diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..e3db3cc Binary files /dev/null and b/.DS_Store differ 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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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5 rows × 24 columns

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" + ], + "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": [ + "
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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
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" + ], + "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", 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\n", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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" + ], + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
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" + ], + "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
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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
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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" + } + ], + "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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" + ], + "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": [ + "
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" + ], + "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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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
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" + ], + "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 Project Submission-checkpoint.ipynb b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb new file mode 100644 index 0000000..c7cbbe1 --- /dev/null +++ b/.ipynb_checkpoints/BT2101 Project Submission-checkpoint.ipynb @@ -0,0 +1,6490 @@ +{ + "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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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 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" + ] + }, + { + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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0 rows × 47 columns

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" + ], + "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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" + ] + }, + "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": [ + "
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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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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
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" + ], + "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
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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
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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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" + ] + }, + { + "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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" + ] + }, + "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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X5kqsPqIaAXdgvWxJKaVUCcvOKZ0utlz2YKOILDfGRBQxyWDgM7vDwVXGmCrGmFr2C3eUUkpdIBHh7ekbeXNrTKksz51PytfhzJcjxdjDzkooxpg7sK5iqFevXv7RSiml8tm3L56Rj/7EvgaBYM7nLcPnzp2N8gWtYYHXZSLyoYh0FJGOYWEu7YpGKaU8nohw9V3zrGQC3N27Yaks151XKDFYrxnNFY717g2llFLn4fffDxLrZ5iyfA/JbUIAeOzKpozt1ZBHSmH57kwo84B7jDEzsd5rnKDtJ0opde7i4lJ59LHFfHs0nsAWViKJDK3ExCEt6RYVWmpxuCyhGGO+BHoDocaYGGACUB5ARN4HFgIDgd1AKjDKVbEopZQ3EhE+/d8Gnp63jfKNqxAYEkIFn3IserAn9UMqlXo8rrzLa3gx4wW421XLV0opb5aakcXQpxex3eRYyaSCD9dfUpd7LosiJNDPLTF53PtQlFLqYrb/eDILNx7h1V92kmOsu5sev7Ipt/eIpFy50rmbqzCaUJRSygMkp2fx3Gd/8fWeEwAElPfh3/2bMLpbBKaUbgsujiYUpZQqQ0SEpPQs4lMy2RObzHfrD7F42zGSM7LzprkmMpTJozriX97HjZGeTROKUkq5mYiwPy6V7zce5j+LdhY4TcbhZHo3COH1+7tSs2rFUo7QOZpQlFLKDQ7Hn+bTlXtZtvMEO48lnzGuUfVAbru0PoEBvtSt6MfkCct46YV+REVVc1O0ztGEopRSpSQ9K5vVe0/ywbJoftsdmze8ac0gLo0MoVfjMNrWDObZCUuZ+tViVq4cjY9POb6aeZ0bo3aeJhSllHKhzOwcnpy7mTX7TrE3NuWMcZ+OvIRejcMoV84gIsyevY0WfaZz9Ggy48ZdQnp6NhUres5rqzShKKVUCUvLzOaT3/by2R/7OJaYnje8Re1g+reoSf8WNWlcIzDv7qwTJ1IYMeJbfvhhN+3a1eS7727kkkvquCn686cJRSmlSsimmHjeXrKLxduP5w3rEhlC90ahjO3VEJ9CnhMJDvYjNjaVN9/sz913d8LX13OuShxpQlFKqfNwMiWDFbtOsGbfSY4mpLN4+7Ezxo/t1ZC7L2tIkH/5Ar+/fPl+XnxxBXPmDCMwsAKrVt3u9gcTL5QmFKWUcpKIsPVwIiM/XU1sckbe8NDACtQPqUhkaCXGXRZF6/DK+PkW/IxIbGwqDz+8iGnTNhARUYV9++Jp2bK6xycT0ISilFKF2hyTwJy/YvjrwCk2xSScMS40sAKP9G9K7yZhVA/2L3ZeIsKnn27g4YcXkZiYzuOPd+epp3pSsWLBVzCeSBOKUkrZEk5nsmbvST5btZ89x5M5FH86b1zdagE0rRlM81rBNKsVxICWtc55/p9/vonmzcN4//2raNGiekmGXiZoQlFKXdR2H09i2u/7+HXHCWJOnT5j3MBWNRnTPZL29aqcV39ZqamZvPTSCsaO7Uh4eDBz5gyjcmV/r6jeKogmFKXURen3PbGM+nQN6Vk5ecPa1K3C0PZ16NU47ILfJ7Jw4S7uvnsh+/bFU6dOEHfddQlVqwZcaNhlmiYUpdRFQUT4YctRfthylPUHTuVdjfRsHMaorhH0bhJWIr32xsQkcv/9PzJnznaaNQtl2bKR9OxZ/4Ln6wk0oSilvNb6A6eYvmo/Ww4lnNFfVqUKPtzZK5Jr2tSmRe3KJbrMF19czoIFu3jppT489FBXKlQoWz0Cu5KxXpzoOTp27Chr1651dxhKqTJu6+EErnr7NwD8fMsRXjWAXo2rM75PFFUrVSjRZa1efYiAAF9atapBXFwqCQnpREZWLdFlXChjzDoR6ejKZegVilLKK2Rm57D07+NsjInnuw2H86q03rqxLYPbuqYbk4SENJ54YgnvvbeWq69uzLx5wwkJqUhISNnsXt7VNKEopTxSdo6waNsxvl1/iB+3Hj1jXI1gP1rUDubpq5tzaWRIiS9bRJg1aysPPPATx4+nMH58JyZO7FPiy/E0mlCUUh7j76OJPDdvGydTMthxLOmMcZc1CaN7ozAGtKxJ7cr+Ln0t7uefb+K2276lY8fazJ8/nA4dartsWZ5EE4pSqsxKSM1k25FENsbEs2bvSZb8bXW6GBbkR99mNYiqHshtXepTI9i/0I4XS0p6ehbR0ado1iyMYcNakJWVw223tcHHxzM7cnQFTShKqTIhJT2Lw/GnWbf/FIu3Hz+rs0WAXo3D+L8ekXRvFFqqsS1dupe77lpAamomu3aNx8/Pl1Gj2pVqDJ5AE4pSyq2iTyTzyOxNrN1/6ozhdaoEUKdqADd0rEtkWCWa1QrGv3zp3oJ7/HgK//73z0yfvonIyKp8+OEg/Pz0sFkY3TJKqVIRn5rB5kMJLNtxgo0x8SSnZ7MvNoXTmdl509xzWRQd6lelRe1gpzpcdKXdu0/SqdNHJCdn8OSTPXjyyR4EBHhPR46uoAlFKVWiUjOy+OavQ/y2KxYfH8PfRxLZcyLlrOkub1qdhmGV8C/vw5juDWhWK9gN0Z4tMTGd4GA/Gjasypgx7Rg9uh3NmoW5OyyPoAlFKXVBRIQvVx/k49+iyckR9sWl5o2rVMGHiNBKRIZVol3dqnRuUI1W4ZVpEFqp1KuvipOSksHzzy/jo4/+YtOmuwgPD+bVV69wd1geRROKUuq8TZy/jU9+25v3uXGNQK7vEE5kWCCjukWUuaRRmO+/38E99/zAgQMJjBnTzqveUVKaNKEopZySkyMcT0pn9/Fk3vllF3/uPZk3bljHcCYMakElD2uwzsrKYdiwr5k7929atAhjxYpRdO9ez91heSzP+vWVUqVKRJgwbyszVx8kIzvnrPERIRWZO65bifeN5WoigjEGX99y1KoVyMsvX84DD3S5qDpydAVNKEqpM6RmZPHn3pM8OGsDp1IzAajgU46+zWpwaWQ1ggPK06RGEK3qVPbIF0WtWhXD3Xcv5KOPBtG+fS2mTr3K3SF5DU0oSikAktIyuWbKSvbGnnlH1sP9m3BXr4YemTwcnTp1mieeWMIHH6yjdu0gTuV7O6O6cC5NKMaYAcBbgA/wsYi8nG98PeB/QBV7msdEZKErY1JKWUSELYcSWbz9GMt3nWD9gfi8cU9d1YzLmlanYVigGyMsObNmbeHee38kNjaV+++/lOee601QkJ+7w/I6LksoxhgfYCrQD4gB1hhj5onINofJngK+EpH3jDHNgYVAhKtiUupiJyL8tPUYM1YfYP2BUySlZeWNqxnsz/jLo7i5s/e9XfDvv2OJiKjCjz/eTLt2tdwdjtdy5RVKJ2C3iEQDGGNmAoMBx4QiQO7TTJWBwy6MR6mL0uaYBOauP8Sh+FR+2vpP/1hB/r7c2SuSPk2q075+Vcp7USeHaWlZvPLKb7RvX4tBg5rwxBM9eOqpntqRo4u5MqHUAQ46fI4BOueb5lngZ2PMeKAS0LegGRlj7gDuAKhXT2/pU6o4xxLTeHHBduZt/OccrUawH42qB9K4RhBPX92cmpXd27WJqyxeHM24cQvYteskDz3UhUGDmlDeQ56H8XSuTCgFteDlf9/wcGCaiPzHGNMFmG6MaSkiZ9yfKCIfAh+C9Qpgl0SrlAcTsV42NX3VftYfiCc5/Z+qrOs6hDO8U1061K/mxghd79ixZB588GdmzNhMVFQ1fv75Fvr1a+jusC4qrkwoMUBdh8/hnF2lNQYYACAifxhj/IFQ4LgL41LKo4kI3/x1iO1HEomOTWHnsaS8190CBJT3YUjb2rQOr8LIrhEef3eWsxYtimb27G0880xPHn+8B/7+ehNraXPlFl8DNDLGNAAOATcCN+Wb5gBwOTDNGNMM8AdOuDAmpTzalF928drPO/M+Vw/yI6p6IJc1qU4lP19u61Kf2lUC3Bhh6dq48Si7dp3kuuuac/PNrejWrS4NGlR1d1gXLZclFBHJMsbcA/yEdUvwf0VkqzHmeWCtiMwDHgI+MsY8gFUdNlJEtEpLXfRycoTkjCzSMrNZsTOWRduOsWj7MbJzrN3jkoiqvH9LB0ICL85bX5OTM5gwYSlvvfUnERFVGDKkKb6+5TSZuJlLrwntZ0oW5hv2jMPf24BuroxBKU+xbv8pZq87yIpdsWdUYeWqHFCe/i1qML5PI+pWq+iGCMuGb7/9m/HjfyAmJpE77mjPpEl98fXVu7fKAq1kVMpNsrJziI5N4Z1fdvPHnlhikzMAaFoziJZ1gmkdXoVmtYIpZ6Bd3ao0r1023hfiTps3H+Nf/5pFq1bVmTXrOrp2rVv8l1Sp0YSiVCnbH5fCj1uO8sHyaE6mWEmkvI/hps71uKlTPVrWqezmCMuWzMxsVqw4QJ8+DWjVqgYLFtxEv36ReitwGaQJRSkXS0nP4s+9cSzadpwfthwh3u5wEWBk1whuubQeUdWD3Bhh2fX77wcZO3Y+W7eeYMeOe4iKqsbAgY3cHZYqhCYUpVxERHj5h7/5YHn0GcMHtKjJte3r0KlBNapU9Kxu30vLyZOneeyxxXz00V/UrRvMN98MIyrKu5+j8QaaUJS6QDk5wpHENA7EpbJ230lW7IoFA3uOJxNnV2ndc1kU/2pfx2s6W3SltLQs2rZ9n8OHk3jooS48+2xvAgM18XoCTShKXYCv1h7kkdmbzhpeq7I/bepWoVmtIO69vBF+vlrfX5yYmETCw4Px9/dl4sTLaNu2Jm3a1HR3WOocaEJR6hxlZefw1pJdfPLbXlIzsgEY1S2CjvWrERbkR5u6lTWBnIPTpzOZNOk3XnllJbNnX8+gQU0YMaKtu8NS58GphGKMqQDUE5HdLo5HqTIpPSubJ77ZwqroOA7F//OMyFWtanH3ZVF6S+95+vnnPYwbt4A9e05xyy2t6dSpjrtDUheg2IRijLkKeB2oADQwxrQFJojIv1wdnFLuEpuczqcr9/Lz1mOkZWVz8OQ/SeSSiKr0a16DEV0j9ErkAowfv5ApU9bQqFE1Fi++lcsvj3R3SOoCOXOF8jxWt/NLAURkgzEmyqVRKVXKsnOEifO38d2GQ6RkZJOR9U+H10H+vlzTpja1qwTw6IAmGHNxdLboCtnZ1nb18SnHpZeGExpakUcf7a4dOXoJZ37FTBGJz7cTaX9byiMlpWVyOiObg6dSWbk7jl3Hk1m37ySHE9LypukWFUKniBAiwypxdetamkBKyF9/HWHs2Pncemtrxo/vzM03t3Z3SKqEOZNQthtjhgHl7J6D7wNWuTYspS5cbgeLCzcd4c+9J5m7/tBZ05QzkCPQrFYwN3QM59oO4QT7l3dDtN4rKSmdZ55ZyttvryYsrCK1aulDnN7KmYRyD/AMkAN8g9V78OOuDEqpC7HnRDLT/9jPtN/3nTWuYVglRnZrgL9vOSLDKnn9S6fc7eef9zB69HccPpzE2LEdeemly6lSxTvfFKmcSyj9ReRR4NHcAcaYa7GSi1Jlwpp9J5n+x/4zXnkLcEXzGlzerDrXtKlDQAVtQC9tFSr4UL16JebMGUbnzuHuDke5mCnu9SPGmL9EpH2+YetEpINLIytEx44dZe3ate5YtCpDRITpq/az4UA83+SryurbrAa392hA5wbVtP2jlGVmZvP663+QmJjOiy9eDlhVjxfLWyPLMvu43dGVyyj0CsUY0x/r9bx1jDGvO4wKxqr+UqpULdl+jK/XxrBydyxJDu9MD/TzJSzIj3eGt6NZrWB89ODlFr/9diCvI8frr2+el0g0mVw8iqryOg5sAdKArQ7Dk4DHXBmUUo6+WnOQT3/fx/YjiQB0iqhGuXJwaWQIY3s1xF+7MXeruLhUHn10MZ98sp569Srz/ffDufrqxu4OS7lBoQlFRNYD640xX4hIWmHTKVXSRISFm4/y0YpoNhyMzxveOrwyr17XhiY19S6hsiQu7jQzZ27hkUe68swzvahUSTtyvFg50yhfxxjzItAcyLs9Q0T0FESVqJT0LKYu3c27v+7JG9akRhAdI6pyf9/GhAVdnO9PL4u2bz/BV19tZcKE3jRuHMKBAw9QrVqAu8NSbuZMQpkGvAC8BlwJjELbUFQJij6RzGNzNrN638m8YUPa1uaBfo2pH1LJjZGp/FJTM3nxxeW8+urvBAZWYHlloFcAACAASURBVMyY9oSHB2syUYBzCaWiiPxkjHlNRPYATxljVrg6MOXdEtMy+WrNQd5asouktH8a2F8Y0pJBbWpTOUAfLixrfvxxN+PGLWDv3nhGjGjDq6/2IyxME776hzMJJd1Y917uMcaMBQ4B1V0blvJW0SeSee77bSzbeSJv2KWR1Xigb2M66W2+ZVZycga33jqXkJAAli4dQe/eEe4OSZVBziSUB4BA4F7gRaAyMNqVQSnvs27/SSYt/Ju1+08BULGCDw/2a8zNnevrA4dlVHZ2Dl9+uYXhw1sSGFiBxYtvpWnTUPz8tCNHVbBiS4aI/Gn/mQTcCmCM0UdeVbGiTyTzn0U7+ftIIntOpABQrVIFXhjSkitb1tSrkTJs3brD3HnnfNatO0JAgC9DhzbXtyeqYhWZUIwxlwB1gN9EJNYY0wKrC5Y+gCYVdYbTGdn8sOUIa/ef4rv1VjfwABV8y9G2bhWevro5HepXdXOUqigJCWk8/fRSpk5dQ/XqlZg5cyjXXtvM3WEpD1HUk/KTgKHARqyG+LlYPQ2/AowtnfCUJ0jPymbayn1M+uHvvGEB5X1oX68K9/dtTM/GYW6MTp2LoUO/4pdf9nL33Zfwwgt9qFxZO3JUzivqCmUw0EZEThtjqgGH7c87Sic0VZbl5AjHktJ4e8kuvlx9MG/4vX2iGN29AVUq6sNtniI6+hRhYRUJCvLjxRf7UK6c4ZJL9FW86twVlVDSROQ0gIicNMb8rclEiQivL9rJO7/sPmP4Va1q8eRVzahdRZ9H8BQZGdm89trvTJy4nHvv7cQrr/TTHoHVBSkqoUQaY3K7qDdAhMNnRORal0amypTk9Cy+WnOQd37ZxanUTABGd2tA4xqBXNs+nAq+5dwcoToXy5fvZ+zY+WzfHst11zXn3ns7uzsk5QWKSihD832e4spAVNm061gSj32zmXX27b4ALWoH88XtnbVay0O98cYfPPjgz0REVGHBgpsYOLCRu0NSXqKoziGXlGYgquxYFR3H0h3HmbZyH+lZVi87zWsFM7JrBFe1rkUlfQ7B4+TkCCkpGQQF+XHVVY05cSKVp57qScWK2iOBKjl6ZFB5Ek5ncv/M9Szd8c9T7J0iqvHStS2Jqq49/HqqrVuPM3bsgrw3JzZuHMJLL13u7rCUF3JpQjHGDADeAnyAj0Xk5QKmGQY8CwiwUURucmVM6kz741J4/JvNxKdmss1+30g5Az/d35OI0EqU99G2EU+VmprJxInLeO21P6hc2Y/Ro9siIvpAqXIZpxOKMcZPRNLPYXofYCrQD4gB1hhj5onINodpGgGPA91E5JQxRvsIKyW/7Yrl8bmbOHjydN6wf7WrQ/v6VbmpUz1966GHW7/+CNde+xX79sUzalRbJk/uR2hoRXeHpbxcsQnFGNMJ+ASrD696xpg2wO0iMr6Yr3YCdotItD2fmVjPtmxzmOb/gKkicgpARI6f+yqocyEiPDZnM7PWWs+O1K0WwItDWtGjUaieuXqB3CuQevUqU69eZf73vyH07Fnf3WGpi4QzVyhvA1cD3wKIyEZjzGVOfK8OcNDhcwyQ/97ExgDGmJVY1WLPisiPTsxbnYe/jyYy/MNVebf9fn9Pd1qFV3ZzVKokZGXlMGXKaubN28GiRbcSElKRZctGujssdZFxJqGUE5H9+c5es534XkGnu1LA8hsBvbH6BlthjGkpIvGOExlj7gDuAKhXr54Ti1aOsrJzmPb7Pl5YsB2AHo1CeevGdlTTV7V6hdWrDzF27HzWrz/KlVdGkZiYTtWq+oCpKn3OJJSDdrWX2O0i44GdTnwvBqjr8Dkcq/uW/NOsEpFMYK8xZgdWglnjOJGIfAh8CNCxY8f8SUkVIDEtk4e+2si2w4kciv+nneSJgU25o2dDN0amSkpycgaPPrqI995bS61aQXz99fUMHdpMqy6V2ziTUO7CqvaqBxwDFtvDirMGaGSMaYD1Uq4bgfx3cH0LDAemGWNCsarAop0LXRVm+h/7mPzjDpLSs/DzLUe7elX4V7s63Ny5vja2e5Hy5cvx66/7GT++ExMn9iE42M/dIamLnDMJJUtEbjzXGYtIljHmHuAnrPaR/4rIVmPM88BaEZlnj7vCGLMNqxrtYRGJO9dlKcuRhNMMnrKS40nWzXj3Xt6IB/o20jNWL7J790mef34ZU6cOJCjIj3Xr7sDfXx8nU2WDESm6BskYswfYAcwCvhGRpNIIrDAdO3aUtWvXujOEMmnSwu18sNy6uAso78M347rSrFawm6NSJSU9PYvJk1fy4osrqFDBhwULbqJHD717SznPGLNORDq6chnOvLGxoTGmK1aV1XPGmA3ATBGZ6crAlHN+3x3LTR//mfd58tDWDLukbhHfUJ5m6dK93HXXAnbsiOOGG1rw+uv9qV1bey5QZY9T18oi8jvwuzHmWeBN4AtAE4obHUk4zZuLduU9T3Jly5q8dWM77fXXy4gIL764gszMHH788Wb6949yd0hKFcqZBxsDsR5IvBFoBnwHdHVxXKoQfx9N5OaP/iQuJSNv2Jy7uuqrdb1ITo7wySd/MWBAFHXrVmb69H9RpYo/AQHakaMq25y5QtkCfA9MFpEVLo5HFeH7jYcZ/+V6wHpP+1s3tKVH4zACtfdfr7Fp0zHGjp3PH3/E8MwzPXnuucuoVUurt5RncOZIFCkiOS6PRBUqLTObp77dwux1MQC8MKQlt1yqDbLeJDk5g+ee+5U33lhF1aoBTJs2mNtua+PusJQ6J4UmFGPMf0TkIWCOMeasW8H0jY2usz8uhVlrDrLhYDzHEtPYcyIFgNDACix+sJe+2MoLPfvsr/znP39w++3tePnlvoSEaEeOyvMUdYUyy/5f39RYSrJzhHFfrOOnrcfyhgX5+9K7SRiXRobwfz0i9cFEL3LwYAIpKZk0bRrKY491Z8iQpnTvrl0LKc9V1BsbV9t/NhORM5KK/cCivtGxBIkIN3+8ilXRJynvY3hneDv6t6ipDyV6oaysHN5++0+eeWYpHTrUZtmykYSGVtRkojyeM20oozn7KmVMAcPUeUjNyOK9X/fw+ar9nErNJDK0Ekse6qWJxEutWhXD2LHz2bjxGFdd1YgpUwa6OySlSkxRbSg3YN0q3MAY843DqCAgvuBvqXORnpVNpxeXkJyeBcBVrWvx+rA2mky81IIFOxk06Etq1w7im2+GMWRIU/2tlVcp6gplNRCH1UvwVIfhScB6VwZ1MUg4nUmb534GoHeTMN4Y1paq2p281xERDh9Ook6dYPr2jeT55y/jvvs6ExSkHTkq71NsX15ljaf35bXxYDxfrT3IF38eAKBt3SrMHddVz1S90M6dcYwbt4CdO+PYtu1uAgP1hEG5j1v78jLGLBORXsaYU5z5YiwDiIhUc2Vg3mjWmgM8OmczAJGhlejVJIwJg1q4OSpV0tLSsnj55d+YNOk3AgJ8mTTpcgIC9OFT5f2KKuW5r/kNLY1AvN1LC7fzod0b8P9Gd6JX4zA3R6Rc4ejRZHr2/JRdu04yfHhLXn+9PzVrBro7LKVKRVG3Dec+HV8XOCwiGcaY7kBr4HMgsRTi82hZ2TnM+SuGmWsOsv6AdR/Dgnu706K2vsfd22RmZlO+vA81alSiZ8/6TJ06kH799M2Y6uLizHX4t8AlxpiGwGfAAmAGcLUrA/NkSWmZPPbNZhZsOpI3rEawH9PHdKZxDe2XyZvk5AgffriOl15awe+/jyE8PJiPP77G3WEp5RbOJJQcEck0xlwLvCkibxtj9C6vfESEn7YeY/a6GBZv/+dJ99u7N+CBfo2ppB04ep2NG49y553z+fPPQ/Tp04DMzGx3h6SUWzn1CmBjzPXArcAQe5j2o+1ARBj56RqW7TyRN+yFIS25uXM9vXvLC4kIDz+8iDffXEW1agFMn/4vbr65lf7W6qLn7JPy47C6r482xjQAvnRtWJ7l05X7WLbzBLUr+/PtPd0IC/TTg4sXM8Zw6tRpxoyxOnKsWjXA3SEpVSY49RyKMcYXyH1V3G4RyXJpVEUoS8+h7D6exE0f/cnxpHT8fMux9qm+BPnrxZs32r8/nvvu+5FnnulF+/a1yMkRymlHncqDlMZzKMW+L9YY0wPYDXwC/BfYaYzp5sqgPMHu48n0fX05x5PSaVevCj/d31OTiRfKzMxm8uSVNG/+LosWRbNjRyyAJhOlCuBMldcbwEAR2QZgjGkGTAdcmunKsqgnFpKVY13ZjehSn+cGt3RzRMoVfv/9IHfeOZ8tW44zeHAT3n77SurV01u+lSqMMwmlQm4yARCR7caYi7YPiQ+X78lLJl+P7cIlEdphgLdavDiahIQ0vv32BgYPburucJQq84ptQzHGTAPSsa5KAG4GKorICNeGVjB3tqEcPJlKj8lLAVj2cG/qh1RySxzKNUSE6dM3ERZWkSuvbER6ehaZmTnaB5fyCmWiDQUYC+wBHgEeBaKBO10ZVFl0MiUjL5m8M7ydJhMv8/ffsfTp8xkjRnzLp59uAMDPz1eTiVLnoMgqL2NMK6AhMFdEJpdOSGXP6r0nGfbBHwCM7BrBoDa13RyRKimnT2fy0ksreOWVlVSqVIEPPria229v7+6wlPJIRfU2/ATWmxn/wup65XkR+W+pRVYGpGVmc+27v7PtiNVt2SURVXn2Gu0d2Jt8//1OXnhhBbfc0prXXutHjRrakaNS56uoK5SbgdYikmKMCQMWYt02fNF4YNaGvGTy/i0dGNCyppsjUiXh6NFkNmw4yoABUVx/fXMiIm6nU6c67g5LKY9XVEJJF5EUABE5YYxxpr3FaxxJOM0PW44CsHfSQH3y3QtkZ+fwwQfrePzxJVSo4MOBA/cTEFBek4lSJaSohBLp8C55AzR0fLe8iFzr0sjc6LsNh7hvptUw++yg5ppMvMBffx1h7Nj5rFlzmL59I3n33YEEBOiDqEqVpKISytB8n6e4MpCyYva6GP799UYAnh/cgtu6RLg3IHXB9u49RadOHxEaWpEZM67lxhtb6kmCUi5Q1Au2lpRmIO4mItw5fR0/b7O6np/xf53p2lBfVumpRITNm4/TunUNGjSoyqefDmbQoCZUqeLv7tCU8loXVbtIUe5wSCbf3t1Nk4kH27v3FFdf/SXt2n3Apk3Wb3rrrW00mSjlYi5NKMaYAcaYHcaY3caYx4qY7jpjjBhj3NI/2B974li07Ri1K/sT/dJA2tat4o4w1AXKyMjm5Zd/o0WLd1m2bB+vvdaP5s3D3B2WUhcNp18jaIzxE5H0c5jeB5gK9ANigDXGmHmO/YLZ0wUB9wJ/OjvvkrT+wCmGf7QKgE9GXqK9yHqo7Owcunb9hHXrjnDttc14883+1K2rHTkqVZqc6b6+kzFmM7DL/tzGGPOOE/PuhPXulGgRyQBmAoMLmG4iMBlIcz7skvPKj38DMPWm9jSrFeyOENQFSEy0znF8fMoxenQ7vv9+OHPmDNNkopQbOFPl9TZwNRAHICIbgcuc+F4d4KDD5xh7WB5jTDugrojML2pGxpg7jDFrjTFrT5w4UdSk5+R4Uhqrok9yWZMwrmpdq8Tmq1xPRJg2bQORkW/x3XfWScG4cZdw9dWN3RyZUhcvZxJKORHZn29YthPfK6juKK9rY/tByTeAh4qbkYh8KCIdRaRjWFjJ1Ym/tXgXgN4a7GG2bTtB797/Y9So72jaNJSGDfUVAkqVBc60oRw0xnQCxG4XGQ/sdOJ7MUBdh8/hwGGHz0FAS+BX+5mAmsA8Y8w1IuLy/ukTUjP54s8DBPr50ruJNtx6ismTV/Lkk78QHOzHxx8PYtSodtrupVQZ4UxCuQur2qsecAxYbA8rzhqgkTGmAXAIuBG4KXekiCQAeffmGmN+Bf5dGskE4NsNhwCr92B9yK3sExGMMdSsGcjNN7fi1Vf7ERamrxBQqiwpNqGIyHGsZHBORCTLGHMP8BPgA/xXRLYaY54H1orIvHOOtoTk5AhTl+4G4J4+Ue4KQznh8OEk7rvvR3r0qMe993bmttvacNttbdwdllKqAMUmFGPMRzi0feQSkTuK+66ILMTqpdhx2DOFTNu7uPmVhBNJ6Vw6aQnZOcLwTnXxL+9TGotV5yg7O4d3313Dk0/+QmZmDl27hrs7JKVUMZyp8lrs8Lc/8C/OvHvLo7y9ZBfZOcJVrWoxcXBLd4ejCrBhw1Fuv30e69Yd4YorGvLuuwO14V0pD+BMldcsx8/GmOnAIpdF5EIJpzOZvmo/LesEM/VmfStfWZWQkMbhw0nMmnUd11+vvT0r5SmcflLeQQOgfkkHUhru/uIvAAa20mdOyhIR4euvt7FrVxxPPtmTXr0iiI6+D3//8ymeSil3ceZJ+VPGmJP2v3isq5MnXB9ayUrLzOa33bFEVQ9kXG9tiC8r9uw5ycCBM7jhhtl8990OMjOtR5w0mSjleYrca41V19AG67ZfgBwROauB3hNMWrgdgDt6RLo5EgWQnp7Fa6/9zgsvrKB8+XK89dYAxo27BF9f7QBbKU9VZEIRETHGzBWRDqUVkCvk5Aj/+8N62F+7WCkbDh5MZOLE5Qwa1IQ33+xPnTraj5pSns6Z08HVxhiPbsH+bXcsAKO7NaCSn1aluMuJEylMmbIagKioamzbdjdff329JhOlvEShR1djjK+IZAHdgf8zxuwBUrD66BIR8Zgk85Jd3XXf5Y3cHMnFKSdH+PTT9TzyyGKSktLp1y+SJk1CiYys6u7QlFIlqKjT9dVAe2BIKcXiEulZ2fx9NAmAyhXLuzmai8+WLce5664F/PbbAXr0qMf7719Nkyb6NkylvFFRCcUAiMieUorFJcZOXwfAIwOauDmSi09GRjZXXDGdjIxs/vvfaxg5sq0+U6KUFysqoYQZYx4sbKSIvO6CeErcuv2nALizZ0M3R3Lx+OWXvfTqVZ8KFXz46qvrado0lNDQiu4OSynlYkU1yvsAgVjdzBf0r8xLzcgiMS2La9rUxke7OHe5mJhEhg79issv/4zPPtsIQPfu9TSZKHWRKOoK5YiIPF9qkbjAiSTr9bAt9S4il8rKymHKlNU8/fRSsrNzmDTpcm6+ubW7w1JKlbJi21A82YGTqQDUq6bvzXClW2+dy8yZW7jyyiimTh1IgwZ695ZSF6OiEsrlpRaFi6zZZ7WftAqv7OZIvE98fBq+vuUIDKzA3XdfwtChzRg6tJk2uit1ESu0DUVETpZmIK6wYtcJ6lYLoE6VAHeH4jVEhJkzt9Cs2VSefvoXwGonue467RVYqYud13aclJMjrD8QT7C/PntSUnbvPkn//p8zfPgcwsODueUWbSdRSv3Da/sh2XQoAYDujfQhupIwY8ZmRo/+Dj8/X6ZMuZKxYzvi4+O15yNKqfPgtQnlz+g4AK7Sd59ckMzMbMqX96Fjx9pcd11zJk/uR+3aHnHXuFKqlHltQpm73upxv3ENPfidj+PHU3jooZ9JScngm29uoHHjED7//Fp3h6WUKsO8ts4iOT0LAP/yPm6OxLPk5AgffriOJk2mMGvWFlq0CCM7O8fdYSmlPIBXXqFsPZxAzKnTjOjikW8qdpvo6FPccss3/PFHDL17R/Dee1fRtKm2QSmlnOOVCWX8l+sB6Ne8ppsj8SyVK/sRH5/G//43hFtvba23ASulzonXVXntOJpE9IkUagb76x1eTpg3bwfXXjuL7OwcQkIqsmXLOG67rY0mE6XUOfO6hDLjT+tVvy8PbeXmSMq2AwcSGDJkJoMHz2TnzjiOHEkGoJx2oqmUOk9eV+X1+Z8HAOjaUK9OCpKVlcObb65iwoRfERFeeaUvDzxwKeX15gWl1AXyuoSSnSO0Ca9MBV+vu/gqEdnZOXz88V/06dOAd965koiIKu4OSSnlJbzqqPvXAaszyAEt9WFGR6dOnebRRxeRlJSOn58vK1eOZt68GzWZKKVKlFcllOU7TwBwRYsabo6kbBARvvhiE02bTuU///mDpUv3ARASUlEb3ZVSJc6rqrwSTmcCULeqviFw5844xo1bwJIle+nUqQ4//XQLbdvqbdRKKdfxqoSybv8pmtYM0vYT4P77f2Tt2sO8++5A7rijg3bkqJRyOa9JKDk5wqaYBG65tJ67Q3GbRYv20LRpKHXrVua9967Cz8+XmjUD3R2WUuoi4dLTVmPMAGPMDmPMbmPMYwWMf9AYs80Ys8kYs8QYc959pew5YT1HUT3I/wIi9kxHjyZz001zuOKKz3nllZUA1K9fRZOJUqpUuSyhGGN8gKnAlUBzYLgxpnm+ydYDHUWkNTAbmHy+y1tld1ffpWHI+c7C4+TkCO+/v5amTacwZ852JkzoxWuvXeHusJRSFylXXqF0AnaLSLSIZAAzgcGOE4jIUhFJtT+uAsLPd2G/77ESyiUR1c53Fh5n0qQV3HXXAjp0qM2mTWN59tne+Pt7TS2mUsrDuPLoUwc46PA5BuhcxPRjgB8KGmGMuQO4A6BevYLbSP7ce5LQwArnFagnSUpKJzY2lQYNqjJ2bEcaNKjK8OEt9TZgpZTbufIKpaAjnBQ4oTG3AB2BVwsaLyIfikhHEekYFhZ21vjoE8mcTMmgZ+Ozx3kLEWHu3O00b/4uN9wwGxEhJKQiN93USpOJUqpMcGVCiQHqOnwOBw7nn8gY0xd4ErhGRNLPZ0HrD8QD0LtJ9fP5epm3f38811wzk2uv/Ypq1QJ4++0rNYkopcocV1Z5rQEaGWMaAIeAG4GbHCcwxrQDPgAGiMjx813Q9FVWD8NdvbBB/o8/DtK373QAXnutH/fddym++pyNUqoMcllCEZEsY8w9wE+AD/BfEdlqjHkeWCsi87CquAKBr+0z7gMics25Lqu8j3W2HhroV1Lhu11iYjrBwX60b1+L0aPb8vDD3ahXr7K7w1JKqUK59JYgEVkILMw37BmHv/uWxHLiUjLo09Q7qrvi4lJ57LHF/PxzNFu3jiMwsALvvDPQ3WEppVSxPL7uJD41g+gTKdSpEuDuUC6IiPDZZxtp2nQqn366gRtuaIE2kyilPInHP7SQ+4R845pBbo7k/CUkpDFkyCx+/XUfXbqE8/77V9O6tfaYrJTyLB6fUNbus96BEhXmed2MiAjGGIKD/QgNrciHH17NmDHt9TW8SimP5PFVXptiEgC4JKKqmyM5Nz/9tJv27T8kJiYRYwxff309//d/HTSZKKU8lscnlK2HrYTi6yHdsx85ksSNN85mwIAvSE3N5PjxFHeHpJRSJcLjq7z2xaXSpIZntJ9MnbqaJ574hfT0LJ57rjePPtoNPz+P/wmUUgrw8IRyNCENgKjqntF+sm7dETp3rsPUqQNp1Mj7HsJUSl3cPDqhLN1hPVzft3nZfAYlMTGdZ55Zyq23tqZDh9q8++5V+Pn5aLcpSimv5NEJZdmOEwD0bly2EoqIMGfOdu6770eOHEmiXr3KdOhQW7uWV0p5NY89wqWkZ/Hj1qPUqRJA1Uplp9v6vXtPcc89P7Bw4S7atq3JN98Mo3Pn837Ni1JKeQyPTSjLd1pXJ2N7N3RzJGf64ovNLF++nzfe6M8993TSjhyVUhcNj00opzOzAegeFermSGDFiv2kp2fTt28kDz/clZEj2xIeHuzusJRSqlR57Onz3PWHAAh2Y7tEbGwqo0d/R8+e03j++WUA+Pn5ajJRSl2UPPYK5cDJVMoZCHFDl/UiwrRpG3j44UUkJKTz6KPdePrpnqUehyr7MjMziYmJIS0tzd2hqIuEv78/4eHhlC9fvtSX7ZEJJS0zmyPxaXRv5J5X/i5cuIvRo+fRrVtd3n//alq2LFt3mamyIyYmhqCgICIiIvR2ceVyIkJcXBwxMTE0aNCg1JfvkVVe8amZZGTn0K956fXIm5qaycqVBwAYOLAR3313I8uXj9JkooqUlpZGSEiIJhNVKowxhISEuO2K2CMTSsypVACqVSyd24V/+GEXLVu+y5VXfkF8fBrGGK65pol25KicoslElSZ3ljePTChJ6VkA1Kri79LlHDqUyPXXf83AgTPw8/Pl+++HU8XFy1RKKU/lkQklITUTAH9fH5ct4/jxFJo3f5f583fywguXsXHjWHr1inDZ8pRyFR8fH9q2bUvLli0ZNGgQ8fHxeeO2bt1Knz59aNy4MY0aNWLixImISN74H374gY4dO9KsWTOaNm3Kv//9b3esQpHWr1/P7bfffsawwYMH06VLlzOGjRw5ktmzZ58xLDDwn34Ad+7cycCBA4mKiqJZs2YMGzaMY8eOXVBsJ0+epF+/fjRq1Ih+/fpx6tSps6ZZunQpbdu2zfvn7+/Pt99+C8CUKVOIiorCGENsbGzed+bPn8+ECRMuKDaXEBGP+tehQweZtHC71H90vhyOT5WSFhOTkPf3W2+tkt2740p8GerisW3bNneHIJUqVcr7+7bbbpMXXnhBRERSU1MlMjJSfvrpJxERSUlJkQEDBsiUKVNERGTz5s0SGRkp27dvFxGRzMxMmTp1aonGlpmZecHzuO6662TDhg15n0+dOiXh4eHStGlTiY6Ozhs+YsQI+frrr8/4bu62OX36tERFRcm8efPyxv3yyy+yefPmC4rt4YcflkmTJomIyKRJk+SRRx4pcvq4uDipWrWqpKSkiIjIX3/9JXv37pX69evLiRMn8qbLycmRtm3b5k2XX0HlDlgrLj4+e+RdXjuOJlLBtxy1Kpfce+QTEtJ46qlf+OCDdaxadTvt29fi3ns7l9j8lXru+61sO5xYovNsXjuYCYNaOD19ly5d2LRpEwAzZsygW7duXHHFFQBUrFiRKVOm0Lt3b+6++24mT57Mk08+SdOmTQHw9fVl3LhxZ80zOTmZ8ePHs3btWowxTJgwgaFDhxIYGEhysvWK7tmzZzN//nymTZvGyJEjqVatGuvXr6dt27bMnTuXDRs2UKVKFQCioqJYuXIl5cqVY+zYrcH2GwAAD6ZJREFUsRw4YN0M8+abb9KtW7czlp2UlMSmTZto06ZN3rA5c+YwaNAgatSowcyZM3n88ceL3S4zZsygS5cuDBo0KG/YZZdd5vR2Lcx3333Hr7/+CsCIESPo3bs3r7zySqHTz549myuvvJKKFSsC0K5duwKnM8bQu3dv5s+fz7Bhwy44zpLikQll6Y4T9GhUMk/Iiwhff72N++//kaNHk7nnnk40bOhZb39UyhnZ2dksWbKEMWPGAFZ1V4cOHc6YpmHDhiQnJ5OYmMiWLVt46KGHip3vxIkTqVy5Mps3bwYosFonv507d7J48WJ8fHzIyclh7ty5jBo1ij///JOIiAhq1KjBTTfdxAMPPED37t05cOAA/fv3Z/v27WfMZ+3atbRs2fKMYV9++SUTJkygRo0aXHfddU4llC1btpy1LQqSlJREjx49Chw3Y8YMmjdvfsawY8eOUatWLQBq1arF8ePHi5z/zJkzefDBB4uNA6Bjx46sWLFCE8qFyO1yJawEHmgUEa699iu+/fZv2revxbx5w+nYsfYFz1epgpzLlURJOn36NG3btmXfvn106NCBfv36AVb5L+yOoHO5U2jx4sXMnDkz73PVqsWfkF1//fX4+FhtoDfccAPPP/88o0aNYubMmdxwww158922bVvedxITE0lKSiIo6J8X6h05coSwsH+eRzt27Bi7d++me/fuGGPw9fVly5YttGzZssB1Otc7ooKCgtiwYcM5fcdZR44cYfPmzfTv39+p6atXr87hw4ddEsv58rhG+czsHAAub3b+z6Bk2knJGEP37nV5++0BrF59uyYT5ZUCAgLYsGED+/fvJyMjg6lTpwLQokUL1q5de8a00dHRBAYGEhT0/+3df3BV9ZnH8fdnEUxAYNewsNuiQseICTEgQhvsACJdxioLhXFEflV27DqCSC2Kww7OrLtaa1tBG9FF1nWSrm2MMhUYUFhkY6kIKGwjKNg00jsBh9UsZllKQX49+8c5SS4hISdwf+Qmz2vmztzz457z5Jl7zzfne855vj0ZPHgwO3fubHX7LTVM8fOaPhfRo0ePhvcjR46kurqa2tpaVq1axZQpUwA4c+YMW7dupbKyksrKSj799NOzGpP6vy1+2+Xl5dTV1TFw4EAGDBhALBZraOxycnLOOnv64osv6NOnT0MuovytR44cOesCevwrvvGr169fPw4ePAgEDUbfvi0/t/bqq68yefLkyE+4Hz9+nOzsxHX7J0LGNSiEN6AM7NPj/Ou14O23YxQWLmf16o8BePDBG7n//m/QJUPGpHfuQvXu3Zvi4mKeeuopTp48yYwZM3jnnXd46623gOBMZv78+Tz88MMALFy4kCeeeIKqqiogOMAvXbr0nO2OHz+eZcuWNUzXH7T79evH3r17G7q0WiKJyZMns2DBAvLy8sjJyWl2u82dGeTl5VFdXd0wXVZWxvr164nFYsRiMXbu3NnQoNx0002Ul5dz4sQJAEpKShquk0yfPp13332XdevWNWxr/fr1Dd149erPUJp7Ne3uApg4cSKlpaUAlJaWMmnSpBbzUFZWxrRp01pc3lRVVdU53X3plnFH0cPHgluGu7WxLHxt7VHuumsVY8eW8uWXp+jZM/U1wJxLt+uvv54hQ4bwyiuvkJ2dzerVq3n88ccZNGgQ1113HSNGjGDevHkAFBYW8swzzzBt2jTy8vIoKCho+G873iOPPEJdXR0FBQUMGTKEiooKAJ588kkmTJjAzTff3HAdoSVTp07l5ZdfbujuAiguLmbHjh0UFhaSn5/P8uXLz/nctddey+HDhzly5AixWIyamhqKiooalg8cOJBevXqxfft2JkyYwKhRo7jhhhsYOnQoW7ZsabhAnp2dzdq1a3n22WfJzc0lPz+fkpKS855RRLFo0SI2btxIbm4uGzduZNGiRUBw7Sf+VudYLMb+/fsZM2bMWZ8vLi6mf//+HDhwgMLCwrM+U1FRwW233XZR8SWaLO6e80zQ64pBdvmMpXzyxK10ifikelnZbu677w3++McTLFx4I4sXj6Z799QXTnOdz969e8nLy0t3GB3a008/Tc+ePc95FqUj++yzz5g+fTqbNm1qdnlz3ztJO81seDLjyrgzlDMGuX0vi9yYAJw6dYaCgr5UVt7LD384zhsT5zqQOXPmcOmlnavHoaamhiVLlqQ7jHNk3F1eJ0+fYfQ1568yfPToCR57bDNXXtmbuXNHMHNmITNnFnpNJec6oKysLGbNmpXuMFJqxIgR6Q6hWRl3hgLwV71arqe1dm0Vgwc/z49/vIWqqkNAcNHPGxOXLpnWrewyWzq/bxl3hgLNX5A/cOD/mD//TV5//WPy8/+SzZtnM2rUVWmIzrlGWVlZHDp0yEvYu5SwcDyUrKz0FLHNyAblki7n/jD37atjw4ZP+NGPxrFgwUi6dUte4Ujnoqq/Q6e2tjbdobhOon7ExnTIyAala/jMyHvvfcrWrfv5/veLGD36KmpqHiAnp3uao3OuUdeuXdMycp5z6ZDUayiSbpH0O0nVkhY1s/xSSeXh8u2SBkTZbk63S5g7dx1FRS+ydOk2jh4NHlTyxsQ559InaQ2KpC7Ac8C3gXxgmqSmj5LeDdSZ2dXA00DLZTjjzPhOOS+8sJP587/B7t1z6NEjNSM3Oueca1kyu7y+DlSb2T4ASa8Ak4D4gjeTgEfD9yuBZZJkrdym0L9PD95YOZVhw87/9K1zzrnUSWaD8lVgf9z0AaDpACMN65jZKUmHgRzgf+JXknQPcE84+eXO/77nwwiVpjuDPjTJVSfmuWjkuWjkuWg0KNk7SGaD0tw9kk3PPKKsg5mtAFYASNqR7PIBmcJz0chz0chz0chz0UjSjtbXujjJvCh/ALgibro/0LR4f8M6ki4BegNfJDEm55xzSZLMBuV9IFfSQEndgDuBNU3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" + ], + "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", 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" + ] + }, + "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": [ + "
