x2xqiaKyD3ADzj1j49Udb2IvAAsV9WZ7ndXicgGnOayR1T10PlOy5j0nFNAb1iBO9vlnAK6py1bDvPCC78yfnxnQkNDWLHiLgoWtBdNmcDK8IZEEfkH2ARMBb5S1disCCwtdkOi8UVqBfQ+LavkqAK6p/j4RF57bREvvbSQAgWCmD27D23b2tVYxncBvSFRVauJSGucpqnnRWQ1MEVVp/gjIGMuRloF9F4tKlMsBxXQPf3yy1aGDJnNpk2H6NWrLmPGXE2FCqGBDsuYM3w6J1bV34HfReQ54A3gM8ASick2cksB3Zuq8tJLC0lISGbOnFu5+urqgQ7JmHP4ckNiUZwbCW8BIoEZQGs/x2WMT3YePslHi7Yyddm/BfQxPRvSpnpYjiygg9Ms9+GHK7nmmupUrlycTz+9gRIlClKoUM48ozK5ny9nJOuAb4HXVHWhn+Mxxidro4/y3oJ/ziqgD2obQZ0KOa+A7mnNmn0MHjyLxYujefbZdjz//OWUL2/NWCZ78yWRRKhqst8jMSYDycnKr5sP8N6Cf3LsHehpOX78NM8/P5+xY5dQsmQhJk7sxu23Nwx0WMb4JM1EIiKvq+pwYLqInHNpl70h0WSV3FhA9/bcc/N5/fXFDBrUmFdeuZLSpe0BiybnSO+MZKr7v70Z0QREbi2gp9i58ygnTiRQu3YYjz/ehu7da9OmjT0CyOQ86b0hcan7Z6SqnpVM3BsN7Q2Kxi9yYwHdU2JiMm+99QfPPvsLTZtW4Ndf+xEWVtiSiMmxfKmRDODcs5KBqXQz5qLk1gK6pyVLohk8eBZ//rmP666rwbhxnQMdkjEXLb0aSS+cS36rishXHl+FAkdSH8qY8+NdQC8aEsygthH0zwUFdG+zZ2+ma9fPqVAhlK++6kn37rVzxRmWMemdkSwFDuE8tXe8R/dYYJU/gzK5n3cBvVyxgjzZuTa3tKiSawro4NxQuHt3LBUrFuPKKyN44YXLuf/+loSG2gMWTe6R4bO2sht71lbO5l1Ar10ulLvbR3Bd/QoUCM75BXRPmzcfYujQ2WzefIgNG4ZRtGiBQIdk8rCAPGtLRH5V1fYiEsPZL6QSQFW1lD8CMrlTbi+ge4qLS+SVV35j1KjfKFQomFGjrqBQIXtCr8m90tu6U16nG5YVgZjcaW30USYsdB7hLpArC+ie9u49Trt2/+Pvvw/Tu3c9xoy5mnLligY6LGP8Kr3Lf1PuZq8M7FbV0yLSBmgATAKOZUF8JgdKKaBPWBDF4qhDFA0JZmCbqrmygJ4iISGJ/PmDKFu2CO3aXcr48Z3p1KlaoMMyJkv48j6S1UBznDck/gjMBqqqahf/h3cuq5FkX/GJScxYvZv3F/xbQB/QJjzXFdA9JScrEyas4OWXF/L77wOpVCl3nmmZnC+g7yMBklU1QUR6AG+o6lsiYldtmTOOnkzgs6XbmbhoG/vdAvrYXg1zZQHd059/7uXuu2fxxx+76NixKgkJSYEOyZiA8OlVuyJyM9AX6O52y52Hl+a8pFZAfz2XFtA9qSqPPPIjb7yxhFKlCvHppzdw6631c/U8G5MeX+9sH4rzGPkoEakKfO7fsEx2ltcK6N5EhJiYUwwc6DxgsWTJ3Fn3McZXPt1HIiLBQMqr2baoaqJfo0qH1UgCI7UCep+WVejXOpwKJXL/jnT79iPcf/8cnn22PU2alCc5WcmXz85ATM4R0BqJiLQFPgV24dxDUk5E+qrqIn8EZLKX1AroufEO9LQkJCQxduwSnn/+VwB69apLkyblLYkY48GXpq2xQGdV3QAgIpE4icUvmc1kD6kV0Mf0dB7hnpsL6J5+/30nd989i3Xr9tOtWy3eeutaqlQpHuiwjMl2fEkkBVKSCICqbhQRe9ZDLhUdc5KPftvGlGU7zhTQR9/ckLY1cncBPTXz5kVx9Ggc33zTi27dagc6HGOyLV/uI5kIxOOchQDcChRW1Tv8G1rqrEbiH+t2HeW9Bf8W0Ls2rMCgtlWpWyHvHIGrKp9+uoYyZQpz7bU1iI9PJCEh2Z6RZXKFQN9HMhi4D3gUp0ayAPg/fwRjspaqMn/zASb8evYd6HmlgO7pr78OMmTIbObP38bNN9fh2mtrEBISTIg9pNeYDKWbSESkPlAN+FpVX8uakIy/pRTQP1gYxeZ9ea+A7unUqQRefnkhr766iCJFCvDee10YNKhJoMMyJkdJ7+m/T+K8CXEl0FxEXlDVj7IsMpPprIB+rm+/3cyLLy7kttsaMHp0J8qWtQcsGnO+0jsjuRVooKonRKQM8B1giSQHsgL62fbuPc7q1Xu55prq3HxzHcLDB9GiRcVAh2VMjpVeIolX1RMAqnpARPLmIWsOZgX0syUlJfPeeyt44omfKFAgiB07HqBQofyWRIy5SOklkgiPd7ULUM3z3e2q2sOvkZkLkloBfcBl4fS/rGqeK6B7WrlyD4MHz2LZst1ceWUEb7/dmUKF8lY9yBh/SS+R3Oj1eZw/AzEXJ7UC+hPX1qZ3y7xXQPe2dWsMLVq8T1hYYSZP7sEtt9TLk016xvhLei+2+ikrAzEX5uipBD77wwro3lSVtWv306BBWapWLcn//teNrl1rUaJEwUCHZkyuYy+SzqFSCuhTl+3gxOkk2lTP2wV0T1u3xnDPPd8zZ84WVq26mwYNytK3b8NAh2VMruXXRCIi1wBvAkHAB6r6Shr93QRMA5qrqt22no7EpGSe/Hot01fusgK6l9OnkxgzZjEvvPAr+fIJo0d3ok6dMoEOy5hcz+dEIiIhqhp/Hv0HAeOBTkA0sExEZno+t8vtLxTnzvk/fB13Xvbegii+WB5Nr2aVuf/KGnm6gO4pKSmZ1q0/ZMWKPfToEckbb1xN5cqWXI3JChk2ootICxFZC/ztfm4oIr48IqUFzrtLolT1NDAF6JZKfyOB14A438POmxZtOcjrczfRpUF5XrmxviUR4Ngx59gmKCgfAwY05ttvezN9ek9LIsZkIV+qsW8BXYBDAKr6J3C5D8NVBHZ6fI52u50hIo2Byqo6K70RichdIrJcRJYfOHDAh0nnPnuOnuK+z1cRUaYor97YIM/XQVSViRNXExHxJjNm/AXA0KHN6dKlZoAjMybv8SWR5FPV7V7dknwYLrU93ZlHDbs3OI4Fhmc0IlWdoKrNVLVZmTJ5r837dGIyQz9bSVxCEu/e1pQiIXn7GokNGw7QocPH9O8/g9q1w6hWrVSgQzImT/Nlj7RTRFoA6tY97gU2+zBcNFDZ43MlYLfH51CgHjDfPbouB8wUkeut4H62l2ZvYNWOI4zv04Tql+TtZ0G99toinnrqZ4oVC+GDD7rSv39je1uhMQHmSyIZgtO8VQXYB8xzu2VkGVBDRKrivKb3FqBPypeqehQIS/ksIvOBhy2JnO2bVbv4ePF2BrapynUNygc6nIBRVUSEcuWKcuut9fnvfztRpkyRQIdljMGHRKKq+3GSwHlR1UQRuQf4Aefy349Udb2IvAAsV9WZ5x1tHrNpbyxPfLWW5uElefzavPmGvt27Y7n//jm0bVuF++5rye23N+T22+2eEGOykwwTiYi8j0dtI4Wq3pXRsKr6Hc5Tgz27PZtGvx0yGl9eEhuXwJBJKygSEsz4Pk3IH5S37lJPSkrm7beX8dRTP5OQkEzr1pUCHZIxJg2+NG3N8/i7IHADZ1+NZTKZqvLItDVsP3ySyYNackmxvPVYj9Wr9zJo0ExWrNjDVVdV4+23O1tB3ZhszJemramen0XkU+BHv0VkeH9hFHPW7+WpzpG0jCgd6HCy3NGjcezeHcvUqTdx88118vylzsZkdxdyHWlV4NLMDsQ4lkQd4tU5m7i2XjkGta0a6HCyhKoybdoG/v77EE891Y727cOJirqfggXz9mXOxuQUvtzZHiMih91/R3DORp70f2h5z75jcdwzeRWXli7MazfljZsO//nnMJ07T6ZXry+ZMWMTCQnOLUqWRIzJOdL9tYqzJ2uIc/kuQLKqnlN4NxcvISmZYZ+t5ER8IpPvbEloLn+HSHx8IqNH/86LLy4kf/58vPnmNQwd2pzgPPzoe2NyqnQTiaqqiHytqk2zKqC8atR3f7F8ewxv3tKImmVDAx2O3+3ceYyRIxfQtWst3njjaipWLBbokIwxF8iXw7+lItLE75HkYbPW7OajRVvp1zqcbo1y7/vDDxw4wbhxSwGoXr0UGzYMY9q0my2JGJPDpXlGIiLBqpoItAHuFJF/gBM4z9BSVbXkkgm27I/l0S/X0KRKCZ7sHBnocPwiOVn53/9W8eij84iNjadTpwhq1QojIqJkoEMzxmSC9Jq2lgJNgO5ZFEueczw+kbs/XUGh/EGMv7VJrnw17rp1+xkyZDa//baDtm2r8O67XahVKyzjAY0xOUZ6iUQAVPWfLIolT1FVHpu+hq0HTzBpYEvKF8997xY5fTqJq676lNOnk/joo+vp169RnrgSzZi8Jr1EUkZEHkrrS1Ud44d48oyPFm1j9po9PHZNbVpXz11H6D//vJX27S+lQIEgvvjiZmrXDiMsrHCgwzLG+El6bSlBQFGcx72n9s9coGXbDjPqu410qlOWwe0jAh1OpomOPsaNN37BFVd8wief/AlAmzZVLIkYk8uld0ayR1VfyLJI8oj9sXEM+2wllUoW4vWeDXNFU09iYjLjxi3lmWd+ISkpmVGjruDWWxsEOixjTBbJsEZiMk9iUjL3Tl7FsbgEPh7QgmK55KbDvn2/ZsqUdVx7bXXGj+9M1ap2NZYxeUl6ieSKLIsij/jvD5v4Y+thxvRsSGT5nH3vxJEjcQQH56No0QIMG9acG2+M5MYbI3PFGZYx5vykWSNR1cNZGUhuN2fdHt5bEMVt/6lCjyY5990aqsqUKeuIjBzPM8/8DDh1kJtusqf0GpNX5b4bF7Khfw4c5+Fpa2hYuQTPdKkT6HAu2JYth7n66kn07j2dSpWKcdttVgcxxlzYY+TNeTh5OpEhk1aQP0h4+9YmhAQHBTqkCzJ58loGDJhBSEgw48Zdy+DBzQjKY29tNMakzhKJH6kqT3y1lr/3H+eTAS2oWCLn3XSYkJBE/vxBNGtWgZtuqsNrr3WiQgW7+tsY8y9LJH70yeLtzFi9m4evqknbGmUCHc552b//BMOHz+XEidN89VUvatYszaRJPQIdljEmG7K2CT9ZsT2GF2dv4IralzC0Q/VAh+Oz5GRlwoQV1Ko1jqlT11G3bhmSkpIDHZYxJhuzMxI/OHg8nmGfraRc8YKM6dmIfPlyxtVMUVEx3HbbVyxeHE2HDuG888511K6dux7fYozJfJZIMllSsnLf56uIOXma6UNaU7xwzrnpsHjxEI4ciePjj7vTt2/eeNWvMebiWdNWJnt97iZ+/+cQI7vXo17F4oEOJ0MzZ26iR4+pJCUlU7p0YdatG8rtt+eOR7cYY7KGJZJMNHf9Xt6e/w+9W1SmZ7PKgQ4nXTt2HKV79yl06zaFzZsPsWfPcYAc0wxnjMk+rGkrk2w7eILh0/6kfsXijOhaN9DhpCkxMZk33ljCiBHzUVVeffVKHnzwP+TPnzPvbzHGBJ4lkkxw6nQSgyetICifc9NhwWy8U05KSuaDD1bSsWNV/u//riU8vESgQzLG5HDWtHWRVJWnvlnLpn2xvNGrEZVLZb93b8TEnOKxx34kNjaekJBgFi0awMyZt1gSMcZkCkskF2ny0h18tXIX93WsQYdalwQ6nLOoKp99tobatcfz+uuL+eWXbQCULl3YiunGmExjTVsX4c+dR3h+5gba1yzD/VfUCHQ4Z9m8+RBDh87mp5+20qJFRX744TYaNSoX6LCMMbmQJZILdPjEaYZ+tpIyoSG80Sv73XT4wANzWL58N2+/3Zm77mpqD1g0xviNJZILkJSs3D9lFQdi4/lySCtKFikQ6JAA+PHHf6hdO4zKlYvzzjvXERISTLlyRQMdljEml/PrYaqIXCMim0Rki4g8nsr3D4nIBhFZIyI/icil/owns7w5bzML/z7I893q0qBS4AvWe/cep0+f6Vx11SRefXURAJdeWsKSiDEmS/gtkYhIEDAeuBaoA/QWEe+3Oq0CmqlqA+BL4DV/xZNZfv5rH2/9vIWbmlbiluaBvekwOVl5993l1K49junTNzJiRHtGj74qoDEZY/Ief56RtAC2qGqUqp4GpgDdPHtQ1V9U9aT7cQmQrd9Bu/PwSR6c+id1yhfjxe71An7l06hRCxkyZDZNm1ZgzZrBPPdcBwoWtNZKY0zW8udepyKw0+NzNNAynf4HAt+n9oWI3AXcBVClSpXMiu+8xCU4Nx2qKu/e1jRgNx3GxsZz8OBJqlYtyeDBzahatSS9ewc+qRlj8i5/npGktmfTVHsUuQ1oBvw3te9VdYKqNlPVZmXKBOYFUSNmrGf97mOM7dWIKqWz/qZDVeXrrzdSp87b9Or1JapK6dKF6dOnviURY0xA+TORRAOeRYRKwG7vnkTkSuAp4HpVjfdjPBds6rIdTF2+k3sur84VkWWzfPrbtx/h+uun0KPHF5QqVYi33rrWkocxJtvwZ9PWMqCGiFQFdgG3AH08exCRxsB7wDWqut+PsVywdbuO8syM9bSpHsaDnWpm+fQXL97JlVd+CsDo0Z24//7/EBxs94QYY7IPvyUSVU0UkXuAH4Ag4CNVXS8iLwDLVXUmTlNWUWCae4S9Q1Wv91dM5+vIydMMnrSCsCIFePOWRgRl4U2Hx47FU6xYCE2alGfAgEY88shlVKmS/d9vYozJe0Q11bJFttWsWTNdvny536eTnKwM/HgZv205yBd3t6JxlZJ+nybAoUMnefzxecydG8X69UMpWjR73OxojMnZRGSFqjbzx7jtWtE0jPtlC79sOsDI7vWyJImoKp9+uobhw+cSE3OKhx5qhZVBjDE5gSWSVPy6+QBj523mhsYVua2l/y83Pno0ju7dpzJ//jZatarEu+92oUGDrC/qG2PMhbBE4iU65iT3T1lFrbKhvHyDfy+tVVVEhGLFQggLK8yECV0YOLBJtnsApDHGpMcu//EQn5jE0M9WkpSkvHNbUwoV8N9Nhz/8sIUmTSYQHX0MEWHatJu5886mlkSMMTmOJRIPz3+7gTXRRxndsyFVw4r4ZRp79sRyyy1fcs01n3HyZAL795/wy3SMMSarWNOW68sV0Uz+YweD21fj6rr+eQHU+PFLefLJn4mPT+T55zvw2GOXERJiq8AYk7PZXgzYsPsYT329llYRpXn4Kv/ddLhixR5atqzI+PGdqVGjtN+mY4wxWSnPJ5KjpxIY8tkKShTOz1u9GxOciW8SPHYsnmef/YW+fRvQtGkF3n77OkJCguzxJsaYXCVPJ5LkZGX4F6vZFXOKqXf/hzKhIZkyXlVl+vSN3H//HPbsiaVKleI0bVrBHvFujMmV8vSe7Z1f/2Hexv2M6FqHppeWypRxbt0awz33fM933/1No0bl+OqrnrRsma1fs2KMMRclzyaSRVsO8vrcTVzfsAL9Wodn2ng/+2wtCxZsZ+zYq7nnnhb2gEVjTK6XJ5+1tefoKa576zdKFynAN8Muo8hFXjm1cOF24uOTuPLKCOLjEzlw4CSVKhW7qHEaY0xm8ueztvLc4fLpxGSGfraS+IQk3rmt6UUlkYMHTzJgwAzatZvICy/8CkBISLAlEWNMnpLnmrZenL2BVTuO8PatTah+SdELGoeqMnHiah555EeOHo3nsccu45ln2mVypCY3SEhIIDo6mri4uECHYvKIggULUqlSJfLnz59l08xTieSbVbv4ZPF2BrWpSuf65S94PN999zcDBszksssq8+67XahX75JMjNLkJtHR0YSGhhIeHm6XfRu/U1UOHTpEdHQ0VatWzbLp5pmmrU17Y3niq7W0CC/FY9fWPu/hT55MYNGiHQB07lyDGTNuYcGC/pZETLri4uIoXbq0JRGTJUSE0qVLZ/kZcJ5IJLFxCQyetIKiBYMZ16cx+c/zpsPvv/+bevXe5tprP+PIkThEhOuvr2UPWDQ+sSRislIgtrdcn0hUlUemrWHH4ZOM79OES4oV9HnYXbuOcfPN0+jceTIhIcF8+21vSpTwfXhjjMkLcn0imbAgijnr9/LEtbVpUdX3mw737z9BnTpvM2vWZl588XL+/HMw7duH+y9QY/wkKCiIRo0aUa9ePbp27cqRI0fOfLd+/Xo6duxIzZo1qVGjBiNHjsTzloDvv/+eZs2aERkZSe3atXn44YcDMQvpWrVqFYMGDTqrW7du3WjVqtVZ3fr168eXX355VreiRf+94Gbz5s107tyZ6tWrExkZSc+ePdm3b99FxXb48GE6depEjRo16NSpEzExMan2t2PHDq666ioiIyOpU6cO27ZtA2DcuHFUr14dEeHgwYNn+p81axYjRoy4qNgylarmqH9NmzZVX/2+5aBWfXyWDpm0XJOTk30aJjr66Jm/33xziW7Zcsjn6RnjbcOGDYEOQYsUKXLm79tvv11ffPFFVVU9efKkRkRE6A8//KCqqidOnNBrrrlGx40bp6qqa9eu1YiICN24caOqqiYkJOj48eMzNbaEhISLHsdNN92kq1evPvM5JiZGK1WqpLVr19aoqKgz3e+44w6dNm3aWcOmLJtTp05p9erVdebMmWe++/nnn3Xt2rUXFdsjjzyio0aNUlXVUaNG6aOPPppqf+3bt9e5c+eqqmpsbKyeOHFCVVVXrlypW7du1UsvvVQPHDhwpv/k5GRt1KjRmf68pbbdAcvVT/vlXHvV1r5jcdz7+SrCw4rw2k0NM2w3PHo0jqef/pn33lvBkiWDaNKkPPfd1zKLojV5wfPfrmfD7mOZOs46FYoxomtdn/tv1aoVa9asAWDy5MlcdtllXHXVVQAULlyYcePG0aFDB4YNG8Zrr73GU089Re3azsUpwcHBDB069JxxHj9+nHvvvZfly5cjIowYMYIbb7yRokWLcvz4cQC+/PJLZs2axcSJE+nXrx+lSpVi1apVNGrUiK+//prVq1dTokQJAKpXr86iRYvIly8fgwcPZscO5yKXN954g8suu+ysacfGxrJmzRoaNmx4ptv06dPp2rUrZcuWZcqUKTzxxBMZLpfJkyfTqlUrunbteqbb5Zdf7vNyTcuMGTOYP38+AHfccQcdOnTg1VdfPaufDRs2kJiYSKdOnYCzz5IaN26c6nhFhA4dOjBr1ix69ux50XFerFyZSBKSkhn22UpOnk7k8ztbUjSdmw5VlWnTNvDAA3PYu/c499zTgmrVSmZhtMZkjaSkJH766ScGDhwIOM1aTZs2PaufatWqcfz4cY4dO8a6desYPnx4huMdOXIkxYsXZ+3atQBpNt942rx5M/PmzSMoKIjk5GS+/vpr+vfvzx9//EF4eDhly5alT58+PPjgg7Rp04YdO3Zw9dVXs3HjxrPGs3z5curVq3dWt88//5wRI0ZQtmxZbrrpJp8Sybp1685ZFqmJjY2lbdu2qX43efJk6tSpc1a3ffv2Ub68c6tB+fLl2b9//znDbd68mRIlStCjRw+2bt3KlVdeySuvvEJQUPpvaG3WrBkLFy60ROIvo777i+XbY3ird2NqlA1Nsz9VpUePL/jmm79o0qQ8M2f2plmzClkYqclLzufMITOdOnWKRo0asW3bNpo2bXrmyFdV0zxTP58rf+bNm8eUKVPOfC5ZMuMDsZtvvvnMjrJXr1688MIL9O/fnylTptCrV68z492wYcOZYY4dO0ZsbCyhof/+pvfs2UOZMmXOfN63bx9btmyhTZs2iAjBwcGsW7eOevXqpTpP53uFU2hoKKtXrz6vYTKSmJjIwoULWbVqFVWqVKFXr15MnDjxTMJPyyWXXMLu3bszNZYLleuK7bPW7OajRVvp1zqc6xumnhQSEpIAZyNq06Yyb711DUuXDrIkYnKlQoUKsXr1arZv387p06cZP348AHXr1sX7uXVRUVEULVqU0NBQ6taty4oVKzIcf1oJybOb930NRYr8+yrrVq1asWXLFg4cOMA333xDjx49AEhOTmbx4sWsXr2a1atXs2vXrrOSSMq8eY576tSpxMTEULVqVcLDw9m2bduZJFe6dOmzzpYOHz5MWFjYmWXhy7zGxsbSqFGjVP95Jr0UZcuWZc+ePYCT9C655Nz7zipVqkTjxo2JiIggODiY7t27s3LlygxjiYuLo1ChQhn2lxVyVSLZsj+WR79cQ9NLS/Jk58hU+5k/fxsNGrzLjBl/ATB8eGvuvbclQZn4QitjsqPixYvz1ltvMXr0aBISErj11lv57bffmDdvHuCcudx33308+uijADzyyCO8/PLLbN68GXB27GPGjDlnvFdddRXjxo078zllZ122bFk2btx4pukqLSLCDTfcwEMPPURkZCSlS5dOdbypnQlERkayZcuWM58///xz5syZw7Zt29i2bRsrVqw4k0g6dOjA1KlTOX36NAATJ048Uwfp06cPv//+O7Nnzz4zrjlz5pxprkuRckaS2j/vZi2A66+/no8//hiAjz/+mG7dup3TT/PmzYmJieHAgQMA/Pzzz6mOy9vmzZvPadYLGH9V8f31L62rtmLjErTj6F+06ci5uufIqXO+37//uN5++9cKz2nVqm/oTz9FpTIWYzJXdrtqS1W1S5cu+sknn6iq6po1a7R9+/ZaLDP+EgAAC3BJREFUs2ZNrVatmj733HNnXeH47bffapMmTbR27doaGRmpDz/88Dnjj42N1dtvv13r1q2rDRo00OnTp6uq6rRp0zQiIkLbt2+vw4YN0zvuuENVU796atmyZQroxIkTz3Q7cOCA9uzZU+vXr6+RkZF69913pzp/9erV02PHjunWrVu1QoUK51yh2bhxY12yZImqqj733HNar149bdiwofbo0UP3799/pr+NGzfq1VdfrdWrV9fIyEjt1auX7t27N91lm5GDBw9qx44dtXr16tqxY0c9dOjQmfkdOHDgmf7mzp2r9evX13r16ukdd9yh8fHxqqr65ptvasWKFTUoKEjLly9/1jDXXXedrlmzJtXpZvVVW7niMfKqyj2TV/H9uj1MGtSS1tXCzvr+88/XMmzYdxw/fppHHmnNU0+1o3DhrHugmcm7Nm7cSGRk6mfHJnOMHTuW0NDQc+4lyc327dtHnz59+Omnn1L9PrXtzh4jn4GPFm1j9to9PHpN7XOSCEBiYjL16l3C6tWDeemlKyyJGJOLDBkyhJCQzHlNdk6xY8cOXn/99UCHcUaOv2pr2bbDjPpuI1fVKcvd7SIAOHHiNCNHLqBKleIMHdqc225rwG23NbBnHhmTCxUsWJC+ffsGOows1bx580CHcJYcfUayPzaOYZ+tpHKpwozu6dx0OGvWZurWfZtXX13E5s2HAKeYZ0nEBEpOaz42OVsgtrcce0aSmJTMPZNXcSwugU8GtuDYwVP0u+8rvv76L+rUKcOCBf1o2/bSQIdp8riCBQty6NAhe5S8yRKqzvtIChbM2ofL5thE8toPm1i69TBjezWkdrliLFiwnR9++IdRo67goYdaUaBA+neFGpMVKlWqRHR09JlLO43xt5Q3JGalHJlI5qzbw4QFUVwVEcaOBdHQuBLt2l3Kjh0PULp04UCHZ8wZ+fPnz9I31RkTCH6tkYjINSKySUS2iMjjqXwfIiJT3e//EJHwjMYZn5jM8C/+pEQifDDsB8aMWcKJE84NRpZEjDEm6/ktkYhIEDAeuBaoA/QWEe/bNQcCMapaHRgLvEoGovYf5/ixeDZ8sIb7hrVg7dohFClSILPDN8YY4yN/Nm21ALaoahSAiEwBugGeD6TpBjzn/v0lME5ERNO57CAxWSmx4Qhf/9yPJk3K+ydyY4wxPvNnIqkI7PT4HA14v+DjTD+qmigiR4HSwEHPnkTkLuAu92P8mh/7rWv6o19izmnC8FpWeZgti3/ZsviXLYt/1fLXiP2ZSFK71tH7TMOXflDVCf/f3v3GyFWVcRz//gQqrUCVNBgRtBAKWmqptZoqiViLBGsoSppuSQusAQ1VNID1hamJ+OcFAXlhBSwFmxYDpLQB3SCkElwoabrQjdBtu0HB0pAmxBKtjYGiWH6+OGeZcdndubuz83efTzLJnTt35jz7ZOaevefMPAdYByCpt1Y/8281kYuSyEVJ5KIkclEiqbfyUWNTy8n2A8DpZfdPAwYXz3/nGEnHAlOBf9QwphBCCOOslh3JTmCGpDMkTQKWAV2DjukCrsrbS4A/jjQ/EkIIofnUbGgrz3lcB2wFjgHW294r6SekcsZdwK+B30h6iXQlsqzAS6+rVcwtKHJRErkoiVyURC5KapaLlisjH0IIobm0dNHGEEIIjRcdSQghhKo0bUdSi/IqrapALm6U1C+pT9ITktq27HGlXJQdt0SSJbXtVz+L5ELS0vze2Cvp/nrHWC8FPiMfkdQt6bn8OVnUiDhrTdJ6SQcl7RnmcUlak/PUJ2nuuDRcqzV8q7mRJuf/CpwJTAJ2ATMHHfMtYG3eXgZsanTcDczFAmBK3l45kXORjzsR2Ab0APMaHXcD3xczgOeAD+T7pzQ67gbmYh2wMm/PBPY3Ou4a5eLzwFxgzzCPLwIeI/2Gbz7wzHi026xXJO+UV7H9H2CgvEq5S4GNeXsLsFDtueBDxVzY7rb9Rr7bQ/rNTjsq8r4A+ClwC/BmPYOrsyK5+AZwh+1DALYP1jnGeimSCwMn5e2pvPs3bW3B9jZG/i3epcC9TnqA90uqutZUs3YkQ5VX+fBwx9j+LzBQXqXdFMlFuatJ/3G0o4q5kPRJ4HTbj9QzsAYo8r44Gzhb0nZJPZIurlt09VUkFzcBKyQdAB4FvlOf0JrOaM8nhTTreiTjVl6lDRT+OyWtAOYBF9Q0osYZMReS3kOqIt1Zr4AaqMj74ljS8NYXSFepT0uaZfufNY6t3ork4nJgg+3bJH2W9Pu1Wbbfrn14TaUm581mvSKJ8iolRXKBpAuB1cBi2/+uU2z1VikXJwKzgCcl7SeNAXe16YR70c/I72y/Zftl4M+kjqXdFMnF1cCDALZ3AMeTCjpONIXOJ6PVrB1JlFcpqZiLPJxzF6kTaddxcKiQC9uHbU+zPd32dNJ80WLbNStW10BFPiO/JX0RA0nTSENd++oaZX0UycUrwEIASR8ndSQTcf3jLuDK/O2t+cBh269W+6JNObTl2pVXaTkFc3ErcAKwOX/f4BXbixsWdI0UzMWEUDAXW4GLJPUDR4Hv2/5746KujYK5+B5wt6QbSEM5ne34j6ekB0hDmdPyfNCPgOMAbK8lzQ8tAl4C3gC+Pi7ttmEuQwgh1FGzDm2FEEJoEdGRhBBCqEp0JCGEEKoSHUkIIYSqREcSQgihKtGRhKYj6aik58tu00c4dvpwlU5H2eaTuXrsrlxS5JwxvMa1kq7M252STi177B5JM8c5zp2S5hR4zvWSplTbdgjDiY4kNKMjtueU3fbXqd3lts8jFQO9dbRPtr3W9r35bidwatlj19juH5coS3HeSbE4rweiIwk1Ex1JaAn5yuNpSX/Kt88Nccy5kp7NVzF9kmbk/SvK9t8l6ZgKzW0DzsrPXZjXsNid13p4b95/s0prwPw877tJ0ipJS0g1z+7LbU7OVxLzJK2UdEtZzJ2SfjnGOHdQVnBP0q8k9SqtPfLjvO+7pA6tW1J33neRpB05j5slnVChnRBGFB1JaEaTy4a1Hs77DgJfsj0X6ADWDPG8a4Ff2J5DOpEfyOUwOoDz8/6jwPIK7V8C7JZ0PLAB6LD9CVIliJWSTga+Bpxrezbws/In294C9JKuHObYPlL28BbgsrL7HcCmMcZ5MakMyoDVtucBs4ELJM22vYZUS2mB7QW5VMoPgQtzLnuBGyu0E8KImrJESpjwjuSTabnjgNvznMBRUt2owXYAqyWdBjxk+0VJC4FPATtz+ZjJpE5pKPdJOgLsJ5UZPwd42fZf8uMbgW8Dt5PWOrlH0u+BwiXrbb8maV+uc/RibmN7ft3RxPk+UjmQ8hXulkr6Julz/SHSAk59g547P+/fntuZRMpbCGMWHUloFTcAfwPOI11Jv2vRKtv3S3oG+AqwVdI1pLLZG23/oEAby8sLPEoacn2bXNvpM6QigMuA64AvjuJv2QQsBV4AHrZtpbN64ThJqwDeDNwBXCbpDGAV8GnbhyRtIBUmHEzA47YvH0W8IYwohrZCq5gKvJrXj7iC9N/4/5F0JrAvD+d0kYZ4ngCWSDolH3Oyiq9p/wIwXdJZ+f4VwFN5TmGq7UdJE9lDfXPqX6Sy9kN5CPgqaY2MTXnfqOK0/RZpiGp+HhY7CXgdOCzpg8CXh4mlBzh/4G+SNEXSUFd3IRQWHUloFXcCV0nqIQ1rvT7EMR3AHknPAx8jLSnaTzrh/kFSH/A4adinIttvkqqjbpa0G3gbWEs6KT+SX+8p0tXSYBuAtQOT7YNe9xDQD3zU9rN536jjzHMvtwGrbO8irc++F1hPGi4bsA54TFK37ddI3yh7ILfTQ8pVCGMW1X9DCCFUJa5IQgghVCU6khBCCFWJjiSEEEJVoiMJIYRQlehIQgghVCU6khBCCFWJjiSEEEJV/gfjqiqqiMW2cAAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3092,10 +3090,10 @@ { "data": { "text/plain": [ - "0.6064648683126901" + "0.6239677471688679" ] }, - "execution_count": 42, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -3106,7 +3104,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -3115,12 +3113,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6727\n", - " 1 0.39 0.41 0.40 2022\n", + " 0 0.83 0.82 0.82 6741\n", + " 1 0.41 0.43 0.42 2008\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", + " accuracy 0.73 8749\n", + " macro avg 0.62 0.62 0.62 8749\n", + "weighted avg 0.73 0.73 0.73 8749\n", "\n" ] } @@ -3138,7 +3136,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3165,7 +3163,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -3175,7 +3173,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3190,7 +3188,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3201,7 +3199,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -3210,8 +3208,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13477\n", - " 1 1.00 1.00 1.00 4019\n", + " 0 1.00 1.00 1.00 13463\n", + " 1 1.00 1.00 1.00 4033\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3233,14 +3231,14 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 782\n" + "Of 2008 Defaulters, the Decision Tree (Random Forest) identified 788\n" ] }, { @@ -3275,14 +3273,14 @@ " \n", " \n", " \n", - " 0\n", - " 6264\n", - " 463\n", + " 0\n", + " 6265\n", + " 476\n", " \n", " \n", - " 1\n", - " 1240\n", - " 782\n", + " 1\n", + " 1220\n", + " 788\n", " \n", " \n", "\n", @@ -3291,11 +3289,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6264 463\n", - "1 1240 782" + "0 6265 476\n", + "1 1220 788" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -3306,19 +3304,19 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2805714285714286\n" + "Optimal Threshold: 0.325\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3335,7 +3333,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -3344,8 +3342,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.93 0.88 6727\n", - " 1 0.63 0.39 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.62 0.39 0.48 2008\n", "\n", " accuracy 0.81 8749\n", " macro avg 0.73 0.66 0.68 8749\n", @@ -3360,7 +3358,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -3391,7 +3389,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -3409,7 +3407,7 @@ " warm_start=False)" ] }, - "execution_count": 52, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3422,7 +3420,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -3431,12 +3429,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.97 0.91 13477\n", - " 1 0.81 0.48 0.60 4019\n", + " 0 0.86 0.96 0.91 13463\n", + " 1 0.79 0.47 0.59 4033\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.84 0.72 0.76 17496\n", - "weighted avg 0.85 0.85 0.84 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", "\n" ] } @@ -3454,14 +3452,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 770\n" + "Of 2008 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 796\n" ] }, { @@ -3496,14 +3494,14 @@ " \n", " \n", " \n", - " 0\n", - " 6289\n", - " 438\n", + " 0\n", + " 6281\n", + " 460\n", " \n", " \n", - " 1\n", - " 1252\n", - " 770\n", + " 1\n", + " 1212\n", + " 796\n", " \n", " \n", "\n", @@ -3512,11 +3510,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6289 438\n", - "1 1252 770" + "0 6281 460\n", + "1 1212 796" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -3527,19 +3525,19 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2100144956690176\n" + "Optimal Threshold: 0.22481097140826298\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3556,7 +3554,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -3565,11 +3563,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.93 0.88 6727\n", - " 1 0.64 0.38 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.63 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.74 0.66 0.69 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3581,7 +3579,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -3599,7 +3597,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": { "scrolled": true }, @@ -3632,22 +3630,22 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " Decision Tree (GINI)\n", - " 0.418892\n", - " 0.609249\n", + " 0.449203\n", + " 0.625991\n", " \n", " \n", - " 1\n", + " 1\n", " Random Forest\n", - " 0.386746\n", - " 0.761906\n", + " 0.392430\n", + " 0.762567\n", " \n", " \n", - " 2\n", + " 2\n", " Gradient Boosted\n", - " 0.380811\n", - " 0.775534\n", + " 0.396414\n", + " 0.772109\n", " \n", " \n", "\n", @@ -3655,12 +3653,12 @@ ], "text/plain": [ " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.418892 0.609249\n", - "1 Random Forest 0.386746 0.761906\n", - "2 Gradient Boosted 0.380811 0.775534" + "0 Decision Tree (GINI) 0.449203 0.625991\n", + "1 Random Forest 0.392430 0.762567\n", + "2 Gradient Boosted 0.396414 0.772109" ] }, - "execution_count": 58, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -3701,7 +3699,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -3710,7 +3708,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3718,7 +3716,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", + " Current function value: 0.440644\n", " Iterations 7\n" ] }, @@ -3737,13 +3735,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838 \n", "\n", "\n", - " Time: 23:33:29 Log-Likelihood: -7727.2\n", + " Time: 00:20:46 Log-Likelihood: -7709.5\n", "\n", "\n", - " converged: True LL-Null: -9429.0\n", + " converged: True LL-Null: -9445.9\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3754,136 +3752,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", + " LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", "\n", "\n", - " SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", + " SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", "\n", "\n", - " AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", + " AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", "\n", "\n", - " PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", + " PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", "\n", "\n", - " PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", + " PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", "\n", "\n", - " PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", + " PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", "\n", "\n", - " PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", + " PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", "\n", "\n", - " PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", + " PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", "\n", "\n", - " PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", + " PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", "\n", "\n", - " BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", + " BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", "\n", "\n", - " BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", + " BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", "\n", "\n", - " BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", + " BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", "\n", "\n", - " BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", + " BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", "\n", "\n", - " BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", + " BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", "\n", "\n", - " BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", + " BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", "\n", "\n", - " PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", + " PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", "\n", "\n", - " PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", + " PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", "\n", "\n", - " PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", + " PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", "\n", "\n", - " PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", + " PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", "\n", "\n", - " PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", + " PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", "\n", "\n", - " PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", + " PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", "\n", "\n", - " GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", + " GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", "\n", "\n", - " UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", + " UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", "\n", "\n", - " HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", + " HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", "\n", "\n", - " MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", + " MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", "\n", "\n", - " SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", + " SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", "\n", "\n", - " PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", + " PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", "\n", "\n", - " PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", + " PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", + " PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", "\n", "\n", - " PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", + " PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", + " PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", + " PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", "\n", "\n", - " PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", + " PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", "\n", "\n", - " PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", + " PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", + " PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", "\n", "\n", - " PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", + " PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", "\n", "\n", - " PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", + " PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", + " PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", "\n", "\n", - " PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", + " PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", "\n", "\n", - " PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", + " PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", + " PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", "\n", "\n", - " PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", + " PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", "\n", "\n", - " PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", + " PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + " PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", "\n", "" ], @@ -3895,62 +3893,62 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805\n", - "Time: 23:33:29 Log-Likelihood: -7727.2\n", - "converged: True LL-Null: -9429.0\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838\n", + "Time: 00:20:46 Log-Likelihood: -7709.5\n", + "converged: True LL-Null: -9445.9\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", - "SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", - "AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", - "PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", - "PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", - "PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", - "PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", - "PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", - "PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", - "BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", - "BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", - "BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", - "BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", - "BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", - "BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", - "PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", - "PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", - "PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", - "PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", - "PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", - "PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", - "GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", - "UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", - "HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", - "MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", - "SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", - "PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", - "PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", - "PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", - "PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", - "PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", - "PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", - "PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", - "PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", - "PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", - "PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", - "PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", - "PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", - "PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", - "PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", - "PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", - "PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", - "PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", - "PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + "LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", + "SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", + "AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", + "PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", + "PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", + "PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", + "PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", + "PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", + "PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", + "BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", + "BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", + "BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", + "BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", + "BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", + "BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", + "PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", + "PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", + "PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", + "PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", + "PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", + "PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", + "GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", + "UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", + "HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", + "MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", + "SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", + "PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", + "PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", + "PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", + "PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", + "PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", + "PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", + "PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", + "PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", + "PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", + "PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", + "PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", + "PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", + "PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", + "PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", + "PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", + "PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", + "PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", + "PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 60, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -3962,7 +3960,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -3971,12 +3969,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13477\n", - " 1 0.68 0.36 0.47 4019\n", + " 0 0.83 0.95 0.89 13463\n", + " 1 0.68 0.37 0.48 4033\n", "\n", - " accuracy 0.81 17496\n", - " macro avg 0.76 0.66 0.68 17496\n", - "weighted avg 0.80 0.81 0.79 17496\n", + " accuracy 0.82 17496\n", + " macro avg 0.76 0.66 0.69 17496\n", + "weighted avg 0.80 0.82 0.79 17496\n", "\n" ] } @@ -3994,7 +3992,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -4002,64 +4000,52 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441658\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441662\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441670\n", + " Current function value: 0.440645\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441678\n", + " Current function value: 0.440647\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441690\n", + " Current function value: 0.440653\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441709\n", + " Current function value: 0.440668\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441720\n", + " Current function value: 0.440694\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441740\n", + " Current function value: 0.440734\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441771\n", + " Current function value: 0.440782\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441820\n", + " Current function value: 0.440827\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441865\n", + " Current function value: 0.440895\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441905\n", + " Current function value: 0.440960\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441966\n", + " Current function value: 0.441031\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.442051\n", + " Current function value: 0.441168\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.442158\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442265\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442384\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442495\n", + " Current function value: 0.441325\n", " Iterations 7\n" ] }, @@ -4072,19 +4058,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17471 \n", + " Model: Logit Df Residuals: 17467 \n", "\n", "\n", - " Method: MLE Df Model: 24 \n", + " Method: MLE Df Model: 28 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826 \n", "\n", "\n", - " Time: 23:38:59 Log-Likelihood: -7741.9\n", + " Time: 00:20:46 Log-Likelihood: -7721.4\n", "\n", "\n", - " converged: True LL-Null: -9429.0\n", + " converged: True LL-Null: -9445.9\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4095,79 +4081,91 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", + " LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", + "\n", + "\n", + " SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", "\n", "\n", - " SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", + " PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", "\n", "\n", - " PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", + " PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", "\n", "\n", - " PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", + " PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", "\n", "\n", - " PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", + " PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", "\n", "\n", - " PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", + " BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", "\n", "\n", - " BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", + " PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", "\n", "\n", - " BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", + " PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", "\n", "\n", - " PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", + " PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", "\n", "\n", - " PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", + " PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", "\n", "\n", - " PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", + " PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", "\n", "\n", - " PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", + " GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", "\n", "\n", - " GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", + " UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", "\n", "\n", - " UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", + " HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", "\n", "\n", - " HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", + " MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", "\n", "\n", - " MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", + " PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", + " PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", "\n", "\n", - " PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", + " PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", + " PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", + " PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", "\n", "\n", - " PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", + " PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", "\n", "\n", - " PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", + " PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", + " PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", "\n", "\n", - " PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", + " PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", "\n", "\n", - " PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + " PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + "\n", + "\n", + " PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + "\n", + "\n", + " PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + "\n", + "\n", + " PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", "\n", "" ], @@ -4177,45 +4175,49 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17471\n", - "Method: MLE Df Model: 24\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789\n", - "Time: 23:38:59 Log-Likelihood: -7741.9\n", - "converged: True LL-Null: -9429.0\n", + "Model: Logit Df Residuals: 17467\n", + "Method: MLE Df Model: 28\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826\n", + "Time: 00:20:46 Log-Likelihood: -7721.4\n", + "converged: True LL-Null: -9445.9\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", - "SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", - "PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", - "PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", - "PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", - "PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", - "BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", - "BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", - "PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", - "PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", - "PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", - "PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", - "GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", - "UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", - "HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", - "MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", - "PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", - "PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", - "PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", - "PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", - "PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", - "PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", - "PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", - "PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", - "PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + "LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", + "SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", + "PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", + "PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", + "PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", + "PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", + "BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", + "PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", + "PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", + "PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", + "PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", + "PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", + "GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", + "UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", + "HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", + "MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", + "PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", + "PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", + "PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", + "PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", + "PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", + "PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", + "PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", + "PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", + "PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + "PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + "PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + "PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 80, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -4233,20 +4235,21 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "25 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", - " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + "29 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " 'PAY_5_Pay_Duly', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", " dtype='object')\n" ] } @@ -4259,7 +4262,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -4268,11 +4271,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.64 0.36 0.47 2022\n", + " 0 0.83 0.94 0.88 6741\n", + " 1 0.64 0.38 0.47 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -4293,19 +4296,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2233371077226231\n" + "Optimal Threshold: 0.22999391096950886\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4318,10 +4321,10 @@ { "data": { "text/plain": [ - "0.771318789360392" + "0.7757068306651365" ] }, - "execution_count": 81, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -4340,7 +4343,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 67, "metadata": { "scrolled": true }, @@ -4351,11 +4354,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.79 0.83 6727\n", - " 1 0.47 0.62 0.54 2022\n", + " 0 0.87 0.80 0.83 6741\n", + " 1 0.47 0.60 0.53 2008\n", "\n", " accuracy 0.75 8749\n", - " macro avg 0.67 0.71 0.68 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", "weighted avg 0.78 0.75 0.76 8749\n", "\n" ] @@ -4379,14 +4382,14 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Logistic Regression identified 1252\n" + "Of 2008 Defaulters, the Logistic Regression identified 1207\n" ] }, { @@ -4421,14 +4424,14 @@ " \n", " \n", " \n", - " 0\n", - " 5341\n", - " 1386\n", + " 0\n", + " 5388\n", + " 1353\n", " \n", " \n", - " 1\n", - " 770\n", - " 1252\n", + " 1\n", + " 801\n", + " 1207\n", " \n", " \n", "\n", @@ -4437,11 +4440,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5341 1386\n", - "1 770 1252" + "0 5388 1353\n", + "1 801 1207" ] }, - "execution_count": 83, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4452,14 +4455,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Logistic Regression identified 738\n" + "Of 2008 Defaulters, the Logistic Regression identified 755\n" ] }, { @@ -4494,14 +4497,14 @@ " \n", " \n", " \n", - " 0\n", - " 6319\n", - " 408\n", + " 0\n", + " 6321\n", + " 420\n", " \n", " \n", - " 1\n", - " 1284\n", - " 738\n", + " 1\n", + " 1253\n", + " 755\n", " \n", " \n", "\n", @@ -4510,11 +4513,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6319 408\n", - "1 1284 738" + "0 6321 420\n", + "1 1253 755" ] }, - "execution_count": 68, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -4532,27 +4535,16 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.22122047856901356\n" + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m optimal =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ @@ -4562,7 +4554,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -4736,7 +4728,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -4748,7 +4740,7 @@ " verbose=False)" ] }, - "execution_count": 95, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -4762,19 +4754,19 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15978089375876542\n" + "Optimal Threshold: 0.1567076896169202\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU1frA8e8hIYGQAinUEEIJhN4iSBURpCjCpYoFBbyKKBaUa0Gxi3DtFyzY+KkgIEgREAWpIkiR0HtooRPSezm/P2YSNyFlA9lsdvN+noeH7MzszDuzZ+adOTNzjtJaI4QQQtyoCvYOQAghhHOQhCKEEKJESEIRQghRIiShCCGEKBGSUIQQQpQISShCCCFKhNMnFKXUvUqp3+wdh70ppYKUUglKKZdSXGawUkorpVxLa5m2pJTar5TqcR3fu64yqJSqrJT6WSkVq5T6sbjfL6uKWy6UUm8qpa4opS6UwLK1UqrRjc5H5K9UE4pS6qRSKtk8sF1QSs1WSnnacpla6zla69ttuYyyyNzWvbI/a61Pa609tdaZ9ozLXkriQKK1bq61Xl/Ecq45WN5AGRwK1AD8tNbDruP7+cX3olLqhLkPRiql5pvDP1dKfZvP9K2UUqlKKV+l1Kvmuj2RZ5qnzOGvlkSMeeZdF3gGaKa1rlnC856tlHqzJOdZViileiilIkt7ufa4QhmgtfYE2gBtgRfsEMMNs+dZt7Oc8RdHOd3e9YAjWuuM4n4xv5iVUg8A9wO9zH0wDPjdHD0bGKyUqpLna6OA5Vrrq+bnI8AD+UxzpLgxWqkeEKW1vmSj+ZcKR9tnrzterXWp/QNOYhTm7M/TgRUWn92Bd4HTwEXgM6CyxfiBQDgQBxwH+prDfYCvgPPAWeBNwMUc9yDwh/n3Z8C7eWJaCkw0/64NLAIuAyeAJyymexVYCHxvLv+hfNbPB/jW/P4p4CWggkUcm4H/AbHAIeC2PN8tbB02Ax8AV81xDYG1QBRwBZgDVDWn/w7IApKBBOA/QDCgAVdzmvXAG+Z844HfAH+LeEaZ6xAFvJz3t8uz3pWB98zpY4E/zGHZy3zA/E2vAJMtvtcB2ALEmOs9A3CzGK+Bx4CjwAlz2EfAGfM32Al0s5jeBXjRLBvx5vi6wEZzXonm9hhhTn8nRnmKAf4EWuUpq88Be4BUwNVyG5ix7zDjuAi8bw4/bS4rwfzXCYsyaE7THFht/pYXgRfz2aavAWlAujmfsRgngC+Z2/kSRlnzMafP3tZjzRg25jPPGcCHheyfh4FRebbnOeAui33ge+Ag0NxiXQ6aw18tYL4uGPv1FSDC/E0ty2K+ZR/ohVGGs8xtMNuc/kfgAkZZ25gdi0W5fsjic95tr4FGwMPmtk0z5/1zAbFr4Akz7ivAf/lnny5wHyykDD3PP+XzAPCvPLFm7+cx5jI7m8PPmL/5A0UdL4EqebZbAsaxrYLF8qOABYBvQeUHqGT+rlFmPNuBGoUe4+2VUIBAYC/wkcX4D4FlgC/gBfwMTLXYgWOB3uaGqQOEmuOWAJ+bG7I6sA14JJ+E0t38YZT5uZq54bM39k5gCuAGNDB/0D4WO1M6MMictnI+6/ctRoLyMn+gI8BYizgygKeBisAIc318rVyHDGACRqGsjLFT9DYLVYBZAD7Mb1vnKTCWCeU40Nic33rgHXNcM4xC2NXcFu+a615QQplpfr8OxoGgsxlX9jK/MJfRGmPHamp+rz1ws7lOwRgHpqfy7MyrMcpDZXPYfYCf+Z1nMA4slcxxkzDKVBNAmcvzszyQWMy7HcYO2tGM+QFzm7lbbL9wjIRUOe82xUiE95t/ewI357ed8ymDXhgHzmcwdlgvoGMB2/VV4HuLz2OAYxhl0xP4Cfguz3K/xShD+ZXP+zCS2CSMqxOXPOMnA2ssPvfBODmqmCehvAhMM4dNx6hlKCyhjMM4gapr/pbryF0WCyv7PYDIPPMbY243d4xjRnhxE4r592zgzSKOWdqM1xcIwtinHzLHWbMP5i1Dw/jneDMC4ySnVp79fDRGmXwT4+A+01zG7RiJyNOK42V+2+0pYCvGsdfd3OY/FFR+gEfMeXqY8bQHvAvdXrZOInlW6CTGgSreDP53/jmrVubGbWgxfSf+OTP9HPggn3nWwDhIWV7JjATW5bMzK/MH6m5+/jew1vy7I3A6z7xfAL6x2JmuOeuzmNbFjKOZxbBHgPUWcZzDTGbmsG0YVRDWrMPpgpZtTjMI2JVnWxeVUF6yGD8eWGX+PSW7oJmfPTDO5K5JKBg7RjLQOp9x2csMzLPOdxewDk8Bi/PszD2LWO/o7GVjnGEPLOTAYJlQPgXeyDPNYeAWi+03Jp/ym51QNmJcRfjnmSbXds6nDI60/J2KWLdXyZ1QfgfGW3xugpHoXS2W26CIed4LrMHY16KA5y3GBZnzCzQ/zyH3Cd+rGIkjCGM/qmj+X5fCE8paYJzF59uztxFFl/0e5Dkw5pl3VXNe2Vdq6yn5hNI3z37yezH2wTFFzD88u8yasR61GNfSXH4Ni2FRGLcLijpeXrPdME7YLGtFahVWfjASd64r96L+2aNeb5DWeo1S6hZgLuCPcTkVgHHg2qmUyp5WYRyowSi0K/OZXz2Mgn3e4nsVMK5EctFaa6XUPIwCuxG4B2NHyJ5PbaVUjMVXXIBNFp+vmacFf4yz+VMWw05hnLVnO6vNX8pifG0r1yHXspVS1YGPgW4YZycVMA6uxWH51EwSxlkvZkw5y9NaJymlogqYhz/Gmfbx4i5HKdUYeB/jbNkDo2DvzPPdvOv9DPCQGaMGvM0YwCgjhcVhqR7wgFJqgsUwN3O++S47j7HA68AhpdQJ4DWt9XIrllucGPOqzbXlK/ugnK2wmNFazwHmKKUqYhwA5yildmmtf9Van1ZKbQTuU0rNMMd3y2cep5VSx4C3MQ6AZyzKbUFxW8ZluQ5W778A5lOKb2Gc6QdgVOuAUQZiCwviBuSNvbYZizX7YN7yOwqYiHEAB2Nf8LeY5KLF38kAWuu8wzwp+niZn3rAYqVUlsWwTAouP99hlNd5SqmqGMfKyVrr9IIWYLfHhrXWGzDOEN41B13B2FjNtdZVzX8+2rh5CMaKNsxnVmcwznD8Lb7nrbVuXsCifwCGKqXqYVyVLLKYzwmLeVTVWntprftbhl3IKl3ByPb1LIYFYdQJZ6ujcu95QRhXLdasQ95lTzWHtdJae2NUZ6hCpi+O8xiXxYDx+CpGNVN+rgAp5P/bFOVTjKqQEHMdXiT3OoDFeiilumHUSQ8Hqmmtq2IcRLK/U1AZyc8Z4K08v7eH1vqH/Jadl9b6qNZ6JEYVzTRgoXlDu6jtXpwY8zrHteUrg9wHIat+d611utb6R4z6/RYWo/4P4/7ZEIz94e8CZvEtRrXdNU+G5eM8xoHJMu5sxd1/78G4l9oL495LsDk8uwwkYhxosxX2ZJi1+0je2M+Zfxe1D+ZahnnM+QJ4HKMqtiqwL5/vWKOo42V+63YG6JenzFfSWlseo3K+Z5aR17TWzTCqse/EKBsFsvd7KB8CvZVSbbTWWRgb+wMz86OUqqOU6mNO+xUwWil1m1KqgjkuVGt9HuOG8ntKKW9zXEPzCugaWutdGPXCXwK/aq2zr0i2AXFKqefM5/9dlFItlFI3WbMi2ngcdwHwllLKyyw8E/nnCgiMg88TSqmKSqlhQFNgZXHXweSFUX0Yo5Sqg1EvbukiRl379VgIDFBKdVZKuWFU7eRb6M3f7WvgfaVUbXO7dVJKuVuxHC+Mm9oJSqlQ4FErps/A+P1clVJTMK5Qsn0JvKGUClGGVkqp7ESYd3t8AYxTSnU0p62ilLpDKeVlRdwope5TSgWY659dhjLN2LIoeNsvB2qaj9q6m2WlozXLxDgZelopVd983P5tYL628ikwpdSD2etolrF+GDfV/7KYbBHGAfQ1jORSkPkYVVcLrFj0AoxyH6iUqoZxYxiA6yj7XhgJKAojcbydZ3w4xtNqHsp4THxsIXFZu49MUkpVMx9hfhJj3bNjKWwfzCv7hOMygFJqNLmTudWsOF5eBPyUUj4WX/sM4/hUz5w+QCk1sKBlKKVuVUq1NK8K4zBOmAt97cCuCUVrfRnjDOdlc9BzGDcdtyql4jDqepuY027DuFn1AcZZ6Qb+OVsbhVFdcQDjknMhRv1gQX7AOMOZaxFLJjAAo37yBMYZwJcYZ0HWmoBxhhSB8aTTXIyDbba/gBBz3m8BQ7XW2VVJxV2H1zBuLMcCKzBu0FqaCryklIpRSj1bjHVAa73fXJd5GGeX8Rg3sFML+MqzGDfDt2Pc9J2GdWXrWYwzzniMnWN+4ZPzK/ALxo3RUxhXRpaX6O9jHLx+w9gBvsK4uQhG/f//mdtjuNZ6B8Y9tBkY2/sYRh22tfoC+5VSCRhPnt2ttU7RWidh/LabzWXdbPklrXU8xo3cARhVgUeBW61c5tcY1RAbMcpoCsbvZK04jKvA0xhJcDrwqNb6D4v4EvknqcwpaEZa62St9RqtdbIVy/0C47fbDfzNtWW1OGX/W4zf/qw5/dY84z/AuN93ESMhFrgOGOWjmfk7LSlkuqUYVbHhGPvaV+bwovbBXLTWBzCehtxixtcS46mu61XY8fIQxnEuwly/2hjldBnwm1IqHmPbFXYyUxPjt4jDuP+ygdwnyNfIftpJ2JhS6kGMm4Vd7R1LcZlnwzEYVVMn7B2PEKVFKaUxyv0xe8fiCOxd5SXKKKXUALPaoArGfa69GE+tCCFEviShiIIMxLj5eA6jmu5uLZezQohCSJWXEEKIEiFXKEIIIUqEQzVYBuDv76+Dg4PtHYYQQjiUnTt3XtFaB9hyGQ6XUIKDg9mxY4e9wxBCCIeilDpV9FQ3Rqq8hBBClAhJKEIIIUqEJBQhhBAlQhKKEEKIEiEJRQghRImQhCKEEKJE2CyhKKW+VkpdUkrtK2C8Ukp9rJQ6ppTao5RqZ6tYhBBC2J4tr1BmYzTxXZB+GG1EhQAPY3S2JIQQooRlZGYVPVEJsNmLjVrrjUqp4EImGQh8azY4uFUpVVUpVcvscEcIIcQN0loz4r1NbLsSXyrLs+c9lDrk7hwpktz9r+dQSj2slNqhlNpx+fLlUglOCCEc2c79l+j8wKJSSyZg36ZX8utSNt+mj7XWs4BZAGFhYdI8shBC5ONyfCo/bDvN4l1nOXElEWobHZZO7NWYJ6fZfvn2TCiRGN2MZgvE6HtDCCFEMWitmb/9DM//tDdnWF3vSkzqGcItrWvhU7kiT5ZCHPZMKMuAx5VS8zD6NY6V+ydCCFE801Yd4tP1x3M+d63oxpdTelKpokupx2KzhKKU+gHoAfgrpSKBV4CKAFrrz4CVQH/gGJAEjLZVLEII4UxS0jP5ZN0xPl77T1f3SQevcneTGkx9/ha7JBOw7VNeI4sYr4HHbLV8IYRwNmdjklkafpbpqw7nDEuOiKXh+RQ+/+QOWrasYcfoHLA/FCGEKG92nLzK91tPsST8n9vMtzYJYGLHYLZuPsPYse2oUCG/55xKlyQUIYQog45fTmD1gYt8tuE4MUnpAHi4ViB583na+HryzTt3ANCyWXV7hpmLJBQhhChDtNa8tGQfc/46nTPM082FuicTWfXdPpo08eOJKbfaMcKCSUIRQgg7S0nPZMbaY4SfieGPY1dyhr8/vDXeV1MZPHgBx9IyeeONW5k0qTPu7mXz0F02oxJCCCcXGZ3E36dj2HD4Mov+jswZ3ry2NxVdKjDrvnZU96nMlStJ9O8fwptv9qRRI187Rlw0SShCCGFDKemZ7Dodw46TV9HA0vCzpKRncTYmOWea+v5VuKNlLZ7qFUJSYjovv7yWu/rMYfPmMfj7ezBv3lD7rUAxSEIRQogSdCkuhaXh57ickMrGI5c5dCF3W1oVFLhWqMCQdoGE1vRiYJvaVPeuhNaahQsP8OSTq7hwIYHx428iNTUTDw/H6bZKEooQQtyA6MQ0pv96mJ93nyMhNSPXOH9PN5rV8mZwuzp0bxxAff8qVHS5NkFcvpzIAw8s4ZdfjtG2bU2WLr2bm27Kt63cMk0SihBCFEN0YhovLt7LmegkzkYnE20+0gvQo0kAgdUq0yqwKne2qoWHm3WHWG9vd65cSeLDD/vw2GMdcHV1nKsSS5JQhBCiAFlZmpNRiaw9dIndkbHsPHmVc7EpOeN7Na2Ou6sL3UL8GdwuELdiJIKNG0/x1lubWLRoOJ6ebmzd+lCZeDnxRkhCEUIIC+djk9kTGcvPu8+xfE/u9mqVgvb1qjHiproMD6tbwBwKd+VKEpMmrWb27HCCg6ty8mQMLVpUd/hkApJQhBDl3LmYZNYeusTJK4ksCT/HlYTUnHE1vN25o2VtOjf0o3vjgGJdgeSlteabb8KZNGk1cXGpvPBCV156qTseHhVLYjXKBEkoQohy58zVJN5ffYQ1By4Sn+dG+sgOdenVtAYdG/jhWcIvEH7//R6aNQvgs8/uoHnzstNkSkmRhCKEcGpnriaxNSKKIxfj2Rpxlb1nY3PGVfOoSIdgX4a0r8NtTWvg6+FWolVPSUnpvP32JsaNCyMw0JtFi4bj41PJKaq38iMJRQjhFBJTM4i4nMixy/GsPXSZXaejOReTTFaeTsNbB/rQsLonQ9sF0rmRv83iWbnyKI89tpKTJ2OoU8eLRx+9iWrVKttseWWBJBQhhMO5HJ/KmegkLsSmMGPtMU5GJZKUlnnNdM1qedOyjg+3Na1Om7pVqVbFLd/3QEpSZGQcTz21ikWLDtK0qT8bNjxI9+71bLrMskISihCiTMvIzOJKQhrbT15l24mr/LLvPFcS0q6ZrnezGnSs70tIDS+a1vQiwMsdpUq/aumttzayYsVR3n67J8880xk3N/v0nmgPyug40XGEhYXpHTt22DsMIYSNZWZp3vnlIF9sOpFreIOAKrSs40PP0OpU9XAjpLontXwq2SV5ZNu27SyVK7vSsmUNoqKSiI1NpUGDanaLJz9KqZ1a6zBbLkOuUIQQZYLWmuOXE/h0fQTL95wjNSMrZ9zD3RvQso4PbepWpa6vhx2jzC02NoUXX/ydTz/dwZ13NmbZspH4+Xng51d2YixNklCEEKUuJT2TTUev8PfpaI5ejGfL8SgS89wD6du8Jm2CqjIirC7VqrjZKdL8aa2ZP38/Tz/9K5cuJTJhQgfeeKOnvcOyO0koQgiby8jMIjEtk1NRiaw5cJGP1x7LGedVyZUKFRTdQvxpU7cqnRv6c3MDX7tWYRXl++/3MGrUEsLCarN8+Ujat69t75DKBEkoQogSdexSAmeuJhGVmMbCnWfYfy6O+JSMa6br1bQGrwxoVqaqsAqTmppBREQ0TZsGMHx4czIyshg1qjUuNn5qzJFIQhFCXJeU9Ewio5M4fTWJ6MR0tkRE8eu+C9e8ee5XxY2neoVQxc0VpaBFHR9a1vGhShntxjY/69ad4NFHV5CUlM7RoxNwd3dl9Oi29g6rzHGcX1QIYTcxSWms2neBqMQ0dp6K5tD5OC7Fp5KR961BjHc/nusXSg1vd3yruFHdq5IdIi4Zly4l8uyzv/Hdd3to0KAas2YNKLP9uZcFsmWEELlkZmn2RMZw9FICG45cZsPhy/l2HNUq0IeBbepQ17cyvlXcqe9XBR8naujw2LGrdOjwBQkJaUye3I3Jk7tRubLzrJ8tSEIRohxLy8jiQmwKy/eeY82Bi6RlZrHvbFyuaXwqV6R1oA8jbgqif8ua+FSuWKZvmN+ouLhUvL3dadiwGmPHtmXMmLY0bRpg77AcgiQUIcqRpLQM5mw9zV8nrnLwfBxnY5Jzja/u5c6gNrW5qb4v7etVI8DTHT9PdztFW7oSE9N4/fUNfPHF3+zZ8yiBgd7897+32zsshyIJRQgndj42mYSUDHacimbBjjPsOh2Ta/wdLWvRIKAKzWv70KNJAJUqlp9mQiz9/PNhHn/8F06fjmXs2LZO1UdJaZKEIoQTycoy3jZ/duEedp+JuWZ8Xd/K/KttIONuaWB1f+fOLCMji+HDf2Tx4kM0bx7Apk2j6do1yN5hOSwpUUI4uKuJaTz87Q7Ox6ZcU4XVq2kNBrWtTSVXF9rXq1bm3ji3F601SilcXStQq5Yn77xzG08/3alcNeRoC5JQhHAwyWmZHDgfx+ZjV/hiU0SulwbvaFWL2j6VuLmBHz2aVMfFSTtyuhFbt0by2GMr+eKLAbRrV4uZM++wd0hOQxKKEA5ga0QULy/Zx4kride8+9Eh2JceoQGM79HITtE5hujoZF588Xc+/3wntWt7ER2dXPSXRLHYNKEopfoCHwEuwJda63fyjA8C/g+oak7zvNZ6pS1jEsIRpGdmset0DB+uOcKpqKScqiyfyhUZ06U+Vdxd6BlanQYBnnaO1DHMn7+PJ55YxZUrSTz11M289loPvLzKx9NrpclmCUUp5QLMBHoDkcB2pdQyrfUBi8leAhZorT9VSjUDVgLBtopJiLJIa01UYhrbT1zltwMXOXIxnqOXEkizaL795ga+TOgZQhcbdlnrzA4dukJwcFVWrbqXtm1r2Tscp2XLK5QOwDGtdQSAUmoeMBCwTCga8Db/9gHO2TAeIewuM0tz9FI8y8LPseNUNBGXE7mSkHrNdHe0qkVYvWq0r1eNlnV8nPpFQltISclg2rQ/aNeuFgMGNOHFF7vx0kvdpSFHG7NlQqkDnLH4HAl0zDPNq8BvSqkJQBWgV34zUko9DDwMEBQkj/QJx5OQmsEzC8L5df/FXMN9q7jRLcSfjvV9qeVTma4h/tTwdty2r8qCNWsiGD9+BUePXuWZZzoxYEATKpbT92tKmy0TSn6nVHlbkhsJzNZav6eU6gR8p5RqobXOyvUlrWcBs8DoAtgm0QpRwrKyNK8vP8Cmo5c5fjkRADeXCvyrbR0GtK5N54Z+VJCnsErMxYsJTJz4G3Pn7qVRI19+++0+evduaO+wyhVbJpRIoK7F50CurdIaC/QF0FpvUUpVAvyBSzaMSwibSk7L5NMNx/n496M5w24LrU5dXw9eGdBMqq9sZPXqCBYuPMCUKd154YVuVKokD7GWNltu8e1AiFKqPnAWuBu4J880p4HbgNlKqaZAJeCyDWMSokQlp2UScSWBiMuJXI5PZUtEFKsP/FOtNbpLMJP7N8VV6u5tYvfuCxw9epWhQ5tx770t6dKlLvXrV7N3WOWWzRKK1jpDKfU48CvGI8Ffa633K6VeB3ZorZcBzwBfKKWexqgOe1BrLVVaokyKSkjlwPk4riSksu1ENPvOxrL3bGyuaSq6KJrU8OLmBr48368pleXNa5tISEjjlVfW8dFHfxEcXJVBg0Jxda0gycTObHpNaL5TsjLPsCkWfx8AutgyBiGu18W4FP46cZVFOyPZEhGV6zHebMPDAmkbVI3AapWp5VOJur4euLtKErGlJUsOMWHCL0RGxvHww+2YOrUXrq5yBVgWSCWjEHlcTUxj2i+HmL/jTK7hfZrXoFfTGjQIqEI9vyr4VXGT+yGlbO/ei/zrX/Np2bI68+cPpXPnukV/SZQaSSii3Ft94CJf/RFBQmoGB8/Hk2k2bVJBwdO9GnN3hyAC5K1qu0lPz2TTptP07Fmfli1rsGLFPfTu3UAeBS6DJKGIcmn3mRjm/HWKBTsic4Z5VXKlV9PquFaowJiu9WlfT+rj7e3PP88wbtxy9u+/zOHDj9OokS/9+4fYOyxRAEkootw4dimeiQt2E5WQltM2VmhNL9rXq8awsLq0qVvVzhGKbFevJvP882v44ou/qVvXm59+Gk6jRr72DksUQRKKcHoZmVms2HueJ+eFA1CnamUe6d6AO1vVpmWgj52jE3mlpGTQps1nnDsXzzPPdOLVV3vg6Sn9uDgCSSjCKaWkZ3LkYjx3zdica/ibg1pw38317BSVKExkZByBgd5UquTKG2/cSps2NWnduqa9wxLFIAlFOI2YpDTzBvsJDl2IzzVucv+mtK5blQ71pdqkrElOTmfq1D+YNm0zCxcOY8CAJjzwQBt7hyWug1UJRSnlBgRprY/ZOB4hrJaVpfl68wk+2xBBTFJaro6nWgf6cHvzmnSo78tNwZJEyqrffjvO+PErOH48mvvua0WHDnXsHZK4AUUmFKXUHcD7gBtQXynVBnhFa/0vWwcnRF6xSemcuprIvO1nWLD9TE4SGdkhiDpVK+Hn6U6/FjWp6iF17mXdhAkrmTFjOyEhvqxZcz+33dbA3iGJG2TNFcrrGM3OrwPQWocrpaSvUVFqMjKz+GzDcVbsvcDB83E5w70ruTKyYxAPdW0g74k4iMxMo7UBF5cK3HxzIP7+Hjz3XFdpyNFJWPMrpmutY/K8ESztbYlS8ffpaO7+fCtp5oEotKYXD3QOJqS6J2FSleVQ/v77POPGLef++1sxYUJH7r23lb1DEiXMmoRyUCk1HKhgthz8JLDVtmGJ8m5vZCz3fLmV+JQMwLgn8tP4LrhI/yEOJz4+lSlT1vHxx9sICPCgVi0ve4ckbMSahPI4MAXIAn7CaD34BVsGJcqvU1GJvLRkH5uOXgHg1iYBPHN7E1rUkfdFHNFvvx1nzJilnDsXz7hxYbz99m1UrSo9UjoraxJKH631c8Bz2QOUUoMxkosQJeJUVCJz/jrNrI0RADSu4cmTtzXmjla17ByZuBFubi5Ur16FRYuG07FjoL3DETamiup+RCn1t9a6XZ5hO7XW7W0aWQHCwsL0jh077LFoYQPbT15l4oJwzlxNzhn2YOdgXr2ruR2jEtcrPT2T99/fQlxcKm+9dRtgPN4tXR3bn3ncDrPlMgq8QlFK9cHonreOUup9i1HeGNVfQly3uJR0hnzyJ0cvJQBGx1SzRoVxS0iAHHwc1B9/nM5pyHHYsGY5iUR+z/KjsCqvS8A+IAXYbzE8HnjelkEJ57b6wEX+/a1xlenuWoHF47vQrLa3naMS1ysqKonnnlvDV1/tIijIh59/Hsmddza2d1jCDgpMKFrrXcAupdQcrXVKKcYknNC5mGR+P3SJ5bvP8deJqwBMHdySEWF15QzWwUVFJTNv3j7+85/OTJlyC1WqyEul5ZU1N+XrKKXeApoBOY9naDQJ3T0AACAASURBVK3lFEQU6WxMMv9ddYgl4ecAcK2gCPL1YNao9oTWlKsSR3Xw4GUWLNjPK6/0oHFjP06ffhpf38r2DkvYmTUJZTbwJvAu0A8YjdxDEQXQWnP6ahITF+zmfEwy52KNi9vQml6M7BDEyA5BuEn/3w4rKSmdt97ayH//+yeenm6MHduOwEBvSSYCsC6heGitf1VKvau1Pg68pJTaZOvAhOM5FZVIv482kZSWmTNseFggw8LqSgONTmDVqmOMH7+CEydieOCB1vz3v70JCKhi77BEGWJNQklVRrsrx5VS44CzQHXbhiUczaELcfT90DjP6NzQj393a0CPJgHkabJHOKiEhDTuv38xfn6VWbfuAXr0CLZ3SKIMsiahPA14Ak8AbwE+wBhbBiUcx6X4FHq+u4GEVKOJlG4h/nw3tqOdoxIlITMzix9+2MfIkS3w9HRjzZr7CQ31x91dGnIU+SuyZGit/zL/jAfuB1BKySuv5VxWlmb07O1sOHIZgHp+Hkz9V0s6NfSzc2SiJOzceY5HHlnOzp3nqVzZlSFDmknviaJIhSYUpdRNQB3gD631FaVUc4wmWHoCklTKoRNXEvn3tzs4Zr6QCPC/kW0Z0Lq2HaMSJSU2NoWXX17HzJnbqV69CvPmDWHw4Kb2Dks4iMLelJ8KDAF2Y9yIX4zR0vA0YFzphCfKikMX4nj2x93sO2v0R1KnamXuvTmIh7s1wNVFntpyFkOGLGDt2hM89thNvPlmT3x8pCFHYb3CrlAGAq211slKKV/gnPn5cOmEJuwtMTWD2X+eZO5fpzkbY7S15e/pxox72nFzA6nachYREdEEBHjg5eXOW2/1pEIFxU03SVe8ovgKSygpWutkAK31VaXUIUkm5cPaQxeZvHgf52P/aSChW4g/T9wWIo//OpG0tEzeffdP3nhjI0880YFp03pLi8DihhSWUBoopbKbqFdAsMVntNaDbRqZKFVaa3aciua1n/fnVGu1qONNWD1fJt/RlIpSreVUNm48xbhxyzl48ApDhzbjiSfkyTxx4wpLKEPyfJ5hy0CE/Ry9GM8j3+0k4koiAH2b1+SJ20KkwUYn9cEHW5g48TeCg6uyYsU99O8fYu+QhJMorHHI30szEGEfW45HMfILo0fn20Kr85++oTSpKV20OpusLE1iYhpeXu7ccUdjLl9O4qWXuuPhUdHeoQknIm8olWNrDlzkIbMZ+Q9HtGFQW7kR64z277/EuHErcnpObNzYj7ffvs3eYQknZNOKcaVUX6XUYaXUMaVUvn2oKKWGK6UOKKX2K6Xm2jIeAZlZmm82n6Db9LU5yWT60FaSTJxQUlI6L7ywhjZtPufgwcvceWcIRfXQKsSNsPoKRSnlrrVOLcb0LsBMoDcQCWxXSi3TWh+wmCYEeAHoorWOVkpJG2E2tDcylgEz/sg1bMUTXWle28dOEQlb2bXrPIMHL+DkyRhGj27D9Om98ff3sHdYwskVmVCUUh2ArzDa8ApSSrUGHtJaTyjiqx2AY1rrCHM+8zDebTlgMc2/gZla62gArfWl4q+CKEpcSjqjvtpG+JkYAEaE1WXi7Y2p4S0vrTkbrTVKKYKCfAgK8uH//m8Q3bvXs3dYopyw5grlY+BOYAmA1nq3UupWK75XBzhj8TkSyPtsYmMApdRmwAV4VWu9yop5Cyst3hXJ0/N3A+DmWoHX7mrOyA5Bdo5KlLSMjCxmzNjGsmWHWb36fvz8PNiw4UF7hyXKGWsSSgWt9ak8zZBnFjSxhfzaLc9bgesKhAA9MNoG26SUaqG1jsk1I6UeBh4GCAqSg6G1pizdx7dbTgHw+sDmjOoUbN+AhE1s23aWceOWs2vXBfr1a0RcXCrVqkmHV6L0WZNQzpjVXtq8LzIBOGLF9yKBuhafAzGab8k7zVatdTpwQil1GCPBbLecSGs9C5gFEBYWJncVi3AhNoVRX//FkYtGA45z/92Rzg397RyVKGkJCWk899xqPv10B7VqefHjj8MYMqSp9EEj7MaahPIoRrVXEHARWGMOK8p2IEQpVR+jU667gXvyTLMEGAnMVkr5Y1SBRVgXushr3rbTzP7zJIcuxAPQpIYX8x6+mWpV3OwcmbCFihUrsH79KSZM6MAbb/TE29vd3iGJcs6ahJKhtb67uDPWWmcopR4HfsW4P/K11nq/Uup1YIfWepk57nal1AGMarRJWuuo4i6rPNNaszT8HP9ZuIe0zCzA6L99TNf6DA+rW8S3haM5duwqr7++gZkz++Pl5c7OnQ9TqZK8TibKBlXUc+lKqePAYWA+8JPWOr40AitIWFiY3rFjhz1DKBM2HLnMzHXH2Hbias4wvypurJ/UA69K8vazs0lNzWD69M289dYm3NxcWLHiHrp1k6e3hPWUUju11mG2XIY1PTY2VEp1xqiyek0pFQ7M01rPs2VgIn/bT15lztZTLAk3bke5VlDc0zGIp3s1lqotJ7Vu3QkefXQFhw9HMWJEc95/vw+1a0vzOKLssepaWWv9J/CnUupV4ENgDiAJpRR9t+Ukn6w/ntOk/M0NfHn29iaESXPyTk1rzVtvbSI9PYtVq+6lT59G9g5JiAJZ82KjJ8YLiXcDTYGlQGcbxyVMWmsGzPgjp0n5Xk1r8EL/UBoGeNo5MmErWVmar776m759G1G3rg/fffcvqlatROXKUpUpyjZrrlD2AT8D07XWm2wcj7Dw5aYIZv95ksjoZHyruLHgkU40qi6JxJnt2XORceOWs2VLJFOmdOe1126lVi2p3hKOwZqE0kBrnWXzSEQukxfvZc5fpwGjf5JP7m1HhQryfoGzSkhI47XX1vPBB1upVq0ys2cPZNSo1vYOS4hiKTChKKXe01o/AyxSSl3zKJj02GgbWmsGzdzM7shYAHZPuR0f6bPC6b366nree28LDz3Ulnfe6YWfnzTkKBxPYVco883/pafGUrL+8CUe/OafRgLCp/SWZOLEzpyJJTExndBQf55/viuDBoXStas0LSQcV4H9oWitt5l/NtVa/275D+PmvCghaRlZTFt1KCeZNPCvQsTb/anqIY8BO6OMjCzef38LTZvO5JFHlgPg7+8hyUQ4PGvuoYzh2quUsfkME9dh31mjjxKtwaWCYuljXWhRR/oncVZbt0Yybtxydu++yB13hDBjRn97hyREiSnsHsoIjEeF6yulfrIY5QXE5P8tURw//R3JxAVG0/IjOwQxoWcjaleVVmKd1YoVRxgw4Adq1/bip5+GM2hQqDTkKJxKYVco24AojFaCZ1oMjwd22TKo8mDWxuO8vfIQAM/e3pjHe4bYOSJhC1przp2Lp04db3r1asDrr9/Kk092xMtLGnIUzqfItrzKGkdvyyv8TAz//nYHl+ON3pQ/u68dfVvUsnNUwhaOHIli/PgVHDkSxYEDj+HpKffEhP3YtS0vpdQGrfUtSqlocneMpQCttZY2P4ohIzOLF37ay487IwHwcHNh1ZPdCZLHQ51OSkoG77zzB1On/kHlyq5MnXoblStLi8DC+RVWyrO7+ZWemW7Qn8eucM+Xf+V8/ujuNgxsU8eOEQlbuXAhge7dv+Ho0auMHNmC99/vQ82a0rqBKB8KTCgWb8fXBc5prdOUUl2BVsD3QFwpxOewMrM0qw9c5NstJ/nzuNHFS9/mNZlxT1tcXQp8Wls4qPT0TCpWdKFGjSp0716PmTP707t3Q3uHJUSpsuY6fAlwk1KqIfAtsAKYC9xpy8AcVUJqBuPn/M3GI5dzhnlXcuWF/k0Z2UHeM3A2WVmaWbN28vbbm/jzz7EEBnrz5Zd32TssIezCmoSSpbVOV0oNBj7UWn+slJKnvPIx569TTF68D4A6VStz781B9Gpag8Y1pHE/Z7R79wUeeWQ5f/11lp4965OenmnvkISwK6u6AFZKDQPuBwaZw6Q9EAtZWZrXlx9g9p8nARjcrg7vDWst7xg4Ka01kyat5sMPt+LrW5nvvvsX997bUn5vUe5Z+6b8eIzm6yOUUvWBH2wbluNISc/kzv/9wbFLCQB8O6YD3RsH2DkqYUtKKaKjkxk71mjIsVo1eRlVCLDyPRSllCuQ3VXcMa11hk2jKkRZeg9Fa83Qz7aw81Q0vZvV4H8j21Kpoou9wxI2cOpUDE8+uYopU26hXbtaZGVp6U5AOJTSeA+lyMeNlFLdgGPAV8DXwBGlVBdbBuUIloafpeu0dew8FQ3ArPvbSzJxQunpmUyfvplmzT5h9eoIDh++AiDJRIh8WFPl9QHQX2t9AEAp1RT4DrBppiurjl9O4JkFuwk/YzRn1i6oKl+MCpP6cyf0559neOSR5ezbd4mBA5vw8cf9CAqShjuFKIg1CcUtO5kAaK0PKqXKbRsSd3y8iZT0LKq4ubD08S40qi5PcDmrNWsiiI1NYcmSEQwcGGrvcIQo86xJKH8rpT7HuCoBuJdy2DhkVpZm4oJwUtKzGNY+kP8Ok+5ZnY3Wmu++20NAgAf9+oXw3HNdmDixk7TBJYSVrHllexxwHPgP8BwQATxiy6DKoum/HmZJ+DkaBlTh5QHN7B2OKGGHDl2hZ89veeCBJXzzTTgA7u6ukkyEKIZCr1CUUi2BhsBirfX00gmp7PlyUwSfbThOkxperHqqm9wvcSLJyem8/fYmpk3bTJUqbnz++Z089FA7e4clhEMqrLXhFzF6Zvwbo+mV17XWX5daZGVARmYWo2dvZ9NR48meeQ/fLMnEyfz88xHefHMT993Xinff7U2NGtKQoxDXq7ArlHuBVlrrRKVUALAS47HhcuHAuTj6f7wJgEoVK7DksS5UqyLVH87gwoUEwsMv0LdvI4YNa0Zw8EN06CCtPwtxowpLKKla60QArfVlpVS5aiL3+Z/2ADC4bR2mDW1FRWkh2OFlZmbx+ec7eeGF33Fzc+H06aeoXLmiJBMhSkhhCaWBRV/yCmho2be81nqwTSOzow1HLrMnMpZWgT68P6KNvcMRJeDvv88zbtxytm8/R69eDfjkk/5UrixN0glRkgpLKEPyfJ5hy0DKknd+Mfp6/0CSiVM4cSKaDh2+wN/fg7lzB3P33S3kXpgQNlBYB1u/l2YgZcULP+3l4Pk4Qmt60TBAbtA6Kq01e/deolWrGtSvX41vvhnIgAFNqFq1kr1DE8JpyY0BCxfjUvhh22kAXruruZ2jEdfrxIlo7rzzB9q2/Zw9ey4CcP/9rSWZCGFjNk0oSqm+SqnDSqljSqnnC5luqFJKK6Xs2j7Y+Dl/AzBtSEs6NvCzZyjiOqSlZfLOO3/QvPknbNhwknff7U2zZtKVgBClxZqmVwBQSrlrrVOLMb0LMBPoDUQC25VSyyzbBTOn8wKeAP6ydt62kJyWmdNy8IibpKteR5OZmUXnzl+xc+d5Bg9uyocf9qFuXWnIUYjSZE3z9R2UUnuBo+bn1kqp/1kx7w4YfadEaK3TgHnAwHymewOYDqRYH3bJGz17GwCT+ze1ZxiimOLijHMcF5cKjBnTlp9/HsmiRcMlmQhhB9ZUeX0M3AlEAWitdwO3WvG9OsAZi8+R5rAcSqm2QF2t9fLCZqSUelgptUMptePy5ctWLLp4PlpzlK0RV6ni5sK/uzco8fmLkqe1ZvbscBo0+IilS42n8saPv4k772xs58iEKL+sSSgVtNan8gzLtOJ7+T2XmdM9pPmi5AfAM0XNSGs9S2sdprUOCwgo2TrxczHJfLDmCC4VFOsnWZMnhb0dOHCZHj3+j9GjlxIa6k/Dhr72DkkIgXX3UM4opToA2rwvMgE4YsX3IoG6Fp8DgXMWn72AFsB6852AmsAypdRdWutS6eNXa82gmZsBWDiuEwFe7qWxWHEDpk/fzOTJa/H2dufLLwcwenRb6T1RiDLCmoTyKEa1VxBwEVhjDivKdiBEKVUfOAvcDdyTPVJrHQv4Z39WSq0Hni2tZAKwNPwcl+JT8arkStugaqW1WHEdtNYopahZ05N7723Jf//bm4CAKvYOSwhhociEorW+hJEMikVrnaGUehz4FXABvtZa71dKvQ7s0FovK3a0JSg1I5On5hv9XqyZeIs9QxGFOHcuniefXEW3bkE88URHRo1qzahR0rmZEGVRkQlFKfUFFvc+smmtHy7qu1rrlRitFFsOm1LAtD2Kml9JevIHI5nc0bIWNbzlhbeyJjMzi08+2c7kyWtJT8+ic+dAe4ckhCiCNVVeayz+rgT8i9xPbzmc3w9eZNX+CwC8K135ljnh4Rd46KFl7Nx5nttvb8gnn/SXG+9COABrqrzmW35WSn0HrLZZRKVg7P8Zt2neHNSCym4udo5G5BUbm8K5c/HMnz+UYcOaSUOOQjgIq9+Ut1AfqFfSgZSWfWdjAejUwI/7bnbY1XAqWmt+/PEAR49GMXlyd265JZiIiCepVOl6iqcQwl6seVM+Wil11fwXg3F18qLtQyt56ZlZ3D1rKwBP9QqxczQC4Pjxq/TvP5cRIxaydOlh0tONV5wkmQjheArda5VR19Aa47FfgCyt9TU36B3FrI0RJKRmcN/NQdL4o52lpmbw7rt/8uabm6hYsQIffdSX8eNvwtVVGsAWwlEVmlC01loptVhr3b60ArKlLzZFADCpT6idIxFnzsTxxhsbGTCgCR9+2Ic6dbztHZIQ4gZZczq4TSnVzuaR2FhsUjoxSen0DK2Oj3T9aheXLycyY4bRCGejRr4cOPAYP/44TJKJEE6iwCsUpZSr1joD6Ar8Wyl1HEjEaKNLa60dKsl8u+UkAKO7BNszjHIpK0vzzTe7+M9/1hAfn0rv3g1o0sSfBg2kdQIhnElhVV7bgHbAoFKKxWYuxqXw3uojVPdyp0tD/6K/IErMvn2XePTRFfzxx2m6dQvis8/upEkT+Q2EcEaFJRQFoLU+Xkqx2MyX5r2T94a3loYES1FaWia33/4daWmZfP31XTz4YBt5p0QIJ1ZYQglQSk0saKTW+n0bxGMTuyONd0+6NpIz49Kwdu0JbrmlHm5uLixYMIzQUH/8/T3sHZYQwsYKuynvAnhiNDOf3z+HEJuUzrYTV+nayF/Ojm0sMjKOIUMWcNtt3/Ltt7sB6No1SJKJEOVEYVco57XWr5daJDay8O9IAIaFSeOCtpKRkcWMGdt4+eV1ZGZmMXXqbdx7byt7hyWEKGVF3kNxZJlZmnd+OUh1L3f6t6xl73Cc1v33L2bevH3069eImTP7U7++PL0lRHlUWEK5rdSisJHtJ6+Snqm5u0MQFV3kDeySFBOTgqtrBTw93XjssZsYMqQpQ4Y0lWpFIcqxAo+yWuurpRmILfx5PAqAsV3r2zkS56G1Zt68fTRtOpOXX14LGPdJhg6VVoGFKO+c+rR97aGLeLi5yJvxJeTYsav06fM9I0cuIjDQm/vuk/skQoh/OHWTrplZkJHlsG1Zlilz5+5lzJiluLu7MmNGP8aNC8NFqhGFEBacNqFciE3h4Pk4xnSR6q4bkZ6eScWKLoSF1Wbo0GZMn96b2rUd5qlxIUQpctqEMvevUwD0bVHTzpE4pkuXEnnmmd9ITEzjp59G0LixH99/P9jeYQkhyjCnrbNISDU6ampfTx5hLY6sLM2sWTtp0mQG8+fvo3nzADIzs+wdlhDCATjtFUr4mWgCvNxxkba7rBYREc199/3Eli2R9OgRzKef3kFoqDRXI4SwjtMmlOT0LDzdnXb1bMLHx52YmBT+7/8Gcf/9reQxYCFEsThllVdKeiYHz8fRuaF081uUZcsOM3jwfDIzs/Dz82DfvvGMGtVakokQoticMqEcvhAPQE3vSnaOpOw6fTqWQYPmMXDgPI4cieL8+QQAad5fCHHdnLJOaJHZIKQ84XWtjIwsPvxwK6+8sh6tNdOm9eLpp2+mYkUXe4cmhHBwTplQ0s2nkhpV97RzJGVPZmYWX375Nz171ud//+tHcHBVe4ckhHASTlnldfB8PCHVPeU+gCk6OpnnnltNfHwq7u6ubN48hmXL7pZkIoQoUU6XUKISUgk/E0OH+r72DsXutNbMmbOH0NCZvPfeFtatOwmAn5+HJFshRIlzuiqvT9cfB+Cm4PKdUI4ciWL8+BX8/vsJOnSow6+/3kebNnJPSQhhO06XUC4npALQr2X5Png+9dQqduw4xyef9Ofhh9tLQ45CCJtzuoRyLiaZBv5VcHctf08trV59nNBQf+rW9eHTT+/A3d2VmjXlwQQhROmw6WmrUqqvUuqwUuqYUur5fMZPVEodUErtUUr9rpSqd6PLPHwhnsY1yldruBcuJHDPPYu4/fbvmTZtMwD16lWVZCKEKFU2SyhKKRdgJtAPaAaMVEo1yzPZLiBMa90KWAhMv5FlpmVkEZeSQUiN8nEgzcrSfPbZDkJDZ7Bo0UFeeeUW3n33dnuHJYQop2x5hdIBOKa1jtBapwHzgIGWE2it12mtk8yPW4HAG1ngjpNGr8XBflVuZDYOY+rUTTz66Arat6/Nnj3jePXVHlSq5HS1mEIIB2HLo08d4IzF50igYyHTjwV+yW+EUuph4GGAoKCgAmfw0pJ9ALQM9ClepA4kPj6VK1eSqF+/GuPGhVG/fjVGjmwhjwELIezOllco+R3h8u2PVyl1HxAG/De/8VrrWVrrMK11WEBAQL4LS8/MIuJKIiHVPZ3yHorWmsWLD9Ks2SeMGLEQrTV+fh7cc09LSSZCiDLBlgklEqhr8TkQOJd3IqVUL2AycJfWOvV6F3bmqlFzdkerWtc7izLr1KkY7rprHoMHL8DXtzIff9xPkogQosyxZZXXdiBEKVUfOAvcDdxjOYFSqi3wOdBXa33pRha27YRx/yS0pnNdnWzZcoZevb4D4N13e/Pkkzfj6irvlAghyh6bJRStdYZS6nHgV8AF+FprvV8p9TqwQ2u9DKOKyxP40TzjPq21vut6lnfiSiIAYU7yhnxcXCre3u60a1eLMWPaMGlSF4KCnPfekBDC8dn0kSCt9UpgZZ5hUyz+7lVSy/p8YwSVK7rg7+leUrO0i6ioJJ5/fg2//RbB/v3j8fR043//62/vsIQQokhO8YxpQmoGAI58W0FrzXff7eGZZ34jOjqZiRM7OfT6CCHKH6dIKGsPGbdfXugXaudIrk9sbAqDBs1n/fqTdOoUyGef3UmrVjXsHZYQQhSLUySURTuNHhp7NKlu50iKR2uNUgpvb3f8/T2YNetOxo5tJ93wCiEcklM8LuRVyZUqbi7U9fWwdyhW+/XXY7RrN4vIyDiUUvz44zD+/e/2kkyEEA7LKRLK8j3nCXGQlxnPn4/n7rsX0rfvHJKS0rl0KdHeIQkhRIlw+Cqv7BcaA6tVtnMkRZs5cxsvvriW1NQMXnutB8891wV3d4f/CYQQAnCChHLkYjwA/VuW/Tfkd+48T8eOdZg5sz8hIX72DkcIIUqUwyeUTUevANC8tredI7lWXFwqU6as4/77W9G+fW0++eQO3N1dpNkUIYRTcviEsv6w8chwUBm6Ia+1ZtGigzz55CrOn48nKMiH9u1rS9PyQgin5vBHuLMxyfhWcSszZ/0nTkTz+OO/sHLlUdq0qclPPw2nY8cb6uZFCCEcgkMnlKS0DNIzNf1b1rR3KDnmzNnLxo2n+OCDPjz+eAdpyFEIUW44dELZfCwKgHZB1ewax6ZNp0hNzaRXrwZMmtSZBx9sQ2Bg2bunI4QQtuTQp88nzRaGWwVWtcvyr1xJYsyYpXTvPpvXX98AgLu7qyQTIUS55NBXKGmZWQAEeJVuC8Naa2bPDmfSpNXExqby3HNdePnl7qUag3Ac6enpREZGkpKSYu9QRDlQqVIlAgMDqVixYqkv26ETyq7T0QB4l/LTUytXHmXMmGV06VKXzz67kxYtHKsNMVG6IiMj8fLyIjg4uMw8PCKck9aaqKgoIiMjqV+/fqkv36GrvNxdXQBKZSdNSkpn8+bTAPTvH8LSpXezceNoSSaiSCkpKfj5+UkyETanlMLPz89uV8MOnVBW7jtPs1q2v1/xyy9HadHiE/r1m0NMTApKKe66q4k05CisJslElBZ7ljWHTShaa7SG1IxMmy3j7Nk4hg37kf795+Lu7srPP4+katVKNlueEEI4ModNKFcT0wDo28I276BcupRIs2afsHz5Ed5881Z27x7HLbcE22RZQtiSi4sLbdq0oUWLFgwYMICYmJiccfv376dnz540btyYkJAQ3njjDbTWOeN/+eUXwsLCaNq0KaGhoTz77LP2WIVC7dq1i4ceeijXsIEDB9KpU6dcwx588EEWLlyYa5inp2fO30eOHKF///40atSIpk2bMnz4cC5evHhDsV29epXevXsTEhJC7969iY6OvmaadevW0aZNm5x/lSpVYsmSJQCMHTuW1q1b06pVK4YOHUpCQgIAM2bM4Jtvvrmh2GzCONN3nH/t27fXWmv9x9HLut5zy/VPf5/RJSkyMjbn748+2qqPHYsq0fmL8ufAgQN2XX6VKlVy/h41apR+8803tdZaJyUl6QYNGuhff/1Va611YmKi7tu3r54xY4bWWuu9e/fqBg0a6IMHD2qttU5PT9czZ84s0djS09NveB5Dhw7V4eHhOZ+jo6N1YGCgDg0N1RERETnDH3jgAf3jjz/m+m72tklOTtaNGjXSy5Ytyxm3du1avXfv3huKbdKkSXrq1Klaa62nTp2q//Of/xQ6fVRUlK5WrZpOTEzUWmsdG/vP8ejpp5/OmVdiYqJu06ZNgfPJr8wBO7SNj88O+5RXdrP1Qb5VSmR+sbEpvPTSWj7/fCdbtz5Eu3a1eOKJjiUybyGyvfbzfg6ciyvReTar7c0rA5pbNW2nTp3Ys2cPAHPnzqVLly7cfvvtAHh4eDBjxgx69OjBY489xvTp05k8eTKhoUbX2q6urowfP/6aeSYkJDBhwgR27NiBUopXXnmFIUOG4OnpmXNGvXDhQpYvX87s2bN58MEH8fX1ZdeuFavFKwAAEEFJREFUXbRp04bFixcTHh5O1arG+2SNGjVi8+bNVKhQgXHjxnH6tPEwzIcffkiXLl1yLTs+Pp49e/bQunXrnGGLFi1iwIAB1KhRg3nz5vHCCy8UuV3mzp1Lp06dGDBgQM6wW2+91aptWpilS5eyfv16AB544AF69OjBtGnTCpx+4cKF9OvXDw8Po21Cb2/jHrHWmuTk5Jz7Ix4eHgQHB7Nt2zY6dOhww3GWFIdNKJfjUwEI9ruxRiG11vz44wGeemoVFy4k8PjjHWjY0L5v3gthC5mZmfz++++MHTsWMKq72rdvn2uahg0bkpCQQFxcHPv27eOZZ54pcr5vvPEGPj4+7N27FyDfap28jhw5wpo1a3BxcSErK4vFixczevRo/vrrL4KDg6lRowb33HMPTz/9NF27duX06dP06dOHgwcP5prPjh07aNGiRa5hP/zwA6+88go1atRg6NChViWUffv2XbMt8hMfH0+3bt3yHTd37lyaNWuWa9jFixepVcvoWqNWrVpcunSp0PnPmzePiRMn5ho2evRoVq5cSbNmzXjvvfdyhoeFhbFp0yZJKCXhb/MdFN8qbtc9D601gwcvYMmSQ7RrV4tly0YSFla7pEIU4hrWXkmUpOTkZNq0acPJkydp3749vXv3BozyX9ATQcV5UmjNmjXMmzcv53O1akWfkA0bNgwXF+Ox/xEjRvD6668zevRo5s2bx4gRI3Lme+DAgZzvxMXFER8fj5fXP72znj9/noCAgJzPFy9e5NixY3Tt2hWlFK6uruzbt48WLVrku07FfSLKy8uL8PDwYn3HWufPn2fv3r306dMn1/BvvvmGzMxMJkyYwPz58xk9ejQA1atX59ChQzaJ5Xo55E35zCzNusOXget7RC49PTPnu1271uXjj/vy/+3dfZRU9X3H8fcnK7IglDQgNITIg4gsu4I8JAE8EYUUjbVQOJaVZ3IwKhDRINj20HO6LY15MthSMECtB/Ak2xUJyoNCo4UQlZWHsgKCIeuyGiyEBSkggsDy7R/37uywzLqzMA87y/d1zpwzc+fOvd/5ntn57v3dO9/fli0PeDFxjVKzZs0oKSnhgw8+4OzZsyxYsACA3Nxctm3bdtG6ZWVltGjRgpYtW5Kbm8v27dvr3H5thSl6Wc3fRVx3XfVQ9YABAygtLaWiooKXXnqJkSNHAnDhwgU2b95MSUkJJSUlfPTRRxcVk6r3Fr3toqIijh07RufOnenUqRPl5eWRYte6deuLjp4+/vhj2rRpE8lFPO/15MmTF51Aj75FF78q7dq14+DBg0BQMNq2rf13ay+88AIjRoyI+Qv3rKws8vPzWbFiRWTZmTNnaNasYc1Um5EF5egnwXBX9z+r/zzyGzeW07PnQl5+Oajsjz8+kEce+QZZWRmZCufi1qpVK+bNm8dTTz3FuXPnGDt2LG+88QavvfYaEBzJTJ8+nSeeeAKAWbNm8eSTT7Jv3z4g+IKfO3fuJdsdOnQo8+fPjzyu+tJu164de/fujQxp1UYSI0aMYMaMGeTk5NC6deuY2411ZJCTk0NpaWnkcWFhIevWraO8vJzy8nK2b98eKSh33HEHRUVFnD0bXCG6ZMmSyHmSMWPG8NZbb7F27drIttatWxcZxqtSdYQS61ZzuAtg2LBhLF26FIClS5cyfPjwWvNQWFjI6NGjI4/NLPLezIzVq1dHzmdBMGxYc7gv7ZJ91j/Rt759+9rCjaXW8W/W2PrdB2u9yqGmw4c/sQkTVhoUWOfO/2Kvv15W94ucS4CGdJWXmdm9995ry5YtMzOznTt32qBBg6xbt2524403WkFBgV24cCGy7urVq61Pnz7WvXt3y8nJsZkzZ16y/ZMnT9qECRMsNzfXevbsaStWrDAzs+XLl1uXLl1s0KBBNm3aNJs4caKZxb7aauvWrQbYkiVLIssqKips1KhRdsstt1hOTo499NBDMd9fXl6enThxwvbv32/t27e/KH4zs969e1txcbGZmRUUFFheXp716tXLRo4caYcPH46st3fvXrvrrrusa9eulpOTY/n5+Xbo0KHPzW1djhw5YoMHD7auXbva4MGD7ejRo5H3O3ny5Mh6VbFXVlZGllVWVtrAgQMtLy/PcnNzbcyYMRdd9dW7d2+rqKiIud90XeWlYD+Zo1+/ftbv0UWse/cQ7z95D1lx/Fq9sHAX06a9wiefnGXWrIHMnn07zZunvnGauzrt3buXnJycdIfRaD399NO0bNnykt+iNGY7duxg7ty5PP/88zGfj/WZk7TdzPolM66MHOcpP3qKptd8Ia5iAnD+/AXy8tpSUvIwP/jBEC8mzjUiU6ZMoWnT1HYcT7cjR44wZ86cdIdxiYy8yuuPJ87QqXXtvz85deosc+Zs4oYbWjF16tcYN64n48b19H5KzjVC2dnZjB8/Pt1hpFTVlXoNTcYdoZw+V8mxT8/x1/1iz9O+Zs0+cnOf4cc/fpN9+4IZHSV5MXFplWlDyy5zpfOzlnFHKKc+Ow9A344XX+t+4MAJpk9/lZUr36NHj+vZtGkS3/xmx3SE6NxFsrOzOXr0qLewd0lnFsyHkp2dnia2GVdQPj1bSXMgp0bb+rKyY6xf/z4//OEQZswYwLXXZqUnQOdq6NChAwcOHKCioiLdobirQNWMjemQcQXl+Olz3HbDF8luksWWLR+xefMfePTR/tx+e0c+/PAxWl9hKxbnEq1JkyZpmT3PuVRL6jkUSXdL+p2kUkl/G+P5ppKKwuffltQpnu12v74FU6eupX//Z5k7t5hTYSt7LybOOZc+SSsokrKABcC3gR7AaEk1f0o6GThmZl2Bp4Ha23BGWfbzbSxatJ3p07/Brl1TuO4K+nk555xLjGQOeX0dKDWzMgBJ/wkMB6Ib3gwHCsL7LwLzJcnquEzherJYvfW79Onz5cRH7Zxz7rIks6B8BfhD1OMDQM0JRiLrmNl5SceB1sCR6JUkPQg8GD787J1D39kdR6fpq0EbauTqKua5qOa5qOa5qHZzsneQzIIS6/rImkce8ayDmS0GFgNI2pbs9gGZwnNRzXNRzXNRzXNRTdK2ute6Msk8KX8A+GrU4w7A/9a2jqRrgFbAx0mMyTnnXJIks6BsBW6S1FnStcD9wKoa66wCJob37wP+u67zJ8455xqmpA15hedEvgesB7KA58zsXUn/RNBGeRXwH8DzkkoJjkzuj2PTi5MVcwbyXFTzXFTzXFTzXFRLei4yrn29c865hinjmkM655xrmLygOOecS4gGW1CS1bYlE8WRixmS9kjaKel1SY22zXJduYha7z5JJqnRXjIaTy4kjQo/G+9K+mWqY0yVOP5GbpC0QdKO8O/knnTEmWySnpN0WNLuWp6XpHlhnnZK6pPQAJI9x/Dl3AhO4r8PdAGuBd4BetRYZyqwMLx/P1CU7rjTmIs7gebh/SlXcy7C9VoCm4BioF+6407j5+ImYAfwp+HjtumOO425WAxMCe/3AMrTHXeScnE70AfYXcvz9wCvEvwGsD/wdiL331CPUCJtW8zsLFDVtiXacGBpeP9FYIga52QTdebCzDaY2afhw2KC3/w0RvF8LgDmAD8BzqQyuBSLJxffBRaY2TEAMzuc4hhTJZ5cGFA150UrLv1NXKNgZpv4/N/yDQeWWaAY+KKkhPWwaqgFJVbblq/Uto6ZnQeq2rY0NvHkItpkgv9AGqM6cyGpN/BVM1uTysDSIJ7PRTegm6Q3JRVLujtl0aVWPLkoAMZJOgC8AjySmtAanPp+n9RLQ50PJWFtWxqBuN+npHFAP2BQUiNKn8/NhaQvEHStnpSqgNIons/FNQTDXncQHLX+VlKemf1fkmNLtXhyMRpYYmY/kzSA4PdveWZ2IfnhNShJ/d5sqEco3ralWjy5QNK3gNnAMDP7LEWxpVpduWgJ5AEbJZUTjBGvaqQn5uP9G3nZzM6Z2X7gdwQFprGJJxeTgRcAzGwzkE3QOPJqE9f3yeVqqAXF27ZUqzMX4TDPIoJi0ljHyaGOXJjZcTNrY2adzKwTwfmkYWaW9KZ4aRDP38hLBBdsIKkNwRBYWUqjTI14cvEhMARAUg5BQbka52ReBUwIr/bqDxw3s4OJ2niDHPKy5LVtyThx5uKnQAtgeXhdwodmNixtQSdJnLm4KsSZi/XAUEl7gEpglpkdTV/UyRFnLh4H/l3S9wmGeCY1xn9AJRUSDHG2Cc8X/QPQBMDMFhKcP7oHKAU+Bb6T0P03wpw655xLg4Y65OWccy7DeEFxzjmXEF5QnHPOJYQXFOeccwnhBcU551xCeEFxDY6kSkklUbdOn7Nup9o6q9ZznxvDbrXvhK1Kbr6MbTwsaUJ4f5Kk9lHPPSupR4Lj3Crp1jhe85ik5le6b+fq4gXFNUSnzezWqFt5ivY71sx6ETQd/Wl9X2xmC81sWfhwEtA+6rkHzGxPQqKsjvMZ4ovzMcALiks6LyguI4RHIr+V9D/hbWCMdXIlbQmPanZKuilcPi5q+SJJWXXsbhPQNXztkHAOjV3hXBNNw+U/UvUcNE+FywokzZR0H0FPtV+E+2wWHln0kzRF0k+iYp4k6d8uM87NRDX2k/RzSdsUzH3yj+Gy6QSFbYOkDeGyoZI2h3lcLqlFHftxLi5eUFxD1CxquGtluOww8Odm1gfIB+bFeN3DwL+a2a0EX+gHwjYb+cBt4fJKYGwd+/9LYJekbGAJkG9mtxB0lpgi6UvACCDXzHoC/xz9YjN7EdhGcCRxq5mdjnr6RWBk1ON8oOgy47yboL1Kldlm1g/oCQyS1NPM5hH0arrTzO4MW7D8PfCtMJfbgBl17Me5uDTI1ivuqnc6/FKN1gSYH54zqCToS1XTZmC2pA7Ar8zs95KGAH2BrWFbmmYExSmWX0g6DZQTtDe/GdhvZvvC55cC04D5BHOtPCtpLRB3q3wzq5BUFvZR+n24jzfD7dYnzusI2oxEz7g3StKDBH/XXyaYSGpnjdf2D5e/Ge7nWoK8OXfFvKC4TPF94I9AL4Ij60smzzKzX0p6G/gLYL2kBwjadS81s7+LYx9joxtJSoo5v07YO+rrBM0G7we+Bwyux3spAkYB7wErzcwUfLvHHSfBrIQ/AhYAIyV1BmYCXzOzY5KWEDRArEnAr81sdD3idS4uPuTlMkUr4GA4f8V4gv/OLyKpC1AWDvOsIhj6eR24T1LbcJ0vSeoY5z7fAzpJ6ho+Hg/8Jjzn0MrMXiE44R3rSquTBO30Y/kV8FcEc3QUhcvqFaeZnSMYuuofDpf9CXAKOC6pHfDtWmIpBm6rek+SmkuKdbTnXL15QXGZ4hlgoqRiguGuUzHWyQd2SyoBuhNMdbqH4Iv3vyTtBH5NMBxUJzM7Q9CNdbmkXcAFYCHBl/OacHu/ITh6qmkJsLDqpHyN7R4D9gAdzWxLuKzecYbnZn4GzDSzdwjmj38XeI5gGK3KYuBVSRvMrILgCrTCcD/FBLly7op5t2HnnHMJ4UcozjnnEsILinPOuYTwguKccy4hvKA455xLCC8ozjnnEsILinPOuYTwguKccy4h/h+DzV6oGiFUawAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gUVffA8e8lgUBIaEnoJfQqvUhHpCuiSFMEBPwpYhexFwQVC6/tBXvhFUUQUEFAVJSuiPQqLbRQAgmQSuqe3x+zCZuQsoFsNpucz/PwkJ2ZnTkzOzNn5s6de42IoJRSSl2rYu4OQCmlVOGgCUUppVSe0ISilFIqT2hCUUoplSc0oSillMoTmlCUUkrliUKfUIwxI40xv7o7DnczxtQ0xsQYY7zycZnBxhgxxnjn1zJdyRizxxjT4yq+d1X7oDGmlDHmJ2NMpDFmQW6/X1Dldr8wxrxijAk3xpzJg2WLMabetc5HZS5fE4ox5qgx5pL9xHbGGDPbGOPnymWKyDci0seVyyiI7Nu6V+pnETkuIn4ikuLOuNwlL04kItJURFbnsJwrTpbXsA8OASoBASIy9Cq+n1l8zxpjjtiPwVBjzHz78I+NMV9lMn1zY0yCMaaCMWaKfd0ezjDNo/bhU/IixgzzrgFMApqISOU8nvdsY8wreTnPgsIY08MYE5rfy3XHHcpAEfEDWgKtgGfcEMM1c+dVd2G54s+NIrq9awEHRCQ5t1/MLGZjzBhgFNDLfgy2BX63j54NDDbGlM7wtdHAUhE5b/98ABiTyTQHchujk2oBESJy1kXzzxeedsxedbwikm//gKNYO3Pq5zeBZQ6ffYAZwHEgDPgIKOUwfhCwHYgCDgP97MPLAp8Dp4GTwCuAl33c3cB6+98fATMyxLQYeNz+d1VgEXAOOAI87DDdFGAh8LV9+fdksn5lga/s3z8GPA8Uc4hjA/BfIBL4F7gxw3ezW4cNwDvAefu4usAfQAQQDnwDlLNPPwewAZeAGOBJIBgQwNs+zWpgmn2+0cCvQKBDPKPt6xABvJDxt8uw3qWA/9injwTW24elLnOM/TcNB55z+F574C/gon29ZwIlHMYL8ABwEDhiH/YecML+G2wBujpM7wU8a983ou3jawBr7fOKtW+P4fbpb8bany4CfwLNM+yrTwE7gQTA23Eb2GPfbI8jDHjbPvy4fVkx9n8dcdgH7dM0BX6z/5ZhwLOZbNOXgUQgyT6f8VgXgM/bt/NZrH2trH361G093h7D2kzmORN4N5vjcz8wOsP2PAXc4nAMfA3sA5o6rMs++/ApWczXC+u4DgdC7L+p476Y6b4P9MLah232bTDbPv0C4AzWvrY2NRaH/foeh88Zt70A9YB77ds20T7vn7KIXYCH7XGHA29x+ZjO8hjMZh96msv7517gtgyxph7nF+3L7GQffsL+m4/J6XwJlM6w3WKwzm3FHJYfAXwHVMhq/wFK2n/XCHs8/wCVsj3HuyuhANWBXcB7DuPfBZYAFQB/4CdgusMBHAn0tm+YakAj+7gfgY/tG7IisAm4L5OE0s3+wxj75/L2DZ+6sbcALwIlgDr2H7Svw8GUBNxqn7ZUJuv3FVaC8rf/QAeA8Q5xJAOPAcWB4fb1qeDkOiQDD2HtlKWwDore9p0qyL4DvJvZts6wwzgmlMNAA/v8VgOv28c1wdoJu9i3xQz7umeVUGbZv18N60TQyR5X6jI/tS+jBdaB1dj+vTbA9fZ1CsY6MT2a4WD+DWt/KGUfdhcQYP/OJKwTS0n7uMlY+1RDwNiXF+B4InGYd2usA7SDPeYx9m3m47D9tmMlpFIZtylWIhxl/9sPuD6z7ZzJPuiPdeKchHXA+gMdstiuU4CvHT6PAw5h7Zt+wPfAnAzL/QprH8ps/7wLK4lNxro78cow/jlgpcPnvlgXR8UzJJRngTfsw97EKmXILqFMwLqAqmH/LVeRfl/Mbt/vAYRmmN84+3bzwTpnbM9tQrH/PRt4JYdzltjjrQDUxDqm77GPc+YYzLgPDeXy+WY41kVOlQzH+VisffIVrJP7LPsy+mAlIj8nzpeZbbdHgY1Y514f+zb/Nqv9B7jPPk9fezxtgDLZbi9XJ5EMK3QU60QVbQ/+dy5fVRv7xq3rMH1HLl+Zfgy8k8k8K2GdpBzvZO4AVmVyMBv7D9TN/vn/gD/sf3cAjmeY9zPAlw4H0xVXfQ7TetnjaOIw7D5gtUMcp7AnM/uwTVhFEM6sw/Gslm2f5lZgW4ZtnVNCed5h/ERghf3vF1N3NPtnX6wruSsSCtaBcQlokcm41GVWz7DOI7JYh0eBHzIczD1zWO8LqcvGusIelM2JwTGhfAhMyzDNfqC7w/Ybl8n+m5pQ1mLdRQRmmCbdds5kH7zD8XfKYd2mkD6h/A5MdPjcECvRezsst04O8xwJrMQ61iKApx3G1bTPr7r98zekv+CbgpU4amIdR8Xt/9cg+4TyBzDB4XOf1G1Ezvt+DzKcGDPMu5x9Xql3aqvJ+4TSL8Nx8nsujsFxOcx/e+o+a4/1oMO46+zLr+QwLALrcUFO58srthvWBZtjqUiV7PYfrMSd7s49p3/uKNe7VURWGmO6A3OBQKzbqSCsE9cWY0zqtAbrRA3WTrs8k/nVwtqxTzt8rxjWnUg6IiLGmHlYO+xa4E6sAyF1PlWNMRcdvuIFrHP4fMU8HQRiXc0fcxh2DOuqPdVJsf9SDuOrOrkO6ZZtjKkIvA90xbo6KYZ1cs0Nx1ozcVhXvdhjSlueiMQZYyKymEcg1pX24dwuxxjTAHgb62rZF2vH3pLhuxnXexJwjz1GAcrYYwBrH8kuDke1gDHGmIcchpWwzzfTZWcwHpgK/GuMOQK8LCJLnVhubmLMqCpX7l+pJ+VU2cWMiHwDfGOMKY51AvzGGLNNRH4RkePGmLXAXcaYmfbxXTOZx3FjzCHgNawT4AmH/TaruB3jclwHp49fAHstxVexrvSDsIp1wNoHIrML4hpkjL2qPRZnjsGM++9o4HGsEzhYx0KgwyRhDn9fAhCRjMP8yPl8mZlawA/GGJvDsBSy3n/mYO2v84wx5bDOlc+JSFJWC3BbtWERWYN1hTDDPigca2M1FZFy9n9lxXp4CNaK1s1kViewrnACHb5XRkSaZrHob4EhxphaWHclixzmc8RhHuVExF9EBjiGnc0qhWNl+1oOw2pilQmnqmbSH3k1se5anFmHjMuebh/WXETKYBVnmGymz43TWLfFgFV9FauYKTPhQDyZ/zY5+RCrKKS+fR2eJf06gMN6GGO6YpVJDwPKi0g5rJNI6ney2kcycwJ4NcPv7Ssi32a27IxE5KCI3IFVRPMGsND+QDun7Z6bGDM6xZX7VzLpT0JO/e4ikiQiC7DK95s5jPof1vOz27GOh61ZzOIrrGK7K2qGZeI01onJMe5UuT1+78R6ltoL69lLsH146j4Qi3WiTZVdzTBnj5GMsZ+y/53TMZhuGfZzzqfAg1hFseWA3Zl8xxk5nS8zW7cTQP8M+3xJEXE8R6V9z76PvCwiTbCKsW/G2jey5O73UN4FehtjWoqIDWtjv2PP/Bhjqhlj+tqn/RwYa4y50RhTzD6ukYicxnqg/B9jTBn7uLr2O6AriMg2rHLhz4BfRCT1jmQTEGWMecpe/9/LGNPMGNPOmRURqzrud8Crxhh/+87zOJfvgMA6+TxsjClujBkKNAaW53Yd7Pyxig8vGmOqYZWLOwrDKmu/GguBgcaYTsaYElhFO5nu9Pbf7QvgbWNMVft262iM8XFiOf5YD7VjjDGNgPudmD4Z6/fzNsa8iHWHkuozYJoxpr6xNDfGpCbCjNvjU2CCMaaDfdrSxpibjDH+TsSNMeYuY0yQff1T96EUe2w2st72S4HK9qq2PvZ9pYMzy8S6GHrMGFPbXt3+NWC+OFkLzBhzd+o62vex/lgP1f92mGwR1gn0ZazkkpX5WEVX3zmx6O+w9vvqxpjyWA+GAbiKfd8fKwFFYCWO1zKM345VW83XWNXEx2cTl7PHyGRjTHl7FeZHsNY9NZbsjsGMUi84zgEYY8aSPpk7zYnzZRgQYIwp6/C1j7DOT7Xs0wcZYwZltQxjzA3GmOvsd4VRWBfM2b524NaEIiLnsK5wXrAPegrroeNGY0wUVllvQ/u0m7AeVr2DdVW6hstXa6Oxiiv2Yt1yLsQqH8zKt1hXOHMdYkkBBmKVTx7BugL4DOsqyFkPYV0hhWDVdJqLdbJN9TdQ3z7vV4EhIpJalJTbdXgZ68FyJLAM6wGto+nA88aYi8aYJ3KxDojIHvu6zMO6uozGeoCdkMVXnsB6GP4P1kPfN3Bu33oC64ozGuvgmJ/95PwC/Iz1YPQY1p2R4y3621gnr1+xDoDPsR4uglX+/z/79hgmIpuxnqHNxNreh7DKsJ3VD9hjjInBqnk2QkTiRSQO67fdYF/W9Y5fEpForAe5A7GKAg8CNzi5zC+wiiHWYu2j8Vi/k7OisO4Cj2MlwTeB+0VkvUN8sVxOKt9kNSMRuSQiK0XkkhPL/RTrt9sBbOXKfTU3+/5XWL/9Sfv0GzOMfwfreV8YVkLMch2w9o8m9t/px2ymW4xVFLsd61j73D48p2MwHRHZi1Ub8i97fNdh1eq6WtmdL//FOs+F2NevKtZ+ugT41RgTjbXtsruYqYz1W0RhPX9ZQ/oL5Cuk1nZSLmaMuRvrYWEXd8eSW/ar4YtYRVNH3B2PUvnFGCNY+/0hd8fiCdxd5KUKKGPMQHuxQWms51y7sGqtKKVUpjShqKwMwnr4eAqrmG6E6O2sUiobWuSllFIqT+gdilJKqTzhUQ2WAQQGBkpwcLC7w1BKKY+yZcuWcBEJcuUyPC6hBAcHs3nzZneHoZRSHsUYcyznqa6NFnkppZTKE5pQlFJK5QlNKEoppfKEJhSllFJ5QhOKUkqpPKEJRSmlVJ5wWUIxxnxhjDlrjNmdxXhjjHnfGHPIGLPTGNPaVbEopZRyPVfeoczGauI7K/2x2oiqD9yL1dmSUkqpPJacYst5ojzgshcbRWStMSY4m0kGAV/ZGxzcaIwpZ4ypYu9wRyml1DU6dTGO295bz9m4xHxZnjvflK9G+s6RQu3Drkgoxph7se5iqFmzZsbRSiml7E6cj+ORedvYfyaa2ESrg0WTkG1Hi3nGnQ/lM+tSNtOmj0XkExFpKyJtg4Jc2hSNUkp5rG83Hafrm6vYevwisZeSiNt6llvK+nFwxsB8Wb4771BCsboZTVUdq+8NpZRSuXDobDQjPtlIeIxVtPXSwCa09C1J4MO+1KiRm17Mr407E8oS4EFjzDysfo0j9fmJUko5JyImgfWHwnlx8R4iLyWlDb+9RCnGdq7tlphcllCMMd8CPYBAY0wo8BJQHEBEPgKWAwOAQ0AcMNZVsSilVGFx8uIlBs3cQHhMQtowW2wSEUuPMHFoU6Y81cVtsbmyltcdOYwX4AFXLV8ppQqLs9HxfLDqMLP/PJo2LNCvBNWjUvhp5haub1WFZUvu5LrrKrkvSDywPxSllCoKIuOS+HDNYWb/eYT4pMvvkfRuXJG7Wteg+3WV2b8/nJvqBzF+fGuKFcusnlP+0oSilFIFgM0mvPXrfk5euMSfhyPSFWm1rFGOO9rXwPdsPI8+vILzLY/QfdEwGjYMpGHDQDdGnZ4mFKWUcoPEZBsLt4Tyx79nORAWzfHzcWnjqpUrRaPK/ozrXJtBraoScTaORx9dwYIFe2nYMIAHH2znxsizpglFKaVcLCE5hZ2hkWw7fgGAUxfj0z0PKVm8GM2qlWFQi2qM71I7XfHV77+HcNtt80lMTGHatBuYPLkTPj4F89RdMKNSSikPdiwilrl/Hyc8JpFFW0OznG5EuxpMuaUpJYt7XTEuKSmF4sW9aNGiMgMG1OeVV3pSr14FV4Z9zTShKKXUNbDZhJDwWGITklmx5wyLtoRyNvry848GlfzwKlaM21tX4/o6AQQHlqakdzG8vTJvqCQqKoEXXviDv/8+yYYN4wgM9GXevCH5tTrXRBOKUkpdhfCYBF5avIdlu658H/u2VtXo2agivRpXolSJK+8+MiMiLFy4l0ceWcGZMzFMnNiOhIQUfH09p9sqTShKKeWkxGQbn60PYc3+c/x95Hza8MGtqnFT8yr4lvCmda1y+Hg7l0RSnTsXy5gxP/Lzz4do1aoyixePoF27ankdvstpQlFKqSycOB/H6v1nmb/Zahh998motHHBAb483b8x/ZpVvubllCnjQ3h4HO++25cHHmiPt7fn3JU40oSilFIORITvt57kjRX/pnsWUszAzc2r4F3M8M7wlhhzbS8Srl17jFdfXceiRcPw8yvBxo33FIiXE6+FJhSllALCouJ5ddk+Nh89z6nIeAA61Q1gTKdgutUPcvpZSE7Cw+OYPPk3Zs/eTnBwOY4evUizZhU9PpmAJhSlVBEUn5TC7/vOciEukU/WhqR7qbBUcS8e6lmPgS2q0qCSf54tU0T48svtTJ78G1FRCTzzTBeef74bvr7F82wZ7qYJRSlV6IkIe05F8eO2kyzaGsqFuKQrphnSpjrtgyswrF2NTOaQN77+eidNmgTx0Uc30bRpRZctx100oSilCq0j4bF8t/kEc/46RkxCctrwPk0q0aluADc2rkSAXwl8S7jmVBgXl8Rrr61jwoS2VK9ehkWLhlG2bMlCUbyVGU0oSqlCIT4phY/XhLB4x0nORSUQ7ZBAAPo3q8w9XevQuma5a36g7ozlyw/ywAPLOXr0ItWq+XP//e0oX76Uy5frTppQlFIeKTnFxqaj5/lx20l2hkby75notHFlSnrTrUEQLauXpX3tADrXC8iXJAIQGhrFo4+uYNGifTRuHMiaNXfTrVutfFm2u2lCUUp5jLjEZBZsDuWvwxGs2HMm3bi2tcrTtX4QD99YL9+SR2ZefXUty5Yd5LXXejJpUidK5FHtME9grI4TPUfbtm1l8+bN7g5DKZVPDp+L4ZuNx5mz8ShJKZfPV4F+PjSq7M/Lg5pSJ7C0W5PIpk0nKVXKm+uuq0RERByRkQnUqVPebfFkxhizRUTaunIZeoeilCowTpyP44PVh/l9Xxg2EcJjEtONb1atDKOur8UtLarl2Xsh1yIyMp5nn/2dDz/czM03N2DJkjsICPAlIMDX3aG5hSYUpZRbiAj/nolm+a7TrDsYzvYTF9ONb1WzHANbVKWYMXRrEES3+oFuvQtxJCLMn7+Hxx77hbNnY3noofZMm9bT3WG5nSYUpVS+2nc6iq83HmPupuM4lrg3qORH3SA/RnaoRae6AQW6au3XX+9k9Ogfadu2KkuX3kGbNlXdHVKBoAlFKeVSYVHxvPf7QcIi4zl4NibdW+l3dwqmT9NKtK1VgRIFvEHEhIRkQkIu0LhxEMOGNSU52cbo0S3wyqJfk6JIE4pSKs9diE1kxZ4zfLouhJBzsWnDW9UsR3BgaSZ0r8P1tQv2XYijVauOcP/9y4iLS+LgwYfw8fFm7NhW7g6rwNGEopTKMz/vOs3D87alq41VK8CX529qQu8mldwY2dU5ezaWJ574lTlzdlKnTnk++WRgge3PvSDQLaOUumpR8Ul8uf4oW45fYO2Bc2nDW9Qox92datG7SWX8PPQEfOjQedq3/5SYmESee64rzz3XlVKlCk9Djq7gmb+0UipfxCYkc+JCHCv3hhERm0h8ko29p61OpnZkqJUF0LdpJd68vQVlPbgF3aioBMqU8aFu3fKMH9+KceNa0bhxkLvD8giaUJRSaaLjk/h203HWHQxn2/GL6RpUBOtlQh/vYkTHJ9Hf3lNhm1rlGdWxFiW8ihWYar1XIzY2kalT1/Dpp1vZufN+qlcvw1tv9XF3WB5FE4pSRdT52ET+OXqevw5HsP9MNJuOnifFdvnZR3nf4vRqXIkOtSvQqIo/bWqVd1mrvO7200/7efDBnzl+PJLx41sVqj5K8lPh3DuUUgDYbEJIeCxnIuM5cSGOf46e52BYDKEX4q7oE6RRZX/qBJWmfkV/7utep9AmD0fJyTaGDVvADz/8S9OmQaxbN5YuXWq6OyyPVfj3GKWKEBHhh20nCYtKYPfJSJbvPk1mzfVVKVuS/s0CGN6uBrUDS1OjvK/HVOHNCyKCMQZv72JUqeLH66/fyGOPdSxSDTm6giYUpQqBpBQb7/9+kDkbj3Exw53HTc2rMKR1dcr6Fqd+RT/8Sxbt4pyNG0N54IHlfPrpQFq3rsKsWTe5O6RCQxOKUh7mxPk4luw4ReiFOELOxRJ5KSldXyBjOwczqU9DfIt7Fam7jpxcuHCJZ5/9nY8/3kLVqv5cuHDJ3SEVOi5NKMaYfsB7gBfwmYi8nmF8TeB/QDn7NE+LyHJXxqSUJzp0NoZf9pzhi/VHiIi93AJvRX8fkm1C/2aVqVfRj3u61PHoKruuMn/+bh5+eAXh4XE8+uj1vPxyD/z9fdwdVqHjsoRijPECZgG9gVDgH2PMEhHZ6zDZ88B3IvKhMaYJsBwIdlVMSnmSuMRkdoZG8u7KA2wMOZ82vFFlfyb3bUjX+kEFvv2rguLff8MJDi7HihUjadWqirvDKbRceYfSHjgkIiEAxph5wCDAMaEIUMb+d1nglAvjUapAOxsVz9qD4RwIi2b+PyeIvJT+Wch/hrbgpuZVKFlcHxznJD4+mTfeWE/r1lUYOLAhzz7bleef76YNObqYKxNKNeCEw+dQoEOGaaYAvxpjHgJKA70ym5Ex5l7gXoCaNbVKnyocklNsLN15mpDwWBZtCeXkxfRl+i2ql+XGxpXo2agizaqVdVOUnmflyhAmTlzGwYPnmTSpIwMHNqS4JuF84cqEktnTwIwVGO8AZovIf4wxHYE5xphmImJL9yWRT4BPwOoC2CXRKpUPbDbh2Pk4Pll7mG83Xb7eCvQrQZta5RnRrgbdGwRRoXQJvPVqOlfCwmJ4/PFfmTt3F/XqVeDXX++id++67g6rSHFlQgkFajh8rs6VRVrjgX4AIvKXMaYkEAicdWFcSuW77zaf4OuNx9gZGplu+JiOtXigZz0CS/tojaxr9NtvISxcuJcXX+zGM890pWRJrcSa31y5xf8B6htjagMngRHAnRmmOQ7cCMw2xjQGSgLnUKoQSEqxsfbAOe7/ZiuJydZNd5ta5akV4MttrarRpV7B6dLWU+3YcYaDB88zZEgTRo68js6da1C7dnl3h1VkuSyhiEiyMeZB4BesKsFfiMgeY8xUYLOILAEmAZ8aYx7DKg67WySz93qV8hyRcUmM/uJvdjjcjVQoXYJPRrWhbXAFN0ZWeMTEJPLSS6t4772/CQ4ux623NsLbu5gmEzdz6T2h/Z2S5RmGvejw916gsytjUMrVzkUnsOPERaITknh12T7CY6z3RBpU8mNIm+rc2LgSdYP83Bxl4fHjj//y0EM/Exoaxb33tmb69F54a/XpAkELGZW6CiLCL3vC+GFbKL/sCbti/NvDWnBbq2papJXHdu0K47bb5nPddRWZP38InTrVyPlLKt9oQlHKCdHxSXy4+jB/HzlPREwCRyPi0sb5+3jz4sAmtKpZnrKlihOkb2DnqaSkFNatO07PnrW57rpKLFt2J71719GqwAWQJhSlspCYbONIeCyzVh1iyY7LFRQD/UrQs1FFmlYtw6jra1GxTEk3Rlm4/fnnCSZMWMqePefYv/9B6tWrwIAB9d0dlsqCJhSl7M7HJnLywiWW7jrFx2tCrhj/8I31eaxXfS3Gygfnz1/i6adX8umnW6lRowzffz+MevW0QkNBpwlFFWmbj55n/P82X9HMCUD74Ar0aVqJ66qVpX3tCppI8kl8fDItW37EqVPRTJrUkSlTeuDnV8LdYSknaEJRRY6IsGBLKE8u3Jlu+ITudalWvhT1gvxoXascPt5aRp+fQkOjqF69DCVLejNt2g20bFmZFi0quzsslQuaUFSREhGTQJtXVqZ97lo/kCm3NNVqvW506VIS06ev5403NrBw4VAGDmzImDEt3R2WugpOJRRjTAmgpogccnE8SuU5EWHpztN8ui4kremT8r7FWfvkDUW+90J3+/XXw0ycuIzDhy9w113Nad++mrtDUtcgx4RijLkJeBsoAdQ2xrQEXhKR21wdnFJX6/utoWw9foFtxy+y51RU2vDO9QIY1rYGg1rqicvdHnpoOTNn/kP9+hVYuXIUN95Yx90hqWvkzB3KVKxm51cBiMh2Y0w9l0al1FVauvMUj87bTrLNasGnnG9xGlcpg7+PN28Pb0H18r5ujrBoS0mx2jTz8irG9ddXJzDQl6ee6qINORYSzvyKSSJyMUMNF21vSxUIYVHxbD12gbmbjrPuYHja8MplSvLV+PY0qOTvxuiUo61bTzNhwlJGjWrOQw91YOTI5u4OSeUxZxLKPmPMMKCYveXgR4CNrg1Lqez9eSicB7/dxnmH/tVrVCjF9bUDGNMpWDukKkCioxN48cVVvP/+JoKCfKlSRZN8YeVMQnkQeBGwAd9jtR78jCuDUiorZ6PjeXnJXpbtOg1YtbRGdwymSdUyVCtXys3RqYx+/fUw48Yt5tSpaCZMaMtrr91IuXLaskBh5UxC6SsiTwFPpQ4wxgzGSi5K5YtDZ2N457cDaYmkfkU/ptzSlM71At0cmcpOiRJeVKxYmkWLhtGhQ3V3h6NczOTU/YgxZquItM4wbIuItHFpZFlo27atbN682R2LVvlMRPh+60leWrKHmITktOGP9qrPo70auDEylZWkpBTefvsvoqISePXVGwGr22PtjdL97Ofttq5cRpZ3KMaYvljd81YzxrztMKoMVvGXUi5xLCKWj9aE8O2m42nDejWuyN2datO5XoA2gVJArV9/PK0hx6FDm6QlEk0mRUd2RV5ngd1APLDHYXg08LQrg1JFU3KKjZmrDvHuyoNpw+7pUpuHe9WnjL6AWGBFRMTx1FMr+fzzbdSsWZaffrqDm2/WO8iiKMuEIiLbgG3GmG9EJD4fY1JFzIGwaD5afZjvt51MG/bl2Hbc0LCiG6NSzoqIuMS8ebt58slOvPhid0qX1oYciypnHspXM8a8CjQB0qpniIhegqirdjY6npl/HOKfoxfYd9p6k5oRccEAACAASURBVL1ymZIMbFGFR3o1wM9HX3QryPbtO8d33+3hpZd60KBBAMePP0aFClrLrqhz5qidDbwCzAD6A2PRZyjqKsUkJLN4+0me+2F32rD+zSoztnNt2gWX1+cjBVxcXBKvvrqWt976Ez+/Eowf35rq1ctoMlGAcwnFV0R+McbMEJHDwPPGmHWuDkwVDskpNlbtP8ehszH8sC2UA2ExaeOeG9CYe7rW1iTiIVasOMTEics4cuQiY8a04K23ehMUVNrdYakCxJmEkmCsI/6wMWYCcBLQwm2Vpci4JLaeuMCXG46y9sC5dOPqBJZmcOtqjOxQi/Ja1u4xYmISGTXqBwICSrFq1Rh69Ah2d0iqAHImoTwG+AEPA68CZYFxrgxKeZ7zsYn8vPs0C7eEsu34xXTjejepxCM31qdeRT9KFtdOqzxFSoqNb7/dzR13NMPPrwQrV46iUaNAfPT5lspCjnuGiPxt/zMaGAVgjNFXXhUACckpTF6wkyU7TgFQsngxBreqRrvaFWgXXIF6FbXjKk+0Zcsp7rtvKVu2nKZUKW9uv72J9p6ocpRtQjHGtAOqAetFJNwY0xSrCZaegCaVIm7u38d5cfHutKbip93ajKFtqutdiAeLjIznhRdWMWvWP1SsWJp5825n8ODG7g5LeYjs3pSfDtwO7MB6EP8DVkvDbwAT8ic8VdDEJCQzffk+tp+43HHVmI61eKp/I3xLaFGIp7v99u/4448jPPBAO155pSdly2pDjsp52Z0BBgEtROSSMaYCcMr+eX/+hKYKms/WhfDKsn1pn+/uFMxT/RpRqoTekXiykJALBAX54u/vw6uv9qRYMUO7dtqjpcq97BJKvIhcAhCR88aYfzWZFE0Hw6J5YsEOdtj7Y+/WIIjZd7fTNpo8XGJiCjNm/Mm0aWt5+OH2vPFGb20RWF2T7BJKHWNMahP1Bgh2+IyIDHZpZMqtklJsvLJ0L//761jasDIlvVn1RA8C/HzcGJnKC2vXHmPChKXs2xfOkCFNePjhDu4OSRUC2SWU2zN8nunKQFTBceriJTq9/kfa58GtqtG3WWVuaFiREt7F3BiZygvvvPMXjz/+K8HB5Vi27E4GDKjv7pBUIZFd45C/52cgyv0OnY2h19tr0j63D67A/Puu1zfZCwGbTYiNTcTf34ebbmrAuXNxPP98N3x9tRVnlXe0Wo7i3zNRTJizhaMRcQDUCSrNCzc1oUfDIE0mhcCePWeZMGFZWs+JDRoE8NprN7o7LFUIuTShGGP6Ae8BXsBnIvJ6JtMMA6YAAuwQkTtdGZNKb+nOUzyzaBfRCcm0qVWeSX0a0KmudqtbGMTFJTFt2hpmzPiLsmV9GDeuJSKiFwnKZZxOKMYYHxFJyMX0XsAsoDcQCvxjjFkiInsdpqkPPAN0FpELxhhtIyyfiAj3zdnCr3vDAJgxtAVD2mgNn8Ji27bTDB78HUePXmTs2Ja8+WZvAgN93R2WKuRyTCjGmPbA51hteNU0xrQA7hGRh3L4anvgkIiE2OczD+vdlr0O0/wfMEtELgCIyNncr4LKrT/+DWPc7M0AlPctzpzxHWhWraybo1J5IfUOpGbNstSsWZb//e9WunWr5e6wVBHhzB3K+8DNwI8AIrLDGHODE9+rBpxw+BwKZKyb2ADAGLMBq1hsioiscGLe6irEJ6Vw56cb2WpvvLFzvQC+Ht9Bi0AKgeRkGzNnbmLJkv389tsoAgJ8WbPmbneHpYoYZxJKMRE5luGkk+LE9zI7S0kmy68P9MBqG2ydMaaZiKRrrtYYcy9wL0DNmjWdWLTKzO0f/smeU1EElC7B/Puup15Ff3eHpPLApk0nmTBhKdu2naF//3pERSVQvrx2eKXynzMvFZywF3uJMcbLGPMocMCJ74UCNRw+V8dqviXjNItFJElEjgD7sRJMOiLyiYi0FZG2QUFBTixaOUpMtvHWL/+y51QUlcr4sPn5XppMCoGYmEQeeGAZ11//GWFhsSxYMJRly+7UZKLcxpk7lPuxir1qAmHASvuwnPwD1DfG1MbqlGsEkLEG14/AHcBsY0wgVhFYiHOhK2dExiXRYuqvaZ8XTuikRVyFRPHixVi9+hgPPdSeadN6UqaMtmCg3MuZhJIsIiNyO2MRSTbGPAj8gvV85AsR2WOMmQpsFpEl9nF9jDF7sYrRJotIRG6XpTJns0laMuneIIj3RrSknK/2kujJDh06z9Spa5g1awD+/j5s2XIvJUvq62SqYDAiGR9rZJjAmMNYRVHzge9FJDo/AstK27ZtZfPmze4MocATEV5f8S8fr7l8s3dk+gC9M/FgCQnJvPnmBl59dR0lSnixbNmddO2qtbeU84wxW0SkrSuX4UyPjXWNMZ2wiqxeNsZsB+aJyDxXBqauzv4z0Uz8ZguHz8UCcG+3Okzu21CTiQdbteoI99+/jP37Ixg+vClvv92XqlX1GZgqeJy6VxaRP4E/jTFTgHeBbwBNKAWIiDD6i02sOxgOQLvg8rxxe3PqBGkXvJ5MRHj11XUkJdlYsWIkffvWc3dISmXJmRcb/bBeSBwBNAYWA51cHJfKBRGh21urOHH+EgBfjWtPtwZaG85T2WzC559vpV+/etSoUZY5c26jXLmSlCqlDTmqgs2ZasO7geuBN0WknohMEpG/XRyXctLeU1HUfmZ5WjLZO7WvJhMPtnNnGF26fMG99y7ls8+2AlClir8mE+URnCnyqiMiNpdHonLFZhMmfH25La56Ff1Y+lAXShbX7ng9UUxMIi+/vJp33tlI+fKlmD17EKNHt3B3WErlSpYJxRjzHxGZBCwyxlxRFUx7bHQPEeHFxXuYs/FyT4rf3deR9rUruDEqda2mTFnNf/7zF/fc04rXX+9FQIA25Kg8T3Z3KPPt/2tPjQVE5KUkJszZwl8h1qs6N11XhTeGNMfPR99D8EQnTkQSG5tEo0aBPP10F269tRFdumjTQspzZddj4yb7n41FJF1Ssb+wqD065pMj4bHcMnM90fHJANQK8GXl490p7qXd8Xqi5GQb77//Ny++uIo2baqyZs3dBAb6ajJRHs+ZS9txXHmXMj6TYSqPHQiLZtaqQyzebjWBVr+iHw/cUI9bW1Vzc2Tqam3cGMqECUvZsSOMm26qz8yZA9wdklJ5JrtnKMOxqgrXNsZ87zDKH7iY+bdUXsjsOcl93erwzIDGboxKXatlyw4wcOC3VK3qz/ffD+PWWxvpC6eqUMnuDmUTEIHVSvAsh+HRwDZXBlXUPblwJwu2hAIw/97raV+7gp54PJSIcOpUNNWqlaFXrzpMnXoDjzzSAX9/bchRFT45tuVV0BTmtrxiE5LpMWM156Ktnpb/ea4XQXri8VgHDkQwceIyDhyIYO/eB/Dz04Y5lfu4tS0vY8waEelujLlA+o6xDCAiovVU80jIuRheW76PlfusHpCD/H34fVJ3ypTUl9k8UXx8Mq+/vp7p09dTqpQ306ffSKlSWhNPFX7Z7eWp3fwG5kcgRdGeU5EM/egv4hIvd4A5rnNtnr+pMcWKaRGXJzpzJoZu3b7k4MHz3HFHM95+uy+VK2t7aqpoyK7acOrb8TWAUyKSaIzpAjQHvgai8iG+Qmn3yUg+XHOYZTtPA1ZDjo/1akCnepq7PVVSUgrFi3tRqVJpunWrxaxZA+jdu667w1IqXzlzH/4j0M4YUxf4ClgGzAVudmVghdGZyHg+XH2I//1l1d6qHViaSX0acHPzqm6OTF0tm0345JMtvPbaOv78czzVq5fhs89ucXdYSrmFMwnFJiJJxpjBwLsi8r4xRmt55UJUfBL3zN7MpqPn04Ytur8jbWrpYyhPtmPHGe67byl//32Snj1rk5SUkvOXlCrEnOoC2BgzFBgF3Gofpk+LnXAgLJo/D4Uz5ae9AFQoXYJXbm1GnyaV8Na33D2WiDB58m+8++5GKlQoxZw5tzFy5HVatVsVec6+KT8Rq/n6EGNMbeBb14bl2RKSU3jk2+2s2HMmbdhN11Vh5p2t9KRTCBhjuHDhEuPHWw05li9fyt0hKVUgOPUeijHGG0jtKu6QiCS7NKpsFOT3UKLik3jgm61pvSYCfD6mLZ3qBlKqhDYr78mOHbvII4+s4MUXu9O6dRVsNtGaeMqjFIg+5Y0xXYE5wEmsd1AqG2NGicgGVwbmaWITkmk+5de0z4/1asDEG+pqA44eLikphXfe2cjLL68BYPjwprRuXUWTiVKZcKbI6x1ggIjsBTDGNMZKMC7NdJ4kNiGZgf9dD8CtLavyzvCWWrRVCPz55wnuu28pu3efZdCghrz/fn9q1izr7rCUKrCcSSglUpMJgIjsM8ZoGxIOWk79laQUoWejirw7opW7w1F5ZOXKECIj4/nxx+EMGtTI3eEoVeDl+AzFGDMbSMC6KwEYCfiKyBjXhpa5gvQM5WJcIi2n/gZAyxrl+GFiJ70z8WAiwpw5OwkK8qV///okJCSTlGTTNrhUoZAfz1CcKeCfABwGngSeAkKA+1wZlCeIik9KSya9m1RiwYSOmkw82L//htOz51eMGfMjX365HQAfH29NJkrlQrZFXsaY64C6wA8i8mb+hFTwJafYuOGt1QBULVuST0a10WTioS5dSuK119bxxhsbKF26BB9/fDP33NPa3WEp5ZGya234WayeGbdiNb0yVUS+yLfICqDkFBuTFuxI60FxeNsavDGkuZujUtfip58O8Mor67jrrubMmNGbSpW0IUelrlZ2dygjgeYiEmuMCQKWA0U2oRwJj+W+OZs5EBYDwB3ta/Dqrde5OSp1Nc6ciWH79jP061ePoUObEBx8D+3ba7fKSl2r7BJKgojEAojIOWNMkX2hIinFxg0zVgNwf4+6PNVPa/x4opQUGx9/vIVnnvmdEiW8OH78UUqVKq7JRKk8kl1CqePQl7wB6jr2LS8ig10aWQHx56Fw7vzsbwD6Na2sycRDbd16mgkTlvLPP6fo1asOH3wwgFKltEk6pfJSdgnl9gyfZ7oykILo8/VHmLbUegVnwHWVmXWnPqz1REeOXKB9+08JDPRl7tzBjBjRTCtRKOUC2XWw9Xt+BlLQhEXFpyWT7yd2onXN8m6OSOWGiLBr11maN69E7drl+fLLQQwc2JBy5Uq6OzSlCq0i+1wkO4nJNga8tw6AqYOaajLxMEeOXODmm7+lVauP2bkzDIBRo1poMlHKxVyaUIwx/Ywx+40xh4wxT2cz3RBjjBhj3N4+WMi5GJpN+YWI2ESeG9CY0R2D3R2SclJiYgqvv76epk0/YM2ao8yY0ZsmTYLcHZZSRYYzbXkBYIzxEZGEXEzvBcwCegOhwD/GmCWO7YLZp/MHHgb+dnberhJyLoae/7Fale1QuwLjutR2c0TKWSkpNjp1+pwtW04zeHBj3n23LzVqaEOOSuWnHO9QjDHtjTG7gIP2zy2MMf91Yt7tsfpOCRGRRGAeMCiT6aYBbwLxzoed937edTotmTzTvxHz7+uIlzZRXuBFRVnXOF5exRg3rhU//XQHixYN02SilBs4U+T1PnAzEAEgIjuAG5z4XjXghMPnUPuwNMaYVkANEVma3YyMMfcaYzYbYzafO3fOiUXnzur9Z7n/m60ATOxRl3u71cnzZai8JSLMnr2dOnXeY/HifwGYOLEdN9/cwM2RKVV0OVPkVUxEjmWoZpnixPcyu7xPa9rY/qLkO8DdOc1IRD4BPgGrtWEnlp0r983ZAsD/xrWnewMtcy/o9u49x/33L2Pt2mN07lyDunUruDskpRTOJZQTxpj2gNifizwEHHDie6FADYfP1YFTDp/9gWbAanuyqgwsMcbcIiL51j79TztOkZBs495udTSZeIA339zAc8/9QZkyPnz22UDGjm2lvScqVUA4k1Duxyr2qgmEASvtw3LyD1DfGFMbq/vgEcCdqSNFJBIITP1sjFkNPJGfyST1xUUf72I82qt+fi1WXQURwRhD5cp+jBx5HW+91ZugoNLuDksp5SDHhCIiZ7GSQa6ISLIx5kHgF8AL+EJE9hhjpgKbRWRJrqPNQ0fCY9NeXPxhYmd8Szhd4U3lo1OnonnkkRV07VqThx/uwOjRLRg9uoW7w1JKZSLHs6gx5lMcnn2kEpF7c/quiCzHaqXYcdiLWUzbI6f55aV3V1qldp+ObkuTqmXyc9HKCSkpNj744B+ee+4PkpJsdOpU3d0hKaVy4Mxl+UqHv0sCt5G+9pbHCTkXk9anSa/GFd0cjcpo+/Yz3HPPErZsOU2fPnX54IMB+uBdKQ/gTJHXfMfPxpg5wG8uiygfPDxvGwCzx7bTRgILoMjIeE6dimb+/CEMHdpEfyOlPMTVPDioDdTK60Dyy8W4RHafjKJ9cAV6NNS7k4JARFiwYC8HD0bw3HPd6N49mJCQRyhZUp9rKeVJnHlT/oIx5rz930Wsu5NnXR+aa7z5y34AxnfVZlUKgsOHzzNgwFyGD1/I4sX7SUqyXnHSZKKU58n2qDVWWUMLrGq/ADYRyfMXC/OLzSbM/fs4AL0bV3JzNEVbQkIyM2b8ySuvrKN48WK8914/Jk5sh7e3NoCtlKfKNqGIiBhjfhCRNvkVkCu9/ZtVs6tjnQB9Gc7NTpyIYtq0tQwc2JB33+1LtWpa004pT+fM5eAmY0yh6Kpw/aFwwGpiReW/c+dimTlzEwD16lVg794HWLBgqCYTpQqJLO9QjDHeIpIMdAH+zxhzGIjFaqNLRMSjkkxMQjLbT1ykc70ASmixSr6y2YQvv9zGk0+uJDo6gd6969CwYSB16mjHZUoVJtkVeW0CWgO35lMsLvX4/O0ADGxe1c2RFC27d5/l/vuXsX79cbp2rclHH91Mw4aBOX9RKeVxsksoBkBEDudTLC4jImw6ep7yvsUZ3q5Gzl9QeSIxMYU+feaQmJjCF1/cwt13t9R3SpQqxLJLKEHGmMezGikib7sgHpf4YsNRLsYlMblvQz2h5YM//jhC9+61KFHCi+++G0qjRoEEBvq6OyyllItl9zDBC/DDamY+s38e480VVgdMIzvUdHMkhVtoaBS33/4dN974FV99tQOALl1qajJRqojI7g7ltIhMzbdIXCQ2IZmEZBtB/j6U8y3h7nAKpeRkGzNnbuKFF1aRkmJj+vQbGTmyubvDUkrlsxyfoXi6rccvADC5T0M3R1J4jRr1A/Pm7aZ//3rMmjWA2rW19pZSRVF2CeXGfIvChdYesPqg76a9Meapixfj8fYuhp9fCR54oB23396Y229vrM+olCrCsnyGIiLn8zMQV1mx5wwAQf4+bo6kcBAR5s3bTePGs3jhhT8A6znJkCHaKrBSRV2hfsNv2c7TnDh/ift71MVLm1q5ZocOnadv36+5445FVK9ehrvu0uckSqnLCnWTrhtDIgAY2znYvYEUAnPn7mLcuMX4+Hgzc2Z/Jkxoi5dXob4eUUrlUqFNKGFR8czZeIxGlf2p6F/S3eF4rKSkFIoX96Jt26oMGdKEN9/sTdWqHlVrXCmVTwptQln171kAxnXRfk+uxtmzsUya9CuxsYl8//1wGjQI4OuvB7s7LKVUAVZoyyx+3G514dJda3flis0mfPLJFho2nMn8+btp2jSIlBSbu8NSSnmAQnuHcj42kbKlilOpjBZ3OSsk5AJ33fU9f/0VSo8ewXz44U00aqQNOSqlnFMoE0psQjIHwmIY1FJbFs6NsmV9uHgxnv/971ZGjWqu1YCVUrlSKIu8jkbEAlDap1Dmyzy1ZMl+Bg+eT0qKjYAAX3bvnsjo0S00mSilcq1QJpR1B62eGQe3qubmSAqu48cjufXWeQwaNI8DByI4fToGQLtGVkpdtUJ5CX82KgGA66qXdXMkBU9yso13393ISy+tRkR4441ePPbY9RQv7uXu0JRSHq5QJpRLSckE+pXAx1tPkhmlpNj47LOt9OxZm//+tz/BweXcHZJSqpAolEVe6w+FU1KvuNNcuHCJp576jejoBHx8vNmwYRxLlozQZKKUylOFLqHEJ6UQFplAlbJaXVhE+OabnTRqNIv//OcvVq06CkBAgK8+dFdK5blCV+S1MzSSxBQbIzvUcncobnXgQAQTJy7j99+P0L59NX755S5atqzs7rCUUoVYoUsofx62ani1qFG0i3MefXQFmzef4oMPBnDvvW20IUellMsVuoSy5ZjVQ2NRLPL67bfDNGoUSI0aZfnww5vw8fGmcmU/d4ellCoiXHrZaozpZ4zZb4w5ZIx5OpPxjxtj9hpjdhpjfjfGXHM5VeiFS5QtVbxIPZQ/cyaGO+9cRJ8+X/PGGxsAqFWrnCYTpVS+cllCMcZ4AbOA/kAT4A5jTJMMk20D2opIc2Ah8Oa1LvdoRCw1KpS61tl4BJtN+OijzTRqNJNFi/bx0kvdmTGjj7vDUkoVUa68Q2kPHBKREBFJBOYBgxwnEJFVIhJn/7gRqH6tCzVAg0pFo7+O6dPXcf/9y2jTpio7d05gypQelCxZ6EoxlVIewpVnn2rACYfPoUCHbKYfD/yc2QhjzL3AvQA1a9bMcgaXElOwCdQJLJ3rYD1FdHQC4eFx1K5dngkT2lK7dnnuuKOZVgNWSrmdK+9QMjvDSaYTGnMX0BZ4K7PxIvKJiLQVkbZBQVn3b/LbvjAAihfCGk0iwg8/7KNJkw8YPnwhIkJAgC933nmdJhOlVIHgyjNvKFDD4XN14FTGiYwxvYDngFtEJOFaFvjp2hAA+jUrXO9bHDt2kVtumcfgwd9RoUIp3n+/vyYRpVSB48oir3+A+saY2sBJYARwp+MExphWwMdAPxE5ey0LOx15iV0nIwn086FWQOEp8vrrrxP06jUHgBkzevPII9fj7V347sCUUp7PZQlFRJKNMQ8CvwBewBcisscYMxXYLCJLsIq4/IAF9ivu4yJyy9Us72CY1fz6E30a5EX4bhcVlUCZMj60bl2FceNaMnlyZ2rW1NaTlVIFl0urBInIcmB5hmEvOvzdK6+Wte34RQCaVvXsk25ERBxPP72SX38NYc+eifj5leC//x3g7rCUUipHhaaO6XebT+BbwoumVcu4O5SrIiLMmbOTSZN+5cKFSzz+eEf0MYlSypMUioRiswknL15iUMuqHtnjYGRkPLfeOp/Vq4/SsWN1PvroZpo3r+TusJRSKlcKRUI5eNZ6fhLsYQ/jRQRjDGXK+BAY6Msnn9zM+PGtPTIpKqVUoagudDQiFoAWNTzn+ckvvxyidetPCA2NwhjDggVD+b//a6PJRCnlsQpFQlm28zTgGU2unD4dzYgRC+nX7xvi4pI4ezbW3SEppVSeKBRFXsk2GwDVyhXsRiFnzdrEs8/+QUJCMi+/3IOnnuqMj0+h+AmUUqpwJJRdJyNpXr1sgX97fMuW03ToUI1ZswZQv36Au8NRSqk8VSgSyoXYJGpW8HV3GFeIikrgxRdXMWpUc9q0qcoHH9yEj49XgU98Sil1NTw+ocQnpRCTkEyrGuXdHUoaEWHRon088sgKTp+OpmbNsrRpU1WblldKFWoef4b7aYfV3qS3V8G46j9y5AIPPvgzy5cfpGXLynz//TA6dLjmbl6UUqrA8/iEcuic9Q7KwBZV3RyJ5ZtvdrF27THeeacvDz7YXhtyVEoVGR6fUELPXwKgbpD7+k9ft+4YCQkp9OpVh8mTO3H33S2pXt0zm4BRSqmr5dGXzyLC9hMX8XHTXUB4eBzjxi2mW7fZTJ26BgAfH29NJkqpIsmj71C2n7jIyYuX6N4g614cXUFEmD17O5Mn/0ZkZAJPPdWZF17olq8xKM+QlJREaGgo8fHx7g5FFRElS5akevXqFC9ePN+X7dEJ5csNRwGY3Ldhvi53+fKDjBu3hM6da/DRRzfTrFnFfF2+8hyhoaH4+/sTHBys1cWVy4kIERERhIaGUrt27XxfvkcXeW05dgEgX5qsj4tLYsOG4wAMGFCfxYtHsHbtWE0mKlvx8fEEBARoMlH5whhDQECA2+6IPTahJCSncPLiJepX9HP5wfrzzwdp1uwD+vf/hosX4zHGcMstDbUhR+UUTSYqP7lzf/PYhHLgjFVduF+zyi5bxsmTUQwduoABA+bi4+PNTz/dQblyJV22PKWU8mQem1AiYhMAaFWznEvmf/ZsLE2afMDSpQd45ZUb2LFjAt27B7tkWUq5kpeXFy1btqRZs2YMHDiQixcvpo3bs2cPPXv2pEGDBtSvX59p06YhImnjf/75Z9q2bUvjxo1p1KgRTzzxhDtWIVvbtm3jnnvuSTds0KBBdOzYMd2wu+++m4ULF6Yb5ud3+XWDAwcOMGDAAOrVq0fjxo0ZNmwYYWFh1xTb+fPn6d27N/Xr16d3795cuHDhimlWrVpFy5Yt0/6VLFmSH3/8EYCRI0fSsGFDmjVrxrhx40hKSgJg6dKlvPTSS9cUm0uIiEf9a9OmjYiIfLk+RGo9tVT2nY6UvBQaenl+7723UQ4disjT+auiZe/eve4OQUqXLp329+jRo+WVV14REZG4uDipU6eO/PLLLyIiEhsbK/369ZOZM2eKiMiuXbukTp06sm/fPhERSUpKklmzZuVpbElJSdc8jyFDhsj27dvTPl+4cEGqV68ujRo1kpCQkLThY8aMkQULFqT7buq2uXTpktSrV0+WLFmSNu6PP/6QXbt2XVNskydPlunTp4uIyPTp0+XJJ5/MdvqIiAgpX768xMbGiojIsmXLxGazic1mkxEjRsgHH3wgIiI2m01atmyZNl1Gme13wGZx8fnZY2t5xSdbTdYH+vnkyfwiI+N5/vk/+PjjLWzceA+tW1fh4Yc75Mm8lQJ4+ac97D0VlafzbFK1DC8NbOr09B07dmTnzp0AzJ07l86dO9OnTx8AfH19mTlzJj169OCBBx7gzTff5LnnnqNRo0YAeHt7M3HixCvmGRMTw0MPPcTmzZsxxvDSSy9x++234+fnR0yMVTS91gVJiQAAEDhJREFUcOFCli5dyuzZs7n77rupUKEC27Zto2XLlvzwww9s376dcuWs0oZ69eqxYcMGihUrxoQJEzh+3KoM8+6779K5c+d0y46Ojmbnzp20aNEibdiiRYsYOHAglSpVYt68eTzzzDM5bpe5c+fSsWNHBg4cmDbshhtucHq7ZmXx4sWsXr0agDFjxtCjRw/eeOONLKdfuHAh/fv3x9fXaux2wIABaePat29PaGgoYD0n6dGjB0uXLmXYsGHXHGde8diEsvbAOQDKlbq2utYiwoIFe3n00RWcORPDgw+2p27dgtPQpFJ5JSUlhd9//53x48cDVnFXmzZt0k1Tt25dYmJiiIqKYvfu3UyaNCnH+U6bNo2yZcuya9cugEyLdTI6cOAAK1euxMvLC5vNxg8//MDYsWP5+++/CQ4OplKlStx555089thjdOnShePHj9O3b1/27duXbj6bN2+mWbNm6YZ9++23vPTSS1SqVIkhQ4Y4lVB27959xbbITHR0NF27ds103Ny5c2nSpEm6YWFhYVSpUgWAKlWqcPbs2WznP2/ePB5//PErhiclJTFnzhzee++9tGFt27Zl3bp1mlCuVUJyCtuOX6RVzXJ4e139YyARYfDg7/jxx39p3boKS5bcQdu2BaNNMFX45OZOIi9dunSJli1bcvToUdq0aUPv3r0Ba//PqkZQbmoKrVy5knnz5qV9Ll8+5wuyoUOH4uXlBcDw4cOZOnUqY8eOZd68eQwfPjxtvnv37k37TlRUFNHR0fj7X+6Z9fTp0wQFXX6xOSwsjEOHDtGlSxeMMXh7e7N7926aNWuW6TrltkaUv78/27dvz9V3nHX69Gl27dpF3759rxg3ceJEunXrli6ZVaxYkVOnTrkklqvlkQ/lNxwK51JSCiM71Lqq7yclpQDWztSlSw3ef78fmzbdo8lEFUqlSpVi+/btHDt2jMTERGbNmgVA06ZN2bx5c7ppQ0JC8PPz+//27j+4qvrM4/j7IwUSMLDbsDC2VIgjYkIMEaENdhSRlrGKsDCOyK9K19YBRGpRHHZwZv1VS1tBiugiyzpJ1zZG3RYYsLDIxlIRUNhGQLBpSu8EHBazmGUp5TfP/nFOkpuQkBu4Nzc3eV4zd+bec84957nP3Jxvzvd7z/MlIyODQYMGsXPnzmb331TDFL2s4X0R3bt3r30+fPhwKioqqKqqYtWqVUyYMAGA8+fPs3XrVsrKyigrK+PTTz+t15jUfLbofZeUlFBdXU1WVhb9+/cnEonUNnaZmZn1rp4+//xzevXqVZuLWD7rsWPH6g2gRz+iG78affr04dChYIryQ4cO0bt30/etvfHGG4wfP/6CO9yfeuopqqqqWLx4cb3lJ0+eJD29bc1Sm5INyoY9wS8vLqXkyrvvRsjLW87q1Z8A8OijN/Pww1+j02Vc6TiXCnr27MnSpUt5/vnnOXPmDFOmTOG9997jnXfeAYIrmTlz5vD4448DMG/ePJ577jnKy8uB4ATf8KQGMHr0aJYtW1b7uuak3adPH/bt21fbpdUUSYwfP565c+eSnZ1NZmZmo/tt7MogOzubioqK2tfFxcWsX7+eSCRCJBJh586dtQ3KbbfdRklJCadPnwagsLCwdpxk8uTJvP/++6xbt652X+vXr6/txqtRc4XS2KNhdxfA2LFjKSoqAqCoqIhx48Y1mYfi4mImTZpUb9nKlSvZsGEDxcXFXHFF/XNUeXn5Bd19yZaSZ9GTZ4MrjF5Xdon5PVVVx7n//lWMHFnEqVNnyciIz2C+c6nkxhtvZPDgwbz++uukp6ezevVqnn32WQYOHMgNN9zAsGHDmD17NgB5eXksWbKESZMmkZ2dTW5ubu1/29GeeOIJqquryc3NZfDgwZSWlgKwcOFCxowZw+233147jtCUiRMn8tprr9V2dwEsXbqUHTt2kJeXR05ODsuXL7/gfddffz1Hjx7l2LFjRCIRKisrKSgoqF2flZVFjx492L59O2PGjOGWW27hpptuIj8/ny1bttQOkKenp7N27VpefPFFBgwYQE5ODoWFhRe9oojF/Pnz2bhxIwMGDGDjxo3Mnz8fCMZ+on/qHIlEOHDgACNGjKj3/hkzZnD48GGGDx9Ofn4+Tz/9dO260tJS7rrrrsuKL95kUb85TwVDhw61vt/5GZ2uEP8+8+aY3lNcvJuHHnqbv/zlNPPm3cyCBbfSrVvrF05zHc++ffvIzs5Odhjt2gsvvEBGRsYF96K0Z4cPH2by5Mls2rSp0fWNfe8k7TSzoYmMK+WuUIygynC/zNjnkD979jy5ub0pK5vBD384yhsT59qRmTNn0rVrx+pxqKysZNGiRckO4wIp9yuvU+GA+nV9Mprc5vjx0zzzzGauvrons2YNY+rUPKZOzfOaSs61Q2lpaUybNi3ZYbSqYcOGJTuERqXcFcqJsEEZfk1mo+vXri1n0KCX+fGPt1BefgQIBv28MXHJkmrdyi61JfP7lnINyrGTZwHI+rvu9ZYfPPh/TJhQwt13F9O9exc2b57OkiV3JCNE52qlpaVx5MgRb1Rcq7BwPpS0tOQUsU25Lq/jp86S1aMrPdLqj4Ps31/Nhg1/4kc/GsXcucPp0qVTkiJ0rk7fvn05ePAgVVVVyQ7FdRA1MzYmQ8o1KJK4qmdwM88HH3zK1q0H+P73C7j11n5UVj5CZgsG651LtM6dOydl5jznkiGhXV6S7pD0B0kVkuY3sr6rpJJw/XZJ/Zvb55lz58m7qgezZq2joGAlixdv4/jx4EYlb0yccy55EtagSOoEvAR8C8gBJklqeCvpA0C1mV0LvAA0XYYzysqFW3jllZ3MmfM1du+eSffusd/g6JxzLjES2eX1VaDCzPYDSHodGAdEF7wZBzwZPn8LWCZJ1swIZp/0rqz98HsMGXLxu2+dc861nkQ2KF8GDkS9Pgg0nGCkdhszOyvpKJAJ/E/0RpIeBB4MX54q++9/2BNDpemOoBcNctWBeS7qeC7qeC7qDEz0ARLZoDR240fDK49YtsHMVgArACTtSHT5gFThuajjuajjuajjuagjaUfzW12eRA7KHwS+EvW6L9CweH/tNpK+APQEPk9gTM455xIkkQ3Kh8AASVmSugD3AWsabLMGuD98fg/wn82NnzjnnGubEtblFY6JzAY2AJ2AV83sY0lPAzvMbA3wr8C/SaoguDK5L4Zdr0hUzCnIc1HHc1HHc1HHc1En4blIufL1zjnn2qaUq+XlnHOubfIGxTnnXFy02QYlEWVbUlUMuZgraa+kXZI2SeqXjDhbQ3O5iNruHkkmqd3+ZDSWXEi6N/xufCzpl60dY2uJ4W/kakmlkn4f/p3cmYw4E03Sq5I+k7SnifWStDTM0y5JQ+IagJm1uQfBIP6fgGuALsBHQE6DbWYBy8Pn9wElyY47ibkYCXQLn8/syLkIt8sANgPbgKHJjjuJ34sBwO+Bvw1f90523EnMxQpgZvg8B4gkO+4E5eJWYAiwp4n1dwK/IbgHsADYHs/jt9UrlNqyLWZ2Gqgp2xJtHFAUPn8LGKX2OYtWs7kws1Iz+2v4chvBPT/tUSzfC4BngJ8AJ1szuFYWSy6+B7xkZtUAZvZZK8fYWmLJhQE9wuc9ufCeuHbBzDZz8Xv5xgE/t8A24G8kxa2GVVttUBor2/LlprYxs7NATdmW9iaWXER7gOA/kPao2VxIuhH4ipmtbc3AkiCW78V1wHWStkjaJqm9zjgXSy6eBKZKOgi8DTzcOqG1OS09n7RIW50PJW5lW9qBmD+npKnAUGBEQiNKnovmQtIVBFWrp7dWQEkUy/fiCwTdXrcRXLX+TlKumf1vgmNrbbHkYhJQaGaLJA0nuP8t18zOJz68NiWh5822eoXiZVvqxJILJH0DWACMNbNTrRRba2suFxlALvCupAhBH/GadjowH+vfyGozO2Nmfwb+QNDAtDex5OIB4A0AM9sKpBEUjuxoYjqfXKq22qB42ZY6zeYi7OZ5haAxaa/95NBMLszsqJn1MrP+ZtafYDxprJklvCheEsTyN7KK4AcbSOpF0AW2v1WjbB2x5KISGAUgKZugQemI8zKvAb4d/tqrADhqZofitfM22eVliSvbknJizMVPgSuBN8PfJVSa2dikBZ0gMeaiQ4gxFxuA0ZL2AueAeWZ2JHlRJ0aMuXgU+BdJPyDo4pneHv8BlVRM0MXZKxwv+iegM4CZLScYP7oTqAD+Cnwnrsdvhzl1zjmXBG21y8s551yK8QbFOedcXHiD4pxzLi68QXHOORcX3qA455yLC29QXJsj6ZyksqhH/4ts27+pyqotPOa7YbXaj8JSJQMvYR8zJH07fD5d0pei1q2UlBPnOD+UlB/Dex6R1O1yj+1cc7xBcW3RCTPLj3pEWum4U8xsMEHR0Z+29M1mttzMfh6+nA58KWrdd81sb1yirIvzZWKL8xHAGxSXcN6guJQQXon8TtJ/hY+bG9lmkKQPwquaXZIGhMunRi1/RVKnZg63Gbg2fO+ocA6N3eFcE13D5QtVNwfN8+GyJyU9JukegppqvwiPmR5eWQyVNFPST6Jini7pxUuMcytRhf0k/bOkHQrmPnkqXDaHoGErlVQaLhstaWuYxzclXdnMcZyLiTcori1Kj+ru+nW47DPgm2Y2BJgILG3kfTOAn5lZPsEJ/WBYZmMi8PVw+TlgSjPHvxvYLSkNKAQmmtkNBJUlZkr6IjAeGGRmecCz0W82s7eAHQRXEvlmdiJq9VvAhKjXE4GSS4zzDoLyKjUWmNlQIA8YISnPzJYS1GoaaWYjwxIsTwDfCHO5A5jbzHGci0mbLL3iOrwT4Uk1WmdgWThmcI6gLlVDW4EFkvoCvzKzP0oaBdwEfBiWpUknaJwa8wtJJ4AIQXnzgcCfzaw8XF8EPAQsI5hrZaWkdUDMpfLNrErS/rCO0h/DY2wJ99uSOLsTlBmJnnHvXkkPEvxdX0UwkdSuBu8tCJdvCY/ThSBvzl02b1BcqvgBcBgYTHBlfcHkWWb2S0nbgbuADZK+S1Cuu8jM/jGGY0yJLiQpqdH5dcLaUV8lKDZ4HzAbuL0Fn6UEuBf4BPi1mZmCs3vMcRLMSrgQeAmYICkLeAwYZmbVkgoJCiA2JGCjmU1qQbzOxcS7vFyq6AkcCuevmEbw33k9kq4B9ofdPGsIun42AfdI6h1u80VJ/WI85idAf0nXhq+nAb8Nxxx6mtnbBAPejf3S6hhBOf3G/Ar4e4I5OkrCZS2K08zOEHRdFYTdZT2A48BRSX2AbzURyzbg6zWfSVI3SY1d7TnXYt6guFTxMnC/pG0E3V3HG9lmIrBHUhlwPcFUp3sJTrz/IWkXsJGgO6hZZnaSoBrrm5J2A+eB5QQn57Xh/n5LcPXUUCGwvGZQvsF+q4G9QD8z+yBc1uI4w7GZRcBjZvYRwfzxHwOvEnSj1VgB/EZSqZlVEfwCrTg8zjaCXDl32bzasHPOubjwKxTnnHNx4Q2Kc865uPAGxTnnXFx4g+Kccy4uvEFxzjkXF96gOOeciwtvUJxzzsXF/wOSZmB5vqrsHQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -4787,10 +4779,10 @@ { "data": { "text/plain": [ - "0.7301560712348498" + "0.7227905615337198" ] }, - "execution_count": 96, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -4802,14 +4794,14 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the SVM default parameters identified 761\n" + "Of 2008 Defaulters, the SVM default parameters identified 768\n" ] }, { @@ -4844,14 +4836,14 @@ " \n", " \n", " \n", - " 0\n", - " 6331\n", - " 396\n", + " 0\n", + " 6321\n", + " 420\n", " \n", " \n", - " 1\n", - " 1261\n", - " 761\n", + " 1\n", + " 1240\n", + " 768\n", " \n", " \n", "\n", @@ -4860,11 +4852,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6331 396\n", - "1 1261 761" + "0 6321 420\n", + "1 1240 768" ] }, - "execution_count": 97, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -4876,7 +4868,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -4885,11 +4877,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.66 0.38 0.48 2022\n", + " 0 0.84 0.94 0.88 6741\n", + " 1 0.65 0.38 0.48 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -4920,80 +4912,69 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 0.01, 'gamma': 0.001}" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001,0.01, 0.1, 1]\n", - " gammas = [0.001, 0.01, 0.1]\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train, y_train,3)\n" + "svc_param_selection(X_train, y_train,2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", " verbose=False)" ] }, - "execution_count": 100, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", "clf_reduced_tuned.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16199599731203465\n" + "Optimal Threshold: 0.15368680599405346\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf4AAAEWCAYAAACHePXKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gV1dbA4d8iCYSQToBACgm9STM0RSmiUlSwY++IlKti93567V1siFy9KhYUFBtNQVGkCwFEpHcSegIhhJC+vz9mgoeQCkkmOWe9z8NDpq+ZMzNr9p49M2KMQSmllFKeoYbTASillFKq8mjiV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jiV0oppTxIlUz8InKDiMxxOg6niUi0iKSJiFclLjNGRIyIeFfWMiuSiKwVkd6nMd1p7YMiUltEpovIERH5uqzTuxMRmScid1byMieKyHPFDH9ORJJEZF9lxlWdiMgOEelXScuaICJPVMayKpJ9zmxWBeIodv/PV2Lit3eC43YC2mfP2L98wiycMWaSMeaiilxGVVTwgDPG7DLG+Btjcp2MyynlcTAZY9oaY+aVsJxTLnbOYB+8CmgA1DXGXH0a0xcW3+Mist0+BhNFZIrd/78i8mkh47cXkUwRCRWRp+x1+1eBce6z+z9VHjFWByISBTwAtDHGhJ/hvHqLSGL5RFZ9lDaxlJYxZrgx5tnyml8+l2M6zf63Q0QeLTCOa247LCIz7X0kf/hEEclymUeaiFxb3rE6obQl/kuNMf5AR6AT8FjFhVRxnCzFuksJuiw8dHs3BjYZY3LKOmFhMYvILcBNQD/7GIwD5tqDJwJXiEidApPdDMwwxhyyuzcBtxQyzqYzia0aagwkG2MOOB2Im2zP6iDYPm6uAp4QkQsLDM/PbQ2B/cA7BYa/Yhe+8v9NqchgK6t2t0xV/caYfcBsrAsAAESkloi8JiK7RGS/XXVT22X4YBH5U0RSRWSriPS3+weJyIcisldEdttVcF72sFtFZKH99wQRec01DhH5QUTG2H83EpFvROSgXSr6l8t4T4nIVBH5XERSgVsLrpMdx6f29DtF5P9EpIZLHItE5B276naDiFxQYNri1mGRiLwhIoeAp0SkqYj8KiLJYlU3ThKRYHv8z4BoYLp9ZflwwZKoWFWnz9rzPSoic0QkzCWem+11SBaRJ6SYKjuxqqRft8c/IiILXX834Ab7N00SkX+7TNdVRJaISIq93uNEpKbLcCMiI0VkM7DZ7veWiCTY+8AKETnPZXwvsUq0W+11WiEiUSIy3x5lteuVtohcYu9PKSKyWETau8xrh4g8IiJ/AcdExNt1G9ixx9tx7BeRsfak+ctKsZfVw3UftKdtKyI/i8ghe9rHC9mmTwNPAtfa87lDRGrY+9ROETlg72tB9vj5v+8dIrIL+LWQn6oLMNsYsxWsY9AY87799xJgN3Cl6/YErgc+cZnHcsBPRNrmrwtQ2+5fqML2X7v/7SKyXqwS0mwRaewyzYX2MXJERMYB4jLsKRH53KW74L4dKiIfi8gee97fu4xb3G/eSURW2vvOFMC3iPXpB/wMNLJ/m4l2/+72PFNEZLW43BYSkdvsdT0qIttE5G67fx3gR5d5pYl1HjqpNCwFagWK2D+LO38Vtb8WXLcQEZlhz+Ow/Xeky/CSzhs3yT/njX8Xtgx7vGHADcDD9jpPt/ufVDPnuh3yt4GIPGDv/3tF5LbTHLeuWLfRUkVkuVjn2xPHaHGMMfHAWlxyV4HhGcBUoE1p5lccEekp1vmuj93dSv45d2wUkWtcxp0oIu+JyCwROQb0sfu9K1YNxFER+UNEmrpMU+T8Ss0YU+w/YAdWaQMgElgDvOUy/E1gGhAKBADTgRftYV2BI8CFWBcZEUAre9j3wH+BOkB9YBlwtz3sVmCh/ff5QAIgdncIcBxoZM9zBdbJtibQBNgGXGyP+xSQDQyxx61dyPp9Cvxgxx6DVQq6wyWOHOB+wAe41l6f0FKuQw4wGvDGOtE2s7dFLaAeVsJ5s7BtbXfHAAbwtrvnAVuBFvb85gEv2cPaAGlAT3tbvGave78iftd37ekjAC/gHDuu/GV+YC+jA5AJtLanOxvobq9TDLAeuM9lvgbrBBuav72BG4G69jQPAPsAX3vYQ1j7VEusRNEBq5o8f17NXObdGTgAdLNjvsXeZrVctt+fQJTLsk9sU2AJcJP9tz/QvbDtXMg+GADstWP3tbu7FbFdnwI+d+m+HdiCtW/6A98CnxVY7qdY+1Bh++eNwCF7O8UBXgWG/xv4xaX7YuAg4OMaD/A48LLd7xWsWrvPgaeKWI9bOXX/HWKvS2u73/8Bi+3xw4BUrJKVD9YxkwPcWcR2OWmbAzOBKVjHtw/Qq6TfHGs/38k/x+dVWPv8c0WsU28g0aU7AkgGBmKdHy60u+vZwwcBTbH2y15AOtC5sHnZ/Sa6LruQ5e3AZf+k5PNXoftrIetVF+vizw9r3/wa+N5l+DxKPm+cb2/TsfbvVtR546R1LOI4PTGOvQ1ygGfs32igvR1DTmPcyfY/PzvuBOxjtJA4Yzh5/+puz+vyInKbH9bF8qfFrWtR//K3AdbxlwB0tfvXsbtvwzpmOgNJQFuXZRwBzrX3B1+73yGs/OkNTAIml2F+JcZc2sSfBhy1V24uVvUJWAfEMaCpy/g9gO323/8F3ihkng2wkkltl37XAb8VctIVYBdwvt19F/Cr/Xc3YFeBeT8GfOxysplfzLp52XG0cel3NzDPJY492Bcddr9lWFWvpVmHXUUt2x5nCLCqsB2xiJ13HvB/LsNHAD/Zfz8JfOkyzA/IopAD2N7BjgMdijlgIgus89Ai1uE+4LsCB0DfEtb7cP6ygY3A4OIOJpfu94BnC4yzkX+SxA7g9kL23/yDez7wNBBWxDoXlfivc/2dSli3pzg5wc0FRrh0t8RKTt4uy21SwjxvAH7BOtaSgUddhkXb84u0uydx8oX5U1gJPhrrOPKx/4+i5MRf8Nj6Efui2GU/SseqQr8ZWOoyTIBESpH4sapZ87BP8AWWWeRvjpWsCh6fiyl94n8E+yLMpd9s4JYipv8euLewedn9JlJy4r/dpbuk81eh+2sp9sGOwGGX7nkUf96Y7DKsDkWcNwpbxyKO0xPj2NvgOCcfWwf456K7VONinauzgZYuw56j5MSfYs/TYBWGXPeVHVi5LQXrgmMPcFaB9ciwh6cAScVsc2P/djsLzONaYEGBcf8L/MdlGZ8WGD4R+J9L90BgQxnmV2LiL21V/xBjTID9w7TCuroHq9TqB6ywq8pSgJ/s/mCdXLYWMr/GWCegvS7T/Rer1HwSY63NZKyTL1jVmJNc5tMofx72fB7HSsr5EopZrzD+KTXk24lVEsi3247BdXijUq7DScsWkfoiMlms2wKpWCfeMMrGtTVyOlZJADumE8szxqRjJYnChGFdWRb22xS7HBFpYVcl7rPX4QVOXYeC6/2AXWV6xN5OQS7TFLWPFKYx8ECB3zsKa90LXXYBd2CVejbYVYWXlHK5ZYmxoEacun95U/p9FGM1NOwHBAPDgWdE5GJ72C6sBHGjWI1uh3ByNT8u423B+r02G2OKXWYRcTUG3nLZ9oewEnwEp+5/pqT1chEFHDLGHC5kWHG/eSMKPz5LqzFwdYF598S6EEFEBojIUrtKNQXrBFzW47Ug121S0vmrVPuriPiJ1dBzp31MzgeC5eT7xaU9b+RfXJanZHNymxfX5Zd23HpYx43r9ivN/hVmT/8gVv7yKTB8iDEmGKu2YxTwu4i4Nvx8zRgTbP8r6be/D/jKGLPGpV9joFuB3/gGwHUZha1HUb9XaeZXorLe4/8d64oi/557EtbVVFuXjRNkrMYSYK1Q01PnRAJWaTnMZbpAY0zbIhb9JXCVWPcTuwHfuMxnu8s8go0xAcaYga5hF7NKSVhXkY1d+kVj3TfNFyEiUmD4nlKuQ8Flv2j3a2+MCcSqxpVixi+LvVi3YgDrHj5WFWBhkrCuZAv7bUryHrABaG6vw+OcvA7gsh5i3c9/BLgGq0QXjFW1lT9NUftIYRKA5wv83n7GmC8LW3ZBxpjNxpjrsC7OXgaminW/tqTtXpYYC9rDqftXDlZDohOhlWZGxphsY8zXwF9AO5dBn2CVuK/EOh5WFjGLT7FuV5zyJEBRiyzQnYB1K8t1+9c2xizG2v9cW0SLazdWbYWfS3fBE1+o2O1dCllmUb/5Xgo/PksrAavE7zrvOsaYl0SkFtZ55jWggb3fzuKf/baw36y4dcznOl2x569i9teCHsCqSepmH5Pn2/0LHpeFKfi7+VH0eaNg/PnSKXm9z9RBrOMm0qVfVBHjnsQYk2uMeR3rnDeimHG+BXKxLv5Ox9XAEBG5z6VfAvB7gd/Y3xhzj+viy7CM0syvRKfzHP+bwIUi0tEYk4d1L/gNEakPICIR+aUR4EPgNhG5QKxGThEi0soYsxeYA7wuIoH2sKYi0quwBRpjVmH98P/DauiUYg9aBqSK1WCmtlgNxdqJSJfSrIixHpP7CnheRALsC4sxWCXxfPWBf4mIj4hcjXV/c1ZZ18EWgF21JCIRWPdtXe3Hus93OqYCl4rIOWI1tnuaIg58+3f7CBgrVuMiL7EatNUqxXICsO7lpolIK6CkHS4A64A9CHiLyJNAoMvw/wHPikhzsbQXkfwTT8Ht8QEwXES62ePWEZFBIhJQirgRkRtFpJ69/vn7UK4dWx5Fb/sZQLhYj8DVsveVbqVZJtZF6/0iEmuXyF8ApphStvoXq5HdIHuZNURkANAW+MNltG+wToJPU0hp38UU4CKsff50TAAek38aCQbZxwRY9+jbisgVYjXY+xcnJ4A/gfPFejdFEC5PBtnH0o/AeLEaqvmISH7yKu43X4K1b/1LrIZyV2DdFy2tz7GOmYvtY8BXrAZmkVg1gbWwE4693V0f79wP1LXXxXUdB4rVUDEcqwRYnGLPX8XsrwUFYBXAUkQkFPhPGbbBVOASsRqk1cS6v15cXijsHPUncL0df3+s2zDlyj5Xf4vVSNrPPvfcXMbZvITVMPGUBqD2vjUYq43J+tMMcw9wAdb+mH+BMQNoIVYDSh/7XxcRaX2ayyiX+ZU58RtjDmKVGPJfuvAIVhXiUrGqmX7BuvrEGLMMqxHCG1ilvN/5p/RzM9bBtQ7rnu9U7Cq2InwJ9AO+cIklF7gU657WdqyS7P+wqpJLazTWlfo2YKE9/49chv8BNLfn/TxwlTEmvyqsrOvwNFZjjCNYJ8pvCwx/Efg/sapwHizDOmCMWWuvy2Ssq/ijWPfHMouY5EGsRnXLsapsX6Z0+8ODWLdbjmKdlEt6vGU21kl9E1Y1bAYnV22NxUpEc7AuKD7EaoAE1n3hT+ztcY2xWubeBYzD2t5bKORJjWL0B9aKSBrwFla7hQz7tsjzwCJ7Wd1dJzLGHMVq+HUpVhXcZqBPKZf5EfAZVvXrdqz1H12GmFOxalV2YZ38XwHuMcacaM1sV8/mJ/9Jhc3EHu+4MeYXY8zxMizfdfrvsPaTyfax/jcwwB6WhFXieQmrqrg5sMhl2p+x9pW/sBq0zSgw+5uwat82YO2399nTFfmbG2OygCvs7sNY9z8LHlPFrU8CMBhr+x7E2i8fAmrYv/m/sPbNw1j7/DSXaTdgnZO22ftMI6zfeTXWveM5lHBslOL8Vej+Wsis3sQ6ZpKApVi3W0u7DdYCI7HOe3vtdS3u/QQfAm3sdc5/8uJeez3yq52/L2riMzQKa9vsw9rWX1L0+a0wM7HW7y6XftPt7ZuKdQ64xd4mp8W+pXYB8IiI3GnvRxcBQ7EuDPZhHUOlKWQVNv9ymV9+S3lVCBG5Fatx0ulW/TjGLl2mYFXJb3c6HqWUKk8i8jIQboy5xelYqpsq+cpedXpE5FK7GqwO1r3JNVilD6WUqtbEen69vV0t3xWr8eN3TsdVHWnidy+Dsap/9mBVtQ41WqWjlHIPAVi3co5h3YJ5HesdLKqMtKpfKaWU8iBa4ldKKaU8iH4oooKFhYWZmJgYp8NQSqlqY8WKFUnGmHolj6lOhyb+ChYTE0N8fLzTYSilVLUhImV5A6MqI63qV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jiV0oppTyIJn6llFLKg2jit4nIRyJyQET+LmK4iMjbIrJFRP4Skc6VHaNSSil1pjTx/2Mi1mcwizIA6/33zYFhwHuVEJNSSnmUjOxcp0Nwe5r4bcaY+VjfpS/KYOBTY1kKBItIw8qJTiml3N+e/Ue5bMwsp8Nwe/rmvtKLABJcuhPtfnsLjigiw7BqBYiOjq6U4JRSqjoyxrD1QBrXjFvEoexcqKPl0Yqmib/0pJB+hX7a0BjzPvA+QFxcnH7+UCmlXPy9+wh7Uo6zZFsyHy/acaK/5OQxrHsMjzsXmkfQxF96iUCUS3ck1nfvlVJKlWD93lQ+XrSdr1ckUvBr8D2bhdGhVk3uv64D3t41NPFXME38pTcNGCUik4FuwBFjzCnV/EoppSzfr9rN2J83kZSWSXrWP4322oT4seG7zRzclcqmVcNp2MDfwSg9jyZ+m4h8CfQGwkQkEfgP4ANgjJkAzAIGAluAdOA2ZyJVSqmq7eDRTB74ejXzNx0EoG6dmgw7vwltQuvw0StLmPJyPC1b1mXapCs16TtAE7/NGHNdCcMNMLKSwlFKqWpj476jfBWfwNfxCRgDRzNzADgrIogPb4mjfqAvhw4dp2nTtzl+PJtnnunNww+fS61amoKcoFtdKaVUmR08msn3q3bz/Kz1J/Xv3bIerRsG0rphIJe2b8ju3Uch0JfQ0Nq8+OIF9OvXhGbNQh2KWoEmfqWUUmWQm2cY9cVKfvx734l+XWJCGN23OXExIfjVtNJKamom9933E+PHx7NgwW107x7J8OFxToWtXGjiV0opVaLcPMP8TQe5beLyE/1euao9l3VohK+P14l+xhimTl3Hvff+xL59adxzTxytWoU5EbIqgiZ+pZRSRcrIzuWTxTt48ccNJ/p1iQnho1u7EODrc9K4xhiuuWYqU6euo2PHcL7/fihdu0ZUdsiqBJr4lVJKneTd37awdFsyCzYnndR/eK+m3HleLGH+tU7qn52di7d3DUSEXr0ac+65UYwa1RVvb30LX1WkiV8ppRQZ2bn0fPk3UtKzyMmz3rAT1ziE5g38aRLmT/924USF+p0y3YIFOxk+fCZPPHE+Q4e2Y9SorpUduiojTfxKKeXBMrJzeXX2Rj5cuP1Ev2HnN+GqsyNp0SCgyOmSktJ5+OGf+fjjP2ncOIjQ0NqVEa4qB5r4lVLKAw14awH7jhzncHr2iX7924Yz/obO1KhR2KdJ/vHVV2u5556ZpKZm8sgj5/LEE+dTp07Nig5ZlRNN/Eop5eby8gxJxzL5df0BPv9jJ4ePZbM75Thgle4bBPpyXdeoE4/ilUQEWrcOY8KES2jXrn5Fhq4qgJiCX0tQ5SouLs7Ex8c7HYZSysMs2ZrMrxv2k3o8hynxCScNqxdQi3Oa1uWxAa0JD/ItcV7p6dk899x86tatzQMPnIMxBmMosWbgdInICmOMPvRfQbTEr5RSbmTmX3t5aOrqkz6K0y4ikPaRwXSLDaV3i/oE+fkUM4eTzZq1mZEjZ7FjRwr33GPlYhFBKibnq0qgiV8ppaq5oxnZrNuTyquzNxK/8zAAzev7896NnYkN88frNErmu3enct99s5k6dR2tW4cxb94t9OoVU86RKydo4ldKqWooOzeP+ZsOMn7eVlbYyR6gfkAt5tx/PsF+Z9bYLjExlVmzNvP883158MFzqFnTq+SJVLWgiV8ppaqZ9+ZtZdyvmzlmV+c3r+/P9d2iaR8ZRPvIYHy8Tu/FOcuX72bevB089NC5dOsWSULC/fqYnhvSxK+UUtVE4uF0er7824nuBy9qwVVnR5WqgV5xjhzJ4N///pXx45fTqFEAd98dR2BgLU36bkoTv1JKVXE7ko7xxA9/n3iFbnSoHzP+1ZNA39I30iuMMYavvlrLfffN5sCBY4wa1ZXnnutLYGCtkidW1ZYmfqWUqoKMMWw5kMaDU/9idUIKAHVqevHq1R0YeFbDclnGvn1p3H77NFq3DmP69OuIi2tULvNVVZsmfqWUqiKMMWzYd5R7J69i0/60E/0jQ2rzznWd6BQdcsbLyMzM4auv1nLjje1p2DCABQtuo0OHBnidZrsAVf1o4ldKKYcdz8rlrk/jWbjln6/hicC1cVEM6RRBl5jQ03okr6B583YwfPgMNm5MJjY2hJ49o+ncuXxqD1T1oYlfKaUcsiflOB8s2MbHi3ac6Hd3ryb0alGPc5qGldtyDh48xoMP/synn64mNjaYWbOup2fP6HKbv6peNPErpVQl2pF0jEVbk3h9ziYOHcs60f/mHo156tK25f4aXGMMfft+ysaNSTz+eE/+/e/z8SvDm/uU+9HEr5RSFcQYw7xNB1m58zBJaZl8uSzhlHE+vCWOC1o3KPdlr1t3kObNQ/Hx8eLtt/vToIE/bdrUK/flqOpHE79SSpWT9KwcVu5M4c+Ew6zalcLcDQdOGh4RXJtjWTm8PbQTHaKCCfT1Rsr5pffHjmXxzDO/M3bsUl5+uR9jxvSgT5/Ycl2Gqt408SulVBnl5RlW7jrM9NV72HIwjZ3J6SQePn7KeLW8a3BHz1iu6xpNeJDvab9Rr7SmT9/IqFE/smvXEW6/vSO33NKhQpenqidN/EopVQJjDMezc5myPIFJf+xiy4G0k4Y3ruvHwLPCqeXtRefGITQJq0OPJnUr7LO1hXnssV946aVFtGlTj/nzb+W88xpX2rJV9aKJXymlCkjPyuH/vvub1IxsUo/n8PeeIyd95jYqtDYXtg7nso6N6BAZVO7V9aWVk5NHZmYOderUZMiQVgQF+TJmTA/9oI4qliZ+pZTHOnwsi/X7UknPzOWv3UdIPZ7N9NV7SHZpbR/XOIQeTerSLiKI0Do1GXBWOPUDzuzd+OXhjz8SufvuGfToEcl7711Ct26RdOsW6XRYqhrQxK+U8gjGGPYcyWDrgTS+WZnIoi3JJKVlFjpuh8ggLmzTgOG9muJdxd5ol5KSweOPz2XChHgaNQrgwgubOh2SqmY08Sul3IoxhiVbk5m0bBcrdx6mTi1vsnLy2HUo/ZRxO0QFc8lZDekaG0qdWt7E1PWrcone1bx5Oxg6dCoHD6Zz773deOaZPgQE6Ad1VNlo4ldKVWtHjmfz9+4j5BnDtyt3892q3SeGhQf6UtvHi3aNAunRpC41vWtwduMQ2jYKpFl9f8fuzZeVMQYRISYmmFatwpg162J91a46bZr4lVJV3oGjGWzal0ZGdi7zNx9k5a7D1BBhT8pxktKyThn/lh6NGdS+EV1jQx2ItvxkZOTw8ssLWbVqH999dy0xMcHMm3er02Gpak4Tv1Kqypm/6SCLtyaTnpXD8h2HWb839ZRx2jQMpEtMKDVE6N4klOYNAvCuIbRuGEidWtX/1DZ37jZGjJjFpk3JDB3ajoyMHGrX1lftqjNX/Y+OciIi/YG3AC/gf8aYlwoMjwY+AYLtcR41xsyq9ECVckOJh9P57+/biN95cpIP9vOhplcNWjcM5LZzY2gdHkjtmjWIDfMvl6/VVUXJyence+9PTJq0hqZNQ5g9+0Yuukgb8Knyo4kfEBEv4F3gQiARWC4i04wx61xG+z/gK2PMeyLSBpgFxFR6sEq5gYzsXGat2cvyHYeZtWYvR45nnxjWJKwOPZrWZXivpkSF+jkYpTO8vWuwaFECTzxxPo891lNL+arcaeK3dAW2GGO2AYjIZGAw4Jr4DRBo/x0E7KnUCJWqxjbuO8qsNXv5ed1+RGDtnn9K9SF+PgztEsVFbRvQt1X5f6ymOli9eh9vvLGUDz64lKAgXzZsGEktN7hdoaom3bMsEYDrZ7MSgW4FxnkKmCMio4E6QL+iZiYiw4BhANHR+s1r5TmMMexITmfS0p1k5+Yx/a+9J316FqwS/eCOjYipW4c7zovFv6Z3pb7atipJS8viqafm8eabSwkNrc3Gjcm0a1dfk76qULp3WQo765gC3dcBE40xr4tID+AzEWlnjMk7ZUJj3gfeB4iLiys4H6XcxpH0bDbsS2Xx1mQ27T/Kj3/vO2l4eKAvLRr4c07TMC5q24BOUSHU1tfJAvDDDxsYPfpHEhJSueuuzrz0Uj9CQ2s7HZbyAJr4LYlAlEt3JKdW5d8B9AcwxiwREV8gDDiAUm5uf2oGv204wPHsXNYkHuGv3UcKfSlOsJ8PvVvU44rOkZzXPKzaPCdf2XJz8/jPf+YRHOzL5MlXcc45USVPpFQ50cRvWQ40F5FYYDcwFLi+wDi7gAuAiSLSGvAFDlZqlEpVEmMMc9bt56vlCew9ksG6Qh6na9kggJt7NKZjVDCN6/rRKSrEY6vsSyM7O5fx45dz880dCAmpzfTp1xEe7o+Pj9aAqMqliR8wxuSIyChgNtajeh8ZY9aKyDNAvDFmGvAA8IGI3I91G+BWY4xW4yu3kt/a/qGpf5GbZ+3eNb1r0Co8gKvjoriiUwS1a3pRy7uGlubLYPHiBIYPn8GaNQfw8fFixIguREUFOR2W8lCa+G32M/mzCvR70uXvdcC5lR2XUhUpLTOHGav38P78bexOOU5mzj9NVlqFBzDpzm7U9dd3wZ+uQ4eO89hjv/D++yuJigrk+++vZfDgVk6HpTycJn6lPMyKnYd4e+4WFm1JIifvn0qrMP9a9G9Wl3ObhdG/XTiBvvr8+Jm6996f+PLLNTzwQA+eeqo3/v41nQ5JKURrqytWXFyciY+PdzoM5eFycvO4d8qfLNycdOJlORHBtWnTKJALWtWnf7twgv00KZWHjRuT8PX1pnHjYHbuTCElJYMOHcKdDqtaEZEVxpg4p+NwV1riV8qNJaVlMvqLVSzZlnyiX68W9bj/whZ0jAp2MDL3k5GRw4svLuCllxYxeHBLvvrqaho3DqZxY6cjU+pkmviVchNZOXk8P3Md6/cdJTfPsCPpGMkuL8956OKWDDu/CT5V+Hvz1dWcOVsZMWImWyBRaBQAACAASURBVLce5oYbzuL11y9yOiSliqSJX6lqLCM7l3d+3Uz8jsP8sf3Qif4do4LpFB1MmH8tujepy2UdGumjdhXk449Xcfvt02jePJRffrmJCy5o4nRIShVLE79S1cSR49n8vukgO5KOMWvNXjbsO3rS8DYNA+ndsh4P99dW4xUtNzeP/fuP0ahRAFdc0Zr9+49x333d8fXVU6qq+nQvVaoKS83I5v3ftzHuty2nDIsIrk2HqCCa1w/gjvNitRV+JVm1ai/Dh88kPT2blSuHERTky6OP9nQ6LKVKzS0Tv4jUBKKNMaeeLZWqovLyDMt2HGLz/qNs2p/Gz+v2sy8148TwC9s0oG+r+pzfoh71/GtR01vv1Vemo0cz+c9/5vHWW38QFubHG29cjLf+BqoacrvELyKDgLFATSBWRDoC/zHGXO5sZEqdyhjDR4t2MP63LSc1xAOo5V2DpvXqcPf5TRncqRG1vPXVrk7ZtCmZvn0/Yc+eo9x999m88MIFhIToB3VU9eR2iR94BuuTur8BGGP+FJFmzoak1Kl2pxznmglL2J1yHIDGdf24uG04fVrW56zIIPz106yOy87OxcfHi9jYYHr1imH06K507x7pdFhKnRF3PLNkG2NSCrxHXN9SpKqE5LRMnpu5noVbkjh4NBOAHk3q8vmd3fDSVvdVRnZ2LmPHLuH991eyYsUwgoN9mTTpCqfDUqpcuGPiXy8i1wA17K/t3QssdTgm5WGMMazfe5RPl+zgz4QUjmXlkHDo+InhYf41GXRWQ66Ki6RPy/rOBapOsXDhLoYPn8HatQcZMqQVmZk5ToekVLlyx8Q/CngSyAO+xfri3mOORqQ8hjGG1+Zs5N3ftp7UP6auH9d1jSY3L4+L2oTTr00DhyJURcnKymXEiJl8+OEqoqOD+OGHoVx2WUunw1Kq3Llj4r/YGPMI8Eh+DxG5AusiQKlyl5aZw9fxCczfdJBFW5PJysmjto8XF7Suzy3nxNAlJtTpEFUp+PjUICkpnYceOof//KcXderotwuUe3K7j/SIyEpjTOcC/VYYY852Ih79SI97yszJ5ftVu3nrl83sOfLPI3ftIgIZ2iWaoV2i8NZX41Z569cfZMyYObz77kCaNAkhL8/oGw6rAP1IT8VymxK/iFwM9AciRGSsy6BArGp/pU7btoNpTF2RSHpWLhv3HT3pozeNgny5p3dTrjo7ito19ZG76uD48Wyef34Br7yyCH//mmzenEyTJiGa9JVHcJvEDxwA/gYygLUu/Y8CjzoSkaq2jDGkZeaweGsyT01by16XUn2DwFo0r+/P9d2iGdwxglCtEq5WfvppCyNHzmLbtsPcfHMHXn31QurXr+N0WEpVGrdJ/MaYVcAqEZlkjMkocQKlXOw7ksGE37ey9WAa2bl5LN126KThzer7c1+/5lzQqoGW6qu5GTM24eNTg19/vZk+fWKdDkepSuc2id9FhIg8D7QBfPN7GmNaOBeSqoo27T/Km79s4tcNB8jI/uduUKvwAM5rHkZ4oC/N6vvTu2V9WoYHOBipOhO5uXmMH7+cuLhG9OgRxUsv9cPHpwa19AVJykO5454/EXgOeA0YANyG3uNXLhZsPsjzM9ef+LpdRHBtWoUHcEXnSAaeFU6Blz+pamzFij3cffcMVqzYy+jRXenRIwp/f701ozybOyZ+P2PMbBF5zRizFfg/EVngdFDKWcYYPv9jF89OX0dWrnUdGNc4hOcub0er8ECHo1PlLTU1kyee+JVx45ZTv34dJk++kmuuaet0WEpVCe6Y+DPFKrJtFZHhwG5AX43mwRZvTeLxb9ewIzkdgGviIhndtzlRoX4OR6Yqyief/Mk77yxj5MguPPdcX4KCfEueSCkP4Y6J/37AH/gX8DwQBNzuaESq0mXm5PLqTxv538LtJ/p1iQlh/A1nUy+gloORqYqybdthdu5MoU+fWIYPj6Nnz2g6dWrodFhKVTlul/iNMX/Yfx4FbgIQEf2clgfIzMnlk8U7+GXdAZbt+KdVfv+24Tx4cUua1fd3MDpVUbKycnnttcU8++x8IiMD2bBhJD4+Xpr0lSqCWyV+EekCRAALjTFJItIW69W9fQFN/m7oaEY2y3cc4rMlO/lt48ET/ZvUq8PVZ0dx13mx+gY9NzZ//k6GD5/B+vVJXHlla958sz9e+nsrVSy3Sfwi8iJwJbAaq0Hfd1hf5nsZGO5kbKr8Hc3I5vU5m5i4eMeJfj5ewsg+zbi9ZyyBvj7OBacqRXz8Hnr1mkhMTDAzZlzHoEH6xK5SpeE2iR8YDHQwxhwXkVBgj9290eG4VDn6bMkOpsQn8PfuVAACfb0Z0acZF7cNJzZM377m7vLyDGvXHuCssxpw9tkN+fDDyxg6tB1+fnqhp1RpuVPizzDGHAcwxhwSkQ2a9N3H4WNZvL9gG+/Nsz53Gx3qR99W9fn3oNb4aNWuR1i79gD33DOT5cv3sGnTKKKigrj99k5Oh6VUteNOib+JiOR/eleAGJdujDFXOBOWOlO/bTzAbR8vP9G9+NG+NAqu7WBEqjKlp2fz7LO/89prSwgMrMW77w4kIkLfvaDU6XKnxH9lge5xjkShyo0xhqsmLGHFzsMA3H1+E0b0aUZQba3W9RTHjmXRvv0Etm07zK23duTVVy8kLEzfv6DUmXCbxG+Mmet0DKr8HM3Ipt/Y39mfmgnAhBs707+dPp7lKVJTMwkMrEWdOjW5667O9OgRSa9eMU6HpZRb0JujqsrJyzOc9dQc9qdm0i02lM3PD9Ck7yFycvJ4662lREW9weLFCQA8+mhPTfpKlSNN/DYR6S8iG0Vki4g8WsQ414jIOhFZKyJfVHaM7m5PynFu/XgZTR6fdaLf5GHdtfGeh1i2bDddu37AfffN5pxzomjYUF+4pFRFcJuq/oJEpJYxJrOU43oB7wIXAonAchGZZoxZ5zJOc+Ax4FxjzGER0ff/l5MN+1L56e99fDB/G8eycqkhcFGbcN4c2lG/lOchHnnkZ159dTENGwbw9ddXc+WVrfW3V6qCuF3iF5GuwIdY7+iPFpEOwJ3GmNHFTNYV2GKM2WbPYzLWewHWuYxzF/CuMeYwgDHmQEXE70ny8gz9xv7OtqRjAITWqcnr13Skf7twhyNTlcEYA4CI0KCBP6NHd+XZZ/sSGKjfUlCqIrld4gfeBi4BvgcwxqwWkT4lTBMBJLh0JwLdCozTAkBEFgFewFPGmJ/KJWIPdeen8WxLOkYt7xq8fGV7BndspKU8D7FlyyFGjpzFbbd1ZOjQdowZ08PpkJTyGO6Y+GsYY3YWSCC5JUxTWLYxBbq9geZAb6z3/i8QkXbGmJRTZiYyDBgGEB0dXcqwPcOxzBw+Wridaav3sPlAGgAbnu2vCd9DZGbm8Oqri3nuufnUrOnF9de3czokpTyOOyb+BLu639j37kcDm0qYJhGIcumOxHrlb8FxlhpjsoHtIrIR60JgeYHxMMa8D7wPEBcXV/ACwmMdOpZFjxfnkpmTB0Cn6GA+u6ObJn0PsWDBTu66azobNyZzzTVteeONi2nUKMDpsJTyOO6Y+O/Bqu6PBvYDv9j9irMcaC4iscBuYChwfYFxvgeuAyaKSBhW1f+2cozbbe09cpyPFm7ngwXbARjcsRGvXd1BW+t7mP37j5GVlcuPP95A//7NnA5HKY/ljok/xxgztCwTGGNyRGQUMBvr/v1Hxpi1IvIMEG+MmWYPu0hE1mHdOnjIGJNc3sG7k4zsXNbvTeXy8YtP9HvpirMY2lVvf3iCvDzDhx+uJDMzl1GjunLlla255JIW+Pq642lHqepD8lvWugsR2QpsBKYA3xpjjjoZT1xcnImPj3cyhEqXkZ3Ll8t28fT0fx6KuKFbNM8ObkeNGlqt7wnWrNnP8OEzWbw4gf79mzFr1vV6S0eVmoisMMbEOR2Hu3K7S29jTFMROQeruv5pEfkTmGyMmexwaB5hTeIRbvl4GYeOZQEwoF04d/SMpVN0iCZ9D3DsWBZPP/07Y8cuISSkNhMnDubmmzto0leqCnG7xA9gjFkMLBaRp4A3gUmAJv4KNnVFIg9+vRqAMRe24IrOEUSG6AdVPMm6dQd5/fUl3H57R156qR916+rvr1RV43aJX0T8sV6+MxRoDfwAnONoUB7ANel/dXcPusaGOhyRqiwJCUf46act3HXX2XTpEsGWLaOJjQ1xOiylVBHcLvEDfwPTgVeMMQucDsbdHT6WxcgvVrJ4q9XO8ZnBbTXpe4icnDzefvsPnnzyNwAGD25F/fp1NOkrVcW5Y+JvYozJczoITzB52S4e/XYNAD5ewo/3nkez+vpctidYujSR4cNnsHr1fgYNas64cQOpX7+O02EppUrBbRK/iLxujHkA+EZETnlUwRhzhQNhuaU1iUe489Pl7E+1voF09dmRvHp1B4ejUpUlJSWDCy/8jKCgWnzzzTVcfnkrbbynVDXiNokf6/E9gHGORuHGUtKzuH3iclbu+uctxUsfu4DwIF8Ho1KVwRjDL79so1+/JgQH+/LDD0Pp0qURAQH6QR2lqhu3SfzGmGX2n62NMSclf/vlPHMrPyr38c2KRB6wG+8BTLqzG+c2C3MwIlVZNm1KZsSImcydu52ZM69n4MDm9O0b63RYSqnT5I7vTL29kH53VHoUbuSHP3efSPpjLmzBjpcGadL3ABkZOTz99DzOOus94uP3MH78QC6+uKnTYSmlzpDblPhF5FqsR/hiReRbl0EBwClf0FOlsyflOPdO/hOAX8b0oll9f4cjUpVlwIBJzJu3g6FD2/HGGxcTHq6/vVLuwG0SP7AMSMb6st67Lv2PAqsciaiaS0nP4pyXfgVgeK+mmvQ9wP79aYSE1KZmTS8eeugcHnusJxddpKV8pdyJ2yR+Y8x2YDvW1/jUGdqedIx+Y38H4LIOjXh0QCuHI1IVKS/P8MEHK3j00bk88si5PPpoTwYObO50WEqpCuA2iV9EfjfG9BKRw4Dr43wCGGOMvlWmlH76ey/DP18JwMCzwnn7uk4OR6Qq0urV+xg+fCZLlybSp08MQ4boRZ5S7sxtEj/Qx/5fW52dhuzcPEZOWsmcdftP9Hvo4paM7KPfTXdn77zzB/ffP5vQ0Np89tnl3HDDWfpMvlJuzm0Sv8vb+qKAPcaYLBHpCbQHPgdSHQuuGhj41gI2H0gjwNebS9o35LZzY2nRQN/C566ys3Px8fGia9cI7rijEy++2I/Q0NpOh6WUqgRuk/hdfA90EZGmwKfATOAL4BJHo6qiVuw8zJXvLQagto8Xq5+8SD+f68Z27kzhX//6iYYN/Zkw4RK6dYukW7dIp8NSSlUid3yOP88Ykw1cAbxpjBkNRDgcU5U0ZsqfJ5J+bFgdVjzRT5O+m8rOzuXVVxfRps14fvllG82ba5MXpTyVO5b4c0TkauAmYIjdz8fBeKqcPxNSGPPVn2w7eAyAGaN70i4iyOGoVEX566/93Hjjt6xZc4DLLmvJO+8MIDpaf2+lPJU7Jv7bgRFYn+XdJiKxwJcOx1RlTPh9Ky/9uAGA81vU4/2bzsbXx8vhqFRFCgysRVZWLt9/fy2DB2uLfaU8nRhzyofsqj0R8Qbym6NvMcbkOBVLXFyciY+Pd2rxJ3lh1nren78N0HftuzNjDJ9//hdz5mzj00+HICLk5Rm9jaOqDRFZYYyJczoOd+V2JX4ROQ/4DNiN9Qx/uIjcZIxZ5Gxkzvp53f4TSX/Z4xdQP1C/qOeONm5M4p57ZvLbbzvo1i2CI0cyCQ721aSvlDrB7RI/8AYw0BizDkBEWmNdCHjs1eOu5HTu+tSqdVj0aF9N+m4oIyOHF15YwMsvL8LPz4cJEwZx111na8JXSp3CHRN/zfykD2CMWS8iNZ0MyEmHjmVxw4dLAbilR2MigvVZbXeUmZnD//63kmuuactrr11Igwb6XQWlVOHcMfGvFJH/YpXyAW7AQz/Sk5WTR/cX5pKVm8cj/VtxT2/92Io72bv3KG+8sZTnn+9LUJAvf/89Ql/Co5QqkTs+xz8c2Ao8DDwCbAPudjQiB6Rn5XDWU7PJys3j8k4RmvTdSG5uHuPHL6dVq3d5660/WLZsN4AmfaVUqbhViV9EzgKaAt8ZY15xOh4nDXl3EZk5edT28eLlK9s7HY4qJytX7mX48BksX76HCy6IZfz4QbRoUdfpsJRS1YjbJH4ReRy4A1iJ9creZ4wxHzkcliOWbktm0/40usSEMHlYD7y0gZdbMMYwbNh0EhJSmTTpCq67rp1+UEcpVWZuk/ix7uW3N8YcE5F6wCzA4xL/B/O38fys9QA8eFFLTfrVnDGGH37YSK9ejQkJqc0XX1xJvXp+hIRotb5S6vS40z3+TGPMMQBjzEHca91KZej7S04k/X8PbE23JloFXJ3t2JHCpZd+yeWXT2HcuGUAtGhRV5O+UuqMuFOJv4mIfGv/LUBTl26MMVc4E1bFm7/pICMmrSQt03pB4bJ/X0D9AH1Wv7rKzs5l7NglPP3079SoIYwdexGjR3dzOiyllJtwp8R/ZYHucY5EUckysnO5+SOrNHhj92geH9gav5ru9LN6ngcemMM77yxjyJBWvP12f6Ki9IM6Sqny4zYZwhgz1+kYKltenqHVEz8BcHOPxjwzuJ3DEanTlZycTkZGDhERgYwZ04N+/Zpw2WUtnQ5LKeWGPO4+uDuZs24fAJ2ig3nq0rYOR6NOhzGGiRP/pGXLcQwfPhOAmJhgTfpKqQqjid8mIv1FZKOIbBGRR4sZ7yoRMSLi+Lv/X7Q/rzvhRn0ne3W0fv1Bevf+hNtu+4GWLcN44YW+ToeklPIAblPVX5CI1DLGZJZyXC/gXeBCIBFYLiLTXN/5b48XAPwL+KO84y2rkZNWsjM5nQHtwmmgH92pdmbM2MQVV0zB378mH3xwKbff3kkv3pRSlcLtSvwi0lVE1gCb7e4OIvJOCZN1BbYYY7YZY7KAycDgQsZ7FngFyCjPmMvqkal/MXPNXgCeuKSNk6GoMkpNta5FzzsvmuHD49i4cRR33tlZk75SqtK4XeIH3gYuAZIBjDGrgT4lTBMBJLh0J9r9ThCRTkCUMWZGSQGIyDARiReR+IMHD5Yl9hIZY/hprXVvf90zF9NIv7ZXLezZc5Rrr53Kued+RHZ2LkFBvrz99gDq1avjdGhKKQ/jjom/hjFmZ4F+uSVMU1hxy5wYKFIDeAN4oDQBGGPeN8bEGWPi6tWrV5pJSm3B5iSOHM9mVJ9m+theNZCbm8c77/xBq1bjmDZtI0OHtsWYkqdTSqmK4o6ZI0FEugLGvnc/GthUwjSJQJRLdySwx6U7AGgHzLPfjR4OTBORy4wx8eUWeSncP+VPAAZ3bFSZi1WnYc+eo1x22ZesWLGXiy5qyvjxA2naNNTpsJRSHs4dE/89WNX90cB+4Be7X3GWA81FJBbYDQwFrs8faIw5AoTld4vIPODBykz6KelZDPtsBcnHsujeJJTmDQIqa9GqjIwxiAj16vlRr14dJk++kmuuaasf1FFKVQlul/iNMQewEndZpskRkVHAbMAL+MgYs1ZEngHijTHTKiDUUvtuVSL3T1kNWM/sf3FndyfDUUUwxjB16jpefHEhc+feTEhIbX788Qanw1JKqZO4XeIXkQ9wuT+fzxgzrLjpjDGzsL7o59rvySLG7X0GIZZJbp45kfRfvao9V8dFlTCFcsK2bYcZOXIWP/20hU6dwklKSteP6SilqiS3S/xYVfv5fIHLObnFfrXyVbwV+qMDWmnSr4Ly8gwvvbSQZ5+dj49PDd5882JGjuyKt7c7tptVSrkDt0v8xpgprt0i8hnws0PhnJHs3Dwe+3YNAHf2jHU4GlUYEVi0KIFLLmnBm29eTEREoNMhKaVUsTyhWBILNHY6iNOxYe9RAPq1ro+3lyf8VNVDUlI6d901jW3bDiMifPPNNXz99dWa9JVS1YLblfhF5DD/3OOvARwCinz3flWVnZvHpeMWAlY1v3JeXp7h449X8fDDv5Camsl55zWmSZMQfH3d7jBSSrkxtzpjifW8VAesR/IA8oypnq9Lufa/SwBoHxlEs/r66J7T/v77APfcM5OFC3fRs2c0EyYMom3b+k6HpZRSZeZWid8YY0TkO2PM2U7HciY27z/Kyl0pBPv58MPIc50ORwH//W8869cf5KOPLuOWWzrqu/WVUtWWO944XiYinZ0O4kx8vtR64/CUYT30pS8OmjVrM3/8kQjAc8/1ZcOGUdx2m35FTylVvblN4heR/NqLnljJf6OIrBSRVSKy0snYyuqTJVbib17f3+FIPFNiYipXXfUVgwZ9wWuvWbdcgoJ8CQvzczgypZQ6c+5U1b8M6AwMcTqQM5FwKB2AS9o31JJlJcvJyWPcuGU88cRv5OTk8cILfXnggXOcDksppcqVOyV+ATDGbHU6kDOxZvcRAK7rGu1wJJ7n009Xc//9sxkwoBnjxg2kSZMQp0NSSqly506Jv56IjClqoDFmbGUGc7o27rOe3W8Y5OtwJJ7hyJEMNm5MpmvXCG66qT0NGtRh4MDm2rZCKeW23CnxewH+2CX/6uqvxBQAokP1fnJFMsYwZcpa7r9/NjVqCNu330vNml4MGtTC6dCUUqpCuVPi32uMecbpIM6EMYbfNh7k7MYh+qa+CrRlyyFGjpzFnDlbiYtrxIQJg6hZ08vpsJRSqlK4U+Kv1iV9gM0H0gBo3VBf2FNRNm5MokOHCdSs6cU77wzgnnvi8NKLLKWUB3GnxH+B0wGcqRGTrKcOb+xeLT8tUKXt3p1KREQgLVrU5Zln+nDjje1p1EgvsJRSnsdtijrGmENOx3AmcnLz2GKX+FuF68deysuBA8e4+ebvaN78nRMf1Xn44XM16SulPJY7lfirtXkbDwJwd68mDkfiHvLyDB9+uJJHHvmFtLQsHnnkXBo21BciKaWUJv4q4s8EqzX/LT1inA3EDWRn59K376csXLiLXr0a8957g2jdup7TYSmlVJWgib+KWLvHenFPo+DaDkdSfWVn5+Lj44WPjxe9ezfmzjs7cfPNHfSZfKWUcuE29/irs/zH+CJDNOmfrmnTNtK8+TssXpwAwLPP9uWWWzpq0ldKqQI08VcBKenZAPRpqd93L6uEhCMMGTKZwYMn4+9fEx8f3aWVUqo4WtVfBRxMywQgNqyOw5FUL+PHL+fhh38mL8/w8sv9uP/+7vj46It4lFKqOJr4q4B1e1IBiK2nib8s0tOz6d07hnHjBhITE+x0OEopVS1ovWgV8Lf9Rb5W4fpseXEOHz7OPffMYMqUvwEYM6YH06dfp0lfKaXKQEv8VcD0v/bQumEgDYO0cV9hjDF88cUaxoyZQ1JSOhER1guOatTQhntKKVVWmvgdtiPpGPtTM7m8U6TToVRJmzYlM2LETObO3U6XLo346acb6NSpodNhKaVUtaWJ32HvzdsKQKdora4uzF9/7Sc+fg/jxw9k2LCz9YM6Sil1hjTxO2z2un0AXNBKH+XLN3fuNnbsSOGOOzpz5ZWt6dMnhrp1/ZwOSyml3IIWnxy0ctdhUtKzubJzJN5akmX//jRuvPFb+vX7jLfe+oPc3DxERJO+UkqVI802Dlq+3fqg4B09Yx2OxFl5eYYJE+Jp2XIcX3+9jiefPJ9ly+7San2llKoAWtXvoJlr9hLs50NLD3+Mb82a/YwYMZPevWN4771BtGwZ5nRISinltrRI5RBjDH8lHiEiuDZeHvhYWlpaFl9/vRaADh3CWbLkDubOvVmTvlJKVTBN/DYR6S8iG0Vki4g8WsjwMSKyTkT+EpG5ItL4TJY3c81eANpHBp3JbKql77/fQOvW7zJ06Dds334YgG7dIvWDOkopVQk08QMi4gW8CwwA2gDXiUibAqOtAuKMMe2BqcArZ7LMX9cfAOCR/q3OZDbVys6dKQwePJnLL59CSIgvCxbcRmxsiNNhKaWUR9F7/JauwBZjzDYAEZkMDAbW5Y9gjPnNZfylwI1nssDFW5OJDKlNsF/NM5lNtXH8eDZdunzAsWPZvPrqhdx7bzf9oI5SSjlAE78lAkhw6U4EuhUz/h3Aj0UNFJFhwDCA6OjoQsfZl5pBv9bu/+z+mjX7adeuPrVr+/D++5fSuXNDoqM97/aGUkpVFVrVbyns5rIpdESRG4E44NWiZmaMed8YE2eMiatXr94pw9fvtb7G16KB+7bmP3ToOMOGTad9+wl88816AIYMaaVJXymlHKYlfksiEOXSHQnsKTiSiPQD/g30MsZknu7C5q7fD0BfN3xbnzGGzz//iwcemMOhQ8d58MEe9O/fzOmwlFJK2TTxW5YDzUUkFtgNDAWudx1BRDoB/wX6G2MOnMnCvlxm3VXoHO1+Ddtuuuk7Jk1aQ/fukfz88yA6dAh3OiSllFIuNPEDxpgcERkFzAa8gI+MMWtF5Bkg3hgzDatq3x/42n7sbJcx5rKyLisjO5fdKcfxr+XtNp+VPX48G2/vGvj4eHH11W0477xo7rrrbLdZP6WUciea+G3GmFnArAL9nnT5u195LGfFTuu59RF9mpbH7Bw3Z85WRoyYyZ13dubRR3syeLDnPJ6olFLVkTbuq2Q7k9MB6N+2eleB7917lKFDp3LxxZ/j5VWDbt0inA5JKaVUKWiJv5KtTkgBoK5/LYcjOX1Tp67jjjumkZmZw9NP9+aRR86lVi3dlZRSqjrQs3UlO5yeRYifD0G1fZwOpcyMMYgI0dFBdO8eybhxA2jevK7TYSmllCoDTfyVbMuBNGLC6jgdRpkcPZrJk0/+RnZ2HuPGDaRr1whmzz6jFxcqpZRyiN7jr2Tbko6RkZ3ndBilYozhm2/W0br1u7z11h8YYzCm0PcaKaWUqia0xF+J0jJzAGjXKNDhSEqWkHCE4cNnMmvWZjp2DOebb66hW7dIp8NSSil1hjTxm1Ht0wAAFQxJREFUV6LvVu0GoH+7qt+iPy/PsGLFHsaOvYjRo7vh7a2VQ0op5Q408VeivxOPAHB+i1Pf318VLFy4iylT/ubttwfQuHEwO3bch6+v7iJKKeVOtBhXiVYlHKZlgwB8vKrWZk9OTufOO6dx3nkfM336JvbtSwPQpK+UUm6oamUgN7c3JYO8KtQ4zhjDxIl/0rLlOD75ZDUPP3wOa9eOoGFD9/1qoFJKeTot0lWyRsG1nQ7hhLS0LB5/fC4tW4YxYcIgzjqrgdMhKaWUqmCa+CtJZk4uRzNzaBfhbIv+9PRsJkyIZ/TorgQE1GLRottp3DhYP6ijlFIeQhN/JVm+3fo4T/P6zlWj//jjZkaOnMX27Sm0aFGXSy5pQWys+30aWCmlVNE08VeSw+lZAESF+lX6svfsOcp99/3E11+vo2XLuvz22y307h1T6XGoqi87O5vExEQyMjKcDkV5AF9fXyIjI/HxqX6vMK/ONPFXkvV7UwGICq38e/zXXfcNy5bt5rnn+vDgg+foB3VUkRITEwkICCAmJgb5//buPTqq6l7g+PcHqAkQAgZJUVoeEiEPIBKgpCACFkQUuVokAlVAbC8PZQmKF5e4qEDxXRXBUi+3C7SSRFCUG4pcwPhCgYZLTAK0KcU0omngQoCABAL53T/OYUhCQiYmMwOZ32etWWvOPq/f7DXJb84+++wtdvvH+I6qcujQIfbv30/Hjh0DHU5QsQzgJ98eOQnANX6alW/Hju/o1KkVrVqFsnjxbTRtegXXX3+1X85tLl8lJSWW9I1fiAgREREcPHgw0KEEHXucz0+aNHKq2tf/UI8eLWH69PX06bOM3/72MwC6dYu0pG+8Zknf+It91wLDrvj9ZE/BMTq3ae6z46sqq1bt5pFHPuRf/zrOtGm9eeqpAT47nzHGmMuTXfH7wdGTpewuOMapM2d9do4FCz4lKWk1bduGsX37r3jtteGEh4f47HzG+Erjxo2Jj48nLi6OESNGcOTIEc+6Xbt2MXjwYG644QaioqKYP39+hRkj169fT69evYiOjqZr16489thjgfgIF7Vz504efPDBCmUjR44kMTGxQtmECRNYvXp1hbLmzc9fPOTm5jJ8+HA6d+5MdHQ0o0ePprCwsE6xHT58mCFDhhAVFcWQIUMoKiq6YJv09HTi4+M9r5CQEN5//30Axo0bR5cuXYiLi+OBBx6gtLQUgLS0NObOnVun2Ew9OjfVqr1880pISNB3d3yj7f8jTVO352t9OnXqjB44cFxVVb/+ukhffXWrlpaerddzmOCye/fuQIegzZo187y///77dcGCBaqq+v3332unTp10w4YNqqp64sQJHTZsmC5evFhVVbOzs7VTp066Z88eVVUtLS3VJUuW1GtspaWldT7GqFGjNDMz07NcVFSk7dq1065du+q+ffs85ePHj9dVq1ZV2Pdc3Zw8eVI7d+6sa9eu9az76KOPNDs7u06xzZo1S5955hlVVX3mmWf08ccfv+j2hw4d0latWumJEydUVXXdunVaVlamZWVleu+99+rrr7+uqqplZWUaHx/v2a68qr5zQIZeAv+/G+rLmvr9IMudnGdYt/qble+TT/KYPHkd7duHs379ODp0aMn06T+tt+Mb8/R/72L3d8fq9Zgx17Zg7ohYr7dPTEwkKysLgJUrV9KvXz+GDh0KQNOmTVm8eDEDBw5k2rRpPP/88zz55JN07doVgCZNmjB16tQLjnn8+HEefvhhMjIyEBHmzp3LL37xC5o3b87x4848FatXryYtLY3ly5czYcIErr76anbu3El8fDxr1qwhMzOTli1bAtC5c2e2bNlCo0aNmDx5Mvn5+QC88sor9OvXr8K5i4uLycrKokePHp6yd999lxEjRhAZGUlKSgpPPPFEjfWycuVKEhMTGTFihKds0KBBXtdrdT744AM+/vhjAMaPH8/AgQN57rnnqt1+9erV3HbbbTRt6jymPHz4cM+6Pn36sH//fsC5lz9w4EDS0tIYPXp0neM0dWOJ3w+KS84A0CKk7s+qHjx4glmzNrJixVeeZG8dZExDdPbsWTZv3sykSZMAp5k/ISGhwjbXX389x48f59ixY+Tk5PDoo4/WeNz58+cTHh5OdnY2QJXN2ZXl5uayadMmGjduTFlZGWvWrGHixIls27aNDh06EBkZydixY5kxYwb9+/cnPz+fW2+9lT179lQ4TkZGBnFxcRXKkpOTmTt3LpGRkYwaNcqrxJ+Tk3NBXVSluLiYm266qcp1K1euJCYmpkJZYWEhbdu2BaBt27YcOHDgosdPSUlh5syZF5SXlpby1ltv8eqrr3rKevXqxWeffWaJ/xJgid8P/n6gmPgft6zzcb744htGjEjm2LFTPPFEf+bMGUDTpjbwhfGN2lyZ16eTJ08SHx9PXl4eCQkJDBkyBHBuS1b3I7c2P343bdpESkqKZ7lVq5pHr7znnnto3LgxAElJScybN4+JEyeSkpJCUlKS57i7d+/27HPs2DGKi4sJCzs/WmdBQQHXXHN+Wu7CwkL27t1L//79ERGaNGlCTk4OcXFxVX6m2v7IDwsLIzMzs1b7eKugoIDs7GxuvfXWC9ZNnTqVAQMGVPjR0aZNG7777jufxGJqxzr3+cH+opNcWYepeEtLnU6BsbHXMHhwRzIz/52FC2+xpG8apNDQUDIzM/nnP//J6dOnWbJkCQCxsbFkZGRU2Hbfvn00b96csLAwYmNj2bFjR43Hr+4HRPmyyiMXNmvWzPM+MTGRvXv3cvDgQd5//33uvvtuAMrKyvjyyy/JzMwkMzOTb7/9tkLSP/fZyh87NTWVoqIiOnbsSIcOHcjLy/P8KImIiKjQGnH48GFat27tqQtvPmtxcXGFjnjlX+V/pJwTGRlJQUEB4CT2Nm3aVHvsd955h7vuuuuCUfeefvppDh48yO9+97sK5SUlJYSGXjqTlAUzS/x+cPjEaa5rVfsv/IkTp5k9exN9+iyjtPQs4eEhrFp1D7Gx1f8xGtNQhIeHs2jRIl588UVKS0sZN24cn3/+OZs2bQKcloHp06fz+OOPAzBr1iwWLlxIbm4u4CTiyskHYOjQoSxevNizfC65RkZGsmfPHk9TfnVEhLvuuouZM2cSHR1NRERElcet6ko7OjqavXv3epaTk5P58MMPycvLIy8vjx07dngS/8CBA0lNTeX0aWe47+XLl3vu448dO5YvvviCdevWeY714Ycfem5fnHPuir+qV+VmfoA777yTFStWALBixQpGjhxZbT0kJyczZsyYCmXLli1jw4YNJCcn06hRxfSSm5t7wW0OExiW+H3s9JkyANrW8tG6detyiY19neee20LPnj+ixO0nYEwwufHGG+nRowcpKSmEhobywQcfsGDBArp06UK3bt3o3bs3Dz30EADdu3fnlVdeYcyYMURHRxMXF+e5ei1vzpw5FBUVERcXR48ePUhPTwfg2Wef5Y477mDw4MGe+9zVSUpK4k9/+pOnmR9g0aJFZGRk0L17d2JiYli6dOkF+3Xt2pWjR49SXFxMXl4e+fn59O3b17O+Y8eOtGjRgm3btnHHHXdw0003kZCQQHx8PFu2bPF0tAsNDSUtLY3XXnuNqKgoYmJiWL58+UWv0L0xe/ZsNm7cSFRUFBs3bmT27NmA0zeh/COIeXl5fPPNN9x8880V9p88eTKFhYUkJiYSHx/PvHnzPOvS09O5/fbb6xSfqR/iPDlhfOVH18dqyD3P8+6Un5HQvuZ7iUeOlDBp0lree28P0dGtWbr0DgYMaO+HSI2BPXv2EB0dHegwGrSXX36ZsLCwC57lb8gKCwsZO3YsmzdvvmBdVd85Edmhqr38FV+wsSt+Hzt60hnAoudPvOvc17z5lRQUFLNw4WAyMydb0jemgZkyZQpXXeWfOTsuFfn5+bz00kuBDsO4rFe/j5WpMqbPTy7aG3f79m+ZO/djVq68m1atQvn88wdo1Mge0TOmIQoJCeG+++4LdBh+1bt370CHYMqxK34/uKdXuyrLjxwpYdq0dfTtu4ysrEL+8Q+nk5ElfRNIdvvP+It91wLDrvj9oNt14RWWVZXU1F3MmLGBAwdOMH36T5k3bxAtWgRX85+59ISEhHDo0CEiIiJsYCjjU6rKoUOHCAmxOUX8zRK/H1xRxTP8b7+dTbt2LUhLG0NCwrUBiMqYC7Vr1479+/fbHOnGL0JCQmjXruoWUeM71qvfx5pdd4Oe+DaXU6fO8MILXzB2bDc6dWrFkSMlhIVdSeM6DOxjjDENkfXq9y3LOi4RGSYifxORvSIyu4r1V4lIqrt+m4h08Oa4jRsJ6elf0737Up56Kp1333VGy2rZMsSSvjHGGL+zzAOISGNgCXAbEAOMEZHKw1pNAopUtTPwMlD9lFXlnC4tY/DgNzlzpoz168cxa1a/mncyxhhjfMQSv6MPsFdV96nqaSAFqDxW5Uhghft+NXCLeNH76WzJGebMuYmcnCkMG9a5XoM2xhhjass69zmuA74pt7wfqDy5vWcbVT0jIkeBCOD/Kh9MRH4N/NpdPLVgwS05CxbUe8yXo9ZUUV9ByurCYfVwntXFeV0CHUBDZonfUdWVe+Vej95s4xSqvgG8ASAiGdZJxWF1cZ7VhcPq4Tyri/NEJKPmrcwPZU39jv3Aj8sttwMqTxzt2UZEmgDhwGG/RGeMMcbUE0v8jr8AUSLSUUSuBO4F1lbaZi0w3n0/CvhI7VlIY4wxlxlr6sdzz/4hYAPQGPijqu4SkXlAhqquBf4LeEtE9uJc6d/r5eHf8EnQlyeri/OsLhxWD+dZXZxndeFDNoCPMcYYE0Ssqd8YY4wJIpb4jTHGmCBiib8e+Gq438uRF3UxU0R2i0iWiGwWkfaBiNMfaqqLctuNEhEVkQb7KJc3dSEio93vxi4RWenvGP3Fi7+Rn4hIuojsdP9OhgciTn8QkT+KyAERyalmvYjIIreuskSkp79jbJBU1V51eOF0BvwH0Am4EvgKiKm0zVRgqfv+XiA10HEHsC4GAU3d91OCuS7c7cKAT4GtQK9Axx3A70UUsBNo5S63CXTcAayLN4Ap7vsYIC/QcfuwPgYAPYGcatYPB9bjjKPSF9gW6Jgbwsuu+OvOZ8P9XoZqrAtVTVfV793FrThjJjRE3nwvAOYDzwMl/gzOz7ypi18BS1S1CEBVD/g5Rn/xpi4UaOG+D+fCMUUaDFX9lIuPhzISeFMdW4GWItLWP9E1XJb4666q4X6vq24bVT0DnBvut6Hxpi7Km4Tza74hqrEuRORG4MeqmubPwALAm+/FDcANIrJFRLaKyDC/Redf3tTFb4Bfish+4M/Aw/4J7ZJU2/8pxgv2HH/d1etwv5c5rz+niPwS6AXc7NOIAueidSEijXBmeZzgr4ACyJvvRROc5v6BOK1An4lInKoe8XFs/uZNXYwBlqvqSyKSiDN+SJyqlvk+vEtOsPzv9Cu74q87G+73PG/qAhH5OfAkcKeqnvJTbP5WU12EAXHAxyKSh3P/cm0D7eDn7d/IB6paqqpfA3/D+SHQ0HhTF5OAdwBU9UsgBGcCn2Dk1f8UUzuW+OvOhvs9r8a6cJu3/4CT9BvqfVyooS5U9aiqtlbVDqraAae/w52q2hAnJ/Hmb+R9nI6fiEhrnKb/fX6N0j+8qYt84BYAEYnGSfwH/RrlpWMtcL/bu78vcFRVCwId1OXOmvrrSH073O9lxcu6eAFoDqxy+zfmq+qdAQvaR7ysi6DgZV1sAIaKyG7gLDBLVQ8FLmrf8LIuHgX+U0Rm4DRrT2igFwqISDLO7Z3Wbp+GucAVAKq6FKePw3BgL/A9MDEwkTYsNmSvMcYYE0Ssqd8YY4wJIpb4jTHGmCBiid8YY4wJIpb4jTHGmCBiid8YY4wJIpb4jakjETkrIpnlXh0usm2H6mYiq+U5P3ZnePvKHea2yw84xmQRud99P0FEri23bpmIxNRznH8RkXgv9nlERJrW9dzGmKpZ4jem7k6qany5V56fzjtOVXvgTAD1Qm13VtWlqvqmuzgBuLbcugdVdXe9RHk+ztfxLs5HAEv8xviIJX5jfMC9sv9MRP7Xff2sim1iRWS720qQJSJRbvkvy5X/QUQa13C6T4HO7r63uPO4Z7tznV/llj/rznWfJSIvumW/EZHHRGQUzrwJb7vnDHWv1HuJyBQReb5czBNE5LUfGOeXlJtgRUR+LyIZIrJLRJ52y6bj/ABJF5F0t2yoiHzp1uMqEWlew3mMMRdhid+Yugst18y/xi07AAxR1Z5AErCoiv0mA6+qajxO4t3vDtGaBPRzy88C42o4/wggW0RCgOVAkqp2wxmZc4qIXA3cBcSqandgQfmdVXU1kIFzZR6vqifLrV4N3F1uOQlI/YFxDsMZmvecJ1W1F9AduFlEuqvqIpyx2Aep6iB3+N45wM/duswAZtZwHmPMRdiQvcbU3Uk3+ZV3BbDYvad9Fmfs+cq+BJ4UkXbAe6r6dxG5BUgA/uIOaRyK8yOiKm+LyEkgD2fq1i7A16qa665fAUwDFgMlwDIRWQd4PQ2wqh4UkX3uOOl/d8+xxT1ubeJshjNEbc9y5aNF5Nc4/4faAjFAVqV9+7rlW9zzXIlTb8aYH8gSvzG+MQMoBHrgtKyVVN5AVVeKyDbgdmCDiDyIMw3pClV9wotzjCs/qY+IRFS1kTs+fB+ciV/uBR4CBtfis6QCo4G/AmtUVcXJwl7HCXwFPAssAe4WkY7AY0BvVS0SkeU4k9FUJsBGVR1Ti3iNMRdhTf3G+EY4UODOoX4fztVuBSLSCdjnNm+vxWny3gyMEpE27jZXi0h7L8/5V6CDiHR2l+8DPnHviYer6p9xOs5V1bO+GGeq4Kq8B/wbzjzxqW5ZreJU1VKcJvu+7m2CFsAJ4KiIRAK3VRPLVqDfuc8kIk1FpKrWE2OMlyzxG+MbrwPjRWQrTjP/iSq2SQJyRCQT6Aq86faknwP8j4hkARtxmsFrpKolOLOXrRKRbKAMWIqTRNPc432C0xpR2XJg6bnOfZWOWwTsBtqr6na3rNZxun0HXgIeU9WvgJ3ALuCPOLcPznkDWC8i6ap6EOeJg2T3PFtx6soY8wPZ7HzGGGNMELErfmOMMSaIWOI3xhhjgoglfmOMMSaIWOI3xhhjgoglfmOMMSaIWOI3xhhjgoglfmOMMSaI/D82qH1m0UTgigAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5006,19 +4987,19 @@ ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", - " \"SVM reduced features and tuning RBF kernel\")" + " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the SVM reduced features and tuning RBF kernel identified 732\n" + "Of 2008 Defaulters, the SVM reduced features and tuning linear kernal identified 696\n" ] }, { @@ -5053,14 +5034,14 @@ " \n", " \n", " \n", - " 0\n", - " 6383\n", - " 344\n", + " 0\n", + " 6405\n", + " 336\n", " \n", " \n", - " 1\n", - " 1290\n", - " 732\n", + " 1\n", + " 1312\n", + " 696\n", " \n", " \n", "\n", @@ -5069,23 +5050,23 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6383 344\n", - "1 1290 732" + "0 6405 336\n", + "1 1312 696" ] }, - "execution_count": 102, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernel\")" + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5094,12 +5075,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6727\n", - " 1 0.68 0.36 0.47 2022\n", + " 0 0.83 0.95 0.89 6741\n", + " 1 0.67 0.35 0.46 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -5112,210 +5093,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[4] = ([\"SVM\" , \n", - " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neural Networks\n", - "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", - "\n", - "#### Theory\n", - "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", - "\n", - ".\n", - "\n", - "\n", - "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", - "\n", - "\n", - "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", - "\n", - "\n", - "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", - "\n", - "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Parameter tuning\n", - "##### Learning rate\n", - "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", - "\n", - "##### Momentum\n", - "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", - "\n", - "##### Loss function\n", - "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", - " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", - " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", - " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", - " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", - " validation_fraction=0.1, verbose=False, warm_start=False)" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, + "cell_type": "code", + "execution_count": 80, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2022 Defaulters, the Neural Network (5x26) identified 788\n" - ] - }, { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06281446