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" + ], + "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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0tuf9LHClMSar2OpM1+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/EoLpfhoYdm89Z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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
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" + ], + "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": [ + "
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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
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" + ], + "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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Edp9IJjUzJ2+eWtWrMrpnEP1D6tGrpX+h6/niiy08+ugS4uPTmDixBxMn9ijxeJ351NPIYqYbYIKztq+UUq6WmJrF1qMJHI5L5e+oOGpUq8K2o0nssqtb8HJ3o5pbFQK83Lm9exDdmvnSyKcmPh7VzrlrKMikSUt57bU/6dGjCe+/fxXt29dzyn6Uu/4olFKqrNoWnciGQ3Es2x3Dqn2nC5ynuX8t2jXypk+rAEZ0aUIz/1rntY20tCxSUrLw9/dg7NhOtGzpy9ixnalSpeikcjE0USil1AVISs9i8fYTbDgUz9qoWBLTsohPzTpnnlu7BRLSwJuwxj4E1/WkZvWqF7XNX3+NZMKERYSF1WfevBtp3dqf1q0LLpYqSZoolFKqEDm5hqS0LCJPnSE1M4ej8WkcjE1h5d5T7D6RnDdfi4Ba1KlVnUGh9bm5axOC/GpRy92N6m4l0/jFsWPJPPjgr8yZs5PWrf24995LSmS9jtJEoZRSBfhx81EemL250OlBfh7c3bsFw8MaUsvdeT+ly5ZFce2135KZmcPUqZczcWIP3J24vYJoolBKVXrpWTlExpxh6a6TJKZlsXx3DAdjUwG4o3tTgut50aquJ5413AjwcqeuVw2nx5SVlUO1alXp2LE+Q4a05IUX+hEc7Ov07RZEE4VSqtI6lpDGte+u4WRSxjnj3d2q0LGJD2/e2JHmAZ6lGlNSUgbPPPM7f/99lDVrxuDv78Hs2SNKNYb8NFEopSqFxLQsFm49zqnkDLYdTWDprpi8ab61qjOuT3M6BdahS2Adpz5BVBhjDHPn7uSBB37lxIkz3HPPJWRk5ODh4fpugzRRKKUqpMS0LFbtO8WRuDRe+XX3v6ZXEQhr4sO4Pi0YGFqv2HcWnOnUqRTuuOMHfvklkk6d6vPjjzdzySWNXBZPfpoolFIVRnZOLsnp2YyeuZ7NRxLOmdaqnidjejZjcLv6eLq74VbV9VfqZ3l7u3P6dCpvvXUFEyZ0xa2EnpYqKZoolFLl3tqoWB6bu5XDcal54zzd3Zg0uDV9W9fFz7M6HtXL1s/dypWHmDZtFfPm3YinZ3XWrr3LJUVejihbR04ppRyUlplDxKE47vxsPTm5Vu8Dw8Ma0r5Rbep612BYhwYuLU4qzOnTqUycuISZMzcTFOTDwYMJtGtXt8wmCdBEoZQqB3JyDesPxrE1OoHNRxJYtO3Ev+ZZcG9POjT2cUF0jjHG8Nlnm5k4cQlJSRk88UQvnn66Nx4e1VwdWrE0USilyqTk9Cx+3x3Dsl0xLN8dQ3LGP720+Xu60zygFtd3bsTlretS19v57zWUhK++2kpoaADvv38VbdvWdXU4DtNEoZQqE+JTMvn7QCyRMWf4YGUUyen/JIbm/rV4eFArwpv60rq+V4k1jeFsqalZvPjiKsaNC6dxY2/mzbuR2rVrlOlipoJoolBKuURaZg5Rp8/w3foj/Lk/ln0xZ86ZfmlzX/q0qstV7RsQ6Ofhoigv3KJF+5gwYREHDybQqJEX48dfQp06NV0d1gXRRKGUKhUxyel88EcUP205RlxKJtm2CuizujStQ++WAQwPa0iAl7tT209ypujoJB588FfmzdtFmzb+/PHHnfTu3dTVYV2U8vlJKKXKjdgzGTz+/TaW7DyZN65L0zr0bRWAe7UqdGzsQ5emdcrUew0XY9q0lSxcuI8XX+zHI4/0oPpFNi1eFojV0Vz5ER4ebiIiIlwdhlLKAc8t2MHMPw/mDT86qBX/6d0cd7fy/+Npb926o9Ss6Ub79vWIjU0lMTGD5s3ruDqsc4jIBmNM+IUsq3cUSqkSlZaZw/Tl+/hm3RHiUjIBmDQ4hLG9mpWbSmhHJSam8+STy3jvvQiGDm3FggUj8fPzwK8c1qkURROFUqpEbD+ayIPfbibSrlJ6QJu6TL2mHQ1ql89K3MIYY/j22x089NBiYmJSuO++rkyd2s/VYTmNJgql1AU5EpfKij0x/Lk/lj/3W12BArSs68nA0Ho8MKBlhStiOuurr7Zy++0/EB7ekJ9/HkmXLg1dHZJTaaJQSjnkRGI6P24+ysp9p1gTGXvOtHre7gT5+zB5WCidA8tW2XxJycjIJioqnjZtArjxxrZkZ+dy++0dqVpBKuGLoolCKVWk1MxsZq87wpSfd+aNa1C7BqENvLn10kC6NPWlds2y3wzFxVi+/ADjxy8kNTWLffvuw93djdGjO7k6rFKjiUIp9S8JqZlM+Wkn+2LOsO1oYt74m8KbMOWathW2SCm/mJgUHn30N778civNm9fhww+HlXp/1WVB5dtjpVShdhxL5Np3/yQzOzdvXNdmvlwW7M/4vi0qzLsOjoiMjKNr1484cyaTp566jKeeuoyaFfzOqTCaKJSqxIwxzN90lKW7TrJ0V0xegvByd2Pi4Nbc0jWwUiUHsPqs9vZ2p0WLOowd24kxYzrRpk2Aq8NyKU0USlVSkTFnGPXJ3xxPTM8bd1X7Btzduzkdm5Td5rqdJSUlkylT/uCjjzaydet4Gjf25rXXBrk6rDJBE4VSlUTsmQyOxKexeMcJ5kREc/pMBgCXtfTn/du6lNu2lUrCTz/t4d57f+Hw4UTGju1ULvqIKE2V98xQqoI7mZTOT1uO8cfeU6yOPE3+1noGhdZjZNdALg8pP/0ilLTs7FxuvHEO8+fvpm3bAFatGk2vXoGuDqvM0UShVAWSkZ3D538e5K2l+0jNzMkb7+/pTrtG3gzt0JCGPjXo0rROpXlyqSDGGEQEN7cqNGjgycsv9+ehh7pXiAb8nEEThVIVQFJ6Fg/N3syqyNN5FdJ9Wwcwoktj+ofUo6b+AOZZuzaaCRMW8dFHw+jcuQEzZlzl6pDKPE0USpVDxhiOJabz6eoDrIk8ze4TyQB0DfJlSPv6DAitR+M6FathuosVH5/Gk08u44MPNtCwoRfx8WmuDqnccGqiEJHBwNtAVeBjY8zL+aYHAp8DPrZ5HjfGLHJmTEqVZ0t2nuTdFZFsOpxwzvhm/rW4tVsgd13W3EWRlW3ffrud++//ldOnU3nwwUt5/vm+eHm5uzqscsNpiUJEqgIzgIFANLBeRBYYY3bazfY08J0x5j0RCQUWAUHOikmp8uRUcgYRB+NYsuskubmGtVFxnEiyHmVt29Cbrs18uap9A8KDfF0cadm3e/dpgoJ8+PXXW+nUqYGrwyl3nHlH0RWINMZEAYjIbGA4YJ8oDOBt+7s2cMyJ8ShVZuXmGnYcS2LN/tNsOZLAL9tPnDNdBFrX86JtQ2+eHRZKU79aLoq0fEhPz+aVV1bTuXMDhg1rzZNPXsbTT/euFA34OYMzE0Uj4IjdcDTQLd88zwG/ich9QC1gQEErEpG7gbsBAgP10TVVMWTn5LJ0Vwwfr4oi4lD8OdN6tPAjyL8WYU186BXsT0OfitWfgzMtXRrFPfcsZN++OB55pDvDhrWmWjWtzL8YzkwUUsC4/P2ujgRmGmP+JyLdgS9FpJ0xJvechYz5EPgQrK5QnRKtUk5mjCEhNYuo02d4/qedbI3+p7G95gG16BXsz9AODekU6EM1vfI9bydPnuHhh39j1qxtBAf78ttvtzFwYAtXh1UhODNRRANN7IYb8++ipbHAYABjzF8iUgPwB2KcGJdSpSY5PYtPVh9g57Ekftt58l/T/9u7Off2C8arhr4JfLGWLIli7tydPPtsb5544jJq1NCHOkuKM4/keqCliDQDjgI3A7fkm+cw0B+YKSJtgBrAKSfGpJRTGGPYczKZrUcS2XY0kbSsHP7aH8vRhH8ewQxt4I0I3NkjiJD63rRvXNuFEVcMW7acYN++OEaMCOXWW9vTs2cTmjWrmB0nuZLTEoUxJltE7gUWYz36+qkxZoeITAEijDELgEeAj0TkIaxiqTuNyd/QgFJlT2Z2LicS0/lh81GW7Dx5Tp8NZ7Ws60loA2/6t6nLjeFNaOKr7zWUlDNnMpk8eTlvv/03QUE+XHNNCG5uVTRJOIlT781s70QsyjfuWbu/dwI9nRmDUiUhIzuHtVFxHIlLZePheL7fePSc6cF1Pekc6MN1nRvTPKAWAZ7uiBRUTacu1g8/7Oa++34hOjqJu+/uzEsvDcDNTet0nEkL8ZQqRHZOLiv3nWLexqMs3Hr8nGluVYQRXRrTp1UA/drUrdTtJpWmbdtOcu2139K+fV2+/XYEPXo0KX4hddE0USiVz8mkdDYdTmDCrI3k5FoloY18anLzJU3o36YejX1r4q2Vz6UmKyuHVasO069fM9q3r8fChbcwcGBzfeS1FGmiUJVaTHI6i7efYG1UHCv3niI5I/uc6X1aBTB5WCjNAzxdFGHl9uefRxg37md27DjFnj33Ehzsy5AhLV0dVqWjiUJVOtuPJjJt4S4iDsWRlfPPsxNdm/niVkUIqe/NJUF16NjER190c5G4uDQef3wpH320kSZNvPn++xsJDtamSlxFE4Wq8NKzcohJyiDyVDJvLNnL9qNJAHjXcGNI+7r0C6nLgDb1KnUPb2VJeno2YWHvc+xYMo880p3nnuuLp2d1V4dVqek3Q1VIObmGzUcSeGvpXtYdiCMj+5+X/d2qCG/eFMawjg1dGKHKLzo6icaNvalRw42pUy8nLKw+HTvWd3VYCk0UqgI5lpBGTHIGf+2P5ZVfd+eNr1mtKs8ODaWhTw1a1vOihdY3lClpaVm89NJqXnllDXPn3sCwYa25444wV4el7DiUKESkOhBojIl0cjxKOSQ31/BXVCzfbzzKij0xxKZknjPdq4YbA0PrMaZnM9o29NZ3Gsqo337bzz33LGT//nhuu60DXbs2cnVIqgDFJgoRuQp4A6gONBORMGCyMeZaZwen1FlZObnEp2Tyw+ajLNhyLK+e4ay2Db3p0cKPzoF1qO1RjfCmvlTXl7DKtPvuW8T06etp2dKXpUtH0b+/drpUVjlyRzEFq3nw5QDGmM0iEuzUqJSyWbTtODOWR7Lj2LmJwa2KMLhdfR4Z1JogPw+9YygncnKsuqKqVatw6aWN8ff3YNKkXtqAXxnnyKeTZYxJyPdF1PaYlFNkZuey/Vgis9cd5lRyBsv3WG1E9mkVQNdmvjTyqcmwjg2pWkUTQ3mzceNxxo37mVGjOnDffd249dYOrg5JOciRRLFLRG4Eqthagn0AWOvcsFRlEp+SyakzGczfdJT3Vuw/Z9plLf15aGArOgdqY2/lVXJyBs8+u5x33llHQIAHDRp4uTokdZ4cSRT3As8CucD3WK3BPuHMoFTFFZeSyfcbo9l9Ipm5G6ILnGdohwb857LmdGziU8rRqZL222/7GTPmR44dS2bcuHBefLE/Pj41XB2WOk+OJIorjDGTgElnR4jIdVhJQ6libTgUx+uL9/JXVOy/prVvVJtLm/sSXNeTAC93Lgny1U58KpDq1atSt24t5s27kW7dGrs6HHWBpLjuH0RkozGmc75xG4wxXZwaWSHCw8NNRESEKzatzoMxhrkbonnmx+2kZ1kVmA1r16BP6wAuaxnAwNB62t1nBZSVlcMbb/xFUlIG06b1B6xHmatonZLL2X63wy9k2ULvKETkCqxuShuJyBt2k7yxiqGUOkduruFEUjqvL97D95v+6a+hfaPaPH5lCD2D/V0YnXK21asP5zXgd8MNoXkJQpNE+VdU0VMMsB1IB3bYjU8GHndmUKr82RqdwNXT15wz7rpOjZhyTTs8tQ2lCi02NpVJk5byySebCAyszU8/jWTo0FauDkuVoEK/wcaYTcAmEfnaGJNeijGpMi4rJ5dtRxN5Z9k+1h2IQ4CUzBwAmvp5MKFvMMM6NqRmde0voDKIjU1j9uztPPZYD559tg+1amkDfhWNI5d6jURkGhAK5D2uYIzRS4ZKJCUjm9GfrWfdwbhzxtfzdie4rieXNvNjWMeGBPnXclGEqjTt2nWK777bweTJfWnVyo/Dhx/C11ebZK+oHEkUM4EXgNeBK4HRaB1FpZGelcPNH65l85GEvHHDwxoSUt9qMqND49r6VnQlkpqaxbRpK3nttT/x9KzO2LGdadzYW5NEBedIovAwxiwWkdeNMfuBp0VklbMDU66XnpVD28mLyck1hNT34uZLmnBHjyBNDJXUr79Gcs89CzlwIHgWAHoAACAASURBVIE77ujIa68NJCBA7yArA0cSRYZYvwz7RWQccBSo69ywlCsZY/h0zUHe+G0PObmGDo1rs+DeXq4OS7nQmTOZjBo1Hz+/mixffgd9+wa5OiRVihxJFA8BnsD9wDSgNjDGmUEp1zl4OoX7Z29ia3QiAFOHt+W2S5u6OCrlCjk5uXzzzXZGjmyHp2d1li4dRUiIP+76FFulU+wnboz52/ZnMjAKQET0FcsKZsOheOZEHGH2+iMA9G4VwMw7L9Fn4CupDRuO8d///syGDcepWdON668P1d7mKrEiE4WIXAI0AlYbY06LSFuspjz6AZosyjljDI/N3crcjdHYv6B/f79gHhrYSusiKqHExHSeeWY5M2asp27dWsyefT3XXdfG1WEpFyvqzeyXgOuBLVgV2POxWo59BRhXOuEpZ8nJNQx68w/2n0oBYGyvZtx0SRNa1dOWPSuz66//jt9/P8CECZfwwgv9qF1bG/BTRd9RDAc6GmPSRMQXOGYb3lM6oamSlpSexc5jSTz9w3YiY84AMKBNXabf0pka1fTluMoqKiqegAAPvLzcmTatH1WqCJdcol2Sqn8UlSjSjTFpAMaYOBHZrUmifJr++z7e/yOKMxnZeeN8a1XnwQEtua1bU62HqKQyM3N4/fU/mTp1Jfff35VXXhmoLbyqAhWVKJqLyNmmxAUIshvGGHOdUyNTFyUhNZO3lu7jaEIaS3aeBOC/fZpT16sGPVr4EVLfS+sgKrGVKw8xbtzP7Np1mhEjQrn//m6uDkmVYUUliuvzDU93ZiCq5Hy6+gBTft6ZN9w1yJdHBrWiW3M/F0alyoo33/yLhx/+jaAgHxYuvIUhQ1q6OiRVxhXVKOCy0gxEXbjcXMNPW48xd0M0q/adzhv/3LBQ7uzZzIWRqbIiN9eQkpKJl5c7V13VilOnUnn66d54eGgnUap4+uZMObdy7ylu/3Rd3nAdj2p0a+bH9Fs64aYdAylgx44Yxo1bmNfTXKtWfrz4Yn9Xh6XKEacmChEZDLwNVAU+Nsa8XMA8NwLPAQbYYoy5xZkxVRTGGG756O+87kWv7dSIe/sF0yLA08WRqbIiNTWLqVP/4PXX/6J2bXfGjAnDGKN1U+q8OZwoRMTdGJNxHvNXBWYAA4FoYL2ILDDG7LSbpyXwBNDTGBMvItqGlAMOxabw4Leb2XTYatF1wb096dDYx8VRqbJk06bjXHfddxw8mMDo0WG8+upA/P09XB2WKqeKTRQi0hX4BKuNp0AR6QjcZYy5r5hFuwKRxpgo23pmY72bsdNunv8AM4wx8QDGmJjz34XKJSY5nT6vrQCgVT1P5o7vgXcNLWdWlrN3DIGBtQkMrM3nn19D797aVpe6OI7cUbwDDAV+ADDGbBGRyx1YrhFwxG44Gsj/DF4rABFZg1U89Zwx5lcH1l3p5OYaxn6+nuV7TgFWMxsPD2rt4qhUWZGdncv06etYsGAPS5aMws/Pgz/+uNPVYakKwpFEUcUYcyhfuWaOA8sVVBBq8g27AS2BvlhtR60SkXbGmAT7mUTkbuBugMDAQAc2XbGkZ+Vw+esrOJ5o9UirLboqe+vWHWXcuJ/ZtOkEV14ZTFJSBnXqaEdCquQ4kiiO2IqfjK3e4T5grwPLRQNN7IYbYzUDkn+etcaYLOCAiOzBShzr7WcyxnwIfAgQHh6eP9lUaGujYrn5w7UAtAioxbzxPfDx0D6JldVHxKRJS3jvvQgaNPBizpwbuP76NlpZrUqcI4liPFbxUyBwElhqG1ec9UBLEWmG1dnRzUD+J5p+AEYCM0XEH6soKsqx0Cu2xNQshk1fzeG4VAA6NvFh/vge2tyGylOtWhVWrDjEffd1ZerUfnh7u7s6JFVBOZIoso0xN5/vio0x2SJyL7AYq/7hU2PMDhGZAkQYYxbYpg0SkZ1YxVkTjTGx57utisb+zWofj2rMHded4LraqquCyMg4pkz5gxkzhuDl5c6GDXdTo4a+DqWcS4wpuiRHRPYDe4Bvge+NMcmlEVhhwsPDTUREhCtDcJrsnFye+XEH36w7DMDEK1oz4fJgF0elyoKMjGxefXUN06atonr1qixceAuXXab1VMpxIrLBGBN+Ics60sNdCxHpgVV09LyIbAZmG2NmX8gGVcFW7zvNbZ9YnQnW9XJn3vgeNPHV594VLF9+gPHjF7JnTyw33dSWN964goYN9Q5TlR6H7lmNMX8Cf4rIc8BbwNeAJooSsu5AXF6SuL9/SyZc3gJ3N+0fQlnvRUybtoqsrFx+/fVWrrhC7zBV6XPkhTtPrBflbgbaAD8CPZwcV6Vy4wd/AfB/IzsxrGNDF0ejXC031/DJJxsZPDiYJk1q8+WX1+LjU4OaNfXFSuUajrQatx24FHjVGBNsjHnEGPO3k+OqNP7Ya71A175RbU0Siq1bT9Kr16fcfffPfPzxRgAaNPDSJKFcypGip+bGmFynR1JJTVtoPd302ehLXByJcqUzZzJ5/vkVvPnmWurUqcnMmcO5/faOrg5LKaCIRCEi/zPGPALME5F/PRqlPdxdnNxcw80frmXvyTN0bOKDv6c+A1+ZPffcCv73v7+4665OvPzyAPz89EEGVXYUdUfxre1/7dmuhBljGPXp36w7GEerep58fZd2Q1kZHTmSSEpKFiEh/jz+eC+uuSaEXr0qXxM1quwrtI7CGHO2N5w2xphl9v+wKrXVBfh1+wm6vriMNZGxNK5Tk8UP9sbTXV+Yqkyys3N5442/aNNmBv/9788A+Pt7aJJQZZYjv1Bj+PddxdgCxqkiLN5xgu/WH2HZbqsl9a5Bvnw+pqu2y1PJrF0bzbhxP7Nly0muuqol06cPcXVIShWrqDqKm7AeiW0mIt/bTfICEgpeShXk5V928/4f+/OGtaOhymnhwr0MG/YNDRt68f33N3LNNSF6oaDKhaLuKNYBsVitvs6wG58MbHJmUBXJ5B+38/lfhxCBvx7vTz1vd/1xqESMMRw7lkyjRt4MGNCcKVMu54EHuuHlpQ8vqPKj0ERhjDkAHMBqLVZdgEOxKXz+1yEA3rm5E/Vr13BxRKo07d0byz33LGTv3lh27pyAp2d1nn66t6vDUuq8FVX09Icxpo+IxHNuh0MCGGOMr9OjK+du/dh6L3HWXd3oEezv4mhUaUlPz+bll1fz0kurqVnTjZde6k/NmvrAgiq/ijp7z3Z3qr9w5ykuJZMR7/1JdHwawXU9NUlUIidOnKF378/Yty+OkSPb8cYbV1C/vqerw1LqohRV9HT2bewmwDFjTKaI9AI6AF8BSaUQX7n06JwtRJ1O4dLm1pNNquLLysqhWrWq1KtXi969mzJjxhAGDmzh6rCUKhGOtPX0A1Y3qC2AL7DeoZjl1KjKsRcX7eL33TGE1Pdi9t3dtRXYCi431/D++xG0aPEO0dFJiAgff3y1JglVoTiSKHJtfVpfB7xljLkPaOTcsMqniINxfLjS6sl1xq2dXRyNcrYtW07Qo8cnjB+/kJYt/cjKynF1SEo5hUNdoYrIDcAo4BrbOG3KMh9jTF5z4csf7Usz/1oujkg5izGGiROX8NZba/H1rcmXX17Lrbe218eeVYXl6JvZ92A1Mx4lIs2Ab5wbVvmSk2u4ZNpScg00rlNTk0QFJyLEx6cxdqzVgF+dOjVdHZJSTlVs0ZMxZjtwPxAhIiHAEWPMNKdHVo58sHI/cSmZACx9uI+Lo1HOcOhQAtdcM5uNG48D8NFHV/PBB8M0SahKodhEISKXAZHAJ8CnwF4R6enswMqT33acBGDPC4OpUU0rryuSrKwcXn11DaGh77JkSRR79pwGoEoVLWZSlYcjRU9vAkOMMTsBRKQN8CUQ7szAyouZaw6w+UgCg0Lr6RNOFcyffx7hv//9me3bYxg+vDXvvHMlgYG1XR2WUqXOkURR/WySADDG7BKR6k6MqdyIjk/luZ+sQzNleDsXR6NK2tKlUSQmpvPDDzcxfHiIq8NRymUcSRQbReQDrLsIgFvRRgEBuP69PwF4ZmiotuNUARhj+PLLrQQEeHDllS2ZNKknDz/cHU9PvS5SlZsj71GMA/YDjwGTgCjgv84Mqjx4Z9k+TiZl4O/pzthezVwdjrpIu3efpl+/L7jjjh/47LPNALi7u2mSUIpi7ihEpD3QAphvjHm1dEIqHz5ZfQCARQ/0cnEk6mKkpWXx4oureOWVNdSqVZ0PPhjKXXfpy5JK2Sv0jkJEnsRqvuNWYImIjCm1qMq4I3GpJKZlMbJrIHW9tMipPPvpp7288MIqbrqpHbt3T+Duu7voE01K5VPUHcWtQAdjTIqIBACLsB6PrdQOnk6h7+srAOjbOsC1wagLcuLEGTZvPsHgwcHccEMoQUF30bWrtkqjVGGKqqPIMMakABhjThUzb6WwLTqRWz5aC8CdPYIYFFrPxRGp85GTk8u7766ndevpjBo1n7S0LEREk4RSxSjqjqK5XV/ZArSw7zvbGHOdUyMrYxLTshg2fTVgJYnnrm7r4ojU+di48Tjjxv3M+vXHGDCgOe++O4SaNbXJMqUcUVSiuD7f8HRnBlKWncnIptuLVo+wDw1oxQMDWro4InU+DhyIp2vXj/D392DWrOu4+eZ22oCfUuehqI6LlpVmIGVVdk4u7SYvBqBnsB/39w92cUTKEcYYtm2LoUOHejRrVofPPhvOsGGt8fHRhw+UOl+Vvt6hOD1e/h0ArxpufH3XpXolWg4cOBDP0KHf0KnTB2zdarXDNWpUR00SSl0gpyYKERksIntEJFJEHi9ivhEiYkSkTLUf9djcLcQkZ+DjUY2tkwe5OhxVjMzMHF5+eTVt277LH38c5PXXBxIaqk+mKXWxHGnCAwARcTfGZJzH/FWBGcBAIBpYLyIL7NuNss3nhdWM+d+Orrs0fBdxhO8iogFY/GBvvZMo43JycunR4xM2bDjOdde14a23rqBJE23AT6mS4Egz411FZBuwzzbcUUT+z4F1dwUijTFRxphMYDYwvID5pgKvAumOh+1cW44k8NjcrQB8ckc49by1yKKsSkqyrl2qVq3CmDGd+Omnkcybd6MmCaVKkCNFT+8AQ4FYAGPMFuByB5ZrBByxG44mX1/bItIJaGKM+bmoFYnI3SISISIRp06dcmDTF+dskvjlgcvo30bflSiLjDHMnLmZ5s3f5scfdwNwzz2XMHRoKxdHplTF40iiqGKMOZRvnCO9yBdUVmPyJopUwerr4pHiVmSM+dAYE26MCQ8IcG6Zc0xSOntOJgPQpoG3U7elLszOnafo2/dzRo/+kZAQf1q08HV1SEpVaI7UURwRka6AsdU73AfsdWC5aKCJ3XBj4JjdsBfQDlhhK/+vDywQkauNMRGOBO8M//1qAwBPX9XGVSGoIrz66hqeeup3vL3d+fjjYYwe3UnbZlLKyRxJFOOxip8CgZPAUtu44qwHWopIM+AocDNwy9mJxphEwP/ssIisAB51ZZJYE3maTYcT8K1Vnbsua+6qMFQBjDGICPXre3Lrre157bWBBATUcnVYSlUKxSYKY0wM1o/8eTHGZIvIvcBioCrwqTFmh4hMASKMMQvOO1onm7fBespp1n+6uTgSddaxY8k88MCvXHZZIPff343bb+/I7bd3dHVYSlUqxSYKEfkIu7qFs4wxdxe3rDFmEVars/bjni1k3r7Frc+Zth9N5PtNR+nRwo+Q+lo34WpnG/B76qnfycrKpUePxq4OSalKy5Gip6V2f9cAruXcp5kqhPu/sXp3ffHa9i6ORG3efIK77lrAhg3HGTSoBe++O0QrrJVyIUeKnr61HxaRL4ElTovIBbYcSSDqdAqXNvclyF/LvV0tMTGdY8eS+fbbEdxwQ6i+7KiUizn8ZradZkDTkg7EVYwx3P1lBNWqCs9f3c7V4VRKxhjmzNnJvn2xPPVUb/r0CSIq6gFq1LiQ01MpVdIceTM7XkTibP8SsO4mnnR+aKXjq7WHOJmUwbg+LWhd38vV4VQ6+/fHMWTILG66aS4//riHrCzrFR1NEkqVHUV+G8W65++I9XgrQK4x5l8V2+XV4h0neObHHQDc3Vsfhy1NGRnZvP76n7zwwiqqVavC228P5p57LsHNTRs0VqqsKTJRGGOMiMw3xnQprYBK0yerDwAwb3x3vGpob2el6ciRJKZOXcmwYa15660raNRInzRTqqxy5PJtnYh0dnokpWzZrpOsOxBHz2A/ujTVJ2pKw6lTKUyfvg6A4GBfdu6cwJw5N2iSUKqMK/SOQkTcjDHZQC/gPyKyH0jBasPJGGPKbfLYcSyRsZ9bL4A/PFAbkXO23FzDZ59t4rHHlpKcnMHAgc1p3dqf5s3ruDo0pZQDiip6Wgd0Bq4ppVhKzbUz/gRg8rBQvZtwsu3bYxg/fiGrVx/msssCef/9obRu7V/8gkqpMqOoRCEAxpj9pRRLqVi26ySZObkAjO7ZzMXRVGyZmTkMGvQlmZk5fPrp1dx5Z5i+E6FUOVRUoggQkYcLm2iMecMJ8Tjdl2utFtP/eqKfiyOpuH7//QB9+jSlevWqfPfdDYSE+OPv7+HqsJRSF6ioyuyqgCdWc+AF/St30rNyWLHnFAPa1KVB7ZquDqfCiY5O4vrrv6N//y/44ostAPTqFahJQqlyrqg7iuPGmCmlFkkpeHOJ1Y1Gp0CtRC1J2dm5TJ++jmeeWU5OTi4vvdSfW2/t4OqwlFIlpNg6iorCGMMHK6NwqyKM79PC1eFUKKNGzWf27O1ceWUwM2YMoVkzTcRKVSRFJYr+pRZFKVi47ThgvYGtPaJdvISEdNzcquDpWZ0JEy7h+uvbcP31bbSyWqkKqNA6CmNMXGkG4kzGGP5vWSQAt11aYdozdAljDLNnb6dNmxk888zvgFUPMWKEtvKqVEVVKRrWmbfxKHtOJjO+bwsa+mgl9oWKjIzjiiu+YuTIeTRu7M1tt2k9hFKVQaVoovPROdYTOOP7at3EhZo1axtjxvyIu7sb06dfybhx4VStWimuM5Sq9Cp8ojiVnAHAVR0a4K0N/523rKwcqlWrSnh4Q0aMCOXVVwfSsGG5fDpaKXWBKnyieOmXXQB0auLj4kjKl5iYFB555DdSUjL5/vubaNXKj6++us7VYSmlXKBClx2kZ+Xw8xbraac7ewS5NphyIjfX8OGHG2jdejrffrudtm0DyLE1eaKUqpwq9B3FV2sPkZmTy7NDQ3HT8vRiRUXFc9tt3/PXX9H07RvEe+9dRUiINuCnVGVXoRPFItu7E7d310diHVG7tjsJCel8/vk1jBrVQR93VUoBFbzoaefxJMKa+OjdRBEWLNjDddd9S05OLn5+Hmzffg+3395Rk4RSKk+F/QWNT8kkPSuX/iF1XR1KmXT4cCLXXDOb4cNns3dvLMePnwHQt9aVUv9SYYueVu47BYCfp7uLIylbsrNzeeuttUyevAJjDK+8MoCHHrqUatWqujo0pVQZVWETxabDCQBc26mRiyMpW3Jycvn4443069eM//u/KwkK0seGlVJFq5BFT8YYlu0+SZsG3tSsrlfK8fFpTJq0hOTkDNzd3VizZgwLFtysSUIp5ZAKmSg2HIrnSFwaQ9rVd3UoLmWM4euvtxISMoP//e8vli8/CICfn4dWViulHFYhi57mREQDcHklrsjeuzeWe+5ZyLJlB+jatRGLF99GWFjlTpxKqQtTIRPF6sjT1KxWlXaNars6FJd58MFfiYg4xrvvDuHuu7toA35KqQtW4RJFUnoWRxPSGNK+8l09L1myn5AQf5o0qc17712Fu7sb9et7ujospVQ559TLTBEZLCJ7RCRSRB4vYPrDIrJTRLaKyDIRuehXqN/4zeoX+4q2lSdRnDhxhltumcegQV/xyitrAGja1EeThFKqRDgtUYhIVWAGcCUQCowUkdB8s20Cwo0xHYC5wKsXu92Zfx4EYHAlqMjOzTW8/34EISHTmTdvF5Mn9+H11we5OiylVAXjzDuKrkCkMSbKGJMJzAaG289gjFlujEm1Da4FGl/MBtMycwAY1rEh7m4V/7HYl15axfjxC+nSpSFbt47juef6UqNGhStNVEq5mDN/VRoBR+yGo4FuRcw/FviloAkicjdwN0BgYGChK0jNzAagQwWuxE5OzuD06VSaNavDuHHhNGtWh5Ej2+njrkopp3HmHUVBv1ymwBlFbgPCgdcKmm6M+dAYE26MCQ8ICCh0gyeS0gGo5V7xrqqNMcyfv4vQ0He56aa5GGPw8/Pgllvaa5JQSjmVMxNFNNDEbrgxcCz/TCIyAHgKuNoYk3ExGzz7/kSQn8fFrKbMOXQogauvns11132Hr29N3nnnSk0OSqlS48xL7/VASxFpBhwFbgZusZ9BRDoBHwCDjTExF7MxY0xeRfalzf0uZlVlyl9/HWHAgC8BeP31gTzwwKW4uek7EUqp0uO0RGGMyRaRe4HFQFXgU2PMDhGZAkQYYxZgFTV5AnNsV8iHjTFXX8j2th1NBKBzoE+FaCo7KSkDb293OnduwJgxYUyc2JPAwIpb96KUKrucWphvjFkELMo37lm7vweU1Lb+2h8LwONXtimpVbpEbGwqjz++lN9+i2LHjnvw9KzO//3fEFeHpZSqxCpMre+i7ScA6NC4fF51G2P48sutPPLIb8THp/Hww93RagilVFlQIRKFMYYtRxJoXc+LGuWwA57ExHSuueZbVqw4SPfujXn//aF06FDP1WEppRRQQRLFRlsnRf3blK/WYo0xiAje3u74+3vw4YdDGTu2c4WoY1FKVRwV4vGZN5dY7TsNDC0/V+GLF0fSufOHREcnISLMmXMD//lPF00SSqkyp9wnitgzGayOPI2PRzU6BdZxdTjFOn48mZtvnsvgwV+TmppFTEyKq0NSSqkilfuip+V7TgFwW7eLbnjW6WbMWMeTT/5ORkY2zz/fl0mTeuJeAd8iV0pVLOX+V2pbtFU/MbJb4W1AlRUbNhynW7dGzJgxhJYtK85LgUqpiq3cJ4p5G48CUN+7hosj+bekpAyefXY5o0Z1oEuXhrz77lW4u1fV5jeUUuVKuU4U6Vk5nMnIZnhYQ6qWoUpgYwzz5u3igQd+5fjxZAIDa9OlS0NtAlwpVS6V61+uyJgzAHRt5uviSP5x4EA89977C4sW7SMsrD7ff38j3bpdVDcbSinlUuU6UXz992EAegX7uziSf3z99TZWrjzEm29ewb33dtUG/JRS5V65TRQ5uYbvN0ZT37sGTf1quTSWVasOkZGRw4ABzZk4sQd33hlG48beLo1JKaVKSrm93D0an0ZGdq5L+8Y+fTqVMWN+pHfvmUyZ8gcA7u5umiSUUhVKub2jWLP/NADhQaX/kp0xhpkzNzNx4hISEzOYNKknzzzTu9TjUOVDVlYW0dHRpKenuzoUVQnUqFGDxo0bU61atRJbZ7lNFDuPJQFweevSb99p0aJ9jBmzgJ49m/D++0Np1658tTGlSld0dDReXl4EBQXpo9HKqYwxxMbGEh0dTbNmzUpsveW26OmHTUcJ9PUotf6xU1OzWLPGqjwfMqQlP/54MytXjtYkoYqVnp6On5+fJgnldCKCn59fid+9lttEkZaVQ0pGdqls65df9tGu3btceeXXJCSkIyJcfXVrbcBPOUyThCotzjjXymWiSM/KITvXcEN4E6du5+jRJG64YQ5DhszC3d2Nn34aiY9P2XsDXCmlnKlcJoqzL9rV8Si5ypr8YmJSCA19l59/3ssLL1zOli3j6NMnyGnbU8pZqlatSlhYGO3atWPYsGEkJCTkTduxYwf9+vWjVatWtGzZkqlTp2KMyZv+yy+/EB4eTps2bQgJCeHRRx91xS4UadOmTdx1113njBs+fDjdu3c/Z9ydd97J3Llzzxnn6emZ9/fevXsZMmQIwcHBtGnThhtvvJGTJ09eVGxxcXEMHDiQli1bMnDgQOLj4/81z/LlywkLC8v7V6NGDX744Ydz5rnvvvvOiXX69Ol89tlnFxXbeTHGlKt/Xbp0Mc8v2GGaTvrZrD8Qa0padHRi3t9vv73WREaW/DZU5bJz506Xbr9WrVp5f99+++3mhRdeMMYYk5qaapo3b24WL15sjDEmJSXFDB482EyfPt0YY8y2bdtM8+bNza5du4wxxmRlZZkZM2aUaGxZWVkXvY4RI0aYzZs35w3Hx8ebxo0bm5CQEBMVFZU3/o477jBz5sw5Z9mzxyYtLc0EBwebBQsW5E37/fffzbZt2y4qtokTJ5qXXnrJGGPMSy+9ZB577LEi54+NjTV16tQxKSkpeePWr19vbrvttnM+x5SUFBMWFlboego654AIc4G/u+XyqadjCWkAhDXxKbF1Jiam8/TTv/PBBxtYu/YuOnduwP33dyux9SsF8PxPO/Ke2CspoQ29mTysrUPzdu/ena1btwIwa9YsevbsyaBBgwDw8PBg+vTp9O3blwkTJvDqq6/y1FNPERISAoCbmxv33HPPv9Z55swZ7rvvPiIiIhARJk+ezPXXX4+npydnzlh3/3PnzuXnn39m5syZ3Hnnnfj6+rJp0ybCwsKYP38+mzdvxsfH+j4HBwezZs0aqlSpwrhx4zh82HqI5K233qJnz57nbDs5OZmtW7fSsWPHvHHz5s1j2LBh1KtXj9mzZ/PEE08Ue1xmzZpF9+7dGTZsWN64yy+/3KFjWpQff/yRFStWAHDHHXfQt29fXnnllULnnzt3LldeeSUeHh4A5OTkMHHiRGbNmsX8+fPz5vPw8CAoKIh169bRtWvXi46zOOUyUWw6Ek9dL3fcql58yZkxhjlzdvLgg79y4sQZ7r23Ky1alP0OkJQ6Xzk5OSxbtoyxY8cCVrFTly5dzpmnRYsWnDlzhqSkJLZv384jjzxS7HqnTp1K7dq12bZtG0CBxSv57d27l6VLl1K1alVyc3OZP38+o0eP5u+//yYoKIh69epxyy23aPPJNwAADs5JREFU8NBDD9GrVy8OHz7MFVdcwa5du85ZT0REBO3atTtn3DfffMPkyZOpV68eI0aMcChRbN++/V/HoiDJyclcdtllBU6bNWsWoaGh54w7efIkDRo0AKBBgwbExMQUuf7Zs2fz8MMP5w1Pnz6dq6++Om8d9sLDw1m1apUmisJ416hWYkniuuu+44cfdtO5cwMWLBhJeHjDEohQqYI5euVfktLS0ggLC+PgwYN06dKFgQMHAv/02V6Q83lyZunSpcyePTtvuE6d4i+0brjhBqpWrQrATTfdxJQpUxg9ejSzZ8/mpptuylvvzp0785ZJSkoiOTkZLy+vvHHHjx8nICAgb/jkyZNERkbSq1cvRAQ3Nze2b99Ou3btCtyn831CyMvLi82bN5/XMo46fvw427Zt44orrgDg2LFjzJkzJ++OJL+6deuye/dup8SSX7mszE7LyqFNfa/iZyxEVlYOYJ0kvXo14Z13BrNu3V2aJFSFVLNmTTZv3syhQ4fIzMxkxowZALRt25aIiIhz5o2KisLT0xMvLy/atm3Lhg0bil1/YQnHflz+5/pr1fqnfbbu3bsTGRnJqVOn+P/27j+4qvLO4/j7swokKNAtLM5a0KQhBUIMyI+alkWK0I4o1co4ImsoOHYdQRasCyM7OLPuYreULujijyJ1Hai2iDoVMcrQTE1LJwJKTfghiKWYAVxmRciyFAQkfPePc3JzCTc3JzH33vz4vmbuzD3nnh/P/ebe++R5nnO+z7p165g8eTIA58+fZ/PmzVRVVVFVVcXHH398QSVR997ij7127VpqamrIzc0lJyeH6urqWCXWu3fvC1o7x44do0+fPrFYRHmvJ06cuGDgOf4RX6nVueKKKzh8+DAQVAR9+zZ+39VLL73EbbfdFrujurKykn379jFgwABycnI4deoUAwYMiG1/+vRpsrOzmyxzq2jp4EamHiNGjLCrHyq1R9bvanQgJ5ny8o9s0KAnbd26PS3a37nmakuD2e+9957179/fzp49a6dOnbLc3FwrKyszs2Bw++abb7bly5ebmdn27dstLy/P9u7da2ZmtbW1tnTp0ouO/9BDD9ncuXNjy8eOHTMzs7y8PNu9e7fV1tba5MmTbfr06WaWeFB53rx5VlJSYhMnToytmzp1qi1ZsiS2XFlZedG59+zZY6NHj44tFxcX29tvvx1b3r9/v+Xl5ZmZ2euvv27jx4+3M2fOmJnZ0qVL7e67746997y8PCstLY3tu2HDBtuxY8dF52yOefPmXTCYPX/+/Ea3ve666+ytt95q9PX4v6OZ2ezZs23NmjUJt23twex216I4HbYG+lzerVn7HTlykunT1zFu3GrOnDlHjx7N29+5juDaa69l6NChvPjii2RnZ/Paa6/x6KOPMnDgQK655hpGjRrF7NmzASgqKuLxxx9n6tSpDB48mMLCwth/x/EefvhhampqKCwsZOjQoZSXlwOwePFiJk2axA033JCwjz3elClTeOGFF2LdTgDLly9n27ZtFBUVUVBQwIoVKy7ab9CgQRw/fpwTJ05QXV3NgQMHKC4ujr2em5tLz5492bp1K5MmTWLMmDGMGDGCYcOGUVFRERtYzs7OprS0lCeeeIL8/HwKCgpYtWpV0hZAFAsWLKCsrIz8/HzKyspYsGABEIytxF/SW11dzcGDBxk7dmzkY1dUVDBhwoQvVL6oZHHXTLcH+UOG2uff/XfemPN3DLmyV6R91qzZyf33v8lf/nKW+fO/ycKF19M9hfdgOBdvz549DB48ONPF6LAee+wxevTocdG9FB1ZZWUly5Yt4/nnn0/4eqLPnKQ/mtnIlpyv3bUozpw7D0BOM+agOHfuPIWFfamquo8f/Wi8VxLOdSAzZ86kW7fO1UPw6aefsmjRorSdr91d9XT23Hmu6tEtaTLAkyfPsmjRJq66qhezZo2ipKSIkpIiz7fjXAeUlZXFtGnTMl2MtKq7ci1d2mGLopbcPo23JkpLP2TIkKf5yU8q+PDDo0Bw9YVXEi6T2lsXr2u/UvFZa5ctikQVxaFD/8ecORt49dUPKCj4GzZtmsGYMVdnoITOXSgrK4ujR496qnGXchbOR5GV1brJS9tdRXHuvJGToKLYv7+GjRv/zI9/PJ4HH/wGXbtekoHSOXexfv36cejQIY4cOZLporhOoG6Gu9bU7ioKINaieOedj9m8+SBz5xZz/fVXc+DAA/Tu3T3DpXPuQl26dGnV2cacS7eUjlFIulHSXkn7JC1I8Ho3SWvD17dKyoly3N5dL2XWrDcoLn6WZcu2cPLk2WC9VxLOOdfqUlZRSLoEeAqYCBQAUyUVNNjsHqDGzAYAjwGNp1WMM+lbq3nmmT8yZ8517Nw5k8su69qaRXfOORcnlV1PXwf2mdl+AEkvArcC8QlRbgUeCZ+/AjwpSZZk2N5qjf79evLm63/P8OHJ7/Z0zjn3xaWyovgKcDBu+RDQcIKH2DZmdk7ScaA38Gn8RpLuBe4NF89sO3LvrggZgTuDPjSIVSfmsajnsajnsag3sKU7prKiSHQdYMOWQpRtMLOVwEoASdtaeht6R+OxqOexqOexqOexqCdpW9NbJZbKwexDQP+45X7Afze2jaRLgV7AsRSWyTnnXDOlsqJ4F8iXlCupK3AnsL7BNuuB6eHz24G3ko1POOecS7+UdT2FYw6zgY3AJcBzZva+pH8jyIu+Hvgv4HlJ+whaEndGOPTKVJW5HfJY1PNY1PNY1PNY1GtxLNpdmnHnnHPp1e6SAjrnnEsvryicc84l1WYrilSl/2iPIsTiQUm7Je2Q9FtJHTZtblOxiNvudkkmqcNeGhklFpLuCD8b70v6VbrLmC4RviNXSSqXVBl+T27KRDlTTdJzkj6RtKuR1yVpeRinHZKGRzpwSyfbTuWDYPD7z8BXga7AdqCgwTazgBXh8zuBtZkudwZjMQ7oHj6f2ZljEW7XA9gEbAFGZrrcGfxc5AOVwF+Hy30zXe4MxmIlMDN8XgBUZ7rcKYrF9cBwYFcjr98EbCC4h60Y2BrluG21RRFL/2FmZ4G69B/xbgVWh89fAcarYyb7bzIWZlZuZqfCxS0E96x0RFE+FwCLgCXA6XQWLs2ixOIfgKfMrAbAzD5JcxnTJUosDOgZPu/Fxfd0dQhmtonk96LdCvzCAluAL0lqMhdSW60oEqX/+Epj25jZOaAu/UdHEyUW8e4h+I+hI2oyFpKuBfqbWWk6C5YBUT4XXwO+JqlC0hZJN6atdOkVJRaPACWSDgFvAv+YnqK1Oc39PQHa7nwUrZb+owOI/D4llQAjgbEpLVHmJI2FpL8iyEI8I10FyqAon4tLCbqfvkXQyvyDpEIz+98Uly3dosRiKrDKzJZK+gbB/VuFZnY+9cVrU1r0u9lWWxSe/qNelFggaQKwELjFzM6kqWzp1lQsegCFwO8kVRP0wa7voAPaUb8jr5nZ52b2EbCXoOLoaKLE4h7gJQAz2wxkESQM7Gwi/Z401FYrCk//Ua/JWITdLc8QVBIdtR8amoiFmR03sz5mlmNmOQTjNbeYWYuTobVhUb4j6wgudEBSH4KuqP1pLWV6RInFAWA8gKTBBBVFZ5ybdj3w/fDqp2LguJkdbmqnNtn1ZKlL/9HuRIzFT4HLgZfD8fwDZnZLxgqdIhFj0SlEjMVG4DuSdgO1wHwzO5q5UqdGxFj8E/BzST8k6GqZ0RH/sZS0hqCrsU84HvMvQBcAM1tBMD5zE7APOAXcHem4HTBWzjnnWlFb7XpyzjnXRnhF4ZxzLimvKJxzziXlFYVzzrmkvKJwzjmXlFcUrs2RVCupKu6Rk2TbnMYyZTbznL8Ls49uD1NeDGzBMe6T9P3w+QxJV8a99qykglYu57uShkXY5wFJ3b/ouV3n5RWFa4s+M7NhcY/qNJ33LjMbSpBs8qfN3dnMVpjZL8LFGcCVca/9wMx2t0op68v5NNHK+QDgFYVrMa8oXLsQthz+IOm98PHNBNsMkfRO2ArZISk/XF8St/4ZSZc0cbpNwIBw3/HhHAY7w1z/3cL1i1U/B8h/hOsekTRP0u0EObd+GZ4zO2wJjJQ0U9KSuDLPkPREC8u5mbiEbpJ+Jmmbgrkn/jVcN4egwiqXVB6u+46kzWEcX5Z0eRPncZ2cVxSuLcqO63Z6NVz3CfBtMxsOTAGWJ9jvPuA/zWwYwQ/1oTBdwxRgdLi+FririfN/F9gpKQtYBUwxs2sIMhnMlPRl4DZgiJkVAY/G72xmrwDbCP7zH2Zmn8W9/AowOW55CrC2heW8kSBNR52FZjYSKALGSioys+UEuXzGmdm4MJXHw8CEMJbbgAebOI/r5NpkCg/X6X0W/ljG6wI8GfbJ1xLkLWpoM7BQUj/g12b2J0njgRHAu2F6k2yCSieRX0r6DKgmSEM9EPjIzD4MX18N3A88STDXxbOS3gAipzQ3syOS9od5dv4UnqMiPG5zynkZQbqK+BnK7pB0L8H3+m8JJujZ0WDf4nB9RXiergRxc65RXlG49uKHwP8AQwlawhdNSmRmv5K0FbgZ2CjpBwRplVeb2T9HOMdd8QkEJSWc3yTMLfR1giRzdwKzgRua8V7WAncAHwCvmpkp+NWOXE6CWdwWA08BkyXlAvOAUWZWI2kVQeK7hgSUmdnUZpTXdXLe9eTai17A4XD+gGkE/01fQNJXgf1hd8t6gi6Y3wK3S+obbvNlRZ9T/AMgR9KAcHka8PuwT7+Xmb1JMFCc6MqjEwRpzxP5NfA9gjkS1obrmlVOM/ucoAupOOy26gmcBI5LugKY2EhZtgCj696TpO6SErXOnIvxisK1F08D0yVtIeh2OplgmynALklVwCCCKR93E/yg/kbSDqCMoFumSWZ2miC75suSdgLngRUEP7ql4fF+T9DaaWgVsKJuMLvBcWuA3cDVZvZOuK7Z5QzHPpYC88xsO8H82O8DzxF0Z9VZCWyQVG5mRwiuyFoTnmcLQayca5Rnj3XOOZeUtyicc84l5RWFc865pLyicM45l5RXFM4555LyisI551xSXlE455xLyisK55xzSf0/8J7VjnFa9qUAAAAASUVORK5CYII=\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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" + ] + }, + "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": [ + "
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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
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" + ], + "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": [ + "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 - 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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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0 rows × 45 columns

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" + ], + "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": [ + "