11234788
\n", - "
" - ], "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6281 446\n", - "1 1234 788" + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" ] }, - "execution_count": 109, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.204745415479134\n" + "Optimal Threshold: 0.15780782271279034\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5327,12 +5147,13 @@ } ], "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -5341,186 +5162,98 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6727\n", - " 1 0.64 0.39 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.64 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.74 0.67 0.69 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the rbf kernel does not increase the AUROC but and the recall increased slightly by 0.02, thus, it can be said that the linear and rbf kernel is similar in performance. However, we will choose the linear kernel as it is less computationally expensive compared to rbf kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall." ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
3Logistic Regression0.6191890.766563
4SVM0.3620180.723867
5Neural Network0.3897130.774088
\n", - "
" - ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.418892 0.609249\n", - "1 Random Forest 0.386746 0.761906\n", - "2 Gradient Boosted 0.380811 0.775534\n", - "3 Logistic Regression 0.619189 0.766563\n", - "4 SVM 0.362018 0.723867\n", - "5 Neural Network 0.389713 0.774088" + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 112, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'linear', probability = True)\n", + "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10\n", - "17496/17496 [==============================] - 2s 100us/step - loss: 0.4586 - accuracy: 0.8061\n", - "Epoch 2/10\n", - "17496/17496 [==============================] - 1s 83us/step - loss: 0.4432 - accuracy: 0.8153\n", - "Epoch 3/10\n", - "17496/17496 [==============================] - 1s 86us/step - loss: 0.4412 - accuracy: 0.8154 0s - loss: 0.4414 - accuracy: 0.\n", - "Epoch 4/10\n", - "17496/17496 [==============================] - 1s 83us/step - loss: 0.4397 - accuracy: 0.8165\n", - "Epoch 5/10\n", - "17496/17496 [==============================] - 1s 82us/step - loss: 0.4385 - accuracy: 0.8156\n", - "Epoch 6/10\n", - "17496/17496 [==============================] - 1s 80us/step - loss: 0.4377 - accuracy: 0.8170\n", - "Epoch 7/10\n", - "17496/17496 [==============================] - 1s 84us/step - loss: 0.4374 - accuracy: 0.8162\n", - "Epoch 8/10\n", - "17496/17496 [==============================] - 1s 82us/step - loss: 0.4362 - accuracy: 0.8173\n", - "Epoch 9/10\n", - "17496/17496 [==============================] - 2s 91us/step - loss: 0.4350 - accuracy: 0.8172\n", - "Epoch 10/10\n", - "17496/17496 [==============================] - 2s 108us/step - loss: 0.4346 - accuracy: 0.8176\n" + "Optimal Threshold: 0.1550813999698536\n" ] }, { "data": { + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "from numpy import loadtxt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "# define the keras model\n", - "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + "auroc = get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5529,23 +5262,114 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.65 0.36 0.47 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.64 0.39 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "# evaluate the keras model\n", - "#recall, accuracy = model.evaluate(df1, target)\n", - "#print('Accuracy: %.2f' % (accuracy*100))\n", - "#print('Recall: %.2f' % (recall*100))\n", - "\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the performance is not as great after using filtered features. Thus, the best SVM model to use is linear kernel with C = 0.01 and gamma = 0.01 using unfiltered features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "print(classification_report(y_test,predictions))" ] }, @@ -5555,8 +5379,334 @@ "metadata": {}, "outputs": [], "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", "evaluation" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=20, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(X_train, y_train, epochs=20, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(X_train, y_train, epochs=10, batch_size=20)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adadelta Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adagrad Optimzier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RMSProp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SGD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### conclude that best optimizer is adagrad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(40, input_dim=44, activation='relu'))\n", + "model.add(Dense(25, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] } ], "metadata": { @@ -5580,7 +5730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb index ff9f477..c68730d 100644 --- a/BT2101_Default - EDIT-MASTER- Reon.ipynb +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAapElEQVR4nO3deZxcZZ3v8c+XgALjAg6LCEhQo4iMsgTEcQNRBBwFRhz16oBeFHVwvDqLouOIG3fQGZfBBQcxF3BUFBFFQTGgiNcrSFBkEZWIKDEBoiyyKYu/+0c9LWWnOl056Uqn6c/79apXn/Oc55zzO51Kffs8p+pUqgpJkrpYZ7oLkCTNXIaIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEpNWU5NAkX27T6yepJFtNd10ASY5OcnybfnSSm6Zw2yckeWOb3ifJ4inc9jOT/HCqtqfRMUTUWZJb+x5/SHJH3/xL1nAtq/3ineTaJE/pm98uyd2TrVdVn6iq53bd77gazk/y0qnY1nhV9dOq2miIGl6d5Owhtveyqnrv6tY16N+uqs6uqies7rY1eutOdwGauarqAWPTSa4GXlFVk774DJJk3aqa9AV7bTNT615dSeZU1T3TXYemn2ciGpkkT05yQZKbkyxN8oEk67ZlY399vibJz4DLWvtzklyZ5KYkHxz/l3mSVyX5SZIbkpyRZMu26Lz28yftTOiAAfVsl+Tctu7yJCcmeWBbdgqwGfD1tv7r2jbn9J1d7dT+Sv9Gko8kuRE4YoK/3A9IcnXbz1FJ0vbzx+GlvprubtPvA3YFjm/7e19r36Ht88YkVww6tr7tPSrJd5LckuSrwMaD9tXmX9lqvCXJVUlekGQn4IPAHq2Ga1vfk5Mck+TrSW4DntTa3jpu/+9ov9+rkrygr338v2P/72yFf7vxw2NJ/iLJt9vz4pIk+/YtO7k9V85qx/KdJNtM9DvSFKsqHz5W+wFcDTxzXNtu9F4U5wCPBBYDr27L1gcKOAPYCNgAeChwK/BXwHrAG4G7gJe2dV4EXAE8ui1/N/DNcdvbaiU1bgc8A7hf29f5wNF9y68FnjKu/93jtvFq4G7gle24NmhtZ4+r46x2XNsCV/Udw9HA8RPto9X00r75BwHLgJe0/e0K3AA8aoJj/D7wb+0Y9wJuH9tf/77ohctNwCPb/JbAY/uO8exx2z257feJ9P74vH9re2tbvk/7vYzt+5lt39tOcFyDfmdb9S3fB1jct/wXwD+2f/dnt+fJtn21XQ/s3JZ/Hjhhuv9PzJaHZyIamar6XlVdWFX3VNXPgOOBp4/rdlRV3VRVdwDPAy6sqq9U1V3AfwA39vV9FfDu6o3t3wW8A3hKks2HrOfHVfWNqrqzqq6l9xf3+HqGcVVVfbwd1x0T9Pm3dlw/Bz4MvLjDfgAOBC6rqk+1/V0IfBl4/viOSR4NbA+8ox3jOcDXJtn+DknWr6pfVdUVk/T9fFVdUFV/qKrfD1h+d9++zwbOBg6a7ACH8NT28/1VdVdVnQUsBF7Y1+dzVfX99rz4NLDjFOxXQzBENDJJtk/y1STXJfkt8DZgk3Hdrumbflj/fFX9AfhV3/JtgI+1IY2bgOX0XriGupie5GFJTknyq1bP8QPqGcY1k3f5kz6/oHdsXWwDPG3smNtxPx/YYkDfhwHLq+p34/a9gqq6kd7ZzeuAa5OcnuRRk9Qy2XEP2nfX4+73MOCXVdV/t9hf0Dt7GnNt3/TtwAPQGmGIaJQ+Tm945ZFV9SDgnUDG9el/YVhGXyAkWYc/faG4BnhZVW3U99igqi4at52J/DtwG7BDq+cV4+oZv42JtjnMvrbum344sLRN3wZs2LfsoZNs+xrg6+OO+QFV9foB+1wGbJJk/XH7HqiqzqiqvWgv0sCxE9QwUW3jDdr3MMc92XaXsuJxPJw//QND08QQ0Sg9ELi5qm5N8jh61xFW5nTgiUn2axfg/4G+C8PAx4C3JnkMQJKNkzwfoA2v3Aw8YpJ6bgV+m+Thbfv9rhu3/vX0LqxP+EK8Em9K8uAkc4HXAp9t7RcDeybZMsnGwJsmqeGLwE5JXphkvST3S7J7G7oa76fAj4F/bf32pHdtYQVt/89JsiHwe3q/l7F3W10HbJ1kvVU85vX69v0M4FnAqX3HfVB6b6jYDnjZ2EpD/Nt9G1gnyeuTrJvkWcDewCmrWJ9GwBDRKL0BeEWSW4GPcO8L6UBVtYzetYNjgF/TOyu5lN6LHFX1GXrXF77QhqMupvdCNeZtwClt2Od5A3bxNuAp9F6wTuPeF7gxRwFHtfVf24Z83gtc1NpWZZz9DOCHwCJ6L3b/3df+FeBH9C42f3Hceh8ADm7vxHpvq+HZwMvpnWkspfeGghVe4NtwzwuBPeldBH9j337HmwO8md4w0G/oXbD/+7bsa/TeKHF9kiWrcMxX0xtevBZYALy8qq5qy95L7yMFy4HjBtQ14b9dGyL7K3rXV34DvB94YbvOpmmWPx1mlNYe7WzkWuC5VfXd6a5H0oo8E9FaJcm+bRhofeBIehdJL5rmsiRNwBDR2uZpwM/pXY/YCziwqu6c3pIkTcThLElSZ56JSJI6m3U3YNxkk01q7ty5012GJM0oF1100a+ratPx7bMuRObOncuiRYumuwxJmlGSDLz7gcNZkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOZt0n1meKuUecMd0l3GdcffRzprsE6T7LMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmcjC5EkWyf5ZpIrklye5H+19ockWZjkyvZz49aeJMckWZzkkiQ7923rkNb/yiSH9LXvkuTSts4xSTKq45EkrWiUZyJ3A/9YVY8FdgcOT7I9cARwTlXNA85p8wD7AvPa4zDgWOiFDnAk8ERgN+DIseBpfQ7rW2+fER6PJGmckYVIVS2rqu+36VuAK4Atgf2BE1u3E4ED2vT+wEnVcz6wUZItgGcDC6vqhqq6EVgI7NOWPaiqvltVBZzUty1J0hqwRq6JJJkL7ARcAGxeVcugFzTAZq3blsA1fastaW0ra18yoF2StIaMPESSPAA4FXh9Vf12ZV0HtFWH9kE1HJZkUZJFy5cvn6xkSdKQRhoiSdajFyCfqqovtObr2lAU7ef1rX0JsHXf6lsBSydp32pA+wqq6riqml9V8zfddNPVOyhJ0h+N8t1ZAT4BXFFV7+9bdDow9g6rQ4Av9bUf3N6ltTtwcxvuOgvYO8nG7YL63sBZbdktSXZv+zq4b1uSpDVg3RFu+8nA3wKXJrm4tb0FOBr4XJJDgV8CL2jLzgT2AxYDtwMvB6iqG5K8C7iw9XtnVd3Qpl8DnABsAHy1PSRJa8jIQqSq/i+Dr1sA7DWgfwGHT7CtBcCCAe2LgB1Wo0xJ0mrwE+uSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJgiTXJ7msr+3tSX6V5OL22K9v2ZuTLE7ykyTP7mvfp7UtTnJEX/u2SS5IcmWSzya536iORZI02CjPRE4A9hnQ/oGq2rE9zgRIsj3wIuBxbZ2PJpmTZA7wEWBfYHvgxa0vwHvatuYBNwKHjvBYJEkDjCxEquo84IYhu+8PnFxVv6+qnwOLgd3aY3FVXVVVdwInA/snCfAM4PNt/ROBA6b0ACRJkxoqRJLsMIX7fG2SS9pw18atbUvgmr4+S1rbRO1/DtxUVXePax8oyWFJFiVZtHz58qk6Dkma9YY9E/lYku8l+bskG63G/o4FHgnsCCwD3tfaM6BvdWgfqKqOq6r5VTV/0003XbWKJUkTGipEquopwEuArYFFST6d5FmrurOquq6q7qmqPwAfpzdcBb0zia37um4FLF1J+6+BjZKsO65dkrQGDX1NpKquBN4KvAl4OnBMkh8n+etht5Fki77ZA4Gxd26dDrwoyf2TbAvMA74HXAjMa+/Euh+9i++nV1UB3wQOausfAnxp2DokSVNj3cm7QJLHAy8HngMsBJ5bVd9P8jDgu8AXBqzzGWAPYJMkS4AjgT2S7Ehv6Olq4FUAVXV5ks8BPwLuBg6vqnvadl4LnAXMARZU1eVtF28CTk7ybuAHwCdW+eglSatlqBABPkxv+OktVXXHWGNVLU3y1kErVNWLBzRP+EJfVUcBRw1oPxM4c0D7Vdw7HCZJmgbDhsh+wB19ZwfrAOtX1e1V9cmRVSdJWqsNe03kbGCDvvkNW5skaRYbNkTWr6pbx2ba9IajKUmSNFMMGyK3Jdl5bCbJLsAdK+kvSZoFhr0m8nrglCRjn8XYAnjhaEqSJM0UQ4VIVV2YZDvgMfQ+Lf7jqrprpJVJktZ6w56JAOwKzG3r7JSEqjppJFVJkmaEYT9s+El697y6GLinNRdgiEjSLDbsmch8YPt2uxFJkoDh3511GfDQURYiSZp5hj0T2QT4UZLvAb8fa6yq542kKknSjDBsiLx9lEVIkmamYd/i+60k2wDzqursJBvSu6uuJGkWG/brcV9J7/vM/6s1bQl8cVRFSZJmhmEvrB8OPBn4LfzxC6o2G1VRkqSZYdgQ+X1V3Tk2076W1rf7StIsN2yIfCvJW4AN2nernwJ8eXRlSZJmgmFD5AhgOXApva+0PZPe961LkmaxYd+d9Qd6X4/78dGWI0maSYa9d9bPGXANpKoeMeUVSZJmjFW5d9aY9YEXAA+Z+nIkSTPJUNdEquo3fY9fVdUHgWeMuDZJ0lpu2OGsnftm16F3ZvLAkVQkSZoxhh3Oel/f9N3A1cDfTHk1kqQZZdh3Z+056kIkSTPPsMNZ/7Cy5VX1/qkpR5I0k6zKu7N2BU5v888FzgOuGUVRkqSZYVW+lGrnqroFIMnbgVOq6hWjKkyStPYb9rYnDwfu7Ju/E5g75dVIkmaUYc9EPgl8L8lp9D65fiBw0siqkiTNCMO+O+uoJF8FntqaXl5VPxhdWZKkmWDY4SyADYHfVtV/AkuSbDuimiRJM8SwX497JPAm4M2taT3gv0dVlCRpZhj2TORA4HnAbQBVtRRveyJJs96wIXJnVRXtdvBJ/mx0JUmSZophQ+RzSf4L2CjJK4GzmeQLqpIsSHJ9ksv62h6SZGGSK9vPjVt7khyTZHGSS/pv+JjkkNb/yiSH9LXvkuTSts4xSbIqBy5JWn3D3gr+P4DPA6cCjwHeVlUfmmS1E4B9xrUdAZxTVfOAc9o8wL7AvPY4DDgWeqEDHAk8EdgNOHIseFqfw/rWG78vSdKITfoW3yRzgLOq6pnAwmE3XFXnJZk7rnl/YI82fSJwLr0L9vsDJ7Uhs/OTbJRki9Z3YVXd0GpZCOyT5FzgQVX13dZ+EnAA8NVh65Mkrb5Jz0Sq6h7g9iQPnoL9bV5Vy9p2lwGbtfYt+dP7cC1pbStrXzKgfaAkhyVZlGTR8uXLV/sgJEk9w35i/XfApe1M4Laxxqp63RTVMeh6RnVoH6iqjgOOA5g/f/6E/SRJq2bYEDmjPVbXdUm2qKplbbjq+ta+BNi6r99WwNLWvse49nNb+1YD+kuS1qCVhkiSh1fVL6vqxCna3+nAIcDR7eeX+tpfm+RkehfRb25Bcxbwv/supu8NvLmqbkhyS5LdgQuAg4HJLvRLkqbYZNdEvjg2keTUVdlwks8A3wUek2RJkkPphcezklwJPKvNA5wJXAUspvfW4b8DaBfU3wVc2B7vHLvIDrwGOL6t8zO8qC5Ja9xkw1n91x4esSobrqoXT7BorwF9Czh8gu0sABYMaF8E7LAqNUmSptZkZyI1wbQkSZOeiTwhyW/pnZFs0KZp81VVDxppdZKktdpKQ6Sq5qypQiRJM8+qfJ+IJEl/whCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM7Wne4CJM0sc484Y7pLuE+5+ujnTHcJq8UzEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKmzaQmRJFcnuTTJxUkWtbaHJFmY5Mr2c+PWniTHJFmc5JIkO/dt55DW/8okh0zHsUjSbDadZyJ7VtWOVTW/zR8BnFNV84Bz2jzAvsC89jgMOBZ6oQMcCTwR2A04cix4JElrxto0nLU/cGKbPhE4oK/9pOo5H9goyRbAs4GFVXVDVd0ILAT2WdNFS9JsNl0hUsDXk1yU5LDWtnlVLQNoPzdr7VsC1/Stu6S1TdS+giSHJVmUZNHy5cun8DAkaXabrlvBP7mqlibZDFiY5Mcr6ZsBbbWS9hUbq44DjgOYP3/+wD6SpFU3LWciVbW0/bweOI3eNY3r2jAV7ef1rfsSYOu+1bcClq6kXZK0hqzxEEnyZ0keODYN7A1cBpwOjL3D6hDgS236dODg9i6t3YGb23DXWcDeSTZuF9T3bm2SpDVkOoazNgdOSzK2/09X1deSXAh8LsmhwC+BF7T+ZwL7AYuB24GXA1TVDUneBVzY+r2zqm5Yc4chSVrjIVJVVwFPGND+G2CvAe0FHD7BthYAC6a6RknScNamt/hKkmYYQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHU240MkyT5JfpJkcZIjprseSZpNZnSIJJkDfATYF9geeHGS7ae3KkmaPWZ0iAC7AYur6qqquhM4Gdh/mmuSpFlj3ekuYDVtCVzTN78EeOL4TkkOAw5rs7cm+ckaqG022AT49XQXMZm8Z7or0DTx+Tm1thnUONNDJAPaaoWGquOA40ZfzuySZFFVzZ/uOqRBfH6uGTN9OGsJsHXf/FbA0mmqRZJmnZkeIhcC85Jsm+R+wIuA06e5JkmaNWb0cFZV3Z3ktcBZwBxgQVVdPs1lzSYOEWpt5vNzDUjVCpcQJEkaykwfzpIkTSNDRJLUmSGiVZbkhCQHTdJnuyQXJ/lBkkd22Mfbk/xTm35Zkod1rVczX//zYYLlmya5oD3fntph+y9L8uE2fYB3vhieIaJROQD4UlXtVFU/W81tvQwwRLQyewE/bs+3b6/mtg6gdxslDcEQuQ9JMjfJFUk+nuTyJF9PskFbtmOS85NckuS0JBu39nOTvCfJ95L8dNBfcen5cJIfJTkD2Kxv2S5JvpXkoiRnJdkiyX7A64FXJPlm6/fF1ufydgeBsfVv7Zs+KMkJ4/Z9EDAf+FQ7s9lgKn9nWnsl+Zd2c9Wzgce0tkcm+Vp7Ln27nfHuCLwX2G/sOZLk2CSL2vPtHX3bvDrJJm16fpJzx+3zL4HnAf/etrXKZ9GzjSFy3zMP+EhVPQ64CXh+az8JeFNVPR64FDiyb511q2o3ei/8/e1jDqT3n/gvgFcCfwmQZD3gQ8BBVbULsAA4qqrOBD4GfKCq9mzb+J+tz3zgdUn+fJiDqarPA4uAl1TVjlV1xzDraWZLsgu9z33tBPw1sGtbdBzw9+259E/AR6vqYuBtwGf7niP/0j6t/njg6UkeP8x+q+r/0fus2T+3ba3uWfR93oz+nIgG+nn7TwVwETA3yYOBjarqW639ROCUvnW+0N9/wDafBnymqu4Blib5Rmt/DLADsDAJ9D6rs2yCul6X5MA2vTW9sPvNqhyYZpWnAqdV1e0ASU4H1qf3B8wp7fkGcP8J1v+bdsa7LrAFveGpS0Za8SxliNz3/L5v+h5gmOGfsXXuYeLnxKAPFAW4vKqetLKNJ9kDeCbwpKq6vQ0hrD9gu+sj3Wv8c24d4Kaq2nFlKyXZlt5Zyq5VdWMbIh17bt3NvSMwPt+mgMNZs0BV3Qzc2He942+Bb61klfHOA16UZE6SLYCxIaqfAJsmeRL0hreSPG7A+g8GbmwBsh2we9+y65I8Nsk69IbNBrkFeOAq1KuZ7zzgwHZ944HAc4HbgZ8neQH88VrdEwas+yDgNuDmJJvT+76hMVcDu7Tp5zOYz7dVYIjMHofQu1h4CbAj8M5VWPc04Ep611KOpQVQ+w6Xg4D3JPkhcDHtesk4XwPWbft+F3B+37IjgK8A32DiobATgI95YX32qKrvA5+l95w6FRh7x9VLgEPb8+1yBnx/UFX9EPhBW74A+E7f4ncA/5nk2/TOvAc5Gfjnrm9Pn2287YkkqTPPRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISKNSJKHJjk5yc/afcfOTPLoJJdNd23SVPET69IIpHdfjtOAE6vqRa1tR2DzaS1MmmKeiUijsSdwV1V9bKyh3dPsmrH5dtflbyf5fnuM3dhyiyTntQ9XXpbkqe1uASe0+UuTvGHNH5K0Is9EpNHYgd4NLVfmeuBZVfW7JPOAz9C7y/H/AM6qqqOSzAE2pHeXgS2rageAJBuNrnRpeIaINH3WAz7chrnuAR7d2i8EFrRb7X+xqi5OchXwiCQfAs4Avj4tFUvjOJwljcbl3Hujv4m8AbgOeAK9M5D7AVTVefRuv/8r4JNJDq6qG1u/c4HDgeNHU7a0agwRaTS+Adw/ySvHGpLsCmzT1+fBwLKq+gO9OyvPaf22Aa6vqo8DnwB2bt/Gt05VnQr8K7DzmjkMaeUczpJGoKqqfQnXB5McAfyO3m3IX9/X7aPAqe3W5t+kd/tygD3o3UX2LuBW4GBgS+D/tFvmA7x55AchDcG7+EqSOnM4S5LUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJn/x+T6PNo2DnX9gAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4], dtype=int64)" + "array([2, 1, 3, 4])" ] }, "execution_count": 14, @@ -1297,7 +1297,7 @@ { "data": { "text/plain": [ - "array([1, 2, 3], dtype=int64)" + "array([1, 2, 3])" ] }, "execution_count": 15, @@ -1391,7 +1391,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1400,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1409,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1418,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1427,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1529,7 +1529,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,7 +1553,7 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", " 1.852734\n", @@ -1577,7 +1577,7 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", " 0.738572\n", @@ -1601,7 +1601,7 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1625,7 +1625,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1649,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1673,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1697,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1894,7 +1894,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1918,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1942,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1966,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1990,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2124,7 +2124,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2132,7 +2132,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2140,7 +2140,7 @@ " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2148,7 +2148,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2156,7 +2156,7 @@ " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2240,7 +2240,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2261,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2282,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2303,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2324,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2604,26 +2604,24 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 88.108919 6.061985e-05\n", - "PAY_0 4383.304779 0.000000e+00\n", - "PAY_2 3808.897634 0.000000e+00\n", - "PAY_3 2929.844986 0.000000e+00\n", - "PAY_4 3128.967849 0.000000e+00\n", - "PAY_5 2916.099105 0.000000e+00\n", - "PAY_6 2553.774724 0.000000e+00\n", - "PAY_0_No_Transactions 75.763632 1.499570e-03\n", - "PAY_0_Pay_Duly 70.548603 5.076335e-03\n", - "PAY_0_Revolving_Credit 432.083997 0.000000e+00\n", - "PAY_2_Pay_Duly 91.371021 2.433442e-05\n", - "PAY_2_Revolving_Credit 208.148061 0.000000e+00\n", - "PAY_3_Pay_Duly 96.953056 4.833864e-06\n", - "PAY_3_Revolving_Credit 111.611983 5.228360e-08\n", - "PAY_4_Pay_Duly 88.985156 4.755470e-05\n", - "PAY_4_Revolving_Credit 87.590881 6.991450e-05\n", - "PAY_5_Pay_Duly 79.941208 5.308188e-04\n", - "PAY_5_Revolving_Credit 62.597352 2.699279e-02\n", - "PAY_6_Pay_Duly 66.360306 1.261937e-02\n", - "PAY_6_Revolving_Credit 63.991063 2.050515e-02\n" + "LIMIT_BAL 85.131154 1.364065e-04\n", + "PAY_0 4436.456236 0.000000e+00\n", + "PAY_2 3950.467050 0.000000e+00\n", + "PAY_3 3110.398233 0.000000e+00\n", + "PAY_4 3096.770229 0.000000e+00\n", + "PAY_5 2977.392709 0.000000e+00\n", + "PAY_6 2449.597828 0.000000e+00\n", + "PAY_0_No_Transactions 75.890061 1.454296e-03\n", + "PAY_0_Revolving_Credit 482.349587 0.000000e+00\n", + "PAY_2_Pay_Duly 76.560100 1.235192e-03\n", + "PAY_2_Revolving_Credit 237.062546 0.000000e+00\n", + "PAY_3_Pay_Duly 79.130634 6.519343e-04\n", + "PAY_3_Revolving_Credit 135.381715 1.723299e-11\n", + "PAY_4_Pay_Duly 74.678430 1.946930e-03\n", + "PAY_4_Revolving_Credit 98.449450 3.100197e-06\n", + "PAY_5_Pay_Duly 72.744346 3.071499e-03\n", + "PAY_5_Revolving_Credit 70.267645 5.406887e-03\n", + "PAY_6_Revolving_Credit 60.999670 3.661838e-02\n" ] } ], @@ -2704,7 +2702,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2724,7 +2722,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -2755,7 +2753,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2790,7 +2788,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -2799,7 +2797,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2813,7 +2811,7 @@ " random_state=None, splitter='best')" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2825,7 +2823,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2834,8 +2832,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13477\n", - " 1 1.00 1.00 1.00 4019\n", + " 0 1.00 1.00 1.00 13463\n", + " 1 1.00 1.00 1.00 4033\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2857,14 +2855,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (GINI) identified 847\n" + "Of 2008 Defaulters, the Decision Tree (GINI) identified 902\n" ] }, { @@ -2899,14 +2897,14 @@ " \n", " \n", " \n", - " 0\n", - " 5368\n", - " 1359\n", + " 0\n", + " 5410\n", + " 1331\n", " \n", " \n", - " 1\n", - " 1175\n", - " 847\n", + " 1\n", + " 1106\n", + " 902\n", " \n", " \n", "\n", @@ -2915,11 +2913,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5368 1359\n", - "1 1175 847" + "0 5410 1331\n", + "1 1106 902" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2930,19 +2928,19 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.5\n" + "Optimal Threshold: 1.0\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2959,7 +2957,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2968,12 +2966,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6727\n", - " 1 0.38 0.42 0.40 2022\n", + " 0 0.83 0.80 0.82 6741\n", + " 1 0.40 0.45 0.43 2008\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.71 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.62 0.63 0.62 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", "\n" ] } @@ -2992,14 +2990,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Entropy) identified 823\n" + "Of 2008 Defaulters, the Decision Tree (Entropy) identified 865\n" ] }, { @@ -3034,14 +3032,14 @@ " \n", " \n", " \n", - " 0\n", - " 5413\n", - " 1314\n", + " 0\n", + " 5508\n", + " 1233\n", " \n", " \n", - " 1\n", - " 1199\n", - " 823\n", + " 1\n", + " 1143\n", + " 865\n", " \n", " \n", "\n", @@ -3050,11 +3048,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5413 1314\n", - "1 1199 823" + "0 5508 1233\n", + "1 1143 865" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3067,19 +3065,19 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.5\n" + "Optimal Threshold: 1.0\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3QVVffw8e8mIaEkoSTUhJDQe40gwYqggCAIgiDS/flgwd4LFsTeX7ErUVBBwIKAigURE5DepafSWxoh/bx/zESvMSQ3kJubsj9rscj0PeXOnjkzc44YY1BKKaXOVRV3B6CUUqp800SilFLqvGgiUUopdV40kSillDovmkiUUkqdF00kSimlzkulSSQiMkZElrk7DncTkWARSRURj1JcZoiIGBHxLK1lupKIbBeRy85hunM6BkWkuoh8JyJJIjK/uNOXJmePr4r6exSRaiKyU0TquzuW8yUii0Skn1PjuuM7EhGJARoAOUAq8ANwuzEmtdSDqeDsbX2TMeZnN8YQAkQDVY0x2e6Kw47FAC2NMXtdvJwQSmidRWQsMBUIL4ntZyfBX4E0u1ciEAW8ZIxZe77zdxcRGQO8Z3d6AN78s44YY3xKIYa7gebGmNvt7jnASCDTYbRdxpjuTszrGSDIGDPBFbE6sfxw4DVjTM+ixnXnHclge8d2AboCD7sxlnPmzqvsinKFXxyVdHs3BXafSxIpJOaD9u/PF7gQ2AmsFJErzj1M9zLGfGaM8bHXawD2Ojr0+xcX7c//AbPz9XvWMQ5nkogzRKSKiLjsHG6MiQLqiUhXZ0Yu9X9ADNDXoftFYIlDtzfwMhAHHAHeBao7DB8CbAKSgX1Af7t/LeAj4BBwAHgG8LCHTQD+sP9+F3g5X0zfAvfYfzcGFgLHsK4q73AY70lgATDHXv5NBaxfLeBTe/pY4DGgikMckcD/A5KwfsBX5Ju2sHWIBF4DTtrDmmNdXZ4AjgOfAbXt8WcDucAZrDu/B4AQwACe9ji/AdPt+aYAy4AAh3jG2etwAng8/77Lt97VgVfs8ZOAP+x+ecscb+/T48CjDtP1AFZhXRkfAt4CvByGG+A2YA8Qbfd7A4i398F64GKH8T2AR+xjI8Ue3gT43Z7XaXt7XG+PPwjreMq7Mu+U71h9ENgCZACejtvAjn2dHccR4FW7f5y9rFT7Xy8cjkF7nPbAT/a+PAI8UsA2fQrrajbLns9krAvAx+ztfBTrWKtlj5+3rSfbMfxewDwvAxIK6P8WsM6hu41DfLuAkcXY13nH1wRgv70fooEx+X+Pdnc4sNae11qsu6+8Yb9RyDF6lmPxbOuYANwPbAUy7X5BwNf883u/zWH8KvxzLB0H5gJ1zrLMZljHlodDvznAk2cZv4W9rcbZcR0DHnI4Jh33+3q7/x/2tliF9bsOseNfbO+nPcAkh2U8A8wD5tvbbh3Q0R72MDAvX0zv4HBuBGbh8Fs96/Y+12RwPv/49w8xyN6pbzgMfx1YBNTFumL6DnjO4YebBPSzd3Ig0MYe9g3WrW1NoD6wBvhf/gMXuATrJJRXtFfH3imN7XmuB6YBXvbBsR+4yh73SXvnDrXHrV7A+n2KlZh87R29G5jsEEc2cDdQFbjeXp+6Tq5DNlYxhyfWD7eFvS28gXpYJ8vXC9rW+U40jolkH9DKnt9vwPP2sHZYB/FF9rZ42V73syWSmfb0gVgn83A7rrxlfmAvozPWSbmtPV13rKtiT3vcv4C7HOZrsE5odfO2N3Aj4G9Pcy9wGKhmD8s7UbQGxF6ev8O8WjjMuxvWybinHfN4e5t5O2y/TViJqHr+bYr1gx5r/+0DXFjQdi7gGPTFSpr3AtXs7p5n2a5PAnMcuicBe7GOTR/gK2B2vuV+inUMFXR8XkbBJ9k+WBceNe1/8cBEext3wzqRtndyX3va80gGWtvTNHKY3nFb1AVOAWPt6Ubb3Xn77DfOcowWco452zomYP2+g+x5edj79xGsY7yFvX+vsMe/DyuBBdr76aO8bV3AvIcAm/P1cyaRvGvPuxvW76KlPfwZICLfNH/Y8bXFOn948s+Fad48jgOXOswjC7jWHv8h+9jxtLdBKuBnj+tlT9vZYXkPAF8WeU53VbIoYifH2CuQYm/IX/jnKlqwsnpzh/F78c+V6HtY5Xb559nA3gmOdy6jgeUFHLiCdbV2id39f8Cv9t89gbh8834YmOXwo/7PVZ7DuB52HO0c+v0P+M0hjoPYSczutwbrR+TMOsSdbdn2OEOBjfm2dVGJ5DGH4bcCP9h/TwO+cBhWA+sq6T+JBCupnnE8CAtYZlC+dR51lnW4C/jaodsAfYpY71N5y8a6eh5ylvHyJ5J3gOn5xtnFPz/EGByu8PJvU6zE/RT5rpDzb+cCjsHRjvupiHV7kn8nkl+AWx26W2OdLDwdltuskPldRsEn2Tb2tIFYFzgr8w1/D3jCyX2dl0gSgeHkS2j5tsVYYE2+4auACUUdo+ewjgnAOIfu3sD+fOM8Dnxg/70n71iwu5tg/UarFDDv8TjcZdn95gDp9nbI+/eRPSwvkTR0GH8DcJ3999kSyTSH7lB739d06PcS8KHDPBzv/DywLpx62d0/ARPtv4cCW/It7xZgWVHHqDvL2IcaY34WkUuBz4EArI1cD+uEtV5E8sYVrA0A1o5cWsD8mmJl3EMO01XBuqr6F2OMEZG5WD/m34EbsHZ43nwai0iiwyQewEqH7v/M00EAVmaPdegXi/XjzHPA2HvJYXhjJ9fhX8u23w55E7gY66q2CtZJtTgOO/ydhnWVix3T38szxqSJyImzzCMA64poX3GXIyKtgFeBMKx974l11ego/3rfC9xkx2gAPzsGsI6RwuJw1BQYLyJTHfp52fMtcNn5TAaeBnaKSDTwlDFmsRPLLU6M+TXmv8eXJ9aFSJ7CYj6bQKxtmYi1XXrm+x14YhWXOrOvMcacFpHrsa7qPxKRSOBeY8zOfKPmXx/472/mbMfouXDcNk2B4AJ+77/ZfwcD34lIrsNwg1Va4BgTWL873wKW97wx5smzBWOMKe66OcbfGDhujDnt0C8W6FDQ+MaYHBE5wD/H9ydYd52zsO7y8z/f8cU6Hgrl9td/jTErgAisYhOwbq3OYN0C17b/1TL/PCyLx3oukF881pVCgMN0fsaY9mdZ9BfAdSLSFOsuZKHDfKId5lHbGONrjBnoGHYhq3Qc6wqhqUO/YKznHXkCxSFT2MMPOrkO+Zf9nN2vkzHGD+tgkELGL45DWLe/gPUaKlZxUkGOY115FbRvivIO1rOilvY6PMK/1wEc1kNELsZ6bjESq7y6NlbxYN40ZztGChIPzMi3v2sYY74oaNn5GWP2GGNGY51YXgAWiEjNwqY5hxjzO8h/j69srOcsf4d2DvO9Fthgn5TigRX5touPMeYWirGvjTE/GmP6YRVr7cQq3ixqfeC/v5mS5Lht4oE9BfzeB9vDE4B++YZXy3fyz7MFaF6Cr9afbR869j8IBNjHXJ78265J3h/2w/lAezqwikW7i0h7rBcUPs+3rLbA5qICdXsisb0O9BORLsaYXKyD7bW8d7FFJFBErrLH/QiYKCJX2G8tBIpIG2PMIayHcK+IiJ89rLl9x/MfxpiNWA+3PgR+NMbkZd01QLKIPGi/v+8hIh1E5AJnVsQYkwN8CcwQEV87Ud3DP3c8YJ107hCRqiIyAmtnLS3uOth8sYoJE0UkEOv5gKMjWGXp52IBMFhEwkXEC6sIJ/8JHgB7v30MvCoije3t1ktEvJ1Yji9WWXqqiLTBup0uavxsrP3nKSLTsO5I8nwITBeRlmLpJCJ5CTD/9vgAmCIiPe1xa4rI1SJS0JXlf4jIjSJSz17/vGMox44tl7Nv+8VAQxG5S0S87WOlyNcsbV8Ad4tIqIj4AM9iPTQ9l7e6xP4NPYF1h/eIQ3ytRGSsfZxWFZELRKSts/taRBqIyDX2SS4D6zjNKSCMpfaybhART/supp0dg6utAjJF5F6xvgHxEJGOIpL3ZtW7wLMiEgxWCYCIXFPQjIwxMVhF5iXyVhbWsRqS76Iz/zKjsR6gP2sfR12w7jA+cxith4gMEZGqWHeHKVgvNGCMScN60eALINIYkz95XwJ8X1SgZSKRGGOOYT0cfNzu9SDWA6HVIpIM/IxVDowxZg3WhnoN6yp0Bf9czYzDKpbYgXWbuQDrSuhsvgD64pCF7UQwGOu15Gisq68Psd6mctZUrOc8+7HKND/H+uHl+RNoac97BlaZaF6RUXHX4SmsB2xJwBKsKwxHzwGPiUiiiNxXjHXAGLPdXpe5WHcnKVjlqxlnmeQ+rIfca7HeIHkB546x+7CKF1OwTuzzihj/R6yDezfWbXw6/77dfxUrmS/DSlAfYT1YBet5wyf29hhpjFmH9YzsLaztvRer/N5Z/YHtIpKK9SbZKGNMuv0DnQFE2su60HEiY0wK1ksSg7GKSPYAlzu5zI+xiiB+xzpG07H2U3E0tmNOxdpfHYHLjDHLHOK7EhiFdfV6GGt/5iULZ/Z1FayXCQ7a41yK9XzjX+xjf5A97gmsB7yDjDHHi7lOxWYn34FYL/HEYP0m3+OfC5NXsb5z+0VEUrDe6ivsovI9rGc+jh4R6yPNvH8F3c0UZB7WueCkiKwpZLzrsc4nh7HOF48YY5Y7DP8aq6TipD3usHwXHZ9g7f9/FWuJSC/gpDFmQ1GBuuWDxMpMRCZgvTJ8kbtjKS776jcRqwgq2t3xKFXWiEg1YCPWA/qjZSCeIj9qFJFmWMVyDY3DR+Ei8i0wM+/iojCV7oM2VTwiMhjrLSHBeo61FevKTSmVjzEmHauoulywn5ncA3xu8tUsYowZ4ux8NJGoogzBuuUVrLLYUUZvY5Uq90SkFtZD+RjgqsLHLmJeek5QSil1PsrEw3allFLlV7kr2goICDAhISHuDkMppcqV9evXHzfG1HPFvMtdIgkJCWHdunXuDkMppcoVEclfe0CJ0aItpZRS50UTiVJKqfOiiUQppdR50USilFLqvGgiUUopdV40kSillDovLkskIvKxiBwVkW1nGS4i8qaI7BWRLSLSzVWxKKWUch1X3pFEYFWxfTYDsKo+bgncjNW4kVJKqRKWnlVQMzAlx2UfJBpjfheRkEJGGQJ8alcAuFpEaotII7txJ6WUUufpUOIZpn26gd/ii9v6dvG488v2QP7dGFGC3e8/iUREbsa6ayE4OLhUglNKqfLIGMOGuERm/rSbX/ccwwBVj6e7dJnuTCQFNR9ZYFXExpj3gfcBwsLCtLpipZTKJyM7h6VbDxERGcPmhCQkK5cz244zpW9LHp8RTtUI1y3bnYkkAYdG6YEg/mmQXimllBOOpqTz+Z9xzFkdx/HUDEL8azB9SHuae3jS5C5fmjQpTivh58adiWQRcLuIzAV6Akn6fEQppZyzJSGRWZExLN5ykKwcQ0Cm4cjXexg/rgtje4WUaiwuSyQi8gVwGRAgIgnAE0BVAGPMu8BSYCCwF0gDJroqFqWUqgiycnL5ftthIiKj2RCXiI+3J938arD8/c1sik3innt68cADvUs9Lle+tTW6iOEGuM1Vy1dKqYriRGoGX6yJY/bqWI4kW8VXTwxux5Zv9/D60ysID2/Cu9+MomPHBm6Jr9y1R6KUUpXFtgNJRETFsGjzQTKzc7m4ZQBPDWpHt0a1qF+vJrsCfGnX0p/Jk7tRpUpB7y+VDk0kSilVhmTn5LJsxxEiImNYE3OSGl4eXB/WhPHhTdm74Qi3XbuALl0asnDhSFq3DqB16wB3h6yJRCmlyoJTpzOZuzae2atiOJiUTpO61Xns6raMCGvC6VPp3HX7D8yfv4PWrf25/fYL3B3uv2giUUopN9p5OJmIyBi+3niAjOxcwpv78+Q17bmibQM8qgi//LKfa6+dR2ZmDtOnX87994fj7V22Tt1lKxqllKoEcnINP/9lFV+t2n+CalWrMKxbIBPCQ2nd0BeArKwcPKp40LlzQwYObMkzz/ShRYu6bo68YJpIlFKqlCSlZfHlung+WRVDwqkzBNauzkMD2nB9WBPq1PQCIDk5g8cf/5U//zxAZOQkAgJqMHfude4NvAiaSJRSysX2Hk1hVmQMX204wJmsHHqE1uWxq9vSt20DPD2sStiNMSxYsIM77/yBw4dTufXWC8jIyKFGjbLfbJQmEqWUcoHcXMPyXUeJiIph5Z7jeHlWYWiXxowPD6F9439XW3Ls2GnGj/+G77/fS9euDfn221FccEGgmyIvPk0kSilVglLSs5i/LoFPVsUQeyKNhn7VuP+q1oy6oAn+Pt4FTuPn583x42m8/vpV3HZbDzw9y/5diCNNJEopVQL2H0vl01WxzF8Xz+nMHLo3rcN9V7amf4eGVPX4b2L4/fdYZsxYycKFI/Hx8WL16pvc+lHh+dBEopRS5yg31/D7nmNERMXw265jeHlUYVDnRkwID6FTUO0Cpzl+PI377/+JiIhNhITUJiYmkQ4d6pfbJAKaSJRSqthSM7L5akMCEVEx7D92mnq+3tzdtxU39Aymnm/BxVfGGGbN2sT99/9EcnIGDz98EY89dgk1alQt5ehLniYSpZRyUuyJ03y6KpYv18aTkpFN56BavH59FwZ2bISXE8815szZQrt29Xj33atp375+KURcOjSRKKVUIYwxRO07wazIaH7ZeRQPEQZ2bMTE3iF0Da5T6LRpaVk8++xKpkwJIyjIj4ULR1KrVrVyXYxVEE0kSilVgLTMbL7eeICIyBj2HE3Fv6YXUy9vwZgLm9LAr1qR0y9duofbbltKTEwigYG+3HLLBdSpU70UIi99mkiUUspB/Mk05qyOZe7aeJLOZNG+sR8vj+jMoE6NqFbVo8jpExKSueuuH1i48C/atg1gxYoJXHJJ01KI3H00kSilKj1jDKv3nyQiKpqfdhxBROjfoSETw0Po3rQOIs4XRc2Y8TtLluzh2Wf7cO+94Xh5FZ18yjuxGiosP8LCwsy6devcHYZSqgJIz8rh200HmBUZw87DKdSpUZXRPYK58cKmNK7tfDHUmjUHqF7dk44dG3DiRBpJSRk0a1b485PSJiLrjTFhrpi33pEopSqdg4lnmLM6li/WxHEqLYs2DX15YXhHhnQJdKr4Kk9SUjqPPPIL77yzjkGDWrFo0Wj8/Wvg71/DhdGXPZpIlFKVgjGG9bGnmBUZww/bD2OMoV+7BkzsHUrP0LrFKr4yxjBv3nbuvvtHjh49zdSpPZg+vY8Loy/bNJEopSq0jOwcvtt8iIioaLYdSMavmic3XRTKjRc2pUndc7tzmDNnC+PGfUNYWGMWLx5N9+6NSzjq8kUTiVKqQjqSnM5nq2P5fE0cx1MzaVnfhxnXduDaroHU8Cr+qS8jI5v9+0/Rtm09Ro5sT3Z2LuPGdcajgHq0KhtNJEqpCmVjnFV8tXTrIXKM4Yo29ZnYO5Tw5v7FKr5ytHx5NLfcsoS0tCz27JmKt7cnEyd2LeHIyy9NJEqpci8zO5elWw8xKyqGzfGJ+Hp7Mj48hHG9mtLUv+Y5z/fo0dPcd98yZs/eQrNmdXj//cFlrr30skC3iFKq3DqWksHnf8Yx589YjqVk0CygJk8Pac+wbkH4nOcJf+/ek/To8QGpqZk8+ujFPProxVSvXv4rWHQFTSRKqXJna0ISs6KiWbz5EJk5uVzWuh4TwkO4pGW9867HKjk5Az8/b5o3r8PkyV2ZNKkrbdvWK6HIKyZNJEqpciErJ5cfth0mIiqG9bGnqOnlwQ09gxnXqynN6vmc9/xPn87k6adX8MEHG9iy5RaCgvx46aUrSyDyik8TiVKqTDuRmsHctfHMXhXL4eR0mvrXYNqgdlwXFoRftZIpavruu13cfvv3xMUlMXly1wrRRkhp0kSilCqTth9MIiIyhm83HyQzO5eLWwYw49oOXN665FoTzM7OZeTI+Xz99U7at6/HypUTueii4BKZd2WiiUQpVWZk5+Ty044jzIqKYU30SapX9WBE9yAmhIfQsoFviS3HGIOI4OlZhUaNfHj++Su4++5elaKCRVfQRKKUcrvEtMy/i68OJJ4hqE51Hh3YlpFhTahVwsVMq1cncNttS/ngg8F069aImTOvLtH5V0aaSJRSbrPrcAoRUTF8vTGB9KxcejXzZ9rgdvRt2wCPEm5F8NSpMzzyyC+89956Gjf25dSpMyU6/8rMpYlERPoDbwAewIfGmOfzDQ8GPgFq2+M8ZIxZ6sqYlFLulZNr+HXnUWZFRhO17wTenlUY1i2Q8eEhtGno55Jlzpu3jTvu+IHjx9O4664Leeqpy/D19XbJsiojlyUSEfEAZgL9gARgrYgsMsbscBjtMeBLY8w7ItIOWAqEuCompZT7JJ3JYv66eD5ZFUP8yTM0rlWNB/u3YdQFTahT08uly9658zghIbX54YcxdO3ayKXLqoxceUfSA9hrjNkPICJzgSGAYyIxQN4lSC3goAvjUUq5wd6jqXwSFcPCDQmkZebQI6QuDw9oy5XtGuDpogoP09OzeeGFP+jWrRGDB7fmkUcu5rHHLtEKFl3ElYkkEIh36E4AeuYb50lgmYhMBWoCfQuakYjcDNwMEBysr+YpVdbl5hpW7D7Gx5HRrNxzHC+PKlzTpTETwkPoEFjLpcv++ef93HrrEvbsOcm99/Zi8ODWVC1GY1Wq+FyZSAp6Upa/Xd/RQIQx5hUR6QXMFpEOxpjcf01kzPvA+2A1teuSaJVS5y0lPYsF6xP4JCqGmBNpNPDz5r4rWzGqRzABPq59JnHkSCr33LOMzz/fSosWdVm27Eb69Wvu0mUqiysTSQLQxKE7iP8WXU0G+gMYY1aJSDUgADjqwriUUiUs+vhpPomKYcH6BFIzsukWXJt7rmzNgA4NqVpKxUk//bSfBQt2MG3aJTz88MVUq6YvpZYWV27ptUBLEQkFDgCjgBvyjRMHXAFEiEhboBpwzIUxKaVKiDGGlXuOMysymuW7jlHVQxjUySq+6tykdqnEsHnzYfbsOcl117VjzJiO9O7dhNDQOqWybPUPlyUSY0y2iNwO/Ij1au/HxpjtIvI0sM4Yswi4F/hARO7GKvaaYIzRoiulyrDTGdl8tSGBiKgY9h07TYCPN3f1bckNPYOp71utVGJITc3kiSeW88YbfxISUpuhQ9vg6VlFk4ibuPTez/4mZGm+ftMc/t4B9HZlDEqpkhF3Io1PV8Uwb108KenZdAqqxWvXd2Zgx0Z4e5bew+xvvtnJ1Knfk5CQzM03d+O55/ri6alvY7mTFiIqpc7KGMOqfSf4ODKGX3YewUOEAR0bMSE8hG7Btc+56dpztXXrEa69dh4dO9Zn3rzrCA9vUvREyuU0kSil/uNMZg5fbzxARFQ0u4+k4l/Ti9svb8GYnk1pWKt0iq/yZGXlsHJlHH36hNKxYwOWLLmBfv2a6Su9ZYgmEqXU3xJOpTF7dSxz18STdCaLdo38eOm6Tgzu3JhqbjhxR0XFM2XKYrZvP8auXbfTokVdBg5sWepxqMJpIlGqkjPGsCb6JLMiY1i24zAiwlXtGzCxdyhhTeuUevEVwMmTZ3jooZ/54IMNNGnix1dfjaRFi7qlHodyjiYSpSqp9KwcFm06yKyoGP46lEztGlX536XNufHCpgTWru6+uNKz6dLlXQ4eTOHee3vx5JOX4ePj2rq41PnRRKJUJXMo6QxzVsfyxZp4Tp7OpHUDX54f1pEhXQKp7saGnRISkgkK8qNaNU+mT7+cLl0a0rlzQ7fFo5yniUSpSsAYw4a4U8yKjOH7bYcxxtC3bQMm9A6hVzN/txRf5TlzJovnnvuDF16IZMGCEQwe3Jrx47u4LR5VfE4lEhHxAoKNMXtdHI9SqgRlZOewePMhIqJi2HogCb9qnky+KJSxFzalSd0a7g6PZcv2ceutS9i37xQ33tiJHj0C3R2SOgdFJhIRuRp4FfACQkWkC/CEMeZaVwenlDo3R5PTmfNnHJ//Gcvx1Exa1PfhmaEdGNYtkBpeZaMgYurUpbz11lpatqzLzz+P5Yormrk7JHWOnDminsaq/n05gDFmk4i0cGlUSqlzsik+kYjIaJZsPUR2rqFP6/pM6B3CRS0C3Fp8lScnx6rY28OjChdeGERAQA0efPAirWCxnHNm72UZYxLzHYRaH5ZSZURmdi7fbzvErMgYNsUn4uPtyY0XNmV8rxBCAmq6O7y/bdhwiClTFjN2bCemTu3JmDGd3B2SKiHOJJK/RGQkUMWuyfdOYLVrw1JKFeV4agaf/xnHnNWxHE3JoFlATZ66pj3Duwfh4112rvBTUjKYNm05b765hnr1atCoka+7Q1IlzJmj7XZgGpALfIVVm+/DrgxKKXV22w4kMSsyhu82HyQzJ5dLW9XjhetCuLRlPapUcX/xlaNly/YxadK3HDyYwpQpYTz77BXUrl26Vawo13MmkVxljHkQeDCvh4gMw0oqSqlSkJWTy7LtR4iIimZtzClqeHkwukcTxoWH0Lyej7vDOysvLw/q16/JwoUj6dkzyN3hKBeRopr/EJENxphu+fqtN8Z0d2lkZxEWFmbWrVvnjkUrVepOns7kizVW8dWhpHSC69ZgfHgII8KC8KtW1d3h/UdWVg6vvrqK5OQMZsy4ArDaby9rd0qVkX3eDnPFvM96RyIiV2E1gxsoIq86DPLDKuZSSrnIX4eSiYiM4ZtNB8jIzuWiFgFMH9KBy9vUx6OMnpT/+CPu7woWR4xo93cC0SRS8RVWtHUU2AakA9sd+qcAD7kyKKUqo5xcw087jjArMpo/o09SrWoVhncPYkJ4CK0alN0H1CdOpPHggz/z0UcbCQ6uxXffjWbQoFbuDkuVorMmEmPMRmCjiHxmjEkvxZiUqlSS0rKYuzaOT1fFciDxDIG1q/PIwDaMDGtC7Rplv7LCEyfOMHfuNh54IJxp0y6lZs2yH7MqWc48bA8UkRlAO+Dv1y2MMXrJodR52H0khYioGL7ecIAzWTlc2Kwujw9qR792Dcps8VWev/46xpdfbueJJy6jVSt/4uLupm5d99UYrNzLmUQSATwDvAwMACaiz0iUOic5uYblO48yKyqayL0n8Paswk9NkmMAACAASURBVNAugUzoHULbRn7uDq9IaWlZzJjxOy+9FIWPjxeTJ3cjKMhPk0gl50wiqWGM+VFEXjbG7AMeE5GVrg5MqYokOT2LL9fG8+mqWOJOptGoVjUe6N+aURcEU7ecFAX98MNebr11CdHRiYwf35mXXupHvXpl58t55T7OJJIMsepH2SciU4ADQH3XhqVUxbDvWCqfRMWwYH0CaZk5XBBShwf7t+HK9g2o6lHF3eE5LTU1k7Fjv8bfvzrLl4/nsstC3B2SKkOcSSR3Az7AHcAMoBYwyZVBKVWe5eYaVuw5RkRkDCt2H8PLowqDOzdmYu8QOgTWcnd4TsvJyeWLL7YxenQHfHy8+PnnsbRpE4B3Gap+RZUNRR4Rxpg/7T9TgLEAIqKfqCqVT2pGNgvWxfPJqliij5+mvq839/ZrxeiewQT4eLs7vGJZv/4g//vfYtavP0T16p4MH95OWytUZ1VoIhGRC4BA4A9jzHERaY9VVUofQJOJUkDM8dN8siqG+esSSM3Ipmtwbd4Y1YUBHRrh5Vl+iq8AkpLSefzx5cycuZb69Wsyd+5whg1r6+6wVBlX2JftzwHDgc1YD9i/xqr59wVgSumEp1TZZIzhj73HmRUZw/JdR/GsIlzdsRETeofSpUltd4d3zoYP/5Jff43mttsu4Jln+lCrllawqIpW2B3JEKCzMeaMiNQFDtrdu0onNKXKnrTMbBZuOMAnUTHsPZpKgI8XU/u05MaewdT3K58n3f37T1GvXg18fb2ZMaMPVaoIF1ygTd4q5xWWSNKNMWcAjDEnRWSnJhFVWcWfTOPTVTHMWxtPcno2HQNr8erIzlzdqRHenh7uDu+cZGbm8PLLUUyf/jt33NGDF17opzX0qnNSWCJpJiJ5VcULEOLQjTFmmEsjU8rNjDGs2n+CiMgYfv7rCCLCgA4Nmdg7lG7BtctE07Xn6vffY5kyZTF//XWc665rxx139HR3SKocKyyRDM/X/ZYrA1GqrDiTmcO3mw4QERXDzsMp1K3pxa2XtWDMhcE0qlX+v+B+7bVV3HPPMkJCarNkyQ0MHNjS3SGpcq6wSht/Kc1AlHK3A4lnmL0qlrlr40hMy6JtIz9evK4T13RuTLWq5bP4Kk9uruH06Ux8fb25+upWHDuWxmOPXUKNGmWvTRNV/uiXRapSM8awNuYUEVHR/Lj9CMYYrmrfkAnhIfQIrVuui6/ybN9+lClTlvzdUmGrVv48++wV7g5LVSAuTSQi0h94A/AAPjTGPF/AOCOBJwEDbDbG3ODKmJQCSM/K4bvNB4mIimH7wWRqVa/KTReHMvbCpgTVqeHu8EpEWloW06ev4OWXV1GrljeTJnXBGFMhkqMqW5xOJCLibYzJKMb4HsBMoB+QAKwVkUXGmB0O47QEHgZ6G2NOiYjW4aVc6nBSOnNWx/L5mjhOns6kdQNfnhvWkaFdAqnuVb6Lrxxt3HiIYcO+JCYmkYkTu/Dii/0ICKgYCVKVPUUmEhHpAXyEVcdWsIh0Bm4yxkwtYtIewF5jzH57PnOxvk3Z4TDO/wEzjTGnAIwxR4u/CkoVzhjDhrhEIqJi+H7rIXKMoW/bBkwMD6FXc/8KdYWed8cRHFyL4OBafPLJUC65pKm7w1IVnDN3JG8Cg4BvAIwxm0XkciemCwTiHboTgPzvGLYCEJFIrOKvJ40xPzgxb6WKlJGdw9Kth5gVGcOWhCR8q3kyITyEcb1CCPavWFfn2dm5vPXWGhYt2sVPP43F378GK1ZMcHdYqpJwJpFUMcbE5rtqy3FiuoIu80wBy28JXIZVd9dKEelgjEn814xEbgZuBggODnZi0aoyO5qSzmer4/jszziOp2bQvF5Npg/twLCugdSsgDXXrllzgClTFrNx42EGDGhBcnIGdeqU/9eUVfnhzK8q3i7eMvZzj6nAbiemSwCaOHQHYVWzkn+c1caYLCBaRHZhJZa1jiMZY94H3gcICwvLn4yUAmBzvFV8tXjLQbJyDH3a1GdCeAgXtQigShlvuvZcpKZm8uCDP/HOO+to1MiX+fNHMHx42wpVVKfKB2cSyS1YxVvBwBHgZ7tfUdYCLUUkFKsxrFFA/jeyvgFGAxEiEoBV1LXfudCVgqycXL7fdpiIyGg2xCXi4+3JmJ5NGR8eQmhAxW69r2rVKvz2WyxTp/Zg+vQ++PmVr6rqVcXhTCLJNsaMKu6MjTHZInI78CPW84+PjTHbReRpYJ0xZpE97EoR2YFVXHa/MeZEcZelKp8TqRl8sSaO2atjOZKcQWhATZ4c3I7h3YPwrVZxP7Lbu/ckTz+9gpkzB+Lr68369TdTrVrFK65T5YsYU3hJkYjsA3YB84CvjDEppRHY2YSFhZl169a5MwTlRtsOJBERFcOizQfJzM7lklb1mBgewqWt6lXI4qs8GRnZvPhiJDNmrMTLy4MlS27g4ov1bSzlPBFZb4wJc8W8nWkhsbmIhGMVTT0lIpuAucaYua4ISKn8snNyWbbjCLMio1kbc4oaXh5cH9aE8eEhtKjv4+7wXG758mhuuWUJu3ad4Prr2/Pqq1fRuLGvu8NS6m9O3RMbY6KAKBF5Engd+AzQRKJc6tTpTL5YG8ecVbEcTEqnSd3qPHZ1W0aENaFW9YpbfOXIGMOMGSvJysrlhx/GcNVVLdwdklL/4cwHiT5YHxKOAtoC3wLhLo5LVWI7DycTERnD1xsPkJGdS+8W/jw1pAN92tTHowIXX+XJzTV89NEG+vdvQZMmtZg9+1pq165G9UqSPFX548wdyTbgO+BFY8xKF8ejKqmcXMPPfx0hIjKGVftPUK1qFYZ1C2JCeAitG1aeYpwtW44wZcpiVq1KYNq0S3jqqctp1KjyrL8qn5xJJM2MMbkuj0RVSklpWXy5Lp5PVsWQcOoMgbWr8/CANlx/QRNq1/Byd3ilJjU1k6ee+o3XXltNnTrViYgYwrhxnd0dllJOOWsiEZFXjDH3AgtF5D+vdmkLiep87DmSQkRUDF9tOMCZrBx6htblsavb0rdtAzw9qrg7vFL35JO/8corq7jppq48/3xf/CtYFS6qYivsjmSe/b+2jKhKRG6uYfmuo0RExbByz3G8PKswtEtjxoeH0L5xLXeHV+ri45M4fTqLNm0CeOihixg6tA0XXaRVAKnyp7AWEtfYf7Y1xvwrmdgfGmoLisopyelZLFiXwCerYog9kUZDv2rcf1VrRvcIpm7NylN8lSc7O5c33/yTadOW0717Y1asmEBAQA1NIqrccuYZyST+e1cyuYB+Sv3L/mOpfBIVw4L1CZzOzCGsaR3uv6o1V7VvSNVKWHwFsHp1AlOmLGbz5iNcfXVL3nproLtDUuq8FfaM5HqsV35DReQrh0G+QGLBU6nKLjfX8PueY0RExfDbrmN4eVRhUOdGTAwPpWNQ5Su+crRkyW4GD/6Cxo19+eqrkQwd2kYrWFQVQmF3JGuAE1i19s506J8CbHRlUKr8Sc3I5qsNCURExbD/2Gnq+Xpzd99W3NAzmHq+lbcyQWMMBw+mEBjoR9++zXj66cu5886e+FbibaIqniLr2iprtK6tsiX2xGk+iYpl/rp4UjKy6dykNpN6hzCgQyO8PCtn8VWe3btPcOutS9i9+wQ7dtyGj0/lex6kyg631LUlIiuMMZeKyCn+3SCVAMYYU9cVAamyzxhD5N4TRERF88vOo3iIcHWnRkwID6FrcB13h+d26enZPP/8Hzz33B9Ur+7Jc89dQfXqWkOvqrgKO7rzmtMNKI1AVNmXlpnN1xsPEBEZw56jqfjX9GLq5S0Yc2FTGvhVc3d4ZcLhw6lccsks9uw5yejRHXj11ato2LDiVyypKrfCXv/N+5q9CXDQGJMpIhcBnYA5QHIpxKfKgPiTacxeHcvcNXEkp2fTIdCPV0Z0ZlDnRnh7erg7vDIhKyuHqlU9aNCgJpdc0pSZMwfSr19zd4elVKlw5n77G+ACEWkOfAosAT4HBrkyMOVexhhW7z9JRFQ0P+04gojQv0NDJoaH0L1pHX3byJaba3j//fU8++xKoqImExTkx4cfXuPusJQqVc4kklxjTJaIDANeN8a8KSL61lYFlZ6Vw7ebDjArMoadh1OoU6MqUy5tztheTWlUq7q7wytTNm8+zP/+t5g//zxAnz6hZGXluDskpdzCqaZ2RWQEMBYYavfT+qwrmIOJZ5i9OpYv1sSRmJZFm4a+vDi8E9d0aUy1qlp85cgYw/33/8Trr6+mbt3qzJ59LWPGdNS7NFVpOftl+61Y1cjvF5FQ4AvXhqVKgzGGdbGniIiM4YfthzHGcGW7hkzoHULP0Lp6YjwLEeHUqTNMnmxVsFinjt6pqcrNqe9IRMQTyGuaba8xJtulURVCvyM5f+lZOSzecoiIqGi2HUjGr5ono3sEM7ZXU4LqaK2zBYmNTeTOO39g2rRL6datEbm5pkK3Ea8qHre22S4iFwOzgQNY35A0FJGxxphIVwSkXOdIcjqfrY7lsz/jOHE6k1YNfHj22o4M7dqYGl76nUNBsrJyeO211Tz11AoArr++Pd26NdIkopQDZ84erwEDjTE7AESkLVZicUlmUyVvQ5xVfLV06yFyjOGKNg2Y2DuE8Ob+WnxViKioeP73v8Vs23aUIUNa8+abAwgOrtz1hSlVEGcSiVdeEgEwxvwlIlrXQxmXmZ3L0q2HmBUVw+b4RHy9PRkfHsK4Xk1p6l/T3eGVCz//vJ+kpHS++eZ6hgxp4+5wlCqzinxGIiIRQAbWXQjAGKCGMWa8a0MrmD4jKdyxlAw++9MqvjqWkkGzejWZGB7CsG5B1PTW4qvCGGOYPXsL9erVYMCAlmRkZJOVlat1ZKkKwa3PSIApwB3AA1jPSH4H/p8rglHnbktCIhGRMSzecojMnFwub12PCb1DubhFgJbnO2HnzuPccssSfvsthhEj2jFgQEu8vT3x1kp6lSpSoYlERDoCzYGvjTEvlk5IyllZObn8sO0wEVExrI89RU0vD27oGcy4Xk1pVk/rd3LGmTNZPPvsSl54IZKaNb14771B3HRTN3eHpVS5Uljtv49gtYS4AauKlKeNMR+XWmTqrE6kZjB3bTyzV8VyODmdEP8aPDG4Hdd1D8K3mn4rWhzffbebZ55ZyY03duLll/vRoIEmYKWKq7A7kjFAJ2PMaRGpBywFNJG40faDSURExvDt5oNkZudyccsAnh3Wgcta1dfiq2I4fDiVTZsO079/C0aMaEdIyE306BHo7rCUKrcKSyQZxpjTAMaYYyJSuVspcpPcXMOP2w8zKyqGNdEnqV7Vg5FhQYzvFULLBr7uDq9cycnJ5b331vPww7/g5eVBXNxdVK9eVZOIUuepsETSzKGtdgGaO7bdbowZ5tLIFAAv/riLd1fsI6hOdR67ui0jwppQq7oWXxXXhg2HmDJlMWvXHqRv32a8/fZAqut2VKpEFJZIhufrfsuVgaj/Whtzkvd+38fwbkG8eF0nPLT46pxER5+iR48PCAioweefD2PUqA76IaZSJaiwhq1+Kc1A1L+dzsjm3i83E1SnOk8Naa9JpJiMMWzdepROnRoQGlqHWbOGMHhwa2rX1pYclSpp+tyjjJqx9C/iT6Xxyogu+OiHhMUSHX2KQYO+oGvX99iy5QgAY8d21iSilIu4NJGISH8R2SUie0XkoULGu05EjIho/V3A8l1H+fzPOG6+uBk9Quu6O5xyIzMzh+ef/4P27d9mxYoYXn65H+3a1XN3WEpVeE5f6oqItzEmoxjjewAzgX5AArBWRBY51ttlj+eL9eX8n87OuyJLTMvkwQVbaNXAh7v7tXJ3OOVGTk4u4eEfsX79IYYNa8vrr19FkyZawaJSpaHIOxIR6SEiW4E9dndnEXGmipQeWG2X7DfGZAJzgSEFjDcdeBFIdz7siuvxb7dz8nQmr47soi0TOiE52bq28fCowqRJXfnuu9EsXDhSk4hSpciZoq03gUHACQBjzGbgciemCwTiHboT7H5/E5GuQBNjzOLCZiQiN4vIOhFZd+zYMScWXT4t2nyQ7zYf5K6+LekQqCfCwhhjiIjYRLNmb/DttzsBuPXWCxg0SO/ilCptziSSKsaY2Hz9cpyYrqDXjP6uatj+wPE14N6iZmSMed8YE2aMCatXr2KWeR9JTufxb7bRpUltplza3N3hlGk7dhzjsss+YeLEb2nTJoDmzfU5klLu5MwzkngR6QEY+7nHVGC3E9MlAE0cuoOAgw7dvkAH4Df7nf6GwCIRucYYU6nqiTfG8MCCLWRk5/DqyM54eujLdGfz4ouRPPror/j5efPhh4OZOLGrVg+jlJs5k0huwSreCgaOAD/b/YqyFmgpIqFYzfSOAm7IG2iMSQIC8rpF5DfgvsqWRAC+WBPPit3HeOqa9lpr71kYYxARGjb0YcyYjrz0Uj/q1dMGupQqC4pMJMaYo1hJoFiMMdkicjvwI+ABfGyM2S4iTwPrjDGLih1tBRR74jTPLNnBRS0CGHthU3eHU+YcPJjCnXf+wMUXB3PHHT0ZN64z48Z1dndYSikHRSYSEfkAh2cbeYwxNxc1rTFmKVatwY79pp1l3MuKml9Fk5NruPfLzXhUEV68rpMW0TjIycnl7bfX8uijv5KVlUt4eJC7Q1JKnYUzRVs/O/xdDbiWf7+Npc7RByv3sy72FK+O7Ezj2tXdHU6ZsWnTYW66aRHr1x/iyiub8/bbA/WBulJlmDNFW/Mcu0VkNvCTyyKqJHYeTubVZbvp374h13bVaswdJSWlc/BgCvPmXceIEe20gkWlyrhzqcQpFNDC/POQmZ3L3fM241fdkxnXak20xhjmz9/Bnj0nePTRS7j00hD277+TatW0jjGlygNnvmw/JSIn7X+JWHcjj7g+tIrrjV9289ehZJ4b1gl/H293h+NW+/adZODAz7n++gV8++0usrKsT5Q0iShVfhT6axXrUrkz1uu7ALnGmP88eFfO2xB3ind+28eI7kH0a9fA3eG4TUZGNi+/HMUzz6ykatUqvPFGf2699QI8PfUbGqXKm0ITiTHGiMjXxpjupRVQRZaWabUx0qhWdaYNbufucNwqPj6Z6dN/Z/Dg1rz++lUEBvq5OySl1Dly5vJvjYh0c3kklcDz3+8k+vhpXh7RGd9qla+Z12PHTvPWW2sAaNGiLjt23Mb8+SM0iShVzp31jkREPI0x2cBFwP+JyD7gNFYdWsYYo8mlGFbuOcanq2KZfFEovZr7uzucUpWba5g1ayMPPPAzKSkZ9OvXjNatA2jWrI67Q1NKlYDCirbWAN2AoaUUS4WVlJbF/fO30KK+D/df1drd4ZSqbduOcsstS/jjjzguvjiYd98dROvWAUVPqJQqNwpLJAJgjNlXSrFUWE9+t51jqRm8P657pWpjJDMzhyuvnE1mZg4ff3wNEyZ0qfSvOitVERWWSOqJyD1nG2iMedUF8VQ4S7ce4uuNB7irb0s6BdV2dzil4tdfo7n00qZ4eXnw5ZcjaNMmgICAGu4OSynlIoU9bPcAfLCqey/onyrC0ZR0Hv16Kx0Da3Hb5S3cHY7LJSQkM3z4l1xxxad8+ulmAC66KFiTiFIVXGF3JIeMMU+XWiQVjDGGR77ayunMHF67vjNVK3AbI9nZubz11hoef3w5OTm5PPfcFYwZ08ndYSmlSkmRz0jUuZm/LoGf/zrK44Pa0aJ+xb6BGzv2a+bO3caAAS2YOXMgoaH6NpZSlUlhieSKUouigok/mcZT323nwmZ1mRge4u5wXCIxMR1Pzyr4+Hhx220XMHx4W4YPb6sP05WqhM5a3mKMOVmagVQUubmG++ZvRkR4eUTnCtfGiDGGuXO30bbtTB5//FfAeg5y3XVaS69SlVXFLbh3k48jo/kz+iTTBrcjqE7Fesi8d+9JrrpqDqNHLyQoyI8bb9TnIEqpc6tGXp3FniMpvPjjLvq2bcCI7hWrRb/PP9/KpEnf4u3tyVtvDWDKlDA8KvALBEop52kiKSFZObnc/eUmfLw9eW5YxwpTzJOVlUPVqh6EhTXmuuva8eKL/WjcuGK/PKCUKh5NJCXkrV/3su1AMu/e2I16vuW/jZGjR09z773LOH06k6++up5WrfyZM2eYu8NSSpVBWjZRAjbHJ/LW8r0M6xpI/w6N3B3OecnNNbz//npat36LefO20b59PXJyct0dllKqDNM7kvOUnpXD3V9uor6vN09c097d4ZyX/ftPceONX7FqVQKXXRbCO+9cTZs2WsGiUqpwmkjO0ws/7GT/sdPMmdyTWtXLdxsjtWp5k5iYziefDGXs2E4V5jmPUsq1tGjrPETtO86syBjG92rKRS3L55X7okW7GDZsHjk5ufj712DbtlsZN66zJhGllNM0kZyj5HSrjZFmATV5aEBbd4dTbHFxSQwdOpchQ+aye/cJDh1KBahwH1AqpVxPi7bO0dPf7eBQ0hkW3hJOda/y08ZIdnYur7++miee+A1jDC+80Je7776QqpWonRSlVMnSRHIOlm0/zIL1CUzt04KuweWrgsKcnFw+/HADffqE8v/+3wBCQipHGylKKdfRoq1iOp6awcNfbaV9Yz+m9mnp7nCccurUGR588CdSUjLw9vYkMnISixaN0iSilCoRmkiKwRjDo19vJSU9m1dHdsHLs2xvPmMMn322hTZtZvLKK6tYvjwGAH//GvowXSlVYrRoqxi+2nCAH7cf4ZGBbWjdsGxXE7J79wluvXUJv/wSTY8egfz444106dLQ3WEppSogTSROOpB4hicXbadHSF0mX9TM3eEU6a67fmDduoO8/fZAbr65u1awqJRyGU0kTsjNNTywYDM5xvDyiM54lNFXZH/6aR9t2gTQpEkt3nnnary9PWnY0MfdYSmlKjiXXqaKSH8R2SUie0XkoQKG3yMiO0Rki4j8IiJNXRnPufp0VQyRe0/w+KB2BPuXvTZGDh9O5YYbFnLllXN44YVIAJo2ra1JRClVKlyWSETEA5gJDADaAaNFpF2+0TYCYcaYTsAC4EVXxXOu9h1L5bnvd3J563qMuqCJu8P5l9xcw7vvrqNNm7dYuPAvnnjiUl5++Up3h6WUqmRceUfSA9hrjNlvjMkE5gJDHEcwxiw3xqTZnauBMtUaVHZOLvd8uZnqXh68MLzs1T313HMrueWWJXTv3pgtW6bw5JOXUa2allYqpUqXK886gUC8Q3cC0LOQ8ScD3xc0QERuBm4GCA4OLqn4ivTOb/usKuJv6Ep9v2qlttzCpKRkcPx4GqGhdZgyJYzQ0DqMHt2hzCU5pVTl4co7koLObKbAEUVuBMKAlwoabox53xgTZowJq1evXgmGeHbbDiTxxi97uKZzYwZ1alwqyyyMMYavv/6Ldu3e5vrrF2CMwd+/BjfcUHFaY1RKlU+uTCQJgONDhSDgYP6RRKQv8ChwjTEmw4XxOC09K4e7523C38eLp4e4v42R2NhErrlmLsOGfUndutV5880BmjyUUmWGK4u21gItRSQUOACMAm5wHEFEugLvAf2NMUddGEuxvPrTbvYcTSVi4gXUruHl1lhWrYqnb9/ZALz8cj/uvPNCPMv4F/VKqcrFZYnEGJMtIrcDPwIewMfGmO0i8jSwzhizCKsoyweYb19hxxljrnFVTM74c/8JPli5nzE9g7msdX23xZGcnIGfnzfdujVi0qQu3H9/b4KDa7ktHqWUOhsxpsDHFmVWWFiYWbdunUvmnZqRTf/Xf8ejirD0joup6V36b0CdOJHGQw/9zLJl+9m+/VZ8fNx7R6SUqhhEZL0xJswV89Z3RR08s3gHBxPP8OX/epV6EjHGMHv2Fu69dxmnTp3hnnt6oY9BlFLlgSYS2687jzB3bTxTLm1OWEjdUl12UlI6Q4fO47ffYujVK4h33x1Ep04NSjUGpZQ6V5pIgJOnM3lgwVbaNPTl7n6l18aIMQYRwc/Pm4CAGrz//iAmT+6mzd0qpcqVSv/6jzGGx77ZStKZTF4d2QVvz9JpcvbHH/fSrdv7JCQkIyLMnz+C//u/7ppElFLlTqVPJIs2H2Tp1sPc3a8V7Rr7uXx5hw6lMGrUAvr3/4y0tCyOHj3t8mUqpZQrVeqircNJ6Tz+zTa6Bdfmf5c0d/nyZs5cwyOP/EpGRjZPPXUZDz7YG283vBmmlFIlqdKexYwx3L9gM1k5hldGdimVNkbWrz9Ez56BzJw5kJYt/V2+PKWUKg2VNpHM+TOOlXuOM31oB0IDarpkGcnJGUybtpyxYzvRvXtj3n77ary9PbR6E6VUhVIpE0nM8dM8u+QvLmlVjxt7lnxtwsYYFi78izvv/IFDh1IIDq5F9+6NtYp3pVSFVOnObDm5hnu+3ERVD+FFF7QxEh19ittv/56lS/fQpUtDvvpqJD17lqlmVpRSqkRVukTy3u/72BCXyBujutCwVsm3MfLZZ1v5/fdYXnvtKm6/vYdWsKiUqvAqVSLZcTCZ137azdUdG3FN55JrY2TlylgyMnLo27cZ998fzoQJXQgKcv2rxEopVRZUmsvljOwc7vlyE7VreDF9aMm0KHj8eBqTJn3LJZdE8PTTKwDw9vbUJKKUqlQqzR3J6z/vYefhFD6eEEbdmudXo64xhoiITdx//08kJWXw4IO9efzxS0ooUlWRZGVlkZCQQHp6urtDUZVEtWrVCAoKomrVqqW2zEqRSNbFnOS9FfsYdUET+rQ5/8oQly7dw6RJi+jduwnvvjuIDh3c126JKtsSEhLw9fUlJCREX/tWLmeM4cSJEyQkJBAaGlpqy63wRVunM7K5d/5mGteuzmOD2p3zfNLSsoiMjANg4MCWfPvtKH7/faImEVWo9PR0/P39NYmoUiEi+Pv7l/odcIVPJM99/xdxJ9N4ZURnfM6xOpLvv99Dhw5vM2DAZyQmpiMiXHNNa61gUTlFk4gqTe443ip0Ilmx+xhzVsdxbCDCdAAAE3hJREFU00Wh9GxW/CpJDhxIZsSI+Qwc+Dne3p58991oatcu+VeGlVKqPKuwiSQxLZMHFmymZX0f7r2ydbGnP3r0NO3avc3ixbt55pnL2bx5CpdeGlLygSrlYh4eHnTp0oUOHTowePBgEhMT/x62fft2+vTpQ6tWrWjZsiXTp0/Hsfnt77//nrCwMNq2bUubNm2477773LEKhdq4cSM33XTTv/oNGTKEXr16/avfhAkTWLBgwb/6+fj4/P337t27GThwIC1atKBt27aMHDmSI0eOnFdsJ0+epF+/frRs2ZJ+/fpx6tSpAseLi4vjyiuvpG3btrRr146YmBgAxowZQ+vWrenQoQOTJk0iKysLgMWLF/PEE0+cV2wlyhhTrv51797dOGPq5xtM84eXmK0JiU6NnychIenvv994Y7XZu/dEsaZXytGOHTvcHYKpWbPm33+PGzfOPPPMM8YYY9LS0v5/e2cfVVW1LfDfDG6pycXS5FnWRaSuCAoqVqYlaql9mEUmpiY67FVqeUuz9xo2UvPdzG6fXvVWr9fAGlck9fqRlnr9uppJiUlAUERIXNKUjALMD/TM98feHA54lINwzgFcvzH2GHuvvfZac8+xz55nzbX2nBoWFqYbN25UVdWjR4/q0KFDdeHChaqqmpmZqWFhYZqTk6OqqhUVFbpo0aIGla2ioqLebYwYMULT09OdxyUlJdqxY0ft0qWL5ufnO8sTExN1+fLl1a6t1M2xY8c0PDxc165d6zy3detWzczMrJdsM2bM0Hnz5qmq6rx58/Tpp592W69///66adMmVVUtKyvTo0ePqqrq+vXr1eFwqMPh0FGjRunixYtVVdXhcGhMTIyzXk3cPXdAmnrpvdwsV22tyzjA2i8PMO2264i6Ktija3799TjPPruVt97aS2rqQ/Ts2YGpU2/wsqSGC4k5H35F9oHSBm2z65W/Z9awSI/r9+nTh4yMDACWLl1K3759GTx4MACtWrVi4cKFxMXFMWXKFF566SVmzpxJly5dAAgMDGTy5MlntFleXs7jjz9OWloaIsKsWbO47777aN26NeXl5QCsWLGCdevWkZSUxPjx47n88svZt28fMTExrFq1ivT0dNq0aQNAeHg4u3bt4qKLLuLRRx+lsNBa5PL666/Tt2/fan2XlZWRkZFBdHS0s2zlypUMGzaMkJAQli1bxjPPPFOrXpYuXUqfPn0YNmyYs2zAgAEe6/VsrFmzhu3btwOQmJhIXFwc8+fPr1YnOzubU6dOcdtttwHVR0l33HGHc//666+nqKgIsOZB4uLiWLduHSNHjqy3nPWl2RmSw6XHeXZ1FtFXt2FyXO05RlSV5cuzeeKJDfz4YzmPPXY9nTtf5gNJDQbfcvr0abZs2cLEiRMBy63Vq1evanU6d+5MeXk5paWlZGVlMX369FrbnTt3LsHBwWRmZgKc1X3jSm5uLps3byYgIACHw8GqVauYMGECn332GaGhoYSEhDB69GiefPJJ+vXrR2FhIUOGDCEnJ6daO2lpaURFRVUrS05OZtasWYSEhDBixAiPDElWVtYZunBHWVkZN998s9tzS5cupWvX6itDDx06RIcOHQDo0KEDhw8fPuO63Nxc2rRpQ3x8PPv37+fWW2/lxRdfJCCgKltrRUUF77//Pm+88YazLDY2lp07dxpD0tCoKv+1MoNjJ0/z6shoAgPOPQWkqsTHf8Dq1V/Ts2cH1q59gNjYhgudYjC4UpeRQ0Ny7NgxYmJiKCgooFevXs5/vqp61hU+dVn5s3nzZpYtW+Y8vuyy2v+I3X///c4XZUJCAs8//zwTJkxg2bJlJCQkONvNzs52XlNaWkpZWRlBQUHOsoMHD3LFFVc4jw8dOkReXh79+vVDRAgMDCQrK4uoKPfRLOq6wikoKIj09PQ6XVMbp06dYufOnezbt49rrrmGhIQEkpKSnAYfYPLkydxyyy3VjFj79u05cOBAg8pyvjSryfaUPf9m2zfFPHN7Fzpf0fqs9SoqTgPWQ9Sv39UsWDCUzz9/yBgRQ7OkZcuWpKen8/3333Py5EkWLVoEQGRkJGlpadXq5ufn07p1a4KCgoiMjGTv3r21tn82g+RaVvO7hksvrcoB1KdPH/Ly8iguLmb16tXEx8cD4HA42L17N+np6aSnp/PDDz9UMyKV9+badkpKCiUlJXTq1InQ0FAKCgqcRq5t27bVRks///wz7dq1c+rCk3stKysjJibG7eZq9CoJCQnh4MGDgGX02rc/87uzjh070qNHD8LCwggMDOSee+7hiy++cJ6fM2cOxcXFvPrqq9WuO378OC1btqxVZl/QbAxJ4ZHfmLsum5s6t2Vcn9Cz1tu+vYDu3d9kzZqvAZg+/SYef/wGAmoZvRgMTZ3g4GAWLFjAyy+/TEVFBWPGjOGTTz5h8+bNgDVymTp1Kk8//TQAM2bM4IUXXiA3NxewXuw1X2YAgwcPZuHChc7jypd1SEgIOTk5TtfV2RAR7r33XqZNm0ZERARt27Z12667kUBERAR5eXnO4+TkZDZs2EBBQQEFBQXs3bvXaUji4uJISUnh5MmTACQlJTnnQUaPHs2nn37K+vXrnW1t2LDB6a6rpHJE4m6r6dYCuPvuu1myZAkAS5YsYfjw4WfU6d27NyUlJRQXFwOwdetWZ1vvvPMOGzduJDk5mYsuqv6Oys3NPcOt5ze8NYvvrc3dqq1Tpx16/98+1ajnNmhRyW9uVzEcPlyu48atUpitnTq9rlu25LutZzA0JI1t1Zaq6l133aXvvfeeqqpmZGRo//799brrrtPOnTvr7Nmz1eFwOOt++OGH2rNnT+3SpYtGREToU089dUb7ZWVlOm7cOI2MjNTu3bvrypUrVVV1+fLlGhYWpv3799cpU6ZoYmKiqrpfPbVnzx4FNCkpyVlWXFysI0eO1G7dumlERIQ+8sgjbu8vKipKS0tLdf/+/XrllVdWk19VtUePHpqamqqqqrNnz9aoqCiNjo7W+Ph4PXz4sLNeTk6ODhkyRMPDwzUiIkITEhL0xx9/PKdua+Onn37SgQMHanh4uA4cOFCPHDnivN+JEyc6623atEm7deumUVFRmpiYqCdOnFBV1YCAAA0LC9Po6GiNjo7WOXPmOK+58847NSMjw22/vl61JVb7TYfY2FitORx/e8d3vPDR17xyfzT39ToziVRyciZTpnxEeflJZsy4iZkzb6FVK98FNDNcuOTk5BAREeFvMZo1r732GkFBQWd8S9KcOXToEKNHj2bLli1uz7t77kRkr6rGekOeJu/P+ebHMl7emMuQyBDie17lts6pUw6iotqTnv4of/7zIGNEDIZmxKRJk7jkkkv8LYZPKSws5JVXXvG3GE6a9Kqtk6ccTPsgnaAWgbxwbzfn5N7RoyeZO3cH11wTzOTJvRk7tjtjxzZ8Wl2DweB/WrRowYMPPuhvMXxK7969/S1CNZr0iOSvW7/lqwOlzIvvRtvW1j+SdetyiYxczPz5u8jNPQJYk3nGiBj8RVNzHxuaNv543prsiGRfYQmLtuUxoldHBkf+B0VFpUyd+jGrVn1N165XsGPHeG6++Q/+FtNwgdOiRQuOHDliQskbfIKqlY+kRQvfBpdtkobk2MnTTP/gSzoEt+S5YdYyufz8EjZu/I558wYxbVofLr44oJZWDAbv07FjR4qKipxLOw0Gb1OZIdGXNMlVW3c9t4SkTwt47ubOlOX9wp/+dCMAR478Rtu2rfwsocFgMDQ+muyqLREZKiLfiEieiPy3m/OXiEiKff4zEQmtrc3yE6dI+rSA0BPw0LAUXn01laNHrQ+MjBExGAwG3+M1QyIiAcAi4HagK/CAiNT89HMiUKKq4cBrwHxqofDIbzh+OcEni79g6tQbyMycxKWXXtzQ4hsMBoPBQ7w5R3I9kKeq+QAisgwYDrgGpBkOzLb3VwALRUT0HP620w7l8txSVu+2Qr0bDAaDwb9405BcBfzb5bgIqJngw1lHVU+JyK9AW+An10oi8jDwsH144stDE7I8iPh8IdCOGrq6gDG6qMLoogqjiyrqnirWQ7xpSNytdaw50vCkDqr6NvA2gIikeWvCqKlhdFGF0UUVRhdVGF1UISJptdc6P7w52V4EXO1y3BGoGTzfWUdEAoFg4GcvymQwGAyGBsabhmQPcK2IdBKRi4FRwNoaddYCifb+CGDrueZHDAaDwdD48Jpry57zeAzYCAQA76rqVyLyPFY447XA/wHvi0ge1khklAdNv+0tmZsgRhdVGF1UYXRRhdFFFV7TRZP7INFgMBgMjYsmHbTRYDAYDP7HGBKDwWAw1ItGa0i8EV6lqeKBLqaJSLaIZIjIFhFptmGPa9OFS70RIqIi0myXfnqiCxEZaT8bX4nIUl/L6Cs8+I1cIyLbRGSf/Tu5wx9yehsReVdEDotI1lnOi4gssPWUISI9G6Rjb+Xwrc+GNTn/HRAGXAx8CXStUWcy8Ka9PwpI8bfcftTFAKCVvT/pQtaFXS8I2AGkArH+ltuPz8W1wD7gMvu4vb/l9qMu3gYm2ftdgQJ/y+0lXdwC9ASyznL+DuBjrG/4bgQ+a4h+G+uIxBleRVVPApXhVVwZDiyx91cAg6R5JnyoVRequk1Vf7MPU7G+2WmOePJcAMwFXgKO+1I4H+OJLv4TWKSqJQCqetjHMvoKT3ShwO/t/WDO/KatWaCqOzj3t3jDgffUIhVoIyL1jjXVWA2Ju/AqNROyVwuvAlSGV2lueKILVyZi/eNojtSqCxHpAVytqut8KZgf8OS5uA64TkR2iUiqiAz1mXS+xRNdzAbGikgR8BHwuG9Ea3TU9X3iEY01sVWDhVdpBnh8nyIyFogF+ntVIv9xTl2IyEVYUaTH+0ogP+LJcxGI5d6Kwxql7hSRKFX9xcuy+RpPdPEAkKSqr4hIH6zv16JU1eF98RoVXnlvNtYRiQmvUoUnukBEbgVmAner6gkfyeZratNFEBAFbBeRAiwf8NpmOuHu6W9kjapWqOp+4Bssw9Lc8EQXE4EPAFR1N9ACK6DjhYZH75O60lgNiQmvUkWturDdOW9hGZHm6geHWnShqr+qajtVDVXVUKz5ortV1WvB6vyIJ7+R1VgLMRCRdliurnyfSukbPNFFITAIQEQisAzJhZj/eC0wzl69dSPwq6oerG+jjdK1pd4Lr9Lk8FAXfwFaA8vt9QaFqnq334T2Eh7q4oLAQ11sBAaLSDZwGpihqkf8J7V38FAX04H/FZEnsVw545vjH08RScZyZbaz54NmAb8DUNU3seaH7gDygN+ACQ3SbzPUpcFgMBh8SGN1bRkMBoOhiWAMicFgMBjqhTEkBoPBYKgXxpAYDAaDoV4YQ2IwGAyGemEMiaHRISKnRSTdZQs9R93Qs0U6rWOf2+3osV/aIUX+eB5tPCoi4+z98SJypcu5d0SkawPLuUdEYjy45gkRaVXfvg2Gs2EMiaExckxVY1y2Ah/1O0ZVo7GCgf6lrher6puq+p59OB640uXcQ6qa3SBSVsm5GM/kfAIwhsTgNYwhMTQJ7JHHThH5wt5uclMnUkQ+t0cxGSJyrV0+1qX8LREJqKW7HUC4fe0gO4dFpp3r4RK7/EWpygHzsl02W0SeEpERWDHP/m732dIeScSKyCQReclF5vEi8tfzlHM3LgH3RORvIpImVu6ROXbZVCyDtk1Ettllg0Vkt63H5SLSupZ+DIZzYgyJoTHS0sWttcouOwzcpqo9gQRggZvrHgXeUNUYrBd5kR0OIwHoa5efBsbU0v8wIFNEWgBJQIKqdsOKBDFJRC4H7gUiVbU78D+uF6vqCiANa+QQo6rHXE6vAOJdjhOAlPOUcyhWGJRKZqpqLNAd6C8i3VV1AVYspQGqOsAOlfIscKutyzRgWi39GAznpFGGSDFc8ByzX6au/A5YaM8JnMaKG1WT3cBMEekI/ENVvxWRQUAvYI8dPqYlllFyx99F5BhQgBVm/I/AflXNtc8vAaYAC7FynbwjIusBj0PWq2qxiOTbcY6+tfvYZbdbFzkvxQoH4prhbqSIPIz1u+6AlcApo8a1N9rlu+x+LsbSm8Fw3hhDYmgqPAkcAqKxRtJnJK1S1aUi8hlwJ7BRRB7CCpu9RFWf8aCPMa4BHkXEbX4bO7bT9VhBAEcBjwED63AvKcBI4GtglaqqWG91j+XEygL4IrAIiBeRTsBTQG9VLRGRJKzAhDUR4J+q+kAd5DUYzolxbRmaCsHAQTt/xINY/8arISJhQL7tzlmL5eLZAowQkfZ2ncvF85z2XwOhIhJuHz8I/MueUwhW1Y+wJrLdrZwqwwpr745/APdg5chIscvqJKeqVmC5qG603WK/B44Cv4pICHD7WWRJBfpW3pOItBIRd6M7g8FjjCExNBUWA4kikorl1jrqpk4CkCUi6UAXrJSi2Vgv3E0ikgH8E8vtUyuqehwrOupyEckEHMCbWC/ldXZ7/8IaLdUkCXizcrK9RrslQDbwB1X93C6rs5z23MsrwFOq+iVWfvavgHex3GWVvA18LCLbVLUYa0VZst1PKpauDIbzxkT/NRgMBkO9MCMSg8FgMNQLY0gMBoPBUC+MITEYDAZDvTCGxGAwGAz1whgSg8FgMNQLY0gMBoPBUC+MITEYDAZDvfh/51w/pInPS40AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -3092,10 +3090,10 @@ { "data": { "text/plain": [ - "0.6064648683126901" + "0.6239677471688679" ] }, - "execution_count": 42, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -3106,7 +3104,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -3115,12 +3113,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.82 0.80 0.81 6727\n", - " 1 0.39 0.41 0.40 2022\n", + " 0 0.83 0.82 0.82 6741\n", + " 1 0.41 0.43 0.42 2008\n", "\n", - " accuracy 0.71 8749\n", - " macro avg 0.60 0.61 0.60 8749\n", - "weighted avg 0.72 0.71 0.72 8749\n", + " accuracy 0.73 8749\n", + " macro avg 0.62 0.62 0.62 8749\n", + "weighted avg 0.73 0.73 0.73 8749\n", "\n" ] } @@ -3138,7 +3136,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3165,7 +3163,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -3175,7 +3173,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3190,7 +3188,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3201,7 +3199,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -3210,8 +3208,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13477\n", - " 1 1.00 1.00 1.00 4019\n", + " 0 1.00 1.00 1.00 13463\n", + " 1 1.00 1.00 1.00 4033\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3233,14 +3231,14 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Random Forest) identified 782\n" + "Of 2008 Defaulters, the Decision Tree (Random Forest) identified 788\n" ] }, { @@ -3275,14 +3273,14 @@ " \n", " \n", " \n", - " 0\n", - " 6264\n", - " 463\n", + " 0\n", + " 6265\n", + " 476\n", " \n", " \n", - " 1\n", - " 1240\n", - " 782\n", + " 1\n", + " 1220\n", + " 788\n", " \n", " \n", "\n", @@ -3291,11 +3289,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6264 463\n", - "1 1240 782" + "0 6265 476\n", + "1 1220 788" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -3306,19 +3304,19 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2805714285714286\n" + "Optimal Threshold: 0.325\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3335,7 +3333,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -3344,8 +3342,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.93 0.88 6727\n", - " 1 0.63 0.39 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.62 0.39 0.48 2008\n", "\n", " accuracy 0.81 8749\n", " macro avg 0.73 0.66 0.68 8749\n", @@ -3360,7 +3358,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -3391,7 +3389,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -3409,7 +3407,7 @@ " warm_start=False)" ] }, - "execution_count": 52, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3422,7 +3420,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -3431,12 +3429,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.97 0.91 13477\n", - " 1 0.81 0.48 0.60 4019\n", + " 0 0.86 0.96 0.91 13463\n", + " 1 0.79 0.47 0.59 4033\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.84 0.72 0.76 17496\n", - "weighted avg 0.85 0.85 0.84 17496\n", + " macro avg 0.82 0.72 0.75 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", "\n" ] } @@ -3454,14 +3452,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 770\n" + "Of 2008 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 796\n" ] }, { @@ -3496,14 +3494,14 @@ " \n", " \n", " \n", - " 0\n", - " 6289\n", - " 438\n", + " 0\n", + " 6281\n", + " 460\n", " \n", " \n", - " 1\n", - " 1252\n", - " 770\n", + " 1\n", + " 1212\n", + " 796\n", " \n", " \n", "\n", @@ -3512,11 +3510,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6289 438\n", - "1 1252 770" + "0 6281 460\n", + "1 1212 796" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -3527,19 +3525,19 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2100144956690176\n" + "Optimal Threshold: 0.22481097140826298\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3556,7 +3554,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -3565,11 +3563,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.93 0.88 6727\n", - " 1 0.64 0.38 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.63 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.74 0.66 0.69 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3581,7 +3579,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -3599,7 +3597,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": { "scrolled": true }, @@ -3632,22 +3630,22 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " Decision Tree (GINI)\n", - " 0.418892\n", - " 0.609249\n", + " 0.449203\n", + " 0.625991\n", " \n", " \n", - " 1\n", + " 1\n", " Random Forest\n", - " 0.386746\n", - " 0.761906\n", + " 0.392430\n", + " 0.762567\n", " \n", " \n", - " 2\n", + " 2\n", " Gradient Boosted\n", - " 0.380811\n", - " 0.775534\n", + " 0.396414\n", + " 0.772109\n", " \n", " \n", "\n", @@ -3655,12 +3653,12 @@ ], "text/plain": [ " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.418892 0.609249\n", - "1 Random Forest 0.386746 0.761906\n", - "2 Gradient Boosted 0.380811 0.775534" + "0 Decision Tree (GINI) 0.449203 0.625991\n", + "1 Random Forest 0.392430 0.762567\n", + "2 Gradient Boosted 0.396414 0.772109" ] }, - "execution_count": 58, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -3701,7 +3699,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -3710,7 +3708,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3718,7 +3716,7 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", + " Current function value: 0.440644\n", " Iterations 7\n" ] }, @@ -3737,13 +3735,13 @@ " Method: MLE Df Model: 43 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838 \n", "\n", "\n", - " Time: 23:33:29 Log-Likelihood: -7727.2\n", + " Time: 00:20:46 Log-Likelihood: -7709.5\n", "\n", "\n", - " converged: True LL-Null: -9429.0\n", + " converged: True LL-Null: -9445.9\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3754,136 +3752,136 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", + " LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", "\n", "\n", - " SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", + " SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", "\n", "\n", - " AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", + " AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", "\n", "\n", - " PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", + " PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", "\n", "\n", - " PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", + " PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", "\n", "\n", - " PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", + " PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", "\n", "\n", - " PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", + " PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", "\n", "\n", - " PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", + " PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", "\n", "\n", - " PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", + " PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", "\n", "\n", - " BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", + " BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", "\n", "\n", - " BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", + " BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", "\n", "\n", - " BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", + " BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", "\n", "\n", - " BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", + " BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", "\n", "\n", - " BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", + " BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", "\n", "\n", - " BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", + " BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", "\n", "\n", - " PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", + " PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", "\n", "\n", - " PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", + " PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", "\n", "\n", - " PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", + " PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", "\n", "\n", - " PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", + " PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", "\n", "\n", - " PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", + " PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", "\n", "\n", - " PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", + " PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", "\n", "\n", - " GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", + " GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", "\n", "\n", - " UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", + " UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", "\n", "\n", - " HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", + " HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", "\n", "\n", - " MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", + " MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", "\n", "\n", - " SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", + " SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", "\n", "\n", - " PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", + " PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", "\n", "\n", - " PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", + " PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", + " PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", "\n", "\n", - " PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", + " PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", "\n", "\n", - " PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", + " PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", + " PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", "\n", "\n", - " PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", + " PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", "\n", "\n", - " PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", + " PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", + " PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", "\n", "\n", - " PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", + " PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", "\n", "\n", - " PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", + " PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", + " PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", "\n", "\n", - " PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", + " PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", "\n", "\n", - " PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", + " PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", + " PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", "\n", "\n", - " PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", + " PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", "\n", "\n", - " PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", + " PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + " PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", "\n", "" ], @@ -3895,62 +3893,62 @@ "Dep. Variable: Y No. Observations: 17496\n", "Model: Logit Df Residuals: 17452\n", "Method: MLE Df Model: 43\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1805\n", - "Time: 23:33:29 Log-Likelihood: -7727.2\n", - "converged: True LL-Null: -9429.0\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838\n", + "Time: 00:20:46 Log-Likelihood: -7709.5\n", + "converged: True LL-Null: -9445.9\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8819 0.115 -7.642 0.000 -1.108 -0.656\n", - "SEX -0.1190 0.041 -2.885 0.004 -0.200 -0.038\n", - "AGE 0.1478 0.101 1.470 0.141 -0.049 0.345\n", - "PAY_0 0.6515 0.060 10.842 0.000 0.534 0.769\n", - "PAY_2 -0.5251 0.099 -5.301 0.000 -0.719 -0.331\n", - "PAY_3 -0.0687 0.121 -0.567 0.571 -0.306 0.169\n", - "PAY_4 -0.2974 0.156 -1.912 0.056 -0.602 0.007\n", - "PAY_5 -0.1408 0.177 -0.797 0.425 -0.487 0.205\n", - "PAY_6 0.4757 0.156 3.045 0.002 0.169 0.782\n", - "BILL_AMT1 -0.8821 0.523 -1.685 0.092 -1.908 0.144\n", - "BILL_AMT2 -0.4042 0.787 -0.513 0.608 -1.947 1.139\n", - "BILL_AMT3 2.1128 0.756 2.796 0.005 0.632 3.594\n", - "BILL_AMT4 0.2009 0.718 0.280 0.779 -1.205 1.607\n", - "BILL_AMT5 -0.9680 0.884 -1.095 0.273 -2.700 0.764\n", - "BILL_AMT6 0.5013 0.810 0.619 0.536 -1.087 2.090\n", - "PAY_AMT1 -0.9423 0.302 -3.125 0.002 -1.533 -0.351\n", - "PAY_AMT2 -1.7093 0.369 -4.636 0.000 -2.432 -0.987\n", - "PAY_AMT3 -0.4924 0.296 -1.661 0.097 -1.074 0.089\n", - "PAY_AMT4 -0.7230 0.307 -2.358 0.018 -1.324 -0.122\n", - "PAY_AMT5 -0.7380 0.283 -2.611 0.009 -1.292 -0.184\n", - "PAY_AMT6 -0.4371 0.254 -1.724 0.085 -0.934 0.060\n", - "GRAD 1.2777 0.220 5.807 0.000 0.846 1.709\n", - "UNI 1.2382 0.219 5.667 0.000 0.810 1.667\n", - "HS 1.1544 0.222 5.196 0.000 0.719 1.590\n", - "MARRIED 0.2329 0.169 1.377 0.169 -0.099 0.564\n", - "SINGLE 0.0305 0.170 0.180 0.857 -0.302 0.363\n", - "PAY_0_No_Transactions -0.0507 0.126 -0.402 0.688 -0.298 0.197\n", - "PAY_0_Pay_Duly 0.0725 0.122 0.593 0.553 -0.167 0.312\n", - "PAY_0_Revolving_Credit -0.8204 0.138 -5.936 0.000 -1.091 -0.549\n", - "PAY_2_No_Transactions -1.4755 0.241 -6.131 0.000 -1.947 -1.004\n", - "PAY_2_Pay_Duly -1.3708 0.228 -6.025 0.000 -1.817 -0.925\n", - "PAY_2_Revolving_Credit -0.8291 0.231 -3.586 0.000 -1.282 -0.376\n", - "PAY_3_No_Transactions -0.4620 0.291 -1.586 0.113 -1.033 0.109\n", - "PAY_3_Pay_Duly -0.4549 0.265 -1.717 0.086 -0.974 0.064\n", - "PAY_3_Revolving_Credit -0.3925 0.255 -1.542 0.123 -0.892 0.107\n", - "PAY_4_No_Transactions -1.1369 0.353 -3.219 0.001 -1.829 -0.445\n", - "PAY_4_Pay_Duly -1.1269 0.332 -3.392 0.001 -1.778 -0.476\n", - "PAY_4_Revolving_Credit -1.0415 0.323 -3.228 0.001 -1.674 -0.409\n", - "PAY_5_No_Transactions -0.3848 0.394 -0.976 0.329 -1.158 0.388\n", - "PAY_5_Pay_Duly -0.5256 0.379 -1.388 0.165 -1.268 0.217\n", - "PAY_5_Revolving_Credit -0.4389 0.369 -1.189 0.234 -1.162 0.285\n", - "PAY_6_No_Transactions 0.8377 0.341 2.458 0.014 0.170 1.505\n", - "PAY_6_Pay_Duly 0.7267 0.335 2.170 0.030 0.070 1.383\n", - "PAY_6_Revolving_Credit 0.4928 0.327 1.509 0.131 -0.147 1.133\n", + "LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", + "SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", + "AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", + "PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", + "PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", + "PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", + "PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", + "PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", + "PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", + "BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", + "BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", + "BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", + "BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", + "BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", + "BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", + "PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", + "PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", + "PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", + "PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", + "PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", + "PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", + "GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", + "UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", + "HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", + "MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", + "SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", + "PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", + "PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", + "PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", + "PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", + "PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", + "PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", + "PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", + "PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", + "PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", + "PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", + "PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", + "PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", + "PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", + "PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", + "PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", + "PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", + "PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", + "PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 60, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -3962,7 +3960,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -3971,12 +3969,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13477\n", - " 1 0.68 0.36 0.47 4019\n", + " 0 0.83 0.95 0.89 13463\n", + " 1 0.68 0.37 0.48 4033\n", "\n", - " accuracy 0.81 17496\n", - " macro avg 0.76 0.66 0.68 17496\n", - "weighted avg 0.80 0.81 0.79 17496\n", + " accuracy 0.82 17496\n", + " macro avg 0.76 0.66 0.69 17496\n", + "weighted avg 0.80 0.82 0.79 17496\n", "\n" ] } @@ -3994,7 +3992,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -4002,64 +4000,52 @@ "output_type": "stream", "text": [ "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.441655\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441658\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441662\n", + " Current function value: 0.440644\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441670\n", + " Current function value: 0.440645\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441678\n", + " Current function value: 0.440647\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441690\n", + " Current function value: 0.440653\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441709\n", + " Current function value: 0.440668\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441720\n", + " Current function value: 0.440694\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441740\n", + " Current function value: 0.440734\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441771\n", + " Current function value: 0.440782\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441820\n", + " Current function value: 0.440827\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441865\n", + " Current function value: 0.440895\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441905\n", + " Current function value: 0.440960\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441966\n", + " Current function value: 0.441031\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.442051\n", + " Current function value: 0.441168\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.442158\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442265\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442384\n", - " Iterations 7\n", - "Optimization terminated successfully.\n", - " Current function value: 0.442495\n", + " Current function value: 0.441325\n", " Iterations 7\n" ] }, @@ -4072,19 +4058,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17471 \n", + " Model: Logit Df Residuals: 17467 \n", "\n", "\n", - " Method: MLE Df Model: 24 \n", + " Method: MLE Df Model: 28 \n", "\n", "\n", - " Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826 \n", "\n", "\n", - " Time: 23:38:59 Log-Likelihood: -7741.9\n", + " Time: 00:20:46 Log-Likelihood: -7721.4\n", "\n", "\n", - " converged: True LL-Null: -9429.0\n", + " converged: True LL-Null: -9445.9\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4095,79 +4081,91 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", + " LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", + "\n", + "\n", + " SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", "\n", "\n", - " SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", + " PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", "\n", "\n", - " PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", + " PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", "\n", "\n", - " PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", + " PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", "\n", "\n", - " PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", + " PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", "\n", "\n", - " PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", + " BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", "\n", "\n", - " BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", + " PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", "\n", "\n", - " BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", + " PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", "\n", "\n", - " PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", + " PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", "\n", "\n", - " PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", + " PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", "\n", "\n", - " PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", + " PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", "\n", "\n", - " PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", + " GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", "\n", "\n", - " GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", + " UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", "\n", "\n", - " UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", + " HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", "\n", "\n", - " HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", + " MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", "\n", "\n", - " MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", + " PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", + " PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", "\n", "\n", - " PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", + " PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", "\n", "\n", - " PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", + " PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", + " PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", "\n", "\n", - " PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", + " PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", "\n", "\n", - " PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", + " PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", "\n", "\n", - " PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", + " PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", "\n", "\n", - " PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", + " PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", "\n", "\n", - " PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + " PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + "\n", + "\n", + " PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + "\n", + "\n", + " PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + "\n", + "\n", + " PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", "\n", "" ], @@ -4177,45 +4175,49 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17471\n", - "Method: MLE Df Model: 24\n", - "Date: Wed, 20 Nov 2019 Pseudo R-squ.: 0.1789\n", - "Time: 23:38:59 Log-Likelihood: -7741.9\n", - "converged: True LL-Null: -9429.0\n", + "Model: Logit Df Residuals: 17467\n", + "Method: MLE Df Model: 28\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826\n", + "Time: 00:20:46 Log-Likelihood: -7721.4\n", + "converged: True LL-Null: -9445.9\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.9101 0.114 -7.982 0.000 -1.133 -0.687\n", - "SEX -0.1315 0.041 -3.232 0.001 -0.211 -0.052\n", - "PAY_0 0.6385 0.038 16.719 0.000 0.564 0.713\n", - "PAY_2 -0.5206 0.082 -6.370 0.000 -0.681 -0.360\n", - "PAY_4 -0.2886 0.078 -3.689 0.000 -0.442 -0.135\n", - "PAY_6 0.2768 0.032 8.542 0.000 0.213 0.340\n", - "BILL_AMT1 -1.2504 0.373 -3.355 0.001 -1.981 -0.520\n", - "BILL_AMT3 1.7957 0.440 4.085 0.000 0.934 2.657\n", - "PAY_AMT1 -0.9294 0.272 -3.415 0.001 -1.463 -0.396\n", - "PAY_AMT2 -2.0171 0.347 -5.817 0.000 -2.697 -1.337\n", - "PAY_AMT4 -1.0689 0.278 -3.842 0.000 -1.614 -0.524\n", - "PAY_AMT5 -0.7517 0.253 -2.977 0.003 -1.247 -0.257\n", - "GRAD 1.3015 0.188 6.913 0.000 0.933 1.671\n", - "UNI 1.2630 0.187 6.755 0.000 0.897 1.629\n", - "HS 1.1983 0.191 6.273 0.000 0.824 1.573\n", - "MARRIED 0.2340 0.042 5.566 0.000 0.152 0.316\n", - "PAY_0_Revolving_Credit -0.8369 0.094 -8.944 0.000 -1.020 -0.653\n", - "PAY_2_No_Transactions -1.6640 0.198 -8.418 0.000 -2.051 -1.277\n", - "PAY_2_Pay_Duly -1.4681 0.186 -7.876 0.000 -1.833 -1.103\n", - "PAY_2_Revolving_Credit -0.9196 0.190 -4.849 0.000 -1.291 -0.548\n", - "PAY_4_No_Transactions -1.3041 0.194 -6.738 0.000 -1.683 -0.925\n", - "PAY_4_Pay_Duly -1.3554 0.179 -7.572 0.000 -1.706 -1.005\n", - "PAY_4_Revolving_Credit -1.1937 0.165 -7.237 0.000 -1.517 -0.870\n", - "PAY_6_No_Transactions 0.4147 0.089 4.658 0.000 0.240 0.589\n", - "PAY_6_Pay_Duly 0.2381 0.078 3.070 0.002 0.086 0.390\n", + "LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", + "SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", + "PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", + "PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", + "PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", + "PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", + "BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", + "PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", + "PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", + "PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", + "PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", + "PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", + "GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", + "UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", + "HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", + "MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", + "PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", + "PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", + "PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", + "PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", + "PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", + "PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", + "PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", + "PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", + "PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", + "PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + "PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + "PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + "PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 80, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -4233,20 +4235,21 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "25 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_4', 'PAY_6', 'BILL_AMT1',\n", - " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4', 'PAY_AMT5', 'GRAD',\n", + "29 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT3',\n", + " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " 'PAY_5_Pay_Duly', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", " dtype='object')\n" ] } @@ -4259,7 +4262,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -4268,11 +4271,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.64 0.36 0.47 2022\n", + " 0 0.83 0.94 0.88 6741\n", + " 1 0.64 0.38 0.47 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -4293,19 +4296,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.2233371077226231\n" + "Optimal Threshold: 0.22999391096950886\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4318,10 +4321,10 @@ { "data": { "text/plain": [ - "0.771318789360392" + "0.7757068306651365" ] }, - "execution_count": 81, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -4340,7 +4343,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 67, "metadata": { "scrolled": true }, @@ -4351,11 +4354,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.79 0.83 6727\n", - " 1 0.47 0.62 0.54 2022\n", + " 0 0.87 0.80 0.83 6741\n", + " 1 0.47 0.60 0.53 2008\n", "\n", " accuracy 0.75 8749\n", - " macro avg 0.67 0.71 0.68 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", "weighted avg 0.78 0.75 0.76 8749\n", "\n" ] @@ -4379,14 +4382,14 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Logistic Regression identified 1252\n" + "Of 2008 Defaulters, the Logistic Regression identified 1207\n" ] }, { @@ -4421,14 +4424,14 @@ " \n", " \n", " \n", - " 0\n", - " 5341\n", - " 1386\n", + " 0\n", + " 5388\n", + " 1353\n", " \n", " \n", - " 1\n", - " 770\n", - " 1252\n", + " 1\n", + " 801\n", + " 1207\n", " \n", " \n", "\n", @@ -4437,11 +4440,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5341 1386\n", - "1 770 1252" + "0 5388 1353\n", + "1 801 1207" ] }, - "execution_count": 83, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4452,14 +4455,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the Logistic Regression identified 738\n" + "Of 2008 Defaulters, the Logistic Regression identified 755\n" ] }, { @@ -4494,14 +4497,14 @@ " \n", " \n", " \n", - " 0\n", - " 6319\n", - " 408\n", + " 0\n", + " 6321\n", + " 420\n", " \n", " \n", - " 1\n", - " 1284\n", - " 738\n", + " 1\n", + " 1253\n", + " 755\n", " \n", " \n", "\n", @@ -4510,11 +4513,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6319 408\n", - "1 1284 738" + "0 6321 420\n", + "1 1253 755" ] }, - "execution_count": 68, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -4532,27 +4535,16 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Threshold: 0.22122047856901356\n" + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m optimal =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ @@ -4562,7 +4554,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -4736,7 +4728,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -4748,7 +4740,7 @@ " verbose=False)" ] }, - "execution_count": 95, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -4762,19 +4754,19 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15978089375876542\n" + "Optimal Threshold: 0.1567076896169202\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4787,10 +4779,10 @@ { "data": { "text/plain": [ - "0.7301560712348498" + "0.7227905615337198" ] }, - "execution_count": 96, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -4802,14 +4794,14 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the SVM default parameters identified 761\n" + "Of 2008 Defaulters, the SVM default parameters identified 768\n" ] }, { @@ -4844,14 +4836,14 @@ " \n", " \n", " \n", - " 0\n", - " 6331\n", - " 396\n", + " 0\n", + " 6321\n", + " 420\n", " \n", " \n", - " 1\n", - " 1261\n", - " 761\n", + " 1\n", + " 1240\n", + " 768\n", " \n", " \n", "\n", @@ -4860,11 +4852,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6331 396\n", - "1 1261 761" + "0 6321 420\n", + "1 1240 768" ] }, - "execution_count": 97, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -4876,7 +4868,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -4885,11 +4877,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.66 0.38 0.48 2022\n", + " 0 0.84 0.94 0.88 6741\n", + " 1 0.65 0.38 0.48 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.66 0.68 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -4920,80 +4912,69 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C': 0.01, 'gamma': 0.001}" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", - " Cs = [0.001,0.01, 0.1, 1]\n", - " gammas = [0.001, 0.01, 0.1]\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", " param_grid = {'C': Cs, 'gamma' : gammas}\n", " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", " grid_search.fit(X, y)\n", " grid_search.best_params_\n", " return grid_search.best_params_\n", - "svc_param_selection(X_train, y_train,3)\n" + "svc_param_selection(X_train, y_train,2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "With 2 folds, it can be found that C = 10, and gamma = 0.01 will have the best svm model with RBF kernel" + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n", + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", " verbose=False)" ] }, - "execution_count": 100, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#train svm model with feature reduction and cost = 10, gamma = 0.01\n", - "clf_reduced_tuned = svm.SVC(kernel = 'rbf', probability = True, C = 1, gamma = 0.01 )\n", + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", "clf_reduced_tuned.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.16199599731203465\n" + "Optimal Threshold: 0.15368680599405346\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5006,19 +4987,19 @@ ], "source": [ "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", - " \"SVM reduced features and tuning RBF kernel\")" + " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2022 Defaulters, the SVM reduced features and tuning RBF kernel identified 732\n" + "Of 2008 Defaulters, the SVM reduced features and tuning linear kernal identified 696\n" ] }, { @@ -5053,14 +5034,14 @@ " \n", " \n", " \n", - " 0\n", - " 6383\n", - " 344\n", + " 0\n", + " 6405\n", + " 336\n", " \n", " \n", - " 1\n", - " 1290\n", - " 732\n", + " 1\n", + " 1312\n", + " 696\n", " \n", " \n", "\n", @@ -5069,23 +5050,23 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6383 344\n", - "1 1290 732" + "0 6405 336\n", + "1 1312 696" ] }, - "execution_count": 102, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#confusion matrix\n", - "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning RBF kernel\")" + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5094,12 +5075,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6727\n", - " 1 0.68 0.36 0.47 2022\n", + " 0 0.83 0.95 0.89 6741\n", + " 1 0.67 0.35 0.46 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.76 0.66 0.68 8749\n", - "weighted avg 0.80 0.81 0.79 8749\n", + " macro avg 0.75 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } @@ -5112,210 +5093,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From this, we can conclude that fitting SVM model with PCA-reduced features but no parameter tuning is most accurate based on Recall value" + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, fitting an SVM with PCA-reduced features with default gamma = 1/13 and C = 1 with kernal = 'rbf' is the best model. However, this is only for rbf kernel." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[4] = ([\"SVM\" , \n", - " classification_report(y_test, clf_reduced_tuned.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neural Networks\n", - "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", - "\n", - "#### Theory\n", - "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", - "\n", - ".\n", - "\n", - "\n", - "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", - "\n", - "\n", - "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", - "\n", - "\n", - "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", - "\n", - "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Parameter tuning\n", - "##### Learning rate\n", - "Learning rate is a coefficient in the update of weights that controls the weight at the end of each batch/epoch and controls how quickly the model is adapted to the problem. Smaller learning rates require more training epoch given smaller changes to the weights each update, which can lead to computationally expensive situations. Whereas larger learning rates is less computationally expensive and require fewer training epochs, but risk converging too quickly to a suboptimal solution.\n", - "\n", - "##### Momentum\n", - "Working alongside learning rate is momentum, where it helps the model have accelerated convergence and avoid the algorithm from getting stuck in a local minimum and avoid futile jumps over narrow valleys.\n", - "\n", - "##### Loss function\n", - "A measure to devaluate the candidate solution. It typically comes in a form of an error function which we seek to minimize. The loss function is used to update the weights after each epoch. The choice of measuring the loss function is therefore important. Some common loss functions are Maximum Likelihood and Cross-Entropy." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", - " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", - " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", - " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", - " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", - " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", - " validation_fraction=0.1, verbose=False, warm_start=False)" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, + "cell_type": "code", + "execution_count": 80, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Of 2022 Defaulters, the Neural Network (5x26) identified 788\n" - ] - }, { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Predicted01
Actual
06281446
11234788
\n", - "
" - ], "text/plain": [ - "Predicted 0 1\n", - "Actual \n", - "0 6281 446\n", - "1 1234 788" + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" ] }, - "execution_count": 109, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.204745415479134\n" + "Optimal Threshold: 0.15780782271279034\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5327,12 +5147,13 @@ } ], "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -5341,186 +5162,98 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6727\n", - " 1 0.64 0.39 0.48 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.64 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.74 0.67 0.69 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the rbf kernel does not increase the AUROC but and the recall increased slightly by 0.02, thus, it can be said that the linear and rbf kernel is similar in performance. However, we will choose the linear kernel as it is less computationally expensive compared to rbf kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall." ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelRecall-1AUROC
0Decision Tree (GINI)0.4188920.609249
1Random Forest0.3867460.761906
2Gradient Boosted0.3808110.775534
3Logistic Regression0.6191890.766563
4SVM0.3620180.723867
5Neural Network0.3897130.774088
\n", - "
" - ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.418892 0.609249\n", - "1 Random Forest 0.386746 0.761906\n", - "2 Gradient Boosted 0.380811 0.775534\n", - "3 Logistic Regression 0.619189 0.766563\n", - "4 SVM 0.362018 0.723867\n", - "5 Neural Network 0.389713 0.774088" + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 112, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'linear', probability = True)\n", + "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10\n", - "17496/17496 [==============================] - 2s 100us/step - loss: 0.4586 - accuracy: 0.8061\n", - "Epoch 2/10\n", - "17496/17496 [==============================] - 1s 83us/step - loss: 0.4432 - accuracy: 0.8153\n", - "Epoch 3/10\n", - "17496/17496 [==============================] - 1s 86us/step - loss: 0.4412 - accuracy: 0.8154 0s - loss: 0.4414 - accuracy: 0.\n", - "Epoch 4/10\n", - "17496/17496 [==============================] - 1s 83us/step - loss: 0.4397 - accuracy: 0.8165\n", - "Epoch 5/10\n", - "17496/17496 [==============================] - 1s 82us/step - loss: 0.4385 - accuracy: 0.8156\n", - "Epoch 6/10\n", - "17496/17496 [==============================] - 1s 80us/step - loss: 0.4377 - accuracy: 0.8170\n", - "Epoch 7/10\n", - "17496/17496 [==============================] - 1s 84us/step - loss: 0.4374 - accuracy: 0.8162\n", - "Epoch 8/10\n", - "17496/17496 [==============================] - 1s 82us/step - loss: 0.4362 - accuracy: 0.8173\n", - "Epoch 9/10\n", - "17496/17496 [==============================] - 2s 91us/step - loss: 0.4350 - accuracy: 0.8172\n", - "Epoch 10/10\n", - "17496/17496 [==============================] - 2s 108us/step - loss: 0.4346 - accuracy: 0.8176\n" + "Optimal Threshold: 0.1550813999698536\n" ] }, { "data": { + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "from numpy import loadtxt\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense\n", - "\n", - "# define the keras model\n", - "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + "auroc = get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5529,23 +5262,114 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6727\n", - " 1 0.65 0.36 0.47 2022\n", + " 0 0.84 0.93 0.88 6741\n", + " 1 0.64 0.39 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.65 0.67 8749\n", + " macro avg 0.74 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] } ], "source": [ - "# evaluate the keras model\n", - "#recall, accuracy = model.evaluate(df1, target)\n", - "#print('Accuracy: %.2f' % (accuracy*100))\n", - "#print('Recall: %.2f' % (recall*100))\n", - "\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the performance is not as great after using filtered features. Thus, the best SVM model to use is linear kernel with C = 0.01 and gamma = 0.01 using unfiltered features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", + "\n", + "#### Training\n", + "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "print(classification_report(y_test,predictions))" ] }, @@ -5555,8 +5379,334 @@ "metadata": {}, "outputs": [], "source": [ + "evaluation.loc[5] = ([\"Neural Network\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", "evaluation" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep Learning\n", + "\n", + "#### Theory\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=20, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the keras model\n", + "#recall, accuracy = model.evaluate(df1, target)\n", + "#print('Accuracy: %.2f' % (accuracy*100))\n", + "#print('Recall: %.2f' % (recall*100))\n", + "\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(X_train, y_train, epochs=20, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(X_train, y_train, epochs=10, batch_size=20)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adadelta Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adagrad Optimzier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RMSProp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SGD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### conclude that best optimizer is adagrad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(40, input_dim=44, activation='relu'))\n", + "model.add(Dense(25, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] } ], "metadata": { @@ -5580,7 +5730,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" } }, "nbformat": 4, From 527506b55954e080102012e372cc2e3b01cbbbdd Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Thu, 21 Nov 2019 11:56:15 +0800 Subject: [PATCH 21/25] Updated from jansons --- .DS_Store | Bin 8196 -> 6148 bytes ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 64 ++++++++++++------ BT2101_Default - EDIT-MASTER- Reon.ipynb | 64 ++++++++++++------ 3 files changed, 86 insertions(+), 42 deletions(-) diff --git a/.DS_Store b/.DS_Store index 7db16079758be136d45eec6582c64cbdcf23e038..e3db3cc1b6be3e11c43c083b4eebab3a7d4ea445 100644 GIT binary patch delta 138 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{MGjUEV6q~50$j=6n2a6dn7&63fj9bgd$Tm4f zpnCFL!Do~23mI$<72U+Nv0(?}Vs;J=L1v(GAQ0dN60RU)HWq$op3E=f2(kxc5DSQA Of>;C=+Z@j`hZz8o=ob?J literal 8196 zcmeHM-D(p-6h6~d-BiRB3M~k-3W67DN&IOM6=IrP1gsXf>V-vmqjuYq=Ssc_K1U zI3~}cIzae1F9R~6dS-wO`b25UkV$!}(940@xdndz^uZaM5#_psuNS5naWU@@D4pwOedC=sB6uDW?+y3p1aRej@IcKa`*oH-KG}# z6yY2`r2EL}7s;bKZBsx_(GSaxA3D(G$L%)C)n7u;B1-MES5n^t{@7c9%>sIh@M;pj zD$)a5ftSbVEs&*p*U)!w48NDg@Vfxpq1aDmxQ7WlwzJD)d055jE$p#CkB0kTZ(}|# zjNxPGJsDW}?4eeN+F^6C)g5l9gzr8cqNYO4Sd6@n*|`{7j|;sfH4t+yVyr}E+#R26 za?s{2cMJv%Xxp&Gb;_{1wSO(_M{F%Ku?Srd*AaG6M*Ms5AMCdvB7UbY;zzLHw3~Hx zW%-M}+{A$gPs*Y-}hoQgMww)(l zrCmzSu7p8kdqJfkls&hCDbJsJfg84SVJmQ(!j2^e3?pGAO3ArS=f=|YlzDThn=(7g z%juN4d~2!OO&D`mGIxs`we44(*WEWe;0eN*L^`i<7v7;U!p{B8j8zV6b)oj*^Y_uT zYGFR_wxTl8*PqEd-}rNC1Yfn#{R^#nwQq$VH~2zF@U>+%0zV8kF`F}#z$q`_#OG-h zxJWf}V^PD;K!4QWQj2Eb$QYQEp67Y~Ul{)V|H#&-9cu" ] @@ -5253,7 +5261,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -5263,10 +5271,10 @@ " precision recall f1-score support\n", "\n", " 0 0.84 0.93 0.88 6741\n", - " 1 0.64 0.39 0.49 2008\n", + " 1 0.62 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.73 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -5280,7 +5288,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the performance is not as great after using filtered features. Thus, the best SVM model to use is linear kernel with C = 0.01 and gamma = 0.01 using unfiltered features." + "As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will use unfiltered features and SVM with rbf kernel." ] }, { @@ -5574,7 +5582,7 @@ "outputs": [], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", @@ -5587,7 +5595,13 @@ "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" ] }, { @@ -5684,7 +5698,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### conclude that best optimizer is adagrad" + "### conclude that best optimizer is adagrad, so now we use filtered data to run tests" ] }, { @@ -5694,18 +5708,26 @@ "outputs": [], "source": [ "model = Sequential()\n", - "model.add(Dense(40, input_dim=44, activation='relu'))\n", - "model.add(Dense(25, activation='relu'))\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Deep Learning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", + " classification_report(y_test, mlp.predict(X_test_filter), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" ] } ], diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb index c68730d..724ff8f 100644 --- a/BT2101_Default - EDIT-MASTER- Reon.ipynb +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -5180,7 +5180,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As shown, the rbf kernel does not increase the AUROC but and the recall increased slightly by 0.02, thus, it can be said that the linear and rbf kernel is similar in performance. However, we will choose the linear kernel as it is less computationally expensive compared to rbf kernel." + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel." ] }, { @@ -5199,43 +5199,51 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 86, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, { "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", - " kernel='linear', max_iter=-1, probability=True, random_state=None,\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 83, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "clf_reduced_tuned_filtered = svm.SVC(kernel = 'linear', probability = True)\n", + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'rbf', probability = True)\n", "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.1550813999698536\n" + "Optimal Threshold: 0.15729612279514055\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5253,7 +5261,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -5263,10 +5271,10 @@ " precision recall f1-score support\n", "\n", " 0 0.84 0.93 0.88 6741\n", - " 1 0.64 0.39 0.49 2008\n", + " 1 0.62 0.40 0.49 2008\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", + " macro avg 0.73 0.66 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -5280,7 +5288,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, the performance is not as great after using filtered features. Thus, the best SVM model to use is linear kernel with C = 0.01 and gamma = 0.01 using unfiltered features." + "As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will use unfiltered features and SVM with rbf kernel." ] }, { @@ -5574,7 +5582,7 @@ "outputs": [], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", @@ -5587,7 +5595,13 @@ "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", + " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" ] }, { @@ -5684,7 +5698,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### conclude that best optimizer is adagrad" + "### conclude that best optimizer is adagrad, so now we use filtered data to run tests" ] }, { @@ -5694,18 +5708,26 @@ "outputs": [], "source": [ "model = Sequential()\n", - "model.add(Dense(40, input_dim=44, activation='relu'))\n", - "model.add(Dense(25, activation='relu'))\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Deep Learning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", + " classification_report(y_test, mlp.predict(X_test_filter), output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" ] } ], From e70b32f51982d4daa4e6829ddbbf3a586eba4509 Mon Sep 17 00:00:00 2001 From: sgxcj777 Date: Thu, 21 Nov 2019 15:48:06 +0800 Subject: [PATCH 22/25] Updated with naive bayes --- ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 77 +++++++++++++++++++ BT2101_Default - EDIT-MASTER- Reon.ipynb | 77 +++++++++++++++++++ 2 files changed, 154 insertions(+) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb index 724ff8f..17c9245 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -5729,6 +5729,83 @@ "\n", "evaluation" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "\n", + "gnb = GaussianNB()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for naive Bayes\n", + "get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#accessing model performance\n", + "print(metrics.classification_report(expected, predicted))\n", + "print(metrics.confusion_matrix(expected, predicted))" + ] } ], "metadata": { diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb index 724ff8f..17c9245 100644 --- a/BT2101_Default - EDIT-MASTER- Reon.ipynb +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -5729,6 +5729,83 @@ "\n", "evaluation" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "\n", + "gnb = GaussianNB()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#plot roc for naive Bayes\n", + "get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#accessing model performance\n", + "print(metrics.classification_report(expected, predicted))\n", + "print(metrics.confusion_matrix(expected, predicted))" + ] } ], "metadata": { From 9bf17447e475cd830f605615f87ce798314cca6a Mon Sep 17 00:00:00 2001 From: reon Date: Thu, 21 Nov 2019 19:45:00 +0800 Subject: [PATCH 23/25] made changes --- ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 2874 ++++++++++++----- BT2101_Default - EDIT-MASTER- Reon.ipynb | 2874 ++++++++++++----- 2 files changed, 4084 insertions(+), 1664 deletions(-) diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb index 17c9245..99725fe 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1258,7 +1258,7 @@ "metadata": {}, "source": [ "### Removing Noise\n", - "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4" ] }, { @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4])" + "array([2, 1, 3, 4, 0], dtype=int64)" ] }, "execution_count": 14, @@ -1278,7 +1278,7 @@ } ], "source": [ - "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)\n", "df[\"EDUCATION\"].unique()" ] }, @@ -1286,28 +1286,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, for Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", - "df[\"MARRIAGE\"].unique()" + "Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, \"Missing\"" ] }, { @@ -1328,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1341,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1391,7 +1370,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1379,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1388,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1397,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1406,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1449,7 +1428,7 @@ "5 -1 0 -1 0 0 0" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1461,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1480,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1529,7 +1508,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,11 +1532,11 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", - " 1.852734\n", - " 1.564717\n", + " 1.850753\n", + " 1.558773\n", " 35.006592\n", " 0.372109\n", " 0.337321\n", @@ -1577,11 +1556,11 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", - " 0.738572\n", - " 0.521936\n", + " 0.738175\n", + " 0.522639\n", " 8.832028\n", " 0.765730\n", " 0.814878\n", @@ -1601,11 +1580,11 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", - " 1.000000\n", - " 1.000000\n", + " 0.000000\n", + " 0.000000\n", " 21.000000\n", " 0.000000\n", " 0.000000\n", @@ -1625,7 +1604,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1628,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1652,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1676,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1728,9 +1707,9 @@ "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", - "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", - "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "mean 149324.899981 1.608954 1.850753 1.558773 35.006592 \n", + "std 116558.616530 0.487994 0.738175 0.522639 8.832028 \n", + "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", @@ -1779,7 +1758,7 @@ "[8 rows x 30 columns]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1805,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1819,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -1894,7 +1873,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1897,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1921,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1945,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1969,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2038,7 +2017,7 @@ "[5 rows x 30 columns]" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2073,7 +2052,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -2082,7 +2061,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -2091,7 +2070,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2115,51 +2094,63 @@ " \n", " \n", " \n", + " MISSING-EDU\n", " GRAD\n", " UNI\n", " HS\n", + " MISSING-MS\n", " MARRIED\n", " SINGLE\n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", @@ -2168,15 +2159,15 @@ "" ], "text/plain": [ - " GRAD UNI HS MARRIED SINGLE\n", - "0 0.0 1.0 0.0 1.0 0.0\n", - "1 0.0 1.0 0.0 0.0 1.0\n", - "2 0.0 1.0 0.0 0.0 1.0\n", - "3 0.0 1.0 0.0 1.0 0.0\n", - "4 0.0 1.0 0.0 1.0 0.0" + " MISSING-EDU GRAD UNI HS MISSING-MS MARRIED SINGLE\n", + "0 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 0.0" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2184,15 +2175,15 @@ "source": [ "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", - "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "onehot.columns= names = [\"MISSING-EDU\",\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MISSING-MS\",\"MARRIED\",\"SINGLE\",\"OTHER-MS\"]\n", "#drop one of each category to prevent dummy variable trap\n", - "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER-MS\"], axis = 1)\n", "onehot.head()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "scrolled": true }, @@ -2240,7 +2231,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2252,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2273,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2294,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2315,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2392,7 +2383,7 @@ "4 0.0 0.0 1.0 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2420,7 +2411,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2429,18 +2420,18 @@ "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", - " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", - " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", - " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", - " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", - " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", - " 'PAY_6_Revolving_Credit'],\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',\n", + " 'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',\n", + " 'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',\n", + " 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',\n", + " 'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],\n", " dtype='object')" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2454,7 +2445,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2504,18 +2495,18 @@ " \n", " \n", "\n", - "

0 rows × 45 columns

\n", + "

0 rows × 47 columns

\n", "" ], "text/plain": [ "Empty DataFrame\n", - "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, MISSING-EDU, GRAD, UNI, HS, MISSING-MS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", "Index: []\n", "\n", - "[0 rows x 45 columns]" + "[0 rows x 47 columns]" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2531,14 +2522,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data has 45 Columns and 26245 Rows\n" + "Data has 47 Columns and 26245 Rows\n" ] } ], @@ -2558,7 +2549,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2568,7 +2559,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "colab": {}, "colab_type": "code", @@ -2576,12 +2567,11 @@ }, "outputs": [], "source": [ - "#using holdout sampling for train test split\n", + "#using holdout sampling for train test split using seed 123\n", + "np.random.seed(123) \n", "ft = df1.drop(\"Y\", axis = 1)\n", "target = df1[\"Y\"]\n", - "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", - "#make the results reproducible (according to instructions)\n", - "np.random.seed(123) " + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)" ] }, { @@ -2595,7 +2585,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2604,24 +2594,23 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 85.131154 1.364065e-04\n", - "PAY_0 4436.456236 0.000000e+00\n", - "PAY_2 3950.467050 0.000000e+00\n", - "PAY_3 3110.398233 0.000000e+00\n", - "PAY_4 3096.770229 0.000000e+00\n", - "PAY_5 2977.392709 0.000000e+00\n", - "PAY_6 2449.597828 0.000000e+00\n", - "PAY_0_No_Transactions 75.890061 1.454296e-03\n", - "PAY_0_Revolving_Credit 482.349587 0.000000e+00\n", - "PAY_2_Pay_Duly 76.560100 1.235192e-03\n", - "PAY_2_Revolving_Credit 237.062546 0.000000e+00\n", - "PAY_3_Pay_Duly 79.130634 6.519343e-04\n", - "PAY_3_Revolving_Credit 135.381715 1.723299e-11\n", - "PAY_4_Pay_Duly 74.678430 1.946930e-03\n", - "PAY_4_Revolving_Credit 98.449450 3.100197e-06\n", - "PAY_5_Pay_Duly 72.744346 3.071499e-03\n", - "PAY_5_Revolving_Credit 70.267645 5.406887e-03\n", - "PAY_6_Revolving_Credit 60.999670 3.661838e-02\n" + "LIMIT_BAL 82.306062 2.883753e-04\n", + "PAY_0 4279.993739 0.000000e+00\n", + "PAY_2 3557.072141 0.000000e+00\n", + "PAY_3 2766.119390 0.000000e+00\n", + "PAY_4 2736.965012 0.000000e+00\n", + "PAY_5 2587.002458 0.000000e+00\n", + "PAY_6 2240.874786 0.000000e+00\n", + "PAY_0_No_Transactions 76.858872 1.147939e-03\n", + "PAY_0_Revolving_Credit 480.805794 0.000000e+00\n", + "PAY_2_Pay_Duly 75.283344 1.684018e-03\n", + "PAY_2_Revolving_Credit 229.527990 0.000000e+00\n", + "PAY_3_Pay_Duly 86.995856 8.229607e-05\n", + "PAY_3_Revolving_Credit 121.059740 2.357071e-09\n", + "PAY_4_Pay_Duly 79.449207 6.014800e-04\n", + "PAY_4_Revolving_Credit 82.276504 2.906105e-04\n", + "PAY_5_Pay_Duly 63.330298 2.338310e-02\n", + "PAY_5_Revolving_Credit 64.659773 1.792035e-02\n" ] } ], @@ -2668,7 +2657,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2702,7 +2691,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2722,7 +2711,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -2753,11 +2742,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + "evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])" ] }, { @@ -2788,7 +2777,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2797,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2811,7 +2800,7 @@ " random_state=None, splitter='best')" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2823,7 +2812,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2832,8 +2821,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13463\n", - " 1 1.00 1.00 1.00 4033\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2855,14 +2844,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (GINI) identified 902\n" + "Of 1987 Defaulters, the Decision Tree (GINI) identified 809\n" ] }, { @@ -2897,14 +2886,14 @@ " \n", " \n", " \n", - " 0\n", - " 5410\n", - " 1331\n", + " 0\n", + " 5482\n", + " 1280\n", " \n", " \n", - " 1\n", - " 1106\n", - " 902\n", + " 1\n", + " 1178\n", + " 809\n", " \n", " \n", "\n", @@ -2913,11 +2902,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5410 1331\n", - "1 1106 902" + "0 5482 1280\n", + "1 1178 809" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2928,19 +2917,19 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 1.0\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2957,7 +2946,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2966,12 +2955,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.80 0.82 6741\n", - " 1 0.40 0.45 0.43 2008\n", + " 0 0.82 0.81 0.82 6762\n", + " 1 0.39 0.41 0.40 1987\n", "\n", " accuracy 0.72 8749\n", - " macro avg 0.62 0.63 0.62 8749\n", - "weighted avg 0.73 0.72 0.73 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] } @@ -2990,14 +2979,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Entropy) identified 865\n" + "Of 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" ] }, { @@ -3032,14 +3021,14 @@ " \n", " \n", " \n", - " 0\n", - " 5508\n", - " 1233\n", + " 0\n", + " 5509\n", + " 1253\n", " \n", " \n", - " 1\n", - " 1143\n", - " 865\n", + " 1\n", + " 1156\n", + " 831\n", " \n", " \n", "\n", @@ -3048,11 +3037,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5508 1233\n", - "1 1143 865" + "0 5509 1253\n", + "1 1156 831" ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -3065,19 +3054,19 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 1.0\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3090,10 +3079,10 @@ { "data": { "text/plain": [ - "0.6239677471688679" + "0.6167794747491347" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3104,7 +3093,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3113,12 +3102,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.82 0.82 6741\n", - " 1 0.41 0.43 0.42 2008\n", + " 0 0.83 0.81 0.82 6762\n", + " 1 0.40 0.42 0.41 1987\n", "\n", - " accuracy 0.73 8749\n", - " macro avg 0.62 0.62 0.62 8749\n", - "weighted avg 0.73 0.73 0.73 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", "\n" ] } @@ -3134,17 +3123,6 @@ "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." ] }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", - " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3163,7 +3141,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3173,7 +3151,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -3188,7 +3166,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 47, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -3199,7 +3177,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3208,8 +3186,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13463\n", - " 1 1.00 1.00 1.00 4033\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3231,14 +3209,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Random Forest) identified 788\n" + "Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713\n" ] }, { @@ -3273,14 +3251,14 @@ " \n", " \n", " \n", - " 0\n", - " 6265\n", - " 476\n", + " 0\n", + " 6371\n", + " 391\n", " \n", " \n", - " 1\n", - " 1220\n", - " 788\n", + " 1\n", + " 1274\n", + " 713\n", " \n", " \n", "\n", @@ -3289,11 +3267,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6265 476\n", - "1 1220 788" + "0 6371 391\n", + "1 1274 713" ] }, - "execution_count": 49, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3304,19 +3282,19 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.325\n" + "Optimal Threshold: 0.27\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEWCAYAAADcsGj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gU1dfA8e9JSAghgZCQEEgh9F4NoKACUkUUu4INOyr23rG+tp+9i72hYkcUUUQQlC69lzQgpJDes/f9Yya6xEA2wGazyfk8Dw/ZqWfqmblz544YY1BKKaXqIh9PB6CUUkodjCYppZRSdZYmKaWUUnWWJimllFJ1liYppZRSdZYmKaWUUnVWnUxSInKBiPzs6Tg8TURiRSRPRHxrcZ5xImJEpFFtzdOdRGS9iAw7jPEOax8UkSYi8r2IZIvIFzUdvza5un/V1+NRRAJEZJOIRHhg3o3s4yyutuddF4hIfxFZ6Mqw1SYpEdklIoX2zrxXRN4TkaAjD/PgjDEfG2NGu3MedZG9rkdW/DbGJBpjgowx5Z6My1Psg7jjkUzDGNPDGDO/mvn8JzEfwT54NtAKCDPGnHMY41eObZiIOOzjL09EkkXkcxEZcKTTdnX/csfxaCe+imUqrLSMeUdzXodwDfCLMWafHdNHIlJix5ApIj+LSOdaisVtROQPESlyXr9HY/+pwfz/k5CNMSuBQhE5ubrxXb2TOtUYEwT0BfoBdx9GrB7nybuD+nJnUhMNdH23BbYYY8pqOuIhYt5tH3/BwLHAJmChiIw4/DA9y058QfZynYy9jE7dDuCm7Xk18GGlbo/b848C9gFvuWG+njDFef0aY5bVZGQR8RGRo13y9jHWNjg0Y8wh/wG7gJFOv58CfnD63Rh4BkgEUoHXgSZO/ScAfwM5wHZgrN29OfA2sAdIAR4FfO1+k4E/7L9fB56pFNO3wC32322AL4E0YCdwg9Nw04CZwEf2/K+oYvmaAx/Y4ycA9wE+TnEsAl4CsrFODiMqjXuoZVgEPAdk2v06APOADCDd3kgh9vAfAg6gEMgD7gDiAAM0soeZDzxiTzcX+Blo6RTPxfYyZAD3V952lZa7CfA/e/hs4A+7W8U8L7G3aTpwr9N4A4E/gSx7uV8G/J36G+A6YCuw0+72ApBkb4MVwAlOw/sC99j7Rq7dPwZYYE8r314f59nDj8fan7KAxUDvSvvqncAaoBho5LwO7NiX23GkAs/a3RPteeXZ/47DaR+0h+kBzLW3ZSpwTxXr9CGgBCi1p3M51oXgffZ63oe1rzW3h69Y15fbMSyoYprDgOQqur8MLHf63dUpvs3AuTXY1hX712Rgh70ddgIXVD4e7d+DgWX2tJYBg536zecQ++hB9sWDLWMycDuwFiixu0UDX/Pv8X6d0/A+/LsvpQMzgBYHmWd7rH3L16nbR8A0p9+nAdlOvzsBv/Hv8fthxbZ0ivcWO95s4FOgsVP/u4C9WOeKy+11H2f3C7Hnn4a1z94NiN3vCuB34EWs/X4bMMieRhLW/njhIdbvH8Dkg/Q7HuuYyAaWAoMqjfcI1vFeaO8vIcC7WMd+MvAw/54vO2Mdt9n2+vnE7r6YA4/ls+zube1ufofcPw7V0+nArzjIo+0N8IJT/+eB74BQrCu974H/czopZAOj7B0oCuhq9/sGeANoCkTYK+jqygcFcKK9ISo2WAt7hbWxp7kCeADwx9rxdgBjnJJUKXC6PWyTKpbvA6ykF2xvhC3A5U5xlAE3A37AefbyhLq4DGXA9VgnyyZAR3tdNAbC7Q36fFXrutJJzDlJbbd3hib27yfsft3tHeB4e108Yy/7wZLUK/b4UViJYrAdV8U837Ln0QfrhN/NHu8YrKv5RvawG4GbKiWpuVj7QxO724VAmD3OrVgHaoDdr+Ik1AUQe35hTtPq6DTt/lgn+kF2zJfY66yx0/r7GyvJNam8TrEOtovsv4OAY6taz1Xsg8FYB+WtQID9e9BB1us04COn35dhnVTa2/P8Cviw0nw/wNqHqto/h1H1CfwkrIuapva/JOBSex33xzpJ9HBxWzeyp5EDdLHHae00vvO6CAX2AxfZ4020f1dss/kcZB89zCS1Auu808SO/W+sROSPdTztwr5wBG7DSo5R9nZ6u2JdVzHtCcDqSt3+SVL2tvoUWOHUvzMwwp53hD2vZyrF+xcQibW/b8G+MMa6uNqDdZw2BT7nwCT1ib1vBNv7yjbgEqckVWavc1/gCawLjhft7TgO67wUWJMkBbS0x5tob8sLsRJwC6fxdgHdsM5/jYBZwKtAoL2cK/j3fPkF1kWij73+h9jdGzkva6UYCoDuRyNJ5WFdFRngV/69+hesTNjBafjj+PcK+g3guSqm2QrrxOd8xzUR+K2Kg0KwrjJPtH9fCcyz/x4EJFaa9t3Au04njP9cnToN62vH0d2p29XAfKc4dmMnSLvbUntncWUZEg82b3uY04FVldZ1dUnqPqf+1wI/2X8/AHzq1C8Q66r+P0nK3okKgT5V9KuYZ3SlZT7/IMtwE/C1028DnFTNcu+vmDfWVf+EgwxXOUm9BjxSaZjNwFCn9XdZFftvRZJagHW307LSMAes5yr2wYnO26maZZvGgUnqV+Bap99dsC4eGjnNt/0hpjeMqk/gXe1xo7AunhZW6v8G8KCL27oiSWUBZ1EpWVZaFxcBSyv1/xP7JHioffQwljEZuNjp9xBgR6Vh7gfesv/eWrEv2L9jsI5RnyqmfQlOd4d2t4+AIns9GKwL3p6HiPtsYFmleM93+v0s8LL99wfAo079utvziMNKAGVAZ6f+12E9LwMrSW106tfPHjfMqVv2wWLFSjYF9nJlVWw/rIuaxZWGXYZ9V2aP94BTvyh7X3K+O7wImGv//QnWMRpVaZqHSlKpON2JV/XP1TLG040xwVg7U1esDAzW3UAgsEJEskQkC/jJ7g7WTrK9ium1tTfMHqfx3sC6OjmAsZZkBtaJAmASVjFZxXTaVEzDns49WAmkQtIhlqsl1lVRglO3BKyNUSHFjsG5fxsXl+GAeYtIhIjMEJEUEcnBOihaUjN7nf4uwLriw47pn/kZYwqwroqq0hLrSqeqbXPI+YhIZxGZZVeiyQEe57/LUHm5bxWRjXaNtyysYtKKcQ62j1SlLXBrpe0dg7XsVc67ksuxroY3icgyERnv4nxrEmNlbfjv/tUI1/fRg4nCOvCzsNbLoErr5QKsK11XtjXGmHysZDcFa5/+QUS6urA88N9j5mD76OFwXjdtgdhKy3kH1nICxALfO/Vbi7WOqqq9tx/rrqWyJ4wxIUA7rIu8ThU9RCTSrrRScfy+x3/3fZeOTw5chxFYF8yHOg+lOv1dCJQbYzIqdTvUer7WGBNi/xvoFFN127Ly+m8MpDqt41f4d1++FeucuFxE1orIJYeIp0Iw1j58UDV6EGaM+R1rwzxjd0rHWjk9nFZAc/Pvg88krOcwlSVhXeG0dBqvmTGmx0Fm/Slwtoi0xbp7+tJpOjudphFijAk2xoxzDvsQi5SOdVXb1qlbLFaZcYUoEZFK/Xe7uAyV5/1/drfexphmWLfXcojha2IPVrEIYFWFxipyqEo61hVjVdumOq9hPZvrZC/DPRy4DOC0HCJyAlYRwLlYxQghWFd9FeMcbB+pShLwWKXtHWiM+bSqeVdmjNlqjJmIdVJ4EpgpIk0PNc5hxFjZbv67f5Vx4EnncLb7GcBKO7kkAb9XWi9BxphrqMG2NsbMMcaMwirq20TVlQYqLw/895g5mpzXTRKwtYrj/VS7fzIwqlL/AGPM3v9OljVAh4NVvzfG7MIq5n9JRBrbnZ/EOuZ72fv+ZP677x/MHqyLnQqxTn/vA8o59HnIHVzZlpXXfwHW4w7nc15vAGPMHmPMFcaY1lh3gm+KSDsOsn/b53Ow7oAP6nBqazwPjBKRvsYYB9aO/FzFuwYiEiUiY+xh3wYuFZERdu2QKBHpaozZg/VA9X8i0szu10FEhlY1Q2PMKqwHitOBOcaYisy7FMgRkTvFej/FV0R6ulq90lhVbz8HHhORYHul3YJ1h1MhArhBRPxE5Bys8tnZNV0GWzBW0WmWiERhPY9xlopVHn04ZgKnishgEfHHKtaq8gCyt9s7wLMi0sZeb8c5HYyHEoz17CLPvtK+xoXhy7C2XyMReQBo5tR/OvCIiHQSS28RqUiuldfHW8AUERlkD9tURE4RkaquiP9DRC4UkXB7+Sv2oXI7NgcHX/ezgEgRuUlEGtv7yiBX5ol1gXWziLQT69WNx4HPzOHV/hP7GHoQqwjoHqf4OovIRfZ+6iciA0Skm6vbWkRaichpdtIuxtpPq6qaPtue1ySxqhafh1V0Naumy3MY/gRK7DvzAHtZeonIMXb/14HHRSQW/im5OK2qCdlJKBHrGWuVjDE/Yu0bV9idgrEeb2SLSAzWMzBXfQ5cJiJd7XX8oNN8SrGO38dFJMg+sd/Mgechd5gF9BCR8+xtOQnrOd/sqgY2xiRhVeB4xumc11FETgQQkXPt8xr8W2Rabp9nM/jv8TUUq0iz9FBB1jhJGWPSsMpX77c73Yn1kO8v+xb4F6xyd4wxS7HKPZ/Dunr+nX8z98VYRW0bsG69Z2JdwR3Mp8BIrHLPiljKgVOxqsbvxLpqnI5VnOSq67F2vB1YZbCfYB3UFZZg3fKnA48BZzvdZtd0GR7CeqidDfyA9aDU2f8B99m30jU5ADDGrLeXZQbWVVsu1hVa8UFGuQ2rOGQZVo2wJ3Ftf7gNq8g1FytpfFbN8HOAH7EeIidgXdU7FyE8i3UA/4yV/N7GekgO1vOd9+31ca4xZjnWM8mXsdb3NqyrWVeNBdaL9R7OC1jPD4rsotHHgEX2vI51HskYk4tV4eVUrOKcrcBwF+f5DlYtsAVY+2gR1naqiTZ2zHlY26sXMMwY87NTfKOB87Gujvdibc+KROTKtvbBKq7ZbQ8zFOt50gHsfX+8PWwGVnHbeGNMeg2XqcbsxD4Oq0LWLqxj8g3+veh5Futxw68ikotVq+xQF6xvYD1TOZRngDvtC78H+bcy2Hf8W6LjSuzfYxWN/Y51LMytNMi1WMWLO+1h3sc6z7qNfS4/DescnoGVGMcbYzIPMdqFWM8vK855X/BvcesgYJmI5GOd264zxiTa/R4EPrGPrzPtbhdgXVgcUkWNOVUFEZmMVTvneE/HUlP2VXsWVrHcTk/Ho1RdIyIBwCqsyhb7PB1PQyIi/YCXXDm31slmkdThEZFTRSTQLk54BuvqeZdno1KqbrLvortpgqp9xphVrl78a5KqXyZgFdfsxiqiPN/orbJSyotpcZ9SSqk6S++klFJK1Vle2ehpy5YtTVxcnKfDUEopr7JixYp0Y0x49UPWHV6ZpOLi4li+fLmnw1BKKa8iIpVbmKjztLhPKaVUnaVJSimlVJ2lSUoppVSdpUlKKaVUnaVJSimlVJ2lSUoppVSd5fYkJSLviMg+EVl3kP4iIi+KyDYRWSMi/d0dk1JKKe9QG3dS72F9IuFgTsZqZ64TcBXWR/WUUkodRQ6HdzaB5/aXeY0xC0Qk7hCDTAA+sBtC/UtEQkSktf1RQaWUUgdR7jDsyy2iqNRBSZmD4rJydmUUsC4lm017c9mfX0J2YSn784vJLarxdzbrhLrQ4kQUB34EL9nudkCSEpGrsO60iI11/vKyUkrVf8YYdmcXsWF3Dn8n7WdVYhark7LIL/nvB5T9G/nQuVUQoYH+5O/NZ++KvfiU6Z3U4arqE+f/WZvGmDeBNwHi4+O9c20rpZQLSssdbNqTy5qULDbtyWXT3hw27c39527I10fo1jqYM/tH07V1MIH+vjRu5Iu/rw+tQwLo3CoYP18fnn56ER8+/hfnndeDZ58dQ1TUZR5espqrC0kqGYhx+h2N9T0kpZSq90rLHSRkFLBtXy6rkrJYlZDFmpQsikodAAQ1bkTXyGAm9G1Dl8hmdIsMpkeb5jTx961yevv25bNuTSr9+rXmuusG0q9fa0aObF+bi3RU1YUk9R0wVURmAIOAbH0epZSqT8odhj3ZhSRmFJCQWcCujHy278tnR1oeiZkFlNmVGvx8hR5tmjNxYCz9Y1vQNyaE6BZNEKmqwOlADofh7bdXcuedvxAZGcS6ddcSGOjn1QkKaiFJicinwDCgpYgkAw8CfgDGmNeB2cA4YBtQAFzq7piUUupoKyotJymzgAQ7ESVm5Nv/F5C8v5CScsc/w/r7+hDXMpAukcGM69WaDhFN6RAeROdWwQT4VX2HdChr1qQyZcos/vwzmWHD4njttVPw8ak+sXmD2qjdN7Ga/ga4zt1xKKXUkcoqKDkwCf3zdwF7c4oOGDa4cSNiwwLp2jqY0T0iaRsWSNvQQGLDAmndvAm+RymJLFmSzJAh79CiRRPef/90Lrqot0t3Xt6iLhT3KaVUneBwGPbmFJGQUUBi5oFJKCEjn5xK1bjDgxvTNjSQwR3DaBvalLZhVhJqGxpIaFN/tyaL5OQcoqObMWBAFI88Mpyrr44nNLSJ2+bnKZqklFINijGGhIwCdqbnk2AXySXYSShpfyElZf8WyzXyEaJaNCE2NJA+MW1oG9rUSkJhgcSGBhLoX/un0MTEbG644Ud+/z2BzZunEhHRlLvvPqHW46gtmqSUUvVaUWk5q5OyWJG4n5UJ+1mRsJ/9BaX/9A/09yU2NJCOEUGM6NaK2NBAu2iuKW1CAmjkWzeaOC0tLeeFF5bw4IPzMcYwbdowWrQI8HRYbqdJSilVLxhjSM0pZnNqLlv25rI5NZfNe3PZuCfnn9pz7cObMrJbK/q3bUHnVkHEhjalZZB7i+WOhry8EoYMeYc1a1IZP74zL710MnFxIZ4Oq1ZoklJKeZ2SMgdb9+WyPiWHdbuz2bgnh817cw94ZhQe3JjOrYK48sT2xLdtQb/YFoQ29fdg1DVXWlqOn58vQUH+jBjRjmnThnL66V3rfFI9mjRJKaXqtLJyB5v25vJ3UhbrUrJZtzubLXvz/qnSXfGy66l92tAlMpjOrax/3paQnBlj+OSTtdx116/8+OMF9OwZwbPPjvF0WB6hSUopVafsyS7k78QsViVl8XdiFmtTsikstdqnCwn0o2eb5lx6fBw92zSnZ1Rz2oYG1pt3ggA2b07n2mtnM2/eTgYOjKIB3TRVSZOUUspjCkrKWJOczd92QlqVtJ/UnGLAeuG1R1Qzzh8YQ7/YFvSrQesL3uqxxxbw8MMLaNKkEa++Oo6rrjoG3zpSccNTNEkppWpFdmEpO9Ly2JqaZ90lJWWxJTWXcrtSQ9uwQI5tH0a/mBD6xragW+tgGjeqeesL3iwvr4Szz+7O//43msjIIE+HUydoklJKHVU5RaWsTspi895ctqdZ7dNtT8snPa/4n2GCAxrRNyaEUd060Dc2hD7RIYQFNfZg1J6xd28et976Mxdf3JsxYzry2GMj6lXR5dGgSUopddiMMexMz2dFwn5WJmaxMmE/W/blYuyP6YQE+tEhPIiTuobTPjyIDuFBdAhvSlxY0wZ9MnY4DG+8sZy77/6VwsIyhgyxPgTRkNfJwWiSUkq5zBjDrowC/tiaxoKt6SzflfnPi7HNAhrRL7YF43q1pn/bEHq0ae7VNezc5e+/9zJlyiyWLEnhpJPa8eqr4+jSpaWnw6qzNEkppQ4pu6CUxdvTWbA1nYVb00jeXwhATGgTRnVvxTFtW9A/tgUdwoP0TsAFS5Yks3NnFh99dAaTJvWq1xVBjgYxxvs+chsfH2+WL1/u6TCUqrd2pefz84a9zN2QyoqE/TiM1ar3cR3COKFzOCd2aknbsKaeDtMrGGP45ptNFBWVMXFiLxwOQ05OMSEhtd+kkYisMMbE1/qMj4DeSSmlMMawNiWbn9en8vOGvWxJzQOge+tmTB3ekaFdwukTHVJn2rHzFgkJWUyd+iOzZm1h2LA4zj+/Jz4+4pEE5a00SSnVQJWUOViyM4Of16cyd0Mqe3OK8PURBsaF8uCpsYzs1oqY0EBPh+mVSkvLee65v3jood8RgWeeGcWNNx6rRXuHQZOUUg1IblEpv29JY+6GVOZt2kduURkBfj4M7RzO7d27cFLXCFpoZYcjtmhREnfe+Qunn96VF14YS2xsc0+H5LU0SSlVz5U7DL9t2scnSxP5Y2s6JeUOQpv6c3LPSEZ1j+T4ji1p4t+wXpp1h4yMAhYsSOCMM7oxbFgcS5dewYABUZ4Oy+tpklKqnsopKuWL5cm8v3gXiZkFRDYL4OLj2jK6RyTHtG1x1D5f3tAZY/jwwzXceuvP5OeXkJh4My1bBmqCOko0SSlVz2xPy+P9xbuYuSKZgpJy4tu24I6xXRjTIxI/rfhwVG3alM411/zA/Pm7OO64aF5/fTwtW+pzvKNJk5RS9YDDYViwNY13F+3i9y1p+Pv6ML5Pay4d3I5e0fo8xB0yMwuJj38TPz9f3nhjPFdc0V/fE3MDTVJKebG84jK+WpnMe4t3sSMtn/Dgxtw8sjOTBsUSHtzw2sKrDatX76VPn0hCQ5vw7rsTGDo0jogIfWfMXTRJKeWFEjLy+eDPBD5flkRucRl9opvz/Hl9GderNf6NtEjPHfbsyeXmm+fw2WfrmTfvYoYPb8c55/TwdFj1niYppbxEak4RP6zZw/drdrMqMYtGPsK4Xq2ZPCSO/rEtPB1evVVe7uC115Zz773zKC4u46GHhjF4cIynw2owNEkpVYel5xXz49o9fL9mD8t2ZWIMdGvdjNvHdOGs/tFENteWC9zJGMO4cZ/w88/bGTmyPa++Oo5OncI8HVaDoklKqTqmpMzBrxtTmbEsiYVb03AY6BgRxE0jOjO+T2s6hOvH8NwtN7eYpk398fERLr64N5Mn9+H883tqixEeoElKqTpi2748Pl+exJcrksnIL6FN8wCuGdaBU/u0oUurYD1B1gJjDF9+uZEbb/yJadOGcuWVx3DBBb09HVaDpklKKQ9KySrk981pfLMqhaW7MmnkI4zs1orzBsZwYqdwfeG2Fu3cuZ+pU39k9uyt9O0bSd++kZ4OSaFJSqlaVVRazpKdmfy+OY0FW9PYts9qbbxdy6bcdXJXzuofrVXHPWD69JXccMOP+Pr68NxzY5g6dSCNtJZknaBJSik3251VyNwNqfy6aR9LdmRQXObAv5EPg9qFcv6AGIZ2DqdjRJAW53mAMQYRISamGWPHduTFF08mOrqZp8NSTjRJKXWUGWPYtDfX+gTGxr2sS8kBoH3LpkwaFMvQzuEMahemjbp6UHp6AXfcMZc2bYJ59NGTGDOmI2PGdPR0WKoKmqSUOkoSMvL5dGkSP6zdTVJmISLQLyaEu07uyqjurbRWXh1gjOG99/7m9tvnkp1dzF13DfF0SKoabk9SIjIWeAHwBaYbY56o1D8WeB8IsYe5yxgz291xKXU0lJY7+GVDKp8sTWTh1nR8fYQTO7Xk2mEdGdEtgohgfY+prtiyJYMrrviOhQsTGTIkhtdfH0/PnhGeDktVw61JSkR8gVeAUUAysExEvjPGbHAa7D7gc2PMayLSHZgNxLkzLqWOVGJGAZ8vT+Kz5Umk5RbTpnkAt4zqzHkDYmjVTBNTXVRSUs6WLRlMn34ql17aTxuD9RLuvpMaCGwzxuwAEJEZwATAOUkZoOJJZXNgt5tjUuqw5BWXMXvNHmauSGbprkxE4KQuEUwaFMuwLhFaXbwO+vHHrfz22y6eemoUPXtGkJBwE40b61MOb+LurRUFJDn9TgYGVRpmGvCziFwPNAVGVjUhEbkKuAogNjb2qAeqVFWKSstZtiuTr1am8OO6PRSVOmgf3pTbx3ThjH5RtAlp4ukQVRVSUnK46aY5zJy5ga5dW3LffSfSrFljTVBeyN1brKpLS1Pp90TgPWPM/0TkOOBDEelpjHEcMJIxbwJvAsTHx1eehlJHRWFJOSsT97NkRwZ/7czk76QsSsocNAtoxFn9oznrmGj6xYRodfE6qqzMwauvLuO+++ZRWurg0UeHc/vtQ/DXmpRey91JKhlwbi44mv8W510OjAUwxvwpIgFAS2Cfm2NTCoCkzAK+WpnCgq1prEnOorTc4CPQM6o5lxzXlkHtwji+U0sC/PREV9dlZhbywAO/MXhwDK+8Mo4OHUI9HZI6Qu5OUsuATiLSDkgBzgcmVRomERgBvCci3YAAIM3NcakGrrisnJ/Xp/LZsiQWbU8HoG9MCJcf355B7UOJb9uC4AA/D0epXJGdXcRbb63klluOIyKiKatWXU1cnN7t1hduTVLGmDIRmQrMwape/o4xZr2IPAwsN8Z8B9wKvCUiN2MVBU42xmhxnnKLjXty+GxZEt/8nUJWQSlRIU24cUQnzomPIUqfL3kVYwyff76em26aQ2pqHoMHxzB4cAzt2um3teoTtz9FtN95ml2p2wNOf28A9I065TY5RaV8v3o3ny1LYk1yNv6+Pozu0YrzBsQwpENLrYrshbZvz+S662YzZ852+vdvzfffTyQ+vo2nw1JuoFVdVL1kjGHpzkw+W57E7LVWrbyukcE8ML47Z/SLokVTf0+HqA6TMYYJE2aQmJjNiy+O5dprB+Drq43B1leapFS9si+3iC9XpPDF8iR2pOcT1LgRZ/aP5rz4GHpHN9fnFF5s4cIEjjmmDYGBfrz33um0aRNMmzbBng5LuZkmKeX1ysodzN+cxoxlSfy2eR/lDsPAuFCuHd6Rcb0iCfTX3dybpaXlc/vtc3n//dX83/+N4K67jteivQZEj17ltRIzCvh0WSJfrkhmX24xLYMac8UJ7Tg3PkYbc60HHA7DO++s4o475pKXV8I99xzPDTdUbgtA1XeapJRXcTgMv29N44PFu5i/JQ0BTuoawbnxMQzvGoGfPpuoN2666SdeemkpJ5wQy+uvj6d793BPh6Q8QJOU8grZBaV8vjyJj5YkkJBRQMugxlx/UicmDYwlsrk26Fpf5OeXUFxcTmhoE668sj/9+7fmkkv66LPEBkyTlKrTtqbm8u7iXXy9MoXC0nIGxLXg1tFdGNsjEn/9vHe98sMPW7juutkMGRLLxx+fSa9erejVq5Wnw1Ie5nKSEhF/INYYs8uKxYkAACAASURBVM2N8SgFwB9b03n99+38sS2dxo18mNC3DZcMjqNHm+aeDk0dZcnJOdx440989dVGuncPZ8qUYzwdkqpDXEpSInIK8CzgD7QTkb7Ag8aYM9wZnGp4dmcV8sisDfy4bi+tmwdwx9gunD8gllB9r6le+umnbZxzzheUlzv4v/8bwS23HKeNwaoDuHon9TDWJzZ+AzDG/C0iHd0WlWpwSsocvP3HTl78dSsGw+1junDFCe1o3EhPWPVRaWk5fn6+9O0bybhxnXjiiRHanJGqkqtJqtQYk1Xp4aW2r6eOmDGG+VvSeHTWBran5TOmRyvuH9+d6BaBng5NuUFWVhH33PMr69btY/78yURGBvHZZ2d7OixVh7mapDaKyLmAj92i+Y3AX+4LSzUE61KyeXz2RhZvzyAuLJB3Lx3A8C4Rng5LuYExhhkz1nHzzXNISyvg+usHUlJSTkCA1t1Sh+bqHjIVeABwAF9htWp+t7uCUvWXMYZNe3N5c8EOvl6VQotAP6ad2p1Jg9pqbb16au/ePC6++Gvmzt1BfHwbZs++gP79W3s6LOUlXE1SY4wxdwJ3VnQQkTOxEpZS1dqZns/3q3fz3erdbNuXR+NGPlwzrAPXDOtAM/1uU73WrFlj0tMLePnlk5kyJV4bg1U1Iq58uklEVhpj+lfqtsIY45G6ovHx8Wb58uWemLWqAYfDMGf9Xt5YsIO/k7IAGNgulFP7tGFcz0jCghp7OELlLvPm7eSZZxbz5Zfn0qSJHw6H0U+i1AH2eTve03HUxCHvpERkDNan3aNE5FmnXs2wiv6U+o+SMgff/J3C679vZ0daPm3DArl3XDfG92lN6+b6YcH6LDU1j9tum8tHH62hQ4cWJCZm06WLfrNLHb7qivv2AeuAImC9U/dc4C53BaW8S05RKauTsliZkMWqpP2sSswiu7CUbq2b8dLEfozr1RpfPUnVaw6H4a23VnDXXb+Sn1/C/fefyN13H0+TJlqUq47MIZOUMWYVsEpEPjbGFNVSTMoLGGNYtC2DNxfuYOHWNIwBEegUEcTJPSMZ0yOSYV3Ctc21BuSDD9bQt28kr712Cl27tvR0OKqecLXiRJSIPAZ0B/5pzdMY09ktUak6yxjDrDV7eG3+djbsySE8uDFTh3dkYLtQ+sSEaCWIBiQvr4THH1/IjTcOolWrIL7/fiItWgTohYk6qlxNUu8BjwLPACcDl6LPpBqcPdmFPPjten7ekEpEcGOeOqs3E/q10VYhGqBvv93E9df/SFJSDh07hnLZZf0IDdXnjeroczVJBRpj5ojIM8aY7cB9IrLQnYGpuiO7sJS3Fuxg+h87cDjgyhPacefYrjTSqsQNTmJiNjfc8CPffruZXr0imDHjbAYPjvF0WKoeczVJFYt1D79dRKYAKYA2DVDP5RaV8u6iXby1cAe5RWVM6NuG20Z3ISZUmyxqqB58cD5z5+7gqadGctNNx+Lnp3fRyr1cfU9qELABaAE8BjQHnjTGLHJveFXT96Tcq7CknA/+3MXrv29nf0Epo7q34qaRnfQzGQ3Un38m0bx5AN27h5OamkdRURlt24Z4Oix1GOrde1IVjDFL7D9zgYsARCTaXUEpz1myI4MbZqwiNaeYEzuHc+uozvSJ0RNSQ7R/fyF33fULb765krPP7s4XX5xDq1ZBng5LNTDVJikRGQBEAX8YY9JFpAdW80gnAZqo6gljDB8tSeSh79YTGxbI51f3Z2C7UE+HpTzAGMPHH6/lllvmkJlZyC23HMu0acM8HZZqoKprceL/gLOA1ViVJb7GagH9SWCK+8NTtSGnqJT/m72RT5cmcVLXCJ4/v69WJW/Apk9fyVVXzWLgwCh+/vki+vaN9HRIqgGr7k5qAtDHGFMoIqHAbvv3ZveHptyptNzBrxv38e3fKfy6aR8lZQ6uG96BW0Z10dYhGqCiojISErLo0qUlF1zQm0aNfLj44j7aGKzyuOqSVJExphDAGJMpIps0QXm/HWl53DBjFetScghr6s+kgbGc2T+K3tH67Kkhmjt3O9deOxuHw7Bp03UEBvpx6aX9PB2WUkD1Saq9iFR8jkOAOKffGGPOdFtk6qgrKi3ns2VJPPnTJvwb+fDixH6M6xmp7zs1UHv35nHLLXP49NN1dOoUyhtvjNcq5arOqS5JnVXp98vuCkS5T8X7Th/8uYv0vBIGdwjj2XP7Etk8oNpxVf20cWMaxx33NoWFZTz44FDuuut4/UquqpOqa2D219oKRB19RaXlfLo0kZfmbSMzv4ThXcK58sT2HNc+TNtXa6Bycopp1qwxXbq05LLL+jFlSjydO4d5OiylDkovneoZh8PwxYokfli7lyU7Miguc3Bc+zDuOrmrvu/UgOXmFvPgg/P58MM1rFt3Da1aBfHss2M8HZZS1XJ7khKRscALgC8w3RjzRBXDnAtMAwyw2hgzyd1x1TfGGJbuzOSpOZtZkbCf9uFNmTQollHdW+mdUwNmjOHrrzdxww0/snt3LldffQyNG+u1qfIeNdpbRaSxMaa4BsP7Aq8Ao4BkYJmIfGeM2eA0TCfgbmCIMWa/iGibgDW0cU8Od321ltVJWYQ19efZc/twRr8oTUwNXElJOWed9TmzZm2hT59WzJx5Lsceq+/fK+/iUpISkYHA21ht9sWKSB/gCmPM9dWMOhDYZozZYU9nBta7VxuchrkSeMUYsx/AGLOvZovQsC3als6UD1fQxN+XR0/vyVn9o2nirzW0GjJjDCKCv78vkZFN+d//RnPDDYNo1EhrcSrv4+pe+yIwHsgAMMasBoa7MF4UkOT0O9nu5qwz0FlEFonIX3bxoHLBN6tSmPzuUtqENOHbqUO48Ni2mqAauEWLEjnmmDdZt8661nvrrdO45ZbjNEEpr+VqcZ+PMSahUvFRuQvjVVXeVLnZ9UZAJ2AYVluAC0WkpzEm64AJiVwFXAUQGxvrYtj1U2m5g2fnbuG1+ds5tn0ob1wUT/Mm2oxRQ5aRUcBdd/3C9OmriI1tTmZmoadDUuqocDVJJdlFfsZ+znQ9sMWF8ZIB5y+iRWM1rVR5mL+MMaXAThHZjJW0ljkPZIx5E3gTrE91uBh3veJwGBZuS+e5uVv4OymLiQNjmXZad/0ybgP38cdruOmmOezfX8jttw/mgQeGEhTk7+mwlDoqXE1S12AV+cUCqcAvdrfqLAM6iUg7rA8lng9Urrn3DTAReE9EWmIV/+1wMa4GIaughJkrkvnorwR2ZRTQMsifVyb155TerT0dmqoD1q9Po1OnUF5/fTy9e7fydDhKHVWuJqkyY8z5NZ24MaZMRKYCc7CqoL9jjFkvIg8Dy40x39n9RovIBqwixNuNMRk1nVd9lF1QykvztvLhXwkUlzkYENeCm0d1ZmzPSL17asAKC0t57LGFnHhiW0aP7sC0acNo1MgHH20YWNVDriapZXYx3GfAV8aYXFdnYIyZDcyu1O0Bp78NcIv9TwGpOUV8siSR9xbvIqeolDP7RXPFCe3o1rqZp0NTHjZnzjauvXY2O3bsx+EwjB7dAX+tLKPqMVe/zNtBRAZjFdc9JCJ/AzOMMTPcGl0DUlBSxidLElm4NZ1F29IpN4bhXSK4bXQXurfR5NTQ7dmTy003zeHzz9fTpUsY8+ZdzPDh7TwdllJu5/LLvMaYxcBiEZkGPA98DGiSOgr2ZBdy1QcrWJuSTceIIC4/vh2TBsXSNqypp0NTdcSsWVv49ttNPPzwMO64Y4i2GqEaDFdf5g3Cegn3fKAb8C0w2I1xNQjrd2fzyKwNLN2ZSYCfL29fEs+IbvrgW1lWrNhNUlIOp5/elcsv78+oUR2Ii9P2F1XD4url2Drge+ApY8xCN8bTIJSVO3h1/nZe/HUrIYH+TB3ekdP7RdE+PMjToak6ICenmPvvn8fLLy+jS5cwTjutCz4+oglKNUiuJqn2xhiHWyNpIBwOwy2fr+a71bs5rU8bHjqtBy2a6jstymrOaObMDdx440/s3ZvHNdfE89hjI7TWnmrQDpmkROR/xphbgS9F5D8v0OqXeWtmR1oeD363noVb07ljbBeuHdbR0yGpOmTFij2ce+5M+vaN5JtvzmfgwMotiCnV8FR3J/WZ/b9+kfcIlZQ5uPz95WTkFTPt1O5MHqI1s5TVUvnixUkMGxZHfHwbZs2ayJgxHbWtPaVshzwSjDFL7T+7GWN+df6HVYFCucAYw/3frGNnej4vTOynCUoBsGBBAn37vs6oUR+SmJgNwCmndNYEpZQTV4+Gy6rodvnRDKQ+e/uPnXy2PInrT+rI8C76uayGLj29gMsu+5ahQ9+jsLCMb745j9jY5p4OS6k6qbpnUudhVTtvJyJfOfUKBrKqHks5W7wtnSd+3MSYHq24ZVRnT4ejPCw/v4RevV4jPb2AO+8cwgMPDCUwUFuwV+pgqnsmtRTrG1LRWF/YrZALrHJXUPXFtn25XPnBcjqEB/HU2X30S7kNWHJyDtHRzWja1J9HHx3OoEHR9Oypd9VKVeeQScoYsxPYidXquaqB/fklTP1kFY39fHn/soH6vacGqqCglEcfXcAzzyxm1qxJjB7dgcsv7+/psJTyGtUV9/1ujBkqIvs58GOFgtU2bKhbo/NSO9Pzuey9ZaTsL+TNi48hsnmAp0NSHjB79lauu242u3ZlMXlyX/r1i/R0SEp5neqK+yo+Ed/S3YHUF1+uSGba9+vx8/XhkysHER+nebwhuuqq73nrrZV069aS+fMvYejQOE+HpJRXqq64r6KViRhgtzGmRESOB3oDHwE5bo7Pq/y0bi+3frGage1CeebsPsSGBXo6JFWLyssdiAg+PsJxx0UTFxfCbbcN1k9pKHUEXK2C/g3Wp+M7AB9gvSP1idui8kJFpeU8MmsD3Vs348PLB2qCamCWLUth4MDpTJ++EoBLL+3HPfecoAlKqSPkapJyGGNKgTOB540x1wPaZouTdxftIiWrkPvGd9Ov5jYg2dlFTJ06m0GDprNnTy4REfp5FaWOJpc/Hy8i5wAXAafb3bS6mm1PdiGv/LaNkd0iGNxBH981FD/8sIUrrvieffvymTp1II8+ehLNmjX2dFhK1SuuJqnLgGuxPtWxQ0TaAZ+6LyzvYYzhvq/XUeZwcP/47p4OR9UiX18foqKC+f77icTHt/F0OErVS65+Pn6diNwAdBSRrsA2Y8xj7g3NO7y7aBe/btrHfad00y/p1nPFxWU8/fRiyssdPPjgMMaO7cjo0R30UxpKuZGrX+Y9AfgQSMF6RypSRC4yxixyZ3B13Zcrknl41gZGd2/FpdpobL02f/4upkyZxebNGUya1AtjzD81+ZRS7uNqcd9zwDhjzAYAEemGlbTi3RVYXffH1nTu+HINQzqG8eLEfvjqyapeSkvL57bb5vLBB6tp1y6E2bMncfLJnTwdllINhqtJyr8iQQEYYzaKSIP9nOz2tDyu+XgFHcODeP3CYwjw09p89VVqaj4zZ27g3ntP4N57T6CJNm+lVK1yNUmtFJE3sO6eAC6gATcw+8yczQC8PTme4AA9adU3a9em8t13m7n33hPp2TOCpKSbCQ1t4umwlGqQXH1PagqwHbgDuBPYAVztrqDqsl3p+fy0fi8XHduW6Bb6wm59kp9fwp13zqV//zd57rm/2LcvH0ATlFIeVO2dlIj0AjoAXxtjnnJ/SHVXWbmDO75cQ+NGPkweHOfpcNRR9P33m5k69UcSE7O57LK+PPXUKMK01RClPK66VtDvwfoC70pggIg8bIx5p1Yiq4PeXLiDpTsz+d85fYhopi2b1xdZWUVcfPE3tGkTzIIFkznhhLaeDkkpZavuTuoCoLcxJl9EwoHZQINMUqk5Rbz223ZCm/pzZn9tEcrblZU5mDFjHZMm9SIkJIB58y6mR48IbWtPqTqmuiRVbIzJBzDGpImIq8+w6hVjDPd+vY6ScgffTB2iX9j1ckuWJHP11bNYvTqVFi0COOWUzvTr19rTYSmlqlBdkmovIl/ZfwvQwek3xpgz3RZZHfL9mj38sjGVe8d1o0N4kKfDUYcpK6uIe+75lddfX07r1sHMnHkO48bpO09K1WXVJamzKv1+2V2B1FUZecVM+249fWJCuOx4bVXCm40f/wl//pnMDTcM4pFHhhMcrI3BKlXXVffRw19rK5C66n9zt5BbVMrTZ/fWViW80NatGURFNSMw0I8nnxxJkyZ+9O+vRXtKeYsG+YzJFcYY3l20kxlLE5k0MJbOrYI9HZKqgeLiMh56aD69er3Gk0/+AcCQIbGaoJTyMm5PUiIyVkQ2i8g2EbnrEMOdLSJGRDzeHmBecRmXv7+ch77fwIhurbhjbFdPh6RqYN68nfTu/TrTpv3OGWd0Y8oUj+9SSqnDVKMkJSI1KsQXEV/gFeBkoDswUUT+89ElEQkGbgCW1GT67vL96t3M27SPG0d04o0Lj6FpY1dbj1Ke9sQTfzBixAeUlzuYM+dCPv30LFq31rtgpbyVq5/qGAi8DTQHYkWkD3CF/Rn5QxmI9e2pHfZ0ZgATgA2VhnsEeAq4rQaxu0W5w/DJkkRiQptw08hOWt3cCzgchoKCUoKC/Bk/vjMFBaXcfffx2hisUvWAq3dSLwLjgQwAY8xqYLgL40UBSU6/k+1u/xCRfkCMMWbWoSYkIleJyHIRWZ6WluZi2DX33eoU1qZkc/uYrpqgvMDq1XsZMuQdrr7a2n169ozg4YeHa4JSqp5wNUn5GGMSKnUrd2G8qs7y5p+e1svBzwG3VjchY8ybxph4Y0x8eHi4C7OuubJyB+8u2kX78Kac2lsfsNdleXkl3HbbzxxzzJts357JySd39HRISik3cPVhS5Jd5Gfs50zXA1tcGC8ZiHH6HQ3sdvodDPQE5tt3LZHAdyJymjFmuYuxHRV5xWVM+XAFa5Kzeeqs3noXVYctXZrC2Wd/TlJSDlde2Z8nnhipLZUrVU+5mqSuwSryiwVSgV/sbtVZBnQSkXZYn54/H5hU0dMYkw20rPgtIvOB22o7QQF8siSBP7al8+RZvTh3QEz1I6haV/HJ9tjY5sTFhTBjxtkMHqzbSqn6zKUkZYzZh5VgasQYUyYiU4E5gC/wjjFmvYg8DCw3xnxX02m6Q0FJGTOWJdEnJoTzBsR6OhxVSWlpOS+8sISff97OTz9dSGRkEAsWXOrpsJRStcDV2n1v4fQsqYIx5qrqxjXGzMZqPd252wMHGXaYK/Ecba/N387O9HzemTzAE7NXh7B4cRJTpsxi7dp9jB/fmdzcYpo318+kKNVQuFrc94vT3wHAGRxYa89r7cst4q2FOzihUzjDu0R4Ohxly8kp5vbbf+bNN1cSHd2Mr78+jwkTuuizQqUaGFeL+z5z/i0iHwJz3RJRLTLGcPeXa3EYuGNMF0+Ho5w0auTD/PkJ3HrrcUybNoygIH9Ph6SU8oDDbUqhHeD1ny+duSKZXzft4/7x3ekZ1dzT4TR4mzen8/jjf/Daa6cQGOjH6tVTCAjQ1j6Uashcek9KRPaLSKb9LwvrLuoe94bmXul5xTz8/QYGtgvl0sFxng6nQSsqKuPBB3+jd+/X+fbbTaxdmwqgCUopVf2dlFgPAfpgVSEHcBhj/lOJwtt89FcCucVlPH5GT3z0Exwe8/PP27n22h/Yvn0/F1zQi2eeGU1kpH5YUillqTZJGWOMiHxtjDmmNgKqDcVl5Xz0VwIndY2gY4Q2Puopxhgefvh3fHyEX365iBEj2ns6JKVUHeNqecpSEelvjFnp1mhqyfzNaaTnlXCJFvPVuvJyB9Onr2TChK5ERgbx2WdnExYWqEV7SqkqHfLMICKNjDFlwPHAlSKyHcjHapPPGGP610KMR92c9Xtp3sSPwR3CPB1Kg7Jq1R6mTPmBpUtTyMws5O67TyAqqpmnw1JK1WHVXb4uBfoDp9dCLLUiv7iMXzfuY0S3CPx89cPEtSE3t5gHHviNF19cSsuWgXz88ZlMnNjT02EppbxAdUlKAIwx22shFrczxjDtu/XkFJVywSCvr0HvNe6++1defXUZV199DI8/PoIWLbQxWKWUa6pLUuEicsvBehpjnj3K8bjV+4t38cWKZKYO78gxbVt4Opx6bdeuLEpLy+nUKYz77juRCy/szbHHRns6LKWUl6muvMsXCML6pEZV/7zKjGVJ9Iluzq2jO3s6lHqrtLScJ5/8g+7dX2Hq1B8BiIwM0gSllDos1d1J7THGPFwrkbhZTlEpm/bmctvoztr+m5ssWpTI1VfPYv36NE4/vSsvvjjW0yEppbycS8+k6oMNu3MA6N5Ga5O5w9dfb+TMMz8nNrY53357Pqedpm0hKqWOXHVJakStRFEL/tiajq+PcEzbUE+HUm8YY9i9O5eoqGaMGdORRx8dzk03HUvTptoYrFLq6DjkMyljTGZtBeJui7en0zcmhOZN/DwdSr2wcWMaw4e/z4knvkdhYSmBgX7ce++JmqCUUkdVg3hRqKzcwcY9ufSO1pbOj1RhYSn33TePPn1eZ82aVO6++3gaN9bWIpRS7tEgzi5/J2VRWFpOvBb1HZGkpGyGDXufHTv2c9FFvXnmmdFERDT1dFhKqXqsQSSpV+dvJ7hxI47v1NLToXil0tJy/Px8iYpqxgknxDJ9+qkMH97O02EppRqAel/ctys9n3mb9nH10Pb6PKqGyssdvPTSEjp2fInU1Dx8fIT33jtdE5RSqtbU+zupb//eDcCZ/fVl0ppYsWI3V189ixUr9jBqVHtKSso9HZJSqgGq10kqq6CEtxbu4KSuEbQJ0fbiXOFwGG6++SdefnkZERFNmTHjLM49t4e+AK2U8oh6naS+WplCXnEZd4zVF0td5eMjZGQUcs018Tz66EmEhAR4OiSlVANWr59Jrd+dQ0RwY7pGaisTh7Jjx35OO+1T1q/fB8AHH5zByy+P0wSllPK4epukisvKmbN+L31jQjwdSp1VUlLO448vpEePV/ntt11s2pQOWHdTSilVF9Tb4r7Ne3PJKy5jQt8oT4dSJy1YkMCUKbPYuDGds87qxvPPjyU6Wu84lVJ1S71NUqsSswC0lYmD+OmnbRQWljFr1kROOUU/XaKUqpvqbXHfD2v20CG8KdH6FVjAagz23XdX8csvOwC4//4TWb/+Wk1QSqk6rV4mqaTMApbuyuTM/tFadRpYv34fQ4e+x2WXfcf7768GoEkTPwID9eVmpVTdVi+L++Zvtmqpje/d2sOReFZBQSmPPPI7zzzzJ82aNebtt09j8uS+ng5LKaVcVi+T1IqE/YQ19Sc2NNDToXjUF1+s54knFjF5cl+efnoULVs27PWhlPI+9S5JFZaUM3dDKqf0bt0gi/pSUnLYsCGNUaM6cNFFfejWLZyBA7WGo1LKO7n9mZSIjBWRzSKyTUTuqqL/LSKyQUTWiMivItL2SOa3KnE/+SXlnNyrYRX1lZU5eOGFv+ja9RUuueQbSkrK8fERTVBKKa/m1iQlIr7AK8DJQHdgooh0rzTYKiDeGNMbmAk8dSTzXJVkVT3vH9PiSCbjVZYtS2HQoOncdNMcjj8+lj/+uAx/f19Ph6WUUkfM3cV9A4FtxpgdACIyA5gAbKgYwBjzm9PwfwEXHskM/9qRQaeIIJo3kJprmzenc+yxb9OqVVM+//xszj67e4Ms5lRK1U/uLu6LApKcfifb3Q7mcuDHqnqIyFUislxElqelpVU5cmFJOUt2ZnJi5/DDjdcrGGNYuzYVgC5dWvLOO6exadNUzjlHWytXStUv7k5SVZ0xTZUDilwIxANPV9XfGPOmMSbeGBMfHl51EvpzRzolZQ6Gdam/SWrbtkzGjv2Y/v3f/KetvUsu6UuzZo09HJlSSh197i7uSwZinH5HA7srDyQiI4F7gaHGmOLDndmCLekE+PkwIC70cCdRZxUXl/H004t59NEF+Pv78txzY+jUqf4tp1JKOXN3kloGdBKRdkAKcD4wyXkAEekHvAGMNcbsO9wZlZU7+HVTKgPbhRHgV78qDZSWljNgwFusXbuPc87pzvPPj6VNm2BPh6WUUm7n1iRljCkTkanAHMAXeMcYs15EHgaWG2O+wyreCwK+sJ+nJBpjTqvpvH7dtI+kzELuObnbUVwCz8rJKaZZs8b4+fly+eX96Nw5jJNP7uTpsJRSqta4/WVeY8xsYHalbg84/T3yaMznx7V7CAn0Y2T3Vkdjch7lcBjefnsld975CzNmnM3o0R248cZjPR2WUkrVunrT4sSfOzIY1jkcP1/vbjN37dpUpkz5gcWLkzjxxLbExOg3npRSDVe9SFLFZeWk5hQT17Kpp0M5Io89toBp034nJCSA996bwMUX99Eq5UqpBq1eJKk9WUUARIV457ejjDGICBERTbnkkj48+eRIwsK0MVillPLusjFbSlYhAFFe9oHDpKRszjzzM956ayUAV155DNOnn6YJSimlbPUiSe1MzwcgpoV3nNzLyhw8++yfdOv2Cj/9tI2SknJPh6SUUnVSvSjuW5Gwn5ZBjb3iU/HLl+/miiu+Y/XqVE45pRMvvzyOuLgQT4ellFJ1Ur1IUssTMhkQ18IrKhlkZhaSnl7Al1+eyxlndPWKmJVSylO8PkntzS4iKbOQyYPbeTqUKhlj+PTTdaSk5HD77UMYPboD27bdQECA1696pZRyO69/JrU62f5+VGzdKzLbujWD0aM/4oILvuLbbzdTXu4A0ASllFIu8voklbLfqtnXNqzuvCNVXFzGQw/Np1ev11i6NIVXXhnH779PxtfLXzRWSqna5vWX9Fv35RIc0IgWdegjh9u37+fRRxdy9tndefbZ0bRurY3BKqXU4fD6JLUmOZu+MSEer4CQmprHl19u5NprB9C9ezibNl1Hhw76KQ2llDoSXl3+ZIxhZ3o+HSOCPBaDw2F4/fXldOnyMjffPIedO/cDaIJSSqmjJx/rZQAAE+xJREFUwKvvpPblFlNQUk57D7XZt3r1XqZM+YG//kpm+PA4Xn31FNq1a+GRWFTdVlpaSnJyMkVFRZ4ORTUAAQEBREdH4+dXdx6DHC6vTlLb0/IAaNey9u+kCgtLGTnyQ0Tgww/P4IILenm8yFHVXcnJyQQHBxMXF6f7iXIrYwwZGRkkJyfTrl3dfDWnJrw6SVU0h9Q+vPbupObN28mwYXE0aeLHzJnn0KtXK0JD635LF8qzioqKNEGpWiEihIWFkZaW5ulQjgqvfia1Iy2fAD8fIpsFuH1eCQlZTJgwgxEjPmDGjHUADB0apwlKuUwTlKot9Wlf8+o7qbXJ2XQID8LHx30bpLS0nOef/4tp034H4OmnR3HOOd3dNj+llFL/8to7qYy8YpYlZDLKzZ+LP/fcmdxxxy+MHNmejRuv47bbBuPn5+vWeSrlDr6+vvTt25eePXty6qmnkpWV9U+/9evXc9JJJ9G5c2c6derEI488gjHmn/4//vgj8fHxdOvWja5du3Lbbbd5YhEOadWqVVxxxRUHdJswYQLHHXfcAd0mT57MzJkzD+gWFPTvc+0tW7Ywbtw4OnbsSLdu3Tj33HNJTU09otgyMzMZNWoUnTp1YtSoUezfv/8/w/z222/07dv3n38BAQF88803gPWc6d5776Vz587/3969R1dVXwkc/24eEgIBFTQLimOE8AgEiIAdGOwQoAI+QHlIBAqBIiJSXQWFGRezVvCxpFJRy0CltOMKTg2JgFHEViovRQpKKGkSiGLEyFDTgJFKeFRCsuePc3LzJjeQ+wr7s9Zd655zzz1n373uzc75nd/5/YiJiWHlypUAbNmyhaSkpCuKLeipasg9Bg4cqNnH/6E3/8cW3ZpToI2tqOicnj17QVVVd+w4qunpuY1+DHN1OXz4cKBD0DZt2niez5gxQ5999llVVT137px27dpVt27dqqqqZ8+e1TFjxuiqVatUVTU7O1u7du2qubnO76CkpERXr17dqLGVlJRc8T4mTZqkmZmZnuVTp05ply5dtFevXnr06FHP+sTERN2wYUOV95bn5vz58xodHa2bN2/2vLZjxw7Nzs6+otgWLVqky5YtU1XVZcuW6eLFiy+5fVFRkV533XV69uxZVVV99dVXdfr06VpaWqqqqoWFhaqqWlZWpnFxcZ7tKqvtOwdkaBD8DW/II2Sb+4r/eRGAto04Dp6q8vvfZ/H443/iwQcH8NxzIxk+PPR7x5jg8tQ7hzj89elG3Wfvzu1IGtvH6+2HDBlCVlYWACkpKQwdOpRRo0YBEB4ezqpVq4iPj2f+/PksX76cJUuW0KtXLwBatGjBI488UmOfZ86c4dFHHyUjIwMRISkpiYkTJ9K2bVvOnHF64m7cuJEtW7aQnJzMzJkzuf766zl48CBxcXGkp6eTmZnJtdc643BGR0ezZ88emjVrxsMPP8yxY8cAePnllxk6dGiVYxcXF5OVlUX//v096zZt2sTYsWOJjIwkNTWVJ598st68pKSkMGTIEMaOHetZN3z4cK/zWpe3336bXbt2AZCYmEh8fDzPP/98ndtv3LiRO++8k/BwZ468V155hZSUFJo1cxq/brzxRsC59hQfH8+WLVuYPHnyFccZjEK2SH179gIA17a+plH299ln3zBv3rvs3JnP4MFdSEjw/gdvTCgpLS1l+/btzJ49G3Ca+gYOHFhlm27dunHmzBlOnz5NTk4Ojz/+eL37feaZZ2jfvj3Z2dkAtTZpVXfkyBG2bdtG8+bNKSsrIz09nVmzZvHxxx8TFRVFZGQkU6dOZcGCBdx+++0cO3aM0aNHk5ubW2U/GRkZxMbGVlm3fv16kpKSiIyMZNKkSV4VqZycnBq5qE1xcTE/+tGPan0tJSWF3r2rXrcuLCykU6dOAHTq1IkTJ05ccv+pqaksXLjQs/zFF1+QlpZGeno6N9xwAytXrqR79+4ADBo0iN27d1uRCjafnygGIKrjlc/Gm5ycydy5WwgPb8maNXczZ85An3bGMFe3hpzxNKbz588TFxdHfn4+AwcO5I477gCcFoS6eoM1pJfYtm3bSE1N9Sxfd139N7bff//9NG/uXONNSEjg6aefZtasWaSmppKQkODZ7+HDhz3vOX36NMXFxUREVIyJWVBQwA033OBZLiwsJC8vj9tvvx0RoUWLFuTk5BAbG1vrZ2pob7iIiAgyMzMb9B5vFRQUkJ2dzejRoz3rvv/+e8LCwsjIyODNN9/kpz/9Kbt37wacs6qvv/7aJ7EEg5DtOLHvaBHtW7ck/JrLr7MlJc607YMGdeaBB2L59NP5zJ07yAqUaZJat25NZmYmX331FRcuXGD16tUA9OnTh4yMjCrbHj16lLZt2xIREUGfPn04cOBAvfuvq9hVXld9xI02bSrucRwyZAh5eXmcPHmSt956iwkTJgBQVlbG3r17yczMJDMzk7/97W9VClT5Z6u877S0NE6dOsUtt9xCVFQU+fn5ngLaoUOHKmd53377LR07dvTkwpvPWlxcXKWTQ+VH5YJaLjIykoKCAsApQuXNdbV54403GD9+fJXRIrp06cLEiRMBGD9+vKepFpyctm7ddG+FCdkiVVqmlz3yeUFBMVOmbCIx0ek5Ext7I+vW3UdkZODGADTGX9q3b8/KlSt54YUXKCkpYdq0aXz00Uds27YNcM64HnvsMRYvXgzAokWLeO655zhy5AjgFI0XX3yxxn5HjRrFqlWrPMvlhSAyMpLc3FxPc15dRITx48ezcOFCYmJi6NChQ637re0MJiYmhry8PM/y+vXree+998jPzyc/P58DBw54ilR8fDxpaWlcuOBcMkhOTvZcd5o6dSp//vOfeffddz37eu+99zxNmOXKz6Rqe1Rv6gMYN24c69atA2DdunXce++9deZh/fr1TJkypcq6++67jx07dgDwwQcf0KNHD89rR44cqdHU2aQEuufG5TziBgzQ7kv+oE9tPlSj98qlXLxYqqtXf6Lt2i3TVq2e0aee2qVlZWUN2ocxlyPYevepqt5zzz362muvqapqVlaWDhs2THv06KHdunXTpUuXVvltvPPOOzpgwADt1auXxsTE6BNPPFFj/8XFxTpjxgzt06eP9uvXTzdt2qSqqhs2bNCuXbvqsGHDdP78+ZqYmKiqtfey279/vwKanJzsWXfy5EmdPHmy9u3bV2NiYnTu3Lm1fr7Y2Fg9ffq0fvnll9q5c+cav+1bb71V9+3bp6qqS5cu1djYWO3fv79OmDBBT5w44dkuNzdXR48erdHR0RoTE6MJCQn697///ZK5rc8333yjI0aM0OjoaB0xYoQWFRV5Pu/s2bM925XHXt6Lr9ypU6f0rrvu0tjYWB08eHCVXox33323ZmVl1ThmU+ndJ07coaVb735aOm4Zb80fStxN3s3I+/nnRUyb9ib793/NyJG38Mord9O9ewcfR2qMIzc3l5iYmECH0aS99NJLRERE1LhXqikrLCxk6tSpbN++vcZrtX3nROSAqg7yV3yNISSb+y5cLKOZQN8ftPf6Pe3ataK4+AKvvz6B99+fbgXKmCZm3rx5tGrVKtBh+NWxY8dYsWJFoMPwqZDs3Vdaplwf1pLml+jgoKqkp39KamoOqamTiIxsy6FDj1inCGOaqLCwMKZPnx7oMPzqtttuC3QIPheSZ1IXy5SIS9zEm5//D8aOXc/EiW9w5EgRJ086o6VbgTKBFIpN6yY0NaXvWkgWqe8vltU6G29JSSnPP/8RvXuvZteufF58cRQZGQ9Zrz0TcGFhYRQVFTWpPx4mOKk680mFhfl+dgh/CMnmvpKLZXRoU7Pt+eLFMtau/QtjxkTzq1+N4aabvL9mZYwvdenShePHjzeZOX5McCufmbcpCMkiVapKnFuAiorOsXz5HpKS4gkPb8knnzxIhw5XPgqFMY2pZcuWTWKWVGP8zefNfSIyRkQ+E5E8EfnPWl5vJSJp7usfi0iUN/uN6tiG5ORMevZcxYoVe/nww68ArEAZY0wT4tMiJSLNgdXAnUBvYIqIVL8dezZwSlWjgZeAuocGrmTxgq3MmvU2PXt25ODBuYwZE92YoRtjjAkCvj6T+iGQp6pHVfUCkApUHw/kXmCd+3wjMFK8GO3x80+/4be/Hcvu3bPo29e3Ex8aY4wJDF9fk/oB8H+Vlo8D/1rXNqp6UUS+AzoA31TeSEQeAh5yF78vYkHOnDkwZ45P4g4lHamWq6uY5aKC5aKC5aJCz0AH0FC+LlK1nRFV74PrzTao6lpgLYCIZITa0B6+YrmoYLmoYLmoYLmoICIZ9W8VXHzd3HccuKnScheg+sQnnm1EpAXQHvjWx3EZY4wJAb4uUvuB7iJyi4hcAzwAbK62zWYg0X0+CdihdsejMcYYfNzc515j+hmwFWgOvKqqh0TkaZwh4zcD/wP8r4jk4ZxBPeDFrtf6LOjQY7moYLmoYLmoYLmoEHK5CMmpOowxxlwdQnLsPmOMMVcHK1LGGGOCVlAXKV8NqRSKvMjFQhE5LCJZIrJdRG4ORJz+UF8uKm03SURURJps92NvciEik93vxiERSfF3jP7ixW/kX0Rkp4gcdH8ndwUiTl8TkVdF5ISI5NTxuojISjdPWSIywN8xNkig56+v64HT0eILoCtwDfBXoHe1bR4B1rjPHwDSAh13AHMxHAh3n8+7mnPhbhcBfAjsAwYFOu4Afi+6AweB69zlGwMddwBzsRaY5z7vDeQHOm4f5eLfgQFATh2v3wX8Eece1cHAx4GO+VKPYD6T8tmQSiGo3lyo6k5VPecu7sO5J60p8uZ7AfAMsBz4pz+D8zNvcjEHWK2qpwBU9YSfY/QXb3KhQDv3eXtq3rPZJKjqh1z6XtN7gdfUsQ+4VkQ6+Se6hgvmIlXbkEo/qGsbVb0IlA+p1NR4k4vKZuP8p9QU1ZsLEbkVuElVt/gzsADw5nvRA+ghIntEZJ+IjPFbdP7lTS6WAj8RkePAH4BH/RNa0Gno35OACub5pBptSKUmwOvPKSI/AQYBw3waUeBcMhci0gxnNP2Z/googLz5XrTAafKLxzm73i0isar6Dx/H5m/e5GIKkKyqK0RkCM79mbGqWub78IJKSP3dDOYzKRtSqYI3uUBEfgwsAcap6vd+is3f6stFBBAL7BKRfJw2981NtPOEt7+Rt1W1RFW/BD7DKVpNjTe5mA28AaCqe4EwnMFnrzZe/T0JFsFcpGxIpQr15sJt4voNToFqqtcdoJ5cqOp3qtpRVaNUNQrn+tw4VQ25gTW94M1v5C2cTjWISEec5r+jfo3SP7zJxTFgJICIxOAUqZN+jTI4bAZmuL38BgPfqWpBoIOqS9A296nvhlQKOV7m4pdAW2CD23fkmKqOC1jQPuJlLq4KXuZiKzBKRA4DpcAiVS0KXNS+4WUuHgd+KyILcJq3ZjbFf2pFZD1O825H9/pbEtASQFXX4FyPuwvIA84BswITqXdsWCRjjDFBK5ib+4wxxlzlrEgZY4wJWlakjDHGBC0rUsYYY4KWFSljjDFBy4qUCVoiUioimZUeUZfYNqquUZ8beMxd7kjaf3WHEup5Gft4WERmuM9nikjnSq/9TkR6N3Kc+0Ukzov3/FxEwq/02Mb4kxUpE8zOq2pcpUe+n447TVX74wxe/MuGvllV16jqa+7iTKBzpdceVNXDjRJlRZy/xrs4fw5YkTIhxYqUCSnuGdNuEfmL+/i3WrbpIyKfuGdfWSLS3V3/k0rrfyMizes53IdAtPveke48RNnufD2t3PW/kIp5vF5w1y0VkSdEZBLOOIqvu8ds7Z4BDRKReSKyvFLMM0Xkvy8zzr1UGiBURF4RkQxx5o96yl33GE6x3CkiO911o0Rkr5vHDSLStp7jGON3VqRMMGtdqakv3V13ArhDVQcACcDKWt73MPArVY3DKRLH3WFwEoCh7vpSYFo9xx8LZItIGJAMJKhqX5yRWuaJyPXAeKCPqvYDnq38ZlXdCGTgnPHEqer5Si9vBCZUWk4A0i4zzjE4wx+VW6Kqg4B+wDAR6aeqK3HGZxuuqsPdIZL+C/ixm8sMYGE9xzHG74J2WCRjcJv7qq1rCaxyr8GU4oxFV91eYImIdAHeVNXPRWQkMBDY7w4b1Rqn4NXmdRE5D+TjTOfQE/hSVY+4r68D5gOrcOar+p2IvAt4PTWIqp4UkaPu2Gmfu8fY4+63IXG2wRkGqPLsqpNF5CGc33cnnAn+sqq9d7C7fo97nGtw8mZMULEiZULNAqAQ6I/TElBjUkNVTRGRj4G7ga0i8iDO9ATrVPVJL44xrfKAtCJS6xxl7nhxP8QZtPQB4GfAiAZ8ljRgMvApkK6qKk7F8DpOnBlofwGsBiaIyC3AE8BtqnpKRJJxBlKtToD3VXVKA+I1xu+suc+EmvZAgTsH0HScs4gqRKQrcNRt4tqM0+y1HZgkIje621wvIjd7ecxPgSgRiXaXpwMfuNdw2qvqH3A6JdTWw64YZ/qQ2rwJ3Iczz1Gau65BcapqCU6z3WC3qbAdcBb4TkQigTvriGUfMLT8M4lIuIjUdlZqTEBZkTKh5tdAoojsw2nqO1vLNglAjohkAr1wpso+jPPH/E8ikgW8j9MUVi9V/SfOSNEbRCQbKAPW4PzB3+Lu7wOcs7zqkoE15R0nqu33FHAYuFlVP3HXNThO91rXCuAJVf0rcBA4BLyK04RYbi3wRxHZqaoncXoernePsw8nV8YEFRsF3RhjTNCyMyljjDFBy4qUMcaYoGVFyhhjTNCyImWMMSZoWZEyxhgTtKxIGWOMCVpWpIwxxgSt/wcCLTpZ3fho6wAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3333,7 +3311,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -3342,11 +3320,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.62 0.39 0.48 2008\n", + " 0 0.83 0.94 0.88 6762\n", + " 1 0.65 0.36 0.46 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.73 0.66 0.68 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3358,12 +3336,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[1] = ([\"Random Forest\" , \n", - " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[0] = ([\"Decision Trees - Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", " auroc])" ] }, @@ -3389,7 +3367,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -3407,7 +3385,7 @@ " warm_start=False)" ] }, - "execution_count": 53, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -3420,7 +3398,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -3429,11 +3407,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13463\n", - " 1 0.79 0.47 0.59 4033\n", + " 0 0.86 0.96 0.91 13442\n", + " 1 0.79 0.46 0.58 4054\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.72 0.75 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", "weighted avg 0.84 0.85 0.83 17496\n", "\n" ] @@ -3452,14 +3430,14 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 796\n" + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" ] }, { @@ -3494,14 +3472,14 @@ " \n", " \n", " \n", - " 0\n", - " 6281\n", - " 460\n", + " 0\n", + " 6381\n", + " 381\n", " \n", " \n", - " 1\n", - " 1212\n", - " 796\n", + " 1\n", + " 1270\n", + " 717\n", " \n", " \n", "\n", @@ -3510,11 +3488,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6281 460\n", - "1 1212 796" + "0 6381 381\n", + "1 1270 717" ] }, - "execution_count": 55, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3525,19 +3503,19 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22481097140826298\n" + "Optimal Threshold: 0.24738247273049666\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3554,7 +3532,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -3563,11 +3541,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.63 0.40 0.49 2008\n", + " 0 0.83 0.94 0.89 6762\n", + " 1 0.65 0.36 0.46 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.69 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3577,27 +3555,16 @@ "print(classification_report(y_test, xgb.predict(X_test)))" ] }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", - " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + "From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm." ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 56, "metadata": { "scrolled": true }, @@ -3624,41 +3591,27 @@ " \n", " \n", " Model\n", - " Recall-1\n", + " F1-1\n", " AUROC\n", " \n", " \n", " \n", " \n", - " 0\n", - " Decision Tree (GINI)\n", - " 0.449203\n", - " 0.625991\n", - " \n", - " \n", - " 1\n", - " Random Forest\n", - " 0.392430\n", - " 0.762567\n", - " \n", - " \n", - " 2\n", - " Gradient Boosted\n", - " 0.396414\n", - " 0.772109\n", + " 0\n", + " Decision Trees - Random Forest\n", + " 0.461339\n", + " 0.768458\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.449203 0.625991\n", - "1 Random Forest 0.392430 0.762567\n", - "2 Gradient Boosted 0.396414 0.772109" + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" ] }, - "execution_count": 59, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -3699,7 +3652,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3708,16 +3661,24 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", - " Iterations 7\n" + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" ] }, { @@ -3729,19 +3690,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17452 \n", + " Model: Logit Df Residuals: 17450 \n", "\n", "\n", - " Method: MLE Df Model: 43 \n", + " Method: MLE Df Model: 45 \n", "\n", "\n", - " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784 \n", "\n", "\n", - " Time: 00:20:46 Log-Likelihood: -7709.5\n", + " Time: 19:35:32 Log-Likelihood: -7781.7\n", "\n", "\n", - " converged: True LL-Null: -9445.9\n", + " converged: False LL-Null: -9471.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3752,136 +3713,142 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", + " LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "\n", + "\n", + " SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "\n", + "\n", + " AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", "\n", "\n", - " SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", + " PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", "\n", "\n", - " AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", + " PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", "\n", "\n", - " PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", + " PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", "\n", "\n", - " PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", + " PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", "\n", "\n", - " PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", + " PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", "\n", "\n", - " PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", + " PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", "\n", "\n", - " PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", + " BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", "\n", "\n", - " PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", + " BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", "\n", "\n", - " BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", + " BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", "\n", "\n", - " BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", + " BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", "\n", "\n", - " BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", + " BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", "\n", "\n", - " BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", + " BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", "\n", "\n", - " BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", + " PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", "\n", "\n", - " BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", + " PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", "\n", "\n", - " PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", + " PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", "\n", "\n", - " PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", + " PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", "\n", "\n", - " PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", + " PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", "\n", "\n", - " PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", + " PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", "\n", "\n", - " PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", + " MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", "\n", "\n", - " PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", + " GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", "\n", "\n", - " GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", + " UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", "\n", "\n", - " UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", + " HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", "\n", "\n", - " HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", + " MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", "\n", "\n", - " MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", + " MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", "\n", "\n", - " SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", + " SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", "\n", "\n", - " PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", + " PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", "\n", "\n", - " PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", + " PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", + " PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", "\n", "\n", - " PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", + " PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", "\n", "\n", - " PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", + " PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", + " PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", "\n", "\n", - " PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", + " PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", "\n", "\n", - " PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", + " PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", + " PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", "\n", "\n", - " PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", + " PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", "\n", "\n", - " PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", + " PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", + " PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", "\n", "\n", - " PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", + " PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", "\n", "\n", - " PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", + " PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", + " PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", "\n", "\n", - " PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", + " PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", "\n", "\n", - " PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", + " PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", + " PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", "\n", "" ], @@ -3891,64 +3858,66 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17452\n", - "Method: MLE Df Model: 43\n", - "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838\n", - "Time: 00:20:46 Log-Likelihood: -7709.5\n", - "converged: True LL-Null: -9445.9\n", + "Model: Logit Df Residuals: 17450\n", + "Method: MLE Df Model: 45\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784\n", + "Time: 19:35:32 Log-Likelihood: -7781.7\n", + "converged: False LL-Null: -9471.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", - "SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", - "AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", - "PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", - "PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", - "PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", - "PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", - "PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", - "PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", - "BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", - "BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", - "BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", - "BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", - "BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", - "BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", - "PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", - "PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", - "PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", - "PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", - "PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", - "PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", - "GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", - "UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", - "HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", - "MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", - "SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", - "PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", - "PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", - "PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", - "PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", - "PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", - "PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", - "PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", - "PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", - "PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", - "PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", - "PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", - "PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", - "PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", - "PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", - "PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", - "PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", - "PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", - "PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", + "LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", + "PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", + "PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", + "PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", + "PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", + "PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", + "PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", + "BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", + "BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", + "BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", + "BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", + "BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", + "BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", + "PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", + "PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", + "PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", + "PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", + "PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", + "PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", + "MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", + "GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", + "UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", + "HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", + "MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", + "MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", + "SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", + "PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", + "PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", + "PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", + "PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", + "PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", + "PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", + "PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", + "PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", + "PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", + "PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", + "PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", + "PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", + "PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", + "PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", + "PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", + "PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", + "PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", + "PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 61, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -3960,7 +3929,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -3969,12 +3938,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13463\n", - " 1 0.68 0.37 0.48 4033\n", + " 0 0.83 0.95 0.88 13442\n", + " 1 0.67 0.36 0.47 4054\n", "\n", - " accuracy 0.82 17496\n", - " macro avg 0.76 0.66 0.69 17496\n", - "weighted avg 0.80 0.82 0.79 17496\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", "\n" ] } @@ -3992,60 +3961,97 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.445360\n", + " Iterations: 35\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445386\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445386\n", + " Iterations 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445387\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445388\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440645\n", + " Current function value: 0.445392\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440647\n", + " Current function value: 0.445397\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440653\n", + " Current function value: 0.445410\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440668\n", + " Current function value: 0.445455\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440694\n", + " Current function value: 0.445512\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440734\n", + " Current function value: 0.445596\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440782\n", + " Current function value: 0.445680\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440827\n", + " Current function value: 0.445770\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440895\n", + " Current function value: 0.445853\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440960\n", + " Current function value: 0.445877\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441031\n", + " Current function value: 0.445963\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441168\n", + " Current function value: 0.446090\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441325\n", + " Current function value: 0.446288\n", " Iterations 7\n" ] }, @@ -4058,19 +4064,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17467 \n", + " Model: Logit Df Residuals: 17468 \n", "\n", "\n", - " Method: MLE Df Model: 28 \n", + " Method: MLE Df Model: 27 \n", "\n", "\n", - " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756 \n", "\n", "\n", - " Time: 00:20:46 Log-Likelihood: -7721.4\n", + " Time: 19:35:33 Log-Likelihood: -7808.3\n", "\n", "\n", - " converged: True LL-Null: -9445.9\n", + " converged: True LL-Null: -9471.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4081,91 +4087,88 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", - "\n", - "\n", - " SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", + " LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", "\n", "\n", - " PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", + " SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", "\n", "\n", - " PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", + " PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", "\n", "\n", - " PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", + " PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", "\n", "\n", - " PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", + " PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", "\n", "\n", - " BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", + " PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", "\n", "\n", - " PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", + " BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", "\n", "\n", - " PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", + " BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", "\n", "\n", - " PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", + " PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", "\n", "\n", - " PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", + " PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", "\n", "\n", - " PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", + " PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", "\n", "\n", - " GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", + " PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", "\n", "\n", - " UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", + " GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", "\n", "\n", - " HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", + " UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", "\n", "\n", - " MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", + " HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", "\n", "\n", - " PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", + " MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", "\n", "\n", - " PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", + " PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", "\n", "\n", - " PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", + " PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", + " PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", "\n", "\n", - " PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", + " PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", "\n", "\n", - " PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", + " PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", + " PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", "\n", "\n", - " PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", + " PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", "\n", "\n", - " PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", + " PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + " PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", "\n", "\n", - " PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + " PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", "\n", "\n", - " PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + " PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", "\n", "\n", - " PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", + " PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", "\n", "" ], @@ -4175,49 +4178,48 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17467\n", - "Method: MLE Df Model: 28\n", - "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826\n", - "Time: 00:20:46 Log-Likelihood: -7721.4\n", - "converged: True LL-Null: -9445.9\n", + "Model: Logit Df Residuals: 17468\n", + "Method: MLE Df Model: 27\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756\n", + "Time: 19:35:33 Log-Likelihood: -7808.3\n", + "converged: True LL-Null: -9471.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", - "SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", - "PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", - "PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", - "PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", - "PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", - "BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", - "PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", - "PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", - "PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", - "PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", - "PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", - "GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", - "UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", - "HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", - "MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", - "PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", - "PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", - "PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", - "PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", - "PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", - "PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", - "PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", - "PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", - "PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", - "PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", - "PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", - "PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", - "PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", + "LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", + "SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", + "PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", + "PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", + "PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", + "PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", + "BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", + "BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", + "PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", + "PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", + "PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", + "PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", + "GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", + "UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", + "HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", + "MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", + "PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", + "PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", + "PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", + "PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", + "PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", + "PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", + "PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", + "PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", + "PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", + "PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", + "PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", + "PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 63, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -4235,21 +4237,21 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "29 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT3',\n", - " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + "28 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_5_Pay_Duly', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", " dtype='object')\n" ] } @@ -4262,7 +4264,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -4271,12 +4273,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6741\n", - " 1 0.64 0.38 0.47 2008\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -4296,19 +4298,19 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22999391096950886\n" + "Optimal Threshold: 0.21650864211883647\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3wU1RbA8d8lgUBIgRQggYRQAqEHiCBVLIggCCqIqIiCz4fYRewdFduzgs+C3YcoIIKADURBFCmC9F5DQiABUgjp5/0xm7iElCVks9nkfD8fPmRnZmfOlJ0zc+fOvUZEUEoppYpTw9UBKKWUqtw0USillCqRJgqllFIl0kShlFKqRJoolFJKlUgThVJKqRJpoiiBMeZ6Y8yPro7D1Ywx4caYNGOMRwUuM8IYI8YYz4papjMZYzYbY/qV4XtlOgaNMXWMMd8aY5KNMbPO9vsVxRjznTFmTBm+18cYs90ZMVVmrvgtAhh3eY/CGLMPaAjkAmnA98AdIpLmyriqItu2vkVEFrswhghgL1BTRHJcFYctFgEiRWSXk5cTQTmtszFmNHAn0LM8tp8tyX0uIk3OdV5lXH657ANjzFPAo0AmkANsASaKyB/nHGQV5m53FENExAeIBjoDD7s4njJx5VVyVblCPxvVdHs3BXaUJUlUg2PkS9t5JAhYCjjljqtKbUcRcYt/wD7gErvPLwEL7T57Aa8AB4AE4B2gjt34ocB6IAXYDVxmG+4PfADEA4eAZwEP27ibgN9sf78DvFIopnnAfba/Q4E5wFGsq8K77KZ7CpgNfG5b/i1FrJ8/8Knt+/uBx4AadnGsAN4CkoFtwMWFvlvSOqwAXgOO2ca1AH4GkoBE4H9APdv0nwF5wCmsO7cHgAhAAE/bNL8Ak23zTQV+BILs4rnRtg5JwOOF912h9a4D/Mc2fTLwm21Y/jLH2PZpIvCo3fe6AX8AJ2zrPRWoZTdegNuBncBe27A3gIO2fbAW6GM3vQfwiO3YSLWNDwOW2eZ10rY9RtqmH4x1PJ0Afgc6FjpWHwQ2YF25etpvA1vsa2xxJACv2oYfsC0rzfavB3bHoG2adsBPtn2ZADxSxDZ9GsgCsm3zGYd1UfiYbTsfwTrW/G3T52/rcbYYlhUxz35AbDH7sKRj18O2fxOxfhd3cOaxdIvt75bAr7bjIBHrhE5R+6BwPLZ99bUthiRgajGxPoV1Z5T/ua1t3sF2w0rat12AdbZjZBbwJfCs/Tay7fvDwGcOzO9BrN9sKrAd2++6hGMkf1/lb79QYL7teNgF/KvQun5l2zepwGYgpkzn34o+4Zf1H6f/0JoAG4E37Ma/bttgAYAv8C0wxW6jJwP9sX4wjYEo27hvgHeBukADYBXwb7uTbH6i6It1kskvrquPdTINtc1zLfAEUAtoDuwBBtjtsGxgmG3aOkWs36dYicfXdjDsAMbZxZED3AvUxPqhJAMBDq5DDlYxhCfWSbilbVt4AcFYP8TXi9rWxRycv2CdUFvZ5vcL8ILdDy8N6G3bFq/Y1r24RDHN9v3GWCeVnra48pf5vm0ZnbBOum1s3+sKnG9bpwhgK3CP3XwF64QakL+9gRuAQNt3JmL9mGvbxk3COqZaA8a2vEC7ebUsdLI4AnS3xTzGts287LbfeqyTV53C2xQrwY22/e0DnF/Udi7iGPTFSooTgdq2z90dPCGOxTqRNLct82v+OZHlL/dTrGOoqOOzH8UnipKO3fFYxTtNsH4ziyk+UXyBVSxUw7Z+vQvtz5ZFxWPbB39jXQzVLfzd4rYL1vH5AlZSyo+n2H1rm34/cDfW7/AqrIRsnyhygBdt09cpZX6tsc4poXb7ocXZHCNYifVt2zpHYyXKi+3WNQMYZFv2FGBlmc6/rk4ADgdqbdw0rMwowBL+uQo2WFcbLeym78E/V5LvAq8VMc+GWCcf+zuPUcDSIn6kButqq6/t87+An21/dwcOFJr3w8BHdjvsjKs0u2k9bHG0tRv2b+AXuzjisCUp27BVwGgH1+FAccu2TTMMWFdoW5eWKB6zGz8B+N729xPAF3bjvLF+TGckCqwTwimgUxHj8pfZpNA6X1vMOtwDzLX7LMBFpaz38fxlY13NDS1musInqf8CkwtNsx24wG77jS3i+M1PFMuwrvqDCk1z2nYu4hgcZb+fSlm3pzg9USwBJth9bo2VwD3tltu8hPn1o4hE4cCx+zO2ixbb50uKOJbyE8WnwHv2+7yEfVAQD9Zv/aj9ditlu2RhXd3nYt199HNk32JdLB7i9N/hb5yeKLKwXXw4ML+WWEnkEqznUvbTlHqMYF2I5AK+duOnAB/bretiu3FtgVOOHD+F/7nbM4phIuKLtUOisMoYwboq9gbWGmNOGGNOYD3sDraND8O6Ai6sKdaVQbzd997Fuio/jVhbeibWjxXgOqwim/z5hObPwzafR7BO4vkOlrBeQfxztZJvP9ZVdr5Dthjsx4c6uA6nLdsY08AYM9MYc8gYk4JVJBbE2Tls93c61lUPtpgKlici6Vg/xqIEYV0JFbVvSlyOMaaVMWaBMeawbR2e58x1KLzeE40xW201gU5gFZnkf6e4Y6QoTYGJhfZ3GNa6F7nsQsZh3Y1tM8asNsYMdnC5ZxNjYaGceXx54vgxWpzSjt3TjodSlvEA1gXZKlstsbEOxhAG7BfHn8d8JSL1sNZ9E9bdab6S9m0oZ/4OC6/PURHJcGR+Yj2YvwfrhH7E9pvMP4YcOUZCgWMikmo3rPB5o/Dvp3ZZnp24W6IAQER+BT7GKtYA69bxFNBOROrZ/vmL9cAKrJ3ZoohZHcS6Ggqy+56fiLQrZtFfAMONMU2x7iLm2M1nr9086omIr4gMsg+7hFVKxLq6a2o3LBzr6iVfY2OMKTQ+zsF1KLzsKbZhHUXED6tIxpQw/dmIxypmAKxqmljFPUVJxLo1LmrflOa/WM9qIm3r8AinrwPYrYcxpg9WefA1QH3biSLZ7jvFHSNFOQg8V2h/e4vIF0UtuzAR2Skio7CS+YvAbGNM3ZK+U4YYC4vjzOMrB6v8uyC0Msy3tGP3tOMB6yRZJBE5LCL/EpFQrLuSt40xLR2I4SAQfrYnQBFJtC3nKWNMiN28itu38Zz5Oyy8PoW3YYnHiojMEJHeWNtPsI6Hko4Re3FAgDHG125Y4fNGuXDLRGHzOtDfGBMtInlYZdmvGWMaABhjGhtjBtim/QC42RhzsTGmhm1clIjEYz2I/Y8xxs82roUx5oKiFigi67BucacDP4jICduoVUCKMeZBW/11D2NMe2PMeY6siIjkYj10es4Y42tLRPdhXennawDcZYypaYwZAbQBFp3tOtj4YhXjnTDGNMYqn7eXgFWWXRazgSHGmJ7GmFpYt8+FT+AA2Pbbh8CrxphQ23brYYzxcmA5vlgP+tKMMVHAbQ5Mn4OtiMIY8wTgZzd+OjDZGBNpLB2NMfkJrvD2eB8Yb4zpbpu2rjHm8kI/2GIZY24wxgTb1j//GMq1xZZH8dt+AdDIGHOPMcbLdqx0d2SZWBc59xpjmhljfLDuwL48i6vw/Nhr2/+zxVvSsfsVcLftN1cPK1kXN+8Rxpj8pHIc68SZa/tc0jG5Cusk/oJtX9Q2xvRyZH1EZBvwA9bdDJS8b/+wxXOHMcbTGDMU6/lnSYqdnzGmtTHmItvxnoF1sZtr2xbFHSP2sR/Eejg+xbbOHbHuRP5HOXPbRCEiR7HKNB+3DXoQ62HdSmMVRSzGKodFRFYBN2M97ErGegCUfwV0I9at8xasg3M2kH91UZQvsMoUZ9jFkgsMwXqYtBfrKms6VtGGo+7Ees6yB6vccwbWSTTfn0Ckbd7PAcNFJL9I52zX4Wmsh2zJwEKsB5v2pgCP2W6V7z+LdUBENtvWZSbWjzcVqxw2s5iv3I/1EHk1Vs2NF3HsuLwfq/gvFevH+GUp0/8AfIf1oHU/1g/TvtjgVayT2o9YCegDrIeRYBUNfGLbHteIyBqsZ1RTsbb3LqxnCY66DNhsjEnDqol1rYhk2IrpngNW2JZ1vv2XbEUM/bGOtcNYNboudHCZH2LVaFuGdYxmYO2ns9EY62Rm/68FJR+772Nt0w1YtYUWYSXs0056NucBf9q2y3zgbhHZaxv3FHb7wP5Ldr+/lljPEWOxKnw46mXgVmNMg5L2rYhkYT3AHod18r4BK3kXd2xTyrHixT8P0w9jXQw+YhtX5DFSxCJGYT23iAPmAk+KyE9nse4OcZsX7qozY8xNWA/8ers6lrNlu3o9gVVEtLe06VXVZowZCLwjIk1LndgNGGP+xFqfj1wdizO57R2FqryMMUOMMd62MtVXsO4Y9rk2KuUKtqLYQbaimsbAk1hXvm7JGHOBMaaRbX3GAB2xKs5UaZoolDMMxboVjsMqLrtW9Na1ujJYRZ3HsYqetmJVoXZXrbHe2UjGep9luO05YZWmRU9KKaVKpHcUSimlSuR2jVYFBQVJRESEq8NQSim3snbt2kQRCS59yjO5XaKIiIhgzZo1rg5DKaXcijFmf+lTFU2LnpRSSpVIE4VSSqkSaaJQSilVIk0USimlSqSJQimlVIk0USillCqR0xKFMeZDY8wRY8ymYsYbY8ybxphdxpgNxpguzopFKaVU2TnzjuJjrKZyizMQqx2gSOBWrI5olFJKlSMRISsn75zm4bQX7kRkmTEmooRJhgKf2hqLW2mMqWeMCakODWwppdS5yMjOJSElg/jkDHYmpLIjIY1anjXIyM5lS3wK/nVqsjMhjZSMbFIzzqpvqiK58s3sxpzecUysbdgZicIYcyvWXQfh4eEVEpxSSrlSYloma/Yd5+dtCeTkCXuOnkRE2J6QSkZ20XcIAXVrUcMYDpJOY9/aJMWncnJPMgFe53aqd2WiKKp7zCKbshWR94D3AGJiYrS5W6WUWzuZmUNCSgbbD6ey80gaexNPkpMnrD94HBGIPX7qjO9ENvAhNSOHPpHBZGTn0r1ZAE3qe9M8uC4tgn2oa5cMRISYmPeJ3Z7IM89cyF13dadmzevLHK8rE0Usp3dM3gSr/wKllKpS0jJz+GbdIRZvTeCX7UeLna5tiB+eHoYOjf0JD/CmWVBdLmvfiHretRxazu+/H6RDhwb4+noxffoQgoK8CQs7mx6Zi+bKRDEfq5PymUB3IFmfTyil3Fl6Vg57E0+SlJbFhtgTLNuZyPqDJ057mHxp24YE+XrRNMCbpoF1iWzoQ3iANzU9yl63KCkpnYceWsz06et48skLeOqpfnTuHFIeqwQ4MVEYY74A+gFBxphYrC4QawKIyDtYnawPwupsPB242VmxKKXUucrJzSP5VDZb4lM4kZ7Noo3xHDiWTi3PGhxOth4sF6WWRw36tQ5meNcm9IkMxr9OzXKLSUT49NO/uf/+nzh+/BSTJvVk0qSe5Tb/fM6s9TSqlPEC3O6s5SullKOycvJISMkgJSObfYnpbI5LZkNsMvuSTiICh06c+cwgX9NAb9o39qd1I1/C6nvTsoEPofXqEB5g/e1Ro6jHseXjwQcX8/LLv9OzZxjvvHM5HTo0dMpy3K4/CqWUKqusnDy2xqewfOdRtiekse7A8SIfHAPUMNC4fh3ah/rTt1UQxhiaB9Wldk0POjWpRwM/Lxr61a7gNYBTp7I5eTKboCBvxo3rTGRkAOPGdaGGExOSJgqlVJUjImyOS2H30TS2xqeSkJLB3HWHzpiuU1g9gn29aBnsQ+P6dYhs4EtND0P7xv6E1qvjgshL9v33u7j99kVERzdizpxraN06iNatg5y+XE0USim3djQ1k7nrYtkcl8Kh46fYdjiVtMyiXzJrFlSXa2LC6Bxej+iwetSu6VHB0ZZNXFwq99zzPbNmbaF160DuuOO8Cl2+JgqllFtISstka3wqf+xJZFt8KikZ2WyLTyXVLil41jA0C6pLWIA3F7QKpn1jP8ICvAn28cIY5xXNONOSJXu48sovycrKZfLkC5k0qSde5/gC3dnSRKGUqlSyc/M4kprJpkPJbD+cyp6jaXyz/sxXrALq1qJ/24Y08q9Nq4a+XNa+kdvcITgiOzuXmjU96NSpEYMGRfLssxfRsmWAS2LRRKGUcqnV+44xa81BMrLz+HHL4TOap6jnXZPuzQII9KnFRVEN6dDYn8gGPk59eOtKKSmZPP74z/z55yFWrBhLUJA3M2cOd2lMmiiUUhVuz9E05q2PY+rSXeTm/dMqT/vGfhgMF0U1oFOYP9Fh9Qmo69hbye5ORJg9ewt33/09hw+nMWHCeWRm5uLt7fpugzRRKKWcRkTYEp/Cwg3xpGRk8+PmBI6kZhaMb1yvDv1aB3NjjwhaN/J1YaSudfToScaM+YbvvttF586NmDfvWs47r7GrwyqgiUIpVa6S07P5/M/9/LglgQ2xJxC7ZjwjG/jQsYk/bUL8OC8igL6tgl0XaCXi5+dFYmI6r78+gNtv74anp+vvIuxpolBKnRMRYf3BE8xbH8ea/cfYdCilYFzPFoEMbN+IC1o1IDzQ24VRVj7Llu3nueeWM2fONfj41GLlylsq7XMXTRRKKYekZeZwOPkUR1Oz2BKfwobYE2yJS2HnkbTTpmsT4sfFUQ246+JIalWyK+PKIDExnUmTfuLjj9cTEVGPfftO0L59g0qbJEAThVKqGCJCQkomi7cmMH35HvYlpZ8xTd1aHpzfPIBuzQK5pE0DOjT2d9v3FZxNRPjoo/VMmvQTKSmZPPxwbx57rC/e3uXXSKCzaKJQShXIzRM2xJ7gm3WHWLYzkb2JJwvGdW8WwNDoxvjU9qRVQx8iG/g6tcG7qujzzzfQtm0w77xzOe3aNXB1OA7TRKFUNZeRncv2w6ks3BjPe8v2FAz3rGG4uVcE/ds25LyIgHPqL6G6Sk/P5vnnlzN+fAxNmvgxZ841+PvXrtTFTEXRRKFUNSEiHDpxip1H0jiQlM7ynYlsjks+rR+Fhn5eXNq2ESPPC6N943PvGa06W7RoJ7ffvoh9+07QuLEvt912HvXrV76GBh2hiUKpKi41I5vXF+9k3vo4EtMyTxuX3wxG75ZBRIfVo0Njf7e72q1sYmNTuOee75kzZytt2gTx66830bdvU1eHdU40UShVxaRmZPPztiPsPpLGtsOp/LgloWDc1V2acGFUcEEHO3UruHG56uC555axcOFOnn/+IiZO7EmtWu7f/pQR+7dh3EBMTIysWbPG1WEoVakcTs7gyfmbWLv/xGl3DTUM9GgRyKAOIVzXLVxrJDnJqlWHqFPHkw4dGpKUlE5ycibNm9d3dVinMcasFZGYsnxXLyeUckMiwrbDqXy/6TDfbohjz9F/aicN7hhC/7YN6dE8kAYu6IGtOklOzuCRR5bw3/+uYfDgVsyfP4rAQG8Cq9jLhZoolKrk8ntr23gomd93J5GQksGmQ8mkZ+UC0DbEj1v7NufStg2JiXBNM9TVjYjw5ZebuffeHzhy5CR33tmNyZMvcnVYTqOJQqlKKPlUNp/+vo/fdiXy595jZ4wf1KERPVoE0TcyiKaBdV0QYfX2+ecbuPHGb4iJCWXBglF07Rrq6pCcShOFUpVIakY2T83fwpy/YguGtWroQ+ew+oyIaUKzoLoE1K2lzxpcIDMzhz17jtOmTTDXXNOOnJw8bryxEx7V4P0STRRKVQLJ6dlc/8HK0xrUe3hgFDf2iKBOFag14+6WLt3LbbctJD09m50778TLy5Obb+7s6rAqjCYKpVwk7sQpFmyI44fNCazdf7xg+Ds3dGVAu4Z611AJHDlykvvv/5HPPttA8+b1ee+9IRXeX3VlUP3WWCkXOngsnb8OHGfJ1iPM//uffqC7RQQwpFMIN5zfVBNEJbFr1zG6dXuftLQsHn20D48+2oc6dSp/A37OoIlCKSfakZDK3HWHOJGexbIdiRw6capgXJ/IIG44vykXtAqmdk0tXqosUlIy8fPzokWL+owb15mxYzvTpk317mBJE4VS5SgnN4/vNx/m1Z92cPBYOtm5/7zQ2jTQm5t7RdC9WQDRYfVp5K/vOFQmJ09m8cwzv/L++3+xYcNtNGnix8svX+rqsCoFTRRKlYOTmTnMWnOQp77dUjAs2NeLoZ1C6dsqmD6RQVqkVIl9++127rjjOw4cSGbcuM5u0UdERdJEoVQZpWfl8My3W1iwIZ60zJyC4QPaNWTy0Pb6VrQbyMnJ45prZjF37jbatQtm+fKb6d073NVhVTqaKJQ6C3l5wqy1B/n0j/1sjvunKuvlHUPo0zKI4V2b4FkN6tW7OxHBGIOnZw1CQnx44YWLuffeHlWiAT9n0EShVCnSMnOYu+4Qv2w7wpJtRwqGdw6vx+UdQri2Wzg+1bDKpLtauTKW229fxPvvD6FLlxCmTbvc1SFVenp0K2UnL0/4bVciq/cd4/fdSexPOkliWlbB+Jim9ekUVo+Jl7bCu5b+fNzJ8eOneOSRJbz77lpCQ305fvxU6V9SgJMThTHmMuANwAOYLiIvFBofDnwC1LNN85CILHJmTEoVZem2I/y2K5HvNsYTZ+vxLbBuLbw8a3BxVAMGdQhhSKdQanlqsZI7+vLLTdx11/ckJqZzzz3n8/TT/fD19XJ1WG7DaYnCGOMBTAP6A7HAamPMfBHZYjfZY8BXIvJfY0xbYBEQ4ayYlLK37XAKj3y9kSOpmcTari4jG/jw7LD2DI0Oxbe21nypKrZtSyQioh7ff389nTuHuDoct+PMO4puwC4R2QNgjJkJDAXsE4UAfra//YE4lHKyWWsO8vyirRxPzwbAr7Yno7qFc9fFLQnxd88+jdXpMjJyePHF3+jSJYQhQ1rzyCN9eOyxvtWiAT9ncGaiaAwctPscC3QvNM1TwI/GmDuBusAlRc3IGHMrcCtAeLhWXVNn72hqJrPXxvLl6gPsS0oHrGYznryiLe1C/V0cnSpPixfvYcKEhezceYyJE3swZEhrauqb7+fEmYmiqLeLCve7Ogr4WET+Y4zpAXxmjGkvInmnfUnkPeA9sLpCdUq0qsrJyc1j3vo4vtt0mMVbrX6jA+vW4v5LWzG6RwT+1bTdnqoqISGN++77kRkzNtKyZQA//ngD/fu3cHVYVYIzE0UsEGb3uQlnFi2NAy4DEJE/jDG1gSDgCEqVUXzyKd79dQ8f/76vYNh5EfUZ26sZl7VvpG9IV1E//bSH2bO38MQTfXn44T7Urq210sqLM7fkaiDSGNMMOARcC1xXaJoDwMXAx8aYNkBt4KgTY1JV2M6EVN5dtofZa61OfyIb+NCjRSAPDYzSqqxV1N9/H2bnzmMMH96W66/vQK9eYTRrVt/VYVU5Tvv1iEiOMeYO4Aesqq8fishmY8wzwBoRmQ9MBN43xtyLVSx1k4ho0ZJymIiwfGciX6w6wHebDgPQvrEfTwxuR7dm2n90VZWWlsWTTy7ljTf+JCKiHsOGReHpWUOThJM49TLL9k7EokLDnrD7ewvQy5kxqKpra3wK4z5eXfDew1VdGnNjjwiiw+q5ODLlTN98s4077/yO2NgUbr21C1OmXIKnvt/iVHo/rtxKZk4us9bEsnJPEgs2xAMwvGsT7rkkkib1vV0cnXK2jRsTuPLKL+nQoQFffjmcnj3DSv+SOmeaKJRbyMjO5dWfdvDhb3vJybNKJ7uE1+OhgW20iKmKy87OZfnyA1x0UTM6dGjIwoXX0b9/c63yWoE0UahKbfW+Y3yz7hBfrj5ITp7QsoEPY3s144roUG2Irxr4/feDjB+/gM2bj7J9+x20bBnAoEGRrg6r2tFfmqqUtsanMGzaCjJzrFdqOjbxZ1zvZlzRKVSrt1YDx46d4qGHFvP++38RFubH119fQ8uWeufoKpooVKWx7XAKM1cdLHj/oaaHIcS/Nl/9uwdhAfr8obrIyMghOvod4uJSmTixB0891Q8fn1quDqta00ShXGrb4RRe/2knP2w5TH7F6E5N/GnZwJd7++sD6uokNjaFJk38qF3bk8mTLyQ6uhGdOjVydVgKTRTKRTbGJnPnF38VtLvUqqEPPVsEcV33cFo19HVxdKoinTqVzZQpv/HiiyuYPXsEQ4a0ZsyYaFeHpew4lCiMMbWAcBHZ5eR4VBV3NDWTB+ds4GdbT3GDO4YwtnczuoTri1LV0Y8/7mbChIXs3n2cG27oSLdujV0dkipCqYnCGHM58CpQC2hmjIkGnhSRK50dnKoaRIT5f8fx5pKd7D56EoDwAG/evzGG1o307qG6uvPORUyduprIyAAWLx7NxRc3d3VIqhiO3FE8g9U8+FIAEVlvjGnp1KhUlbH7aBr3frmeDbHJAPRoHsi/L2hOv9YNXByZcoXcXKsWm4dHDc4/vwlBQd48+GBvbcCvknNk72SLyIlCVRK1PSZVoozsXB7+eiNz1x0C4Pru4Tw4MAo/7TWu2vrrr3jGj1/A6NEdufPO7lx/fUdXh6Qc5Eii2GqMuQaoYWsJ9m5gpXPDUu4qNSOb1xfv5IPf9hYMm39HLzo20faXqqvU1EyeeGIpb765iuBgb0JCtLjR3TiSKO4AngDygK+xWoN92JlBKfeyL/Ekb/28i1X7kjh4zOp7OjzAm9svbMGIrmHUqKEvyFVXP/64m7Fj5xEXl8r48TE8//zF1KtX29VhqbPkSKIYICIPAg/mDzDGXIWVNFQ1dvxkFpNmbyjoPQ6gW7MAbrugBRdG6TMIBbVqedCgQV3mzLmG7t2buDocVUamtO4fjDF/iUiXQsPWikhXp0ZWjJiYGFmzZo0rFq1sTqRnMWPVAd79dQ/Jp7Lp0TyQBwdGafPeiuzsXF599Q9SUjJ57rmLAcjLE72rrARs5+2Ysny32DsKY8wArG5KGxtjXrUb5YdVDKWqkbw84cctCbz8w7aCKq4AX/zrfHq0CHRhZKqy+O23AwUN+I0Y0bYgQWiScH8lFT0dATYBGcBmu+GpwEPODEpVLn8fPMG/Pl3DkdRMAPq1DubKzo25vEMInh7aYUx1l5SUzoMPLuaDD9YRHu7Pt9+OYvDgVq4OS5WjYhOFiKwD1hlj/iciGRUYk6oERIQp323jl+1H2JGQBsCw6FAmD2uPr1ZxVXaSkk4xc+YmHnigJ088cQF162oDflWNI2hZmaAAACAASURBVA+zGxtjngPaAgXVFURELxmqqJV7krhh+p8FHQTdcWFLRp4Xpi24qgJbtx7lq6828+ST/WjVKpADB+4lIKCOq8NSTuJIovgYeBZ4BRgI3Iw+o6hyRISv1hzknV/3sDfRegYxoF1DXh/ZmTq1tCcxZUlPz+a555bx8su/4+NTi3HjutCkiZ8miSrOkUThLSI/GGNeEZHdwGPGmOXODkxVjJSMbKYs2sbirQkctT2DuLB1MM8Mba93EOo033+/iwkTFrJ37wnGjOnEyy/3Jzi4rqvDUhXAkUSRaaz2O3YbY8YDhwCtJO/mRIR56+N4btFWjqZmEuRTi8nD2jMsOlSfQagzpKVlMXr0XAID67B06Rj69YtwdUiqAjmSKO4FfIC7gOcAf2CsM4NSzvX77kSmLNrGxkNWQ31vjerM4I4h2sWoOk1ubh5ffLGJUaPa4+NTi8WLRxMVFYSX9lVe7ZS6x0XkT9ufqcBoAGOMvmLpZkSEmasPsnznURZtPAzAyJgwJg9rTy1PreKqTrd2bRz//vcC1q6Np04dT66+uq32NleNlZgojDHnAY2B30Qk0RjTDqspj4sATRZuICUjm7eW7GTuujgS06xnEEOjQ5k0oLV2M6rOkJycweOPL2XatNU0aFCXmTOv5qqr2rg6LOViJb2ZPQW4Gvgb6wH2XKyWY18ExldMeKqscnLzePPnXcz4cz+JaVkA9G4ZxPQxMdSuqbWYVNGuvvorfv55L7fffh7PPnsR/v7agJ8q+Y5iKNBJRE4ZYwKAONvn7RUTmiqL7YdTufOLvwpekgP4z4hOXBEdSk19i1oVYc+e4wQHe+Pr68Vzz11EjRqG887TLknVP0pKFBkicgpARI4ZY7Zpkqi8jqZm8tGKvbz9y24AWjf0ZeR5YVzXPVzvIFSRsrJyeeWV35k8eRl33dWNF1/sry28qiKVlCiaG2PymxI3QITdZ0TkKqdGphySkZ3Lf3/ZzRtLdgLg4+XJ9DExnN9cG+pTxVu2bD/jxy9g69ZEhg9vy113dXd1SKoSKylRXF3o81RnBqLOTm6eMHP1AR6duwmAIJ9avDy8E/1aB2s1V1Wi1177g/vu+5GIiHosXHgdgwZFujokVcmV1CjgkooMRDlu9tpY7p/1d8HnV0Z04srOjfHQ5pxVMfLyhJMns/D19eLyy1tx9Gg6jz3WF29vfblSlU7fnHEju46k8fDXG1i97zgAl3cM4blh7annra11quJt3nyE8eMXFvQ016pVIM8/f7Grw1JuxKmJwhhzGfAG4AFMF5EXipjmGuApQIC/ReQ6Z8bkjjJzcvl56xFu+99fAFzRKZTJQ9vjr1eDqgTp6dlMnvwrr7zyB/7+XowdG42IaNGkOmsOJwpjjJeIZJ7F9B7ANKA/EAusNsbMF5EtdtNEAg8DvUTkuDFG25AqZGNsMle+vaKgye+bekbw1BXtXByVquzWrYvnqqu+Yt++E9x8czQvvdSfoCB9wVKVTamJwhjTDfgAq42ncGNMJ+AWEbmzlK92A3aJyB7bfGZivZuxxW6afwHTROQ4gIgcOftVqLqWbE1g/OdryckTxvVuxt2XROKnDfapEuTfMYSH+xMe7s8nnwyjb9+mrg5LuTlH7ijeBAYD3wCIyN/GmAsd+F5j4KDd51igcB28VgDGmBVYxVNPicj3Dsy7SsvKyWPcJ6tZvjMRgE/HdqNvq2AXR6Uqs5ycPKZOXcX8+dv56afRBAZ68+uvN7k6LFVFOJIoaojI/kLlmrkOfK+oglApYvmRQD+stqOWG2Pai8iJ02ZkzK3ArQDh4eEOLNp9bTqUzC2frOFwSga+tT1ZdFcf7RdClWjVqkOMH7+AdesOM3BgS1JSMqlfXzsSUuXHkTYdDtqKn8QY42GMuQfY4cD3YoEwu89NsJoBKTzNPBHJFpG9wHasxHEaEXlPRGJEJCY4uOpeWS/aGM+Qqb9xOCWDm3pGsOHJSzVJqGKlpWVx++0LOf/86SQknGTWrBEsXHidJglV7hy5o7gNq/gpHEgAFtuGlWY1EGmMaYbV2dG1QOEaTd8Ao4CPjTFBWEVRexwLverIyxMmL9zCRyv2AbD4vr60bODr2qBUpVezZg1++WU/d97ZjcmTL8LPz8vVIakqypFEkSMi157tjEUkxxhzB/AD1vOHD0VkszHmGWCNiMy3jbvUGLMFqzhrkogkne2y3NnW+BRu+WQNh06collQXR4eGKVJQhVr165jPPPMr0ybNghfXy/Wrr2V2rX1dSjlXEak8GODQhMYsxurSOhL4GsRSa2IwIoTExMja9ascWUI5ebdX3cz5bttAIzqFsbzV3bQOu6qSJmZObz00gqee245tWp5sHDhdfTpo7WZlOOMMWtFJKYs33Wkh7sWxpieWEVHTxtj1gMzRWRmWRaoLO/8upsXbEni9ZHRDOuszTqroi1dupfbblvI9u1JjBzZjldfHUBoqN51qorj0D2riPwO/G6MeQp4HfgfoImijL5Zd4gXvttGiH9tvp7QkxB/ffioiiYiPPfccrKz8/j+++sZMKClq0NS1ZAjL9z5YL0ody3QBpgH9HRyXFXWZ3/s4/F5mwkLqMP0G8/TJKHOkJcnfPDBX1x2WUvCwvz57LMrqVevNnXq6MuWyjUcqR67CTgfeElEWorIRBH508lxVTm5ecLDX2/g8Xmb8a9Tkxm3nE/rRlp8oE63YUMCvXt/yK23LmD6dKttr5AQX00SyqUcKXpqLiJ5To+kCjt4LJ0+Ly0FwMuzBsseuBB//eErO2lpWTz99C+89tpK6tevw8cfD+XGGzu5OiylgBIShTHmPyIyEZhjjDmjapT2cOeYTYeSGfzWbwA09PPi94cu1n4j1BmeeuoX/vOfP7jlls688MIlBAbqi5aq8ijpjuJL2//as10ZLdoYz90z1+FRw/DKiI5c2Vn7I1b/OHgwmZMns4mKCuKhh3ozbFgUvXtX7SZqlHsq9hmFiKyy/dlGRJbY/8N6qK1KkJmTy51frCOwrhcL7+qtSUIVyMnJ49VX/6BNm2n8+98LAAgK8tYkoSotRx5mjy1i2LjyDqQqyc0T/vXpWnLzhKeHtiOqkZ+rQ1KVxMqVscTEvMfEiT/Sr18En3wyzNUhKVWqkp5RjMSqEtvMGPO13Shf4ETR31JHUzMZ98lqNsQmM6pbOAPaNXJ1SKqSWLhwB0OGfEFoqC9ff30Nw4ZF6Zv4yi2U9IxiFZCE1errNLvhqcA6ZwblrrJz87jl0zVsiE3m0UFt+Fff5q4OSbmYiBAXl0rjxn5ccklznnnmQu6+uzu+vtqAn3IfxSYKW7Pfe7Fai1WlyM0T/v3ZWv4+eILHLm/DLX00SVR3O3YkMWHCQnbsSGLLltvx8anFY4/1dXVYSp21koqefhWRC4wxxzm9wyEDiIgEOD06N3HwWDqXvPormTl5jL+ghSaJai4jI4cXXviNKVN+o04dT6ZMuZg6dbSFV+W+Sjp687s7DaqIQNyV/ct0/VoH88CA1i6OSLnS4cNp9O37ETt3HmPUqPa8+uoAGjXycXVYSp2Tkoqe8t/GDgPiRCTLGNMb6Ah8DqRUQHyVWlZOHkOmWi/TTejXggcui3JxRMpVsrNzqVnTg4YN69K3b1OmTRtE//4tXB2WUuXCkeqx32B1g9oC+BTrHYoZTo3KTbzw3TZOpGdrkqjG8vKEd95ZQ4sWbxIbm4IxhunTr9AkoaoURxJFnohkA1cBr4vInUC17zxhyqKtfLhiLw18vZikxU3V0t9/H6Znzw+47baFREYGkp2d6+qQlHIKh7pCNcaMAEYD+W8HVesW7e6euY556+NoF+rH1xN6al34akZEmDTpJ15/fSUBAXX47LMruf567Z1QVV2OJIqxwASsZsb3GGOaAV84N6zK6VRWLuM+Wc3vu5Pw9fJk7oRe1PJ05KZMVSXGGI4fP8W4cVYDfvXra58iqmortc9sAGOMJ5DftdYuEclxalQlcFWf2aeycrn4P78Ql5zBqG5hPD64Ld61tMpjdbF//wnuvvt7nnjiArp0CSEvT6ihrQArN3IufWaXejlsjOkD7AI+AD4EdhhjepVlYe7q+Mksrn1/JXHJGVzdpQlTruqoSaKayM7O5aWXVtC27dv89NMetm9PBNAkoaoVR852rwGDRGQLgDGmDfAZUKbM5G5ycvPo/9oyEtMyufa8MF64uqOrQ1IV5PffD/Lvfy9g06YjDB3amjffHEh4uL+rw1KqwjmSKGrlJwkAEdlqjKnlxJgqlVd+3EFiWibjL2jBQwO1Cmx1snjxHpKTM/jmm5EMHar7XlVfpT6jMMZ8DGRi3UUAXA94i8gY54ZWtIp8RvHZH/t4fN5mBnVoxLTrumitlipORPjssw0EB3szcGAkmZk5ZGfn4eNTba6LVBXm1GcUwHhgN/AA8CCwB/h3WRbmThZvSeDxeZsJ8a/Nc8O06mNVt21bIhdd9CljxnzDRx+tB8DLy1OThFKUUvRkjOkAtADmishLFROS6336xz6e/nYL9bxr8vWEntSvqyeLqurUqWyef345L764grp1a/Huu4O55ZYurg5LqUql2DsKY8wjWM13XA/8ZIwpqqe7KudwcgZPzNtMbp7w9W09CfHXOvJV2bff7uDZZ5czcmR7tm27nVtv7ao1mpQqpKQ7iuuBjiJy0hgTDCzCqh5bpd0/6288ahhm3NKd5sHa6mdVdPhwGuvXH+ayy1oyYkRbIiJuoVu3at8qjVLFKukZRaaInAQQkaOlTFslzFkby2+7ErmxR1O6Nw90dTiqnOXm5vH226tp3Xoqo0fP5dSpbIwxmiSUKkVJdxTN7frKNkAL+76zReQqp0ZWwZLSMpk462/8ansy8VJt5K+q+euveMaPX8Dq1XFccklz3n57EHXqVOsmy5RyWEmJ4upCn6c6MxBX2pmQyrXvrQRg6nVd8PHSt66rkr17j9Ot2/sEBXkzY8ZVXHtte63FptRZKKnjoiUVGYirpGRk0/+1ZQC8NLwjfVsFuzgiVR5EhI0bj9CxY0OaNavPRx8NZciQ1tSrV9vVoSnldqr8c4fSPDBrAwBvjerMNTFhLo5GlYe9e48zePAXdO78Lhs2JAAwenQnTRJKlZFTE4Ux5jJjzHZjzC5jzEMlTDfcGCPGmAptP+r4ySx+2HKYYF8vhnQKrchFKyfIysrlhRd+o127t/n113288kp/2rbVO0SlzpXDhfHGGC8RyTyL6T2AaUB/IBZYbYyZb99ulG06X+Au4E9H511eFm6MRwT+M6JTRS9albPc3Dx69vyAtWvjueqqNrz++gDCwrQBP6XKgyPNjHczxmwEdto+dzLGvOXAvLth9V2xR0SygJnA0CKmmwy8BGQ4Hnb5eHfZboJ8vOjVMqiiF63KSUqKde3i4VGDsWM78+23o5gz5xpNEkqVI0eKnt4EBgNJACLyN3ChA99rDBy0+xxLob62jTGdgTARWVDSjIwxtxpj1hhj1hw9etSBRZdu15E0Dh47RdtQPzz0TVy3IyJ8/PF6mjd/g3nztgEwYcJ5DB7cysWRKVX1OJIoaojI/kLDHOlFvqizb0FTtcaYGlh9XUwsbUYi8p6IxIhITHBw+ZQ5v/PrbgDuvKhlKVOqymbLlqP06/cJN988j6ioIFq0CHB1SEpVaY48ozhojOkGiO25w53ADge+FwvYVyNqAsTZffYF2gO/2Oq0NwLmG2OuEBGntyO+L/EkXp41OC9CTzLu5KWXVvDooz/j5+fF9OlDuPnmzto2k1JO5kiiuA2r+CkcSAAW24aVZjUQaYxpBhwCrgWuyx8pIslAwcMBY8wvwP0VkSRy84Q1+48zLFprOrkLEcEYQ6NGPlx/fQdefrk/wcF1XR2WUtVCqYlCRI5gneTPiojkGGPuAH4APIAPRWSzMeYZYI2IzD/raMvJL9uPANC6kZ+rQlAOiotL5e67v6dPn3Duuqs7N97YiRtv1FpqSlWkUhOFMeZ97J4t5BORW0v7rogswmp11n7YE8VM26+0+ZWXyQusGrrXnx9eUYtUZym/Ab9HH/2Z7Ow8evZs4uqQlKq2HCl6Wmz3d23gSk6vzeRWDiSlsy8pne7NAvCrrY3CVUbr1x/mllvms3ZtPJde2oK33x6kD6yVciFHip6+tP9sjPkM+MlpETnZgo3W8/TJw9q7OBJVnOTkDOLiUvnyy+GMGNFWG/BTysXK0kxqM6BpeQdSUeavj8PLswaRDbRTospCRJg1aws7dybx6KN9ueCCCPbsuZvatbUVX6UqA0fezD5ujDlm+3cC627iEeeHVv42xyWz7XAql3cI0avUSmL37mMMGjSDkSNnM2/edrKzrVd0NEkoVXmU+Gs01tm0E1b1VoA8ETnjwbY7EBGenm89xL5dX7JzuczMHF555XeefXY5NWvW4I03LmPChPPw9Kz2DRorVemUmChERIwxc0Wka0UF5Cy/7Upk1b5j3NwrghbaF7bLHTyYwuTJyxgypDWvvz6Axo21qrJSlZUjl2+rjDFdnB6Jk723bA8A91ysbQG5ytGjJ5k6dRUALVsGsGXL7cyaNUKThFKVXLF3FMYYTxHJAXoD/zLG7AZOYrXhJCLiNskjOzeP5TsT6dE8EH9vrRJb0fLyhI8+WscDDywmNTWT/v2b07p1EM2b13d1aEopB5RU9LQK6AIMq6BYnGbToWQA+rTS5sQr2qZNR7jttoX89tsB+vQJ5513BtO6te4HpdxJSYnCAIjI7gqKxWnW7j8OQN9I7e2sImVl5XLppZ+RlZXLhx9ewU03RWttM6XcUEmJItgYc19xI0XkVSfE4xSfr9xPiH9t2oVqWXhF+PnnvVxwQVNq1fLgq69GEBUVRFCQt6vDUkqVUUkPsz0AH6zmwIv65xYOHvunyQ69mnWu2NgUrr76Ky6++FM+/fRvAHr3DtckoZSbK+mOIl5EnqmwSJzkv7YOim7t28LFkVRdOTl5TJ26iscfX0pubh5TplzM9dd3dHVYSqlyUuozCne3MyEVgDYhbnMT5HZGj57LzJmbGDiwJdOmDaJZM63NpFRVUlKiuLjConCSvDzh4LFTtG/sp8VO5ezEiQw8PWvg41OL228/j6uvbsPVV7fR7axUFVTsMwoROVaRgTjD5rgUDqdkMLB9iKtDqTJEhJkzN9GmzTQef/xnwHoOMXy4tvKqVFVVpRvWWXfQqhbbo0WgiyOpGnbtOsaAAZ8zatQcmjTx44Yb9DmEUtVBlW6i862fd9HIrzadw+q5OhS3N2PGRsaOnYeXlydTpw5k/PgYPDyq9HWGUsqmyiaKo6mZHE3NpH/bhlokcg6ys3OpWdODmJhQhg9vy0sv9Sc0VCsGKFWdVNlE8fnK/QBc3137xS6LI0dOMnHij5w8mcXXX4+kVatAPv/8KleHpZRygSpbdpDfvlPvltqu0NnIyxPee28trVtP5csvN9GuXTC5uXmuDksp5UJV9o5i/cETXNKmAZ5aju6wPXuOc8MNX/PHH7H06xfBf/97OVFRmmiVqu6qZKLYfjiVpJNZNAuq6+pQ3Iq/vxcnTmTwySfDGD26oz7bUUoBVbToKb+TosEdQ10cSeU3f/52rrrqS3Jz8wgM9GbTpgnceGMnTRJKqQJVMlEknczEu5YHnbRabLEOHEhm2LCZDB06kx07koiPTwOgRg1NEEqp01W5oqfcPOGX7UcZ3FHfxi5KTk4er7++kief/AUR4cUXL+Hee8+nZk0PV4emlKqkqlyi+HnbEQBaN9S6/kXJzc1j+vS/uOiiZrz11kAiIvSuSylVsipX9PTpH/sAGNMrwpVhVCrHj5/iwQd/IjU1Ey8vT1asGMv8+ddqklBKOaRKJYpTWbms2JXI6POb4le7pqvDcTkR4X//20BU1DT+858/WLp0HwCBgd76sFop5bAqVfQ0delO8gQujNK+sXfsSGLChIUsWbKXbt0a88MPNxAd3cjVYSml3FCVShRb4lIAuKBVAxdH4nr33PM9a9bE8fbbg7j11q7agJ9SqsyqTKLYfTSNpduPcmXnxnhU0yqeP/20m6ioIMLC/Pnvfy/Hy8uTRo18XB2WUsrNOfUy0xhzmTFmuzFmlzHmoSLG32eM2WKM2WCMWWKMaVrWZb21ZCcAd1zU8hwidk+HD6dx3XVzuPTSz3nxxRUANG1aT5OEUqpcOC1RGGM8gGnAQKAtMMoY07bQZOuAGBHpCMwGXirr8n7blUS3ZgG0CK4+J8e8POGdd9YQFTWVOXO28uSTF/DKK5e6OiylVBXjzDuKbsAuEdkjIlnATGCo/QQislRE0m0fVwJNyrqwpJOZ1PSoXkVOU6Ys57bbFtK1aygbNoznqaf6Ubt2lSlNVEpVEs48qzQGDtp9jgW6lzD9OOC7okYYY24FbgUIDz+zf4k9R9MQgYjAqt8IYGpqJomJ6TRrVp/x42No1qw+o0a11+quSimnceYdRVFnLilyQmNuAGKAl4saLyLviUiMiMQEB59Z9XVHQioAXcLrlznYyk5EmDt3K23bvs3IkbMREQIDvbnuug6aJJRSTuXMRBELhNl9bgLEFZ7IGHMJ8ChwhYhklmVB+dViz28RWJavV3r795/giitmctVVXxEQUIc33xyoyUEpVWGcWfS0Gog0xjQDDgHXAtfZT2CM6Qy8C1wmIkfKuqBV+44BEOJXu8zBVlZ//HGQSy75DIBXXunP3Xefj6envhOhlKo4TksUIpJjjLkD+AHwAD4Ukc3GmGeANSIyH6uoyQeYZbtCPiAiV5ztsnLzhAa+XlWqieyUlEz8/Lzo0iWEsWOjmTSpF+Hh/q4OSylVDTm1ioyILAIWFRr2hN3fl5THcuKTM+gcXjUauEtKSuehhxbz44972Lx5Aj4+tXjrrUGuDkspVY25fRmGiBB7/BRN6nu7OpRzIiJ8+unfREVN46OP1jNyZDv0MYRSqjJw+0r32201ngJ9ark4krJLTs5g2LAv+eWXffTo0YR33hlMx44NXR2WUkoBVSBR/LYzEYBeLYJcHMnZExGMMfj5eREU5M177w1m3LguVepZi1LK/bl90dOvO44CEBXiXj3a/fDDLrp0eY/Y2BSMMcyaNYJ//aurJgmlVKXj1oniVFYuy3cmcnWXJnh5ukefz/HxqVx77Wwuu+x/pKdnc+TISVeHpJRSJXLroqfFWxMAaN/Yz8WROGbatFU88sjPZGbm8PTT/XjwwV54ebn1LlBKVQNufZbaEHsCgMs7hLg4EsesXRtP9+6NmTZtEJGRVfMtcqVU1ePWiWLXkTTq1PSgQSV9IzslJZMnnljK6NEd6do1lLffvhwvLw9tfkMp5VbcOlHsPJJG31aVr7aTiDBnzlbuvvt74uNTCQ/3p2vXUG0CXCnlltz2YXZunvWiXYh/HVeHcpq9e48zePAXjBgxiwYN6vLHH+O4774erg5LKaXKzG0vcTfHJQPQwM/LxZGc7n//28iyZft57bUB3HFHN23ATynl9tw2USSdzAKgbYjrazwtX76fzMxcLrmkOZMm9eSmm6Jp0sT1cSmlVHlw28vdlbuTAGjdyHUv2iUmpjN27Dz69v2YZ575FQAvL09NEkqpKsVt7yh+sr1D0cgFNZ5EhI8/Xs+kST+RnJzJgw/24vHH+1Z4HMo9ZGdnExsbS0ZGhqtDUdVA7dq1adKkCTVr1iy3ebptovDy9CCgbi2XVDVdtGgnY8fOp1evMN55ZzDt2zeo8BiU+4iNjcXX15eIiAitGq2cSkRISkoiNjaWZs2aldt83bboaWt8Cv1andl/trOkp2ezYsUBAAYNimTevGtZtuxmTRKqVBkZGQQGBmqSUE5njCEwMLDc717dMlEctz3I9qmg9xK++24n7du/zcCB/+PEiQyMMVxxRWttwE85TJOEqijOONbcMlHsSUwDoG+kc+8oDh1KYcSIWQwaNAMvL0++/XYU9epVzrfAlVLKWdwyUfy+y6rx1MjfeSftI0dO0rbt2yxYsINnn72Qv/8ezwUXRDhteUo5i4eHB9HR0bRv354hQ4Zw4sSJgnGbN2/moosuolWrVkRGRjJ58mREpGD8d999R0xMDG3atCEqKor777/fFatQonXr1nHLLbecNmzo0KH06HH6i6433XQTs2fPPm2Yj49Pwd87duxg0KBBtGzZkjZt2nDNNdeQkJBwTrEdO3aM/v37ExkZSf/+/Tl+/PgZ0yxdupTo6OiCf7Vr1+abb74BYMmSJXTp0oXo6Gh69+7Nrl27AJg6dSofffTROcV2VkTErf517dpVRn/wpzR9cIFkZudKeYuNTS74+403VsquXUnlvgxVvWzZssWly69bt27B3zfeeKM8++yzIiKSnp4uzZs3lx9++EFERE6ePCmXXXaZTJ06VURENm7cKM2bN5etW7eKiEh2drZMmzatXGPLzs4+53kMHz5c1q9fX/D5+PHj0qRJE4mKipI9e/YUDB8zZozMmjXrtO/mb5tTp05Jy5YtZf78+QXjfv75Z9m4ceM5xTZp0iSZMmWKiIhMmTJFHnjggRKnT0pKkvr168vJkydFRCQyMrLg+Jk2bZqMGTNGRKx9FR0dXex8ijrmgDVSxvOuW9Z6Sj6VTVhAHWqV41vPyckZPPbYz7z77lpWrryFLl1CuOuu7uU2f6UAnv52M1viUsp1nm1D/XhySDuHpu3RowcbNmwAYMaMGfTq1YtLL70UAG9vb6ZOnUq/fv24/fbbeemll3j00UeJiooCwNPTkwkTJpwxz7S0NO68807WrFmDMYYnn3ySq6++Gh8fH9LSrGLi2bNns2DBAj7++GNuuukmAgICWLduHdHR0cydO5f169dTr149AFq2bMmKFSuoUaMG48eP58ABqxLJ66+/Tq9evU5bdmpqKhs2bKBTp04Fw+bMmcOQIUNo2LAhM2fO5OGHHy51u8yYMYMePXowuTg7ggAAD9RJREFUZMiQgmEXXnihQ9u0JPPmzeOXX34BYMyYMfTr148XX3yx2Olnz57NwIED8fb2BqznDSkp1vGSnJxMaGgoYO2riIgIVq1aRbdu3c45ztK4XaIQgb8PnmBUt/Bymp8wa9YW7rnnew4fTuOOO7rRokX9cpm3UpVJbm4uS5YsYdy4cYBV7NS1a9fTpmnRogVpaWmkpKSwadMmJk6cWOp8J0+ejL+/Pxs3bgQosnilsB07drB48WI8PDzIy8tj7ty53Hzzzfz5559ERETQsGFDrrvuOu6991569+7NgQMHGDBgAFu3bj1tPmvWrKF9+/anDfviiy948sknadiwIcOHD3coUWzatOmMbVGU1NRU+vTpU+S4GTNm0LZt29OGJSQkEBJidYMQEhLCkSNHSpz/zJkzue+++wo+T58+nUGDBlGnTh38/PxYuXJlwbiYmBiWL1+uiaIoWbl5AAT71DrneYkIV131Fd98s40uXUKYP38UMTGh5zxfpYrj6JV/eTp16hTR0dHs27ePrl270r9/f+CfPtuLcjY1ZxYvXszMmTMLPtevX/qF1ogRI/DwsHqlHDlyJM888ww333wzM2fOZOTIkQXz3bJlS8F3UlJSSE1Nxdf3n9YY4uPjCQ7+p1JLQkICu3btonfv3hhj8PT0ZNOmTbRv377IdTrbGkK+vr6sX7/+rL7jqPj4eDZu3MiAAQMKhr322mssWrSI7t278/LLL3Pfffcxffp0ABo0aMD/27v/6KjqM4/j788i8rupwsIRQQIiGhKDsFjBtkShiz/qwsLxEJEfZQ+uClRqEdzdw56z7LLa2lrssuAitR7A07IRBUVAWHSxVCHyYwmgQCk/osXFEpAFRBBInv3j3kyGMJkMaWaSSZ7XOXPOzJ07937nOTPzzP1+732+e/bsSUpbKku7wewLYaLo3aXm//rPny8Fgg/Jt77Vmdmz72bTpoc8SbgGqUWLFhQVFfHxxx9z7tw55s6dC0B2djZbtmy5aN0DBw7QunVr2rRpQ3Z2Nlu3bq12+1UlnOhllc/rb9WqVeR+//792bdvHyUlJbz++usMHz4cgLKyMjZu3EhRURFFRUV8+umnFyWJ8vcWve2CggKOHz9O165dyczMpLi4OJLE2rZte9HRzueff067du0isUjkvZ46deqigefoW3RSK9ehQwcOHz4MBImgffuqr7t65ZVXGDZsWOSK6pKSErZv385ttwVd4Pn5+WzYsCGy/tmzZ2nRIjXVs9MuUZwvDc7IuKaGZzy9+24xubnzeOONIBM/8cTtPPbYbTRpknahcO6yZGRkMHv2bJ599lnOnz/PqFGjeO+993j77beB4Mhj8uTJPPnkkwBMmzaNp59+mr179wLBD/esWbMu2e7gwYOZM2dO5HH5j3GHDh3YvXt3pGupKpIYNmwYU6ZMISsri7Zt28bcbqx/8llZWZEzgSDodlq9ejXFxcUUFxezdevWSKK44447KCgo4Ny54DqsBQsWRMYhHnzwQTZs2MDKlSsj21q9enWkO61c+RFFrFvlbieAIUOGsHDhQgAWLlzI0KFDq4zD4sWLGTlyZOTxVVddxYkTJyLxX7t2LVlZWZHn9+7de0m3W9LUdBS8rm6dbsi2Ln+3wv548kyVI/6xHDnyhY0du8xghnXt+nN7550D1b/IuVpQn856MjO77777bNGiRWZmtmPHDsvLy7MePXrY9ddfbzNmzLCysrLIum+++ab16dPHbrrpJsvKyrKpU6desv1Tp07Z2LFjLTs723Jzc+21114zM7MlS5ZYt27dLC8vzyZNmhQ5YyfW2UebN282wBYsWBBZVlJSYiNGjLCbb77ZsrKy7JFHHon5/nJycuzkyZN28OBB69ix40XtNzPr3bu3FRYWmpnZjBkzLCcnx3r16mXDhw+3I0eORNbbvXu33XXXXda9e3fLysqy/Px8++yzz+LGtjpHjx61gQMHWvfu3W3gwIF27NixyPsdP358ZL3ytpeWXnwm59KlSy0nJ8dyc3MtLy/P9u/ff9H7Kikpibnf2j7rSRZ1znQ66Nwjx5oMf4Z9T93DFQkeBSxevJNJk1bxxRfnmDbtdqZPH0DLlrVXMMu5eHbv3n3RP0FXu5577jnatGlzybUUDdm2bduYNWsWL7/8csznY33mJG01s7412V/a9becLzXatW6WcJIAuHChjJyc9hQVPcpTTw3yJOFcAzJhwgSaNatfE5gl29GjR5k5c2bK9pd2Zz19daGULm1bxl3n9OlzzJy5nuuuy2DixFsZPTqX0aNzvd6Ocw1Q8+bNGTNmTF03I6XKz1xLlbQ7ovjyXCnXfr3qkf4VK/aSnf08zzzzPnv3BqU+JHmScHUq3bp4XfpKxmct7Y4oAC6UlV2y7NChk0ye/BbLlu2hZ88/Z/36cXz7213qoHXOXax58+YcO3bMS427pLNwPormzWu3Dl5aJopbM6++ZNmBA8dZs2Y/P/rRIKZM6c+VVzapg5Y5d6lOnTpx6NAhSkpK6roprhEon+GuNqVlorgmI+h62rTpUzZu/AM/+EE/BgzowiefPE7basYvnEu1pk2b1upsY86lWlLHKCTdLel3kvZJ+vsYzzeTVBA+/4GkzES22y2jBRMnrqRfvxeZNauQ0+FERp4knHOu9iUtUUhqAswF7gF6AiMlVb50cTxw3My6A88BVZdVjDLg1l/wwgtbmTz5NnbunECrVn963SfnnHOxJbPr6RvAPjM7ACDpP4GhQHRBlKHAjPD+q8AcSbI4w/Z2oYzOmRmsWjWKPn2uSU7LnXPORSQzUVwL/CHq8SGg8gQPkXXM7IKkE0Bb4Gj0SpIeBh4OH3615ejDHyZQEbgxaEelWDViHosKHosKHosKN9b0hclMFLHOA6x8pJDIOpjZfGA+gKQtNb0MvaHxWFTwWFTwWFTwWFSQtKX6tWJL5mD2IaBz1ONOwP9WtY6kK4AM4PMktsk559xlSmai2AzcIKmrpCuBB4DlldZZDnwvvH8/8N/xxiecc86lXtK6nsIxh+8Da4AmwEtm9pGkfyEod7sc+CXwsqR9BEcSDySw6fnJanMa8lhU8FhU8FhU8FhUqHEs0q7MuHPOudRKu6KAzjnnUssThXPOubjqbaJIVvmPdJRALKZI2iVph6R3JDXYsrnVxSJqvfslmaQGe2pkIrGQNCL8bHwk6depbmOqJPAduU7SOknbwu/JvXXRzmST9JKkI5I+rOJ5SZodxmmHpD4Jbbimc6gm80Yw+L0f6AZcCWwHelZaZyIwL7z/AFBQ1+2uw1jcCbQM709ozLEI12sDrAcKgb513e46/FzcAGwDrgoft6/rdtdhLOYDE8L7PYHium53kmIxAOgDfFjF8/cCbxFcw9YP+CCR7dbXI4pI+Q8zOweUl/+INhRYGN5/FRikhlnsv9pYmNk6M/syfFhIcM1KQ5TI5wJgJvAT4GwqG5diicTib4G5ZnYcwMyOpLiNqZJILAz4Wng/g0uv6WoQzGw98a9FGwosskAh8HVJ1dZCqq+JIlb5j2urWsfMLgDl5T8amkRiEW08wT+GhqjaWEjqDXQ2sxWpbFgdSORz0QPoIel9SYWS7k5Z61IrkVjMAEZLOgSsAh5LTdPqncv9PQHq73wUtVb+owFI+H1KGg30BfKS2qK6EzcWkv6MoArxuFQ1qA4l8rm4gqD76Q6Co8zfSsoxs/9LcttSLZFYjAQWmNnPJPUnuH4rx8wunS6zYavR72Z9PaLw8h8VEokFkr4DTAeGmNlXKWpbqlUXizZADvCupGKCPtjlDXRAO9HvyBtmdt7MDgK/I0gcDU0isRgPvAJgZhuB5gQFAxubhH5PKquvicLLf1SoNhZhd8sLBEmiofZDQzWxMLMTZtbOzDLNLJNgvGaImdW4GFo9lsh35HWCEx2Q1I6gK+pASluZGonE4hNgEICkLIJE0Rjnpl0OjA3PfuoHnDCzw9W9qF52PVnyyn+knQRj8VOgNbAkHM//xMyG1FmjkyTBWDQKCcZiDTBY0i6gFJhmZsfqrtXJkWAsngB+IemHBF0t4xriH0tJiwm6GtuF4zH/BDQFMLN5BOMz9wL7gC+Bv0louw0wVs4552pRfe16cs45V094onDOOReXJwrnnHNxeaJwzjkXlycK55xzcXmicPWOpFJJRVG3zDjrZlZVKfMy9/luWH10e1jy4sYabONRSWPD++MkdYx67kVJPWu5nZsl3ZLAax6X1PJP3bdrvDxRuProjJndEnUrTtF+R5lZL4Jikz+93Beb2TwzWxQ+HAd0jHruITPbVSutrGjn8yTWzscBTxSuxjxRuLQQHjn8VtL/hLfbY6yTLWlTeBSyQ9IN4fLRUctfkNSkmt2tB7qHrx0UzmGwM6z13yxc/mNVzAHybLhshqSpku4nqLn1q3CfLcIjgb6SJkj6SVSbx0n69xq2cyNRBd0k/YekLQrmnvjncNlkgoS1TtK6cNlgSRvDOC6R1Lqa/bhGzhOFq49aRHU7LQuXHQH+0sz6APnA7BivexT4NzO7heCH+lBYriEf+Ga4vBQYVc3+/wrYKak5sADIN7ObCSoZTJB0NTAMyDazXOBfo19sZq8CWwj++d9iZmeinn4VGB71OB8oqGE77yYo01Fuupn1BXKBPEm5ZjaboJbPnWZ2Z1jK4x+B74Sx3AJMqWY/rpGrlyU8XKN3JvyxjNYUmBP2yZcS1C2qbCMwXVInYKmZ/V7SIOAvgM1heZMWBEknll9JOgMUE5ShvhE4aGZ7w+cXApOAOQRzXbwoaSWQcElzMyuRdCCss/P7cB/vh9u9nHa2IihXET1D2QhJDxN8r68hmKBnR6XX9guXvx/u50qCuDlXJU8ULl38EPgj0IvgSPiSSYnM7NeSPgC+C6yR9BBBWeWFZvYPCexjVHQBQUkx5zcJawt9g6DI3APA94GBl/FeCoARwB5gmZmZgl/thNtJMIvbj4G5wHBJXYGpwK1mdlzSAoLCd5UJWGtmIy+jva6R864nly4ygMPh/AFjCP5NX0RSN+BA2N2ynKAL5h3gfkntw3WuVuJziu8BMiV1Dx+PAX4T9ulnmNkqgoHiWGcenSIoex7LUuCvCeZIKAiXXVY7zew8QRdSv7Db6mvAaeCEpA7APVW0pRD4Zvl7ktRSUqyjM+ciPFG4dPE88D1JhQTdTqdjrJMPfCipCLiJYMrHXQQ/qP8laQewlqBbplpmdpaguuYSSTuBMmAewY/uinB7vyE42qlsATCvfDC70naPA7uALma2KVx22e0Mxz5+Bkw1s+0E82N/BLxE0J1Vbj7wlqR1ZlZCcEbW4nA/hQSxcq5KXj3WOedcXH5E4ZxzLi5PFM455+LyROGccy4uTxTOOefi8kThnHMuLk8Uzjnn4vJE4ZxzLq7/B3+7nCQKdPVEAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4321,10 +4323,10 @@ { "data": { "text/plain": [ - "0.7757068306651365" + "0.7734028258005102" ] }, - "execution_count": 66, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -4343,7 +4345,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 64, "metadata": { "scrolled": true }, @@ -4354,8 +4356,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.80 0.83 6741\n", - " 1 0.47 0.60 0.53 2008\n", + " 0 0.88 0.78 0.83 6762\n", + " 1 0.46 0.62 0.53 1987\n", "\n", " accuracy 0.75 8749\n", " macro avg 0.67 0.70 0.68 8749\n", @@ -4374,7 +4376,7 @@ "source": [ "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", "\n", - "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.\n", "\n", "\n", "Calculate the confusion matrices for both cut offs to better compare their performance." @@ -4382,14 +4384,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Logistic Regression identified 1207\n" + "Of 1987 Defaulters, the Logistic Regression identified 1235\n" ] }, { @@ -4424,14 +4426,14 @@ " \n", " \n", " \n", - " 0\n", - " 5388\n", - " 1353\n", + " 0\n", + " 5303\n", + " 1459\n", " \n", " \n", - " 1\n", - " 801\n", - " 1207\n", + " 1\n", + " 752\n", + " 1235\n", " \n", " \n", "\n", @@ -4440,11 +4442,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5388 1353\n", - "1 801 1207" + "0 5303 1459\n", + "1 752 1235" ] }, - "execution_count": 68, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -4455,14 +4457,14 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Logistic Regression identified 755\n" + "Of 1987 Defaulters, the Logistic Regression identified 715\n" ] }, { @@ -4497,14 +4499,14 @@ " \n", " \n", " \n", - " 0\n", - " 6321\n", - " 420\n", + " 0\n", + " 6421\n", + " 341\n", " \n", " \n", - " 1\n", - " 1253\n", - " 755\n", + " 1\n", + " 1272\n", + " 715\n", " \n", " \n", "\n", @@ -4513,11 +4515,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6321 420\n", - "1 1253 755" + "0 6421 341\n", + "1 1272 715" ] }, - "execution_count": 69, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -4530,39 +4532,112 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is evident that the lower cutoff is better able to detect defualts." + "It is evident that the lower cutoff is better able to detect defaults. " ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 67, "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m optimal =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "optimal = \n", "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , \n", - " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[1] = [\"Logistic Regression\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)[\"1\"][\"f1-score\"],\n", " auroc]" ] }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, { "cell_type": "markdown", "metadata": { @@ -4728,7 +4803,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -4740,7 +4815,7 @@ " verbose=False)" ] }, - "execution_count": 72, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -4754,19 +4829,19 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.1567076896169202\n" + "Optimal Threshold: 0.16469105377809068\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4779,10 +4854,10 @@ { "data": { "text/plain": [ - "0.7227905615337198" + "0.7138537062929152" ] }, - "execution_count": 73, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -4794,14 +4869,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the SVM default parameters identified 768\n" + "Of 1987 Defaulters, the SVM default parameters identified 713\n" ] }, { @@ -4836,14 +4911,14 @@ " \n", " \n", " \n", - " 0\n", - " 6321\n", - " 420\n", + " 0\n", + " 6432\n", + " 330\n", " \n", " \n", - " 1\n", - " 1240\n", - " 768\n", + " 1\n", + " 1274\n", + " 713\n", " \n", " \n", "\n", @@ -4852,11 +4927,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6321 420\n", - "1 1240 768" + "0 6432 330\n", + "1 1274 713" ] }, - "execution_count": 74, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -4868,7 +4943,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -4877,12 +4952,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.94 0.88 6741\n", - " 1 0.65 0.38 0.48 2008\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -4912,9 +4987,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -4937,7 +5023,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -4949,7 +5035,7 @@ " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -4962,19 +5048,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15368680599405346\n" + "Optimal Threshold: 0.15996357777982226\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAEWCAYAAADsCgQrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3gV1dbA4d9KD6TQg5BACL13EBRpoiAiCuoVKaJYALH3hmL7FLti73oRvCoCIkpTUESqKEjvEHogCel1f3/MBA4hDVImOWe9z8NDpq+ZM7NnTdtbjDEopZRSSuXm5XQASimllCqfNElQSimlVJ40SVBKKaVUnjRJUEoppVSeNElQSimlVJ40SVBKKaVUnsplkiAiw0VkvtNxOE1E6olIooh4l+EyI0XEiIhPWS2zNInIBhHpdQ7TndM+KCKBIvKDiMSLyDdnO707EZHFInJzGS/zMxF5toDhz4pIjIgcKsu4KhIR2S0iF5fRst4TkSdKYb6nlWMi8pOI3FDSyzkXdlyNykEcBR4rOQpNEuwdJsU+WR2yZxxUMmHmzRgz1RhzSWkuozzKfXAaY/YaY4KMMVlOxuWUkjiYjDEtjTGLC1nOGYlRMfbBq4EwoLox5ppzmD6v+B4VkV32MRgtIl/b/d8XkS/yGL+NiKSJSDURecpetztzjXO33f+pkoixIhCRCOA+oIUxpnYx59VLRKJLJrKKo6gnlqIyxow1xjxTUvMrYDkDjDGfl/Zy3FFR7yQMMsYEAe2A9sAjpRdS6XHy6thdrszPhodu7/rAVmNM5tlOmFfM9tXPSOBi+xjsBCyyB38GDBGRyrkmGwXMMcYct7u3ArmvokbZ/c85tgqoPnDMGHPE6UDcZHuqApT2b1xmd5iNMQX+A3ZjFVA53ZOBH126/YGXgb3AYeA9INBl+GDgb+AEsAPob/cPBT4GDgL7gWcBb3vYaGCp/fd7wMu5YpoF3Gv/XQf4DjgK7ALudBnvKeBb4L/28m/OY/1CgS/s6fcAjwNeLnH8AbwFxAObgb65pi1oHf4AXgOO28MaAr8Ax4AYYCpQxR7/SyAbSAESgQeBSMAAPvY4i4Fn7PkmAPOBGi7xjLLX4RjwRO7fLtd6BwKv2OPHA0vtfjnLvMH+TWOAx1ym6wL8CcTZ6z0F8HMZboDbgW3ALrvfG8A++zdYA/RwGd8beNTeNxLs4RHAb/a8kuzt8R97/Mux9qc4YBnQJte++hCwDkgDfFy3gR37ajuOw8Crdv+99rIS7X/dcNkH7XFaAgvs3/Iw8Gge23QSkA5k2PMZg5WIP25v5yNY+1qoPX7Oth5jx/BbHvOcArxewPG5BRiVa3seAK5wOQb+C2wCWrqsyya7/1P5zHc0ufZfu/9N9rSxwDygvss0/bCOkXg77iXYx1xOHC7j5qx7zr5dDfjUjj0WmOkybkG/eXvgL6x952tgek6sudbnYqxjK9v+bT6z+59vzzMO+Afo5TLNjfa6JgA7gdvs/pVzzSsRqxz6zHXZQC8gupD9s6DyK8/9NY91qwrMsecRa/8d7jJ8MQWXGyM5VW48Rj7lBnAr1r6dbq/zDy7HfCOX8U5uh5xtgHUH5whWmXHjOY5bHfjB3h6rsMrUpflsk0jOLDtz9sXRWOXdy/b22gUMKGK5nm8Znt9vnEdsJ7cXcCFW2djb7m7GqXJmC3Btrm31LjAXq1y82O73NvCj/duuABq6TFPY/M44Vs6It9ARTi9kw4H1wBsuw18HZmMd5MH2j/h/Ljt5PFbh4QXUBZrZw2YC72MdcLWAlZw6CEdzKkm4yN6I4nJApGAdXF5YJ5WJgB8QhXUwX+pSMGUAV9rjBuaxfl9gJR3BWDvWVmCMSxyZwD2AL/Afe32qFXEdMoE7sAqDQKCRvS38gZpYJ8LX89rWBezoO4Am9vwWAy/Yw1pgHbgX2tviZXvd80sS3ranr4t1Yulux5WzzA/tZbTF2tmb29N1xCpYfexxNwF35zoAFmDtD4F2vxFYB7gPVgFwCAiwhz2AtU81BcReXvV8Cp8OWIVHVzvmG+xt5u+y/f7GSjICc29TrORmpP13EHB+Xts5j30wGKvAuA8IsLu75rNdn+L0k+FNwHasfTMImAF8mWu5X2DtQ3ntnyOwDvAHsO4ieOca/hiw0KX7UqyTha9rPFiJ2It2v8lYdwMLSxJy779X2uvS3O73OLDMHr8GVuF9Ndaxco89fVGThB+xTvJV7el7FvabY+3nezh1fF6Ntc/nWfBx5km7LlZhfxlW+dDP7q5pDx+IdVIQoCeQDHTIa155Fbp5LG83LvsnhZdfee6veaxXdWAoUAlr3/yG05OsxRReblxkb9NX7d8tv3LjtHXM5zg9OY69DTKBp+3f6DJ7O1Y9h3Gn2/8q2XHv49yThAzgFqx9ahxWcppzjimoXC9KGX5aGZRHbMaez6X2OnSx+1e2u2/EOr46YCUiLV22VTxwAda+E2D3O451rvXBSlqmn8X8SixJSMTKUgzWrc6cq1/BymhcM5dunLqCfB94LY95hmGdeFzvOAwDfs2jgBasq6yL7O5bgF/sv7sCe3PN+xHgU5eC6YyrM5dxve04Wrj0uw1Y7BLHyZ3H7rcSK/MuyjrszW/Z9jhXAmtzbevCkoTHXYaPB362/54ITHMZVgkr48/risALK9FqW8DBFZ5rna/LZx3uBr7PdQD0KWS9Y3OWjZXdDi7oYHLpfhd4Jtc4Wzh1QtkN3JTH/puTJPyGdbVfI9c4p23nPPbBYa6/UyHr9hSnnwwXAeNduptiFVA+LsuNKmSew4GFWMfaMeBhl2H17PmF291TOT2JfworGaiHdRz52v9HUHiSkPvY+gk7gXbZj5KxbuOPApa7DBOsq8JCkwTgPKyr8qp5xJHvb451Yst9fC6j6EnCQ9gJm0u/ecAN+Uw/E7grr3nZ/T6j8CThJpfuwsqvPPfXIuyD7YBYl+7FFFxuTHcZVpl8yo281jGf4/TkOPY2SOH0Y+sIpxL0Io2LVVZnAE1dhhXnTsJ2l3Er2ePWppByPY/l5FWG35TXuLm21yNYCW5rl/7/AX7PNe77wJMu2+qLPH6Pj1y6LwM2n8X8Ck0SivrM5EpjzEIR6Ql8hXXVEIeVSVUC1ohIzrhi/6BgFURz85hffazC6qDLdF5YWc9pjDFGRKZj/VC/AddjFW4586kjInEuk3gDv7t0nzFPFzU4dTWSYw/WFUaO/cbeoi7D6xRxHU5btojUAt4EemBl/F5YJ8yz4fpWdjLWFQZ2TCeXZ4xJFpFj+cyjBlYWuuNslyMiTbCuNjph/fY+WFdDrnKv933AzXaMBgixYwBrHykoDlf1gRtE5A6Xfn72fPNcdi5jsK5SNovILmCSMWZOEZZ7NjHmVocz9y8frMIoR0ExY4yZCkwVEV+sQmmqiKw1xswzxuwVkd+AESIyxR7eI4957BWR7cDzwDZjzD6X/TY/ueOqD7whIq+49BOs4yX3/mdEpMD1chEBHDfG5HUsFPSbG/I+PouqPnCNiAxy6ecL/AogIgOAJ7GuwL2w9vf1ZzH/vLhuk8LKryLtryJSCeuxUH+sOzEAwSLibU699FzUciOpgHLjXB0zp7+j47r8oo5bE+u4ybd8PUsnt4ddVmIvpxoFlOtFLMOLEtfdWCd81/2pPtA11/7gg/UouqB55/fbFmV+hTqrTyCNMUuwso+X7V4xWJlfS2NMFftfqLFesAJrhRrmMat9WNlaDZfpQowxLfNZ9DTgahGpj5V9f+cyn10u86hijAk2xlzmGnYBqxSDlZ3Wd+lXD+s5VI66cnppWg/r6qUo65B72f9n92tjjAnBupUsBYx/Ng5iPQ4CrE/xsG5D5iUGSCXv36Yw72I9d25sr8OjnL4O4LIeItID64rtWqwrxSpYt8xypslvH8nLPuC5XL93JWPMtLyWnZsxZpsxZhjWLcQXgW/tl/4K2+5nE2NuBzhz/8rEesZ8MrSizMgYk2GM+QbreWcrl0GfY13JD8U6Hv7KZxZfYD0yOeOLiPwWmat7H9ZtV9ftH2iMWYa1/0XkjGgfMxEu0yZhnWRzuH5dsA+oJiJV8oihoN/8IHkfn0W1D+tOguu8KxtjXhARf6xy5mUgzN5v53Jqv83rNytoHXO4Tldg+VXA/prbfVh3qLrax+RFdv9Cs0DO/N0qkX+5kTv+HMkUvt7FdRTruAl36ReRz7jFUVi5XlgZDkU7nq8BrhSRu3Mte0mu/SHIGDPuLOd9NvMr1LnUk/A60E9E2hljsrGeXb9mZ1iISF0RudQe92PgRhHpKyJe9rBmxpiDWC/PvCIiIfawhvadijMYY9Zi7SQfAfOMMTmZ0UrghIg8JNb36d4i0kpEOhdlRews+3/AcyISbCch93LqTgVYB+idIuIrItdgPY+de7brYAvGenQTJyJ1sZ4zuzqM9VzyXHwLDBKR7iLih3WbMs9Cwv7dPgFeFZE69nbrZheMhQnGevacKCLNsJ7nFTZ+Jtbv5yMiE7HuJOT4CHhGRBqLpY2I5BRSubfHh8BYEelqj1tZRAaKSHAR4kZERohITXv9c/ahLDu2bPLf9nOA2vZng/72vtK1KMvESnDvEZEGYn06/DzwtSni1w8iMjpnHe19bADWi4crXEb7DqvAnISVMOTna+ASrH3+XLwHPCIiLe3YQu1jAqx3ClqKyBD7re47Of1k8TdwkVh1f4Ti8oWUfSz9BLwjIlXtYy3nRFfQb/4n1r51p4j4iMgQrGezRfVfrGPmUvsYCBDr08ZwrLsV/tgnJ3u7u34Sexiobq+L6zpeJtanp7WxrhYLUmD5VcD+mlsw1sVanIhUw7r7UVTfApeLyIV2ufE0BZ8X8iqj/gaut+Pvj/UoqETZZfUM4CkRqWSXPaNKYTmFleuFleFFdQDoi7Xvjrf7zQGaiMhI+xjwFZHOItL8HJdRIvM76yTBGHMU60okpwKMh7BeZlouIiewnp02tcddifXSxGtYV49LOHVVNQrrQNyIdbvmW6xnk/mZhvU251cusWQBg7Cewe3CukL+COvt1KK6A+sKYCfWG69fYZ1Ac6wAGtvzfg642hiTczvubNdhEtbLI/FYheqMXMP/D3hcROJE5P6zWAeMMRvsdZmOdXWQgPU8Ly2fSe7HunW6CuvFlxcp2v5wP9YjnwSsAvzrQsafh3UC2Ip1KziV02+ZvYp10pqPlXx8jPVyFVjPsT+3t8e1xpjVWO+kTMHa3tuxni8WVX9gg4gkYn1xcZ0xJtUYk4z12/5hL+t814mMMQlYLysNwrq1tw3oXcRlfoJ1e+83rH00Fet3KqoTWHdr9mKdKCYD44wxS13iS+JUojA1vxkZY1KMMQuNMSlnsXzX6b/H2k+m28f6v8AAe1gM1tXRC1jvTTTGeps+Z9oFWPvKOqzHU7lvm4/Euqu3GWu/vdueLt/f3BiTDgyxu2OxnsHmPqYKWp99WF9fPYqVDOzDKvS97N/8Tqx9MxZrn5/tMu1mrDJpp73P1MH6nf/Bei49n0KOjSKUX3nur3nM6nWsYyYGWA78fBbbYAPW10hfYZUbsVjvkuTnY6CFvc4z7X532esRh/X+zMz8Ji6mCVjb5hDWtp5G/uVbcRRUrhdWhheZMWYvVqLwkIjcbO9zlwDXYSURh7COt6JcvOU1/xKZX87bnCoPIjIa62WXC52O5WzZV61xWI8Fdjkdj1JKlSQReRGobYy5welY3Fm5rJZZnRsRGWTfiquM9Sx1PdZVjVJKVWgi0sx+HCki0gXrxc7vnY7L3WmS4F4GY91WOoB1u/c6o7eKlFLuIRjr9n4S1mOgV7DquFGlSB83KKWUUipPeidBKaWUUnnSRkYcUKNGDRMZGel0GEopVaGsWbMmxhhT0+k4PIkmCQ6IjIxk9erVToehlFIVioicTY2aqgTo4wallFJK5UmTBKWUUkrlSZMEpZRSSuVJkwSllFJK5UmTBKWUUkrlSZMEpZRSSuVJk4QCiMgnInJERP7NZ7iIyJsisl1E1olIh7KOUSmllCotmiQU7DOs5lrzMwCrjYTGwK3Au2UQk1JKeZzMrGynQ/BIWplSAYwxv4lIZAGjDAa+sBtRWi4iVUTkPGPMwTIJUCml3ND2Iwn8u/8E3/0VTVJaJn/tjXM6JI+lSULx1AX2uXRH2/3OSBJE5Fasuw3Uq1evTIJTSqnyJDvbsOd4MtsOJxAdm8LRxDRW7TrOxoMn8PYSsrINyelZp09kDMnb4wkJ0NOVE3SrF4/k0S/PZjWNMR8AHwB06tRJm95USrm9zYdO8PfeOBZuOsKKXcdISM08Y5zKft5U9vchqkZlWtUNxc/Hi2xjuLBRDR4cO5d//oxm4sSe3HtvN/y/cmAlPJwmCcUTDUS4dIcDBxyKRSmlHGGM4WhiGruOJnEsKZ0Zf0WzcNORM8br06wWjcOCaBtehSZhwYSF+BMc4HvaOCtX7qdJk+pUqRLAB6/3JyjIjwYNqpbVqqhcNEkontnABBGZDnQF4vV9BKWUOzPGkJaZzbroeN5bsoMNB+I5fCLtjPF8vIRuDaszvGt9zo+qRmigLyJ53Xy1xMWl8uiji3jvvdU88EB3XnyxH61bh5Xmqqgi0CShACIyDegF1BCRaOBJwBfAGPMeMBe4DNgOJAM3OhOpUkqVvPjkDBZtPsz+2BRSMrL4eOku0jLP/Mqgd9Oa1K0aSKs6oTStHUx41UrUDPYv0jKMMUyf/i/33DOPo0eTueuurjz++EUlvSrqHGmSUABjzLBChhvg9jIKRymlSl1qRhZD3llGYlome48nn+zv6y34entRI8iPUd0iCQnwoVNkNVrVDS3W8iZNWsKkSUvo3LkOP/00nPbtzyvuKqgSpEmCUkp5uNSMLJbvPMYP/xzku7+iAfDz9mLk+fXpWL8q3RtVp1ZwQIktLy0tk/j4NGrVqszo0e2oVasyt93WEW9vrbqnvNEkQSmlPEhqRhZ/7Yll25FEjiWmsXzncVbuPn7aOHf0acR9lzQtleUvWrST8ePn0rBhVebOHU5kZBXGj+9cKstSxadJglJKuTljDL9vi+GLP/ewcNPh04YF+HoREuBD3+ZhjO/VkMZhwaUSw+HDidx333ymTl1Pw4ZVueuurqWyHFWyNElQSik3kp1t+HTZbjYdPMFfe2PJzjbsPnbq3YJgfx+GdgxnSIe61K9emdBA3wLmVjKWLt3LoEHTSEpK54knLuKRRy4ksAyWq4pPkwSllKrAElIz2HE0ib/3xjJt5T62HE44OaxxrSAyjeG6zhFU8vPh5h4NqFMlsMxiy8jIwtfXm9ata3HJJQ2ZNKkXzZrVKLPlq+LTJEEppSqY1Iws3ly0jS/+3ENi2um1GAYH+HB9l3pM6NPojIqKykpiYjqTJi1m4cJdrFx5M6GhAXz99dWOxKKKR5MEpZSqIA7GpzB/w2GenL3hZL+24aFc2zmCRjWDaF+vKn4+zn4hMGvWZu644yf27TvBLbd0IC3NupugKiZNEpRSqpxLy8xiwldrWbDReunQx0voHFmNz2/q4nhSkCM2NoXRo2cxe/YWWrWqxbRpQ7ngAm3MrqLTJEEppcqZY4lprIuO5/dtMfy27SjbjySeHPbmsPZc3vo8vLzyr+LYCUFBfhw9msTkyRdz993n690DN6FJglJKOcwYw/Kdx1mz5ziLNh9h7d6404a3rBPCdV3qMaJrvQLbPyhrf/65jyefXMzXX19N1aqBLF16U7lLXlTxaJKglFJlaO+xZNbvj2f1nuP8tTeOf/fHk5V9euvxUTUqc1vPKDpFVqNB9crl7sQbG5vCww8v5IMP/iI8PIRdu+KoWjWw3MWpik+TBKWUKmXbjyTy0/qD/BMdf0ZlRmEh/nSLqk7XqOp0jqxKgxpBeJfTk60xhqlT13PvvfM4fjyFe+89n0mTehMU5Od0aKqUaJKglFKlICE1gz+2xzBx1gaOJJxqSrlZ7WBu6RHFBY1qUDu05NpDKCvTpv1LVFRV5s8fSbt2tZ0OR5UyTRKUUqqEHE1IY8fRRL5bE803a6JP9vf38eKDUZ3o2qAaARXshb7U1ExeeGEpo0a1JSqqKlOnDiEkxF8fLXgITRKUUuocZWcb3l2yg/kbDvFPdPwZw+/q25j/dI4o01oOS9KCBTsYP34u27cfJyTEn3vv7UaVKhXv7oc6d5okKKXUWdodk8Tnf+7m0z92n+zXrHYwEdUqcUXbOoRXDaRdRJVy9SXC2Th0KJF7753HtGn/0rhxNRYuHEnfvlFOh6UcoEmCUkoVweItR1i46TC/b4thj91gUnjVQK7pGMEtFzWgkp/7FKeTJ//Bd99t4qmnevLQQxcSEOA+66bOjhhjCh9LlahOnTqZ1atXOx2GUqoI4pMzuOXL1azcdRyAkAAf6lWvxL39mtCnWZjD0ZWctWsPYgx06HAecXGpHDmSRJMm1Z0O6zQissYY08npODyJpodKKZUHYwz/7j/BoClLAahe2Y+PR3emXUQVhyMrWQkJaUyc+CtvvrmS3r0jWbhwFFWqBOi7BwrQJEEppU6KT85g3sZDfPbHbjYePHGy/xVt6/DyNW3LTTsJJcEYw/ffb+bOO3/iwIEEbrutI88/39fpsFQ5o0mCUsqjxSal88aibXz+525cn77WDgmgf6vajDi/Po1qBTkWX2mZMWMTV1/9DW3bhvHtt9dy/vnhToekyiFNEpRSHiU727BmbyyvL9zKH9uPnTbs2k7hdKhXlcvanEdIgK9DEZaejIwstm07TosWNbniiqZ89NEgbrihHT5udIdElSxNEpRSbu9EagYrdx5nwcbDzPx7P2mZ2QAE+HpxcfMwLm4eRp/mtdwyMcixdOlexo6dQ0xMMjt23Enlyn6MGdPB6bBUOadJglLK7cQkpjHr7wPsOZbEF3/uOW1YZT9vejSvxYP9m9EkLNihCMvOsWPJPPTQQj7+eC0RESF88MEgKlfWthZU0WiSoJSq8DKysnlz0Tb+3HGM1XtiTxtWyc+b6kF+DO9anx6Na9CyTqhDUZa9ffvi6dDhA2JjU3jgge5MnNhTG2NSZ0WTBKVUhbPlUAIvzdvMwfhUNhw4cdqwJmFBhIUEMKxLPXo1relWlRwV1YkTaYSE+BMeHsLNN7dn2LDWtGnjPnU6qLLjeUePUqrCiUlMY/bfB1i89Shrdh8nKT3r5LB+LcIIC/GnYc0ghnWpV+EaUCpJKSkZPPfc70yZspK1a2+jQYOq/N//Xex0WKoC0yRBKVXu7DyayMpdx/lh3YEzvkDo0bgGnSOr0TmyGt0alq8aAZ00b952xo+fy86dsYwc2UYfK6gSoUmCUqpcSEnPYuqKPbwyfyspGafuFNQK9qd13VCu6RROr6a1PPpOQV6ysw0jRsxg2rR/adq0Or/8MorevRs4HZZyE5okKKUck5qRxcH4VKJjkxn58cqT/WsG+/PCkNZ0ql+N0Eru+1licRhjEBG8vITzzgvi6ad78eCDF+Dvr8W6Kjm6NymlytS2wwm8sWgbRxPSWGE3mpTjvNAAfr2/l94tKMSaNQcYN+5HXn+9P927R/DKK5c6HZJyU5okKKXKzJo9sQx9dxkA1Sr70ax2MO3rVaVnk5rUq1aJFnVCHI6wfDtxIo0nnviFKVNWUatWZeLjU50OSbk5TRIKISL9gTcAb+AjY8wLuYbXAz4HqtjjPGyMmVvmgSpVzqRlZvHb1hh2xSRy+EQaU1fsITXDqunw2StbMeL8+g5HWLHMnLmZ22+fy8GDCYwf35lnn+2jLTWqUqdJQgFExBt4G+gHRAOrRGS2MWajy2iPA/8zxrwrIi2AuUBkmQerlMNOpGbw/pId/L4thnXR8WcMD/T1plfTmtzXrymtwz2nQqOSsm3bMcLCKjNz5n/o3Lmu0+EoD6FJQsG6ANuNMTsBRGQ6MBhwTRIMkHOPNBQ4UKYRKuWgXTFJLNp0mA9/38nhE2kn+7euG0r96pVoVTeUi5vXokGNILy9xMFIK5709CxeeWUZjRtX5+qrW3DPPd24555u2hiTKlOaJBSsLrDPpTsa6JprnKeA+SJyB1AZyLPmEhG5FbgVoF69eiUeqFJlJSU9i8//3M0LP20+rX941UDG9WrI0A7h+uJhMf322x7Gjp3Dpk0xjBvXiauvbqHJgXKEJgkFy+vSx+TqHgZ8Zox5RUS6AV+KSCtjTPZpExnzAfABQKdOnXLPQ6ly79/98by5aBvzNx4+2S+iWiDPX9WadhFVCHbjFhTLSkxMMg8+uIBPP/2byMgqzJkzjIEDmzgdlvJgmiQULBqIcOkO58zHCWOA/gDGmD9FJACoARwpkwiVKiXxyRks2xHDuv3xvLt4x8n+ft5ePDawOaO61UdEHyGUpCVLdvPll+t4+OELeOKJnlTSOiKUwzRJKNgqoLGINAD2A9cB1+caZy/QF/hMRJoDAcDRMo1SqRJijOHnfw/x8Iz1xKdknDasW1R1Hr2sub50WMI2bDjCunWHGTasNUOGNGfr1gk0aFDV6bCUAjRJKJAxJlNEJgDzsD5v/MQYs0FEngZWG2NmA/cBH4rIPViPIkYbY/RxgqpQVuw8xvNzN/GPy1cJDWpU5pYeUXSNqkb9apXw8dZn4iUpOTmDZ55Zwssv/0nt2kEMGdIcf38fTRBUuaJJQiHsOg/m5uo30eXvjcAFZR2XUsWRnpnNCz9tZvnOY2w8eKqp5bAQf/o0C+Oefo2pFazf4JeWuXO3cfvtc9m9O47Ro9vx0kv9tDplVS7pXqmUm8vONqzdF0d8SjppGdk8PWcjB11q6hvSvi6+3l5c0ymcTpHVHIzUM2zdeozLL/+KZs1qsHjxDfTsGel0SErlS5MEpdxQWmYWHyzZyeGEVP67fG+e49x/SRNuuSgKfx/9XLG0ZWZms2TJbvr2jaJJk+rMnTucPn0a4Oen216Vb5okKFXBnUjNIDYpnb3Hk1m/P549Mcl8vfpU9R51qwRSPciPJwe1oJKfD0H+PoRXDdQvE8rIqlX7GTv2R/766yDr14+jVata9O/fyOmwlCoSTRKUqkDSM7OJTU7nh38OsGjTEQ7Gp7D7WPIZ44WF+DO0Qzj3X9IUL63p0A9ImeAAACAASURBVBHx8ak89tgvvPPOKmrXDuJ//7uali1rOh2WUmdFkwSlyjljDL9uOcK3a6KZu/7QacOqVPLl8jbn0al+VWqFBBAW4k/LOqFa46HDMjKy6NjxA3btimPChC48+2wfQkL8nQ5LqbOmSYJS5dieY0kMfvsP4pKtOgv8vL0YcX592tWrQu+mNbWWw3ImOvoEdesG4+vrzaRJvWjatAadOtVxOiylzpkmCUqVM7tikthwIJ55Gw7zwz9WBZ/9WoTxwpDWVA/Sq9HyKC0tk5deWsZzz/3OZ58N5j//acXw4W2cDkupYvOYJEFE/IB6xpjtTseilKvtRxLZezyJr1bsZeGmM2vzvqx1bd4Z3tGByFRRLF68m3HjfmTz5hiuuaYFPXrUdzokpUqMRyQJIjIQeBXwAxqISDvgSWPMVc5GpjzZv/vjueGTlRxLSj+tf7eo6oy5sAFtIkK1QqNy7sEHF/DSS8to0KAKc+dez4ABjZ0OSakS5RFJAvA0VhPPvwIYY/4WEf0GSZWptMwsrnjrDw7EpZCQlnmyf90qgTx7ZSsiqlWiQY3KeOvXCOVadrYhO9vg4+NF1651efTRC3nssYu0MSblljwlScgwxsTl+i5c21dQpSolPYsdRxNZsyeWb9dEs37/qXYRbr0oCh8voW/zWnSsr7UcVhTr1x9m7NgfueKKJjz00IUMHdqCoUNbOB2WUqXGU5KETSJyLeBlt+h4F7Dc4ZiUm0rLzGL4hytYvSf2tP7NzwthXK+GDGpznlZkVMEkJaXz9NNLePXV5YSG+hMe3snpkJQqE56SJEwAJgLZwAysVh0fcTQi5VZik9KZs+4Ac9YdZMWu4yf7339JEzrUq0rbiCpU1gZ8KqTFi3czevRM9uyJZ8yY9rz44sVUr17J6bCUKhOeUmpdaox5CHgop4eIDMFKGJQ6JxlZ2Xy9ah8LNx1m8ZajJ/sHB/gwrEs9HhnQTO8YuIHAQB9CQvz5/fcbufDCek6Ho1SZEmPc/9G8iPxljOmQq98aY4wj35V16tTJrF692olFq2IyxjD7nwN8vWofy3YcO9k/yN+HaztF8NCAptpgUgWXmZnNW2+tYP/+BF5++RLAellRq7d2nl1u67OeMuTWdxJE5FKgP1BXRF51GRSC9ehBqUJlZmXz0vwt7DqaxPyNh0/29/fxYlS3+tx/qSYG7mLFimhuu20O//xzmEGDmpCZmY2Pj5cmCMpjuXWSABwB/gVSgQ0u/ROAhx2JSFUIWdmGr1bu5X+r9p32VcIFjarTLao6w7rU09oP3UhcXCqPPLKQ999fQ506wXz33bVcdZU+LlLKrZMEY8xaYK2ITDXGpDodjyrfjDGsi47nt61HeWXB1pP9O9avSss6ITx6WXNtOMlNxcenMnXqeu66qytPP92b4GBNAJUCN08SXNQVkeeAFsDJKuyMMU2cC0mVB8YYDp1IZfrKfXy5fA/HXWo/PD+qGp/f1EUfJbiprVuP8eWX//D0072pX78Ku3ffTbVqgU6HpVS54ilJwmfAs8DLwADgRvSdBI/2195Y3lq0jV9dvkoAaFY7mJeubkuLOiFa86GbSk3N5MUXl/L880sJCPDhxhvbExVVVRMEpfLgKUlCJWPMPBF52RizA3hcRH53OihV9uasO8BTszcSk5gGQICvF/1b1qZP8zD6NQ8j0E/vGrizRYt2Mn78XLZuPcawYa149dVLqV07yOmwlCq3PCVJSBPrDaQdIjIW2A/UcjgmVYZmrt3Px0t3nXwJsWlYMG8P70CjWnqC8BSpqZmMHPk9lSv7MX/+CPr1a+h0SEqVe56SJNwDBAF3As8BocBNjkakysS/++MZ8fEK4pIzALi0ZRgvX9OW4ABtjMcTZGcbpk1bzzXXtCQgwId580bQuHF1AgI8pehTqng84kgxxqyw/0wARgKISLhzEanStismiZfnb+HHdQcB6NG4Bi8ObUOdKvrc2VP8888hxo79keXLo8nONowc2ZbWrcOcDkupCsXtkwQR6QzUBZYaY2JEpCVW9cx9AE0U3Mz3a6P5cd0hFm46VenRi0Nb85/OWp2up0hMTOeppxbz+uvLqVYtkC++uJIRI9o4HZZSFZJbJwki8n/AUOAfrJcVv8dqAfJFYKyTsamSdfhEKjd/vvrkOwc9Gtfgpgsa0KNxDXy8vRyOTpWlYcO+Y86crdxySwdeeOFi/WpBqWJw67YbRGQj0NEYkyIi1YADQFtjzBYn49K2G0pORlY2/12+h0k/bASgXrVKfDO2G2EhAYVMqdzJ3r3xBAf7UbVqIH/9dZDU1Ey6d49wOixVwrTthrLn1ncSgFRjTAqAMea4iGx2OkFQJWfNnuMMfffPk929m9bk0xu7OBiRKmsZGVm88cYKnnxyMTfd1I633rqMDh3OczospdyGuycJUSKS0xy0AJEu3RhjhjgTliquV+dv4c1ftgPQoV4V3h7egfNC9bayJ1m2bB9jx85h/fojDBrUhPvv7+50SEq5HXdPEobm6p7iSBSqRP3fT5t4f8lOAN64rh2D29V1OCJV1t59dxXjx88lIiKEmTP/w+DBzZwOSSm35NZJgjFmkdMxqJITm5RO+2cWnOxeeG9PrQzJgxhjSEhIJyTEnwEDGvPAA92ZOLEnQUF+ToemlNty6yRBuY/MrGx6TP4VsB4vfHZTF0K0QiSPsWVLDOPG/Yi/vw9z515PZGQVJk/u53RYSrk9/TasECLSX0S2iMh2EXk4n3GuFZGNIrJBRL4q6xjd3fKdx+j50mIS0zIBmDH+Ak0QPERKSgYTJ/5KmzbvsXbtIa68sqnTISnlUTzqToKI+Btj0s5ifG/gbaAfEA2sEpHZxpiNLuM0Bh4BLjDGxIqItglRgr5asZdHv18PQJcG1Zh6c1eHI1Jl5d9/j3DlldPZsSOW4cNb88orlxAWpo+XlCpLHpEkiEgX4GOsNhvqiUhb4GZjzB2FTNoF2G6M2WnPZzowGNjoMs4twNvGmFgAY8yRko7fU73963Zemmd9sfrVLV3p3rCGwxGpsmCMQUSIiAghPDyE99+/nL59o5wOSymP5CmPG94ELgeOARhj/gF6F2G6usA+l+5ou5+rJkATEflDRJaLSP8SiNfjLd5y5GSCsPSh3pogeICsrGzeeWcVF130GRkZWYSGBrB48WhNEJRykKckCV7GmD25+mUVYTrJo1/uKip9gMZAL2AY8JGIVDljRiK3ishqEVl99OjRIizac01fuZfRn64CYMr17QmvWsnhiFRpW7v2IN27f8Ltt8/F39+buLhUp0NSSuEhjxuAffYjB2O/Z3AHsLUI00UDrnW7hmNV7Zx7nOXGmAxgl4hswUoaVrmOZIz5APgArGqZz2ktPEDPl35lz7FkAP53Wze6NKjmcESqNKWkZPDoo4t4882V1KhRialThzBsWCtE8srPlVJlzVOShHFYjxzqAYeBhXa/wqwCGotIA2A/cB1wfa5xZmLdQfhMRGpgPX7YWUJxe4x5Gw7xyIz1HE9KB2DdU5foFwwewNfXmyVL9nDrrR14/vm+VK2qtWYqVZ54SpKQaYy57mwnMsZkisgEYB7gDXxijNkgIk8Dq40xs+1hl9iNSWUBDxhjjpVk8O7u1QVbeXPRNgD6NKvFByM7asuNbmz37jgmTvyVN97oT9WqgSxbNoaAAE8pipSqWNy6FcgcIrID2AJ8DcwwxiQ4GY+2AnnK+KlrmLv+EADfjO1G50h9vOCuMjKyePXVP5k0aQleXsKsWdfpS4nqrGgrkGXPI9J3Y0xDEemO9bhgkoj8DUw3xkx3ODSP9uFvO08mCCsf7Ustbd7ZbS1dupexY+ewYcNRrrqqGW+80Z+IiFCnw1JKFcJj7ukaY5YZY+4EOgAngKkOh+Sx0jOzefT79Tw3dxMAi+/vpQmCm3vxxT9ISEhn9uzrmDHjP5ogKFVBeMSdBBEJwqoE6TqgOTAL0HZlHfDzvwe5c9rfpGdlA/DDhAuJrFHZ4ahUSTPG8Pnn/3DRRfWJiqrKRx8NIijIj8qVtTEmpSoSj0gSgH+BH4DJxpjfnQ7G02RkZTNx1gZ+XHeAE6lW+wu3XhTF7b0aEVpJv2BwN5s2HWXs2B/57bc9PPBAdyZP7qfVKStVQXlKkhBljMl2OghPZIxh6LvLWBcdD8CQ9nW595ImWkGSG0pOzuC5537jpZeWERTkx4cfDuKmm9o7HZZSqhjcOkkQkVeMMfcB34nIGZ9xGGOGOBCWx9h44AR3TV/LtiOJAOx8/jK8vLSSHHf1wgtLef75pYwa1ZaXXupHrVr6GEmpis6tkwSsTx4BpjgahQe67oM/Wb7zOABNwoL4fvwFmiC4oQMHEjh+PIVWrWpx333d6NOnAb16RTodllKqhLh1kmCMWWn/2dwYc1qiYFeStKjso3JvccnpdH/hF5LTraYxvh/fnfb1qjoclSppOY0xPfbYLzRrVoMVK24mNDRAEwSl3IynfAJ5Ux79xpR5FB7g4e/Wk5yexXmhAex4/jJNENzQmjUH6Nr1I+6882e6dYvgq6+GalsLSrkpt76TICL/wfrssYGIzHAZFAzEOROVe8rONkyc/S8/bzhEo1pBLLjnIj1xuKFfftlFv35fUqtWZaZPH8q117bU31kpN+bWSQKwEjiG1Xrj2y79E4C1jkTkhlIzsuj83EIS7M8bPx3dWU8cbsQYw/79CYSHh9CjRz0mTerFhAldqFJFK8BSyt15RNsN5Y07td2QmJZJ31cWc/hEGpHVK/Hz3RcR4OvtdFiqhOzcGcuECXP566+DbN48QRMD5Shtu6HsufWdBBFZYozpKSKxgGs2JIAxxmhrQsWQlW1o9eQ8ANqEhzLr9gv0DoKbSE/P4uWXl/HMM7/h4+PFs8/2JihIa0tUytO4dZIA9Lb/r+FoFG5ozZ5Yhr67DIBLWoTx/siOmiC4iWPHkunR41M2bYph6NDmvPFGf+rWDXE6LKWUA9z66waXWhYjAG9jTBbQDbgN0JpeztGaPcdPJgh9m9XSBMFNZGRYn61WqxZIz571mTNnGN9+e60mCEp5MLdOElzMBIyINAS+wGrk6StnQ6qYktIyGfrunwB8OKoTH+tLihVedrbhk0/WEhX1Jjt3xiIivPvu5Qwc2MTp0JRSDvOUJCHbGJMBDAFeN8bcAdR1OKYKacTHKwAY1a0+/VqEORyNKq4NG47Qs+dnjBkzm8jIKmRlaRMnSqlT3P2dhByZInINMBK40u6nzQ+epXkbDrF2bxwR1QKZdEVLp8NRxWCM4fHHf2Hy5GWEhvrzySdXcMMN7bTqbKXUaTwlSbgJGI/VVPROEWkATHM4pgpj59FERn68kv1xKQC8OLSNPmKo4ESE+Pg0Roxow0sv9aNGDW2VUyl1Jo+pJ0FEfIBGdud2Y0ymU7FUlHoSsrIN/12+hydnbwCgdd1QHuzflB6NazocmToX0dEnuOeeedx9d1cuuKAe2dlG7xyoCkXrSSh7HnEnQUR6AF8C+7HqSKgtIiONMX84G1n5Nu6/a5i/8TAAjw9szs09ohyOSJ2LzMxspkxZyRNP/EpmZjaXXdaICy6opwmCUqpQHpEkAK8BlxljNgKISHOspEEz0nxsP5JwMkHY/twAfLw95R1X97Jq1X5uu20Oa9ceYsCARkyZchlRUdrollKqaDwlSfDLSRAAjDGbRESrj3NxIC6FT//YxfYjiRxJSGPDgRMAfH5TF00QKrDFi3dz+HAS33xzDUOHNtd3SZRSZ8Uj3kkQkc+ANKy7BwDDgUrGmBuciKc8vJOQnpnN6j3HWbTpCDPX7udYUvrJYSEBPnSKrEanyKqM79WogLmo8sYYw9dfbyAw0IfBg5uRkZFFSkomISH+ToemVLHpOwllz1PuJIwF7gQexHon4TfgLUcjctCiTYcZ8/npSUpUzco8eGlTLm1ZW682K6jt249z++1zmT9/BwMHNmbw4Gb4+nrjqw1uKaXOkdsnCSLSGmgIfG+Mmex0PE5bui3mZIIw8vz6DD+/Hs1qa7W7FVlaWiaTJ//Bc8/9jp+fN2+9NYBx4/RiSylVfG6dJIjIo8AY4C+gs4g8bYz5xOGwHPPx0l08M8d6NeP+S5owoU9jhyNSJWHBgp1MnLiYa69tyWuvXUqdOsFOh6SUchNunSRgvXvQxhiTJCI1gbmARyYJKelZJxOE+fdcRJMwPZFUZEeOJLFq1X4GDmzCwIGNWbHiZrp00ZrGlVIly91fW08zxiQBGGOO4v7rmydjDM0n/gzAmAsbaIJQgWVnGz78cA3Nmk1h+PAZJCSkISKaICilSoW730mIEpEZ9t8CNHTpxhgzxJmwyk5KehY9Jv8KQLPawTw+sLnDEalztX79YcaO/ZFly/bRs2d93n13IMHB+tWCUqr0uHuSMDRX9xRHonDQsA+XE5OYBsDM2y/QLxcqqAMHEujU6UOCg/347LPBjBrVVn9LpVSpc+skwRizyOkYnPTR7zv5e18cdUIDWPpQH62GtwJat+4wbdqEUadOMJ9+OphLL21I9eraGJNSqmx45DN6d2eM4c1F23j2x00A/HT3RZogVDD79sVz1VVf07bte6xatR+A669vrQmCUqpMaZJQCBHpLyJbRGS7iDxcwHhXi4gREcc/UO/98mJeXbAVgC9u6kJooK/DEamiyszM5tVX/6R587eZN287L754Me3a1XY6LKWUh3Lrxw25iYi/MSbtLMb3Bt4G+gHRwCoRme3aDoQ9XjBWjY4rSjLec7Fy13F2H0sGYOPTl1LJz6N+4grNGEOvXp/xxx/7GDiwMVOmXEZkZBWnw1JKeTCPuJMgIl1EZD2wze5uKyJFqZa5C7DdGLPTGJMOTAcG5zHeM8BkILWkYj4XS7fFcO37f+Il8Mt9PTVBqCBOnEjDGIOIMHp0O7777lp++GGYJghKKcd5RJIAvAlcDhwDMMb8A/QuwnR1gX0u3dF2v5NEpD0QYYyZU9CMRORWEVktIquPHj16NrEXydz1BxnxsXUjY9btFxJVM6jEl6FKljGGr75aT+PGb/G//20A4OabOzBkiLbWqJQqHzwlSfAyxuzJ1S+rCNPlVVKfbDZTRLyA14D7CpuRMeYDY0wnY0ynmjVrFmHRRZedbXhkxnoA3hvRkdbhoSU6f1Xytm49Rr9+XzJ8+AwiI6vQtGkNp0NSSqkzeMr96H0i0gUw9nsGdwBbizBdNBDh0h0OHHDpDgZaAYvtK7/awGwRucIYU2ZtQU+et4X4lAwm9G5E/1b6klt599ZbK7j//gUEBvrwzjuXceutHfH29pR8XSlVkXhKkjAO65FDPeAwsNDuV5hVQGMRaQDsB64Drs8ZaIyJB05eAorIYuD+skwQfvjnAO8t2QHAHX0bldVi1TnIee+gdu0ghgxpzmuvXUrt2vpYSClVfnlEkmCMOYJ1gj/b6TJFZAIwD/AGPjHGbBCRp4HVxpjZJRzqWXvUfszw5yN98PfxdjgalZfDhxO59975tG5di4cfvpBrrmnJNde0dDospZQqlEckCSLyIS7vEuQwxtxa2LTGmLlYrUe69puYz7i9zjHEc/LdmmgS0jKJqBbIeaGBZbloVQTZ2YYPPljDww8vJDk5g5YtS/ZdFKWUKm0ekSRgPV7IEQBcxelfLVRIL8/fAsBXN5/vcCQqtw0bjjBmzGxWrNhP796RvPPOQJo105cTlVIVi0ckCcaYr127ReRLYIFD4ZSIz5ft5mB8KqO7RxJRTavqLW8SE9PZsyeeL7+8iuHDW+snjUqpCskjkoQ8NADqOx3EuYpPyeDJ2dZ39Xf00ZcVy4uZMzezdu1BJk3qTdeu4ezadRcBAZ56iCml3IFHfHclIrEictz+F4d1F+FRp+M6VxNn/QvAhN6NqB7k73A0as+eOK64YhpXXfU1s2ZtITU1E0ATBKVUhef2pZhY93nbYn3CCJBtjDnjJcaKIjMrm1l/W1U13H9pU4ej8WwZGVm89tpyJk1aAsBLL/Xjrru64uurX5kopdyD2ycJxhgjIt8bYzo6HUtJeP+3nQAM6VC3kDFVaTt4MJFJk5Zw8cVRvPXWAOrV05oulVLuxSMeNwArRaSD00EU13+X7+GledYXDc9d2drhaDzT8eMpvPHGcowx1KsXyvr145g16zpNEJRSbsmt7ySIiI8xJhO4ELhFRHYASVhtMhhjTIVJHBZuPMzjM613Ed64rh2BfnpLuywZY/jvf9dx333zOX48hV69ImnbtjZRUVWdDk0ppUqNWycJwEqgA3Cl04EU15fLrfapPr6hE32bhzkcjWfZvDmGceN+ZPHi3Zx/fjgLFgykbVttI0Mp5f7cPUkQAGPMDqcDKa5dMUn4eXtpglDGMjOzGTBgKnFxqbz33kBuuaUjXl5a54FSyjO4e5JQU0TuzW+gMebVsgzmXKVmZHEwPoX29fTWdllZvHg33btH4OfnzVdfDSEqqiphYdoYk1LKs7j7i4veQBBWk855/asQlu2IISPLcEO3SKdDcXsHDyYwbNh39O79OR9+uAaAbt0iNEFQSnkkd7+TcNAY87TTQRTXzLVWvQgXNta6/0tLVlY277+/hkceWURaWiaTJvVizJgK816rUkqVCndPEtzi4XFKRhbeXkJooK/TobitW2/9gU8++ZuLL47inXcuo3Hj6k6HpJRSjnP3JKGv0wEUV3pmNgs2HqZXU21muKQlJKRhDISE+DN2bCf69o1i2LBW2hiTUkrZ3PqdBGPMcadjKK4DcSkANKsd4nAk7sMYw3ffbaR587d58EGrMdDOnety/fXaWqNSSrly6yTBHUxbuReAfi1qORyJe9i1K5bLL5/G1Vd/Q82albnxxnZOh6SUUuWWuz9uqNCysw3frImmfvVKdKxfzelwKrwZMzYxYsQMvLyEV1+9hDvu6IqPj+bJSimVH00SyrEdRxM5npTO7b0bOR1KhZaRkYWvrzcdO57H4MHNmDz5YiIitK0FpZQqjF5GlWPbjyQC0C6iisORVEwxMcmMGTOLwYOnY4yhfv0qTJs2VBMEpZQqIk0SyrENB04AEBbi73AkFYsxhs8++5tmzabwxRfraN26FllZxumwlFKqwtHHDeXY79uO4ust1K0S6HQoFca+ffGMGPE9v/22h+7dI3jvvYG0bq3tXSil1LnQJKGcMsbwT3Q8reqG6Gd5ZyEkxJ9jx5L58MNB3HRTe22MSSmlikEfN5RTcckZgL6PUBQ//7ydwYOnk5GRRWhoAOvWjePmmztogqCUUsWkSUI5td+uRKmpVqKUrwMHErj22m8YMGAqW7bEsH9/AoAmB0opVUL0cUM5tfmQdcJrWUeThNyysrJ5551VPPbYL6SnZ/HMM7154IHu+Pvr7qyUUiVJS9VyyBjDM3M2AtAkrMK0aF1mjIGPP15Lt24RvP32ZTRqpBVNKaVUadDHDeXQ5kMJxKdk0CWyGkF6dQzAiRNpPPLIQmJjU/Dx8WLRolH8/PNwTRCUUqoUaZJQDs36+wAAd/dr7HAkzjPG8M03G2jWbAovvvgH8+fvAKB69Ur61YdSSpUyvUwthzYfsipR6hzp2VfJO3fGcvvtc/n55+20b1+bWbOuo3Pnuk6HpZRSHkOThHLo8Ik0qlTyxdfbs2/0PPDAApYu3cvrr1/K7bd30caYlFKqjGmSUM6kZ2az6eAJOtav6nQojliyZDcREaFERVXljTf6IwJ16+oXHkop5QS9NCuEiPQXkS0isl1EHs5j+L0islFE1onIIhGpX5zlfb82GoAW53nWiTEmJpnRo2fSq9fnPPvsbwCEh4dogqCUUg7SJKEAIuINvA0MAFoAw0SkRa7R1gKdjDFtgG+BycVZZmJaFgB3XewZLy1mZxs++WQtTZtOYerU9TzyyIVMmXKZ02EppZRCk4TCdAG2G2N2GmPSgenAYNcRjDG/GmOS7c7lQHhxFjjv30MAhAb6Fmc2Fcbrry9nzJjZtGxZk7//vo3nn+9LpUqese5KKVXe6TsJBasL7HPpjga6FjD+GOCnvAaIyK3ArQD16tXLdwabDp3Az8fLrV9aTE7O4ODBBBo2rMaYMe2pWbMSI0a00U8alVKqnHHfM1HJyOusZfIcUWQE0Al4Ka/hxpgPjDGdjDGdatasmefCUtKzSEjN5IKG1c813nLvxx+30rLlO1x11ddkZxtCQwMYObKtJghKKVUOaZJQsGggwqU7HDiQeyQRuRh4DLjCGJN2rgvbdsRqr6F9Pff7siE6+gRDh/6Pyy+fRmCgD1OmXKYNMSmlVDmnjxsKtgpoLCINgP3AdcD1riOISHvgfaC/MeZIcRb247qDAG73+eNffx2kZ8/PyMzM5vnn+3Dffd3x8/N2OiyllFKF0CShAMaYTBGZAMwDvIFPjDEbRORpYLUxZjbW44Ug4Bv7lvleY8wV57I8f7uyoC4N3KOmxRMn0ggJ8adNmzBuvLEdd999PlFR7pUAKaWUO9MkoRDGmLnA3Fz9Jrr8fXFJLWvv8WRqBPlV+JcW4+JSeeyxRXz//WY2brydKlUCePPNAU6HpZRS6ixpklCObD6UQFhIgNNhnDNjDF9/vYF77pnHkSNJ3HFHF7y99b0DpZSqqDRJKEcOn0ilaiU/p8M4J0lJ6QwZ8j/mz99Bp051mDNnGB071nE6LKWUUsWgSUI5cSwxjdjkDLo3rOF0KGfFGIOIUKmSLzVqVOKttwYwblwnvCv4IxOllFL6CWS5sWzHMQAGtT3P4UiK7tdfd9Gx4wfs3BmLiDB16hAmTOiiCYJSSrkJLc3Lid0xSQB0qAB1JBw5ksSoUd/Tp88XxMencfRoktMhKaWUKgX6uKGcmLF2P5X9vKkZ7O90KAX6+OO/eOCBBSQmpvP44z149NEeBHpIOxNKKeVpNEkoB1IzstgVk8T5UdXKffXEa9ceok2bMN59dyDNm+ddvbRSlD/wlgAAFQRJREFUSin3oElCORCXnAHA5W3K39cASUnpTJq0hCuvbEb37hG88sol+Pl5l/tkRimlVPFpklAOJKRaSUJl//JVVfEPP2xhwoSf2Ls3nipVAujePQJ/f91llFLKU2iJXw78sT0GgEDf8pEk7NsXz513/szMmZtp1aoWS5feyAUX5N+8tVJKKfekSUI58Ns2K0no16K2w5FYvvlmI/PmbefFFy/mnnvOx7ecJC9KKaXKliYJ5YCXQJC/D94ONp28fHk0cXGp9O/fiDvu6MLQoc2pX7+KY/EopZRyntaTUA5Ex6bQLsKZE3JsbApjx86he/ePeeKJXzHG4OvrrQmCUkopvZNQHqRkZFE9qGzbbDDG8NVX67n33vnExCRz993nM2lSL/1qQeUpIyOD6OhoUlNTnQ5FeYCAgADCw8Px9dU6WJymSUI5EJ+Sgb9P2d7UWbx4NyNGfE+XLnX5+efhtG9fcaqDVmUvOjqa4OBgIiMjNZFUpcoYw7Fjx4iOjqZBgwZOh+Px9HGDwzKzsolLziAru/SXlZqaye+/7wGgV69IZs26jmXLbtIEQRUqNTWV6tWra4KgSp2IUL16db1rVU5okuCwwwlpAERUCyzV5SxcuJM2bd7l0kv/y5EjSYgIV1zRVBtjUkWmCYIqK7qvlR96hnDYwbgUAFrVCS2V+R86lMjw4TPo1+9LjIFZs66jVq3KpbIspZRS7kWTBIct2HQYgCqVSv4FndjYFFq2fIdvv93IxIkXsX79OPr1a1jiy1GqLHh7e9OuXTtatWrFoEGDiIuLOzlsw4YN9OnThyZNmtC4cWOeeeYZjPn/9u49OqrqXuD49wcoSSQiD2FR4RpMKCQEiCYgAbyCiBR5XSk1UB+AWAWF2yKwxKWrVLGFYqleREppq7RSCILlIS2rRYgv5NFAQoiJYAoRUAoIAUITRMLv/nEOk0mYJBOSmYnh91lr1ppzzj7n/GZnMvObs/fZWz3bN2zYQFJSErGxsXTq1Ilp06aF4iVUKiMjg0cffbTMuuHDh5OcnFxm3dixY1m1alWZdU2aNPE837dvH/feey8xMTHExsZy//33c/To0RrFdvLkSQYMGECHDh0YMGAABQUFl5VJS0sjISHB8wgLC2PNmjVlykyePLlMrAsWLOCNN96oUWwmsCxJCLHt+08C0LVt7d1y+MUXZwBo1iycWbP6kZU1geef70dYmPVTNd9e4eHhZGZmkp2dTfPmzXnttdcAKC4uZtiwYcyYMYN9+/axe/duPv74YxYuXAhAdnY2kyZNYunSpeTm5pKdnc0tt9xSq7FduHChxsf4xS9+weTJkz3Lp06dYteuXZw6dYoDBw74dYxz584xePBgJk6cSF5eHrm5uUycOJHjx4/XKLY5c+bQv39/PvvsM/r378+cOXMuK9OvXz8yMzPJzMxk8+bNREREcM8993i2p6enl0nsAB555BHmz59fo9hMYNm3RoidKjpPx9aRXFsLdzecPXuen/40jVdf3cH774+lV692PPFE91qI0phSz7/zCTlfnqnVY8Z953pmDu3sd/nk5GSysrIAWLZsGb179/Z8IUVERLBgwQL69u3Lk08+ydy5c3n22Wfp1KkTAI0aNeKJJ5647Jhnz55l8uTJpKenIyLMnDmT73//+zRp0oSzZ88CsGrVKtavX8+SJUsYO3YszZs3JyMjg4SEBFavXk1mZiY33OAk/DExMWzZsoUGDRowYcIEDh48CMArr7xC7969y5y7sLCQrKwsunXr5ln39ttvM3ToUFq3bk1qairPPPNMlfWybNkykpOTGTp0qGddv379/K7Xiqxdu5b33nsPgDFjxtC3b19++ctfVlh+1apVDBo0iIiICABKSkqYPn06y5YtY/Xq1Z5yERERREVFsWPHDnr06FHjOE3tsyQhhM5+fYH8E0WkJLWr0XFUlbVr9zJ58gYOHz7D448nEhvbspaiNKZuKSkpYdOmTYwfPx5wmhoSExPLlImOjubs2bOcOXOG7Oxspk6dWuVxZ82aRdOmTdmzZw+Az0vq5e3bt493332Xhg0bcvHiRVavXs24cePYvn07UVFRtG7dmh/+8IdMmTKFPn36cPDgQQYOHEhubm6Z46SnpxMfH19m3fLly5k5cyatW7dm5MiRfiUJ2dnZl9WFL4WFhdxxxx0+ty1btoy4uLgy644ePUqbNs5dUG3atOHYsWOVHj81NZWnnnrKs7xgwQKGDRvmOYa3pKQkPvzwQ0sS6ihLEkJoY86/AbipWc3ubHjggb+wfHk2Xbq04q23RpKcXLOkw5jKVOcXf20qLi4mISGB/Px8EhMTGTBgAOAkyRX1hq9OL/l3332X1NRUz3KzZs2q3OcHP/gBDRs6c5ukpKTwwgsvMG7cOFJTU0lJSfEcNycnx7PPmTNnKCwsJDIy0rPuyJEj3HjjjZ7lo0ePkpeXR58+fRARGjVqRHZ2NvHx8T5fU3XvBoiMjCQzM7Na+/jryJEj7Nmzh4EDBwLw5ZdfsnLlSs+ViPJatWrFp59+GpBYTM1Zn4QQen+v0074cPLN1d73woWLno5ZvXq141e/GsDOnY9ZgmDqrUt9Ej7//HPOnz/v6ZPQuXNn0tPTy5Tdv38/TZo0ITIyks6dO7Nz584qj19RsuG9rvy9+9ddV3qnUHJyMnl5eRw/fpw1a9YwYsQIAC5evMjWrVs97fVffPFFmQTh0mvzPvaKFSsoKCigffv2REVFkZ+f70lgWrRoUeYqx8mTJ2nZsqWnLvx5rYWFhWU6GXo/vBOaS1q3bs2RI0cAJwlo1apVhcd+6623uO+++zyjJWZkZJCXl0dMTAxRUVEUFRURExPjKX/u3DnCwwN7C7ipAVW1R5AfiYmJevHiRb356fUaNWO9VtdHH32u8fELdfnyPdXe15grkZOTE+oQ9LrrrvM837Vrl7Zr107Pnz+vRUVF2r59e924caOqqhYVFengwYN1/vz5qqq6e/dujY6O1r1796qqaklJic6bN++y4z/99NP64x//2LN88uRJVVWNjo7WnJwcLSkp0REjRuiYMWNUVXXMmDG6cuXKMseYNm2aPvjggzpo0CDPutGjR+vcuXM9yxkZGZedOzc3V3v37u1Z7tmzp3788cee5f3792t0dLSqqr7zzjvav39//frrr1VVdd68eTpu3DjPa4+Ojtb160s/VzZs2KBZWVmXnbM6pk2bprNnz1ZV1dmzZ+v06dMrLHv77bfr5s2bK9zu/XdUVZ00aZIuX778snK+3nNAutaBz/Cr6WFXEkIk85DTy3dMcpTf+5w8WcyPfrSOPn3e4PTpc9xwQ1iAojOmbrv11lvp1q0bqamphIeHs3btWl588UU6duxIly5d6N69O5MmTQKga9euvPLKK4wePZrY2Fji4+M9v4q9PffccxQUFBAfH0+3bt1IS0sDnJ79Q4YM4a677vLZpu4tJSWFpUuXepoaAObPn096ejpdu3YlLi6ORYsWXbZfp06dOH36NIWFheTn53Pw4EF69uzp2d6+fXuuv/56tm/fzpAhQ7jjjjtITEwkISGBLVu2eDoRhoeHs379el599VU6dOhAXFwcS5YsqfSXvz9mzJjBxo0b6dChAxs3bmTGjBmA05fC+7bN/Px8Dh06xJ133un3sbds2cLdd99do/hM4IiTnJlgSkpK0lE//zML0vJ4Z1IfurSteiClt9/OYcKEv1JQUMyUKT2ZObMvTYI8KZS5euXm5hIbGxvqMOq1l19+mcjIyMvGSqjPMjIy+PWvf82bb7552TZf7zkR2amqScGKz1ifhJD5w0cHuDGysV8JwiUxMc3ZufMxXnrpHksQjKlnJk6cSOPGjUMdRlB99dVXzJo1K9RhmErY3Q0hFHFtwwq3FRd/w+zZH9G0aWOmTu3FiBGx3HdfLA0a2JjmxtRHYWFhPPTQQ6EOI6gu3aFi6i67khAixd+UMKSr7/bNf/zjX3Tp8htmzfqAvXtPAE4Pa0sQTChZ06QJFnuv1R2WJITApfd/xLVlL+QcOVLIqFGrGDhwKQ0bNmDTpodZvHiojyMYE1xhYWGcOHHCPrxNwKkqJ06cICzMOmbXBdbcEAJF551x3huWuzJw6NAZ1q3by/PP9+Xpp3vTuLH9eUzd0LZtWw4fPlzjOQCM8UdYWBht27YNdRgGSxJCouh8CQJ0j2rOrl1HSEs7wNSpvejR4yYOHZpCixYRoQ7RmDKuueYa2rdvH+owjDFBZs0NVRCR74nIXhHJE5EZPrY3FpEV7vbtIhJV1TFPF38DwBsvb6N7998xb95WTp92RluzBMEYY0xdYUlCJUSkIfAaMAiIA0aLSFy5YuOBAlWNAV4GKp4azVX8TQkAC+fv4PHHE8nJeZKmTa39zRhjTN1izQ2V6wHkqep+ABFJBYYD3oObDwd+5j5fBSwQEdEqenhFbjvG1q3juf12a3czxhhTN1mSULmbgENey4eB2ysqo6oXROQ00AL4yruQiDwGPOYufp3970eyvUZdvZq1pFxdXcWsLkpZXZSyuijVMdQBXG0sSaicr4EJyl8h8KcMqroYWAwgIuk2tKjD6qKU1UUpq4tSVhelRCS96lKmNlmfhModBrznXm4LfFlRGRFpBDQFTgYlOmOMMSaALEmo3D+BDiLSXkSuBUYB68qVWQeMcZ+PBDZX1R/BGGOM+Taw5oZKuH0MJgF/BxoCr6vqJyLyAs685uuAPwBvikgezhWEUX4cenHAgv72sbooZXVRyuqilNVFKauLILOpoo0xxhjjkzU3GGOMMcYnSxKMMcYY45MlCQEUiCGdv638qIunRCRHRLJEZJOI3ByKOIOhqrrwKjdSRFRE6u3tb/7UhYjc7743PhGRZcGOMVj8+B/5LxFJE5EM9//k3lDEGWgi8rqIHBOR7Aq2i4jMd+spS0RuC3aMVxVVtUcAHjgdHf8F3AJcC+wG4sqVeQJY5D4fBawIddwhrIt+QIT7fOLVXBduuUjgA2AbkBTquEP4vugAZADN3OVWoY47hHWxGJjoPo8D8kMdd4Dq4r+B24DsCrbfC2zAGaOmJ7A91DHX54ddSQgcz5DOqnoeuDSks7fhwB/d56uA/iLia3Cmb7sq60JV01S1yF3chjMmRX3kz/sCYBYwFzgXzOCCzJ+6+BHwmqoWAKjqsSDHGCz+1IUC17vPm3L5mC31gqp+QOVjzQwH/qSObcANItImONFdfSxJCBxfQzrfVFEZVb0AXBrSub7xpy68jcf5pVAfVVkXInIr0E5V1wczsBDw533xXeC7IrJFRLaJyPeCFl1w+VMXPwMeFJHDwN+AycEJrc6p7ueJqQEbJyFwam1I53rA79cpIg8CScCdAY0odCqtCxFpgDOb6NhgBRRC/rwvGuE0OfTFubr0oYjEq+qpAMcWbP7UxWhgiarOE5FknPFZ4lX1YuDDq1Ouls/NOsGuJASODelcyp+6QETuBp4Fhqnq10GKLdiqqotIIB54T0Tycdpc19XTzov+/o+sVdVvVPUAsBcnaahv/KmL8cBbAKq6FQjDmfzpauPX54mpHZYkBI4N6VyqyrpwL7H/FidBqK/tzlBFXajqaVVtqapRqhqF0z9jmKrWx4lt/PkfWYPTqRURaYnT/LA/qFEGhz91cRDoDyAisThJwvGgRlk3rAMedu9y6AmcVtUjoQ6qvrLmhgDRwA3p/K3jZ128BDQBVrp9Nw+q6rCQBR0gftbFVcHPuvg7cI+I5AAlwHRVPRG6qAPDz7qYCvxORKbgXF4fWx9/VIjIcpzmpZZu/4uZwDUAqroIpz/GvUAeUASMC02kVwcbltkYY4wxPllzgzHGGGN8siTBGGOMMT5ZkmCMMcYYnyxJMMYYY4xPliQYY4wxxidLEowJABEpEZFMr0dUJWWjKprxrprnfM+dRXC3O4xxxys4xgQRedh9PlZEvuO17fciElfLcf5TRBL82OcnIhJR03MbY6rHkgRjAqNYVRO8HvlBOu8DqtoNZ+Kwl6q7s6ouUtU/uYtjge94bXtUVXNqJcrSOBfiX5w/ASxJMCbILEkwJkjcKwYfisgu99HLR5nOIrLDvfqQJSId3PUPeq3/rYg0rOJ0HwAx7r79RSRDRPaIyOsi0thdP0dEctzz/Mpd9zMRmSYiI3Hm0Pize85w9wpAkohMFJG5XjGPFZFXrzDOrXhNziMivxGRdBH5RESed9f9L06ykiYiae66e0Rkq1uPK0WkSRXnMcZcAUsSjAmMcK+mhtXuumPAAFW9DUgB5vvYbwLwf6qagPMlfdgdgjcF6O2uLwEeqOL8Q4E9IhIGLAFSVLULziirE0WkOXAf0FlVuwIveu+sqquAdJxf/AmqWuy1eRUwwms5BVhxhXF+D2fo5UueVdUkoCtwp4h0VdX5OGPz91PVfu7wzM8Bd7t1mQ48VcV5jDFXwIZlNiYwit0vSm/XAAvcNvgSnHkIytsKPCsibYG/qOpnItIfSAT+6Q5ZHY6TcPjyZxEpBvJxphLuCBxQ1X3u9j8CTwILgHPA70Xkr4Df01Kr6nER2e+Om/+Ze44t7nGrE+d1OEMQ3+a1/n4ReQzns6kNEAdkldu3p7t+i3uea3HqzRhTyyxJMCZ4pgBHgW44V/HOlS+gqstEZDswGPi7iDyKMzXuH1X1GT/O8YD3ZFAi0sJXIXeugB44EwaNAiYBd1XjtawA7gc+BVarqorzje13nMBuYA7wGjBCRNoD04DuqlogIktwJjEqT4CNqjq6GvEaY66ANTcYEzxNgSOqehF4COdXdBkicguw373Evg7nsvsmYKSItHLLNBeRm/0856dAlIjEuMsPAe+7bfhNVfVvOJ0Cfd1hUIgzdbUvfwH+BxiNkzBQ3ThV9RucZoOeblPF9cB/gNMi0hoYVEEs24Del16TiESIiK+rMsaYGrIkwZjgWQiMEZFtOE0N//FRJgXIFpFMoBPwJ/eOgueAf4hIFrAR51J8lVT1HM4seStFZA9wEViE84W73j3e+zhXOcpbAiy61HGx3HELgBzgZlXd4a6rdpxuX4d5wDRV3Q1kAJ8Ar+M0YVyyGNggImmqehznzovl7nm24dSVMaaW2SyQxhhjjPHJriQYY4wxxidLEowxxhjjkyUJxhhjjPHJkgRjjDHG+GRJgjHGGGN8siTBGGOMMT5ZkmCMMcYYn/4fwWKZEnRc0PgAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4992,14 +5078,14 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the SVM reduced features and tuning linear kernal identified 696\n" + "Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638\n" ] }, { @@ -5034,14 +5120,14 @@ " \n", " \n", " \n", - " 0\n", - " 6405\n", - " 336\n", + " 0\n", + " 6492\n", + " 270\n", " \n", " \n", - " 1\n", - " 1312\n", - " 696\n", + " 1\n", + " 1349\n", + " 638\n", " \n", " \n", "\n", @@ -5050,11 +5136,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6405 336\n", - "1 1312 696" + "0 6492 270\n", + "1 1349 638" ] }, - "execution_count": 78, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -5066,7 +5152,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -5075,12 +5161,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6741\n", - " 1 0.67 0.35 0.46 2008\n", + " 0 0.83 0.96 0.89 6762\n", + " 1 0.70 0.32 0.44 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " macro avg 0.77 0.64 0.66 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", "\n" ] } @@ -5098,7 +5184,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5110,7 +5196,7 @@ " verbose=False)" ] }, - "execution_count": 80, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -5123,19 +5209,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15780782271279034\n" + "Optimal Threshold: 0.15910713557498266\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5153,7 +5239,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -5162,12 +5248,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.64 0.40 0.49 2008\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.38 0.48 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.67 0.69 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", "\n" ] } @@ -5180,14 +5266,88 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel." + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 82, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### SVM with filtered features" + "evaluation.loc[2] = ([\"SVM-RBF (Grid Search)\" , \n", + " classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" ] }, { @@ -5199,14 +5359,14 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "//anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", " \"avoid this warning.\", FutureWarning)\n" ] }, @@ -5219,7 +5379,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 86, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -5231,19 +5391,19 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15729612279514055\n" + "Optimal Threshold: 0.16104553371241384\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5252,16 +5412,26 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6689738476077944" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "auroc = get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + "get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5270,12 +5440,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.62 0.40 0.49 2008\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.73 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -5313,12 +5483,12 @@ "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", "\n", "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + "Here we create an instance of our model, with 5 layers of 26 neurons each, approximately 2/3 of the columns in our training data. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -5327,7 +5497,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": { "scrolled": true }, @@ -5338,16 +5508,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mlp.fit(X_train,y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -5356,59 +5543,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Neural Network (5x26) identified 704\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06449313
11283704
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6449 313\n", + "1 1283 704" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,predictions,\"Neural Network (5x26)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2145780241518794\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.69 0.35 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,predictions))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" + "### Model Tuning\n", + "We define the following baseline model using Keras using the optimiser, \"adam\" , which is the default. After experimenting with different optimizers, we found that \"adam\" did indeed perform the best." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4787 - accuracy: 0.7982\n", + "Epoch 2/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4465 - accuracy: 0.8112\n", + "Epoch 3/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4433 - accuracy: 0.8124\n", + "Epoch 4/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4420 - accuracy: 0.8143\n", + "Epoch 5/20\n", + "17496/17496 [==============================] - 2s 117us/step - loss: 0.4409 - accuracy: 0.8133\n", + "Epoch 6/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4396 - accuracy: 0.8136\n", + "Epoch 7/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4388 - accuracy: 0.8141\n", + "Epoch 8/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4381 - accuracy: 0.8138\n", + "Epoch 9/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4376 - accuracy: 0.8150\n", + "Epoch 10/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4372 - accuracy: 0.8156\n", + "Epoch 11/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4356 - accuracy: 0.8156\n", + "Epoch 12/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4357 - accuracy: 0.8137\n", + "Epoch 13/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4356 - accuracy: 0.8143\n", + "Epoch 14/20\n", + "17496/17496 [==============================] - 2s 124us/step - loss: 0.4355 - accuracy: 0.8140\n", + "Epoch 15/20\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4340 - accuracy: 0.8149\n", + "Epoch 16/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4341 - accuracy: 0.81510s - loss: 0.4311 \n", + "Epoch 17/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4335 - accuracy: 0.8163\n", + "Epoch 18/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4335 - accuracy: 0.8150\n", + "Epoch 19/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4322 - accuracy: 0.8160\n", + "Epoch 20/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4320 - accuracy: 0.8147\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from numpy import loadtxt\n", "from keras.models import Sequential\n", @@ -5416,7 +5752,7 @@ "\n", "# define the keras model\n", "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", @@ -5429,33 +5765,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25378615\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.39 0.49 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.69 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "# evaluate the keras model\n", "#recall, accuracy = model.evaluate(df1, target)\n", "#print('Accuracy: %.2f' % (accuracy*100))\n", "#print('Recall: %.2f' % (recall*100))\n", "\n", + "get_roc(model, y_test, X_test, \"Adam\")\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", - "print(classification_report(y_test,predictions))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(X_train, y_train, epochs=20, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", "print(classification_report(y_test,predictions))" ] }, @@ -5463,20 +5818,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam optimizer" + "Experimenting with lowering the cutoff for the neural network," ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25378615\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.79 0.83 6762\n", + " 1 0.46 0.63 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], "source": [ - "model.fit(X_train, y_train, epochs=10, batch_size=20)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", + "optimal_adam = get_optimal(model, y_train, X_train, \"Adam\")\n", + "get_roc(model, y_test, X_test, \"Adam\")\n", + "predictions = list(model.predict(X_test).ravel() > optimal_adam)\n", "print(classification_report(y_test,predictions))" ] }, @@ -5484,86 +5872,587 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam optimizer" + "The performance is quite impressive with a recall of 0.62 on defaults, when we lowered the classification cut off. The ROC of 0.77 is also on par with other models. However, with some experimentation, we found that the neural network performed the best with 3 hidden layers." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], - "source": [ + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 132us/step - loss: 0.4825 - accuracy: 0.7861\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4477 - accuracy: 0.8113\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 92us/step - loss: 0.4456 - accuracy: 0.8115\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4441 - accuracy: 0.8134\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4428 - accuracy: 0.8138\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 90us/step - loss: 0.4423 - accuracy: 0.8140\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4411 - accuracy: 0.8130\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4406 - accuracy: 0.8144\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4396 - accuracy: 0.8148\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 111us/step - loss: 0.4390 - accuracy: 0.8150\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2291201\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.79 0.83 6762\n", + " 1 0.47 0.62 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam3 = get_optimal(model, y_train, X_train, \"3-layer adam\")\n", + "predictions = list(model.predict(X_test).ravel() > optimal_adam3)\n", + "auroc = get_roc(model, y_test, X_test, \"3-layer adam\")\n", "\n", "print(classification_report(y_test,predictions))" ] }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 3 Layer Adam0.5321260.771859
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[3] = ([\"Neural Network - 3 Layer Adam\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adamax Optimizer" + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ - "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", "\n", - "print(classification_report(y_test,predictions))" + "gnb = GaussianNB(var_smoothing = 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GaussianNB(priors=None, var_smoothing=0.1)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.03496484204782862\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[11931 1511]\n", + " [ 2233 1821]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.89 0.86 13442\n", + " 1 0.55 0.45 0.49 4054\n", + "\n", + " accuracy 0.79 17496\n", + " macro avg 0.69 0.67 0.68 17496\n", + "weighted avg 0.77 0.79 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "#accessing model performance\n", + "print(metrics.confusion_matrix(expected, predicted))\n", + "print(classification_report(y_train,gnb.predict(X_train)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adadelta Optimizer" + "We will now try searching for the smoothing parameter to achieve a better ROC and recall on default." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.45112314307016005 3\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.87 0.86 13442\n", + " 1 0.54 0.51 0.52 4054\n", + "\n", + " accuracy 0.79 17496\n", + " macro avg 0.70 0.69 0.69 17496\n", + "weighted avg 0.78 0.79 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "best = 0\n", + "smooth = 0\n", + "for i in range(1,100):\n", + " gnb = GaussianNB(var_smoothing = i/100)\n", + " gnb.fit(X_train, y_train)\n", + " x = classification_report(y_train, gnb.predict(X_train), output_dict = True)[\"1\"][\"f1-score\"] \n", + " #arc = get_roc(gnb, y_train, X_train, \"NB\")\n", + " y = classification_report(y_train, gnb.predict(X_train), output_dict = True)[\"0\"][\"f1-score\"]\n", + " if x*y > best:\n", + " best = x*y\n", + " smooth = i\n", + "\n", + "print(best, smooth)\n", + " \n", + "gnb = GaussianNB(var_smoothing = smooth/100)\n", + "gnb.fit(X_train, y_train) \n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.019734239369418004\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gUVffA8e8hCSEhjRQ6IYTeWwBBQEQQQRBFQbEDyosgNuS1vjZUxK4/UOxYAQWVKgoKiijSew09EGpIISH9/v6YJWxiygLZbDY5n+fhYXfqmdnJnJk7d+4VYwxKKaVUQSq4OgCllFKlmyYKpZRShdJEoZRSqlCaKJRSShVKE4VSSqlCaaJQSilVKE0UJUxEbhORX1wdh6uJSLiInBERjxJcZ4SIGBHxLKl1OpOIbBWRHhcx30UdgyLiIyLzRCRBRL670Pmd4WL3gbow5TpRiMh+ETlrO2EdFZFpIuLnzHUaY742xlztzHWURrZ93evcd2PMQWOMnzEmy5VxuYotYTW4lGUYY5obY5YVsZ5/JcdLOAZvAqoBIcaYwRcxf97Yethim5Jn+J8icrcjy3BkH1xEXOf22Rnbv2Mi8p6IeBXnetxJuU4UNgOMMX5AG6At8ISL47korrxKLitX6BeinO7vusAuY0zmhc5YSMzJwJ0iEnEJcTlLkO3c0BLoDIxxcTwuo4nCxhhzFPgZK2EAICLeIvK6iBy0XVVMFREfu/EDRWSDiCSKyB4RucY2PFBEPhGRWBE5LCIvnitiEZG7ReRP2+epIvK6fRwiMkdEHrF9rikis0XkhIjsE5EH7KZ7TkRmichXIpII3J13m2xxfGGb/4CIPC0iFeziWCEi/2crStghIlflmbewbVghIm+JSBzwnIjUF5HfROSUiJwUka9FJMg2/ZdAODDPdoX237xXuiKyTEQm2JabJCK/iEioXTx32rbhlIj8L+8dSp7t9hGRN2zTJ9iuUH3sJrnN9pueFJGn7ObrKCJ/i0i8bbsni0hFu/FGRMaIyG5gt23YOyJyyHYMrBWRbnbTe4jIk7ZjI8k2vo6I/GGbZKNtf9xsm76/7XiKF5G/RKSV3bL2i8hjIrIJSBYRT/t9YIt9jS2OYyLypm3Wc+uKt62rs/0xaJu3uYgsFpE427xP5rNPnweeAW62LWeEiFSwHVMHROS47VgLtE1/7vcdISIHgd/y+62AeGAa8GwBv2WBx5Xdfukl1t/KWREJthvX1jaPl+37cBHZLiKnReRnEalbQEy5GGOOA4uBZnbLftzud90mIjfYhnvb9mNLu2mr2mILs30v7Hd+TKy/tyQR2Sl2f5MuZYwpt/+A/UAv2+fawGbgHbvxbwNzgWDAH5gHTLSN6wgkAL2xEm4toIlt3I/AB0BloCqwCviPbdzdwJ+2z92BQ4DYvlcBzgI1bctci/XHWRGIBPYCfWzTPgdkANfbpvXJZ/u+AObYYo8AdgEj7OLIBB4GvICbbdsT7OA2ZAJjAU/AB2hg2xfeQBjWCert/Pa17XsEYABP2/dlwB6gkW15y4BXbOOaAWeArrZ98bpt23sV8LtOsc1fC/AAutjiOrfOj2zraA2kAU1t87UHLrNtUwSwHXjIbrkG64QRfG5/A7cDIbZ5xgFHgUq2ceOxjqnGgNjWF2K3rAZ2y24HHAc62WK+y7bPvO323wagjt26c/Yp8Ddwh+2zH3BZfvs5n2PQH4i1xV7J9r1TAfv1OeAru+/DgWisY9MP+B74Ms96v8A6hvI7PnsAMUB1IBFobBv+J3C37bPDxxVWMrrXbtxrwFTb5+ttsTa1/VZPA38VsJ259hnW3+NGYLjdNIM5/3d6M9adUQ3buPeASXbTPgjMK+p3xjpODgE17eKo7+rzpDFGEwXWCSjJdmD8inW7CdYfdrL9D4V1+7nP9vkD4K18llkN6+TjYzdsKLDU9tn+j1SAg0B32/d7gd9snzsBB/Ms+wngM9vn54A/Ctk2D1sczeyG/QdYZhfHEWxJyjZsFXCHg9twsKB126a5HlifZ18XlSieths/Glhk+/wMMN1unC+QTj6JwvaHexZonc+4c+usnWebbylgGx4CfrD7boCeRWz36XPrBnYCAwuYLm+ieB+YkGeancAVdvtveJ7xOfsU6wT6PBBawDYXlCiG2v9ORWzbc+ROFL8Co+2+N8ZK4J52640sZHk9gBjb51eBmbbPOYniQo4r4B7O//0I1kn33N/WT9gukuyOkxSgbiHHSbztnwH+AgIK2ZYN535rrL/dQ0AF2/c1wJCifmespHgc6AV4OfKblNQ/LXqC640x/lgHbRPgXHFHGNYJaa3tFjEeWGQbDtaV3Z58llcX6wo91m6+D7CuynMx1lEyA+uPFeBW4Gu75dQ8twzbcp7EOomfc6iQ7QrFuvo+YDfsANZV9jmHbTHYj6/p4DbkWrft9nqG7bY5EfiK8/vSUUftPqdgXaViiylnfcaYFOBUAcsIxboyzu+3KXQ9ItJIROaLVbEhEXiZf29D3u0eZyvOSLDtp0C7eQo6RvJTFxiX5/eug7Xt+a47jxFYd2M7RGS1iPR3cL0XEmNeNfn38eWJ48eovUlAHxFpbT/wAo+rWUBnEamJdbdugOW2cXWBd+z2bRxWMqmV75IsocaYIKzzwAqsv/9zcd1pV3wUD7Q4F5cx5h+si8wrRKQJVgKYaxdHvr+zMSYa6+LkOeC4bbvtf3+X0URhY4z5Haus9Nwzg5NYV6bNjTFBtn+Bxnq4BdYfQP18FnUI62o81G6+AGNM8wJWPR24yVZe2gmYbbecfXbLCDLG+Btj+tmHXcgmncS6uqtrNywcOGz3vZaISJ7xRxzchrzrnmgb1soYE4BVJCOFTH8hYrGKBgHrGQRWcU9+TgKp5P/bFOV9YAfQ0LYNT5J7G8BuO8R6HvEYMASoYjupJNjNU9Axkp9DwEt5fm9fY8z0/NadlzFmtzFmKFYynwTMEpHKhc1zETHmdYR/H1+ZwDH70BxZkDHmFFZR74Q8o4o6ruyXEQ/8gvV73Ip1F3pu/Yewik7t96+PMeYvB2I7i3Vu6Cwioba/1Y+A+7GKEoOALXni+twW6x3ALGNMql0cBf7OxphvjDFdsfarwfotXU4TRW5vA71FpI0xJhvrYHhLRKoCiEgtEeljm/YTYJiIXGV7qFdLRJoYY2KxDtY3RCTANq6+iFyR3wqNMeuBE8DHwM+2gx2sIpFE28MtH7EejLYQkQ6ObIixqp1+C7wkIv62g/sRrCuyc6oCD4iIl4gMxiq/XXih22Djj1WMFy8itbDK5+0dwyrLvhizgAEi0kWsh8vPU/DJIhv4FHjT9oDTQ6wHuN4OrMcfq6z8jO1K8D4Hps/E+v08ReQZIMBu/MfABBFpKJZWInIuweXdHx8Bo0Skk23ayiJyrYj4OxA3InK7iITZtv/cMZRliy2bgvf9fKC6iDxkexDrLyKdHFkn1kXOwyJST6xq5S9jFR9dcK0omzexnic1tRtW1HGV1zfAncCNts/nTAWeEJHmkFNZw6EqvrZj5w6sO9FTWM9cDNa+RUSGYd1R2PsSuAErWXxhN7zA31lEGotIT9v6UrEuVEtF9XFNFHaMMSewftT/2QY9hvUAbKXttncJVjksxphVwDDgLayryN85f3V1J1axzzasMutZQI1CVj0dq1wy58C2negHYNXC2od1pfwxVtGGo8Zi3QLvxSr3/QbrJHrOP0BD27JfAm6yXdldzDY8j/WgLgFYgPVg095E4Gnb7fajF7ANGGO22rZlBtbdRRJWWW5aAbM8ivUQeTVWEcMkHDvWH8W6Ek3C+oOeWcT0P2OVfe/CKnZJJXdRy5tYyfoXrAT0CdZDdLCKFz637Y8hxpg1WM+oJmPt72jyqclWiGuArSJyBngH67lLqq2Y7iVghW1dl9nPZIxJwnpYPADrRLgbuNLBdX6KdUL8A+sYTcX6nS6KMSYR61lFsN3goo6rvOZiHdPHjDEb7Zb9A9ZxMMP2t7wF6FvEsuJt+/MY1vPJ64xlG/AGVgWCY1jVZ1fk2ZYYYB25i78o4nf2Bl7B+ns8inUh968aaK5wrraNKmfEeqHpHtttrluxXb3GYxUR7XN1PErlR0Q+BY4YY552dSyXqty9KKXck4gMwKplI1jPkTZj1XhRqtQR6wXCQVgv8bo9LXpS7mIg1sPTI1hFC7cYvR1WpZCITMAq2nqtrNzxatGTUkqpQukdhVJKqUK53TOK0NBQExER4eowlFLKraxdu/akMSas6Cn/ze0SRUREBGvWrHF1GEop5VZE5EDRU+VPi56UUkoVShOFUkqpQmmiUEopVShNFEoppQqliUIppVShNFEopZQqlNMShYh8KlY/ulsKGC8i8q6IRIvIJhFp56xYlFJKXTxn3lFMw2r6uCB9sdrsaQiMxOo0RimlVDEyxpCemX1Jy3DaC3fGmD9sLSgWZCDwha1ht5UiEiQiNWyd5iillLpACWcz+GT5XvadSmHZjuOkZ2WTnpl9Sd1LgmvfzK5F7k5eYmzD/pUoRGQk1l0H4eHhJRKcUkqVJmmZWaw7EE9KeiZpmdlsPpxA4tkM9p1MJjPbsGZ/HNl2GaFWQCVOHUsmfv0xqob6XtK6XZko8uvKMt/EZ4z5EPgQICoqSpu7VUqVWSnpmWyPTeJsehZ7T55hw6F4Vu+P41Dc2QLnaVTNjw4RwYT5e9OtYSg3tqtNp44fE7PzJC+8cCUPPNAJL6/bLzomVyaKGKCO3ffaWH0NKKVUufTE95uYvupQvuM6RFShc2QIVzSuip+3Jz5eHtQJ9kHk/DX3X38domWTqnh6VODjjwcQGupLnToX0nty/lyZKOYC94vIDKATkKDPJ5RS5c3JM2m8t3QPP244TFxyOhEhvtzZOYJmNQPw8/akZpAPwZUrFrqMU6dSePzxJXz88XqeffYKnnuuB23bFtbF/YVxWqIQkelADyBURGKAZwEvAGPMVGAh0A+rc/EUYJizYlFKKVcyxrA9NonYhLNsPBTP8uiTRB8/Q1pmdq4aSR0jgvnqnk5U9HSsQqoxhi++2Mijjy7m9OmzjB/fhfHjuxR7/M6s9TS0iPEGGOOs9SullKvsPJrEtL/2kZFlWLUvjoNxKbnGB/p44ePlQYuagXSKDKZeaGUGtql1wet57LElvPbaX3TpUoepU6+lZctqxbUJubhdfxRKKVVaZWUbPluxjxcXbAcgoJInof7eNK0RQNvwIPq3rEFkmB/VAytd9DrOns0gOTmD0FBfRoxoS8OGwYwY0Y4KFfKrH1Q8NFEopdRFOBJ/loWbY0k8m8HZjCy+XRNDwtmMnPHv3daOfi2L7zkBwKJF0YwZs5A2baoze/YQGjcOpXHj0GJdR340USillAPWHohj3sZYNh9OYO2B07nG+Xh54O1VgVpBPtx+WV3u7FyXyt7Fd3o9ciSJhx5axHffbaNx4xDuv79DsS3bEZoolFKqAMcSU5mx6hAfLd/LmbTMnOFNawQQGVaZ61rX5IpGYVTy8nBaDL/+upcbbphJenoWEyZcyfjxXfAuxiTkCE0USillJzvbsDz6JM/N3cq+k8k5w7vUD2Hc1Y1pWSvQ4VpJlyIjIwsvLw9at65Ov34NefHFnjRoEOz09eZHE4VSqlxLy8zit+3HiTl9lm9WHcyVHAC+HNGRy+uHOvVhsb3ExDT+97/f+Oefw6xYMZzQUF9mzLipRNZdEE0USqly6bcdx5i97jALNp1/z9e3ogftwoNoU6cKI7rVo1aQT4nFY4xh1qxtPPjgIo4ePcPo0R1IS8vC19f13QZpolBKlWlHE1LZfyqZtMxs0jKy2HI4gVlrYziSkApAJa8K3NM1kps71KFqgDfens573lCQEyeSueuuH/npp2jatq3OnDm30KHDhb9X4SyaKJRSZcqqfXH8GX2SxduOsT02scDpejWtyis3tiLUz7sEo8tfQIA3J0+m8PbbfRgzpiOeJfAM5EJoolBKub1lO4+zaMtRZqzO3aBex3rB1AupTFREFSLD/PD2rEAlLw/qh1XO1ZieK/zxxwFeemk5s2cPwc+vIitX3lNiz0EulCYKpZRbysjK5r2le3hrya5cwztEVGHC9S2oH+aHl0fpujIHOHkyhfHjFzNt2gYiIoLYvz+eFi2qltokAZoolFJuJDMrm/mbYnlo5oZcw/u2qM6T/ZpSK8in1J5wjTF89tkGxo9fTGJiGk880ZWnn+6Or6+Xq0MrkiYKpVSplpGVzdRle/hj9wlW7z//RnTXBqH0aVGd2zuFu7wYyVFffbWJZs3CmDr1Wpo3r+rqcBymiUIpVSot332C5+dtI/r4mZxhfZpXo3pAJe7pFkmd4Evr3rMkpKRk8PLLyxk1KoratQOYPXsIgYGVSu1dT0E0USilSoVtRxJZuvM48SnpzFobw+mU8w3sPdSrIfd2iyzW9pOcbeHC3YwZs5D9++OpVcuf++7rQJUqJfdeRnFyn72ulCpTUtIzmb8plq2HE/j87wO5xlXyqkCjan7839B2NK7u76IIL05MTCIPPbSI2bO307RpKL//fjfdu9d1dViXRBOFUqrEZGUblmw/xvLdJ/hq5cGc4VV8vagZ5MNz1zWnbZ0gPEthbSVHvfTSHyxYsJuXX+7JuHFdqFix5F/gK25idTTnPqKiosyaNWtcHYZS6gJsOZzAVysP/Os9h/uvbMCgdrWIDPNzUWTFY9Wqw/j4eNKyZTVOnUohISGNyMgqrg4rFxFZa4yJuph59Y5CKVXsjielMu7bjRxNSGXfyWQys60L0rbhQbQLr8JdnSOoGVTJre8cABISUnnyyV95//019O/fiLlzhxIS4ktISOl/0H4hNFEopYpNZlY2r/+yi6m/78kZ1rFeMC1qBjKoXS1a1Ap0YXTFxxjDzJlbefjhnzl+PJmxYzsyYUJPV4flNJoolFIXLTMrm+mrD7FqXxxr9scRa2toD2D45fV46tqmeLhZVVBHfPXVJu6880eiomoyf/5Q2rev6eqQnEoThVLqgi3aEsvkpdFsOXy+0b36YZVpGx5Ej0ZVGdk9Ep8y8BDXXlpaJnv3nqZp0zCGDGlOZmY2d97ZGg83Lz5zhCYKpZRD9p44w3drY3h/2fliJT9vT8Zc2YBbO4UT6FP6m6K4WEuX7uO++xaQkpLB7t1j8fb2ZNiwtq4Oq8RoolBK5etsehZxKemcTk5n8NS/OZuRlTOue6MwXh/ciqr+lVwYofMdP57Mo4/+wpdfbiIysgoffjigxPurLg3K3xYrpfKVnW34bcdxZqw+yM5jSRyKO/uvaWaMvIxO9YLdpm2lSxEdHUfHjh9x5kw6Tz3Vjaee6oZPGb5rKowmCqXKMWMMP205yoLNsbm6BK1c0YM6wT5c17omjasHEFK5Il3qh5SLBJGYmEZAgDf161dhxIi2DB/elqZNw1wdlktpolCqnPl561EWbzsGwKy1MTnDKwhERQTz7i1tqR5YtouU8pOcnM4LL/zORx+tY9Om+6hdO4DXXrva1WGVCpoolCoHTp5JY86GI3zzzwH2nEjOGd64mj8tagXy9LVNqVK5ogsjdK1583Zy//0/cfBgAiNGtHWLPiJKkiYKpcogYww7jibx3rI9rN4Xx9HE8+83XBYZzPu3tS/XieGczMxshgz5jh9+2EHz5mEsXz6Mrl3DXR1WqaOJQik3l5mVzbGkNDbHxLNw81GW7jxOUmpmzvhQP2+i6lahf6sa3NwhvMy933AxjDGICJ6eFahRw49XXrmKhx/uXCYa8HMGTRRKuZHMrGw2HU7g+3UxbD2SyMkzaf+qnVTRowJNawTQv1UNmtbwp2eTai6KtnRauTKGMWMW8tFHA2jXrgZTplzr6pBKPU0USpVyxhgWbj7KL9uOMmfDkVzj6gT7MCSqNsGVvWlc3Y8OEcHUrlK2GqQrLqdPn+XJJ3/lgw/WUrOmP6dP/7v6r8qfUxOFiFwDvAN4AB8bY17JMz4c+BwIsk3zuDFmoTNjUqq0M8awaMtRvvj7ACnpmWyMScgZV0FgZPf6XNuyBs1rBrhdl5quMnPmFh54YBEnT6bw0EOX8fzzPfD393Z1WG7DaYlCRDyAKUBvIAZYLSJzjTHb7CZ7GvjWGPO+iDQDFgIRzopJqdLsj10nWLApljkbD5OakZ0zvF/L6lTy8uDhXo3cop/o0mjHjpNERASxaNFttG1bw9XhuB1n3lF0BKKNMXsBRGQGMBCwTxQGCLB9DgRy31crVcakZWax4WA8KelZbD+ayFuLdxHkW5ETSWk50zSs6kfdEF9evqElVQPK3/sMxSE1NZNJk/6kXbsaDBjQmCef7MbTT3cvFw34OYMzE0UtwL47qxigU55pngN+EZGxQGWgV34LEpGRwEiA8HCtuqbcy2cr9rHuYDy/7zxOol1tpHNOJKVxd5cIRGBYl3qEl7FOb0rakiV7GT16Abt3xzFuXGcGDGiMl5fWZroUzkwU+RWe5u13dSgwzRjzhoh0Br4UkRbGmOxcMxnzIfAhWF2hOiVapYrJ2gOnmbU2hg2H4tkee74Z7mY1AqhS2Yt24VXo0bgqft6e1A3xpZKexIrFsWNneOSRX/jmm800aBDML7/cTu/e9V0dVpngzEQRA9Sx+16bfxctjQCuATDG/C0ilYBQ4LgT41Kq2O04msiE+dtYuTeOrOzz1zJ1Q3xpWNWPt29pi185bHW0JC1evJdZs7bxzDPdeeKJblSqpPu7uDhzT64GGopIPeAwcAtwa55pDgJXAdNEpClQCTjhxJiUKja7jyUx4vM1HIxLyTX8P1dEMrB1LZrVDChgTlVcNm48yu7dcdx0UzNuu60ll19eh3r1qrg6rDLHaYnCGJMpIvcDP2NVff3UGLNVRF4A1hhj5gLjgI9E5GGsYqm7jTFatKRKhdSMLGJOp5Cakc2huBTOpGWyKSaBnceSWLUvLte0I7rW49pWNWgXriepknDmTDrPPruUd975h4iIIK6/vgmenhU0STiJU+/NbO9ELMwz7Bm7z9uAy50Zg1JFMcbw+64T/L3nFEcSUvl7zynOpGXkqqKaVyWvCjSu5s/4Pk3o2jC0BKNVP/64g7FjfyImJpGRI9sxcWIvPD21NpMzaSGeKreMMayIPsXtn/yTa3jlih7UD/OjV9NqeHkIDav5U8nLg5DKFakZ5EOQj5e+6OYimzcf44YbZtKyZVVmzryJLl3qFD2TumSaKFS5k5aZxdcrD/LSwu05D54viwzmtZtaU7uKT7nonMedZGRksXz5QXr2rEfLltVYsOBWeveO1CqvJUgThSrzEs5msP9kMusPnmby0j2cPHP+5bZB7Wpxb7dImtbQB8+l0V9/HWLUqPls3XqCnTvvp0GDYPr1a+jqsModTRSqTDkcf5bV++LYFptIWkYW/+yLY8fRpFzTBPp4MbZnAwa3r0OgdlBTKsXFneXxx5fw0UfrqFMngO+/H0KDBsGuDqvc0kSh3FpyWiZHE1NZvO0YCzfHssmuAT2AmoGVqBXkw4NXNaRd3SDCgytTUR98lmqpqZm0aTOVI0eSGDeuM8891wM/P+1kyZU0USi3kp6ZzckzaXy0fC+LthwlNiE11/iagZV4qHcjejapSpCPF57ato/biIlJpHbtACpV8mTChCtp06Y6rVtXd3VYCk0UqpRbtCWWg3EpJJzNYMm24+w8lrsYqVmNAAa1q0XdkMp0axiqzWG4obNnM5g48U8mTVrBrFmDGTCgMXfd1cbVYSk7DiUKEakIhBtjop0cj1IYY9hwKJ47P1lFUlruRvRqBfnQp3l1OkUG07tpNa2m6uZ++WUPo0cvYM+e09x+eys6dqzl6pBUPopMFCJyLfAmUBGoJyJtgGeNMTc4OzhVfhxLTGXviWR+XH+YmWvONzoc5OvFnDGXU7uKLx6aFMqUsWMXMnnyaho2DGbJkju46qpIV4ekCuDIHcULWM2DLwUwxmwQkQZOjUqVeSnpmfy5+yQ7jibx9T8HOJZ4vspqtQBvGlXz5/G+TWheM9CFUarilpVlve3u4VGByy6rTWioL4891lUb8CvlHPl1Mowx8XleQtL2mNRFOZ6Yyp/RJ3nk2425hreuE8TIbpHUDfGlRS1NDmXRunWxjBo1nzvuaMXYsZ247bZWrg5JOciRRLFdRIYAFWwtwT4IrHRuWKosmbn6ICuiT7HvZDKbD5+vvjq0Yzj/6R5JqL+3NsFdhiUlpfHMM0t5991VhIX5UqOGv6tDUhfIkb/O+4FngGzge6zWYJ9wZlDKvSWmZrDvRDJ/7z3Fx8v3cvJMOgANqvrRp3k1+rW0WlnV/p/Lvl9+2cPw4XM4ciSJUaOiePnlqwgK0u5d3Y0jiaKPMeYx4LFzA0RkEFbSUAqAjKxs5m08wvfrDvNn9Mlc43o1rcorN7Yi1M/bRdEpV6lY0YOqVSsze/YQOnWq7epw1EWSorp/EJF1xph2eYatNca0d2pkBYiKijJr1qxxxapVPjKysvnwj7188ff+nAfS/t6ejL6yAR0iqtCydiDenvpuQ3mRkZHFm2/+TWJiGi+9dBUA2dlGqzGXArbzdtTFzFvgHYWI9MHqprSWiLxpNyoAqxhKlXMJKRl0fHkJaZnW4dCjcRhv39yGIF9tbqE8+vPPgzkN+A0e3CwnQWiScH+FFT0dB7YAqcBWu+FJwOPODEqVfou2HGXUV2sB6BgRzBcjOupb0eXUqVMpPPbYEj75ZD3h4YHMmzeU/v0buTosVYwKTBTGmPXAehH52hiTWtB0qnyIS07nwKlkNhyK55WfduTcRXSMCOabeztpm0rl2KlTZ5kxYwv//W8XnnnmCipX1jvKssaRh9m1ROQloBmQU13BGKOXDOXAscRUnp2zlUVbj+YaXjOwEvMf6EawnhTKpe3bT/Dtt1t59tkeNGoUwsGDDxMc7OPqsJSTOJIopgEvAq8DfYFh6DOKMi8tM4tHvt3Igk2xOcOevrYprWoH0aJWAL4V9b2H8iglJYOXXvqD1177Cz+/iowY0Y7atQM0SZRxjvy1+xpjfhaR140xe4CnRWS5swNTJS8r27D3xBkmL41mzoYjAHh5CC/f0JKb2tfWLkLLuRdWHXsAACAASURBVEWLohk9egH79sVz112tee213oSFVXZ1WKoEOJIo0sQ6Q+wRkVHAYaCqc8NSJeVEUhqLtx3jrSW7OJF0vr0lHy8PXhvcij7Nq+Olzx/KvTNn0rnjjh8ICfFh6dK76NEjwtUhqRLkSKJ4GPADHgBeAgKB4c4MSjnX8t0nWLAplsXbjnEqOT1neK0gH/q2qE6PxlXpUj9EqzWWc1lZ2UyfvoWhQ1vg51eRJUvuoEmTULy1uZVyp8hf3Bjzj+1jEnAHgIjoK5Zu6qUF2/ho+T4AGlXzo3ujMK5qWpUOEcFUC9CmFZRl7doj/Oc/81m7NhYfH09uvLGZ9jZXjhWaKESkA1AL+NMYc1JEmmM15dET0GThRlbuPcUtH55vy3He/V1pWVtbaVW5JSSk8r//LWXKlNVUrVqZGTNuZNCgpq4OS7lYYW9mTwRuBDZiPcD+Aavl2EnAqJIJT12qRVtiGfXVupzvDav68fU9naiqdw8qHzfe+C2//baPMWM68OKLPQkM1ONEFX5HMRBobYw5KyLBwBHb950lE5q6FIu3HeOj5XtZtS8OgODKFZk1qjORYX4ujkyVNnv3niYszBd/f29eeqknFSoIHTpol6TqvMISRaox5iyAMSZORHZoknAPkxbt4P1lewAI9fPmpwe7EeavLbeq3NLTs3j99b+YMOEPHnigI5Mm9dYWXlW+CksUkSJyrilxASLsvmOMGeTUyNQFi09Jp80Li3O+TxvWgS71Q6noqdVbVW5//HGAUaPms337SW66qRkPPNDJ1SGpUqywRHFjnu+TnRmIujR/7TnJrR9ZFdSa1wxg2rCOeheh8vXWW3/zyCO/EBERxIIFt9KvX0NXh6RKucIaBfy1JANRF8cYwxu/7GLy0mgAhl0ewbMDmrs4KlXaZGcbkpPT8ff35tprG3HiRApPP90dX18vV4em3IC+OePGDp5KYeSXa9hxNAmA125qxeCoOi6OSpU2W7ceZ9SoBTk9zTVqFMLLL1/l6rCUG3Fq4bWIXCMiO0UkWkTy7cNCRIaIyDYR2Soi3zgznrLkUFwK3V9byo6jSdzUvjabn7tak4TKJSUlgyeeWEKbNh+wffsJ+vdvSFE9WiqVH4fvKETE2xiTVvSUOdN7AFOA3kAMsFpE5hpjttlN0xB4ArjcGHNaRLQNKQd88fd+nplj9SV1/5UNeLRPY9cGpEqd9etjGTToW/bvj2fYsDa8+mpvQkN9XR2WclNFJgoR6Qh8gtXGU7iItAbuMcaMLWLWjkC0MWavbTkzsN7N2GY3zb3AFGPMaQBjzPEL34Ty5dvVh3KSxKd3R9GzSTUXR6RKE2MMIkJ4eCDh4YF8/vn1dO9e19VhKTfnSNHTu0B/4BSAMWYjcKUD89UCDtl9j7ENs9cIaCQiK0RkpYhc48Byy631B0/z39mbAJg/tqsmCZUjMzObt99eyVVXfUFWVjYhIb78/vvdmiRUsXCk6KmCMeZAnr4IshyYL7+mR/MWkHoCDYEeWG1HLReRFsaY+FwLEhkJjAQIDw93YNVlS2pGFle98TuH488CMK53I1rU0naalGXVqsOMGjWf9euP0rdvAxIT06hSRTsSUsXHkTuKQ7biJyMiHiLyELDLgfliAPunq7WxmgHJO80cY0yGMWYfsBMrceRijPnQGBNljIkKCwtzYNVlR/TxM7SbsJjD8Wfx8fLgzSGtGXuV1ntXVh8RY8Ys4LLLPubYsWS++24wCxbcqklCFTtH7ijuwyp+CgeOAUtsw4qyGmgoIvWwOju6Bbg1zzQ/AkOBaSISilUUtdex0Mu29MxsRny+muW7TwJWk+C/PHyFi6NSpYmXVwWWLTvA2LEdmTChJwEB+oKlcg5HEkWmMeaWC12wMSZTRO4HfgY8gE+NMVtF5AVgjTFmrm3c1SKyDas4a7wx5tSFrqssevz7TSzffZKWtQIZ36cx3RuVrzsplb/o6DheeOF3pkzph7+/N2vXjqRSJX0dSjmXFFWvWkT2YBUJzQS+N8YklURgBYmKijJr1qxxZQhOlZSaQcvnfgGgX8vqvHdbexdHpEqDtLRMXn11BS+9tJyKFT1YsOBWunXTB9XKcSKy1hgTdTHzFvmMwhhTH3gRaA9sFpEfReSC7zBU0dYfPJ2TJFrVDmTy0HYujkiVBkuX7qN166k888wyrr++CTt23K9JQpUoh97MNsb8ZYx5AGgHJAJfOzWqcmjXsSRueO8vAHo1rcr393XRPqsVxhheemk5GRnZLFp0GzNm3ETNmv6uDkuVM468cOeH9aLcLUBTYA7QxclxlStPfL+Z6asOAvDCwObc2TnCtQEpl8rONnzyyTquuaYBdeoE8uWXNxAUVAkfH23AT7mGI3cUW4DLgFeNMQ2MMeOMMf84Oa5yISvbMOrLtTlJYvq9l2mSKOc2bTpG166fMnLkfD7+2OrCtkYNf00SyqUcqS4RaYzJdnok5cz22ET6vrM85/tXIzrRuX6ICyNSrnTmTDrPP7+Mt95aSZUqPkybNpA772zt6rCUAgpJFCLyhjFmHDBbRP5VNUp7uLt49kVNrWoHMmtUF+2Frpx77rllvPHG39xzT1teeaUXISHagJ8qPQq7o5hp+197titGU5ZG5ySJR3o34gF9y7rcOnQogeTkDJo0CeXxx7ty/fVN6Nq1/DVRo0q/wnq4W2X72NQYkytZ2F6k0x7wLoAxhgdmbGDeRqsVk7+f6EmNQG1qoTzKzMzm3Xf/4ZlnltK+fU1+//1uQkN9NUmoUsuR8o7h+QwbUdyBlHWjvlrLvI1HuCwymN/H99AkUU6tXBlDVNSHjBv3Cz16RPD559e7OiSlilTYM4qbsarE1hOR7+1G+QPx+c+l8vP0j5v5eesxAKYN60glLw8XR6RcYcGCXQwYMJ2aNf35/vshXH99E/K0yqxUqVTYM4pVWH1Q1Mbqqe6cJGC9M4MqS37dfoyvVlrPJJb/90pNEuWMMYYjR5KoVSuAXr0ieeGFK3nwwU74+2sDfsp9FNnWU2njTm09Ld99gjs+sR71/DbuCiLD/FwckSpJu3adYvToBezadYpt28bg51fR1SGpcuxS2noqrOjpd2PMFSJymtwdDglgjDHBF7PC8mJF9MmcJPFUv6aaJMqR1NRMXnnlTyZO/BMfH08mTrwKHx9t4VW5r8KO3nPdnYaWRCBlyUsLtvHR8n0APH1tU+7pFuniiFRJOXr0DN27f8bu3XEMHdqCN9/sQ/XqepGg3Fth1WPPvY1dBzhijEkXka5AK+ArrMYBVR7rDp7OSRLfjepMhwi98SoPMjKy8PLyoFq1ynTvXpcpU/rRu3d9V4elVLFwpHrsj1jdoNYHvsBqGPAbp0blphJSMhhkawF29n2aJMqD7GzD1KlrqF//XWJiEhERPv74Ok0SqkxxJFFkG2MygEHA28aYsUAt54blnt5bFg1An+bVaF9Xk0RZt3HjUbp0+YT77ltAw4YhZGRkuTokpZzCoa5QRWQwcAdw7u0gbcoyj8PxZ/ngj73UD6vM1Nu1V7qyzBjD+PGLefvtlQQH+/Dllzdw220t9Z0IVWY5kiiGA6OxmhnfKyL1gOnODcv9TP/Helfi+eta6AmjjBMRTp8+y4gRVgN+VaroW/aqbHOkK9QtwAPAGhFpAhwyxrzk9MjczIo9JwHoFKlFTmXRgQPxXH/9DNatiwXgo4+u44MPBmiSUOVCkYlCRLoB0cAnwKfALhG53NmBuZMpS6NZfzCe3s2q4eWhzYWXJRkZWbz66gqaNXuPxYv3snOndUGg3dSq8sSRoqe3gH7GmG0AItIU+BK4qDf8yprUjCxe+3knAK/fpB3NlCV//XWI//xnPlu2HGfgwMa8+25fwsMDXR2WUiXOkURR8VySADDGbBcRbYvAZvi01YD19nWgrz7jL0uWLNlLQkIqP/54MwMHNnF1OEq5jCOJYp2IfIB1FwFwG9ooIADHE1P5a88pAn28GNG1nqvDUZfIGMOXX24iLMyXvn0b8thjl/PII521jSZV7jlSoD4K2AP8F3gM2Av8x5lBuYuHZm4AYML1LbTM2s3t2HGSnj2/4K67fuSzz6zf1dvbU5OEUhRxRyEiLYH6wA/GmFdLJiT38OP6w/y15xQA1zSv7uJo1MU6ezaDl19ezqRJK6hcuSIffNCfe+5p5+qwlCpVCms99kmsnuzWAR1E5AVjzKclFlkpZYyh///9ydYjVlNX3/6nMxU9taaTu5o3bxcvvric229vxeuv96ZaNW3AT6m8CrujuA1oZYxJFpEwYCFW9dhy7aGZG3KShPYx4Z6OHj3Dhg1HueaaBgwe3IyIiHvo2FFbpVGqIIUlijRjTDKAMeaEiJT7y+ZpK/YxZ8MRAn28WP1UL72TcDNZWdl88MFannjiVypW9ODgwYfw8fHSJKFUEQpLFJF2fWULUN++72xjzCCnRlbKzNt4hOfmWbWE593fVZOEm1m3LpZRo+azevURevWK5L33+uHjo9WZlXJEYYnixjzfJzszkNLsp82xjJ1u1Qhe+cRVVA+s5OKI1IXYt+80HTt+RGioL998M4hbbtH2uJS6EIV1XPRrSQZSWhljuO/rdQAseqibJgk3YYxh8+bjtGpVjXr1qvDZZwMZMKAxQUH6+yl1obT8pBALN8dS74mFAPRrWZ0m1QNcHJFyxL59p+nffzpt237Apk3HALjjjtaaJJS6SE5NFCJyjYjsFJFoEXm8kOluEhEjIqWm/ajUjCxG2+4kujYI5a2b27g4IlWU9PQsXnnlT5o3f4/ff9/P66/3plmzMFeHpZTbc6QJDwBExNsYk3YB03sAU4DeQAywWkTm2rcbZZvOH6sZ838cXXZJeN724PqhXg15qFcjF0ejipKVlU2XLp+wdm0sgwY15e23+1Cnjjbgp1RxcKSZ8Y4ishnYbfveWkT+z4FldwSijTF7jTHpwAxgYD7TTQBeBVIdD9u54pLTmb7K6ojowasaujgaVZjEROvaxcOjAsOHt2XevKHMnj1Ek4RSxciRoqd3gf7AKQBjzEbgSgfmqwUcsvseQ56+tkWkLVDHGDO/sAWJyEgRWSMia06cOOHAqi/Ni/Otu4k3h7TW2jGllDGGadM2EBn5DnPm7ABg9OgO9O+vd39KFTdHEkUFY8yBPMMc6UU+vzOsyRlpvcD3FjCuqAUZYz40xkQZY6LCwpxb5pyakcX36w/TIaIKg9rVduq61MXZtu0EPXp8zrBhc2jSJJT69bVXQaWcyZFnFIdEpCNgbM8dxgK7HJgvBqhj9702cMTuuz/QAlhmu2qvDswVkeuMMWscCd4ZHvnWajl0cPs6RUypXOHVV1fw1FO/ERDgzccfD2DYsLbacq9STuZIorgPq/gpHDgGLLENK8pqoKGI1AMOA7cAt54baYxJAELPfReRZcCjrkwSWw4nsHDzUQJ9vBjSQRNFaWKMQUSoXt2P225ryWuv9SYsrLKrw1KqXCgyURhjjmOd5C+IMSZTRO4HfgY8gE+NMVtF5AVgjTFm7gVH62RP/bgFgFcGtXRxJOqcI0eSePDBRXTrFs4DD3Tizjtbc+ed2uWsUiWpyEQhIh9h92zhHGPMyKLmNcYsxGp11n7YMwVM26Oo5TnT6v1xbDwUT7eGofRtWcOVoSis6q7vvbeap576jYyMbLp00edFSrmKI0VPS+w+VwJuIHdtJreXlpnF4Kl/A/Bwb60142obNhzlnnvmsnZtLFdfXZ/33uunD6yVciFHip5m2n8XkS+BxU6LyAVmrz0MQFTdKrQLr+LiaFRCQipHjiQxc+ZNDB7cTKsoK+ViDr+ZbaceULe4A3EVYwxP/rAZgA/uaO/iaMonYwzffbeN3btP8dRT3bniigj27n2QSpUu5vBUShU3R97MPi0icbZ/8Vh3E086P7SS8coi62Wt8GBfQvy8XRxN+bNnTxz9+n3DzTfPYs6cnWRkWK/oaJJQqvQo9K9RrHv+1ljVWwGyjTH/erDtruZtPMIHv+8FYPEj3V0cTfmSlpbJ66//xYsvLsfLqwLvvHMNo0d3wFM7hFKq1Ck0URhjjIj8YIwpc2Uy6ZnZOZ0RPdK7Ed6eHi6OqHw5dCiRCRP+YMCAxrz9dh9q1dIm3JUqrRy5fFslIu2cHkkJm2QrcnqoV0Me0Ib/SsSJE8lMnrwKgAYNgtm2bQzffTdYk4RSpVyBdxQi4mmMyQS6AveKyB4gGasNJ2OMcdvkMX3VQT75cx+NqvnxQE9NEs6WnW347LP1/Pe/S0hKSqN370gaNw4lMlJrmCnlDgoreloFtAOuL6FYSswT31u1nKbfe5m2E+RkW7Yc5777FvDnnwfp1i2cqVP707hxaNEzKqVKjcIShQAYY/aUUCwlIjE1A4D+rWpoLScnS0/P4uqrvyQ9PYtPP72Ou+9uo+9EKOWGCksUYSLySEEjjTFvOiEep1t34DQAnerpm77O8ttv+7jiirpUrOjBt98OpkmTUEJDfV0dllLqIhX2MNsD8MNqDjy/f25n25FE7v5sNQBREZooiltMTCI33vgtV131BV98sRGArl3DNUko5eYKu6OINca8UGKRlIDRX68F4J6u9WhaQ2vaFJfMzGwmT17F//63lKysbCZOvIrbbmvl6rCUUsWkyGcUZcW8jUfYfyqFnk2q8nT/Zq4Op0y5444fmDFjC337NmDKlH7Uq6e1mZQqSwpLFFeVWBQl4NzLdU/0beLiSMqG+PhUPD0r4OdXkTFjOnDjjU258cam+rBaqTKowGcUxpi4kgzEmdbstzalV9NqNKzmlo9XSg1jDDNmbKFp0yn873+/AdZziJtu0lZelSqrykXDOm8v2Q3A8wObuzgS9xYdHUefPl8xdOhsatcO4Pbb9TmEUuVBmW+iMyvb8Gf0ScL8vakV5OPqcNzWN99sZvjwOXh7ezJ5cl9GjYrCw6NcXGcoVe6V+UTxZ/RJAG7pUMfFkbinjIwsvLw8iIqqyU03NePVV3tTs6YW3ylVnpT5RPHm4l0AXNe6posjcS/HjyczbtwvJCen8/33N9OoUQhffTXI1WEppVygTJcdpGVmsfFQPD5eHvoQ20HZ2YYPP1xL48aTmTlzC82bh5GVle3qsJRSLlSm7yg++XMfgDYj7qC9e09z++3f8/ffMfToEcH7719LkybagJ9S5V2ZThTLdp4A4GZ9PuGQwEBv4uNT+fzz67njjlZa3VUpBZTxoqe1B05Tu4oPwZUrujqUUmvu3J0MGjSTrKxsQkJ82bJlNHfe2VqThFIqR5lNFNnZhqxsQ2SYn6tDKZUOHkzg+utnMHDgDHbtOkVs7BkA7Z9DKfUvZbbo6bcdxwFoFx7k4khKl8zMbN5+eyXPPrsMYwyTJvXi4Ycvw8tL+wxXSuWvzCaKGasPAlYHReq8rKxsPv54HT171uP//q8vERGaSJVShSuTRU/GGJZsP05FjwpEhmrR0+nTZ3nsscUkJaXh7e3JihXDmTv3Fk0SSimHlMlE8fPWowA82qdRuS5zN8bw9debaNJkCm+88TdLl+4HICTEVx9WK6UcViaLns5Vix3cvvxWi9216xSjRy/g11/30bFjLX7++XbatKnu6rCUUm6oTCaKrUcSqeLrRZVyXC32oYcWsWbNEd57rx8jR7bXBvyUUhetzCWKtMwsNh9OIKpu+etlbfHiPTRpEkqdOoG8//61eHt7Ur26PqNRSl0ap15misg1IrJTRKJF5PF8xj8iIttEZJOI/CoidS91nY/P3gxA90Zhl7oot3H06BluvXU2V1/9FZMmrQCgbt0gTRJKqWLhtEQhIh7AFKAv0AwYKiJ5O6teD0QZY1oBs4BXL3W9v+86gUcF4b4e9S91UaVedrZh6tQ1NGkymdmzt/Pss1fw+utXuzospVQZ48w7io5AtDFmrzEmHZgBDLSfwBiz1BiTYvu6Eqh9qSuNS07n8gaheJWDMvmJE5dz330LaN++Jps2jeK553pQqVKZK01USrmYM88qtYBDdt9jgE6FTD8C+Cm/ESIyEhgJEB4eXuACUtIzAWhUtewWuSQlpXHyZAr16lVh1Kgo6tWrwtChLbS6q1LKaZx52Z3fmcvkO6HI7UAU8Fp+440xHxpjoowxUWFhBT97OBhn3ZzUDfG94GBLO2MMP/ywnWbN3uPmm2dhjCEkxJdbb22pSUIp5VTOTBQxgP2LDLWBI3knEpFewFPAdcaYtEtZ4cMzNwJQNaDSpSym1DlwIJ7rrpvBoEHfEhzsw7vv9tXkoJQqMc4seloNNBSResBh4BbgVvsJRKQt8AFwjTHm+KWu8JDtjuLqZtUudVGlxt9/H6JXry8BeP313jz44GV4epb95y9KqdLDaYnCGJMpIvcDPwMewKfGmK0i8gKwxhgzF6uoyQ/4znaFfNAYc93FrG/fyWTOpGUyvk/jMnG1nZiYRkCAN+3a1WD48DaMH3854eGBrg5LKVUOObWKjDFmIbAwz7Bn7D73Kq51LdhklWo1rxlQXIt0iVOnUnj88SX88stetm4djZ9fRf7v//q5OiylVDlWZupSzt8UC0DbOu75RrYxhi+/3MS4cb9w+vRZHnmkM2XgxkgpVQaUiUQxbcU+dhxNolfTqgT6erk6nAuWkJDK9dfPZNmy/XTuXJupU/vTqlXZec6ilHJvZSJRvLhgOwBvDG7j4kgujDEGESEgwJvQUF8+/LA/I0a0K9dNoyulSh+3rz5zJi2TzGxDRIivW91N/PxzNO3afUhMTCIiwnffDebee9trklBKlTpunyiOJ6YCcG/3SBdH4pjY2CRuuWUW11zzNSkpGRw/nuzqkJRSqlBuX/T0zT9W39hhft4ujqRoU6as4sknfyMtLZPnn+/BY49djre32/8ESqkyzu3PUrG2O4qeTaq6OJKirV0bS6dOtZgypR8NG4a4OhyllHKI2yeKP3aeoENEFTxLYWuxiYlpPPPMUu64oxXt29fkvfeuxdvbo0y8EKiUKj/cOlGkpGeSlJZJtVLWtpMxhtmzt/Pgg4uIjU0iPDyQ9u1rahPgSim35NZnrgW2l+y6NQx1cSTn7dt3mvvv/4mFC3fTpk11vv9+CJ06XXI3G0op5TJunSi+X3cYgGta1HBxJOd9/fVm/vjjAG+91Yf77++oDfgppdyeWyeKw/FnAQj0ce37E8uXHyAtLYtevSIZP74Ld9/dhtq13bvNKaWUOsdtL3cTUzM4GJfC3V0iXBbDyZMpDB8+h+7dp/HCC78D4O3tqUlCKVWmuO0dxZz1VrFTu7ol3wigMYZp0zYwfvxiEhLSeOyxy/nf/7qXeByq9MvIyCAmJobU1FRXh6LKiUqVKlG7dm28vIqvpMVtE8WmmAQA+jQv+cbzFi7czfDhc7n88jpMndqfFi1K/zscyjViYmLw9/cnIiJCq0UrpzPGcOrUKWJiYqhXr16xLddti57+2RdHi1oBeHt6lMj6UlIyWLHCegu8X7+GzJlzC3/8MUyThCpUamoqISEhmiRUiRARQkJCiv0O1m0TxcG4FNIysktkXT/9tJsWLd6jb9+viY9PRUS47rrG2oCfcogmCVWSnHG8uW2iAIgMq+zU5R8+nMjgwd/Rr983eHt7Mm/eUIKCStfLfUop5WxumSiOJ1m3Va1qBzlvHceTadbsPebP38WLL17Jxo2juOKKCKetTyln8fDwoE2bNrRo0YIBAwYQHx+fM27r1q307NmTRo0a0bBhQyZMmIAxJmf8Tz/9RFRUFE2bNqVJkyY8+uijrtiEQq1fv5577rkn17CBAwfSuXPnXMPuvvtuZs2alWuYn59fzuddu3bRr18/GjRoQNOmTRkyZAjHjh27pNji4uLo3bs3DRs2pHfv3pw+ffpf0yxdupQ2bdrk/KtUqRI//vhjTsz16tXLGbdhwwYA5s+fz7PPPntJsV0QY4xb/Wvfvr15Z8kuU/ex+ebzv/aZ4hYTk5Dz+Z13Vpro6FPFvg5Vfmzbts3VIZjKlSvnfL7zzjvNiy++aIwxJiUlxURGRpqff/7ZGGNMcnKyueaaa8zkyZONMcZs3rzZREZGmu3btxtjjMnIyDBTpkwp1tgyMjIueRk33XST2bBhQ87306dPm9q1a5smTZqYvXv35gy/6667zHfffZdr3nP75uzZs6ZBgwZm7ty5OeN+++03s3nz5kuKbfz48WbixInGGGMmTpxo/vvf/xY6/alTp0yVKlVMcnJygTEbY0x2drZp06ZNznR55XfcAWvMRZ533bLWU2yCdUcxqF3xNY2RkJDK00//xgcfrGXlynto164GDzzQqdiWr9Tz87ay7UhisS6zWc0Anh3Q3OHpO3fuzKZNmwD45ptvuPzyy7n66qsB8PX1ZfLkyfTo0YMxY8bw6quv8tRTT9GkSRMAPD09GT169L+WeebMGcaOHcuaNWsQEZ599lluvPFG/Pz8OHPmDACzZs1i/vz5TJs2jbvvvpvg4GDWr19PmzZt+OGHH9iwYQNBQVYJQYMGDVixYgUVKlRg1KhRHDxoVSJ5++23ufzyy3OtOykpiU2bNtG6deucYbNnz2bAgAFUq1aNGTNm8MQTTxS5X7755hs6d+7MgAEDcoZdeeWVDu/XgsyZM4dly5YBcNddd9GjRw8mTZpU4PSzZs2ib9+++Pr6FrpcEaFHjx7Mnz+fIUOGXHKcRXHLoqfMLOshtl8x9OVgjOHbb7fStOkUpkxZzahRUdSvX/LvZijlbFlZWfz6669cd911gFXs1L59+1zT1K9fnzNnzpCYmMiWLVv+NT4/EyZMIDAwkM2bN7Np0yZ69uxZ5Dy7du1iyZIlvPXWWwwcOJAffvgBgH/++YeIiAiqVavGgw8+yMMPP8zq1auZPXv2v4qXANasWUOLFi1yDZs+fTpDhw5l6NChTJ8+vchYAIe3NSkpKVcxkf2/bdu2/Wv6Y8eOUaOG1cRQjRo1/w8YrgAADoNJREFUOH78eKHLnzFjBkOHDs017KmnnqJVq1Y8/PDDpKWl5QyPiopi+fLljmzeJXPLO4rNhxOoXwwPso0xDBr0LT/+uIN27Wowd+5QoqJqFkOESv3bhVz5F6ezZ8/Spk0b9u/fT/v27enduzdwvs/2/FxIzZklS5YwY8aMnO9VqhR9oTV48GA8PKyq7TfffDMvvPACw4YNY8aMGdx88805y7U/+SYmJpKUlIS/v3/OsNjYWMLCwnK+Hzt2jOjoaLp27YqI4OnpyZYtW2jRokW+23ShNYT8/f1znhMUt9jYWDZv3kyfPn1yhk2cOJHq1auTnp7OyJEjmTRpEs888wwAVatW5ciRI06JJS+3vKM4fPosZ9IyL3r+jIwswDpIunatw7vvXsOqVfdoklBlko+PDxs2bODAgQOkp6czZcoUAJo3b86aNWtyTbt37178/Pzw9/enefPmrF27tsjlF5Rw7IflrddfufL5C73OnTsTHR3NiRMn+PHHHxk0aBAA2dnZ/P3332zYsIENGzZw+PDhXEni3LbZL3vmzJmcPn2aevXqERERwf79+3OSWEhISK6HyXFxcYSGhubsC0e29ULvKKpVq0ZsrNXKdWxsLFWrFvze1bfffssNN9yQ643qGjVqICJ4e3szbNgwVq1alTMuNTUVHx+fImMuDm6ZKJLSMmlS/eLaU1q2bD+tWk1lzpwdAIwb14WxY/+/vfsPrqo+8zj+/hSBBAtpxdXZivxwTCExCYjQpnYQkcpYpVAdR4RAZceuI8hSy4pDB2bqVmtpK2gjushaJ+naIqujyIBCkaZFEdCwVaJgU0ozITsdRZallqr8yLN/nJPkGm5uTmLuzzyvmTtz7znnnvPkmZvzvef7Pff5fpk+GTjxkXM9qaCggMrKSh544AFOnjxJRUUFr7zyCi+99BIQXHksXLiQu+++G4DFixdz//33U19fDwQn7pUrV56x3ylTprBq1arW1y0n4/PPP5/9+/fT3Nzc2rUUjySuv/56Fi1aRFFREYMHD46733jf5IuKijhw4EDr67Vr17J582YaGhpoaGhgz549rQ3FlVdeybp16zhx4gQAVVVVreMQs2bN4tVXX2XTpk2t+9q8eTN1dXWfOF7LFUW8R3Fx8RnxTZs2jerqagCqq6uZPn16h3lo6TKL1dLImBnr16//RDdbfX39Gd1uyZJ1Z8e/fnQSgJILutZQHD58nFtuWc+kSdV8/PEpBg7M/Dm2netpl156KaNHj+app54iPz+f559/nvvuu4+RI0dSWlrK+PHjWbBgAQBlZWU89NBDzJw5k6KiIkpKSlpPXLGWLVvG0aNHKSkpYfTo0dTU1ACwfPlypk6dylVXXdXaT9+RGTNm8OSTT7Z2OwFUVlZSW1tLWVkZxcXFrF69+oz3jRo1imPHjvHBBx/Q0NBAY2Mj5eXlretHjBjBoEGD2L17N1OnTmXChAlcdtlljBkzhh07drQOLOfn57Nx40YefvhhCgsLKS4upqqqKuEVQBRLlixh69atFBYWsnXrVpYsWQIEYyuxYy4NDQ0cOnSIiRMnfuL9FRUVlJaWUlpayvvvv8+yZcta19XU1HDdddd9qviiksXcM50Nho8qNb65nJfvnsSF5yS+M6DF2rV13HHHC/ztbydYvPhyli69ggED0lua3PUO+/fvp6ioKN1h5LQHH3yQgQMHxh3szlXvvvsus2bNYtu2bXHXx/vcSdpjZuO6c7ysu6I4caqZgf3PYsjno/fNnTrVTEnJebzxxu388IeTvZFwLofMmzeP/v17Vw9BY2MjK1asSNnxsu6upxOnmhk6eEDCuxWOHz/BvfduZ+jQAubPH8/s2WXMnl3mNXecy0F5eXnMmTMn3WGk1Pjx41N6vOy7ojjdzNAEXU4bN9ZzySWP8uMf76C+/ggQDJZ5I+HSJdu6d112S8bnLTuvKOI0FE1Nf2Xhwhd57rl3KC7+B7Zvn8uECcPSEKFzbfLy8jhy5IiXGncpYeF8FHl5PVu8NOsaCoO4g9gHDx5ly5Y/8aMfTWbRoq/Qr19q5qlwLpEhQ4bQ1NTE4cOH0x2K6yVaZrjrSVnXUAAMGxw0FK+99j/s3HmI73ynnCuuGEZj450MHhztTijnUqFv3749OtOYc+mQ1DEKSddI+oOkA5KWxFnfX9K6cP1uScOj7PdzZ/Vh/vxNlJc/zsqVuzh+PPgBjTcSzjnX85LWUEjqAzwCfB0oBmZKav/TxVuBo2Z2MfAg0HFZxRhXX/4Ejz22h4ULv0xd3TzOPrtfT4bunHMuRjK7nr4EHDCzgwCSngKmA7EFUaYD94TPnwFWSZIlGLa308aFwwp4YVMFY8cm/rWnc865Ty+ZDcUFwKGY101A+wkeWrcxs1OSjgGDgfdjN5J0G3Bb+PLj2sO3vRWhInBvcC7tctWLeS7aeC7aeC7ajOzuG5PZUMS7F7D9lUKUbTCzNcAaAEm13f0Zeq7xXLTxXLTxXLTxXLSRVNv5VvElczC7Cbgw5vUQoH3x9NZtJJ0FFAD/m8SYnHPOdVEyG4rXgUJJIyT1A24GNrTbZgNwS/j8RuA3icYnnHPOpV7Sup7CMYcFwBagD/CEmb0t6QcEk3xvAH4O/KekAwRXEjdH2PWaZMWchTwXbTwXbTwXbTwXbbqdi6wrM+6ccy61sq4ooHPOudTyhsI551xCGdtQJKv8RzaKkItFkvZJ2itpm6ScLZvbWS5itrtRkknK2Vsjo+RC0k3hZ+NtSb9KdYypEuF/ZKikGkm/D/9Prk1HnMkm6QlJ70l6q4P1klQZ5mmvpLGRdmxmGfcgGPz+E3AR0A94Eyhut818YHX4/GZgXbrjTmMuJgEDwufzenMuwu0GAtuBXcC4dMedxs9FIfB74PPh6/PSHXcac7EGmBc+LwYa0h13knJxBTAWeKuD9dcCLxL8hq0c2B1lv5l6RdFa/sPMTgAt5T9iTQeqw+fPAJOVmwX/O82FmdWY2d/Dl7sIfrOSi6J8LgDuBX4CfJTK4FIsSi7+GXjEzI4CmNl7KY4xVaLkwoBB4fMCzvxNV04ws+0k/i3adOAXFtgFfE5Sp7WQMrWhiFf+44KOtjGzU0BL+Y9cEyUXsW4l+MaQizrNhaRLgQvNbGMqA0uDKJ+LLwJflLRD0i5J16QsutSKkot7gNmSmoAXgH9JTWgZp6vnEyBz56PosfIfOSDy3ylpNjAOmJjUiNInYS4kfYagCvHcVAWURlE+F2cRdD9dSXCV+bKkEjP7vyTHlmpRcjETqDKzFZK+QvD7rRIza05+eBmlW+fNTL2i8PIfbaLkAklfA5YC08zs4xTFlmqd5WIgUAL8VlIDQR/shhwd0I76P/K8mZ00sz8DfyBoOHJNlFzcCvwXgJntBPIICgb2NpHOJ+1lakPh5T/adJqLsLvlMYJGIlf7oaGTXJjZMTM718yGm9lwgvGaaWbW7WJoGSzK/8h6ghsdkHQuQVfUwZRGmRpRctEITAaQVETQUPTG+Wk3AN8K734qB46Z2V86e1NGdj1Z8sp/ZJ2Iufgp8Fng6XA8v9HMpqUt6CSJmIteIWIutgBTJO0DTgOLzexI+qJOjoi5+FfgPyR9l6CrZW4ufrGUtJagq/HccDzm+0BfADNbTTA+cy1wAPg78E+R9puDuXLOOdeDMrXryTnnXIbwhsI551xC3lA455xLyBsK55xzCXlD4ZxzLiFvKFzGkXRa0hsxj+EJth3eUaXMLh7zt2H10TfDkhcju7GP2yV9K3w+V9IXYtY9Lqm4h+N8XdKYCO+5U9KAT3ts13t5Q+Ey0YdmNibm0ZCi41aY2WiCYpM/7eqbzWy1mf0ifDkX+ELMum+b2b4eibItzkeJFuedgDcUrtu8oXBZIbxyeFnSf4ePy+Nsc4mk18KrkL2SCsPls2OWPyapTyeH2w5cHL53cjiHQV1Y679/uHy52uYAeSBcdo+kuyTdSFBz65fhMfPDK4FxkuZJ+klMzHMlPdzNOHcSU9BN0r9LqlUw98S/hcsWEjRYNZJqwmVTJO0M8/i0pM92chzXy3lD4TJRfky303PhsveAq81sLDADqIzzvtuBn5nZGIITdVNYrmEG8NVw+WmgopPjfwOok5QHVAEzzKyUoJLBPEnnANcDl5hZGXBf7JvN7BmgluCb/xgz+zBm9TPADTGvZwDruhnnNQRlOlosNbNxQBkwUVKZmVUS1PKZZGaTwlIey4CvhbmsBRZ1chzXy2VkCQ/X630Ynixj9QVWhX3ypwnqFrW3E1gqaQjwrJn9UdJk4DLg9bC8ST5BoxPPLyV9CDQQlKEeCfzZzOrD9dXAHcAqgrkuHpe0CYhc0tzMDks6GNbZ+WN4jB3hfrsS59kE5SpiZyi7SdJtBP/X/0gwQc/edu8tD5fvCI/TjyBvznXIGwqXLb4LvAuMJrgSPmNSIjP7laTdwHXAFknfJiirXG1m34twjIrYAoKS4s5vEtYW+hJBkbmbgQXAVV34W9YBNwHvAM+ZmSk4a0eOk2AWt+XAI8ANkkYAdwHjzeyopCqCwnftCdhqZjO7EK/r5bzryWWLAuAv4fwBcwi+TX+CpIuAg2F3ywaCLphtwI2Szgu3OUfR5xR/Bxgu6eLw9Rzgd2GffoGZvUAwUBzvzqMPCMqex/Ms8E2CORLWhcu6FKeZnSToQioPu60GAceBY5LOB77eQSy7gK+2/E2SBkiKd3XmXCtvKFy2eBS4RdIugm6n43G2mQG8JekNYBTBlI/7CE6ov5a0F9hK0C3TKTP7iKC65tOS6oBmYDXBSXdjuL/fEVzttFcFrG4ZzG6336PAPmCYmb0WLutynOHYxwrgLjN7k2B+7LeBJwi6s1qsAV6UVGNmhwnuyFobHmcXQa6c65BXj3XOOZeQX1E455xLyBsK55xzCXlD4ZxzLiFvKJxzziXkDYVzzrmEvKFwzjmXkDcUzjnnEvp/fI6k4OE1iPUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.88 0.87 6762\n", + " 1 0.54 0.50 0.52 1987\n", + "\n", + " accuracy 0.79 8749\n", + " macro avg 0.70 0.69 0.69 8749\n", + "weighted avg 0.79 0.79 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_test,gnb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 106, "metadata": {}, "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Naive Bayes\" , \n", + " classification_report(y_test, gnb.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
3Neural Network - 3 Layer Adam0.5321260.771859
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5223060.745084
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "5 Naive Bayes 0.522306 0.745084" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.sort_values([\"AUROC\"], ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Tuning Neural Network with different optimisers \n", + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4697 - accuracy: 0.7966\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 122us/step - loss: 0.4483 - accuracy: 0.8123\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4456 - accuracy: 0.8117\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4439 - accuracy: 0.8117\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4428 - accuracy: 0.8133\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 88us/step - loss: 0.4417 - accuracy: 0.8149 1s - loss: 0.448 - ETA: \n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 87us/step - loss: 0.4415 - accuracy: 0.8144\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4408 - accuracy: 0.8138 0s - loss: 0.4418 - ETA: 0s - loss: 0.4399 - accuracy: 0.\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 91us/step - loss: 0.4401 - accuracy: 0.8145\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 87us/step - loss: 0.4395 - accuracy: 0.8145 0s - loss: 0.4370 - accura\n", + "Optimal Threshold: 0.26521116\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5572,94 +6461,254 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adagrad Optimzier" + "### Adadelta Optimizer" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 131us/step - loss: 0.4686 - accuracy: 0.7957\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4494 - accuracy: 0.8114\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4468 - accuracy: 0.81200s - loss: 0.4466 - accuracy: 0.\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4452 - accuracy: 0.8133\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4436 - accuracy: 0.8118\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4430 - accuracy: 0.8129\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4423 - accuracy: 0.8127\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4422 - accuracy: 0.8136\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 120us/step - loss: 0.4415 - accuracy: 0.8130\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4410 - accuracy: 0.8132\n", + "Optimal Threshold: 0.19084787\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))\n", - "\n", - "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" + "print(classification_report(y_test,predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### RMSProp" + "### Adagrad Optimzier" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4590 - accuracy: 0.8029\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4508 - accuracy: 0.8095 0s - l\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4487 - accuracy: 0.8114 0s - loss:\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4473 - accuracy: 0.8132\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4463 - accuracy: 0.8126 0s - los\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4456 - accuracy: 0.8131\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4450 - accuracy: 0.8134\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4445 - accuracy: 0.8128\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4439 - accuracy: 0.8124 0s - loss: 0\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4434 - accuracy: 0.8138 0s - loss: 0.4451 - \n", + "Optimal Threshold: 0.28107572\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU1dfA8e8hIQkhBUjoCQYI0nsEqSJFBUFQEUXFAv4UQWyIDRUUEXt7QbGjKIKAKAIiIL1Jkd47hE6AVNLv+8cMcRNCCJDNZpPzeR4eslPP7M7umbn3zr1ijEEppZS6mGKuDkAppVTBpolCKaVUjjRRKKWUypEmCqWUUjnSRKGUUipHmiiUUkrlSBPFRYjIfSIyx9VxuJqIVBGROBHxyMd9homIERHP/NqnM4nIFhFpdwXrXdE5KCIlROQPEYkWkcmXu35+EpGvReRlV8fh7kTkTREZl8tll4rIQ5ezfbdIFCKyX0TO2T9Yx0RknIj4OXOfxpifjDE3OXMfBZH9Xnc8/9oYc9AY42eMSXNlXK5iJ6zwq9mGMaauMWbhJfZzQXK8inOwJ1AeCDLG3HUF6zvGdZ/9vYuzv4PpDq/jrmbbAMaYR4wxb13tdrISkXARMQ6vRUQ+E5GtIlIxr/d3GXF1tD/nX7JMj7Cnz3NVbDlxi0Rh62aM8QMaAY2Bl1wczxVx5VVyYblCvxxF9P2+BthpjEm93BWzxmwnKz/7u9cZOHL+tT2twBORYsA3QCvgBmPM0ctcP68/x+PADSJSymHaA8DOPN5PnnGnRAGAMeYY8BdWwgBARLxF5H0ROSgix0VkrIiUcJjfXUTWi0iMiOwRkVvs6YEi8o2IHBWRw/btm4c97yERWWr/PVZE3neMQ0R+F5Fn7b8richUETkpIvtE5EmH5YaLyBQR+VFEYoCHsh6THccP9voHROQV++Q+H8cyEfk/uyhhu4h0yLJuTsewTEQ+EpHTwHARqS4i80UkSkROichP509YERkPVAH+sK8Yn896pSsiC0VkhL3dWBGZIyLBDvE8YB9DlIi8mvUOJctxlxCRD+zlo8W6JS7hsMh99md6SkSGOqzXTERWiMhZ+7hHi4iXw3wjIgNFZBewy572iYgcss+BtSLSxmF5DxF52T43Yu35oSKy2F5kg/1+3G0v39U+n86KyHIRaeCwrf0i8oKIbATiRcTT8T2wY19jx3FcRD60Vz2/r7P2vlo4noP2unVFZK6InLbXvaDIRkReB14D7ra3009Eitnn1AEROWGfa4H28uc/334ichCYn91ndTH28RkRCXOY9qOIDLf/7mgf//P2+X1ERB64wmXLishM+71bJSJvicjCS8TnAfwANABuNMacdJj3iFjfpzMi8qeIhGY5pgEishvYbk8fLSKR9v5Xi0hLh21dLyL/Onyu7+UQViLwB3D+fPLEuguckCX21va5Em0fb3OHedVEZIl9vv4FBGVZt5WIrLTP0fUi0jan9+mSjDEF/h+wH+ho/x0CbAI+cZj/MTAdKAP42x/CKHteMyAa6ISVGCsDtex5vwFfACWBcsAq4DF73kPAUvvvtsAhQOzXpYFzQCV7m2uxvpxeQDVgL3CzvexwIAXoYS9bIpvj+wH43Y49DOvKop9DHKnAM0BxrJMrGiiTy2NIBQYBnkAJINx+L7yBslg/UB9n917br8MAA3jarxcCe4Br7e0tBN6259UB4oDW9nvxvn3sHS/yuY6x168MeAAt7bjO7/Mrex8NgSSgtr1eU+B6+5jCgG3A0w7bNcBcrPOhhD3tfqwvkycwGDgG+NjzhmCdUzUBsfcX5LCtcIdtNwFOAM3tmB+03zNvh/dvPRDqsO+M9xRYAfSx//YDrs/ufc7mHPQHjtqx+9ivm1/kfR0O/Ojwui+wG+vc9AN+BcZn2e8PWOfQBeenw3baAZFZpnna64c5TPsRGG7/3RHrHByGdf7eBsQDAVew7BTgJ/ucqAccBhZeJNZwO64pwHIgMMv8nsAO+zP3tN+zJVmOaTbWd/3859gH65zyBF6w93/+c18N9Hb4rC722XS0z4e2wDJ72m3ATKA/MM+eFoz1Pe9t7+9+IAoobc9fBbyH9X25Eet7N86eF2ovezPWb84twCn+O6eXAg9d1m+wK374L/ef/cbGAbH2B/g3UMqeJ/bJVN1h+RbAPvvvL4CPstlmeawfnxIO03oDC7L5kgpwEGhrv/4fMN/+uzlwMMu2XwK+c/jSLs7h2DzsOOo4THvs/BfAjuMIdpJyOEn65PIYDl5s3/YyPYB1Wd7rSyWKVxzmDwBm23+/BvzsMM8XSCabRGGfwOeAhtnMO7/PkCzHfM9FjuFpYJrDawO0v8Rxnzm/b6wfjO4XWS5rovgcGJFlmR1YRRrn37++2Zy/5xPFYuB1IPgix3yxRNHb8XO6xLENJ3Oi+BsY4PC6JlYC93TYb7VcbLcdV5Yo4gAPh/mngYjLWRYrcaSS+Xv+NpdOFDHAU9nMnws8mOU4krAuWs4fU9sc3gvB+j2qa79ejnX+B13iPTyfKATrgrI6VjK7m8yJ4mFgeZZ1V2MljGpY3ytfh3m/8F+iGIr9+5PlHLjP/vuyE4U7FT31MMb4Y52stbAyLlhXxb7AWvs26yzWlUBZe34o1hVwVtdgnXxHHdb7AuuqPBNjvbsTsb6sAPdiXdmc306l89uwt/My1o/4eYdyOK5grKvvAw7TDmCdsOcdtmNwnF8pl8eQad8iUk5EJopVTBWD9UUN5vIcc/g7AesqFTumjP0ZYxKwrmyyE4x1ZZzdZ5PjfkTkWhGZIVbDhhjgLS48hqzHPVhEttm38WeBQId1LnaOZOcaYHCWzzsU69iz3XcW/bDuxrbbxRddc7nfy4kxq0pceH55kvtz9GqdMpkbQzieM7ldtjzWRZVjnLmJ+VbgTcciLNs1wBiHz/AUkI5VYpHt9u0ise0iEo11oVGS/86hh7HuqHfYxURdcgrK/j7/CDwFtMEqUXCU9TOD/34XKgFR9vfLcZ7jsfXOco5eT+Zz9LK4U6IAwBizCBiHVawB1gd8Diuzl7L/BZr/KtoOYWXtrA5hXUEEO6wXYIype5Fd/wz0FJFrsO4ipjpsZ5/DNkoZY/yNMY4nism6MQensK7urnGYVgXrtva8yiIiWeYfyeUxZN33KHtaA2NMANYViuSw/OU4isMXTaz6hqCLLHsKq6w2u8/mUj7HKjeuYR/Dy2Q+BnA4DrHqI14AemHdupfCuq0/v87FzpHsHAJGZvm8fY0xP2e376yMMbuMMb2xkvk7wBQRKZnTOlcQY1ZHuPD8SsWqVM0I7Uo2bKwK8ySsi7XzKlzJti7hOBf+kIdeaiVjzBKgO/CZ2HVMtkNYxbuOn2MJY8w/jquf/0NEbgSeBe4ESmEVScVhn0PGmB3GmHuwPtcPgKki4nOJ8H4ABgLTjTGJWeZl/czgv9+Fo0CQZK7Pq5Ll2L7LcmwljTE51ZvkyO0She1joJOINDLGpGOVZX8kIuUARKSyiNxsL/sN8LCIdLAr9SqLSC1jtXyYA3wgIgH2vOoickN2OzTGrANOAl8DfxljztqzVgExYlVglhCrYrSeiFyXmwOxr55+AUaKiL+diJ7Futo4rxzwpIgUF5G7gNrArMs9Bps/1gl+VkQqY5XPOzqOdWt7JaYA3USkpViVy69z4Q84APbn9i3woViNATzEqsD1zsV+/LGKFOJEpBbweC6WT8X6/DxF5DUgwGH+18AIEakhlgYicj7BZX0/vgL6i0hze9mSInKriPjnIm5E5H4RKWsf//lzKM2OLZ2Lv/czgAoi8rRYjTf8HSs3L+Fn4BkRqSpWs/K3gEnmClpFXcQGrIYHHiJyK1YdVZ4yxqRg1ce9bn/P6mJd5ORm3flYdRLfiEgPe/JYYKiI1AYQkVIi0jOHzZw/h05h3cUPx7qjwF6/j4gE259rNFaSSb9EXLuxSkhey2b2DKCuiNwtVuX6vVjFabOMMXuAjViNU7zsiupbHdYdD9wuIp3sz8RHRG4UkaJzRwFgrJYLPwCv2pNewKqsW2kXRczDKofFGLMK67bwI6wPcBH/ZeoHsIp9tmLdSk4Bcmpj/TNWGWNG6wT7h74bViusfVgn0tdYRRu5NQirnmUvVvnhBKwf0fP+AWrY2x4J9DTGnC/SudxjeB2rQjYaqwLt1yzzRwGv2Lesz13GMWCM2WIfy0Ssq55YrIrfpIus8hxWJfJqrLLod8jdOfkcVvFfLNYP96RLLP8X8CdWI4EDWHcyjsUKH2Il6zlYCegbrApTsH4Qvrffj17GmDVYdVSjsd7v3WTTki0HtwBbxHoG4ROsepdEuxhhJLDM3tf1jisZY2KxGiF0wyqS24VViZkb32L9eCzGOkcTsT6nvPIkcDtW4rsLq2GJMzyOdYd6HPgO6/t4sXMrE2PMbKxz5kcR6WKMmYz1uU+2fzM2YlX+XswsrN+VXVh1DDFY5/h5XYBtIhKLVdpxtzEmORdxLTHZNNe1f+Nuw/pti8JqzNLVGHPaXuQerOa+p7HqJMY7rLsf6/N4FesC5CBWI4gr/r0/34pHFVBiPUH5iDEmz6/SnM2+ej2LVUS0z9XxqMJFRD7AatTSz9WxFHZueUehCi4R6SYivna5+/tYdwz7XRuVKgxEpI6I1LeL/K7HKimY5uq4igJNFCqvdceqiDuCVVx2j9HbVpU3ArDqKeKxip3eNsbMcG1IRYMWPSmllMqR3lEopZTKkdt1EhccHGzCwsJcHYZSSrmVtWvXnjLGlL30khdyu0QRFhbGmjVrXB2GUkq5FRHJ+qR3rmnRk1JKqRxpolBKKZUjTRRKKaVypIlCKaVUjjRRKKWUypEmCqWUUjlyWqIQkW/FGp9380Xmi4h8KiK7RWSjiDRxVixKKaWunDPvKMZhdal8MZ2x+gKqATyKNRiNUkqpPJCUmkZiyn//robTHrgzxiwWkbAcFukO/GB3GLfSHjikYnZ9syulVFEXl5TK6bhkYhJT2B8VjzGQnJrOnpNxLN51koTkNFLS0klNMxyNzjpg3tVx5ZPZlck8eEykPe2CRCEij2LddVClSpWss5VSyq2lpFk/+IfPnGNDZDTGGJJT01l74Awp6YYth6NJTc+5A9dgP29ahQfh7VkML89iRJ6IZ9vySLZvj6JixYsNUZ47rkwU2Q2Rme07YYz5EvgSICIiQru7VUq5rcSUNP49eIZ1B8+yat9pluw6SXY5wKd4MTxEKCZC4yqlCC3tS80K/oSU9sWjmFC9bEmKe1hJoZRvcXy9/vs5N8YQEfEVO3ac4o03buTJJ5tTvHiuRo7NlisTRSSZB0cPwRrDQCml3F5cUiqzNh0lKi6ZTYfPkp4OS3adJD45c31BvcoBNK8aRGnf4tQPKUWlQB/Cy/khku1w8zlavvwQ9euXw9/fm6+/7kZwsC+hoZczKnP2XJkopgNPiMhEoDkQrfUTSil3kpSaxsRVh4g+l8KGQ2fxKCas3n8aj2LFOBWXeThvf29PQsv40iAkkNY1ylK9bElqlvfH0+Pq2xRFRSXw4ovz+PrrdQwbdgPDh7ejceOKV73d85yWKETkZ6AdECwikcAwoDiAMWYs1mDlXbAGp0/AGtZQKaUKtMSUNBZsP8GP/xxg2e6ojOnFBNINRFxTmuS0dLo3qkRYkC+3NqhEgI9nniSErIwx/PDDBp57bi5nzpxjyJCWDBnSMs/348xWT70vMd8AA521f6WUulLGGI7HJHEqLomk1HS2H4thxoajnI5PZsfx2EzLPnZDNQZ3qomXZ/4/v/zCC/N4773ltGwZytixt1K/fnmn7MftxqNQSqm8dOTsOQ6eTiAuMZVNh6P5dV0kh06fu+jyHWuXI7SML/9rU41KpUrkY6SWc+dSiI9PITjYl379GlOjRhn69WtCsWKXX6eRW5oolFJFSnJqOhsjz7JsdxS/rDnE4bMXJoXW4cE0q1qGCgE+lAvwxtvTgzoVAwj0Le6CiP8ze/ZuBg6cRaNGFZg6tRc1awZTs2aw0/eriUIpVaidS05j1qajLNp5kq1HY9h9Ii7T/GZhZbj7ulCqli1JgE9xqgWXdOrV+ZU4ciSWp5+ezeTJW6lZM4gnnrguX/eviUIp5dbOJiQTm5hKYkoau0/EkZyWzsq9URw8ncDp+BS2HY3JWLaUb3Huv74K5fx9aBUeRKPQ0ngUsKSQ1d9/7+X22yeRnJzGiBE3MmRIS7y98/enWxOFUsotGGNISE7jx5UHWL3/NEejEzkdn5xjdxV1KwVwfbUy1KsUyKAONQgs4dqio8uRkpJG8eIeNGxYgS5davDmm+0JDy/jklg0USilCqTT8cn8tPIAH8zdmdH01FHD0FJEhJXB27MYEdeUpqS3J8VECC/nh5+PJ+X9vZ3SJNXZYmKSePXV+fzzz2GWLetLcLAvEyf2dGlMmiiUUi5ljOHfg2fYdjSW2ZuPceB0POnpZFQy+3p5EFiiOHdFhOJTvBilSnhxz3WhBa4e4WoZY5gyZStPPTWbY8fiGDDgOpKS0vD1dX2y00ShlMo3xhj+2nKcn/45QEJyGidiE7Ntitq+VjnaXhvM9dWC6N6osgsizV8nT8bz4IO/8eefu2ncuAK//34P111XcI5bE4VSyqlS09LZfCSGncdiefX3zSSlpmfM61SnPPUqBRJaxpcu9StSo5wfvl4eV9TPkTsLCPDm1KkEPv74ZgYObIanCx7ey4kmCqVUnkpMSWPu1uP8ve04m49c2By1dsUAvnkwwiUPqxUkixcfYOTIJUyd2gs/Py9WrnykwBanaaJQSl211LR0Jqw6yNJdp5iz9XjG9PIB3rSpEUydigHccG1Z6lYKdPlDa6526lQCQ4bMZdy49YSFlWL//rPUq1euwCYJ0EShlLpCSalp/LL6EMt2RzF7y7GM6c2rlqFxldI8fkP1Ip8UHBlj+O679QwZMpeYmCReeqk1r7zSFl83eI80USilcs0Yw9hFexm3fB/HY/7rRrusv3XnMPy2ugT4FPwfPlf58ceN1KlTlrFjb6Vu3XKuDifXNFEopS4qOiGFE7GJzN12nP2n4vllTWTGvGZhZejRuDI9m4a4pOdUd5CQkMJbby2hf/8IQkICmDq1F4GBPgW6mCk7miiUUplsPhzNX1uO8fnCPReM01xMrAfdPruvCRUDi3Zl9KXMmrWLgQNnsX//WSpX9ufxx6+jdGn3fM80USil2HIkmq8W7+W39ZlHI65TMYAHWlxDWX9vWtcIxtvTw0URuo/IyBiefno2U6duo3btYBYteoi2ba9xdVhXRROFUkXUkbPn+H7Ffv7afIz9UQkAeHkWo3FoKZ7qUIP6IYH4a33DZRs5cjEzZ+7irbfaM3hwS7y83D+5ijXQnPuIiIgwa9ascXUYSrmljZFnmbo2kl/XHSY2MTVjeo1yfrzevS4tqzt/bIPCaNWqw5Qo4Un9+uWJikogOjqJatVKuzqsTERkrTEm4krW1TsKpQq5Q6cTGPXnNv7ediLTU9GtwoN4qGVVOtQq2G34C7Lo6ERefvlvPv98DV27Xsv06b0JCvIlKMjX1aHlKU0UShVCaemGZbtP8faf29nqMB7DgHbV6VSnPHUrBWpLpatgjGHSpC0888xfnDgRz6BBzRgxor2rw3IaTRRKFQKRZxL4a8tx/tkbxZJdpziXkpYxr17lAF64pRatw4OLXB9KzvLjjxt54IHfiIioxIwZvWnatJKrQ3IqTRRKubFJqw/y86pDrD90NmNasJ8XDUICaRRailsbVKRBSCkXRlh4JCWlsnfvGWrXLkuvXnVJTU3ngQca4uGGY15cLk0USrmR5XtO8X9/7yYxNY0th2NITrPqHFqHB/NAi2voULt8gR/a0x0tWLCPxx+fSUJCCrt2DcLb25OHH27s6rDyjSYKpQq4yDMJfLFoL5PWHCLZrowu7VucG2uVpUxJbwa0q05omcJVeVpQnDgRz3PPzWH8+I1Uq1aaL7/slu/jVRcERe+IlXIDO47F8sXiPWyKjGaXQzfdDUNLMer2+tSpFODC6IqG3btP06zZV8TFJTN0aBuGDm1DCTcaczsvaaJQqoDZczKOmz9eDIC/tyfBfl682Lk2PZuGuDiyoiEmJomAAG+qVy9Nv36N6du3MbVrl3V1WC6liUIpF4pNTGHdwbPEJ6UyfcMRlu4+lfEgXNcGFRl9bxMXR1h0xMcn88Ybi/jqq3/ZuPFxQkICeO+9m1wdVoGgiUKpfGCMYcWeKKb8G8m55DSW7jpFCS8PTsQmXbBs02tK82yna2kVrk9J55c//tjBE0/8ycGD0fTr19gtxojIT5oolHKS6IQUlu4+xU//HGD5nqhM8xqGlsLboxhdG1SirL837WqWxc/bk5DSJfRZh3yUmppOr16TmTZtO3XrlmXJkodp3bqKq8MqcDRRKJVHIs8kcORsIkt3n2Lswj0ZTVfBerahbqVAht9Wl6rBJV0YpQLrDk9E8PQsRsWKfrz9dgeeeaZFoejAzxk0USh1FZJT0/lhxX6m/nuYbQ5dZQBUK1uS/jdUp0W1IG2+WoCsXBnJwIGz+OqrbjRpUpExY251dUgFniYKpS6TMYbvl+9n+B9bM02vVzmAQe1rEFTSi3qVA/EprlenBcmZM+d4+eW/+eKLtVSq5M+ZM+dcHZLbcGqiEJFbgE8AD+BrY8zbWeZXAb4HStnLvGiMmeXMmJS6EsYYvl6yjwmrDrLvVHzGdC/PYjzZPpw7moRQqZR7jl5WFEyatJknn5zNqVMJPP309bz+ejv8/b1dHZbbcFqiEBEPYAzQCYgEVovIdGOM42XYK8AvxpjPRaQOMAsIc1ZMSl2u5btP8fG8XazafzpjWsOQQBpXKc3THWtQytfLhdGp3Nq+/RRhYaWYPfs+Gjeu6Opw3I4z7yiaAbuNMXsBRGQi0B1wTBQGOP+IaSCQeRxGpVxg36l4Rs/fzR8bj2R0mVHcQ7j7ulCev6UWATrqW4GXmJjKO+8spUmTinTrVpOXX27DK6+0LRId+DmDMxNFZeCQw+tIoHmWZYYDc0RkEFAS6JjdhkTkUeBRgCpVtOmayntp6YbJaw4xesFuIh3KrtvXKsfI2+tRMVCLldzFvHl7GTBgJrt2nWbw4BZ061aT4lpfdFWcmSiyawyeddzV3sA4Y8wHItICGC8i9Ywx6ZlWMuZL4EuwhkJ1SrSqSNp5PJZnJq1ny5H/Wix1rF2evq3CaKkPvLmV48fjePbZOUyYsInw8DLMmXM/nTpVd3VYhYIzE0UkEOrwOoQLi5b6AbcAGGNWiIgPEAyccGJcqoiLT0rl84V7WLXvdEbdQ9XgkvRoVJlH21ajhLald0tz5+5lypStvPZaW156qQ0+PtqoM684851cDdQQkarAYeAe4N4syxwEOgDjRKQ24AOcdGJMqogxxnAgKoFdJ+KYt/U4CSlp/LHhv+uVjrXL07tZKB1ql3dhlOpKbdhwjF27TtOzZx3uu68+rVqFUrVqaVeHVeg4LVEYY1JF5AngL6ymr98aY7aIyBvAGmPMdGAw8JWIPINVLPWQMUaLltRVS0s3fLt0H5/8vYu4pNSM6f4+ntSrHED7muV4uuO1FNNBftxSXFwyw4Yt4JNP/iEsrBQ9etTC07OYJgknceq9mf1MxKws015z+Hsr0MqZMaiiIzEljR9XHmDfqXh++udgxvSgkl681q0O1cv6Ua9yoAsjVHnht9+2M2jQn0RGxvDoo00YNaojnp7amsmZtBBPub34pFS+WbqPD+fuzJhWKdCHepUD+bR3Y31CuhDZtOk4t98+ifr1yzFpUk9atgy99ErqqmmiUG4rMSWNrxbv5QOHBNGlfgU+ursR3p6aHAqLlJQ0liw5SPv2ValfvzwzZ95Lp07VtMlrPtJEodxOYkoan/y9i88X7smYNqh9OIPa18BLiyAKleXLD9G//wy2bDnJjh1PEB5ehi5darg6rCJHE4VyK18s2sOoP7dnvO7drAovd6mFvz4tXaicPn2OF1+cx1df/UtoaAC//tqL8PAyrg6ryNJEodzGlLWRGUni0bbV6BURSng5PxdHpfJaYmIqjRqN5ciRWAYPbsHw4e3w89M+tVxJE4UqsNLTDcv2nGLOluOMX3kgY/qGYTcRWELvIAqbyMgYQkIC8PHxZMSIG2nUqAING1ZwdVgKTRSqgDl89hwzNhxh9pZjrDt4NtO8djXLMrxbXU0Shcy5cymMGrWUd95ZxpQpd9GtW00efLCRq8NSDnKVKETEC6hijNnt5HhUEXU8JpH+P67NlBx8vTy4o0ll+rWupsOHFlJz5uxhwICZ7Nlzhvvvb0CzZpVdHZLKxiUThYjcCnwIeAFVRaQRMMwYc7uzg1OFW0JyKmMX7mHl3tOZxnv4sFdDbmtYCU/tErpQGzRoFqNHr6ZGjTLMm9eHDh2quTokdRG5uaN4A6t78AUAxpj1IhLu1KhUoTd78zH6/7g243VQSS8eb1edR9roj0VhlpZmdQzt4VGM668PITjYlxdeaK0d+BVwufl0UowxZ0Uy9Ymj/TGpK/LFoj2MX3kgY8yH0DIlWPTcjdrnUhHw779H6d9/Bn36NGDQoObcd18DV4ekcik3iWKbiPQCitk9wT4FrHRuWKqwSUxJ476v/2HtgTMAdKhVjqc61qBBSCkXR6acLTY2iddeW8Cnn66ibFlfKlb0d3VI6jLlJlE8AbwGpAO/YvUG+5Izg1KFgzGGhTtOMmn1IWZvOQaAv7cnC4e0I8hPB7YvCubM2UPfvr9z5Egs/ftH8NZbHShVysfVYanLlJtEcbMx5gXghfMTROQOrKShVLY2H46m6/8tzTRtaJfaPNKmKlmKMVUh5uXlQblyJZk6tRfNm4e4Ohx1heRSwz+IyL/GmCZZpq01xjR1amQXERERYdasWeOKXatcOhiVQNv3FgAQXs6P7x66jsqlSmg9RBGQkpLGhx+uICYmiZEjOwDWg5P62bue/bsdcSXrXvSOQkRuxhqmtLKIfOgwKwCrGEqpTN7+czsT/jlATKI1UNCg9uEMvqmmi6NS+WXp0oMZHfjddVedjAShScL95VT0dALYDCQCWxymx8IUz0IAACAASURBVAIvOjMo5V7mbj3OgJ/WkpJm3Z32aFSJdjXL0aOxPjxVFERFJfDCC/P45pt1VKkSyB9/9KZr12tdHZbKQxdNFMaYdcA6EfnJGJOYjzGpAi41LZ1xy/fzy5pD7DwelzG9UWgpJj56vQ4UVMRERZ1j4sTNPP98S1577QZKltQO/Aqb3FRmVxaRkUAdIKO5gjFGLxmKoP/7e1emgYIahARyfbUgHmldlXIB2pqlqNi27SS//LKFYcPace21QRw8+AxlypRwdVjKSXKTKMYBbwLvA52Bh9E6iiInNjGFHmOWsedkPAAPtwqj/w3VKa/JoUhJSEhh5MjFvPfecvz8vOjXrwkhIQGaJAq53CQKX2PMXyLyvjFmD/CKiCxxdmCq4IhLSuWmjxZzNDqRcv7eTH28JaFlfF0dlspns2fvZsCAmezbd5YHH2zIe+91omxZ7ayxKMhNokgSq+H7HhHpDxwGyjk3LOVqkWcSWLDjJH9uOsryPVEAPHfTtQy8MVyfgyiC4uKS6dNnGkFBJViw4EHatQtzdUgqH+UmUTwD+AFPAiOBQKCvM4NS+c8Yw7xtJ/hnbxRfL92XaV6DkEDubVaFe5pVcVF0yhXS0tL5+efN9O5dDz8/L+bN60OtWsF4e2sHfkXNJT9xY8w/9p+xQB8AEdFHLAuR137fzOQ1kZxLScuY1vSa0gxoV51W4cHaiqkIWrv2CI89NoO1a49SooQnd95ZR0ebK8JyTBQich1QGVhqjDklInWxuvJoD2iycHPR51J49bfNTN9wBIBXbq1N5/oVqRDgg4c+JFUkRUcn8uqrCxgzZjXlypVk4sQ7ueOO2q4OS7lYTk9mjwLuBDZgVWBPw+o59h2gf/6Ep5xlzILdvPfXDgCC/byY0r8lYTqKXJF3552/MH/+PgYOvI4332xPYKC2alM531F0BxoaY86JSBngiP16R/6Eppxh/aGzjFmwm7lbjwPwZo963Ne8ilZQF2F7956hbFlf/P29GTmyPcWKCdddp0/Vq//klCgSjTHnAIwxp0VkuyYJ92SMYdLqQ/z4zwE2H44BoO21ZXmtax3Cy/m5ODrlKsnJabz//nJGjFjMk0824513OmkPrypbOSWKaiJyvitxAcIcXmOMucOpkamrtvbAaYZO28z2Y7EZ00r5FueTexpzw7VlXRiZcrXFiw/Qv/8Mtm07Rc+edXjyyeauDkkVYDklijuzvB7tzEBU3vpq8V5GztoGQO2KAbSpEcyAdtUp5av98BR1H320gmefnUNYWClmzryXLl1quDokVcDl1Cng3/kZiLp6xhg2REbz6d+7mL/9BB7FhPF9m9EyPNjVoSkXS083xMcn4+/vza23XsvJkwm88kpbfH2Luzo05Qb0yRk3Z4xh+oYjTFx1iBV7ozLNW/5ie+2LSbFlywn695+ZMdLctdcG8dZbHVwdlnIjTk0UInIL8AngAXxtjHk7m2V6AcMBA2wwxtzrzJgKk8SUNGq9OjvjdUkvD26uV4EHW4TRICRQWzIVcQkJKYwYsYj3319BYKA3ffs2whij54W6bLlOFCLibYxJuozlPYAxQCcgElgtItONMVsdlqkBvAS0MsacERHtQyoXNh+OZuamo/y48gAAPsWLsfKlDlr/oDKsW3eUO+74hf37z/Lww414991OBAdrR47qylwyUYhIM+AbrD6eqohIQ+ARY8ygS6zaDNhtjNlrb2ci1rMZWx2W+R8wxhhzBsAYc+LyD6Ho2HIkmtf/2MqqfacB8Pf2pFdECO/c2UCvEhVAxh1DlSqBVKkSyPff96Bt22tcHZZyc7m5o/gU6Ar8BmCM2SAiN+ZivcrAIYfXkUDWNnjXAojIMqziqeHGmNmoCxyIiufWT5cC0LJ6EA+2DOPmutr3jrKkpqYzevQqpk/fwdy5fQgK8mXRoodcHZYqJHKTKIoZYw5kuWJNu9jCDrK7xDXZ7L8G0A6r76glIlLPGHM204ZEHgUeBahSpej0YHrk7Dle+W0zB6LiMwYM+l+bqgy9tY6LI1MFyapVh+nffwbr1h2jc+dwYmKSKF1aBxJSeSc3ieKQXfxk7HqHQcDOS6wD1h1EqMPrEKxuQLIus9IYkwLsE5EdWIljteNCxpgvgS8BIiIisiabQicxJY05W48zdNomYhNTKenlQed6FehSvyLdGlZydXiqgIiLS+aFF+by+edrqFjRn8mT7+LOO2trMaTKc7lJFI9jFT9VAY4D8+xpl7IaqCEiVbEGO7oHyNqi6TegNzBORIKxiqL25i70wict3fDc5A1MW3c4Y9r7dzWkZ1PtVkFdqHjxYixceIBBg5oxYkR7AgK8XR2SKqRykyhSjTH3XO6GjTGpIvIE8BdW/cO3xpgtIvIGsMYYM92ed5OIbMUqzhpijIm6+FYLr6TUNPp8syqjovrRttV4pE1VyvnrcxDqP7t3n+aNNxYxZkwX/P29Wbv2UXx89HEo5VxiTM4lOSKyB9gBTAJ+NcbE5riCk0VERJg1a9a4MoQ8l5iSRvO3/ib6XArXVyvDNw9eR0kdRUw5SEpK5d13lzFy5BK8vDyYOfNe2rTR1kwq90RkrTEm4krWzc0Id9VFpCVW0dHrIrIemGiMmXglO1QWYwyzNx/j/Tk7MiqqO9erwGf3NdEyZpXJggX7ePzxmezYEcXdd9flww9vplIlf1eHpYqQXF22GmOWA8tFZDjwMfAToIniKjwxYR0zNx0FoE7FAHo3C6VPizDXBqUKHGMMI0cuISUlndmz7+Pmm8NdHZIqgnLzwJ0f1oNy9wC1gd+Blk6Oq1DrMWYZ6w9ZLYAXDWnHNUE6spz6T3q64Ztv/uWWW8IJDQ1k/PjbKVXKhxIltAM/5Rq5uaPYDPwBvGuMWeLkeAqtpNQ0Hv/xXzYfjuZErNUTyu8DW2mSUJls3Hic/v1nsGJFJK+91pbXX7+RihW1mEm5Vm4SRTVjTLrTIymkTsYm8dj4Nfx78L9nCHs0qsSIHvXw99ErRGWJi0vm9dcX8tFHKyldugTjxnXngQcaujospYAcEoWIfGCMGQxMFZELmkbpCHeXNmrWNr5YbD0WUjHQh0faVOPhlmEUK6aV1Sqz4cMX8sEHK3jkkca8/XZHgoK0Az9VcOR0RzHJ/l9HtrsCi3aezEgSfVtV5dWu+sSsyuzQoWji41OoVSuYF19sTY8etWjduuh0UaPcR7GLzTDGrLL/rG2M+dvxH1altrqImRuP8uC31tv38/+u57VudTRJqAypqel8+OEKatcew2OPzQAgONhXk4QqsC6aKBz0zWZav7wOpLCYtekoAyf8C8CAdtVpUT3IxRGpgmTlykgiIr5k8OA5tGsXxvff93B1SEpdUk51FHdjNYmtKiK/OszyB85mv1bRdiouiQE/WUniu4ev48aaOg6T+s/MmTvp1u1nKlXy59dfe9GjRy2901RuIac6ilVAFFavr2McpscC65wZlDsyxnDb/1njRXzau7EmCQVY58WRI7FUrhxAx47VeOONG3nqqeb4+2sHfsp9XDRRGGP2AfuweotVl/C/H9ZyJDqRkl4e3KZdgStg584oBgyYyc6dUWzdOhA/Py9eeaWtq8NS6rLlVPS0yBhzg4icIfOAQwIYY0wZp0fnBowx9PlmFUt3nwJg7audXByRcrXExFTefnspo0YtpUQJT0aN6kCJEtrJo3JfOZ2954c7Dc6PQNzVwAn/snT3KXy9PFg05EZ8inu4OiTlQseOxdG27Xfs2nWa3r3r8eGHN1Ohgp+rw1LqquRU9HT+aexQ4IgxJllEWgMNgB+BmHyIr0CbsjaSWZuOAbDipQ4Eal88RVZKShrFi3tQvnxJ2ra9hjFjutCpU3VXh6VUnshN89jfsIZBrQ78gPUMxQSnRuUG0tMNQ6dtAmDNKx01SRRR6emGsWPXUL36p0RGxiAifP31bZokVKGSm0SRbo9pfQfwsTFmEFDZuWEVfBNXHyIpNZ1eESEE+2kLlqJow4ZjtGz5DY8/PpMaNYJISUlzdUhKOUWuhkIVkbuAPsD5p4OK9OVzUmoaL0/bRIniHoy8vb6rw1H5zBjDkCFz+fjjlZQpU4Lx42/nvvvq6zMRqtDKTaLoCwzA6mZ8r4hUBX52blgFW9dPrecl7r4ulOIeubkpU4WJiHDmzDn69bM68CtduoSrQ1LKqS75K2eM2Qw8CawRkVrAIWPMSKdHVkD99M8Bdp2Io2FoKYZ1q+PqcFQ+OXDgLD16TOTff61RCb/66ja++KKbJglVJFwyUYhIG2A38A3wLbBTRFo5O7CCaPX+0wydthmAbx+M0KKGIiAlJY13311GnTqfMXfuXnbssJ6X0a7iVVGSm6Knj4AuxpitACJSGxgPRDgzsIImMSWNu8auAKwuOoK0ArvQW778EI89NoPNm0/QvXtNPv20M1WqBLo6LKXyXW4Shdf5JAFgjNkmIl5OjKlA+mzBbgCeu+la7aKjiJg3by/R0Yn89tvddO9ey9XhKOUyYswFg9dlXkBkHJCEdRcBcB/ga4x50LmhZS8iIsKsWbMm3/Z3NPocLUbNB8CjmLBh2E34eWt3DIWRMYbx4zdStqwvnTvXICkplZSUdPz8itx1kSqERGStMeaKSoJy02SnP7AHeB54AdgLPHYlO3M3aekmI0lUCPDhr6fbapIopLZvP0X79j/w4IO/8d136wHw9vbUJKEUlyh6EpH6QHVgmjHm3fwJqeAYYxc3eXkUY8VL7bXyuhA6dy6Ft95awjvvLKNkSS+++KIrjzzSxNVhKVWgXPSOQkRexuq+4z5grohkN9JdoXX47Dk+nLsTgPnP3aBJopD644+dvPnmEu6+ux7btw/k0UebaosmpbLI6Y7iPqCBMSZeRMoCs7CaxxYJ/cevBeCVW2sTUtrXxdGovHTsWBzr1x/jllvCueuuOoSFPUKzZkW+VxqlLiqnOookY0w8gDHm5CWWLVT+3HSUTYejKe4hPNKmmqvDUXkkLS2dzz5bTc2ao+nTZxrnzqUgIpoklLqEnO4oqjmMlS1Adcexs40xdzg1MheavDYSgGUvtndxJCqv/PvvUfr3n8Hq1Ufo2LEan33WhRLa469SuZJTorgzy+vRzgykoJi6NpL520/Qu1ko5fx9XB2OygP79p2hWbOvCA72ZcKEO7jnnnpa56TUZchp4KK/8zOQguC3dYcZPHkDAA+1rOriaNTVMMawadMJGjQoT9Wqpfnuu+5061aTUqU0+St1uYpMvcOlrD90lqcnWe3nf/7f9dSs4O/iiNSV2rfvDF27/kzjxl+wceNxAPr0aahJQqkr5NREISK3iMgOEdktIi/msFxPETEi4pL+o87EJ9NjzDIAfnmsBS2qB7kiDHWVkpPTePvtpdSt+xmLFu3n/fc7UadOWVeHpZTby/VjxiLibYxJuozlPYAxQCcgElgtItMd+42yl/PH6sb8n9xuOy+diEmk2VtWKdtDLcNoVrWMK8JQVyktLZ2WLb9h7dqj3HFHbT7++GZCQ7UDP6XyQm66GW8mIpuAXfbrhiLyf7nYdjNgtzFmrzEmGZgIdM9muRHAu0Bi7sPOOz+sOADAfc2rMPy2uq4IQV2FmBjr2sXDoxh9+zbmjz96M3VqL00SSuWh3BQ9fQp0BaIAjDEbgBtzsV5l4JDD60iyjLUtIo2BUGPMjJw2JCKPisgaEVlz8uTJXOw6d/aejGPsoj0E+3nzRvd6ebZd5XzGGMaNW0+1ap/w++/bARgw4Dq6dr3WxZEpVfjkJlEUM8YcyDItN6PIZ9f+MKOrWhEphjXWxeBLbcgY86UxJsIYE1G2bN6UOaempXPn58tJTTe8f1cDPLTbBrexdetJ2rX7nocf/p1atYKpXl2LC5VyptzUURwSkWaAsesdBgE7c7FeJBDq8DoEOOLw2h+oByy027RXAKaLyG3GGKf3I/7a9C2cSUgh4prStKtZztm7U3nk3XeXMXTofAICvPn66248/HBj7ZtJKSfLTaJ4HKv4qQpwHJhnT7uU1UANEakKHAbuAe49P9MYEw0En38tIguB5/IjSQBM+Oeg9f//rs+P3amrZIxBRKhQwY/77qvPe+91omzZkq4OS6ki4ZKJwhhzAutH/rIYY1JF5AngL8AD+NYYs0VE3gDWGGOmX3a0eeTtP60y7WplS+LlqY+SFGRHjsTy1FOzadOmCk8+2ZwHHmjIAw80dHVYShUpl0wUIvIVDnUL5xljHr3UusaYWVi9zjpOe+0iy7a71PbygjGGsYv2APDHE63zY5fqCpzvwG/o0PmkpKTTsmWIq0NSqsjKTdHTPIe/fYDbydyaya30/molAG1qBFNSR6srkNavP8Yjj0xn7dqj3HRTdT77rItWWCvlQrkpeprk+FpExgNznRaRE323bB8r954GYNzDzVwcjbqY6OhEjhyJZdKkntx1Vx3twE8pF7uSS+qqwDV5HYizJaak8fof1kPhq4d21OawBYgxhsmTt7JrVxRDh7blhhvC2Lv3KXx89I5PqYIgN09mnxGR0/a/s1h3Ey87P7S8Nd5+Avv2xpUp6+/t4mjUeXv2nKZLlwncffcUfv99Bykp1iM6miSUKjhy/DaKdc/fEKt5K0C6MeaCim138N6cHYjAB3dpi5mCICkplfffX86bby6hePFifPLJLQwYcB2e2gpNqQInx0RhjDEiMs0Y0zS/AnKGkTO3kpyaTs+mIfpwVgFx6FAMI0Ysplu3mnz88c1Urhzg6pCUUheRm8u3VSLSxOmROMnZhGS+WrIPgME3aT9ArnTyZDyjR68CIDy8DFu3DmTy5Ls0SShVwF30jkJEPI0xqUBr4H8isgeIx+rDyRhj3CJ5jF20F4B372xAxcASLo6maEpPN3z33Tqef34esbFJdOpUjZo1g6lWrbSrQ1NK5UJORU+rgCZAj3yKxSn+PXAGXy8P7orQB7ZcYfPmEzz++EyWLj1ImzZVGDu2KzVrBl96RaVUgZFTohAAY8yefIrFKbYejaFl9WBti+8Cyclp3HTTeJKT0/j229t46KFG+jko5YZyShRlReTZi800xnzohHjyVFJqGnFJqaSlp7s6lCJl/vx93HDDNXh5efDLL3dRq1YwwcG+rg5LKXWFcqrM9gD8sLoDz+5fgffrv1ar3pvrVnBxJEVDZGQMd975Cx06/MAPP2wAoHXrKpoklHJzOd1RHDXGvJFvkTjBnC3HAOjeqPIlllRXIzU1ndGjV/HqqwtIS0tn1KgO3HdfA1eHpZTKI5eso3BnK/eepqy/NyW8PFwdSqHWp880Jk7cTOfO4YwZ04WqVbU1k1KFSU6JokO+ReEEZ+KTOZeSRvdGlVwdSqF09mwinp7F8PPzYuDA67jzztrceWdtraxWqhC6aB2FMeZ0fgaS15bsPgVAk2v06jYvGWOYOHEztWuP4dVX5wNWPUTPntrLq1KFVaHtWGfb0RgAWoVrm/28snv3aW6++Ud6955KSEgA99+v9RBKFQWFtovOH5bvB6BigI9rAykkJkzYRN++v+Pt7cno0Z3p3z8CD49Ce52hlHJQKBPF6fhk4pPTCAvy1U4Ar1JKShrFi3sQEVGJnj3r8O67nahUyS1aRyul8kihTBTni50eaVPNxZG4rxMn4hk8eA7x8cn8+uvdXHttED/+eIerw1JKuUChLDuIOZcCQJ1K2ivp5UpPN3z55Vpq1hzNpEmbqVu3LGlp+mS7UkVZobyjWLTzJABVyugTwZdj794z3H//r6xYEUm7dmF8/vmt1KqljQGUKuoKZaLYEBmNRzEh2E+HPL0cgYHenD2byPff96BPnwba3FUpBRTCoidjDNuOxtCkSilXh+IWpk/fwR13TCItLZ2gIF82bx7AAw801CShlMpQ6BLF54usXtFrlNeWOTk5eDCaHj0m0r37RHbujOLo0TgAbSWmlLpAoSt6GjN/NwAvda7l4kgKptTUdD7+eCXDhi3EGMM773TkmWeup3hx7Q9LKZW9QpUo0tIN8clpVA0uib9PcVeHUyClpaXz9df/0r59Vf7v/zoTFqZFdEqpnBWqoqf1h84A0LOpDnvq6MyZc7zwwlxiY5Pw9vZk2bK+TJ9+jyYJpVSuFKpE8e+BswA0q1rGxZEUDMYYfvppI7VqjeGDD1awYMF+AIKCfLWyWimVa4Wq6OmjeTsBqF850MWRuN7OnVEMGDCTv//eR7Nmlfnrr/tp1EhH+lNKXb5CkyhOxCaSkJxGxUAffLRilqefns2aNUf47LMuPPpoU+3ATyl1xQpNopi/7QQAg2+q6eJIXGfu3D3UqhVMaGggn39+K97enlSo4OfqsJRSbs6pl5kicouI7BCR3SLyYjbznxWRrSKyUUT+FpFrrnRf24/FAtCpdvmriNg9HTsWx733TuWmm37knXeWAXDNNaU0SSil8oTTEoWIeABjgM5AHaC3iNTJstg6IMIY0wCYArx7pfubt+041YJLEuhbdJrFpqcbxo5dQ61ao5k6dRvDht3A++/f5OqwlFKFjDPvKJoBu40xe40xycBEoLvjAsaYBcaYBPvlSuCK2rWmpxuiz6UUqSQBMGrUEh5/fCZNm1Zi48b+DB/eDh+fQlOaqJQqIJz5q1IZOOTwOhJonsPy/YA/s5shIo8CjwJUqVLlgvl7TsYRm5hKh1rlrjhYdxEbm8SpUwlUrVqa/v0jqFq1NL1719Pmrkopp3HmHUV2v1wm2wVF7gcigPeym2+M+dIYE2GMiShbtuwF86f8GwlA3UqFt1msMYZp07ZRp85n3H33FIwxBAX5cu+99TVJKKWcypmJIhIIdXgdAhzJupCIdASGArcZY5KuZEcr9kRR0suDGwvpHcWBA2e57baJ3HHHL5QpU4JPP+2syUEplW+cWfS0GqghIlWBw8A9wL2OC4hIY+AL4BZjzIkr3VFcYirh5QpnC58VKw7RseN4AN5/vxNPPXU9np76TIRSKv847RfHGJMKPAH8BWwDfjHGbBGRN0TkNnux9wA/YLKIrBeR6Ze7n5S0dPaeiqd5taA8i70giImxbq6aNKlI376N2LZtIIMHt9QkoZTKd05tImOMmQXMyjLtNYe/O17tPtYfsvp3qhToc7WbKhCiohJ48cV5zJmzly1bBuDn58X//V8XV4ellCrC3L4t5bqDVo+xrWu499jOxhjGj9/I4MFzOHPmHM8+2wKthlBKFQRunyj2nowHIKS0r4sjuXLR0Yn06DGJhQv306JFCGPHdqVBg6L3hLlSqmBy+0Sx+Ug0gFt2BGiMQUQICPAmONiXL7/sSr9+TXQ4UqVUgeLWNaNJqWlsPhxDaJkSrg7lsv31126aNPmSyMgYRITJk+/if/9rqklCKVXguHWi2HokBoC7I0IvsWTBcfRoLPfcM4VbbvmJhIQUTpyId3VISimVI7cuetpsJwp3aRo7ZswqXn55PklJqbz+ejteeKEV3t5u/REopYoAt/6Vijxj9SdYq4K/iyPJnbVrj9K8eWXGjOlCjRrukdyUUsqtE8XBqATK+Xvj71Mwe42NiUnitdcW0KdPA5o2rcRnn92Kt7eHdr+hlHIrbp0o/tx8jIYhBa8jQGMMU6du46mnZnP0aCxVqgTStGkl7QJcKeWW3PaXKyE5FSh4z0/s23eGJ574k1mzdtGoUQV+/bUXzZtf0TAbSilVILhtoog+lwLAdWGlXRxJZj/9tInFiw/w0Uc388QTzbRvJqWU23PbRLHlsNXiqXRJLxdHAkuWHCApKY2OHasxZEhLHnqoESEhAa4OSyml8oTbXu6+9ec2AOpXdl0dxalTCfTt+ztt247jjTcWAeDt7alJQilVqLjlHYUxhmPRifh6eVCtbP6PQ2GMYdy49QwZMpfo6CReeKEVr77aNt/jUAVfSkoKkZGRJCYmujoUVUT4+PgQEhJC8eJ51xrULRPFliMxJCSn8fwtNV2y/1mzdtG373RatQpl7Niu1KtXOEfWU1cvMjISf39/wsLCtFm0cjpjDFFRUURGRlK1atU8265bFj1tsTsCrFMx/4p4EhJSWLbsIABdutTg99/vYfHihzVJqBwlJiYSFBSkSULlCxEhKCgoz+9g3TJRbLYrshuFlsqX/f355y7q1fuMzp1/4uzZRESE226rqR34qVzRJKHykzPON7dMFDuPxwJQyte5LZ4OH47hrrsm06XLBLy9Pfnjj96UKlU4RtJTSqnccstE4eVZjNK+zu2248SJeOrU+YwZM3by5ps3smFDf264Icyp+1TKGTw8PGjUqBH16tWjW7dunD17NmPeli1baN++Pddeey01atRgxIgRGGMy5v/5559ERERQu3ZtatWqxXPPPeeKQ8jRunXreOSRRzJN6969Oy1atMg07aGHHmLKlCmZpvn5/dcYZufOnXTp0oXw8HBq165Nr169OH78+FXFdvr0aTp16kSNGjXo1KkTZ86cuWCZBQsW0KhRo4x/Pj4+/PbbbwC0adMmY3qlSpXo0aMHADNmzGDYsGFXFdtlMca41b+mTZuaWq/8aXp/ucI4Q2RkdMbfn3yy0uzeHeWU/aiiYevWra4OwZQsWTLj7wceeMC8+eabxhhjEhISTLVq1cxff/1ljDEmPj7e3HLLLWb06NHGGGM2bdpkqlWrZrZt22aMMSYlJcWMGTMmT2NLSUm56m307NnTrF+/PuP1mTNnTEhIiKlVq5bZu3dvxvQHH3zQTJ48OdO659+bc+fOmfDwcDN9+vSMefPnzzebNm26qtiGDBliRo0aZYwxZtSoUeb555/PcfmoqChTunRpEx8ff8G8O+64w3z//ffGGGPS09NNo0aNsl3OmOzPO2CNucLfXbds9XQuJY1zKWl5us3o6EReeWU+X3yxlpUrH6FJk4o8+WTzPN2HKtpe/2NLxhgqeaVOpQCGdaub6+VbtGjBxo0bAZgwYQKtWrXipptuAsDX6Z14DQAAEDxJREFU15fRo0fTrl07Bg4cyLvvvsvQoUOpVasWAJ6engwYMOCCbcbFxTFo0CDWrFmDiDBs2DDuvPNO/Pz8iIuLA2DKlCnMmDGDcePG8dBDD1GmTBnWrVtHo0aNmDZtGuvXr6dUKavOMTw8nGXLllGsWDH69+/PwYNWI5KPP/6YVq1aZdp3bGwsGzdupGHDhhnTpk6dSrdu3ShfvjwTJ07kpZdeuuT7MmHCBFq0aEG3bt0ypt144425fl8v5vfff2fhwoUAPPjgg7Rr14533nnnostPmTKFzp074+ubuWui2NhY5s+fz3fffQdY9RDt2rVjxowZ9OrV66rjvBS3SxRp6dZtcbOqZfJke8YYJk/eytNPz+bYsTieeKIZ1asXrG5BlMoLaWlp/P333/Tr1w+wip2aNm2aaZnq1asTFxdHTEwMmzdvZvDgwZfc7ogRIwgMDGTTpk0A2RavZLVz507mzZuHh4cH6enpTJs2jYcffph//vmHsLAwypcvz7333sszzzxD69atOXjwIDfffDPbtm3LtJ01a9ZQr169TNN+/vlnhg0bRvny5enZs2euEsXmzZsveC+yExsbS5s2bbKdN2HCBOrUqZNp2vHjx6lYsSIAFStW5MSJEzluf+LEiTz77LMXTJ82bRodOnQg4P/bu/vgqOpzgePfR94SIHA1CFMLGjpSzIshQmiDBREpaJULF0aN4aXi6PUKIrUoDh2cuVatpa2gjeBFrtdJem2TgFOBAYUL3FgQeTHcRkCgaUrXgKaYYi6mFAgvT/84J5sl2exuYnY3u3k+Mzuz5+x5+eWZzXn2/M45z69P452e2dnZ7NixwxKFP3VnnWKA7XFrrKoybdpq1q49wvDhX2P9+jyys6/5yts1xp/W/PJvT2fOnCErKwuPx8OIESOYMGEC0Dhmuz+tuXNm69atFBcXe6evvDL4D6177rmHLl2cce5zc3N59tlneeCBByguLiY3N9e73UOHDnnX+fLLL6mrqyMpqXH8merqaq6++mrv9IkTJ6isrGT06NGICF27duXgwYNkZGT4/Ztae4dQUlIS5eXlrVonVNXV1Rw4cIDbb7+92WdFRUXNrsP079+fzz77LCxtaSrmLmbXX7wEQHZK288ozrvdViLC6NGDyM+/g717H7IkYeJSYmIi5eXlfPLJJ9TX17NixQoA0tPTKSsru2zZo0eP0rt3b5KSkkhPT2ffvn1Bt99SwvGd1/S+/l69ennfjxo1isrKSmpqali7di3Tpk0D4NKlS+zatYvy8nLKy8v59NNPL0sSDX+b77ZLSkqora1l8ODBpKSk4PF4vEksOTn5srOdL774gn79+nljEcrfWldXd9mFZ9+Xb1JrMGDAAKqrqwEnEfTv3/JzV6tXr2bq1KnNnqg+efIke/fu5a677rps/tmzZ0lMTAza5vYQc4ni3AXnID8gqUeb1n/vPQ+ZmStZt+4IAE88cTOPPfZtunSJuVAY0yp9+/YlPz+fF198kfPnzzNjxgzef/99tm7dCjhnHvPnz+epp54CYOHChbzwwgtUVFQAzoF72bJlzbY7ceJEli9f7p1uOBgPGDCAw4cPe7uWWiIiTJ06lQULFpCamkpycrLf7fr7JZ+amkplZaV3uqioiE2bNuHxePB4POzbt8+bKG699VZKSkqor68HoKCgwHsdYvr06XzwwQds3LjRu61NmzZ5u9MaNJxR+Hs17XYCmDx5MoWFhQAUFhYyZcqUFuNQVFREXl5es/lr1qxh0qRJJCRcfmt+RUVFs263cIm5o+O585dI6tGVrq08sNfUnOb++9cyblwh585dIKmNicaYWHbTTTcxbNgwiouLSUxMZN26dTz//PMMHTqUG2+8kZEjRzJv3jwAMjMzefnll8nLyyM1NZWMjAzvr2NfTz/9NLW1tWRkZDBs2DBKS0sBWLJkCZMmTeK2227z9tO3JDc3lzfffNPb7QSQn59PWVkZmZmZpKWlsXLlymbr3XDDDZw6dYq6ujo8Hg9VVVXk5OR4Px88eDB9+vRhz549TJo0iTFjxjBixAiysrLYuXOn98JyYmIiGzZs4JVXXmHIkCGkpaVRUFAQ8AwgFIsWLWLLli0MGTKELVu2sGjRIsC5tuLbleTxeDh27Bhjx45tto3i4mK/CaS0tLTZWUa4iPrcMx0LklNSdei/LeeDH40PeZ2iogM8+ug7/O1v9SxceDOLF99CzzA/h2EMwOHDh0lNTY12M+LaSy+9RFJSUrM+/Hh24sQJpk+fzrZt2/x+7u97JyL7VDW7LfuLuTOK0+cucF1yr+AL+rhw4RIZGf0pL3+En/xkvCUJY+LInDlz6NGjc/UQVFVVsXTp0ojtL+buerriCqFrl8B3Kpw+Xc9zz23n2mv7MnfuSGbOzGTmzEyruWNMHEpISGDWrFnRbkZEjRw5MqL7i7kziouXlNQAt8Zu2FBBevqr/OxnO6moOAk4F8ssSZhoibXuXRPbwvF9i7kzCoD6C5eazTt+/Evmz3+Xt98+Qlra1WzfPpsxY66LQuuMaZSQkMDJkyet1LiJCHXHo2h6h9RXFZOJ4vr+zUe1O3q0ls2b/8RPfzqeBQtG0b17lyi0zJjLDRw4kOPHj1NTUxPtpphOomGEu/YUk4niql5OefG9ez9l165j/OAHOdxyy3VUVT1OcnLPIGsbEzndunVr15HGjImGsF6jEJE7ROQPIlIpIov8fN5DRErcz/eISEoo2x3Uuwdz524kJ+d1li3bzenTzgM0liSMMab9hS1RiEgXYAXwPSANyBORpo8uPgjUqur1wEtAy2UVfUy4+Q1ee20f8+d/mwMH5tCrV3gHMDLGmM4snF1P3wIqVfUogIgUA1MA34IoU4Bn3PdvActFRDTIZftBA/vyzsYZDB8e+GlPY4wxX104E8XXgWM+08eBpgM8eJdR1QsicgpIBv7qu5CIPAw87E6eK/vLwwdDqAjcGfSjSaw6MYtFI4tFI4tFo6FtXTGcicLfvYBNzxRCWQZVXQWsAhCRsrY+hh5vLBaNLBaNLBaNLBaNRKQs+FL+hfNi9nFgkM/0QKBp8XTvMiLSFegLfBHGNhljjGmlcCaKD4EhIjJYRLoD9wHrmyyzHrjffX838L/Brk8YY4yJrLB1PbnXHOYBm4EuwBuq+rGIPIszyPd64L+A/xaRSpwziftC2PSqcLU5BlksGlksGlksGlksGrU5FjFXZtwYY0xkxVxRQGOMMZFlicIYY0xAHTZRhKv8RywKIRYLROSQiOwXkW0iErdlc4PFwme5u0VERSRub40MJRYicq/73fhYRH4T6TZGSgj/I9eKSKmI/N79P7kzGu0MNxF5Q0Q+F5GDLXwuIpLvxmm/iAwPacOq2uFeOBe//wR8A+gOfASkNVlmLrDSfX8fUBLtdkcxFuOAnu77OZ05Fu5yScB2YDeQHe12R/F7MQT4PXClO90/2u2OYixWAXPc92mAJ9rtDlMsbgGGAwdb+PxO4F2cZ9hygD2hbLejnlF4y3+oaj3QUP7D1xSg0H3/FjBe4rPgf9BYqGqpqv7dndyN88xKPArlewHwHPBz4GwkGxdhocTiX4EVqloLoKqfR7iNkRJKLBRoGPGsL82f6YoLqrqdwM+iTQF+pY7dwD+JSNBaSB01Ufgr//H1lpZR1QtAQ/mPeBNKLHw9iPOLIR4FjYWI3AQMUtUNkWxYFITyvfgm8E0R2Skiu0Xkjoi1LrJCicUzwEwROQ68AzwWmaZ1OK09ngAddzyKdiv/EQdC/jtFZCaQDYwNa4uiJ2AsROQKnCrEsyPVoCgK5XvRFaf76Vacs8wdIpKhqv8f5rZFWiixyAMKVHWpiIzCeX4rQ1WbD5cZ39p03OyoZxRW/qNRKLFARL4LLAYmq+q5CLUt0oLFIgnIAN4TEQ9OH+z6OL2gHer/yDpVPa+qfwb+gJM44k0osXgQWA2gqruABJyCgZ1NSMeTpjpqorDyH42CxsLtbnkNJ0nEaz80BImFqp5S1X6qmqKqKTjXayarapuLoXVgofyPrMW50QER6YfTFXU0oq2MjFBiUQWMBxCRVJxE0RnHp10PfN+9+ykHOKWq1cFW6pBdTxq+8h8xJ8RY/ALoDaxxr+dXqerkqDU6TEKMRacQYiw2AxNF5BBwEVioqiej1+rwCDEWTwD/KSI/xOlqmR2PPyxFpAinq7Gfez3m34FuAKq6Euf6zJ1AJfB34IGQthuHsTLGGNOOOmrXkzHGmA7CEoUxxpiALFEYY4wJyBKFMcaYgCxRGGOMCcgShelwROSiiJT7vFICLJvSUqXMVu7zPbf66EduyYuhbdjGIyLyfff9bBG5xuez10UkrZ3b+aGIZIWwzuMi0vOr7tt0XpYoTEd0RlWzfF6eCO13hqoOwyk2+YvWrqyqK1X1V+7kbOAan88eUtVD7dLKxna+SmjtfBywRGHazBKFiQnumcMOEfk/93Wzn2XSRWSvexayX0SGuPNn+sx/TUS6BNndduB6d93x7hgGB9xa/z3c+UukcQyQF915z4jIkyJyN07NrV+7+0x0zwSyRWSOiPzcp82zReSVNrZzFz4F3UTkP0SkTJyxJ37szpuPk7BKRaTUnTdRRHa5cVwjIr2D7Md0cpYoTEeU6NPt9LY773NggqoOB3KBfD/rPQL8UlWzcA7Ux91yDbnAd9z5F4EZQfb/z8ABEUkACoBcVb0Rp5LBHBG5CpgKpKtqJvC878qq+hZQhvPLP0tVz/h8/BYwzWc6FyhpYzvvwCnT0WCxqmYDmcBYEclU1XycWj7jVHWcW8rjaeC7bizLgAVB9mM6uQ5ZwsN0emfcg6WvbsByt0/+Ik7doqZ2AYtFZCDwW1X9o4iMB0YAH7rlTRJxko4/vxaRM4AHpwz1UODPqlrhfl4IPAosxxnr4nUR2QiEXNJcVWtE5KhbZ+eP7j52utttTTt74ZSr8B2h7F4ReRjn//prOAP07G+ybo47f6e7n+44cTOmRZYoTKz4IXACGIZzJtxsUCJV/Y2I7AHuAjaLyEM4ZZULVfVHIexjhm8BQRHxO76JW1voWzhF5u4D5gG3teJvKQHuBY4Ab6uqinPUDrmdOKO4LQFWANNEZDDwJDBSVWtFpACn8F1TAmxR1bxWtNd0ctb1ZGJFX6DaHT9gFs6v6cuIyDeAo253y3qcLphtwN0i0t9d5ioJfUzxI0CKiFzvTs8Cfuf26fdV1XdwLhT7u/OoDqfsuT+/Bf4FZ4yEEndeq9qpqudxupBy3G6rPsBp4JSIDAC+10JbdgPfafibRKSniPg7OzPGyxKFiRWvAveLyG6cbqfTfpbJBQ6KSDlwA86Qj4dwDqj/IyL7gS043TJBqepZnOqaa0TkAHAJWIlz0N3gbu93OGc7TRUAKxsuZjfZbi1wCLhOVfe681rdTvfax1LgSVX9CGd87I+BN3C6sxqsAt4VkVJVrcG5I6vI3c9unFgZ0yKrHmuMMSYgO6MwxhgTkCUKY4wxAVmiMMYYE5AlCmOMMQFZojDGGBOQJQpjjDEBWaIwxhgT0D8AU4eGWi4MiuoAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### SGD" + "### RMSProp" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4728 - accuracy: 0.7986\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4467 - accuracy: 0.8118\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4456 - accuracy: 0.8131\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4442 - accuracy: 0.8128\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4437 - accuracy: 0.8132\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4438 - accuracy: 0.8136\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4433 - accuracy: 0.8133\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4438 - accuracy: 0.8145 0s - loss: 0.442\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4428 - accuracy: 0.8142\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4433 - accuracy: 0.8133\n", + "Optimal Threshold: 0.22694841\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5668,28 +6717,83 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam" + "### SGD" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.5018 - accuracy: 0.7766\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 90us/step - loss: 0.4568 - accuracy: 0.8002 0s - l\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4531 - accuracy: 0.8077\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4508 - accuracy: 0.8094\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4495 - accuracy: 0.8109\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4479 - accuracy: 0.8113 0s - loss: 0.4496 - \n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4469 - accuracy: 0.8121\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4466 - accuracy: 0.8126\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4461 - accuracy: 0.8121\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4454 - accuracy: 0.81180s - loss: 0.4\n", + "Optimal Threshold: 0.27847895\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.41 0.50 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.67 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5698,14 +6802,150 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### conclude that best optimizer is adagrad, so now we use filtered data to run tests" + "#### We conclude that best optimizer is adagrad. Testing it on the test set." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4657 - accuracy: 0.7962\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4526 - accuracy: 0.8109\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4506 - accuracy: 0.8133\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 124us/step - loss: 0.4496 - accuracy: 0.8134\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4490 - accuracy: 0.8135\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4484 - accuracy: 0.8130\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4480 - accuracy: 0.8134\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4476 - accuracy: 0.8130\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4474 - accuracy: 0.8135\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4471 - accuracy: 0.8130\n", + "Optimal Threshold: 0.26529908\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.69 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 3 Layer Adam0.5321260.771859
5Naive Bayes0.5223060.745084
6Neural Network - adagrad0.3598390.763871
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859\n", + "5 Naive Bayes 0.522306 0.745084\n", + "6 Neural Network - adagrad 0.359839 0.763871" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model = Sequential()\n", "model.add(Dense(12, input_dim=17, activation='relu'))\n", @@ -5719,92 +6959,62 @@ "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test_filter, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network - adagrad (filtered features)\")\n", "\n", "print(classification_report(y_test,predictions))\n", "\n", - "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", - " classification_report(y_test, mlp.predict(X_test_filter), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", " auroc])\n", "\n", "evaluation" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Naive Bayes\n", - "#### Theory\n", - "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", - "##### Bayes Theorem:\n", - "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", - "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", - "One assumption about naive bayes is that the predictors/features are independent." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training the Naive bayes model" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4652 - accuracy: 0.7974\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4507 - accuracy: 0.8062\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - ETA: 0s - loss: 0.4479 - accuracy: 0.81 - 2s 95us/step - loss: 0.4475 - accuracy: 0.8112\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4457 - accuracy: 0.8122\n", + "Epoch 5/10\n", + " 6760/17496 [==========>...................] - ETA: 1s - loss: 0.4413 - accuracy: 0.8142" + ] + } + ], "source": [ - "from sklearn import datasets\n", - "from sklearn import metrics\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Neural Network - adagrad (all features)\")\n", "\n", - "gnb = GaussianNB()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#training naive bayes model\n", - "gnb.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#classifying values\n", - "predicted = gnb.predict(X_train)\n", - "expected = y_train" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot roc for naive Bayes\n", - "get_roc(gnb, y_test, X_test, \"Naive Bayes\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#accessing model performance\n", - "print(metrics.classification_report(expected, predicted))\n", - "print(metrics.confusion_matrix(expected, predicted))" + "print(classification_report(y_test,predictions))\n", + "\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation\n" ] } ], @@ -5829,7 +7039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb index 17c9245..99725fe 100644 --- a/BT2101_Default - EDIT-MASTER- Reon.ipynb +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -225,7 +225,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 20000\n", " 2\n", " 2\n", @@ -249,7 +249,7 @@ " 1\n", " \n", " \n", - " 2\n", + " 2\n", " 120000\n", " 2\n", " 2\n", @@ -273,7 +273,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 90000\n", " 2\n", " 2\n", @@ -297,7 +297,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 50000\n", " 2\n", " 2\n", @@ -321,7 +321,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 50000\n", " 1\n", " 2\n", @@ -513,7 +513,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -772,7 +772,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " LIMIT_BAL\n", " AGE\n", " PAY_0\n", @@ -799,10 +799,10 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 \\\n", + " 0 1 2 3 4 5 6 7 8 \\\n", "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", "\n", - " 9 10 11 12 13 14 15 \\\n", + " 9 10 11 12 13 14 15 \\\n", "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", "\n", " 16 17 18 19 \n", @@ -857,7 +857,7 @@ " \n", " \n", " \n", - " LIMIT_BAL\n", + " LIMIT_BAL\n", " 30000.0\n", " 167484.322667\n", " 129747.661567\n", @@ -868,7 +868,7 @@ " 1000000.0\n", " \n", " \n", - " AGE\n", + " AGE\n", " 30000.0\n", " 35.485500\n", " 9.217904\n", @@ -879,7 +879,7 @@ " 79.0\n", " \n", " \n", - " PAY_0\n", + " PAY_0\n", " 30000.0\n", " -0.016700\n", " 1.123802\n", @@ -890,7 +890,7 @@ " 8.0\n", " \n", " \n", - " PAY_2\n", + " PAY_2\n", " 30000.0\n", " -0.133767\n", " 1.197186\n", @@ -901,7 +901,7 @@ " 8.0\n", " \n", " \n", - " PAY_3\n", + " PAY_3\n", " 30000.0\n", " -0.166200\n", " 1.196868\n", @@ -912,7 +912,7 @@ " 8.0\n", " \n", " \n", - " PAY_4\n", + " PAY_4\n", " 30000.0\n", " -0.220667\n", " 1.169139\n", @@ -923,7 +923,7 @@ " 8.0\n", " \n", " \n", - " PAY_5\n", + " PAY_5\n", " 30000.0\n", " -0.266200\n", " 1.133187\n", @@ -934,7 +934,7 @@ " 8.0\n", " \n", " \n", - " PAY_6\n", + " PAY_6\n", " 30000.0\n", " -0.291100\n", " 1.149988\n", @@ -945,7 +945,7 @@ " 8.0\n", " \n", " \n", - " BILL_AMT1\n", + " BILL_AMT1\n", " 30000.0\n", " 51223.330900\n", " 73635.860576\n", @@ -956,7 +956,7 @@ " 964511.0\n", " \n", " \n", - " BILL_AMT2\n", + " BILL_AMT2\n", " 30000.0\n", " 49179.075167\n", " 71173.768783\n", @@ -967,7 +967,7 @@ " 983931.0\n", " \n", " \n", - " BILL_AMT3\n", + " BILL_AMT3\n", " 30000.0\n", " 47013.154800\n", " 69349.387427\n", @@ -978,7 +978,7 @@ " 1664089.0\n", " \n", " \n", - " BILL_AMT4\n", + " BILL_AMT4\n", " 30000.0\n", " 43262.948967\n", " 64332.856134\n", @@ -989,7 +989,7 @@ " 891586.0\n", " \n", " \n", - " BILL_AMT5\n", + " BILL_AMT5\n", " 30000.0\n", " 40311.400967\n", " 60797.155770\n", @@ -1000,7 +1000,7 @@ " 927171.0\n", " \n", " \n", - " BILL_AMT6\n", + " BILL_AMT6\n", " 30000.0\n", " 38871.760400\n", " 59554.107537\n", @@ -1011,7 +1011,7 @@ " 961664.0\n", " \n", " \n", - " PAY_AMT1\n", + " PAY_AMT1\n", " 30000.0\n", " 5663.580500\n", " 16563.280354\n", @@ -1022,7 +1022,7 @@ " 873552.0\n", " \n", " \n", - " PAY_AMT2\n", + " PAY_AMT2\n", " 30000.0\n", " 5921.163500\n", " 23040.870402\n", @@ -1033,7 +1033,7 @@ " 1684259.0\n", " \n", " \n", - " PAY_AMT3\n", + " PAY_AMT3\n", " 30000.0\n", " 5225.681500\n", " 17606.961470\n", @@ -1044,7 +1044,7 @@ " 896040.0\n", " \n", " \n", - " PAY_AMT4\n", + " PAY_AMT4\n", " 30000.0\n", " 4826.076867\n", " 15666.159744\n", @@ -1055,7 +1055,7 @@ " 621000.0\n", " \n", " \n", - " PAY_AMT5\n", + " PAY_AMT5\n", " 30000.0\n", " 4799.387633\n", " 15278.305679\n", @@ -1066,7 +1066,7 @@ " 426529.0\n", " \n", " \n", - " PAY_AMT6\n", + " PAY_AMT6\n", " 30000.0\n", " 5215.502567\n", " 17777.465775\n", @@ -1169,7 +1169,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1181,7 +1181,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -1193,7 +1193,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1258,7 +1258,7 @@ "metadata": {}, "source": [ "### Removing Noise\n", - "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be replaced with Education = Others, which has value 4" + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4" ] }, { @@ -1269,7 +1269,7 @@ { "data": { "text/plain": [ - "array([2, 1, 3, 4])" + "array([2, 1, 3, 4, 0], dtype=int64)" ] }, "execution_count": 14, @@ -1278,7 +1278,7 @@ } ], "source": [ - "df['EDUCATION'].replace([0,5,6], 4, regex=True, inplace=True)\n", + "df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)\n", "df[\"EDUCATION\"].unique()" ] }, @@ -1286,28 +1286,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, for Marriage" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['MARRIAGE'].replace([0], 3, regex=True, inplace=True)\n", - "df[\"MARRIAGE\"].unique()" + "Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, \"Missing\"" ] }, { @@ -1328,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1341,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1391,7 +1370,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 1\n", " 1\n", " -1\n", @@ -1400,7 +1379,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " -1\n", " 1\n", " 0\n", @@ -1409,7 +1388,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0\n", " 0\n", " 0\n", @@ -1418,7 +1397,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0\n", " 0\n", " 0\n", @@ -1427,7 +1406,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " -1\n", " 0\n", " -1\n", @@ -1449,7 +1428,7 @@ "5 -1 0 -1 0 0 0" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1461,7 +1440,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1480,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1529,7 +1508,7 @@ " \n", " \n", " \n", - " count\n", + " count\n", " 26245.000000\n", " 26245.000000\n", " 26245.000000\n", @@ -1553,11 +1532,11 @@ " 26245.000000\n", " \n", " \n", - " mean\n", + " mean\n", " 149324.899981\n", " 1.608954\n", - " 1.852734\n", - " 1.564717\n", + " 1.850753\n", + " 1.558773\n", " 35.006592\n", " 0.372109\n", " 0.337321\n", @@ -1577,11 +1556,11 @@ " -0.428158\n", " \n", " \n", - " std\n", + " std\n", " 116558.616530\n", " 0.487994\n", - " 0.738572\n", - " 0.521936\n", + " 0.738175\n", + " 0.522639\n", " 8.832028\n", " 0.765730\n", " 0.814878\n", @@ -1601,11 +1580,11 @@ " 0.900723\n", " \n", " \n", - " min\n", + " min\n", " 10000.000000\n", " 1.000000\n", - " 1.000000\n", - " 1.000000\n", + " 0.000000\n", + " 0.000000\n", " 21.000000\n", " 0.000000\n", " 0.000000\n", @@ -1625,7 +1604,7 @@ " -2.000000\n", " \n", " \n", - " 25%\n", + " 25%\n", " 50000.000000\n", " 1.000000\n", " 1.000000\n", @@ -1649,7 +1628,7 @@ " -1.000000\n", " \n", " \n", - " 50%\n", + " 50%\n", " 120000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1673,7 +1652,7 @@ " 0.000000\n", " \n", " \n", - " 75%\n", + " 75%\n", " 210000.000000\n", " 2.000000\n", " 2.000000\n", @@ -1697,7 +1676,7 @@ " 0.000000\n", " \n", " \n", - " max\n", + " max\n", " 500000.000000\n", " 2.000000\n", " 4.000000\n", @@ -1728,9 +1707,9 @@ "text/plain": [ " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", - "mean 149324.899981 1.608954 1.852734 1.564717 35.006592 \n", - "std 116558.616530 0.487994 0.738572 0.521936 8.832028 \n", - "min 10000.000000 1.000000 1.000000 1.000000 21.000000 \n", + "mean 149324.899981 1.608954 1.850753 1.558773 35.006592 \n", + "std 116558.616530 0.487994 0.738175 0.522639 8.832028 \n", + "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", @@ -1779,7 +1758,7 @@ "[8 rows x 30 columns]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1805,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1819,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -1894,7 +1873,7 @@ " \n", " \n", " \n", - " 1\n", + " 1\n", " 0.020408\n", " 2\n", " 2\n", @@ -1918,7 +1897,7 @@ " -2\n", " \n", " \n", - " 2\n", + " 2\n", " 0.224490\n", " 2\n", " 2\n", @@ -1942,7 +1921,7 @@ " 1\n", " \n", " \n", - " 3\n", + " 3\n", " 0.163265\n", " 2\n", " 2\n", @@ -1966,7 +1945,7 @@ " 0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.081633\n", " 2\n", " 2\n", @@ -1990,7 +1969,7 @@ " 0\n", " \n", " \n", - " 5\n", + " 5\n", " 0.081633\n", " 1\n", " 2\n", @@ -2038,7 +2017,7 @@ "[5 rows x 30 columns]" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2073,7 +2052,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -2082,7 +2061,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -2091,7 +2070,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -2115,51 +2094,63 @@ " \n", " \n", " \n", + " MISSING-EDU\n", " GRAD\n", " UNI\n", " HS\n", + " MISSING-MS\n", " MARRIED\n", " SINGLE\n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", - " 2\n", + " 2\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", " \n", - " 4\n", + " 4\n", + " 0.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " \n", @@ -2168,15 +2159,15 @@ "" ], "text/plain": [ - " GRAD UNI HS MARRIED SINGLE\n", - "0 0.0 1.0 0.0 1.0 0.0\n", - "1 0.0 1.0 0.0 0.0 1.0\n", - "2 0.0 1.0 0.0 0.0 1.0\n", - "3 0.0 1.0 0.0 1.0 0.0\n", - "4 0.0 1.0 0.0 1.0 0.0" + " MISSING-EDU GRAD UNI HS MISSING-MS MARRIED SINGLE\n", + "0 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 0.0" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2184,15 +2175,15 @@ "source": [ "#one hot encoding for EDUCATION and MARRIAGE\n", "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", - "onehot.columns= names = [\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MARRIED\",\"SINGLE\",\"OTHER_MS\"]\n", + "onehot.columns= names = [\"MISSING-EDU\",\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MISSING-MS\",\"MARRIED\",\"SINGLE\",\"OTHER-MS\"]\n", "#drop one of each category to prevent dummy variable trap\n", - "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER-MS\"], axis = 1)\n", "onehot.head()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "scrolled": true }, @@ -2240,7 +2231,7 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2261,7 +2252,7 @@ " 0.0\n", " \n", " \n", - " 1\n", + " 1\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2282,7 +2273,7 @@ " 0.0\n", " \n", " \n", - " 2\n", + " 2\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2303,7 +2294,7 @@ " 1.0\n", " \n", " \n", - " 3\n", + " 3\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -2324,7 +2315,7 @@ " 1.0\n", " \n", " \n", - " 4\n", + " 4\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -2392,7 +2383,7 @@ "4 0.0 0.0 1.0 " ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2420,7 +2411,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2429,18 +2420,18 @@ "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", - " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'GRAD', 'UNI', 'HS', 'MARRIED',\n", - " 'SINGLE', 'PAY_0_No_Transactions', 'PAY_0_Pay_Duly',\n", - " 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly',\n", - " 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly',\n", - " 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly',\n", - " 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions', 'PAY_5_Pay_Duly',\n", - " 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly',\n", - " 'PAY_6_Revolving_Credit'],\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',\n", + " 'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',\n", + " 'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',\n", + " 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',\n", + " 'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],\n", " dtype='object')" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2454,7 +2445,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2504,18 +2495,18 @@ " \n", " \n", "\n", - "

0 rows × 45 columns

\n", + "

0 rows × 47 columns

\n", "" ], "text/plain": [ "Empty DataFrame\n", - "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, GRAD, UNI, HS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, MISSING-EDU, GRAD, UNI, HS, MISSING-MS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", "Index: []\n", "\n", - "[0 rows x 45 columns]" + "[0 rows x 47 columns]" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2531,14 +2522,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data has 45 Columns and 26245 Rows\n" + "Data has 47 Columns and 26245 Rows\n" ] } ], @@ -2558,7 +2549,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2568,7 +2559,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "colab": {}, "colab_type": "code", @@ -2576,12 +2567,11 @@ }, "outputs": [], "source": [ - "#using holdout sampling for train test split\n", + "#using holdout sampling for train test split using seed 123\n", + "np.random.seed(123) \n", "ft = df1.drop(\"Y\", axis = 1)\n", "target = df1[\"Y\"]\n", - "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=0.33333)\n", - "#make the results reproducible (according to instructions)\n", - "np.random.seed(123) " + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)" ] }, { @@ -2595,7 +2585,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2604,24 +2594,23 @@ "text": [ "Significant values are:\n", " 0 pval\n", - "LIMIT_BAL 85.131154 1.364065e-04\n", - "PAY_0 4436.456236 0.000000e+00\n", - "PAY_2 3950.467050 0.000000e+00\n", - "PAY_3 3110.398233 0.000000e+00\n", - "PAY_4 3096.770229 0.000000e+00\n", - "PAY_5 2977.392709 0.000000e+00\n", - "PAY_6 2449.597828 0.000000e+00\n", - "PAY_0_No_Transactions 75.890061 1.454296e-03\n", - "PAY_0_Revolving_Credit 482.349587 0.000000e+00\n", - "PAY_2_Pay_Duly 76.560100 1.235192e-03\n", - "PAY_2_Revolving_Credit 237.062546 0.000000e+00\n", - "PAY_3_Pay_Duly 79.130634 6.519343e-04\n", - "PAY_3_Revolving_Credit 135.381715 1.723299e-11\n", - "PAY_4_Pay_Duly 74.678430 1.946930e-03\n", - "PAY_4_Revolving_Credit 98.449450 3.100197e-06\n", - "PAY_5_Pay_Duly 72.744346 3.071499e-03\n", - "PAY_5_Revolving_Credit 70.267645 5.406887e-03\n", - "PAY_6_Revolving_Credit 60.999670 3.661838e-02\n" + "LIMIT_BAL 82.306062 2.883753e-04\n", + "PAY_0 4279.993739 0.000000e+00\n", + "PAY_2 3557.072141 0.000000e+00\n", + "PAY_3 2766.119390 0.000000e+00\n", + "PAY_4 2736.965012 0.000000e+00\n", + "PAY_5 2587.002458 0.000000e+00\n", + "PAY_6 2240.874786 0.000000e+00\n", + "PAY_0_No_Transactions 76.858872 1.147939e-03\n", + "PAY_0_Revolving_Credit 480.805794 0.000000e+00\n", + "PAY_2_Pay_Duly 75.283344 1.684018e-03\n", + "PAY_2_Revolving_Credit 229.527990 0.000000e+00\n", + "PAY_3_Pay_Duly 86.995856 8.229607e-05\n", + "PAY_3_Revolving_Credit 121.059740 2.357071e-09\n", + "PAY_4_Pay_Duly 79.449207 6.014800e-04\n", + "PAY_4_Revolving_Credit 82.276504 2.906105e-04\n", + "PAY_5_Pay_Duly 63.330298 2.338310e-02\n", + "PAY_5_Revolving_Credit 64.659773 1.792035e-02\n" ] } ], @@ -2668,7 +2657,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2702,7 +2691,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2722,7 +2711,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -2753,11 +2742,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "evaluation = pd.DataFrame(columns=['Model', 'Recall-1', 'AUROC'])" + "evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])" ] }, { @@ -2788,7 +2777,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2797,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2811,7 +2800,7 @@ " random_state=None, splitter='best')" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -2823,7 +2812,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2832,8 +2821,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13463\n", - " 1 1.00 1.00 1.00 4033\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -2855,14 +2844,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (GINI) identified 902\n" + "Of 1987 Defaulters, the Decision Tree (GINI) identified 809\n" ] }, { @@ -2897,14 +2886,14 @@ " \n", " \n", " \n", - " 0\n", - " 5410\n", - " 1331\n", + " 0\n", + " 5482\n", + " 1280\n", " \n", " \n", - " 1\n", - " 1106\n", - " 902\n", + " 1\n", + " 1178\n", + " 809\n", " \n", " \n", "\n", @@ -2913,11 +2902,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5410 1331\n", - "1 1106 902" + "0 5482 1280\n", + "1 1178 809" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2928,19 +2917,19 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 1.0\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2957,7 +2946,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2966,12 +2955,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.80 0.82 6741\n", - " 1 0.40 0.45 0.43 2008\n", + " 0 0.82 0.81 0.82 6762\n", + " 1 0.39 0.41 0.40 1987\n", "\n", " accuracy 0.72 8749\n", - " macro avg 0.62 0.63 0.62 8749\n", - "weighted avg 0.73 0.72 0.73 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", "\n" ] } @@ -2990,14 +2979,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Entropy) identified 865\n" + "Of 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" ] }, { @@ -3032,14 +3021,14 @@ " \n", " \n", " \n", - " 0\n", - " 5508\n", - " 1233\n", + " 0\n", + " 5509\n", + " 1253\n", " \n", " \n", - " 1\n", - " 1143\n", - " 865\n", + " 1\n", + " 1156\n", + " 831\n", " \n", " \n", "\n", @@ -3048,11 +3037,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 5508 1233\n", - "1 1143 865" + "0 5509 1253\n", + "1 1156 831" ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -3065,19 +3054,19 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 1.0\n" + "Optimal Threshold: 0.5\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdd3QVVffw8e8mIaEkoSTUhJDQe40gwYqggCAIgiDS/flgwd4LFsTeX7ErUVBBwIKAigURE5DepafSWxoh/bx/zESvMSQ3kJubsj9rscj0PeXOnjkzc44YY1BKKaXOVRV3B6CUUqp800SilFLqvGgiUUopdV40kSillDovmkiUUkqdF00kSimlzkulSSQiMkZElrk7DncTkWARSRURj1JcZoiIGBHxLK1lupKIbBeRy85hunM6BkWkuoh8JyJJIjK/uNOXJmePr4r6exSRaiKyU0TquzuW8yUii0Skn1PjuuM7EhGJARoAOUAq8ANwuzEmtdSDqeDsbX2TMeZnN8YQAkQDVY0x2e6Kw47FAC2NMXtdvJwQSmidRWQsMBUIL4ntZyfBX4E0u1ciEAW8ZIxZe77zdxcRGQO8Z3d6AN78s44YY3xKIYa7gebGmNvt7jnASCDTYbRdxpjuTszrGSDIGDPBFbE6sfxw4DVjTM+ixnXnHclge8d2AboCD7sxlnPmzqvsinKFXxyVdHs3BXafSxIpJOaD9u/PF7gQ2AmsFJErzj1M9zLGfGaM8bHXawD2Ojr0+xcX7c//AbPz9XvWMQ5nkogzRKSKiLjsHG6MiQLqiUhXZ0Yu9X9ADNDXoftFYIlDtzfwMhAHHAHeBao7DB8CbAKSgX1Af7t/LeAj4BBwAHgG8LCHTQD+sP9+F3g5X0zfAvfYfzcGFgLHsK4q73AY70lgATDHXv5NBaxfLeBTe/pY4DGgikMckcD/A5KwfsBX5Ju2sHWIBF4DTtrDmmNdXZ4AjgOfAbXt8WcDucAZrDu/B4AQwACe9ji/AdPt+aYAy4AAh3jG2etwAng8/77Lt97VgVfs8ZOAP+x+ecscb+/T48CjDtP1AFZhXRkfAt4CvByGG+A2YA8Qbfd7A4i398F64GKH8T2AR+xjI8Ue3gT43Z7XaXt7XG+PPwjreMq7Mu+U71h9ENgCZACejtvAjn2dHccR4FW7f5y9rFT7Xy8cjkF7nPbAT/a+PAI8UsA2fQrrajbLns9krAvAx+ztfBTrWKtlj5+3rSfbMfxewDwvAxIK6P8WsM6hu41DfLuAkcXY13nH1wRgv70fooEx+X+Pdnc4sNae11qsu6+8Yb9RyDF6lmPxbOuYANwPbAUy7X5BwNf883u/zWH8KvxzLB0H5gJ1zrLMZljHlodDvznAk2cZv4W9rcbZcR0DHnI4Jh33+3q7/x/2tliF9bsOseNfbO+nPcAkh2U8A8wD5tvbbh3Q0R72MDAvX0zv4HBuBGbh8Fs96/Y+12RwPv/49w8xyN6pbzgMfx1YBNTFumL6DnjO4YebBPSzd3Ig0MYe9g3WrW1NoD6wBvhf/gMXuATrJJRXtFfH3imN7XmuB6YBXvbBsR+4yh73SXvnDrXHrV7A+n2KlZh87R29G5jsEEc2cDdQFbjeXp+6Tq5DNlYxhyfWD7eFvS28gXpYJ8vXC9rW+U40jolkH9DKnt9vwPP2sHZYB/FF9rZ42V73syWSmfb0gVgn83A7rrxlfmAvozPWSbmtPV13rKtiT3vcv4C7HOZrsE5odfO2N3Aj4G9Pcy9wGKhmD8s7UbQGxF6ev8O8WjjMuxvWybinHfN4e5t5O2y/TViJqHr+bYr1gx5r/+0DXFjQdi7gGPTFSpr3AtXs7p5n2a5PAnMcuicBe7GOTR/gK2B2vuV+inUMFXR8XkbBJ9k+WBceNe1/8cBEext3wzqRtndyX3va80gGWtvTNHKY3nFb1AVOAWPt6Ubb3Xn77DfOcowWco452zomYP2+g+x5edj79xGsY7yFvX+vsMe/DyuBBdr76aO8bV3AvIcAm/P1cyaRvGvPuxvW76KlPfwZICLfNH/Y8bXFOn948s+Fad48jgOXOswjC7jWHv8h+9jxtLdBKuBnj+tlT9vZYXkPAF8WeU53VbIoYifH2CuQYm/IX/jnKlqwsnpzh/F78c+V6HtY5Xb559nA3gmOdy6jgeUFHLiCdbV2id39f8Cv9t89gbh8834YmOXwo/7PVZ7DuB52HO0c+v0P+M0hjoPYSczutwbrR+TMOsSdbdn2OEOBjfm2dVGJ5DGH4bcCP9h/TwO+cBhWA+sq6T+JBCupnnE8CAtYZlC+dR51lnW4C/jaodsAfYpY71N5y8a6eh5ylvHyJ5J3gOn5xtnFPz/EGByu8PJvU6zE/RT5rpDzb+cCjsHRjvupiHV7kn8nkl+AWx26W2OdLDwdltuskPldRsEn2Tb2tIFYFzgr8w1/D3jCyX2dl0gSgeHkS2j5tsVYYE2+4auACUUdo+ewjgnAOIfu3sD+fOM8Dnxg/70n71iwu5tg/UarFDDv8TjcZdn95gDp9nbI+/eRPSwvkTR0GH8DcJ3999kSyTSH7lB739d06PcS8KHDPBzv/DywLpx62d0/ARPtv4cCW/It7xZgWVHHqDvL2IcaY34WkUuBz4EArI1cD+uEtV5E8sYVrA0A1o5cWsD8mmJl3EMO01XBuqr6F2OMEZG5WD/m34EbsHZ43nwai0iiwyQewEqH7v/M00EAVmaPdegXi/XjzHPA2HvJYXhjJ9fhX8u23w55E7gY66q2CtZJtTgOO/ydhnWVix3T38szxqSJyImzzCMA64poX3GXIyKtgFeBMKx974l11ego/3rfC9xkx2gAPzsGsI6RwuJw1BQYLyJTHfp52fMtcNn5TAaeBnaKSDTwlDFmsRPLLU6M+TXmv8eXJ9aFSJ7CYj6bQKxtmYi1XXrm+x14YhWXOrOvMcacFpHrsa7qPxKRSOBeY8zOfKPmXx/472/mbMfouXDcNk2B4AJ+77/ZfwcD34lIrsNwg1Va4BgTWL873wKW97wx5smzBWOMKe66OcbfGDhujDnt0C8W6FDQ+MaYHBE5wD/H9ydYd52zsO7y8z/f8cU6Hgrl9td/jTErgAisYhOwbq3OYN0C17b/1TL/PCyLx3oukF881pVCgMN0fsaY9mdZ9BfAdSLSFOsuZKHDfKId5lHbGONrjBnoGHYhq3Qc6wqhqUO/YKznHXkCxSFT2MMPOrkO+Zf9nN2vkzHGD+tgkELGL45DWLe/gPUaKlZxUkGOY115FbRvivIO1rOilvY6PMK/1wEc1kNELsZ6bjESq7y6NlbxYN40ZztGChIPzMi3v2sYY74oaNn5GWP2GGNGY51YXgAWiEjNwqY5hxjzO8h/j69srOcsf4d2DvO9Fthgn5TigRX5touPMeYWirGvjTE/GmP6YRVr7cQq3ixqfeC/v5mS5Lht4oE9BfzeB9vDE4B++YZXy3fyz7MFaF6Cr9afbR869j8IBNjHXJ78265J3h/2w/lAezqwikW7i0h7rBcUPs+3rLbA5qICdXsisb0O9BORLsaYXKyD7bW8d7FFJFBErrLH/QiYKCJX2G8tBIpIG2PMIayHcK+IiJ89rLl9x/MfxpiNWA+3PgR+NMbkZd01QLKIPGi/v+8hIh1E5AJnVsQYkwN8CcwQEV87Ud3DP3c8YJ107hCRqiIyAmtnLS3uOth8sYoJE0UkEOv5gKMjWGXp52IBMFhEwkXEC6sIJ/8JHgB7v30MvCoije3t1ktEvJ1Yji9WWXqqiLTBup0uavxsrP3nKSLTsO5I8nwITBeRlmLpJCJ5CTD/9vgAmCIiPe1xa4rI1SJS0JXlf4jIjSJSz17/vGMox44tl7Nv+8VAQxG5S0S87WOlyNcsbV8Ad4tIqIj4AM9iPTQ9l7e6xP4NPYF1h/eIQ3ytRGSsfZxWFZELRKSts/taRBqIyDX2SS4D6zjNKSCMpfaybhART/supp0dg6utAjJF5F6xvgHxEJGOIpL3ZtW7wLMiEgxWCYCIXFPQjIwxMVhF5iXyVhbWsRqS76Iz/zKjsR6gP2sfR12w7jA+cxith4gMEZGqWHeHKVgvNGCMScN60eALINIYkz95XwJ8X1SgZSKRGGOOYT0cfNzu9SDWA6HVIpIM/IxVDowxZg3WhnoN6yp0Bf9czYzDKpbYgXWbuQDrSuhsvgD64pCF7UQwGOu15Gisq68Psd6mctZUrOc8+7HKND/H+uHl+RNoac97BlaZaF6RUXHX4SmsB2xJwBKsKwxHzwGPiUiiiNxXjHXAGLPdXpe5WHcnKVjlqxlnmeQ+rIfca7HeIHkB546x+7CKF1OwTuzzihj/R6yDezfWbXw6/77dfxUrmS/DSlAfYT1YBet5wyf29hhpjFmH9YzsLaztvRer/N5Z/YHtIpKK9SbZKGNMuv0DnQFE2su60HEiY0wK1ksSg7GKSPYAlzu5zI+xiiB+xzpG07H2U3E0tmNOxdpfHYHLjDHLHOK7EhiFdfV6GGt/5iULZ/Z1FayXCQ7a41yK9XzjX+xjf5A97gmsB7yDjDHHi7lOxWYn34FYL/HEYP0m3+OfC5NXsb5z+0VEUrDe6ivsovI9rGc+jh4R6yPNvH8F3c0UZB7WueCkiKwpZLzrsc4nh7HOF48YY5Y7DP8aq6TipD3usHwXHZ9g7f9/FWuJSC/gpDFmQ1GBuuWDxMpMRCZgvTJ8kbtjKS776jcRqwgq2t3xKFXWiEg1YCPWA/qjZSCeIj9qFJFmWMVyDY3DR+Ei8i0wM+/iojCV7oM2VTwiMhjrLSHBeo61FevKTSmVjzEmHauoulywn5ncA3xu8tUsYowZ4ux8NJGoogzBuuUVrLLYUUZvY5Uq90SkFtZD+RjgqsLHLmJeek5QSil1PsrEw3allFLlV7kr2goICDAhISHuDkMppcqV9evXHzfG1HPFvMtdIgkJCWHdunXuDkMppcoVEclfe0CJ0aItpZRS50UTiVJKqfOiiUQppdR50USilFLqvGgiUUopdV40kSillDovLkskIvKxiBwVkW1nGS4i8qaI7BWRLSLSzVWxKKWUch1X3pFEYFWxfTYDsKo+bgncjNW4kVJKqRKWnlVQMzAlx2UfJBpjfheRkEJGGQJ8alcAuFpEaotII7txJ6WUUufpUOIZpn26gd/ii9v6dvG488v2QP7dGFGC3e8/iUREbsa6ayE4OLhUglNKqfLIGMOGuERm/rSbX/ccwwBVj6e7dJnuTCQFNR9ZYFXExpj3gfcBwsLCtLpipZTKJyM7h6VbDxERGcPmhCQkK5cz244zpW9LHp8RTtUI1y3bnYkkAYdG6YEg/mmQXimllBOOpqTz+Z9xzFkdx/HUDEL8azB9SHuae3jS5C5fmjQpTivh58adiWQRcLuIzAV6Akn6fEQppZyzJSGRWZExLN5ykKwcQ0Cm4cjXexg/rgtje4WUaiwuSyQi8gVwGRAgIgnAE0BVAGPMu8BSYCCwF0gDJroqFqWUqgiycnL5ftthIiKj2RCXiI+3J938arD8/c1sik3innt68cADvUs9Lle+tTW6iOEGuM1Vy1dKqYriRGoGX6yJY/bqWI4kW8VXTwxux5Zv9/D60ysID2/Cu9+MomPHBm6Jr9y1R6KUUpXFtgNJRETFsGjzQTKzc7m4ZQBPDWpHt0a1qF+vJrsCfGnX0p/Jk7tRpUpB7y+VDk0kSilVhmTn5LJsxxEiImNYE3OSGl4eXB/WhPHhTdm74Qi3XbuALl0asnDhSFq3DqB16wB3h6yJRCmlyoJTpzOZuzae2atiOJiUTpO61Xns6raMCGvC6VPp3HX7D8yfv4PWrf25/fYL3B3uv2giUUopN9p5OJmIyBi+3niAjOxcwpv78+Q17bmibQM8qgi//LKfa6+dR2ZmDtOnX87994fj7V22Tt1lKxqllKoEcnINP/9lFV+t2n+CalWrMKxbIBPCQ2nd0BeArKwcPKp40LlzQwYObMkzz/ShRYu6bo68YJpIlFKqlCSlZfHlung+WRVDwqkzBNauzkMD2nB9WBPq1PQCIDk5g8cf/5U//zxAZOQkAgJqMHfude4NvAiaSJRSysX2Hk1hVmQMX204wJmsHHqE1uWxq9vSt20DPD2sStiNMSxYsIM77/yBw4dTufXWC8jIyKFGjbLfbJQmEqWUcoHcXMPyXUeJiIph5Z7jeHlWYWiXxowPD6F9439XW3Ls2GnGj/+G77/fS9euDfn221FccEGgmyIvPk0kSilVglLSs5i/LoFPVsUQeyKNhn7VuP+q1oy6oAn+Pt4FTuPn583x42m8/vpV3HZbDzw9y/5diCNNJEopVQL2H0vl01WxzF8Xz+nMHLo3rcN9V7amf4eGVPX4b2L4/fdYZsxYycKFI/Hx8WL16pvc+lHh+dBEopRS5yg31/D7nmNERMXw265jeHlUYVDnRkwID6FTUO0Cpzl+PI377/+JiIhNhITUJiYmkQ4d6pfbJAKaSJRSqthSM7L5akMCEVEx7D92mnq+3tzdtxU39Aymnm/BxVfGGGbN2sT99/9EcnIGDz98EY89dgk1alQt5ehLniYSpZRyUuyJ03y6KpYv18aTkpFN56BavH59FwZ2bISXE8815szZQrt29Xj33atp375+KURcOjSRKKVUIYwxRO07wazIaH7ZeRQPEQZ2bMTE3iF0Da5T6LRpaVk8++xKpkwJIyjIj4ULR1KrVrVyXYxVEE0kSilVgLTMbL7eeICIyBj2HE3Fv6YXUy9vwZgLm9LAr1qR0y9duofbbltKTEwigYG+3HLLBdSpU70UIi99mkiUUspB/Mk05qyOZe7aeJLOZNG+sR8vj+jMoE6NqFbVo8jpExKSueuuH1i48C/atg1gxYoJXHJJ01KI3H00kSilKj1jDKv3nyQiKpqfdhxBROjfoSETw0Po3rQOIs4XRc2Y8TtLluzh2Wf7cO+94Xh5FZ18yjuxGiosP8LCwsy6devcHYZSqgJIz8rh200HmBUZw87DKdSpUZXRPYK58cKmNK7tfDHUmjUHqF7dk44dG3DiRBpJSRk0a1b485PSJiLrjTFhrpi33pEopSqdg4lnmLM6li/WxHEqLYs2DX15YXhHhnQJdKr4Kk9SUjqPPPIL77yzjkGDWrFo0Wj8/Wvg71/DhdGXPZpIlFKVgjGG9bGnmBUZww/bD2OMoV+7BkzsHUrP0LrFKr4yxjBv3nbuvvtHjh49zdSpPZg+vY8Loy/bNJEopSq0jOwcvtt8iIioaLYdSMavmic3XRTKjRc2pUndc7tzmDNnC+PGfUNYWGMWLx5N9+6NSzjq8kUTiVKqQjqSnM5nq2P5fE0cx1MzaVnfhxnXduDaroHU8Cr+qS8jI5v9+0/Rtm09Ro5sT3Z2LuPGdcajgHq0KhtNJEqpCmVjnFV8tXTrIXKM4Yo29ZnYO5Tw5v7FKr5ytHx5NLfcsoS0tCz27JmKt7cnEyd2LeHIyy9NJEqpci8zO5elWw8xKyqGzfGJ+Hp7Mj48hHG9mtLUv+Y5z/fo0dPcd98yZs/eQrNmdXj//cFlrr30skC3iFKq3DqWksHnf8Yx589YjqVk0CygJk8Pac+wbkH4nOcJf+/ek/To8QGpqZk8+ujFPProxVSvXv4rWHQFTSRKqXJna0ISs6KiWbz5EJk5uVzWuh4TwkO4pGW9867HKjk5Az8/b5o3r8PkyV2ZNKkrbdvWK6HIKyZNJEqpciErJ5cfth0mIiqG9bGnqOnlwQ09gxnXqynN6vmc9/xPn87k6adX8MEHG9iy5RaCgvx46aUrSyDyik8TiVKqTDuRmsHctfHMXhXL4eR0mvrXYNqgdlwXFoRftZIpavruu13cfvv3xMUlMXly1wrRRkhp0kSilCqTth9MIiIyhm83HyQzO5eLWwYw49oOXN665FoTzM7OZeTI+Xz99U7at6/HypUTueii4BKZd2WiiUQpVWZk5+Ty044jzIqKYU30SapX9WBE9yAmhIfQsoFviS3HGIOI4OlZhUaNfHj++Su4++5elaKCRVfQRKKUcrvEtMy/i68OJJ4hqE51Hh3YlpFhTahVwsVMq1cncNttS/ngg8F069aImTOvLtH5V0aaSJRSbrPrcAoRUTF8vTGB9KxcejXzZ9rgdvRt2wCPEm5F8NSpMzzyyC+89956Gjf25dSpMyU6/8rMpYlERPoDbwAewIfGmOfzDQ8GPgFq2+M8ZIxZ6sqYlFLulZNr+HXnUWZFRhO17wTenlUY1i2Q8eEhtGno55Jlzpu3jTvu+IHjx9O4664Leeqpy/D19XbJsiojlyUSEfEAZgL9gARgrYgsMsbscBjtMeBLY8w7ItIOWAqEuCompZT7JJ3JYv66eD5ZFUP8yTM0rlWNB/u3YdQFTahT08uly9658zghIbX54YcxdO3ayKXLqoxceUfSA9hrjNkPICJzgSGAYyIxQN4lSC3goAvjUUq5wd6jqXwSFcPCDQmkZebQI6QuDw9oy5XtGuDpogoP09OzeeGFP+jWrRGDB7fmkUcu5rHHLtEKFl3ElYkkEIh36E4AeuYb50lgmYhMBWoCfQuakYjcDNwMEBysr+YpVdbl5hpW7D7Gx5HRrNxzHC+PKlzTpTETwkPoEFjLpcv++ef93HrrEvbsOcm99/Zi8ODWVC1GY1Wq+FyZSAp6Upa/Xd/RQIQx5hUR6QXMFpEOxpjcf01kzPvA+2A1teuSaJVS5y0lPYsF6xP4JCqGmBNpNPDz5r4rWzGqRzABPq59JnHkSCr33LOMzz/fSosWdVm27Eb69Wvu0mUqiysTSQLQxKE7iP8WXU0G+gMYY1aJSDUgADjqwriUUiUs+vhpPomKYcH6BFIzsukWXJt7rmzNgA4NqVpKxUk//bSfBQt2MG3aJTz88MVUq6YvpZYWV27ptUBLEQkFDgCjgBvyjRMHXAFEiEhboBpwzIUxKaVKiDGGlXuOMysymuW7jlHVQxjUySq+6tykdqnEsHnzYfbsOcl117VjzJiO9O7dhNDQOqWybPUPlyUSY0y2iNwO/Ij1au/HxpjtIvI0sM4Yswi4F/hARO7GKvaaYIzRoiulyrDTGdl8tSGBiKgY9h07TYCPN3f1bckNPYOp71utVGJITc3kiSeW88YbfxISUpuhQ9vg6VlFk4ibuPTez/4mZGm+ftMc/t4B9HZlDEqpkhF3Io1PV8Uwb108KenZdAqqxWvXd2Zgx0Z4e5bew+xvvtnJ1Knfk5CQzM03d+O55/ri6alvY7mTFiIqpc7KGMOqfSf4ODKGX3YewUOEAR0bMSE8hG7Btc+56dpztXXrEa69dh4dO9Zn3rzrCA9vUvREyuU0kSil/uNMZg5fbzxARFQ0u4+k4l/Ti9svb8GYnk1pWKt0iq/yZGXlsHJlHH36hNKxYwOWLLmBfv2a6Su9ZYgmEqXU3xJOpTF7dSxz18STdCaLdo38eOm6Tgzu3JhqbjhxR0XFM2XKYrZvP8auXbfTokVdBg5sWepxqMJpIlGqkjPGsCb6JLMiY1i24zAiwlXtGzCxdyhhTeuUevEVwMmTZ3jooZ/54IMNNGnix1dfjaRFi7qlHodyjiYSpSqp9KwcFm06yKyoGP46lEztGlX536XNufHCpgTWru6+uNKz6dLlXQ4eTOHee3vx5JOX4ePj2rq41PnRRKJUJXMo6QxzVsfyxZp4Tp7OpHUDX54f1pEhXQKp7saGnRISkgkK8qNaNU+mT7+cLl0a0rlzQ7fFo5yniUSpSsAYw4a4U8yKjOH7bYcxxtC3bQMm9A6hVzN/txRf5TlzJovnnvuDF16IZMGCEQwe3Jrx47u4LR5VfE4lEhHxAoKNMXtdHI9SqgRlZOewePMhIqJi2HogCb9qnky+KJSxFzalSd0a7g6PZcv2ceutS9i37xQ33tiJHj0C3R2SOgdFJhIRuRp4FfACQkWkC/CEMeZaVwenlDo3R5PTmfNnHJ//Gcvx1Exa1PfhmaEdGNYtkBpeZaMgYurUpbz11lpatqzLzz+P5Yormrk7JHWOnDminsaq/n05gDFmk4i0cGlUSqlzsik+kYjIaJZsPUR2rqFP6/pM6B3CRS0C3Fp8lScnx6rY28OjChdeGERAQA0efPAirWCxnHNm72UZYxLzHYRaH5ZSZURmdi7fbzvErMgYNsUn4uPtyY0XNmV8rxBCAmq6O7y/bdhwiClTFjN2bCemTu3JmDGd3B2SKiHOJJK/RGQkUMWuyfdOYLVrw1JKFeV4agaf/xnHnNWxHE3JoFlATZ66pj3Duwfh4112rvBTUjKYNm05b765hnr1atCoka+7Q1IlzJmj7XZgGpALfIVVm+/DrgxKKXV22w4kMSsyhu82HyQzJ5dLW9XjhetCuLRlPapUcX/xlaNly/YxadK3HDyYwpQpYTz77BXUrl26Vawo13MmkVxljHkQeDCvh4gMw0oqSqlSkJWTy7LtR4iIimZtzClqeHkwukcTxoWH0Lyej7vDOysvLw/q16/JwoUj6dkzyN3hKBeRopr/EJENxphu+fqtN8Z0d2lkZxEWFmbWrVvnjkUrVepOns7kizVW8dWhpHSC69ZgfHgII8KC8KtW1d3h/UdWVg6vvrqK5OQMZsy4ArDaby9rd0qVkX3eDnPFvM96RyIiV2E1gxsoIq86DPLDKuZSSrnIX4eSiYiM4ZtNB8jIzuWiFgFMH9KBy9vUx6OMnpT/+CPu7woWR4xo93cC0SRS8RVWtHUU2AakA9sd+qcAD7kyKKUqo5xcw087jjArMpo/o09SrWoVhncPYkJ4CK0alN0H1CdOpPHggz/z0UcbCQ6uxXffjWbQoFbuDkuVorMmEmPMRmCjiHxmjEkvxZiUqlSS0rKYuzaOT1fFciDxDIG1q/PIwDaMDGtC7Rplv7LCEyfOMHfuNh54IJxp0y6lZs2yH7MqWc48bA8UkRlAO+Dv1y2MMXrJodR52H0khYioGL7ecIAzWTlc2Kwujw9qR792Dcps8VWev/46xpdfbueJJy6jVSt/4uLupm5d99UYrNzLmUQSATwDvAwMACaiz0iUOic5uYblO48yKyqayL0n8Paswk9NkmMAACAASURBVNAugUzoHULbRn7uDq9IaWlZzJjxOy+9FIWPjxeTJ3cjKMhPk0gl50wiqWGM+VFEXjbG7AMeE5GVrg5MqYokOT2LL9fG8+mqWOJOptGoVjUe6N+aURcEU7ecFAX98MNebr11CdHRiYwf35mXXupHvXpl58t55T7OJJIMsepH2SciU4ADQH3XhqVUxbDvWCqfRMWwYH0CaZk5XBBShwf7t+HK9g2o6lHF3eE5LTU1k7Fjv8bfvzrLl4/nsstC3B2SKkOcSSR3Az7AHcAMoBYwyZVBKVWe5eYaVuw5RkRkDCt2H8PLowqDOzdmYu8QOgTWcnd4TsvJyeWLL7YxenQHfHy8+PnnsbRpE4B3Gap+RZUNRR4Rxpg/7T9TgLEAIqKfqCqVT2pGNgvWxfPJqliij5+mvq839/ZrxeiewQT4eLs7vGJZv/4g//vfYtavP0T16p4MH95OWytUZ1VoIhGRC4BA4A9jzHERaY9VVUofQJOJUkDM8dN8siqG+esSSM3Ipmtwbd4Y1YUBHRrh5Vl+iq8AkpLSefzx5cycuZb69Wsyd+5whg1r6+6wVBlX2JftzwHDgc1YD9i/xqr59wVgSumEp1TZZIzhj73HmRUZw/JdR/GsIlzdsRETeofSpUltd4d3zoYP/5Jff43mttsu4Jln+lCrllawqIpW2B3JEKCzMeaMiNQFDtrdu0onNKXKnrTMbBZuOMAnUTHsPZpKgI8XU/u05MaewdT3K58n3f37T1GvXg18fb2ZMaMPVaoIF1ygTd4q5xWWSNKNMWcAjDEnRWSnJhFVWcWfTOPTVTHMWxtPcno2HQNr8erIzlzdqRHenh7uDu+cZGbm8PLLUUyf/jt33NGDF17opzX0qnNSWCJpJiJ5VcULEOLQjTFmmEsjU8rNjDGs2n+CiMgYfv7rCCLCgA4Nmdg7lG7BtctE07Xn6vffY5kyZTF//XWc665rxx139HR3SKocKyyRDM/X/ZYrA1GqrDiTmcO3mw4QERXDzsMp1K3pxa2XtWDMhcE0qlX+v+B+7bVV3HPPMkJCarNkyQ0MHNjS3SGpcq6wSht/Kc1AlHK3A4lnmL0qlrlr40hMy6JtIz9evK4T13RuTLWq5bP4Kk9uruH06Ux8fb25+upWHDuWxmOPXUKNGmWvTRNV/uiXRapSM8awNuYUEVHR/Lj9CMYYrmrfkAnhIfQIrVuui6/ybN9+lClTlvzdUmGrVv48++wV7g5LVSAuTSQi0h94A/AAPjTGPF/AOCOBJwEDbDbG3ODKmJQCSM/K4bvNB4mIimH7wWRqVa/KTReHMvbCpgTVqeHu8EpEWloW06ev4OWXV1GrljeTJnXBGFMhkqMqW5xOJCLibYzJKMb4HsBMoB+QAKwVkUXGmB0O47QEHgZ6G2NOiYjW4aVc6nBSOnNWx/L5mjhOns6kdQNfnhvWkaFdAqnuVb6Lrxxt3HiIYcO+JCYmkYkTu/Dii/0ICKgYCVKVPUUmEhHpAXyEVcdWsIh0Bm4yxkwtYtIewF5jzH57PnOxvk3Z4TDO/wEzjTGnAIwxR4u/CkoVzhjDhrhEIqJi+H7rIXKMoW/bBkwMD6FXc/8KdYWed8cRHFyL4OBafPLJUC65pKm7w1IVnDN3JG8Cg4BvAIwxm0XkciemCwTiHboTgPzvGLYCEJFIrOKvJ40xPzgxb6WKlJGdw9Kth5gVGcOWhCR8q3kyITyEcb1CCPavWFfn2dm5vPXWGhYt2sVPP43F378GK1ZMcHdYqpJwJpFUMcbE5rtqy3FiuoIu80wBy28JXIZVd9dKEelgjEn814xEbgZuBggODnZi0aoyO5qSzmer4/jszziOp2bQvF5Npg/twLCugdSsgDXXrllzgClTFrNx42EGDGhBcnIGdeqU/9eUVfnhzK8q3i7eMvZzj6nAbiemSwCaOHQHYVWzkn+c1caYLCBaRHZhJZa1jiMZY94H3gcICwvLn4yUAmBzvFV8tXjLQbJyDH3a1GdCeAgXtQigShlvuvZcpKZm8uCDP/HOO+to1MiX+fNHMHx42wpVVKfKB2cSyS1YxVvBwBHgZ7tfUdYCLUUkFKsxrFFA/jeyvgFGAxEiEoBV1LXfudCVgqycXL7fdpiIyGg2xCXi4+3JmJ5NGR8eQmhAxW69r2rVKvz2WyxTp/Zg+vQ++PmVr6rqVcXhTCLJNsaMKu6MjTHZInI78CPW84+PjTHbReRpYJ0xZpE97EoR2YFVXHa/MeZEcZelKp8TqRl8sSaO2atjOZKcQWhATZ4c3I7h3YPwrVZxP7Lbu/ckTz+9gpkzB+Lr68369TdTrVrFK65T5YsYU3hJkYjsA3YB84CvjDEppRHY2YSFhZl169a5MwTlRtsOJBERFcOizQfJzM7lklb1mBgewqWt6lXI4qs8GRnZvPhiJDNmrMTLy4MlS27g4ov1bSzlPBFZb4wJc8W8nWkhsbmIhGMVTT0lIpuAucaYua4ISKn8snNyWbbjCLMio1kbc4oaXh5cH9aE8eEhtKjv4+7wXG758mhuuWUJu3ad4Prr2/Pqq1fRuLGvu8NS6m9O3RMbY6KAKBF5Engd+AzQRKJc6tTpTL5YG8ecVbEcTEqnSd3qPHZ1W0aENaFW9YpbfOXIGMOMGSvJysrlhx/GcNVVLdwdklL/4cwHiT5YHxKOAtoC3wLhLo5LVWI7DycTERnD1xsPkJGdS+8W/jw1pAN92tTHowIXX+XJzTV89NEG+vdvQZMmtZg9+1pq165G9UqSPFX548wdyTbgO+BFY8xKF8ejKqmcXMPPfx0hIjKGVftPUK1qFYZ1C2JCeAitG1aeYpwtW44wZcpiVq1KYNq0S3jqqctp1KjyrL8qn5xJJM2MMbkuj0RVSklpWXy5Lp5PVsWQcOoMgbWr8/CANlx/QRNq1/Byd3ilJjU1k6ee+o3XXltNnTrViYgYwrhxnd0dllJOOWsiEZFXjDH3AgtF5D+vdmkLiep87DmSQkRUDF9tOMCZrBx6htblsavb0rdtAzw9qrg7vFL35JO/8corq7jppq48/3xf/CtYFS6qYivsjmSe/b+2jKhKRG6uYfmuo0RExbByz3G8PKswtEtjxoeH0L5xLXeHV+ri45M4fTqLNm0CeOihixg6tA0XXaRVAKnyp7AWEtfYf7Y1xvwrmdgfGmoLisopyelZLFiXwCerYog9kUZDv2rcf1VrRvcIpm7NylN8lSc7O5c33/yTadOW0717Y1asmEBAQA1NIqrccuYZyST+e1cyuYB+Sv3L/mOpfBIVw4L1CZzOzCGsaR3uv6o1V7VvSNVKWHwFsHp1AlOmLGbz5iNcfXVL3nproLtDUuq8FfaM5HqsV35DReQrh0G+QGLBU6nKLjfX8PueY0RExfDbrmN4eVRhUOdGTAwPpWNQ5Su+crRkyW4GD/6Cxo19+eqrkQwd2kYrWFQVQmF3JGuAE1i19s506J8CbHRlUKr8Sc3I5qsNCURExbD/2Gnq+Xpzd99W3NAzmHq+lbcyQWMMBw+mEBjoR9++zXj66cu5886e+FbibaIqniLr2iprtK6tsiX2xGk+iYpl/rp4UjKy6dykNpN6hzCgQyO8PCtn8VWe3btPcOutS9i9+wQ7dtyGj0/lex6kyg631LUlIiuMMZeKyCn+3SCVAMYYU9cVAamyzxhD5N4TRERF88vOo3iIcHWnRkwID6FrcB13h+d26enZPP/8Hzz33B9Ur+7Jc89dQfXqWkOvqrgKO7rzmtMNKI1AVNmXlpnN1xsPEBEZw56jqfjX9GLq5S0Yc2FTGvhVc3d4ZcLhw6lccsks9uw5yejRHXj11ato2LDiVyypKrfCXv/N+5q9CXDQGJMpIhcBnYA5QHIpxKfKgPiTacxeHcvcNXEkp2fTIdCPV0Z0ZlDnRnh7erg7vDIhKyuHqlU9aNCgJpdc0pSZMwfSr19zd4elVKlw5n77G+ACEWkOfAosAT4HBrkyMOVexhhW7z9JRFQ0P+04gojQv0NDJoaH0L1pHX3byJaba3j//fU8++xKoqImExTkx4cfXuPusJQqVc4kklxjTJaIDANeN8a8KSL61lYFlZ6Vw7ebDjArMoadh1OoU6MqUy5tztheTWlUq7q7wytTNm8+zP/+t5g//zxAnz6hZGXluDskpdzCqaZ2RWQEMBYYavfT+qwrmIOJZ5i9OpYv1sSRmJZFm4a+vDi8E9d0aUy1qlp85cgYw/33/8Trr6+mbt3qzJ59LWPGdNS7NFVpOftl+61Y1cjvF5FQ4AvXhqVKgzGGdbGniIiM4YfthzHGcGW7hkzoHULP0Lp6YjwLEeHUqTNMnmxVsFinjt6pqcrNqe9IRMQTyGuaba8xJtulURVCvyM5f+lZOSzecoiIqGi2HUjGr5ono3sEM7ZXU4LqaK2zBYmNTeTOO39g2rRL6datEbm5pkK3Ea8qHre22S4iFwOzgQNY35A0FJGxxphIVwSkXOdIcjqfrY7lsz/jOHE6k1YNfHj22o4M7dqYGl76nUNBsrJyeO211Tz11AoArr++Pd26NdIkopQDZ84erwEDjTE7AESkLVZicUlmUyVvQ5xVfLV06yFyjOGKNg2Y2DuE8Ob+WnxViKioeP73v8Vs23aUIUNa8+abAwgOrtz1hSlVEGcSiVdeEgEwxvwlIlrXQxmXmZ3L0q2HmBUVw+b4RHy9PRkfHsK4Xk1p6l/T3eGVCz//vJ+kpHS++eZ6hgxp4+5wlCqzinxGIiIRQAbWXQjAGKCGMWa8a0MrmD4jKdyxlAw++9MqvjqWkkGzejWZGB7CsG5B1PTW4qvCGGOYPXsL9erVYMCAlmRkZJOVlat1ZKkKwa3PSIApwB3AA1jPSH4H/p8rglHnbktCIhGRMSzecojMnFwub12PCb1DubhFgJbnO2HnzuPccssSfvsthhEj2jFgQEu8vT3x1kp6lSpSoYlERDoCzYGvjTEvlk5IyllZObn8sO0wEVExrI89RU0vD27oGcy4Xk1pVk/rd3LGmTNZPPvsSl54IZKaNb14771B3HRTN3eHpVS5Uljtv49gtYS4AauKlKeNMR+XWmTqrE6kZjB3bTyzV8VyODmdEP8aPDG4Hdd1D8K3mn4rWhzffbebZ55ZyY03duLll/vRoIEmYKWKq7A7kjFAJ2PMaRGpBywFNJG40faDSURExvDt5oNkZudyccsAnh3Wgcta1dfiq2I4fDiVTZsO079/C0aMaEdIyE306BHo7rCUKrcKSyQZxpjTAMaYYyJSuVspcpPcXMOP2w8zKyqGNdEnqV7Vg5FhQYzvFULLBr7uDq9cycnJ5b331vPww7/g5eVBXNxdVK9eVZOIUuepsETSzKGtdgGaO7bdbowZ5tLIFAAv/riLd1fsI6hOdR67ui0jwppQq7oWXxXXhg2HmDJlMWvXHqRv32a8/fZAqut2VKpEFJZIhufrfsuVgaj/Whtzkvd+38fwbkG8eF0nPLT46pxER5+iR48PCAioweefD2PUqA76IaZSJaiwhq1+Kc1A1L+dzsjm3i83E1SnOk8Naa9JpJiMMWzdepROnRoQGlqHWbOGMHhwa2rX1pYclSpp+tyjjJqx9C/iT6Xxyogu+OiHhMUSHX2KQYO+oGvX99iy5QgAY8d21iSilIu4NJGISH8R2SUie0XkoULGu05EjIho/V3A8l1H+fzPOG6+uBk9Quu6O5xyIzMzh+ef/4P27d9mxYoYXn65H+3a1XN3WEpVeE5f6oqItzEmoxjjewAzgX5AArBWRBY51ttlj+eL9eX8n87OuyJLTMvkwQVbaNXAh7v7tXJ3OOVGTk4u4eEfsX79IYYNa8vrr19FkyZawaJSpaHIOxIR6SEiW4E9dndnEXGmipQeWG2X7DfGZAJzgSEFjDcdeBFIdz7siuvxb7dz8nQmr47soi0TOiE52bq28fCowqRJXfnuu9EsXDhSk4hSpciZoq03gUHACQBjzGbgciemCwTiHboT7H5/E5GuQBNjzOLCZiQiN4vIOhFZd+zYMScWXT4t2nyQ7zYf5K6+LekQqCfCwhhjiIjYRLNmb/DttzsBuPXWCxg0SO/ilCptziSSKsaY2Hz9cpyYrqDXjP6uatj+wPE14N6iZmSMed8YE2aMCatXr2KWeR9JTufxb7bRpUltplza3N3hlGk7dhzjsss+YeLEb2nTJoDmzfU5klLu5MwzkngR6QEY+7nHVGC3E9MlAE0cuoOAgw7dvkAH4Df7nf6GwCIRucYYU6nqiTfG8MCCLWRk5/DqyM54eujLdGfz4ouRPPror/j5efPhh4OZOLGrVg+jlJs5k0huwSreCgaOAD/b/YqyFmgpIqFYzfSOAm7IG2iMSQIC8rpF5DfgvsqWRAC+WBPPit3HeOqa9lpr71kYYxARGjb0YcyYjrz0Uj/q1dMGupQqC4pMJMaYo1hJoFiMMdkicjvwI+ABfGyM2S4iTwPrjDGLih1tBRR74jTPLNnBRS0CGHthU3eHU+YcPJjCnXf+wMUXB3PHHT0ZN64z48Z1dndYSikHRSYSEfkAh2cbeYwxNxc1rTFmKVatwY79pp1l3MuKml9Fk5NruPfLzXhUEV68rpMW0TjIycnl7bfX8uijv5KVlUt4eJC7Q1JKnYUzRVs/O/xdDbiWf7+Npc7RByv3sy72FK+O7Ezj2tXdHU6ZsWnTYW66aRHr1x/iyiub8/bbA/WBulJlmDNFW/Mcu0VkNvCTyyKqJHYeTubVZbvp374h13bVaswdJSWlc/BgCvPmXceIEe20gkWlyrhzqcQpFNDC/POQmZ3L3fM241fdkxnXak20xhjmz9/Bnj0nePTRS7j00hD277+TatW0jjGlygNnvmw/JSIn7X+JWHcjj7g+tIrrjV9289ehZJ4b1gl/H293h+NW+/adZODAz7n++gV8++0usrKsT5Q0iShVfhT6axXrUrkz1uu7ALnGmP88eFfO2xB3ind+28eI7kH0a9fA3eG4TUZGNi+/HMUzz6ykatUqvPFGf2699QI8PfUbGqXKm0ITiTHGiMjXxpjupRVQRZaWabUx0qhWdaYNbufucNwqPj6Z6dN/Z/Dg1rz++lUEBvq5OySl1Dly5vJvjYh0c3kklcDz3+8k+vhpXh7RGd9qla+Z12PHTvPWW2sAaNGiLjt23Mb8+SM0iShVzp31jkREPI0x2cBFwP+JyD7gNFYdWsYYo8mlGFbuOcanq2KZfFEovZr7uzucUpWba5g1ayMPPPAzKSkZ9OvXjNatA2jWrI67Q1NKlYDCirbWAN2AoaUUS4WVlJbF/fO30KK+D/df1drd4ZSqbduOcsstS/jjjzguvjiYd98dROvWAUVPqJQqNwpLJAJgjNlXSrFUWE9+t51jqRm8P657pWpjJDMzhyuvnE1mZg4ff3wNEyZ0qfSvOitVERWWSOqJyD1nG2iMedUF8VQ4S7ce4uuNB7irb0s6BdV2dzil4tdfo7n00qZ4eXnw5ZcjaNMmgICAGu4OSynlIoU9bPcAfLCqey/onyrC0ZR0Hv16Kx0Da3Hb5S3cHY7LJSQkM3z4l1xxxad8+ulmAC66KFiTiFIVXGF3JIeMMU+XWiQVjDGGR77ayunMHF67vjNVK3AbI9nZubz11hoef3w5OTm5PPfcFYwZ08ndYSmlSkmRz0jUuZm/LoGf/zrK44Pa0aJ+xb6BGzv2a+bO3caAAS2YOXMgoaH6NpZSlUlhieSKUouigok/mcZT323nwmZ1mRge4u5wXCIxMR1Pzyr4+Hhx220XMHx4W4YPb6sP05WqhM5a3mKMOVmagVQUubmG++ZvRkR4eUTnCtfGiDGGuXO30bbtTB5//FfAeg5y3XVaS69SlVXFLbh3k48jo/kz+iTTBrcjqE7Fesi8d+9JrrpqDqNHLyQoyI8bb9TnIEqpc6tGXp3FniMpvPjjLvq2bcCI7hWrRb/PP9/KpEnf4u3tyVtvDWDKlDA8KvALBEop52kiKSFZObnc/eUmfLw9eW5YxwpTzJOVlUPVqh6EhTXmuuva8eKL/WjcuGK/PKCUKh5NJCXkrV/3su1AMu/e2I16vuW/jZGjR09z773LOH06k6++up5WrfyZM2eYu8NSSpVBWjZRAjbHJ/LW8r0M6xpI/w6N3B3OecnNNbz//npat36LefO20b59PXJyct0dllKqDNM7kvOUnpXD3V9uor6vN09c097d4ZyX/ftPceONX7FqVQKXXRbCO+9cTZs2WsGiUqpwmkjO0ws/7GT/sdPMmdyTWtXLdxsjtWp5k5iYziefDGXs2E4V5jmPUsq1tGjrPETtO86syBjG92rKRS3L55X7okW7GDZsHjk5ufj712DbtlsZN66zJhGllNM0kZyj5HSrjZFmATV5aEBbd4dTbHFxSQwdOpchQ+aye/cJDh1KBahwH1AqpVxPi7bO0dPf7eBQ0hkW3hJOda/y08ZIdnYur7++miee+A1jDC+80Je7776QqpWonRSlVMnSRHIOlm0/zIL1CUzt04KuweWrgsKcnFw+/HADffqE8v/+3wBCQipHGylKKdfRoq1iOp6awcNfbaV9Yz+m9mnp7nCccurUGR588CdSUjLw9vYkMnISixaN0iSilCoRmkiKwRjDo19vJSU9m1dHdsHLs2xvPmMMn322hTZtZvLKK6tYvjwGAH//GvowXSlVYrRoqxi+2nCAH7cf4ZGBbWjdsGxXE7J79wluvXUJv/wSTY8egfz444106dLQ3WEppSogTSROOpB4hicXbadHSF0mX9TM3eEU6a67fmDduoO8/fZAbr65u1awqJRyGU0kTsjNNTywYDM5xvDyiM54lNFXZH/6aR9t2gTQpEkt3nnnary9PWnY0MfdYSmlKjiXXqaKSH8R2SUie0XkoQKG3yMiO0Rki4j8IiJNXRnPufp0VQyRe0/w+KB2BPuXvTZGDh9O5YYbFnLllXN44YVIAJo2ra1JRClVKlyWSETEA5gJDADaAaNFpF2+0TYCYcaYTsAC4EVXxXOu9h1L5bnvd3J563qMuqCJu8P5l9xcw7vvrqNNm7dYuPAvnnjiUl5++Up3h6WUqmRceUfSA9hrjNlvjMkE5gJDHEcwxiw3xqTZnauBMtUaVHZOLvd8uZnqXh68MLzs1T313HMrueWWJXTv3pgtW6bw5JOXUa2allYqpUqXK886gUC8Q3cC0LOQ8ScD3xc0QERuBm4GCA4OLqn4ivTOb/usKuJv6Ep9v2qlttzCpKRkcPx4GqGhdZgyJYzQ0DqMHt2hzCU5pVTl4co7koLObKbAEUVuBMKAlwoabox53xgTZowJq1evXgmGeHbbDiTxxi97uKZzYwZ1alwqyyyMMYavv/6Ldu3e5vrrF2CMwd+/BjfcUHFaY1RKlU+uTCQJgONDhSDgYP6RRKQv8ChwjTEmw4XxOC09K4e7523C38eLp4e4v42R2NhErrlmLsOGfUndutV5880BmjyUUmWGK4u21gItRSQUOACMAm5wHEFEugLvAf2NMUddGEuxvPrTbvYcTSVi4gXUruHl1lhWrYqnb9/ZALz8cj/uvPNCPMv4F/VKqcrFZYnEGJMtIrcDPwIewMfGmO0i8jSwzhizCKsoyweYb19hxxljrnFVTM74c/8JPli5nzE9g7msdX23xZGcnIGfnzfdujVi0qQu3H9/b4KDa7ktHqWUOhsxpsDHFmVWWFiYWbdunUvmnZqRTf/Xf8ejirD0joup6V36b0CdOJHGQw/9zLJl+9m+/VZ8fNx7R6SUqhhEZL0xJswV89Z3RR08s3gHBxPP8OX/epV6EjHGMHv2Fu69dxmnTp3hnnt6oY9BlFLlgSYS2687jzB3bTxTLm1OWEjdUl12UlI6Q4fO47ffYujVK4h33x1Ep04NSjUGpZQ6V5pIgJOnM3lgwVbaNPTl7n6l18aIMQYRwc/Pm4CAGrz//iAmT+6mzd0qpcqVSv/6jzGGx77ZStKZTF4d2QVvz9JpcvbHH/fSrdv7JCQkIyLMnz+C//u/7ppElFLlTqVPJIs2H2Tp1sPc3a8V7Rr7uXx5hw6lMGrUAvr3/4y0tCyOHj3t8mUqpZQrVeqircNJ6Tz+zTa6Bdfmf5c0d/nyZs5cwyOP/EpGRjZPPXUZDz7YG283vBmmlFIlqdKexYwx3L9gM1k5hldGdimVNkbWrz9Ez56BzJw5kJYt/V2+PKWUKg2VNpHM+TOOlXuOM31oB0IDarpkGcnJGUybtpyxYzvRvXtj3n77ary9PbR6E6VUhVIpE0nM8dM8u+QvLmlVjxt7lnxtwsYYFi78izvv/IFDh1IIDq5F9+6NtYp3pVSFVOnObDm5hnu+3ERVD+FFF7QxEh19ittv/56lS/fQpUtDvvpqJD17lqlmVpRSqkRVukTy3u/72BCXyBujutCwVsm3MfLZZ1v5/fdYXnvtKm6/vYdWsKiUqvAqVSLZcTCZ137azdUdG3FN55JrY2TlylgyMnLo27cZ998fzoQJXQgKcv2rxEopVRZUmsvljOwc7vlyE7VreDF9aMm0KHj8eBqTJn3LJZdE8PTTKwDw9vbUJKKUqlQqzR3J6z/vYefhFD6eEEbdmudXo64xhoiITdx//08kJWXw4IO9efzxS0ooUlWRZGVlkZCQQHp6urtDUZVEtWrVCAoKomrVqqW2zEqRSNbFnOS9FfsYdUET+rQ5/8oQly7dw6RJi+jduwnvvjuIDh3c126JKtsSEhLw9fUlJCREX/tWLmeM4cSJEyQkJBAaGlpqy63wRVunM7K5d/5mGteuzmOD2p3zfNLSsoiMjANg4MCWfPvtKH7/faImEVWo9PR0/P39NYmoUiEi+Pv7l/odcIVPJM99/xdxJ9N4ZURnfM6xOpLvv99Dhw5vM2DAZyQmpiMiXHNNa61gUTlFk4gqTe443ip0Ilmx+xhzVsdxbCDCdAAAE3hJREFU00Wh9GxW/CpJDhxIZsSI+Qwc+Dne3p58991oatcu+VeGlVKqPKuwiSQxLZMHFmymZX0f7r2ydbGnP3r0NO3avc3ixbt55pnL2bx5CpdeGlLygSrlYh4eHnTp0oUOHTowePBgEhMT/x62fft2+vTpQ6tWrWjZsiXTp0/Hsfnt77//nrCwMNq2bUubNm2477773LEKhdq4cSM33XTTv/oNGTKEXr16/avfhAkTWLBgwb/6+fj4/P337t27GThwIC1atKBt27aMHDmSI0eOnFdsJ0+epF+/frRs2ZJ+/fpx6tSpAseLi4vjyiuvpG3btrRr146YmBgAxowZQ+vWrenQoQOTJk0iKysLgMWLF/PEE0+cV2wlyhhTrv51797dOGPq5xtM84eXmK0JiU6NnychIenvv994Y7XZu/dEsaZXytGOHTvcHYKpWbPm33+PGzfOPPPMM8YYY9LS0v5/e2cfVVW1LfDfDG6pycXS5FnWRaSuCAoqVqYlaql9mEUmpiY67FVqeUuz9xo2UvPdzG6fXvVWr9fAGlck9fqRlnr9uppJiUlAUERIXNKUjALMD/TM98feHA54lINwzgFcvzH2GHuvvfZac8+xz55nzbX2nBoWFqYbN25UVdWjR4/q0KFDdeHChaqqmpmZqWFhYZqTk6OqqhUVFbpo0aIGla2ioqLebYwYMULT09OdxyUlJdqxY0ft0qWL5ufnO8sTExN1+fLl1a6t1M2xY8c0PDxc165d6zy3detWzczMrJdsM2bM0Hnz5qmq6rx58/Tpp592W69///66adMmVVUtKyvTo0ePqqrq+vXr1eFwqMPh0FGjRunixYtVVdXhcGhMTIyzXk3cPXdAmnrpvdwsV22tyzjA2i8PMO2264i6Ktija3799TjPPruVt97aS2rqQ/Ts2YGpU2/wsqSGC4k5H35F9oHSBm2z65W/Z9awSI/r9+nTh4yMDACWLl1K3759GTx4MACtWrVi4cKFxMXFMWXKFF566SVmzpxJly5dAAgMDGTy5MlntFleXs7jjz9OWloaIsKsWbO47777aN26NeXl5QCsWLGCdevWkZSUxPjx47n88svZt28fMTExrFq1ivT0dNq0aQNAeHg4u3bt4qKLLuLRRx+lsNBa5PL666/Tt2/fan2XlZWRkZFBdHS0s2zlypUMGzaMkJAQli1bxjPPPFOrXpYuXUqfPn0YNmyYs2zAgAEe6/VsrFmzhu3btwOQmJhIXFwc8+fPr1YnOzubU6dOcdtttwHVR0l33HGHc//666+nqKgIsOZB4uLiWLduHSNHjqy3nPWl2RmSw6XHeXZ1FtFXt2FyXO05RlSV5cuzeeKJDfz4YzmPPXY9nTtf5gNJDQbfcvr0abZs2cLEiRMBy63Vq1evanU6d+5MeXk5paWlZGVlMX369FrbnTt3LsHBwWRmZgKc1X3jSm5uLps3byYgIACHw8GqVauYMGECn332GaGhoYSEhDB69GiefPJJ+vXrR2FhIUOGDCEnJ6daO2lpaURFRVUrS05OZtasWYSEhDBixAiPDElWVtYZunBHWVkZN998s9tzS5cupWvX6itDDx06RIcOHQDo0KEDhw8fPuO63Nxc2rRpQ3x8PPv37+fWW2/lxRdfJCCgKltrRUUF77//Pm+88YazLDY2lp07dxpD0tCoKv+1MoNjJ0/z6shoAgPOPQWkqsTHf8Dq1V/Ts2cH1q59gNjYhgudYjC4UpeRQ0Ny7NgxYmJiKCgooFevXs5/vqp61hU+dVn5s3nzZpYtW+Y8vuyy2v+I3X///c4XZUJCAs8//zwTJkxg2bJlJCQkONvNzs52XlNaWkpZWRlBQUHOsoMHD3LFFVc4jw8dOkReXh79+vVDRAgMDCQrK4uoKPfRLOq6wikoKIj09PQ6XVMbp06dYufOnezbt49rrrmGhIQEkpKSnAYfYPLkydxyyy3VjFj79u05cOBAg8pyvjSryfaUPf9m2zfFPHN7Fzpf0fqs9SoqTgPWQ9Sv39UsWDCUzz9/yBgRQ7OkZcuWpKen8/3333Py5EkWLVoEQGRkJGlpadXq5ufn07p1a4KCgoiMjGTv3r21tn82g+RaVvO7hksvrcoB1KdPH/Ly8iguLmb16tXEx8cD4HA42L17N+np6aSnp/PDDz9UMyKV9+badkpKCiUlJXTq1InQ0FAKCgqcRq5t27bVRks///wz7dq1c+rCk3stKysjJibG7eZq9CoJCQnh4MGDgGX02rc/87uzjh070qNHD8LCwggMDOSee+7hiy++cJ6fM2cOxcXFvPrqq9WuO378OC1btqxVZl/QbAxJ4ZHfmLsum5s6t2Vcn9Cz1tu+vYDu3d9kzZqvAZg+/SYef/wGAmoZvRgMTZ3g4GAWLFjAyy+/TEVFBWPGjOGTTz5h8+bNgDVymTp1Kk8//TQAM2bM4IUXXiA3NxewXuw1X2YAgwcPZuHChc7jypd1SEgIOTk5TtfV2RAR7r33XqZNm0ZERARt27Z12667kUBERAR5eXnO4+TkZDZs2EBBQQEFBQXs3bvXaUji4uJISUnh5MmTACQlJTnnQUaPHs2nn37K+vXrnW1t2LDB6a6rpHJE4m6r6dYCuPvuu1myZAkAS5YsYfjw4WfU6d27NyUlJRQXFwOwdetWZ1vvvPMOGzduJDk5mYsuqv6Oys3NPcOt5ze8NYvvrc3dqq1Tpx16/98+1ajnNmhRyW9uVzEcPlyu48atUpitnTq9rlu25LutZzA0JI1t1Zaq6l133aXvvfeeqqpmZGRo//799brrrtPOnTvr7Nmz1eFwOOt++OGH2rNnT+3SpYtGREToU089dUb7ZWVlOm7cOI2MjNTu3bvrypUrVVV1+fLlGhYWpv3799cpU6ZoYmKiqrpfPbVnzx4FNCkpyVlWXFysI0eO1G7dumlERIQ+8sgjbu8vKipKS0tLdf/+/XrllVdWk19VtUePHpqamqqqqrNnz9aoqCiNjo7W+Ph4PXz4sLNeTk6ODhkyRMPDwzUiIkITEhL0xx9/PKdua+Onn37SgQMHanh4uA4cOFCPHDnivN+JEyc6623atEm7deumUVFRmpiYqCdOnFBV1YCAAA0LC9Po6GiNjo7WOXPmOK+58847NSMjw22/vl61JVb7TYfY2FitORx/e8d3vPDR17xyfzT39ToziVRyciZTpnxEeflJZsy4iZkzb6FVK98FNDNcuOTk5BAREeFvMZo1r732GkFBQWd8S9KcOXToEKNHj2bLli1uz7t77kRkr6rGekOeJu/P+ebHMl7emMuQyBDie17lts6pUw6iotqTnv4of/7zIGNEDIZmxKRJk7jkkkv8LYZPKSws5JVXXvG3GE6a9Kqtk6ccTPsgnaAWgbxwbzfn5N7RoyeZO3cH11wTzOTJvRk7tjtjxzZ8Wl2DweB/WrRowYMPPuhvMXxK7969/S1CNZr0iOSvW7/lqwOlzIvvRtvW1j+SdetyiYxczPz5u8jNPQJYk3nGiBj8RVNzHxuaNv543prsiGRfYQmLtuUxoldHBkf+B0VFpUyd+jGrVn1N165XsGPHeG6++Q/+FtNwgdOiRQuOHDliQskbfIKqlY+kRQvfBpdtkobk2MnTTP/gSzoEt+S5YdYyufz8EjZu/I558wYxbVofLr44oJZWDAbv07FjR4qKipxLOw0Gb1OZIdGXNMlVW3c9t4SkTwt47ubOlOX9wp/+dCMAR478Rtu2rfwsocFgMDQ+muyqLREZKiLfiEieiPy3m/OXiEiKff4zEQmtrc3yE6dI+rSA0BPw0LAUXn01laNHrQ+MjBExGAwG3+M1QyIiAcAi4HagK/CAiNT89HMiUKKq4cBrwHxqofDIbzh+OcEni79g6tQbyMycxKWXXtzQ4hsMBoPBQ7w5R3I9kKeq+QAisgwYDrgGpBkOzLb3VwALRUT0HP620w7l8txSVu+2Qr0bDAaDwb9405BcBfzb5bgIqJngw1lHVU+JyK9AW+An10oi8jDwsH144stDE7I8iPh8IdCOGrq6gDG6qMLoogqjiyrqnirWQ7xpSNytdaw50vCkDqr6NvA2gIikeWvCqKlhdFGF0UUVRhdVGF1UISJptdc6P7w52V4EXO1y3BGoGTzfWUdEAoFg4GcvymQwGAyGBsabhmQPcK2IdBKRi4FRwNoaddYCifb+CGDrueZHDAaDwdD48Jpry57zeAzYCAQA76rqVyLyPFY447XA/wHvi0ge1khklAdNv+0tmZsgRhdVGF1UYXRRhdFFFV7TRZP7INFgMBgMjYsmHbTRYDAYDP7HGBKDwWAw1ItGa0i8EV6lqeKBLqaJSLaIZIjIFhFptmGPa9OFS70RIqIi0myXfnqiCxEZaT8bX4nIUl/L6Cs8+I1cIyLbRGSf/Tu5wx9yehsReVdEDotI1lnOi4gssPWUISI9G6Rjb+Xwrc+GNTn/HRAGXAx8CXStUWcy8Ka9PwpI8bfcftTFAKCVvT/pQtaFXS8I2AGkArH+ltuPz8W1wD7gMvu4vb/l9qMu3gYm2ftdgQJ/y+0lXdwC9ASyznL+DuBjrG/4bgQ+a4h+G+uIxBleRVVPApXhVVwZDiyx91cAg6R5JnyoVRequk1Vf7MPU7G+2WmOePJcAMwFXgKO+1I4H+OJLv4TWKSqJQCqetjHMvoKT3ShwO/t/WDO/KatWaCqOzj3t3jDgffUIhVoIyL1jjXVWA2Ju/AqNROyVwuvAlSGV2lueKILVyZi/eNojtSqCxHpAVytqut8KZgf8OS5uA64TkR2iUiqiAz1mXS+xRNdzAbGikgR8BHwuG9Ea3TU9X3iEY01sVWDhVdpBnh8nyIyFogF+ntVIv9xTl2IyEVYUaTH+0ogP+LJcxGI5d6Kwxql7hSRKFX9xcuy+RpPdPEAkKSqr4hIH6zv16JU1eF98RoVXnlvNtYRiQmvUoUnukBEbgVmAner6gkfyeZratNFEBAFbBeRAiwf8NpmOuHu6W9kjapWqOp+4Bssw9Lc8EQXE4EPAFR1N9ACK6DjhYZH75O60lgNiQmvUkWturDdOW9hGZHm6geHWnShqr+qajtVDVXVUKz5ortV1WvB6vyIJ7+R1VgLMRCRdliurnyfSukbPNFFITAIQEQisAzJhZj/eC0wzl69dSPwq6oerG+jjdK1pd4Lr9Lk8FAXfwFaA8vt9QaFqnq334T2Eh7q4oLAQ11sBAaLSDZwGpihqkf8J7V38FAX04H/FZEnsVw545vjH08RScZyZbaz54NmAb8DUNU3seaH7gDygN+ACQ3SbzPUpcFgMBh8SGN1bRkMBoOhiWAMicFgMBjqhTEkBoPBYKgXxpAYDAaDoV4YQ2IwGAyGemEMiaHRISKnRSTdZQs9R93Qs0U6rWOf2+3osV/aIUX+eB5tPCoi4+z98SJypcu5d0SkawPLuUdEYjy45gkRaVXfvg2Gs2EMiaExckxVY1y2Ah/1O0ZVo7GCgf6lrher6puq+p59OB640uXcQ6qa3SBSVsm5GM/kfAIwhsTgNYwhMTQJ7JHHThH5wt5uclMnUkQ+t0cxGSJyrV0+1qX8LREJqKW7HUC4fe0gO4dFpp3r4RK7/EWpygHzsl02W0SeEpERWDHP/m732dIeScSKyCQReclF5vEi8tfzlHM3LgH3RORvIpImVu6ROXbZVCyDtk1Ettllg0Vkt63H5SLSupZ+DIZzYgyJoTHS0sWttcouOwzcpqo9gQRggZvrHgXeUNUYrBd5kR0OIwHoa5efBsbU0v8wIFNEWgBJQIKqdsOKBDFJRC4H7gUiVbU78D+uF6vqCiANa+QQo6rHXE6vAOJdjhOAlPOUcyhWGJRKZqpqLNAd6C8i3VV1AVYspQGqOsAOlfIscKutyzRgWi39GAznpFGGSDFc8ByzX6au/A5YaM8JnMaKG1WT3cBMEekI/ENVvxWRQUAvYI8dPqYlllFyx99F5BhQgBVm/I/AflXNtc8vAaYAC7FynbwjIusBj0PWq2qxiOTbcY6+tfvYZbdbFzkvxQoH4prhbqSIPIz1u+6AlcApo8a1N9rlu+x+LsbSm8Fw3hhDYmgqPAkcAqKxRtJnJK1S1aUi8hlwJ7BRRB7CCpu9RFWf8aCPMa4BHkXEbX4bO7bT9VhBAEcBjwED63AvKcBI4GtglaqqWG91j+XEygL4IrAIiBeRTsBTQG9VLRGRJKzAhDUR4J+q+kAd5DUYzolxbRmaCsHAQTt/xINY/8arISJhQL7tzlmL5eLZAowQkfZ2ncvF85z2XwOhIhJuHz8I/MueUwhW1Y+wJrLdrZwqwwpr745/APdg5chIscvqJKeqVmC5qG603WK/B44Cv4pICHD7WWRJBfpW3pOItBIRd6M7g8FjjCExNBUWA4kikorl1jrqpk4CkCUi6UAXrJSi2Vgv3E0ikgH8E8vtUyuqehwrOupyEckEHMCbWC/ldXZ7/8IaLdUkCXizcrK9RrslQDbwB1X93C6rs5z23MsrwFOq+iVWfvavgHex3GWVvA18LCLbVLUYa0VZst1PKpauDIbzxkT/NRgMBkO9MCMSg8FgMNQLY0gMBoPBUC+MITEYDAZDvTCGxGAwGAz1whgSg8FgMNQLY0gMBoPBUC+MITEYDAZDvfh/51w/pInPS40AAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gUZdfA4d9DAqEkoYWaEBJq6B2kiIiigCBIE0SQ4uuL2F4L9q6IvX1gp9hBwIKAiCgKofdeJQFC6C2N1D3fHzPRJYRkQ7LZlHNfFxfZ2Slnys6Z8swZIyIopZRSV6qEpwNQSilVuGkiUUoplSuaSJRSSuWKJhKllFK5oolEKaVUrmgiUUoplSvFJpEYY4YbYxZ7Og5PM8YEG2PijDFe+TjNEGOMGGO882ua7mSM2WGM6XYFw13RNmiMKWOM+dkYc94YMzunw+cnV7evovp7NMaUNsbsNsZU9XQsuWWMmWeM6eFSv554jsQYEwlUA9KAOGARcK+IxOV7MEWcvazvFJElHowhBIgASopIqqfisGMRoL6I7HfzdELIo3k2xowA7gM65cXys5PgH0CC3ekcsBJ4Q0TW5Xb8nmKMGQ58bH/0Anz4dx4REd98iOFBoK6I3Gt//goYAiQ79bZHRNq4MK6XgSARGeWOWF2YfifgHRHpkF2/njwj6Wuv2JZAK+AJD8ZyxTx5lF1UjvBzopgu79rA3itJIlnEHG3//vyAq4DdwHJjzHVXHqZnicjXIuJrz1cv7Hl06nYRN63P/wJfZuj2inMcriQRVxhjShhj3LYPF5GVQBVjTCtXes73f0AkcL3T59eBBU6ffYA3gUPAceAjoIzT9/2AzUAM8DfQ0+5eHpgKHAWOAC8DXvZ3o4Bw+++PgDczxPQT8JD9d01gLnAS66jyfqf+ngfmAF/Z078zk/krD3xhD38QeBoo4RTHCuD/gPNYP+DrMgyb1TysAN4Bztjf1cU6ujwNnAK+BirY/X8JOIALWGd+jwIhgADedj9/Ai/Z440FFgMBTvGMtOfhNPBMxnWXYb7LAG/Z/Z8Hwu1u6dO8w16np4CnnIZrD6zCOjI+CkwGSjl9L8A9wD4gwu72HnDYXgcbgKud+vcCnrS3jVj7+1rAMntc8fbyuNXuvw/W9pR+ZN48w7b6GLAVSAK8nZeBHft6O47jwNt290P2tOLsfx1x2gbtfpoAv9nr8jjwZCbL9AWso9kUezxjsQ4An7aX8wmsba283X/6sh5rx7Ask3F2A6Iy6T4ZWO/0Ocwpvj3AkBys6/TtaxRwwF4PEcDwjL9H+3MnYJ09rnVYZ1/p3/1JFtvoZbbFy81jFDAB2AYk292CgB/49/d+j1P/Jfh3WzoFzAQqXmaadbC2LS+nbl8Bz1+m/3r2shppx3USeNxpm3Re7xvs7uH2sliF9bsOseOfb6+nfcAYp2m8DMwCZtvLbj3QzP7uCWBWhpg+xGnfCEzH6bd62eV9pckgN/+4+IcYZK/U95y+fxeYB1TCOmL6GZjk9MM9D/SwV3IgEGZ/9yPWqW05oCqwFvhvxg0X6Iq1E0q/tFfRXik17XFuAJ4FStkbxwHgRrvf5+2V29/ut0wm8/cFVmLys1f0XmCsUxypwINASeBWe34quTgPqViXObyxfrj17GXhA1TB2lm+m9myzrCjcU4kfwMN7PH9Cbxqf9cYayPuYi+LN+15v1wimWIPH4i1M+9kx5U+zU/tabTA2ik3sodrg3VU7G33uwv4n9N4BWuHVil9eQO3A5XtYR4GjgGl7e/SdxQNAWNPr7LTuOo5jbs11s64gx3zHfYy83FafpuxElGZjMsU6wc9wv7bF7gqs+WcyTboh5U0HwZK2587XGa5Pg985fR5DLAfa9v0Bb4Hvsww3S+wtqHMts9uZL6T7Y514FHO/ncYGG0v49ZYO9ImLq5rb3scMUBDe5gaTsM7L4tKwFlghD3cMPtz+jr7k8tso1nsYy43j1FYv+8ge1xe9vp9Emsbr2ev3+vs/h/BSmCB9nqamr6sMxl3P2BLhm6uJJKP7HG3xvpd1Le/fxmYkWGYcDu+Rlj7D2/+PTBNH8cp4BqncaQAt9j9P25vO972MogD/O1+S9nDtnCa3qPAd9nu092VLLJZyZH2DMTaC/J3/j2KNlhZva5T/x3590j0Y6zrdhnHWc1eCc5nLsOApZlsuAbraK2r/fk/wB/23x2AQxnG/QQw3elHfclRnlO/XnYcjZ26/Rf40ymOaOwkZndbi/UjcmUeDl1u2nY//YFNGZZ1donkaafvxwOL7L+fBb51+q4s1lHSJYkEK6lecN4IM5lmUIZ5HnqZefgf8IPTZwG6ZzPfZ9OnjXX03O8y/WVMJB8CL2XoZw///hAjcTrCy7hMsRL3C2Q4Qs64nDPZBoc5r6ds5u15Lk4kvwPjnT43xNpZeDtNt04W4+tG5jvZMHvYQKwDnOUZvv8YeM7FdZ2eSM4BA8mQ0DIsixHA2gzfrwJGZbeNXsE8RgEjnT53Bg5k6OcZ4FP7733p24L9uRbWb7REJuO+A6ezLLvbV0CivRzS/021v0tPJNWd+t8IDLL/vlwiedbpc6i97ss5dXsD+MxpHM5nfl5YB04d7c+/AaPtv/sDWzNM725gcXbbqCevsfcXkSXGmGuAb4AArIVcBWuHtcEYk96vwVoAYK3IhZmMrzZWxj3qNFwJrKOqi4iIGGNmYv2YlwG3Ya3w9PHUNMaccxrEC1ju9PmScToJwMrsB526HcT6caY7IvZacvq+povzcNG07dYh7wNXYx3VlsDaqebEMae/E7COcrFj+md6IpJgjDl9mXEEYB0R/Z3T6RhjGgBvA22x1r031lGjs4zz/TBwpx2jAP52DGBtI1nF4aw2cIcx5j6nbqXs8WY67QzGAi8Cu40xEcALIjLfhenmJMaManLp9uWNdSCSLquYLycQa1mew1ouHTL8DryxLpe6sq4RkXhjzK1YR/VTjTErgIdFZHeGXjPOD1z6m7ncNnolnJdNbSA4k9/7n/bfwcDPxhiH0/eCdbXAOSawfnd+mUzvVRF5/nLBiEhO5805/prAKRGJd+p2EGiaWf8ikmaMOcK/2/fnWGed07HO8jPe3/HD2h6y5PHmvyLyFzAD67IJWKdWF7BOgSvY/8rLvzfLDmPdF8joMNaRQoDTcP4i0uQyk/4WGGSMqY11FjLXaTwRTuOoICJ+ItLbOewsZukU1hFCbaduwVj3O9IFGqdMYX8f7eI8ZJz2JLtbcxHxx9oYTBb958RRrNNfwGqGinU5KTOnsI68Mls32fkQ615RfXsenuTieQCn+TDGXI1132II1vXqCliXB9OHudw2kpnDwMQM67usiHyb2bQzEpF9IjIMa8fyGjDHGFMuq2GuIMaMorl0+0rFus/yT2hXMN5bgI32Tukw8FeG5eIrIneTg3UtIr+KSA+sy1q7sS5vZjc/cOlvJi85L5vDwL5Mfu997e+jgB4Zvi+dYeefbitQNw+b1l9uHTp3jwYC7G0uXcZlVyv9D/vmfKA9HFiXRdsYY5pgNVD4JsO0GgFbsgvU44nE9i7QwxjTUkQcWBvbO+ltsY0xgcaYG+1+pwKjjTHX2a0WAo0xYSJyFOsm3FvGGH/7u7r2Gc8lRGQT1s2tz4BfRSQ9664FYowxj9nt972MMU2NMe1cmRERSQO+AyYaY/zsRPUQ/57xgLXTud8YU9IYMxhrZS3M6TzY/LAuE54zxgRi3R9wdhzrWvqVmAP0NcZ0MsaUwrqEk3EHD4C93qYBbxtjatrLraMxxseF6fhhXUuPM8aEYZ1OZ9d/Ktb68zbGPIt1RpLuM+AlY0x9Y2lujElPgBmXx6fAOGNMB7vfcsaYm4wxmR1ZXsIYc7sxpoo9/+nbUJodm4PLL/v5QHVjzP+MMT72tpJtM0vbt8CDxphQY4wv8ArWTdMradVl7N/Qc1hneE86xdfAGDPC3k5LGmPaGWMaubqujTHVjDE32zu5JKztNC2TMBba07rNGONtn8U0tmNwt1VAsjHmYWM9A+JljGlmjElvWfUR8IoxJhisKwDGmJszG5GIRGJdMs+TVllY22pIhoPOjNOMwLqB/oq9HbXEOsP42qm39saYfsaYklhnh7FYDRoQkQSshgbfAitEJGPy7gr8kl2gBSKRiMhJrJuDz9idHsO6IbTaGBMDLMG6DoyIrMVaUO9gHYX+xb9HMyOxLkvsxDrNnIN1JHQ53wLX45SF7UTQF6tZcgTW0ddnWK2pXHUf1n2eA1jXNL/B+uGlWwPUt8c9EeuaaPolo5zOwwtYN9jOAwuwjjCcTQKeNsacM8Y8koN5QER22PMyE+vsJBbr+mrSZQZ5BOsm9zqsFiSv4do29gjW5cVYrB37rGz6/xVr496LdRqfyMWn+29jJfPFWAlqKtaNVbDuN3xuL48hIrIe6x7ZZKzlvR/r+r2regI7jDFxWC3JhopIov0DnQissKd1lfNAIhKL1UiiL9Ylkn3AtS5OcxrWJYhlWNtoItZ6yomadsxxWOurGdBNRBY7xXcDMBTr6PUY1vpMTxaurOsSWI0Jou1+rsG6v3ERe9vvY/d7GusGbx8ROZXDecoxO/n2xmrEE4n1m/yYfw9M3sZ6zu13Y0wsVqu+rA4qP8a65+PsSWM9pJn+L7OzmczMwtoXnDHGrM2iv1ux9ifHsPYXT4rIUqfvf8C6UnHG7ndAhoOOz7HW/0WXtYwxHYEzIrIxu0A98kBicWaMGYXVZLiLp2PJKfvo9xzWJagIT8ejVEFjjCkNbMK6QX+iAMST7UONxpg6WJflqovTQ+HGmJ+AKekHF1kpdg+0qZwxxvTFaiVksO5jbcM6clNKZSAiiViXqgsF+57JQ8A3kqGyiIj0c3U8mkhUdvphnfIarGuxQ0VPY5Uq9Iwx5bFuykcCN2bddzbj0n2CUkqp3CgQN9uVUkoVXoXu0lZAQICEhIR4OgyllCpUNmzYcEpEqrhj3IUukYSEhLB+/XpPh6GUUoWKMSZj9YA8o5e2lFJK5YomEqWUUrmiiUQppVSuaCJRSimVK5pIlFJK5YomEqWUUrnitkRijJlmjDlhjNl+me+NMeZ9Y8x+Y8xWY0xrd8WilFLKfdx5RjIDq8T25fTCKn1cH7gL6+VGSiml8lhiSmavgck7bnsgUUSWGWNCsuilH/CFXQBwtTGmgjGmhv1yJ6WUUrl0IiaRp2dsYOnhnL59O2c8+WR7IBe/jCjK7nZJIjHG3IV11kJwcHC+BKeUUoXVrqMxvLdoD7/uOo6UMHifTnTr9Dx5sz2z10dmWopYRD4RkbYi0rZKFbeUilFKqULN4RB+33Wc2z5dTa/3lrNo5zEStp9mRKXy7P5kgFun7ckzkiicXkoPBPHvC+mVUkq5ICE5lbkbopi+IpIDp+Kp6ufDYz3DaFK6FKH3+1OrVk7eEn5lPJlI5gH3GmNmAh2A83p/RCmlXBN97gKfr4rk2zWHiElMxT9FOPnLAUYObsbd3ermayxuSyTGmG+BbkCAMSYKeA4oCSAiHwELgd7AfiABGO2uWJRSqqjYfPgcU8MjWLjtKCJCmG9pNszeR9SeMzz0UEcee7RzvsfkzlZbw7L5XoB73DV9pZQqKlLTHCzeeZyp4RFsOHgWPx9vRncK4eiyKD54OpxOnWqxZNNgmjWr5pH4Ct37SJRSqriISUzhu3WHmb4ikiPnLlCrUhmeuLEhNzaoSkigP3vqV6Vl/cqMHduaEiUya7+UPzSRKKVUAXPwdDzTV0Qye/1h4pPTaB9SiWf6NMZxOJb77lzI4pbVmTt3CA0bBtCwYYCnw9VEopRSBYGIsDbiDFPDI/ht13G8jKFvi5qM6RxK5RIl+N//FjF79k4aNqzMvfe283S4F9FEopRSHpSc6mDBtmimhkew/UgMFcqWZHy3uozsGEI1/9L8/vsBOt8yi+TkNF566VomTOiEj0/B2nUXrGiUUqqYOBufzDdrD/H5ykhOxCZRt0o5Jt7SlAGtgihTyosUuz5WixbV6d27Pi+/3J169Sp5OOrMaSJRSql8tP9ELNNWRPL9xigSUxxcXT+A1wY155r6VShRwhATk8TjExazZs0RVqwYQ0BAWWbOHOTpsLOkiUQppdxMRFi+7xRTwyP4a+9JSnmXYECrQEZ3DqVhdb9/+pk9ewcPPLCIY8fiGD++HUlJaZQtW/BfG6WJRCml3CQxJY0fNx1h2ooI9h6PI8DXh4d6NGB4h2Aq+/r809/Jk/HccceP/PLLflq1qs5PPw2lXbtAD0aeM5pIlFIqj52ITeSrVQf5as0hzsQn06iGP28ObkHfFjXw8fa6pH9/fx9OnUrg3Xdv5J572uPtXfDPQpxpIlFKqTyyI/o808Ij+XlLNCkOB9eFVWVMl1A61qmMMRc/MLhs2UEmTlzO3LlD8PUtxerVd3r0ocLc0ESilFK54HAIv+8+wdTwA6w+cIYyJb0Y2r4WozuHEhpQ7pL+T51KYMKE35gxYzMhIRWIjDxH06ZVC20SAU0kSil1ReKTUpmzIYrpKyKIPJ1AzfKleaJXGEPbBVO+bMlL+hcRpk/fzIQJvxETk8QTT3Th6ae7UjaTfgsbTSRKKZUD0ecu8PnKSL5da5Vvb1mrAv93Q0N6Nq1OSa+s72189dVWGjeuwkcf3USTJlXzKWL300SilFIu2HjoLNPCI/hl+zFEhF5NazCmSyhtale87DAJCSm88spyxo1rS1CQP3PnDqF8+dKF+jJWZjSRKKXUZaSmOVi04xhTwyPYdOgcfj7ejOkcwh2dQgiqWDbLYRcu3Mc99ywkMvIcgYF+3H13OypWLJNPkecvTSRKKZXB+QspzFp3iM9XHuTIuQvUrlyW5/s2ZlDbWvhmU+cqKiqG//1vEXPn7qJRowD++msUXbvWzqfIPUMTiVJK2dLLt3+3/jAJyWl0CK3Ec30bc12jani5eDlq4sRlLFiwj1de6c7DD3eiVKlLnxspaoz1osLCo23btrJ+/XpPh6GUKiJEhDV2+fYlu47jXcLQt3lNxnQJpWlgeZfGsXbtEcqU8aZZs2qcPp3A+fNJ1Klz+XsnnmCM2SAibd0xbj0jUUoVS8mpDn7eEs20FRHsiI6hYtmS3NOtHiM61qaaf2mXxnH+fCJPPvk7H364nj59GjBv3jAqVy5L5cpZ3z8pajSRKKWKlTPxyXy9+iBfrD7Iydgk6lX15ZVbmjGgdSClS7p2GUpEmDVrBw8++CsnTsRz333teeml7m6OvODSRKKUKhb2HY9l2ooIvt94hKRUB10bVOHNwaF0rR9wSfmS7Hz11VZGjvyRtm1rMn/+MNq0qemmqAsHTSRKqSJLRFhml29ftvckPt4lGNA6kDGdQ6lfzS9H40pKSuXAgbM0alSFIUOakJrqYOTIFnhl8xBicaCJRClV5CSmpPH9Rqt8+/4TcVTx8+HhHg24LUP5dlctXRrB3XcvICEhhX377sPHx5vRo1u5IfLCSROJUqrIOBGTyBerDvL1moOcTUihcQ1/3hrcgj6XKd+e7fhOxPPII4v58sut1KlTkU8+6Vvg3pdeEOgSUUoVetuPnGdaeAQ/b40m1SFc36gaY7uE0iG0Uo7vf6Tbv/8M7dt/SlxcMk89dTVPPXU1ZcoU/gKL7qCJRClVKKU5hN93HWdqeARrIs5QtpQXwzvUZlSnEEIyKd/uqpiYJPz9fahbtyJjx7ZizJhWNGpUJQ8jL3o0kSilCpW4pFRmrz/MjJWRHDydQGCFMjzZO4xb2wVTPhdnDPHxybz44l98+ulGtm69m6Agf95444Y8jLzo0kSilCoUos4m8PnKSGauO0xsYiqtgisw4caG9GxSHe9ctpz6+ec93HvvLxw6dJ6xY1sViXeE5CdNJEqpAm3DQat8+6IdxwDo2bQ6Y7uE0jo49yVIUlMdDBkymx9+2E2TJlVYvnw0XboE53q8xY0mEqVUgZOa5uCX7Vb59s2Hz+FX2ps7u4QyslMIgRVyX4pdRDDG4O1dgho1fHn11et48MGOxaLAojtoIlFKFRjnE1L4dt0hvlgZSfT5REIql+WFm5swqE0Q5fKo2e3q1VHcc89CPv20L61b12DKlJvyZLzFmSYSpZTHRZyKZ/qKCOZsiCIhOY2r6lTihX5N6R5W1eXy7dk5e/YCTz75Ox9/vIGaNf04e/ZCnoxXuTmRGGN6Au8BXsBnIvJqhu+Dgc+BCnY/j4vIQnfGpJQqGESEVQdOMy08gt93n7DKt7eoydguoTSp6Vr5dlfNmrWd++9fxKlTCfzvf1fxwgvd8PPL+RPuKnNuSyTGGC9gCtADiALWGWPmichOp96eBr4TkQ+NMY2BhUCIu2JSSnleUmoaP285yrTwCHYejaFSuVLcd209br+qNlVdLN+eU7t3nyIkpAKLFg2nVasabplGcebOM5L2wH4ROQBgjJkJ9AOcE4kA/vbf5YFoN8ajlPKg03FJfL3mEF+sOsipuCTqV/Xl1QHN6N/K9fLtrkpMTOW118Jp3boGffs25Mknr+bpp7tqgUU3cWciCQQOO32OAjpk6Od5YLEx5j6gHHB9ZiMyxtwF3AUQHKxN85QqTPYci2VaeAQ/bD5CcqqDaxpUYWyXUK6+gvLtrliy5ADjxy9g374zPPxwR/r2bUjJPE5U6mLuTCSZbSEZ3+s7DJghIm8ZYzoCXxpjmoqI46KBRD4BPgHrVbtuiVYplWccDuGvfSeZFh7B8n2n8PEuwcDWQYztEkK9qjkr3+6q48fjeOihxXzzzTbq1avE4sW306NHXbdMS13MnYkkCqjl9DmISy9djQV6AojIKmNMaSAAOOHGuJRSbnIhOY3vN0UxLTyCv0/GU9XPhwk3NmRY+2AqlSvl1mn/9tsB5szZybPPduWJJ66mdGltlJpf3Lmk1wH1jTGhwBFgKHBbhn4OAdcBM4wxjYDSwEk3xqSUcoPjMYl8sSqSb9Yc4mxCCk0D/Xnn1hbc1Kwmpbzdd19iy5Zj7Nt3hkGDGjN8eDM6d65FaGjun3hXOeO2RCIiqcaYe4FfsZr2ThORHcaYF4H1IjIPeBj41BjzINZlr1EiopeulCokth85z9TwCObb5dt72OXb2+eifLsr4uKSee65pbz33hpCQirQv38Y3t4lNIl4iFvP/exnQhZm6Pas0987gc7ujEEplbfSHMJvO48zbUUEayPOUM4u3z66cwi1K195+XZX/fjjbu677xeiomK4667WTJp0Pd5uPOtR2dOLiEopl8QlpfLdOqt8+6EzVvn2p29qxJB2tfAvnT/VcrdtO84tt8yiWbOqzJo1iE6damU/kHI7TSRKqSwdPmOVb5+17jCxSam0qV2Rx3uFcUPjarku3+6KlJQ0li8/RPfuoTRrVo0FC26jR4862qS3ANFEopS6hIiw8dBZpoZHsGj7MYwx9G5Wg7FdQmlZq0K+xbFy5WHGjZvPjh0n2bPnXurVq0Tv3vXzbfrKNZpIlFL/SElzsHDbUaatiGTL4XP4l/bmP13rcEfHEGrmQfl2V505c4HHH1/Cp59upFYtf77/fgj16lXKt+mrnNFEopTifEIK36w9xBerIjl6PpHQgHK82K8JA1vnXfl2VyUmptKy5UdER8fy8MMdef75bvj6uvcZFJU7mkiUKsYOnIxj+opI5myI4kJKGp3qVubl/k25tmFVSuRR+XZXRUXFEBTkT+nS3rz00rW0bFmdFi2q52sM6spoIlGqmBERVv19mql2+fZSXiW4uWVNxnQOpXFN/+xHkMcuXEhh0qRwXnttBXPmDKZv34bccUfLfI9DXTmXEokxphQQLCL73RyPUspNklLT+GlzNNPCI9h9LJbK5Upx/3X1uf2qYKr6uad8e3YWL/6b8eMX8PffZ7n99ua0bx/okThU7mSbSIwxNwFvA6WAUGNMS+A5EbnF3cEppXLvVFwSX60+yFerD3IqLpmG1fx4bWAz+rXM+/LtOXHffQuZPHkd9etXYsmSEVx3XR2PxaJyx5Uzkhexyr8vBRCRzcaYem6NSimVa7uPxTAtPIIfN0eTnOrg2oZVGNulDp3rVXZr+ZKspKVZhb29vEpw1VVBBASU5bHHumiBxULOlbWXIiLnMmx4Wg9LqQLI4RD+2nuSqeERhO8/RemSJRjcJojRnUOpV9XXo7Ft3HiUcePmM2JEc+67rwPDhzf3aDwq77iSSHYZY4YAJexKvg8Aq90bllIqJxKSU/l+4xGmrYjgwMl4qvlb5dtvax9MRTeXb89ObGwSzz67lPffX0uVKmWpUcM97yNRnuNKIrkXeBZwAN9jVfN9wp1BKaVcc+x8Ip+vssq3n7+QQrPA8rx7a0t6N6vh1vLtrlq8+G/GjPmJ6OhYxo1ryyuvXEeFCp65sa/cx5VEcqOIPAY8lt7BGDMAK6kopTxga9Q5poZHsGDrURwi3NC4OmOvDqVt7Yoeu/+RmVKlvKhatRxz5w6hQ4cgT4ej3MRk9/oPY8xGEWmdodsGEWnj1sguo23btrJ+/XpPTFopj7LKtx9jangE6yLP4uvjzZC2tRjVKYTgymU9HR5gFVh8++1VxMQkMXHidYB13ya/H25Ul7L3223dMe7LnpEYY27Eeg1uoDHmbaev/LEucyml8kFsYgrfrY9ixsoIDp+5QFBFq3z7re1q4ZdP5dtdER5+6J8Ci4MHN/4ngWgSKfqyurR1AtgOJAI7nLrHAo+7MyillFW+ffqKSL5bf5i4pFTa1q7Ik70a0SOfyre76vTpBB57bAlTp24iOLg8P/88jD59Gng6LJWPLptIRGQTsMkY87WIJOZjTEoVWyLC+oNnmbo8gsU7j1HCqXx7i3ws354Tp09fYObM7Tz6aCeeffYaynm4lZjKf67cbA80xkwEGgP/NLcQET3kUCqPpJdvnxoewdao85QvU5K7utbljk61qVE+/8q3u2rXrpN8990OnnuuGw0aVObQoQepVKngxanyhyuJZAbwMvAm0AsYjd4jUSpPnEtItsq3rzzIsZhE6gSU46X+TRnYOpCypQre094JCSlMnLiMN95Yia9vKcaObU1QkL8mkWLOlS21rIj8aox5U0T+Bp42xix3d2BKFd27UnMAACAASURBVGV/n4xjWngEczdGkZjioHO9yrwyoCndGuR/+XZXLVq0n/HjFxARcY477mjBG2/0oEqVcp4OSxUAriSSJGM1TP/bGDMOOAJUdW9YShU9IsKK/aeZGn6ApXtOUsqrBP1a1mRMl1Aa1cj/8u05EReXzIgRP1C5chmWLr2Dbt1CPB2SKkBcSSQPAr7A/cBEoDwwxp1BKVWUJKakMW9zNNNW/Fu+/YHr6nP7VbWp4ufj6fAuKy3NwbffbmfYsKb4+pZiyZIRhIUF4JPPb0xUBV+2W4SIrLH/jAVGABhj9BFVpbJxMtYq3/71Gqt8e1h1P14f1JybW9T0aPl2V2zYEM1//zufDRuOUqaMNwMHNta3FarLyjKRGGPaAYFAuIicMsY0wSqV0h3QZKJUJnYdjWFqeATzNkeTnOage1hVxnYJpVNdz5Vvd9X584k888xSpkxZR9Wq5Zg5cyADBjTydFiqgMvqyfZJwEBgC9YN9h+wKv++BozLn/CUKhwcDmHpnhNMDY9g5d+nKVPSiyHtrPLtdat4tnx7Tgwc+B1//BHBPfe04+WXu1O+vBZYVNnL6oykH9BCRC4YYyoB0fbnPfkTmlIFX0JyKnM3RDF9RSQHTsVT3b80j/a0yrdXKFs4Hsw7cOAsVaqUxc/Ph4kTu1OihKFdO33lrXJdVokkUUQuAIjIGWPMbk0iSlmOnr/A5ysP8u1aq3x7i6DyvDfUKt9esgCVL8lKcnIab765kpdeWsb997fntdd6aIVedUWySiR1jDHppeINEOL0GREZ4NbIlCqANh+2yrcv3HYUEeHGJtUZ2yWUNgWsfHt2li07yLhx89m16xSDBjXm/vs7eDokVYhllUgGZvg82Z2BKFVQpaY5WLzzOFPDI9hw0CrfPqpTCKM6hVCrUsEo354T77yzioceWkxISAUWLLiN3r3rezokVchlVbTx9/wMRKmCJiYxhe/WHWb6ikiOnLtArUpleKZPY4a0DSpQ5dtd4XAI8fHJ+Pn5cNNNDTh5MoGnn+5K2bKFaz5UwZTti60KGn2xlXK3Q6cTmL4ygtnro4hLSqV9SCXGdAmlR+NqeBXQ8iVZ2bHjBOPGLfjnTYWqePLIi63ygjGmJ/Ae4AV8JiKvZtLPEOB5QIAtInKbO2NSKjMiwrrIs0wNP8DincfxMoY+zWswtksdmgWV93R4VyQhIYWXXvqLN99cRfnyPowZ0xIRKVT3clTh4HIiMcb4iEhSDvr3AqYAPYAoYJ0xZp6I7HTqpz7wBNBZRM4aY7SGl8pXyakOFmyLZlp4JNuOnKdC2ZLcfU1dRnYMoXohfoZi06ajDBjwHZGR5xg9uiWvv96DgIDCdz9HFQ7ZJhJjTHtgKlaNrWBjTAvgThG5L5tB2wP7ReSAPZ6ZWM+m7HTq5z/AFBE5CyAiJ3I+C0rl3Nl4u3z7qkiOxyRRp0o5Xu7flIGtgyhTqmCXL8lK+hlHcHB5goPL8/nn/enatbanw1JFnCtnJO8DfYAfAURkizHmWheGCwQOO32OAjK2MWwAYIxZgXX563kRWeTCuJW6IvtPxDFtRQTf2+Xbr64fwKsDm3NN/SoFtny7K1JTHUyevJZ58/bw228jqFy5LH/9NcrTYaliwpVEUkJEDma4rprmwnCZ/Soz3tn3BuoD3bBqdy03xjQVkXMXjciYu4C7AIKDg12YtFL/EhHC959iangEf+45SSnvEtzSMpAxXUJpWN3P0+Hl2tq1Rxg3bj6bNh2jV696xMQkUbGivmhK5R9XEslh+/KW2Pc97gP2ujBcFFDL6XMQVpmVjP2sFpEUIMIYswcrsaxz7klEPgE+AavVlgvTVorElDR+3HSEaSsi2Hs8jgBfHx68vgHDrwomwLfglm93VVxcMo899hsffrieGjX8mD17MAMHNtKb6SrfuZJI7sa6vBUMHAeW2N2ysw6ob4wJxXoZ1lAgY4usH4FhwAxjTADWpa4DroWuVOZOxCby1aqDfLXmEGfirfLtbwxqzs0ta+LjXXjvf2RUsmQJ/vzzIPfd156XXuqOv3/hT46qcHIlkaSKyNCcjlhEUo0x9wK/Yt3/mCYiO4wxLwLrRWSe/d0NxpidWJfLJojI6ZxOSymAndFW+faft0ST4nBwXVhVxnQOpWMhKN/uqv37z/Dii38xZUpv/Px82LDhLkqX1hdNKc/K9oFEY8zfwB5gFvC9iMTmR2CXow8kKmcOh/DHbqt8+6oDVvn2wW2t8u2hAUXnfeJJSam8/voKJk5cTqlSXixYcBtXX62tsZTrPPpAoojUNcZ0wro09YIxZjMwU0RmuiMgpVwRn5TK3I1W+faIU/HUKF+ax3uFMaxdMOWLWNmPpUsjuPvuBezZc5pbb23C22/fSM2ahb+RgCo6XDonFpGVwEpjzPPAu8DXgCYSle+iz13g81WRfLvmEDGJqbSoVYH3h7WiV9PqhaZ8e06ICBMnLiclxcGiRcO58cZ6ng5JqUu48kCiL9aDhEOBRsBPQCc3x6XURTYdOsvU8Ah+2X4MEaFnU6t8e+vgwlW+3RUOhzB16kZ69qxHrVrl+fLLW6hQoTRlyhStMy1VdLhyRrId+Bl4XUSWuzkepf6Rmubg1x3HmRp+gI2HzuHn482YziGM7Fg4y7e7YuvW44wbN59Vq6J49tmuvPDCtdSooZexVMHmSiKpIyIOt0eilC0mMYVZaw8zY6VVvj24Ulme69uYwW1r4etTNFsoxcUl88ILf/LOO6upWLEMM2b0Y+TIFp4OSymXXPZXaYx5S0QeBuYaYy5p2qVvSFR57eDpeKaviGT2+sPEJ6fRPrQSz/ZtzPWNCmf59px4/vk/eeutVdx5ZyteffV6KlcummdcqmjK6vBulv2/vhlRuY2IsCbiDFPDI1iyyyrf3rdFTcZ2CaVpYOEs3+6qw4fPEx+fQlhYAI8/3oX+/cPo0kVLAKnCJ6s3JK61/2wkIhclE/tBQ32DorpiyakO5m+NZmp4BDuiY6hQtiTju1nl26v5F97y7a5ITXXw/vtrePbZpbRpU5O//hpFQEBZTSKq0HLlgvMYLj0rGZtJN6WydSY+mW/WHOSLVQc5EZtEvaq+vHJLM25pFVioy7e7avXqKMaNm8+WLce56ab6TJ7c29MhKZVrWd0juRWryW+oMeZ7p6/8gHOZD6VU5vYdj2Xaiki+3xhFUqpVvv31Qc3pWsjLt+fEggV76dv3W2rW9OP774fQv39YkWu6rIqnrM5I1gKnsar2TnHqHgtscmdQqmgQEZbts8q3L9trlW8f0Moq396gWvFo0ioiREfHEhjoz/XX1+HFF6/lgQc64OenBRZV0ZFtra2CRmttFXyJKWn8sOkI08Ij2HfCKt8+smNthncIpnIRKN/uqr17TzN+/AL27j3Nzp334OtbytMhqWLMI7W2jDF/icg1xpizXPxCKgOIiFRyR0Cq8DoRk8iXqw/ytV2+vXENf94a3II+LWoUqfLt2UlMTOXVV8OZNCmcMmW8mTTpOsqUKZrPvygFWV/aSn+dbkB+BKIKrx3R5/8p357qEK4Lq8bYLqFcVadSsbsHcOxYHF27TmffvjMMG9aUt9++kerVfT0dllJulVXz3/Sn2WsB0SKSbIzpAjQHvgJi8iE+VUClOYTfdx1nangEayLOULaUF7e1D2ZUESvf7qqUlDRKlvSiWrVydO1amylTetOjR11Ph6VUvnDlfSSbgXZYb0j8DVgAhIpIH/eHdym9R+JZ8UmpzF5/mOkrIzl4OoGa5UtzR6cQhhbB8u2ucDiETz7ZwCuvLGflyrEEBfl7OiSlMuXR95EADhFJMcYMAN4VkfeNMdpqq5g5cu4Cn6+M5Nu1h4hNTKVlrQo8ckNDejWtjncRLN/uii1bjvHf/85nzZojdO8eSkpKmqdDUsojXHrVrjFmMDAC6G93K36HnsXURrt8+6LtxwAuKt9eXIkIEyb8xrvvrqZSpTJ8+eUtDB/erNjdD1IqnatPto/HKiN/wBgTCnzr3rCUJ6WmOfhl+zGmhkew+fA5/Ep7M7ZLKHd0CiGwQhlPh+dxxhjOnr3A2LFWgcWKFXWZqOLNpedIjDHeQPqr2faLSKpbo8qC3iNxn/MXUpi59hCfr4wk+nwitSuXZXSnEAYV4fLtrjp48BwPPLCIZ5+9htata+BwSLF5Il8VDR69R2KMuRr4EjiC9QxJdWPMCBFZ4Y6AVP6LPBXP9BURzN4QRUJyGh1CK/H8zU24rhiUb89OSkoa77yzmhde+AuAW29tQuvWNTSJKOXElcPMd4DeIrITwBjTCCuxuCWzqfwhIqw+YJVv/333cbxLWOXbx3Qu+uXbXbVy5WH++9/5bN9+gn79GvL++70IDtZlo1RGriSSUulJBEBEdhljtNZDIZWUmsbPW44yLTyCnUdjqFi2JPdeW48RV9WmahEv355TS5Yc4Pz5RH788Vb69QvzdDhKFViuPEcyA0jCOgsBGA6UFZE73Bta5vQeyZU5HZfE12sO8eXqg5yMTaJ+VV/GdAnlllaBlC5ZfMqXZEVE+PLLrVSpUpZeveqTlJRKSopDa2SpIsHTz5GMA+4HHsW6R7IM+D93BKPy3t7jsUwLj+CHTUdISnXQtUEV3hwcStf6Adpc1cnu3ae4++4F/PlnJIMHN6ZXr/r4+HjjU3xqTCp1xbJMJMaYZkBd4AcReT1/QlK5JSL8tfckU8MjWL7vFD7eJRjQOogxnUOoX0zKt7vqwoUUXnllOa+9toJy5Urx8cd9uPPO1p4OS6lCJavqv09ivQlxI9DOGPOiiEzLt8hUjl1ITuP7TVFMXxHJ/hNxVPXz4ZEbGnBbh9pUKqeXZzLz8897efnl5dx+e3PefLMH1appgUWlciqrM5LhQHMRiTfGVAEWAppICqDjMYl8sSqSb9Yc4mxCCk1q+vP2kBb0aV6TUt7Fs3xJVo4di2Pz5mP07FmPwYMbExJyJ+3bB3o6LKUKrawSSZKIxAOIyEljjO6RCpjtR6zy7fO3WuXbr29klW/vEFr8yre7Ii3Nwccfb+CJJ36nVCkvDh36H2XKlNQkolQuZZVI6ji9q90AdZ3f3S4iA9wamcpUmkNYYpdvXxtxhnKlvBjeoTajOoUQUgzLt7tq48ajjBs3n3Xrorn++jp88EFvypTRknFK5YWsEsnADJ8nuzMQlbW49PLtKyI5dCaBwApleKp3I4a0q0V53SFmKSLiLO3bf0pAQFm++WYAQ4c21TM2pfJQVi+2+j0/A1GX99vO4zw0azOxSam0Dq7AYz3DuLFJtWJbvt0VIsK2bSdo3rwaoaEVmT69H337NqRCBX3oUqm8Vrwr8RUC5xKSefKHbZT18eKLse1pVYzLt7sqIuIs9977C4sW7WfTpv/SvHk1Roxo4emwlCqy3HpIa4zpaYzZY4zZb4x5PIv+BhljxBij9buciAiPz93GuYRkPhvZTpNINpKT03j11XCaNPmAv/6K5M03e9C4cRVPh6VUkefyGYkxxkdEknLQvxcwBegBRAHrjDHznOt22f35YT05v8bVcRcXM9cdZtGOYzzRK4xmQVosMCtpaQ46dZrKhg1HGTCgEe++eyO1aukyUyo/ZHtGYoxpb4zZBuyzP7cwxrhSIqU91rtLDohIMjAT6JdJfy8BrwOJrodd9O0/EceLP++kc73K/OfqOp4Op8CKibGObby8SjBmTCt+/nkYc+cO0SSiVD5y5dLW+0Af4DSAiGwBrnVhuEDgsNPnKLvbP4wxrYBaIjI/qxEZY+4yxqw3xqw/efKkC5Mu3JJS03hg5iZKlyzB20Na6rsvMiEizJixmTp13uOnn3YDMH58O/r0aeDhyJQqflxJJCVE5GCGbmkuDJfZ3u+fUsP2A47vAA9nNyIR+URE2opI2ypViv4177cW72VHdAyvD2pBNS3tfomdO0/SrdvnjB79E2FhAdStW8nTISlVrLlyj+SwMaY9IPZ9j/uAvS4MFwXUcvocBEQ7ffYDmgJ/2m36qwPzjDE3i0ixrRO/fN9JPll2gNuvCqZH42qeDqfAef31FTz11B/4+/vw2Wd9GT26lZ6xKeVhriSSu7EubwUDx4EldrfsrAPqG2NCsV7TOxS4Lf1LETkPBKR/Nsb8CTxSnJPI6bgkHvpuC/Wq+vJU78aeDqdAERGMMVSv7svw4c14440eVKmiT/IrVRBkm0hE5ARWEsgREUk1xtwL/Ap4AdNEZIcx5kVgvYjMy3G0RZiI8NjcrZxPSOHz0e0pU0pfNgUQHR3LAw8s4uqrg7n//g6MHNmCkSP1mRClCpJsE4kx5lOc7m2kE5G7shtWRBZiVQ127vbsZfrtlt34irKvVh9kya4TPNunMY1r+ns6HI9LS3PwwQfreOqpP0hJcdCpU5CnQ1JKXYYrl7aWOP1dGriFi1tjqVzaezyWlxfs4poGVRjdOcTT4Xjc5s3HuPPOeWzYcJQbbqjLBx/01hvqShVgrlzamuX82RjzJfCb2yIqZhJT0rj/2034lfbmzcEttJggcP58ItHRscyaNYjBgxvrMlGqgLuSWluhQO28DqS4em3RbnYfi2X6qHZU8SueLwgXEWbP3sm+fad56qmuXHNNCAcOPEDp0loKTqnCwJUn288aY87Y/85hnY086f7Qir6lu08wfUUkozqFcG1YVU+H4xF//32G3r2/4dZb5/DTT3tISbEeUdIkolThkeWv1VjXFFpgNd8FcIjIJTfeVc6djE1iwpwthFX34/FeYZ4OJ98lJaXy5psrefnl5ZQsWYL33uvJ+PHt8NZXAytV6GSZSEREjDE/iEib/AqoOHA4hEdmbyE2MZVv/nMVpUsWv6a+hw/H8NJLy+jbtyHvvnsjgYHaUk2pwsqVw7+1xpjWbo+kGJmxMpK/9p7k6Zsa0aCan6fDyTcnT8YzefJaAOrVq8TOnfcwe/ZgTSJKFXKXPSMxxniLSCrQBfiPMeZvIB6rhpaIiCaXK7DraAyv/rKb6xtV5farikebBYdDmD59E48+uoTY2CR69KhDw4YB1Kmj71dRqijI6tLWWqA10D+fYinyLiRbTX3Lly3JawObF4tmrdu3n+DuuxcQHn6Iq68O5qOP+tCwYUD2AyqlCo2sEokBEJG/8ymWIm/iwp3sOxHHl2PbU9m36Df1TU5O44YbviQ5OY1p025m1KiWxSJ5KlXcZJVIqhhjHrrclyLythviKbJ+23mcr1Yf4j9Xh3J1/aJdCv+PPyK45pralCrlxXffDSYsLICAgLKeDksp5SZZ3Wz3Anyxyr1n9k+56HhMIo/O2UKTmv48cmNDT4fjNlFRMQwc+B3XXfcFX3yxBYAuXYI1iShVxGV1RnJURF7Mt0iKKIdDePi7LVxISeO9oa3w8S56TX1TUx1MnryWZ55ZSlqag0mTrmP48OaeDksplU+yvUeicuez8AOE7z/FpAHNqFfV19PhuMWIET8wc+Z2evWqx5QpvQkN1dZYShUnWSWS6/ItiiJq+5HzvPHrHno2qc7QdrWyH6AQOXcuEW/vEvj6luKee9oxcGAjBg5spDfTlSqGLnuPRETO5GcgRU1Ccir3f7uJyuV8eHVgsyKzgxURZs7cTqNGU3jmmT8A6z7IoEFapVep4koLG7nJiz/vJOJ0PG/f2oIKZUt5Opw8sX//GW688SuGDZtLUJA/t9+u90GUUldWRl5l45dtR5m57jB3d6tLp7pF4+G7b77ZxpgxP+Hj483kyb0YN64tXl56HKKU0kSS56LPXeDx77fRIqg8D/Vo4Olwci0lJY2SJb1o27YmgwY15vXXe1Czprb+Vkr9SxNJHkpzCA/O2kxKmoP3hraiZCE+Yj9xIp6HH15MfHwy339/Kw0aVOarrwZ4OiylVAFUePd0BdBHf/3NmogzvHBzE0ICynk6nCvicAiffLKBhg0nM2vWdpo0qUJamsPTYSmlCjA9I8kjmw+f453f9tKneQ0GtQnydDhX5MCBs9x++/esWhVFt24hfPjhTYSFFY17PEop99FEkgfiklJ5YOYmqvmXZuIthbepb/nyPpw7l8jnn/dnxIjiUZ1YKZV7emkrDzz30w4On0ngnVtbUr5MSU+HkyPz5u1hwIBZpKU5qFy5LNu3j2fkyBaaRJRSLtNEkkvztkQzd2MU93avT/vQSp4Ox2WHDp2nf/+Z9Os3k717T3P0aBwAJUpoAlFK5Yxe2sqFw2cSeOqHbbQOrsD93et5OhyXpKY6ePfd1Tz33J+ICK+9dj0PPngVJYvhe+OVUnlDE8kVSk1z8OCszYjAe0Nb4V1ImvqmpTn47LONdO8eyv/9Xy9CQip4OiSlVCFXOPZ+BdCUpX+z/uBZXu7flFqVCvb7Ns6evcBjj/1GbGwSPj7erFgxhnnzhmoSUUrlCU0kV2B95Bne+30vt7QKpH+rQE+Hc1kiwtdfbyUsbApvvbWKpUsjAahcuazeTFdK5Rm9tJVDMYkpPDBzM4EVy/BivyaeDuey9u49zfjxC/j99wjatw/k119vp2XL6p4OSylVBGkiyQER4ekftnMsJpHZ4zriV7rgNvX93/8WsX59NB980Ju77mqjBRaVUm6jiSQHfth0hHlbonm4RwNaBxe8twD+9tvfhIUFUKtWeT788CZ8fLypXr1ovpVRKVVwuPUw1RjT0xizxxiz3xjzeCbfP2SM2WmM2WqM+d0YU9ud8eTGwdPxPPvTDtqHVGL8tQWrqe+xY3HcdttcbrjhK157bQUAtWtX0CSilMoXbkskxhgvYArQC2gMDDPGNM7Q2yagrYg0B+YAr7srntxISXPwwMzNGAPvDG2JVwF5aM/hED76aD1hYZOZO3cXzz13DW++eYOnw1JKFTPuvLTVHtgvIgcAjDEzgX7AzvQeRGSpU/+rgdvdGM8Ve2/JPjYfPsfk21oRWKGMp8P5x6RJy3n66aV07x7KBx/0pmFDLbColMp/7kwkgcBhp89RQIcs+h8L/JLZF8aYu4C7AIKDg/MqPpesPnCaKX/uZ3CbIPo0r5mv085MbGwSp04lEBpakXHj2hIaWpFhw5pqc16llMe48x5JZns2ybRHY24H2gJvZPa9iHwiIm1FpG2VKlXyMMSsnU9I4cFZmwmpXI7nb/ZsU18R4YcfdtG48QfceuscRITKlcty222Ft9qwUqpocGciiQJqOX0OAqIz9mSMuR54CrhZRJLcGE+OiAhP/LCVk7FJvHtrS8r5eK6B28GD57j55pkMGPAdlSqV4f33e2nyUEoVGO7cO64D6htjQoEjwFDgNucejDGtgI+BniJywo2x5Njs9VEs3HaMx3qG0aKW50qJrFp1mOuv/xKAN9/swQMPXIW3tz4TopQqONyWSEQk1RhzL/Ar4AVME5EdxpgXgfUiMg/rUpYvMNs+wj4kIje7KyZXHTgZx3PzdtCxTmX+27WOR2KIiUnC39+H1q1rMGZMSyZM6ExwcHmPxKKUUlkxIpnetiiw2rZtK+vXr3fb+JNTHQz8cCWHzybwywNXU6N8/rbSOn06gccfX8LixQfYsWM8vr6l8nX6SqmiyRizQUTaumPc+mR7Bm/9todtR87z0e1t8jWJiAhffrmVhx9ezNmzF3jooY7obRClVGGgicTJiv2n+GTZAYa1D6Zn0/wrcHj+fCL9+8/izz8j6dgxiI8+6kPz5tXybfpKKZUbmkhsZ+KTeei7zdQJKMczfRrlyzRFBGMM/v4+BASU5ZNP+jB2bGt93a1SqlDR5j9YO/TH5m7lTHwy7w1tRdlS7s+vv/66n9atPyEqKgZjDLNnD+Y//2mjSUQpVehoIgG+WXuI33Ye57GeYTQNdG/LqKNHYxk6dA49e35NQkIKJ07Eu3V6SinlbsX+0tb+E7G8NH8nV9cPYEznULdOa8qUtTz55B8kJaXywgvdeOyxzvh48EFHpZTKC8V6L5aUmsZ9326mbClv3hrcwu2XlTZsOEqHDoFMmdKb+vUru3VaSimVX4p1Inl90R52HY1h6h1tqepfOs/HHxOTxLPPLmXEiOa0aVOTDz64CR8fLy1vopQqUoptIvlzzwmmhkcwsmNtrmuUt01tRYS5c3fxwAOLOHo0luDg8rRpU5PSpYvt4lZKFWHFcs92Ki6JR2ZvpUE1X57snbdNfSMiznLvvb+wcOE+WraszvffD6FDh6A8nYZSShUkxS6RiAgTZm8hJjGFr+5sT+mSXnk6/q+/3sayZQd5550buffe9lpgUSlV5BW7RPLFqoMs3XOS5/s2Jqy6f56Mc/nygyQlpXH99XWYMKETo0a1JCgob8atlFIFXbE6XN59LIaJC3dxbcMq3NEpJNfjO3UqgTFjfqJr1xm8+OJfAPj4eGsSUUoVK8XmjCQxJY37v92Ef+mSvDG4Ra5aTokIM2ZsZsKE3zh/PonHHuvMM890zcNoVVGRkpJCVFQUiYmJng5FFROlS5cmKCiIkiVL5ts0i00imbRwF3uPxzFjdDsCfH1yNa6FC/cxZsw8OneuxUcf9aFp06p5FKUqaqKiovDz8yMkJESbfSu3ExFOnz5NVFQUoaHufcDaWbG4tPX7ruN8vuogY7uE0q3hle30ExJSWLHiEAC9e9fnp5+GsmzZaE0iKkuJiYlUrlxZk4jKF8YYKleunO9nwEU+kZyISWTCnK00quHPoz0bXtE4fvllH02bfkCvXl9z7lwixhhuvrmhFlhULtEkovKTJ7a3Ip1IHA7h4dlbSEhO5f2hLfHxzllT3yNHYhg8eDa9e3+Dj483P/88jAoV8v4JeKWUKsyKdCKZtiKC5ftO8fRNjalfzS9Hw544EU/jxh8wf/5eXn75WrZsGcc114S4J1Cl3MjLy4uWLVvStGlT+vbty7lz5/75bseOHXTv3p0GDRpQv359XnrpJZxfv/3LL7/Qtm1bGjVqRFhYGI888ognZiFLmzZt4s4777yoW79+8aSy1wAAEtdJREFU/ejYseNF3UaNGsWcOXMu6ubr6/vP33v37qV3797Uq1ePRo0aMWTIEI4fP56r2M6cOUOPHj2oX78+PXr04OzZs5n2d+jQIW644QYaNWpE48aNiYyMBGD48OE0bNiQpk2bMmbMGFJSUgCYP38+zz33XK5iy1MiUqj+tWnTRlyxLeqc1Htygdz5+TpxOBwuDSMiEhV1/p+/33tvtezff9rlYZXKaOfOnZ4OQcqVK/fP3yNHjpSXX35ZREQSEhKkTp068uuvv4qISHx8vPTs2VMmT54sIiLbtm2TOnXqyK5du0REJCUlRaZMmZKnsaWkpOR6HIMGDZLNmzf/8/ns2bMSFBQkYWFhcuDAgX+633HHHTJ79uyLhk1fNhcuXJB69erJvHnz/vnujz/+kG3btuUqtgkTJsikSZNERGTSpEny6KOPZtrfNddcI4sXLxYRkdjYWImPjxcRkQULFojD4RCHwyFDhw6VDz74QEREHA6HtGzZ8p/+MspsuwPWi5v2y0Wy1daF5DQemLmJSuVK8drA5i5dMzx/PpGnn/6Djz/ewOrVd9K6dQ3uv79DPkSriosXft7BzuiYPB1n45r+PNe3icv9d+zYka1btwLwzTff0LlzZ2644QYAypYty+TJk+nWrRv33HMPr7/+Ok899RRhYWEAeHt7M378+EvGGRcXx3333cf69esxxvDcc88xcOBAfH19iYuLA2DOnDnMnz+fGTNmMGrUKCpVqsSmTZto2bIlP/zwA5s3b6ZChQoA1KtXjxX/3965BldVZAv4WwSRh5moICkQMQR0DAkkQEAQlKfgoICmkCAgj8JRHory8o7lrRLkjsqMqHCBy3i9VtA7hAwiD1FBQRgQAQGJIQaNGYjcKI+AKAF5BLLuj705OQkn5IRwTh6sr2pX7e7du3vtVfv0Or269+rNm6lRowZjxoxh/35nkcsbb7xB586di7Sdl5dHWloasbGxnrylS5fSr18/wsPDWbx4Mc8991ypelm0aBGdOnWiX79+nrzu3bv7rdeSWLFiBRs2bABgxIgRdOvWjZkzZxYpk5GRwblz57j33nuBoqOkvn37es47dOhATk4O4MyDdOvWjVWrVjFo0KByy1leqqUhmfFhBnuPnOR/R9/JjfVqXbKsqrJkSQbPPLOagwdP8OSTHWje/IYgSWoYweP8+fOsW7eO0aNHA45bq127dkXKNG/enBMnTnD8+HHS09OZPHlyqfXOmDGDsLAwdu/eDVCi+8abzMxM1q5dS0hICAUFBSxbtoxRo0axbds2IiIiCA8PZ8iQIUycOJEuXbqwf/9++vTpw549e4rUs2PHDmJiYorkJScn88ILLxAeHs7AgQP9MiTp6ekX6cIXeXl53H333T6vLVq0iJYtWxbJO3ToEI0aNQKgUaNGHD58+KL7MjMzuf7660lISGDfvn306tWLV155hZCQwjnd/Px83n33XWbPnu3Ji4+PZ9OmTWZIAsHq9IMs2rafJ7pG0rlFg0uWVVUSEv7B8uXf0rZtI1aufIT4+MZBktS42ijLyOFKcurUKeLi4sjOzqZdu3aef76qWuJovSwrf9auXcvixYs96RtuKP2P2MMPP+zpKBMTE3nxxRcZNWoUixcvJjEx0VNvRkaG557jx4+Tl5dHaGjhfOeBAwe46aabPOlDhw6RlZVFly5dEBFq1qxJeno6MTExPp+prCucQkNDSU1NLdM9pXHu3Dk2bdrErl27aNq0KYmJiSQlJXkMPsC4ceO45557ihixhg0b8tNPP11RWS6XajXZfvDX0/zp/TRa3RzG5HtLXuqbn38ecF6iLl1uYc6c+/jyy8fMiBjVkjp16pCamsoPP/zA2bNnmTdvHgDR0dHs2LGjSNm9e/dy3XXXERoaSnR0NDt37iy1/pIMknde8e8a6tWr5znv1KkTWVlZ5Obmsnz5chISEgAoKChgy5YtpKamkpqayo8//ljEiFx4Nu+6U1JSOHbsGM2aNSMiIoLs7GyPkatfv36R0dLPP/9MgwYNPLrw51nz8vKIi4vzeXgbvQuEh4dz4MABwDF6DRte/N1ZkyZNaNOmDZGRkdSsWZMHH3yQr776ynN9+vTp5Obm8tprrxW57/Tp09SpU6dUmYNBtTEk5wuUiSmpnMkvYPbgOGqVEHV3w4ZsWrdewIoV3wIwefJdPPXUnYSEVBtVGIZPwsLCmDNnDq+++ir5+fkMHTqUzz//nLVr1wLOyGXChAk8++yzAEydOpWXXnqJzMxMwOnYi3dmAL1792bu3Lme9IXOOjw8nD179nhcVyUhIjz00ENMmjSJqKgo6tev77NeXyOBqKgosrKyPOnk5GRWr15NdnY22dnZ7Ny502NIunXrRkpKCmfPngUgKSnJMw8yZMgQvvjiCz788ENPXatXr/a46y5wYUTi6yju1gLo378/CxcuBGDhwoUMGDDgojLt27fn2LFj5ObmAvDZZ5956nrrrbdYs2YNycnJ1KhRtI/KzMy8yK1XYQRqFj9QR0mrtuavz9Jb/22Vpny53+f1w4dP6PDhyxSmabNmb+i6dXt9ljOMK0llW7WlqvrAAw/oO++8o6qqaWlp2rVrV7399tu1efPmOm3atCKrHD/44ANt27at3nHHHRoVFaVTpky5qP68vDwdPny4RkdHa+vWrXXp0qWqqrpkyRKNjIzUrl276vjx43XEiBGq6nv11Pbt2xXQpKQkT15ubq4OGjRIW7VqpVFRUfrEE0/4fL6YmBg9fvy47tu3Txs3bnzRKs02bdro1q1bVVV12rRpGhMTo7GxsZqQkKCHDx/2lNuzZ4/26dNHW7RooVFRUZqYmKgHDx68pG5L48iRI9qjRw9t0aKF9ujRQ48ePep53tGjR3vKffLJJ9qqVSuNiYnRESNG6JkzZ1RVNSQkRCMjIzU2NlZjY2N1+vTpnnvuv/9+TUtL89lusFdtiVN/1SE+Pl6LD8fTcn4hYf4X9I4OZ96QthcNs5OTdzN+/EecOHGWqVPv4vnn76Fu3eAFNDOuXvbs2UNU1JXdPM0oyuuvv05oaOhF35JUZw4dOsSQIUNYt26dz+u+3jsR2amq8YGQp8r7c06eOceE5F00DL2Wlx/yvdT33LkCYmIakpo6hj//uacZEcOoRowdO5Zrry1fINaqxv79+5k1a1ZFi+Ghyq/amrbyG374+TeS/9iRMNdAnDx5lhkzNtK0aRjjxrVn2LDWDBvm3/ckhmFULWrXrs2jjz5a0WIElfbt21e0CEWo0iOSVWk/sWRnDuO7taBjpDNBt2pVJtHR85k5czOZmUcBZzLPjIhRUVQ197FRtamI963Kjkh+/OUUz72/m7hbrufpXreRk3OcCRM+Ztmyb2nZ8iY2bhzJ3XffWtFiGlc5tWvX5ujRoxZK3ggKqs5+JLVrBze4bJU0JOcLlImLUykoUGYPjuOakBrs3XuMNWv+xcsv92TSpE7UqlW2SL+GEQiaNGlCTk6OZ2mnYQSaCzskBpMquWprxMxkZn2ayZPxTbnmx5M8/XRHAI4e/Y369etWsISGYRiVjyq7aktE7hOR70QkS0T+5OP6tSKS4l7fJiIRpdX529nzvLH2exrlw7OD3ue117Zy8qTzgZEZEcMwjOATMEMiIiHAPOAPQEvgEREp/unnaOCYqrYAXgdmUgrZR06Sf/wM2+fvYsKEO9m9eyz1SgnMaBiGYQSOQM6RdACyVHUvgIgsBgYA3gFpBgDT3PP3gLkiInoJf9v5AuXG746zfPNo2rZtFBjJDcMwDL8JpCG5Gfg/r3QOUHyDD08ZVT0nIr8C9YEj3oVE5HHgcTd55utDI9P9iPh8NdCAYrq6ijFdFGK6KMR0UUjJkWzLSSANia+1jsVHGv6UQVXfBN4EEJEdgZowqmqYLgoxXRRiuijEdFGIiOwovdTlEcjJ9hzgFq90E6B48HxPGRGpCYQBPwdQJsMwDOMKE0hDsh24TUSaiUgtYDCwsliZlcAI93wg8Nml5kcMwzCMykfAXFvunMeTwBogBHhbVb8RkRdxwhmvBP4HeFdEsnBGIoP9qPrNQMlcBTFdFGK6KMR0UYjpopCA6aLKfZBoGIZhVC6qdNBGwzAMo+IxQ2IYhmGUi0prSAIRXqWq4ocuJolIhoikicg6Eam2YY9L04VXuYEioiJSbZd++qMLERnkvhvfiMiiYMsYLPz4jTQVkfUissv9nfStCDkDjYi8LSKHRSS9hOsiInNcPaWJSNsr0nCg9vAtz4EzOf8vIBKoBXwNtCxWZhywwD0fDKRUtNwVqIvuQF33fOzVrAu3XCiwEdgKxFe03BX4XtwG7AJucNMNK1ruCtTFm8BY97wlkF3RcgdIF/cAbYH0Eq73BT7G+YavI7DtSrRbWUcknvAqqnoWuBBexZsBwEL3/D2gp1TPDR9K1YWqrlfV39zkVpxvdqoj/rwXADOAvwCngylckPFHF38E5qnqMQBVPRxkGYOFP7pQ4HfueRgXf9NWLVDVjVz6W7wBwDvqsBW4XkTKHWuqshoSX+FVbi6pjKqeAy6EV6lu+KMLb0bj/OOojpSqCxFpA9yiqquCKVgF4M97cTtwu4hsFpGtInJf0KQLLv7oYhowTERygI+Ap4IjWqWjrP2JX1TWja2uWHiVaoDfzykiw4B4oGtAJao4LqkLEamBE0V6ZLAEqkD8eS9q4ri3uuGMUjeJSIyq/hJg2YKNP7p4BEhS1Vki0gnn+7UYVS0IvHiVioD0m5V1RGLhVQrxRxeISC/geaC/qp4JkmzBpjRdhAIxwAYRycbxAa+sphPu/v5GVqhqvqruA77DMSzVDX90MRr4B4CqbgFq4wR0vNrwqz8pK5XVkFh4lUJK1YXrzvkbjhGprn5wKEUXqvqrqjZQ1QhVjcCZL+qvqgELVleB+PMbWY6zEAMRaYDj6tobVCmDgz+62A/0BBCRKBxDcjXuf7wSGO6u3uoI/KqqB8pbaaV0bWngwqtUOfzUxV+B64Al7nqD/arav8KEDhB+6uKqwE9drAF6i0gGcB6YqqpHK07qwOCnLiYD/y0iE3FcOSOr4x9PEUnGcWU2cOeDXgCuAVDVBTjzQ32BLOA3YNQVabca6tIwDMMIIpXVtWUYhmFUEcyQGIZhGOXCDIlhGIZRLsyQGIZhGOXCDIlhGIZRLsyQGJUOETkvIqleR8QlykaUFOm0jG1ucKPHfu2GFPn9ZdQxRkSGu+cjRaSx17W3RKTlFZZzu4jE+XHPMyJSt7xtG0ZJmCExKiOnVDXO68gOUrtDVTUWJxjoX8t6s6ouUNV33ORIoLHXtcdUNeOKSFko53z8k/MZwAyJETDMkBhVAnfksUlEvnKPu3yUiRaRL91RTJqI3ObmD/PK/5uIhJTS3EaghXtvT3cPi93uXg/XuvmvSOEeMK+6edNEZIqIDMSJefZ3t8067kgiXkTGishfvGQeKSL/eZlybsEr4J6I/JeI7BBn75Hpbt4EHIO2XkTWu3m9RWSLq8clInJdKe0YxiUxQ2JURup4ubWWuXmHgXtVtS2QCMzxcd8YYLaqxuF05DluOIxEoLObfx4YWkr7/YDdIlIbSAISVbUVTiSIsSJyI/AQEK2qrYH/8L5ZVd8DduCMHOJU9ZTX5feABK90IpBymXLehxMG5QLPq2o80BroKiKtVXUOTiyl7qra3Q2V8u9AL1eXO4BJpbRjGJekUoZIMa56TrmdqTfXAHPdOYHzOHGjirMFeF5EmgDvq+r3ItITaAdsd8PH1MExSr74u4icArJxwoz/Htinqpnu9YXAeGAuzl4nb4nIh4DfIetVNVdE9rpxjr5329js1lsWOevhhAPx3uFukIg8jvO7boSzgVNasXs7uvmb3XZq4ejNMC4bMyRGVWEicAiIxRlJX7RplaouEpFtwP3AGhF5DCds9kJVfc6PNoZ6B3gUEZ/727ixnTrgBAEcDDwJ9CjDs6QAg4BvgWWqquL06n7LibML4CvAPCBBRJoBU4D2qnpMRJJwAhMWR4BPVfWRMshrGJfEXFtGVSEMOODuH/Eozr/xIohIJLDXdeesxHHxrAMGikhDt8yN4v+e9t8CESLSwk0/CvzTnVMIU9WPcCayfa2cysMJa++L94EHcfbISHHzyiSnqubjuKg6um6x3wEngV9FJBz4QwmybAU6X3gmEakrIr5Gd4bhN2ZIjKrCfGCEiGzFcWud9FEmEUgXkVTgDpwtRTNwOtxPRCQN+BTH7VMqqnoaJzrqEhHZDRQAC3A65VVuff/EGS0VJwlYcGGyvVi9x4AM4FZV/dLNK7Oc7tzLLGCKqn6Nsz/7N8DbOO6yC7wJfCwi61U1F2dFWbLbzlYcXRnGZWPRfw3DMIxyYSMSwzAMo1yYITEMwzDKhRkSwzAMo1yYITEMwzDKhRkSwzAMo1yYITEMwzDKhRkSwzAMo1z8PzngWc20mOiYAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -3090,10 +3079,10 @@ { "data": { "text/plain": [ - "0.6239677471688679" + "0.6167794747491347" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3104,7 +3093,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3113,12 +3102,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.82 0.82 6741\n", - " 1 0.41 0.43 0.42 2008\n", + " 0 0.83 0.81 0.82 6762\n", + " 1 0.40 0.42 0.41 1987\n", "\n", - " accuracy 0.73 8749\n", - " macro avg 0.62 0.62 0.62 8749\n", - "weighted avg 0.73 0.73 0.73 8749\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", "\n" ] } @@ -3134,17 +3123,6 @@ "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." ] }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[0] = ([\"Decision Tree (GINI)\" , \n", - " classification_report(y_test, tree.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3163,7 +3141,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3173,7 +3151,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -3188,7 +3166,7 @@ " verbose=0, warm_start=False)" ] }, - "execution_count": 47, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -3199,7 +3177,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3208,8 +3186,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 13463\n", - " 1 1.00 1.00 1.00 4033\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", "\n", " accuracy 1.00 17496\n", " macro avg 1.00 1.00 1.00 17496\n", @@ -3231,14 +3209,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Random Forest) identified 788\n" + "Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713\n" ] }, { @@ -3273,14 +3251,14 @@ " \n", " \n", " \n", - " 0\n", - " 6265\n", - " 476\n", + " 0\n", + " 6371\n", + " 391\n", " \n", " \n", - " 1\n", - " 1220\n", - " 788\n", + " 1\n", + " 1274\n", + " 713\n", " \n", " \n", "\n", @@ -3289,11 +3267,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6265 476\n", - "1 1220 788" + "0 6371 391\n", + "1 1274 713" ] }, - "execution_count": 49, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3304,19 +3282,19 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.325\n" + "Optimal Threshold: 0.27\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3333,7 +3311,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -3342,11 +3320,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.62 0.39 0.48 2008\n", + " 0 0.83 0.94 0.88 6762\n", + " 1 0.65 0.36 0.46 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.73 0.66 0.68 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3358,12 +3336,12 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[1] = ([\"Random Forest\" , \n", - " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[0] = ([\"Decision Trees - Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", " auroc])" ] }, @@ -3389,7 +3367,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -3407,7 +3385,7 @@ " warm_start=False)" ] }, - "execution_count": 53, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -3420,7 +3398,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -3429,11 +3407,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.96 0.91 13463\n", - " 1 0.79 0.47 0.59 4033\n", + " 0 0.86 0.96 0.91 13442\n", + " 1 0.79 0.46 0.58 4054\n", "\n", " accuracy 0.85 17496\n", - " macro avg 0.82 0.72 0.75 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", "weighted avg 0.84 0.85 0.83 17496\n", "\n" ] @@ -3452,14 +3430,14 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 796\n" + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" ] }, { @@ -3494,14 +3472,14 @@ " \n", " \n", " \n", - " 0\n", - " 6281\n", - " 460\n", + " 0\n", + " 6381\n", + " 381\n", " \n", " \n", - " 1\n", - " 1212\n", - " 796\n", + " 1\n", + " 1270\n", + " 717\n", " \n", " \n", "\n", @@ -3510,11 +3488,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6281 460\n", - "1 1212 796" + "0 6381 381\n", + "1 1270 717" ] }, - "execution_count": 55, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -3525,19 +3503,19 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22481097140826298\n" + "Optimal Threshold: 0.24738247273049666\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3554,7 +3532,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -3563,11 +3541,11 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.63 0.40 0.49 2008\n", + " 0 0.83 0.94 0.89 6762\n", + " 1 0.65 0.36 0.46 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.69 8749\n", + " macro avg 0.74 0.65 0.68 8749\n", "weighted avg 0.79 0.81 0.79 8749\n", "\n" ] @@ -3577,27 +3555,16 @@ "print(classification_report(y_test, xgb.predict(X_test)))" ] }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[2] = ([\"Gradient Boosted\" , \n", - " classification_report(y_test, xgb.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "From the accuracy and AUROC, we observe that the gradient boosted tree performs similarly to the random forest ensemble. " + "From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm." ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 56, "metadata": { "scrolled": true }, @@ -3624,41 +3591,27 @@ " \n", " \n", " Model\n", - " Recall-1\n", + " F1-1\n", " AUROC\n", " \n", " \n", " \n", " \n", - " 0\n", - " Decision Tree (GINI)\n", - " 0.449203\n", - " 0.625991\n", - " \n", - " \n", - " 1\n", - " Random Forest\n", - " 0.392430\n", - " 0.762567\n", - " \n", - " \n", - " 2\n", - " Gradient Boosted\n", - " 0.396414\n", - " 0.772109\n", + " 0\n", + " Decision Trees - Random Forest\n", + " 0.461339\n", + " 0.768458\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model Recall-1 AUROC\n", - "0 Decision Tree (GINI) 0.449203 0.625991\n", - "1 Random Forest 0.392430 0.762567\n", - "2 Gradient Boosted 0.396414 0.772109" + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" ] }, - "execution_count": 59, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -3699,7 +3652,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3708,16 +3661,24 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", - " Iterations 7\n" + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" ] }, { @@ -3729,19 +3690,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17452 \n", + " Model: Logit Df Residuals: 17450 \n", "\n", "\n", - " Method: MLE Df Model: 43 \n", + " Method: MLE Df Model: 45 \n", "\n", "\n", - " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784 \n", "\n", "\n", - " Time: 00:20:46 Log-Likelihood: -7709.5\n", + " Time: 19:35:32 Log-Likelihood: -7781.7\n", "\n", "\n", - " converged: True LL-Null: -9445.9\n", + " converged: False LL-Null: -9471.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -3752,136 +3713,142 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", + " LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "\n", + "\n", + " SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "\n", + "\n", + " AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", "\n", "\n", - " SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", + " PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", "\n", "\n", - " AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", + " PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", "\n", "\n", - " PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", + " PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", "\n", "\n", - " PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", + " PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", "\n", "\n", - " PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", + " PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", "\n", "\n", - " PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", + " PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", "\n", "\n", - " PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", + " BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", "\n", "\n", - " PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", + " BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", "\n", "\n", - " BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", + " BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", "\n", "\n", - " BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", + " BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", "\n", "\n", - " BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", + " BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", "\n", "\n", - " BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", + " BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", "\n", "\n", - " BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", + " PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", "\n", "\n", - " BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", + " PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", "\n", "\n", - " PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", + " PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", "\n", "\n", - " PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", + " PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", "\n", "\n", - " PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", + " PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", "\n", "\n", - " PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", + " PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", "\n", "\n", - " PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", + " MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", "\n", "\n", - " PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", + " GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", "\n", "\n", - " GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", + " UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", "\n", "\n", - " UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", + " HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", "\n", "\n", - " HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", + " MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", "\n", "\n", - " MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", + " MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", "\n", "\n", - " SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", + " SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", "\n", "\n", - " PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", + " PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", "\n", "\n", - " PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", + " PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", "\n", "\n", - " PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", + " PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", "\n", "\n", - " PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", + " PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", "\n", "\n", - " PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", + " PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", + " PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", "\n", "\n", - " PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", + " PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", "\n", "\n", - " PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", + " PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", + " PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", "\n", "\n", - " PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", + " PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", "\n", "\n", - " PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", + " PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", + " PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", "\n", "\n", - " PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", + " PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", "\n", "\n", - " PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", + " PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", "\n", "\n", - " PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", + " PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", "\n", "\n", - " PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", + " PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", "\n", "\n", - " PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", + " PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", "\n", "\n", - " PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", + " PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", "\n", "" ], @@ -3891,64 +3858,66 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17452\n", - "Method: MLE Df Model: 43\n", - "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1838\n", - "Time: 00:20:46 Log-Likelihood: -7709.5\n", - "converged: True LL-Null: -9445.9\n", + "Model: Logit Df Residuals: 17450\n", + "Method: MLE Df Model: 45\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784\n", + "Time: 19:35:32 Log-Likelihood: -7781.7\n", + "converged: False LL-Null: -9471.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8518 0.115 -7.395 0.000 -1.078 -0.626\n", - "SEX -0.1133 0.041 -2.743 0.006 -0.194 -0.032\n", - "AGE 0.1139 0.101 1.131 0.258 -0.084 0.311\n", - "PAY_0 0.6280 0.060 10.471 0.000 0.510 0.746\n", - "PAY_2 -0.4827 0.097 -4.954 0.000 -0.674 -0.292\n", - "PAY_3 -0.2082 0.129 -1.610 0.107 -0.462 0.045\n", - "PAY_4 -0.1967 0.168 -1.168 0.243 -0.527 0.133\n", - "PAY_5 0.0009 0.178 0.005 0.996 -0.347 0.349\n", - "PAY_6 0.3822 0.141 2.704 0.007 0.105 0.659\n", - "BILL_AMT1 -1.4295 0.560 -2.555 0.011 -2.526 -0.333\n", - "BILL_AMT2 1.2398 0.794 1.562 0.118 -0.316 2.795\n", - "BILL_AMT3 1.3438 0.724 1.856 0.063 -0.075 2.763\n", - "BILL_AMT4 0.1902 0.714 0.266 0.790 -1.209 1.590\n", - "BILL_AMT5 -1.0799 0.893 -1.210 0.226 -2.829 0.670\n", - "BILL_AMT6 0.5115 0.810 0.632 0.528 -1.075 2.098\n", - "PAY_AMT1 -1.3273 0.313 -4.238 0.000 -1.941 -0.713\n", - "PAY_AMT2 -1.9003 0.395 -4.807 0.000 -2.675 -1.126\n", - "PAY_AMT3 -0.7195 0.303 -2.371 0.018 -1.314 -0.125\n", - "PAY_AMT4 -0.1019 0.283 -0.361 0.718 -0.656 0.452\n", - "PAY_AMT5 -0.9443 0.290 -3.259 0.001 -1.512 -0.376\n", - "PAY_AMT6 -0.6896 0.265 -2.599 0.009 -1.210 -0.170\n", - "GRAD 1.2393 0.222 5.578 0.000 0.804 1.675\n", - "UNI 1.2243 0.221 5.543 0.000 0.791 1.657\n", - "HS 1.1995 0.225 5.337 0.000 0.759 1.640\n", - "MARRIED 0.1146 0.161 0.711 0.477 -0.201 0.431\n", - "SINGLE -0.0347 0.162 -0.214 0.830 -0.352 0.283\n", - "PAY_0_No_Transactions -0.0118 0.125 -0.094 0.925 -0.258 0.234\n", - "PAY_0_Pay_Duly 0.1005 0.122 0.826 0.409 -0.138 0.339\n", - "PAY_0_Revolving_Credit -0.9197 0.138 -6.665 0.000 -1.190 -0.649\n", - "PAY_2_No_Transactions -1.4280 0.236 -6.041 0.000 -1.891 -0.965\n", - "PAY_2_Pay_Duly -1.2864 0.223 -5.756 0.000 -1.724 -0.848\n", - "PAY_2_Revolving_Credit -0.6996 0.228 -3.071 0.002 -1.146 -0.253\n", - "PAY_3_No_Transactions -0.8235 0.306 -2.694 0.007 -1.423 -0.224\n", - "PAY_3_Pay_Duly -0.7340 0.281 -2.610 0.009 -1.285 -0.183\n", - "PAY_3_Revolving_Credit -0.7860 0.271 -2.901 0.004 -1.317 -0.255\n", - "PAY_4_No_Transactions -0.7024 0.376 -1.869 0.062 -1.439 0.034\n", - "PAY_4_Pay_Duly -0.7729 0.357 -2.166 0.030 -1.472 -0.074\n", - "PAY_4_Revolving_Credit -0.7492 0.347 -2.157 0.031 -1.430 -0.068\n", - "PAY_5_No_Transactions -0.2798 0.396 -0.707 0.479 -1.055 0.496\n", - "PAY_5_Pay_Duly -0.4715 0.380 -1.241 0.215 -1.216 0.273\n", - "PAY_5_Revolving_Credit -0.2721 0.370 -0.736 0.462 -0.996 0.452\n", - "PAY_6_No_Transactions 0.7096 0.316 2.244 0.025 0.090 1.329\n", - "PAY_6_Pay_Duly 0.6792 0.308 2.202 0.028 0.075 1.284\n", - "PAY_6_Revolving_Credit 0.4325 0.299 1.448 0.148 -0.153 1.018\n", + "LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", + "PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", + "PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", + "PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", + "PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", + "PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", + "PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", + "BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", + "BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", + "BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", + "BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", + "BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", + "BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", + "PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", + "PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", + "PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", + "PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", + "PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", + "PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", + "MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", + "GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", + "UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", + "HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", + "MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", + "MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", + "SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", + "PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", + "PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", + "PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", + "PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", + "PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", + "PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", + "PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", + "PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", + "PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", + "PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", + "PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", + "PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", + "PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", + "PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", + "PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", + "PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", + "PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", + "PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 61, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -3960,7 +3929,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -3969,12 +3938,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 13463\n", - " 1 0.68 0.37 0.48 4033\n", + " 0 0.83 0.95 0.88 13442\n", + " 1 0.67 0.36 0.47 4054\n", "\n", - " accuracy 0.82 17496\n", - " macro avg 0.76 0.66 0.69 17496\n", - "weighted avg 0.80 0.82 0.79 17496\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", "\n" ] } @@ -3992,60 +3961,97 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.445360\n", + " Iterations: 35\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445386\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445386\n", + " Iterations 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445387\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440644\n", + " Current function value: 0.445388\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440645\n", + " Current function value: 0.445392\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440647\n", + " Current function value: 0.445397\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440653\n", + " Current function value: 0.445410\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440668\n", + " Current function value: 0.445455\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440694\n", + " Current function value: 0.445512\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440734\n", + " Current function value: 0.445596\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440782\n", + " Current function value: 0.445680\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440827\n", + " Current function value: 0.445770\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440895\n", + " Current function value: 0.445853\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.440960\n", + " Current function value: 0.445877\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441031\n", + " Current function value: 0.445963\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441168\n", + " Current function value: 0.446090\n", " Iterations 7\n", "Optimization terminated successfully.\n", - " Current function value: 0.441325\n", + " Current function value: 0.446288\n", " Iterations 7\n" ] }, @@ -4058,19 +4064,19 @@ " Dep. Variable: Y No. Observations: 17496 \n", "\n", "\n", - " Model: Logit Df Residuals: 17467 \n", + " Model: Logit Df Residuals: 17468 \n", "\n", "\n", - " Method: MLE Df Model: 28 \n", + " Method: MLE Df Model: 27 \n", "\n", "\n", - " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826 \n", + " Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756 \n", "\n", "\n", - " Time: 00:20:46 Log-Likelihood: -7721.4\n", + " Time: 19:35:33 Log-Likelihood: -7808.3\n", "\n", "\n", - " converged: True LL-Null: -9445.9\n", + " converged: True LL-Null: -9471.2\n", "\n", "\n", " Covariance Type: nonrobust LLR p-value: 0.000 \n", @@ -4081,91 +4087,88 @@ " coef std err z P>|z| [0.025 0.975] \n", "\n", "\n", - " LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", - "\n", - "\n", - " SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", + " LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", "\n", "\n", - " PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", + " SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", "\n", "\n", - " PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", + " PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", "\n", "\n", - " PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", + " PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", "\n", "\n", - " PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", + " PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", "\n", "\n", - " BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", + " PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", "\n", "\n", - " PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", + " BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", "\n", "\n", - " PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", + " BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", "\n", "\n", - " PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", + " PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", "\n", "\n", - " PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", + " PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", "\n", "\n", - " PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", + " PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", "\n", "\n", - " GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", + " PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", "\n", "\n", - " UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", + " GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", "\n", "\n", - " HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", + " UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", "\n", "\n", - " MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", + " HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", "\n", "\n", - " PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", + " MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", "\n", "\n", - " PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", + " PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", "\n", "\n", - " PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", + " PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", "\n", "\n", - " PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", + " PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", "\n", "\n", - " PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", + " PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", "\n", "\n", - " PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", + " PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", "\n", "\n", - " PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", + " PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", "\n", "\n", - " PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", + " PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", "\n", "\n", - " PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", + " PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", "\n", "\n", - " PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", + " PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", "\n", "\n", - " PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", + " PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", "\n", "\n", - " PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", + " PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", "\n", "\n", - " PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", + " PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", "\n", "" ], @@ -4175,49 +4178,48 @@ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: Y No. Observations: 17496\n", - "Model: Logit Df Residuals: 17467\n", - "Method: MLE Df Model: 28\n", - "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1826\n", - "Time: 00:20:46 Log-Likelihood: -7721.4\n", - "converged: True LL-Null: -9445.9\n", + "Model: Logit Df Residuals: 17468\n", + "Method: MLE Df Model: 27\n", + "Date: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756\n", + "Time: 19:35:33 Log-Likelihood: -7808.3\n", + "converged: True LL-Null: -9471.2\n", "Covariance Type: nonrobust LLR p-value: 0.000\n", "==========================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------\n", - "LIMIT_BAL -0.8831 0.114 -7.764 0.000 -1.106 -0.660\n", - "SEX -0.1171 0.041 -2.875 0.004 -0.197 -0.037\n", - "PAY_0 0.5934 0.038 15.655 0.000 0.519 0.668\n", - "PAY_2 -0.4504 0.091 -4.946 0.000 -0.629 -0.272\n", - "PAY_3 -0.2507 0.086 -2.912 0.004 -0.419 -0.082\n", - "PAY_6 0.2312 0.031 7.431 0.000 0.170 0.292\n", - "BILL_AMT3 0.7006 0.186 3.762 0.000 0.336 1.066\n", - "PAY_AMT1 -1.2024 0.294 -4.097 0.000 -1.778 -0.627\n", - "PAY_AMT2 -1.9469 0.379 -5.138 0.000 -2.690 -1.204\n", - "PAY_AMT3 -0.7334 0.280 -2.621 0.009 -1.282 -0.185\n", - "PAY_AMT5 -0.9177 0.262 -3.499 0.000 -1.432 -0.404\n", - "PAY_AMT6 -0.7636 0.264 -2.891 0.004 -1.281 -0.246\n", - "GRAD 1.3330 0.184 7.262 0.000 0.973 1.693\n", - "UNI 1.3192 0.182 7.230 0.000 0.962 1.677\n", - "HS 1.3082 0.186 7.019 0.000 0.943 1.673\n", - "MARRIED 0.1747 0.042 4.148 0.000 0.092 0.257\n", - "PAY_0_Revolving_Credit -1.0207 0.093 -10.950 0.000 -1.203 -0.838\n", - "PAY_2_No_Transactions -1.3481 0.221 -6.091 0.000 -1.782 -0.914\n", - "PAY_2_Pay_Duly -1.1818 0.205 -5.766 0.000 -1.584 -0.780\n", - "PAY_2_Revolving_Credit -0.6119 0.207 -2.959 0.003 -1.017 -0.207\n", - "PAY_3_No_Transactions -0.9409 0.234 -4.021 0.000 -1.400 -0.482\n", - "PAY_3_Pay_Duly -0.8320 0.202 -4.123 0.000 -1.227 -0.436\n", - "PAY_3_Revolving_Credit -0.8628 0.188 -4.599 0.000 -1.230 -0.495\n", - "PAY_4_No_Transactions -0.4574 0.145 -3.161 0.002 -0.741 -0.174\n", - "PAY_4_Pay_Duly -0.4684 0.114 -4.105 0.000 -0.692 -0.245\n", - "PAY_4_Revolving_Credit -0.4575 0.076 -6.025 0.000 -0.606 -0.309\n", - "PAY_5_Pay_Duly -0.2341 0.085 -2.752 0.006 -0.401 -0.067\n", - "PAY_6_No_Transactions 0.3031 0.092 3.308 0.001 0.124 0.483\n", - "PAY_6_Pay_Duly 0.2868 0.086 3.350 0.001 0.119 0.455\n", + "LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", + "SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", + "PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", + "PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", + "PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", + "PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", + "BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", + "BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", + "PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", + "PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", + "PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", + "PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", + "GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", + "UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", + "HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", + "MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", + "PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", + "PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", + "PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", + "PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", + "PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", + "PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", + "PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", + "PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", + "PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", + "PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", + "PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", + "PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", "==========================================================================================\n", "\"\"\"" ] }, - "execution_count": 63, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -4235,21 +4237,21 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "29 Columns left:\n", - "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT3',\n", - " 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + "28 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", - " 'PAY_5_Pay_Duly', 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", " dtype='object')\n" ] } @@ -4262,7 +4264,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -4271,12 +4273,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.94 0.88 6741\n", - " 1 0.64 0.38 0.47 2008\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -4296,19 +4298,19 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.22999391096950886\n" + "Optimal Threshold: 0.21650864211883647\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4321,10 +4323,10 @@ { "data": { "text/plain": [ - "0.7757068306651365" + "0.7734028258005102" ] }, - "execution_count": 66, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -4343,7 +4345,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 64, "metadata": { "scrolled": true }, @@ -4354,8 +4356,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.80 0.83 6741\n", - " 1 0.47 0.60 0.53 2008\n", + " 0 0.88 0.78 0.83 6762\n", + " 1 0.46 0.62 0.53 1987\n", "\n", " accuracy 0.75 8749\n", " macro avg 0.67 0.70 0.68 8749\n", @@ -4374,7 +4376,7 @@ "source": [ "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", "\n", - "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters.\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.\n", "\n", "\n", "Calculate the confusion matrices for both cut offs to better compare their performance." @@ -4382,14 +4384,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Logistic Regression identified 1207\n" + "Of 1987 Defaulters, the Logistic Regression identified 1235\n" ] }, { @@ -4424,14 +4426,14 @@ " \n", " \n", " \n", - " 0\n", - " 5388\n", - " 1353\n", + " 0\n", + " 5303\n", + " 1459\n", " \n", " \n", - " 1\n", - " 801\n", - " 1207\n", + " 1\n", + " 752\n", + " 1235\n", " \n", " \n", "\n", @@ -4440,11 +4442,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 5388 1353\n", - "1 801 1207" + "0 5303 1459\n", + "1 752 1235" ] }, - "execution_count": 68, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -4455,14 +4457,14 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the Logistic Regression identified 755\n" + "Of 1987 Defaulters, the Logistic Regression identified 715\n" ] }, { @@ -4497,14 +4499,14 @@ " \n", " \n", " \n", - " 0\n", - " 6321\n", - " 420\n", + " 0\n", + " 6421\n", + " 341\n", " \n", " \n", - " 1\n", - " 1253\n", - " 755\n", + " 1\n", + " 1272\n", + " 715\n", " \n", " \n", "\n", @@ -4513,11 +4515,11 @@ "text/plain": [ "Predicted False True \n", "Actual \n", - "0 6321 420\n", - "1 1253 755" + "0 6421 341\n", + "1 1272 715" ] }, - "execution_count": 69, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -4530,39 +4532,112 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is evident that the lower cutoff is better able to detect defualts." + "It is evident that the lower cutoff is better able to detect defaults. " ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 67, "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m optimal =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "optimal = \n", "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "evaluation.loc[3] = [\"Logistic Regression\" , \n", - " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[1] = [\"Logistic Regression\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)[\"1\"][\"f1-score\"],\n", " auroc]" ] }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, { "cell_type": "markdown", "metadata": { @@ -4728,7 +4803,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -4740,7 +4815,7 @@ " verbose=False)" ] }, - "execution_count": 72, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -4754,19 +4829,19 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.1567076896169202\n" + "Optimal Threshold: 0.16469105377809068\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4779,10 +4854,10 @@ { "data": { "text/plain": [ - "0.7227905615337198" + "0.7138537062929152" ] }, - "execution_count": 73, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -4794,14 +4869,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the SVM default parameters identified 768\n" + "Of 1987 Defaulters, the SVM default parameters identified 713\n" ] }, { @@ -4836,14 +4911,14 @@ " \n", " \n", " \n", - " 0\n", - " 6321\n", - " 420\n", + " 0\n", + " 6432\n", + " 330\n", " \n", " \n", - " 1\n", - " 1240\n", - " 768\n", + " 1\n", + " 1274\n", + " 713\n", " \n", " \n", "\n", @@ -4852,11 +4927,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6321 420\n", - "1 1240 768" + "0 6432 330\n", + "1 1274 713" ] }, - "execution_count": 74, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -4868,7 +4943,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -4877,12 +4952,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.94 0.88 6741\n", - " 1 0.65 0.38 0.48 2008\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -4912,9 +4987,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "def svc_param_selection(X, y, nfolds):\n", @@ -4937,7 +5023,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -4949,7 +5035,7 @@ " verbose=False)" ] }, - "execution_count": 76, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -4962,19 +5048,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15368680599405346\n" + "Optimal Threshold: 0.15996357777982226\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4992,14 +5078,14 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Of 2008 Defaulters, the SVM reduced features and tuning linear kernal identified 696\n" + "Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638\n" ] }, { @@ -5034,14 +5120,14 @@ " \n", " \n", " \n", - " 0\n", - " 6405\n", - " 336\n", + " 0\n", + " 6492\n", + " 270\n", " \n", " \n", - " 1\n", - " 1312\n", - " 696\n", + " 1\n", + " 1349\n", + " 638\n", " \n", " \n", "\n", @@ -5050,11 +5136,11 @@ "text/plain": [ "Predicted 0 1\n", "Actual \n", - "0 6405 336\n", - "1 1312 696" + "0 6492 270\n", + "1 1349 638" ] }, - "execution_count": 78, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -5066,7 +5152,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -5075,12 +5161,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.83 0.95 0.89 6741\n", - " 1 0.67 0.35 0.46 2008\n", + " 0 0.83 0.96 0.89 6762\n", + " 1 0.70 0.32 0.44 1987\n", "\n", " accuracy 0.81 8749\n", - " macro avg 0.75 0.65 0.67 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " macro avg 0.77 0.64 0.66 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", "\n" ] } @@ -5098,7 +5184,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -5110,7 +5196,7 @@ " verbose=False)" ] }, - "execution_count": 80, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -5123,19 +5209,19 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15780782271279034\n" + "Optimal Threshold: 0.15910713557498266\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5153,7 +5239,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -5162,12 +5248,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.64 0.40 0.49 2008\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.38 0.48 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.74 0.67 0.69 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", "\n" ] } @@ -5180,14 +5266,88 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel." + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 82, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### SVM with filtered features" + "evaluation.loc[2] = ([\"SVM-RBF (Grid Search)\" , \n", + " classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" ] }, { @@ -5199,14 +5359,14 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "//anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", " \"avoid this warning.\", FutureWarning)\n" ] }, @@ -5219,7 +5379,7 @@ " shrinking=True, tol=0.001, verbose=False)" ] }, - "execution_count": 86, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -5231,19 +5391,19 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimal Threshold: 0.15729612279514055\n" + "Optimal Threshold: 0.16104553371241384\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5252,16 +5412,26 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6689738476077944" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "auroc = get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + "get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", " \"SVM reduced features and tuning linear kernel\")" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5270,12 +5440,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.93 0.88 6741\n", - " 1 0.62 0.40 0.49 2008\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", "\n", - " accuracy 0.81 8749\n", - " macro avg 0.73 0.66 0.68 8749\n", - "weighted avg 0.79 0.81 0.79 8749\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", "\n" ] } @@ -5313,12 +5483,12 @@ "This process is repeated iteratively until the model converges (i.e. it cannot be improved further).\n", "\n", "#### Training\n", - "Here we create an instance of our model, with 5 layers of 26 neurons each, identical to that of our training data. " + "Here we create an instance of our model, with 5 layers of 26 neurons each, approximately 2/3 of the columns in our training data. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -5327,7 +5497,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": { "scrolled": true }, @@ -5338,16 +5508,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n", + " beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(26, 26, 26, 26, 26), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n", + " random_state=None, shuffle=True, solver='adam', tol=0.0001,\n", + " validation_fraction=0.1, verbose=False, warm_start=False)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mlp.fit(X_train,y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -5356,59 +5543,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Neural Network (5x26) identified 704\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06449313
11283704
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6449 313\n", + "1 1283 704" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "confusion(y_test,predictions,\"Neural Network (5x26)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2145780241518794\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.69 0.35 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "print(classification_report(y_test,predictions))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluation.loc[5] = ([\"Neural Network\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Deep Learning\n", - "\n", - "#### Theory\n", - "\n" + "### Model Tuning\n", + "We define the following baseline model using Keras using the optimiser, \"adam\" , which is the default. After experimenting with different optimizers, we found that \"adam\" did indeed perform the best." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4787 - accuracy: 0.7982\n", + "Epoch 2/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4465 - accuracy: 0.8112\n", + "Epoch 3/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4433 - accuracy: 0.8124\n", + "Epoch 4/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4420 - accuracy: 0.8143\n", + "Epoch 5/20\n", + "17496/17496 [==============================] - 2s 117us/step - loss: 0.4409 - accuracy: 0.8133\n", + "Epoch 6/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4396 - accuracy: 0.8136\n", + "Epoch 7/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4388 - accuracy: 0.8141\n", + "Epoch 8/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4381 - accuracy: 0.8138\n", + "Epoch 9/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4376 - accuracy: 0.8150\n", + "Epoch 10/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4372 - accuracy: 0.8156\n", + "Epoch 11/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4356 - accuracy: 0.8156\n", + "Epoch 12/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4357 - accuracy: 0.8137\n", + "Epoch 13/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4356 - accuracy: 0.8143\n", + "Epoch 14/20\n", + "17496/17496 [==============================] - 2s 124us/step - loss: 0.4355 - accuracy: 0.8140\n", + "Epoch 15/20\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4340 - accuracy: 0.8149\n", + "Epoch 16/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4341 - accuracy: 0.81510s - loss: 0.4311 \n", + "Epoch 17/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4335 - accuracy: 0.8163\n", + "Epoch 18/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4335 - accuracy: 0.8150\n", + "Epoch 19/20\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4322 - accuracy: 0.8160\n", + "Epoch 20/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4320 - accuracy: 0.8147\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from numpy import loadtxt\n", "from keras.models import Sequential\n", @@ -5416,7 +5752,7 @@ "\n", "# define the keras model\n", "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", @@ -5429,33 +5765,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25378615\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.39 0.49 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.66 0.69 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], "source": [ "# evaluate the keras model\n", "#recall, accuracy = model.evaluate(df1, target)\n", "#print('Accuracy: %.2f' % (accuracy*100))\n", "#print('Recall: %.2f' % (recall*100))\n", "\n", + "get_roc(model, y_test, X_test, \"Adam\")\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", - "print(classification_report(y_test,predictions))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(X_train, y_train, epochs=20, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", "print(classification_report(y_test,predictions))" ] }, @@ -5463,20 +5818,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam optimizer" + "Experimenting with lowering the cutoff for the neural network," ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25378615\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.79 0.83 6762\n", + " 1 0.46 0.63 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], "source": [ - "model.fit(X_train, y_train, epochs=10, batch_size=20)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", - "\n", + "optimal_adam = get_optimal(model, y_train, X_train, \"Adam\")\n", + "get_roc(model, y_test, X_test, \"Adam\")\n", + "predictions = list(model.predict(X_test).ravel() > optimal_adam)\n", "print(classification_report(y_test,predictions))" ] }, @@ -5484,86 +5872,587 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam optimizer" + "The performance is quite impressive with a recall of 0.62 on defaults, when we lowered the classification cut off. The ROC of 0.77 is also on par with other models. However, with some experimentation, we found that the neural network performed the best with 3 hidden layers." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], - "source": [ + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 132us/step - loss: 0.4825 - accuracy: 0.7861\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4477 - accuracy: 0.8113\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 92us/step - loss: 0.4456 - accuracy: 0.8115\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4441 - accuracy: 0.8134\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4428 - accuracy: 0.8138\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 90us/step - loss: 0.4423 - accuracy: 0.8140\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4411 - accuracy: 0.8130\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4406 - accuracy: 0.8144\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4396 - accuracy: 0.8148\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 111us/step - loss: 0.4390 - accuracy: 0.8150\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.2291201\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.79 0.83 6762\n", + " 1 0.47 0.62 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.71 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam3 = get_optimal(model, y_train, X_train, \"3-layer adam\")\n", + "predictions = list(model.predict(X_test).ravel() > optimal_adam3)\n", + "auroc = get_roc(model, y_test, X_test, \"3-layer adam\")\n", "\n", "print(classification_report(y_test,predictions))" ] }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 3 Layer Adam0.5321260.771859
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[3] = ([\"Neural Network - 3 Layer Adam\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adamax Optimizer" + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ - "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(1, activation='sigmoid'))\n", - "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", - "# fit the keras model on the dataset\n", - "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", - "predictions = list(model.predict(X_test).ravel() > 0.5)\n", - "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", "\n", - "print(classification_report(y_test,predictions))" + "gnb = GaussianNB(var_smoothing = 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GaussianNB(priors=None, var_smoothing=0.1)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.03496484204782862\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[11931 1511]\n", + " [ 2233 1821]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.89 0.86 13442\n", + " 1 0.55 0.45 0.49 4054\n", + "\n", + " accuracy 0.79 17496\n", + " macro avg 0.69 0.67 0.68 17496\n", + "weighted avg 0.77 0.79 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "#accessing model performance\n", + "print(metrics.confusion_matrix(expected, predicted))\n", + "print(classification_report(y_train,gnb.predict(X_train)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Adadelta Optimizer" + "We will now try searching for the smoothing parameter to achieve a better ROC and recall on default." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.45112314307016005 3\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.87 0.86 13442\n", + " 1 0.54 0.51 0.52 4054\n", + "\n", + " accuracy 0.79 17496\n", + " macro avg 0.70 0.69 0.69 17496\n", + "weighted avg 0.78 0.79 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "best = 0\n", + "smooth = 0\n", + "for i in range(1,100):\n", + " gnb = GaussianNB(var_smoothing = i/100)\n", + " gnb.fit(X_train, y_train)\n", + " x = classification_report(y_train, gnb.predict(X_train), output_dict = True)[\"1\"][\"f1-score\"] \n", + " #arc = get_roc(gnb, y_train, X_train, \"NB\")\n", + " y = classification_report(y_train, gnb.predict(X_train), output_dict = True)[\"0\"][\"f1-score\"]\n", + " if x*y > best:\n", + " best = x*y\n", + " smooth = i\n", + "\n", + "print(best, smooth)\n", + " \n", + "gnb = GaussianNB(var_smoothing = smooth/100)\n", + "gnb.fit(X_train, y_train) \n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.019734239369418004\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.88 0.87 6762\n", + " 1 0.54 0.50 0.52 1987\n", + "\n", + " accuracy 0.79 8749\n", + " macro avg 0.70 0.69 0.69 8749\n", + "weighted avg 0.79 0.79 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_test,gnb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 106, "metadata": {}, "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Naive Bayes\" , \n", + " classification_report(y_test, gnb.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
3Neural Network - 3 Layer Adam0.5321260.771859
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5223060.745084
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "5 Naive Bayes 0.522306 0.745084" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.sort_values([\"AUROC\"], ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Tuning Neural Network with different optimisers \n", + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4697 - accuracy: 0.7966\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 122us/step - loss: 0.4483 - accuracy: 0.8123\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4456 - accuracy: 0.8117\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4439 - accuracy: 0.8117\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4428 - accuracy: 0.8133\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 88us/step - loss: 0.4417 - accuracy: 0.8149 1s - loss: 0.448 - ETA: \n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 87us/step - loss: 0.4415 - accuracy: 0.8144\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 86us/step - loss: 0.4408 - accuracy: 0.8138 0s - loss: 0.4418 - ETA: 0s - loss: 0.4399 - accuracy: 0.\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 91us/step - loss: 0.4401 - accuracy: 0.8145\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 87us/step - loss: 0.4395 - accuracy: 0.8145 0s - loss: 0.4370 - accura\n", + "Optimal Threshold: 0.26521116\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", - "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5572,94 +6461,254 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adagrad Optimzier" + "### Adadelta Optimizer" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 131us/step - loss: 0.4686 - accuracy: 0.7957\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4494 - accuracy: 0.8114\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4468 - accuracy: 0.81200s - loss: 0.4466 - accuracy: 0.\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4452 - accuracy: 0.8133\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4436 - accuracy: 0.8118\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4430 - accuracy: 0.8129\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4423 - accuracy: 0.8127\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4422 - accuracy: 0.8136\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 120us/step - loss: 0.4415 - accuracy: 0.8130\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4410 - accuracy: 0.8132\n", + "Optimal Threshold: 0.19084787\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))\n", - "\n", - "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", - " classification_report(y_test, mlp.predict(X_test), output_dict = True)[\"1\"][\"recall\"],\n", - " auroc])\n", - "\n", - "evaluation" + "print(classification_report(y_test,predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### RMSProp" + "### Adagrad Optimzier" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4590 - accuracy: 0.8029\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4508 - accuracy: 0.8095 0s - l\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 94us/step - loss: 0.4487 - accuracy: 0.8114 0s - loss:\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4473 - accuracy: 0.8132\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4463 - accuracy: 0.8126 0s - los\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4456 - accuracy: 0.8131\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4450 - accuracy: 0.8134\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4445 - accuracy: 0.8128\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4439 - accuracy: 0.8124 0s - loss: 0\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4434 - accuracy: 0.8138 0s - loss: 0.4451 - \n", + "Optimal Threshold: 0.28107572\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", - "print(classification_report(y_test,predictions))" + "print(classification_report(y_test,predictions))\n", + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### SGD" + "### RMSProp" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4728 - accuracy: 0.7986\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4467 - accuracy: 0.8118\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4456 - accuracy: 0.8131\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4442 - accuracy: 0.8128\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4437 - accuracy: 0.8132\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4438 - accuracy: 0.8136\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4433 - accuracy: 0.8133\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4438 - accuracy: 0.8145 0s - loss: 0.442\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4428 - accuracy: 0.8142\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4433 - accuracy: 0.8133\n", + "Optimal Threshold: 0.22694841\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5668,28 +6717,83 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Adam" + "### SGD" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.5018 - accuracy: 0.7766\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 90us/step - loss: 0.4568 - accuracy: 0.8002 0s - l\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4531 - accuracy: 0.8077\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4508 - accuracy: 0.8094\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4495 - accuracy: 0.8109\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4479 - accuracy: 0.8113 0s - loss: 0.4496 - \n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 93us/step - loss: 0.4469 - accuracy: 0.8121\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4466 - accuracy: 0.8126\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4461 - accuracy: 0.8121\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4454 - accuracy: 0.81180s - loss: 0.4\n", + "Optimal Threshold: 0.27847895\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.41 0.50 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.67 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], "source": [ "model = Sequential()\n", - "model.add(Dense(12, input_dim=44, activation='relu'))\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# compile the keras model\n", - "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", "# fit the keras model on the dataset\n", "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", "\n", "print(classification_report(y_test,predictions))" ] @@ -5698,14 +6802,150 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### conclude that best optimizer is adagrad, so now we use filtered data to run tests" + "#### We conclude that best optimizer is adagrad. Testing it on the test set." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4657 - accuracy: 0.7962\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4526 - accuracy: 0.8109\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4506 - accuracy: 0.8133\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 124us/step - loss: 0.4496 - accuracy: 0.8134\n", + "Epoch 5/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4490 - accuracy: 0.8135\n", + "Epoch 6/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4484 - accuracy: 0.8130\n", + "Epoch 7/10\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4480 - accuracy: 0.8134\n", + "Epoch 8/10\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4476 - accuracy: 0.8130\n", + "Epoch 9/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4474 - accuracy: 0.8135\n", + "Epoch 10/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4471 - accuracy: 0.8130\n", + "Optimal Threshold: 0.26529908\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.69 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 3 Layer Adam0.5321260.771859