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" + ], + "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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" + ], + "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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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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ModelRecall-1AUROC
0Decision Tree0.4100480.610882
1Random Forest0.3631580.767925
2Gradient Boosted0.3492820.775518
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" + ], + "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
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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
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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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" + ], + "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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4goMHY3jllZt57LF2uLred80x2TNRRJK3Y/JaGP0XCCFEuZKelc3vu8/x9Ybj7LJIBDmq+brj6uxESkY2tzWrQeUKbky6LRQPV+c80/3992maNauKj487X37ZF39/L2rXvpoemQtmz0SxCKOT8tlAOyBB6ieEEGVZYlomh88ns3hXFGmZJtIzs/ll+5k803i4OvHULY25vUUgHm7O+Li7oFRBBTD/iY1N4ZlnlvPll9uZPPkmXnqpCy1b1ii2uG2WKJRSPwFdAH+lVCRGF4iuAFrrTzE6We+N0dl4CjDcVrEIIYQ9aK05E5/K0/N2cTI2hTPxqXnG16rkSYOq3ijg/vZ1uK15IJUruF3V/L/7bidPPfUXcXGpTJjQgQkTOhTzWtj2qachRYzXwBhbLV8IIewhPSubj1YcYfeZBNYcupBn3IBWNWlQ1Zv29arQopYfTk6F3ykUZeLE5bz11t906FCbTz+9jWbNql3X/K7E4fqjEEKI0iI9K5v1h2NYvOssF5LS2RUZT2JaVu74+gEVCK3hy8BWtejSOKDIIiRrpKZmculSJv7+Xowc2ZKGDSszcmSr6046hZFEIYQQVsjMNhGTnM6p2BROxqbw595zrDgQnWeaDvWrYNKaHmHVGda+Di7OxftO8x9/HGHMmKWEh1dn/vzBNG7sT+PG/sW6jIJIohBClFtaa5LSs4hOTCc6KY2jFy6RkWUiM9tERpaJg+eTcFaKv4/GEJOccdn3K1dwY+gNdejfsiZ1/a/vXYXCREUl8fjjfzB37j4aN67Co4+2sdmyCiKJQghRrkQnpbF011nWH4nh3+MXSbIoKrqS0Bq+VPJyo3/LmlSp4Eaj6j7U9POkmq+HzeNdseIYd9wxh4yMbKZMuZkJEzrg7l6yp25JFEKIMsdk0uyNSmTe1tOciU9DKdh6Mo7MLBNJ6f8lhoqertzRsiZtgivj5eZMvYAKBPi44+fphquzwtlJFUu9wrXIzMzG1dWZFi2q07t3Q159tSsNGlS2SyySKIQQZUJMcjoLt59hx+l4Fu/K+0pWvYAK1KniRVa2Jry2Hy2D/LiteQ3cXZyvMDf7SUxM54UXVvLvv2fYsGEE/v5ezJ49yK4xSaIQQjiMpLRMFmw/w5qDF3B2UmRkm4iMS+XipQwuXspbh3BDvco82aMxLWpXLJUJIT+tNfPm7WPcuD84dy6Z0aPbkJ6ejZeX/bsNkkQhhCh1tM4pOookPSub9CwTx2Musf1UfO40Lk6KsEBf/DxdcXFSdA+tSu1KXozqUh/XYn7ayNYuXLjEsGEL+f33I7RsWZ1ff72bNm1q2jusXJIohBB2F5OczrELl/j3WCybTlxk3eGY3HHOTorqvh64uzhxc+MAuoZW487WtS5r58iR+fq6ExOTwvvv38qYMW1xcSldiU4ShRCiRGitSc8yEZ+SyeHoJHZFJhCbnMHXG45fNm3XkKrUruTJ7eE1aV2nkh2itb21a08ydeo65s8fjLe3Gxs3PmjTl+auhyQKIUSxyTZp9kYlEBWfRmRcCgfOJRGbnM6/xy+SknF5L2serk74e7sRXrsSfVvUoLqvB63qVHK4oqOrEROTwoQJfzFz5g6Cg/04cSKepk2rltokAZIohBDXKCvbxK4zCSzYdobIuBR2n0kkJjm9wGmDq3hR1ceDDg2qUNHTFQ9XZ7o0DqBGRc8Sjtp+tNZ8880OJkz4i8TEdJ59thPPP38jXl6u9g6tSJIohBBX5dD5JD5Yfpglu/M+gtq5oT9hgTVxc3aic8MA/LxcCarshbuLk93eRShtfvhhF2FhAXz66W00aVLV3uFYTRKFEKJAmdkmVh2IZtm+82Rmm0jPNLHvbCKnLqbkThNRpxLv3RVOrUqekgwKkJKSyWuvrWPUqAhq1fJl/vzBVKzoUaqLmQoiiUIIgdaazSfiWHUwmn+PxXI+Mf2yvhMaVfPG19OFBlW9mdgzhB5htmnSuqxYuvQwY8Ys5cSJeGrW9OGRR9pQqZJjFrVJohCinErNyGbp7rP8tiuK1Qfz9pvQp3kNbg4JwM/Tjbva1KZ2ZS87Rel4IiMTefzxP5g/fz+hof6sWfMAN95Yx95hXRdJFEKUAyaTJuZSOn/sOcfKA9FsPxVPQmpm7ng/L1daB1Xi4RvrERboi49H6a9gLa2mTl3LkiWHee21rowf3wE3N8d/30MZHc05joiICL1lyxZ7hyFEqbPu8AW2nIhjz5kEdkbG4+rsRJZJk5ltvLtgqaKnKy2D/OgRVo0hbYIcrsy8tNm06Qyeni40a1aN2NgUEhLSqVevdL3/oZTaqrWOuJbvyh2FEA4sPSubz9cc452/DuUZ7uHqRGgNX0Kq++Dq7ISrsxNaQ4Oq3nQPrUrVEmgeuzxISEjjuedW8MknW+jTpxGLFg2hShUvqlQpW0V1kiiEcCBaay5eyuDXHVH8tiuKnafjMWlQCsJr+/HOnS2oF+Bt7zDLPK01c+bs5Ykn/iQ6+hJjx7ZlypSu9g7LZiRRCOEADp1P4pn5u9hm0SgeQPfQavRuVp1+4TVxluKjEvPDD7u4//6FREQEsnjxEFq3DrR3SDYliUKIUmxfVCKfrT3KrzuiAHBzduLeG4JoXM2H7mHV8Pd2t3OE5Ud6ehbHjsURGhrA4MFNyMoycf/9LXAuw82N5JBEIUQpkpCSycHzSaw5FM2MVUdzh7s5O/HB3eH0albDjtGVX6tWHeeRR5aQkpLJ4cNjcXd3YfjwlvYOq8RIohDCjlIzslm8K4ptp+LYfCKOI9HJecaH1fDl1Tua0iqodD1BU15ER1/iqaeW8f33u6hXrxKff963xPurLg3K3xoLYUcnYi6x7vAFjseksO7wBQ7nSwz+3m4Max/MjY0CaFzdp0z1ueBojhy5SNu2X5CcnMGkSZ2ZNKkznp7l8/0SSRRC2JDJpFl1MJqXf9vH+cQ00rNMueP8vd2o6efJPe2CuLddEH5ebnaMVORITEzH19ed+vUrMXJkS0aMaEloaIC9w7IrSRRC2Mg3G47z8m/78gzr2aQ6A1vXoltI6e5/oDy6dCmDV15ZwxdfbGPXrkeoVcuXt966xd5hlQqSKIQoRolpmXy04jBrDl3g0HmjWOm25jV4sU8Y1eQlt1Lrt98O8uijv3PqVAIjR7Z0iD4iSpIkCiGuk9aabafimLpkf573HG5vEcjLtzehUgUpUiqtsrJMDB48lwULDtCkSQDr1g2nU6cge4dV6kiiEOIa/bHnLB+sOMKh80lkm4w206r6uDOsQzBjbm5g5+hEYbTWKKVwcXGiRg1vXn+9G0880b5MNOBnC5IohLgKWmve/esQH608kjusqo87991Qh9Z1KtGxgb8doxPW2LgxkjFjlvLFF31p1aoGM2bcZu+QSj1JFEJY4VxCGqsORvPsL7sBcHFS1K7sxfcj21KrUtlqAK6siotL5bnnVvDZZ1sJDPQhLi616C8JwMaJQinVE/gAcAa+1Fq/nm98EPAt4Gee5hmt9VJbxiSEtbTW7D+bxNiftnH0wqXc4Q2qerPs8RvlqSUHMmfOHh577A9iYlJ4/PEbePnlLvj4SPMn1rJZolBKOQMzgB5AJLBZKbVIa235vODzwM9a60+UUmHAUiDYVjEJUZRsk2b1wWgOnEvi7WUHyemupZ5/BR7r1pAujQPkfQcHdOBADMHBfvzxx720bCnNoFwtW95RtAWOaK2PASilZgP9AMtEoQFf898VgSgbxiNEof7ad56HvvuvU6yKnq74e7vxQp8wOjXwx6UcNP5WVqSlZfHGG+tp1aoGffs25rnnOvP88zeWiwb8bMGWiaImcNricyTQLt80LwHLlFJjgQpA94JmpJR6GHgYIChIHl0Txet8Yhqd31xFhvmt6Z5NqvNMrxBqV/aSprsd0PLlxxg9egmHD19k/Pj29O3bGFdpCuW62DJRFPQLy9/v6hBgptb6HaVUe+B7pVRTrbUpz5e0/hz4HIyuUG0SrShXMrNNTF95hE/WHM1NEAD/PNuVGhU97RiZuFbnzyfz5JPLmDVrNw0aVGbZsvvo0aO+vcMqE2yZKCKB2hafa3F50dJIoCeA1vofpZQH4A9E2zAuUY5tPnGRD1ccZt3hmNxhIdV9eLx7I3qEVZM7CAf211/HmDdvHy++eCPPPtsZDw95qLO42HJLbgYaKqXqAmeAu4F78k1zCugGzFRKhQIewAUbxiTKmWyTJvZSOidiUnh+4e7cZjUCK3rQvr4/U+9oKi20OrCdO89x+PBFBg0K4957m9GxY23q1pUm2YubzRKF1jpLKfUo8CfGo69fa633KqVeAbZorRcB44EvlFJPYBRLPaC1lqIlcd2S0jIZ+tUmdpzO23VoTT9PZtzbivDafnaKTBSH5OQMJk9exQcf/EtwsB/9+4fg4uIkScJGbHpvZn4nYmm+YS9a/L0P6GjLGET5orVm4Y4zPDFnJwDOTop72gbRrFZFAit60qF+FXn/wcEtXHiAsWN/JzIykYcfbsW0ad1xcZGnmWxJCvGEw8s2aTYei2Xp7rMs2hFFUnoWAP3CA/ng7vLTXWV5sHv3ee64Yw7NmlVlzpxBdOhQu+gviesmiUI4pPSsbF5YuIe1h2I4l5iWO9zb3YWhN9RhYq8QvMthl5VlUWZmNuvWnaJr17o0a1aNJUvuoUePevLIawmSX5JwKOcS0hj70zY2n4jLHTagZU1qV/aiT/MaNKzmY8foRHH7++/TjBq1mL17L3Dw4KM0aFCZ3r0b2jusckcShXAIaZnZzNsayfML9+QOG3VTfZ7s0Qg3KZ8ucy5eTOWZZ5bzxRfbqF3bl19+GUyDBpXtHVa5JYlClFqX0rNYffACH608zIFzSbnD3xzUnMERUjZdVqWlZREe/ilRUUmMH9+el17qgre3tK9lT5IoRKmTnpXNjFVH+XDF4dxhNf08GdWlPn2a1ZAe48qoyMhEatXyxcPDhSlTbiY8vDotWlS3d1gCSRSiFImKT+Xbf07w07+nSEz778mlSb1DqSr9TZdZqamZTJu2njfe2MC8eXfSt29jhg0Lt3dYwoJViUIp5QYEaa2PFDmxEFfhbEIqj/ywjT1nEsgy/feu5X03BDGlX1OUknceyrJly44yevQSjh6N4777mtO2bU17hyQKUGSiUErdBrwLuAF1lVLhwGSt9R22Dk6UXbsi43l89g6OxRgdArk5O9G7WTVuaxZI97CquLvIo49l3dixS5k+fTMNG1Zm+fKhdOtWz94hiSuw5o7iFYzmwVcBaK13KKWk53hxTS6lZ/H67wf4fuNJAFoG+TG+R2M6NZS+psuD7GyjpV5nZyduuKEW/v5eTJzYSRrwK+Ws2TuZWuv4fEUA0h6TuCoZWSZGfrs5T6ut349sS+eGAXaMSpSkbdvOMmrUYoYObc7Yse24997m9g5JWMmaRLFfKTUYcDK3BDsO2GjbsERZ8fOW07zx+wFiL2UAxpvTbw5qzs2Nq+LpJsVL5UFSUjovvriKDz/cRECAFzVqyEuRjsaaRPEo8CJgAn7BaA32WVsGJRxfbHI6bV9bQba5grpVkB89wqozvGOwNOtdjixbdpQRI34lKiqJUaMieO21bvj5yRNsjsaaRHGr1noiMDFngFJqAEbSECKX1prfdp3lwxWHORJt9PvQJrgSrw9sTv0AbztHJ+zBzc2ZqlUrMH/+YNq1q2XvcMQ1UkV1/6CU2qa1bpVv2FatdWubRnYFEREResuWLfZYtLiCU7Ep/Ln3HFOX7s8dVte/Av3CA3m8eyM7RiZKWmZmNu+++w+JielMndoNAJNJS9PupYD5vB1xLd+94h2FUupWjG5Kayql3rUY5YtRDCXKuT/3nuPj1UfZadE5UM8m1XmmVwjB/hXsGJmwh/XrT+U24HfnnWG5CUKShOMrrOgpGtgDpAF7LYYnAc/YMihR+j06axuLd50FoG3dygzvEEzHhv74erjaOTJR0mJjU5g4cTlffbWdoKCK/PbbEPr0kTvJsuSKiUJrvR3YrpT6UWuddqXpRPmRlJbJqoMX+O7vE2w5aTTz/el9renZVNrjKc9iY1OZPXsPTz/dgRdfvIkK0hZXmWNNZXZNpdRUIAzIfVxBay2XDOVEcvDLeJsAACAASURBVHoWj/ywNc87EPX8K7BobCfpHKic2r//Aj//vJfJk7vQqFEVTp16gsqVPe0dlrARa37lM4FXgbeBXsBwpI6izDsSncT6wzGsOBCdmyBaBvnRq2l1+jQPJNBPTgrlUUpKJlOnruWtt/7G29uNkSNbUauWrySJMs6aROGltf5TKfW21voo8LxSap2tAxP2sepANK8s3sdxcxtMAHWqeDHm5gbSB0Q598cfRxg9egnHj8czbFgL3nqrBwEB8tBCeWBNokhXRvsdR5VSo4AzQFXbhiVK0tmEVMbO2s6eqATSMo2bxT7NazCwdS3a16siL8gJkpMzGDp0AVWqeLJq1TC6dAm2d0iiBFmTKJ4AvIHHgKlARWCELYMSJWflgfOMmPnfeykPdqrLoIhahFT3tWNUojTIzjbx0097GDKkKd7ebixfPpSQEH/cpV6q3Clyj2ut/zX/mQQMBVBKySuWDi4jy8SImZtZf8Sof3i6Z2Meuam+9P8gANi6NYr//W8xW7eexdPThYEDw6S3uXKs0EShlGoD1ATWa61jlFJNMJry6ApIsnBAqRnZDPtmE5uOX8wd9uuYjrSo7WfHqERpkZCQxgsvrGLGjM1UrVqB2bMHMmBAqL3DEnZW2JvZ04CBwE6MCuwFGC3HvgGMKpnwRHGIu5TB73vO8d0/JzhwLil3+EOd6zKueyN5xFXkGjjwZ1auPM6YMW149