5Naive Bayes0.5223060.745084
6Neural Network - adagrad0.3598390.763871
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 3 Layer Adam 0.532126 0.771859\n", + "5 Naive Bayes 0.522306 0.745084\n", + "6 Neural Network - adagrad 0.359839 0.763871" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model = Sequential()\n", "model.add(Dense(12, input_dim=17, activation='relu'))\n", @@ -5719,92 +6959,62 @@ "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", - "auroc = get_roc(model, y_test, X_test_filter, \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network - adagrad (filtered features)\")\n", "\n", "print(classification_report(y_test,predictions))\n", "\n", - "evaluation.loc[6] = ([\"Deep Learning Neural Network with Keras\" , \n", - " classification_report(y_test, mlp.predict(X_test_filter), output_dict = True)[\"1\"][\"recall\"],\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", " auroc])\n", "\n", "evaluation" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Naive Bayes\n", - "#### Theory\n", - "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", - "##### Bayes Theorem:\n", - "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", - "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", - "One assumption about naive bayes is that the predictors/features are independent." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Training the Naive bayes model" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4652 - accuracy: 0.7974\n", + "Epoch 2/10\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4507 - accuracy: 0.8062\n", + "Epoch 3/10\n", + "17496/17496 [==============================] - ETA: 0s - loss: 0.4479 - accuracy: 0.81 - 2s 95us/step - loss: 0.4475 - accuracy: 0.8112\n", + "Epoch 4/10\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4457 - accuracy: 0.8122\n", + "Epoch 5/10\n", + " 6760/17496 [==========>...................] - ETA: 1s - loss: 0.4413 - accuracy: 0.8142" + ] + } + ], "source": [ - "from sklearn import datasets\n", - "from sklearn import metrics\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Neural Network - adagrad (all features)\")\n", "\n", - "gnb = GaussianNB()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#training naive bayes model\n", - "gnb.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#classifying values\n", - "predicted = gnb.predict(X_train)\n", - "expected = y_train" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot roc for naive Bayes\n", - "get_roc(gnb, y_test, X_test, \"Naive Bayes\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#accessing model performance\n", - "print(metrics.classification_report(expected, predicted))\n", - "print(metrics.confusion_matrix(expected, predicted))" + "print(classification_report(y_test,predictions))\n", + "\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation\n" ] } ], @@ -5829,7 +7039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, From b2ab54d3c35dda66eda6faa482adee282f1dc017 Mon Sep 17 00:00:00 2001 From: reon Date: Fri, 22 Nov 2019 00:34:51 +0800 Subject: [PATCH 24/25] FINAL PUSH --- ...BT2101 Project Submission-checkpoint.ipynb | 6555 +++++++++++++++++ ...fault - EDIT-MASTER- Reon-checkpoint.ipynb | 4 +- BT2101 Project Submission.ipynb | 6555 +++++++++++++++++ BT2101_Default - EDIT-MASTER- Reon.ipynb | 4 +- 4 files changed, 13114 insertions(+), 4 deletions(-) create mode 100644 .ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb create mode 100644 BT2101 Project Submission.ipynb diff --git a/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb new file mode 100644 index 0000000..9aa3491 --- /dev/null +++ b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb @@ -0,0 +1,6555 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "By Reon Ho, Lam Cheng Jun, Janson Chew, and Bryan Koh\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAapElEQVR4nO3deZxcZZ3v8c+XgALjAg6LCEhQo4iMsgTEcQNRBBwFRhz16oBeFHVwvDqLouOIG3fQGZfBBQcxF3BUFBFFQTGgiNcrSFBkEZWIKDEBoiyyKYu/+0c9LWWnOl056Uqn6c/79apXn/Oc55zzO51Kffs8p+pUqgpJkrpYZ7oLkCTNXIaIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEpNWU5NAkX27T6yepJFtNd10ASY5OcnybfnSSm6Zw2yckeWOb3ifJ4inc9jOT/HCqtqfRMUTUWZJb+x5/SHJH3/xL1nAtq/3ineTaJE/pm98uyd2TrVdVn6iq53bd77gazk/y0qnY1nhV9dOq2miIGl6d5Owhtveyqnrv6tY16N+uqs6uqies7rY1eutOdwGauarqAWPTSa4GXlFVk774DJJk3aqa9AV7bTNT615dSeZU1T3TXYemn2ciGpkkT05yQZKbkyxN8oEk67ZlY399vibJz4DLWvtzklyZ5KYkHxz/l3mSVyX5SZIbkpyRZMu26Lz28yftTOiAAfVsl+Tctu7yJCcmeWBbdgqwGfD1tv7r2jbn9J1d7dT+Sv9Gko8kuRE4YoK/3A9IcnXbz1FJ0vbzx+GlvprubtPvA3YFjm/7e19r36Ht88YkVww6tr7tPSrJd5LckuSrwMaD9tXmX9lqvCXJVUlekGQn4IPAHq2Ga1vfk5Mck+TrSW4DntTa3jpu/+9ov9+rkrygr338v2P/72yFf7vxw2NJ/iLJt9vz4pIk+/YtO7k9V85qx/KdJNtM9DvSFKsqHz5W+wFcDTxzXNtu9F4U5wCPBBYDr27L1gcKOAPYCNgAeChwK/BXwHrAG4G7gJe2dV4EXAE8ui1/N/DNcdvbaiU1bgc8A7hf29f5wNF9y68FnjKu/93jtvFq4G7gle24NmhtZ4+r46x2XNsCV/Udw9HA8RPto9X00r75BwHLgJe0/e0K3AA8aoJj/D7wb+0Y9wJuH9tf/77ohctNwCPb/JbAY/uO8exx2z257feJ9P74vH9re2tbvk/7vYzt+5lt39tOcFyDfmdb9S3fB1jct/wXwD+2f/dnt+fJtn21XQ/s3JZ/Hjhhuv9PzJaHZyIamar6XlVdWFX3VNXPgOOBp4/rdlRV3VRVdwDPAy6sqq9U1V3AfwA39vV9FfDu6o3t3wW8A3hKks2HrOfHVfWNqrqzqq6l9xf3+HqGcVVVfbwd1x0T9Pm3dlw/Bz4MvLjDfgAOBC6rqk+1/V0IfBl4/viOSR4NbA+8ox3jOcDXJtn+DknWr6pfVdUVk/T9fFVdUFV/qKrfD1h+d9++zwbOBg6a7ACH8NT28/1VdVdVnQUsBF7Y1+dzVfX99rz4NLDjFOxXQzBENDJJtk/y1STXJfkt8DZgk3Hdrumbflj/fFX9AfhV3/JtgI+1IY2bgOX0XriGupie5GFJTknyq1bP8QPqGcY1k3f5kz6/oHdsXWwDPG3smNtxPx/YYkDfhwHLq+p34/a9gqq6kd7ZzeuAa5OcnuRRk9Qy2XEP2nfX4+73MOCXVdV/t9hf0Dt7GnNt3/TtwAPQGmGIaJQ+Tm945ZFV9SDgnUDG9el/YVhGXyAkWYc/faG4BnhZVW3U99igqi4at52J/DtwG7BDq+cV4+oZv42JtjnMvrbum344sLRN3wZs2LfsoZNs+xrg6+OO+QFV9foB+1wGbJJk/XH7HqiqzqiqvWgv0sCxE9QwUW3jDdr3MMc92XaXsuJxPJw//QND08QQ0Sg9ELi5qm5N8jh61xFW5nTgiUn2axfg/4G+C8PAx4C3JnkMQJKNkzwfoA2v3Aw8YpJ6bgV+m+Thbfv9rhu3/vX0LqxP+EK8Em9K8uAkc4HXAp9t7RcDeybZMsnGwJsmqeGLwE5JXphkvST3S7J7G7oa76fAj4F/bf32pHdtYQVt/89JsiHwe3q/l7F3W10HbJ1kvVU85vX69v0M4FnAqX3HfVB6b6jYDnjZ2EpD/Nt9G1gnyeuTrJvkWcDewCmrWJ9GwBDRKL0BeEWSW4GPcO8L6UBVtYzetYNjgF/TOyu5lN6LHFX1GXrXF77QhqMupvdCNeZtwClt2Od5A3bxNuAp9F6wTuPeF7gxRwFHtfVf24Z83gtc1NpWZZz9DOCHwCJ6L3b/3df+FeBH9C42f3Hceh8ADm7vxHpvq+HZwMvpnWkspfeGghVe4NtwzwuBPeldBH9j337HmwO8md4w0G/oXbD/+7bsa/TeKHF9kiWrcMxX0xtevBZYALy8qq5qy95L7yMFy4HjBtQ14b9dGyL7K3rXV34DvB94YbvOpmmWPx1mlNYe7WzkWuC5VfXd6a5H0oo8E9FaJcm+bRhofeBIehdJL5rmsiRNwBDR2uZpwM/pXY/YCziwqu6c3pIkTcThLElSZ56JSJI6m3U3YNxkk01q7ty5012GJM0oF1100a+ratPx7bMuRObOncuiRYumuwxJmlGSDLz7gcNZkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOZt0n1meKuUecMd0l3GdcffRzprsE6T7LMxFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmcjC5EkWyf5ZpIrklye5H+19ockWZjkyvZz49aeJMckWZzkkiQ7923rkNb/yiSH9LXvkuTSts4xSTKq45EkrWiUZyJ3A/9YVY8FdgcOT7I9cARwTlXNA85p8wD7AvPa4zDgWOiFDnAk8ERgN+DIseBpfQ7rW2+fER6PJGmckYVIVS2rqu+36VuAK4Atgf2BE1u3E4ED2vT+wEnVcz6wUZItgGcDC6vqhqq6EVgI7NOWPaiqvltVBZzUty1J0hqwRq6JJJkL7ARcAGxeVcugFzTAZq3blsA1fastaW0ra18yoF2StIaMPESSPAA4FXh9Vf12ZV0HtFWH9kE1HJZkUZJFy5cvn6xkSdKQRhoiSdajFyCfqqovtObr2lAU7ef1rX0JsHXf6lsBSydp32pA+wqq6riqml9V8zfddNPVOyhJ0h+N8t1ZAT4BXFFV7+9bdDow9g6rQ4Av9bUf3N6ltTtwcxvuOgvYO8nG7YL63sBZbdktSXZv+zq4b1uSpDVg3RFu+8nA3wKXJrm4tb0FOBr4XJJDgV8CL2jLzgT2AxYDtwMvB6iqG5K8C7iw9XtnVd3Qpl8DnABsAHy1PSRJa8jIQqSq/i+Dr1sA7DWgfwGHT7CtBcCCAe2LgB1Wo0xJ0mrwE+uSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1NnIQiTJgiTXJ7msr+3tSX6V5OL22K9v2ZuTLE7ykyTP7mvfp7UtTnJEX/u2SS5IcmWSzya536iORZI02CjPRE4A9hnQ/oGq2rE9zgRIsj3wIuBxbZ2PJpmTZA7wEWBfYHvgxa0vwHvatuYBNwKHjvBYJEkDjCxEquo84IYhu+8PnFxVv6+qnwOLgd3aY3FVXVVVdwInA/snCfAM4PNt/ROBA6b0ACRJkxoqRJLsMIX7fG2SS9pw18atbUvgmr4+S1rbRO1/DtxUVXePax8oyWFJFiVZtHz58qk6Dkma9YY9E/lYku8l+bskG63G/o4FHgnsCCwD3tfaM6BvdWgfqKqOq6r5VTV/0003XbWKJUkTGipEquopwEuArYFFST6d5FmrurOquq6q7qmqPwAfpzdcBb0zia37um4FLF1J+6+BjZKsO65dkrQGDX1NpKquBN4KvAl4OnBMkh8n+etht5Fki77ZA4Gxd26dDrwoyf2TbAvMA74HXAjMa+/Euh+9i++nV1UB3wQOausfAnxp2DokSVNj3cm7QJLHAy8HngMsBJ5bVd9P8jDgu8AXBqzzGWAPYJMkS4AjgT2S7Ehv6Olq4FUAVXV5ks8BPwLuBg6vqnvadl4LnAXMARZU1eVtF28CTk7ybuAHwCdW+eglSatlqBABPkxv+OktVXXHWGNVLU3y1kErVNWLBzRP+EJfVUcBRw1oPxM4c0D7Vdw7HCZJmgbDhsh+wB19ZwfrAOtX1e1V9cmRVSdJWqsNe03kbGCDvvkNW5skaRYbNkTWr6pbx2ba9IajKUmSNFMMGyK3Jdl5bCbJLsAdK+kvSZoFhr0m8nrglCRjn8XYAnjhaEqSJM0UQ4VIVV2YZDvgMfQ+Lf7jqrprpJVJktZ6w56JAOwKzG3r7JSEqjppJFVJkmaEYT9s+El697y6GLinNRdgiEjSLDbsmch8YPt2uxFJkoDh3511GfDQURYiSZp5hj0T2QT4UZLvAb8fa6yq542kKknSjDBsiLx9lEVIkmamYd/i+60k2wDzqursJBvSu6uuJGkWG/brcV9J7/vM/6s1bQl8cVRFSZJmhmEvrB8OPBn4LfzxC6o2G1VRkqSZYdgQ+X1V3Tk2076W1rf7StIsN2yIfCvJW4AN2nernwJ8eXRlSZJmgmFD5AhgOXApva+0PZPe961LkmaxYd+d9Qd6X4/78dGWI0maSYa9d9bPGXANpKoeMeUVSZJmjFW5d9aY9YEXAA+Z+nIkSTPJUNdEquo3fY9fVdUHgWeMuDZJ0lpu2OGsnftm16F3ZvLAkVQkSZoxhh3Oel/f9N3A1cDfTHk1kqQZZdh3Z+056kIkSTPPsMNZ/7Cy5VX1/qkpR5I0k6zKu7N2BU5v888FzgOuGUVRkqSZYVW+lGrnqroFIMnbgVOq6hWjKkyStPYb9rYnDwfu7Ju/E5g75dVIkmaUYc9EPgl8L8lp9D65fiBw0siqkiTNCMO+O+uoJF8FntqaXl5VPxhdWZKkmWDY4SyADYHfVtV/AkuSbDuimiRJM8SwX497JPAm4M2taT3gv0dVlCRpZhj2TORA4HnAbQBVtRRveyJJs96wIXJnVRXtdvBJ/mx0JUmSZophQ+RzSf4L2CjJK4GzmeQLqpIsSHJ9ksv62h6SZGGSK9vPjVt7khyTZHGSS/pv+JjkkNb/yiSH9LXvkuTSts4xSbIqBy5JWn3D3gr+P4DPA6cCjwHeVlUfmmS1E4B9xrUdAZxTVfOAc9o8wL7AvPY4DDgWeqEDHAk8EdgNOHIseFqfw/rWG78vSdKITfoW3yRzgLOq6pnAwmE3XFXnJZk7rnl/YI82fSJwLr0L9vsDJ7Uhs/OTbJRki9Z3YVXd0GpZCOyT5FzgQVX13dZ+EnAA8NVh65Mkrb5Jz0Sq6h7g9iQPnoL9bV5Vy9p2lwGbtfYt+dP7cC1pbStrXzKgfaAkhyVZlGTR8uXLV/sgJEk9w35i/XfApe1M4Laxxqp63RTVMeh6RnVoH6iqjgOOA5g/f/6E/SRJq2bYEDmjPVbXdUm2qKplbbjq+ta+BNi6r99WwNLWvse49nNb+1YD+kuS1qCVhkiSh1fVL6vqxCna3+nAIcDR7eeX+tpfm+RkehfRb25Bcxbwv/supu8NvLmqbkhyS5LdgQuAg4HJLvRLkqbYZNdEvjg2keTUVdlwks8A3wUek2RJkkPphcezklwJPKvNA5wJXAUspvfW4b8DaBfU3wVc2B7vHLvIDrwGOL6t8zO8qC5Ja9xkw1n91x4esSobrqoXT7BorwF9Czh8gu0sABYMaF8E7LAqNUmSptZkZyI1wbQkSZOeiTwhyW/pnZFs0KZp81VVDxppdZKktdpKQ6Sq5qypQiRJM8+qfJ+IJEl/whCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM7Wne4CJM0sc484Y7pLuE+5+ujnTHcJq8UzEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKmzaQmRJFcnuTTJxUkWtbaHJFmY5Mr2c+PWniTHJFmc5JIkO/dt55DW/8okh0zHsUjSbDadZyJ7VtWOVTW/zR8BnFNV84Bz2jzAvsC89jgMOBZ6oQMcCTwR2A04cix4JElrxto0nLU/cGKbPhE4oK/9pOo5H9goyRbAs4GFVXVDVd0ILAT2WdNFS9JsNl0hUsDXk1yU5LDWtnlVLQNoPzdr7VsC1/Stu6S1TdS+giSHJVmUZNHy5cun8DAkaXabrlvBP7mqlibZDFiY5Mcr6ZsBbbWS9hUbq44DjgOYP3/+wD6SpFU3LWciVbW0/bweOI3eNY3r2jAV7ef1rfsSYOu+1bcClq6kXZK0hqzxEEnyZ0keODYN7A1cBpwOjL3D6hDgS236dODg9i6t3YGb23DXWcDeSTZuF9T3bm2SpDVkOoazNgdOSzK2/09X1deSXAh8LsmhwC+BF7T+ZwL7AYuB24GXA1TVDUneBVzY+r2zqm5Yc4chSVrjIVJVVwFPGND+G2CvAe0FHD7BthYAC6a6RknScNamt/hKkmYYQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHU240MkyT5JfpJkcZIjprseSZpNZnSIJJkDfATYF9geeHGS7ae3KkmaPWZ0iAC7AYur6qqquhM4Gdh/mmuSpFlj3ekuYDVtCVzTN78EeOL4TkkOAw5rs7cm+ckaqG022AT49XQXMZm8Z7or0DTx+Tm1thnUONNDJAPaaoWGquOA40ZfzuySZFFVzZ/uOqRBfH6uGTN9OGsJsHXf/FbA0mmqRZJmnZkeIhcC85Jsm+R+wIuA06e5JkmaNWb0cFZV3Z3ktcBZwBxgQVVdPs1lzSYOEWpt5vNzDUjVCpcQJEkaykwfzpIkTSNDRJLUmSGiVZbkhCQHTdJnuyQXJ/lBkkd22Mfbk/xTm35Zkod1rVczX//zYYLlmya5oD3fntph+y9L8uE2fYB3vhieIaJROQD4UlXtVFU/W81tvQwwRLQyewE/bs+3b6/mtg6gdxslDcEQuQ9JMjfJFUk+nuTyJF9PskFbtmOS85NckuS0JBu39nOTvCfJ95L8dNBfcen5cJIfJTkD2Kxv2S5JvpXkoiRnJdkiyX7A64FXJPlm6/fF1ufydgeBsfVv7Zs+KMkJ4/Z9EDAf+FQ7s9lgKn9nWnsl+Zd2c9Wzgce0tkcm+Vp7Ln27nfHuCLwX2G/sOZLk2CSL2vPtHX3bvDrJJm16fpJzx+3zL4HnAf/etrXKZ9GzjSFy3zMP+EhVPQ64CXh+az8JeFNVPR64FDiyb511q2o3ei/8/e1jDqT3n/gvgFcCfwmQZD3gQ8BBVbULsAA4qqrOBD4GfKCq9mzb+J+tz3zgdUn+fJiDqarPA4uAl1TVjlV1xzDraWZLsgu9z33tBPw1sGtbdBzw9+259E/AR6vqYuBtwGf7niP/0j6t/njg6UkeP8x+q+r/0fus2T+3ba3uWfR93oz+nIgG+nn7TwVwETA3yYOBjarqW639ROCUvnW+0N9/wDafBnymqu4Blib5Rmt/DLADsDAJ9D6rs2yCul6X5MA2vTW9sPvNqhyYZpWnAqdV1e0ASU4H1qf3B8wp7fkGcP8J1v+bdsa7LrAFveGpS0Za8SxliNz3/L5v+h5gmOGfsXXuYeLnxKAPFAW4vKqetLKNJ9kDeCbwpKq6vQ0hrD9gu+sj3Wv8c24d4Kaq2nFlKyXZlt5Zyq5VdWMbIh17bt3NvSMwPt+mgMNZs0BV3Qzc2He942+Bb61klfHOA16UZE6SLYCxIaqfAJsmeRL0hreSPG7A+g8GbmwBsh2we9+y65I8Nsk69IbNBrkFeOAq1KuZ7zzgwHZ944HAc4HbgZ8neQH88VrdEwas+yDgNuDmJJvT+76hMVcDu7Tp5zOYz7dVYIjMHofQu1h4CbAj8M5VWPc04Ep611KOpQVQ+w6Xg4D3JPkhcDHtesk4XwPWbft+F3B+37IjgK8A32DiobATgI95YX32qKrvA5+l95w6FRh7x9VLgEPb8+1yBnx/UFX9EPhBW74A+E7f4ncA/5nk2/TOvAc5Gfjnrm9Pn2287YkkqTPPRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISKNSJKHJjk5yc/afcfOTPLoJJdNd23SVPET69IIpHdfjtOAE6vqRa1tR2DzaS1MmmKeiUijsSdwV1V9bKyh3dPsmrH5dtflbyf5fnuM3dhyiyTntQ9XXpbkqe1uASe0+UuTvGHNH5K0Is9EpNHYgd4NLVfmeuBZVfW7JPOAz9C7y/H/AM6qqqOSzAE2pHeXgS2rageAJBuNrnRpeIaINH3WAz7chrnuAR7d2i8EFrRb7X+xqi5OchXwiCQfAs4Avj4tFUvjOJwljcbl3Hujv4m8AbgOeAK9M5D7AVTVefRuv/8r4JNJDq6qG1u/c4HDgeNHU7a0agwRaTS+Adw/ySvHGpLsCmzT1+fBwLKq+gO9OyvPaf22Aa6vqo8DnwB2bt/Gt05VnQr8K7DzmjkMaeUczpJGoKqqfQnXB5McAfyO3m3IX9/X7aPAqe3W5t+kd/tygD3o3UX2LuBW4GBgS+D/tFvmA7x55AchDcG7+EqSOnM4S5LUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJn/x+T6PNo2DnX9gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEYCAYAAADmugmLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dfbRdVXnv8e9PEKS8lMRImhLqoR3RK8KQQoZJL7Y91EEIYA0doyroNSfIbVovjNqWcWuwtlBBG22xJdXLJbZpkqvyMlpfooIhl+EptQMwwYsEREygUY5JSWkAE17aJn3uH2tusrKz93nZZ7/MvfbvM8Yee++518tc2U/Os9dcc86liMDMzCxHr+h1BczMzJpxkjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy5aTlJmZZctJyszMslWZJCVph6QXJe2T9Iykr0k6JX22VtL16fWQpJB0ZINtXCvpsy3ufzTt9+i68rVpf2+vK/+LVL5M0odSvfdJeknSgdL7R9Ly10naKmm/pGtbqaMdVOV4kXSSpFsk7ZT0nKR/lLSglXraQVWOmbT8NyT9i6QfS/qOpCWt1LPdKpOkkl+NiOOAOcBTwF92Y6eShoBfBAJ4e4NFvg+MlJY/EngH8DhARHwsIo5Ldf8t4N7a+4h4Y1ptO/D7wNc6dRwDqKrxchywGTgbmAmsA74m6biOHdTgqGrMAHwAmBMRJwDLgc9KmtOhQ5q0qiUpACLiJeBvgdO6tMulwH3AWkqBUvIV4BxJM9L7xcBDwD9PdgcRsS4i7gT2Tq+qVq9q8RIRT0TEJyNiV0QciIjVwFHA66ddcwOqFzMAEfFQROyvvQVeCZzSaoXbpZJJStJPAO+i+FK7YSnwufQ4X9Lsus9fAjYAl5SWX9+lutkEqh4vks6kSFLbW92GHaqqMSPpq5JeAu4HRoEtrVa4XaqWpL4k6Vngx8B5wJ92eoeS3gK8Frg9Ih6gOL1+d4NF1wNLJf0k8MvAlzpdN5tQ5eNF0gnA/wH+OCKea63WVlLpmImItwHHAxcCGyPiP1uueJtULUldHBEnAkcDVwJ/L+mnOrzPEeCuiHg6vf88DU7HI+KbwGuADwNfjYgXO1wvm1il40XSMRTNQPdFxJ+0XmUrqXTMpO38R7q0cH59Z4xeOKz3SRVExAHgC5JuBt7Sqf2kPwLvBI6QVGv7PRo4UdKbIuI7dat8Fvgj4NxO1cmmrorxknqAfQn4EfCbrdfaGqlizDRwJPBzbdjOtFTtTAoAFZYAM4BHmyx2tKRXlR61f4tX1JUf3WR9gIuBAxQXT89MjzcA/0DRJlxvFUUTwT0tHNMrJb2K4js7MtXtiKluxw5XtXiR9EqKi/ovAktzaLKpmgrGzH+RdIGkY9Lfmv8G/BLw91PZTidU7UzqK5IOUPRM+QEwEhGPSGq07L669+el50vTo+ZHwNwm+xsB/iYiflgulPQpYJWkD5bLI2IPcPdkDqSBz3DoKf4fAJdR9Pax1lQ1Xv4r8DaKJPVs6XguiIh/aGF7dlBVY0bAtRTJ8ACwDXhXRHy7hW21lXxnXjMzy1Ulm/vMzKwaqtbc13aSfgb4bpOPT6s/DbfB5nixqXLMjM/NfWZmlq2+PZOaNWtWDA0NTXr5559/nmOPPbZzFcpA7RgfeOCBpyPiNb2uT04mipdBiI96jpfx9VvMdLM+3YyZvk1SQ0NDbNky+Rk7RkdHGR4e7lyFMlA7Rkk/6HVdcjNRvAxCfNRzvIyv32Kmm/XpZsy444SZmWXLScrMzLLlJGVmZtnq22tSkzG04uD9Aa86Yz/LSu93rLyoF1WyDJTjoqYWH44La6RZzAx3vyoDx2dSZmaWLScpMzPLlpOUmfWUpDWSdkt6uFR2raQfSXowPS4sfXa1pO2SHpN0fql8cSrbLmlFqfxUSfdL2ibpNklHde/obLomTFKSTpH0DUmPSnpE0gdS+UxJm9IXv0nSjFQuSatSoDwk6azStkbS8tskjZTKz5a0Na2zSk2mFDazSloLLG5Q/ucRcWZ63AEg6TSKW6S/Ma3zvyQdkW5b82ngAoqZvC9NywJ8PG1rHvAMcHlHj8baajJnUvuBqyLiDcBC4Ir05a8A7k5f/N3pPRRBMi89lgM3QZHUgGuABcCbgWtqiS0ts7y0XqOANbMKioh7gD2TXHwJcGtE/FtE/BOwneLvyZuB7RHxRET8O3ArsCT94P0VivtrAayjuEeT9YkJk1RE7KrdUyQi9lLc4OtkimBZlxYrf/FLgPVRuI/iDpJzgPOBTRGxJyKeATYBi9NnJ0TEvVFMJLgeB5GZwZWpNWZN6QftycCTpWXGUlmz8lcDz0bE/rpy6xNT6oIuaQj4eeB+YHZE7IIikUk6KS021SA6Ob2uLzezwXUTcB3FzQWvA24A3kdxc756QeMf3DHO8g1JWk7RqsPs2bMZHR0Fiu7m9WYfw8uf52Dfvn1Z1addJp2kJB0H/B3wOxHx43EuGzULiqmWN6pDwwBqphxYs4859H0Vv8yqBqkNnoh4qvZa0meAr6a3Y8AppUXnAjvT60blT1O05hyZzqbKyzfa72pgNcD8+fOjNhfesibjpN45oHP3ddOkkpSkV1IkqM9FxBdS8VOS5qSzqDnA7lTeLIjG4JCxb3OB0VQ+t8Hyh2kWQM0sqxvMe8PWg4e74z3jr9uPqhqkNnhqf1vS218Daj3/NgCfl/RJ4KcprmF/i+LH7jxJp1Lcjv0S4N0REZK+Afw6xXWqEeDL3TsSm67J9O4T8NfAoxHxydJHGyi+cDj0i98ALE29/BYCz6Vg2wgskjQjtS8vAjamz/ZKWpj2tRQHkdnAkHQLcC/wekljki4HPpF6/D4EnAv8LkBEPALcTnGTwK8DV0TEgXSWdCXF35lHgdvTsgAfBH5P0naKa1R/3cXDs2mazJnUOcB7ga2SHkxlHwJWArengPoh8I702R3AhRS9bl4ALgOIiD2SrgM2p+U+EhG1Hj3vp+iGegxwZ3qY2QCIiEsbFDdNJBHxUeCjDcrvoPj7U1/+BEXvP+tDEyapiPgmja8bAby1wfIBXNFkW2uANQ3KtwCnT1QX6w+S1gBvA3ZHxOmp7FrgN4B/SYt9qDT25WqKsSsHgN+OiI2pfDFwI3AE8FcRsTKVn0rRdDMT+Dbw3tTt2MwqxjNOWCesxYMzzawNnKSs7Tw408zapdK36rDsXClpKbCFYhaTZyjGxN1XWqY8Tq5+XN0CpjA4c6pjXq46Y/9AdeH3kAXrB05S1i1dH5w51TEvN2w9spJDE5rxkAXrB05S1hW9GpxpZv3N16SsK9KA75r6wZmXSDo69dqrDc7cTBqcmW6tcAmwIfUerQ3OBA/ONKs0n0lZ26XBmcPALEljFLPfD0s6k6Jpbgfwm1AMzpRUG5y5nzQ4M22nNjjzCGBN3eDMWyVdD/w/PDjTrLKcpKztPDjTBsVQg+ubNTtWXtTFmlSXm/vMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTPrKUlrJO2W9HCpbKakTZK2pecZqVySVknaLukhSWeV1hlJy2+TNFIqP1vS1rTOqnTjTOsTTlJm1mtrgcV1ZSuAuyNiHnB3eg9wAcVM+fMobmh5ExRJjWIi4wUU8zpeU0tsaZnlpfXq92UZc5Iys56KiHuAPXXFS4B16fU64OJS+foo3Edxb7E5wPnApojYk+74vAlYnD47ISLuTbd5WV/alvUBz4JuZjmaHRG7ACJil6STUvnJwJOl5cZS2XjlYw3KG5K0nOKsi9mzZzM6OgoUd24+rILHNC6vqa3bLfv27ev6PrvBScrM+kmj60nRQnlDEbEaWA0wf/78GB4eBmBZg1tyXHXGfm7Y2vxP6I73DDf9rBNGR0ep1bdK3NxnZjl6qnY35/S8O5WPAaeUlpsL7JygfG6DcusTTlLWdu6tZW2wAah95yPAl0vlS1PcLASeS82CG4FFkmak2FoEbEyf7ZW0MMXJ0tK2rA84SVknrMW9tWySJN0C3Au8XtKYpMuBlcB5krYB56X3UNyp+QlgO/AZ4H8ARMQe4Dpgc3p8JJUBvB/4q7TO48Cd3Tguaw9fk7K2i4h7JA3VFS8BhtPrdcAo8EFKvbWA+yTVemsNk3prAUiq9dYaJfXWSuW13lr+w9OnIuLSJh+9tcGyAVzRZDtrgDUNyrcAp0+njtY7TlLWLV3vrdVKT60q9o5qpqq9waxanKSs1zrWW6uVnlrd7pHVS1XtDWbV4mtS1i3urWVmU+YkZd3i3lpmNmVu7rO2S721hoFZksYoeumtBG5PPbd+CLwjLX4HcCFFz6sXgMug6K0lqdZbCw7vrbUWOIaiw4Q7TZhVlJOUtV0/99YaanC9CmDHyos6sTszm8CEzX0emGlmZr0ymWtSa/HATDMz64EJk5Sn0Tczs15p9ZpUVtPoN1MetFk/rX4VBzF6cKaZVU27O070ZBr9ZsqDNuun1a/ioE0PzjSzqml1nJQHZpqZWce1mqQ8MNPMzDpuwuY+D8w0M7NemTBJ9fPATDMz62+eu8/MzLLlJGVmZtlykjIzs2w5SZmZWbacpMwsW5J2pAmoH5S0JZW1bYJry5+TlJnl7tyIODMi5qf37Zzg2jLnJGVm/aYtE1x3u9LWGt/00LpK0g5gL3AA2B8R89Mv3duAIWAH8M6IeCbNQnIjxQDxF4BlEfHttJ0R4MNps9dHxDqsigK4S1IAN6f5O9s1wfVhmk1iXZ6cuqZ+0up63Z7suaoTTDtJWS+cGxFPl97Xmm9WSlqR3n+QQ5tvFlA03ywoNd/Mp/gj9oCkDelXslXLORGxMyWiTZK+N86y057Iutkk1ssa3LG5ftLqet2exLqqE0y7uc9y4OYbaygidqbn3cAXKa4ptWuCa+sDA3smNdTglxHAjpUXdbkmA6drzTf93HTTDbk3D0k6FnhFROxNrxcBH+HgBNcrOXyC6ysl3Upx5v1ciqeNwMdKnSUWAVd38VBsGgY2SVnPdK35pp+bbrqhD5qHZgNfLC5NciTw+Yj4uqTNtG+Ca8uck5R1Vbn5RtIhzTfpV+9km2+G68pHO1x167KIeAJ4U4Pyf6VNE1xb/nxNyrpG0rGSjq+9pmh2eZg23Z+si4diZl3iMynrJjffmNmUOElZ17j5xsymys19ZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtzzhhNgnNbu0Cvr2LWSf5TMrMzLLlJGVmZtlykjIzs2z5mlQdX3swM8uHk5SZWQc0+8HrH7tT4+Y+MzPLVjZJStJiSY9J2i5pRa/rY3lzvNhUOWb6UxbNfZKOAD4NnAeMAZslbYiI7/a2Zpaj3OLFzTr5yy1mbPKySFLAm4Ht6fbiSLoVWAJkFUD+Y5SNvo4XcMz0QDYx47iYmlyS1MnAk6X3Y8CC+oUkLQeWp7f7JD022R38NswCnp5OJZvRxzux1ZbUjvG1va5Ih7U9XjoZH41kEjODEi/QJzEzzbjoZgx3LWZySVJqUBaHFUSsBla3tANpS0TMb2XdfjEIx5i0PV4G6N/uZQN2zJWPmdzq0y65dJwYA04pvZ8L7OxRXSx/jhebKsdMn8olSW0G5kk6VdJRwCXAhh7XyfLleLGpcsz0qSya+yJiv6QrgY3AEcCaiHikzbtpqZmwzwzCMXYqXgbi367OwBzzgMRMbvVpC0Uc1ixrZmaWhVya+8zMzA7jJGVmZtkaiCSV43QoktZI2i3p4VLZTEmbJG1LzzNSuSStSvV/SNJZpXVG0vLbJI2Uys+WtDWts0qSWt1HleUYG404XvIy3bjx9zkFEVHpB8VF0seBnwWOAr4DnJZBvX4JOAt4uFT2CWBFer0C+Hh6fSFwJ8VYj4XA/al8JvBEep6RXs9In30L+IW0zp3ABa3so8qPXGPD8ZL3ox1x4+9zCv9Wva5AFwLqF4CNpfdXA1f3ul6pLkN1QfoYMCe9ngM8ll7fDFxavxxwKXBzqfzmVDYH+F6p/OXlprqPXv8bDWpsOF7yfbQrbvx9Tu4xCM19jaZDOblHdZnI7IjYBZCeT0rlzY5hvPKxBuWt7KPK+v2YHS+90alj9/fZwCAkqUlNh5K5Zscw1fJW9lFlVT1mx0tndfvYB/r7HIQk1U/ToTwlaQ5Aet6dypsdw3jlcxuUt7KPKuv3Y3a89Eanjt3fZwODkKT6aTqUDUCth84I8OVS+dLUA2ch8Fw6Vd8ILJI0I/XSWUTRVr4L2CtpYerVs7RuW1PZR5X1U2w04njpjU7Fjb/PRnp9UawbD4qeK9+n6JHzB72uT6rTLcAu4D8ofsVcDrwauBvYlp5npmVFccO2x4GtwPzSdt4HbE+Py0rl84GH0zqf4uDsIlPeR5UfOcaG4yX/x3Tjxt/n5B+eFsnMzLI1CM19ZmbWp5ykzMwsW05SZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLJVmSQlaYekFyXtk/SMpK9JOiV9tlbS9en1kKSQdGSDbVwr6bMt7n807ffouvK1aX9vryv/i1S+TNKHUr33SXpJ0oHS+0fq1vvltN71rdTTClWPl7rj2yfprlbqaQdVPWbSOh+Q9E+Snpf0qKTXtVLXdqpMkkp+NSKOA+YATwF/2Y2dShoCfhEI4O0NFvk+B++GSQred1DcYIyI+FhEHJfq/lvAvbX3EfHG0nqvBG4E7u/QoQyaSscL6fjSY1FnjmbgVDZmJP13ipsvXgQcB7wNeLpTxzRZVUtSAETES8DfAqd1aZdLgfuAtZQCpeQrwDnpFs8Ai4GHgH+e4n6uAu4CvtdaNa2RCseLdUjVYkbSK4BrgN+NiO9G4fGI2DPtmk9TJZOUpJ8A3kXxpXbDUuBz6XG+pNl1n78EbAAuKS2/fio7kPRailtFf2R6VbV6VYyX5HOS/kXSXZLe1HJt7TAVjJm56XG6pCdTk98fp+TVUz2vQJt9SdKzwI+B84A/7fQOJb0FeC1we0Q8QHF6/e4Gi64Hlkr6SeCXgS9NcVergD+MiH3Tqa8dosrx8h5gKO3rG8BGSSe2Wm97WVVjZm56XgScAZwLXErR/NdTVUtSF0fEicDRwJXA30v6qQ7vcwS4KyJqbbefp8HpeER8E3gN8GHgqxHx4mR3IOlXgeMj4rY21NcOqmS8pPX/MSJejIgXIuJPgGcprmnY9FQ1ZmrLfiIino2IHcDNwIUt17pNDut9UgURcQD4gqSbgbd0aj+SjgHeCRwhqdb2ezRwoqQ3RcR36lb5LPBHFL9SpuKtwPzSPn4SOCDpjIhY0mL1LalgvDQSgNqwHaOSMfMY8O8UcZKVqp1JAaDCEmAG8GiTxY6W9KrSo/Zv8Yq68qObrA9wMXCA4uLpmenxBuAfKNqE662iaCK4Z4qH9IfA60r72AB8BrhsituxBqoWL5J+RtI5ko5KdfqfwCzgH6eyHWuuajETES8AtwG/L+l4SXOB3wC+OpXtdELVzqS+IukAxa+BHwAjEfGI1PAHZP21nfPS86XpUfMjDrbX1hsB/iYiflgulPQpYJWkD5bLU0+ZuydzIHXr7QX2lrb/IvB8Dj1v+lwl4wU4HrgJ+DmKC+oPAhdExL+2sC07VFVjBormy9XATorm4c8Aa1rcVtsoIruzOzMzM6CizX1mZlYNVWvuaztJPwN8t8nHp9Wfhttgc7zYVDlmxufmPjMzy1bfnknNmjUrhoaGprTO888/z7HHHtuZCmW03wceeODpiHhN13bYByaKl17FRg77d7w01srfmPH0OsbaoXYM3YyZvk1SQ0NDbNmyZUrrjI6OMjw83JkKZbRfST/o2s76xETx0qvYyGH/jpfGWvkbM55ex1g71I6hmzHjjhNmZpYtJykzM8vWhElK0imSvpFugPWIpA+k8pmSNknalp5npHJJWiVpu6SHJJ1V2tZIWn6bpPK9T86WtDWts0pNRsaZmdlgmcw1qf3AVRHxbUnHAw9I2gQsA+6OiJWSVgArgA8CFwDz0mMBxcj3BZJmUtyvZD7FaO0HJG2IiGfSMssppr2/g+JeKHdO9+CGVnztkPdXnbGfZalsx8qLprt561P1cQEHY8NxYe3S7O+PY2xqJjyTiohdEfHt9HovxTxVJwNLgHVpsXUUc0yRytenm2bdRzER4hzgfGBTROxJiWkTsDh9dkJE3BtFf/j1pW1ZH5K0RtJuSQ+Xyq6V9CNJD6bHhaXPrk5n0Y9JOr9UvjiVbU8/hGrlp0q6P52R3ybpqO4dnZl105R696m4hfHPU9y+fHZE7IIikUk6KS12MvBkabWxVDZe+ViD8kb7X05xxsXs2bMZHR0dt75XnbH/kPezjzlYNtG67bRv376u7i8Da4FPcfhN1/48Iv6sXCDpNIobtb0R+Gng/0p6Xfr40xTznY0Bm9OZ93eBj6dt3Srpf1Pc8+amTh2MmfXOpJOUpOOAvwN+JyJ+PM5lo0YfNLtNwHjlhxdGrKaYAJH58+fHRN05lzU43b5ha3HIO94z/rrtVIWup1MREfekHzSTsQS4NSL+DfgnSduBN6fPtkfEEwCSbgWWSHoU+BUO3vRtHXAtTlJmlTSp3n2SXkmRoD4XEV9IxU+lpjrS8+5UPgacUlp9LsWsuuOVz21QbtVzZepMs6bW0Yapn3m/Gng2IvbXlZtZBU14JpV62v018GhEfLL00QaKaeRXpucvl8qvTL98FwDPpebAjcDHSn+cFgFXR8QeSXslLaRoRlwK/GUbjs3ychNwHcVZ8nXADcD7aH4m3egH1JTOvJs1D9c3A8PBpuBeNcsOYJOw2aRMprnvHOC9wFZJD6ayD1Ekp9slXQ78EHhH+uwOilsObwdeIN2YLyWj64DNabmPlO6H9H6K6xjHUPTqm3bPPstLRDxVey3pMxy8mVqzM2yalD9N0RnnyHQ21fTMu1nzcH0zMBxsCu5mM3DZoDUJm03WhEkqIr5J89tOv7XB8gFc0WRba2hwE62I2AKcPlFdrH9JmlPraAP8GlDr+bcB+LykT1J0nJgHfIsi5uZJOpXipnCXAO+OiJD0DeDXgVs59Cze+pCkUyg62fwU8J/A6oi4MQ1buQ0YAnYA74yIZ1Lrzo0UP4ZfAJbVeiCn8ZcfTpu+PiLWpfKzOfhD+A7gA+HZtfuCZ5ywtpN0C3Av8HpJY+ls+xNpwPZDwLnA7wJExCPA7RS3Kvg6cEVEHEhnSVcCGymGPdyeloViPN7vpU4Wr6Zojrb+VRuL+QZgIXBF6vW5gmIs5jyKu83WhiGUx2IuJ3WaKY3FXEDR+eaa0uWF2ljM2nqLu3Bc1gZ9O8Gs5SsiLm1Q3DSRRMRHgY82KL+D4ldvffkTHOwBaH0unWHXhrPsTT04a2Mxh9Ni64BRih8oL4/FBO6TVBuLOUwaiwmQJh1YLGmUNBYzldfGYvqyQh/wmZSZZWO8sZhAx8diWn58JmVmWchhLOZUJwwYT7PJBPq5F2cveqE6SZlZz403FjMNYZnsWMzhuvJRpjAWc6oTBoyn2WQCvepB2g696IXq5j4z66lJjMWEw8diLk13XFhIGotJ0clmkaQZqcPEImBj+myvpIVpX0txj9C+4TMpM+s1j8W0ppykzKynPBbTxuPmPjMzy5aTlJmZZctJyszMsuUkZWZm2XKSMjOzbDlJmZlZtpykzMwsW05SZmaWLScpMzPLlpOUmZlly9MimZUM1c1cXbNj5UVdromZgc+kzMwsY05SZmaWLScpMzPLlpOUtZ2kNZJ2S3q4VDZT0iZJ29LzjFQuSaskbZf0kKSzSuuMpOW3SRoplZ8taWtaZ5XGuc+4mfU3JynrhLXA4rqyFcDdETEPuDu9B7gAmJcey4GboEhqwDXAAuDNwDW1xJaWWV5ar35fZlYRTlLWdhFxD7CnrngJsC69XgdcXCpfH4X7gBMlzQHOBzZFxJ6IeAbYBCxOn50QEfemm9+tL23LzCrGXdCtW2ZHxC6AiNgl6aRUfjLwZGm5sVQ2XvlYg/LDSFpOccbF7NmzGR0dBeCqM/YfXrljGpfX1NbtlH379nV8H2b9yEnKeq3R9aRoofzwwojVwGqA+fPnx/DwMADLGoyFuuqM/dywtfl/hx3vGW76WTuMjo5Sq5+ZHeTmPuuWp1JTHel5dyofA04pLTcX2DlB+dwG5WZWQU5S1i0bgFoPvRHgy6XypamX30LgudQsuBFYJGlG6jCxCNiYPtsraWHq1be0tC0zqxg391nbSboFGAZmSRqj6KW3Erhd0uXAD4F3pMXvAC4EtgMvAJcBRMQeSdcBm9NyH4mIWmeM91P0IDwGuDM9zKyCnKSs7SLi0iYfvbXBsgFc0WQ7a4A1Dcq3AKdPp45m1h/c3GdmZtlykjIzs2w5SZmZWbacpMzMLFsTJilPFmpmZr0ymTOptXiyUDMz64EJk5QnCzUzs15pdZxU1ycLheYThjZTP2FoeRLRbk7m6clDzcxa0+7BvB2bLBSaTxjaTP1EouVJRDs9YWiZJw81a07SGuBtwO6IOD2VzQRuA4aAHcA7I+KZdM36RopZSl4AlkXEt9M6I8CH02avj4h1qfxsDs5QcgfwgdRyY32g1d59nizUzNplLb7ubU20mqQ8WaiZtYWve9t4Jmzu82ShZtYDfXHdezzNron38/XpXlxfnzBJebJQM8tIVte9x9Psmng3r4e3Wy+ur3vGCTPLka97G+AkZWZ58nVvA3w/KTPrMV/3tvE4SZlZT/m6t43HzX1mZpatgT2TGqrreVOzY+VFXa6JmZk14zMp6ypJO9KtWR6UtCWVte3WL2ZWLU5S1gvnRsSZETE/vW/nFDhmViFOUpaDtkyB0+1Km1nnDew1KeuZAO6SFMDNaYR/u6bAOUSzKW7qp6uBQ2/j0kinp4Lx7VzMGnOSsm47JyJ2pkS0SdL3xll2WlPdNJvipn66Gjj0Ni6NdHoqG9/OxawxN/dZV0XEzvS8G/gixTWldk2BY2YV467mbUoAAATPSURBVCRlXSPpWEnH115TTF3zMG2aAqeLh2JmXeLmPuum2cAXiynUOBL4fER8XdJm2jcFjplViJOUdU1EPAG8qUH5v9KmKXDMctdsIgHwZAKNuLnPzMyy5SRlZmbZcpIyM7NsOUmZmVm2nKTMzCxbTlJmZpYtJykzM8uWk5SZmWXLScrMzLLlJGVmZtnytEhmk+CpbMx6w2dSZmaWLScpMzPLlpOUmZlly0nKzMyy5SRlZmbZcu++Ou7FZWaWD59JmZlZtpykzMwsW27uMzPLRLPLDYN8qSGbJCVpMXAjcATwVxGxssdVsozlFC/+w9IfcooZm7wskpSkI4BPA+cBY8BmSRsi4ru9rdmh/McoD/0SL5YPx0z/yiJJAW8GtkfEEwCSbgWWAH0RQE5eXdcX8eKeolnpi5hpZpD/xuSSpE4Gniy9HwMW1C8kaTmwPL3dJ+mxqezkt2EW8HSrlZwqffzll13dL/DaLu6rF9oeLz2MjZpux0hZ1eMFuvQ3ZjydiLEGcdRptWPoWszkkqTUoCwOK4hYDaxueSfSloiY3+r6/bbfCmt7vPT6O+r1/gdAV/7GjFuBCnzHvTiGXLqgjwGnlN7PBXb2qC6WP8eLTZVjpk/lkqQ2A/MknSrpKOASYEOP62T5crzYVDlm+lQWzX0RsV/SlcBGiu6hayLikQ7sqiOn8Rnvt5I6FC+9/o56vf9K6+LfmPFU4Tvu+jEo4rBmWTMzsyzk0txnZmZ2GCcpMzPL1sAkKUmLJT0mabukFVNYb4ekrZIelLQllc2UtEnStvQ8I5VL0qq0j4cknVXazkhafpukkVL52Wn729O6Gm8f1n6txkZpfceINTXd+JrGftdI2i3p4VJZz+JyvH2MKyIq/6C4UPo48LPAUcB3gNMmue4OYFZd2SeAFen1CuDj6fWFwJ0UYzIWAven8pnAE+l5Rno9I332LeAX0jp3AheMtw8/8okNx4gf3Yivaez7l4CzgIdLZT2Ly2b7mOgxKGdSL0+JEhH/DtSmRGnVEmBder0OuLhUvj4K9wEnSpoDnA9siog9EfEMsAlYnD47ISLujeJbXF+3rUb7sPZqd2zUOEYMOhdfE4qIe4A9dcW9jMtm+xjXoCSpRlOinDzJdQO4S9IDKqZMAZgdEbsA0vNJE+xnvPKxJvVqtg9rr+nERo1jxJppR3y1Uy/jsqV/iyzGSXXBpKZEaeKciNgp6SRgk6TvtbCfqZZb97TjO3CMWDP98v11Iy5b+rcYlDOplqdEiYid6Xk38EWK0/enaqep6Xn3BPsZr3xuk3o124e117Sny3GM2Dhym46pl3HZ0r/FoCSplqZEkXSspONrr4FFwMNp3VovlxHgy+n1BmBp6sWyEHgune5uBBZJmpF6uiwCNqbP9kpamHrGLK3bVqN9WHtNa7ocx4hNILfpmHoZl832Mb5u9DLJ4UHRs+T7FD1t/mCS6/wsRW+c7wCP1NYDXg3cDWxLzzNTuShurPY4sBWYX9rW+4Dt6XFZqXw+xR+1x4FPcXAWkIb78COP2HCM+NGN+Jrmfm8BdgH/QXEWc3kv43K8fYz38LRIZmaWrUFp7jMzsz7kJGVmZtlykjIzs2w5SZmZWbacpMzMLFtOUmZmli0nKTMzy9b/Bx/Qh33ndxUNAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4, 0], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, \"Missing\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8507531.55877335.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000000.0000000.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.850753 1.558773 35.006592 \n", + "std 116558.616530 0.487994 0.738175 0.522639 8.832028 \n", + "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MISSING-EDUGRADUNIHSMISSING-MSMARRIEDSINGLE
00.00.01.00.00.01.00.0
10.00.01.00.00.00.01.0
20.00.01.00.00.00.01.0
30.00.01.00.00.01.00.0
40.00.01.00.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " MISSING-EDU GRAD UNI HS MISSING-MS MARRIED SINGLE\n", + "0 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 0.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"MISSING-EDU\",\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MISSING-MS\",\"MARRIED\",\"SINGLE\",\"OTHER-MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER-MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',\n", + " 'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',\n", + " 'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',\n", + " 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',\n", + " 'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 47 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, MISSING-EDU, GRAD, UNI, HS, MISSING-MS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 47 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 47 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split using seed 123\n", + "np.random.seed(123) \n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 82.306062 2.883753e-04\n", + "PAY_0 4279.993739 0.000000e+00\n", + "PAY_2 3557.072141 0.000000e+00\n", + "PAY_3 2766.119390 0.000000e+00\n", + "PAY_4 2736.965012 0.000000e+00\n", + "PAY_5 2587.002458 0.000000e+00\n", + "PAY_6 2240.874786 0.000000e+00\n", + "PAY_0_No_Transactions 76.858872 1.147939e-03\n", + "PAY_0_Revolving_Credit 480.805794 0.000000e+00\n", + "PAY_2_Pay_Duly 75.283344 1.684018e-03\n", + "PAY_2_Revolving_Credit 229.527990 0.000000e+00\n", + "PAY_3_Pay_Duly 86.995856 8.229607e-05\n", + "PAY_3_Revolving_Credit 121.059740 2.357071e-09\n", + "PAY_4_Pay_Duly 79.449207 6.014800e-04\n", + "PAY_4_Revolving_Credit 82.276504 2.906105e-04\n", + "PAY_5_Pay_Duly 63.330298 2.338310e-02\n", + "PAY_5_Revolving_Credit 64.659773 1.792035e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3333333333333333\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree, y_train, X_train, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (GINI) identified 809\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054821280
11178809
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5482 1280\n", + "1 1178 809" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.81 0.82 6762\n", + " 1 0.39 0.41 0.40 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
055091253
11156831
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5509 1253\n", + "1 1156 831" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.81 0.82 6762\n", + " 1 0.40 0.42 0.41 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")\n", + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06371391
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6371 391\n", + "1 1274 713" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.27\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6762\n", + " 1 0.65 0.36 0.46 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc_rf = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")\n", + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13442\n", + " 1 0.79 0.46 0.58 4054\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06381381
11270717
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6381 381\n", + "1 1270 717" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24738247273049666\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")\n", + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Trees - Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc_rf])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17450
Method: MLE Df Model: 45
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784
Time: 00:13:23 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649
SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016
AGE 0.2097 0.100 2.095 0.036 0.013 0.406
PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726
PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365
PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037
PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081
PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325
PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731
BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821
BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706
BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396
BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992
BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338
BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799
PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640
PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114
PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186
PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072
PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185
PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145
MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648
GRAD 1.3518 0.220 6.148 0.000 0.921 1.783
UNI 1.3056 0.219 5.971 0.000 0.877 1.734
HS 1.2342 0.223 5.547 0.000 0.798 1.670
MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06
MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426
SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245
PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067
PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284
PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705
PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026
PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947
PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350
PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106
PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247
PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203
PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205
PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252
PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160
PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711
PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500
PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467
PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335
PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343
PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17450\n", + "Method: MLE Df Model: 45\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784\n", + "Time: 00:13:23 Log-Likelihood: -7781.7\n", + "converged: False LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", + "PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", + "PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", + "PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", + "PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", + "PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", + "PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", + "BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", + "BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", + "BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", + "BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", + "BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", + "BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", + "PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", + "PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", + "PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", + "PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", + "PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", + "PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", + "MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", + "GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", + "UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", + "HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", + "MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", + "MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", + "SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", + "PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", + "PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", + "PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", + "PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", + "PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", + "PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", + "PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", + "PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", + "PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", + "PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", + "PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", + "PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", + "PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", + "PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", + "PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", + "PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", + "PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", + "PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 13442\n", + " 1 0.67 0.36 0.47 4054\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs average on both the train and test set with an accuracy of about 0.8 but recall and f1 are still below 0.5. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.445360\n", + " Iterations: 35\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445387\n", + " Iterations 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445388\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445392\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445397\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445410\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445455\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445512\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445596\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445680\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445770\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445853\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445877\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445963\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446090\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446288\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17468
Method: MLE Df Model: 27
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756
Time: 00:14:16 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676
SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036
PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692
PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397
PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111
PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276
BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672
BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867
PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693
PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450
PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350
PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277
GRAD 1.3465 0.175 7.687 0.000 1.003 1.690
UNI 1.2982 0.174 7.462 0.000 0.957 1.639
HS 1.2384 0.178 6.960 0.000 0.890 1.587
MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318
PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799
PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159
PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010
PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421
PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397
PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509
PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471
PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173
PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361
PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288
PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477
PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17468\n", + "Method: MLE Df Model: 27\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756\n", + "Time: 00:14:16 Log-Likelihood: -7808.3\n", + "converged: True LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", + "SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", + "PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", + "PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", + "PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", + "PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", + "BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", + "BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", + "PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", + "PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", + "PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", + "PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", + "GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", + "UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", + "HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", + "MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", + "PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", + "PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", + "PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", + "PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", + "PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", + "PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", + "PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", + "PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", + "PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", + "PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", + "PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", + "PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21650864211883647\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.78 0.83 6762\n", + " 1 0.46 0.62 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we used that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 1235\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053031459
17521235
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5303 1459\n", + "1 752 1235" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 715\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06421341
11272715
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6421 341\n", + "1 1272 715" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defaults. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = [\"Logistic Regression (Optimal Cutoff)\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16469105377809068\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")\n", + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM default parameters identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06432330
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6432 330\n", + "1 1274 713" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15996357777982226\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.96 0.89 6762\n", + " 1 0.70 0.32 0.44 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.77 0.64 0.66 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06492270
11349638
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6492 270\n", + "1 1349 638" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15910713557498266\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.38 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[2] = ([\"SVM-RBF (Grid Search)\" , \n", + " classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'rbf', probability = True)\n", + "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16104553371241384\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAEWCAYAAADsCgQrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gVVfrA8e+bnpBGCQQSQu9NiqBYABFpIoroil1xFevPsnbXFdvaK/ayqIvgWkBFFLAAIkrvVXoCISE9IT33/P6YAS8hDUgyyb3v53l4yJ36ztwz574zc2aOGGNQSimllCrNx+kAlFJKKVU3aZKglFJKqTJpkqCUUkqpMmmSoJRSSqkyaZKglFJKqTJpkqCUUkqpMtXJJEFErhCReU7H4TQRiRORHBHxrcV1thYRIyJ+tbXOmiQiG0Vk8AnMd0JlUESCReRbEckUkc+Pd35PIiILROSGWl7nVBF5soLxT4pIiogcqM246hMR2S0i59bSut4WkX/WwHKPqsdE5HsRuaa613Mi7Lja14E4KjxWDqs0SbALTJ79Y3XAXnBo9YRZNmPMNGPMeTW5jrqo9MFpjNlrjAk1xpQ4GZdTquNgMsZ0M8YsqGQ9xyRGJ1EGxwPNgMbGmEtOYP6y4ntIRHbZx2CCiHxmD39HRD4uY/qeIlIgIo1E5DF72+4oNc2d9vDHqiPG+kBEWgL3AF2NMdEnuazBIpJQPZHVH1X9YakqY8wkY8wT1bW8CtYz0hjzUU2vxxNV9UrCGGNMKHAK0Bt4sOZCqjlOnh17ypn58fDS/d0K2GaMKT7eGcuK2T77uQo41z4G+wE/2aOnAuNEpEGp2a4GZhtj0uzP24DSZ1FX28NPOLZ6qBWQaoxJdjoQD9mfqgI1/R3X2hVmY0yF/4DdWBXU4c/PAd+5fQ4EXgD2AknA20Cw2/ixwBogC9gBjLCHRwAfAInAPuBJwNcedy2w2P77beCFUjF9Ddxt/90C+BI4COwC7nCb7jHgC+C/9vpvKGP7IoCP7fn3AI8APm5x/Aa8DmQCW4ChpeataBt+A14G0uxx7YCfgVQgBZgGRNrTfwK4gDwgB7gPaA0YwM+eZgHwhL3cbGAe0MQtnqvtbUgF/ln6uyu13cHAi/b0mcBie9jhdV5jf6cpwMNu8/UHfgcy7O2eAgS4jTfArcCfwC572KtAvP0drATOcpveF3jILhvZ9viWwCJ7WYfs/fE3e/rzscpTBrAE6FmqrN4PrAMKAD/3fWDHvsKOIwl4yR6+115Xjv3vdNzKoD1NN2C+/V0mAQ+VsU8nA4VAkb2ciViJ+CP2fk7GKmsR9vSH9/VEO4ZFZSxzCvBKBcfnVuDqUvtzP3CB2zHwX2Az0M1tWzbbwx8rZ7nXUqr82sOvt+dNB+YCrdzmGYZ1jGTacS/EPuYOx+E27eFtP1y2GwH/sWNPB2a5TVvRd94bWIVVdj4DZhyOtdT2nIt1bLns72aqPfw0e5kZwFpgsNs819nbmg3sBG6yhzcotawcrHpoqvu6gcFAQiXls6L6q8zyWsa2NQRm28tIt/+OdRu/gIrrjav4q954mHLqDeBGrLJdaG/zt27HfHu36Y7sh8P7AOsKTjJWnXHdCU7bGPjW3h/LserUxeXsk9YcW3ceLovXYtV3L9j7axcwsor1erl1eHnfcRmxHdlfwJlYdeMQ+3Nn/qpntgKXltpXbwFzsOrFc+1hbwDf2d/tUqCd2zyVLe+YY+WYeCud4OhKNhZYD7zqNv4V4BusgzzM/hL/7VbIM7EqDx8gBuhsj5sFvIN1wDUFlvHXQXgtfyUJZ9s7UdwOiDysg8sH60flUSAAaIt1MA93q5iKgAvtaYPL2L6PsZKOMKyCtQ2Y6BZHMXAX4A/8zd6eRlXchmLgdqzKIBhob++LQCAK64fwlbL2dQUFfQfQ0V7eAuAZe1xXrAP3THtfvGBve3lJwhv2/DFYPywD7bgOr/M9ex29sAp7F3u+vlgVq5897WbgzlIHwHys8hBsD7sS6wD3w6oADgBB9rh7scpUJ0Ds9TUup/Lpg1V5DLBjvsbeZ4Fu+28NVpIRXHqfYiU3V9l/hwKnlbWfyyiDYVgVxj1AkP15QDn79TGO/jG8HtiOVTZDga+AT0qt92OsMlRW+bwS6wC/F+sqgm+p8Q8DP7p9Ho71Y+HvHg9WIvasPew5rKuBlSUJpcvvhfa2dLGHPQIssadvglV5j8c6Vu6y569qkvAd1o98Q3v+QZV951jlfA9/HZ/jscp8mRUfx/5ox2BV9qOw6odh9ucoe/xorB8FAQYBuUCfspZVVqVbxvp241Y+qbz+KrO8lrFdjYGLgRCssvk5RydZC6i83jjb3qcv2d9befXGUdtYznF6ZBp7HxQDj9vf0Sh7PzY8gWln2P9C7LjjOfEkoQj4O1aZuhkrOT38G1NRvV6VOvyoOqiM2Iy9nOH2NvS3hzewP1+HdXz1wUpEurntq0zgDKyyE2QPS8P6rfXDSlpmHMfyqi1JyMHKUgzWpc7DZ7+CldG4Zy6n89cZ5DvAy2UssxnWD4/7FYcJwC9lVNCCdZZ1tv3578DP9t8DgL2llv0g8B+3iumYszO3aX3tOLq6DbsJWOAWx5HCYw9bhpV5V2Ub9pa3bnuaC4HVpfZ1ZUnCI27jbwF+sP9+FJjuNi4EK+Mv64zAByvR6lXBwRVbapsvK2cb7gRmljoAzqlku9MPrxsrux1b0cHk9vkt4IlS02zlrx+U3cD1ZZTfw0nCIqyz/SalpjlqP5dRBie4f0+VbNtjHP1j+BNwi9vnTlgVlJ/bettWsswrgB+xjrVU4AG3cXH28mLtz9M4Ool/DCsZiMM6jvzt/1tSeZJQ+tj6HjuBditHuViX8a8G/nAbJ1hnhZUmCUBzrLPyhmXEUe53jvXDVvr4XELVk4T7sRM2t2FzgWvKmX8W8H9lLcseNpXKk4Tr3T5XVn+VWV6rUAZPAdLdPi+g4npjhtu4BpRTb5S1jeUcp0emsfdBHkcfW8n8laBXaVqsuroI6OQ27mSuJGx3mzbEnjaaSur1MtZTVh1+fVnTltpfD2IluD3chv8N+LXUtO8A/3LbVx+X8X287/Z5FLDlOJZXaZJQ1XsmFxpjfhSRQcCnWGcNGViZVAiwUkQOTyv2FwpWRTSnjOW1wqqsEt3m88HKeo5ijDEiMgPri1oEXI5VuR1eTgsRyXCbxRf41e3zMct004S/zkYO24N1hnHYPmPvUbfxLaq4DUetW0SaAq8BZ2Fl/D5YP5jHw71Vdi7WGQZ2TEfWZ4zJFZHUcpbRBCsL3XG86xGRjlhnG/2wvns/rLMhd6W3+x7gBjtGA4TbMYBVRiqKw10r4BoRud1tWIC93DLXXcpErLOULSKyC5hsjJldhfUeT4ylteDY8uWHVRkdVlHMGGOmAdNExB+rUpomIquNMXONMXtFZBFwpYhMscefVcYy9orIduBp4E9jTLxbuS1P6bhaAa+KyItuwwTreCld/oyIVLhdbloCacaYso6Fir5zQ9nHZ1W1Ai4RkTFuw/yBXwBEZCTwL6wzcB+s8r7+OJZfFvd9Uln9VaXyKiIhWLeFRmBdiQEIExFf81ej56rWG4cqqDdOVKo5uo2O+/qrOm0U1nFTbv16nI7sD7uuxF5PIyqo16tYh1clrjuxfvDdy1MrYECp8uCHdSu6omWX991WZXmVOq5HII0xC7GyjxfsQSlYmV83Y0yk/S/CWA2swNqgdmUsKh4rW2viNl+4MaZbOaueDowXkVZY2feXbsvZ5baMSGNMmDFmlHvYFWxSClZ22sptWBzWfajDYuTo2jQO6+ylKttQet3/tof1NMaEY11KlgqmPx6JWLeDAOtRPKzLkGVJAfIp+7upzFtY95072NvwEEdvA7hth4ichXXGdinWmWIk1iWzw/OUV0bKEg88Ver7DjHGTC9r3aUZY/40xkzAuoT4LPCF3eivsv1+PDGWtp9jy1cx1j3mI6FVZUHGmCJjzOdY9zu7u436COtM/mKs42FVOYv4GOuWyTFPRJS3ylKf47Euu7rv/2BjzBKs8tfy8IT2MdPSbd5DWD+yh7k/XRAPNBKRyDJiqOg7T6Ts47Oq4rGuJLgvu4Ex5hkRCcSqZ14Amtnldg5/lduyvrOKtvEw9/kqrL8qKK+l3YN1hWqAfUyebQ+vNAvk2O8thPLrjdLxH5ZL5dt9sg5iHTexbsNaljPtyaisXq+sDoeqHc+XABeKyJ2l1r2wVHkINcbcfJzLPp7lVepE3pPwCjBMRE4xxriw7l2/bGdYiEiMiAy3p/0AuE5EhoqIjz2uszEmEavxzIsiEm6Pa2dfqTiGMWY1ViF5H5hrjDmcGS0DskTkfrGeT/cVke4icmpVNsTOsv8HPCUiYXYScjd/XakA6wC9Q0T8ReQSrPuxc453G2xhWLduMkQkBus+s7skrPuSJ+ILYIyIDBSRAKzLlGVWEvb39iHwkoi0sPfb6XbFWJkwrHvPOSLSGet+XmXTF2N9f34i8ijWlYTD3geeEJEOYukpIocrqdL74z1gkogMsKdtICKjRSSsCnEjIleKSJS9/YfLUIkdm4vy9/1sINp+bDDQLisDqrJOrAT3LhFpI9ajw08Dn5kqPv0gItce3ka7jI3Eani41G2yL7EqzMlYCUN5PgPOwyrzJ+Jt4EER6WbHFmEfE2C1KegmIuPsVt13cPSPxRrgbLHe/RGB2xNS9rH0PfCmiDS0j7XDP3QVfee/Y5WtO0TET0TGYd2brar/Yh0zw+1jIEisRxtjsa5WBGL/ONn73f2R2CSgsb0t7ts4SqxHT6OxzhYrUmH9VUF5LS0M62QtQ0QaYV39qKovgPNF5Ey73nicin8Xyqqj1gCX2/GPwLoVVK3suvor4DERCbHrnqtrYD2V1euV1eFVtR8YilV2b7GHzQY6ishV9jHgLyKnikiXE1xHtSzvuJMEY8xBrDORwy/AuB+rMdMfIpKFde+0kz3tMqxGEy9jnT0u5K+zqquxDsRNWJdrvsC6N1me6VitOT91i6UEGIN1D24X1hny+1itU6vqdqwzgJ1YLV4/xfoBPWwp0MFe9lPAeGPM4ctxx7sNk7Eaj2RiVapflRr/b+AREckQkX8cxzZgjNlob8sMrLODbKz7eQXlzPIPrEuny7EavjxL1crDP7Bu+WRjVeCfVTL9XKwfgG1Yl4LzOfqS2UtYP1rzsJKPD7AaV4F1H/sje39caoxZgdUmZQrW/t6OdX+xqkYAG0UkB+uJi8uMMfnGmFys7/Y3e12nuc9kjMnGaqw0BuvS3p/AkCqu80Osy3uLsMpoPtb3VFVZWFdr9mL9UDwH3GyMWewW3yH+ShSmlbcgY0yeMeZHY0zecazfff6ZWOVkhn2sbwBG2uNSsM6OnsFqN9EBqzX94XnnY5WVdVi3p0pfNr8K66reFqxye6c9X7nfuTGmEBhnf07Hugdb+piqaHvisZ6+eggrGYjHqvR97O/8DqyymY5V5r9xm3cLVp200y4zLbC+57VY96XnUcmxUYX6q8zyWsaiXsE6ZlKAP4AfjmMfbMR6GulTrHojHastSXk+ALra2zzLHvZ/9nZkYLWfmVXezCfpNqx9cwBrX0+n/PrtZFRUr1dWh1eZMWYvVqJwv4jcYJe584DLsJKIA1jHW1VO3spafrUs73BrTlUGEbkWq7HLmU7Hcrzss9YMrNsCu5yORymlqpOIPAtEG2OucToWT1YnX8usToyIjLEvxTXAupe6HuusRiml6jUR6WzfjhQR6Y/VsHOm03F5Ok0SPMtYrMtK+7Eu915m9FKRUsozhGFd3j+EdRvoRax33KgapLcblFJKKVUmvZKglFJKqTJpJyMOaNKkiWndurXTYSilVL2ycuXKFGNMlNNxeBNNEhzQunVrVqxY4XQYSilVr4jI8bxRU1UDvd2glFJKqTJpkqCUUkqpMmmSoJRSSqkyaZKglFJKqTJpkqCUUkqpMmmSoJRSSqkyaZJQARH5UESSRWRDOeNFRF4Tke0isk5E+tR2jEoppVRN0SShYlOxumstz0isPhI6ADcCb9VCTEop5XXyi0qcDsEr6cuUKmCMWSQirSuYZCzwsd2J0h8iEikizY0xibUSoFJKebj0Q4VM+Xw9H2w54HQoXkmThJMTA8S7fU6whx2TJIjIjVhXG4iLi6uV4JRSqj7aeTCHj5bsZkN8JisTMpwOx6vp7YaTI2UMK7NbTWPMu8aYfsaYflFR+upxpZQqLTOviPcW7eScFxfy0e97WJmQQcGODPoHBTJz0ulOh+eV9ErCyUkAWrp9jgX2OxSLUkrVK8YYFv2Zwuq96Ww9kM2Pm5MoKrHOs2bceBoNDhUTGhpAmzYNHY7Ue2mScHK+AW4TkRnAACBT2yMopVTl0g4VMub1xezLyDsyLLIYdszbxTUjO3Ja28YORqcO0yShAiIyHRgMNBGRBOBfgD+AMeZtYA4wCtgO5ALXOROpUkrVH5v2ZzHqtV8BaBEZxKXNGvLMQz+zPjmXO+7oz+OPDHI4QnWYJgkVMMZMqGS8AW6tpXCUUqreSztUyKXv/A7AvcM7kbwgnruu/5ZTT23B93OuoHfv5g5HqNxpkqCUUqrGGWN445ftvDBvGwCPDu/M9UPasbtNE5o1C+Wmm/ri66tt6esasU6GVW3q16+fWbFihdNhKKVUjVu1N523F+xg3qakI8N8Eg/R6WAR38+54riWJSIrjTH9qjtGVT69kqCUUqra/bY9hS9WJjBz9T4AYiODMfHZLHlnLe1aR3LnG6McjlBVhSYJSimlqk1xiYvJ327ikz/2ANA7LpJxrZpwx5WzOHSokH8+dBYPPngmwcH+DkeqqkKTBKWUUtUiOSufi99eQnxaHl2bh/Pi+J50iYkgMzOf2ee1Y/LkwXTu3MTpMNVx0FYiSimlTlp2fhFnPPsz8Wl53DG4PV0Tcrn8/BkUFZUQERHEZ5+N1wShHtIrCUoppU5YYbGLl+Zv491FO3AZ6BoZwis3ziE+Pou//70PBQUl+Pv7Oh2mOkGaJCillDpuBcUlvP/rLmYs30t8Wh49W4ST/ccBvn92Bd27N2X69Is54wztzK6+0yRBKaVUlRljWLknnTs/W0NCeh7+vsKbV/RhWOemDJoxleeeO5c77zxNrx54CE0SlFJKVdk9/1vLV/ZjjUNbNWLvzO2c/o9I/P19Wbz4enx8yuocV9VXmiQopZQ6RonLsDkxi7UJGaRkF5KeW8jWA9n8vjOVXjERhG5K58Nb5hEbG86uXRk0bBisCYIH0iRBKaXUERv3Z/LBr7uOXC04LCzIjxaRwZzdJIwfnvydtNQ87r77NCZPHkJoaIBD0aqapkmCUkopthzI4qZPVrInNReAdlENGNWjORf1jqFloxD8fX0wxnD++dNp26Yh8+ZexSmnRDsctappmiQopZSXyy8q4d7P17EnNZd/nNeREd2jad80zBqXX8xTTyzi6qt70bZtQ6ZNG0d4eKDeWvASmiQopZQXO5hdwDkvLiA7v5grBsRx2zkdjoybP38Ht9wyh+3b0wgPD+Tuu08nMjLIwWhVbdMkQSmlvNT36xO5+39rySsqYXSP5jw+tjsABw7kcPfdc5k+fQMdOjTixx+vYujQtg5Hq5ygSYJSSnmhFbvTuOXTVYT4+/LWFX0Y2aP5kXHPPfcbX365mcceG8T9959JUJD+VHgrMcY4HYPX6devn1mxYoXTYSilvFB+UQkfLN7F83O3ArDsoaE0DQ9i9epEjIE+fZqTkZFPcvIhOnZs7HC0RxORlcaYfk7H4U00PVRKKS/xxcoEHv92I1n5xQDMvGUgwSLcddcPvPbaMoYMac2PP15NZGSQtj1QgCYJSinl8XanHOLpOZuZtymJ2IbBvHLZKZzRrgnffbuNMXd8z/792dx0U1+efnqo06GqOkaTBKWU8jDGGHILS1i5J51/f7+FzYlZADQJDeCDa06lU3QYX365ifHjP6dXr2Z88cWlnHZarMNRq7pIkwSllPIQM1cn8MqPf5KWU0h2gXVLISLYn0dGd6F7TAR9W0by559pEB3GBRd04v33x3DNNafg5+fjcOSqrtIkQSml6rG18Rn8tCWZmasTiE+zemU8q0MU/ds0olFIAOd1a0ZkSACLF++l9wWfkZKSy44dd9CgQQATJ/ZxOnxVx2mSoJRS9VBSVj5vLdjB1CW7AWjfNJTBnaJ49bLeRAT7H5kuNTWXG+74gQ8+WE3LluG8++4YGjTQvhZU1WiSoJRS9UReYQn/WbKLHzYcYP2+TIyBqLBAPr1hAB2ahR0zfXx8Jn36vEt6eh733juQRx8dpJ0xqeOiSYJSStUDWflFTJy6nOW702nbpAET+sdxzemt6RR9bHKQlVVAeHggsbHh3HBDbyZM6EHPns0ciFrVd5okKKVUHTdr9T6e/G4TqYcKufCUFrz8t1MQObaDpby8Ip566lemTFnG6tU30aZNQ/7973MdiFh5Ck0SlFKqDiosdrFo20G+W5/IzNX76NYinA+vPZWesZFlTj937nZuuWUOO3emc9VVPfW2gqoWmiQopVQdsjc1l2lL9/D5ygTSDhUiAo0aBPD6hN60jQo9ZnqXy3DllV8xffoGOnVqzM8/X82QIW0ciFx5Ik0SlFLKYYXFLuZtOsCMZfEs3p6CCPSKjeRfY7oyons0gX6+x8xjjEFE8PERmjcP5fHHB3PffWcQGKjVuqo+WpqUUsohOw/mMGN5PF+uTCD1UCExkcHcdW5HLj01luYRweXOt3Llfm6++TteeWUEAwe25MUXh9di1MqbaJKglFK1KLewmHkbk5i+bC9Ld6Xh6yOc26Upl/WP4+wOUfj6HNsg8bCsrAL++c+fmTJlOU2bNiAzM78WI1feSJOESojICOBVwBd43xjzTKnxccBHQKQ9zQPGmDm1HqhSqk5LzMzj5fnbWLQthQNZ+cQ1CuHe4Z24pG8sTcMr73Fx1qwt3HrrHBITs7nlllN58slztKdGVeM0SaiAiPgCbwDDgARguYh8Y4zZ5DbZI8D/jDFviUhXYA7QutaDVUrVSbtSDvHvOZv5aUsyJS6Dr4/w9pV9OK9rND4VXDUo7c8/U2nWrAGzZv2NU0+NqcGIlfqLJgkV6w9sN8bsBBCRGcBYwD1JMEC4/XcEsL9WI1RK1UnxablMX7aXdxbtpMRlGN2zOVef1oo+rRri71t5h0qFhSW8+OISOnRozPjxXbnrrtO5667TtTMmVas0SahYDBDv9jkBGFBqmseAeSJyO9AAKPPNJSJyI3AjQFxcXLUHqpRyXonLsGBrMv/9Yw8Lth1EgHM6N+OmQW05tXWjKi9n0aI9TJo0m82bU7j55n6MH99VkwPlCE0SKlbWtUBT6vMEYKox5kUROR34RES6G2NcR81kzLvAuwD9+vUrvQylVD2WW1jMf37bzadL97IvI4+mYYHcPqQ9l/WPo0Vk+U8plJaSkst9983nP/9ZQ+vWkcyePYHRozvWYORKVUyThIolAC3dPsdy7O2EicAIAGPM7yISBDQBkmslQqWUo5btSuOfszawNSmbM9s34ZHRXTi3a7Mq3VIobeHC3XzyyToeeOAM/vnPQYSE+Fc+k1I1SJOEii0HOohIG2AfcBlwealp9gJDgaki0gUIAg7WapRKqVq1Jj6D37anMH9TEmviM4gM8eeFS3oxvm/scS9r48Zk1q1LYsKEHowb14Vt226jTZuGNRC1UsdPk4QKGGOKReQ2YC7W440fGmM2isjjwApjzDfAPcB7InIX1q2Ia40xejtBKQ+z82AOK/aks3xXGl+uSsBloE2TBozrE8MTY7vT4DjfdJibW8QTTyzkhRd+Jzo6lHHjuhAY6KcJgqpTNEmohP3Ogzmlhj3q9vcm4IzajkspVfP2Z+Qxe91+vlm7nw37sgAI9vflsv5x3De8E5EhJ9aJ0pw5f3LrrXPYvTuDa689heefH6avU1Z1kpZKpZRyk5pTwJwNB/h2zX6W7U4DoGdsBI+M7sI5nZvSIjKYIP9j+1Koqm3bUjn//E/p3LkJCxZcw6BBraspcqWqnyYJSimvl51fxLyNSXyzdj+Lt6dQ4jK0bxrK3cM6MqZXC9o0aXBSyy8udrFw4W6GDm1Lx46NmTPnCs45pw0BASeebChVGzRJUEp5pfyiEn7Zksw3a/fz05ZkCotdxEQG8/ez2nJBrxZ0aR6GSNXfiFie5cv3MWnSd6xalcj69TfTvXtTRoxoXw1boFTN0yRBKeU1ikpcLN6ewrdr9zNvYxI5BcU0CQ1gwqktueCUFvSJa1gtiQFAZmY+Dz/8M2++uZzo6FD+97/xdOsWVS3LVqq2aJKglPJoLpdh+e40vlm7n+83HCDtUCFhQX6M6hHNBb1iOK1tI/xO4J0GFSkqKqFv33fZtSuD227rz5NPnkN4eGC1rkOp2qBJglLK4xhj2LAvi2/W7mP2ukQSM/MJ8vfh3C7NuKBXCwZ1iiLQr/rbAyQkZBETE4a/vy+TJw+mU6cm9OvXotrXo1Rt0SRBKeVRXC7Dw7M2MH3ZXvx8hEEdo3hgZGfO7dLsuN9lUFUFBcU8//wSnnrqV6ZOHcvf/tadK67oWSPrUqo2eU2SICIBQJwxZrvTsSilakZmXhH3/G8tP25OIjzIj0X3DTnhdxlU1YIFu7n55u/YsiWFSy7pyllntarR9SlVm7wiSRCR0cBLQADQRkROAf5ljLnI2ciUUicjOSufHzYe4KfNySRl5bPlQDYAVwyI457zTvxlR1V1333zef75JbRpE8mcOZczcmSHGl2fUrXNK5IE4HGsLp5/ATDGrBERfQZJqXroQGY+329I5Pv1B1i+Jw1joF1UA9pFhXJG+yYM7dKUge2a1Nj6XS6Dy2Xw8/NhwIAYHnroTB5++GztjEl5JG9JEoqMMRmlHm3S/hWUqge2J+cwc3UCy3enk5yVz+7UXAA6NQvjzqEdGdUjmg7NwmollvXrk5g06TsuuKAj999/Jhdf3JWLL+5aK+tWygnekiRsFpFLAR+7R8f/A/5wOCalVDnyi0r4aXMyT8zexIGsfHx9hJ6xEXSPiWB831hGdG9O+6ahtZar+wEAACAASURBVBbPoUOFPP74Ql566Q8iIgKJje1Xa+tWyknekiTcBjwKuICvsHp1fNDRiJRSRzHGsGJPOl+tSmD2ukSy84sJC/JjXO8Y7h/ZmWbhQY7EtWDBbq69dhZ79mQycWJvnn32XBo3DnEkFqVqm7ckCcONMfcD9x8eICLjsBIGpZTDXC7D+LeXsGpvBsH+vozsHs24PrGc3q4xvj7V8wbEExUc7Ed4eCC//nodZ54Z52gsStU2Mcbzb82LyCpjTJ9Sw1YaY/o6EU+/fv3MihUrnFi1UnVKYbGLFXvSePTrjWxPzmF831gmX9Ctxt5nUBXFxS5ef30p+/Zl88IL5wFWEuPjcLKijtTbeq+nFnn0lQQRGQ6MAGJE5CW3UeFYtx6UUrVsd8ohFm47yKJtB/l9Zyq5hSUE+Pnwz/O7ct3A1o7+GC9dmsBNN81m7dokxozpSHGxCz8/H00QlNfy6CQBSAY2APnARrfh2cADjkSklJfJKShmyfYUFv15kEXbUtibZj2dENcohHF9Yji7QxSnt2tMWJBzjxBmZOTz4IM/8s47K2nRIowvv7yUiy7qXG2dPSlVX3l0kmCMWQ2sFpFpxph8p+NRyltk5xfx2fJ4Zq9LZMO+TIpdhpAAXwa2a8wNZ7Xh7A5RtG7SwOkwj8jMzGfatPX83/8N4PHHhxAWpp0xKQUeniS4iRGRp4CuwJEm0saYjs6FpJTnMMaQkJ7H8t1pfLkqgd+2pwLQoWkoN5zVlrM7NqFfq0YE+FVvb4snY9u2VD75ZC2PPz6EVq0i2b37Tho1CnY6LKXqFG9JEqYCTwIvACOB69A2CUqdMGMMGblFzF63n993prJidzrJ2QUANG4QwLUDWzOsazMGtmtc5y7Z5+cX8+yzi3n66cUEBflx3XW9adu2oSYISpXBW5KEEGPMXBF5wRizA3hERH51Oiil6osSlyEhPZc/k3KYvymJ7zckkpVfDECLiCBOb9eYfq0a0rdVIzpFhzn+2GJ5fvppJ7fcModt21KZMKE7L700nOjo2nspk1L1jbckCQVinc7sEJFJwD6gqcMxKVWnGGPYnJhN2qFC0nML2XEwh+3J1r9dKYcoKLYuvjUI8GV4t2g6RYfRr7WVGNQH+fnFXHXVTBo0CGDevCsZNqyd0yEpVed5S5JwFxAK3AE8BUQA1zsakVIOc7kMO1NyWBufydqEDBZsPXjkyQMAEYhtGEz7qFDO6tCE9k1Dad80jK7NwwkO8HUw8qpzuQzTp6/nkku6ERTkx9y5V9KhQ2OCgryl6lPq5HjFkWKMWWr/mQ1cBSAisc5FpFTtMsaQnF3A1gPZrEvIYM76A+xNyyWnwLpl0CDAl24xEfRv04iL+8TSsIE/rRo1qDfJQFnWrj3ApEnf8ccfCbhchquu6kWPHs2cDkupesXjkwQRORWIARYbY1JEpBvW65nPATRRUB4nKSufL1YmUFjsIiu/iI37stialE1mXtGRaXx9hNPbNmbsKS3o1TKSdlGhdbYdwfHKySnksccW8Morf9CoUTAff3whV17Z0+mwlKqXPDpJEJF/AxcDa7EaK87E6gHyWWCSk7EpVd2KS1x8/PseXpq/7cgVgkA/H7q1CGd0z+Z0ahZGx2ZhdIoOo1GDAIejrTkTJnzJ7Nnb+Pvf+/DMM+fqUwtKnQSP7rtBRDYBfY0xeSLSCNgP9DLGbHUyLu27QVWXohIXy3alMXfjAeZuPEBSVgGDOkbx+NhutGpcd15WVNP27s0kLCyAhg2DWbUqkfz8YgYObOl0WKqaad8Ntc+jryQA+caYPABjTJqIbHE6QVDqZBSXuI683jgxM4+lu9LIyC0iyN+HQR2juLhPLMO6Nqtz7yaoKUVFJbz66lL+9a8FXH/9Kbz++ij69GnudFhKeQxPTxLaisjh7qAFaO32GWPMOGfCUqpimblF7E49xO7UQ+xIzmH7wRx2JB9iV8ohCktcNAjwpVlEEEM6NWV4t2gGdYyq140MT8SSJfFMmjSb9euTGTOmI//4x0CnQ1LK43h6knBxqc9THIlCqUqkHypkxZ50lu9OY+bqfRy0314I4CPQqnED2kU1YHDnKPrGNWRwp6Z16hXHte2tt5Zzyy1zaNkynFmz/sbYsZ2dDkkpj+TRSYIx5ienY1CqNJfLsC8jjxV70li+O53lu9L4MzkHgABfH3rGRnBa28aM6dmc1k0a0KpxCIF+3nWVoCzGGLKzCwkPD2TkyA7ce+9AHn10EKGhntsIUymneXTDxbpKGy56j6z8Ir5es5+Vu9PYlpRDcnYB6bmFlLis4y400I++rRrSv00jTm3diJ6xEQT5a0JQ2tatKdx883cEBvoxZ87lXtPmQh1NGy7WPo++klAdRGQE8CrgC7xvjHmmjGkuBR4DDLDWGHN5rQapHJeVX8Te1Fz2puUSn2b9v2F/FlsSsygodtE8IohO0WH0ahlBw5AAWkQG0zsuks7R4R7zfoKakJdXxL//vZhnn/2NkBB/nnlmqNMhKeVVvCpJEJFAY0xB5VMemd4XeAMYBiQAy0XkG2PMJrdpOgAPAmcYY9JFRPuE8EDJWflk5BVxqKCY5OwCEjPySMzMZ19GHgnpeaxLyMDldlEuMsSflg1DGNWjOdef0YYesRHOBV9PbdiQzIUXzmDHjnSuuKIHL754Hs2aaWdMStUmr0gSRKQ/8AFWnw1xItILuMEYc3sls/YHthtjdtrLmQGMBTa5TfN34A1jTDqAMSa5uuNXtc8YQ0K61W7gq1X7+PXPlGOmCfDzoXlEEM0jgrjqtFac3q4xLRuF0LJRCOFB/g5E7RmMMYgILVuGExsbzjvvnM/QoW2dDkspr+QVSQLwGnA+MAvAGLNWRIZUYb4YIN7tcwIwoNQ0HQFE5DesWxKPGWN+OOmIVa1yuQzbD+awck86S3emsmRHKsn2EwZNQgO5Z1hH2kQ1ICTAl6jQIJpHBtG4QYDeG69GJSUu3nlnJdOnb+Dnn68mIiKIBQuudTospbyatyQJPsaYPaUq9JIqzFfWL0Dplp5+QAdgMFZfEL+KSHdjTMZRCxK5EbgRIC4urophq5qSU1DM2vgMVu5JZ+WedFbtTSc733qVcaMGAZzVoQn9WlndIHeKDtN2AzVs9epEJk36jmXL9jF0aBsyMvKJivKeN0YqVVd5S5IQb99yMHY7g9uBbVWYLwFwf7drLNarnUtP84cxpgjYJSJbsZKG5e4TGWPeBd4F6+mGE9oKdUKMMcSn5bFyb5qdFGSw9UAWLmN1h9yxaRjn92xB31YN6duqIa0bh+gVglqSl1fEQw/9xGuvLaNJkxCmTRvHhAnddf8rVUd4S5JwM9YthzggCfjRHlaZ5UAHEWkD7AMuA0o/uTALmABMFZEmWLcfdlZT3OoEGWOYs/4AP21J4vcdqSRm5gPWI4e94yI575wO9GnVkFNaRhIRrO0HnOLv78vChXu48cY+PP30UBo21M6YlKpLvCVJKDbGXHa8MxljikXkNmAuVnuDD40xG0XkcWCFMeYbe9x5dmdSJcC9xpjU6gxeHZ+M3EIe/Go93284QGSIP2e0b8KprRoyoG1jOjbTWwdO2707g0cf/YVXXx1Bw4bBLFkykaAgb6mKlKpfvOJlSiKyA9gKfAZ8ZYzJdjIefZlS9SosdrFyTzrzNyWRkVvIvE1J5BWVcO/wTtx4Vlt8NCmoE4qKSnjppd+ZPHkhPj7C119fpk8tqOOiL1OqfV6Rvhtj2onIQKzbBZNFZA0wwxgzw+HQ1EnalpTNdf9Zzr6MvCPDxvRqwa1D2tE5OtzByJS7xYv3MmnSbDZuPMhFF3Xm1VdH0LKlvjtCqbrOK5IEAGPMEmCJiDwGvAJMAzRJqIdScgqYtzGJ+ZsOsGJPOocKinn1slM4rW1jmoYFaqO3OujZZ38jO7uQb765jDFjOjkdjlKqirwiSRCRUKyXIF0GdAG+BrRf2Xokt7CY+ZuSmLnaerFRicsQFRbI2R2ieGBkZ1o2CnE6ROXGGMNHH63l7LNb0bZtQ95/fwyhoQE0aKCdMSlVn3hFkgBsAL4FnjPG/Op0MKpqDmYX8Nv2FJbuSuPrNfvILSwhJjKYm85uy7CuzegeE4G/r/d2l1xXbd58kEmTvmPRoj3ce+9AnntumL5OWal6yluShLbGGJfTQaiKJWXl88fOVLYlZZOaYzVATDtUSICfDwPaNOKWwe0Z0KaRNkSso3Jzi3jqqUU8//wSQkMDeO+9MVx/fW+nw1JKnQSPThJE5EVjzD3AlyJyzGMcxphxDoSlbMUlLn7bkcrPm5PIyCvi+w0HKCx24ecjNGoQQIemodw3ohM9YiIJ8NMrBnXdM88s5umnF3P11b14/vlhNG2qb0xUqr7z6CQB65FHgCmORqGO4nIZXv95O5/8sZuUnEJCAnyJCPZndI/m3HBWGzo0DdOkoJ7Yvz+btLQ8undvyj33nM4557Rh8ODWToellKomHp0kGGOW2X92McYclSjYL0n6qfaj8m6b9mfx0Mz1rInPoGOzUJ68sDtDOjcl0M/X6dDUcSgpcfHmm8t5+OGf6dy5CUuX3kBERJAmCEp5GI9OEtxcz7FXEyaWMUzVkKSsfF6ev41Za/ZR4jL8a0xXrjm9tbYvqIdWrtzPTTfNZuXKRM47rx1vvDFKHztVykN5dJIgIn/DeuyxjYh85TYqDMgoey5VHUpchjXxGWTmFbI9OYc3F+wgv6iE0T1acPd5HYmJ1Hf010c//7yLYcM+oWnTBsyYcTGXXtpNEwSlPJhHJwnAMiAVq/fGN9yGZwOrHYnIw+UXlbBg60He+GU76/dlHhl+WttGPHVRD9pF6aNw9Y0xhn37somNDeess+KYPHkwt93Wn8jIIKdDU0rVMK/ou6Gu8cS+G1JzCvj391uYsz6R3MISosODuPu8jnRsFkajkADiGuvLjuqjnTvTue22OaxalciWLbdpYqAcpX031D6PvpIgIguNMYNEJB1wz4YEMMaYRg6F5jGy84v4YPEu3v91F7mFxVzcJ5YLe8cwoE0j/PRFR/VWYWEJL7ywhCeeWISfnw9PPjmE0FB9W6JS3sajkwRgiP1/E0ej8EDrEjL4dOle5m48QHpuESO7R3PPeR1p3zTM6dDUSUpNzeWss/7D5s0pXHxxF159dQQxMdpZllLeyKOTBLe3LLYE9htjCkXkTKAn8F8gy7Hg6pnMvCLWJWSwJTGbHzcnsWx3GqGBfgxo04g7hnagZ2yk0yGqk1RUVIK/vy+NGgUzaFArnn9+GKNHd3Q6LKWUg7yiTYLdNfSpQBwwH/gOaGOMOd+JeOpLm4TUnAIWb0/h1z9TjrQ1AOgcHcbwbtFMPKsN4UH+DkepTpbLZZg6dQ3/+tcCFi68lrZtGzodklJl0jYJtc+jryS4cRljikRkHPCKMeY1EdGnG8qxPTmbh2duYOWedIpdhohgf0Z2b864PjG0jWpA8wh9fNFTbNyYzKRJ37F48V7OPDOOkhLt4kQp9RdvSRKKReQS4CrgQnuYngKXkl9UwjPfb2Hqkt0AjOsdw9UDW9MjJgJffemRRzHG8MgjP/Pcc0uIiAjkww8v4JprTtGXWymljuItScL1wC1YXUXvFJE2wHSHY6oziktcLPrzIPd9sZ6UnAL6xEXy+uV99IVHHkxEyMws4More/L888No0kQfUVVKHcsr2iQAiIgf0N7+uN0YU+xULHWpTUJGbiETP1rByj3pNAsP5Maz2zHxzDZOh6VqQEJCFnfdNZc77xzAGWfE4XIZvXKg6hVtk1D7vOJKgoicBXwC7MN6R0K0iFxljPnN2ciclZiZxwVTfiM1p4DbhrRn0uB2hAZ6RZHwKsXFLqZMWcY///kLxcUuRo1qzxlnxGmCoJSqlLf8IrwMjDLGbAIQkS5YSYPXZqQul+HSd37nYHYBb1zeh9E9mzsdkqoBy5fv46abZrN69QFGjmzPlCmj9OkFpVSVeUuSEHA4QQAwxmwWEa9+fdzz87YSn5bHdWe01gTBgy1YsJukpEN8/vklXHxxF+2MSSl1XLyiTYKITAUKsK4eAFwBhBhjrnEiHqfbJHy2fC/3f7mejs1CmXvn2frD4UGMMXz22UaCg/0YO7YzRUUl5OUVEx4e6HRoSp00bZNQ+7zl5fqTgB3AfcD9wE7gJkcjcoAxhvd/3cn9X64nLMiPj68foAmCB9m+PY0RI6YxYcKXvPfeKgD8/X01QVBKnTCPv90gIj2AdsBMY8xzTsfjlKISF1e8v5Rlu9II8PPhq5sHEh2hPfp5goKCYp577jeeeupXAgJ8ef31kdx8s55sKaVOnkcnCSLyEDARWAWcKiKPG2M+dDisWpeclc/5ry8mObuAdlEN+P7/zibAz1suInm++fN38uijC7j00m68/PJwWrTQTraUUtXDo5MErLYHPY0xh0QkCpgDeE2SYIxh1pp93PXZWgCuOq0Vj47pir924VzvJScfYvnyfYwe3ZHRozuwdOkN9O8f43RYSikP4+lJQoEx5hCAMeagiHjVr+Ok/65k7sYkGob40zk6nCcu7O50SOokuVyGDz5Yxf33/4jLZYiPv4uwsEBNEJRSNcLTk4S2IvKV/bcA7dw+Y4wZ50xYNe/PpGzmbkwCYOF9Q7S3Rg+wfn0SkyZ9x5Il8Qwa1Iq33hpNWJg2SlRK1RxPTxIuLvV5iiNR1LLkrHwmvLcUgBk3nqYJggfYvz+bfv3eIywsgKlTx3L11b30yRSlVI3z6CTBGPOT0zHUts2JWYx89VcAXvnbKZzWtrHDEamTsW5dEj17NqNFizD+85+xDB/ejsaNtTMmpVTt8Kp79J7uu3WJRxKE87o248Leep+6voqPz+Siiz6jV6+3Wb58HwCXX95DEwSlVK3SJKESIjJCRLaKyHYReaCC6caLiBERRx5Qz8gt5NZPrRfoPDOuB+9erc/J10fFxS5eeul3unR5g7lzt/Pss+dyyinRToellPJSHn27oTQRCTTGFBzH9L7AG8AwIAFYLiLfuPcDYU8XBtwBLK3OeI/HnZ+tAeCOc9pzWf84p8JQJ8EYw+DBU/ntt3hGj+7AlCmjaN060umwlFJezCuuJIhIfxFZD/xpf+4lIq9XYdb+wHZjzE5jTCEwAxhbxnRPAM8B+dUV8/FIySng9x2pANx5bkcnQlAnISurAGMMIsK1157Cl19eyrffTtAEQSnlOK9IEoDXgPOBVABjzFpgSBXmiwHi3T4n2MOOEJHeQEtjzOyKFiQiN4rIChFZcfDgweOJvVIzV+2joNjFF5NOx8dHW7zXF8YYPv10PR06vM7//rcRgBtu6MO4cdpbo1KqbvCWJMHHGLOn1LCSKsxXVk19pNtM++VMLwP3VLYgY8y7xph+xph+UVFRVVh11ayJz+CVH7fROTqMvq0aVttyVc3ati2VYcM+4YorvqJ160g6dWridEhKKXUMb2mTEC8i/QFjtzO4HdhWhfkSgJZun2OB/W6fw4DuwAL7zC8a+EZELjDG1Hhf0IXFLm74aAW+PsL71/TTs8964vXXl/KPf8wnONiPN98cxY039sVXX5WtlKqDvCVJuBnrlkMckAT8aA+rzHKgg4i0AfYBlwGXHx5pjMkEjpwCisgC4B+1kSAAPD57Iyk5BTw/viexDfXRuLrucLuD6OhQxo3rwssvDyc6OtTpsJRSqlxekSQYY5KxfuCPd75iEbkNmAv4Ah8aYzaKyOPACmPMN9UcapXFp+Xy3z/2claHJozvG+tUGKoKkpJyuPvuefTo0ZQHHjiTSy7pxiWXdHM6LKWUqpRXJAki8h5ubQkOM8bcWNm8xpg5WL1Hug97tJxpB59giMdt7Bu/AXDf8M56m6GOcrkM7767kgce+JHc3CK6dau+tihKKVUbvCJJwLq9cFgQcBFHP7VQr8xYtpe0Q4UAdI8JdzgaVZaNG5OZOPEbli7dx5AhrXnzzdF07qyNE5VS9YtXJAnGmM/cP4vIJ8B8h8I5KWvjM3jgq/UAzLvrbL2KUEfl5BSyZ08mn3xyEVdc0UO/J6VUveQVSUIZ2gCtnA7iRDwx23rZ49TrTqVjszCHo1HuZs3awurViUyePIQBA2LZtev/CAry1kNMKeUJvOK5KxFJF5E0+18G1lWEh5yO63hl5RexOj6D24a0Z3Cnpk6Ho2x79mRwwQXTueiiz/j6663k5xcDaIKglKr3PL4WE+s6by+sRxgBXMaYYxox1gfzNiZR4jKc3VEbwNUFRUUlvPzyH0yevBCA558fxv/93wD8/X0djkwppaqHxycJxhgjIjONMX2djuVkffz7bgB6x+k7/euCxMQcJk9eyLnntuX110cSFxfhdEhKKVWtvOJ2A7BMRPo4HcTJyCkoZl1CJp2aheGvb+dzTFpaHq+++gfGGOLiIli//ma+/voyTRCUUh7Jo68kiIifMaYYOBP4u4jsAA5h9clgjDH1JnFYF58BwAMjOzsciXcyxvDf/67jnnvmkZaWx+DBrenVK5q2bbW/DKWU5/LoJAFYBvQBLnQ6kJO1ZEcqPgI9Y/WMtbZt2ZLCzTd/x4IFuznttFjmzx9Nr17RToellFI1ztOTBAEwxuxwOpCTYYxhzvpETmvbmMahgU6H41WKi12MHDmNjIx83n57NH//e1/tjlsp5TU8PUmIEpG7yxtpjHmpNoM5UduSctiZcojrz2zjdCheY8GC3Qwc2JKAAF8+/XQcbds2pFkz7YxJKeVdPL0FnC8QitWlc1n/6oU56xMRgeHd9BJ3TUtMzGbChC8ZMuQj3ntvJQCnn95SEwSllFfy9CsJicaYx50O4mR9sHgX/Vs3IipMbzXUlJISF++8s5IHH/yJgoJiJk8ezMSJ9aZdq1JK1QhPTxLq/c3jHQdzyCkopk2TBk6H4tFuvPFbPvxwDeee25Y33xxFhw6NnQ5JKaUc5+lJwlCnAzhZfyblAHBul2YOR+J5srMLMAbCwwOZNKkfQ4e2ZcKE7toZk1JK2Ty6TYIxJs3pGE7WrNXW26TbROmVhOpijOHLLzfRpcsb3Hef1RnoqafGcPnl2lujUkq58+gkob4rLHbxw8YDxEQG0y5KG85Vh1270jn//OmMH/85UVENuO66U5wOSSml6ixPv91Qr7384zYAffSxmnz11WauvPIrfHyEl146j9tvH4Cfn+bJSilVHk0S6rC3FljvgJqoScJJKSoqwd/fl759mzN2bGeee+5cWrbUN1cqpVRl9DSqjlqfkAnA+L6xDkdSf6Wk5DJx4teMHTsDYwytWkUyffrFmiAopVQVaZJQR70wbysAdw3r6HAk9Y8xhqlT19C58xQ+/ngdPXo0paTEOB2WUkrVO3q7oQ4yxrBw20EAYiKDHY6mfomPz+TKK2eyaNEeBg5sydtvj6ZHD318VCmlToQmCXXQartb6It6xzgcSf0THh5Iamou7703huuv762dMSml1EnQ2w110N7UXABuGtTW4Ujqhx9+2M7YsTMoKiohIiKIdetu5oYb+miCoJRSJ0mThDroj52pALTQWw0V2r8/m0sv/ZyRI6exdWsK+/ZlA2hyoJRS1URvN9RBG/Zn0jk6jPAgf6dDqZNKSly8+eZyHn74ZwoLS3jiiSHce+9AAgO1OCulVHXSWrWOMcawJyWXcX20PUJ5jIEPPljN6ae35I03RtG+fSOnQ1JKKY+ktxvqmNXxGWQXFNOlebjTodQpWVkFPPjgj6Sn5+Hn58NPP13NDz9coQmCUkrVIE0S6phpf+zF31cY3Kmp06HUCcYYPv98I507T+HZZ39j3jzrLZSNG4doZ0xKKVXD9HZDHbMuIYNBHZsSHRHkdCiO27kznVtvncMPP2ynd+9ovv76Mk49VW/DKKVUbdEkoY7Jyi+iYYg2WAS49975LF68l1deGc6tt/bXzpiUUqqWaZJQh2TkFpKUVUDrJg2cDsUxCxfupmXLCNq2bcirr45ABGJitH2GUko5QU/NKiEiI0Rkq4hsF5EHyhh/t4hsEpF1IvKTiLQ60XWt2psOQN9WDU8i4vopJSWXa6+dxeDBH/Hkk4sAiI0N1wRBKaUcpElCBUTEF3gDGAl0BSaISNdSk60G+hljegJfAM+d6PrW7M3A10foFRt5oouod1wuw4cfrqZTpylMm7aeBx88kylTRjkdllJKKTRJqEx/YLsxZqcxphCYAYx1n8AY84sxJtf++Adwwn07pxwqpGFIAMEBvicccH3zyit/MHHiN3TrFsWaNTfx9NNDCdE2GUopVSdom4SKxQDxbp8TgAEVTD8R+L6sESJyI3AjQFxcXJkzZ+cXE+IFCUJubhGJidm0a9eIiRN7ExUVwpVX9tRHGpVSqo7RKwkVK+tXy5Q5ociVQD/g+bLGG2PeNcb0M8b0i4qKKnNlG/dl0qFp6InGWi989902unV7k4su+gyXyxAREcRVV/XSBEEppeogTRIqlgC0dPscC+wvPZGInAs8DFxgjCk40ZUlZxfQqrFnPtmQkJDFxRf/j/PPn05wsB9TpozSjpiUUqqO09sNFVsOdBCRNsA+4DLgcvcJRKQ38A4wwhiTfKIrysovIqegmLAgz/tKVq1KZNCgqRQXu3j66XO4556BBHjBbRWllKrvPO8XqRoZY4pF5DZgLuALfGiM2SgijwMrjDHfYN1eCAU+ty+Z7zXGXHC86/pmjXWBYmgXz3kdc1ZWAeHhgfTs2YzrrjuFO+88jbZtve/xTqWUqq80SaiEMWYOMKfUsEfd/j63OtazaNtBmoYF0tMDHn/MyMjn4Yd/YubMLWzadCuRkUG89tpIp8NSSil1nDRJqANScwr4aUsy1w1s7XQoJ8UYw2efbeSuu+aSnHyI22/vj6+vtjtQSqn6SpOEOuDP5BxKXIZBncp+6qE+OHSokHHj/se8eTvo168Fs2dPoG/fFk6HpZRS6iRoklAHYvxHgAAAFddJREFU7E2z3sUU1yjE4UiOnzEGESEkxJ8mTUJ4/fWR3HxzP3x99cEZpZSq77QmrwPi03LxEWgRGex0KMfll1920bfvu+zcmY6IMG3aOG67rb8mCEop5SG0Nq8D9qbl0iIyGP968uOanHyIq6+eyTnnfExmZgEHDx5yOiSllFI1QG831AF7UnPrza2GDz5Yxb33zicnp5BHHjmLhx46i+Bg7WtBKaU8kSYJDisoLmFzYhZXDDjhHqZr1erVB+jZsxlvvTWaLl3qb0NLpZRSldMkwWG/bDlIQbGLge0aOx1KmQ4dKmTy5IVceGFnBg5syYsvnkdAgK/2taCUUl5AkwSHLfrzIGGBfgyug48/fvvtVm677Xv27s0kMjKIgQNbEhioRUYppbyF1vgO23XwEP/f3r1HVVnlDRz//gQVDNTE4LWs0CQFUTEvI17eQQlrvJY54qWLvt20tJVZM7ZqcsxWY6XVa9Y4zkzhjKM4WqYvTZYalXkdVFSEiRglpEwdRIWEFN3vH8/D4QBHOajnHITfZ62z1jnPZT+/sz14fmfv/ex9S2gQ/nVo0OKhQyd54ol1fPjhv4iODuWrrybRr5/r5a2VUkrVX5ok+FjxT2WEBDXxdRiVrFyZySef5PDKK7czfXofGjfWxZiUUqoh0iTBh86fN+QW/Ejn65v7OhS2bcvnxIlS7ryzA9Om9eaeeyK5+earfx0JpZRSl67utHE3QMVnyigqLeOW64J8FkNhYQmTJ6fQt++f+c1vUjHG0LixnyYISimltCXBl0rOnAMgsIn3m/ONMSxbto+nnvqU//znNE8+2YfZs+P0rgVVzdmzZ8nPz6e0tNTXoagGIiAggLZt29K4sc7B4muaJPhQeZLQzAdJwuef53Lvvavp3fsG1q2bQPfubbweg7o65OfnExwcTHh4uCaRyuOMMRQUFJCfn0+7du18HU6Dp90NPvT9iRIArm3mnYGLpaVlbNr0LQBxceGsWTOWLVv+RxMEdVGlpaWEhIRogqC8QkQICQnRlqs6QpMEH9p96AQAt910rcevtWHDAbp2/T133LGUo0d/REQYMaKjLsak3KIJgvIm/bzVHfoN4UP5hadpdU0Tmgd6rtfnhx+KmTDhAxIS/ooxsGbNWEJDr/HY9ZRSStUfmiT40NZ/F9A6qInHsubCwhI6d36HVasyeeGF/2bfvikkJNzikWsp5Ul+fn7ExMQQHR3N8OHDOXHihGPf/v37GTRoELfeeisRERHMmTMHY4xj/8cff0zPnj2JjIykU6dOPP300754Cxe1e/duHnrooUrbRo4cSWxsbKVtEydOZNWqVZW2BQVV3B2VnZ3NkCFD6NChA5GRkYwZM4YjR45cVmzHjx8nISGBiIgIEhISKCwsdHlcXl4egwcPJjIykqioKHJzcwEYMGAAMTExxMTEcP3113PXXXcBkJKSwqxZsy4rNuV5miT40MmSs5w3NR9XW999dwqAa68NZM6cgezdO5nZswcSEKDjVNXVKTAwkPT0dDIyMmjVqhVvv/02ACUlJYwYMYKZM2eSnZ3Nnj172LJlC++88w4AGRkZTJ06laVLl5KVlUVGRgbt27e/orGVlZVddhkvv/wy06ZNc7w+ceIEu3bt4sSJExw8eNCtMkpLSxk6dChTpkwhJyeHrKwspkyZwrFjxy4rtrlz5xIfH88333xDfHw8c+fOdXnc/fffzzPPPENWVhY7duwgNDQUgE2bNpGenk56ejqxsbGMGjUKgKFDh7J27VpOnz59WfEpz9JvDR/54WQphafP8ujPr9wv++LiM7zwQipvvbWDL76YSN++N/LYY72uWPlKzf6//WR+f+qKlhl1fXNmDe/s9vGxsbHs3bsXgGXLltGvXz8GDx4MQLNmzVi4cCFxcXE8/vjjvPrqqzz33HN06tQJAH9/fx577LFqZRYXFzNt2jTS0tIQEWbNmsU999xDUFAQxcXFAKxatYqUlBSSkpKYOHEirVq1Yvfu3cTExLB69WrS09Np2dKaX6RDhw5s3ryZRo0aMXnyZPLy8gB488036devX6VrFxUVsXfvXrp16+bY9v777zN8+HDCwsJITk7m2WefrbFeli1bRmxsLMOHD3dsGzhwoNv1eiFr1qzh888/B+CBBx4gLi6OV155pdIxmZmZlJWVkZCQAFRu3ShXVFTEZ599xnvvvQdY4w7i4uJISUlhzJgxlx2n8gxNEnzk9fVfAzCwY+hll2WMYc2ar5k27WPy80/x6KM9iIxsfdnlKlXXnDt3jo0bN/Lggw8CVldDjx49Kh1zyy23UFxczKlTp8jIyGDGjBk1ljtnzhxatGjBvn37AC7YpO4sOzubDRs24Ofnx/nz51m9ejWTJk1i+/bthIeHExYWxvjx45k+fTr9+/cnLy+PO+64g6ysrErlpKWlER0dXWnb8uXLmTVrFmFhYYwePdqtJCEjI6NaXbhSVFTEgAEDXO5btmwZUVFRlbYdOXKENm2sO6DatGnD0aNHq52XnZ1Ny5YtGTVqFAcPHuT2229n7ty5+PlV3N69evVq4uPjad68YobZnj17smnTJk0S6jBNEnzk+I9nAej4X8GXXdaECR+wfHkGXbqE8ve/jyY29sbLLlMpV2rzi/9KKikpISYmhtzcXHr06OH4xWqMueCYntqM9dmwYQPJycmO19deW/MdR7/85S8dX4KJiYm8+OKLTJo0ieTkZBITEx3lZmZmOs45deoURUVFBAdX/N0fPnyY666rWAX2yJEj5OTk0L9/f0QEf39/MjIyiI6OdvmeajumKTg4mPT09FqdU5OysjI2bdrE7t27uemmm0hMTCQpKcmRzIGV+FQddxEaGsr3339/RWNRV5aOSfABY2BD1hHiO116K0JZ2XnH4Ky+fW9k3rwEdu58RBMEVS+Vj0n49ttvOXPmjGNMQufOnUlLS6t07IEDBwgKCiI4OJjOnTuzc+fOGsu/ULLhvK3qffvXXFNxl1BsbCw5OTkcO3aMDz/80NHvfv78ebZu3erok//uu+8qJQjl78257BUrVlBYWEi7du0IDw8nNzfXkcCEhIRUauU4fvw4rVu3dtSFO++1qKjIMZCw6sM5oSkXFhbG4cOHASuhKR9r4Kxt27Z0796d9u3b4+/vz1133cWuXbsc+wsKCtixYwdDhw6tdF5paSmBgYE1xqx8R5MEHyg9a820eFNIs0s6f/PmPLp3/wMrVuwHYOrU3syY0VdXa1T1XosWLViwYAHz5s3j7NmzTJgwga+++ooNGzYAVovDE088wa9+9SsAnnnmGV5++WWys7MB60v79ddfr1bu4MGDWbhwoeN1+RdxWFgYWVlZju6ECxER7r77bp566ikiIyMJCQlxWa6rX/CRkZHk5OQ4Xi9fvpx169aRm5tLbm4uO3fudCQJcXFxrFixgjNnzgCQlJTkGHcwfvx4tmzZwkcffeQoa926dY4ulHLlLQmuHlW7GgBGjBjBkiVLAFiyZAkjR46sdkyvXr0oLCx0DJL87LPPKpW1cuVKhg0bRkBAQKXzsrOzq3W1qLpFkwQfKP7JGg09pEvtZjo8fryEhx9eS//+73HyZCktWwbUfJJS9Uz37t3p1q0bycnJBAYGsmbNGl566SU6duxIly5d6NWrF1OnTgWga9euvPnmm4wbN47IyEiio6Mdv4qdPf/88xQWFhIdHU23bt1ITU0FrJH9w4YNY9CgQY5++QtJTExk6dKljq4GgAULFpCWlkbXrl2Jiopi0aJF1c7r1KkTJ0+epKioiNzcXPLy8ujTp49jf7t27WjevDnbt29n2LBhDBgwgB49ehATE8PmzZsdgwgDAwNJSUnhrbfeIiIigqioKJKSklz+8q+NmTNnsn79eiIiIli/fj0zZ84ErLEU5d0Hfn5+zJs3j/j4eLp06YIxhocffthRRnJyMuPGjatWdmpqarXWBVW3iPP9xMo7QttHmWZjXiP7pV/QxN+9PO399zOZPPkjCgtLmD69D7NmxREU5J3pnFXDlpWVRWRkpK/DqNfeeOMNgoODq/XZ12dHjhxh/PjxbNy40eV+V587EdlpjOnpjfiURVsSfODsufM08W/kdoJQrkOHVuzc+QivvTZYEwSl6pEpU6bQtGlTX4fhVXl5ecyfP9/XYaga6N0NPnD6zDkG1XDrY0nJWX73u69o0aIpM2b0ZdSoSO6+O5JGjXROc6Xqm4CAAO677z5fh+FVvXrpHC5XA21J8JGAxheu+k8//TdduvyeOXO+5OuvCwBrYJQmCMpXtFtSeZN+3uoOTRJ8pMfN1e/DPny4iLFjV3HHHUvx82vExo33s3jxcBdnK+U9AQEBFBQU6H/cyiuMMRQUFFS7E0L5hnY3+Ehgk+pVf+jQKdau/ZrZs+P49a/70bSp/vMo32vbti35+fmXvQaAUu4KCAigbdu2vg5DoUmCz1zTxJrTYNeuw6SmHmTGjL707n0Dhw5NJ+QS509QyhMaN25Mu3btfB2GUsoHtLuhBiJyp4h8LSI5IjLTxf6mIrLC3r9dRMLdKdffwJNPrqNXrz8yf/5WTp60ZlzTBEEppVRdoUnCRYiIH/A28AsgChgnIlWnJHsQKDTGdADeAF7BDRPHrGLBgu08+mgPMjMfp0UL7X9TSilVt2h3w8X1BnKMMQcARCQZGAk4T3A+Evit/XwVsFBExNQwyuu6wCZ8sPVBfvYz7XdTSilVN2mScHE3AIecXucDP7vQMcaYMhE5CYQA/3E+SEQeAR6xX/6054fJGU4zrzZkralSVw2Y1kUFrYsKWhcVOvo6gIZGk4SLczUxQdUWAneOwRizGFgMICJpOrWoReuigtZFBa2LCloXFUQkreaj1JWkYxIuLh9wXnu5LVB18XPHMSLiD7QAjnslOqWUUsqDNEm4uH8CESLSTkSaAGOBtVWOWQs8YD8fDXxW03gEpZRS6mqg3Q0XYY8xmAp8AvgB7xpj9ovIi0CaMWYt8GfgryKSg9WCMNaNohd7LOirj9ZFBa2LCloXFbQuKmhdeJkuFa2UUkopl7S7QSmllFIuaZKglFJKKZc0SfAgT03pfDVyoy6eEpFMEdkrIhtF5GZfxOkNNdWF03GjRcSISL29/c2duhCRMfZnY7+ILPN2jN7ixt/ITSKSKiK77b+TIb6I09NE5F0ROSoiGRfYLyKywK6nvSJym7djbFCMMfrwwANroOO/gfZAE2APEFXlmMeARfbzscAKX8ftw7oYCDSzn09pyHVhHxcMfAlsA3r6Om4ffi4igN3AtfbrUF/H7cO6WAxMsZ9HAbm+jttDdfHfwG1AxgX2DwE+xpqjpg+w3dcx1+eHtiR4jmNKZ2PMGaB8SmdnI4El9vNVQLyIuJqc6WpXY10YY1KNMaftl9uw5qSoj9z5XADMAV4FSr0ZnJe5UxcPA28bYwoBjDFHvRyjt7hTFwZobj9vQfU5W+oFY8yXXHyumZHAX4xlG9BSRNp4J7qGR5MEz3E1pfMNFzrGGFMGlE/pXN+4UxfOHsT6pVAf1VgXItIduNEYk+LNwHzAnc/FrcCtIrJZRLaJyJ1ei8673KmL3wL3ikg+8A9gmndCq3Nq+/+Jugw6T4LnXLEpnesBt9+niNwL9AR+7tGIfOeidSEijbBWE53orYB8yJ3PhT9Wl0McVuvSJhGJNsac8HBs3uZOXYwDkowx80UkFmt+lmhjzHnPh1enNJT/N+sEbUnwHJ3SuYI7dYGI3A48B4wwxvzkpdi8raa6CAaigc9FJBerz3VtPR286O7fyBpjzFljzEHga6ykob5xpy4eBP4OYIzZCgRgLf7U0Lj1/4m6MjRJ8Byd0rlCjXVhN7H/AStBqK/9zlBDXRhjThpjWhtjwo0x4VjjM0YYY+rjwjbu/I18iDWoFRFpjdX9cMCrUXqHO3WRB8QDiEgkVpJwzKtR1g1rgfvtuxz6ACeNMYd9HVR9pd0NHmI8N6XzVcfNungNCAJW2mM384wxI3wWtIe4WRcNgpt18QkwWEQygXPAM8aYAt9F7Rlu1sUM4I8iMh2reX1iffxRISLLsbqXWtvjL2YBjQGMMYuwxmMMAXKA08Ak30TaMOi0zEoppZRySbsblFJKKeWSJglKKaWUckmTBKWUUkq5pEmCUkoppVzSJEEppZRSLmmSoJQHiMg5EUl3eoRf5NjwC614V8trfm6vIrjHnsa44yWUMVlE7refTxSR6532/UlEoq5wnP8UkRg3znlSRJpd7rWVUrWjSYJSnlFijIlxeuR66boTjDHdsBYOe622JxtjFhlj/mK/nAhc77TvIWNM5hWJsiLOd3AvzicBTRKU8jJNEpTyErvFYJOI7LIffV0c01lEdtitD3tFJMLefq/T9j+IiF8Nl/sS6GCfGy8iu0Vkn4i8KyJN7e1zRSTTvs48e9tvReRpERmNtYbG3+xrBtotAD1FZIqIvOoU80QReesS49yK0+I8IvJ7EUkTkf0iMtve9gRWspIqIqn2tsEistWux5UiElTDdZRSl0CTBKU8I9Cpq2G1ve0okGCMuQ1IBBa4OG8y8L/GmBisL+l8ewreRKCfvf0cMKGG6w8H9olIAJAEJBpjumDNsjpFRFoBdwOdjTFdgZecTzbGrALSsH7xxxhjSpx2rwJGOb1OBFZcYpx3Yk29XO45Y0xPoCvwcxHpaoxZgDU3/0BjzEB7eubngdvtukwDnqrhOkqpS6DTMivlGSX2F6WzxsBCuw/+HNY6BFVtBZ4TkbbAB8aYb0QkHugB/NOesjoQK+Fw5W8iUgLkYi0l3BE4aIzJtvcvAR4HFgKlwJ9E5CPA7WWpjTHHROSAPW/+N/Y1Ntvl1ibOa7CmIL7NafsYEXkE6/+mNkAUsLfKuX3s7Zvt6zTBqjel1BWmSYJS3jMdOAJ0w2rFK616gDFmmYhsB4YCn4jIQ1hL4y4xxjzrxjUmOC8GJSIhrg6y1wrojbVg0FhgKjCoFu9lBTAG+Bew2hhjxPrGdjtOYA8wF3gbGCUi7YCngV7GmEIRScJaxKgqAdYbY8bVIl6l1CXQ7galvKcFcNgYcx64D+tXdCUi0h44YDexr8Vqdt8IjBaRUPuYViJys5vX/BcQLiId7Nf3AV/YffgtjDH/wBoU6OoOgyKspatd+QC4CxiHlTBQ2ziNMWexug362F0VzYEfgZMiEgb84gKxbAP6lb8nEWkmIq5aZZRSl0mTBKW85x3gARHZhtXV8KOLYxKBDBFJBzoBf7HvKHge+FRE9gLrsZria2SMKcVaJW+liOwDzgOLsL5wU+zyvsBq5agqCVhUPnCxSrmFQCZwszFmh72t1nHaYx3mA08bY/YAu4H9wLtYXRjlFgMfi0iqMeYY1p0Xy+3rbMOqK6XUFaarQCqllFLKJW1JUEoppZRLmiQopZRSyiVNEpRSSinlkiYJSimllHJJkwSllFJKuaRJglJKKaVc0iRBKaWUUi79PxQVEU/K8xDWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6689738476077944" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will stick to using unfiltered features and SVM with rbf kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training\n", + "Here we create an instance of our model, specifically a Sequential model, and add layers one at a time until we are happy with our network architecture. We will be training the model on our feature-selected dataset to speed up computation by reducing dimensionality. We have found that the performance difference between the 2 datasets are negligible.\n", + "\n", + "We ensure the input layer has the right number of input features, and is specified when creating the first layer with the input_dim argument and setting it to 17 (The size of the feature selected dataset).\n", + "Additionaly, we start off using a fully-connected network structure with three layers, and attempt to increase the number of layers at later part ater fully optimising our model.\n", + "\n", + "Fully connected layers are defined using the Dense class. We specify the number of neurons or nodes in the layer as the first argument, and specify the activation function using the activation argument. The rectified linear unit activation function (Relu) is usedon the first two layers and the Sigmoid function in the output layer.\n", + "\n", + "Conventionally, Sigmoid and Tanh activation functions were preferred for all layers. However, better performance is achieved using the ReLU activation function. We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map (binary) to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.\n", + "\n", + "The model expects rows of data with 17 variables (the input_dim=17 argument)\n", + "The first hidden layer has 17 nodes and uses the relu activation function.\n", + "The second hidden layer has 17 nodes and uses the relu activation function.\n", + "The output layer has one node and uses the sigmoid activation function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compiling\n", + "\n", + "The model uses the efficient numerical libraries as the backend, and in this case Tensorflow is used. The backend automatically chooses the best way to represent the network for training and making predictions to run.\n", + "\n", + "We must specify the loss function to use to evaluate a set of weights, the optimizer is used to search through different weights for the network and any optional metrics we would like to collect and report during training.\n", + "\n", + "After experimenting with the various loss functions, such as hinge loss and binary cross entropy, we found that entropy performed the best for our dataset.\n", + "\n", + "We have also found that among all the optimizers (Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam) the optimizer \"adam\" is the most efficient yet yields the best results.\n", + "\n", + "Additionaly, for this problem, we will run for a small number of epochs (20) and use a relatively small batch size of 10. This means that each epoch will involve (20/10) 2 updates to the model weights. After we have finalised the best optimizer, we will then increase the numebr of epochs to increase the number of updates to obtain a better result. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4708 - accuracy: 0.7956\n", + "Epoch 2/20\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4502 - accuracy: 0.8116\n", + "Epoch 3/20\n", + "17496/17496 [==============================] - 2s 113us/step - loss: 0.4477 - accuracy: 0.8125\n", + "Epoch 4/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4461 - accuracy: 0.8130\n", + "Epoch 5/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4450 - accuracy: 0.8133\n", + "Epoch 6/20\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4443 - accuracy: 0.81450s - loss: 0.4424 - \n", + "Epoch 7/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4437 - accuracy: 0.8150\n", + "Epoch 8/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4435 - accuracy: 0.8144\n", + "Epoch 9/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4433 - accuracy: 0.8147\n", + "Epoch 10/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4429 - accuracy: 0.8142\n", + "Epoch 11/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4418 - accuracy: 0.8132\n", + "Epoch 12/20\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4416 - accuracy: 0.81450s - loss: 0.4413 - accuracy: \n", + "Epoch 13/20\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4419 - accuracy: 0.8138\n", + "Epoch 14/20\n", + "17496/17496 [==============================] - 2s 128us/step - loss: 0.4417 - accuracy: 0.8140\n", + "Epoch 15/20\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4415 - accuracy: 0.8142\n", + "Epoch 16/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 17/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4409 - accuracy: 0.8152\n", + "Epoch 18/20\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4413 - accuracy: 0.8145\n", + "Epoch 19/20\n", + "17496/17496 [==============================] - 2s 126us/step - loss: 0.4403 - accuracy: 0.8145\n", + "Epoch 20/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4405 - accuracy: 0.8156\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# optimisers to try: Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam\n", + "# verdict : Adam\n", + "\n", + "# Loss function to try: Binary Cross Entropy, Hinge, Logcosh\n", + "# verdict: Binary Cross Entropy\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=20, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.39 0.49 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.69 8749\n", + "weighted avg 0.80 0.81 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 20 epoch\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experimenting with lowering the cutoff for the neural network," + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.81 0.84 6762\n", + " 1 0.48 0.60 0.54 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.79 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam = get_optimal(model, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > optimal_adam)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance is quite impressive when we lowered the classification cut off. The ROC of 0.76 is also on par with other models. Now we ramp up the number of epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4863 - accuracy: 0.7969\n", + "Epoch 2/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4511 - accuracy: 0.8122\n", + "Epoch 3/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4485 - accuracy: 0.8124\n", + "Epoch 4/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4472 - accuracy: 0.81200s - loss: 0.4465 \n", + "Epoch 5/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4461 - accuracy: 0.8123\n", + "Epoch 6/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4450 - accuracy: 0.8124\n", + "Epoch 7/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4432 - accuracy: 0.8138\n", + "Epoch 8/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4428 - accuracy: 0.8139\n", + "Epoch 9/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4422 - accuracy: 0.8132\n", + "Epoch 10/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4420 - accuracy: 0.8140\n", + "Epoch 11/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4411 - accuracy: 0.8137\n", + "Epoch 12/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4410 - accuracy: 0.8146\n", + "Epoch 13/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4412 - accuracy: 0.8143\n", + "Epoch 14/50\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 15/50\n", + "17496/17496 [==============================] - 2s 120us/step - loss: 0.4402 - accuracy: 0.8149\n", + "Epoch 16/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4402 - accuracy: 0.8146\n", + "Epoch 17/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4406 - accuracy: 0.8141\n", + "Epoch 18/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4400 - accuracy: 0.8152\n", + "Epoch 19/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8140\n", + "Epoch 20/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4398 - accuracy: 0.8140\n", + "Epoch 21/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4396 - accuracy: 0.8153\n", + "Epoch 22/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4401 - accuracy: 0.8138\n", + "Epoch 23/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4394 - accuracy: 0.8150\n", + "Epoch 24/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4397 - accuracy: 0.8152\n", + "Epoch 25/50\n", + "17496/17496 [==============================] - 2s 117us/step - loss: 0.4396 - accuracy: 0.8148\n", + "Epoch 26/50\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4396 - accuracy: 0.8144\n", + "Epoch 27/50\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4393 - accuracy: 0.8135\n", + "Epoch 28/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4390 - accuracy: 0.8152\n", + "Epoch 29/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4392 - accuracy: 0.8138\n", + "Epoch 30/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8141\n", + "Epoch 31/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8149\n", + "Epoch 32/50\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4390 - accuracy: 0.8147\n", + "Epoch 33/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4388 - accuracy: 0.8145\n", + "Epoch 34/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4385 - accuracy: 0.8153\n", + "Epoch 35/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4389 - accuracy: 0.8150\n", + "Epoch 36/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8146\n", + "Epoch 37/50\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4386 - accuracy: 0.8142\n", + "Epoch 38/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4386 - accuracy: 0.8143\n", + "Epoch 39/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4379 - accuracy: 0.8148\n", + "Epoch 40/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4385 - accuracy: 0.8148\n", + "Epoch 41/50\n", + "17496/17496 [==============================] - 2s 116us/step - loss: 0.4386 - accuracy: 0.8149\n", + "Epoch 42/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4380 - accuracy: 0.8150\n", + "Epoch 43/50\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4382 - accuracy: 0.8144\n", + "Epoch 44/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4381 - accuracy: 0.8152\n", + "Epoch 45/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4378 - accuracy: 0.8151\n", + "Epoch 46/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4374 - accuracy: 0.8153\n", + "Epoch 47/50\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4382 - accuracy: 0.8152\n", + "Epoch 48/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4379 - accuracy: 0.8166\n", + "Epoch 49/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4381 - accuracy: 0.8144\n", + "Epoch 50/50\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4378 - accuracy: 0.8154\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model50 = Sequential()\n", + "model50.add(Dense(12, input_dim=17, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model50.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model50.fit(X_train_filter, y_train, epochs=50, batch_size=10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the accuracy did not increase much at all from the 20th to 50th epoch. In such a situation we will choose the 20 epoch model for its faster computation." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.20843479\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.82 0.84 6762\n", + " 1 0.48 0.59 0.53 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.70 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam50 = get_optimal(model50, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "get_roc(model50, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 50 epoch\")\n", + "predictions50 = list(model50.predict(X_test_filter).ravel() > optimal_adam50)\n", + "print(classification_report(y_test,predictions50))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep...0.5358340.741382
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep... 0.535834 0.741382\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[3] = ([\"Neural Network - 17x8x8x1 Adam, Entropy, 20 Epochs\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "\n", + "gnb = GaussianNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GaussianNB(priors=None, var_smoothing=1e-09)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.9999935527715175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.11 0.20 13442\n", + " 1 0.25 0.96 0.39 4054\n", + "\n", + " accuracy 0.31 17496\n", + " macro avg 0.58 0.54 0.29 17496\n", + "weighted avg 0.76 0.31 0.24 17496\n", + "\n" + ] + } + ], + "source": [ + "#accessing model performance\n", + "#print(metrics.confusion_matrix(expected, predicted))\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that while the recall is 0.96, the f1 is 0.39 and the overall accuracy is atrocious. \n", + "\n", + "We will now try searching for the smoothing parameter to achieve a better ROC and F1 on default. After some experimentation we found that 0.01 is a good value for this parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.85 0.85 13442\n", + " 1 0.52 0.52 0.52 4054\n", + "\n", + " accuracy 0.78 17496\n", + " macro avg 0.69 0.69 0.69 17496\n", + "weighted avg 0.78 0.78 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "gnb = GaussianNB(var_smoothing = 0.01)\n", + "#experimenting with smoothing constant of naive bayes model on the training set.\n", + "gnb.fit(X_train, y_train)\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smoothing constant of 0.01 allowed us to acheieve a recall and f1 of 0.52 on the training set. Applied on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.86 0.86 6762\n", + " 1 0.52 0.53 0.53 1987\n", + "\n", + " accuracy 0.78 8749\n", + " macro avg 0.69 0.69 0.69 8749\n", + "weighted avg 0.78 0.78 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_test,gnb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Naive Bayes\" , \n", + " classification_report(y_test, gnb.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
3Neural Network 17x8x8x10.5358340.761854
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "3 Neural Network 17x8x8x1 0.535834 0.761854\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.sort_values([\"AUROC\"], ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "Stop Running", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stop Running\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mException\u001b[0m: Stop Running" + ] + } + ], + "source": [ + "raise(Exception(\"Stop Running\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Tuning Neural Network with different optimisers \n", + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adadelta Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adagrad Optimzier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RMSProp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SGD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We conclude that best optimizer is adagrad. Testing it on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network - adagrad (filtered features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Neural Network - adagrad (all features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb index 99725fe..3103496 100644 --- a/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -5969,9 +5969,9 @@ } ], "source": [ - "optimal_adam3 = get_optimal(model, y_train, X_train, \"3-layer adam\")\n", + "optimal_adaS3 = get_optimal(model, y_train, X_train, \"3-layer adagrad\")\n", "predictions = list(model.predict(X_test).ravel() > optimal_adam3)\n", - "auroc = get_roc(model, y_test, X_test, \"3-layer adam\")\n", + "auroc = get_roc(model, y_test, X_test, \"3-layer adagrad\")\n", "\n", "print(classification_report(y_test,predictions))" ] diff --git a/BT2101 Project Submission.ipynb b/BT2101 Project Submission.ipynb new file mode 100644 index 0000000..9aa3491 --- /dev/null +++ b/BT2101 Project Submission.ipynb @@ -0,0 +1,6555 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "By Reon Ho, Lam Cheng Jun, Janson Chew, and Bryan Koh\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4, 0], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, \"Missing\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8507531.55877335.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000000.0000000.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.850753 1.558773 35.006592 \n", + "std 116558.616530 0.487994 0.738175 0.522639 8.832028 \n", + "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MISSING-EDUGRADUNIHSMISSING-MSMARRIEDSINGLE
00.00.01.00.00.01.00.0
10.00.01.00.00.00.01.0
20.00.01.00.00.00.01.0
30.00.01.00.00.01.00.0
40.00.01.00.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " MISSING-EDU GRAD UNI HS MISSING-MS MARRIED SINGLE\n", + "0 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 0.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"MISSING-EDU\",\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MISSING-MS\",\"MARRIED\",\"SINGLE\",\"OTHER-MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER-MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',\n", + " 'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',\n", + " 'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',\n", + " 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',\n", + " 'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 47 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, MISSING-EDU, GRAD, UNI, HS, MISSING-MS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 47 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 47 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split using seed 123\n", + "np.random.seed(123) \n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 82.306062 2.883753e-04\n", + "PAY_0 4279.993739 0.000000e+00\n", + "PAY_2 3557.072141 0.000000e+00\n", + "PAY_3 2766.119390 0.000000e+00\n", + "PAY_4 2736.965012 0.000000e+00\n", + "PAY_5 2587.002458 0.000000e+00\n", + "PAY_6 2240.874786 0.000000e+00\n", + "PAY_0_No_Transactions 76.858872 1.147939e-03\n", + "PAY_0_Revolving_Credit 480.805794 0.000000e+00\n", + "PAY_2_Pay_Duly 75.283344 1.684018e-03\n", + "PAY_2_Revolving_Credit 229.527990 0.000000e+00\n", + "PAY_3_Pay_Duly 86.995856 8.229607e-05\n", + "PAY_3_Revolving_Credit 121.059740 2.357071e-09\n", + "PAY_4_Pay_Duly 79.449207 6.014800e-04\n", + "PAY_4_Revolving_Credit 82.276504 2.906105e-04\n", + "PAY_5_Pay_Duly 63.330298 2.338310e-02\n", + "PAY_5_Revolving_Credit 64.659773 1.792035e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3333333333333333\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3gU5fbA8e8hgQDShGChGZok9CZdQRFBBPEiUkSaKNKs6I+reBXFyrVwvYCIjSuKICLSVARFQQUpgnQQgkLoIJDQQsr5/TGTsIRNsgnZbMr5PA8PmX7m3dk5M+87+46oKsYYY0xqCgQ6AGOMMTmbJQpjjDFpskRhjDEmTZYojDHGpMkShTHGmDRZojDGGJMmSxReiEhvEfk20HEEmohUEpGTIhKUjdsMExEVkeDs2qY/icgmEWmTieUydQyKSBERmSciJ0RkZkaXz06+Hl959fsoIoVFZKuIXJGN23xMRF7I8IKqmqP/AX8CZ4CTwAFgClAs0HHlxX9uWd8c4BjCAAWCc0B5KFAtN+0z0AdYmVXlB7QBEt3v30kgCvgMuC7Qn88l7ldvj306k2IfT2ZTDI8C41OMawJ8BRx3/20CxgCl3On3AT94zB8F7AeKeowbDCx2/w52j60wd7gosBcok5FYc8sdRWdVLQbUBxoATwY4nkwJ5FVyXrlCz4h8Wt7XANtVNT6jC6YR8z73+1ccaAZsBZaJSNvMhxlYqvqJqhZz9+tW3H30GHcBP32eDwBTPbZxPfA98ANwraqWAjq6k2unsZ5CwHBfNqiqp4FvcS4ofBfozO5D1v0Tj6tcYCywwGM4BHgN2A0cBCYBRTymdwHWAdHATqCDO74k8D5ONt4LvAAEudP6Az+5f08CXksR0xzgMffvcsAs4DCwC3jIY77RwOfAx+727/OyfyWBj9zl/wKeBgp4xPEz8F/gBM4XtG2KZdPah5+BN4G/3WlVcQ7Eo8AR4BPOX6lMxbmqSrp7+z9SXOniHMBj3PXG4BxwoR7x9HX34Sjwr5SfXYr9LgK87s5/AvjJHZe0zX7uZ3oEGJXiims5ztXWfmA8UMhjugLDgD+AXe64/wB73M9gDXC9x/xBwFPusRHjTq8ILHXXdcotjx7u/J1wjqfjwC9A3RTH6khgPRCLczWXXAZu7KvdOA4Cb7jjd7vbSrqibY7HMejOUwtY5H6WB4GnvJTpc8A5IM5dz0Cc6uWn3XI+hHOslXTnTyrrgW4MS72ssw0Q5WX8eGC1x3C4R3zbgO4Z+KyTjq/+QKT7OewCeqf8PrrDLYBV7rpWAS08pv1AGsdoKsdiavsYBTwBbADOueMqALM5/30f5jF/Ac4fS0eA6cDlqWyzCs6xFeQxbgXwZjqxerujGOlur4Q7LtU7CndcP2BRhs7DmTl5Z+c/LvyiVXA/tP94TB8HzAVK41zxzANe9vhingDauR9ieSDcnfYl8A5wGXAFzu36AykPTOAGnJOMuMOX45xMy7nrXAM8g5PVq7gHent33tE4X9o73HmLeNm/j3AST3GcL852YKBHHPE4t6gFgR7u/pT2cR/igQfdg6UIUM0tixCgLM7JcJy3sk5xIvFMFDuBa931/QC84k6riXNyauWWxWvuvqeWKCa4y5fHOVm3cONK2ua77jbq4Zx0I9zlGuFc1Qa7824BHvFYr+KcsEonlTdwD1DGXWYEThVmYXda0omgBiDu9sp4rKuax7ob4pxsm7ox93PLLMSj/NbhJJoiKcsUJ8H1cf8uBjTzVs5ejsHiOElxBFDYHW6aSrmOBj72GL4X2IFzbBYDvgCmptjuRzjHkLfjsw3eT6I34VxYXOb+2wMMcMu4Ic6Jq5aPn3Wwu45ooIa7zNUey3uWRWngGM4VcTDQyx1O+sx+IJVjNI1zTGr7GIXz/a7grivI/XyfwjnGq7mfb1t3/sdxElR593N6P6msvay7C/C7x3AJtzxbpROrt0TRBuccONodl16iaAIcytB52B8n96z8534QJ3GuDhT4jvNXwYKTlat6zN+c81eS7+AlQwNX4px8PO88egFLvByYgnO1dYM7fD/wvft3U2B3inU/CXzo8aW96CrNY94gN46aHuMeSDoQ3Dj24SYpd9xK90viyz7sTm3b7jx3AGtTlHV6ieJpj+lDgW/cv58BPvWYVhTn6vaiRIGTNM8A9bxMS9pmhRT73DOVfXgEmO0xrMBN6ez3saRt41z9dkllvpSJ4m1gTIp5tgGtPcrvXi/Hb1KiWIpz1R+aYp4LytnLMdjL83NKZ99Gc2Gi+A4Y6jFcAyeBB3tst0oa62uD95NouLtseZwLmGUppr8DPOvjZ52UKI4Dd5IiYaUoiz7AyhTTlwP90ztGM7GPUUBfj+GWQGSKef4FvOv+/UfSseAOV8T5jhbwsu5+XHiXlFQWnsfbG26ZnAL+6Y5LLVHUc+ctQ/qJIgL3DsnXf7mljeIOVS2OUyDhQKg7vizOCWmNiBwXkePAN+54cD6onV7Wdw3OFfp+j+Xewbkqv4A6JTsd58sKcDdOlU3SesolrcNdz1M4J/Eke9LYr1CcK5O/PMb9hfPlS7LXjcFzejkf9+GCbYvIFSIyXUT2ikg0TpVYKBlzwOPv0zhXqbgxJW9PnbrQo6msIxTnisvbZ5PmdkTkWhGZLyIH3H14iYv3IeV+jxCRLe6TQMdxquySlkntGPHmGmBEis+7Is6+e912CgNxrnS3isgqEenk43YzEmNK5bj4+ArG92M0NeVxTkDHccqlaYpy6Q1chW+fNap6CifhDMY5pheISLgP+wMXf2dSO0Yzw7NsrgEqpdjP/8PZT4BKwDyPaRtwysjbU03HcO4Mk/ztznt10ghVfUyddop5OJ9ZqlT1d2ChG096iuN8bj7LLYkCAFX9Eeepp9fcUUdwrlZqqWop919JPd8YtQenXj6lPTiZPtRjuRKqWiuVTX8KdBORa3DuImZ5rGeXxzpKqWpxVe3osaymXJmHIzhXd9d4jKuE096QpLyISIrp+3zch5TbftkdV1dVS+BUyUga82fEfpxbdMB5TBPn6sabI8BZvH826Xkbp62mursPT3HhPoDHfrgNhCOB7jj1xaVwqu+SlkntGPFmD/Biis+7qKp+6m3bKanqH6raC+fE8SrwuYhcltYymYgxpX1cfHzF47RzJIeWifX+A/jNPcHvAX5MUS7FVHUIGfisVXWhqrbDOVluxal+TG9/4OLvTFbyLJs9wB9evu+d3elRQLsU0wur6oGLV8t6oGrSo8GqGo3TftX1EmJ9BhjC+cSVmgjg94ysOFclCtc4oJ2I1FfVRJyD6c2kZ5FFpLyItHfnfR8YICJtRaSAOy1cVffjNHK9LiIl3GlVRaS1tw2q6lqcxqv3gIWqmpSNVwLRIjLSfX49SERqi8h1vuyIqibgPGr4oogUdxPRYzhX+kmuAB4SkYIichfOh/xVRvfBVRynGu+4iJTHqZ/3dBCnLjszPgc6i0gLESmEU8WS8gQOgPu5fQC8ISLl3HJrLiIhPmynOE5d9kn3inOID/PH43x+wSLyDE59cJL3gDEiUl0cdUUkKcGlLI93gcEi0tSd9zIRuU1EPK8MUyUi94hIWXf/k46hBDe2RFIv+/nAVSLyiIiEuMdKU1+2iXOR86iIVBaRYjh3YDM0c09FifsdehanCuQpj/iuFZE+7nFaUESuE5EIXz9rEblSRG53E2csznGa4CWMr9xt3S0iwSLSA6d9bH5G9ycTlgPn3DvUwu6+1BGRRu70ScBLIlIJku/gb/e2IlX9E6dKu5HH6CeAQSLyfyJS1l1HRS5OjF6p6jaci9gH05m1NfC1L+tMkusShaoexml8+5c7aiROY90KtypiMU49LKq6EqeB7U2cq8gfOV/ofXGqfTbj3AZ+jsdtnxefAjcD0zxiSQA64zy2uwvn6uk9nKoNXz2IUwcZifM0yDScL1aSX4Hq7rpfBLqpalKVTkb34TmchsYTwAKchk1PLwNPu7fOj2dgH1DVTe6+TMe5u4jBafiNTWWRx3FuzVfh3Ha/im/H4+M41X8xOCfuGenMvxDnS7Edp4riLBdWJ7yBk6y/xUlA7+M0XIJT3/8/tzy6q+pqnDaq8TjlvQOn/txXHYBNInIS50msnqp61q2mexH42d1WM8+FVDUG5yGEzjjVKn8AN/q4zQ9wnmhbinOMniX9E0lK5dyYT+J8XnWANqr6rUd8twA9ca74D+B8nknJwJfPugBOY/0+d57WOO0LF3CP/U7uvEdxqlo6qeqRDO5ThrnJtSNOY/CfON/Jdzh/4fEGTtX3dyISg/NUXFoXje/g8ZiqW2NyM85nu8Otvvoa55w20ccwnyONqjb3Tr8DzjnUZ0lP8pgcSET64zxS2yrQsWSUe/V6HKeKaFeg4zEmpxGRwsBanAbwQ9m0zUeBsqr6VLoze8h3P8Iy/iMinXGeshGcdqQNOFdexpgUVPUsTlVydm7zzcwsl+uqnkyO1gWn6mAfTnVZT7VbVmNyPat6MsYYkya7ozDGGJOmXNdGERoaqmFhYYEOwxhjcpU1a9YcUdWy6c95sVyXKMLCwli9enWgwzDGmFxFRFL+ot1nVvVkjDEmTZYojDHGpMkShTHGmDRZojDGGJMmSxTGGGPSZInCGGNMmvyWKETkAxE5JCIbU5kuIvKWiOwQkfUi0tBfsRhjjMk8f/6OYgpOd8ypdWd7K05/QNVxXgb0tvt/mlThbJy3buqNMcb4g98ShaouFZGwNGbpAnzkdhq3QkRKicjV7gt5UrVx3wnC//VNFkZqjDEmLYH8ZXZ5LnyBTJQ77qJEISKDgEEAha6qxsgO3l6la4wxJsmxY2eYM2crW7ce5eqrL+W14YFNFN5ek+m1K1tVnQxMBihZMVyHtMns64ONMSbvU1UaN36XbduO8PzzN/LQQ00pWPCeTK8vkIkiCqjoMVwB5z0GxhhjMuGXX/ZQp84VFC8ewnvvdSY0tCgVK2bkzczeBfLx2LlAX/fpp2bAifTaJ4wxxlzs6NHT3H//XFq2/IDXX18OQIMGV2dJkgA/3lGIyKdAGyBURKKAZ4GCAKo6CfgK50XlO4DTwAB/xWKMMXmRqvLRR7/z+OOLOHbsDE880YInnmiR5dvx51NPvdKZrsAwf23fGGPyupEjF/Pvf/9CixYVmTTpNurUudIv28l176Mwxpj87MyZOE6diiM0tCgDBzagevXSDBzYkAIFvD0flDWsCw9jjMklvvlmB7Vrv80DD8wHoEaNUO6/v5FfkwRYojDGmBxv374Yunefya23fkLBggUYPvy6bN2+VT0ZY0wO9t13kfzjHzM4dy6BMWNu5IknWhASkr2nbksUxhiTA8XFJVCwYBD16l1Fx47VeeGFm6hWrXRAYrGqJ2OMyUGio2N5+OGvuf76D0lISCQ0tCjTp3cLWJIASxTGGJMjqCozZ24iPHw8//3vSho3LkdsbM7oKduqnowxJsAOHz5Fv35f8vXXO2jQ4CrmzOnJddeVD3RYySxRGGNMgJUoEcKRI6cZN649w4Y1ITg4Z1X25KxojDEmn1i69C/at/+YkyfPERISzIoV9/Hww81yXJIASxTGGJOtjhw5zYABc2jdegrbtx/lzz+PA/j9R3OXwqqejDEmG6gqH364jieeWER0dCxPPtmKp5++gaJFCwY6tHRZojDGmGzy8cfrqVmzLJMm3UatWlcEOhyfWaIwxhg/OX06jpdeWsbgwY2pUKEEs2Z1p2TJwjm6mskba6Mwxhg/+OqrP6hVayIvvriMefO2AXD55UVyXZIAu6MwxpgsFRUVzSOPfMOsWVuIiAjlxx/7c8MN1wQ6rEtiicIYY7LQiy8uZcGCP3jppZsYMaIFhQoFBTqkSybOi+Zyj5IVw/XEnq2BDsMYY5KtXLmXIkWCqVPnSo4ePc2JE7FUqXJ5oMO6gIisUdXGmVnW2iiMMSaTTpw4y7BhC2jW7D1GjfoegDJliua4JHGprOrJGGMySFWZMWMTjz66kEOHTvHgg00YM+amQIflN5YojDEmgz7+eD19+35J48blmD+/F40alQt0SH5licIYY3wQGxtPZOQxIiLK0r17LeLjE+nbtx5BQXm/Bj/v76ExxlyiJUt2Ua/eJNq3/5jY2HhCQoIZMKBBvkgSYInCGGNSdejQKfr2nc1NN31EXFwikyd3zvb3VecE+W+PjTHGBzt2/E2TJu9y8uQ5Ro26nlGjrqdIkZzfgZ8/WKIwxhgP0dGxlCgRQtWqlzNwYAPuvbcBERFlAx1WQFnVkzHGAKdOnWPkyEWEhY0jKioaEeHf/74l3ycJsDsKY4xh3rxtDB/+Nbt3n2DgwAa54h0R2ckShTEm34qPT6R795nMnr2VWrXKsmzZAFq1qhTosHIcSxTGmHxHVRERgoMLcPXVxXjllbY8+mjzPNGBnz9YG4UxJl9ZsSKKxo3f5bff9gMwYcJtjBzZypJEGixRGGPyhWPHzjBkyHxatHifgwdPcuzYmUCHlGv4NVGISAcR2SYiO0Tkn16mVxKRJSKyVkTWi0hHf8ZjjMmfZszYSHj4BCZP/o1HHmnGli3DaNu2SqDDyjX81kYhIkHABKAdEAWsEpG5qrrZY7angc9U9W0RqQl8BYT5KyZjTP60desRwsJK8c03vWnQ4OpAh5Pr+POOogmwQ1UjVfUcMB3okmIeBUq4f5cE9vkxHmNMPnH2bDzPPfdD8ruqn3rqen755V5LEpnkz0RRHtjjMRzljvM0GrhHRKJw7iYe9LYiERkkIqtFZHV8Qrw/YjXG5BGLF0dSt+7bjB79Iz/++BcABQsG5ZsO/PzBnyUnXsalfO9qL2CKqlYAOgJTReSimFR1sqo2VtXGwUH2RK8x5mIHD56kd+8vaNduKqrw7bf38NprtwQ6rDzBn2fdKKCix3AFLq5aGgh0AFDV5SJSGAgFDvkxLmNMHrRoUSSff76ZZ565gSefvJ7Che2iMqv4syRXAdVFpDKwF+gJ3J1int1AW2CKiEQAhYHDfozJGJOH/P77Af7442+6datJ7951aNmyIpUr5633VecEfqt6UtV4YDiwENiC83TTJhF5XkRud2cbAdwvIr8DnwL9VTVl9ZQxxlzg5MlzjBixkEaNJvPPfy4mPj4REbEk4SeS287LJSuG64k9WwMdhjEmQL78cisPPvg1UVHRDBrUkJdfvpnSpYsEOqwcT0TWqGrjzCxrlXjGmFxjw4aD/OMfM6hT5wpmzOhGixYV01/IXDJLFMaYHC0uLoFly3Zz002VqVPnShYsuJt27apQsKD1zZRd7MFiY0yO9csve2jUaDLt2k1lx46/AejYsboliWxmicIYk+P8/fcZBg2aR8uWH3D8+Fm++KI71aqVDnRY+ZZVPRljcpSzZ+OpX38S+/bFMGJEc0aPbkOxYoUCHVa+ZonCGJMjREVFU6FCCQoXDmbMmBupX/8q6tW7KtBhGazqyRgTYGfOxPHMM0uoWvWt5E78+vWrb0kiB/HpjkJECgGVVHWHn+MxxuQj3367k6FDF7Bz5zHuuacuTZqk7DfU5ATp3lGIyG3ABmCRO1xfRGb7OzBjTN724INf0b79xxQoICxe3IepU//BlVcWC3RYxgtf7iieB5oCSwBUdZ2IVPNrVMaYPCkhIRGAoKACNGtWgdDQoowc2co68MvhfPl04lT1uMgFvYbnrn4/jDEB99tv+xk8eD59+tTlwQeb0rt33UCHZHzkS2P2FhHpDhQQkcoiMg5Y4ee4jDF5RExMLI8++g3XXfcuu3ef4Oqriwc6JJNBvtxRDAeeARKBL3B6g33Sn0EZY/KGb7/dyb33zmHfvhgGD27MSy+1pVSpwoEOy2SQL4mivaqOBEYmjRCRrjhJwxhjUlWoUBBXXHEZs2Z1p2nTCoEOx2RSut2Mi8hvqtowxbg1qtrIr5GlwroZNybniotL4I03lhMdHcuLL7YFIDFRKVDA25uRTXbySzfjItIe5zWl5UXkDY9JJXCqoYwxJtlPP+1m8OD5bNp0mLvuqpmcICxJ5H5pVT0dAjYCZ4FNHuNjgH/6MyhjTO5x9OhpRo5czPvvr6VSpZLMm9eLTp2uDXRYJgulmihUdS2wVkQ+UdWz2RiTMSYXOXr0DNOnb+T//q8FzzzTmssusw788hpfGrPLi8iLQE0g+XEFVbVLBmPyqS1bDvPZZ5t49tk2XHttGXbvftReR5qH+fI7iinAh4AAtwKfAdP9GJMxJoc6fTqOUaO+o169SfznP78SFRUNYEkij/MlURRV1YUAqrpTVZ8GbvRvWMaYnOabb3ZQu/ZEXnrpJ+6+uw7btg2nQoUSgQ7LZANfqp5ixem/Y6eIDAb2Alf4NyxjTE5y8uQ5+vSZTZkyRViypB9t2oQFOiSTjXxJFI8CxYCHgBeBksC9/gzKGBN4CQmJfPrpRnr1qk2xYoVYvLgP4eGhhIRYB375TbqfuKr+6v4ZA/QBEBH7iaUxediaNft44IH5rFmznyJFgrnzzpr2IqF8LM02ChG5TkTuEJFQd7iWiHyEdQpoTJ504sRZHnroa5o0eY+9e2OYPv1OunaNCHRYJsDS+mX2y8CdwO/A0+7Lih4GXgUGZ094xpjsdOedn/H997sYNuw6XnjhJkqWtA78TNpVT12Aeqp6RkRKA/vc4W3ZE5oxJjtERh6jbNmiFC8ewosv3kSBAsJ119krSc15aVU9nVXVMwCq+jew1ZKEMXnHuXMJvPTSMmrVmsgLLywFoGnTCpYkzEXSuqOoIiJJXYkLEOYxjKp29Wtkxhi/Wbr0LwYPns+WLUfo1q0mDz3UNNAhmRwsrURxZ4rh8f4MxBiTPd58czmPPfYtYWGlWLDgbjp2rB7okEwOl1angN9lZyDGGP9JTFROnTpH8eIh3HbbtRw+fJqnn76BokULBjo0kwuk++KinMZeXGRMxmzadIjBgxckv2nO5E+X8uIiX/p6yjQR6SAi20Rkh4h4fYeFiHQXkc0isklEpvkzHmPyk9On43jyycXUr/8OW7YcplOn6uS2C0OTM/j8W3wRCVHV2AzMHwRMANoBUcAqEZmrqps95qkOPAm0VNVjImJ9SBmTBdau3U/Xrp/x55/HGTCgPmPHtiM0tGigwzK5VLp3FCLSREQ2AH+4w/VE5L8+rLsJsENVI1X1HE7X5F1SzHM/MEFVjwGo6qEMRW+MuUDSHUOlSiWpVKkkP/7Ynw8+6GJJwlwSX6qe3gI6AUcBVPV3fOtmvDywx2M4yh3n6VrgWhH5WURWiEgHH9ZrjEkhPj6RceNW0LbtRyQkJFKmTFF+/LE/N9xwTaBDM3mAL4migKr+lWJcgg/LeXujesoK0mCgOtAG6AW8JyKlLlqRyCARWS0iq+MT4n3YtDH5x8qVe2nS5F0efXQhhQsHEx3tcw2xMT7xJVHsEZEmgIpIkIg8Amz3YbkooKLHcAWcbkBSzjNHVeNUdRewDSdxXEBVJ6tqY1VtHBxkXRwbA847IoYNW0CzZu9x8OApZs68iwUL7ubyy+1tcyZr+ZIohgCPAZWAg0Azd1x6VgHVRaSyiBQCegJzU8zzJW41lttD7bVApG+hG5O/FSxYgB9++IsHH2zCli3D6NatJs47xozJWr5cnseras+MrlhV40VkOLAQCAI+UNVNIvI8sFpV57rTbhGRzTjVWU+o6tGMbsuY/GLHjr95/vkfmTChI8WLh7BmzSAKF7a7bONf6f7gTkR24lQJzQC+UNWY7AgsNfaDO5MfxcbGM3bsz7z44jIKFQpiwYK7uf56a6g2vvPrD+5UtSrwAtAI2CAiX4pIhu8wjDGZs2TJLurVm8Qzz/zAHXeEs3XrcEsSJlv59MtsVf1FVR8CGgLRwCd+jcoYAzi/i3jxxWXExSXyzTe9mT69G+XKFQ90WCafSbdyU0SK4fxQricQAcwBWvg5LmPyrcRE5f33f6NDh2pUrFiSqVP/QalShSlSxDrwM4Hhyx3FRpwnncaqajVVHaGqv/o5LmPypfXrD9Kq1QcMGjSf9977DYCrry5uScIElC+PS1RR1US/R2JMPnby5Dmee+4H3nxzBZdfXoQpU7rQt2+9QIdlDJBGohCR11V1BDBLRC56NMrecGdM1hk9+gdef305993XgFdeuZkyZaxvJpNzpPp4rIg0UdWVItLW2/RAvdjIHo81ecWePSc4dSqO8PBQjhw5zdatR2jVqlKgwzJ5lF8ej1XVle6fEar6nec/nEZtY0wmxMcn8sYby4mImMADD8wHIDS0qCUJk2P50ph9r5dxA7M6EGPygxUromjceDIjRnxLmzZh/O9/dwQ6JGPSlVYbRQ+cR2Iri8gXHpOKA8f9HZgxec2CBdvp3PlTypUrzhdfdOeOO8KtbyaTK6T11NNKnHdQVMB5U12SGGCtP4MyJq9QVfbti6F8+RLcfHMVnn/+Rh5+uCnFi4cEOjRjfJZuX085jTVmm9xi+/ajDB26gO3bj7J58zCKFSsU6JBMPnYpjdlpVT39qKqtReQYF75wSABV1dKZ2aAxed3Zs/G88spPvPzyTxQpEszLL7elSBHr4dXkXmkdvUmvOw3NjkCMyQsOHDjJDTd8yB9//E2vXrV54432XHVVsUCHZcwlSTVRePwauyKwT1XPiUgroC7wMU7ngMYYIC4ugYIFg7jyysu44YZrmDChI+3aVQ10WMZkCV8ej/0S5zWoVYGPcH5DMc2vURmTSyQmKpMmraZq1beIiopGRHjvvdstSZg8xZdEkaiqcUBXYJyqPgiU929YxuR8v/9+gBYt3mfIkAVUr16GuLiEQIdkjF/49CpUEbkL6AMk/TrIurI0+Zaq8sQTixg3bgWlSxdh6tR/0Lt3HftNhMmzfEkU9wJDcboZjxSRysCn/g3LmJxLRDh27AwDBzod+F1+eZFAh2SMX/n0OwoRCQaquYM7VDXer1GlwX5HYQLhr7+O8/DD3/DMM61p2PBqEhOVAgXsDsLkHn59Z7aIXA/sAN4HPgC2i0jLzGzMmNwmLi6BsWN/pmbNiSxaFMm2bUcALEmYfMWXqqc3gY6quhlARCKAqUCmMpMxucUvv+zhgQfms3HjIbp0qcFbb91KpQT04eIAAB6QSURBVEolAx2WMdnOl0RRKClJAKjqFhGxvghMnrd4cSQnTpzlyy970KVLeKDDMSZg0m2jEJEpQCzOXQRAb6Coqvbzb2jeWRuF8RdVZerU9ZQtW5Rbb61ObGw8cXGJ1keTyRP82kYBDAZ2Av8HjAQigQcyszFjcqqtW49w000f0a/fl3z44ToAQkKCLUkYQzpVTyJSB6gKzFbVsdkTkjHZ58yZOF56aRmvvvozl11WiHfe6cR99zUMdFjG5Cip3lGIyFM43Xf0BhaJiLc33RmTq82bt50XXlhGjx612bp1GIMGNbInmoxJIdU2ChHZBDRR1VMiUhb4SlWvy9bovLA2CnOpDhw4ybp1B+jQoRqqyqpV+2jSxHqlMXmbv9ooYlX1FICqHk5nXmNyvISERCZOXEWNGuPp02c2Z87EISKWJIxJR1ptFFU83pUtQFXPd2erale/RmZMFvrtt/0MHjyfVav2cfPNVZg4sSNFiliXZcb4Iq1EcWeK4fH+DMQYf9m16xhNmrxLaGhRpk3rSs+eta0DP2MyIK0XF32XnYEYk5VUlQ0bDlG37pVUrnw5H37Yhc6da1CqVOFAh2ZMrmPtDibP2bXrGJ06fUqDBu+wfv1BAPr0qWdJwphM8muiEJEOIrJNRHaIyD/TmK+biKiIWP9RJtPOnUvglVd+olatifz445+89lo7atYsG+iwjMn1fOnrCQARCVHV2AzMHwRMANoBUcAqEZnr2W+UO19x4CHgV1/XbUxKCQmJtGjxPmvW7Kdr1wjGjWtPxYrWgZ8xWcGXbsabiMgG4A93uJ6I/NeHdTfBeXdFpKqeA6YDXbzMNwYYC5z1PWxjHNHRzrVLUFAB7r23AfPm9WLWrO6WJIzJQr5UPb0FdAKOAqjq78CNPixXHtjjMRxFindti0gDoKKqzk9rRSIySERWi8jq+ISAvTPJ5CCqypQp66hS5T/MmeP8AHPo0Ovo1OnaAEdmTN7jS6IooKp/pRjny1vkvT1/mPwzcBEpgPOuixHprUhVJ6tqY1VtHBzkc22ZyaM2bz5Mmzb/Y8CAOYSHh1K1aulAh2RMnubLWXePiDQB1G13eBDY7sNyUUBFj+EKwD6P4eJAbeAH95n2q4C5InK7qq72JXiT/4wd+zOjRn1PiRIhvPdeZwYMaGB9MxnjZ74kiiE41U+VgIPAYndcelYB1UWkMrAX6AncnTRRVU8AoUnDIvID8LglCeONqiIiXHVVMXr3rsO//92OsmUvC3RYxuQL6SYKVT2Ec5LPEFWNF5HhwEIgCPhAVTeJyPPAalWdm+FoTb6zb18MDz/8DddfX4mHHmpK37716Nu3XqDDMiZfSTdRiMi7eLQtJFHVQektq6pfAV+lGPdMKvO2SW99Jv9I6sBv1KjviYtLpEWLCoEOyZh8y5eqp8UefxcG/sGFTzMZk6XWrTvAfffNZc2a/dxyS1UmTuxoDdbGBJAvVU8zPIdFZCqwyG8RmXzvxImz7NsXw4wZ3bjrrprWgZ8xAZaZZ00rA9dkdSAm/1JVZs7czB9/HGXUqBto3TqMyMiHKVzYHoU2Jifw5ZfZx0Tkb/ffcZy7iaf8H5rJD3bu/JuOHafRo8fnzJmzjbg45yc6liSMyTnS/DaKc89fD+fxVoBETe3dqcZkQGxsPK+99gsvvLCMggUL8J//dGDo0OsIDrYOjY3JadJMFKqqIjJbVRtlV0Amf9izJ5oxY5bSuXMNxo1rT/nyJQIdkjEmFb5cvq0UkYZ+j8TkeYcPn2L8+JUAVKtWms2bhzFz5l2WJIzJ4VK9oxCRYFWNB1oB94vITuAUTh9OqqqWPIxPEhOVDz9cy//932JiYmJp164KNWqEUqXK5YEOzRjjg7SqnlYCDYE7sikWkwdt3HiIIUMW8NNPu7n++kpMmtSJGjVC01/QGJNjpJUoBEBVd2ZTLCaPOXcugVtumcq5cwl88MHt9O9f334TYUwulFaiKCsij6U2UVXf8EM8Jg/4/vtdtG59DYUKBfHZZ3cRHh5KaGjRQIdljMmktBqzg4BiON2Be/tnzAWioqK5887PaNv2Iz766HcAWrWqZEnCmFwurTuK/ar6fLZFYnKt+PhExo9fyb/+tYSEhERefrktvXvXDXRYxpgskm4bhTHp6dNnNtOnb+TWW6sxYUJHKle2p5mMyUsktR9ai0hpVf07m+NJV8mK4Xpiz9ZAh5HvHT9+luDgAhQrVoifftrNgQMnufPOCGusNiaHEpE1qto4M8um2kaRE5OECTxVZfr0jURETOBf//oecNohunWzXl6NyausYx3jsx07/qZ9+4/p1WsWFSqU4J57rB3CmPzAuug0Ppk2bQP33juHkJBgxo+/lcGDGxMUZNcZxuQHlihMmuLiEihYMIjGjcvRrVtNxo5tR7ly9nS0MflJqo3ZOZU1ZmePQ4dOMWLEt5w6dY4vvugR6HCMMZfIL43ZJn9KTFQmT15DjRrjmTFjI7VqlSUhITHQYRljAsiqnkyyyMhj3HPPFyxfHkWbNmG8/fZthIdbB37G5HeWKEyykiVDOH78LP/73x306VPXHnc1xgBW9ZTvzZ27ja5dZ5CQkEiZMkXZuHEoffvWsyRhjElmiSKf2r37BHfcMZ0uXaazfftR9u8/CUCBApYgjDEXsqqnfCY+PpFx41bw7LM/oKq8+urNPPpoMwoWDAp0aMaYHMoSRT6TkJDIe+/9xk03Vea//72VsLBSgQ7JGJPDWdVTPnDs2BlGjlxETEwsISHB/Pzzvcyd29OShDHGJ5Yo8jBV5ZNP1hMePoHXX1/OkiV/AlCmTFFrrDbG+MyqnvKo7duPMnToAr77bhdNmpRn4cJ7qF//qkCHZYzJhSxR5FGPPPINq1fvY+LEjgwa1Mg68DPGZJolijxk0aKdhIeHUrFiSd5++zZCQoK56qpigQ7LGJPL+fUyU0Q6iMg2EdkhIv/0Mv0xEdksIutF5DsRucaf8eRVBw6c5O67Z3HLLR/z6qs/A3DNNaUsSRhjsoTfEoWIBAETgFuBmkAvEamZYra1QGNVrQt8Doz1Vzx5UWKiMmnSasLDxzNr1haefbY1r712S6DDMsbkMf68o2gC7FDVSFU9B0wHunjOoKpLVPW0O7gCqODHePKcl19expAhC2jUqBzr1w9m9Og2FC5stYnGmKzlz7NKeWCPx3AU0DSN+QcCX3ubICKDgEEARa+umlXx5UoxMbEcOXKaypUvZ/DgxlSufDm9etW2x12NMX7jzzsKb2cur29JEpF7gMbAv71NV9XJqtpYVRsHB+XPK2ZVZfbsLdSsOZEePT5HVSlTpih3313HkoQxxq/8mSiigIoewxWAfSlnEpGbgVHA7aoa68d4cq2//jrO7bdPp2vXzyhdughvvXWrJQdjTLbx5+X5KqC6iFQG9gI9gbs9ZxCRBsA7QAdVPeTHWHKt5cv3cPPNUwF47bV2PPxwM4KD7TcRxpjs47dEoarxIjIcWAgEAR+o6iYReR5YrapzcaqaigEz3Svk3ap6u79iyk2io2MpUSKEhg2v5t576/PEEy2pVKlkoMMyxuRDouq12SDHKlkxXE/s2RroMPzm6NHT/POfi/n220g2bRpKsWKFAh2SMSYPEJE1qto4M8vmz5bhHEhVmTp1PSNGfMuxY2d47LHmWDOEMSYnsESRA5w4cZY77pjBDz/8SfPmFZg0qRN1614Z6LCMMQawRBFQqoqIUKJECKGhRZk8uRMDBza015EaY3IUe3wmQBYu3EHDhpOJiopGRJg58y7uv7+RJQljTI5jiSKb7d8fQ8+en9OhwyecPh3HoUOnAh2SMcakyaqestGECSt56qnviY2N57nn2jByZEtCQuwjMMbkbHaWykZr1uynadPyTJjQkerVywQ6HGOM8YklCj+Kjo7lmWeW0KdPXRo1KsfEibcREhJk3W8YY3IVSxR+oKrMmrWFhx/+hv37Y6hUqSSNGpWzLsCNMbmSnbmy2K5dxxg+/Gu++uoP6te/ii++6E7TpvaaDWNM7mWJIot98skGli79izffbM/w4U2sAz9jTK5nfT1lgWXL/iI2NoGbb65CbGw8hw+fpkKFEoEOyxhjkl1KX092uXsJjhw5zb33zuGGG6bw/PM/AhASEmxJwhiTp1jVUyaoKlOmrOOJJxZx4kQsI0e25F//uiHQYZkcKi4ujqioKM6ePRvoUEw+ULhwYSpUqEDBggWzbJ2WKDLhq6/+4N5759KyZUUmTepE7dpXBDokk4NFRUVRvHhxwsLC7NFo41eqytGjR4mKiqJy5cpZtl6revLR6dNx/PzzbgA6dqzOnDk9Wbp0gCUJk66zZ89SpkwZSxLG70SEMmXKZPndqyUKH3z99R/Urj2RW2/9hOPHzyIi3H57DevAz/jMkoTJLv441ixRpGHv3mjuumsmHTtOIyQkmHnzelGqVOFAh2WMMdnKEkUqDh06Rc2aE5k/fzsvvHAjv/8+mNatwwIdljEZFhQURP369alduzadO3fm+PHjydM2bdrETTfdxLXXXkv16tUZM2YMno/Mf/311zRu3JiIiAjCw8N5/PHHA7ELaVq7di333XffBeO6dOlC8+bNLxjXv39/Pv/88wvGFStWLPnv7du307FjR6pVq0ZERATdu3fn4MGDlxTbzJkzqVWrFgUKFGD16tWpzvfNN99Qo0YNqlWrxiuvvJI8fteuXTRt2pTq1avTo0cPzp07B8D48eP58MMPLym2DFHVXPWvRIUa6k9RUSeS//7Pf1bojh1H/bo9k/dt3rw5oNu/7LLLkv/u27evvvDCC6qqevr0aa1SpYouXLhQVVVPnTqlHTp00PHjx6uq6oYNG7RKlSq6ZcsWVVWNi4vTCRMmZGlscXFxl7yObt266bp165KHjx07phUqVNDw8HCNjIxMHt+vXz+dOXPmBcsmlc2ZM2e0WrVqOnfu3ORp33//vW7YsOGSYtu8ebNu3bpVW7duratWrfI6T3x8vFapUkV37typsbGxWrduXd20aZOqqt5111366aefqqrqAw88oBMnTlRV57OqX79+mttNCVitmTzv2lNPrhMnzvL009/zzjtrWLHiPho2vJqHHmoa6LBMHvPcvE1s3hedpeusWa4Ez3au5dO8zZs3Z/369QBMmzaNli1bcssttwBQtGhRxo8fT5s2bRg2bBhjx45l1KhRhIeHAxAcHMzQoUMvWufJkyd58MEHWb16NSLCs88+y5133kmxYsU4efIkAJ9//jnz589nypQp9O/fn9KlS7N27Vrq16/P7NmzWbduHaVKlQKgWrVq/PzzzxQoUIDBgweze7fzEMm4ceNo2bLlBduOiYlh/fr11KtXL3ncrFmz6Ny5M1deeSXTp0/nySefTLdcpk2bRvPmzencuXPyuBtvvNGnMk1LREREuvOsXLmSatWqUaVKFQB69uzJnDlziIiI4Pvvv2fatGkA9OvXj9GjRzNkyBCKFi1KWFgYK1eupEmTJpccZ3ryfaJQVWbO3Mwjj3zDgQMnGT68CVWrXh7osIzJcgkJCXz33XcMHDgQcKqdGjVqdME8VatW5eTJk0RHR7Nx40ZGjBiR7nrHjBlDyZIl2bBhAwDHjh1Ld5nt27ezePFigoKCSExMZPbs2QwYMIBff/2VsLAwrrzySu6++24effRRWrVqxe7du2nfvj1btmy5YD2rV6+mdu3aF4z79NNPefbZZ7nyyivp1q2bT4li48aNF5WFNzExMVx//fVep02bNo2aNWumu46U9u7dS8WKFZOHK1SowK+//srRo0cpVaoUwcHByeP37t2bPF/jxo1ZtmyZJQp/U1W6dv2ML7/cSsOGVzN3bi8aNy4X6LBMHubrlX9WOnPmDPXr1+fPP/+kUaNGtGvXDjj/znZvMvLkzOLFi5k+fXry8OWXp3+hdddddxEUFARAjx49eP755xkwYADTp0+nR48eyevdvHlz8jLR0dHExMRQvHjx5HH79++nbNmyycMHDx5kx44dtGrVChEhODiYjRs3Urt2ba/7lNEnhIoXL866desytEx61Es3SiKS6vgkV1xxBVu3Zk93RvmyMTsuLgFwCr1Vq4q89VYHVq68z5KEyZOKFCnCunXr+Ouvvzh37hwTJkwAoFatWhc1sEZGRlKsWDGKFy9OrVq1WLNmTbrrTy3heI5L+Vz/ZZddlvx38+bN2bFjB4cPH+bLL7+ka9euACQmJrJ8+XLWrVvHunXr2Lt37wVJImnfPNc9Y8YMjh07RuXKlQkLC+PPP/9MTmJlypS54G7n77//JjQ0NLksfNnXmJgY6tev7/WfZ1LLiAoVKrBnz57k4aioKMqVK0doaCjHjx8nPj7+gvFJzp49S5EiRTK1zQzLbONGoP5damP2kiW7NDx8vH755ZZLWo8xvspJjdm//fabVqxYUc+dO6enT5/WypUr66JFi1TVady+7bbb9K233lJV1d9//12rVq2q27ZtU1XVhIQEff311y9a/8iRI/Xhhx9OHv77779VVbVq1aq6efNmTUhI0K5du2q/fv1U1Xuj8uOPP6733HOP3nrrrcnjevXqpWPHjk0eXrt27UXb3rJli7Zs2TJ5uFmzZvrLL78kD0dGRmrVqlVVVXXevHnatm1bjY2NVVXV119/XQcMGJC871WrVtX58+cnL/v111/r+vXrL9pmZqTVmB0XF6eVK1fWyMjI5MbsjRs3qqrTUO/ZmO35MMHw4cOTp6WU1Y3ZAT/xZ/RfZhPFoUMntW/f2QqjtXLlcfrdd5HpL2RMFshJiUJVtVOnTvrRRx+pqur69eu1devWeu2112rVqlV19OjRmpiYmDzvvHnztGHDhhoeHq4RERH6+OOPX7T+mJgY7du3r9aqVUvr1q2rs2bNUlXVmTNnapUqVbR169Y6bNiwNBPFqlWrFNApU6Ykjzt8+LB2795d69SpoxEREfrAAw943b/atWtrdHS07tq1S8uVK3dB/KqqDRo00BUrVqiq6ujRo7V27dpar1497dq1qx46dCh5vi1btmj79u21WrVqGhERoT169NADBw6kWbbp+eKLL7R8+fJaqFAhveKKK/SWW25RVdW9e/dekBQXLFig1atX1ypVqiQ/laaqunPnTr3uuuu0atWq2q1bNz179uwF+3X48GGv283qRJEvuhn/9NMNDBv2FSdPnuOJJ1owatQNFC2adR1mGZOWLVu2+PT0i8mcN998k+LFi1/0W4q8bO3atbzxxhtMnTrV63Rvx5x1M56O+PhEate+gnXrBvPii20tSRiThwwZMoSQkJBAh5Gtjhw5wpgxY7Jte3nyjuLUqXOMGbOUSpVKMnTodclPD1h/OyYQ7I7CZDe7o0jH/PnbqVVrIq+++jPbtx8FnARhScIEUm67IDO5lz+OtTzzO4qoqGgeeuhrZs/eSs2aZVm6tD/XX39NoMMyhsKFC3P06FHratz4narzPorChbO289I8kygiI4+xcOFOXn65LY891pxChYICHZIxgPOcfFRUFIcPHw50KCYfSHrDXVbK1W0UK1fuZfnyPTz8cDMAjh49TZkyRQMZnjHG5Eg5to1CRDqIyDYR2SEi//QyPUREZrjTfxWRMF/We/z4WYYOXUCzZu/xxhsrOHXK6XrXkoQxxmQ9vyUKEQkCJgC3AjWBXiKSssesgcAxVa0GvAm8mt564xMSCQ8fzzvvrOGhh5qyYcMQLrusUFaHb4wxxuXPNoomwA5VjQQQkelAF8CzQ5QuwGj378+B8SIimkZ9WFxcAhXDSvLVV71p2PBq/0RujDEmmT8TRXlgj8dwFJDyBQ/J86hqvIicAMoARzxnEpFBwCB3MHb1kUEbfegROD8IJUVZ5WNWFudZWZxnZXFejcwu6M9E4e05wJR3Cr7Mg6pOBiYDiMjqzDbI5DVWFudZWZxnZXGelcV5IpL6u1jT4c/G7CigosdwBWBfavOISDBQEvjbjzEZY4zJIH8milVAdRGpLCKFgJ7A3BTzzAX6uX93A75Pq33CGGNM9vNb1ZPb5jAcWAgEAR+o6iYReR6nu9u5wPvAVBHZgXMn0dOHVU/2V8y5kJXFeVYW51lZnGdlcV6myyLX/eDOGGNM9spznQIaY4zJWpYojDHGpCnHJgp/df+RG/lQFo+JyGYRWS8i34lInu02N72y8Jivm4ioiOTZRyN9KQsR6e4eG5tEZFp2x5hdfPiOVBKRJSKy1v2edAxEnP4mIh+IyCER2ZjKdBGRt9xyWi8iDX1acWbfoerPfziN3zuBKkAh4HegZop5hgKT3L97AjMCHXcAy+JGoKj795D8XBbufMWBpcAKoHGg4w7gcVEdWAtc7g5fEei4A1gWk4Eh7t81gT8DHbefyuIGoCGwMZXpHYGvcX7D1gz41Zf15tQ7iuTuP1T1HJDU/YenLsD/3L8/B9pK3uzsP92yUNUlqnraHVyB85uVvMiX4wJgDDAWOJudwWUzX8rifmCCqh4DUNVD2RxjdvGlLBQo4f5dkot/05UnqOpS0v4tWhfgI3WsAEqJSLp9IeXUROGt+4/yqc2jqvFAUvcfeY0vZeFpIM4VQ16UblmISAOgoqrOz87AAsCX4+Ja4FoR+VlEVohIh2yLLnv5UhajgXtEJAr4Cngwe0LLcTJ6PgFy7ouLsqz7jzzA5/0UkXuAxkBrv0YUOGmWhYgUwOmFuH92BRRAvhwXwTjVT21w7jKXiUhtVT3u59iymy9l0QuYoqqvi0hznN9v1VbVRP+Hl6Nk6ryZU+8orPuP83wpC0TkZmAUcLuqxmZTbNktvbIoDtQGfhCRP3HqYOfm0QZtX78jc1Q1TlV3AdtwEkde40tZDAQ+A1DV5UBhnA4D8xufzicp5dREYd1/nJduWbjVLe/gJIm8Wg8N6ZSFqp5Q1VBVDVPVMJz2mttVNdOdoeVgvnxHvsR50AERCcWpiorM1iizhy9lsRtoCyAiETiJIj++m3Yu0Nd9+qkZcEJV96e3UI6selL/df+R6/hYFv8GigEz3fb83ap6e8CC9hMfyyJf8LEsFgK3iMhmIAF4QlWPBi5q//CxLEYA74rIozhVLf3z4oWliHyKU9UY6rbHPAsUBFDVSTjtMx2BHcBpYIBP682DZWWMMSYL5dSqJ2OMMTmEJQpjjDFpskRhjDEmTZYojDHGpMkShTHGmDRZojA5jogkiMg6j39hacwbllpPmRnc5g9u76O/u11e1MjEOgaLSF/37/4iUs5j2nsiUjOL41wlIvV9WOYRESl6qds2+ZclCpMTnVHV+h7//sym7fZW1Xo4nU3+O6MLq+okVf3IHewPlPOYdp+qbs6SKM/HORHf4nwEsERhMs0ShckV3DuHZSLym/uvhZd5aonISvcuZL2IVHfH3+Mx/h0RCUpnc0uBau6ybd13GGxw+/oPcce/IuffAfKaO260iDwuIt1w+tz6xN1mEfdOoLGIDBGRsR4x9xeR/2YyzuV4dOgmIm+LyGpx3j3xnDvuIZyEtURElrjjbhGR5W45zhSRYulsx+RzlihMTlTEo9pptjvuENBOVRsCPYC3vCw3GPiPqtbHOVFHud019ABauuMTgN7pbL8zsEFECgNTgB6qWgenJ4MhIlIa+AdQS1XrAi94LqyqnwOrca7866vqGY/JnwNdPYZ7ADMyGWcHnG46koxS1cZAXaC1iNRV1bdw+vK5UVVvdLvyeBq42S3L1cBj6WzH5HM5sgsPk++dcU+WngoC4906+QScfotSWg6MEpEKwBeq+oeItAUaAavc7k2K4CQdbz4RkTPAnzjdUNcAdqnqdnf6/4BhwHicd128JyILAJ+7NFfVwyIS6faz84e7jZ/d9WYkzstwuqvwfENZdxEZhPO9vhrnBT3rUyzbzB3/s7udQjjlZkyqLFGY3OJR4CBQD+dO+KKXEqnqNBH5FbgNWCgi9+F0q/w/VX3Sh2309uxAUES8vt/E7VuoCU4ncz2B4cBNGdiXGUB3YCswW1VVnLO2z3HivMXtFWAC0FVEKgOPA9ep6jERmYLT8V1KAixS1V4ZiNfkc1b1ZHKLksB+9/0BfXCupi8gIlWASLe6ZS5OFcx3QDcRucKdp7T4/k7xrUCYiFRzh/sAP7p1+iVV9SuchmJvTx7F4HR77s0XwB0470iY4Y7LUJyqGodThdTMrbYqAZwCTojIlcCtqcSyAmiZtE8iUlREvN2dGZPMEoXJLSYC/URkBU610ykv8/QANorIOiAc55WPm3FOqN+KyHpgEU61TLpU9SxO75ozRWQDkAhMwjnpznfX9yPO3U5KU4BJSY3ZKdZ7DNgMXKOqK91xGY7Tbft4HXhcVX/HeT/2JuADnOqsJJOBr0Vkiaoexnki61N3OytwysqYVFnvscYYY9JkdxTGGGPSZInCGGNMmixRGGOMSZMlCmOMMWmyRGGMMSZNliiMMcakyRKFMcaYNP0/REiaL3rgxjMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree, y_train, X_train, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (GINI) identified 809\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054821280
11178809
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5482 1280\n", + "1 1178 809" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.81 0.82 6762\n", + " 1 0.39 0.41 0.40 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
055091253
11156831
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5509 1253\n", + "1 1156 831" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.81 0.82 6762\n", + " 1 0.40 0.42 0.41 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")\n", + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06371391
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6371 391\n", + "1 1274 713" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.27\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6762\n", + " 1 0.65 0.36 0.46 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc_rf = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")\n", + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13442\n", + " 1 0.79 0.46 0.58 4054\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06381381
11270717
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6381 381\n", + "1 1270 717" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24738247273049666\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")\n", + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Trees - Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc_rf])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17450
Method: MLE Df Model: 45
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784
Time: 00:13:23 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649
SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016
AGE 0.2097 0.100 2.095 0.036 0.013 0.406
PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726
PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365
PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037
PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081
PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325
PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731
BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821
BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706
BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396
BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992
BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338
BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799
PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640
PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114
PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186
PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072
PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185
PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145
MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648
GRAD 1.3518 0.220 6.148 0.000 0.921 1.783
UNI 1.3056 0.219 5.971 0.000 0.877 1.734
HS 1.2342 0.223 5.547 0.000 0.798 1.670
MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06
MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426
SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245
PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067
PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284
PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705
PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026
PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947
PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350
PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106
PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247
PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203
PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205
PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252
PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160
PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711
PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500
PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467
PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335
PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343
PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17450\n", + "Method: MLE Df Model: 45\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784\n", + "Time: 00:13:23 Log-Likelihood: -7781.7\n", + "converged: False LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", + "PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", + "PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", + "PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", + "PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", + "PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", + "PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", + "BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", + "BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", + "BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", + "BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", + "BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", + "BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", + "PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", + "PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", + "PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", + "PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", + "PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", + "PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", + "MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", + "GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", + "UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", + "HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", + "MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", + "MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", + "SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", + "PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", + "PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", + "PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", + "PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", + "PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", + "PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", + "PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", + "PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", + "PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", + "PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", + "PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", + "PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", + "PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", + "PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", + "PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", + "PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", + "PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", + "PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 13442\n", + " 1 0.67 0.36 0.47 4054\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs average on both the train and test set with an accuracy of about 0.8 but recall and f1 are still below 0.5. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.445360\n", + " Iterations: 35\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445387\n", + " Iterations 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445388\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445392\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445397\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445410\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445455\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445512\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445596\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445680\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445770\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445853\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445877\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445963\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446090\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446288\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17468
Method: MLE Df Model: 27
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756
Time: 00:14:16 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676
SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036
PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692
PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397
PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111
PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276
BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672
BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867
PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693
PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450
PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350
PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277
GRAD 1.3465 0.175 7.687 0.000 1.003 1.690
UNI 1.2982 0.174 7.462 0.000 0.957 1.639
HS 1.2384 0.178 6.960 0.000 0.890 1.587
MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318
PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799
PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159
PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010
PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421
PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397
PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509
PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471
PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173
PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361
PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288
PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477
PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17468\n", + "Method: MLE Df Model: 27\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756\n", + "Time: 00:14:16 Log-Likelihood: -7808.3\n", + "converged: True LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", + "SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", + "PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", + "PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", + "PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", + "PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", + "BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", + "BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", + "PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", + "PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", + "PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", + "PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", + "GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", + "UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", + "HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", + "MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", + "PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", + "PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", + "PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", + "PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", + "PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", + "PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", + "PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", + "PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", + "PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", + "PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", + "PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", + "PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21650864211883647\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.78 0.83 6762\n", + " 1 0.46 0.62 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we used that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 1235\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053031459
17521235
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5303 1459\n", + "1 752 1235" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 715\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06421341
11272715
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6421 341\n", + "1 1272 715" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defaults. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gU1frA8e8hCSkkBEjoEAIECL1FkCoiSFEUaYqKBbiIiBW52PWKiihXvf7AXrEAAoIIiIiKFEGK9N4hhBYgnfT398ds4iaEZIFsNpu8n+fhITszO/NO2XlnzjlzxogISiml1KWUcXUASimlijdNFEoppfKliUIppVS+NFEopZTKlyYKpZRS+dJEoZRSKl+aKPJhjLnLGLPU1XG4mjEmxBiTYIzxKMJlhhpjxBjjWVTLdCZjzA5jTLcr+N4VHYPGGF9jzI/GmFhjzOzL/X5RMcb8ZIy59wq+18UYs8cZMRVnrvgtAhh3eY7CGHMYqApkAAnAEmCsiCS4Mq6SyLatR4rIMhfGEAocArxEJN1VcdhiEaCBiOx38nJCKaR1NsYMAx4GOhbG9rMlua9FpNbVzusKl18o+8AY8xLwLJACpAM7gXEisuaqgyzB3O2Oop+I+AOtgNbA0y6O54q48iq5pFyhX45Sur3rAHuvJEmUgmNklu08Egz8DjjljqtEbUcRcYt/wGGgh93nN4BFdp+9gSnAUeAU8AHgazf+VmAzEAccAHrbhgcCnwIngOPAK4CHbdx9wCrb3x8AU3LF9APwhO3vGsBc4AzWVeEjdtO9BMwBvrYtf2Qe6xcITLd9/wjwHFDGLo7VwP8BscBu4IZc381vHVYDbwPnbOPqA78BZ4Fo4Buggm36r4BM4ALWndu/gVBAAE/bNMuBibb5xgNLgWC7eO6xrcNZ4Pnc+y7XevsC/7VNHwussg3LWua9tn0aDTxr9712wBogxrbeU4GyduMFeAjYBxyyDfsfcMy2DzYCXeym9wCesR0b8bbxtYEVtnkl2rbH7bbpb8Y6nmKAP4EWuY7VCcBWrCtXT/ttYIt9gy2OU8BbtuFHbctKsP3rgN0xaJumKfCLbV+eAp7JY5v+B0gF0mzzGYF1UficbTufxjrWAm3TZ23rEbYYVuQxz25A5CX2YX7Hrodt/0Zj/S7GcvGxNNL2dxjwh+04iMY6oZPXPsgdj21ffW+L4Sww9RKxvoR1Z5T1uYlt3pXthuW3b9sAm2zHyGxgFvCK/Tay7fuTwFcOzG8C1m82HtiD7XedzzGSta+ytl8NYIHteNgP/CvXun5n2zfxwA4g4orOv0V9wr/Sf+T8odUCtgH/sxv/jm2DVQICgB+BSXYbPRboifWDqQmE28bNBz4EygFVgHXAA3Yn2axE0RXrJJNVXFcR62RawzbPjcALQFmgHnAQ6GW3w9KA/rZpffNYv+lYiSfAdjDsBUbYxZEOPA54Yf1QYoFKDq5DOlYxhCfWSTjMti28gcpYP8R38trWlzg4l2OdUBva5rcceN3uh5cAdLZtiym2db9Uophm+35NrJNKR1tcWcv82LaMllgn3ca277UFrrWtUyiwC3jMbr6CdUKtlLW9gbuBINt3xmH9mH1s48ZjHVONAGNbXpDdvMJynSxOA+1tMd9r22bedttvM9bJyzf3NsVKcMNsf/sD1+a1nfM4BgOwkuI4wMf2ub2DJ8ThWCeSerZlfs8/J7Ks5U7HOobyOj67celEkd+xOxqreKcW1m9mGZdOFDOwioXK2Navc679GZZXPLZ9sAXrYqhc7u9eartgHZ+vYyWlrHguuW9t0x8BHsX6HQ7ASsj2iSIdmGyb3reA+TXCOqfUsNsP9S/nGMFKrO/Z1rkVVqK8wW5dk4G+tmVPAtZe0fnX1QnA4UCtjZuAlRkF+JV/roIN1tVGfbvpO/DPleSHwNt5zLMq1snH/s5jKPB7Hj9Sg3W11dX2+V/Ab7a/2wNHc837aeBzux120VWa3bQetjia2A17AFhuF0cUtiRlG7YOGObgOhy91LJt0/QHNuXa1gUliufsxo8Bltj+fgGYYTfOD+vHdFGiwDohXABa5jEua5m1cq3zHZdYh8eAeXafBehewHqfz1o21tXcrZeYLvdJ6n1gYq5p9gDX2W2/4Xkcv1mJYgXWVX9wrmlybOc8jsGh9vupgHV7iZyJ4ldgjN3nRlgJ3NNuufXymV838kgUDhy7v2G7aLF97pHHsZSVKKYDH9nv83z2QXY8WL/1M/bbrYDtkop1dZ+BdffRzZF9i3WxeJycv8NV5EwUqdguPhyYXxhWEumBVS9lP02BxwjWhUgGEGA3fhLwhd26LrMb1wS44Mjxk/ufu9VR9BeRAKwdEo5VxgjWVbEfsNEYE2OMicGq7K5sG18b6wo4tzpYVwYn7L73IdZVeQ5ibemZWD9WgDuximyy5lMjax62+TyDdRLPciyf9Qrmn6uVLEewrrKzHLfFYD++hoPrkGPZxpgqxpiZxpjjxpg4rCKxYC7PSbu/k7CuerDFlL08EUnC+jHmJRjrSiivfZPvcowxDY0xC40xJ23r8BoXr0Pu9R5njNllawkUg1VkkvWdSx0jeakDjMu1v2tjrXuey85lBNbd2G5jzHpjzM0OLvdyYsytBhcfX544foxeSkHHbo7joYBl/BvrgmydrZXYcAdjqA0cEcfrY74TkQpY674d6+40S377tgYX/w5zr88ZEUl2ZH5iVcw/hnVCP237TWYdQ44cIzWAcyISbzcs93kj9+/H50rqTtwtUQAgIn8AX2AVa4B163gBaCoiFWz/AsWqsAJrZ9bPY1bHsK6Ggu2+V15Eml5i0TOAQcaYOlh3EXPt5nPIbh4VRCRARPrah53PKkVjXd3VsRsWgnX1kqWmMcbkGh/l4DrkXvYk27AWIlIeq0jG5DP95TiBVcwAWM00sYp78hKNdWuc174pyPtYdTUNbOvwDDnXAezWwxjTBas8eAhQ0XaiiLX7zqWOkbwcA17Ntb/9RGRGXsvOTUT2ichQrGQ+GZhjjCmX33euIMbcorj4+ErHKv/ODu0K5lvQsZvjeMA6SeZJRE6KyL9EpAbWXcl7xpgwB2I4BoRc7glQRKJty3nJGFPdbl6X2rcnuPh3mHt9cm/DfI8VEflWRDpjbT/BOh7yO0bsRQGVjDEBdsNynzcKhVsmCpt3gJ7GmFYikolVlv22MaYKgDGmpjGml23aT4H7jTE3GGPK2MaFi8gJrIrY/xpjytvG1TfGXJfXAkVkE9Yt7ifAzyISYxu1DogzxkywtV/3MMY0M8Zc48iKiEgGVqXTq8aYAFsiegLrSj9LFeARY4yXMWYw0BhYfLnrYBOAVYwXY4ypiVU+b+8UVln2lZgD9DPGdDTGlMW6fc59AgfAtt8+A94yxtSwbbcOxhhvB5YTgFXRl2CMCQcedGD6dGxFFMaYF4DyduM/ASYaYxoYSwtjTFaCy709PgZGG2Pa26YtZ4y5KdcP9pKMMXcbYyrb1j/rGMqwxZbJpbf9QqCaMeYxY4y37Vhp78gysS5yHjfG1DXG+GPdgc26jKvwrNh97P/Z4s3v2P0OeNT2m6uAlawvNe/BxpispHIe68SZYfuc3zG5Dusk/rptX/gYYzo5sj4ishv4GetuBvLft2ts8Yw1xngaY27Fqv/MzyXnZ4xpZIzpbjvek7EudjNs2+JSx4h97MewKscn2da5BdadyDcUMrdNFCJyBqtM83nboAlYlXVrjVUUsQyrHBYRWQfcj1XZFYtVAZR1BXQP1q3zTqyDcw6QdXWRlxlYZYrf2sWSAfTDqkw6hHWV9QlW0YajHsaqZzmIVe75LdZJNMtfQAPbvF8FBolIVpHO5a7Df7Aq2WKBRVgVm/YmAc/ZbpWfvIx1QER22NZlJtaPNx6rHDblEl95EqsSeT1Wy43JOHZcPolV/BeP9WOcVcD0PwM/YVW0HsH6YdoXG7yFdVJbipWAPsWqjASraOBL2/YYIiIbsOqopmJt7/1YdQmO6g3sMMYkYLXEukNEkm3FdK8Cq23Lutb+S7Yihp5Yx9pJrBZd1zu4zM+wWrStwDpGk7H20+WoiXUys/9Xn/yP3Y+xtulWrNZCi7ESdo6Tns01wF+27bIAeFREDtnGvYTdPrD/kt3vLwyrHjESq8GHo94ERhljquS3b0UkFasCewTWyfturOR9qWObAo4Vb/6pTD+JdTH4jG1cnsdIHosYilVvEQXMA14UkV8uY90d4jYP3JVmxpj7sCr8Ors6lstlu3qNwSoiOlTQ9KpkM8b0AT4QkToFTuwGjDF/Ya3P566OxZnc9o5CFV/GmH7GGD9bmeoUrDuGw66NSrmCrSi2r62opibwItaVr1syxlxnjKlmW597gRZYDWdKNE0UyhluxboVjsIqLrtD9Na1tDJYRZ3nsYqedmE1oXZXjbCe2YjFep5lkK2esETToiellFL50jsKpZRS+XK7TquCg4MlNDTU1WEopZRb2bhxY7SIVC54you5XaIIDQ1lw4YNrg5DKaXcijHmSMFT5U2LnpRSSuVLE4VSSql8aaJQSimVL00USiml8qWJQimlVL40USillMqX0xKFMeYzY8xpY8z2S4w3xph3jTH7jTFbjTFtnBWLUkqpK+fMO4ovsLrKvZQ+WP0ANQBGYb2IRimlVCESEVLTM69qHk574E5EVhhjQvOZ5FZguq2zuLXGmArGmOqloYMtpZQqLOcSU4lOSOHLPw/j6+VBQko6m47GcDA6AWMMaemZV/XKSnDtk9k1yfnimEjbsIsShTFmFNZdByEhIUUSnFJKFQfxyWkkpKRz8EwiS3ecZHtUHMlpGaSmZ3I+KZXohNQc01fw88KzjKF2oC+nj8RyetsZqgb5XVUMrkwUeb0eM8/EJyIfAR8BREREaHe3SqkSR0RITssk9kIaczZa19BTlu7Nc9oKfl50qBeEj5cHZT3K0KF+EOW8PenRuArGGESEiIiPObwnmpdfvp5HHmmPl9fdVxybKxNFJDlfTF4L6/0FSilVYokIC7ZEsebAWVLTM4lLTmPzsViiE/J+o2rNCr6M7R5GWY8yNKwaQNMa5SlTJs/X0PPnn8do3rwKAQHefPJJP4KD/ahd+3LeyJw3VyaKBVgvKZ8JtAditX5CKVWSJKaks+7wOVbti2b/6QTiktPYdDQme3xFPy8qlStLRT8vGlXzp0agL81rBVLGGAa1rYWPl4dDyzl7NomnnlrGJ59s4sUXr+Oll7rRunX1QlsPpyUKY8wMoBsQbIyJxHoFoheAiHyA9ZL1vlgvG08C7ndWLEopVVgyMoXI80msP3yeswkpLN9zBmMgPUNIy8wkPUPYdjyWsh5lSM3I2dqoZgVfWodUoH5lf57qE06wv/dVxSIiTJ++hSef/IXz5y8wfnxHxo/veFXzzIszWz0NLWC8AA85a/lKKVVYktMyWLT1BMt2neKn7ScvGh/s701YlXL4e3niWcbQs0lVUtMzaR1SgUBfL65vVIU6QX4Yk3eR0ZWaMGEZb775Jx071uaDD26iefOqhTr/LG73PgqllHIWESHy/AWSUjM4FJ2ACMzffJyfd5zKnibY35vbWtfg+vAqtKxVAV8vj0vWGTjDhQtpJCamERzsx4gRrWnQoBIjRrRxagyaKJRSpcbp+GROxaaQkp5BSnomq/dHkyHCukPn2HE87qKiIntjrw9j1HX1KO/jVYQR57RkyX4eemgxrVpVY+7cITRqFEyjRsFOX64mCqVUiRR7IY3ZG44Rk5TGusPn2HE8lsTUjDynDfDxpLyvF+3rViIkyI9mNQLx9DBUDvAmwNuT+pX9i/SuIbeoqHgee2wJs2fvpFGjIMaOvaZIl6+JQilVIiSmpLNwaxTrD59n7cGzRJ6/kD3Oo4yhWnkf2tX1p2eTatSs6EtZjzIE+HjStEb5Qq87KEy//nqQ226bRWpqBhMnXs/48R3x9i7aU7cmCqWUW8nIFPacjGfpzpNsPx7LrhPxJKamE5OUlmO6NiEV6N+6Jne3r+PSu4ErlZaWgZeXBy1bVqNv3wa88kp3wsIquSQWTRRKqWLtwJkEvll7lI1HzhGdkMrxmAs5xrcLrURCSjqD29YiJKgct7SsQaCv6+oRrlZcXArPP/8bf/11nNWrhxMc7MfMmYNcGpMmCqVUsSMiRMUm8/jMzaw7fC57eO1KvgxtF5LdhUVEaEX8ypaM05iIMGfOTh59dAknTyYwZsw1pKRk4Ofn+tcGlYwtrJRye6fjk3l67jaOnEti/+mE7OHtQivxxI0NaV+3UrGuS7gaZ84kcu+98/npp/20bl2NH364g2uuqenqsLJpolBKuUR6RiZrD55j98k4vv3rKAejE7PHXVuvEs1rBtIpLJhujaq4MMqiUb68N9HRSbzzTi8eeqgdnp6uv4uwp4lCKVUkTscl8/OOk2w+Fsv+MwlsORaTY3ylcmV5pHsY93Wq66IIi9aKFUd49dWVzJ07BH//sqxdO7LYVrprolBKOUV6RibfbYjkp+0nWLkvOsc4b88yhAb50aNxVQZH1KZ+5XJ4ehSvq2hniY5OYvz4X/jii82Ehlbg8OEYmjWrUmyTBGiiUEoVsrjkNNYcOMsDX23MMfzGJlW5uWUNbgivQrkifg6gOBARPv98M+PH/0JcXApPP92Z557rip9f8W+hVfr2llKq0JyOS2bepuOsO3SOw2cTOXAmMcf4oe1q89xNTUplYsjL119vpUmTynzwwU00beo+dS+695RSDsnMFL5ae4Qtx2L488BZzielkpL+T99I3p5laFy9PE1rWP+ua1iZepX9XRix6yUlpfHaaysZPTqCWrXKM3fuEAIDfYp1MVNeNFEopfKVlpHJr7tOM/rrf4qSwqsFEBxQlkZVy9O7WbXsV3CqfyxevI+HHlrM4cMx1KwZwIMPXkPFir6uDuuKaKJQSl3S939H8sR3W7I/39a6Ji/2a0IFv7IujKp4i4yM47HHljB37i4aNw7mjz/uo2vXOq4O66poolBKkZEpnEtMJT45jW3HY1l36BzrD59j7ynrwbe+zavxdJ/G1K7k5+JIi79XX13BokX7eO217owb15GyZR17nWlxZqwXzbmPiIgI2bBhg6vDUMptnYpLZsn2kySmpnMkOonjMRdYtT86z2mb1wzkyV6NuK5h5SKO0r2sW3ccX19PmjevytmzScTGplCvXkVXh5WDMWajiERcyXf1jkKpEuxCagZrDkaTlJrB7hPxLN5+goO5WiYB1K9cjhoVfOnfqibeXmVoV7cSVQJ8XBCxe4mNTeaZZ37l/fc3cPPNDVmwYChBQX4EBZWsOy9NFEqVADFJqfyy8xSzN0Ry6GwiGZlCWnom8SnpF03bsKo/D3Stz41Nq+Lv7amV0FdARJg1awePP/4zp08n8vDD7Zg4sburw3IaTRRKubFdJ+J44YftrD98Psfwfi1rUNnfG28v6+U81zeqgr+3J1XL+1C2mPUj5I6+/nor99wzn4iIGixcOJS2bWu4OiSn0kShlBtZe/AsM9YdZdnOU5QpY4hP/ueO4ak+4dx9bR389eE2p0hJSefgwfM0blyZIUOakp6eyT33tMSjFHQ9okeUUsVcTFIqN7276qIX9tSvXI77OobSrVEV2oRU0CIkJ/r990M8+OAikpLS2LfvYby9Pbn//tauDqvIaKJQqpg5FZfMnI2R/L77NFsiY0jL+Kdl4v2dQrmrfR3CqpTuJ56LyunTiTz55FK++mor9epV5KOP+hX5+6qLg9K3xkoVMyLC/M3HWbT1BKv2R5Oc9k+3GO3qViK8WgAhlfwY0bmu3jUUof37z9Gu3cckJKTy7LNdePbZLvi68StWr4YmCqVcRET4dNUhpq85wtFzSQC0rVORKgHe9G5WjX4tarhdn0AlQVxcCuXLe1O/fkVGjGjN8OGtady4dD9HoolCqSIkInzz11EWbI7K8S7ooe1q8+9e4VQsp11juEpiYiovv/wHH3/8N1u3PkitWuV5880bXR1WsaCJQqkiEJecxtPfb2PR1hPZwwJ9vejXsjoTeocT4FM6izSKix9/3MPYsT9x9GgsI0a0dot3RBQlTRRKOUlaRiaLt53g/eUH2H0yHoAKfl7cfk1txt/YqNS80a04S0/PZMiQ2cybt5umTSuzcuX9dO4c4uqwih1NFEoVotT0TB6ftZnNx2JyNGf1K+vBmG71Gdu9gQujU1lEBGMMnp5lqF7dn9dfv4HHH+9QIjrwcwZNFEoVgtPxydz9yV/Zva0C9GhchWtCK9GraTVCg8u5MDplb+3aSB56aDEff9yPNm2qM23aTa4OqdjTRKHUFVq9P5plu07x45YoohNSASjv48kD19VnVNd6eGnRUrFy/vwFnnnmVz78cCM1agRw/vyFgr+kACcnCmNMb+B/gAfwiYi8nmt8CPAlUME2zVMistiZMSl1pTIzhRnrjzLv7+PsPBFHUmpG9rh7OtShS4PK9GxS1YURqkuZNWs7jzyyhOjoJB577Fr+859uBAR4uzost+G0RGGM8QCmAT2BSGC9MWaBiOy0m+w54DsRed8Y0wRYDIQ6KyalrsTRs0l8uuogM9YfI9XuHdF9m1fjyRsbEVLJTyumi7ndu6MJDa3AkiV30bp1dVeH43aceUfRDtgvIgcBjDEzgVsB+0QhQHnb34FAlBPjUcoh6w+f47NVh0hNz2RLZCzRCSnZ4+5qH8LY7mFUD3TPdx+XFsnJ6UyevIo2barTr18jnnmmC88917VUdODnDM5MFDWBY3afI4H2uaZ5CVhqjHkYKAf0yGtGxphRwCiAkBBtuqac48jZREZ//Te7TsRlD+tYP4ghEbVoV7cS1zWsrF1ouIFlyw4yZswi9u07x7hxHejXrxFeXtqa6Wo4M1Hk9YvK/d7VocAXIvJfY0wH4CtjTDMRyczxJZGPgI/AehWqU6JVpdb247F8uOIgP26xbmgDfb2YPboDDasGuDgydTlOnUrgiSeW8u232wgLq8TSpXfTs2d9V4dVIjgzUUQCte0+1+LioqURQG8AEVljjPEBgoHTToxLKZLTMvhp+wne+/0A+05bTVobVvVnWIdQhl1bx8XRqSvxyy8HmTNnJy+80JWnn+6Cj4826iwsztyS64EGxpi6wHHgDuDOXNMcBW4AvjDGNAZ8gDNOjEmVclExF3jwm7/Zciwme1jV8t5MHtiCbo2quDAydSW2bDnJvn3nGDSoCXfd1ZxOnWpTt25FV4dV4jgtUYhIujFmLPAzVtPXz0RkhzHmZWCDiCwAxgEfG2MexyqWuk9EtGhJFZqMTGHvqXhW749mytI9ObrwfqBrPUZ1rUeQvzaTdDcJCam8+OLv/O9/fxEaWoH+/cPx9CyjScJJnHpvZnsmYnGuYS/Y/b0T6OTMGFTp9MPm4/y45QS/7zlNRuY/1x41K/hyb8c6DO9UV5u0uqn583fz8MM/ERkZx6hRbZg0qQee+h5wp9JCPFWinI5L5vkftvPzjlOA1Qlf/1Y16daoMhGhlfR90m5u27ZT3HbbLJo3r8KsWYPo2LF2wV9SV01/NcqtiQhHzyUxf1MUc/4+xrFzVrcMjauX5/sHO+Krnby5vbS0DFauPEr37nVp3rwqixbdSc+e9bTJaxHSRKHc0snYZBZsOc5ri3fnGN6ydgXG39iITmFB+sxDCfDnn8cYPXohO3acYc+esYSFVaJvX+2Bt6hpolBu5VRcMqOmb2BLZGz2sN5NqzGobS2urR+kRUslxLlzF3jqqWV8/PHf1K5dnu+/H0JYWCVXh1Vq6a9KFXsiwu6T8dz9yV+cTbR6aa1fuRwPdgujd7NqmhxKmOTkdFq1+oCoqHjGjevASy91w99fXxHrSvoLU8Xa7pNx9J+2Okez1g/ubkuvplW1aKmEiYyMo1at8vj4eDJx4vW0alWNli2ruToshSYKVcykZWSy9uBZpizdy47jsaTbmrYG+nrx5fB2tKgZSJkymiBKkgsX0pg0aRWTJ69mzpzB9OvXiHvvbeXqsJQdhxKFMaYsECIi+50cjyqlohNSePfXfUxfcyR7WFC5stzYtCr3d6qr/S6VUEuXHmDMmEUcOHCeu+9uQbt2NV0dkspDgYnCGHMT8BZQFqhrjGkFvCgitzk7OFWyiQjrDp3jiz8P89P2kwAEeHvSs0lVxnYPo15lfxdHqJzp4YcXM3Xqeho0qMSyZcO44YZ6rg5JXYIjdxQvY3UP/juAiGw2xoQ5NSpV4r2zbC/vLNuX/blbo8rccU1tejfTl8qUZBkZVl2Th0cZrr22FsHBfkyY0Fk78CvmHNk7aSISk6viUPtjUlfkROwFbpm6mjPx1suABrapxTN9w7W/pVLg779PMHr0QoYNa8HDD7fnrrtauDok5SBHEsUuY8wQoIytJ9hHgbXODUuVJCLCp6sOsXTnKdYdOgdAjUAflj5xnTZtLQXi41N44YXfeffddVSu7Ef16lrf5G4c+ZWOBV4AMoHvsXqDfdqZQamS43B0It2mLM/+fHOL6tzQuAq3tqyprZdKgaVLDzB8+A9ERcUzenQEr712AxUq+Lg6LHWZHEkUvURkAjAha4AxZgBW0lAqTwu2RPH56kNsOmq996FWRV9+efw67XuplClb1oMqVcoxd+4Q2rev5epw1BUyBb3+wRjzt4i0yTVso4i0dWpklxARESEbNmxwxaKVA+KS03hi1haW7bJ6b21Y1Z+Hrg/j1lba7LE0SEvL4K231hAXl8Krr94AQGam6N1jMWA7b0dcyXcveUdhjOmF9ZrSmsaYt+xGlccqhlIqh9ikNFq+vBQAY2DF+OupXcnPxVGporJq1dHsDvwGD26SnSA0Sbi//IqeTgPbgWRgh93weOApZwal3Muvu07x2KzNxCenA9CxfhDfjGyvXWyUEmfPJjFhwjI+/XQTISGB/PjjUG6+uaGrw1KF6JKJQkQ2AZuMMd+ISHIRxqTcxPI9p3lkxibibAmibZ2K9G9dk7vbh2iSKEXOnr3AzJnb+fe/O/LCC9dRrpx24FfSOFKZXdMY8yrQBMhuriAieslQSokIY775O/tp6rAq/kwe2IK2dfR9xaXFrl1n+O67Hbz4YrR3IgEAACAASURBVDcaNgzi6NHHqVTJ19VhKSdxJFF8AbwCTAH6APejdRSlUnRCCmO++Tv7WQiAX8ddR33taqPUSEpK49VXV/Dmm3/i71+WESPaUKtWeU0SJZwjicJPRH42xkwRkQPAc8aYlc4OTBUf8clpjPxyA3/ZJYjeTavx5uAWBPh4uTAyVZSWLNnPmDGLOHQohnvvbcmbb/akcuVyrg5LFQFHEkWKsQqcDxhjRgPHgSrODUu5UlxyGp+sOMi7v+3Ho4whI/OfJtTP3dSYkV2087bSJiEhlWHD5hEU5Mvvv99Lt26hrg5JFSFHEsXjgD/wCPAqEAgMd2ZQynWS0zLo9fYKTsQm4+NVhtT0TB7v0ZCQIF/6t6qpldSlSEZGJjNmbGfo0Gb4+5dl2bJhhIcH463drpQ6Be5xEfnL9mc8MAzAGKOPWJZA6RmZhD+/BID2dSsx64EOLo5IucrGjVE88MBCNm48ga+vJwMHNtG3zZViZfIbaYy5xhjT3xgTbPvc1BgzHe0UsETZfTKOez5bR9izPwFQPdCHGf+61sVRKVeIjU3mkUd+ol27Tzh+PJ6ZMwcyYEBjV4elXCy/J7MnAQOBLVgV2POweo6dDIwumvCUM2RkCntOxjN74zFW7D3DgTOJAJQr68HILvV4sFt9fZq2lBo48Dt+++0QDz10Da+80p3AQO3AT+Vf9HQr0FJELhhjKgFRts97iiY0VdgSU9J56vtt/LglKnuYr5cHbUIq8HTfxlwTWsmF0SlXOXjwPJUr+xEQ4M2rr3anTBnDNddo31zqH/klimQRuQAgIueMMbs1Sbinc4mpTJi7lV92nsoe1qNxVR7sVo82IRW1grqUSk3NYMqUP5k4cQWPPNKOyZN7ag+vKk/5JYp6xpisrsQNEGr3GREZ4NTI1FXJyBQ+X32Iz1cf5njMBQCCypXl+ZubcGurGpocSrkVK44wevRCdu2KZtCgJjzySHtXh6SKsfwSxcBcn6c6MxBVODIzhSe+28z8zf8UL7ULrcSjPRrQKSzYhZGp4uLtt9fwxBNLCQ2twKJFd9K3bwNXh6SKufw6Bfy1KANRVyc1PZP//LiDb/46mj2sZ5OqTBnckkBffXq6tMvMFBITUwkI8Oammxpy5kwSzz3XFT8/PTZUwfTJmRJg2c5TjJz+z8ucbmxSlQ+HtdXiJQXAjh2nGT16Ufab5ho2DOK1125wdVjKjTg1URhjegP/AzyAT0Tk9TymGQK8BAiwRUTudGZMJc0DX23g5x1WJfUtLWvw3yEt8fLI9/EYVUokJaUxceIfTJmyhsBAb4YPb4WI6AWEumwOJwpjjLeIpFzG9B7ANKAnEAmsN8YsEJGddtM0AJ4GOonIeWOM9iHloEPRiYz55m92nYgDYNkT1xFWRXtxVZZNm04wYMB3HD4cw/33t+KNN3oSHKxvG1RXpsBEYYxpB3yK1cdTiDGmJTBSRB4u4KvtgP0ictA2n5lYz2bstJvmX8A0ETkPICKnL38VSp8Z647y9PfbAKha3ptZozoQGqy9eCqy7xhCQgIJCQnkyy/707VrHVeHpdycI3cU7wI3A/MBRGSLMeZ6B75XEzhm9zkSyN0GryGAMWY1VvHUSyKyxIF5l0oiwr+mb2TZLquo6dt/tadjfW3JpCA9PZOpU9exYMEefvllGEFBfvzxx32uDkuVEI4kijIiciRXuWaGA9/LqyBUcn32BBoA3YBawEpjTDMRickxI2NGAaMAQkJCHFh0ybIjKpYFm6OYvuYIF9KsTT99eDtNEgqAdeuOM3r0QjZtOkmfPmHExaVQsaK+SEgVHkcSxTFb8ZPY6h0eBvY68L1IoLbd51pY3YDknmatiKQBh4wxe7ASx3r7iUTkI+AjgIiIiNzJpsTafjyWCXO3siPKqocI9vemZe1APhwWoU1eFQkJqUyY8Avvv7+B6tUDmD17MAMHNtbKalXoHEkUD2IVP4UAp4BltmEFWQ80MMbUxXrZ0R1A7hZN84GhwBe2HmobAgcdC73kSs/I5PkfdjBjnfVMhF9ZD94a0orezbSbZ/UPL68yLF9+hIcfbsfEid0pX97b1SGpEsqRRJEuIndc7oxFJN0YMxb4Gav+4TMR2WGMeRnYICILbONuNMbsxCrOGi8iZy93WSXJvE2RPD5rS/bn9+5qQ9/m1V0YkSpO9u8/x8sv/8G0aX0JCPBm48ZR+Pjo41DKuYxI/iU5xpgDwB5gFvC9iMQXRWCXEhERIRs2bCh4QjeTkSn8a/oGftttNfy6rXVN3hzUAk99JkIBKSnpvPHGal59dSVly3qwaNGddOmirZmU44wxG0Uk4kq+68gb7uobYzpiFR39xxizGZgpIjOvZIHqYgu2RPHIjE0AtKwVyMf3RFClvL4HQFl+//0QDz64iD17znL77U15661e1KgR4OqwVCni0D2riPwJ/GmMeQl4B/gG0ERRCOyfrB7Quib/HdJSKyNVNhHh1VdXkpaWyZIld9GrV5irQ1KlkCMP3PljPSh3B9AY+AHo6OS4SryMTOGWqauyWzSt/Pf11K6kT84qqwO/Tz/9m969w6hdO5CvvrqNChV88NWWbspFHCkA3w5cC7whImEiMk5E/nJyXCVaZqYw4L3V7IiKI9DXi32v9tEkoQDYuvUUnTt/xqhRC/nkk78BqF49QJOEcilHip7qiUim0yMpJexbNfVoXJWP79FeXpX1TMR//rOct99eS8WKvnzxxa3cc09LV4elFJBPojDG/FdExgFzjTEXNY3SN9xdvqfmbmXmeqtXk3s61OGFm5toklAAvPTScv773zWMHNma11/vQVCQ3mGq4iO/O4pZtv/1zXaF4PWfdjNz/TECfb346dEu1KigXSyUdseOxZKYmEZ4eDBPPdWZ/v3D6dy59HVRo4q/S9ZRiMg625+NReRX+39YldrKQcO/WM8HfxygUrmyrJxwvSaJUi49PZO33lpD48bTeOCBhQAEB/tpklDFliOV2cPzGDaisAMpqTYeOZ/9EN3ap2+gvI9WSpZma9dGEhHxEePGLaVbt1C+/LK/q0NSqkD51VHcjtUktq4x5nu7UQFATN7fUvaOnUti4Pt/AvD1iPaU9dSnrEuzRYv20q/fDGrUCOD774fQv3+41lEpt5BfHcU64CxWr6/T7IbHA5ucGVRJsOnoeW57z0oSd7YPoXMD7RK8NBIRoqLiqVmzPD161OPll6/n0UfbExCgHfgp91FgX0/FTXHv6yk5LYMpP+/hk1WHAHigaz2e6qNXjqXR3r1nGTNmEXv3nmXnzofw9y/r6pBUKeaUvp6MMX+IyHXGmPPkfOGQAUREKl3JAku6m95dyYEziQDMHHUt19YLcnFEqqglJ6fz+uurmDRpFb6+nkyadAO+vtrDq3Jf+R29Wa871TITB720YEd2kjg0qa/eRZRCJ08m0LXr5+zbd46hQ5vx1lu9qFbN39VhKXVVLpko7J7Grg1EiUiqMaYz0AL4Gogrgvjcxq+7TvHFn4cB2PxCT00SpUxaWgZeXh5UrVqOrl3rMG1aX3r2rO/qsJQqFI40w5mP9RrU+sB0rGcovnVqVG4kLSOTwR/8yYgvrXqT/xvamgp+WhZdWmRmCh98sIH69d8lMjIOYwyffHKLJglVojhScJopImnGmAHAOyLyrjFGWz1htWjp9uZyjsdcAGDhw51pVjPQxVGporJly0keeGAhf/11nO7d65KWluHqkJRyCodehWqMGQwMA7KeDtKnxoAJc7dyPOYCYVX8WfbEda4ORxUREWH8+F945521VKrky1df3cZddzXX4kZVYjmSKIYDY7C6GT9ojKkLzHBuWMXf8j2n+W5DJACLHuns4mhUUTLGcP78BUaMsDrwq1hRu2RRJVuBdRQish14BNhgjAkHjonIq06PrBiLTUrjvs/XA/DfwS3x9vRwcUTK2Y4ciaF//5n8/fcJAD7++BY+/LCfJglVKhSYKIwxXYD9wKfAZ8BeY0wnZwdWnI36yqq4njywOQPb1nJxNMqZ0tIyeOON1TRp8h6//HKQPXuiAShTRouZVOnhSNHT20BfEdkJYIxpDHwFXNETfu5u2u/7+evQOSLqVOT2a7S3z5Lszz+P8cADC9m+/TS33tqId9/tQ0iINlZQpY8jiaJsVpIAEJFdxphS2f5TRHjz5z0APHuT9rRe0i1bdpDY2GTmz7+dW28Nd3U4SrmMI4nib2PMh1h3EQB3UQo7BUxOyyD8+SUADG1Xm9YhFV0ckSpsIsJXX22lcmU/+vRpwIQJnXjiiQ7aR5Mq9Rx54G40cAD4NzABOAg84Mygipv1h8/R+IUl2Z9f6d/chdEoZ9i9O5ru3adz773z+fzzzQB4e3tqklCKAu4ojDHNgfrAPBF5o2hCKn4Gf7AGgHE9GzK2e5i2ly9BLlxI47XXVjJ58mrKlSvLhx/ezMiRbVwdllLFSn69xz6D9Sa7v4FrjDEvi8hnRRZZMfD12iN8uOIAADe3qM7DNzRwcUSqsP34415eeWUld9/dgilTelK1qnbgp1Ru+d1R3AW0EJFEY0xlYDFW89hS4cGvN/LT9pMA3NOhDk/10crMkuLkyQQ2bz5J795hDB7chNDQkbRrV9PVYSlVbOWXKFJEJBFARM4YY0rNezxnrDvKT9tPUj3Qh6WPdyVA33NdImRkZPLhhxt5+ulfKVvWg6NHH8PX10uThFIFyC9R1LN7V7YB6tu/O1tEBjg1MhfZGRXH099vI8DHkwVjO2uSKCH+/vsEo0cvZP36KHr0qMd77/XF11f3rVKOyC9RDMz1eaozAykOMjKFvu+uBOCrEe2prO81LhEOHTpPu3YfExzsx7ffDuCOO5ppgwSlLkN+Ly76tSgDKQ4Wbo0CoKKfF61qV3BxNOpqiAjbtp2mRYuq1K1bkc8/v5V+/RpRoYKPq0NTyu2UmnqHgmRkCuO+2wLADw9pb7Du7NCh89x88wxat/6QrVtPATBsWEtNEkpdIacmCmNMb2PMHmPMfmPMU/lMN8gYI8YYl/Uf9ejMTaRnCj2bVCUkyM9VYairkJqaweuvr6Jp0/f444/DTJnSkyZNKrs6LKXcniNdeABgjPEWkZTLmN4DmAb0BCKB9caYBfb9RtmmC8DqxvwvR+dd2D5bdYiFW63uo9+/Sx+2ckcZGZl07PgpGzeeYMCAxrzzTi9q19YO/JQqDI50M97OGLMN2Gf73NIY838OzLsdsF9EDopIKjATuDWP6SYCbwDJjoddeHafjOPlhVbu+uL+a/D00NI4dxIXZ127eHiUYfjw1vz441Dmzh2iSUKpQuTIWfFd4GbgLICIbAGud+B7NYFjdp8jbcOyGWNaA7VFZGF+MzLGjDLGbDDGbDhz5owDi3bM6bhker9jtXJaMf56ujWqUmjzVs4lInzxxWbq1fsfP/ywG4AxY67h5psbujgypUoeRxJFGRE5kmuYI2+Rz6v9oWSPtB7gexsYV9CMROQjEYkQkYjKlQuvzLnz5N8BuCa0otZLuJGdO8/QrduX3H//D4SHB1O/fiVXh6RUieZIHcUxY0w7QGz1Dg8Dex34XiRQ2+5zLSDK7nMA0AxYbmvTXg1YYIy5RUQ2OBL81Xhr6R5SMzKpF1yO2aM7OntxqpC88cZqnn32N8qX9+aTT/px//2t9W1zSjmZI4niQazipxDgFLDMNqwg64EGxpi6wHHgDuDOrJEiEgsEZ302xiwHniyKJBF7IY13f9sPwEf3tHX24lQhEBGMMVSr5s9ddzXnzTd7UrlyOVeHpVSpUGCiEJHTWCf5yyIi6caYscDPgAfwmYjsMMa8DGwQkQWXHW0hmbX+KACjutYjrEqAq8JQDoiKiufRR5fQpUsIjzzSnnvuack997R0dVhKlSoFJgpjzMfY1S1kEZFRBX1XRBZj9TprP+yFS0zbraD5FQYR4bNVhwF4oqdWfBZXGRmZvPfeep599jfS0jLp2LGWq0NSqtRypOhpmd3fPsBt5GzN5FbmbIzkZFwyIzvXxcfLw9XhqDxs3nySkSMXsHHjCW68sT7vvddXK6yVciFHip5m2X82xnwF/OK0iJxs7cFzAPoSomIsNjaZqKh4Zs0axODBTbQDP6VczOEns+3UBeoUdiBFQUSY+3ck7epWIlC7mC42RITZs3eyb99Znn22K9ddF8rBg4/i43Mlh6dSqrA58mT2eWPMOdu/GKy7iWecH1rhW7rT6iCuvraWKTYOHDhH377fcvvtc/jhhz2kpVmP6GiSUKr4yPfXaKx7/pZYzVsBMkXkooptd/H1Wuu5wSd6NnJxJColJZ0pU/7klVdW4uVVhv/9rzdjxlyDp6d2oaJUcZNvohARMcbMExG3f9jgUHQiK/dF07NJVX0hUTFw7FgcEyeuoF+/RrzzTi9q1izv6pCUUpfgyOXbOmOM23epunib1TvsmG71XRxJ6XXmTCJTp64DICysEjt3PsTs2YM1SShVzF3yjsIY4yki6UBn4F/GmANAIlYfTiIibpM81h8+x5s/78GvrIe+uc4FMjOFzz/fxL//vYz4+BR69qxHo0bB1KtX0dWhKaUckF/R0zqgDdC/iGJxmlHTrV5B7r62jja1LGLbt5/mwQcXsWrVUbp0CeGDD26mUaPggr+olCo28ksUBkBEDhRRLE6xLTKW80lp3H1tCM/0bezqcEqV1NQMbrzxK1JTM/jss1u4775WmqiVckP5JYrKxpgnLjVSRN5yQjyFLqul0+jrtG6iqPz22yGuu64OZct68N13gwkPDyY4WLtxV8pd5VeZ7QH4Y3UHntc/t7D7VDxlDNSqqCcqZ4uMjGPgwO+44YbpTJ++BYDOnUM0SSjl5vK7ozghIi8XWSROkJiSzpZjMXSsH+TqUEq09PRMpk5dx/PP/05GRiaTJt3AXXe1cHVYSqlCUmAdhTubt8l6TnBwhPY86kzDhs1j5szt9OkTxrRpfalbV1szKVWS5JcobiiyKJzkwxUHMAZ6N63u6lBKnJiYZDw9y+DvX5aHHrqGgQMbM3BgY62sVqoEumQdhYicK8pACtuxc0kcO3eBm5pXx7esdideWESEmTO307jxNJ5//jfAqocYNEh7eVWqpCqxHeusPXgWgPs6hro2kBJk//5z9Or1NUOHzqVWrfLcfbfWQyhVGpTYLjonL9mDMdC4unYPURi+/XYbw4f/gLe3J1On9mH06Ag8PErsdYZSyk6JTBRnE1KITkjBx6sM5bxL5CoWmbS0DLy8PIiIqMGgQU14442e1KjhNq2jlVKFoESeRbNaO73av7mLI3Ffp08nMm7cUhITU/n++9tp2DCIr78e4OqwlFIuUCLLDj5acRCA68OruDgS95OZKXz00UYaNZrKrFnbadq0MhkZma4OSynlQiXujiIpNZ3T8SmEVfGnUrmyrg7HrRw8eJ677/6eNWsi6dYtlPffv4nwcO3AT6nSrsQliufn7wBgVJd6Lo7E/QQGehMTk8yXX/Zn2LAW2txVKQWUwKKnX3aeBGBQW30a2xELFuxhwIBZZGRkEhTkx/btY7jnnpaaJJRS2UpUojgdl0xccjpt61SkTBk90eXn6NFY+vefya23zmTv3rOcOJEAoNtNKXWRElX0lFWJ/cLNTVwcSfGVnp7JO++s5cUXlyMiTJ7cg8cfvxYvL316XSmVtxKVKKJiLwDQUl93ekkZGZl88snfdO9el//7vz6Ehuq2Ukrlr8QUPSWmpLN420m6Nars6lCKnfPnLzBhwi/Ex6fg7e3J6tXDWbDgDk0SSimHlJhEsWBLFAC9mlZzcSTFh4jwzTdbCQ+fxn//u4bffz8MQFCQn1ZWK6UcVmKKnj5bdQiAvs20S3GAvXvPMmbMIn799RDt2tXk55/vplUrTaJKqctXIhJFekYm+04nEOxflkA/L1eHUyw89tgSNmyI4r33+jJqVFvtwE8pdcVKRKJYvucMACNL+UN2v/xygPDwYGrXDuT992/C29uTatX8XR2WUsrNOfUy0xjT2xizxxiz3xjzVB7jnzDG7DTGbDXG/GqMqXMlyzkRlwxQaiuyT55M4M4753LjjV8zefJqAOrUqaBJQilVKJyWKIwxHsA0oA/QBBhqjMn9gMMmIEJEWgBzgDeuZFm7TsQBULOC7xXH644yM4UPPthAePhU5s7dxYsvXseUKTe6OiylVAnjzDuKdsB+ETkoIqnATOBW+wlE5HcRSbJ9XAtcUb8bv+w8Rb3gcgT4lK76iUmTVvLgg4to27YGW7eO5qWXuuHjUyJKE5VSxYgzzyo1gWN2nyOB9vlMPwL4Ka8RxphRwCiAkJCQHOMyMoUz8SnUDS53VcG6i/j4FKKjk6hbtyKjR0dQt25Fhg5tps1dlVJO48w7irzOXJLnhMbcDUQAb+Y1XkQ+EpEIEYmoXDlnPcSaA9a7sRtWLdnl8SLCvHm7aNLkPW6/fQ4iQlCQH3fe2VyThFLKqZyZKCKB2nafawFRuScyxvQAngVuEZGUy11IVv3E7REhBUzpvo4cieGWW2YyYMB3VKrky7vv9tHkoJQqMs4seloPNDDG1AWOA3cAd9pPYIxpDXwI9BaR01eykOhEK7eEVSmZdxRr1hyjR4+vAJgypSePPnotnp76TIRSqug4LVGISLoxZizwM+ABfCYiO4wxLwMbRGQBVlGTPzDbdoV8VERuuZzlLN9tPUPh41WyTp5xcSmUL+9NmzbVGT68FePHdyIkJNDVYSmlSiGnNpERkcXA4lzDXrD7u8dVzp9T8cmElqC+i86eTeKpp5axdOlBduwYg79/Wf7v//q6OiylVCnm1pfhZxJSiElKo29z9+/fSUSYPn0L4eHT+Pzzzdx+e1NKSO5TSrk5t250v/+09Va2Bm7e4ik2Npn+/WexfPlhOnSoxQcf3EyLFlVdHZZSSgFunii2RsYCEBrkns9QiAjGGMqX9yY42I+PPrqZESPa6OtIlVLFilsXPS3edgKAxtXLuziSy/fzz/tp0+YjIiPjMMYwe/Zg/vWvtpoklFLFjlsnisPRiRgDPm70vucTJ+K544459O79DUlJaZw+nejqkJRSKl9uXfQUl5xOj8buU5Y/bdo6nnnmN1JS0vnPf7oxYUInvL3dehcopUoBtz1LRZ63+hJ0p647Nm48Qfv2NZk2rS8NGgS5OhyllHKI2yaKw9FWomhULcDFkVxaXFwKL7zwO8OGtaBt2xq8995NeHt7lJhnPpRSpYPbJootkTEAtK1T0cWRXExEmDt3F48+uoQTJ+IJCQmkbdsa2gW4Usotue2ZK/ZCGmU9ylCrop+rQ8nh0KHzjB37E4sX76NVq2p8//0Q2re/otdsKKVUseC2ieJwdCK+ZYtfa6dvvtnGihVHePvtXowd20478FNKuT23TRSZIsQnp7k6DABWrjxCSkoGPXrUY/z4jtx3Xytq1XK/ZzuUUiovbnu5u/lYDC1qVXBpDNHRSQwf/gNdu37Byy//AYC3t6cmCaVUieK2dxQeZQzJaRkuWbaI8MUXmxk//hdiY1OYMKETzz/f1SWxqOItLS2NyMhIkpOTXR2KKiV8fHyoVasWXl5ehTZPt00Up+JS6NqgcsETOsHixfsYPnwBnTrV5oMPbqZZsyouiUMVf5GRkQQEBBAaGqrNopXTiQhnz54lMjKSunXrFtp83bLoaZutM0CvIqwoTkpKY/XqowD07duAH364gxUr7tckofKVnJxMUFCQJglVJIwxBAUFFfodrFsmir2n4gG4sUnRdN/x00/7aNbsPfr0+YaYmGSMMdxySyPtwE85RJOEKkrOON7cMlEs2XESgCY1nFtpfPx4HIMHz6Zv32/x9vbkxx+HUqGCj1OXqZRSxY1bJoqtkTFUD/ShSoDzTtqnTyfSpMl7LFy4l1deuZ4tW0Zz3XWhTlueUs7i4eFBq1ataNasGf369SMmJiZ73I4dO+jevTsNGzakQYMGTJw4ERHJHv/TTz8RERFB48aNCQ8P58knn3TFKuRr06ZNjBw5MsewW2+9lQ4dOuQYdt999zFnzpwcw/z9/+krbu/evfTt25ewsDAaN27MkCFDOHXq1FXFdu7cOXr27EmDBg3o2bMn58+fv2ia33//nVatWmX/8/HxYf78+QB06dIle3iNGjXo378/AAsXLuTFF1+8qtgui4i41b+2bdtKnQkL5Zapq8QZIiNjs//+3//Wyv79Z52yHFU67Ny509UhSLly5bL/vueee+SVV14REZGkpCSpV6+e/PzzzyIikpiYKL1795apU6eKiMi2bdukXr16smvXLhERSUtLk2nTphVqbGlpaVc9j0GDBsnmzZuzP58/f15q1aol4eHhcvDgwezh9957r8yePTvHd7O2zYULFyQsLEwWLFiQPe63336Tbdu2XVVs48ePl0mTJomIyKRJk+Tf//53vtOfPXtWKlasKImJiReNGzBggHz55ZciIpKZmSmtWrXKczqRvI87YINc4XnX7Vo9ZWRaVzstagYW6nxjY5N57rnf+PDDjaxdO5I2barzyCPtC3UZqnT7z4872BkVV6jzbFKjPC/2a+rw9B06dGDr1q0AfPvtt3Tq1Ikbb7wRAD8/P6ZOnUq3bt146KGHeOONN3j22WcJDw8HwNPTkzFjxlw0z4SEBB5++GE2bNiAMYYXX3yRgQMH4u/vT0KC9briOXPmsHDhQr744gvuu+8+KlWqxKZNm2jVqhXz5s1j8+bNVKhgPRcVFhbG6tWrKVOmDKNHj+boUasRyTvvvEOnTp1yLDs+Pp6tW7fSsmXL7GFz586lX79+VK1alZkzZ/L0008XuF2+/fZbOnToQL9+/bKHXX/99Q5v10v54YcfWL58OQD33nsv3bp1Y/LkyZecfs6cOfTp0wc/v5xdE8XHx/Pbb7/x+eefA1Y9RLdu3Vi4cCFDhgy56jgL4naJIuvZicKqnxARZs/eyWOPotNbEgAADx9JREFULeHkyQTGjm1H/frFr6NBpa5WRkYGv/76KyNGjACsYqe2bdvmmKZ+/fokJCQQFxfH9u3bGTduXIHznThxIoGBgWzbtg0gz+KV3Pbu3cuyZcvw8PAgMzOTefPmcf/99/PXX38RGhpK1apVufPOO3n88cfp3LkzR48epVevXuzatSvHfDZs2ECzZs1yDJsxYwYvvvgiVatWZdCgQQ4liu3bt1+0LfISHx9Ply5d8hz37bff0qRJkxzDTp06RfXq1QGoXr06p0+fznf+M2fO5Iknnrho+Lx587jhhhsoX/6f815ERAQrV67URJGXtAzrjqJ+5at/D4WIMGDAd8yfv5s2baqzYMFQIiJqXPV8lcrL5Vz5F6YLFy7QqlUrDh8+TNu2benZsyfwzzvb83I5LWeWLVvGzJkzsz9XrFjwhdbgwYPx8LD6arv99tt5+eWXuf/++5k5cya333579nx37tyZ/Z24uDji4+MJCPjn1QInTpygcuV/nqc6deoU+/fvp3Pnzhhj8PT0ZPv27TRr1izPdbrcFkIBAQFs3rz5sr7jqBMnTrBt2zZ69ep10bgZM2ZcVA9TpUoVoqKinBJLbm5XmS1YiaLGVbQ+SrPdlRhj6Ny5Nu++25t160ZqklAlkq+vL5s3b+bIkSOkpqYybdo0AJo2bcqGDRtyTHvw4EH8/f0JCAigadOmbNy4scD5Xyrh2A/L3a6/XLly2X936NCB/fv3c+bMGebPn8+AAQMAyMzMZM2aNWzevJnNmzdz/PjxHEkia93s5z1r1izOnz9P3bp1CQ0N5fDhw9lJLCgoKMfdzrlz5wgODs7eFo6s6/+3d/fBVdV3Hsffn2IhQSHdQmFq0YJjigkhIEKb2qGIuIwPWVg6jhgeKh1dRxBpi+KwizPrrtbSVtCNaCN1naRrGxCnFgYQFmksFXkQtgoINqU0A9lhkM1SliLIg9/945wk1zzcnMTcp+T7mrkz95x7Hr75zs353fP7nfM9p06d+sTAc+wrtlGrN3DgQI4ePQoEDcGAAa3fd/Xyyy8zZcqUZndU19XVsXPnTm677bZPzD979izZ2dltxtwZMq6h+Dgco+h1Sccqx77xRg2FhWWsXv0+AA8+eD0PPPA1evTIuFQ41y45OTmUlpby5JNPcv78eaZPn86bb77J66+/DgRnHvPmzePhhx8GYMGCBTzxxBNUV1cDwYF76dKlzbY7ceJEli1b1jBdfzAeOHAgBw4caOhaao0kpkyZwvz588nLy6Nfv34tbrelX/J5eXkcPHiwYbqyspINGzZQU1NDTU0Nu3fvbmgobrjhBlauXMm5c+cAKC8vbxiHmDZtGm+99Rbr1q1r2NaGDRsautPq1Z9RtPRq2u0EMGnSJCoqKgCoqKhg8uTJreahsrKSkpKSZvNXrVpFcXExWVmf/HFcXV3drNstUTLu6Hj+ovEZQd/s9vWaHT9+mrvu+jXjx1fw0UcX6NOnV4IidC59XXvttYwYMYIVK1aQnZ3N6tWrefzxxxk6dCjDhw9nzJgxzJ07F4DCwkKefvppSkpKyMvLo6CgoOHXcaxHHnmEEydOUFBQwIgRI6iqqgJg8eLFFBcXc+ONNzb007dm6tSpvPTSSw3dTgClpaXs2rWLwsJC8vPzKSsra7beNddcw8mTJzl16hQ1NTUcPnyYoqKihs+HDBlC37592bFjB8XFxYwdO5brrruOkSNHsnXr1oaB5ezsbNauXcszzzxDbm4u+fn5lJeXxz0DiGLhwoVs2rSJ3NxcNm3axMKFC4FgbCW2K6mmpoYjR44wbty4ZttYsWJFiw1IVVVVs7OMRJHFXDOdCQZelW+D7yllxz/dFHmdysq93H//ev7613MsWHA9ixZ9k969O69glnOtOXDgAHl5eakOo0t76qmn6NOnT7M+/K7s2LFjTJs2jc2bN7f4eUvfO0m7zWx0R/aXcWcUfzlzniH9L217wRgXLnxMQcEA3nnnPn7wgwneSDjXhcyePZtevbpXD8Hhw4dZsmRJ0vaXcVc9AeRkxz/Qnz59jsce28KVV+YwZ84YZswoZMaMQq+541wXlJWVxcyZM1MdRlKNGTMmqfvLuDMKgFsKWu/vXLu2mmHDnuNHP9pKdXUdEAyWeSPhUiXTunddZkvE9y0jzygu/1zzS8Jqa/+PefNe49VX3yc//wts2TKLsWO/nILonGuUlZVFXV2dlxp3SWHh8yiaXiH1aWVkQ9H/sp7N5h06dIKNG//ED384gfnzv07Pnh27fNa5zjRo0CBqa2s5fvx4qkNx3UT9E+46U8Zd9dTri7lWW72PL/Tpxc6d/822bUf47neDy+Hq6j6kX7/ebWzBOee6n7S96knSzZL+IOmgpIUtfN5L0srw8x2SBkfa8LmLzJmzjqKiF1i6dDunTwc30Hgj4ZxznS9hDYWkHsCzwC1APlAiqemti3cDJ8zsauApoPWyijGGD3uO55/fzbx5X2Pv3tlcemnzrijnnHOdI5FjFF8FDprZIQBJK4DJQGxBlMnAo+H7V4BlkmTx+sMMrrgih/XrpzNqVPy7PZ1zzn16iWwovgQciZmuBZo+4KFhGTO7IOkk0A/4n9iFJN0L3BtOfrTr2L37IlQE7g760yRX3ZjnopHnopHnotHQjq6YyIaipWsBm54pRFkGM1sOLAeQtKujAzJdjeeikeeikeeikeeikaRdbS/VskQOZtcCV8RMDwKaFk9vWEbSJUAO8L8JjMk551w7JbKheBvIlTREUk/gTmBNk2XWAHeF728HfhN3fMI551zSJazrKRxzmAtsBHoAL5rZe5L+leAh32uAfwf+Q9JBgjOJOyNsenmiYs5AnotGnotGnotGnotGHc5Fxt1w55xzLrkysiigc8655PGGwjnnXFxp21AkrPxHBoqQi/mS9kvaI2mzpC5bNretXMQsd7skk9RlL42MkgtJd4Tfjfck/TLZMSZLhP+RKyVVSfp9+H9yayriTDRJL0r6QNK+Vj6XpNIwT3skjYq0YTNLuxfB4PefgKuAnsC7QH6TZeYAZeH7O4GVqY47hbkYD/QO38/uzrkIl+sDbAG2A6NTHXcKvxe5wO+BvwmnB6Q67hTmYjkwO3yfD9SkOu4E5eKbwChgXyuf3wq8RnAPWxGwI8p20/WMoqH8h5mdA+rLf8SaDFSE718BJqhrFvxvMxdmVmVmH4aT2wnuWemKonwvAB4DfgycTWZwSRYlF/8APGtmJwDM7IMkx5gsUXJhQN/wfQ7N7+nqEsxsC/HvRZsM/NwC24HPSWqzFlK6NhQtlf/4UmvLmNkFoL78R1cTJRex7ib4xdAVtZkLSdcCV5jZ2mQGlgJRvhdfAb4iaauk7ZJuTlp0yRUlF48CMyTVAuuBB5ITWtpp7/EESN8HF3Va+Y8uIPLfKWkGMBoYl9CIUiduLiR9hqAK8axkBZRCUb4XlxB0P91AcJb5O0kFZvaXBMeWbFFyUQKUm9kSSV8nuH+rwMw+Tnx4aaVDx810PaPw8h+NouQCSTcBi4BJZvZRkmJLtrZy0QcoAN6QVEPQB7umiw5oR/0fWW1m583sz8AfCBqOriZKLu4GXgYws21AFkHBwO4m0vGkqXRtKLz8R6M2cxF2tzxP0Eh01X5oaCMXZnbSzPqb2WAzG0wwXjPJzDpcDC2NRfkf+TXBhQ5I6k/QFXUoqVEmR5RcHAYmAEjKI2gouuPzadcA3w6vfioCTprZ0bZWSsuuJ0tc+Y+MEzEXPwEuA1aF4/mHzWxSyoJOkIi56BYi5mIjMFHSfuAisMDM6lIXdWJEzMWDwM8kfZ+gq2VWV/xhKamSoKuxfzge88/AZwHMrIxgfOZW4CDwIfCdSNvtgrlyzjnXidK168k551ya8IbCOedcXN5QOOeci8sbCuecc3F5Q+Gccy4ubyhc2pF0UdI7Ma/BcZYd3FqlzHbu842w+ui7YcmLoR3Yxn2Svh2+nyXp8pjPXpCU38lxvi1pZIR1viep96fdt+u+vKFw6eiMmY2MedUkab/TzWwEQbHJn7R3ZTMrM7Ofh5OzgMtjPrvHzPZ3SpSNcT5HtDi/B3hD4TrMGwqXEcIzh99J+q/wdX0LywyTtDM8C9kjKTecPyNm/vOSerSxuy3A1eG6E8JnGOwNa/33CucvVuMzQJ4M5z0q6SFJtxPU3PpFuM/s8ExgtKTZkn4cE/MsSc90MM5txBR0k/RTSbsUPHviX8J58wgarCpJVeG8iZK2hXlcJemyNvbjujlvKFw6yo7pdno1nPcB8LdmNgqYCpS2sN59wL+Z2UiCA3VtWK5hKvCNcP5FYHob+/87YK+kLKAcmGpmwwkqGcyW9HlgCjDMzAqBx2NXNrNXgF0Ev/xHmtmZmI9fAb4VMz0VWNnBOG8mKNNRb5GZjQYKgXGSCs2slKCWz3gzGx+W8ngEuCnM5S5gfhv7cd1cWpbwcN3emfBgGeuzwLKwT/4iQd2iprYBiyQNAn5lZn+UNAG4Dng7LG+STdDotOQXks4ANQRlqIcCfzaz6vDzCuB+YBnBsy5ekLQOiFzS3MyOSzoU1tn5Y7iPreF22xPnpQTlKmKfUHaHpHsJ/q+/SPCAnj1N1i0K528N99OTIG/OtcobCpcpvg8cA0YQnAk3eyiRmf1S0g7gNmCjpHsIyipXmNk/RtjH9NgCgpJafL5JWFvoqwRF5u4E5gI3tuNvWQncAbwPvGpmpuCoHTlOgqe4LQaeBb4laQjwEDDGzE5IKicofNeUgE1mVtKOeF03511PLlPkAEfD5wfMJPg1/QmSrgIOhd0tawi6YDYDt0saEC7zeUV/pvj7wGBJV4fTM4Hfhn36OWa2nmCguKUrj04RlD1vya+Avyd4RsLKcF674jSz8wRdSEVht1Vf4DRwUtJA4JZWYtkOfKP+b5LUW1JLZ2fONfCGwmWK54C7JG0n6HY63cIyU4F9kt4BriF45ON+ggPqf0raA2wi6JZpk5mdJaiuuUrSXuBjoIzgoLs23N5vCc52mioHyuoHs5ts9wSwH/iyme0M57U7znDsYwnwkJm9S/B87PeAFwm6s+otB16TVGVmxwmuyKoM97OdIFfOtcqrxzrnnIvLzyicc87F5Q2Fc865uLyhcM45F5c3FM455+LyhsI551xc3lA455yLyxsK55xzcf0/iwArX694W9IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = [\"Logistic Regression (Optimal Cutoff)\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16469105377809068\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")\n", + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM default parameters identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06432330
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6432 330\n", + "1 1274 713" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15996357777982226\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.96 0.89 6762\n", + " 1 0.70 0.32 0.44 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.77 0.64 0.66 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06492270
11349638
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6492 270\n", + "1 1349 638" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15910713557498266\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAEWCAYAAAB7bd4AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3QV1RbA4d9OgUBIQgmETui9B6kCokhTUZ4NO4KCvVd4yBN7b9hF7FhQREBAEARFkN57DzWBhJCE1HveHzOJl5AGJJnce/e3FotM3zN3ZvbMmTNnxBiDUkoppbyHn9MBKKWUUqpoaXJXSimlvIwmd6WUUsrLaHJXSimlvIwmd6WUUsrLaHJXSimlvEypTO4icr2IzHE6DqeJSF0RSRQR/xJcZqSIGBEJKKllFicR2SAivc9iurPaB0WknIj8IiLHReT7M53em4jIAhEZUcLLnCQiz+Qz/BkRiRWRQyUZlycRkd0iclEJLet9EflvCS0r33ObWD4VkTgR+SeX4beIyJ/FH2nB7PVolN84BSZ3+4c+aSeZQ/bBU6HowjydMeYrY8zFxbmM0ijnQWWM2WuMqWCMyXQyLqcUZgcuiDGmpTFmQQHLOe2gP4d98EogAqhijLnqLKbPLb4nRWSXfQxGi8i3dv8PROTzXMZvIyKpIlJZRMbZ63ZvjnHut/uPK4oYPYGI1AEeAloYY6qf47x6i0h00UTmOQq6eDpTxphRxpjxRTW/c9QD6AvUNsac53Qw56qwd+6XGmMqAO2A9sATxRdS8XHybtRb7oTPhI9u73rAVmNMxplOmFvMInIzcCNwkX0MRgHz7MGTgCEiEpxjspuA6caYY3b3VuDmXMbZei6xeaB6wFFjzBGnA/GS7ekxCrm96wG7jTFJpSCWc2eMyfcfsBvrxJLV/RIww627LPAKsBc4DLwPlHMbPhhYDSQAO4D+dv8w4BPgILAfeAbwt4fdAvxp//0+8EqOmH4GHrT/rglMAWKAXcC9buONA34AvrSXPyKX9QsDPren3wOMAfzc4vgLeBs4DmwGLswxbX7r8BfwOnDMHtYQ+B04CsQCXwEV7fG/AFzASSAReBSIBAwQYI+zABhvz/cEMAcId4vnJnsdjgL/zfnb5VjvcsCr9vjHgT/tflnLvNn+TWOB0W7TnQf8DcTb6/0OUMZtuAHuArYBu+x+bwL77N9gBXC+2/j+wJP2vnHCHl4HWGjPK8neHtfY41+CtT/FA4uBNjn21ceAtUAqEOC+DezYl9txHAZes/vvtZeVaP/rits+aI/TEvjN/i0PA0/msk3/B6QB6fZ8hmNdQI+xt/MRrH0tzB4/a1sPt2NYmMs83wHeyOf43ALclGN7HgAuczsGvgQ2AS3d1mWT3X9cHvO9hRz7r93/VnvaOGA2UM9tmr5Yx8hxO+4/sI+5rDjcxs1a96x9uzLwqR17HDDVbdz8fvP2wEqsfedbYHJWrDnW5yKsY8tl/zaT7P5d7HnGA2uA3m7TDLPX9QSwExhp9w/OMa9ErPPQJPdlA72B6AL2z/zOX7nur7msWyVguj2POPvv2m7DF5D/eeNG/j1vjCaP8wZwO9a+nWav8y9ux3wjt/Gyt0PWNsAqMTmCdc4YdpbjVgF+sbfHMqxz6p95bJNIchxbbv1ux9rPDgIP2eMPB1KATHvd/pfHMeF+TngZ67yZdTznd2zkdl40wCi7XxwwARC3aQqaX6Pc1j17nPwGuu2QWSfH2sA64E234W8A07AOzhB74z/vtnMexzro/YBaQDN72FTgA6wDpRrwD/8ePNkbEeiJlRjEbUc+iXVQ+GElg7FAGaAB1kHYz+2Ekg5cbo9bLpf1+xzrYiHE/vG3AsPd4sgAHgACgWvs9alcyHXIAO7BOojLAY3sbVEWqIq1w72R27bO4wS4ACsJNrHntwB4wR7WAmun7GFvi1fsdc8ruU+wp6+FlRC62XFlLfMjexltsU5Eze3pOmKdEAPscTcB9+fY6X7D2h/K2f1uwDowA7AO3ENAkD3sEax9qikg9vKq5HHS6IB10He2Y77Z3mZl3bbfaqyLg3I5tynWRcmN9t8VgC65bedc9sEQ7BMBEGR3d85ju47j1CR2K7Ada9+sAPwIfJFjuZ9j7UO57Z83YCXXR7Du2v1zDB8NzHXr7od1kg90jwfrAupFu99LWKVvBSX3nPvv5fa6NLf7jQEW2+OHY510r8Q6Vh6wpy9scp+BlZwr2dP3Kug3x9rP9/Dv8Xkl1j5/WnJ3Tx5u3bWwEtpArPNDX7u7qj18ENYFuQC9gGSgQ27zypmo8ljebtz2Two+f+W6v+ayXlWA/wDlsfbN7zn14mgBBZ83etrb9DX7d8vrvHHKOuZxnGaPY2+DDOBp+zcaaG/HSmcx7mT7X3k77n0UnNyzjy23ft/Y/VpjHStZ54db8pqf+3D7d/sIK+GWt4fleWzkc140WBdiFYG6diz9z2B+RZLcE7Gu+AxWkWDW3aZg3Vk1dBu/K/9emXwAvJ7LPCOwEob7Hf5QYH4uJ1bBuvLqaXffBvxu/90Z2Jtj3k8An7qdUE67G3Ib19+Oo4Vbv5HAArc4DnDq1dQ/WFe6hVmHvXkt2+0HXJVjWxeU3Me4Db8TmGX/PRb4xm1Yeawr7NyuwP2wLpDa5nNQ1M6xztfmsQ73Az/l2On6FLDecVnLxrrzHJzHeDlPGu8B43OMs4V/E8Fu4NZc9t+sg3ch1t11eI5xTtnOueyDQ91/pwLWbRynJrF5wJ1u3U2xElCA23IbFDDP64G5WMfaUeBxt2F17fnVtru/4tSL73FYSbwu1nEUaP9fh4KTe85j61fsC1+3/SgZqzjzJmCJ2zDBugsrMLkDNbDugivlEkeevzlWQsp5fC6m8Mn9MewLLbd+s4Gb85h+KnBfbvOy+02i4OR+q1t3QeevXPfXQuyD7YA4t+4F5H/emOw2LJg8zhu5rWMex2n2OPY2OMmpx9YR/r2wLtS4WOfqdKCp27DC3Lk3yKVfM7d+LwGf5Dzm8zkmlmJdhE7h1BLLPI8Nt23UJ8f8DNDDrfs77GO7kPPLN7kX9pn75caYEHvjN8O6Sgfr7rM8sEJE4kUkHphl9wfrBLIjl/nVwzrJHHSb7gOsu99TGGtNJmOdYAGuwzqBZc2nZtY87Pk8iZV4s+zLZ73C+ffqP8serCv6LPvtGNyH1yzkOpyybBGpJiKTRWS/iCRgnVzDOTPutXyTsa7osWPKXp4xJhkrEeQmHOsONLffJt/liEgTEZluV65MAJ7j9HXIud4PicgmuwZ5PNbjjKxp8tpHclMPeCjH710Ha91zXXYOw7HuXjaLyDIRuaSQyz2TGHOqyen7VwCF30cxVuW+i7Cu8EcBT4tIP3tYVpHjDXZF18uBz3KZx16sO4HngG3GmHyXmUdc9YA33bb9MawkXovT9z9T0Hq5qQMcM8bE5TIsv9+8Jrkfn4VVD7gqx7x7YF1sICIDRGSJiByzhw3kzI/XnNy3SUHnr0LtryJS3q5cucc+JhcCFeXUt2wKe97IuoAsSkfNqXVQ3Jdf2HGrYh037tvvbPbhnP2yzueF1QjrUfP/jDFpbv3zOzbyiyWv36Uw88vXGb0KZ4z5A+tK6xW7VyzWlVZLY0xF+1+YsSr+gLUyDXOZ1T6su95wt+lCjTEt81j0N8CVIlIP62p3itt8drnNo6IxJsQYM9A97HxWKRbrarCeW7+6WM/Ps9QSEckx/EAh1yHnsp+3+7UxxoRiFblKPuOfiYNYj00A65UsrOK63MRiPV/K7bcpyHtYz1Ub2+vwJKeuA7ith4icj3WHdDXWnVlFrEcbWdPktY/kZh/wbI7fu7wx5pvclp2TMWabMWYo1gXYi8APdmW0grb7mcSY0wFO378ysJ6hZodWmBkZY9KNMd9jPbNt5TboM6w75/9gHQ8r85jF51iPFk6rYZ/XInN078N67OS+/csZYxZj7X91ska0j5k6btMmYd0IZHGvrb4PqCwiFXOJIb/f/CC5H5+FtQ/rzt193sHGmBdEpCzWeeYVIMLeb2fy736b22+W3zpmcZ8u3/NXPvtrTg9hlQh1to/Jnnb/nMdlbnL+buXJ+7yRM/4syRS83ucqBuu4qe3Wr04e47rLLV736bLO54W1Casuxq8i0tStf37HRn6x5KUw88vX2bzn/gbQV0TaGWNcWM8eXheRagAiUivrrgKrstkwEblQRPzsYc2MMQexKnW8KiKh9rCGItIrtwUaY1Zh/bgfA7ONMfH2oH+ABBF5TKz3i/1FpJWIdCrMihjrFbPvgGdFJMS+eHgQ6446SzXgXhEJFJGrsJ6BzDzTdbCFYD3iiBeRWljPUd0dxnrudjZ+AC4VkW4iUgarOC/Xg9v+3SYCr4lITXu7dbVPaAUJwXq2migizYA7CjF+BtbvFyAiY4FQt+EfA+NFpLFY2ohI1skl5/b4CBglIp3tcYNFZJCIhBQibkTkBhGpaq9/1j6UacfmIu9tPx2oLtbrY2XtfaVzYZaJdWH6gIjUt++snwO+NYWsTS/Wu7WD7GX6icgArApxS91Gm4J1wvofudy1u/kWuBhrnz8b7wNPiEhLO7Yw+5gA65l5SxEZIlZt4Hs59SS/GugpVtsNYbi9cWMfS78C74pIJftYy0pQ+f3mf2PtW/eKSICIDMGq51NYX2IdM/3sYyBIrFfcamOV6JXFTir2dnd/NfIwUMVeF/d1HCjWK4jVsR5Z5Sff81c++2tOIVg3WfEiUhl46gy2wQ/AJSLSwz5vPE3+eSG3c9Rq4Do7/v5Yj0yKlH2u/hEYJ1ZJRTOsC9qz8V97Hi2xEvW3ZxjLN1g3NXNFJOuiP79j42yc8/zOOLkbY2KwrvyzGh54DKu4b4lYRUJzsa4iMcb8g7XxXse6W/uDf+9ibsI6gDZiPYP9Abs4LA/fYNV4/dotlkzgUqxnTLuw7kg/xir2Lax7sK64d2JVlvgaK/FlWQo0tuf9LHClMSar2OpM1+F/WBWEjmOdDH/MMfx5YIxYRTEPn8E6YIzZYK/LZKyr8RNYz6tS85jkYayKbMuwinxepHD7w8NYj0ZOYJ14CzowZmOduLdiFYGlcGrx1GtYyWYO1kXDJ1iVX8B6TvuZvT2uNsYsx6pz8Q7W9t6O9RyssPoDG0QkEasG/7XGmBT7EcazwF/2srq4T2SMOYFV2epSrGK0bcAFhVzmRKw3IRZi7aMpWL9TYSVgnUj2Yp3gXwLuMMZkN6ZhF6VmJfivcpuJPd5JY8xcY8zJM1i++/Q/Ye0nk+1jfT0wwB4WC1wFvIBVrNsYq3Z21rS/Ye0ra7EqkU3PMfsbsUrRNmPtt/fb0+X5m9vFokPs7jisCq85j6n81mcfVhHrk1hJfB/WBbef/Zvfi7VvxmHt89Pcpt2MdU7aae8zNbF+5zVYz9bnUMCxUYjzV677ay6zegPrmIkFlmA9Gi3sNtiAVYv7a6zzRhxWXYm8fAK0sNd5qt3vPns94rHqh0zNa+JzdDfWtjmEta2/Ie/zW37+wNqP5mG9iXXGjVUZYz7DuhD6XUQi8zs2zkZRzC+rBrrKhYjcglUhqIfTsZwp+y4xHqv4fJfT8SilVFESkReB6saYm52OpTQqlc3PqrMjIpfaxU3BWM8K12HdRSillEcTkWb2YzsRkfOwKhz+5HRcpZUmd+8yGKtyyAGsYtFrjRbNKKW8QwjWY5ckrMclr2K1UaJyocXySimllJfRO3ellFLKy+jHC4pYeHi4iYyMdDoMpZTyKCtWrIg1xlQteExVGJrci1hkZCTLly93OgyllPIoInImrQuqAmixvFJKKeVlNLkrpZRSXkaTu1JKKeVlNLkrpZRSXkaTu1JKKeVlNLkrpZRSXsank7uITBSRIyKyPo/hIiJvich2EVkrIh1KOkallFLqTPl0cgcmYX1WMS8DsNpobwzcDrxXAjEppZRS58Snk7sxZiHWt8zzMhj43FiWABVFJL/vtSullDoDy9YdovFDvzgdhtfRFuryVwvY59Ydbfc76D6SiNyOdWdP3bp1Syw4pZTyNMYYDh5P4bkZm5i77hApGAj06fvMYqHJPX+SS7/TPqNnjPkQ+BAgKipKP7OnlFJuMjJdrNoXz7MzNrF6X/wpw6omZPDULR249EWHgvNSmtzzFw3UceuujfWtdKWUUrlISEln88ETfLlkD/En00lMSWfd/uOkZ1r3PWX8hWs61aVzvUoExpyk38WNHI7YO2lyz9804G4RmQx0Bo4bYw4WMI1SSvmMmBOpfLd8H18v3cvRpFRS0l2nDO/aoArnN66KiTnJLx+uItzfn/+NH4CfX24Fo6qo+HRyF5FvgN5AuIhEA08BgQDGmPeBmcBAYDuQDAxzJlKllHJebGIqO2OS+HvHUWZvOER0XDIJKRnZw0ODAhjSoTataobRqlYobWpXZN26w4waNYPFi/fRq1c93ntvkCb2EuDTyd0YM7SA4Qa4q4TCUUqpUmPr4RPsjk1ixZ44VuyJY+3+46RlnHpXXjm4DNdE1eH8JuEMal0DkVOT9rJl++nWbSIVKwYxadJgbrqp7WnjqOLh08ldKaV82dHEVOZtPsKOmER2xiSxck8cQYH+xCamkpojkdeqWI42tcPo1iicNrXCaFEzlED/3Gu5R0cnULt2KB071mT8+Au47bYOVKlSviRWSdk0uSullJcyxnAsKY2dsUls2H8cgJ2xSczecIjk1ExOpGacMn6DqsFUKBtA76ZVOZmeSZ9m1WhbuyJ1KhcuMe/bd5x7753F/Pm72LLlbiIiKvD44z2KfL1UwTS5K6WUhzLGsH5/ArFJqWw/nEi6y8WmgyfYGZPIhgMJ+U7bJKICN7WIoF6VYC5oWo3wCmXOusg8I8PFW28tZezY+bhchqee6kWlSuXOal6qaGhyV0qpUizTZTh+Mp3dR5M4fDyFA8dTSM90Memv3RxKSMlzuguaViXA349OkZUoVyaAljVDiawSTFCgH0EB/kVWqS0pKY3u3SeyZs1hBg1qzDvvDCQysmKRzFudPU3uSilVihhj+H5FNF8t3cvGA/++H56X6zvX5bK2NQktF0jNsHKElgsokUpr6emZBAb6ExxchgsvrM/Ysb244opmWmGulNDkrpRSDsh0GVLSM0lOy2T+5iMcTUrjx5XRbDuSmD1OZJXyNKoWQpcGlQn096N5jVAqlQ+kVqVyRXr3fSaMMXzzzXoee2wuM2deR+vWEbz6ar8Sj0PlT5O7UkoVs5T0TGauO8iy3XGs2htHfHJ6rkXqQYF+dKhbkb4tqnNNpzpUDi7jQLR527btKHfeOZO5c3fSqVNNfV+9FNPkrpRSxWTW+kNMmL+ddXZN9SyRVcozqE0N6lcJplpoWYIC/enbPIKK5QNLbbH2888vYty4PwgKCmDChIGMHNkR/zxehVPO0+SulFJF6K/tsUxfe4Bf1x8iPjkdgAbhwbSrW5Exg1pQqRQn8PwkJqYxZEhzXnvtYmrUCHE6HFUATe5KKXUODh1P4dO/drFgSwxbDp84ZVjbOhUZe0lzOtar7FB0Z+/w4UQeemgO11/fmgEDGjN+fB8thvcgmtyVUqqQTqZlsuHAcfbHn+Szxbs5ZL+alqVRtQp0b1iFm7pF0rBqBQcjPXsul+Gjj1bw+OPzSEpKo0uX2gCa2D2MJnellMpHcloGd3+9ilV744izi9mzhJQNYEArq/Jb76bVHIqw6Kxde5iRI6ezZEk0F1wQybvvDqJZs3Cnw1JnQZO7UkrlsGJPHFNX7Wft/uOs2Ref3f+ytjVpWTOUqMhKRIQGUbuSd7WXvnRpNDt2HOOLL67g+utbe2TdAGXR5K6UUljvnW88kMAjP6xh8yHr2XlYuUAC/YX7L2rCiPPrUzbA3+Eoi97PP28mKSmd665rzfDhHbjqqpZUrBjkdFjqHGlyV0r5rJT0TH5df5DJ/+xj+5FEjialAdaras8NaU23ht5bJL1nTzz33juLadO20LNnPYYObYWfn2hi9xKa3JVSPiHmRCoLthzhu+X7cBmr6D2nR/s35ZLWNanrxZ8nTU/P5I03ljBu3B8AvPxyX+67r7MWwXsZTe5KKa9zPNn60MrSXUfZfOgE8zcfOaUyXHiFMvRtEUFQoD9dG1RhUJsahJULdDDikvP339E8+uhcLrusKW+/PYC6dcOcDkkVA03uSimP5HIZEtMy2BObzKwNB0lMyWDF3jj2xCaf9p3yqiFl6dawCv1bVadfy+pEhPpW0fOxYydZsGA3Q4Y0p2fPevzzzwg6darldFiqGGlyV0qVasYY9sefZNPBEyzaFsNf22PJcBn2HE0+bdwaYUGUL+tP90bhdG9UhcYRIbSqFUaFsr55qjPG8OWXa3nooTmcOJHG3r33U7VqsCZ2H+Cbe7xSqlRzuQxfLNnD9LUH2HLoBAkpp96Jd6xXia4NqlAmwI9m1UNpUDWYTpGV8deGVrJt2RLLHXfMYP783XTpUpv33x9E1arBToelSogmd6VUqeByGX5YGc1PK/fz986j2f0bhAczvEcDIsPL07JmGA3Cg7W1tALExZ0kKuojAgL8eP/9Qdx2W0fdZj5Gk7tSyhHGGHbEJPH63K3sOJKY/W45QNOIEG7oUpcbutTTWtxnYM2aQ7RtW51Klcrx6aeDOf/8ukREeGYzuOrcaHJXSpWohJR0bvx4KWui//0ManAZf/7ToTYRoWW576LGXtlYTHE6ePAEDz44h8mT1zN37o1ceGEDrryyhdNhKQdpcldKlZgXZ23mvQU7sruH96hP76ZV6dEoXO/Qz0JmposPPljBE0/MIyUlg3HjetG9e12nw1KlgCZ3pVSxWr//ODPWHTwlqT/SrymjejXUCnDn6JJLvmHWrO1ceGF93n13EE2aVHE6JFVKaHJXShWLA/EnefKndSzYEpPdr3JwGX5/qBcVy5dxMDLPlpiYRvnygfj5CTfe2IYbb2zD0KGttORDnUKTu1LqnCWlZrArNomlu47x8+r9rHV7nh5eoSwvX9mG8xuHE+Dv52CUns0Yw08/bebee3/lv//tyciRUVx3XWunw1KllCZ3pdRZW7U3jrd/387vm4+c0j8itCwDWtWgT7Nq9GxS1aHovMfu3fHcffdMZszYRtu2EbRrV93pkFQpp8ldKXXGth4+wYcLd/LDimgAGlWrQOtaYVzVsTYtaoZqsXsRmjhxFXffPRM/P+G11y7mnns6ExCgJSAqf5rclVKFkpCSzqd/7ub1uVuz+1UPDeL1a9rRtaFW5CpqxhhEhDp1QunXrxFvvdWfOnX0Iy+qcDS5K6VOk5CSTlxSGgknM9h8KIFP/tx1SiMzfZpVY8yg5tQPD9aKXEXs6NFkHn30NyIiKvDccxfSt29D+vZt6HRYysNocldKAZCW4eKd37fxzvztuMzpw2uEBfFg3yb0a1Wd0CDf+DxqSTLG8Nlna3j44TkcP57KY491dzok5cF8OrmLSH/gTcAf+NgY80KO4XWBz4CK9jiPG2NmlnigShWj2MRUpq7azzMzNmX3O79xOH2aVSMiNIhygf60q1ORSsH6HL24bNt2lBEjfmHhwj1061aH998fROvWEU6HpTyYzyZ3EfEHJgB9gWhgmYhMM8ZsdBttDPCdMeY9EWkBzAQiSzxYpYrJszM28tGiXdndjapVYPb9PbVxmRKWlpbJli2xfPTRpdx6a3v9yIs6Zz6b3IHzgO3GmJ0AIjIZGAy4J3cDhNp/hwEHSjRCpYrJ75sP88j3azmalAbAK1e1ZUj7WppUStCsWduZN28nL798MS1bVmPPnvsp66PfnVdFz5f3pFrAPrfuaKBzjnHGAXNE5B4gGLgotxmJyO3A7QB162q7zqr0McYwb9MRVu+L5+M/d5KS7gKgZ5OqvDCkNTUrlnM4Qt9x4MAJHnhgNt99t4GmTaswZkxPwsKCNLGrIuXLe1Nutyg5qxENBSYZY14Vka7AFyLSyhjjOmUiYz4EPgSIiorKpSqSUiXL5TJ88ucudsYmselgAqv3xZ8yvEWNUCYN60S10CCHIvQ9mZku3n13GaNH/05aWibjx1/AI49006SuioUv71XRQB237tqcXuw+HOgPYIz5W0SCgHDgCEqVIsYY1kQf54cV+/hzWyy7jyZnD6tXpTy1K5XjgqbVuL1nA2pXKqevrzng2LGTjB27gK5d6zBhwkAaNarsdEjKi/lycl8GNBaR+sB+4Frguhzj7AUuBCaJSHMgCIhBKYe5XIa1+4+zbv9x5mw4xNKdx0jL/LdAqWeTqrStHcbwHvW1tTgHHT+ewkcfreTBB7tStWowK1feTmRkRb24UsXOZ5O7MSZDRO4GZmO95jbRGLNBRJ4GlhtjpgEPAR+JyANYRfa3GGO02F05Ij3Txe+bj/D279tYvz/hlGHVQspyRftaXNauJi1raitmTjPG8MMPG7nvvlkcOpRIly616dGjLvXrV3I6NOUjfDa5A9jvrM/M0W+s298bAW1JQjkqJT2T3i8v4FBCSna/oEA/bulWn15NqtKiZihh5bRRmdJi58447rprJrNmbad9++r8/PO1dOpUy+mwlI/x6eSuVGn3xZI9/HfqesBqx/2qqNoMPa+u1m4vpYwxDB48mT174nnzzf7ceWcn/ciLcoQmd6VKkcU7Yvlt42FW7oljjf1N9JCyAVzTqQ6jBzXXZ7Wl1J9/7qVDhxqULx/IpEmDqV69ArVqhRY8oVLFRJO7Ug4yxrDnaDJ3fLWSTQdPfY7etnYY7etW4qGLmxCibbmXSjExSTz66FwmTVrNs8/24cknz6djx5pOh6WUJnelnDJr/SFGfbnilH7Dukdyfed6RFYpT4C/FueWVi6X4dNPV/Hoo3NJSEjliSd6cP/9XZwOS6lsmtyVKmGLtsVw4yf/ZHe3rR3GHb0b0a9lhBa7e4gHH5zNm28upUePurz//iBatqzmdEhKnUKTu1IlZPGOWK77aGl2d5XgMnw+/Dx9dc1DJCenk5KSQeXK5bjttg60bRvBzTe30/b4VamkyV2pYmSM4Yslexj784bsfjXDgvj45k60qKkVrjzFzJnbuOuumXTpUptvvvkPLVtW07t1Vap5RXIXkQ7BXCEAACAASURBVDJAXWPMdqdjUSrL8t3HuP7jpaRmWC3HRdWrxCtXtSUyPNjhyFRhRUcncP/9s5gyZRPNm4dzxx1RToekVKF4fHIXkUHAa0AZoL6ItAOeMsZc4Wxkylf9uu4gj05Zy4mUDAA616/M+zd0pFKwNgPrSWbP3s6VV35PRoaL557rw0MPdaNMGX+nw1KqUDw+uQNPY32qdT6AMWa1iDRyNiTla2JOpDLulw0s3XmM2MRUANrVqcgL/2lNs+pa/O5J0tMzCQz0p1276gwc2Jjnn7+QBg202VjlWbwhuacbY+Jz1DLW9t9Vscuw23qfs/EwP6yIBqBycBl6NArnjt4N6d4o3OEI1Zk4fjyFJ5+cx5o1h1m4cBgRERX49tsrnQ5LqbPiDcl9k4hcDfjZX3i7D1jicEzKixljWLA1hmGfLjul/+B2NXnz2vYORaXOljGGb7/dwAMPzObIkSTuvrsTaWmZBAV5w+lR+Spv2HvvBsYCLuBHrK+8PeFoRMornUzL5PYvlrNoW2x2v2bVQ3jj2nY0jQjRd9Q90OHDidx001TmzNlBVFRNpk8fqi3MKa/gDcm9nzHmMeCxrB4iMgQr0St1zpbsPMrUVfuZvGxfdr9bukUyslcDaoTpB1w8WWhoWWJjk3n77QHccUcU/toqoPIS3pDcx3B6Ih+dSz+lzthtny/nt42HAQjwE27v2YC7+zSifBlvOHR80/z5u3jppcVMmXI15csHsmzZbdoQjfI6HnuGEpF+QH+gloi85jYoFKuIXqmzkpbh4vW5W/lo4U4yXIYqwWWYdk8PaulnVj3akSNJPPzwHL74Yi0NGlRiz554mjevqoldeSWPTe7AEWA9kAJscOt/AnjckYiURzLGsO1IIu8t2MGMtQdJy/z32vCGLnUZd2lL/YiLB3O5DJ98spLHHptLYmIaY8acz5NPnk+5cvqlPeW9PDa5G2NWAatE5CtjTIrT8SjPs3pfPJP+2sXU1QdO6d+tYRW6NKjCzd0iCdME4BU+/3wtbdpE8N57g2jevKrT4ShV7Dw2ubupJSLPAi2AoKyexpgmzoWkSqOMTBeJqRnM33KEB75dk90/KNCPvi2qc01UHTrVr0TZAG2FzNMlJaXx3HOLuOeezlSvXoGff76WSpWC9I0G5TO8IblPAp4BXgEGAMPQZ+7KjTGGCfO388bcbWS4/m3fqGuDKrx0ZRvqVC7vYHSqqE2btoV77vmVvXuPU79+JUaM6EDlylpfQvkWb0ju5Y0xs0XkFWPMDmCMiCxyOihVOsQlpXHec3NJzzT4CTzYtwk1woLo3bQaVUPKOh2eKkL79h3n3ntnMXXqZlq1qsaffw6je/e6ToellCO8IbmnilXWtkNERgH7Af0Wo487mpjKIz+s5ffNRwBoW6cin996nj5D92Ljxi1g9uztvPjiRTzwQBcCA/XxivJdYoxnN8MuIp2BjUAl4FkgDHjRGPOXE/FERUWZ5cuXO7Fon2eMYd3+47z+21bmb4kBoFbFctx/UWOuiqrjcHSqOCxZEk1ISBlatqzGkSNJJCenExlZ0emw1FkQkRXGGP2mbhHx+Dt3Y8xS+88TwI0AIlLbuYiUE3bGJHLNh0uIOWF9kS24jD9jL23BNZ20WNYbxcWd5Ikn5vHhhyu44ormTJlyNdWqBTsdllKlhkcndxHpBNQC/jTGxIpIS6xmaPsAmuB9xIItR7jF/ohL29phPD+kDS1q6mdWvZExhq+/XseDD84hNjaZ++/vwv/+19vpsJQqdTw2uYvI88B/gDVYleh+wvoi3IvAKCdjUyXjeHI636/YxzMzNgHw1tD2XNZWP/rhzSZOXMWIEb/QqVNNZs26nvbtazgdklKlkscmd2Aw0NYYc1JEKgMH7O4tDselikmmy/DPrmM8M2MjGw4knDJs9MDmmti9VEpKBnv2xNO0aTjXXdcaPz/hppva6kdelMqHJyf3FGPMSQBjzDER2ayJ3Tst2hbDV0v2MnfT4ez31IPL+HNp25p0iqxMr6ZVCa+gr7V5o7lzd3LnnTPIyHCxefPdlCsXyLBh7Z0OS6lSz5OTewMRyfrymwCRbt0YY4Y4E5YqCpkuw5ip61i0LZbouJMAVCofyPAe9enXsjqNI0IcjlAVp8OHE3nwwTl8/fU6GjWqzAcfXEKZMvpqm1KF5cnJ/T85ut9xJApV5N6at43Xftua3d29URVeGKItyfmKzZtj6dLlY06ezGDs2J488cT5BAV58qlKqZLnsUeMMWae0zGoovfZ4t3ZiX1Y90ieHNicQH226hMSElIJDS1LkyZVuPXW9owc2ZGmTcOdDkspj+SxyV15l++W7+OFXzdzLCmNoEA/pt/Tg0bVtOjdFyQmpvHUU/P57LM1rF9/J9WrV+C11/o5HZZSHs2nk7uI9AfeBPyBj40xL+QyztXAOMAAa4wx15VokF5u+5ET3P75CnbGJgFwUfMIXrumLaFB2kystzPG8PPP1kdeoqMTuP32DpQtq8/VlSoKXpPcRaSsMSb1DMb3ByYAfYFoYJmITDPGbHQbpzHwBNDdGBMnItpmfRFJSEnn40W7eGveNsBqJnba3d2porXefUJaWiZXXfU906ZtoXXranz33ZV07apNBCtVVDw+uYvIecAnWG3K1xWRtsAIY8w9BUx6HrDdGLPTns9krHfnN7qNcxswwRgTB2CMOVLU8fuajQcSuPnTf7KbiS0X6M9rV7dlQGttjMQXGGMQEcqU8ad69WBeeaUv997bWT/yolQR8/jkDrwFXAJMBTDGrBGRCwoxXS1gn1t3NNA5xzhNAETkL6yi+3HGmFnnHLEPOpmWyWd/7+aFXzdn93vq0hbc3DUSPz9xLjBVYhYv3sc99/zKpEmDad06gg8+uNTpkJTyWt6Q3P2MMXusr75myyzEdLlllJyfyAsAGgO9sdqqXyQirYwx8afMSOR24HaAunX1QyU5pWW4aD7232sibSbWtxw7dpLHH5/LRx+tpE6dUI4dO+l0SEp5PW9I7vvsonljP0e/B9hawDRg3am7P+SrjdWEbc5xlhhj0oFdIrIFK9kvcx/JGPMh8CFYn3w9q7XwUr9tPMwdX64AoH54MPMe7KV36j7k66/Xcf/9szh27CQPP9yVp57qTYUKZZwOSymv5w3J/Q6sovm6wGFgrt2vIMuAxiJSH9gPXAvkrAk/FRgKTBKRcKxi+p1FFLfX++/U9XyxZA8AA1pV5+2h7TWx+5iNG2No2LAyv/02iLZtqzsdjlI+Q4zx7BtNEalsjDl2ltMOBN7Aep4+0RjzrIg8DSw3xkwTq6z/VaA/VlH/s8aYyfnNMyoqyixfvvxswvEa248k8uPKaN5dsAOA6ff0oFWtMIejUiXh5Ml0nntuEd2716V//0akpWUSEOCnF3WqQCKywhgT5XQc3sIb7tyX2cXl3wI/GmNOFHZCY8xMYGaOfmPd/jbAg/Y/VQjfLdvHo1PWAhBSNoAvRnTWxO4j5szZwZ13zmDHjjgef7w7/fs30vbglXKIxyd3Y0xDEemGVaz+PxFZDUwu6A5bFa2jiam8t2AHH/+5C4CPboqib4sIh6NSJeHgwRM88MBsvv12A02aVGHevJvo06e+02Ep5dM8PrkDGGMWA4tFZBxWMftXgCb3EmCM4cVZW3j/jx3Z/RY83JvI8GAHo1IlaebMbUydupn//a83jz3WnbJlveK0opRH8/ijUEQqYDU+cy3QHPgZ6OZoUD7CGMOdX63k1/WHALjrgoaM7NVQm471AStXHmTPnniuuKI5w4a158ILGxAZWdHpsJRSNo9P7sB64BfgJWPMIqeD8RUH4k/S7YXfs7tX/bcvlYL1FSdvl5CQytix83n77X9o3Lgyl13WFH9/P03sSpUy3pDcGxhjXE4H4Uv+2XWMqz/4G4D2dSsy8eZOmti9nDGGKVM2cd99szh48ASjRkXx3HMX4q+f41WqVPLY5C4irxpjHgKmiMhp7/MZY4Y4EJbXMsYwY91BPvhjJ+v2Hwegf8vqvHdDB3K0Dqi80MqVB7nqqu9p1646P/54NZ0713Y6JKVUPjw2uWO9+gbwjqNR+IC/tsdyy6f/kJ5pXUNVKBvAZ7eeR8d6lRyOTBWntLRMFi/eR+/ekXTsWJPp04fSr18jAgL0bl2p0s5jk7sx5h/7z+bGmFMSvIjcDcwr+ai8S0JKOn1e+YPYxH+/pLtm7MWEldcKc95u0aI93HHHDLZsOcr27fdQr15FBg1q4nRYSqlC8oZL8Ftz6Te8xKPwMmN/Xk+bcXOyE/v0e3qw+4VBmti9XGxsMsOH/0zPnpNITEzjp5+uoV49rSynlKfx2Dt3EbkG6/W3+iLyo9ugECA+96lUQY4mphL17FyyWiV+7orWXNG+FuW0pTGvl5ycTps27xETk8yjj3Zj7NheBGtFSaU8kscmd+Af4CjW19wmuPU/AaxyJCIPN3fjYUZ8brWLX7dyeeY91ItArQ3t9aKjE6hdO5Ty5QMZP/4CzjuvFq1ba+uCSnkyj03uxphdwC6sr8Cpc2CMYfhny/l98xHAulu/rrN+l97bJSen8+yzC3n55cVMmzaU/v0bMXx4B6fDUkoVAY9N7iLyhzGml4jEAe6vwgnWN18qOxSaRzlyIoU7vlzJij1xAHx6SycuaFbN4ahUcZs1azt33jmDXbviuemmtnToUMPpkJRSRchjkztwgf1/uKNReKjdsUnc+tkydsYkZffbPL4/QYH6bN3bjRz5Cx9+uJKmTaswf/7N9O4d6XRISqki5rHJ3a1VujrAAWNMmoj0ANoAXwIJjgVXyq3aG8cV7y4GoHalcjw+oBmDWtfQxmi8WGamCxHBz0/o2rUOdeuG8fDD3fQjL0p5KW84sqcCnUSkIfA5MAP4GrjE0ahKocTUDF78dTNfLNkDwOXtavLGte0djkoVt+XLDzBq1HRGjOjAqFFR3HJLO6dDUkoVM29I7i5jTLqIDAHeMMa8JSJaW97N7A2HeHXOFrYeTgQgKNCPiTd3olsjfaLhzY4fT2HMmN+ZMGEZEREViIjQz/Aq5Su8IblniMhVwI3A5XY/bWkFqxb8U9M28Pnf1p169dAgLmlTg9GDmmsRvJebOXMbI0ZM49ChRO66qxPPPNOHsLAgp8NSSpUQb0jutwJ3Yn3ydaeI1Ae+cTimUuHNeduyE/uCh3sTGa53br4iIMCPGjVCmDZtKFFRNZ0ORylVwsSY0z6o5nFEJABoZHduN8ZkOBVLVFSUWb58uVOLz3Y0MZWOz1hNAPz52AXUrlTe4YhUcUpNzeCVVxaTnu5i3LjeALhcBj8/LaFRnkFEVhhjopyOw1t4/J27iJwPfAHsx3rHvbqI3GiM+cvZyJyzMyaRPq/+AcCw7pGa2L3cH3/sZtSoGWzeHMvQoa0wxmTXjFdK+SaPT+7A68BAY8xGABFpjpXsffIKMD45LTuxP3VpC4Z1r+9wRKq4xMYm8/DDc/jsszXUr1+RGTOuY+DAxk6HpZQqBbwhuZfJSuwAxphNIuKTX7vIdBnaPf0bAP1aRmhi93KHDyfy/fcbefLJHowe3ZPy+sU+pZTNG5L7ShH5AOtuHeB6fPTDMc/O2ARAixqhfHCjTxZceL31648wdepmxozpScuW1di37wEqVy7ndFhKqVLGG5L7KOBe4FGsZ+4LgbcdjaiEfbdsH98t38fyPXF0aVCZb27r4nRIqoglJaUxfvxCXn31b0JDyzJiRAeqV6+giV0plSuPTu4i0hpoCPxkjHnJ6XhKWmxiKv1eX8jRpDQAujSozBvXtNd32L3MjBlbueuumezZc5xhw9rx0kt9CQ/XSpJKqbx5bHIXkSeB4cBKrOZnnzbGTHQ4rBJjjOGSt/7kaFIaTSIq8Nmt51EjTO/ivM3x4ynceONPVK9egT/+uIWePes5HZJSygN4bHLHerbexhiTJCJVgZmAzyT30VPXcyghhdqVyjHngV5Oh6OKUEaGi8mT13Pdda0JCwti3rybaNmyGmXK6Bf7lFKF48nJPdUYkwRgjIkRET+nAyopO2MS+XrpXgDmPaSJ3Zv8889+Ro2azqpVhwgLK8ullzalfXv91rpS6sx4cnJvICI/2n8L0NCtG2PMEGfCKl4ul+Hi1xcC8M1tXSgboHdz3iA+PoXRo+fx3nvLqVEjhO+/v4pLLmnidFhKKQ/lycn9Pzm633EkihKUkp5Jq6dmk+EyDGxdna4Nqzgdkioil132DX/9tY977jmP8eP7EBpa1umQlFIezGOTuzFmntMxlLRHflhLhstQu1I5Xrtav8nt6bZvP0bNmiGULx/I889fSFBQAB076kdelFLnzmeeU3u6iX/u4pc1BwCY/3BvggK1ON5TpaZm8PTTf9Cq1bs8//wiALp3r6uJXSlVZHw6uYtIfxHZIiLbReTxfMa7UkSMiDjS7Ft8chpPT7da2F35374E+vv0z+bR5s/fRZs27/PUUwu4/PJm3HFHJ6dDUkp5Ia/JEiJyRg8pRcQfmAAMAFoAQ0WkRS7jhWC1gLe0KOI8G6N/Wg/AhOs6UDnYJ5vN9wovvfQXffp8TkaGi1mzrmfy5CupWTPE6bCUUl7I45O7iJwnIuuAbXZ3WxEpTPOz52F9+32nMSYNmAwMzmW88cBLQEpRxXwmjpxIYca6gwAMbF3diRDUOXC5DImJVguCgwY1ZsyY81m//g769WvkcGRKKW/m8ckdeAu4BDgKYIxZA1xQiOlqAfvcuqPtftlEpD1QxxgzPb8ZicjtIrJcRJbHxMScSewFuu+b1QBMvr2LNivrYdauPUyPHhO57bZfAGjZshrjx/ehXDn9eptSqnh5Q3L3M8bsydEvsxDT5ZYpTfZAq1Gc14GHCpqRMeZDY0yUMSaqatWqhVh04cxaf5C/dx4lKNCPLg30tTdPkZSUxiOPzKFDhw/Ytu0YAwboXbpSqmR57KtwbvaJyHmAsZ+j3wNsLcR00UAdt+7awAG37hCgFbDAvmOuDkwTkcuMMcuLJPJ8pGe6GPXlSgCm3NGtuBenisiyZfu58srv2bv3OCNGtOfFF/vql9uUUiXOG5L7HVhF83WBw8Bcu19BlgGNRaQ+sB+4Frgua6Ax5jgQntUtIguAh0sisQNc8e5fAFwTVYeWNcNKYpHqHBhjEBHq1g2jXr0wvv56CN2713U6LKWUj/L45G6MOYKVmM90ugwRuRuYDfgDE40xG0TkaWC5MWZaEYdaaG/O3cb6/QlUDSnLi1e2cSoMVQjp6Zm89dZSZs3awezZNxARUYGFC4c5HZZSysd5fHIXkY9we1aexRhze0HTGmNmYn1Nzr3f2DzG7X2WIZ6xhdusSnk/39W9pBapzsKSJdGMHDmdtWsPM2hQY06cSCUsLMjpsJRSyvOTO1YxfJYg4ApOrQXvUfbHn2TFnjgubVuTmhX1WW1pdOJEKo888hsffriCWrVC+fHHq7n88mb6NoNSqtTw+ORujPnWvVtEvgB+cyicczZlRTQAFzar5nAkKi8BAX4sWLCbBx7owrhxvQkJ0Y+8KKVKF49P7rmoD9RzOoiz9dpvVkX/S9tqO+OlydatR3n22UW8994gypcPZPXqUQQFeePho5TyBh7/nruIxInIMftfPNZd+5NOx3U2Ztot0ZXx98PfT4t4S4OUlAzGjVtA69bv8fPPm1m79jCAJnalVKnm0WcosR5ytsV6lQ3AZYw5rXKdp3j4+zUA/Hy3VqQrDX77bQd33jmT7duPcd11rXn11YupXr2C02EppVSBPDq5G2OMiPxkjOnodCzn6nhyOn4iNKwaTPMaoU6H4/OMMTz99EIA5sy5gb59GzockVJKFZ5HJ3fbPyLSwRiz0ulAzsWzMzeSmJrBh5d7/HWKx3K5DB99tILLLmtKjRohTJ78H6pUKa9F8Eopj+OxZy0RCTDGZAA9gNtEZAeQhNVmvDHGdHA0wDPgchnmb4mhWfUQujUML3gCVeRWrz7EqFHTWbp0P7GxyYwe3ZNatbQERSnlmTw2uQP/AB2Ay50O5FztPZZMzIlUbj+/gdOh+JwTJ1J56qkFvPnmUsLDy/Pll1dw3XWtnQ5LKaXOiScndwEwxuxwOpBz9fnf1kftWuqdYokbPfp33n77H0aO7Mjzz19IpUracJBSyvN5cnKvKiIP5jXQGPNaSQZzLib+tQs/QYvkS8iePfGkpmbSpEkVRo8+n6FDW9G1a52CJ1RKKQ/hye+5+wMVsD7Nmts/j7BqbxwAUZGVHY7E+6WnZ/LSS3/RosW73HWX9UmBiIgKmtiVUl7Hk+/cDxpjnnY6iHO19fAJAMYMau5wJN5t8eJ9jBw5nfXrjzB4cFPeemuA0yEppVSx8eTk7hVNuP2x1foCXIOq2jhKcfn5581cfvm31KkTytSp1zB4cDOnQ1JKqWLlycn9QqcDKAor9sQRUjaACmU9+acofYwxHDhwglq1Qrn44oaMH38B99/fhQoVyjgdmlJKFTuPfeZujDnmdAznKuZEKocTUulQr5LToXiVzZtj6dPnc3r0+JTk5HTKlQtkzJiemtiVUj7DY5O7N7jq/cUAXNRcP+9aFE6eTOe///2dNm3eY/XqQzzxRA9tXU4p5ZP0zOeQTJdh99FkKgeX4caukU6H4/H270+gV69J7NgRxw03tOHVVy+mWrVgp8NSSilHaHJ3yOp91itwA1tXdzgSz5aenklgoD81aoRw/vn1+PDDS+nTp77TYSmllKO0WN4hszdY3wUf2VO/NnY2MjNdvPPOPzRq9DaHDiXi5yd8+ulgTexKKYXeuTvmp1X7aVStAnUql3c6FI+zcuVBRo6czvLlB7joogakpWU6HZJSSpUqmtwdsGLPMWJOpNK2dkWnQ/EoLpfhoYdm89Zb/1C1anm+/noI117bChGvaPJAKaWKjCZ3Bzw+ZR0Az13RyuFIPIufn3D06ElGjerIs89eSMWKQU6HpJRSpZI+cy9hxhi2HUmkcnAZqoVqcirIrl1xDB48mXXrrDoKkyZdzoQJgzSxK6VUPjS5l7D1+xMAGNBKa8nnJy0tk+efX0TLlu8yb95ONm+OBay7d6WUUvnTYvkS9ud2K0ldqA3X5GnRoj2MGjWDjRtjGDKkOW+80Y86dcKcDksppTyGJvcS9v4fOwDo1USTe15mz95BUlIav/wylEsuaeJ0OEop5XG0WL6EVSgbQNkAP/y1eDmbMYZJk1bz22/Whc/o0eezYcOdmtiVUuosaXIvQSnpmeyPP8nA1jWcDqXU2Lgxht69P2PYsJ+ZNGkNAOXKBRIcrB95UUqps6XF8iXoL/t5u961Q3JyOs88s5CXX15MaGhZPv74UoYNa+90WEop5RU0uZegtdHHARjeQ5tInTJlI88//yc339yWl1/uS9Wq+pEXpZQqKprcS9APK6IBaFytgsOROGP//gQ2bIjh4osbcv31bWjaNJzzzqvldFhKKeV1fPqZu4j0F5EtIrJdRB7PZfiDIrJRRNaKyDwRqXe2y8p0GfbHn6RTZCUC/H1rs2dmunjrraU0bz6Bm2+eSmpqBn5+ooldKaWKiW9lGTci4g9MAAYALYChItIix2irgChjTBvgB+Cls13ex4t2AtCtYfjZzsIjLV9+gM6dP+a++2bRtWsd/vxzGGXLaoGRUkoVJ18+y54HbDfG7AQQkcnAYGBj1gjGmPlu4y8BbjjbhU38axcAI3s1ONtZeJytW4/SufPHREQE8+23V3LVVS30Iy9KKVUCfDm51wL2uXVHA53zGX848GtuA0TkduB2gLp16+Y68eGEVKoEl6F8Ge/e5MYY1q8/QuvWETRpUoWJEy/j8subERambcErpVRJ8dlieSC3W0iT64giNwBRwMu5DTfGfGiMiTLGRFWtWvW04clpGQD0bHL6MG+yY8cxBg78mg4dPmTTphgAbr65nSZ2pZQqYd59G5m/aKCOW3dt4EDOkUTkImA00MsYk3o2C8p6Ba5dHe/8fntqagavvLKYZ55ZRGCgH6++ejFNmlRxOiyllPJZvpzclwGNRaQ+sB+4FrjOfQQRaQ98APQ3xhw52wUt3nEUgE6Rlc862NIqI8NF584fs2bNYa68sgVvvNGPWrVCnQ5LKaV8ms8md2NMhojcDcwG/IGJxpgNIvI0sNwYMw2rGL4C8L1dEWyvMeayM13WiZR0ABpHeM/77QkJqYSGliUgwI9bb21Po0aVGTiwsdNhKaWUwoeTO4AxZiYwM0e/sW5/X1QUy9l7NJmQsgEEesH77S6XYeLEVTz22Fy++moI/fs34t5786uHqJRSqqT5dHIvKbtik3Kvqedh1q8/wqhR0/nrr32cf35d6tXTb6wrpVRppMm9mJ1My2RnbBLnN/bsxmuef34RY8cuICysLJ9+Opibb26r76wrpVQppcm9mC3aZr0S5qnJ3RiDiFCtWjA33dSGF1/sS3h4eafDUkoplQ/Pfwhcyq3fb70Gd3GL6g5HcmaioxP4z3++44MPVgAwfHgHPvlksCZ2pZTyAJrci5nLftheu1I5ZwMppIwMF6+//jfNm0/g11+3kZ6e6XRISimlzpAWyxezH1ZEE1zG3yO+BLdixQFGjPiF1asPMWBAIyZMGEj9+pWcDksppdQZ0uRezMqX8ScuOc3pMAolLi6FmJgkfvjhKoYMaa4V5pRSykNpci9mO2OTGNSmhtNh5MoYw7ffbmDv3uM8+mh3LrqoAdu330tQkO4WSinlyUp/WbEHO5Zk3bEHl/F3OJLTbd9+jH79vmTo0Cn8/PMWMjJcAJrYlVLKC2hyL0ZZX4OLKkVtyqemZvD003/QqtW7LF26n3feGcDChbcQEKC7glJKeQu9TStG248kAlChbOnZzLt2xfPMMwsZMqQ5r73Wj5o1Q5wOSSmlVBErPVnHC2V96rV1LWebaT18OJEpUzZx552daNYsnE2b7qJhw9JTDpwhuwAAFApJREFUmqCUUqpoaVlsMdpwwErutSo68467y2X48MMVNGs2gfvvn8XOnXEAmtiVUsrL6Z17MXG5DH9tP0qb2mH4+ZX8K2Vr1x5m1Kjp/P13NL17R/Lee4No0EDfWVenS09PJzo6mpSUFKdDUT4gKCiI2rVrExgY6HQoXk2TezHZHpNIYmoGA1uX/GtwKSkZ9O37BS6X4bPPLufGG9voO+sqT9HR0YSEhBAZGan7iSpWxhiOHj1KdHQ09evXdzocr6bJvZhsPJAAQPMaoSW2zN9/30Xv3pEEBQXw/fdX0apVNSpX9oxmb5VzUlJSNLGrEiEiVKlShZiYGKdD8Xr6zL2YZLVK1yA8uNiXtXfvcS6/fDIXXvg5X3+9DoCePetpYleFpoldlRTd10qG3rkXkwVbYihfxr9YK9Olp2fy5ptLeeqpBQC89NJFXHNNy2JbnlJKKc+gd+7FZOXeOEKCAoq1Mt21107hkUd+o0+f+mzceCePPNKdwMDS1xqeUgXx9/enXbt2tGrViksvvZT4+PjsYRs2bKBPnz40adKExo0bM378eIwx2cN//fVXoqKiaN68Oc2aNePhhx92YhXytWrVKkaMGHFKv8GDB9O1a9dT+t1yyy388MMPp/SrUKFC9t9bt25l4MCBNGrUiObNm3P11Vdz+PDhc4rt2LFj9O3bl8aNG9O3b1/i4uJOG2f+/Pm0a9cu+19QUBBTp07Njrl+/frZw1avXg3A9OnTeeqpp84pNnUOjDH6rwj/dezY0bhcLlPvsenmho+XmKJ27FiySUpKM8YYM3/+LvPjjxuNy+Uq8uUo37Fx40anQzDBwcHZf990003mmWeeMcYYk5ycbBo0aGBmz55t/t/evUdHXV0LHP9uiJJEAiJIpKU1QSJJCCEKVLi014CKqDwuSI2BUsAHgq8WrrhwwSqtugRRRBEEub2uQBUSBXkUW72AoSryChoTBKUpRkQREFMIyCMm+/7x+zGZhIRM4jxksj9rzVrze+85a2b2nPM7c46q6vHjx7V///46d+5cVVUtLCzUDh066K5du1RVtaysTOfNm+fX2MrKyn7wOYYNG6b5+fme5ZKSEm3fvr0mJibqnj17POtHjRqlr732WpVjz5TNiRMntGPHjrp69WrPtrffflsLCwt/UGyTJk3S6dOnq6rq9OnT9eGHHz7n/ocPH9ZWrVrp8ePHa41ZVbWiokLT0tI8+3mr6T0H5OmP4Ds8XB7WLB8AB46eAqBTrP9Gf1NVXnmlkIkT3+KOO65ixozrSU+P89v5jQH4018/9nQG9Zfkn7Rg2kDfbxf16tWLgoICAJYsWULv3r3p168fANHR0cydO5f09HTuu+8+Zs6cyZQpU0hMTAQgIiKCe++996xzHjt2jAceeIC8vDxEhGnTpnHrrbfSvHlzjh1zRpJctmwZa9asISsri9GjR3PJJZfw4YcfkpaWxooVK8jPz+fiiy8GoGPHjmzcuJEmTZowbtw49u7dC8Czzz5L7969q1y7tLSUgoICunbt6lm3fPlyBg4cSGxsLNnZ2TzyyCN1lsuSJUvo1asXAwcO9Kzr06ePz+Vam1WrVrFhwwYARo0aRXp6Ok8++WSt+y9btoybbrqJ6Ojoc55XREhPT2fNmjXcdtttPzhOUz/WLB8AZwav6Z3Qxi/n+/TTb7j++r8wcuQK4uNbcfvtKX45rzE/NuXl5axfv55BgwYBTpN8t27dquxzxRVXcOzYMY4ePcqOHTvO2l6Txx57jJYtW1JYWEhBQQF9+/at85jdu3ezbt06Zs+ezeDBg1mxYgUAW7ZsIS4ujtjYWH73u98xYcIEtm3bxvLly89qegfIy8sjJaXqZ3bp0qVkZmaSmZnJ0qVL64wF8Pm1lpaWVmlC937s3LnzrP0PHDhAu3bOX3bbtWvHwYMHz3n+7OxsMjMzq6ybMmUKqampTJgwgVOnTnnWd+/enXfffdeXl2f8zGruAfCvQ05NwB8198WLP+Luu/9KVFQE8+ffwt13X03TpvabzARGfWrY/nTixAnS0tIoLi6mW7du3HDDDYDTYlVb7+r69Lpet24d2dnZnuVWreoe0OnXv/41TZs6fVgyMjJ49NFHGTNmDNnZ2WRkZHjO650wjx49SmlpKTExlZ/9/fv3c+mll3qWDxw4QFFREb/85S8RESIiItixYwcpKSk1vqb69i6PiYnx3Pf2t/3791NYWMiNN97oWTd9+nQuu+wyTp8+zdixY3nyySf5wx/+AEDbtm356quvAhKLOTfLEgHw2TfHAWgb06zB5ygrKwegW7d2ZGR05pNP7mfcuO6W2E1YioqKIj8/n88//5zTp08zb948ADp37kxeXl6Vfffs2UPz5s2JiYmhc+fObN++vc7z1/YjwXtd9RH6Lrqo8m+svXr1oqioiEOHDrFy5UqGDh0KQEVFBZs2bSI/P5/8/Hy+/PLLKon9zGvzPndOTg4lJSXEx8cTFxdHcXGx54dH69atq3Ro+/bbb2nTpo2nLHx5rfWtucfGxrJ//37ASd5t27at9dyvvvoqQ4YMqTK6XLt27RARmjVrxpgxY9i6datn28mTJ4mKsr/khoJligCIaNKEiCZCRAMS8ddfH2P48OWMHOk0AXbu3JbFi4dw2WXN6zjSmPNfy5YtmTNnDk8//TRlZWWMGDGC9957j3Xr1gFODf/BBx/k4YcfBmDSpEk88cQT7N69G3CS7TPPPHPWefv168fcuXM9y2cSaGxsLLt27aKiosLT7F4TEWHIkCFMnDiRpKQkWrduXeN5a6oxJyUlUVRU5FleunQpb775JsXFxRQXF7N9+3ZPck9PTycnJ4fTp51xMrKysjz31YcPH87777/PG2+84TnXm2++SWFhYZXrnam51/RITk4+K75BgwaxaNEiABYtWsTgwYNrLYcztxO8nflhoKqsXLmyyi2I3bt3n3VLwgSHJfcAOFR6ikvrWWsvL69g/vxtJCbOZfnyXSQmtqGiQus+0Jgwc9VVV9G1a1eys7OJiopi1apVPP7443Tq1IkuXbrQo0cP7r//fgBSU1N59tlnyczMJCkpiZSUFE+y8TZ16lRKSkpISUmha9eu5ObmAjBjxgwGDBhA3759Pfeda5ORkcHLL7/saZIHmDNnDnl5eaSmppKcnMyCBQvOOi4xMZEjR45QWlpKcXExe/fupWfPnp7t8fHxtGjRgi1btjBgwAB+9atf0a1bN9LS0ti4caOnc1tUVBRr1qzh+eefJyEhgeTkZLKyss5Z0/bF5MmTWbt2LQkJCaxdu5bJkycDTl8B7z4ExcXFfPHFF1x77bVVjh8xYgRdunShS5cufPPNN0ydOtWzLTc3l1tuueUHxWcaRpx/IBh/6d69uzbPeIqW0Rey6r7edR8AFBV9y4gRr7N165f07RvP/Pm3cOWVrQMcqTGOXbt2kZSUFOowwtrs2bOJiYmpscNduDpw4ADDhw9n/fr1Z22r6T0nIttVtXuw4gt3VnP3MwWKD39H1/a+z+HeokUzjh49xcsvD2HdupGW2I0JM+PHj6dZs4b3wTkf7d27l1mzZoU6jEbLesv72fGT3wPnHlPeuTf1CUuW7CAnZxht217Exx/fG5KpYY0xgRcZGcnIkSNDHUZQ9ejRI9QhNGpWc/ezE24v92s61Fz7/vzzfzNoUDZDh77K7t2HOXjQ6Vlvid2Ekt2eM8Fi77XgsOTuZyXfnSb6wqZccWnV3u1lZeXMnLmR5OQXyM39jKefvoHt28daL3gTcpGRkRw+fNi+dE3AqTrzuUdGRoY6lLBnzfJ+Vl6hdLj0Ii6MqPq7qbxcWbhwO/36XcFzz/Xn5z/3/Z68MYHUvn179u3bZ3Nsm6CIjIykffv2oQ4j7Fly97PvK5Rr4p0m+cOHv2PmzI1Mm5ZOdPQFbNlyF61bn3s8ZmOC7YILLiA+Pj7UYRhj/KhRN8uLSH8R+VREikRkcg3bm4lIjrt9i4jE+XLepk2ExYs/IjFxHrNmbWLDhmIAS+zGGGOCotEmdxFpCswDbgKSgUwRqT58051Aiap2BGYDtU+V5GX5ogJGjVpJQsIlfPDBPdx8c4I/QzfGGGPOqdEmd+AXQJGq7lHV00A2UH3cxcHAIvf5MuA68WEWhy+KvuXFFwfw3nt3kJoa69egjTHGmLo05nvuPwW+8FreB1xT2z6q+r2IHAFaA9947yQiY4Gx7uKpQ1/fu+Oee+CeewIS9/mkDdXKqhGzsqhkZVHJyqJSp1AHEE4ac3KvqQZe/b9AvuyDqi4EFgKISJ4NoeiwsqhkZVHJyqKSlUUlEcmrey/jq8bcLL8P+JnXcnug+sTDnn1EJAJoCXwblOiMMcaYBmrMyX0bkCAi8SJyIXA7sLraPquBUe7zYcDbaiN9GGOM+ZFrtM3y7j30+4G3gKbAS6r6sYg8CuSp6mrgf4G/iEgRTo39dh9OvTBgQZ9/rCwqWVlUsrKoZGVRycrCj2zKV2OMMSbMNOZmeWOMMSYsWXI3xhhjwowl9wYK1NC15yMfymKiiOwUkQIRWS8il4cizmCoqyy89hsmIioiYfs3KF/KQkRuc98bH4vIkmDHGCw+fEZ+LiK5IvKh+zm5ORRxBpqIvCQiB0VkRy3bRUTmuOVUICJXBzvGsKGq9qjnA6cD3r+ADsCFwEdAcrV97gUWuM9vB3JCHXcIy6IPEO0+H9+Yy8LdLwZ4B9gMdA913CF8XyQAHwKt3OW2oY47hGWxEBjvPk8GikMdd4DK4j+Bq4EdtWy/Gfg7zhgjPYEtoY75fH1Yzb1hAjZ07XmozrJQ1VxV/c5d3IwzpkA48uV9AfAYMBM4GczggsyXsrgbmKeqJQCqejDIMQaLL2WhQAv3eUvOHnMjLKjqO5x7rJDBwGJ1bAYuFpF2wYkuvFhyb5iahq79aW37qOr3wJmha8ONL2Xh7U6cX+bhqM6yEJGrgJ+p6ppgBhYCvrwvrgSuFJGNIrJZRPoHLbrg8qUs/gj8RkT2AX8DHghOaD869f0+MbVotP9z/4H8NnRtGPD5dYrIb4DuwLUBjSh0zlkWItIEZ3bB0cEKKIR8eV9E4DTNp+O05rwrIimq+u8AxxZsvpRFJpClqrNEpBfO+BopqloR+PB+VBrL92bAWc29YWzo2kq+lAUicj0wBRikqqeCFFuw1VUWMUAKsEFEinHuKa4O0051vn5GVqlqmap+BnyKk+zDjS9lcSfwKoCqbgIicSaVaWx8+j4xdbPk3jA2dG2lOsvCbYp+ESexh+t9VaijLFT1iKq2UdU4VY3D6X8wSFXDccIMXz4jK3E6WyIibXCa6fcENcrg8KUs9gLXAYhIEk5yPxTUKH8cVgO/dXvN9wSOqOr+UAd1PrJm+QbQwA1de97xsSyeApoDr7l9Cveq6qCQBR0gPpZFo+BjWbwF9BORnUA5MElVD4cu6sDwsSz+G/gfEZmA0ww9OhwrAyKyFOc2TBu3f8E04AIAVV2A09/gZqAI+A4YE5pIz382/KwxxhgTZqxZ3hhjjAkzltyNMcaYMGPJ3RhjjAkzltyNMcaYMGPJ3RhjjAkzltyNaQARKReRfK9H3Dn2jattFqx6XnODO7PYR+6QrZ0acI5xIvJb9/loEfmJ17Y/i0iyn+PcJiJpPhzzexGJ/qHXNsY4LLkb0zAnVDXN61EcpOuOUNWuOJMSPVXfg1V1gaoudhdHAz/x2naXqu70S5SVcb6Ab3H+HrDkboyfWHI3xk/cGvq7IvKB+/iPGvbpLCJb3dp+gYgkuOt/47X+RRFpWsfl3gE6usde584DXujOl93MXT/DnSu9QESedtf9UUQeEpFhOOP8v+JeM8qtcXcXkfEiMtMr5tEi8nwD49yE18QfIjJfRPLEmb/9T+66B3F+ZOSKSK67rp+IbHLL8TURaV7HdYwxXiy5G9MwUV5N8ivcdQeBG1T1aiADmFPDceOA51Q1DSe57nOHG80Aervry4ERdVx/IFAoIpFAFpChql1wRp0cLyKXAEOAzqqaCjzufbCqLgPycGrYaap6wmvzMmCo13IGkNPAOPvjDDN7xhRV7Q6kAteKSKqqzsEZP7yPqvZxh6KdClzvlmUeMLGO6xhjvNjws8Y0zAk3wXm7AJjr3mMuxxkrvbpNwBQRaQ+8rqr/FJHrgG7ANnd43iicHwo1eUVETgDFONOCdgI+U9Xd7vZFwH3AXJz54v8sIm8APk8xq6qHRGSPO7b3P91rbHTPW584L8IZbvVqr/W3ichYnO+edkAyUFDt2J7u+o3udS7EKTdjjI8suRvjPxOAA0BXnFaxk9V3UNUlIrIFuAV4S0TuwpnmcpGqPuLDNUZ4TzQjIq1r2skdz/wXOJOR3A7cD/Stx2vJAW4DPgFWqKqKk2l9jhP4CJgBzAOGikg88BDQQ1VLRCQLZ4KU6gRYq6qZ9YjXGOPFmuWN8Z+WwH53Du6ROLXWKkSkA7DHbYpejdM8vR4YJiJt3X0uEZHLfbzmJ0CciHR0l0cC/3DvUbdU1b/hdFarqcd6Kc40tDV5HfgvnHnGc9x19YpTVctwmtd7uk36LYDjwBERiQVuqiWWzUDvM69JRKJFpKZWEGNMLSy5G+M/LwCjRGQzTpP88Rr2yQB2iEg+kAgsdnuoTwX+T0QKgLU4TdZ1UtWTODNnvSYihUAFsAAnUa5xz/cPnFaF6rKABWc61FU7bwmwE7hcVbe66+odp3svfxbwkKp+BHwIfAy8hNPUf8ZC4O8ikquqh3B68i91r7MZp6yMMT6yWeGMMcaYMGM1d2OMMSbMWHI3xhhjwowld2OMMSbMWHI3xhhjwowld2OMMSbMWHI3xhhjwowld2OMMSbM/D/bFtEvhs3GPAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.38 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[2] = ([\"SVM-RBF (Grid Search)\" , \n", + " classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'rbf', probability = True)\n", + "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16104553371241384\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6689738476077944" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will stick to using unfiltered features and SVM with rbf kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training\n", + "Here we create an instance of our model, specifically a Sequential model, and add layers one at a time until we are happy with our network architecture. We will be training the model on our feature-selected dataset to speed up computation by reducing dimensionality. We have found that the performance difference between the 2 datasets are negligible.\n", + "\n", + "We ensure the input layer has the right number of input features, and is specified when creating the first layer with the input_dim argument and setting it to 17 (The size of the feature selected dataset).\n", + "Additionaly, we start off using a fully-connected network structure with three layers, and attempt to increase the number of layers at later part ater fully optimising our model.\n", + "\n", + "Fully connected layers are defined using the Dense class. We specify the number of neurons or nodes in the layer as the first argument, and specify the activation function using the activation argument. The rectified linear unit activation function (Relu) is usedon the first two layers and the Sigmoid function in the output layer.\n", + "\n", + "Conventionally, Sigmoid and Tanh activation functions were preferred for all layers. However, better performance is achieved using the ReLU activation function. We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map (binary) to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.\n", + "\n", + "The model expects rows of data with 17 variables (the input_dim=17 argument)\n", + "The first hidden layer has 17 nodes and uses the relu activation function.\n", + "The second hidden layer has 17 nodes and uses the relu activation function.\n", + "The output layer has one node and uses the sigmoid activation function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compiling\n", + "\n", + "The model uses the efficient numerical libraries as the backend, and in this case Tensorflow is used. The backend automatically chooses the best way to represent the network for training and making predictions to run.\n", + "\n", + "We must specify the loss function to use to evaluate a set of weights, the optimizer is used to search through different weights for the network and any optional metrics we would like to collect and report during training.\n", + "\n", + "After experimenting with the various loss functions, such as hinge loss and binary cross entropy, we found that entropy performed the best for our dataset.\n", + "\n", + "We have also found that among all the optimizers (Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam) the optimizer \"adam\" is the most efficient yet yields the best results.\n", + "\n", + "Additionaly, for this problem, we will run for a small number of epochs (20) and use a relatively small batch size of 10. This means that each epoch will involve (20/10) 2 updates to the model weights. After we have finalised the best optimizer, we will then increase the numebr of epochs to increase the number of updates to obtain a better result. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4708 - accuracy: 0.7956\n", + "Epoch 2/20\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4502 - accuracy: 0.8116\n", + "Epoch 3/20\n", + "17496/17496 [==============================] - 2s 113us/step - loss: 0.4477 - accuracy: 0.8125\n", + "Epoch 4/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4461 - accuracy: 0.8130\n", + "Epoch 5/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4450 - accuracy: 0.8133\n", + "Epoch 6/20\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4443 - accuracy: 0.81450s - loss: 0.4424 - \n", + "Epoch 7/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4437 - accuracy: 0.8150\n", + "Epoch 8/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4435 - accuracy: 0.8144\n", + "Epoch 9/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4433 - accuracy: 0.8147\n", + "Epoch 10/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4429 - accuracy: 0.8142\n", + "Epoch 11/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4418 - accuracy: 0.8132\n", + "Epoch 12/20\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4416 - accuracy: 0.81450s - loss: 0.4413 - accuracy: \n", + "Epoch 13/20\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4419 - accuracy: 0.8138\n", + "Epoch 14/20\n", + "17496/17496 [==============================] - 2s 128us/step - loss: 0.4417 - accuracy: 0.8140\n", + "Epoch 15/20\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4415 - accuracy: 0.8142\n", + "Epoch 16/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 17/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4409 - accuracy: 0.8152\n", + "Epoch 18/20\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4413 - accuracy: 0.8145\n", + "Epoch 19/20\n", + "17496/17496 [==============================] - 2s 126us/step - loss: 0.4403 - accuracy: 0.8145\n", + "Epoch 20/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4405 - accuracy: 0.8156\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# optimisers to try: Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam\n", + "# verdict : Adam\n", + "\n", + "# Loss function to try: Binary Cross Entropy, Hinge, Logcosh\n", + "# verdict: Binary Cross Entropy\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=20, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAEWCAYAAABMlWzFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gV1dbA4d9KgRBIAqEmQAhVCB1DsaAgIlgQFRvYUCyo6LVevehVFBtcu+C1i1hAPwVBQVCpgoVeRFqoSShJSEJ6Pfv7YybcwzEkJ0AyKet9njw5Z+qaMzN71uzZMyPGGJRSSimlKhMfpwNQSimllPKkCYpSSimlKh1NUJRSSilV6WiCopRSSqlKRxMUpZRSSlU6mqAopZRSqtKplAmKiNwgIj86HYfTRCRCRDJExLcC5xkpIkZE/CpqnuVJRLaIyICTGO+ktkERqSMi34nIURH5v7KOXxWIyF4RudDpOE6ViEwQkc+cjqM60t9WebKPK+3KMk6pCYpdGGXbB8pDIjJNROqdfJilM8Z8boy5qDznURl5FvzGmP3GmHrGmEIn43LKyWzQnowxnY0xS0uZz9+SslPYBq8GmgINjTHXnMT4nrENsGOb6tF9hYiMPtXpn252+WBEpI9bt3Yi4tUDl0RktIisKL8IT46IDBSRJXbiudejX9GJhPufEZGHvZz2tSKyVUTSReQvEbmiDHHdJyJ7RCRNRNaIyLlejtdFRBaKSFIZ1o2IyG4R+cvb+Cqavb+4ilkfZ3kxbqU+ORORl0Vkp72dbBORmz369xCRtSKSZf/v4VSsp4u3NSjDjDH1gB5AT+Bf5RdS+XFyw6usG315qqG/dytghzGmoKwjlhBzJnCziESeQlynGkNZJAPPnYbplJuTWM5M4CPgUc8ebicS9exysivgAr7xIo7mwGfAQ0CwPf0vRKSJF+P2BV7CSopDgA+B2V7WuOYDXwFjvBi2yHlAE6CNiPQuw3gV7YD7+rD/fjsdE3a4HM8EhmGt61uAN0TkbDuuWsAcrG2pAfAJMMfuXnUZY0r8A/YCF7p9nwzMc/teG3gZ2A8cBt4B6rj1Hw5sANKAXcBQu3vRDnUQiMcq0HztfqOBFfbnd4CXPWKaAzxkfw7HKggSgT3A/W7DTQC+xlppacDtxSxfCDDdHn8f8CTg4xbHSuAt4CiwDRjkMW5Jy7ASeI3/FdhtgcXAESAJ+Byobw//KVahlg1kAP8EIgED+NnDLAUm2tNNB34EGrnFc7O9DEeAf3uuO4/lrgO8Yg9/FFhhdyua5y32Ok0CnnAbrw/wG5BqL/cUoJZbfwPcC+wE9tjd3gBi7XWwFujvNrwvMN7eNtLt/i2B5fa0Mu3f4zp7+MuwtqdU4Fegm8e2+hiwCcgF/Nx/Azv2NXYch4FX7e777Xll2H9n4bYN2sN0Bn6y1+VhYHwxv+kzQB5W4Z+BVfj7YG1T+4AErG0txB6+6LceY8ewvJhpDgDisLbBj926rwBGu32/DdgKpAALgVYe8/BzG3Yp9r5AGbfT4soEj3inAa8Ch4Dz7W7tAFPafgN0AnKAQvv3SwVa2/+L9skPgAS3aX0GPOBWFsy1lyMGuKOkssDu9pnd3x+YgVWW1Cpu2ezhLgT2llJmPg0scfv+X+Brt++TgEWAAH3dl8funwicZX+eD7zi1u9L4CP783XAKrd+de11HQbUwtpP7nPbz1YCT3nM67h1U8pyfWRvC7OAKR79WgPLsPbhn7DKhc/c+v+fvU0cxdq3O3tsM28DP9jrfSXQDHgda3veBvT0MsYBQFwJ/ZdygjKUE5cDnvuHN/v0ncABrG38YbtfMyALq3a1KJ4z7fXt783yeSzLXLdpX4S1L4lb//3Yx9tixvXm2HWi415J+1mx5bndzwBjsY4NKcBU93iLjdOLH2Ev/yvgWwCbgTfc+r9uBxsKBAHfAS/a/frYCzjYXqnNgY52v2+Bd7F2qibAKuAutx+oKEE5D+vgJvb3BlgH8XB7mmuBp7B2yDbAbmCIW6GUD1xhD1unmOWbjpXwBNkb1w5gjFscBcCDWAXYdfbyhHq5DAXAfVgHyjpYhcFgrKSuMdaO+npxv7XHxu6eoOwCOtjTWwq8ZPeLwtqpzrV/i5ftZT/RgWSqPX5zrI3qbDuuonm+b8+jO9bBvpPbDtXPXqZIrIPiA27TNVgFVGjR7w3cCDS0x3kYq6AKsPs9irVNnYFVYHfH3oHtabVzm3YvrAKhrx3zLfZvVtvt99uAleDU8fxNsRKrm+zP9YB+xf3OxWyDQdgFDRBgf+97gt91AscXzLdh7cRt7HnOAj71mO90rG2ouO1zAFaC0gzrwHqG3f1YgoK1fcdgHeD9sArPX0tYtqUcn6Cc0nbqEe80rMLufrffzzNB8Wrf9yhoz7Q/b8faxzu59etpf16GdaALwKrtTcQuWCmmLChaV/bneXbsvqWUh94kKLs4PnkMxCpXRgP9sZK+Fm4F+jLgcvvzFfb6rmv3b4a1zV8A3GAve5DdLxir/CvaH+4D1vO/srIL1oGgE/AE8Lvn8nmumxKWKRBr+7sEGGEvg/uJyW9YiWltrDI7nb/vB0F2/9eBDR7bTBJW2RKAlRzvwTrh8rW3pyWlxei+v5TQfyknLkMjKb4c8Nw/vNmnZ2Bt312xtsOiMmg+cLfb9F8D3vJm2TyWow5WmVR0wv8g8IPHMN9jJzDFjO/NsetEx72S9rPSyvPvgfpAhD1esQnUsTi9+CH2Yh340u0ZLOJ/Z/2CdYbb1m34s/jfmfO7wGvFTLMp1kHPvaZlZNFGyPEHB8EqhM6zv98BLLY/9wX2e0z7X9hnmlgF0N/OSt2G9bXjiHLrdhew1C2OAxyfla4CbvJyGfafaN72MFcA6z1+69ISlCfd+t8DLLA/PwXM8ChQ8ijmQIJVQGcD3YvpVzTPFh7LfP0JluEBYLbbdwNcUMpypxTNG+uAM/wEw3kmKP8FJnoMs53/nanvBW4rZvstKhyWY9VyNPIY5rjfuZhtcKT7eipl2SZwfMG8CLjH7fsZWAdKP7f5tilhegOwC1ys2ssv7c/uCcoP2Em12/rNwrrcVNyyLeX4BOWUtlOPYadhHVBqY+23F+N2EKQM+75b/0+xLoE0s9f3ZKwzsWO1K1hJaSH2wdse70Vgmtt6We4x3QlYJ1fLgDcp5WzOHqfEBAUrAckA6nl074N1xrkPGOnRb4w9ToG93i716H8V1klaEnCuW3fBOlvNt8dNAnp7jPsw1hlwCtC+mHi9TVBuxDqg+NnrNhW40u4XYc+/rtvwX+C2H3hMq769TRbVOkwD3nfrfx+w1e17VyDVy/1vAFZNdKrHX1HCt5QTl6GRFF8OeB5jvNmnO7r1nwx8aH++Dlhpf/bFOlnr482yecTwCbCA/yWj/wZmegzzOTChmHG92QdPdNwrbT8rrTx3336/Ah4vaTm9bYNyhTEmyF75HYFGdvfGWAfCtSKSKiKp9o/W2O7fEitb9dQKKzM76Dbeu1iZ3HGMtSQz7R8QYBTWD180nfCiadjTGY+1AorElrBcjbBqG/a5dduHVatQJN6Owb1/uJfLcNy8RaSJiMwUkXgRScM6e2tE2Rxy+5yFlcFjx3RsfsaYLKwq+uI0wsp+i1s3Jc5HRDqIyPd2g+k04AX+vgyey/2w3QjwqP07hbiNc6JtpDitgIc91ndLrGUvdt4exmCdOW0TkdUicpmX8y1LjJ7C+fv25Yf326i7ScAQEenu0b0V1vXoot8kGevg1dxzAidw2rdTY0wuVlX6RDsW91i92vfdLMMqe87DSjKXAufbf78YY1xYv3OyMSbdbTzPfbm437kf0A3rLNoU07+sbgG+McZkuHc0xqzCqv0QrIIZALtR/GSs5auFtUwfeDRw/B7rYLbdGOPegPh2rLP5zva4NwLfi4j7/vAJ1kFzvjFm5yku11fGmAJ73c6yu4H126cYYzLdhj+2zYuIr4i8JCK77O1pr93LfZs67PY5u5jvZbkx44Axpr7Hn3tsJypDT8RzuynrPl10zACrtj5KRNpg1VIetbcNr4nIf7Bqx65122YzsGrU3AVjVSx48mYfPNFxr7T9rLSysky/fZluMzbGLMPKdl+2OyVhbTyd3TaEEGM1FANrJbUtZlKxWBlcI7fxgo0xnU8w6xnA1SLSCqvW5Bu36ezx2BCDjDGXuIddwiIlYWW+rdy6RWBdkyvSXETEo/8BL5fBc94v2t26GWOCsQoUKWH4sjiIdQkOsG53xbqsUpwkrGv9xa2b0vwX64ysvb0M4zl+GcBtOUSkP1a7kGuBBsaY+ljVhUXjnGgbKU4s8LzH+g40xswobt6ejDE7jTEjsXbEScDXIlK3pHFOIkZPB/j79lXA8QWwV+vdGHMEq3p8YjHx3eXxu9QxxvyKVcMJ1olEkWaek/b4Xtp26q2PsZLRKz1iLWm/Ke63WIZVMzHA/rwCOAfrYL7MHuYAECoiQW7jee7LxU37R6zlXSQiTYvp7zV7n7sGKynw7HcvVs3DAaz2ZUV6YNXsrDHGuIwxq4E/sGpqijyPdSk1TERGunXvDnxnjNlhj7sAqxw4222Yt7ESnCHe3uFTTOwtsC4x3WifmBzCaph7iYg0sufZwN6XikS4fR6F1RbxQqztIbJo0icTTzk60X7o2d2bfbqlR/8DAMaYHKwE9QasGolPyxKgiDyDVSt5kTEmza3XFqCbx7Gqm93dkzfHrhMd90rbz06lrPybk3kOyuvAYBHpYZ+5vA+8VtTqXESai8gQe9gPgVtFZJCI+Nj9OhpjDmIVDK+ISLDdr62InF/cDI0x67GqFz8AFhpjUu1eq4A0EXlMrOdP+Ip1+5xXLcyNdfvuV8DzIhJkJ0APYZ0xFmkC3C8i/iJyDdb13PllXQZbEHbjP7v1vucdAYexrmuejK+BYSJytt1y+xlOUADY6+0j4FURCbd/t7NEpLYX8wnCuhadISIdgbu9GL4Au3pYRJ7i+Ez/A2CiiLQXSzcRKUqsPH+P94GxItLXHrauiFzqsbOckIjcKCKN7eUv2oYK7dhcnPi3/x5oJiIPiEhte1vp6808sZLrB0WktVi357+AdZmmzHf52F7FOgB1cuv2DvAvEekMICIh9raKMSYRq/C40V7Pt1F6AVLaduoVexknYCWoRd1K228OAy3E7e4D+8w/GytRWm4XzIex2kIss4eJxWo0/aKIBIhIN6was6La1pLinIx1SWKRfcD9GzvOAKwzT7Hn4XmHxJVY29USj3E7YF32uhHroPRPtxqS1UD/ou8i0hMrGdtkfz8PuBWrPcbNwFv2Oika91IRaWPvD4Oxagj/tMe9Catdx2isNkGf2Ntg0S3DAVg1L9jLc6L9/yasNjRnYCVUPez5xGFdrtqH1fj8GRGpZSdCw9zGD8I6IB7BSpRfOMF8vCLWrezTTmUaJ1BaOVDEm3363yISaO+Tt2I1bi4yHWudXI7bsUb+d5tzZHEzFZF/YSV7g+2TFXdLscqy++0yapzdfbHndLw8dp3ouFfaflZSeV5mZU5Q7AJvOtY1L7AKnxjgd7Gq737G2pCLqjVvxWoIdBSrMCnKPG/G2jn+wro++jVW6/MTmYGVgX/hFksh1o7QA6tRVRLWDxRShkW6D+ssczfWmdkXWAfvIn8A7e1pPw9c7bZxlHUZnsFq6HkUq1HeLI/+LwJPilXt9kgZlgFjzBZ7WWZindGkYzWuyz3BKI9gNWZajXVJYBLebQ+PYO0k6VgJw5clD85CrDYSO7CqAnM4vvrzVawk8UesxOdDrAZgYB3cPrF/j2uNMWuw2iBNwfq9Y7B2dG8NBbaISAbWnUXXG2Ny7MthzwMr7Xn1cx/Jrs4cjLWtHcJqhT7Qy3l+hHWWtBxrG83BWk8nxT44T8ZqhFzUbTbW+ptp74N/Yp1lFbkDK8k4gnU54NdSZlPadloWM7C2R3cl7TeLsc76DolIkts4y4Ajxpj9bt8Fq1FokZFYZ+cHgNnA08aYn7wJ0hgzEavh4M8iElrMIOdhJUnzsc4Ys7G2WXe3ANPdq8bFui31M2CSMWajnWyNBz4Vkdp2rfQErNq8dKza4ReMMT+KSDBWWTvOGBNvX975EPhYRMTuNxPr4JSG1Y7mLmPMNhGJwDqZvNkYk2GM+QIriXjNDq2VvQxFZ9jZWO0HinML8LYx5pD7H1ZiXHSZZxRW7XYy1l1M093Gn46178djrfPfTzAfb7XEusvkRMLl789BGVHaREsrB9x4s08vwyqfFmHdhXpsWzHGrMRKhNYZY/Z6LFfR71ScF7C2vZ1uyzXenmYeVluxm7GS5NuwmmbknWBapR27SjrulbSflVSel5mY03LZtXoS60FYtxtjTqpq1El2Zp+KdSlmj9PxKKXUqbJrrTZiXX7MdzoeT3btxx6s24ZPWEsqIouBL4wxH7h1exJINMa8W95xlqQyHfdq3MPDqjMRGcb/nq/wMlYNyV4nY1JKqdPFrhHoVOqAlZhYTRB6YbXLOcYYU6kfbuiESvkuHnXShvO/hkztsS5haBWZUkpVAiLyCVYziAc87oRRxdBLPEoppZSqdLQGRSmllFKVjrZBqYIaNWpkIiMjnQ5DKaWqlLVr1yYZYxqXPqSqDDRBqYIiIyNZs2aN02EopVSVIiL7Sh9KVRZ6iUcppZRSlY4mKEoppZSqdDRBUUoppVSlowmKUkoppSodTVCUUkopVelogqKUUkqpSkcTlHIkIh+JSIKI/HmC/iIib4pIjIhsEpFeFR2jUkopVRlpglK+pgFDS+h/MdY7c9oDdwL/rYCYlFKqRjHGkFfgcjoMVUb6oLZyZIxZbr9++0SGA9PtF/r9LiL1RSTMGHOwQgJUSqkqoNBlSMvOJzu/kPxCl/1n2JOUSXJmHitjkkjNyqfAZXUvdBnyC10kZ+aRlpNPbr4Lfetc1aMJirOaA7Fu3+Psbn9LUETkTqxaFiIiIiokOKWUqmjGGGKTs1mzL5kpi2NISM8lI7egxHEa1atN60aB+Pn4EOAv+Pv64OcjRAQFsGZlLId2JhPWpG4FLYE6XTRBcZYU063YRN8Y8x7wHkB0dLSeDCilqixjDClZ+Ww9mMZfB9LYl5zJ4bRcDqRms+9I1rGEJDjAj4jQQAZHNSWkjj91a/vi5+ODv58PtXwFY6BzeAgtQ+sgIn+bR7du77BvXyrPPXcB997bGz+/G5xYXHWSNEFxVhzQ0u17C+CAQ7EopdRpV1Qjsik+lc1xR9kUd5Q/44+S7lYrUj/Qn2bBATQLCaB3ZCitGgbSqmEgAzo0wcenuPO4E1uxYj+9eoURGOjPtGnDadasHs2bB5/uxVIVQBMUZ80FxonITKAvcFTbnyilqrKEtBzW7U9l7b5kNsSmsuNwBkez8wGo5etDx7AgLu8RTutGdekUFkynsGBC69Y65fkmJmby6KM/8cknG3nhhQv417/6c+aZ4ac8XeUcTVDKkYjMAAYAjUQkDnga8AcwxrwDzAcuAWKALOBWZyJVSqmyK3QZNsWlsm5/Kuv3p7B+fyrxqdkA+PsK7ZoEcUnXMLo2D6FbixA6NA2ilt/pvXnU5TJ8/PF6/vnPn0lPz2X8+HP5xz/6ndZ5KGdoglKOjDEjS+lvgHsrKByllDpt1u5L4a5P15KUkQtAeEgAPVs14NZzIukZ0YDO4cEE+PuWexwPPbSQN974g/79I3jnncuIimpc7vNUFUMTFKWUUl5xuQyfr9rPwj8PsSImCYBbzmrF3QPa0SwkoMLiyMzMIze3kNDQOtxxRy969GjGLbd0/1tDWVW1aYKilFLqOOk5+ayMSWLN3hTiUrJJSM8hIT2XxPRccu0Hnl0X3ZJxF7SjZWhghcb2/fc7GDduPmef3ZIvvhhB585N6Ny5SYXGoCqGJihKKVWDHDqawx97jpCTX0h2XiF/HkgjPiWblKw8UrPySc3OIyffSkJq+fnQKjSQJsG16R0ZSpOg2kSFBzOkc7MKuXzjLi4ujfvv/4HZs7cRFdWYsWOjK3T+quJpgqKUUtVUocuw/VA66/ansM5uxLonKfO4Yfx9hS7NQ2gZGkjX5v40qFuLkDr+1A/0Z0SvFhWeiBRnwYIYrrnm/ygsdPHii4N46KGzqFXL+bhU+dIERSmlqjhjDEkZeexPzmR/chYxCRms35/KxthUMvMKAWhYtxY9IxpwTXQLosKCad80iAA/H+rW9qsUSUhx8vML8ff3pWfPZlx6aXtefHEQrVs3cDosVUE0QVFKqSokIS2HORsOcOBoNrHJ2cQmZ7E/OYvs/MJjw/j6CB2bBXFVrxb0alWfXhENiAgNrDKNSFNTc3jiiUVs3HiY5ctvpWnTesycebXTYakKpgmKUkpVUvmFLtbuS2HN3mQS03OZt/nQsdt669bypUWDQFqGBnJOu0a0ahhIRKj1vUWDOpW2VqQkxhhmzvyTBx9cSGJiFvfd14e8vEICAvRQVRPpWldKqUoiJTOPnQkZ7EnKYMm2RFbGJB17JHy92tZ7aXq0DOHJS6OIbFS9Xn536FAGN988m59+2k10dDjz5o3SJ8HWcJqgKKWUA3LyC9lxOJ3th9L562AaX/yx/9gtvADNggO4rHsY53dowjntGhIU4O9gtOUvJKQ2SUlZTJlyMWPHRuPre3qfOKuqHk1QlFKqnBW6DPEp2SzedpjthzPYeTidTXFHySu0EpLafj5ENqxL3dq+XNmzOX1aN6RD03pVps3IyVq8eA+TJ69k1qzrCAz0Z82aO8v8ckBVfWmCopRSp1nRG3x/33OEP3Yns3xnIonpucf6d20ewnW9W3JW24ac0SyIyIZ18a1BB+aEhEwefvhHPvtsE23aNGDfvlQ6dWqsyYk6jiYoSil1igpdht2JGexKzGT2+jg2xR3l4NEcABoE+tOndSi9I0Pp0zqUjs2CT/sL86oKl8vwwQfreOyxn8nMzOPJJ/szfnx/6tSp3pev1MnRBEUppU5CTn4he5IyWb8/lXeX72Lfkaxj/To2C+KeAW3p26Yh7RrX05oBN9Onb6RHj2b897+X0rFjI6fDUZWYJihKKVWKhPQclm5PZFdCBjEJGcQkZhCbnIXLWP3rB/rz/JVd6BweQosGdWhUr7azAVciGRl5vPDCL9x/f1+aNavHd9+NpH79gGrfvkadOk1QlFKqGHkFLjbGpfL7riO88tMOAGr5+tC6UV26hIcwvEdz2jWpR7vG9WjTuG6VfO5IeZszZxv33fcDsbFptG3bgDFjetGgQR2nw1JVhCYoSinl5uDRbGati+c/C7cf69aleTB39G/DpV3D8NPbX0u1f/9R7r//B+bM2U6XLk2YMWME55wT4XRYqorRBEUpVaO5XIZ1+1P49Pd9rNmbQnxq9rF+/xx6BiN7R9Cgbi0HI6x6JkxYyk8/7Wby5At54IF++GvtkjoJYoxxOgZVRtHR0WbNmjVOh6FUlXY0K5+Zq/fz4g/bjnW7KKop/do0pE/rUKLCgrVxaxn89lssISEBREU1JiEhk+zsfFq1qu90WMcRkbXGmGin41De0RoUpVSNczQ7n6v+u5JdiZmc2aoBbRvX5eazIunSPMTp0KqclJRsHn/8Z957bx0jRnTi66+vpUmT6vUYfuUMTVCUUjVGek4+v+46wovzt7L3SBaTR3Tj2t4tnQ6rSjLG8Pnnm3n44R85ciSLhx7qx4QJA5wOS1UjmqAopaotYwx/HUxj2Y5Elm1PZO2+FApchrq1fLlnQFtNTk7BRx+t5/bbv6NPn+YsXHgjPXo0czokVc1ogqKUqlay8gpYvC2BJdsSj3vEfFRYMHec14bzOzSmV0SDGvs011ORk1PAvn2pnHFGI0aN6oqvrw833dRNX+ynyoUmKEqpKssYQ2ZeIXEpWew/ksWWA2l8vHIPaTkFNAj0p3/7xpzfoTH9OzSiSVCA0+FWaT/9tIt77pmPy2XYtu1e6tTxZ/ToHk6HpaoxTVCUUlXSviOZPPzVRtbsSzmu+wUdm3DneW3oHRlao17AV14OHcrgoYcWMmPGn7RrF8o771yqtw2rCqEJilKqyohPzWbR1sMs3Z7Iypgkavv58OCFHWhYrxbh9QPo3qI+DfUx86fNtm1J9Ov3AdnZBTz99Pk8/vi5BAToYUNVDN3SlFKVlstlWB+bwkcr97IrIYNth9IBiGwYyCVdw/jn0DMIC9FHp59uaWm5BAfXpkOHhowZ05M77zyTM87QF/upiqUJilKqUjHG8Gd8Gt9vOsD3mw4Sn5qNr48Q4OfD+Es6MqhTU9o2rud0mNVSenouTz+9lOnTN/Lnn/fQrFk9XnlliNNhqRpKExSlVKVQ6DLM3RjPW4tj2J2YiZ+PcF6HxjwypAMXdmpKUIC/0yFWW8YYvv12G/ffv4C4uDTuuutMvZSjHKdboFLKcXM3HmDK4p3sOJxBp7BgXrqqK0O7NKN+oL4Dp7zl5RVy9dVf8d13O+jWrSlffXU1Z52lz4dRztMERSnliNjkLGas2s/ibQnH2pa8NbInl3YN03fgVABjDCJCrVq+hIXV4+WXB/OPf/TDT58PoyoJTVCUUhVuwZ8HGfvZOgD6tg7liUs6cV2flgTrZZwKsXLlfsaN+4Hp06+ga9emvPvuMKdDUupvNEFRSlWIg0ezWfDnIX7YfIhVe5OJCA3kw1uiad80yOnQaowjR7J4/PGf+eCD9bRsGUxKSo7TISl1QpqglDMRGQq8AfgCHxhjXvLoHwF8AtS3h3ncGDO/wgNV6jQqKHRxOD2X7LxClm5P4Ic/D7HWfqDaGU2DeODC9tzQtxWNg/SZJRXl88838cADC0lJyebRR8/mqafOp149beOjKi9NUMqRiPgCU4HBQBywWkTmGmP+chvsSeArY8x/RSQKmA9EVniwSp0CYwzr9qewNymL33cf4eeth0nJyj/Wv1NYMA8P7sDFXcNo10RvEXbCX38l0r59KO+8cxndujV1OhylSqUJSvnqA8QYY3YDiMhMYDjgnqAYINj+HAIcqNAIlToFR7PzeXtJDD9vPcyuxEwAggL8GNSxCX1aN6SWnw9ntmpA60Z1HY605snOzueFF37hnHMiGDq0HU8/PYCJE320AQckG9UAACAASURBVLKqMjRBKV/NgVi373FAX49hJgA/ish9QF3gwuImJCJ3AncCREREnPZAlfJWUW3J12vjmbFqPwDnd2jMzWdF0r99I1qGBuKvb7d11MKFMdxzz3x2707h8cfPYejQdtSqpe/PUVWLJijlq7hTFePxfSQwzRjzioicBXwqIl2MMa7jRjLmPeA9gOjoaM9pKFXudhxOZ86GeOZsOEBcSja1/Xzo374Rt53bmoFnNHE6PAUcOJDOQw8t5Msvt9ChQ0MWLbqZCy5o7XRYSp0UTVDKVxzg/sSjFvz9Es4YYCiAMeY3EQkAGgEJFRKhUiWIT83mu40H+HZ9PNsOpeMjcG77xjx4YQcu6qxPd61sfvhhJ99+u41nnx3AP/95DrVraxGvqi7desvXaqC9iLQG4oHrgVEew+wHBgHTRKQTEAAkVmiUSrlJzsxj/uaDzN1wgFV7kwHoGVGfZy7vzCVdw/TOm0pm3bqD7NuXypVXduLWW3syaFAbIiPrOx2WUqdME5RyZIwpEJFxwEKsW4g/MsZsEZFngTXGmLnAw8D7IvIg1uWf0cYYvYSjKlRWXgE//XWYuRsOsGxHIgUuQ7sm9Xjkog5c3r05EQ0DnQ5ReUhLy+Xf/17MlCmr6dChIZdffga+vj6anKhqQxOUcmY/02S+R7en3D7/BZxT0XEplV/oYsXOJL7dEM+PWw6TnV9IWEgAY85tzeU9wokKC0ZE7/iobIwxfPPNVv7xjwUcPJjO3XdH8/zzg/DVhsmqmtEERakaxOUyrN2fwpwN8czbdJCUrHzqB/pzZa/mDO8eTu/IUL0NtZJbu/Yg11zzf/To0YzZs6+jT5/mToekVLnQBEWpai6/0MWPWw6zbn8KC/48RHxqNgH+PgyOasbw7uGc16ExtfQFcZVaXl4hv/4ay4ABkURHhzNv3iguuqitvthPVWuaoChVzbhchsSMXPYnZ7EyJonPft9HUkYe/r7COe0a8ciQDlwU1Yy6eodHlbB8+T7uvnseO3YcISbmPlq1qs8ll7R3Oiylyp2WUEpVE2k5+byycDszV8eSW/C/x+j0b9+I285pTf/2jfDTdgpVRlJSFv/85098/PEGWrUKYfbs62jVShvAqppDExSlqrDE9FzeWbaLJdsT2G0/an5Erxb0jKhPeP0AOoUFExZSx+EoVVllZeXTtet/SUrK4rHHzuHf/z6PunX1xX6qZtEERakqJiYhgx//OsTyHYn8vtt6TsmFnZpwVpuGDOrUhAs66ovgqqq4uDRatAgmMNCf55+/gD59mtOliz6lV9VMmqAoVQWk5eQzZ8MBvlody+b4owC0aFCHNo3r8tRlUQzQR81XaVlZ+Tz33HJefvlXvvtuJEOGtOO223o6HZZSjtIExUsiUguIMMbEOB2LqhmMMazdl8KMVbHM23yAnHwXncKC+fdlUVzWLYymwQFOh6hOgx9+2Mm9985nz55UbrmlO716hTkdklKVgiYoXhCRS4FXgVpAaxHpATxtjLnS2chUdZSSmcc36+L4cnUsOxMyqFvLlyt7tmBkn5Z0bR6iD0+rRu666zvee28dHTs2YsmSWxgwINLpkJSqNDRB8c6zQF9gCYAxZoOItHM2JFUdbYxN5eaPVnE0O5+eEfWZPKIbl3YL01uCq5GCAhc+PoKPj3DWWS2JiAjhkUfO1hf7KeVB9wjv5BtjUj3OXPV9OeqUZeUVsCcpk8VbE1i0LYGNcakEB/gz885+9GvT0Onw1Gm2enU8Y8fO4847e3HXXdGMHt3D6ZCUqrQ0QfHOVhG5FvCx30z8D+B3h2NSVVihy/Dl6lhe/WkHSRm5AHRtHsK4ge0Yc25r6gfqLaXVydGjOTz55GKmTl1Ns2b1aNq0ntMhKVXpaYLinXHAU4ALmIX1duJ/ORqRqpIOHs3mq9VxfLl6PweO5uDnI0wZ1ZOwkDr0iqiv7Uuqofnzd3L77XM5dCiDceP6MHHiQEJCtIGzUqXRBMU7Q4wxjwGPFXUQkauwkhWlSlToMizfkcjnf+xn8bbDuIz1dNd/XxbFhVFN8denu1Zr/v4+hIcHMXfuSKKjw50OR6kqQ4zRphSlEZF1xpheHt3WGmPOdCKe6Ohos2bNGidmrcrgcFoOX66O5cvVscSnZtOoXi2uiW7JyN4RRDQMdDo8VU5ycwt4+eVfKShw8fTTAwDr/Uj6lmjn2eV2tNNxKO9oDUoJRGQIMBRoLiKvuvUKxrrco9TfxCZn8fTcLSzelgDAOe0aMv6STgyOaqpvDa7mli7dy913z2PbtiRGjeqKMQYR0eREqZOgCUrJEoA/gRxgi1v3dOBxRyJSlVKhy7Bo62Een7WZ5Mw8AGr5+jD73rPpHB7icHSqvCUmZvLIIz8xffpGWreuz/z5o7j4Yn3jsFKnQhOUEhhj1gPrReRzY0yO0/GoysXlMny36QAfrdjDtkPpx94gPPrsSPq2DmVI52Z65lxDJCZm8fXXfzF+/Lk88cR5BAb6Ox2SUlWeJijeaS4izwNRwLHm98aYDs6FpJxS6DLM2RDP1CUx7ErMpGOzIG4+qxWdwoLpHRlKy1BtX1ITbN58mDlztvPkk+cRFdWY2NgHCQ3VN0crdbpoguKdacBzwMvAxcCtaBuUGuv9X3bz0g/baNO4Lq9f14PLu4drTUkNkpmZx7PPLuPVV38nJKQ2d9zRi6ZN62lyotRppgmKdwKNMQtF5GVjzC7gSRH5xemgVMXJyC3gq9WxfLMuji0H0ujYLIg5486htp+v06GpCvT99zsYN24++/YdZcyYnkyadCEN9Y4spcqFJijeyRXrCVq7RGQsEA/o++1riDV7k3noq43sT86ief06nNuuEa9e112Tkxrm6NEcbrppNs2bB/HLL7dy7rkRToekVLWmCYp3HgTqAfcDzwMhwG2ORqTK1fIdiXywYg/r9qWQkVtAiwZ1+PLOfvTV9+PUKAUFLmbM2MwNN3QjJCSAJUtuISqqMbVqaXKqVHnTBMULxpg/7I/pwE0AItLCuYhUefgz/ijLdiSyeFsCa/el4O8rDOsWTpfmIVwT3YKgAL0zoyb544847rrrezZuPEz9+gEMG3YGPXo0czospWoMTVBKISK9gebACmNMkoh0xnrk/QWAJilVXF6Bi/mbD/Lm4p3sTswEICosmCcv7cSN/VoR4K9nyjVNamoO48cv4p131hAWFsTXX1/DZZfpDXtKVTRNUEogIi8CI4CNWA1jZ2O9yXgSMNbJ2NSpcbkMn/y2l6lLdpGUkUtkw0BGnx3JuAva0ahebafDUw4aNmwGv/4ay/339+XZZwcSHKzbg1JO0ASlZMOB7saYbBEJBQ7Y37c7HJc6CQWFLjbEpnLzR6twGUNOvovekQ14dnhnhupD1Wq0mJhkwsODCAz0Z9KkCwkI8KNXrzCnw1KqRtMEpWQ5xphsAGNMsohs0+Sk6jHG8PKP25m6ZNexbo3q1WbSiE5c3j0c6wYtVRPl5hYwadJKXnjhFx599GwmTryAs89u6XRYSik0QSlNGxGZZX8WINLtO8aYq5wJS3lr+6F0Jszdwm+7jwAw9vy23HJ2K8JC9KFaNd3ixXu4++557NhxhOuv78I99/R2OiSllBtNUEo2wuP7FEeiUGWyKzGDNXuTmb0+nt93JxNYy5eJV3RhVJ8IfPUyjgImTVrB448vom3bBixYcANDhrRzOiSllAdNUEpgjFnkdAzKe2k5+Tw9Zwuz18cDEBTgR7cWIbxxfU9aN6rrcHTKaS6XITMzj6Cg2lx2WQcyMvIYP74/dero7eNKVUaaoKgqLyEth/tmrGfV3mSMgRv6RjCsezjdW9Snjj5QSwEbNx5i7Nh5REbWZ8aMEXTu3ISJEy9wOiylVAk0QSlnIjIUeAPwBT4wxrxUzDDXAhMAA2w0xoyq0CCrqMzcAr5ZF8d/FmwnPbeA4AA/Phrdm+jIUKdDU5VERkYeEyYs5fXXfyc0tA533x3tdEhKKS9pglIGIlLbGJNbhuF9ganAYCAOWC0ic40xf7kN0x74F3COMSZFRPQdP6VISM/h09/28dbiGADOadeQicO70KZxPYcjU5XJ6tXxjBjxFbGxadxxRy9eeulCfeOwUlWIJiheEJE+wIdY7+CJEJHuwO3GmPtKGbUPEGOM2W1PZybWs1X+chvmDmCqMSYFwBiTcLrjrw6MMfzf2jhmrtrPhthUXAaaBQfw5GWduLRrmN4qrI4xxiAiRESE0Lp1A2bOvFpvHVaqCtIExTtvApcB3wIYYzaKyEAvxmsOxLp9jwP6egzTAUBEVmJdBppgjFlwyhFXI4Uuw6BXlrL3SBYAt5/bmsFRTekdGaoPV1PH5OcX8sYbf7Bw4S4WLryRpk3rsWzZaKfDUkqdJE1QvONjjNnncZZe6MV4xR09jcd3P6A9MADr3T6/iEgXY0zqcRMSuRO4EyAioua85n1z3FEmLdjG3iNZDI5qypvX99SGr+pvfvstlrFj57Fp02GGDetAenouISEBToellDoFmqB4J9a+zGPsdiX3ATu8GC8OcK9bboH1uHzPYX43xuQDe0RkO1bCstp9IGPMe8B7ANHR0Z5JTrVjjOHRrzfx9do4AC7tFsZ/ru6myYk6Tnp6Lo888iPvvbeOFi2CmT37OoYPP0Mv+SlVDWiC4p27sS7zRACHgZ/tbqVZDbQXkdZAPHA94HmHzrfASGCaiDTCuuSz+zTFXWW9/8tuvl4bh7+vsOihAUQ0DHQ6JFUJ+fn5sGzZPh56qB/PPDOQevVqOR2SUuo00QTFOwXGmOvLOpIxpkBExgELsdqXfGSM2SIizwJrjDFz7X4XichfWJeNHjXGHDmdwVc1i7Ye5rWfdtK+ST3mjjtXa03UcbZvT+L553/hnXcuIzDQnw0bxhIQoEWZUtWNGFPtrxacMhHZBWwHvgRmGWPSnYwnOjrarFmzxskQys3kBdt4e+kuGgfV5vv7zqVpsLYjUJacnAJefPEXXnppJXXq+LFgwY3069fC6bBUFSIia40x+jCcKkJPO7xgjGkrImdjXaJ5RkQ2ADONMTMdDq1ayC0oZOqSXSzbnsDGuKOEhwSw5NEB1PbTmhNl+emnXdxzz3xiYpK54YauvPLKRTRtqs+9Uao60wTFS8aYX4FfRWQC8DrwOaAJyinanZjB7dPXsDsxExEY3iOcJy7ppMmJOsYYw8SJyxGBn3++iUGD2jgdklKqAmiC4gURqYf1gLXrgU7AHOBsR4OqBlbtSebad38DYFj3cCaN6EpgLd0kFRQWunj//XUMH34GYWFBzJx5NaGhdbStiVI1iO7t3vkT+A6YbIz5xelgqoPfdx/h+vd+B2DiFV24qV8rhyNSlcX69QcZO3Yeq1bFk5yczfjx/QkPD3I6LKVUBdMExTttjDEup4OoDowxjJ+9mRmrrAfsTh3Vi0u7hTkclaoM0tNzeeqpJbz55ioaNQrks8+uZNSork6HpZRyiCYoJRCRV4wxDwPfiMjfbncyxlzlQFhVljGGf8zcwNyN1rPq5t1/Lp3DQxyOSlUWTzyxmClTVnHXXWfywguDaNBAX+ynVE2mCUrJvrT/T3E0imrAGMMrP+5g7sYDXN49nFeu7Y6/r4/TYSmH7d2bSl5eIR06NOSJJ/ozalRXvXVYKQWAHiFKYIxZZX/sZIxZ5P6H1VhWeWnWunimLIlhaOdm/Oeabpqc1HD5+YVMmrSCqKipjBs3H4CmTetpcqKUOkaPEt65rZhuYyo8iioqIS2HV37cTh1/X6aM6qm3ENdwK1bsp2fPd3n88UUMGdKODz+83OmQlFKVkF7iKYGIXId1a3FrEZnl1isISC1+LOVu/f4U7puxntTsfGbc2Q8/rTmp0WbP3spVV31FREQIc+Zcz+WXn+F0SEqpSkoTlJKtAo5gvYV4qlv3dGC9IxFVITEJGVz59q80CPRnxh396N6yvtMhKQcYYzhwIJ3mzYMZOrQdzz03kH/8o5++2E8pVSJNUEpgjNkD7MF6e7HyUqHL8MEvu3l3+W58BD65rQ/dWmhyUhNt3ZrI3XfPY9++o2zZcg+Bgf488cR5ToellKoCtL69BCKyzP6fIiLJbn8pIpLsdHyV1bRf9/LiD9vwEfhodG9NTmqg7Ox8nnxyMd27v8OmTYcZP/5cfQqsUqpMtMQo2UD7fyNHo6giCl2G5+b9xccr9xIc4MfKxy/QBrE1UFxcGuefP43du1O46aZuvPzyRTRpUtfpsJRSVYwmKCVwe3psS+CAMSZPRM4FugGfAWmOBVfJFBS6eGtxDB+v3MvZbRsy8YoumpzUMPn5hfj7+xIeHsR557Xigw+GMXBga6fDUkpVUXqJxzvfAkZE2gLTsZ6B8oWzIVUeeQUuxn62jjcW7aRPZCjTbu1D28b1nA5LVZDCQhdTpqyiXbu3OHQoAx8f4eOPh2tyopQ6JVqD4h2XMSZfRK4CXjfGvCkiNf4uHmMMkxZs59v18RxKy6FNo7p8dntfavlp3ltTrF17gLvu+p61aw8yeHAb8vIKnQ5JKVVNaILinQIRuQa4CbjC7ubvYDyO2xibyks/bOO33Udo3agu027tzfkdGiMiToemKoDLZXjwwQVMmbKaJk3qMnPmCK69trOuf6XUaaMJinduA+4BJhtjdotIa2CGwzE5IjO3gEe/3sj8zYcI8PdhwrAobuzXSh/AVsP4+AjJyTmMHXsmzz8/iPr1A5wOSSlVzYgxf3tJryqGiPgB7eyvMcaYAqdiiY6ONmvWrKnw+bpchvtmrmfepoM8cGF7Rp8dSf1AfdhWTbF7dwoPPLCA5567gG7dmuJyGXx8tMZEVR0istYYE+10HMo7WoPiBRHpD3wKxAMCNBORm4wxK52NrOIUugy3fLSKFTFJPDy4A/cNau90SKqC5OUV8vLLvzJx4nL8/HzYseMI3bo11eREKVWuNEHxzmvAJcaYvwBEpBNWwlJjMvGPVuxhRUwS7ZvUY9wF7UofQVULy5fvY+zY79m6NYkRIzrx+utDadEi2OmwlFI1gCYo3qlVlJwAGGO2ikiNubYRm5zFtF/30qhebRY+cJ42hKxBFi6MITu7gO+/H8mll3ZwOhylVA2ibVC8ICLTgFysWhOAG4BAY8wtTsRT0W1QbvzgDzbGpfLpmL700Bf+VWsul+GTTzbQokUwgwe3JTs7H2MgMLBG37Smqgltg1K16K0X3hkL7AL+CTwG7AbucjSiCvL77iOsiEnitnNaa3JSzW3ZksCAAdO47ba5fPLJRgDq1PHX5EQp5Qi9xFMKEekKtAVmG2MmOx1PRTqQms3z87YCcMvZkc4Go8pNVlY+Eycu4+WXfyM4uDYffDCMW2/t6XRYSqkaThOUEojIeGAMsA7oLSLPGmM+cjisCpGWk89lb60gLTufN67vQWjdGtPkpsaZNWsrL720ktGjezB58oU0bqwv9lNKOU8TlJLdAHQzxmSKSGNgPlAjEpR3l+0iOTOPN67vwfAezZ0OR51m8fFpbNmSyEUXtWXUqK6ccUZDevfW9ayUqjy0DUrJco0xmQDGmERqyO+1/0gWH6/cS8vQOpqcVDMFBS7eeON3OnacyujR35KbW4CPj2hyopSqdLQGpWRtRGSW/VmAtm7fMcZc5UxY5edodj7n/WcJADNH9XI4GnU6rV4dz9ix81i37iBDh7Zj6tRLqF1biwClVOWkpVPJRnh8n+JIFBXo7SUxANw/qD3dWuhdO9XFjh1H6NfvQ5o2rctXX13N1VdH6fNslFKVmiYoJTDGLHI6hopkjOGbdXEA3K9Pi63yjDFs3pxAt25N6dChIR99dDlXXNGRkBB9sZ9SqvKrEW0qlHce+mojSRl53DOgrb6duIqLiUlm6NDPOfPM99i6NRGAW27pocmJUqrK0KNQORORoSKyXURiROTxEoa7WkSMiDjylMOYhHRmr4/noqimPDrkDCdCUKdBbm4Bzz23nC5d3ua332J57bUhdOjQ0OmwlFKqzPQSTxmISG1jTG4ZhvcFpgKDgThgtYjMdX+vjz1cEHA/8MfpjLcsLn1zBQB3nd9G2yZUUfn5hfTp8wGbNh3m2ms789prQwgPD3I6LKWUOilag+IFEekjIpuBnfb37iLylhej9gFijDG7jTF5wExgeDHDTQQmAzmnK+ayOHg0m9wCF2EhAZzZKtSJENQpSEuzcmZ/f1/GjOnJDz/cwJdfXq3JiVKqStMExTtvApcBRwCMMRuBgV6M1xyIdfseZ3c7RkR6Ai2NMd+XNCERuVNE1ojImsTExLLEXqoJc7cA8Pp1PU7rdFX5crkMH3ywjsjI11mwwL776v6+DB2qDZyVUlWfJije8THG7PPoVujFeMVdKzn2+mgR8QFeAx4ubULGmPeMMdHGmOjGjRt7MWvvJGfmsXDLYc7r0Ji+bbStQlWxefNh+vf/mDvu+I5u3ZoSGam3hCulqhdtg+KdWBHpAxi7Xcl9wA4vxosDWrp9bwEccPseBHQBltrtPpoBc0XkcmPMmtMSeSke/moDAHf0b10Rs1OnwfPPL2fChGWEhNRm2rTh3Hxzd203pJSqdjRB8c7dWJd5IoDDwM92t9KsBtqLSGsgHrgeGFXU0xhzFGhU9F1ElgKPVFRysnZfMku2J3JJ12b0b3/6amVU+TDGICI0bVqPm2/uxuTJg2nYMNDpsJRSqlzoJR4vGGMSjDHXG2Ma2X/XG2OSvBivABgHLAS2Al8ZY7aIyLMicnl5x11KbIz4728A3DtQ2yxUZrGxR7nqqi959921ANx+ey8+/HC4JidKqWpNa1C8ICLv49Z2pIgx5s7SxjXGzMd6C7J7t6dOMOyAkwyxTIwxPPzVRgCu6tmczuEhFTFbVUYFBS7efPMPnnpqCS6XYeDASKdDUkqpCqMJind+dvscAFzJ8XfnVCkTv9/KrPXxtG1cl8lXd3M6HFWMtWsPMGbMXDZuPMwll7RnypSLad26gdNhKaVUhdEExQvGmC/dv4vIp8BPDoVzSpbvSOSjlXvoHdmAL+7op4+0r6SSk7NJSsrim2+u5corO2ojWKVUjaMJyslpDbRyOoiyKih08cx3W/D3Fd4a2Qt/TU4qDWMMM2f+yf79R3nssXMZPLgtMTH3ExCgu6hSqmbSI5QXRCRFRJLtv1Ss2pPxTsdVVqv2JrMrMZPrerekmb40rtLYufMIF130GaNGzWLu3B0UFLgANDlRStVoWgKWQqy69e5YtwkDuIwxf2swWxXMWGU1mxk3sL3DkSiAnJwCJk1awQsvrCAgwI+pUy/hrrvOxFdrtpRSShOU0hhjjIjMNsac6XQspyIxPZf5mw8yvEe41p5UEnv2pPD8878wYkQUr756EWFh+u4cpZQqoqdq3lklIr2cDuJU/LIzkUKX4Y7+bZwOpUY7fDiDt99eDUCnTo3Ztm0cM2aM0OREKaU8aA1KCUTEz37Y2rnAHSKyC8jEeseOMcZUmaRlxc4kQuvWIios2OlQaiSXy/D++2t5/PFFZGXlM3RoO9q0aUCbNnrrsFJKFUcTlJKtAnoBVzgdyKkwxvDdpgMM6dwMHx+9XbWibdx4iLFj5/H773EMHBjJ229fqomJUkqVQhOUkgmAMWaX04Gcip+3JpBfaDinXaPSB1anVXZ2PoMHfwrA9OlXcOON3fSZJkop5QVNUErWWEQeOlFPY8yrFRnMyTDGMGHuFgL8fbiyZ3Onw6kxFi3azcCBralTx5+vv76WLl2aEBpax+mwlFKqytBGsiXzBeoBQSf4q/Q2xKYSn5rN2PPbEuDv63Q41d6+fakMHz6TCy/8lBkzNgNw3nmtNDlRSqky0hqUkh00xjzrdBCnYu7GAwBc0UNrT8pTfn4hb7zxB08/vRSAyZMv5NprOzsblFJKVWGaoJSsSjcWcLkMX/yxH4BWDQMdjqZ6u/76b5g1ayvDhnXgrbcuplWr+k6HpJRSVZomKCUb5HQAp2LG6v3kFri4+swW2jCzHCQnZxMQ4EdgoD/33deHm27qxhVXdHQ6LKWUqha0DUoJjDHJTsdwKhZtTQDg35dFORxJ9WKM4dNPN9Kx4xSefXYZAAMGRGpyopRSp5EmKNWUy2VYvSeZi7s0I6SOv9PhVBvbtycxaNB0br75W9q2DWXkyC5Oh6SUUtWSXuKpplKz80nPLaB3ZKjToVQbn3yygTvv/J7AQH/eeedS7rjjTH3wnVJKlRNNUKqpXYkZgDaOPR3y8wvx9/eld+/mXHddZ/7zn8E0bVrP6bCUUqpa0wSlmvph8yEAujQPcTiSquvgwXQeeuhHAGbMGEFUVGOmT7/S4aiUUqpm0DYo1dSRzFwAmgYHOBxJ1VNY6OLtt1fTseNUZs3aSseODTHGOB2WUkrVKFqDUk0dSM2mYd1aTodR5ezceYQbb5zNqlXxDBrUmrffvpQOHRo6HZZSStU4mqBUQ4npuWyMO8rI3i2dDqXKCQkJIC0tl88/v4qRI7vo82OUUsohmqBUQ1+vjSOvwMXoc1o7HUqlZ4xh9uxtzJjxJ19+eTVNmtRly5Z79O4cpZRymLZBqYY2xKYQ2TCQ1o3qOh1KpbZ3byrDhs1gxIiv2LnzCAkJmQCanCilVCWgCUo1czgth192JtGthb4L5kTy8wuZNGkFUVFTWbp0L6+8chFr1txJs2Z667BSSlUWeomnmvnij/3k5Bdy/6D2TodSaRUUuHj//XUMGdKON98cSsuWeiu2UkpVNlqDUs0s2Z5Aq4Z1addEawPcHTmSxWOP/URWVj516vjzxx+3M3v2dZqcKKVUJaUJSjXj7+tDTn6h02FUGsYYPvlkAx07TuWVV35j2bK9ADTUJ+wqpVSlpglKNZOSmUevVg2cDqNS2Lo1kYEDP2H06Dl06NCQ9evv4uKL9dKXUkpVBdoGpZpJysjVB7TZxo37gU2bDvPee5cxZkwvvTtHKaWqCjwcHQAAGRpJREFUEE1QqpGjWfmk5RQQFlLH6VAcs2BBDN27NyUsLIj33x9GvXq1aNJEb7dWSqmqRi/xlDMRGSoi20UkRkQeL6b/QyLyl4hsEpFFItLqZOe1KT4VgI5hQacQcdV04EA61133NRdf/Dn/+c+vALRp00CTE6WUqqI0QSlHIuILTAUuBqKAkSIS5THYeiDaGNMN+BqYfLLzO3Q0B4BWoTWnAWhhoYspU1bRqdNU5szZxrPPDuDFFwc5HZZSSqlTpJd4ylcfIMYYsxtARGYCw4G/igYwxixxG/534MaTndm+I1n4CLRqWHNqDZ55ZhkTJy7noovaMnXqJbRrF+p0SEoppU4DTVDKV3Mg1u17HNC3hOHHAD8U10NE7gTuBIiIiCh25K0H0witWwvfat4YNC0tl+TkbCIj63Pvvb3p3Lkx117bWV/sp5RS1Yhe4ilfxR0xTbEDitwIRAP/Ka6/MeY9Y0y0MSa6cePGf+t/IDWbRdsSuKxb+KnEW6kZY/i//9tCx45TuPHGWZj/b+/Oo6uq7gWOf38EJAwBIWAEowYIQ0iQMLVS4mMqUEkBUUoAqyBYCqK2KFZ8uB5UX11U6/AooLWVBTwfg6AgUNQKglIFSjAhBGJDhAhhCoRAAiRM+b0/ziUDBHID5E78Pmudte45d99zfnfnJveXvffZW5WwsLokJNiqw8YYE2gsQalaWcCdpfbDgQOXFhKRnwJTgIGqeuZaLvTPXUcBGNIp/Fpe7vN2784lPn4BQ4cu5fbb6/Lmm/0sKTHGmABmXTxVawvQUkSaAfuBYcCI0gVEpAPwF+Bnqpp9rRfal3saICCnuP/qqx/o1+99qlevxltv9WPChB9Rvbrl1sYYE8gsQalCqnpeRJ4EPgOCgDmqukNEXgISVXUFTpdOXWCJq0Vgr6oOrOy10g/n07xRHYJrBN3Ad+BdeXlnqFevJl26NGXMmA5MnhxHeHg9b4dljDHGAyxBqWKquhpYfcmx/yr1+Kc34joHjhdSv3aNG3Eqrzt69DTPPfc5X36ZSWrqE9SuXYOZM/t7OyxjjDEeZO3kAeDoyTPsOHCC5o38u3unqEiZMyeJ1q1n8v77KSQkRGPDTIwx5uZkLSgBYPX2gxQpPNr1mieh9bpjxwp44IFFbNiwl7i4u3jnnXiio2/zdljGGGO8xBKUAJCd59z4E3NHfS9HUnmqiohw663BhIbW5r33BjJqVKwt7GeMMTc56+IJAFsyj9E4pKbfTdC2evUuOnZ8l0OHTlKtmrBsWQKjR3ew5MQYY4wlKP7u4IkCNu85xrAud1Zc2EdkZeUxZMgHxMcv4MyZ82Rnn/J2SMYYY3yMdfH4ua0/5ALwkxaNvBxJxVSVGTM28+KL6zh/vohXXunFs8/+hFtuCZxbo40xxtwYlqD4ub3HnAnaYu+81cuRVExE2LLlAHFxdzFrVn+aN2/g7ZCMMcb4KEtQ/NyhE4XUC65OLR9thThxopAXX/yCsWM70a5dGH/720Bq1gyyaeqNMcZclSUofu67Q/ncFVrb22FcRlX54IMd/Pa3n5GdfYrWrRvRrl0YwcH2kTPGGFMx+7bwc4fzCn2ueycj4xgTJqzmH//4nk6dmrBq1XA6dQrcVZaNMcbceHYXjx+7UKQcPF5ILR9bf2f+/G1s3LiPP//5fjZvftySE2OMMZVmLSh+7NTZ85y9UMSdDb3fxbNu3R5EhB49InjhhTjGjetM06Yh3g7LGGOMn7IWFD+2P7cAgMZ1a3othuzsUzz66DJ69ZrPH/6wAYBatWpYcmKMMea6WAuKH/twaxY1goReUZ5fs6aoSHnvvW95/vk1nDx5lilT7mPKlPs8HofxfefOnSMrK4vCwkJvh2JuEsHBwYSHh1OjRmCs8H6zsgTFj61PP0JcZCMaeaEF5eOPv2Ps2FV07343b78dT1RUY4/HYPxDVlYWISEhRERE2O3lpsqpKjk5OWRlZdGsWTNvh2Oug3Xx+Kmz54vIPHqKtk3reeyap06dZePGfQAMGtSGjz8exrp1Iy05MVdVWFhIaGioJSfGI0SE0NBQa7ELAJag+Kl9uac5X6S0aFzXI9dbufLftG07m/79F5Cff4Zq1YSBA1vbl45xi31OjCfZ5y0wWILip7btOw5AeIOqvYNn374TDB68mIEDFxEScgsrVw4nJMR7g3KNMcbcHCxB8VOH8pzmy3Z31K+ya+zfn0fbtrP57LMMpk/vTVLSr4mLu6vKrmdMVQkKCiI2NpaYmBgGDBjA8ePHi5/bsWMHvXr1olWrVrRs2ZKXX34ZVS1+/pNPPqFz585ERUXRpk0bJk2a5I23cFVJSUk8/vjjZY4NGjSIrl27ljk2atQoli5dWuZY3bolrbDp6en079+fyMhIoqKiGDp0KIcPH76u2I4dO0afPn1o2bIlffr0ITc397Iy69atIzY2tngLDg5m+fLlgDOmZMqUKbRq1YqoqChmzJgBwKpVq5g6dep1xWZ8myUofur0mQtUEwiuceN/hPv35wFwxx31eOmlHuzcOYHnn4+jho9NCGeMu2rVqkVycjKpqak0bNiQWbNmAVBQUMDAgQOZPHky6enpbNu2jW+++YbZs2cDkJqaypNPPsn7779PWloaqampNG/e/IbGdv78+es+xyuvvMJTTz1VvH/8+HG+/fZbjh8/zp49e9w6R2FhIfHx8YwfP56MjAzS0tIYP348R44cua7Ypk+fTu/evdm1axe9e/dm+vTpl5Xp2bMnycnJJCcn88UXX1C7dm369u0LwNy5c9m3bx/fffcdaWlpDBs2DID4+HhWrFjB6dOnrys+47vsLh4/lVd4jjo1q9/Qvtbc3AL+8z/X8t57SWzdOpZ27cKYOLFrxS80xk2/X7mDnQfybug52zatx9QB0W6X79q1KykpKQAsWLCAbt26FX8Z1q5dm5kzZ9KjRw8mTJjAq6++ypQpU2jTpg0A1atX54knnrjsnCdPnuSpp54iMTEREWHq1Kk89NBD1K1bl5MnTwKwdOlSVq1axdy5cxk1ahQNGzYkKSmJ2NhYli1bRnJyMrfe6ixbERkZyddff021atUYN24ce/fuBeCtt96iW7duZa6dn59PSkoK7du3Lz724YcfMmDAAMLCwli0aBEvvPBChfWyYMECunbtyoABA4qP9ezZ0+16vZKPP/6Y9evXAzBy5Eh69OjBH//4xyuWX7p0Kffffz+1azvd12+//TYLFiygWjXnn7HbbnOmVXAmhuzBqlWrGDp06HXHaXyPJSh+at2/s2kddmMmQ1NVFi5MZeLEzzh69DS/+c2PiYjwrfV9jLkRLly4wNq1axkzZgzgdO906tSpTJkWLVpw8uRJ8vLySE1N5dlnn63wvC+//DL169dn+/btAOV2Y1wqPT2dNWvWEBQURFFREcuWLeOxxx5j8+bNREREEBYWxogRI5g4cSJxcXHs3buXfv36kZaWVuY8iYmJxMTElDm2cOFCpk6dSlhYGEOGDHErQUlNTb2sLsqTn5/PffeVP+fRggULaNu2bZljhw8fpkmTJgA0adKE7Ozsq55/0aJFPPPMM8X733//PYsXL2bZsmU0btyYGTNm0LJlSwA6d+7Mhg0bLEEJUJag+Kl9xwpoc/v132JcVKQMGLCQ1at30aVLUz799GE6dGhyAyI05nKVaem4kQoKCoiNjSUzM5NOnTrRp08fwEnOr9QKWZnWyTVr1rBo0aLi/QYNGlT4ml/84hcEBTndpgkJCbz00ks89thjLFq0iISEhOLz7ty5s/g1eXl55OfnExJS8s/JwYMHady45Fb/w4cPk5GRQVxcHCJC9erVSU1NJSYmptz3VNlW2JCQEJKTkyv1GncdPHiQ7du3069fv+JjZ86cITg4mMTERD766CNGjx7Nhg3OrNW33XYbBw4cqJJYjPfZGBQ/VOQawHc9qxifO3cBgGrVhPvuu4vZs/uzceMYS05MQLo4BuWHH37g7NmzxWNQoqOjSUxMLFN29+7d1K1bl5CQEKKjo9m6dWuF579SolP62KXzctSpU6f4cdeuXcnIyODIkSMsX76cBx98EICioiI2btxYPD5j//79ZZKTi++t9LkXL15Mbm4uzZo1IyIigszMzOLkKTQ0tEzrzrFjx2jUqFFxXbjzXvPz88sMaC29lU6mLgoLC+PgwYOAk4Bc7KIpzwcffMDgwYPLzAAbHh7OQw89BMDgwYOLu+fAqdNatWpVGLPxT5ag+KHTZ53kol7wtTWArVmzm+jo2XzyyS4AJk+OY/z4LgQF2cfBBLb69eszY8YM/vSnP3Hu3Dkefvhh/vnPf7JmzRrAaWl5+umn+d3vfgfAc889xyuvvEJ6ejrgJAxvvPHGZeft27cvM2fOLN6/mASEhYWRlpZW3IVzJSLC4MGDeeaZZ4iKiiI0NLTc85bXchEVFUVGRkbx/sKFC/n000/JzMwkMzOTrVu3FicoPXr0YPHixZw9exZwBqBeHGcyYsQIvvnmG/7+978Xn+vTTz8t7ra66GILSnnbpd07AAMHDmTevHkAzJs3j0GDBl2xHhYuXMjw4cPLHHvggQf44osvAPjyyy9p1apV8XPp6emXdW+ZAKKqtvnZ1rxNO737+VW6N+eUVsahQ/k6YsSHCtM0MnKGrl+/p1KvN+Za7Ny509shaJ06dcrs//znP9f58+erqmpKSop2795dW7VqpS1atNBp06ZpUVFRcdmVK1dqx44dtU2bNhoVFaWTJk267Pz5+fn66KOPanR0tN5zzz364YcfqqrqkiVLtHnz5tq9e3edMGGCjhw5UlVVR44cqUuWLClzji1btiigc+fOLT525MgRHTp0qLZr106joqL017/+dbnvLyYmRvPy8nTPnj3atGnTMvGrqnbo0EE3bdqkqqrTpk3TmJgYbd++vT744IOanZ1dXC4tLU379eunkZGRGhUVpQkJCXro0KGr1m1Fjh49qr169dLIyEjt1auX5uTkFL/fMWPGFJe7GPuFCxfKvD43N1f79++vMTExeu+992pycnLxc/Hx8ZqSklLudcv73AGJ6gN/w21zbxPnZ2b8ye0tojX4F6+y6w/3U8PNVo/587fx9NOfUFBwnsmTu/HCC/cRfI0tMMZURlpaGlFRUd4OI6C9+eabhISEXDYXSiA7fPgwI0aMYO3ateU+X97nTkS2qmpnT8Rnrp+16fsxd5MTcMacdOzYhG3bxvH73/e05MSYADJ+/Hhq1ry5Znjeu3cvr7/+urfDMFXIvqX8UJEqzRrVuWqZkyfPMm3aelq3DuVXv+rE6NEdGD26g61RYUwACg4O5pFHHvF2GB7VpUsXb4dgqpi1oPih02cvcHu94Cs+v3z5d0RFzeL11zeSnp4DOIPwLDkx3mJdycaT7PMWGCxB8UPnLhTRO+ryW/X27j3BoEGLGDx4MQ0aBPP116N57bW+XojQmBLBwcHk5OTYl4bxCFUlJyeH4OAr/xNn/IN18fipu0Mv7+JJT89h7drdvPZaH37zmx/b2jnGJ4SHh5OVlXXda7oY467g4GDCw8O9HYa5TnYXjx+q2aSlbtr8Lzrc1YBvvtlHUtJBJkz4EQA5OacJDa3t5QiNMcb32F08/sW6eKqYiPxMRP4tIhkiMrmc52uKyGLX85tFJMKd89ZBGDt2Jd26zeGNNzZRWOisiGrJiTHGmEBgCUoVEpEgYBZwP9AWGC4il061OAbIVdVI4E3gyst8lhLX+a/MmZPEs892Zdu2cXbbsDHGmIBi32pV60dAhqruBhCRRcAgoPSCFYOAaa7HS4GZIiJ6tb43hRYtGvL55/G0b397lQRujDHGeJMlKFXrDmBfqf0s4MdXKqOq50XkBBAKHC1dSETGAmNdu2c2HX48NTa2SmL2N424pK5uYlYXJawuSlhdlGjt7QCM+yxBqVrlTTxyacuIO2VQ1XeBdwFEJNEGejmsLkpYXZSwuihhdVFCRBIrLmV8hY1BqVpZwJ2l9sOBA1cqIyLVgfrAMY9EZ4wxxvgoS1Cq1hagpYg0E5FbgGHAikvKrABGuh4PAb646vgTY4wx5iZgXTxVyDWm5EngMyAImKOqO0TkJZxlv1cA7wH/KyIZOC0nw9w49btVFrT/sbooYXVRwuqihNVFCasLP2ITtRljjDHG51gXjzHGGGN8jiUoxhhjjPE5lqD4sKqaJt8fuVEXz4jIThFJEZG1InK3N+L0hIrqolS5ISKiIhKwt5i6UxciMtT12dghIgs8HaOnuPE7cpeIrBORJNfvSX9vxFnVRGSOiGSLSOoVnhcRmeGqpxQR6ejpGI2bVNU2H9xwBtV+DzQHbgG2AW0vKfME8I7r8TBgsbfj9mJd9ARqux6Pv5nrwlUuBPgK2AR09nbcXvxctASSgAau/du8HbcX6+JdYLzrcVsg09txV1Fd/AfQEUi9wvP9gU9w5qC6F9js7ZhtK3+zFhTfVTxNvqqeBS5Ok1/aIGCe6/FSoLeIlDfxm7+rsC5UdZ2qnnbtbsKZcyYQufO5AHgZeBUo9GRwHuZOXfwKmKWquQCqmu3hGD3FnbpQoJ7rcX0un5MpIKjqV1x9LqlBwHx1bAJuFZEmnonOVIYlKL6rvGny77hSGVU9D1ycJj/QuFMXpY3B+Q8pEFVYFyLSAbhTVVd5MjAvcOdz0QpoJSJfi8gmEfmZx6LzLHfqYhrwSxHJAlYDT3kmNJ9T2b8nxktsHhTfdcOmyQ8Abr9PEfkl0BnoXqURec9V60JEquGsij3KUwF5kTufi+o43Tw9cFrVNohIjKoer+LYPM2duhgOzFXV10WkK878SzGqWlT14fmUm+Xvpt+zFhTfZdPkl3CnLhCRnwJTgIGqesZDsXlaRXURAsQA60UkE6ePfUWADpR193fkY1U9p6p7gH/jJCyBxp26GAN8AKCqG4FgnIUEbzZu/T0x3mcJiu+yafJLVFgXrm6Nv+AkJ4E6zgAqqAtVPaGqjVQ1QlUjcMbjDFTVQFwkzZ3fkeU4A6gRkUY4XT67PRqlZ7hTF3uB3gAiEoWToBzxaJS+YQXwqOtunnuBE6p60NtBmctZF4+P0qqbJt/vuFkXrwF1gSWuccJ7VXWg14KuIm7WxU3Bzbr4DOgrIjuBC8Bzqprjvairhpt18SzwVxGZiNOlMSoQ/6ERkYU4XXqNXONtpgI1AFT1HZzxN/2BDOA08Jh3IjUVsanujTHGGONzrIvHGGOMMT7HEhRjjDHG+BxLUIwxxhjjcyxBMcYYY4zPsQTFGGOMMT7HEhRjfJCIXBCR5FJbxFXKRlxp5dZKXnO9azXcba6p4VtfwznGicijrsejRKRpqef+JiJtb3CcW0Qk1o3X/FZEal/vtY0xnmMJijG+qUBVY0ttmR667sOq2h5nEcrXKvtiVX1HVee7dkcBTUs997iq7rwhUZbEORv34vwtYAmKMX7EEhRj/ISrpWSDiHzr2n5STploEfmXq9UlRURauo7/stTxv4hIUAWX+wqIdL22t4gkich2EZkjIjVdx6eLyE7Xdf7kOjZNRCaJyBCcNZH+z3XNWq6Wj84iMl5EXi0V8ygR+fM1xrmRUgu9icjbIpIoIjtE5PeuY0/jJErrRGSd61hfEdnoqsclIlK3gusYYzzMEhRjfFOtUt07y1zHsoE+qtoRSABmlPO6ccD/qGosToKQ5ZrWPAHo5jp+AXi4gusPALaLSDAwF0hQ1XY4s0+PF5GGwGAgWlXvAf679ItVdSmQiNPSEauqBaWeXgo8WGo/AVh8jXH+DGc6+4umqGpn4B6gu4jco6ozcNZa6amqPV1T3r8I/NRVl4nAMxVcxxjjYTbVvTG+qcD1JV1aDWCma8zFBZx1ZS61EZgiIuHAR6q6S0R6A52ALa5lAGrhJDvl+T8RKQAygaeA1sAeVU13PT8PmADMBAqBv4nI34FV7r4xVT0iIrtd66Dscl3ja9d5KxNnHZxp3TuWOj5URMbi/G1rArQFUi557b2u41+7rnMLTr0ZY3yIJSjG+I+JwGGgPU7rZ+GlBVR1gYhsBuKBz0TkcZzl5eep6gtuXOPh0gsLikhoeYVca7/8CGfxuWHAk0CvSryXxcBQ4DtgmaqqONmC23EC24DpwCzgQRFpBkwCuqhqrojMxVkQ71ICfK6qwysRrzHGw6yLxxj/UR84qKpFwCM4rQdliEhzYLerW2MFTlfHWmCIiNzmKtNQRO5285rfAREiEunafwT40jVmo76qrsYZgFrenTT5QMgVzvsR8AAwHCdZobJxquo5nK6ae13dQ/WAU8AJEQkD7r9CLJuAbhffk4jUFpHyWqOMMV5kCYox/mM2MFJENuF075wqp0wCkCoiyUAbYL7rzpkXgX+ISArwOU73R4VUtRBntdclIrIdKALewfmyX+U635c4rTuXmgu8c3GQ7CXnzQV2Aner6r9cxyodp2tsy+vAJFXdBiQBO4A5ON1GF70LfCIi61T1CM4dRgtd19mEU1fGGB9iqxkbY4wxxudYC4oxxhhjfI4lKMYYY4zxOZagGGOMMcbnWIJijDHGGJ9jCYoxxhhjfI4lKMYYY4zxOZagGGOMMcbn/D/E6cQGCAsN0AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.39 0.49 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.69 8749\n", + "weighted avg 0.80 0.81 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 20 epoch\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experimenting with lowering the cutoff for the neural network," + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.81 0.84 6762\n", + " 1 0.48 0.60 0.54 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.79 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam = get_optimal(model, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > optimal_adam)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance is quite impressive when we lowered the classification cut off. The ROC of 0.76 is also on par with other models. Now we ramp up the number of epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4863 - accuracy: 0.7969\n", + "Epoch 2/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4511 - accuracy: 0.8122\n", + "Epoch 3/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4485 - accuracy: 0.8124\n", + "Epoch 4/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4472 - accuracy: 0.81200s - loss: 0.4465 \n", + "Epoch 5/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4461 - accuracy: 0.8123\n", + "Epoch 6/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4450 - accuracy: 0.8124\n", + "Epoch 7/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4432 - accuracy: 0.8138\n", + "Epoch 8/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4428 - accuracy: 0.8139\n", + "Epoch 9/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4422 - accuracy: 0.8132\n", + "Epoch 10/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4420 - accuracy: 0.8140\n", + "Epoch 11/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4411 - accuracy: 0.8137\n", + "Epoch 12/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4410 - accuracy: 0.8146\n", + "Epoch 13/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4412 - accuracy: 0.8143\n", + "Epoch 14/50\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 15/50\n", + "17496/17496 [==============================] - 2s 120us/step - loss: 0.4402 - accuracy: 0.8149\n", + "Epoch 16/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4402 - accuracy: 0.8146\n", + "Epoch 17/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4406 - accuracy: 0.8141\n", + "Epoch 18/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4400 - accuracy: 0.8152\n", + "Epoch 19/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8140\n", + "Epoch 20/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4398 - accuracy: 0.8140\n", + "Epoch 21/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4396 - accuracy: 0.8153\n", + "Epoch 22/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4401 - accuracy: 0.8138\n", + "Epoch 23/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4394 - accuracy: 0.8150\n", + "Epoch 24/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4397 - accuracy: 0.8152\n", + "Epoch 25/50\n", + "17496/17496 [==============================] - 2s 117us/step - loss: 0.4396 - accuracy: 0.8148\n", + "Epoch 26/50\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4396 - accuracy: 0.8144\n", + "Epoch 27/50\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4393 - accuracy: 0.8135\n", + "Epoch 28/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4390 - accuracy: 0.8152\n", + "Epoch 29/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4392 - accuracy: 0.8138\n", + "Epoch 30/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8141\n", + "Epoch 31/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8149\n", + "Epoch 32/50\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4390 - accuracy: 0.8147\n", + "Epoch 33/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4388 - accuracy: 0.8145\n", + "Epoch 34/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4385 - accuracy: 0.8153\n", + "Epoch 35/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4389 - accuracy: 0.8150\n", + "Epoch 36/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8146\n", + "Epoch 37/50\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4386 - accuracy: 0.8142\n", + "Epoch 38/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4386 - accuracy: 0.8143\n", + "Epoch 39/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4379 - accuracy: 0.8148\n", + "Epoch 40/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4385 - accuracy: 0.8148\n", + "Epoch 41/50\n", + "17496/17496 [==============================] - 2s 116us/step - loss: 0.4386 - accuracy: 0.8149\n", + "Epoch 42/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4380 - accuracy: 0.8150\n", + "Epoch 43/50\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4382 - accuracy: 0.8144\n", + "Epoch 44/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4381 - accuracy: 0.8152\n", + "Epoch 45/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4378 - accuracy: 0.8151\n", + "Epoch 46/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4374 - accuracy: 0.8153\n", + "Epoch 47/50\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4382 - accuracy: 0.8152\n", + "Epoch 48/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4379 - accuracy: 0.8166\n", + "Epoch 49/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4381 - accuracy: 0.8144\n", + "Epoch 50/50\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4378 - accuracy: 0.8154\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model50 = Sequential()\n", + "model50.add(Dense(12, input_dim=17, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model50.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model50.fit(X_train_filter, y_train, epochs=50, batch_size=10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the accuracy did not increase much at all from the 20th to 50th epoch. In such a situation we will choose the 20 epoch model for its faster computation." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.20843479\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.82 0.84 6762\n", + " 1 0.48 0.59 0.53 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.70 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam50 = get_optimal(model50, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "get_roc(model50, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 50 epoch\")\n", + "predictions50 = list(model50.predict(X_test_filter).ravel() > optimal_adam50)\n", + "print(classification_report(y_test,predictions50))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep...0.5358340.741382
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep... 0.535834 0.741382\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[3] = ([\"Neural Network - 17x8x8x1 Adam, Entropy, 20 Epochs\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "\n", + "gnb = GaussianNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GaussianNB(priors=None, var_smoothing=1e-09)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.9999935527715175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.11 0.20 13442\n", + " 1 0.25 0.96 0.39 4054\n", + "\n", + " accuracy 0.31 17496\n", + " macro avg 0.58 0.54 0.29 17496\n", + "weighted avg 0.76 0.31 0.24 17496\n", + "\n" + ] + } + ], + "source": [ + "#accessing model performance\n", + "#print(metrics.confusion_matrix(expected, predicted))\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that while the recall is 0.96, the f1 is 0.39 and the overall accuracy is atrocious. \n", + "\n", + "We will now try searching for the smoothing parameter to achieve a better ROC and F1 on default. After some experimentation we found that 0.01 is a good value for this parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.85 0.85 13442\n", + " 1 0.52 0.52 0.52 4054\n", + "\n", + " accuracy 0.78 17496\n", + " macro avg 0.69 0.69 0.69 17496\n", + "weighted avg 0.78 0.78 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "gnb = GaussianNB(var_smoothing = 0.01)\n", + "#experimenting with smoothing constant of naive bayes model on the training set.\n", + "gnb.fit(X_train, y_train)\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smoothing constant of 0.01 allowed us to acheieve a recall and f1 of 0.52 on the training set. Applied on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.86 0.86 6762\n", + " 1 0.52 0.53 0.53 1987\n", + "\n", + " accuracy 0.78 8749\n", + " macro avg 0.69 0.69 0.69 8749\n", + "weighted avg 0.78 0.78 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_test,gnb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Naive Bayes\" , \n", + " classification_report(y_test, gnb.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
3Neural Network 17x8x8x10.5358340.761854
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "3 Neural Network 17x8x8x1 0.535834 0.761854\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.sort_values([\"AUROC\"], ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "Stop Running", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stop Running\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mException\u001b[0m: Stop Running" + ] + } + ], + "source": [ + "raise(Exception(\"Stop Running\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Tuning Neural Network with different optimisers \n", + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adadelta Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adagrad Optimzier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RMSProp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SGD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We conclude that best optimizer is adagrad. Testing it on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network - adagrad (filtered features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Neural Network - adagrad (all features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/BT2101_Default - EDIT-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb index 99725fe..3103496 100644 --- a/BT2101_Default - EDIT-MASTER- Reon.ipynb +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -5969,9 +5969,9 @@ } ], "source": [ - "optimal_adam3 = get_optimal(model, y_train, X_train, \"3-layer adam\")\n", + "optimal_adaS3 = get_optimal(model, y_train, X_train, \"3-layer adagrad\")\n", "predictions = list(model.predict(X_test).ravel() > optimal_adam3)\n", - "auroc = get_roc(model, y_test, X_test, \"3-layer adam\")\n", + "auroc = get_roc(model, y_test, X_test, \"3-layer adagrad\")\n", "\n", "print(classification_report(y_test,predictions))" ] From 31226b4f6925e35c1a1b9cbd67c20be4774c9c25 Mon Sep 17 00:00:00 2001 From: reon Date: Fri, 22 Nov 2019 01:25:39 +0800 Subject: [PATCH 25/25] commit --- ...BT2101 Project Submission-checkpoint.ipynb | 67 +- BT2101 Project Submission.ipynb | 67 +- BT2101_Notebook.ipynb | 6555 +++++ bt2101_notebook.html | 20204 ++++++++++++++++ 4 files changed, 26761 insertions(+), 132 deletions(-) create mode 100644 BT2101_Notebook.ipynb create mode 100644 bt2101_notebook.html diff --git a/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb index 9aa3491..c7cbbe1 100644 --- a/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb +++ b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb @@ -1151,7 +1151,7 @@ "\n", "This means we must conduct some data transformation.\n", "\n", - "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "According to the datasets' author, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", "\n" ] }, @@ -6252,71 +6252,6 @@ "### Adamax Optimizer" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test,predictions))" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/BT2101 Project Submission.ipynb b/BT2101 Project Submission.ipynb index 9aa3491..c7cbbe1 100644 --- a/BT2101 Project Submission.ipynb +++ b/BT2101 Project Submission.ipynb @@ -1151,7 +1151,7 @@ "\n", "This means we must conduct some data transformation.\n", "\n", - "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "According to the datasets' author, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", "\n" ] }, @@ -6252,71 +6252,6 @@ "### Adamax Optimizer" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mlp.fit(X_train,y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = mlp.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "confusion(y_test,predictions,\"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test,predictions))" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/BT2101_Notebook.ipynb b/BT2101_Notebook.ipynb new file mode 100644 index 0000000..9aa3491 --- /dev/null +++ b/BT2101_Notebook.ipynb @@ -0,0 +1,6555 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-4Rm0wjQMUHi" + }, + "source": [ + "# BUILDING A DEFUALT DETECTION MODEL\n", + "\n", + "---\n", + "\n", + "By Reon Ho, Lam Cheng Jun, Janson Chew, and Bryan Koh\n", + "\n", + "\n", + "\n", + "## Table of Contents\n", + "1. Problem Description (Brief Write Up)\n", + "2. Exploratory Data Analysis (EDA)\n", + "3. Data Pre-processing\n", + "4. Model Selection\n", + "5. Evaluation\n", + "6. Discussion and Possible Improvements\n", + "\n", + "## 1. Problem Description\n", + "\n", + "The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end. \n", + "\n", + "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).\n", + "\n", + "The 23 explanatory attributes and their explanations (from the data provider) are as follows:\n", + "\n", + "### X1 - X5: Indivual attributes of customer\n", + "\n", + "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n", + "\n", + "X2: Gender (1 = male; 2 = female). \n", + "\n", + "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n", + "\n", + "X4: Marital status (1 = married; 2 = single; 3 = others). \n", + "\n", + "X5: Age (year). \n", + "\n", + "### X6 - X11: Repayment history from April to Septemeber 2005\n", + "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n", + "\n", + "\n", + "X6 = the repayment status in September, 2005\n", + "\n", + "X7 = the repayment status in August, 2005\n", + "\n", + "X8 = the repayment status in July, 2005\n", + "\n", + "X9 = the repayment status in June, 2005\n", + "\n", + "X10 = the repayment status in May, 2005\n", + "\n", + "X11 = the repayment status in April, 2005. \n", + "\n", + "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n", + "\n", + "X12 = amount of bill statement in September, 2005; \n", + "\n", + "X13 = amount of bill statement in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X17 = amount of bill statement in April, 2005. \n", + "\n", + "### X18 - X23: Amount of previous payment (NT dollar)\n", + "X18 = amount paid in September, 2005\n", + "\n", + "X19 = amount paid in August, 2005\n", + "\n", + ". . .\n", + "\n", + "X23 = amount paid in April, 2005. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aM_aIU6UPHe4" + }, + "source": [ + "## EDA\n", + "\n", + "In this section we will explore the data set, its shape and its features to get an idea of the data.\n", + "\n", + "### Importing packages and the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Is0wEkk3LJCt" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "x_Z7u_9vRC5m" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KhmX9KWWyrUW" + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n", + "df = pd.read_csv(url, header = 1, index_col = 0)\n", + "# Dataset is now stored in a Pandas Dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "colab_type": "code", + "id": "FhJ2eAxVQhBm", + "outputId": "7f79bb40-f08f-4709-e7d4-1f747bb8af2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
\n", + "

5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 20000 2 2 1 24 2 2 -1 -1 \n", + "2 120000 2 2 2 26 -1 2 0 0 \n", + "3 90000 2 2 2 34 0 0 0 0 \n", + "4 50000 2 2 1 37 0 0 0 0 \n", + "5 50000 1 2 1 57 -1 0 -1 0 \n", + "\n", + " PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 \\\n", + "ID ... \n", + "1 -2 ... 0 0 0 0 689 0 \n", + "2 0 ... 3272 3455 3261 0 1000 1000 \n", + "3 0 ... 14331 14948 15549 1518 1500 1000 \n", + "4 0 ... 28314 28959 29547 2000 2019 1200 \n", + "5 0 ... 20940 19146 19131 2000 36681 10000 \n", + "\n", + " PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \n", + "ID \n", + "1 0 0 0 1 \n", + "2 1000 0 2000 1 \n", + "3 1000 1000 5000 0 \n", + "4 1100 1069 1000 0 \n", + "5 9000 689 679 0 \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#rename the target variable to \"Y\" for convenience\n", + "df[\"Y\"] = df[\"default payment next month\"] \n", + "df = df.drop(\"default payment next month\", axis = 1)\n", + "df0 = df #backup of df\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "zcuPyfM86AKj", + "outputId": "89bb2e37-a3ba-43e5-99a7-6917f24acc3f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 24 Columns and 30000 Rows\n" + ] + } + ], + "source": [ + "size = df.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "colab_type": "code", + "id": "QVaSnvJP3VbO", + "outputId": "4bf72e64-2d0c-41c3-85b5-3bd6e70920d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for null values\n", + "df.isnull().any().sum() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eVYXnIGH9Zq6" + }, + "source": [ + "From the above analyses, we observe that:\n", + "1. The data indeed has 30000 rows and 24 columns\n", + "2. There are no null values\n", + "\n", + "We will now explore the features more in depth." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "W6hhPNl1Slau" + }, + "source": [ + "### Exploring the features" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1Sp2F3gzXX2F" + }, + "source": [ + "**1) Exploring target attribute:**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "colab_type": "code", + "id": "DCSEICWwXWgX", + "outputId": "9545da56-f31b-48f2-a271-db0e18677beb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaults : 22.12 %\n", + "non defaults : 77.88000000000001 %\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "All = df.shape[0]\n", + "default = df[df['Y'] == 1]\n", + "nondefault = df[df['Y'] == 0]\n", + "\n", + "x = len(default)/All\n", + "y = len(nondefault)/All\n", + "\n", + "print('defaults :',x*100,'%')\n", + "print('non defaults :',y*100,'%')\n", + "\n", + "# plotting target attribute against frequency\n", + "labels = ['non default','default']\n", + "classes = pd.value_counts(df['Y'], sort = True)\n", + "classes.plot(kind = 'bar', rot=0)\n", + "plt.title(\"Target attribute distribution\")\n", + "plt.xticks(range(2), labels)\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Frequency\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tysR0WHw4SGU" + }, + "source": [ + "**2) Exploring categorical attributes**\n", + "\n", + "Categorical attributes are:\n", + "- Sex\n", + "- Education\n", + "- Marriage" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "colab_type": "code", + "id": "s61SSRII00UB", + "outputId": "69df981f-8c36-43a9-d155-a6553adbba0b", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 60.373333\n", + "1 39.626667\n", + "Name: SEX, dtype: float64\n", + "--------------------------------------------------------\n", + "2 46.766667\n", + "1 35.283333\n", + "3 16.390000\n", + "5 0.933333\n", + "4 0.410000\n", + "6 0.170000\n", + "0 0.046667\n", + "Name: EDUCATION, dtype: float64\n", + "--------------------------------------------------------\n", + "2 53.213333\n", + "1 45.530000\n", + "3 1.076667\n", + "0 0.180000\n", + "Name: MARRIAGE, dtype: float64\n" + ] + } + ], + "source": [ + "print(df[\"SEX\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"EDUCATION\"].value_counts().apply(lambda r: r/All*100))\n", + "print(\"--------------------------------------------------------\")\n", + "print(df[\"MARRIAGE\"].value_counts().apply(lambda r: r/All*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Uudv5XE828nb" + }, + "source": [ + "**Findings**\n", + "\n", + "- Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2\n", + "- Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3\n", + "- Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4 " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "colab_type": "code", + "id": "U3IJzhwwe5KK", + "outputId": "cb61e112-a3ec-4a37-c1a0-0ffc9ebcbf89", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total target attributes:\n", + "non defaults : 77.88000000000001 %\n", + "defaults : 22.12 %\n", + "--------------------------------------------------------\n", + "SEX Male Female\n", + "Y \n", + "non defaults 75.832773 79.223719\n", + "defaults 24.167227 20.776281\n", + "--------------------------------------------------------\n", + "EDUCATION 0 1 2 3 4 5 \\\n", + "Y \n", + "non defaults 100.0 80.765234 76.265146 74.842384 94.308943 93.571429 \n", + "defaults 0.0 19.234766 23.734854 25.157616 5.691057 6.428571 \n", + "\n", + "EDUCATION 6 \n", + "Y \n", + "non defaults 84.313725 \n", + "defaults 15.686275 \n", + "--------------------------------------------------------\n", + "MARRIAGE unknown married single others\n", + "Y \n", + "non defaults 90.740741 76.528296 79.071661 73.993808\n", + "defaults 9.259259 23.471704 20.928339 26.006192\n" + ] + } + ], + "source": [ + "#proportion of target attribute (for reference)\n", + "print('Total target attributes:')\n", + "print('non defaults :',y*100,'%')\n", + "print('defaults :',x*100,'%')\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with Sex\n", + "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(sex_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with education\n", + "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(education_target)\n", + "print(\"--------------------------------------------------------\")\n", + "#analysing default payment with marriage\n", + "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n", + "print(marriage_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kOriUQ0wxbhD" + }, + "source": [ + "**Conclusion**\n", + "\n", + "From the analyses above we conclude that\n", + "\n", + "1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "77GAylGWnPJO" + }, + "source": [ + "**3) Analysis of Numerical Attributes**\n", + "\n", + "The numerical attributes are:\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "HEcCl5Rj-N0T", + "outputId": "a59f7092-366e-47ec-c67b-e18f02d84ac4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 \\\n", + "0 LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \n", + "\n", + " 9 10 11 12 13 14 15 \\\n", + "0 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 \n", + "\n", + " 16 17 18 19 \n", + "0 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#printing numerical attributes\n", + "pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "LIMIT_BAL 30000.0 167484.322667 129747.661567 10000.0 50000.00 \n", + "AGE 30000.0 35.485500 9.217904 21.0 28.00 \n", + "PAY_0 30000.0 -0.016700 1.123802 -2.0 -1.00 \n", + "PAY_2 30000.0 -0.133767 1.197186 -2.0 -1.00 \n", + "PAY_3 30000.0 -0.166200 1.196868 -2.0 -1.00 \n", + "PAY_4 30000.0 -0.220667 1.169139 -2.0 -1.00 \n", + "PAY_5 30000.0 -0.266200 1.133187 -2.0 -1.00 \n", + "PAY_6 30000.0 -0.291100 1.149988 -2.0 -1.00 \n", + "BILL_AMT1 30000.0 51223.330900 73635.860576 -165580.0 3558.75 \n", + "BILL_AMT2 30000.0 49179.075167 71173.768783 -69777.0 2984.75 \n", + "BILL_AMT3 30000.0 47013.154800 69349.387427 -157264.0 2666.25 \n", + "BILL_AMT4 30000.0 43262.948967 64332.856134 -170000.0 2326.75 \n", + "BILL_AMT5 30000.0 40311.400967 60797.155770 -81334.0 1763.00 \n", + "BILL_AMT6 30000.0 38871.760400 59554.107537 -339603.0 1256.00 \n", + "PAY_AMT1 30000.0 5663.580500 16563.280354 0.0 1000.00 \n", + "PAY_AMT2 30000.0 5921.163500 23040.870402 0.0 833.00 \n", + "PAY_AMT3 30000.0 5225.681500 17606.961470 0.0 390.00 \n", + "PAY_AMT4 30000.0 4826.076867 15666.159744 0.0 296.00 \n", + "PAY_AMT5 30000.0 4799.387633 15278.305679 0.0 252.50 \n", + "PAY_AMT6 30000.0 5215.502567 17777.465775 0.0 117.75 \n", + "\n", + " 50% 75% max \n", + "LIMIT_BAL 140000.0 240000.00 1000000.0 \n", + "AGE 34.0 41.00 79.0 \n", + "PAY_0 0.0 0.00 8.0 \n", + "PAY_2 0.0 0.00 8.0 \n", + "PAY_3 0.0 0.00 8.0 \n", + "PAY_4 0.0 0.00 8.0 \n", + "PAY_5 0.0 0.00 8.0 \n", + "PAY_6 0.0 0.00 8.0 \n", + "BILL_AMT1 22381.5 67091.00 964511.0 \n", + "BILL_AMT2 21200.0 64006.25 983931.0 \n", + "BILL_AMT3 20088.5 60164.75 1664089.0 \n", + "BILL_AMT4 19052.0 54506.00 891586.0 \n", + "BILL_AMT5 18104.5 50190.50 927171.0 \n", + "BILL_AMT6 17071.0 49198.25 961664.0 \n", + "PAY_AMT1 2100.0 5006.00 873552.0 \n", + "PAY_AMT2 2009.0 5000.00 1684259.0 \n", + "PAY_AMT3 1800.0 4505.00 896040.0 \n", + "PAY_AMT4 1500.0 4013.25 621000.0 \n", + "PAY_AMT5 1500.0 4031.50 426529.0 \n", + "PAY_AMT6 1500.0 4000.00 528666.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of PAY_0 to PAY_6**\n", + "\n", + "We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on. \n", + "\n", + "However, the presence of -2, -1 in these columns indicates that\n", + "1. There is anomalous data, OR \n", + "2. The numbers do not strictly correspond to the number of months of payment delay. \n", + "\n", + "This means we must conduct some data transformation.\n", + "\n", + "According to **(link)**, the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 669 + }, + "colab_type": "code", + "id": "awXnqvLOS-wB", + "outputId": "a77b53b8-011e-4f53-b7b7-20d80bbc1777", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_histograms(df, variables, n_rows, n_cols, n_bins):\n", + " fig=plt.figure()\n", + " for i, var_name in enumerate(variables):\n", + " ax=fig.add_subplot(n_rows,n_cols,i+1)\n", + " df[var_name].hist(bins=n_bins,ax=ax)\n", + " ax.set_title(var_name)\n", + " fig.tight_layout() # Improves appearance a bit.\n", + " plt.show()\n", + "\n", + "PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]\n", + "BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]\n", + "PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n", + "\n", + "draw_histograms(PAY, PAY.columns, 2, 3, 10)\n", + "draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)\n", + "draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6c_Gz6wUrJ8" + }, + "source": [ + "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.\n", + "\n", + "Now that we have an idea of the features, we will move on to feature selection and data preparation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AQBksEyEf4Sf" + }, + "source": [ + "## Data Preprocessing\n", + "\n", + "It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.\n", + "1. Removing Noise - Inconsistencies\n", + "2. Dealing with negative values of PAY_0 to PAY_6\n", + "3. Outliers\n", + "4. One Hot Encoding\n", + "5. Train Test Split\n", + "6. Feature selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing Noise\n", + "First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1, 3, 4, 0], dtype=int64)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)\n", + "df[\"EDUCATION\"].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, \"Missing\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Separating negative and positive values for PAY_0 to PAY_6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.\n", + "\n", + "The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(0,7):\n", + " try:\n", + " df[\"S_\" + str(i)] = [x if x < 1 else 1 for x in df[\"PAY_\" + str(i)]]\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dummy variables for negative values\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
\n", + "
" + ], + "text/plain": [ + " S_0 S_2 S_3 S_4 S_5 S_6\n", + "ID \n", + "1 1 1 -1 -1 -2 -2\n", + "2 -1 1 0 0 0 1\n", + "3 0 0 0 0 0 0\n", + "4 0 0 0 0 0 0\n", + "5 -1 0 -1 0 0 0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Dummy variables for negative values')\n", + "df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#attributes representing positive values\n", + "for col in [\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"]:\n", + " df[col].replace([0,-1,-2], 0, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Outliers\n", + "Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8507531.55877335.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000000.0000000.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", + "

8 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 149324.899981 1.608954 1.850753 1.558773 35.006592 \n", + "std 116558.616530 0.487994 0.738175 0.522639 8.832028 \n", + "min 10000.000000 1.000000 0.000000 0.000000 21.000000 \n", + "25% 50000.000000 1.000000 1.000000 1.000000 28.000000 \n", + "50% 120000.000000 2.000000 2.000000 2.000000 34.000000 \n", + "75% 210000.000000 2.000000 2.000000 2.000000 41.000000 \n", + "max 500000.000000 2.000000 4.000000 3.000000 59.000000 \n", + "\n", + " PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean 0.372109 0.337321 0.324633 0.278224 0.238750 \n", + "std 0.765730 0.814878 0.811491 0.786314 0.743923 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 8.000000 8.000000 8.000000 8.000000 8.000000 \n", + "\n", + " ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y \\\n", + "count ... 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean ... 2787.425071 2778.830673 2822.285007 0.230177 \n", + "std ... 4835.081906 4751.263287 5271.198100 0.420954 \n", + "min ... 0.000000 0.000000 0.000000 0.000000 \n", + "25% ... 150.000000 82.000000 0.000000 0.000000 \n", + "50% ... 1200.000000 1218.000000 1143.000000 0.000000 \n", + "75% ... 3118.000000 3140.000000 3069.000000 0.000000 \n", + "max ... 45171.000000 44197.000000 51000.000000 1.000000 \n", + "\n", + " S_0 S_2 S_3 S_4 S_5 \\\n", + "count 26245.000000 26245.000000 26245.000000 26245.000000 26245.000000 \n", + "mean -0.133587 -0.300438 -0.327300 -0.364412 -0.395999 \n", + "std 0.879876 0.883472 0.895264 0.886115 0.877789 \n", + "min -2.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", + "25% -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + " S_6 \n", + "count 26245.000000 \n", + "mean -0.428158 \n", + "std 0.900723 \n", + "min -2.000000 \n", + "25% -1.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 1.000000 \n", + "\n", + "[8 rows x 30 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy import stats\n", + "#we are only concerned with the ordinal data\n", + "o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis=1))\n", + "#rows where the absolute z score of all columns are less than 2.58 (critical value)\n", + "rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)\n", + "df = df[rows]\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Scaling\n", + "The models used subsequently may have difficulty converging before the maximum number of iterations allowed\n", + "is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "cols = df.drop(['Y','EDUCATION', 'MARRIAGE', \"SEX\",\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\",\"PAY_0\", \"PAY_2\", \"PAY_3\", \"PAY_4\", \"PAY_5\", \"PAY_6\"], axis =1)\n", + "df1 = df.copy()\n", + "df1[cols.columns] = scaler.fit_transform(cols)\n", + "df = df1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
\n", + "

5 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\n", + "ID \n", + "1 0.020408 2 2 1 0.078947 2 2 0 0 \n", + "2 0.224490 2 2 2 0.131579 0 2 0 0 \n", + "3 0.163265 2 2 2 0.342105 0 0 0 0 \n", + "4 0.081633 2 2 1 0.421053 0 0 0 0 \n", + "5 0.081633 1 2 1 0.947368 0 0 0 0 \n", + "\n", + " PAY_5 ... PAY_AMT4 PAY_AMT5 PAY_AMT6 Y S_0 S_2 S_3 S_4 S_5 S_6 \n", + "ID ... \n", + "1 0 ... 0.000000 0.000000 0.000000 1 1 1 -1 -1 -2 -2 \n", + "2 0 ... 0.022138 0.000000 0.039216 1 -1 1 0 0 0 1 \n", + "3 0 ... 0.022138 0.022626 0.098039 0 0 0 0 0 0 0 \n", + "4 0 ... 0.024352 0.024187 0.019608 0 0 0 0 0 0 0 \n", + "5 0 ... 0.199243 0.015589 0.013314 0 -1 0 -1 0 0 0 \n", + "\n", + "[5 rows x 30 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One-Hot Encoding for Categorical attributes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the \"Education\" column for our dataset.\n", + "\n", + "A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).\n", + "\n", + "One hot encoding will allow our models to treat these columns explicitly as categorical features.\n", + "\n", + "The following categorical columns will be one-hot encoded\n", + "\n", + "1. EDUCATION\n", + "2. MARRIAGE\n", + "3. S0 - S6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "onenc = OneHotEncoder(categories='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MISSING-EDUGRADUNIHSMISSING-MSMARRIEDSINGLE
00.00.01.00.00.01.00.0
10.00.01.00.00.00.01.0
20.00.01.00.00.00.01.0
30.00.01.00.00.01.00.0
40.00.01.00.00.01.00.0
\n", + "
" + ], + "text/plain": [ + " MISSING-EDU GRAD UNI HS MISSING-MS MARRIED SINGLE\n", + "0 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "2 0.0 0.0 1.0 0.0 0.0 0.0 1.0\n", + "3 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 0.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for EDUCATION and MARRIAGE\n", + "onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())\n", + "onehot.columns= names = [\"MISSING-EDU\",\"GRAD\",\"UNI\",\"HS\",\"OTHER-EDU\",\"MISSING-MS\",\"MARRIED\",\"SINGLE\",\"OTHER-MS\"]\n", + "#drop one of each category to prevent dummy variable trap\n", + "onehot = onehot.drop([\"OTHER-EDU\", \"OTHER-MS\"], axis = 1)\n", + "onehot.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
\n", + "
" + ], + "text/plain": [ + " PAY_0_No_Transactions PAY_0_Pay_Duly PAY_0_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 1.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_2_No_Transactions PAY_2_Pay_Duly PAY_2_Revolving_Credit \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_3_No_Transactions PAY_3_Pay_Duly PAY_3_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 1.0 0.0 \n", + "\n", + " PAY_4_No_Transactions PAY_4_Pay_Duly PAY_4_Revolving_Credit \\\n", + "0 0.0 1.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_5_No_Transactions PAY_5_Pay_Duly PAY_5_Revolving_Credit \\\n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 \n", + "\n", + " PAY_6_No_Transactions PAY_6_Pay_Duly PAY_6_Revolving_Credit \n", + "0 1.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 0.0 1.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#one hot encoding for S_0 to S_6\n", + "onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())\n", + "onehot_PAY.columns= onenc.fit(df[[\"S_0\", \"S_2\", \"S_3\", \"S_4\", \"S_5\", \"S_6\"]]).get_feature_names()\n", + "#drop one of each category to prevent dummy variable trap\n", + "#onehot = onehot.drop([\"OTHER-EDU\", \"OTHER_MS\"], axis = 1)\n", + "names = []\n", + "for X in range(0,7):\n", + " if X == 1:\n", + " continue\n", + " names.append(\"PAY_\"+str(X)+\"_No_Transactions\")\n", + " names.append(\"PAY_\"+str(X)+\"_Pay_Duly\")\n", + " names.append(\"PAY_\"+str(X)+\"_Revolving_Credit\")\n", + " try:\n", + " onehot_PAY = onehot_PAY.drop(\"x\" + str(X) +\"_1\", axis =1)\n", + " except:\n", + " onehot_PAY = onehot_PAY.drop(\"x1_1\", axis =1)\n", + "onehot_PAY.columns = names\n", + "onehot_PAY.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n", + " 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',\n", + " 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n", + " 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',\n", + " 'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',\n", + " 'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',\n", + " 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',\n", + " 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',\n", + " 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',\n", + " 'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',\n", + " 'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],\n", + " dtype='object')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)\n", + "df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
\n", + "

0 rows × 47 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [LIMIT_BAL, SEX, AGE, PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, Y, MISSING-EDU, GRAD, UNI, HS, MISSING-MS, MARRIED, SINGLE, PAY_0_No_Transactions, PAY_0_Pay_Duly, PAY_0_Revolving_Credit, PAY_2_No_Transactions, PAY_2_Pay_Duly, PAY_2_Revolving_Credit, PAY_3_No_Transactions, PAY_3_Pay_Duly, PAY_3_Revolving_Credit, PAY_4_No_Transactions, PAY_4_Pay_Duly, PAY_4_Revolving_Credit, PAY_5_No_Transactions, PAY_5_Pay_Duly, PAY_5_Revolving_Credit, PAY_6_No_Transactions, PAY_6_Pay_Duly, PAY_6_Revolving_Credit]\n", + "Index: []\n", + "\n", + "[0 rows x 47 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check for perfect collinearity\n", + "corr = df1.corr()\n", + "for i in range(len(corr)):\n", + " corr.iloc[i,i] = 0\n", + "#corr[corr == 1] = 0\n", + "corr[corr.eq(1).any(1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has 47 Columns and 26245 Rows\n" + ] + } + ], + "source": [ + "size = df1.shape\n", + "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Test Split\n", + "\n", + "Before we conduct feature selection and model selection, we split the data using a train test split according to the project description." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import *\n", + "from sklearn.model_selection import *" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VOB68z_hM1jW" + }, + "outputs": [], + "source": [ + "#using holdout sampling for train test split using seed 123\n", + "np.random.seed(123) \n", + "ft = df1.drop(\"Y\", axis = 1)\n", + "target = df1[\"Y\"]\n", + "X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter method for feature selection\n", + "The filter method for feature selection entails selecting relevant attributes before moving on to learning phase.\n", + "We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Significant values are:\n", + " 0 pval\n", + "LIMIT_BAL 82.306062 2.883753e-04\n", + "PAY_0 4279.993739 0.000000e+00\n", + "PAY_2 3557.072141 0.000000e+00\n", + "PAY_3 2766.119390 0.000000e+00\n", + "PAY_4 2736.965012 0.000000e+00\n", + "PAY_5 2587.002458 0.000000e+00\n", + "PAY_6 2240.874786 0.000000e+00\n", + "PAY_0_No_Transactions 76.858872 1.147939e-03\n", + "PAY_0_Revolving_Credit 480.805794 0.000000e+00\n", + "PAY_2_Pay_Duly 75.283344 1.684018e-03\n", + "PAY_2_Revolving_Credit 229.527990 0.000000e+00\n", + "PAY_3_Pay_Duly 86.995856 8.229607e-05\n", + "PAY_3_Revolving_Credit 121.059740 2.357071e-09\n", + "PAY_4_Pay_Duly 79.449207 6.014800e-04\n", + "PAY_4_Revolving_Credit 82.276504 2.906105e-04\n", + "PAY_5_Pay_Duly 63.330298 2.338310e-02\n", + "PAY_5_Revolving_Credit 64.659773 1.792035e-02\n" + ] + } + ], + "source": [ + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "\n", + "selector = SelectKBest( score_func = chi2, k=10)\n", + "selector.fit(X_train, y_train)\n", + "np.set_printoptions(precision=10)\n", + "chi2data = pd.DataFrame(selector.scores_)\n", + "chi2data[\"pval\"] = 1 - stats.chi2.cdf(chi2data, 43)\n", + "chi2data.index = X_train.columns\n", + "\n", + "print(\"Significant values are:\")\n", + "print(chi2data[chi2data[\"pval\"] < 0.05])\n", + "\n", + "cols = chi2data[chi2data[\"pval\"] < 0.05].index\n", + "X_train_filter = X_train[cols]\n", + "X_test_filter = X_test[cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mbhlIlQzZz7c" + }, + "source": [ + "## Model Selection\n", + "\n", + "In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.\n", + "\n", + "We will be attempting to fit the following models:\n", + "\n", + "\n", + "- Decision Tree \n", + "- Logistic Regression\n", + "- Support Vector Machine\n", + "- Neural Network\n", + "\n", + "To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def get_roc(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver operating characteristic for ' + name)\n", + " plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))\n", + " plt.legend(loc=\"lower right\")\n", + " \n", + " #find- best threshold\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " print(\"Optimal Threshold: \" + str(optimal_threshold))\n", + " \n", + " plt.show()\n", + " \n", + " return auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def get_optimal(model, y_test, X_test, name):\n", + " try:\n", + " fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]\n", + " tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]\n", + " thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]\n", + " except:\n", + " fpr = roc_curve(y_test,model.predict(X_test))[0]\n", + " tpr = roc_curve(y_test,model.predict(X_test))[1]\n", + " thresholds = roc_curve(y_test,model.predict(X_test))[2]\n", + " optimal_idx = np.argmax(tpr - fpr)\n", + " optimal_threshold = thresholds[optimal_idx]\n", + " return optimal_threshold " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def confusion(y_test, predictions, name):\n", + " conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])\n", + " print(\"Of \" + str(conf[0][1] + conf[1][1]) + \" Defaulters, the \" + name + \" identified \" + str(conf[1][1])) \n", + " return conf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation \n", + "We will select the model based on the model evaluation. The key metrics we will compute are:\n", + "\n", + "1. Accuracy\n", + "2. Recall\n", + "3. AUROC\n", + "\n", + "Because of the nature of a default detection problem, we would like to prioritise **recall** for defaults. \n", + "This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)\n", + "\n", + "However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "H89tM6NvaN17" + }, + "source": [ + "### Decision Trees\n", + "\n", + "#### Theory:\n", + "The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.\n", + "\n", + "Below is a binary decision tree that has been split for a few iterations.\n", + "\n", + "![image.png](https://elf11.github.io/images/decisionTree.png)\n", + "\n", + "Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:\n", + "\n", + "![image.png](https://miro.medium.com/max/664/1*otdoiyIwxJI-UV0ukkyutw.png)\n", + "\n", + "The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the *weighted sum* of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.\n", + "\n", + "#### Training\n", + "We will now construct a simple decision tree using the GINI index." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier()\n", + "tree.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.3333333333333333\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree, y_train, X_train, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_train, tree.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (GINI) identified 809\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
054821280
11178809
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5482 1280\n", + "1 1178 809" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, tree.predict(X_test), \"Decision Tree (GINI)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.81 0.82 6762\n", + " 1 0.39 0.41 0.40 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.61 0.61 8749\n", + "weighted avg 0.72 0.72 0.72 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(tree, y_test, X_test, \"Decision Tree (GINI)\")\n", + "print(classification_report(y_test, tree.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
055091253
11156831
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5509 1253\n", + "1 1156 831" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree2 = DecisionTreeClassifier(criterion = \"entropy\")\n", + "tree2.fit(X_train, y_train)\n", + "confusion(y_test, tree2.predict(X_test), \"Decision Tree (Entropy)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.5\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.81 0.82 6762\n", + " 1 0.40 0.42 0.41 1987\n", + "\n", + " accuracy 0.72 8749\n", + " macro avg 0.61 0.62 0.61 8749\n", + "weighted avg 0.73 0.72 0.73 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(tree2, y_test, X_test, \"Decision Tree (Entropy)\")\n", + "print(classification_report(y_test, tree2.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "#### Theory\n", + "Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees. \n", + "\n", + "Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently. \n", + "\n", + "#### Training\n", + "To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "randf = RandomForestClassifier(n_estimators=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=200,\n", + " n_jobs=None, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "randf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 13442\n", + " 1 1.00 1.00 1.00 4054\n", + "\n", + " accuracy 1.00 17496\n", + " macro avg 1.00 1.00 1.00 17496\n", + "weighted avg 1.00 1.00 1.00 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, randf.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06371391
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6371 391\n", + "1 1274 713" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, randf.predict(X_test), \"Decision Tree (Random Forest)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.27\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.94 0.88 6762\n", + " 1 0.65 0.36 0.46 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.74 0.65 0.67 8749\n", + "weighted avg 0.79 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc_rf = get_roc(randf, y_test, X_test, \"Decision Tree (Random Forest)\")\n", + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gradient Boosted Trees Classifier\n", + "\n", + "#### Theory\n", + "In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.\n", + " \n", + "#### Training\n", + "For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='deviance', max_depth=4,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=300,\n", + " n_iter_no_change=None, presort='auto',\n", + " random_state=None, subsample=1.0, tol=0.0001,\n", + " validation_fraction=0.1, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)\n", + "xgb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 13442\n", + " 1 0.79 0.46 0.58 4054\n", + "\n", + " accuracy 0.85 17496\n", + " macro avg 0.82 0.71 0.74 17496\n", + "weighted avg 0.84 0.85 0.83 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train, xgb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set," + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06381381
11270717
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6381 381\n", + "1 1270 717" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test, xgb.predict(X_test), \"Decision Tree (Gradient Boosted Trees)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24738247273049666\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(xgb, y_test, X_test, \"Decision Tree (XGBoost)\")\n", + "print(classification_report(y_test, xgb.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[0] = ([\"Decision Trees - Random Forest\" , \n", + " classification_report(y_test, randf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc_rf])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n", + "#### Theory\n", + "Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model. \n", + "\n", + "Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default. \n", + "\n", + "In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a5e86f014eb1f0744e280eb0d68485cb8c0a6c3)\n", + "\n", + "We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:\n", + "\n", + "![image.png](https://wikimedia.org/api/rest_v1/media/math/render/svg/614e0c64d59f0ff2e926deafcb2de6e502394fac)\n", + "\n", + "We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.\n", + "\n", + "The logistic regression equation produces a \"squashed\" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).\n", + "\n", + "![image.png](https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Logistic-curve.svg/1280px-Logistic-curve.svg.png)\n", + "\n", + "\n", + "#### Training\n", + "We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17450
Method: MLE Df Model: 45
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784
Time: 00:13:23 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649
SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016
AGE 0.2097 0.100 2.095 0.036 0.013 0.406
PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726
PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365
PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037
PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081
PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325
PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731
BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821
BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706
BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396
BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992
BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338
BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799
PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640
PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114
PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186
PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072
PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185
PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145
MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648
GRAD 1.3518 0.220 6.148 0.000 0.921 1.783
UNI 1.3056 0.219 5.971 0.000 0.877 1.734
HS 1.2342 0.223 5.547 0.000 0.798 1.670
MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06
MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426
SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245
PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067
PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284
PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705
PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026
PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947
PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350
PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106
PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247
PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203
PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205
PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252
PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160
PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711
PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500
PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467
PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335
PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343
PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17450\n", + "Method: MLE Df Model: 45\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784\n", + "Time: 00:13:23 Log-Likelihood: -7781.7\n", + "converged: False LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649\n", + "SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016\n", + "AGE 0.2097 0.100 2.095 0.036 0.013 0.406\n", + "PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726\n", + "PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365\n", + "PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037\n", + "PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081\n", + "PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325\n", + "PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731\n", + "BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821\n", + "BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706\n", + "BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396\n", + "BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992\n", + "BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338\n", + "BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799\n", + "PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640\n", + "PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114\n", + "PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186\n", + "PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072\n", + "PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185\n", + "PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145\n", + "MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648\n", + "GRAD 1.3518 0.220 6.148 0.000 0.921 1.783\n", + "UNI 1.3056 0.219 5.971 0.000 0.877 1.734\n", + "HS 1.2342 0.223 5.547 0.000 0.798 1.670\n", + "MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06\n", + "MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426\n", + "SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245\n", + "PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067\n", + "PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284\n", + "PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705\n", + "PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026\n", + "PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947\n", + "PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350\n", + "PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106\n", + "PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247\n", + "PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203\n", + "PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205\n", + "PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252\n", + "PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160\n", + "PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711\n", + "PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500\n", + "PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467\n", + "PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335\n", + "PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343\n", + "PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.88 13442\n", + " 1 0.67 0.36 0.47 4054\n", + "\n", + " accuracy 0.81 17496\n", + " macro avg 0.75 0.65 0.68 17496\n", + "weighted avg 0.79 0.81 0.79 17496\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_train,list(glm.predict(X_train)>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logisitc model with all features performs average on both the train and test set with an accuracy of about 0.8 but recall and f1 are still below 0.5. We will now try removing all the insignificant features to see how that affects the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.444770\n", + " Iterations: 35\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Maximum number of iterations has been exceeded.\n", + " Current function value: 0.445360\n", + " Iterations: 35\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445387\n", + " Iterations 7\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", + " \"Check mle_retvals\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.445388\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445392\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445397\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445410\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445455\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445512\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445596\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445680\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445770\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445853\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445877\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.445963\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446090\n", + " Iterations 7\n", + "Optimization terminated successfully.\n", + " Current function value: 0.446288\n", + " Iterations 7\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17468
Method: MLE Df Model: 27
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756
Time: 00:14:16 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676
SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036
PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692
PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397
PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111
PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276
BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672
BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867
PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693
PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450
PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350
PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277
GRAD 1.3465 0.175 7.687 0.000 1.003 1.690
UNI 1.2982 0.174 7.462 0.000 0.957 1.639
HS 1.2384 0.178 6.960 0.000 0.890 1.587
MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318
PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799
PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159
PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010
PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421
PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397
PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509
PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471
PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173
PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361
PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288
PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477
PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y No. Observations: 17496\n", + "Model: Logit Df Residuals: 17468\n", + "Method: MLE Df Model: 27\n", + "Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756\n", + "Time: 00:14:16 Log-Likelihood: -7808.3\n", + "converged: True LL-Null: -9471.2\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "==========================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------\n", + "LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676\n", + "SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036\n", + "PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692\n", + "PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397\n", + "PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111\n", + "PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276\n", + "BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672\n", + "BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867\n", + "PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693\n", + "PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450\n", + "PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350\n", + "PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277\n", + "GRAD 1.3465 0.175 7.687 0.000 1.003 1.690\n", + "UNI 1.2982 0.174 7.462 0.000 0.957 1.639\n", + "HS 1.2384 0.178 6.960 0.000 0.890 1.587\n", + "MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318\n", + "PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799\n", + "PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159\n", + "PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010\n", + "PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421\n", + "PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397\n", + "PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509\n", + "PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471\n", + "PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173\n", + "PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361\n", + "PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288\n", + "PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477\n", + "PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402\n", + "==========================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remove the most insignificant attribute, and retrain\n", + "train_log = X_train.copy()\n", + "glm = sm.Logit(y_train,train_log).fit()\n", + "while max(glm.pvalues) > 0.01:\n", + " least = glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]\n", + " train_log = train_log.drop(least,axis = 1)\n", + " glm = sm.Logit(y_train,train_log).fit()\n", + "glm.summary() " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28 Columns left:\n", + "Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',\n", + " 'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',\n", + " 'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',\n", + " 'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',\n", + " 'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',\n", + " 'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',\n", + " 'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "count = len(glm.pvalues.index)\n", + "print(str(count) + \" Columns left:\")\n", + "print(glm.pvalues.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.65 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony). \n", + "\n", + "We now Calculate the AUROC for the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21650864211883647\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.78 0.83 6762\n", + " 1 0.46 0.62 0.53 1987\n", + "\n", + " accuracy 0.75 8749\n", + " macro avg 0.67 0.70 0.68 8749\n", + "weighted avg 0.78 0.75 0.76 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "get_roc(glm, y_train, X_train[glm.pvalues.index], \"Logistic Regression\")\n", + "print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the optimal cut off was found to be ~0.22 using the training data, we used that as our cut off for our evaluation of the test set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults. \n", + "\n", + "The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.\n", + "\n", + "\n", + "Calculate the confusion matrices for both cut offs to better compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 1235\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
053031459
17521235
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 5303 1459\n", + "1 752 1235" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Logistic Regression identified 715\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PredictedFalseTrue
Actual
06421341
11272715
\n", + "
" + ], + "text/plain": [ + "Predicted False True \n", + "Actual \n", + "0 6421 341\n", + "1 1272 715" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, \"Logistic Regression\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident that the lower cutoff is better able to detect defaults. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], \"Logistic Regression\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[1] = [\"Logistic Regression (Optimal Cutoff)\" , \n", + " classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iCxBcin11EI8" + }, + "source": [ + "### Support Vector Machine\n", + "#### Theory\n", + "Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).\n", + "\n", + " \n", + "\n", + "Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the \"maximum-margin hyperplane\" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.\n", + "\n", + "Any hyperplane can be written as the set of points X satisfying\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "\n", + "where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.\n", + "\n", + "#### Margin\n", + "A margin is a separation of line to the closest class points.\n", + "Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin.\n", + "A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.\n", + "\n", + "##### Hard Margin\n", + "If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the \"margin\", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:\n", + "\n", + " Minimize ||W|| subject to:\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "##### Soft Margin\n", + "Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.\n", + "\n", + "We then wish to minimize\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.\n", + "\n", + "#### Computing SVM classifier\n", + "We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.\n", + "\n", + "##### Primal\n", + "Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.\n", + "\n", + "We can rewrite the optimization problem as follows\n", + " \n", + "\n", + "
\n", + "
\n", + "\n", + "This is called the primal problem.\n", + "\n", + "##### Dual\n", + "By solving for the Lagrangian dual of the above problem, one obtains the simplified problem\n", + " \n", + "\n", + "
\n", + "
\n", + " \n", + "\n", + "
\n", + "
\n", + "This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.\n", + "\n", + "Here, the variables C are defined such that\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Tuning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Kernel\n", + "For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.\n", + "\n", + "Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regularization (C value)\n", + "The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.\n", + "\n", + "For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "Left: low regularization value, right: high regularization value\n", + "\n", + "\n", + "The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gamma\n", + "The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation. \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply SVM model\n", + "For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM without parameter tuning\n", + "First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is \"rbf\" and cost = 1.0 and gamma = 1/n where n is the number of samples." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import svm\n", + "#train svm model without standardization and no parameter tuning\n", + "clf_original = svm.SVC( probability = True, gamma = 'scale')\n", + "clf_original.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16469105377809068\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.95 0.89 6762\n", + " 1 0.68 0.36 0.47 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for svm\n", + "get_roc(clf_original, y_test, X_test, \"SVM default parameters\")\n", + "print(classification_report(y_test, clf_original.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM default parameters identified 713\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06432330
11274713
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6432 330\n", + "1 1274 713" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_original.predict(X_test), \"SVM default parameters\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with Parameter tuning\n", + "One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn. \n", + "\n", + "Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.\n", + "\n", + "GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.01, 'gamma': 0.01}" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "def svc_param_selection(X, y, nfolds):\n", + " Cs = [0.01, 0.1, 1]\n", + " gammas = [0.01, 0.1, 1]\n", + " param_grid = {'C': Cs, 'gamma' : gammas}\n", + " grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')\n", + " grid_search.fit(X, y)\n", + " grid_search.best_params_\n", + " return grid_search.best_params_\n", + "svc_param_selection(X_train, y_train,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel\n", + "clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )\n", + "clf_reduced_tuned.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15996357777982226\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAEWCAYAAADsCgQrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3gV1dbA4d8iHVIhFEkhhN6rNEFAQMGGYrmiiCgWsJdrVz6xXXvFrlzUi4AdRRQEpYnSVYr0ltBCIAmB9GR/f8wkHEIapExyznqfh4dMXzNnZs+amT2zxRiDUkoppVRhtZwOQCmllFLVkyYJSimllCqSJglKKaWUKpImCUoppZQqkiYJSimllCqSJglKKaWUKlK1TBJE5BoRmet0HE4TkWgROSoiXlW4zBgRMSLiXVXLrEwisl5EBpzGdKe1D4pIgIh8LyIpIvLFqU7vTkRkgYjcWMXLnCIiT5cw/GkRSRSR/VUZV00iIjtFZHAVLetdEXm8EuZ7QjkmIj+KyHUVvZzTYcfVvBrEUeKxkq/UJMHeYdLtk9V+e8aBFRNm0YwxU40x51bmMqqjwgenMWa3MSbQGJPrZFxOqYiDyRjTzhizoJTlnJQYlWMfvBxoCNQzxlxxGtMXFd8jIrLDPgbjRWSG3f89EfmkiPE7ikimiNQVkSfsdbuz0Dh32/2fqIgYawIRiQLuA9oaYxqVc14DRCS+YiKrOcp6YikrY8w4Y8xTFTW/EpYzzBjzcWUvxx2V9U7CRcaYQKAz0AV4uPJCqjxOXh27y5X5qfDQ7d0E2GyMyTnVCYuK2b76uRYYbB+D3YH59uApwAgRqVNostHALGPMYbt7M1D4Kmq03f+0Y6uBmgCHjDEJTgfiJttTlaCyf+Mqu8NsjCnxH7ATq4DK734B+MGl2w94CdgNHADeBQJchg8H/gSOANuAoXb/EOAjYB+wB3ga8LKHjQGW2H+/C7xUKKaZwL32342Br4CDwA7gTpfxngC+BP5nL//GItYvBPjEnn4X8BhQyyWO34A3gRRgIzCo0LQlrcNvwKvAYXtYM+AX4BCQCEwFQu3xPwXygHTgKPAAEAMYwNseZwHwlD3fVGAuEO4Sz2h7HQ4Bjxf+7QqtdwDwsj1+CrDE7pe/zOvs3zQReNRluh7A70Cyvd6TAF+X4Qa4DdgC7LD7vQ7E2b/BKqCfy/hewCP2vpFqD48CFtnzOmZvj3/Z41+ItT8lA0uBjoX21QeBv4FMwNt1G9ixr7TjOAC8YvffbS/rqP2vNy77oD1OO+Bn+7c8ADxSxDadCGQB2fZ8xmIl4o/Z2zkBa18LscfP39Zj7RgWFTHPScBrJRyfm4DRhbbnXuBil2Pgf8A/QDuXdfnH7v9EMfMdQ6H91+5/gz1tEjAHaOIyzRCsYyTFjnsh9jGXH4fLuPnrnr9v1wX+a8eeBHzrMm5Jv3kXYDXWvjMDmJ4fa6H1GYx1bOXZv80Uu38ve57JwF/AAJdprrfXNRXYDtxi969TaF5HscqhKa7LBgYA8aXsnyWVX0Xur0WsWxgwy55Hkv13pMvwBZRcblzL8XLjUYopN4CbsfbtLHudv3c55pu7jFewHfK3AdYdnASsMuP60xy3HvC9vT1WYJWpS4rZJjGcXHbm74tjsMq7l+zttQMYVsZyvdgyvLjfuIjYCrYX0BerbBxod7fmeDmzCbiy0LZ6B5iNVS4Otvu9Bfxg/7bLgGYu05Q2v5OOlZPiLXWEEwvZSGAt8LrL8NeA77AO8iD7R/yPy06eglV41AIigNb2sG+B97AOuAbAco4fhGM4niScbW9EcTkg0rEOrlpYJ5UJgC8Qi3Uwn+dSMGUDl9jjBhSxfp9gJR1BWDvWZmCsSxw5wD2AD/Ave33qlnEdcoA7sAqDAKC5vS38gPpYJ8LXitrWJezo24CW9vwWAM/Zw9piHbh97W3xkr3uxSUJb9nTR2CdWPrYceUv8wN7GZ2wdvY29nTdsApWb3vcf4C7Cx0AP2PtDwF2v1FYB7g3VgGwH/C3h92PtU+1AsReXr1iCp+uWIVHTzvm6+xt5uey/f7ESjICCm9TrOTmWvvvQKBXUdu5iH0wCKvAuA/wt7t7FrNdn+DEk+ENwFasfTMQ+Br4tNByP8Hah4raP0dhHeD3Y91F8Co0/FFgnkv3eVgnCx/XeLASseftfi9g3Q0sLUkovP9eYq9LG7vfY8BSe/xwrML7cqxj5R57+rImCT9gneTD7On7l/abY+3nuzh+fF6Otc8XWfBx8kk7AquwPx+rfBhid9e3h1+AdVIQoD+QBnQtal5FFbpFLG8nLvsnpZdfRe6vRaxXPeAyoDbWvvkFJyZZCyi93Djb3qav2L9bceXGCetYzHFaMI69DXKAJ+3f6Hx7O4adxrjT7X+17bjjOP0kIRu4CWufGo+VnOafY0oq18tShp9QBhURm7Hnc569Dj3s/nXs7uuxjq+uWIlIO5dtlQKchbXv+Nv9DmOda72xkpbppzC/CksSjmJlKQbrVmf+1a9gZTSumUtvjl9Bvge8WsQ8G2KdeFzvOIwEfi2igBasq6yz7e6bgF/sv3sCuwvN+2Hgvy4F00lXZy7jetlxtHXpdwuwwCWOgp3H7rccK/MuyzrsLm7Z9jiXAGsKbevSkoTHXIbfCvxk/z0BmOYyrDZWxl/UFUEtrESrUwkHV2Shdb6qmHW4G/im0AFwTinrnZS/bKzsdnhJB5NL9zvAU4XG2cTxE8pO4IYi9t/8JGER1tV+eKFxTtjOReyDI11/p1LW7QlOPBnOB2516W6FVUB5uyw3tpR5XgPMwzrWDgEPuQyLtucXaXdP5cQk/gmsZCAa6zjysf+PovQkofCx9SN2Au2yH6Vh3cYfDfzhMkywrgpLTRKAM7CuysOKiKPY3xzrxFb4+FxK2ZOEB7ETNpd+c4Dripn+W+CuouZl95tC6UnCDS7dpZVfRe6vZdgHOwNJLt0LKLncmO4yrA7FlBtFrWMxx2nBOPY2SOfEYyuB4wl6mcbFKquzgVYuw8pzJ2Gry7i17XEbUUq5XsRyiirDbyhq3ELb62GsBLeDS/9/AYsLjfse8H8u2+qTIn6PD126zwc2nsL8Sk0SyvrM5BJjzDwR6Q98hnXVkIyVSdUGVolI/rhi/6BgFUSzi5hfE6zCap/LdLWwsp4TGGOMiEzH+qEWAVdjFW7582ksIskuk3gBi126T5qni3COX43k24V1hZFvj7G3qMvwxmVchxOWLSINgDeAflgZfy2sE+apcK2VnYZ1hYEdU8HyjDFpInKomHmEY2Wh2051OSLSEutqozvWb++NdTXkqvB63wfcaMdogGA7BrD2kZLicNUEuE5E7nDp52vPt8hlFzIW6yplo4jsACYaY2aVYbmnEmNhjTl5//LGKozylRQzxpipwFQR8cEqlKaKyBpjzBxjzG4RWQSMEpFJ9vB+Rcxjt4hsBZ4Fthhj4lz22+IUjqsJ8LqIvOzST7COl8L7nxGREtfLRRRw2BhT1LFQ0m9uKPr4LKsmwBUicpFLPx/gVwARGQb8H9YVeC2s/X3tKcy/KK7bpLTyq0z7q4jUxnosNBTrTgxAkIh4meOVnstabhwrodw4XYfMiXV0XJdf1nHrYx03xZavp6hge9hlJfZy6lJCuV7GMrwscd2NdcJ33Z+aAD0L7Q/eWI+iS5p3cb9tWeZXqlN6BdIYsxAr+3jJ7pWIlfm1M8aE2v9CjFXBCqwValbErOKwsrVwl+mCjTHtiln0NOByEWmClX1/5TKfHS7zCDXGBBljzncNu4RVSsTKTpu49IvGeg6VL0JOLE2jsa5eyrIOhZf9H7tfR2NMMNatZClh/FOxD+txEGC9iod1G7IoiUAGRf82pXkH67lzC3sdHuHEdQCX9RCRflhXbFdiXSmGYt0yy5+muH2kKHHAM4V+79rGmGlFLbswY8wWY8xIrFuIzwNf2pX+StvupxJjYXs5ef/KwXrGXBBaWWZkjMk2xnyB9byzvcugj7Gu5C/DOh5WFzOLT7AemZz0RkRxiyzUHYd129V1+wcYY5Zi7X9R+SPax0yUy7THsE6y+VzfLogD6opIaBExlPSb76Po47Os4rDuJLjOu44x5jkR8cMqZ14CGtr77WyO77dF/WYlrWM+1+lKLL9K2F8Luw/rDlVP+5g82+5fahbIyb9bbYovNwrHny+N0te7vA5iHTeRLv2iihm3PEor10srw6Fsx/MVwCUicnehZS8stD8EGmPGn+K8T2V+pTqd7yS8BgwRkc7GmDysZ9ev2hkWIhIhIufZ434EXC8ig0Sklj2stTFmH1blmZdFJNge1sy+U3ESY8warJ3kQ2COMSY/M1oOHBGRB8V6P91LRNqLyJllWRE7y/4ceEZEguwk5F6O36kA6wC9U0R8ROQKrOexs091HWxBWI9ukkUkAus5s6sDWM8lT8eXwEUi0kdEfLFuUxZZSNi/22TgFRFpbG+33nbBWJogrGfPR0WkNdbzvNLGz8H6/bxFZALWnYR8HwJPiUgLsXQUkfxCqvD2+AAYJyI97XHriMgFIhJUhrgRkVEiUt9e//x9KNeOLY/it/0soJH92qCfva/0LMsysRLce0SkqVivDj8LzDBlfPtBRMbkr6O9jw3Dqni4zGW0r7AKzIlYCUNxZgDnYu3zp+Nd4GERaWfHFmIfE2DVKWgnIiPsWt13cuLJ4k/gbLG+/RGCyxtS9rH0I/C2iITZx1r+ia6k3/x3rH3rThHxFpERWM9my+p/WMfMefYx4C/Wq42RWHcr/LBPTvZ2d30l9gBQz14X13U8X6xXTxthXS2WpMTyq4T9tbAgrIu1ZBGpi3X3o6y+BC4Ukb52ufEkJZ8Xiiqj/gSutuMfivUoqELZZfXXwBMiUtsue0ZXwnJKK9dLK8PLai8wCGvfvdXuNwtoKSLX2seAj4icKSJtTnMZFTK/U04SjDEHsa5E8j+A8SBWZaY/ROQI1rPTVva4y7EqTbyKdfW4kONXVaOxDsQNWLdrvsR6NlmcaVi1OT9ziSUXuAjrGdwOrCvkD7Fqp5bVHVhXANuxarx+hnUCzbcMaGHP+xngcmNM/u24U12HiViVR1KwCtWvCw3/D/CYiCSLyL9PYR0wxqy312U61tVBKtbzvMxiJvk31q3TFVgVX56nbPvDv7Ee+aRiFeAzShl/DtYJYDPWreAMTrxl9grWSWsuVvLxEVblKrCeY39sb48rjTErseqkTMLa3luxni+W1VBgvYgcxXrj4ipjTIYxJg3rt/3NXlYv14mMMalYlZUuwrq1twUYWMZlTsa6vbcIax/NwPqdyuoI1t2a3VgniheA8caYJS7xHeN4ojC1uBkZY9KNMfOMMemnsHzX6b/B2k+m28f6OmCYPSwR6+roOax6Ey2watPnT/sz1r7yN9bjqcK3za/Fuqu3EWu/vduertjf3BiTBYywu5OwnsEWPqZKWp84rLevHsFKBuKwCv1a9m9+J9a+mYS1z3/nMu1GrDJpu73PNMb6nf/Cei49l1KOjTKUX0Xur0XM6jWsYyYR+AP46RS2wXqst5E+wyo3krDqkhTnI6Ctvc7f2v3ustcjGav+zLfFTVxOt2Ntm/1Y23oaxZdv5VFSuV5aGV5mxpjdWInCgyJyo73PnQtchZVE7Mc63spy8VbU/Ctkfvm1OVURRGQMVmWXvk7Hcqrsq9ZkrMcCO5yORymlKpKIPA80MsZc53Qs7qxafpZZnR4Ruci+FVcH61nqWqyrGqWUqtFEpLX9OFJEpAdWxc5vnI7L3WmS4F6GY91W2ot1u/cqo7eKlFLuIQjr9v4xrMdAL2N940ZVIn3coJRSSqki6Z0EpZRSShVJGxlxQHh4uImJiXE6DKWUqlFWrVqVaIyp73QcnkSTBAfExMSwcuVKp8NQSqkaRURO5YuaqgLo4wallFJKFUmTBKWUUkoVSZMEpZRSShVJkwSllFJKFUmTBKWUUkoVSZMEpZRSShVJk4QSiMhkEUkQkXXFDBcReUNEtorI3yLStapjVEoppSqLJgklm4LVXGtxhmG1kdACuBl4pwpiUkopj5OTm+d0CB5JP6ZUAmPMIhGJKWGU4cAndiNKf4hIqIicYYzZVyUBKqWUG8nNM+xLSWfxlkSS07KZ9fde6vh6szvhKPvTspwOzyNpklA+EUCcS3e83e+kJEFEbsa620B0dHSVBKeUUtVZRnYu8/9JYP7GA/y0bj9pWbknjeOdk0fqrlTqBPs6EKHSJKF8pIh+RTaraYx5H3gfoHv37tr0plLKYySnZbFh3xHW7znC3A372ZpwlOT0bAo3Qty6URCXdImgcWgAZ7cI58LzprJ61V4mTOjPvff2xm+yM/F7Mk0SyiceiHLpjgT2OhSLUko5whjD6t3JfLBoO8eycli5MwnvWkJOniE9++S7A+GBvvSOrUfv2HoE+ntzYcfG1A/yA2D58j20bFKX0Nq+vPP2+QQG+tK0aVhVr5KyaZJQPt8Bt4vIdKAnkKL1EZRSnmDxloO8t3A7e1PS2X7w2AnD+jYPJzMnl85Rofh61yIrJ49GIQE0q1+H9hEhhAf6nTS/5OQMHnlkPu++u5L77+/D888PoUOHhlW1OqoYmiSUQESmAQOAcBGJB/4P8AEwxrwLzAbOB7YCacD1zkSqlFKV46+4ZFbvTiI3z5CZk8fB1EymLN15wjiDWjegXeNgrjwzisiw2qc0f2MM06ev45575nDwYBp33dWTxx47uwLXQJWHJgklMMaMLGW4AW6ronCUUqpSpaRns2BTAnPXH+BYVg6LNh8kr4gaVEF+3sQ2COSda7rSODSgXMucOHEhEycu5MwzG/Pjj9fQpcsZ5ZqfqliaJCillIc6fCyLKUt3kpyWxYwVcWTmnPgtgl6xdakf5M8V3SLpHB2Kr1ctfL1qUatWUXW2yy4zM4eUlEwaNKjDmDGdadCgDrfc0g0vL/10T3WjSYJSSnmA/SkZrN2Twj/7jrBgUwLr9h4hyyUpaBTsT3Td2gzr0OiEioQVbf787dx662yaNQtj9uxriIkJ5dZbz6yUZany0yRBKaXcUOLRTBZuOsiyHYf4ds1esgp9sTDY35vz2jXivHYNGdb+DLzKeXegNAcOHOW+++YydepamjUL4667elbq8lTF0CRBKaXcQHpWLhv2HWH22n38ujGB7YknvnHQr0U4l3eLpH1ECFFhtfH1rrpb+0uW7Oaii6Zx7FgWjz9+Ng8/3JeAAJ8qW746fZokKKVUDbPt4FF+WrefjOxcdh9O48d1+094dNAgyI9RvaJp0SCIEV0jCPJ35oScnZ2Lj48XHTo04NxzmzFx4gBatw53JBZ1ejRJUEqpai4rJ49jmTks23GYb9bEM2f9gYJhdXy9iK5bm1YNg+geE0a3JmG0axxS6Y8PSnL0aBYTJy5g3rwdLF9+IyEh/syYcblj8ajTp0mCUkpVU0cysun5zPyTvloYERrAf0Z0oF+LcEScSwaKMnPmRu6440fi4o5w001dycy07iaomkmTBKWUqmZW707i9qmr2ZuSUdBvwoVt8fGuxTmtGxBRzm8TVIakpHTGjJnJd99ton37BkybdhlnnaWN2dV0miQopVQ1sj8lgxFvLwUgum5txvVvxsgeUdXujkFhgYG+HDx4jBdeGMzdd/fSuwduQpMEpZRy0NHMHDbuO8LzP21kR+IxEo9mAdbbCJ+Ord6vCf7+exz/938LmDHjcsLCAliy5IZyf2hJVS+aJCilVBUyxvDH9sO8+csWVu5KOuGthIjQAG4+O5aeTesyqE31bdwoKSmdhx6ax/vvryYyMpgdO5IJCwvQBMENaZKglFKVJC/PsHF/Kn/GJZOUlsXqXUnM35hQMLyWwFnN63FW83CGtT+DpuF1HIy2dMYYpk5dy733zuHw4XTuvbcXEycOJDDQ1+nQVCXRJEEppSrBnuR0znrul5P6hwf60TkqhIeGtaF5g0AHIiufadPWERsbxty519K5cyOnw1GVTJMEpZSqQL9sPMANU1YWdHvXEj4Z24OWDYMICfDBp4Y1YpSRkcNzzy1h9OhOxMaGMXXqCIKD/fTRgofQJEEppU5Dbp5h84FUlm47xNaEVNbsTmbXobSCbxoMbFWfUb2aVOu6BaX5+edt3HrrbLZuPUxwsB/33tub0FB/p8NSVUiTBKWUOgXZuXm8NGcTny3fTWpGTkH/sNo+1Av0pVn9QB4a1po2ZwQ7GGX57N9/lHvvncO0aeto0aIu8+Zdy6BBsU6HpRygSYJSShUjL8+w63AaOxKPEp+UzrIdh/nh730Fw8f0iWFwm4Z0jAoh2KH2ESrDCy/8xldf/cMTT/TnwQf74u+vpwpPJcYYp2PwON27dzcrV64sfUSlVJUyxjB3wwH++9sOVuxMIjfv5PLRu5YQXbc2c+85G+8aVr+gJGvW7MMY6Nr1DJKTM0hIOEbLlvWcDusEIrLKGNPd6Tg8iaaHSimP9tWqeOas38+uQ2lsOpBa0L9fi3D8vL3oGBlCu8bBNArxp1n9QPzd7EuCqamZTJjwK2+8sZyBA2OYN280oaH+WvdAAZokKKU80O5DaXy4ZDtLtiay/eAxAHy8hL7Nw2kXEcz1fZrSKMS9T5LGGL75ZiN33vkje/emcsst3Xj22UFOh6WqGU0SlFIeIeFIBrd/toblOw+f0P/ctg15+Pw21f5DRhXt66//4fLLv6BTp4Z8+eWV9OoV6XRIqhrSJEEp5bb2Jqfz47r9LN5ykAWbDgIQ4OPFJV0ac267Rpzdoj5eHvS+f3Z2Llu2HKZt2/pcfHErPvzwIq67rjPe3u5Tt0JVLE0SlFJuY/3eFJZsSWTZjsP84vL5Y4B6dXwZ1asJ9wxp6VB0zlqyZDfjxs0iMTGNbdvupE4dX8aO7ep0WKqa0yRBKVWj7Tp0jClLd7Jh7xGW7Tj+KCHAx4vY+nUYP6AZnSJDiapb28EonXPoUBoPPjiPjz5aQ1RUMO+/fxF16mhbC6psNElQStU4c9fvZ9GWg8z8c+8JHzRq1ziYiRe3o1uTMEQ85zFCceLiUuja9X2SktK5//4+TJjQXxtjUqdEkwSlVLWXmpHNhr1H+N+y3Xz/196C/jH1ahMRGsDEi9vRo2ldTQxsR45kEhzsR2RkMDfe2IWRIzvQsWPN/Ty0co4mCUqpamlrQiqTf9vJP/uOsGZ38gnDukSHMvm6MwnT2+YnSE/P5plnFjNp0nLWrLmFpk3D+M9/BjsdlqrBNElQSlUbxhhW7Uri7hl/Ep+UXtC/T7N6DGrTkK7RobRuFEyAr3t90KgizJmzlVtvnc327Ulce21HfaygKoQmCUopxxlj+HjpTp74fkNBv/BAP94d1ZXuMXUdjKz6y8szjBr1NdOmraNVq3r88stoBg5s6nRYyk1okqCUckzCkQwWb0nk6R82kJSWDcCAVvV5eFgbWjUKcji66s0Yg4hQq5ZwxhmBPPnkAB544Cz8/LRYVxVH9yalVJUxxrD7cBqTl+zgi1XxpGXlFgw7v0Mj/nNpR0Jqu09ripVl1aq9jB//A6+9NpQ+faJ4+eXznA5JuSlNEpRSlSY3z7BkayK/bkzg922HTmhACaBTVChj+zalb/Nw6molxFIdOZLJ44//wqRJK2jQoA4pKRlOh6TcnCYJpRCRocDrgBfwoTHmuULDo4GPgVB7nIeMMbOrPFClHJabZ1i9O4kDRzLYfOAo05bv5mBq5gnjdI0OpX1ECOe0bkD/lvX1lcVT8O23G7ntttns25fKrbeeydNPn6MtNapKp0lCCUTEC3gLGALEAytE5DtjzAaX0R4DPjfGvCMibYHZQEyVB6uUQ4wxPP/TJt5duO2E/gE+Xgxp25Cu0WGc264hMfXqeFQ7CRVty5ZDNGxYh2+//RdnnhnhdDjKQ2iSULIewFZjzHYAEZkODAdckwQDBNt/hwB7UcqN5eTmsSXhKPtTMnjr162s3JVUMOzf57bkrObhhAf6ERkWoHcKyiErK5eXX15Kixb1uPzyttxzT2/uuae3NsakqpQmCSWLAOJcuuOBnoXGeQKYKyJ3AHWAIr9cIiI3AzcDREdHV3igSlW2+KQ0vlwVz2vztpzQP8jPmzFnxXDT2bEE+2ulw4qwaNEuxo2bxT//JDJ+fHcuv7ytJgfKEZoklKyoyyBTqHskMMUY87KI9AY+FZH2xpi8EyYy5n3gfYDu3bsXnodS1U5unmHK0p2kpGXx9oJt5ORZu21IgA8XdjyDga0a0KJhINF1a+sdgwqSmJjGAw/8zH//+ycxMaHMmjWSCy7wzFYrVfWgSULJ4oEol+5ITn6cMBYYCmCM+V1E/IFwIAGlapjs3DyWbjvEY9+uJe7w8S8e1vb1omloAI9d2Jb+Les7GKF7W7hwJ59++jcPPXQWjz/en9r6OqhymCYJJVsBtBCRpsAe4Crg6kLj7AYGAVNEpA3gDxys0iiVOg3GGLYkHOXT33fx47p9gJB49PjbCIF+3jw4tBWXdIkgSB8jVJr16xP4++8DjBzZgREj2rB58+00bRrmdFhKAZoklMgYkyMitwNzsF5vnGyMWS8iTwIrjTHfAfcBH4jIPViPIsYYY/RxgqqWVu9O4o/th1i69RBLtiaeMKxzVCj9WoTTrH4dLurUmCb16jgUpWdIS8vmqacW8tJLv9OoUSAjRrTBz89bEwRVrWiSUAr7mwezC/Wb4PL3BuCsqo5LqbJIy8phwaaDLNx0kBkrj9fB9fESIkIDOK9dIwa2rs9ZzcKppa8nVpnZs7dw222z2bkzmTFjOvPii0P0c8qqWtK9Uik3Mm/DAaaviGP17iSMMQXtIeS7rGsko3s3oWNkiFY2dMjmzYe48MLPaN06nAULrqN//xinQ1KqWJokKFXDGWP4ad1+xk9dXdCvcYg/DYL9ubK71YLiNT2bUD/IT5tYdkhOTh4LF+5k0KBYWrasx+zZ13DOOU3x1d9DVXOaJChVgyUezaT70/MKuuvV8eWLcb2JrR/oYFTK1YoVexg37gdWr97H2rXjad++AUOHNnc6LKXKRJMEpWqY3DzDC3M28sXKeA4fywKgW5Mw3hjZhYjQAIejU/lSUjJ49NFfeN0/4YEAACAASURBVPvtFTRqFMjnn19Ou3b6+qiqWTRJUKoGyMszzN+YwBPfrWdP8vHvF5zVvB4DWjZgbN+mWvGwGsnOzqVbt/fZsSOZ22/vwdNPn0NwsJ/TYSl1yjRJUKqaysjO5f1F25n3zwH+jk8p6B/g48Vzl3VgWPsz8NVP9VYr8fFHiIgIwsfHi4kTB9CqVTjduzd2OiylTpsmCUpVI8YY/th+mMdnrmNrwtGC/i0aBHJW83DGD2hGw2BtHri6yczM4cUXl/LMM4uZMmU4//pXe665pqPTYSlVbh6TJIiILxBtjNnqdCxKFZZ0LIuPf9/JpF+2FrSR4OMlPHJ+G67t1QRvL71jUF0tWLCT8eN/YOPGRK64oi39+jVxOiSlKoxHJAkicgHwCuALNBWRzsD/GWMudTYy5emWbT/Ef3/byU/r9xf0G9ymAeP6N6N7TF0HI1Nl8cADP/Pii0tp2jSU2bOvZtiwFk6HpFSF8ogkAXgSq4nnXwGMMX+KiL6DpKrc8h2HWbUriUm/bCEzJ6/grkHzBoEMbFWfB4a2xkfvGlRreXmGvDyDt3ctevaM4JFH+vLoo2drY0zKLXlKkpBtjEku9IU5bV9BVYm/45P5Ye0+Ply8g9y847uddy3hnsEt6dcynK7R+r3+mmDt2gOMG/cDF1/ckgcf7Mtll7XlssvaOh2WUpXGU5KEf0TkSqCW3aLjXcAfDsek3FRenmHO+v18vjKO+KR0trhUQGwfEcyLl3cium5t6ui3+muMY8eyePLJhbzyyh+EhPgRGdnd6ZCUqhKeUkrdDkwA8oCvsVp1fNjRiJTbOJqZw9z1+9mTlM7Xa/awI/FYwbAgP28u7HgG158VQ+eoMLz0WwY1zoIFOxkz5lt27Uph7NguPP/8YOrVq+10WEpVCU9JEs4zxjwIPJjfQ0RGYCUMSp2SnNw8Plu+my9XxXPgSAYHjmSeMLxZ/ToMatOQUT2bEK0nkxovIMCb4GA/Fi++nr59o50OR6kqJca4/6N5EVltjOlaqN8qY0w3J+Lp3r27WblypROLVqfJGMO6PUeYsXI3367Zy9HMHAAiQgMY1KYBZ8bUpUfTutSt46sVD2u4nJw83nxzGXv2pPLSS+cC1iMk/aKl8+xyW5/1VCG3vpMgIucBQ4EIEXnFZVAw1qMHpYqVl2f4YlUcn/y+i/V7j5ww7KZ+TRk/oDl16/g6FJ2qDMuWxXPLLbP4668DXHRRS3Jy8vD2rqUJgvJYbp0kAAnAOiADWO/SPxV4yJGIVLVmjGHmn3t5f9F2Nuw7nhicEeJPvxbhXNsrhnaNg/Wk4WaSkzN4+OF5vPfeKho3DuKrr67k0ktbU+iNKKU8jlsnCcaYNcAaEZlqjMlwOh5VfR3NzGHOuv3c98VfBf06RYXSp1k9ru4RTVRdrVvgzlJSMpg6dS133dWTJ58cSFCQNsakFLh5kuAiQkSeAdoCBR++N8a0dC4k5bSM7Fw+XLydqct2sy/leA7ZK7YuEy9uT6tGQQ5Gpyrb5s2H+PTTv3jyyYE0aRLKzp13U7euNrWtlCtPSRKmAE8DLwHDgOvROgkeKTfP8P1fe/ls2W6W7zxc0D/Y35sb+8VyaZcIvWvg5jIycnj++SU8++wS/P29uf76LsTGhmmCoFQRPCVJqG2MmSMiLxljtgGPichip4NSVSc3z/DB4u089+PGgn6+3rV4eFhrrunZRJtc9hDz52/n1ltns3nzIUaObM8rr5xHo0aBToelVLXlKUlCplg1kLaJyDhgD9DA4ZhUFTmYmsmZz8wr6I4MC2D2Xf0I9tdv7XuSjIwcrr32G+rU8WXu3FEMGdLM6ZCUqvY8JUm4BwgE7gSeAUKAGxyNSFWq1IxsZqyIY+afe1m7JwWw3lBY+tA5WmPdg+TlGaZNW8sVV7TD39+bOXNG0aJFPfz9PaXoU6p8POJIMcYss/9MBa4FEJFI5yJSleXr1fG8MX8LOw+lFfSLqVebEV0juXOQNuPrSf76az/jxv3AH3/Ek5dnuPbaTnTo0NDpsJSqUdw+SRCRM4EIYIkxJlFE2mF9nvkcQBMFN5CXZ1i45SDvL9zO79sPARAe6MvwzhHcO6SlNqTkYY4ezeKJJxbw2mt/ULduAJ98cgmjRnV0OiylaiS3Lj1F5D/AZcBfWJUVv8FqAfJ5YJyTsanyM8aqjPjs7OOVEQe3acAbI7tQ29etd21VgpEjv2LWrM3cdFNXnntusL61oFQ5uHXbDSKyAehmjEkXkbrAXqCTMWaTk3Fp2w3lt2l/Kue9tqige1SvaG45u5m+vuihdu9OISjIl7CwAFav3kdGRg59+kQ5HZaqYNp2Q9Vz98utDGNMOoAx5rCIbHQ6QVDlk5dneOL79Xzy+y4AWjUM4svxvQnSNxU8UnZ2Lq+/voz/+78F3HBDZ95883y6dj3D6bCUchvuniTEikh+c9ACxLh0Y4wZ4UxY6nRMXrKDJ2dtKOj+YHR3hrTVimieaunSOMaNm8XatQlcdFFL/v3vPk6HpJTbcfck4bJC3ZMciUKV2yPfrOWzZbsB6N4kjE/H9iTA18vhqJRT3nlnBbfeOpuoqGC+/fZfDB/e2umQlHJLbp0kGGPmOx2DKp/1e1MYO2Ul+49YbSssfegcGodqRTRPZIwhNTWL4GA/hg1rwf3392HChP4EBmpz3UpVFrdOElTN9snvO5kw02rhu2fTurx9TVfqBWrrfJ5o06ZExo//AT8/b2bPvpqYmFBeeGGI02Ep5fb0g/WlEJGhIrJJRLaKyEPFjHOliGwQkfUi8llVx+iOJn6/viBBeGNkF2bc0lsTBA+Unp7NhAm/0rHju6xZs59LLmnldEhKeRSPupMgIn7GmMxTGN8LeAsYAsQDK0TkO2PMBpdxWgAPA2cZY5JERNuEKIfs3DxumLKCxVsSAVj8wEB9rdFDrVuXwCWXTGfbtiSuuaYDL798Lg0bamNMSlUlj0gSRKQH8BFWmw3RItIJuNEYc0cpk/YAthpjttvzmQ4MBza4jHMT8JYxJgnAGJNQ0fF7grjDacz6ex8fLt7OoWNZAMy5+2xNEDyQMQYRISoqmMjIYN5770IGDYp1OiylPJJHJAnAG8CFwLcAxpi/RGRgGaaLAOJcuuOBnoXGaQkgIr8BXsATxpifyh2xB1izO4kZK+KYviLuhP5PXNSWMWc1dSgq5ZTc3Dzee28V06at45dfRhMS4s+CBWOcDkspj+YpSUItY8yuQq3/5ZZhuqKaCyz8iUpvoAUwAKstiMUi0t4Yk3zCjERuBm4GiI6OLmPY7mnVriSe/H49f8VbrTM2qVebTpGhXNypMT1j6+qHkTzQmjX7GDfuB5Yv38OgQU1JTs6gfv06ToellMfzlCQhzn7kYOx6BncAm8swXTzg+m3XSKxPOxce5w9jTDawQ0Q2YSUNK1xHMsa8D7wP1meZT2st3MCvmxK4/r/WpjmndQMeGNqK1o2CHY5KOSU9PZtHHpnPG28sJzy8NlOnjmDkyPbanLdS1YSnJAnjsR45RAMHgHl2v9KsAFqISFNgD3AVcHWhcb4FRgJTRCQc6/HD9gqK26289etWXpxjfRV71h19aR8R4nBEymk+Pl4sXLiLm2/uyrPPDiIsTL+BoVR14ilJQo4x5qpTncgYkyMitwNzsOobTDbGrBeRJ4GVxpjv7GHn2o1J5QL3G2MOVWTw7mDW33sLEoQZN/fSBMGD7dyZzIQJv/L660MJCwtg6dKx+Pt7SlGkVM3i1q1A5hORbcAmYAbwtTEm1cl4PKkVyO/+2sv9X/xFZk4eAB+O7s5gbW/BI2Vn5/LKK78zceJCatUSZs68St9aUKdEW4Gseh6RvhtjmolIH6zHBRNF5E9gujFmusOhubUdice4c9oaAMb0ieGW/rGcEaK3kz3RkiW7GTduFuvXH+TSS1vz+utDiYrSu0lKVXcekSQAGGOWAktF5AngNWAqoElCJZr4vfXFxKcuac+1vZo4HI1y0vPP/0ZqahbffXcVF12kX01UqqbwiCRBRAKxPoJ0FdAGmAlou7KVZNHmg9z4yUqycvIY3KahJggeyBjDxx//xdlnNyE2NowPP7yIwEBf6tTRxpiUqkk8pe2GdUAv4AVjTHNjzH3GmGVOB+WO3vp1K6MnLycrJ49+LcJ5+YpOToekqtg//xxkwICPuf76mbz7rlX3pmHDQE0QlKqBPOJOAhBrjMlzOgh3lpWTR8vHfizofu1fnbmkS4SDEamqlpaWzTPPLOLFF5cSGOjLBx9cxA03dHE6LKVUObh1kiAiLxtj7gO+EpGTXuMwxoxwICy3sm5PCn/FJ/PoN+sK+i28fwBN6unX8jzNc88t4dlnlzB6dCdefHEIDRroPqBUTefWSQLWK48AkxyNwg0dzczh5k9WsnTb8U9C3HJ2LA+f38bBqFRV27s3lcOH02nfvgH33debc85pyoABMU6HpZSqIG6dJBhjltt/tjHGnJAo2B9Jml/1UdV8G/cfYehriwu6J4/pTrfouoTU1jYXPEVubh5vv72CRx/9hdatw1m27EZCQvw1QVDKzXhKxcUbiug3tsqjcAN/bD9UkCD0bR7Ojv+czzmtG2qC4EFWrdpLz54fcuedP9G7dxSffXaZtrWglJty6zsJIvIvrNcem4rI1y6DgoDkoqdSxVm+4zBXvf8HABMubMsNfbU5Z0/zyy87GDLkUxo0qMP06Zdx5ZXtNEFQyo25dZIALAcOYbXe+JZL/1RgjSMR1VBr41O46v3fAXjx8o5c0T2qlCmUuzDGsGdPKpGRwfTrF83EiQO4/fYehIb6Ox2aUqqSeUTbDdVNTWu74bkfN/Luwm2AVf/gnNba9oKn2L49idtvn83q1fvYuPF2TQyUo7Tthqrn1ncSRGShMaa/iCQBrtmQAMYYU9eh0GqMA0cyChKEd0d10wTBQ2Rl5fLSS0t56qlFeHvX4umnBxIYqB9DUsrTuHWSAAy0/w93NIoaKjfPMOjlhQC8dEUnhrZv5HBEqiocOpRGv37/5Z9/Ernssja8/vpQIiKCnQ5LKeUAt367weUri1GAlzEmF+gN3ALol15K8cb8LRzNzOHK7pFc3i3S6XBUJcvOzgWgbt0A+vdvwqxZI/nyyys1QVDKg7l1kuDiW8CISDPgE6xGnj5zNqTqbenWRCb/tgOAJ4e3dzgaVZny8gyTJ68hNvYNtm9PQkR4550LueCClk6HppRymKckCXnGmGxgBPCaMeYOQBsWKMHVHy4jNSOHd0d1w9/Hy+lwVCVZvz6B/v2nMHbsd8TEhJKbq02cKKWOc/c6CflyROQK4FrgEruffv2nGLd/thqAc9s21HoIbsoYw2OP/cILLywlJMSPyZMv5rrrOlOrln7zQCl1nKckCTcAt2I1Fb1dRJoC0xyOqVp6Y/4WZv29z/p7pLbg565EhJSUTEaN6siLLw4hPLy20yEppaohj/lOgoh4A83tzq3GmBynYqmu30kwxtD04dkArHl8CGF19JU3dxIff4R77pnD3Xf35KyzosnLM3rnQNUo+p2EqucRdRJEpB+wFfgImAxsFpGznI2q+vliZTwAF3Y8QxMEN5KTk8drr/1BmzZvMWvWZjZvtlru1ARBKVUaT3nc8CpwvjFmA4CItAE+BTw+Iz1wJIPFWxLZm5zOKz9vBuDRC7S5Z3exYsUebrllFmvW7GfYsOZMmnQ+sbFhToellKohPCVJ8M1PEACMMf+IiEdfKn+8dCdP/7CB7NwTHzeN7BHFGSEBDkWlKtqCBTs5cOAYX3xxBZdd1kYbY1JKnRKPqJMgIlOATKy7BwDXALWNMdc5EY+TdRIOHc1k/P9Ws3znYQD6tQhndO8YOkaGEFbbF19vj3gC5baMMcyYsZ6AAG+GD29NdnYu6ek5BAf7OR2aUuWmdRKqnqfcSRgH3Ak8gNVuwyLgTUcjqmJpWTnc9/lf/LhuPwAtGgTy/R199RsIbmTr1sPcdtts5s7dxgUXtGD48Nb4+Hjho7+xUuo0uX2SICIdgGbAN8aYF5yOxwlbDqQy5NVFANSt48ur/+rM2S3C9dazm8jMzOGFF37jmWcW4+vrxZtvDmP8eL3YUkqVn1snCSLyCDAWWA2cKSJPGmMmOxxWlRv3v1UAXN0zmqeHt9da7W7m55+3M2HCAq68sh2vvnoejRsHOR2SUspNuHWSgFX3oKMx5piI1AdmY70C6REOHc1k9OTlbDt4jIs7NebZSzs4HZKqIAkJx1ixYg8XXNCSCy5owbJlN9Kjh35pXClVsdw9Scg0xhwDMMYcFBGPqZV36Ggm3Z6eB0DT8Do8cXE7hyNSFSEvz/DRR6t58MF55OUZ4uLuISjITxMEpVSlcPckIVZEvrb/FqCZSzfGmBHOhFW50rNyCxKEh4e15uazY7X+gRtYu/YA48b9wNKlcfTv34R33rmAoCB9a0EpVXncPUm4rFD3JEeiqGJTl+0CoGfTutzSv5nD0aiKsHdvKt27f0BQkC9Tpgxn9OhOmvgppSqdWycJxpj5TsdQlf6MS2bi9+tZszsZgHdHdXM4IlVef/99gI4dG9K4cRD//e9wzjuvGfXqaWNMSqmq4THP6N1dRnYul7z1G2t2J9Mrti5LHhyo7S/UYHFxKVx66Qw6dXqXFSv2AHD11R00QVBKVSlNEkohIkNFZJOIbBWRh0oY73IRMSLiyAvqd0xbA8A9g1sy/ebeRIbpyaQmysnJ45VXfqdNm7eYM2crzz8/mM6dGzkdllLKQ7n144bCRMTPGJN5CuN7AW8BQ4B4YIWIfOfaDoQ9XhDWFx2XVWS8ZfXIN2v5ecMBAO4c1LyUsVV1ZYxhwIAp/PZbHBdc0IJJk84nJibU6bCUUh7MI+4kiEgPEVkLbLG7O4lIWT7L3APYaozZbozJAqYDw4sY7yngBSCjomIuq+/+2stny3YD8NPd/bQyWw105EgmxhhEhDFjOvPVV1fy/fcjNUFQSjnOI5IE4A3gQuAQgDHmL2BgGaaLAOJcuuPtfgVEpAsQZYyZVdKMRORmEVkpIisPHjx4KrEXKzs3jwe//BuA5Y8OonWj4AqZr6oaxhg++2wtLVq8yeefrwfgxhu7MmKEttaolKoePCVJqGWM2VWoX24ZpiuqpC5oNtP+ONOrwH2lzcgY874xprsxpnv9+vXLsOjSvTl/C+nZuYzr34wGQf4VMk9VNTZvPsSQIZ9yzTVfExMTSqtW4U6HpJRSJ/GUOglxItIDMHY9gzuAzWWYLh6IcumOBPa6dAcB7YEF9pVfI+A7EbnYGFOpbUFvOZDKG79sBeC+c1tW5qJUBXvzzWX8+98/ExDgzdtvn8/NN3fDy8tT8nWlVE3iKUnCeKxHDtHAAWCe3a80K4AWItIU2ANcBVydP9AYkwIUXAKKyALg35WdIAC8Nm8LAF+N742PnmBqhPx6B40aBTJiRBteffU8GjUKdDospZQqlkckCcaYBKwT/KlOlyMitwNzAC9gsjFmvYg8Caw0xnxXwaGWyf/+2MUPa/cxpG1DujWp60QI6hQcOHCUe++dS4cODXjoob5ccUU7rrhC29JQSlV/HpEkiMgHuNQlyGeMubm0aY0xs7Faj3TtN6GYcQecZoin5N2F2wB46fJOVbE4dZry8gzvv7+Khx6aR1paNu3aVUxdFKWUqioekSRgPV7I5w9cyolvLdQYxhjik9Jp1TCIkNo+ToejirF+fQJjx37HsmV7GDgwhrffvoDWrbVyolKqZvGIJMEYM8O1W0Q+BX52KJxy+Wq19Ynec9s1dDgSVZKjR7PYtSuFTz+9lGuu6aCvNCqlaiSPSBKK0BRo4nQQp2PZ9kMAjOkT42wg6iTffruRNWv2MXHiQHr2jGTHjrvw9/fUQ0wp5Q48olq8iCSJyGH7XzLWXYRHnI7rdPy0fj/1g/yoF+jndCjKtmtXMhdfPI1LL53BzJmbyMjIAdAEQSlV47l9KSbWfd5OWK8wAuQZY06qxFgTJB7NJDUjh77N9dl2dZCdncurr/7BxIkLAXjxxSHcdVdPfHy8HI5MKaUqhtsnCcYYIyLfGGO6OR1Led3y6SoAhnU4w+FIFMC+fUeZOHEhgwfH8uabw4iODnE6JKWUqlAe8bgBWC4iXZ0OorxW7UoiPNCPizs1djoUj3X4cDqvv/4Hxhiio0NYu3Y8M2depQmCUsotufWdBBHxNsbkAH2Bm0RkG3AMq00GY4ypMYnDrxsTAOjRNMzhSDyTMYb//e9v7rtvLocPpzNgQAydOjUiNlZ/D6WU+3LrJAFYDnQFLnE6kPJ6Z4H1AaVbBzR3OBLPs3FjIuPH/8CCBTvp1SuSn3++gE6dGjkdllJKVTp3TxIEwBizzelAyuPQ0UyW7zxMs/p1aB+ht7WrUk5OHsOGTSU5OYN3372Am27qRq1a+s0DpZRncPckob6I3FvcQGPMK1UZzOn6YPEOAK7uWSM/7VAjLViwkz59ovD19eKzz0YQGxtGw4baGJNSyrO4e8VFLyAQq0nnov5Vexv2HuHdhduIrlubsX2bOh2O29u3L5WRI79i4MCP+eAD622S3r2jNEFQSnkkd7+TsM8Y86TTQZTHf378B4CHhrV2OBL3lpubx3vvreLhh+eTmZnDxIkDGDu2xtRrVUqpSuHuSUKNfnicmZPL4i2JnBkTxvn6bYRKdfPN3zN58p8MHhzL22+fT4sW9ZwOSSmlHOfuScIgpwMoj5/W7Qfg4s4RDkfinlJTMzEGgoP9GDeuO4MGxTJyZHttjEkppWxuXSfBGHPY6RjK481ftgIwuE0DhyNxL8YYvvpqA23avMUDD1iNgZ55ZgRXX62tNSqllCu3ThJqut2H0+gUGcIZIQFOh+I2duxI4sILp3H55V9Qv34drr++s9MhKaVUteXujxtqrH0p6WTl5DG4TUOnQ3EbX3/9D6NGfU2tWsIrr5zLHXf0xNtb82SllCqOJgnV1Nz1BwBo1kBfvSuv7OxcfHy86NbtDIYPb80LLwwmKko/SqWUUqXRy6hq6vOVcQCc01rrI5yuxMQ0xo6dyfDh0zHG0KRJKNOmXaYJglJKlZEmCdXU+r1H6BQZgr+Pl9Oh1DjGGKZM+ZPWrSfxySd/06FDA3JzjdNhKaVUjaOPG6qhdXtSAIitr48aTlVcXAqjRn3DokW76NMninffvYAOHbReh1JKnQ5NEqqhf/YdAWBMnxhnA6mBgoP9OHQojQ8+uIgbbuiijTEppVQ56OOGamjxlkREoGXDGtG8hON++mkrw4dPJzs7l5AQf/7+ezw33thVEwSllConTRKqoT3J6fh7exHgq/URSrJ3bypXXvkFw4ZNZdOmRPbsSQXQ5EAppSqIPm6oZowxrNqVRO9YbTugOLm5ebz99goeffQXsrJyeeqpgdx/fx/8/HR3VkqpiqSlajUTdzgdgIgw/cpicYyBjz5aQ+/eUbz11vk0b17X6ZCUUsot6eOGamb2un0AXNpFG3VydeRIJg8/PI+kpHS8vWsxf/5ofvrpGk0QlFKqEmmSUM0s3HQQr1rCWc3DnQ6lWjDG8MUX62ndehLPP/8bc+duA6BevdraGJNSSlUyfdxQjRhj+H37IYL89WcB2L49idtum81PP22lS5dGzJx5FWeeqXdYlFKqqujZqBo5fCwL0Fcf891//88sWbKb1147j9tu66GNMSmlVBXTJKEaWbTlIAA39Yt1OBLnLFy4k6ioEGJjw3j99aGIQEREsNNhKaWUR9JLs1KIyFAR2SQiW0XkoSKG3ysiG0TkbxGZLyJNTndZq3clA9Cnuee9/piYmMaYMd8yYMDHPP30IgAiI4M1QVBKKQdpklACEfEC3gKGAW2BkSLSttBoa4DuxpiOwJfAC6e7vN+3HwIg2N/ndGdR4+TlGSZPXkOrVpOYOnUtDz/cl0mTznc6LKWUUmiSUJoewFZjzHZjTBYwHRjuOoIx5ldjTJrd+QcQeboLy8zJpZWH1Ud47bU/GDv2O9q1q8+ff97Cs88OonZtz0mSlFKqOtM6CSWLAOJcuuOBniWMPxb4sagBInIzcDNAdHT0ScMPH8si7nC6RzTqlJaWzb59qTRrVpexY7tQv35tRo3qqK80KqVUNaN3EkpW1FnLFDmiyCigO/BiUcONMe8bY7obY7rXr1//pOHz/jkAQNcmYacdbE3www+badfubS69dAZ5eYaQEH+uvbaTJghKKVUNaZJQsnggyqU7EthbeCQRGQw8ClxsjMk8nQVt2Gs1Dz2w1ckJhDuIjz/CZZd9zoUXTiMgwJtJk87XhpiUUqqa08cNJVsBtBCRpsAe4CrgatcRRKQL8B4w1BiTcLoL2ppwFIAgN6y0uHr1Pvr3n0JOTh7PPnsO993XB19t4VIppao9TRJKYIzJEZHbgTmAFzDZGLNeRJ4EVhpjvsN6vBAIfGHfMt9tjLn4VJe1+UAqwW72pcUjRzIJDvajY8eGXH99Z+6+uxexse79OEUppdyJe52VKoExZjYwu1C/CS5/Dy7vMrYmHCUhNZPBbRqWd1bVQnJyBo8+Op9vvtnIhg23ERrqzxtvDHM6LKWUUqdIk4RqYPtB61HDJV0aOxxJ+RhjmDFjPffcM4eEhGPccUcPvLy03oFSStVUmiRUA5v2pwLQvEGgw5GcvmPHshgx4nPmzt1G9+6NmTVrJN261eykRymlPJ0mCdXAviMZADSrX/OSBGMMIkLt2j6Eh9fmzTeHMX58d7y89MUZpZSq6bQkrwbmbThAh4gQfGrYifXXX3fQrdv7bN+ehIgwdeoIbr+9hyYISinlJrQ0rwbSsnIJqkFvNiQkHGP06G8455xPSEnJ5ODBY06HpJRSqhLUnDOTm9qfksHRzBx6xdaMlh8/+mg199//M0ePZvHYY/145JF+BAS437cdlFJKaZLguI37rS8ttj2jZjSJvGbNfjp2bMg7wQFEYAAAFNRJREFU71xAmzbu+XVIpZRSFk0SHBaflA5Ag2A/hyMp2rFjWUycuJBLLmlNnz5RvPzyufj6emlbC0op5QE0SXDYnPX78aoltKmGdxK+/34Tt9/+I7t3pxAa6k+fPlH4+ekuo5RSnkJLfId51RLCavtWqzcb4uJSuPPOn/j22420b9+AJUuu56yzTm7eWimllHvTJMFh6/YcoVn9Ok6HcYIvvtjAnDlbef75wdxzTy98fLQxJqWU8kSaJFQDRzJynA6BP/6IJzk5g6FDm3PHHT247LI2NGkS6nRYSimlHFR97nF7qKycXHrEONcyYlJSOuPGzaJPn494/PFfMcbg4+OlCYJSSim9k+CkjfuPcCQjhwDfqv8ZjDF89tla7r13LomJadx9dy8mThygby2ok2RnZxMfH09GRobToSgP4e/vT2RkJD4++g0Wp2mS4KCvVsUDMKBV1X9vYMGCnYwa9Q09ekTw00/X0KXLGVUeg6oZ4uPjCQoKIiYmRpNIVemMMRw6dIj4+HiaNm3qdDgeTx83OGhHovU546r62mJGRg6LF+8CYMCAGGbOvIqlS2/QBEGVKCMjg3r16mmCoKqEiFCvXj29c1VNaJLgoHn/JBBSRZ80njdvOx07vsN55/2PhIRjiAgXX9xKG2NSZaIJgqpKur9VH3qGcEhalvVGQ2V/jnn//qNcc83XDBnyKcbAzJlX0aBB9XrlUimlVPWkSYJDUtKzARjStmGlLSMpKZ127d7myy83MGHC2axdO54hQ5pV2vKUqixeXl507tyZ9v/f3r1HR1XdCxz//nhIAglBoEQqVkITTUICwYAlYK8RC+IDuFBKDFSBpaWgaEXE4oIlVVjFF8UitJT2eqHXQtAgjxsrFjC+KA9DiQkEpClGoOYCQoSAQRB+949zMpnAJJmQZAbC77PWWWvmnD3n/GZnJvObvffsnZDAoEGD+OqrrzzHdu7cSb9+/bjhhhuIiYlh5syZqKrn+Ntvv03Pnj2Ji4sjNjaWJ554IhhPoVrbt2/nwQcfrLRvyJAhpKSkVNo3ZswYMjMzK+0LCwvz3N6zZw933XUX0dHRxMXFMWLECA4ePFin2I4ePUr//v2JiYmhf//+lJSUXFAmOzubpKQkzxYSEsKqVas8MUdFRXmO5ebmApCVlcWMGTPqFJsJAFW1LcBbcnKybvv8qF7/yyzNzNmv9e3AgWOe2wsWbNXduw/X+zXMlaOgoCDYIWirVq08t++//36dNWuWqqp+/fXX2qVLF33nnXdUVfXkyZM6cOBAnT9/vqqq5ufna5cuXXTXrl2qqnrmzBldsGBBvcZ25syZOp9j+PDhmpub67lfUlKinTp10tjYWN27d69n/+jRo/WNN96o9NjyuikrK9Po6Ghds2aN59i7776r+fn5dYptypQpOnv2bFVVnT17tj755JPVlj9y5IheffXVevLkySpjVlU9d+6cJiUlecqdz9frDsjRS+B/+JW02a8bgqTw0AkAOrYJqbdznjhxmqefzuaVV7by/vtj6NPnOh56qFe9nd+YZ/53JwVfHK/Xc8Z/tzUzBnX1u3xKSgp5eXkALF26lL59+zJgwAAAWrZsyfz580lNTeXhhx/mhRdeYNq0acTGxgLQrFkzHnrooQvOeeLECR555BFycnIQEWbMmMGPf/xjwsLCOHHCea9mZmaSlZXF4sWLGTNmDG3btmX79u0kJSWxcuVKcnNzadPGmV8kOjqajRs30qRJE8aPH8++ffsAePnll+nbt2+la5eWlpKXl0f37t09+1asWMGgQYOIjIwkIyODp556qsZ6Wbp0KSkpKQwaNMiz77bbbvO7XquyevVq3nvvPQBGjx5Namoqzz//fJXlMzMzufPOO2nZsmW15xURUlNTycrKYsSIEXWO0zQM624Ikt3FpQDEXlP3MQmqyqpVu4mLW8DcuZt54IEexMW1r/N5jbnUnD17lg0bNjB48GDA6WpITk6uVOb73/8+J06c4Pjx4+zYseOC477MnDmTiIgI8vPzycvLo1+/fjU+Zs+ePaxfv565c+cyZMgQVq5cCcCWLVvo3LkzkZGR/OIXv2DSpEl8/PHHrFix4oIuBYCcnBwSEhIq7Vu2bBnp6emkp6ezbNmyGmMB/H6upaWllboGvLeCgoILyh88eJCOHZ1fQHXs2JFDhw5Ve/6MjAzS09Mr7Zs2bRrdunVj0qRJfPPNN579PXv25MMPP/Tn6ZkgsZaEIPnsS+fbSdtWV9X5XKNGvcmyZTtITOzA668PJyXlujqf0xhfavONvz6VlZWRlJREUVERycnJ9O/fH3AS5KpGwtdmhPz69evJyMjw3L/66ppnQf3JT35C06bOuiZpaWk8++yzjB07loyMDNLS0jzn9f7gPX78OKWlpYSHh3v2FRcX853vVMyVcvDgQQoLC7nlllsQEZo1a8aOHTtISEjw+Zxq+0uA8PBwz7iA+lZcXEx+fj533HGHZ9/s2bO55pprOH36NOPGjeP555/n6aefBqBDhw588cUXDRKLqR/WkhAkm/ceJfaa8JoLVuHbb8/hdNFBnz7X8dJL/dm2bZwlCKZRCg0NJTc3l88//5zTp0+zYMECALp27UpOTk6lsnv37iUsLIzw8HC6du3Ktm3bajx/VcmG977zf7ffqlXFr4RSUlIoLCzk8OHDrFq1imHDhgFw7tw5Nm3aRG5uLrm5ufz73/+ulCCUPzfvcy9fvpySkhKioqLo3LkzRUVFngSmXbt2lQYOHj16lPbt23vqwp/nWtuWhMjISIqLiwEnCejQoUOV53799dcZOnRopZkSO3bsiIjQokULxo4dy9atWz3HTp06RWhoaI0xm+CxJCEIVKHszFm6dYq4qMdv3LiPHj3+wPLlOwGYOPFmJk/uY6s1mkYvIiKCefPm8dJLL3HmzBlGjRrFRx99xPr16wGnxeHRRx/lySefBGDKlCn8+te/Zs+ePYDzof2b3/zmgvMOGDCA+fPne+6XfxBHRkaya9cuzp075+lO8EVEGDp0KI8//jhxcXG0a9fO53l9fYOPi4ujsLDQc3/ZsmWsXbuWoqIiioqK2LZtmydJSE1NZfny5Zw+fRqAxYsXe8YdjBw5kr///e+89dZbnnOtXbuW/Pz8Stcrb0nwtcXHx18Q3+DBg1myZAkAS5YsYciQIVXWQ3k3ibfyBMPpFl1VqWtlz549F3S1mEuLJQlBUPK18wbveX3bWj3u6NEyfvazNdxyy39z7Ngp2tTjoEdjLhc9evSge/fuZGRkEBoayurVq5k1axY33ngjiYmJ9OrVi4kTJwLQrVs3Xn75ZdLT04mLiyMhIcHzoeVt+vTplJSUkJCQQPfu3cnOzgbgueee45577qFfv36efvmqpKWl8dprr3m6GgDmzZtHTk4O3bp1Iz4+noULF17wuNjYWI4dO0ZpaSlFRUXs27eP3r17e45HRUXRunVrtmzZwj333MMPf/hDkpOTSUpKYuPGjZ5BhKGhoWRlZfHKK68QExNDfHw8ixcvrvabvz+mTp3KunXriImJYd26dUydOhVwxlJ4j7EoKipi//793HrrrZUeP2rUKBITE0lMTOTLL79k+vTpnmPZ2dncfffddYrPNCwpb7I2gdPme7HaZuQcds8cSIif3/5XrChg/Pi3KCkpY9Kk3syYkUpYWN3HMxhTk127dhEXFxfsMBq1uXPnEh4e7nNgY2N18OBBRo4cyYYNG3we9/W6E5FtqtozEPEZh7UkBIEI3BAZ5neCUC46ui3bto3jxRcHWIJgTCMyYcIEWrRoEewwAmrfvn3MmTMn2GGYGtivG4JAFa5tU/1gnbKyM8ye/RERES2YPLkPw4bFMXRoHE2a2JzmxjQ2ISEh3HfffcEOI6B69bI5XC4H1pIQBKrQvJqFlf72t3+RmPh7Zs78gE8/PQI4A6MsQTDBYt2SJpDs9XbpsCQhCE59e5YmPn5uVVxcyr33ZnLHHa/RtGkTNmy4n0WLBvk4gzGBExISwpEjR+wftwkIVeXIkSOEhNjA7EuBdTcESYvmF+Zn+/cfZ82aT3nmmVR++cu+tGhhfx4TfJ06deLAgQMcPnw42KGYK0RISAidOnUKdhgGSxKCJrK1kyX/4x/FZGd/xuTJfbj55mvZv38S7dpVP+e5MYHUvHlzoqKigh2GMSYIrLuhBiIyUEQ+FZFCEZnq43gLEVnuHt8iIp39OW9025Y89thaevX6I3PmbOLYMWfGNUsQjDHGXCosSaiGiDQFFgB3AvFAuoicPyXZA0CJqkYDc4Gql0fzMmnMGubN28LPf55MQcHDRERY/5sxxphLi3U3VO9moFBV9wKISAYwBPCe4HwI8Cv3diYwX0REaxjl1SH0Kt7c9AA/+IH1uxljjLk0WZJQvWuB/V73DwA/qKqMqn4rIseAdsCX3oVEZBwwzr37Te7/jd/hNfPqlaw959XVFczqooLVRQWriwo3BjuAK40lCdXzNTHB+S0E/pRBVRcBiwBEJMemFnVYXVSwuqhgdVHB6qKCiOTUXMrUJxuTUL0DgPfay52A8xc/95QRkWZABHA0INEZY4wxDciShOp9DMSISJSIXAXcC6w5r8waYLR7ezjwbk3jEYwxxpjLgXU3VMMdYzAReAdoCryqqjtF5FkgR1XXAP8F/I+IFOK0INzrx6kXNVjQlx+riwpWFxWsLipYXVSwuggwWyraGGOMMT5Zd4MxxhhjfLIkwRhjjDE+WZLQgBpqSufLkR918biIFIhInohsEJHrgxFnINRUF17lhouIikij/fmbP3UhIiPc18ZOEVka6BgDxY/3yPdEJFtEtrvvk7uCEWdDE5FXReSQiOyo4riIyDy3nvJE5KZAx3hFUVXbGmDDGej4L6ALcBXwCRB/XpmHgIXu7XuB5cGOO4h1cRvQ0r094UquC7dcOPABsBnoGey4g/i6iAG2A1e79zsEO+4g1sUiYIJ7Ox4oCnbcDVQX/wHcBOyo4vhdwNs4c9T0BrYEO+bGvFlLQsPxTOmsqqeB8imdvQ0Blri3M4HbRcTX5EyXuxrrQlWzVfVr9+5mnDkpGiN/XhcAM4EXgFOBDC7A/KmLnwELVLUEQFUPBTjGQPGnLhRo7d6O4MI5WxoFVf2A6ueaGQL8WR2bgTYi0jEw0V15LEloOL6mdL62qjKq+i1QPqVzY+NPXXh7AOebQmNUY12ISA/gOlXNCmRgQeDP6+IG4AYR2Sgim0VkYMCiCyx/6uJXwE9F5ADwV+CRwIR2yant/xNTBzZPQsOptymdGwG/n6eI/BToCdzaoBEFT7V1ISJNcFYTHROogILIn9dFM5wuh1Sc1qUPRSRBVb9q4NgCzZ+6SAcWq+ocEUnBmZ8lQVXPNXx4l5Qr5f/mJcFaEhqOTelcwZ+6QER+BEwDBqvqNwGKLdBqqotwIAF4T0SKcPpc1zTSwYv+vkdWq+oZVf0M+BQnaWhs/KmLB4DXAVR1ExCCs/jTlcav/yemfliS0HBsSucKNdaF28T+B5wEobH2O0MNdaGqx1S1vap2VtXOOOMzBqtqY1zYxp/3yCqcQa2ISHuc7oe9AY0yMPypi33A7QAiEoeTJBwOaJSXhjXA/e6vHHoDx1S1ONhBNVbW3dBAtOGmdL7s+FkXLwJhwBvu2M19qjo4aEE3ED/r4orgZ128AwwQkQLgLDBFVY8EL+qG4WddTAb+KCKTcJrXxzTGLxUisgyne6m9O/5iBtAcQFUX4ozHuAsoBL4GxgYn0iuDTctsjDHGGJ+su8EYY4wxPlmSYIwxxhifLEkwxhhjjE+WJBhjjDHGJ0sSjDHGGOOTJQnGNAAROSsiuV5b52rKdq5qxbtaXvM9dxXBT9xpjG+8iHOMF5H73dtjROS7Xsf+JCLx9RznxyKS5MdjHhORlnW9tjGmdixJMKZhlKlqktdWFKDrjlLV7jgLh71Y2wer6kJV/bN7dwzwXa9jD6pqQb1EWRHn7/AvzscASxKMCTBLEowJELfF4EMR+Ye79fFRpquIbHVbH/JEJMbd/1Ov/X8QkaY1XO4DINp97O0isl1E8kXkVRFp4e5/TkQK3Ou85O77lYg8ISLDcdbQ+It7zVC3BaCniEwQkRe8Yh4jIq9cZJyb8FqcR0R+LyI5IrJTRJ5x9z2Kk6xki0i2u2+AiGxy6/ENEQmr4TrGmItgSYIxDSPUq6thpbvvENBfVW8C0oB5Ph43HvitqibhfEgfcKfgTQP6uvvPAqNquP4gIF9EQoDFQJqqJuLMsjpBRNoCQ4GuqtoNmOX9YFXNBHJwvvEnqWqZ1+FMYJjX/TRg+UXGORBn6uVy01S1J9ANuFVEuqnqPJy5+W9T1dvc6ZmnAz9y6zIHeLyG6xhjLoJNy2xMwyhzPyi9NQfmu33wZ3HWITjfJmCaiHQC3lTVf4rI7UAy8LE7ZXUoTsLhy19EpAwowllK+EbgM1Xd4x5fAjwMzAdOAX8SkbcAv5elVtXDIrLXnTf/n+41NrrnrU2crXCmIL7Ja/8IERmH87+pIxAP5J332N7u/o3uda7CqTdjTD2zJMGYwJkEHAS647TinTq/gKouFZEtwN3AOyLyIM7SuEtU9Sk/rjHKezEoEWnnq5C7VsDNOAsG3QtMBPrV4rksB0YAu4GVqqrifGL7HSfwCfAcsAAYJiJRwBNAL1UtEZHFOIsYnU+AdaqaXot4jTEXwbobjAmcCKBYVc8B9+F8i65ERLoAe90m9jU4ze4bgOEi0sEt01ZErvfzmruBziIS7d6/D3jf7cOPUNW/4gwK9PULg1Kcpat9eRP4TyAdJ2GgtnGq6hmcboPebldFa+AkcExEIoE7q4hlM9C3/DmJSEsR8dUqY4ypI0sSjAmc3wGjRWQzTlfDSR9l0oAdIpILxAJ/dn9RMB34m4jkAetwmuJrpKqncFbJe0NE8oFzwEKcD9ws93zv47RynG8xsLB84OJ55y0BCoDrVXWru6/WcbpjHeYAT6jqJ8B2YCfwKk4XRrlFwNsikq2qh3F+ebHMvc5mnLoyxtQzWwXSGGOMMT5ZS4IxxhhjfLIkwRhjjDE+WZJgjDHGGJ8sSTDGGGOMT5YkGGOMMcYnSxKMMcYY45MlCcYYY4zx6f8BMk65sWW5chkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.96 0.89 6762\n", + " 1 0.70 0.32 0.44 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.77 0.64 0.66 8749\n", + "weighted avg 0.80 0.81 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Predicted01
Actual
06492270
11349638
\n", + "
" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6492 270\n", + "1 1349 638" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#confusion matrix\n", + "confusion(y_test,clf_reduced_tuned.predict(X_test), \"SVM reduced features and tuning linear kernal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel\n", + "clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)\n", + "clf_reduced_tuned_rbf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15910713557498266\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.38 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.76 0.66 0.69 8749\n", + "weighted avg 0.80 0.82 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, \n", + " \"SVM reduced features and tuning rbf kernel\")\n", + "print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[2] = ([\"SVM-RBF (Grid Search)\" , \n", + " classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SVM with filtered features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\reonh\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=True, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_reduced_tuned_filtered = svm.SVC(kernel = 'rbf', probability = True)\n", + "clf_reduced_tuned_filtered.fit(X_train_filter, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16104553371241384\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.6689738476077944" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.95 0.89 6762\n", + " 1 0.67 0.37 0.48 1987\n", + "\n", + " accuracy 0.82 8749\n", + " macro avg 0.75 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 0.79 8749\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will stick to using unfiltered features and SVM with rbf kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Networks\n", + "We will now use the train and test sets as defined above and attempt to implement a neural network model on the data\n", + "\n", + "#### Theory\n", + "A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function. \n", + "\n", + ".\n", + "\n", + "\n", + "![image.png](https://www.researchgate.net/profile/Leslaw_Plonka/publication/260080460/figure/fig1/AS:340931325775876@1458295770470/A-simple-neural-network-diagram.png)\n", + "\n", + "\n", + "The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.\n", + "\n", + "\n", + "This process is repeated iteratively until the model converges (i.e. it cannot be improved further)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training\n", + "Here we create an instance of our model, specifically a Sequential model, and add layers one at a time until we are happy with our network architecture. We will be training the model on our feature-selected dataset to speed up computation by reducing dimensionality. We have found that the performance difference between the 2 datasets are negligible.\n", + "\n", + "We ensure the input layer has the right number of input features, and is specified when creating the first layer with the input_dim argument and setting it to 17 (The size of the feature selected dataset).\n", + "Additionaly, we start off using a fully-connected network structure with three layers, and attempt to increase the number of layers at later part ater fully optimising our model.\n", + "\n", + "Fully connected layers are defined using the Dense class. We specify the number of neurons or nodes in the layer as the first argument, and specify the activation function using the activation argument. The rectified linear unit activation function (Relu) is usedon the first two layers and the Sigmoid function in the output layer.\n", + "\n", + "Conventionally, Sigmoid and Tanh activation functions were preferred for all layers. However, better performance is achieved using the ReLU activation function. We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map (binary) to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.\n", + "\n", + "The model expects rows of data with 17 variables (the input_dim=17 argument)\n", + "The first hidden layer has 17 nodes and uses the relu activation function.\n", + "The second hidden layer has 17 nodes and uses the relu activation function.\n", + "The output layer has one node and uses the sigmoid activation function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compiling\n", + "\n", + "The model uses the efficient numerical libraries as the backend, and in this case Tensorflow is used. The backend automatically chooses the best way to represent the network for training and making predictions to run.\n", + "\n", + "We must specify the loss function to use to evaluate a set of weights, the optimizer is used to search through different weights for the network and any optional metrics we would like to collect and report during training.\n", + "\n", + "After experimenting with the various loss functions, such as hinge loss and binary cross entropy, we found that entropy performed the best for our dataset.\n", + "\n", + "We have also found that among all the optimizers (Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam) the optimizer \"adam\" is the most efficient yet yields the best results.\n", + "\n", + "Additionaly, for this problem, we will run for a small number of epochs (20) and use a relatively small batch size of 10. This means that each epoch will involve (20/10) 2 updates to the model weights. After we have finalised the best optimizer, we will then increase the numebr of epochs to increase the number of updates to obtain a better result. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4708 - accuracy: 0.7956\n", + "Epoch 2/20\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4502 - accuracy: 0.8116\n", + "Epoch 3/20\n", + "17496/17496 [==============================] - 2s 113us/step - loss: 0.4477 - accuracy: 0.8125\n", + "Epoch 4/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4461 - accuracy: 0.8130\n", + "Epoch 5/20\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4450 - accuracy: 0.8133\n", + "Epoch 6/20\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4443 - accuracy: 0.81450s - loss: 0.4424 - \n", + "Epoch 7/20\n", + "17496/17496 [==============================] - 2s 96us/step - loss: 0.4437 - accuracy: 0.8150\n", + "Epoch 8/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4435 - accuracy: 0.8144\n", + "Epoch 9/20\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4433 - accuracy: 0.8147\n", + "Epoch 10/20\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4429 - accuracy: 0.8142\n", + "Epoch 11/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4418 - accuracy: 0.8132\n", + "Epoch 12/20\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4416 - accuracy: 0.81450s - loss: 0.4413 - accuracy: \n", + "Epoch 13/20\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4419 - accuracy: 0.8138\n", + "Epoch 14/20\n", + "17496/17496 [==============================] - 2s 128us/step - loss: 0.4417 - accuracy: 0.8140\n", + "Epoch 15/20\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4415 - accuracy: 0.8142\n", + "Epoch 16/20\n", + "17496/17496 [==============================] - 2s 118us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 17/20\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4409 - accuracy: 0.8152\n", + "Epoch 18/20\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4413 - accuracy: 0.8145\n", + "Epoch 19/20\n", + "17496/17496 [==============================] - 2s 126us/step - loss: 0.4403 - accuracy: 0.8145\n", + "Epoch 20/20\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4405 - accuracy: 0.8156\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import loadtxt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "# optimisers to try: Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam\n", + "# verdict : Adam\n", + "\n", + "# Loss function to try: Binary Cross Entropy, Hinge, Logcosh\n", + "# verdict: Binary Cross Entropy\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=20, batch_size=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 6762\n", + " 1 0.65 0.39 0.49 1987\n", + "\n", + " accuracy 0.81 8749\n", + " macro avg 0.75 0.66 0.69 8749\n", + "weighted avg 0.80 0.81 0.80 8749\n", + "\n" + ] + } + ], + "source": [ + "get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 20 epoch\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experimenting with lowering the cutoff for the neural network," + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.23287344\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.81 0.84 6762\n", + " 1 0.48 0.60 0.54 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.71 0.69 8749\n", + "weighted avg 0.79 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam = get_optimal(model, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy\")\n", + "predictions = list(model.predict(X_test_filter).ravel() > optimal_adam)\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance is quite impressive when we lowered the classification cut off. The ROC of 0.76 is also on par with other models. Now we ramp up the number of epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4863 - accuracy: 0.7969\n", + "Epoch 2/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4511 - accuracy: 0.8122\n", + "Epoch 3/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4485 - accuracy: 0.8124\n", + "Epoch 4/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4472 - accuracy: 0.81200s - loss: 0.4465 \n", + "Epoch 5/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4461 - accuracy: 0.8123\n", + "Epoch 6/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4450 - accuracy: 0.8124\n", + "Epoch 7/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4432 - accuracy: 0.8138\n", + "Epoch 8/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4428 - accuracy: 0.8139\n", + "Epoch 9/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4422 - accuracy: 0.8132\n", + "Epoch 10/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4420 - accuracy: 0.8140\n", + "Epoch 11/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4411 - accuracy: 0.8137\n", + "Epoch 12/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4410 - accuracy: 0.8146\n", + "Epoch 13/50\n", + "17496/17496 [==============================] - 2s 97us/step - loss: 0.4412 - accuracy: 0.8143\n", + "Epoch 14/50\n", + "17496/17496 [==============================] - 2s 98us/step - loss: 0.4415 - accuracy: 0.8141\n", + "Epoch 15/50\n", + "17496/17496 [==============================] - 2s 120us/step - loss: 0.4402 - accuracy: 0.8149\n", + "Epoch 16/50\n", + "17496/17496 [==============================] - 2s 103us/step - loss: 0.4402 - accuracy: 0.8146\n", + "Epoch 17/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4406 - accuracy: 0.8141\n", + "Epoch 18/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4400 - accuracy: 0.8152\n", + "Epoch 19/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8140\n", + "Epoch 20/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4398 - accuracy: 0.8140\n", + "Epoch 21/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4396 - accuracy: 0.8153\n", + "Epoch 22/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4401 - accuracy: 0.8138\n", + "Epoch 23/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4394 - accuracy: 0.8150\n", + "Epoch 24/50\n", + "17496/17496 [==============================] - 2s 119us/step - loss: 0.4397 - accuracy: 0.8152\n", + "Epoch 25/50\n", + "17496/17496 [==============================] - 2s 117us/step - loss: 0.4396 - accuracy: 0.8148\n", + "Epoch 26/50\n", + "17496/17496 [==============================] - 2s 106us/step - loss: 0.4396 - accuracy: 0.8144\n", + "Epoch 27/50\n", + "17496/17496 [==============================] - 2s 95us/step - loss: 0.4393 - accuracy: 0.8135\n", + "Epoch 28/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4390 - accuracy: 0.8152\n", + "Epoch 29/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4392 - accuracy: 0.8138\n", + "Epoch 30/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8141\n", + "Epoch 31/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8149\n", + "Epoch 32/50\n", + "17496/17496 [==============================] - 2s 127us/step - loss: 0.4390 - accuracy: 0.8147\n", + "Epoch 33/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4388 - accuracy: 0.8145\n", + "Epoch 34/50\n", + "17496/17496 [==============================] - 2s 115us/step - loss: 0.4385 - accuracy: 0.8153\n", + "Epoch 35/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4389 - accuracy: 0.8150\n", + "Epoch 36/50\n", + "17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8146\n", + "Epoch 37/50\n", + "17496/17496 [==============================] - 2s 107us/step - loss: 0.4386 - accuracy: 0.8142\n", + "Epoch 38/50\n", + "17496/17496 [==============================] - 2s 102us/step - loss: 0.4386 - accuracy: 0.8143\n", + "Epoch 39/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4379 - accuracy: 0.8148\n", + "Epoch 40/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4385 - accuracy: 0.8148\n", + "Epoch 41/50\n", + "17496/17496 [==============================] - 2s 116us/step - loss: 0.4386 - accuracy: 0.8149\n", + "Epoch 42/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4380 - accuracy: 0.8150\n", + "Epoch 43/50\n", + "17496/17496 [==============================] - 2s 108us/step - loss: 0.4382 - accuracy: 0.8144\n", + "Epoch 44/50\n", + "17496/17496 [==============================] - 2s 101us/step - loss: 0.4381 - accuracy: 0.8152\n", + "Epoch 45/50\n", + "17496/17496 [==============================] - 2s 105us/step - loss: 0.4378 - accuracy: 0.8151\n", + "Epoch 46/50\n", + "17496/17496 [==============================] - 2s 104us/step - loss: 0.4374 - accuracy: 0.8153\n", + "Epoch 47/50\n", + "17496/17496 [==============================] - 2s 99us/step - loss: 0.4382 - accuracy: 0.8152\n", + "Epoch 48/50\n", + "17496/17496 [==============================] - 2s 110us/step - loss: 0.4379 - accuracy: 0.8166\n", + "Epoch 49/50\n", + "17496/17496 [==============================] - 2s 112us/step - loss: 0.4381 - accuracy: 0.8144\n", + "Epoch 50/50\n", + "17496/17496 [==============================] - 2s 109us/step - loss: 0.4378 - accuracy: 0.8154\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model50 = Sequential()\n", + "model50.add(Dense(12, input_dim=17, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(8, activation='relu'))\n", + "model50.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model50.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model50.fit(X_train_filter, y_train, epochs=50, batch_size=10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that the accuracy did not increase much at all from the 20th to 50th epoch. In such a situation we will choose the 20 epoch model for its faster computation." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.20843479\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.82 0.84 6762\n", + " 1 0.48 0.59 0.53 1987\n", + "\n", + " accuracy 0.76 8749\n", + " macro avg 0.68 0.70 0.69 8749\n", + "weighted avg 0.78 0.76 0.77 8749\n", + "\n" + ] + } + ], + "source": [ + "optimal_adam50 = get_optimal(model50, y_train, X_train_filter, \"Neural Network 17x8x8x1 Adam Entropy\")\n", + "get_roc(model50, y_test, X_test_filter, \"Neural Network 17x8x8x1 Adam, Entropy, 50 epoch\")\n", + "predictions50 = list(model50.predict(X_test_filter).ravel() > optimal_adam50)\n", + "print(classification_report(y_test,predictions50))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep...0.5358340.741382
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "3 Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep... 0.535834 0.741382\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.loc[3] = ([\"Neural Network - 17x8x8x1 Adam, Entropy, 20 Epochs\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naive Bayes\n", + "#### Theory\n", + "Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.\n", + "##### Bayes Theorem:\n", + "![image.png](https://miro.medium.com/max/510/1*tjcmj9cDQ-rHXAtxCu5bRQ.png)\n", + "Using the Bayes theorem, we can find the probability of A happening, given that B has occured.\n", + "One assumption about naive bayes is that the predictors/features are independent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training the Naive bayes model" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB\n", + "\n", + "gnb = GaussianNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GaussianNB(priors=None, var_smoothing=1e-09)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#training naive bayes model\n", + "gnb.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "#classifying values\n", + "predicted = gnb.predict(X_train)\n", + "expected = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.9999935527715175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.11 0.20 13442\n", + " 1 0.25 0.96 0.39 4054\n", + "\n", + " accuracy 0.31 17496\n", + " macro avg 0.58 0.54 0.29 17496\n", + "weighted avg 0.76 0.31 0.24 17496\n", + "\n" + ] + } + ], + "source": [ + "#accessing model performance\n", + "#print(metrics.confusion_matrix(expected, predicted))\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that while the recall is 0.96, the f1 is 0.39 and the overall accuracy is atrocious. \n", + "\n", + "We will now try searching for the smoothing parameter to achieve a better ROC and F1 on default. After some experimentation we found that 0.01 is a good value for this parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.85 0.85 13442\n", + " 1 0.52 0.52 0.52 4054\n", + "\n", + " accuracy 0.78 17496\n", + " macro avg 0.69 0.69 0.69 17496\n", + "weighted avg 0.78 0.78 0.78 17496\n", + "\n" + ] + } + ], + "source": [ + "gnb = GaussianNB(var_smoothing = 0.01)\n", + "#experimenting with smoothing constant of naive bayes model on the training set.\n", + "gnb.fit(X_train, y_train)\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_train,gnb.predict(X_train)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smoothing constant of 0.01 allowed us to acheieve a recall and f1 of 0.52 on the training set. Applied on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.038218795916133315\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.86 0.86 6762\n", + " 1 0.52 0.53 0.53 1987\n", + "\n", + " accuracy 0.78 8749\n", + " macro avg 0.69 0.69 0.69 8749\n", + "weighted avg 0.78 0.78 0.78 8749\n", + "\n" + ] + } + ], + "source": [ + "#plot roc for naive Bayes\n", + "auroc = get_roc(gnb, y_test, X_test, \"Naive Bayes\")\n", + "print(classification_report(y_test,gnb.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "evaluation.loc[5] = ([\"Naive Bayes\" , \n", + " classification_report(y_test, gnb.predict(X_test), output_dict = True)[\"1\"][\"f1-score\"],\n", + " auroc])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
3Neural Network 17x8x8x10.5358340.761854
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5263420.741382
\n", + "
" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458\n", + "1 Logistic Regression (Optimal Cutoff) 0.527665 0.765244\n", + "3 Neural Network 17x8x8x1 0.535834 0.761854\n", + "2 SVM-RBF (Grid Search) 0.482247 0.748465\n", + "5 Naive Bayes 0.526342 0.741382" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation.sort_values([\"AUROC\"], ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "Stop Running", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stop Running\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mException\u001b[0m: Stop Running" + ] + } + ], + "source": [ + "raise(Exception(\"Stop Running\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Tuning Neural Network with different optimisers \n", + "### Adamax Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = \"logistic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = mlp.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "confusion(y_test,predictions,\"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adadelta Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adagrad Optimzier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RMSProp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SGD" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Tuning Keras Model\")\n", + "\n", + "print(classification_report(y_test,predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### We conclude that best optimizer is adagrad. Testing it on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=17, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train_filter, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test_filter).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test_filter, \"Neural Network - adagrad (filtered features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(12, input_dim=46, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(8, activation='relu'))\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "# compile the keras model\n", + "model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])\n", + "# fit the keras model on the dataset\n", + "model.fit(X_train, y_train, epochs=10, batch_size=10)\n", + "predictions = list(model.predict(X_test).ravel() > 0.5)\n", + "#confusion(y_test, model.predict(X_test[sig.index]), \"Deep Learning Keras Model\")\n", + "auroc = get_roc(model, y_test, X_test, \"Neural Network - adagrad (all features)\")\n", + "\n", + "print(classification_report(y_test,predictions))\n", + "\n", + "\n", + "evaluation.loc[6] = ([\"Neural Network - adagrad\" , \n", + " classification_report(y_test, predictions, output_dict = True)[\"1\"][\"recall\"],\n", + " auroc])\n", + "\n", + "evaluation\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "BT2101 disrudy ", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/bt2101_notebook.html b/bt2101_notebook.html new file mode 100644 index 0000000..03a12ae --- /dev/null +++ b/bt2101_notebook.html @@ -0,0 +1,20204 @@ + + + + +bt2101_notebook + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
+

BUILDING A DEFUALT DETECTION MODEL

+

By Reon Ho, Lam Cheng Jun, Janson Chew, and Bryan Koh

+

Table of Contents

    +
  1. Problem Description (Brief Write Up)
    2. +
  2. Exploratory Data Analysis (EDA)
    3. +
  3. Data Pre-processing
    4. +
  4. Model Selection
    5. +
  5. Evaluation
    6. +
  6. Discussion and Possible Improvements
    7. +
+

1. Problem Description

The goal of this project is to predict a binary target feature (default or not) valued 0 (= not default) or 1 (= default). This project will cover the entire data science pipeline, from data analysis to model evaluation. We will be trying several models to predict default status, and choosing the most appropriate one at the end.

+

The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan, and each data sample is described by 23 feature attributes and the binary target feature (default or not).

+

The 23 explanatory attributes and their explanations (from the data provider) are as follows:

+

X1 - X5: Indivual attributes of customer

X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.

+

X2: Gender (1 = male; 2 = female).

+

X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).

+

X4: Marital status (1 = married; 2 = single; 3 = others).

+

X5: Age (year).

+

X6 - X11: Repayment history from April to Septemeber 2005

The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.

+

X6 = the repayment status in September, 2005

+

X7 = the repayment status in August, 2005

+

X8 = the repayment status in July, 2005

+

X9 = the repayment status in June, 2005

+

X10 = the repayment status in May, 2005

+

X11 = the repayment status in April, 2005.

+

X12 - X17: Amount of bill statement (NT dollar) from April to September 2005

X12 = amount of bill statement in September, 2005;

+

X13 = amount of bill statement in August, 2005

+

. . .

+

X17 = amount of bill statement in April, 2005.

+

X18 - X23: Amount of previous payment (NT dollar)

X18 = amount paid in September, 2005

+

X19 = amount paid in August, 2005

+

. . .

+

X23 = amount paid in April, 2005.

+ +
+
+
+
+
+
+

EDA

In this section we will explore the data set, its shape and its features to get an idea of the data.

+

Importing packages and the dataset

+
+
+
+
+
+
In [1]:
+
+
+
import pandas as pd
+
+ +
+
+
+ +
+
+
+
In [2]:
+
+
+
import matplotlib.pyplot as plt
+import seaborn as sns
+
+ +
+
+
+ +
+
+
+
In [3]:
+
+
+
import numpy as np
+
+ +
+
+
+ +
+
+
+
In [4]:
+
+
+
url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'
+df = pd.read_csv(url,  header = 1, index_col = 0)
+# Dataset is now stored in a Pandas Dataframe
+
+ +
+
+
+ +
+
+
+
In [5]:
+
+
+
#rename the target variable to "Y" for convenience
+df["Y"] = df["default payment next month"] 
+df = df.drop("default payment next month", axis = 1)
+df0 = df #backup of df
+df.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[5]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
ID
1200002212422-1-1-2...000068900001
212000022226-12000...3272345532610100010001000020001
3900002223400000...1433114948155491518150010001000100050000
4500002213700000...2831428959295472000201912001100106910000
55000012157-10-100...2094019146191312000366811000090006896790
+

5 rows × 24 columns

+
+
+ +
+ +
+
+ +
+
+
+
In [6]:
+
+
+
size = df.shape
+print("Data has {} Columns and {} Rows".format(size[1], size[0]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Data has 24 Columns and 30000 Rows
+
+
+
+ +
+
+ +
+
+
+
In [7]:
+
+
+
#check for null values
+df.isnull().any().sum()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[7]:
+ + + + +
+
0
+
+ +
+ +
+
+ +
+
+
+
+

From the above analyses, we observe that:

+
    +
  1. The data indeed has 30000 rows and 24 columns
    2. +
  2. There are no null values
    3. +
+

We will now explore the features more in depth.

+ +
+
+
+
+
+
+

Exploring the features

+
+
+
+
+
+
+

1) Exploring target attribute:

+ +
+
+
+
+
+
In [8]:
+
+
+
All = df.shape[0]
+default = df[df['Y'] == 1]
+nondefault = df[df['Y'] == 0]
+
+x = len(default)/All
+y = len(nondefault)/All
+
+print('defaults :',x*100,'%')
+print('non defaults :',y*100,'%')
+
+# plotting target attribute against frequency
+labels = ['non default','default']
+classes = pd.value_counts(df['Y'], sort = True)
+classes.plot(kind = 'bar', rot=0)
+plt.title("Target attribute distribution")
+plt.xticks(range(2), labels)
+plt.xlabel("Class")
+plt.ylabel("Frequency")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
defaults : 22.12 %
+non defaults : 77.88000000000001 %
+
+
+
+ +
+ +
Out[8]:
+ + + + +
+
Text(0, 0.5, 'Frequency')
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

2) Exploring categorical attributes

+

Categorical attributes are:

+
    +
  • Sex
    • +
  • Education
    • +
  • Marriage
    • +
+ +
+
+
+
+
+
In [9]:
+
+
+
print(df["SEX"].value_counts().apply(lambda r: r/All*100))
+print("--------------------------------------------------------")
+print(df["EDUCATION"].value_counts().apply(lambda r: r/All*100))
+print("--------------------------------------------------------")
+print(df["MARRIAGE"].value_counts().apply(lambda r: r/All*100))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
2    60.373333
+1    39.626667
+Name: SEX, dtype: float64
+--------------------------------------------------------
+2    46.766667
+1    35.283333
+3    16.390000
+5     0.933333
+4     0.410000
+6     0.170000
+0     0.046667
+Name: EDUCATION, dtype: float64
+--------------------------------------------------------
+2    53.213333
+1    45.530000
+3     1.076667
+0     0.180000
+Name: MARRIAGE, dtype: float64
+
+
+
+ +
+
+ +
+
+
+
+

Findings

+
    +
  • Categorical variable SEX does not seem to have any missing/extra groups, and it is separated into Male = 1 and Female = 2
    • +
  • Categorical variable MARRIAGE seems to have unknown group = 0, which could be assumed to be missing data, with other groups being Married = 1, Single = 2, Others = 3
    • +
  • Categorical variable EDUCATION seems to have unknown group = 0,5,6, with other groups being graduate school = 1, university = 2, high school = 3, others = 4
    • +
+ +
+
+
+
+
+
In [10]:
+
+
+
#proportion of target attribute (for reference)
+print('Total target attributes:')
+print('non defaults :',y*100,'%')
+print('defaults :',x*100,'%')
+print("--------------------------------------------------------")
+#analysing default payment with Sex
+sex_target = pd.crosstab(df["Y"], df["SEX"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: "Male", 2: "Female"}, index = {0: "non defaults", 1: "defaults"})
+print(sex_target)
+print("--------------------------------------------------------")
+#analysing default payment with education
+education_target = pd.crosstab(df["Y"], df["EDUCATION"]).apply(lambda r: r/r.sum()*100).rename(index = {0: "non defaults", 1: "defaults"})
+print(education_target)
+print("--------------------------------------------------------")
+#analysing default payment with marriage
+marriage_target = pd.crosstab(df["Y"], df["MARRIAGE"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: "unknown",1: "married", 2: "single", 3: "others"},index = {0: "non defaults", 1: "defaults"})
+print(marriage_target)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Total target attributes:
+non defaults : 77.88000000000001 %
+defaults : 22.12 %
+--------------------------------------------------------
+SEX                Male     Female
+Y                                 
+non defaults  75.832773  79.223719
+defaults      24.167227  20.776281
+--------------------------------------------------------
+EDUCATION         0          1          2          3          4          5  \
+Y                                                                            
+non defaults  100.0  80.765234  76.265146  74.842384  94.308943  93.571429   
+defaults        0.0  19.234766  23.734854  25.157616   5.691057   6.428571   
+
+EDUCATION             6  
+Y                        
+non defaults  84.313725  
+defaults      15.686275  
+--------------------------------------------------------
+MARRIAGE        unknown    married     single     others
+Y                                                       
+non defaults  90.740741  76.528296  79.071661  73.993808
+defaults       9.259259  23.471704  20.928339  26.006192
+
+
+
+ +
+
+ +
+
+
+
+

Conclusion

+

From the analyses above we conclude that

+
    +
  1. The categorical data is noisy - EDUCATION and MARRIAGE contains unexplained/anomalous data.
    2. +
+ +
+
+
+
+
+
+

3) Analysis of Numerical Attributes

+

The numerical attributes are:

+ +
+
+
+
+
+
In [11]:
+
+
+
#printing numerical attributes
+pd.DataFrame(df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis = 1).columns).transpose()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[11]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
012345678910111213141516171819
0LIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
+
+
+ +
+ +
+
+ +
+
+
+
In [12]:
+
+
+
df.drop(['SEX', 'EDUCATION', 'MARRIAGE','Y'], axis=1).describe().transpose()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[12]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
countmeanstdmin25%50%75%max
LIMIT_BAL30000.0167484.322667129747.66156710000.050000.00140000.0240000.001000000.0
AGE30000.035.4855009.21790421.028.0034.041.0079.0
PAY_030000.0-0.0167001.123802-2.0-1.000.00.008.0
PAY_230000.0-0.1337671.197186-2.0-1.000.00.008.0
PAY_330000.0-0.1662001.196868-2.0-1.000.00.008.0
PAY_430000.0-0.2206671.169139-2.0-1.000.00.008.0
PAY_530000.0-0.2662001.133187-2.0-1.000.00.008.0
PAY_630000.0-0.2911001.149988-2.0-1.000.00.008.0
BILL_AMT130000.051223.33090073635.860576-165580.03558.7522381.567091.00964511.0
BILL_AMT230000.049179.07516771173.768783-69777.02984.7521200.064006.25983931.0
BILL_AMT330000.047013.15480069349.387427-157264.02666.2520088.560164.751664089.0
BILL_AMT430000.043262.94896764332.856134-170000.02326.7519052.054506.00891586.0
BILL_AMT530000.040311.40096760797.155770-81334.01763.0018104.550190.50927171.0
BILL_AMT630000.038871.76040059554.107537-339603.01256.0017071.049198.25961664.0
PAY_AMT130000.05663.58050016563.2803540.01000.002100.05006.00873552.0
PAY_AMT230000.05921.16350023040.8704020.0833.002009.05000.001684259.0
PAY_AMT330000.05225.68150017606.9614700.0390.001800.04505.00896040.0
PAY_AMT430000.04826.07686715666.1597440.0296.001500.04013.25621000.0
PAY_AMT530000.04799.38763315278.3056790.0252.501500.04031.50426529.0
PAY_AMT630000.05215.50256717777.4657750.0117.751500.04000.00528666.0
+
+
+ +
+ +
+
+ +
+
+
+
+

Analysis of PAY_0 to PAY_6

+

We observe that the minimum value of PAY_0 to PAY_6 is -2. The dataset's author has explained these factors (PAY_0 to PAY_6) as the number of months of payment delay, that is, 1= payment delay of one month; 2= payment delay of two months and so on.

+

However, the presence of -2, -1 in these columns indicates that

+
    +
  1. There is anomalous data, OR
    2. +
  2. The numbers do not strictly correspond to the number of months of payment delay.
    3. +
+

This means we must conduct some data transformation.

+

According to (link), the numeric value in these attributes shows the past history of a credit card holder, where -2 means: No consumption of credit card, -1 means that holder paid the full balance, and 0 means the use of revolving credit.

+ +
+
+
+
+
+
In [13]:
+
+
+
def draw_histograms(df, variables, n_rows, n_cols, n_bins):
+    fig=plt.figure()
+    for i, var_name in enumerate(variables):
+        ax=fig.add_subplot(n_rows,n_cols,i+1)
+        df[var_name].hist(bins=n_bins,ax=ax)
+        ax.set_title(var_name)
+    fig.tight_layout()  # Improves appearance a bit.
+    plt.show()
+
+PAY = df[['PAY_0','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]
+BILLAMT = df[['BILL_AMT1','BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']]
+PAYAMT = df[['PAY_AMT1','PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]
+
+draw_histograms(PAY, PAY.columns, 2, 3, 10)
+draw_histograms(BILLAMT, BILLAMT.columns, 2, 3, 10)
+draw_histograms(PAYAMT, PAYAMT.columns, 2, 3, 10)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

We observe that the "repayment status" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default. In fact the later the status (pay_0 is later than pay_6), the more correlated it is.

+

Now that we have an idea of the features, we will move on to feature selection and data preparation.

+ +
+
+
+
+
+
+

Data Preprocessing

It was previously mentioned that our data had a bit of noise, so we will clean up the data in this part. Additionally, we will conduct some feature selection.

+
    +
  1. Removing Noise - Inconsistencies
    2. +
  2. Dealing with negative values of PAY_0 to PAY_6
    3. +
  3. Outliers
    4. +
  4. One Hot Encoding
    5. +
  5. Train Test Split
    6. +
  6. Feature selection
    7. +
+ +
+
+
+
+
+
+

Removing Noise

First, we found in our data exploration that education has unknown groups 0, 5 and 6. These will be dealt with using the identification method. 0 will be assumed to be missing data and identified. Groups 5 and 6 will be subsumed by Education = Others, with value 4

+ +
+
+
+
+
+
In [14]:
+
+
+
df['EDUCATION'].replace([5,6], 4, regex=True, inplace=True)
+df["EDUCATION"].unique()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[14]:
+ + + + +
+
array([2, 1, 3, 4, 0], dtype=int64)
+
+ +
+ +
+
+ +
+
+
+
+

Similarly, for Marriage, we will use the identification method to deal with missing data. So 0 will be treated as a new category, "Missing"

+ +
+
+
+
+
+
+

Separating negative and positive values for PAY_0 to PAY_6

+
+
+
+
+
+
+

Second, we are going to extract the negative values of PAY_0 to PAY_6 as another categorical feature. This way, PAY_0 to PAY_6 can be thought of purely as the months of delay of payments.

+

The negative values will form a categorical variable. e.g. negative values of PAY_0 will form the categorical variable S_0.

+ +
+
+
+
+
+
In [15]:
+
+
+
for i in range(0,7):
+    try:
+        df["S_" + str(i)] = [x  if x < 1 else 1 for x in df["PAY_" + str(i)]]
+    except:
+        pass
+
+ +
+
+
+ +
+
+
+
In [16]:
+
+
+
print('Dummy variables for negative values')
+df[["S_0", "S_2", "S_3", "S_4", "S_5", "S_6"]].head()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Dummy variables for negative values
+
+
+
+ +
+ +
Out[16]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
S_0S_2S_3S_4S_5S_6
ID
111-1-1-2-2
2-110001
3000000
4000000
5-10-1000
+
+
+ +
+ +
+
+ +
+
+
+
In [17]:
+
+
+
#attributes representing positive values
+for col in ["PAY_0", "PAY_2", "PAY_3", "PAY_4", "PAY_5", "PAY_6"]:
+    df[col].replace([0,-1,-2], 0, regex=True, inplace=True)
+
+ +
+
+
+ +
+
+
+
+

Outliers

Next, we would like to remove outliers from the continuous variables. Assuming that all the data points are normally distributed, we will consider a point an outlier if it falls outside the 99% interval of a distribution. (Critical value = 2.58)

+ +
+
+
+
+
+
In [18]:
+
+
+
from scipy import stats
+#we are only concerned with the ordinal data
+o = pd.DataFrame(df.drop(['Y','EDUCATION', 'MARRIAGE', "SEX","S_0", "S_2", "S_3", "S_4", "S_5", "S_6","PAY_0", "PAY_2", "PAY_3", "PAY_4", "PAY_5", "PAY_6"], axis=1))
+#rows where the absolute z score of all columns are less than 2.58 (critical value)
+rows = (np.abs(stats.zscore(o)) < 2.58).all(axis=1)
+df = df[rows]
+df.describe()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[18]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
count26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000...26245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.00000026245.000000
mean149324.8999811.6089541.8507531.55877335.0065920.3721090.3373210.3246330.2782240.238750...2787.4250712778.8306732822.2850070.230177-0.133587-0.300438-0.327300-0.364412-0.395999-0.428158
std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
min10000.0000001.0000000.0000000.00000021.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000
25%50000.0000001.0000001.0000001.00000028.0000000.0000000.0000000.0000000.0000000.000000...150.00000082.0000000.0000000.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000
50%120000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.000000...1200.0000001218.0000001143.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
75%210000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.000000...3118.0000003140.0000003069.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
max500000.0000002.0000004.0000003.00000059.0000008.0000008.0000008.0000008.0000008.000000...45171.00000044197.00000051000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
+

8 rows × 30 columns

+
+
+ +
+ +
+
+ +
+
+
+
+

Feature Scaling

The models used subsequently may have difficulty converging before the maximum number of iterations allowed +is reached if the data is not normalized. Additionaly, Multi-layer Perceptron is sensitive to feature scaling, so we will use StandardScaler for standardization. We only want to scale the numerical factors.

+ +
+
+
+
+
+
In [19]:
+
+
+
from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+cols = df.drop(['Y','EDUCATION', 'MARRIAGE', "SEX","S_0", "S_2", "S_3", "S_4", "S_5", "S_6","PAY_0", "PAY_2", "PAY_3", "PAY_4", "PAY_5", "PAY_6"], axis =1)
+df1 = df.copy()
+df1[cols.columns] = scaler.fit_transform(cols)
+df = df1
+
+ +
+
+
+ +
+
+
+
In [20]:
+
+
+
df1.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[20]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...PAY_AMT4PAY_AMT5PAY_AMT6YS_0S_2S_3S_4S_5S_6
ID
10.0204082210.07894722000...0.0000000.0000000.000000111-1-1-2-2
20.2244902220.13157902000...0.0221380.0000000.0392161-110001
30.1632652220.34210500000...0.0221380.0226260.0980390000000
40.0816332210.42105300000...0.0243520.0241870.0196080000000
50.0816331210.94736800000...0.1992430.0155890.0133140-10-1000
+

5 rows × 30 columns

+
+
+ +
+ +
+
+ +
+
+
+
+

One-Hot Encoding for Categorical attributes

+
+
+
+
+
+
+

In some models, categorical variables which are encoded numerically will be erroneously treated as ordinal data. To understand why this is a problem, consider the "Education" column for our dataset.

+

A logistic regression model, for example, will assume that the difference in odds of default between education = 1 and education = 2 is the same as the difference between education = 2 and 3. This is wrong because the difference in odds between a graduate degree and university (1 and 2) is likely to be different from that between univeristy education and high school education (2 and 3).

+

One hot encoding will allow our models to treat these columns explicitly as categorical features.

+

The following categorical columns will be one-hot encoded

+
    +
  1. EDUCATION
    2. +
  2. MARRIAGE
    3. +
  3. S0 - S6
    4. +
+ +
+
+
+
+
+
In [21]:
+
+
+
from sklearn.preprocessing import OneHotEncoder
+
+ +
+
+
+ +
+
+
+
In [22]:
+
+
+
onenc = OneHotEncoder(categories='auto')
+
+ +
+
+
+ +
+
+
+
In [23]:
+
+
+
#one hot encoding for EDUCATION and MARRIAGE
+onehot = pd.DataFrame(onenc.fit_transform(df[['EDUCATION', 'MARRIAGE']]).toarray())
+onehot.columns= names = ["MISSING-EDU","GRAD","UNI","HS","OTHER-EDU","MISSING-MS","MARRIED","SINGLE","OTHER-MS"]
+#drop one of each category to prevent dummy variable trap
+onehot = onehot.drop(["OTHER-EDU", "OTHER-MS"], axis = 1)
+onehot.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[23]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MISSING-EDUGRADUNIHSMISSING-MSMARRIEDSINGLE
00.00.01.00.00.01.00.0
10.00.01.00.00.00.01.0
20.00.01.00.00.00.01.0
30.00.01.00.00.01.00.0
40.00.01.00.00.01.00.0
+
+
+ +
+ +
+
+ +
+
+
+
In [24]:
+
+
+
#one hot encoding for S_0 to S_6
+onehot_PAY = pd.DataFrame(onenc.fit_transform(df[['S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6']]).toarray())
+onehot_PAY.columns= onenc.fit(df[["S_0", "S_2", "S_3", "S_4", "S_5", "S_6"]]).get_feature_names()
+#drop one of each category to prevent dummy variable trap
+#onehot = onehot.drop(["OTHER-EDU", "OTHER_MS"], axis = 1)
+names = []
+for X in range(0,7):
+    if X == 1:
+        continue
+    names.append("PAY_"+str(X)+"_No_Transactions")
+    names.append("PAY_"+str(X)+"_Pay_Duly")
+    names.append("PAY_"+str(X)+"_Revolving_Credit")
+    try:
+        onehot_PAY = onehot_PAY.drop("x" + str(X) +"_1", axis =1)
+    except:
+        onehot_PAY = onehot_PAY.drop("x1_1", axis =1)
+onehot_PAY.columns = names
+onehot_PAY.head()
+
+ +
+
+
+ +
+
+ + +
+ +
Out[24]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PAY_0_No_TransactionsPAY_0_Pay_DulyPAY_0_Revolving_CreditPAY_2_No_TransactionsPAY_2_Pay_DulyPAY_2_Revolving_CreditPAY_3_No_TransactionsPAY_3_Pay_DulyPAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.0
10.01.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.0
20.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
30.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.0
40.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.0
+
+
+ +
+ +
+
+ +
+
+
+
In [25]:
+
+
+
df1 = df.drop(['EDUCATION', 'MARRIAGE','S_0', 'S_2', 'S_3', 'S_4', 'S_5', 'S_6'], axis = 1)
+df1 = pd.concat([df1.reset_index(drop=True), onehot], axis=1)
+df1 = pd.concat([df1.reset_index(drop=True), onehot_PAY], axis=1)
+df1.columns
+
+ +
+
+
+ +
+
+ + +
+ +
Out[25]:
+ + + + +
+
Index(['LIMIT_BAL', 'SEX', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',
+       'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4',
+       'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',
+       'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Y', 'MISSING-EDU', 'GRAD', 'UNI',
+       'HS', 'MISSING-MS', 'MARRIED', 'SINGLE', 'PAY_0_No_Transactions',
+       'PAY_0_Pay_Duly', 'PAY_0_Revolving_Credit', 'PAY_2_No_Transactions',
+       'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit', 'PAY_3_No_Transactions',
+       'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit', 'PAY_4_No_Transactions',
+       'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit', 'PAY_5_No_Transactions',
+       'PAY_5_Pay_Duly', 'PAY_5_Revolving_Credit', 'PAY_6_No_Transactions',
+       'PAY_6_Pay_Duly', 'PAY_6_Revolving_Credit'],
+      dtype='object')
+
+ +
+ +
+
+ +
+
+
+
In [26]:
+
+
+
#check for perfect collinearity
+corr = df1.corr()
+for i in range(len(corr)):
+    corr.iloc[i,i] = 0
+#corr[corr == 1] = 0
+corr[corr.eq(1).any(1)]
+
+ +
+
+
+ +
+
+ + +
+ +
Out[26]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LIMIT_BALSEXAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...PAY_3_Revolving_CreditPAY_4_No_TransactionsPAY_4_Pay_DulyPAY_4_Revolving_CreditPAY_5_No_TransactionsPAY_5_Pay_DulyPAY_5_Revolving_CreditPAY_6_No_TransactionsPAY_6_Pay_DulyPAY_6_Revolving_Credit
+

0 rows × 47 columns

+
+
+ +
+ +
+
+ +
+
+
+
In [27]:
+
+
+
size = df1.shape
+print("Data has {} Columns and {} Rows".format(size[1], size[0]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Data has 47 Columns and 26245 Rows
+
+
+
+ +
+
+ +
+
+
+
+

Train Test Split

Before we conduct feature selection and model selection, we split the data using a train test split according to the project description.

+ +
+
+
+
+
+
In [28]:
+
+
+
from sklearn.metrics import *
+from sklearn.model_selection import *
+
+ +
+
+
+ +
+
+
+
In [29]:
+
+
+
#using holdout sampling for train test split using seed 123
+np.random.seed(123) 
+ft = df1.drop("Y", axis = 1)
+target = df1["Y"]
+X_train,X_test,y_train,y_test = train_test_split(ft,target,test_size=1/3)
+
+ +
+
+
+ +
+
+
+
+

Filter method for feature selection

The filter method for feature selection entails selecting relevant attributes before moving on to learning phase. +We will utitlise univariate feature selection to reduce the features to the fewer more significant attributes.

+ +
+
+
+
+
+
In [30]:
+
+
+
from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import chi2
+
+selector = SelectKBest( score_func = chi2, k=10)
+selector.fit(X_train, y_train)
+np.set_printoptions(precision=10)
+chi2data = pd.DataFrame(selector.scores_)
+chi2data["pval"] = 1 - stats.chi2.cdf(chi2data, 43)
+chi2data.index = X_train.columns
+
+print("Significant values are:")
+print(chi2data[chi2data["pval"] < 0.05])
+
+cols = chi2data[chi2data["pval"] < 0.05].index
+X_train_filter = X_train[cols]
+X_test_filter = X_test[cols]
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Significant values are:
+                                  0          pval
+LIMIT_BAL                 82.306062  2.883753e-04
+PAY_0                   4279.993739  0.000000e+00
+PAY_2                   3557.072141  0.000000e+00
+PAY_3                   2766.119390  0.000000e+00
+PAY_4                   2736.965012  0.000000e+00
+PAY_5                   2587.002458  0.000000e+00
+PAY_6                   2240.874786  0.000000e+00
+PAY_0_No_Transactions     76.858872  1.147939e-03
+PAY_0_Revolving_Credit   480.805794  0.000000e+00
+PAY_2_Pay_Duly            75.283344  1.684018e-03
+PAY_2_Revolving_Credit   229.527990  0.000000e+00
+PAY_3_Pay_Duly            86.995856  8.229607e-05
+PAY_3_Revolving_Credit   121.059740  2.357071e-09
+PAY_4_Pay_Duly            79.449207  6.014800e-04
+PAY_4_Revolving_Credit    82.276504  2.906105e-04
+PAY_5_Pay_Duly            63.330298  2.338310e-02
+PAY_5_Revolving_Credit    64.659773  1.792035e-02
+
+
+
+ +
+
+ +
+
+
+
+

Model Selection

In this part, we will fit machine learning models learnt in BT2101 to this classification problem, and pick the model that can produce the best results.

+

We will be attempting to fit the following models:

+
    +
  • Decision Tree
    • +
  • Logistic Regression
    • +
  • Support Vector Machine
    • +
  • Neural Network
    • +
+

To make things easier, we define a get_roc function that will plot an ROC curve for all the models we evaluate, and a confusion matrix function.

+ +
+
+
+
+
+
In [31]:
+
+
+
def get_roc(model, y_test, X_test, name):
+    try:
+        fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]
+        tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]
+        thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]
+    except:
+        fpr = roc_curve(y_test,model.predict(X_test))[0]
+        tpr = roc_curve(y_test,model.predict(X_test))[1]
+        thresholds = roc_curve(y_test,model.predict(X_test))[2]
+    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.title('Receiver operating characteristic for ' + name)
+    plt.plot(fpr,tpr,label='ROC curve (AUC = %0.2f)' % (auc(fpr, tpr)))
+    plt.legend(loc="lower right")
+    
+    #find- best threshold
+    optimal_idx = np.argmax(tpr - fpr)
+    optimal_threshold = thresholds[optimal_idx]
+    print("Optimal Threshold: " + str(optimal_threshold))
+    
+    plt.show()
+    
+    return auc(fpr, tpr)
+
+ +
+
+
+ +
+
+
+
In [32]:
+
+
+
def get_optimal(model, y_test, X_test, name):
+    try:
+        fpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[0]
+        tpr = roc_curve(y_test,model.predict_proba(X_test)[:,1])[1]
+        thresholds = roc_curve(y_test,model.predict_proba(X_test)[:,1])[2]
+    except:
+        fpr = roc_curve(y_test,model.predict(X_test))[0]
+        tpr = roc_curve(y_test,model.predict(X_test))[1]
+        thresholds = roc_curve(y_test,model.predict(X_test))[2]
+    optimal_idx = np.argmax(tpr - fpr)
+    optimal_threshold = thresholds[optimal_idx]
+    return optimal_threshold
+
+ +
+
+
+ +
+
+
+
In [33]:
+
+
+
def confusion(y_test, predictions, name):
+    conf = pd.crosstab(y_test,predictions, rownames=['Actual'], colnames=['Predicted'])
+    print("Of " + str(conf[0][1] + conf[1][1]) + " Defaulters, the " + name + " identified " + str(conf[1][1])) 
+    return conf
+
+ +
+
+
+ +
+
+
+
+

Evaluation

We will select the model based on the model evaluation. The key metrics we will compute are:

+
    +
  1. Accuracy
    2. +
  2. Recall
    3. +
  3. AUROC
    4. +
+

Because of the nature of a default detection problem, we would like to prioritise recall for defaults. +This means we will place more importance in correctly identifying a defaulter than avoiding misclassifying a non-defaulter. (Assumming that the bank loses more money when lending to a defaulter than not lending to a non-defaulter)

+

However, simply predicting every data point as a defaulter will give us 100% recall. We have to also consider accuracy and AUROC to get a better idea of how our model performs.

+ +
+
+
+
+
+
In [34]:
+
+
+
evaluation = pd.DataFrame(columns=['Model', 'F1-1', 'AUROC'])
+
+ +
+
+
+ +
+
+
+
+

Decision Trees

Theory:

The decision tree algorithm aims to recursively split the data points in the training set until the data points are completely separated or well separated. At each iteration, the tree splits the datasets by the feature(s) that give the maximum reduction in heterogeneity, which is calculated by a heterogeneity index.

+

Below is a binary decision tree that has been split for a few iterations.

+

image.png

+

Since the target for this project is binary (fraud = yes or no) we will be building a binary decision tree, using the the GINI Index as the Heterogeneity index. The GINI is given by:

+

image.png

+

The GINI index measures how heterogenous a single node is (0 being completely homogenous and 1 being heterogenous). For each possible split, we will calculate the weighted sum of the GINI indices of the child nodes, and choose the split that results in the maximum information gain. i.e. reduction in the weighted sum of the GINI Index.

+

Training

We will now construct a simple decision tree using the GINI index.

+ +
+
+
+
+
+
In [35]:
+
+
+
from sklearn.tree import DecisionTreeClassifier
+
+ +
+
+
+ +
+
+
+
In [36]:
+
+
+
tree = DecisionTreeClassifier()
+tree.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[36]:
+ + + + +
+
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+                       max_features=None, max_leaf_nodes=None,
+                       min_impurity_decrease=0.0, min_impurity_split=None,
+                       min_samples_leaf=1, min_samples_split=2,
+                       min_weight_fraction_leaf=0.0, presort=False,
+                       random_state=None, splitter='best')
+
+ +
+ +
+
+ +
+
+
+
In [43]:
+
+
+
get_roc(tree, y_train, X_train, "Decision Tree (GINI)")
+print(classification_report(y_train, tree.predict(X_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.3333333333333333
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       1.00      1.00      1.00     13442
+           1       1.00      1.00      1.00      4054
+
+    accuracy                           1.00     17496
+   macro avg       1.00      1.00      1.00     17496
+weighted avg       1.00      1.00      1.00     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

The training set accuracy is 1, which means the datapoints are completely separated by the decision tree. We evaluate on the test set below.

+ +
+
+
+
+
+
In [40]:
+
+
+
confusion(y_test, tree.predict(X_test), "Decision Tree (GINI)")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Decision Tree (GINI) identified 809
+
+
+
+ +
+ +
Out[40]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
054821280
11178809
+
+
+ +
+ +
+
+ +
+
+
+
In [42]:
+
+
+
auroc = get_roc(tree, y_test, X_test, "Decision Tree (GINI)")
+print(classification_report(y_test, tree.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.5
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.82      0.81      0.82      6762
+           1       0.39      0.41      0.40      1987
+
+    accuracy                           0.72      8749
+   macro avg       0.61      0.61      0.61      8749
+weighted avg       0.72      0.72      0.72      8749
+
+
+
+
+ +
+
+ +
+
+
+
In [44]:
+
+
+
tree2 = DecisionTreeClassifier(criterion = "entropy")
+tree2.fit(X_train, y_train)
+confusion(y_test, tree2.predict(X_test), "Decision Tree (Entropy)")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Decision Tree (Entropy) identified 831
+
+
+
+ +
+ +
Out[44]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
055091253
11156831
+
+
+ +
+ +
+
+ +
+
+
+
In [45]:
+
+
+
get_roc(tree2, y_test, X_test, "Decision Tree (Entropy)")
+print(classification_report(y_test, tree2.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.5
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.81      0.82      6762
+           1       0.40      0.42      0.41      1987
+
+    accuracy                           0.72      8749
+   macro avg       0.61      0.62      0.61      8749
+weighted avg       0.73      0.72      0.73      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

There is negligible difference in using GINI or Entropy for decision trees. For the sake of simplicity, we will use GINI for the ensemble methods.

+ +
+
+
+
+
+
+

Random Forest Classifier

Theory

Random Forest is an ensemble method for the decision tree algorithm. It works by randomly choosing different features and data points to train multiple trees (that is, to form a forest) - and the resulting prediction is decided by the votes from all the trees.

+

Decision Trees are prone to overfitting on the training data, which reduces the performance on the test set. Random Forest mitigates this by training multiple trees. Random Forest is a form of bagging ensemble where the trees are trained concurrently.

+

Training

To keep things consistent, our Random Forest classifier will also use the GINI Coefficient.

+ +
+
+
+
+
+
In [46]:
+
+
+
from sklearn.ensemble import RandomForestClassifier
+randf = RandomForestClassifier(n_estimators=200)
+
+ +
+
+
+ +
+
+
+
In [47]:
+
+
+
randf.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[47]:
+ + + + +
+
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+                       max_depth=None, max_features='auto', max_leaf_nodes=None,
+                       min_impurity_decrease=0.0, min_impurity_split=None,
+                       min_samples_leaf=1, min_samples_split=2,
+                       min_weight_fraction_leaf=0.0, n_estimators=200,
+                       n_jobs=None, oob_score=False, random_state=None,
+                       verbose=0, warm_start=False)
+
+ +
+ +
+
+ +
+
+
+
In [48]:
+
+
+
print(classification_report(y_train, randf.predict(X_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       1.00      1.00      1.00     13442
+           1       1.00      1.00      1.00      4054
+
+    accuracy                           1.00     17496
+   macro avg       1.00      1.00      1.00     17496
+weighted avg       1.00      1.00      1.00     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

The training set has also been 100% correctly classified by the random forest model. Evaluating with the test set:

+ +
+
+
+
+
+
In [49]:
+
+
+
confusion(y_test, randf.predict(X_test), "Decision Tree (Random Forest)")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Decision Tree (Random Forest) identified 713
+
+
+
+ +
+ +
Out[49]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
06371391
11274713
+
+
+ +
+ +
+
+ +
+
+
+
In [53]:
+
+
+
auroc_rf = get_roc(randf, y_test, X_test, "Decision Tree (Random Forest)")
+print(classification_report(y_test, randf.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.27
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.94      0.88      6762
+           1       0.65      0.36      0.46      1987
+
+    accuracy                           0.81      8749
+   macro avg       0.74      0.65      0.67      8749
+weighted avg       0.79      0.81      0.79      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

The random forest ensemble performs much better than the decision tree alone. The accuracy and AUROC are both superior to the decision tree alone.

+ +
+
+
+
+
+
+

Gradient Boosted Trees Classifier

Theory

In this part we train a gradient boosted trees classifier. It is a boosting ensemble method for decision trees, which means that the trees are trained consecutively, where each new tree added is trained to correct the error from the previous tree.

+

Training

For consistency our xgBoost ensemble will use n_estimators = 300 as we have done for the random forest ensemble.

+ +
+
+
+
+
+
In [55]:
+
+
+
from sklearn.ensemble import GradientBoostingClassifier
+xgb = GradientBoostingClassifier(n_estimators=300, max_depth = 4)
+xgb.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[55]:
+ + + + +
+
GradientBoostingClassifier(criterion='friedman_mse', init=None,
+                           learning_rate=0.1, loss='deviance', max_depth=4,
+                           max_features=None, max_leaf_nodes=None,
+                           min_impurity_decrease=0.0, min_impurity_split=None,
+                           min_samples_leaf=1, min_samples_split=2,
+                           min_weight_fraction_leaf=0.0, n_estimators=300,
+                           n_iter_no_change=None, presort='auto',
+                           random_state=None, subsample=1.0, tol=0.0001,
+                           validation_fraction=0.1, verbose=0,
+                           warm_start=False)
+
+ +
+ +
+
+ +
+
+
+
In [56]:
+
+
+
print(classification_report(y_train, xgb.predict(X_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.86      0.96      0.91     13442
+           1       0.79      0.46      0.58      4054
+
+    accuracy                           0.85     17496
+   macro avg       0.82      0.71      0.74     17496
+weighted avg       0.84      0.85      0.83     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

We observe that the ensemble did not fully separate the data in the training set. (The default maximum depth is 3, so that might be a factor). Evaluating on the test set,

+ +
+
+
+
+
+
In [57]:
+
+
+
confusion(y_test, xgb.predict(X_test), "Decision Tree (Gradient Boosted Trees)")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717
+
+
+
+ +
+ +
Out[57]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
06381381
11270717
+
+
+ +
+ +
+
+ +
+
+
+
In [58]:
+
+
+
auroc = get_roc(xgb, y_test, X_test, "Decision Tree (XGBoost)")
+print(classification_report(y_test, xgb.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.24738247273049666
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

From both the accuracy metrics and the AUROC, we observe that the gradient boosted tree performs similarly to the random forest classifier. We will choose Random Forest as our model of choice using the decision tree algorithm.

+ +
+
+
+
+
+
In [59]:
+
+
+
evaluation.loc[0] = (["Decision Trees - Random Forest" , 
+                      classification_report(y_test, randf.predict(X_test), output_dict = True)["1"]["f1-score"],
+                      auroc_rf])
+
+ +
+
+
+ +
+
+
+
In [60]:
+
+
+
evaluation
+
+ +
+
+
+ +
+
+ + +
+ +
Out[60]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + +
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
+
+
+ +
+ +
+
+ +
+
+
+
+

Logistic Regression

Theory

Logistic regression is a regression technnique used to predict binary target variables. It works on the same principles as a linear regression model.

+

Our binary target (default vs non-default) can be expressed in terms of odds of defaulting, which is the ratio of the probability of default and probability of non-default.

+

In the logistic regression model, we log the odds (log-odds) and equate it to a weighted sum of regressors.

+

image.png

+

We then find weights for the regressors that best fits the data. Since the binary target (default or not) follows a bernoulli distribution, each data point has the following probability distribution function:

+

image.png

+

We would like to update p for each data point such that the log product (joint probability) of the above function for all data points is maximised. In other words, we are maximising the log-likelihood function.

+

The logistic regression equation produces a "squashed" curve like the one below. We then pick a cutoff value for the y axis to classify a data point as 0 (non-default) or 1 (default).

+

image.png

+

Training

We will adopt a top-down approach for training our logistic regression model, i.e. include all regressors first and then remove the most insignificant ones at each iteration to achieve the best fit.

+ +
+
+
+
+
+
In [61]:
+
+
+
import statsmodels.api as sm
+
+ +
+
+
+ +
+
+
+
In [62]:
+
+
+
glm = sm.Logit(y_train,X_train).fit()
+glm.summary()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Warning: Maximum number of iterations has been exceeded.
+         Current function value: 0.444770
+         Iterations: 35
+
+
+
+ +
+ +
+ + +
+
C:\Users\reonh\Anaconda3\lib\site-packages\statsmodels\base\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  "Check mle_retvals", ConvergenceWarning)
+
+
+
+ +
+ +
Out[62]:
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + +
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17450
Method: MLE Df Model: 45
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1784
Time: 00:13:23 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8737 0.115 -7.605 0.000 -1.099 -0.649
SEX -0.0964 0.041 -2.343 0.019 -0.177 -0.016
AGE 0.2097 0.100 2.095 0.036 0.013 0.406
PAY_0 0.6116 0.058 10.521 0.000 0.498 0.726
PAY_2 -0.5528 0.096 -5.763 0.000 -0.741 -0.365
PAY_3 -0.2063 0.124 -1.662 0.096 -0.450 0.037
PAY_4 -0.2327 0.160 -1.452 0.146 -0.547 0.081
PAY_5 -0.0302 0.181 -0.166 0.868 -0.385 0.325
PAY_6 0.4319 0.153 2.825 0.005 0.132 0.731
BILL_AMT1 -1.9057 0.554 -3.442 0.001 -2.991 -0.821
BILL_AMT2 1.1700 0.784 1.493 0.135 -0.366 2.706
BILL_AMT3 1.9680 0.729 2.700 0.007 0.540 3.396
BILL_AMT4 -0.4328 0.727 -0.595 0.552 -1.858 0.992
BILL_AMT5 -0.3910 0.882 -0.443 0.658 -2.120 1.338
BILL_AMT6 0.2306 0.800 0.288 0.773 -1.338 1.799
PAY_AMT1 -1.2427 0.308 -4.041 0.000 -1.845 -0.640
PAY_AMT2 -1.8767 0.389 -4.823 0.000 -2.639 -1.114
PAY_AMT3 -0.4002 0.299 -1.339 0.181 -0.986 0.186
PAY_AMT4 -0.5031 0.293 -1.715 0.086 -1.078 0.072
PAY_AMT5 -0.7629 0.295 -2.589 0.010 -1.341 -0.185
PAY_AMT6 -0.6658 0.266 -2.504 0.012 -1.187 -0.145
MISSING-EDU -14.2753 1898.465 -0.008 0.994 -3735.198 3706.648
GRAD 1.3518 0.220 6.148 0.000 0.921 1.783
UNI 1.3056 0.219 5.971 0.000 0.877 1.734
HS 1.2342 0.223 5.547 0.000 0.798 1.670
MISSING-MS -30.7439 1.14e+06 -2.7e-05 1.000 -2.23e+06 2.23e+06
MARRIED 0.0794 0.177 0.449 0.653 -0.267 0.426
SINGLE -0.1024 0.177 -0.577 0.564 -0.450 0.245
PAY_0_No_Transactions -0.1746 0.123 -1.415 0.157 -0.416 0.067
PAY_0_Pay_Duly 0.0483 0.120 0.402 0.688 -0.187 0.284
PAY_0_Revolving_Credit -0.9702 0.135 -7.181 0.000 -1.235 -0.705
PAY_2_No_Transactions -1.4826 0.233 -6.359 0.000 -1.940 -1.026
PAY_2_Pay_Duly -1.3804 0.221 -6.244 0.000 -1.814 -0.947
PAY_2_Revolving_Credit -0.7926 0.226 -3.514 0.000 -1.235 -0.350
PAY_3_No_Transactions -0.6881 0.297 -2.317 0.021 -1.270 -0.106
PAY_3_Pay_Duly -0.7811 0.272 -2.869 0.004 -1.315 -0.247
PAY_3_Revolving_Credit -0.7137 0.261 -2.740 0.006 -1.224 -0.203
PAY_4_No_Transactions -0.9092 0.360 -2.529 0.011 -1.614 -0.205
PAY_4_Pay_Duly -0.9199 0.341 -2.699 0.007 -1.588 -0.252
PAY_4_Revolving_Credit -0.8088 0.331 -2.442 0.015 -1.458 -0.160
PAY_5_No_Transactions -0.0741 0.401 -0.185 0.853 -0.860 0.711
PAY_5_Pay_Duly -0.2557 0.386 -0.663 0.507 -1.011 0.500
PAY_5_Revolving_Credit -0.2701 0.376 -0.718 0.473 -1.008 0.467
PAY_6_No_Transactions 0.6784 0.335 2.025 0.043 0.022 1.335
PAY_6_Pay_Duly 0.7000 0.328 2.134 0.033 0.057 1.343
PAY_6_Revolving_Credit 0.5159 0.320 1.615 0.106 -0.110 1.142
+
+ +
+ +
+
+ +
+
+
+
In [64]:
+
+
+
print(classification_report(y_train,list(glm.predict(X_train)>0.5)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.95      0.88     13442
+           1       0.67      0.36      0.47      4054
+
+    accuracy                           0.81     17496
+   macro avg       0.75      0.65      0.68     17496
+weighted avg       0.79      0.81      0.79     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

The logisitc model with all features performs average on both the train and test set with an accuracy of about 0.8 but recall and f1 are still below 0.5. We will now try removing all the insignificant features to see how that affects the model performance.

+ +
+
+
+
+
+
In [65]:
+
+
+
#remove the most insignificant attribute, and retrain
+train_log =  X_train.copy()
+glm = sm.Logit(y_train,train_log).fit()
+while max(glm.pvalues) > 0.01:
+    least =  glm.pvalues[glm.pvalues == max(glm.pvalues)].index[0]
+    train_log = train_log.drop(least,axis = 1)
+    glm = sm.Logit(y_train,train_log).fit()
+glm.summary()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Warning: Maximum number of iterations has been exceeded.
+         Current function value: 0.444770
+         Iterations: 35
+
+
+
+ +
+ +
+ + +
+
C:\Users\reonh\Anaconda3\lib\site-packages\statsmodels\base\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  "Check mle_retvals", ConvergenceWarning)
+
+
+
+ +
+ +
+ + +
+
Warning: Maximum number of iterations has been exceeded.
+         Current function value: 0.445360
+         Iterations: 35
+Optimization terminated successfully.
+         Current function value: 0.445386
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445386
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445387
+         Iterations 7
+
+
+
+ +
+ +
+ + +
+
C:\Users\reonh\Anaconda3\lib\site-packages\statsmodels\base\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+  "Check mle_retvals", ConvergenceWarning)
+
+
+
+ +
+ +
+ + +
+
Optimization terminated successfully.
+         Current function value: 0.445388
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445392
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445397
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445410
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445455
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445512
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445596
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445680
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445770
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445853
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445877
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.445963
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.446090
+         Iterations 7
+Optimization terminated successfully.
+         Current function value: 0.446288
+         Iterations 7
+
+
+
+ +
+ +
Out[65]:
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + +
Logit Regression Results
Dep. Variable: Y No. Observations: 17496
Model: Logit Df Residuals: 17468
Method: MLE Df Model: 27
Date: Fri, 22 Nov 2019 Pseudo R-squ.: 0.1756
Time: 00:14:16 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
coef std err z P>|z| [0.025 0.975]
LIMIT_BAL -0.8984 0.113 -7.922 0.000 -1.121 -0.676
SEX -0.1153 0.041 -2.847 0.004 -0.195 -0.036
PAY_0 0.6189 0.037 16.520 0.000 0.545 0.692
PAY_2 -0.5692 0.088 -6.463 0.000 -0.742 -0.397
PAY_3 -0.2710 0.082 -3.313 0.001 -0.431 -0.111
PAY_6 0.2151 0.031 6.899 0.000 0.154 0.276
BILL_AMT1 -1.3934 0.368 -3.784 0.000 -2.115 -0.672
BILL_AMT3 2.0154 0.435 4.638 0.000 1.164 2.867
PAY_AMT1 -1.2565 0.287 -4.371 0.000 -1.820 -0.693
PAY_AMT2 -2.1865 0.376 -5.816 0.000 -2.923 -1.450
PAY_AMT5 -0.8702 0.265 -3.279 0.001 -1.390 -0.350
PAY_AMT6 -0.7982 0.266 -3.000 0.003 -1.320 -0.277
GRAD 1.3465 0.175 7.687 0.000 1.003 1.690
UNI 1.2982 0.174 7.462 0.000 0.957 1.639
HS 1.2384 0.178 6.960 0.000 0.890 1.587
MARRIED 0.2359 0.042 5.643 0.000 0.154 0.318
PAY_0_Revolving_Credit -0.9811 0.093 -10.583 0.000 -1.163 -0.799
PAY_2_No_Transactions -1.5901 0.220 -7.230 0.000 -2.021 -1.159
PAY_2_Pay_Duly -1.4026 0.200 -7.010 0.000 -1.795 -1.010
PAY_2_Revolving_Credit -0.8163 0.202 -4.051 0.000 -1.211 -0.421
PAY_3_No_Transactions -0.8432 0.228 -3.701 0.000 -1.290 -0.397
PAY_3_Pay_Duly -0.8926 0.196 -4.566 0.000 -1.276 -0.509
PAY_3_Revolving_Credit -0.8227 0.179 -4.586 0.000 -1.174 -0.471
PAY_4_No_Transactions -0.4537 0.143 -3.172 0.002 -0.734 -0.173
PAY_4_Pay_Duly -0.5711 0.107 -5.328 0.000 -0.781 -0.361
PAY_4_Revolving_Credit -0.4353 0.075 -5.806 0.000 -0.582 -0.288
PAY_6_No_Transactions 0.3028 0.089 3.399 0.001 0.128 0.477
PAY_6_Pay_Duly 0.2489 0.078 3.197 0.001 0.096 0.402
+
+ +
+ +
+
+ +
+
+
+
In [66]:
+
+
+
count = len(glm.pvalues.index)
+print(str(count) + " Columns left:")
+print(glm.pvalues.index)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
28 Columns left:
+Index(['LIMIT_BAL', 'SEX', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_6', 'BILL_AMT1',
+       'BILL_AMT3', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT5', 'PAY_AMT6', 'GRAD',
+       'UNI', 'HS', 'MARRIED', 'PAY_0_Revolving_Credit',
+       'PAY_2_No_Transactions', 'PAY_2_Pay_Duly', 'PAY_2_Revolving_Credit',
+       'PAY_3_No_Transactions', 'PAY_3_Pay_Duly', 'PAY_3_Revolving_Credit',
+       'PAY_4_No_Transactions', 'PAY_4_Pay_Duly', 'PAY_4_Revolving_Credit',
+       'PAY_6_No_Transactions', 'PAY_6_Pay_Duly'],
+      dtype='object')
+
+
+
+ +
+
+ +
+
+
+
In [67]:
+
+
+
print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>0.5)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.95      0.89      6762
+           1       0.68      0.36      0.47      1987
+
+    accuracy                           0.82      8749
+   macro avg       0.76      0.65      0.68      8749
+weighted avg       0.80      0.82      0.79      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

Since there is not much change to the model performance on both the train and test set when we reduce the features, we will use the reduced logistic regression model from this point onwards (Principle of Parsimony).

+

We now Calculate the AUROC for the train set.

+ +
+
+
+
+
+
In [68]:
+
+
+
optimal_log = get_optimal(glm, y_train, X_train[glm.pvalues.index], "Logistic Regression")
+get_roc(glm, y_train, X_train[glm.pvalues.index], "Logistic Regression")
+print(classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])> optimal_log)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.21650864211883647
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.88      0.78      0.83      6762
+           1       0.46      0.62      0.53      1987
+
+    accuracy                           0.75      8749
+   macro avg       0.67      0.70      0.68      8749
+weighted avg       0.78      0.75      0.76      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

Since the optimal cut off was found to be ~0.22 using the training data, we used that as our cut off for our evaluation of the test set.

+ +
+
+
+
+
+
+

Unfortunately, the training accuracy has gone down when we used the optimal cutoff. However, accuracy may be misleading in a dataset like ours where most of the targets are non-defaults.

+

The recall here is more important - detecting defaulters is more useful than detecting non-defaulters. With a higher recall, our model with lower cutoff is able to correctly catch more defaulters. We note that this increase in recall is greater than the increase in F-1.

+

Calculate the confusion matrices for both cut offs to better compare their performance.

+ +
+
+
+
+
+
In [70]:
+
+
+
confusion(y_test,glm.predict(X_test[glm.pvalues.index])>optimal_log, "Logistic Regression")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Logistic Regression identified 1235
+
+
+
+ +
+ +
Out[70]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
PredictedFalseTrue
Actual
053031459
17521235
+
+
+ +
+ +
+
+ +
+
+
+
In [71]:
+
+
+
confusion(y_test,glm.predict(X_test[glm.pvalues.index])>0.50, "Logistic Regression")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the Logistic Regression identified 715
+
+
+
+ +
+ +
Out[71]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
PredictedFalseTrue
Actual
06421341
11272715
+
+
+ +
+ +
+
+ +
+
+
+
+

It is evident that the lower cutoff is better able to detect defaults.

+ +
+
+
+
+
+
In [72]:
+
+
+
auroc = get_roc(glm, y_test, X_test[glm.pvalues.index], "Logistic Regression")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.24907536768337235
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [75]:
+
+
+
evaluation.loc[1] = ["Logistic Regression (Optimal Cutoff)" , 
+                     classification_report(y_test,list(glm.predict(X_test[glm.pvalues.index])>optimal_log), output_dict = True)["1"]["f1-score"],
+                     auroc]
+
+ +
+
+
+ +
+
+
+
In [76]:
+
+
+
evaluation
+
+ +
+
+
+ +
+
+ + +
+ +
Out[76]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
+
+
+ +
+ +
+
+ +
+
+
+
+

Support Vector Machine

Theory

Support vector machines attempt to find an optimal hyperplane that is able to separate the two classes in n-dimensional space. This is done by finding the hyperplane that maximises the distance between itself and support vectors (data points that lie closest to the decision boundary).

+

+

Given a training dataset of form (X,Y), where X is a vector of attributes of the sample, and where Y are either 1 or -1, each indicating the class to which the point X belongs. We want to find the "maximum-margin hyperplane" that divides the group of points X which Y = 1 from the group of points for which Y = -1, which is defined so that the distance between the hyperplane and the nearest point X from either group is maximized.

+

Any hyperplane can be written as the set of points X satisfying

+ + +
+ +

where W is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that W is not necessarily a unit vector. The parameter b/||W|| determines the offset of the hyperplane from the origin along the normal vector W.

+

Margin

A margin is a separation of line to the closest class points. +Very importrant characteristic of SVM classifier. SVM to core tries to achieve a good margin. +A good margin is one where this separation is larger for both the classes. Images below gives to visual example of good and bad margin. A good margin allows the points to be in their respective classes without crossing to other class.

+ + + +

Our goal is to maximize the margin. Among all possible hyperplanes meeting the constraints, we will choose the hyperplane with the smallest ‖w‖ because it is the one which will have the biggest margin.

+
Hard Margin

If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the "margin", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by an equation and we can put this together to get the optimization problem:

+

Minimize ||W|| subject to:

+ + +
+
Soft Margin

Often, the data are not linearly separable. Thus, to extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function,

+ + +
+

This function is zero if the constraint in (1) is satisfied, in other words, if Xlies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.

+

We then wish to minimize

+ + +
+

where the parameter lambda determines the trade-off between increasing the margin size and ensuring that the X lie on the correct side of the margin. Thus, for sufficiently small values of lambda , the second term in the loss function will become negligible, hence, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not.

+

Computing SVM classifier

We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for lambda yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing (2) to a quadratic programming problem, is detailed below.

+
Primal

Minimizing (2) can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.

+

We can rewrite the optimization problem as follows

+ + +
+

This is called the primal problem.

+
Dual

By solving for the Lagrangian dual of the above problem, one obtains the simplified problem

+ + +
+
+ + +
+
+This is called the dual problem. Since the dual maximization problem is a quadratic function of the C subject to linear constraints, it is efficiently solvable by quadratic programming algorithms. + +Here, the variables C are defined such that + + +
+
+
+
+
+
+
+
+

Parameter Tuning

+
+
+
+
+
+
+

Kernel

For a dataset that is non-linearly separable, a way is to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.

+

Generally, Linear Kernel is the best choice as SVM is already a linear model and has the lowest computational complexity. More often, if the dataset is not linearly separable, Radial Basis Function is the next most common kernel to be used.

+ +
+
+
+
+
+
+

Regularization (C value)

The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.

+

For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points.

+

+

+

+</tr></table> +Left: low regularization value, right: high regularization value

+

The tradeoff is however, a larger value of C might result in overfitting of the model, which is undersirable as it does not generalise the model for other data sets. Consequently, a smaller value of C might result in too many misclassified data points, which means that the model is low in accuracy in the first place. Thus, choosing the right balance of the C value is important.

+ + + + +
+
+
+

Gamma

The gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. In other words, with low gamma, points far away from plausible separation line are considered in the calculation for the separation line. Where as high gamma means the points close to plausible line are considered in the calculation.

+
+ + +

Similarly to regularization, there is a tradeoff between high and low values of Gamma. Higher values of Gamma may result in too strict rules and thus the model cannot find a linearly separable line. Whereas lower values of Gamma may result in including too much noise into the model by including further away points, which reduces the model accuracy. Thus, finding the right balance of Gamma is also important.

+ +
+
+
+
+
+
+

Apply SVM model

For this dataset, we will perform SVM to the model and access its accuracy progressively, starting from no parameter tuning.

+ +
+
+
+
+
+
+

SVM without parameter tuning

First, we train our SVM model without parameter tuning. Note that the default kernel for sklearn svm is "rbf" and cost = 1.0 and gamma = 1/n where n is the number of samples.

+ +
+
+
+
+
+
In [78]:
+
+
+
from sklearn import svm
+#train svm model without standardization and no parameter tuning
+clf_original = svm.SVC( probability = True, gamma = 'scale')
+clf_original.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[78]:
+ + + + +
+
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
+    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
+    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,
+    verbose=False)
+
+ +
+ +
+
+ +
+
+
+
In [79]:
+
+
+
#plot roc for svm
+get_roc(clf_original, y_test, X_test, "SVM default parameters")
+print(classification_report(y_test, clf_original.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.16469105377809068
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.95      0.89      6762
+           1       0.68      0.36      0.47      1987
+
+    accuracy                           0.82      8749
+   macro avg       0.76      0.66      0.68      8749
+weighted avg       0.80      0.82      0.79      8749
+
+
+
+
+ +
+
+ +
+
+
+
In [80]:
+
+
+
#confusion matrix
+confusion(y_test,clf_original.predict(X_test), "SVM default parameters")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the SVM default parameters identified 713
+
+
+
+ +
+ +
Out[80]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
06432330
11274713
+
+
+ +
+ +
+
+ +
+
+
+
+

Based on AUROC and Recall, the SVM model with default parameters seem to do average compared to the other models tested. Now let's search for the best parameters to tune the model.

+ +
+
+
+
+
+
+

SVM with Parameter tuning

One way to find the best parameters for the model is using grid search via GridSearchCV package from sklearn.

+

Grid search is the process of performing hyper parameter tuning in order to determine the optimal values for a given model. This is significant as the performance of the entire model is based on the hyper parameter values specified.

+

GridSearchSV works by using a cross validation process to determine the hyper parameter value set which provides the best accuracy levels. We will start with the linear kernel and move on to rbf if necessary.

+ +
+
+
+
+
+
In [81]:
+
+
+
from sklearn.model_selection import GridSearchCV
+def svc_param_selection(X, y, nfolds):
+    Cs = [0.01, 0.1, 1]
+    gammas = [0.01, 0.1, 1]
+    param_grid = {'C': Cs, 'gamma' : gammas}
+    grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds, scoring = 'recall')
+    grid_search.fit(X, y)
+    grid_search.best_params_
+    return grid_search.best_params_
+svc_param_selection(X_train, y_train,2)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[81]:
+ + + + +
+
{'C': 0.01, 'gamma': 0.01}
+
+ +
+ +
+
+ +
+
+
+
+

With 5 folds, it can be found that C = 0.01 , and gamma = 0.01 will have the best svm model with kernel

+ +
+
+
+
+
+
In [82]:
+
+
+
#train svm model with feature reduction and cost = 0.01, gamma = 0.01, linear kernel
+clf_reduced_tuned = svm.SVC(kernel = 'linear', probability = True, C = 0.01, gamma = 0.01 )
+clf_reduced_tuned.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[82]:
+ + + + +
+
SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,
+    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='linear',
+    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,
+    verbose=False)
+
+ +
+ +
+
+ +
+
+
+
In [83]:
+
+
+
auroc = get_roc(clf_reduced_tuned, y_test, X_test, 
+        "SVM reduced features and tuning linear kernel")
+print(classification_report(y_test, clf_reduced_tuned.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.15996357777982226
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.83      0.96      0.89      6762
+           1       0.70      0.32      0.44      1987
+
+    accuracy                           0.81      8749
+   macro avg       0.77      0.64      0.66      8749
+weighted avg       0.80      0.81      0.79      8749
+
+
+
+
+ +
+
+ +
+
+
+
In [84]:
+
+
+
#confusion matrix
+confusion(y_test,clf_reduced_tuned.predict(X_test), "SVM reduced features and tuning linear kernal")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Of 1987 Defaulters, the SVM reduced features and tuning linear kernal identified 638
+
+
+
+ +
+ +
Out[84]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Predicted01
Actual
06492270
11349638
+
+
+ +
+ +
+
+ +
+
+
+
+

As shown, the AUROC actually increased with tuning of parameters. Next, we will experiment with the RBF kernel

+ +
+
+
+
+
+
In [85]:
+
+
+
#train svm model with feature reduction and cost = 0.1, gamma = 0.1, rbf kernel
+clf_reduced_tuned_rbf = svm.SVC(kernel = 'rbf', probability = True, C = 0.1, gamma = 0.1)
+clf_reduced_tuned_rbf.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[85]:
+ + + + +
+
SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,
+    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',
+    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,
+    verbose=False)
+
+ +
+ +
+
+ +
+
+
+
In [86]:
+
+
+
auroc = get_roc(clf_reduced_tuned_rbf, y_test, X_test, 
+        "SVM reduced features and tuning rbf kernel")
+print(classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.15910713557498266
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.84      0.95      0.89      6762
+           1       0.67      0.38      0.48      1987
+
+    accuracy                           0.82      8749
+   macro avg       0.76      0.66      0.69      8749
+weighted avg       0.80      0.82      0.80      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

As shown, the rbf kernel increases the AUROC and the recall increased to 0.40, thus, it can be said that the rbf kernel is better than the linear kernel. We will choose the RBF SVM as our best performing support vector machine.

+ +
+
+
+
+
+
In [87]:
+
+
+
evaluation.loc[2] = (["SVM-RBF (Grid Search)" , 
+                      classification_report(y_test, clf_reduced_tuned_rbf.predict(X_test), output_dict = True)["1"]["f1-score"],
+                      auroc])
+
+evaluation
+
+ +
+
+
+ +
+
+ + +
+ +
Out[87]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
+
+
+ +
+ +
+
+ +
+
+
+
+

SVM with filtered features

+
+
+
+
+
+
+

We will now apply the best selected kernel (linear kernel) on filtered features to access AUROC and recall.

+ +
+
+
+
+
+
In [88]:
+
+
+
clf_reduced_tuned_filtered = svm.SVC(kernel = 'rbf', probability = True)
+clf_reduced_tuned_filtered.fit(X_train_filter, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
C:\Users\reonh\Anaconda3\lib\site-packages\sklearn\svm\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
+  "avoid this warning.", FutureWarning)
+
+
+
+ +
+ +
Out[88]:
+ + + + +
+
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
+    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
+    kernel='rbf', max_iter=-1, probability=True, random_state=None,
+    shrinking=True, tol=0.001, verbose=False)
+
+ +
+ +
+
+ +
+
+
+
In [89]:
+
+
+
get_roc(clf_reduced_tuned_filtered, y_test, X_test_filter, 
+        "SVM reduced features and tuning linear kernel")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.16104553371241384
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
Out[89]:
+ + + + +
+
0.6689738476077944
+
+ +
+ +
+
+ +
+
+
+
In [90]:
+
+
+
print(classification_report(y_test, clf_reduced_tuned_filtered.predict(X_test_filter)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.84      0.95      0.89      6762
+           1       0.67      0.37      0.48      1987
+
+    accuracy                           0.82      8749
+   macro avg       0.75      0.66      0.68      8749
+weighted avg       0.80      0.82      0.79      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

As we can see, the performance is not as great after using filtered features. The AUROC decreased while the recall remained the same. Thus, we will stick to using unfiltered features and SVM with rbf kernel.

+ +
+
+
+
+
+
+

Neural Networks

We will now use the train and test sets as defined above and attempt to implement a neural network model on the data

+

Theory

A neural network is comprised of many layers of perceptrons that take in a vector as input and outputs a value. The outputs from one layer of perceptrons are passed into the next layer of perceptrons as input, until we reach the output layer. Each perceptron combines its input via an activation function.

+

.

+

image.png

+

The network is at first randomly initialised with random weights on all its layers. Training samples are then passed into the network and predictions are made. The training error (difference between the actual value and the predicted value) is used to recalibrate the neural network by changing the weights. The change in weights is found via gradient descent, and then backpropogated through the neural network to update all layers.

+

This process is repeated iteratively until the model converges (i.e. it cannot be improved further).

+ +
+
+
+
+
+
+

Training

Here we create an instance of our model, specifically a Sequential model, and add layers one at a time until we are happy with our network architecture. We will be training the model on our feature-selected dataset to speed up computation by reducing dimensionality. We have found that the performance difference between the 2 datasets are negligible.

+

We ensure the input layer has the right number of input features, and is specified when creating the first layer with the input_dim argument and setting it to 17 (The size of the feature selected dataset). +Additionaly, we start off using a fully-connected network structure with three layers, and attempt to increase the number of layers at later part ater fully optimising our model.

+

Fully connected layers are defined using the Dense class. We specify the number of neurons or nodes in the layer as the first argument, and specify the activation function using the activation argument. The rectified linear unit activation function (Relu) is usedon the first two layers and the Sigmoid function in the output layer.

+

Conventionally, Sigmoid and Tanh activation functions were preferred for all layers. However, better performance is achieved using the ReLU activation function. We use a sigmoid on the output layer to ensure our network output is between 0 and 1 and easy to map (binary) to either a probability of class 1 or snap to a hard classification of either class with a default threshold of 0.5.

+

The model expects rows of data with 17 variables (the input_dim=17 argument) +The first hidden layer has 17 nodes and uses the relu activation function. +The second hidden layer has 17 nodes and uses the relu activation function. +The output layer has one node and uses the sigmoid activation function.

+ +
+
+
+
+
+
+

Compiling

The model uses the efficient numerical libraries as the backend, and in this case Tensorflow is used. The backend automatically chooses the best way to represent the network for training and making predictions to run.

+

We must specify the loss function to use to evaluate a set of weights, the optimizer is used to search through different weights for the network and any optional metrics we would like to collect and report during training.

+

After experimenting with the various loss functions, such as hinge loss and binary cross entropy, we found that entropy performed the best for our dataset.

+

We have also found that among all the optimizers (Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam) the optimizer "adam" is the most efficient yet yields the best results.

+

Additionaly, for this problem, we will run for a small number of epochs (20) and use a relatively small batch size of 10. This means that each epoch will involve (20/10) 2 updates to the model weights. After we have finalised the best optimizer, we will then increase the numebr of epochs to increase the number of updates to obtain a better result.

+ +
+
+
+
+
+
In [91]:
+
+
+
from numpy import loadtxt
+from keras.models import Sequential
+from keras.layers import Dense
+
+# optimisers to try: Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax and Nadam
+# verdict : Adam
+
+# Loss function to try: Binary Cross Entropy, Hinge, Logcosh
+# verdict: Binary Cross Entropy
+
+# define the keras model
+model = Sequential()
+model.add(Dense(12, input_dim=17, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train_filter, y_train, epochs=20, batch_size=10)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Using TensorFlow backend.
+
+
+
+ +
+ +
+ + +
+
Epoch 1/20
+17496/17496 [==============================] - 2s 110us/step - loss: 0.4708 - accuracy: 0.7956
+Epoch 2/20
+17496/17496 [==============================] - 2s 107us/step - loss: 0.4502 - accuracy: 0.8116
+Epoch 3/20
+17496/17496 [==============================] - 2s 113us/step - loss: 0.4477 - accuracy: 0.8125
+Epoch 4/20
+17496/17496 [==============================] - 2s 96us/step - loss: 0.4461 - accuracy: 0.8130
+Epoch 5/20
+17496/17496 [==============================] - 2s 105us/step - loss: 0.4450 - accuracy: 0.8133
+Epoch 6/20
+17496/17496 [==============================] - 2s 109us/step - loss: 0.4443 - accuracy: 0.81450s - loss: 0.4424 - 
+Epoch 7/20
+17496/17496 [==============================] - 2s 96us/step - loss: 0.4437 - accuracy: 0.8150
+Epoch 8/20
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4435 - accuracy: 0.8144
+Epoch 9/20
+17496/17496 [==============================] - 2s 103us/step - loss: 0.4433 - accuracy: 0.8147
+Epoch 10/20
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4429 - accuracy: 0.8142
+Epoch 11/20
+17496/17496 [==============================] - 2s 118us/step - loss: 0.4418 - accuracy: 0.8132
+Epoch 12/20
+17496/17496 [==============================] - 2s 112us/step - loss: 0.4416 - accuracy: 0.81450s - loss: 0.4413 - accuracy: 
+Epoch 13/20
+17496/17496 [==============================] - 2s 97us/step - loss: 0.4419 - accuracy: 0.8138
+Epoch 14/20
+17496/17496 [==============================] - 2s 128us/step - loss: 0.4417 - accuracy: 0.8140
+Epoch 15/20
+17496/17496 [==============================] - 2s 115us/step - loss: 0.4415 - accuracy: 0.8142
+Epoch 16/20
+17496/17496 [==============================] - 2s 118us/step - loss: 0.4415 - accuracy: 0.8141
+Epoch 17/20
+17496/17496 [==============================] - 2s 108us/step - loss: 0.4409 - accuracy: 0.8152
+Epoch 18/20
+17496/17496 [==============================] - 2s 127us/step - loss: 0.4413 - accuracy: 0.8145
+Epoch 19/20
+17496/17496 [==============================] - 2s 126us/step - loss: 0.4403 - accuracy: 0.8145
+Epoch 20/20
+17496/17496 [==============================] - 2s 110us/step - loss: 0.4405 - accuracy: 0.8156
+
+
+
+ +
+ +
Out[91]:
+ + + + +
+
<keras.callbacks.callbacks.History at 0x17a5fc89b38>
+
+ +
+ +
+
+ +
+
+
+
In [92]:
+
+
+
get_roc(model, y_test, X_test_filter,  "Neural Network 17x8x8x1 Adam, Entropy, 20 epoch")
+predictions = list(model.predict(X_test_filter).ravel() > 0.5)
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.23287344
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.84      0.94      0.89      6762
+           1       0.65      0.39      0.49      1987
+
+    accuracy                           0.81      8749
+   macro avg       0.75      0.66      0.69      8749
+weighted avg       0.80      0.81      0.80      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

Experimenting with lowering the cutoff for the neural network,

+ +
+
+
+
+
+
In [93]:
+
+
+
optimal_adam = get_optimal(model, y_train, X_train_filter, "Neural Network 17x8x8x1 Adam Entropy")
+auroc = get_roc(model, y_test, X_test_filter, "Neural Network 17x8x8x1 Adam, Entropy")
+predictions = list(model.predict(X_test_filter).ravel() > optimal_adam)
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.23287344
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.87      0.81      0.84      6762
+           1       0.48      0.60      0.54      1987
+
+    accuracy                           0.76      8749
+   macro avg       0.68      0.71      0.69      8749
+weighted avg       0.79      0.76      0.77      8749
+
+
+
+
+ +
+
+ +
+
+
+
+

The performance is quite impressive when we lowered the classification cut off. The ROC of 0.76 is also on par with other models. Now we ramp up the number of epochs.

+ +
+
+
+
+
+
In [94]:
+
+
+
model50 = Sequential()
+model50.add(Dense(12, input_dim=17, activation='relu'))
+model50.add(Dense(8, activation='relu'))
+model50.add(Dense(8, activation='relu'))
+model50.add(Dense(8, activation='relu'))
+model50.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model50.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+# fit the keras model on the dataset
+model50.fit(X_train_filter, y_train, epochs=50, batch_size=10)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Epoch 1/50
+17496/17496 [==============================] - 2s 112us/step - loss: 0.4863 - accuracy: 0.7969
+Epoch 2/50
+17496/17496 [==============================] - 2s 102us/step - loss: 0.4511 - accuracy: 0.8122
+Epoch 3/50
+17496/17496 [==============================] - 2s 103us/step - loss: 0.4485 - accuracy: 0.8124
+Epoch 4/50
+17496/17496 [==============================] - 2s 105us/step - loss: 0.4472 - accuracy: 0.81200s - loss: 0.4465 
+Epoch 5/50
+17496/17496 [==============================] - 2s 103us/step - loss: 0.4461 - accuracy: 0.8123
+Epoch 6/50
+17496/17496 [==============================] - 2s 119us/step - loss: 0.4450 - accuracy: 0.8124
+Epoch 7/50
+17496/17496 [==============================] - 2s 112us/step - loss: 0.4432 - accuracy: 0.8138
+Epoch 8/50
+17496/17496 [==============================] - 2s 103us/step - loss: 0.4428 - accuracy: 0.8139
+Epoch 9/50
+17496/17496 [==============================] - 2s 104us/step - loss: 0.4422 - accuracy: 0.8132
+Epoch 10/50
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4420 - accuracy: 0.8140
+Epoch 11/50
+17496/17496 [==============================] - 2s 97us/step - loss: 0.4411 - accuracy: 0.8137
+Epoch 12/50
+17496/17496 [==============================] - 2s 104us/step - loss: 0.4410 - accuracy: 0.8146
+Epoch 13/50
+17496/17496 [==============================] - 2s 97us/step - loss: 0.4412 - accuracy: 0.8143
+Epoch 14/50
+17496/17496 [==============================] - 2s 98us/step - loss: 0.4415 - accuracy: 0.8141
+Epoch 15/50
+17496/17496 [==============================] - 2s 120us/step - loss: 0.4402 - accuracy: 0.8149
+Epoch 16/50
+17496/17496 [==============================] - 2s 103us/step - loss: 0.4402 - accuracy: 0.8146
+Epoch 17/50
+17496/17496 [==============================] - 2s 100us/step - loss: 0.4406 - accuracy: 0.8141
+Epoch 18/50
+17496/17496 [==============================] - 2s 110us/step - loss: 0.4400 - accuracy: 0.8152
+Epoch 19/50
+17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8140
+Epoch 20/50
+17496/17496 [==============================] - 2s 100us/step - loss: 0.4398 - accuracy: 0.8140
+Epoch 21/50
+17496/17496 [==============================] - 2s 104us/step - loss: 0.4396 - accuracy: 0.8153
+Epoch 22/50
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4401 - accuracy: 0.8138
+Epoch 23/50
+17496/17496 [==============================] - 2s 112us/step - loss: 0.4394 - accuracy: 0.8150
+Epoch 24/50
+17496/17496 [==============================] - 2s 119us/step - loss: 0.4397 - accuracy: 0.8152
+Epoch 25/50
+17496/17496 [==============================] - 2s 117us/step - loss: 0.4396 - accuracy: 0.8148
+Epoch 26/50
+17496/17496 [==============================] - 2s 106us/step - loss: 0.4396 - accuracy: 0.8144
+Epoch 27/50
+17496/17496 [==============================] - 2s 95us/step - loss: 0.4393 - accuracy: 0.8135
+Epoch 28/50
+17496/17496 [==============================] - 2s 100us/step - loss: 0.4390 - accuracy: 0.8152
+Epoch 29/50
+17496/17496 [==============================] - 2s 115us/step - loss: 0.4392 - accuracy: 0.8138
+Epoch 30/50
+17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8141
+Epoch 31/50
+17496/17496 [==============================] - 2s 102us/step - loss: 0.4397 - accuracy: 0.8149
+Epoch 32/50
+17496/17496 [==============================] - 2s 127us/step - loss: 0.4390 - accuracy: 0.8147
+Epoch 33/50
+17496/17496 [==============================] - 2s 115us/step - loss: 0.4388 - accuracy: 0.8145
+Epoch 34/50
+17496/17496 [==============================] - 2s 115us/step - loss: 0.4385 - accuracy: 0.8153
+Epoch 35/50
+17496/17496 [==============================] - 2s 102us/step - loss: 0.4389 - accuracy: 0.8150
+Epoch 36/50
+17496/17496 [==============================] - 2s 100us/step - loss: 0.4391 - accuracy: 0.8146
+Epoch 37/50
+17496/17496 [==============================] - 2s 107us/step - loss: 0.4386 - accuracy: 0.8142
+Epoch 38/50
+17496/17496 [==============================] - 2s 102us/step - loss: 0.4386 - accuracy: 0.8143
+Epoch 39/50
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4379 - accuracy: 0.8148
+Epoch 40/50
+17496/17496 [==============================] - 2s 110us/step - loss: 0.4385 - accuracy: 0.8148
+Epoch 41/50
+17496/17496 [==============================] - 2s 116us/step - loss: 0.4386 - accuracy: 0.8149
+Epoch 42/50
+17496/17496 [==============================] - 2s 104us/step - loss: 0.4380 - accuracy: 0.8150
+Epoch 43/50
+17496/17496 [==============================] - 2s 108us/step - loss: 0.4382 - accuracy: 0.8144
+Epoch 44/50
+17496/17496 [==============================] - 2s 101us/step - loss: 0.4381 - accuracy: 0.8152
+Epoch 45/50
+17496/17496 [==============================] - 2s 105us/step - loss: 0.4378 - accuracy: 0.8151
+Epoch 46/50
+17496/17496 [==============================] - 2s 104us/step - loss: 0.4374 - accuracy: 0.8153
+Epoch 47/50
+17496/17496 [==============================] - 2s 99us/step - loss: 0.4382 - accuracy: 0.8152
+Epoch 48/50
+17496/17496 [==============================] - 2s 110us/step - loss: 0.4379 - accuracy: 0.8166
+Epoch 49/50
+17496/17496 [==============================] - 2s 112us/step - loss: 0.4381 - accuracy: 0.8144
+Epoch 50/50
+17496/17496 [==============================] - 2s 109us/step - loss: 0.4378 - accuracy: 0.8154
+
+
+
+ +
+ +
Out[94]:
+ + + + +
+
<keras.callbacks.callbacks.History at 0x17a5fe12630>
+
+ +
+ +
+
+ +
+
+
+
+

We observe that the accuracy did not increase much at all from the 20th to 50th epoch. In such a situation we will choose the 20 epoch model for its faster computation.

+ +
+
+
+
+
+
In [95]:
+
+
+
optimal_adam50 = get_optimal(model50, y_train, X_train_filter, "Neural Network 17x8x8x1 Adam Entropy")
+get_roc(model50, y_test, X_test_filter, "Neural Network 17x8x8x1 Adam, Entropy, 50 epoch")
+predictions50 = list(model50.predict(X_test_filter).ravel() > optimal_adam50)
+print(classification_report(y_test,predictions50))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.20843479
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.87      0.82      0.84      6762
+           1       0.48      0.59      0.53      1987
+
+    accuracy                           0.76      8749
+   macro avg       0.68      0.70      0.69      8749
+weighted avg       0.78      0.76      0.77      8749
+
+
+
+
+ +
+
+ +
+
+
+
In [107]:
+
+
+
evaluation.loc[3] = (["Neural Network - 17x8x8x1 Adam, Entropy, 20 Epochs" , 
+                      classification_report(y_test, predictions, output_dict = True)["1"]["f1-score"],
+                      auroc])
+
+evaluation
+
+ +
+
+
+ +
+
+ + +
+ +
Out[107]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
3Neural Network - 17x8x8x1 Adam, Entropy, 20 Ep...0.5358340.741382
5Naive Bayes0.5263420.741382
+
+
+ +
+ +
+
+ +
+
+
+
+

Naive Bayes

Theory

Naive Bayes classifier is a probabilistic machine learning model used for classification. The crux of the classifier is based on the Bayes theorem.

+
Bayes Theorem:

image.png +Using the Bayes theorem, we can find the probability of A happening, given that B has occured. +One assumption about naive bayes is that the predictors/features are independent.

+ +
+
+
+
+
+
+

Training the Naive bayes model

+
+
+
+
+
+
In [97]:
+
+
+
from sklearn import datasets
+from sklearn import metrics
+import matplotlib.pyplot as plt
+import time
+from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
+
+gnb = GaussianNB()
+
+ +
+
+
+ +
+
+
+
In [98]:
+
+
+
#training naive bayes model
+gnb.fit(X_train, y_train)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[98]:
+ + + + +
+
GaussianNB(priors=None, var_smoothing=1e-09)
+
+ +
+ +
+
+ +
+
+
+
In [99]:
+
+
+
#classifying values
+predicted = gnb.predict(X_train)
+expected = y_train
+
+ +
+
+
+ +
+
+
+
In [100]:
+
+
+
#plot roc for naive Bayes
+auroc = get_roc(gnb, y_test, X_test, "Naive Bayes")
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.9999935527715175
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [101]:
+
+
+
#accessing model performance
+#print(metrics.confusion_matrix(expected, predicted))
+print(classification_report(y_train,gnb.predict(X_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.91      0.11      0.20     13442
+           1       0.25      0.96      0.39      4054
+
+    accuracy                           0.31     17496
+   macro avg       0.58      0.54      0.29     17496
+weighted avg       0.76      0.31      0.24     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

We observe that while the recall is 0.96, the f1 is 0.39 and the overall accuracy is atrocious.

+

We will now try searching for the smoothing parameter to achieve a better ROC and F1 on default. After some experimentation we found that 0.01 is a good value for this parameter.

+ +
+
+
+
+
+
In [102]:
+
+
+
gnb = GaussianNB(var_smoothing = 0.01)
+#experimenting with smoothing constant of naive bayes model on the training set.
+gnb.fit(X_train, y_train)
+auroc = get_roc(gnb, y_test, X_test, "Naive Bayes")
+print(classification_report(y_train,gnb.predict(X_train)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.038218795916133315
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.86      0.85      0.85     13442
+           1       0.52      0.52      0.52      4054
+
+    accuracy                           0.78     17496
+   macro avg       0.69      0.69      0.69     17496
+weighted avg       0.78      0.78      0.78     17496
+
+
+
+
+ +
+
+ +
+
+
+
+

Smoothing constant of 0.01 allowed us to acheieve a recall and f1 of 0.52 on the training set. Applied on the test set:

+ +
+
+
+
+
+
In [103]:
+
+
+
#plot roc for naive Bayes
+auroc = get_roc(gnb, y_test, X_test, "Naive Bayes")
+print(classification_report(y_test,gnb.predict(X_test)))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Optimal Threshold: 0.038218795916133315
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + +
+
              precision    recall  f1-score   support
+
+           0       0.86      0.86      0.86      6762
+           1       0.52      0.53      0.53      1987
+
+    accuracy                           0.78      8749
+   macro avg       0.69      0.69      0.69      8749
+weighted avg       0.78      0.78      0.78      8749
+
+
+
+
+ +
+
+ +
+
+
+
In [104]:
+
+
+
evaluation.loc[5] = (["Naive Bayes" , 
+                      classification_report(y_test, gnb.predict(X_test), output_dict = True)["1"]["f1-score"],
+                      auroc])
+
+ +
+
+
+ +
+
+
+
In [105]:
+
+
+
evaluation.sort_values(["AUROC"], ascending = False)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[105]:
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
3Neural Network 17x8x8x10.5358340.761854
2SVM-RBF (Grid Search)0.4822470.748465
5Naive Bayes0.5263420.741382
+
+
+ +
+ +
+
+ +
+
+
+
In [106]:
+
+
+
raise(Exception("Stop Running"))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
+---------------------------------------------------------------------------
+Exception                                 Traceback (most recent call last)
+<ipython-input-106-c8b15946227a> in <module>
+----> 1 raise(Exception("Stop Running"))
+
+Exception: Stop Running
+
+
+ +
+
+ +
+
+
+
+

Appendix: Tuning Neural Network with different optimisers

Adamax Optimizer

+
+
+
+
+
+
In [ ]:
+
+
+
from sklearn.neural_network import MLPClassifier
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = "logistic")
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
mlp.fit(X_train,y_train)
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
predictions = mlp.predict(X_test)
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
confusion(y_test,predictions,"Neural Network (5x26)")
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
auroc = get_roc(mlp, y_test, X_test, "Neural Network (5x26)")
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adamax', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Tuning Keras Model")
+
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
+

Adadelta Optimizer

+
+
+
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Tuning Keras Model")
+
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
+

Adagrad Optimzier

+
+
+
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Tuning Keras Model")
+
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
+

RMSProp

+
+
+
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Tuning Keras Model")
+
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
+

SGD

+
+
+
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Tuning Keras Model")
+
+print(classification_report(y_test,predictions))
+
+ +
+
+
+ +
+
+
+
+

We conclude that best optimizer is adagrad. Testing it on the test set.

+
+
+
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=17, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train_filter, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test_filter).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test_filter, "Neural Network - adagrad (filtered features)")
+
+print(classification_report(y_test,predictions))
+
+evaluation.loc[6] = (["Neural Network - adagrad" , 
+                      classification_report(y_test, predictions, output_dict = True)["1"]["recall"],
+                      auroc])
+
+evaluation
+
+ +
+
+
+ +
+
+
+
In [ ]:
+
+
+
model = Sequential()
+model.add(Dense(12, input_dim=46, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(8, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+# compile the keras model
+model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['accuracy'])
+# fit the keras model on the dataset
+model.fit(X_train, y_train, epochs=10, batch_size=10)
+predictions = list(model.predict(X_test).ravel() > 0.5)
+#confusion(y_test, model.predict(X_test[sig.index]), "Deep Learning Keras Model")
+auroc = get_roc(model, y_test, X_test, "Neural Network - adagrad (all features)")
+
+print(classification_report(y_test,predictions))
+
+
+evaluation.loc[6] = (["Neural Network - adagrad" , 
+                      classification_report(y_test, predictions, output_dict = True)["1"]["recall"],
+                      auroc])
+
+evaluation
+
+ +
+
+
+ +
+
+
+ + + + + +