dWuVKwoDfiJwu8o+gEttNapSqnKQJT588GSCU1cr8xsE2/8foAv1x/PHdawqjd3tw1iZKe6doxMlCbHjsUREOCFj487U6d2xclJ0aaNdEkq/lNYokjTWqcCaK0vKqUOSJJwDMnpWXy08jCfrTkGgJuLEy/0CePWJtWo6iNXiMKQkZHN22//zZQpa3nssba88UYPaeFVFKiwRFFPKZXTlLgCgi0+o7UeYNPIxDU5Ep3E6B+3ceh8Mm7OTtx3Qx0m3RaKszTMJiysXXuSUaMWs39/DIMGhfHYY+3sHZIoxQpLFAPzfZ5uy0DE9Tl8PomxP23PrYN4oEMwz/UOxc3Fyc6RidLmvff+4cknlxEc7MeSJffQu3dDe4ckSrnCGgVcUZKBiKuXmW1i5oYTfL/xJKcupgDg7KRYPLYToTXkPQjxH5NJc+lSBj4+7tx2WyMuXEjh+edvxMtLWvsVRZPHXRxUfEoG4a/8lfu5RW0/Xr69CeHymKvIZ+/eaEaNWpLb01yjRlV47bVu9g5LOBCbJgqlVE/gA8AZ+FJr/XoB0wwGXgI0sFNrfY8tY3J0WmuW74/moe+Mt6m7hlRlxj2t8HSTZjZEXikpmUyZsoa33/6HihXdGTEiHK21vFQprprViUIp5a61Tr+K6Z2BGUAPIBLYrJRapLXeZzFNQ+BZoKPWOk4pJW1IFSItM5vbp6/n0HmjP+qwGr58/UAbO0clSqPt288yYMDPnDgRz/Dh4bz5Zg/8/b3sHZZwUEUmCqVUW+ArjDaegpRSLYAHtdZji/hqW+CI1vqYeT6zMd7N2GcxzUPADK11HIDWOvrqV6F8iElOp/20FWRma5oE+vL5/RHUlKa+RT45dwxBQRUJCqrIt9/258Yb69g7LOHgrHkk5kOgDxALoLXeCdxsxfdqAqctPkeah1lqBDRSSm1QSm00F1WJfLJNmrZTl5OZrQnwcWfRo50kSYg8srJMvP/+Rrp1+47sbBNVqnixZs0DkiREsbAmUThprU/mG5ZtxfcKKgjV+T67AA2BLsAQ4Eul1GW1sUqph5VSW5RSWy5cuGDFosuOT9ccpd1rKzBp6B8eyOZJ3eWdCJHHpk1naNv2C5544k88PFxITLS6hFgIq1hTR3HaXPykzfUOY4FDVnwvErDs6aYWRjMg+afZqLXOBI4rpQ5iJI7NlhNprT8HPgeIiIjIn2zKpMxsEx1eX8mFJONH36JWRSb3bWLnqERpkpycwcSJf/HJJ1uoUcOHuXPvZODAUKmsFsXOmkTxCEbxUxBwHlhuHlaUzUBDpVRdjM6O7gbyP9G0EONOYqZSyh+jKOqYdaGXXf8ei2Xkt1tITs8CYMeLPfDzkg7rRV6urk6sXn2SsWPbMmVKV3x93e0dkiijrEkUWVrru692xlrrLKXUo8CfGI/Hfq213quUegXYorVeZB53i1JqH0Zx1gStdezVLqssOBOfymtL9rNk99ncYd1CqvLlsAi5QhS5jhy5yCuvrGHGjN74+LizdevDeHjI61DCtpTWhZfkKKWOAgeBOcAvWuukQr9gYxEREXrLli1FT+hAtp+K446P/8793KVxABNubUyTwIp2jEqUJunpWbz55gamTl2Hm5szS5bcQ+fOUlEtrKeU2qq1jriW71rTw119pVQHjKKjl5VSO4DZWuvZ17JAkdfWkxcZ+Mk/AEzqHcpDN9azc0SitFm16jiPPLKEgwdjueuuJrz77q0EBvrYOyxRjljVYpzW+m+t9WNAKyAR+NGmUZUTO0/H5yaJdwe3kCQhLqO1ZurUdWRmmvjjj3uZPXuQJAlR4qx54c4b40W5u4FQ4Fegg43jKhfGzNoGwCf3tqJXsxp2jkaUFiaT5quvttGzZwNq167I99/fgZ+fB56e0oCfsA9r7ij2ADcAb2qtG2itx2ut/7VxXGXeu8sOEhmXSru6lSVJiFy7dp2nU6evefjhxXz5pXEhUaOGjyQJYVfWPC5RT2ttsnkk5cg7yw7y0cojAEzp39TO0YjSIDk5g5dfXs17722kUiVPZs7sx/33t7B3WEIAhSQKpdQ7WuvxwHyl1GWPRkkPd1fPZNI8+N0WVh6Ixs3Zib+f7Yq/tzz7LuCll1bzzjv/8OCDLXn99e5UqSIN+InSo7A7ijnm/6Vnu2KQbdL0+Wg9+88mUtPPk0WPdqSKJIly7fTpBC5dyiQkxJ9nnulE//4hdOoUZO+whLjMFesotNabzH+Gaq1XWP7DqNQWVtBa89X64/R4bw37zybyYKe6rJ94sySJciwry8S77/5DaOgM/ve/xQD4+3tJkhClljV1FCO4/K5iZAHDRAGGz9zM6oNGQ4Yh1X14tre0xVOebdwYyahRi9m58zy33daQ6dN72zskIYpUWB3FXRiPxNZVSv1iMcoHiLd1YGXB6oPRrD54gTpVvFjx5E24OFv12oooo5YsOUTfvj8RGOjDL78Mpn//ELloEA6hsDuKTRh9UNTC6KkuRxKw3ZZBlQXfbzzJCwv3APD1A20kSZRTWmuiopKoWdOX7t3r8corNzNuXDt8fKToUTiOItt6Km0coa2nLScuMuhT443rxWM70bSmtNlUHh06FMvo0Us4dCiWffvG4O0tLQAL+7FJW09KqTVa65uUUnHk7XBIAVprXflaFljWmUyaD83vSKydcDNB8phjuZOWlsXrr69n2rT1eHq6MG1aNzw9pYVX4bgKO3pzujv1L4lAyoI1hy4w7GvjYbHAih6SJMqhc+eSufHGbzh8+CJDhjTl3XdvpXp1b3uHJcR1uWKisHgbuzYQpbXOUEp1ApoDP2A0DijMDp9Pyk0S/cMDGde9kZ0jEiUpMzMbV1dnqlWrwI031mHGjN706FHf3mEJUSysqWFdiNENan3gO4x3KGbZNCoH8+uOM/R4by0A990QxPt3t6SufwU7RyVKgsmk+fTTLdSv/yGRkYkopfjyy9slSYgyxZqCU5PWOlMpNQB4X2v9oVJKnnoy+21nFONm7wDgq2ERdAutZueIREnZufMc//vfYv799wxdu9YlMzPb3iEJYRNWdYWqlLoTGAr0Nw+TpiyBtYcuMPYnI2duntSdAHnksVzQWjNhwl+8//5GKlf25Pvv7+Dee5vJOxGizLL2zezRGM2MH1NK1QV+sm1YpZvJpBk3Zwe/7YzCw9WJZ3uFSpIoR5RSxMWlMnKk0YBfpUqe9g5JCJuy6j0KpZQL0MD88YjWOsumURWiNLxHMWHuTuZujaR5rYp8N6Itfl7yfHxZd/JkPOPG/cGLL95Eq1Y1MJk0Tk5yByEcx/W8R1FkZbZSqjNwBPgK+Bo4pJTqeC0LKws2Hotl7tZIAH4d01GSRBmXmZnNm29uICzsY/766xgHD8YASJIQ5Yo1RU/vAb211vsAlFKhwPfANWUmR5aZbWLkzM0AfHl/hJRJl3F//32a//1vMXv2RNOvX2M+/LAXQUHylr0of6xJFG45SQJAa71fKVUuL6PHztrOpYxsXugTRvcwebqprFu+/BgJCWksXHgX/fqF2DscIezGmkSxTSn1GcZdBMC9lMNGAU/EXOKPvecAGN4h2L7BCJvQWvP997sICPCiV6+GTJzYkSefbC9tNIlyz5oX7kYBR4GngYnAMeB/tgyqtPl0zVG6vL0agOn3tJTy6TLowIEYunb9jmHDFvLNN8Z7Me7uLpIkhKCIOwqlVDOgPrBAa/1myYRUuuw8Hc/rvx8AoF94ILc1q2HniERxSk3N5LXX1vHGGxuoUMGNzz7rw4MPtrJ3WEKUKoW1HvscRk9224A2SqlXtNZfl1hkpcD5xDT6zdgAwILRHWgZVMnOEYni9ttvh3j11XXcd19z3n67B9WqSQN+QuRX2B3FvUBzrfUlpVQAsBTj8dhywWTStHttBQDjujWUJFGGnDuXzI4d5+jZswF33hlGcPCDtG1b095hCVFqFVZHka61vgSgtb5QxLRlztcbjgPQNaQqT/SQlmDLguxsEx9/vJnGjaczdOgCUlMzUUpJkhCiCIXdUdSz6CtbAfUt+87WWg+waWR29MXaY0xduh+A1+5oZudoRHHYtu0so0YtZvPmKLp3r8fHH/fG01OaLBPCGoUlioH5Pk+3ZSClxdaTcblJ4vdxnale0cPOEYnrdfx4HG3bfoG/vxezZg3g7rubysuSQlyFwjouWlGSgZQGiWmZDPzkb8B48zq0hq+dIxLXSmvN7t3RNG9ejbp1K/HNN/3o27cxfn6S+IW4WuWq3qEwWmuav7QMgKE31JE3rx3Y8eNx9OnzEy1bfsauXecBGDq0hSQJIa6RTROFUqqnUuqgUuqIUuqZQqYbpJTSSim7tR/1w7+nAHBzdmJK/6b2CkNch4yMbF5/fT1NmnzMmjUnePvtHoSFBdg7LCEcnjVNeACglHLXWqdfxfTOwAygBxAJbFZKLbJsN8o8nQ/wGPCvtfMubklpmbywcA8AmyZ1s1cY4jpkZ5vo0OErtm49y4ABobz//q3Uri0N+AlRHKxpZrytUmo3cNj8uYVS6iMr5t0Wo++KY1rrDGA20K+A6aYAbwJp1oddvJqZi5x6N6suzYY7mMRE49rF2dmJESNa8ttvQ5g/f7AkCSGKkTVFTx8CfYBYAK31TuBmK75XEzht8TnSPCyXUqolUFtrvbiwGSmlHlZKbVFKbblw4YIVi7ZeSsZ/fTDNuEeabnAUWmtmztxBvXof8OuvRhMro0e3oU8feedFiOJmTaJw0lqfzDfMml7kC3r+MLc7PaWUE0ZfF+OLmpHW+nOtdYTWOiIgoHjLnMf8uA2AyX3D5JFJB7Fv3wW6dPmW4cN/JSTEn/r1K9s7JCHKNGvqKE4rpdoC2lzvMBY4ZMX3IoHaFp9rAVEWn32ApsBq8wm6OrBIKXW71rpE+jo9E5/KqoPGHcoD0nS4Q3jzzQ1MmrQSX193vvyyL8OHS2u+QtiaNYniEYzipyDgPLDcPKwom4GGSqm6wBngbuCenJFa6wTAP+ezUmo18FRJJYmUjCw6vr4SgFf7ywtYpZ3WGqUU1at7c++9zXjrrR4EBFSwd1hClAtFJgqtdTTGSf6qaK2zlFKPAn8CzsDXWuu9SqlXgC1a60VXHW0xmmV+HLZX0+rcd0Mde4YiChEVlcS4cX/QuXMQjz3Wjvvvb8H997ewd1hClCtFJgql1BdY1C3k0Fo/XNR3tdZLMVqdtRz24hWm7VLU/IqL1ppXlxjNdHw4pGVJLVZchZwG/CZNWklmpokOHWrZOyQhyi1rip6WW/ztAdxB3qeZHM7GYxcBaFHbD1dneTm9tNmx4xwPPriIrVvPcsst9fn4495SYS2EHVlT9DTH8rNS6nvgL5tFVAJ+2RYJwKTeoXaORBQkISGNqKgk5swZxJ13ytNoQtib1W9mW6gLOGyhvtaauVsj8XJzpm1duUotDbTWzJ27j8OHY5k06UZuuimYY8fG4eFxLYenEKK4WfNmdpxS6qL5XzzG3cRztg/NNqaa6yZ6Nqlu50gEwNGjF+ndexZ33TWPX389SGam8YqOJAkhSo9Cf43KuOdvgfF4K4BJa31ZxbYj+XK90XPdq3dIw3/2lJ6exdtv/82rr67D1dWJDz7oyejRbXBxkTojIUqbQhOF1lorpRZorVuXVEC2lGa+Wm1WsyJebnLFak+nTycyZcpa+vZtzPvv30rNmtL3hxCllTWXb5uUUmWiEaTFu84CMKi1PGppDxcuXGL69E0ANGhQmX37xjB37p2SJIQo5a54Wa2UctFaZwGdgIeUUkeBSxhtOGmttcMljxX7jU5sBkfULmJKUZxMJs0332zn6aeXk5SUTo8e9Wjc2J969SrZOzQhhBUKK3/ZBLQC+pdQLDa3/kgMfl6ueLo52zuUcmPPnmgeeWQJ69efonPnID79tA+NG/sX/UUhRKlRWKJQAFrroyUUi02tPXSBpLQsafyvBGVkZHPLLd+TkZHN11/fzgMPhMs7EUI4oMISRYBS6skrjdRav2uDeGzmoe+MtgZHdKxr50jKvpUrj3PTTXVwc3Pm55/vJCTEH39/L3uHJYS4RoVVZjsD3hjNgRf0z2GkZGSRnmWiaU1fgqrICctWIiMTGTjwZ7p1+47vvtsJQKdOQZIkhHBwhd1RnNVav1JikdjQV+uMdyfuvyHYvoGUUVlZJqZP38QLL6wiO9vEtGnduPfe5vYOSwhRTIqsoygLch6LvTNCHou1haFDFzB79h569WrAjBm9qVtXnmYSoiwpLFF0K7EobMhk0hyPuUQ9/wpSkVqM4uPTcHFxwtvbjTFj2jBwYCgDB4bKNhaiDLpiHYXW+mJJBmIrj83eTka2iVubSttOxUFrzezZewgNncELLxg9BHbqFMSgQdLKqxBlVZlvWOdIdDIAT93S2M6ROL4jRy5y660/MGTIfGrV8uW++6QeQojyoEw3eJSakc2Bc0nc3iIQZye52r0es2btZsSIX3F3d2H69F6MGhWBs3T6JES5UKYTxcbjsQC0kX4nrllmZjaurs5ERAQyaFAYb77Zg8BAh3o6Wghxncp0ojhqLnaKqCNP4Vyt6OhLjB+/jEuXMvjll7to1KgKP/wwwN5hCSHsoEyXHRw+bySKOvKSndVMJs3nn2+lcePpzJmzhyZNAsjONtk7LCGEHZXpO4o5W07j7+0mfU9Y6dixOO677xf++SeSLl2C+eST2wgJkQb8hCjvyuwZdM+ZBABulS5PrVaxojvx8Wl8+21/hg5tLo+7CiGAMlz0lPNY7IBWNe0cSem2aNFBBgyYQ3a2iSpVvNizZzT3399CkoQQIleZTRQ5zXaE1aho50hKp1OnEujffzb9+s3m0KFYzp41EquTPEYshMinTBY9ZWWbWL7/PM5OSjopyicry8T7729k8uTVaK15443uPPHEDbi6ynYSQhSsTCaKt5YdBODuNtLlaX7Z2Sa+/HIbXbvW5aOPehEc7GfvkIQQpVyZK3rSWvPZmmMAvNg3zM7RlA5xcalMnPgXSUnpuLu7sGHDCBYtuluShBDCKmUuUXy08ggAw9rXwd2lfBenaK358cddhITM4J13/mHVqhMAVKniJZXVQgirlamip4SUTN796xAAT5bzRgAPHYpl9OglrFhxnLZta/Lnn/cRHi6PCgshrl6ZShRrD18AYMKtjano6WrnaOzr8cf/YMuWKD7+uDcPP9xaGvATQlyzMpUotp6MA2BwRPmsxP7rr6OEhPhTu3ZFPvnkNtzdXahe3dveYQkhHJxNLzOVUj2VUgeVUkeUUs8UMP5JpdQ+pdQupdQKpVSd61nenM2nCa3hS4CP+/XMxuGcO5fMPffM55ZbfuCNNzYAUKeOnyQJIUSxsFmiUEo5AzOAXkAYMEQplf8xpO1AhNa6OTAPePNal2cyaVIzs6np53Gts3A4JpPm00+3EBIynfnz9zN58k28/fYt9g5LCFHG2PKOoi1wRGt9TGudAcwG+llOoLVepbVOMX/cCNS61oXl1E90Dal2rbNwONOmreORR5bQunUgu3aN4qWXuuDhUaZKE4UQpYAtzyo1gdMWnyOBdoVMPxL4vaARSqmHgYcBgoKCCvzyop1RAPQIK9uJIikpnZiYFOrWrcSoURHUrVuJIUOayuOuQgibseUdRUFnLl3ghErdB0QAbxU0Xmv9udY6QmsdERAQUODCtp+Kx8fdpczWT2itWbBgP2FhH3PXXfPQWlOlihf33NNMkoQQwqZsmSgiAcvHj2oBUfknUkp1ByYBt2ut0691YcdjLtGwWtmsvD15Mp7bb5/NgAE/U7myJx9+2EuSgxCixNiy6Gkz0FApVRc4A9wN3GM5gVKqJfAZ0FNrHX2tC9p/NhGAOlUqXHOwpdU//5yme/fvAXj77R6MG3cDLi7yToQQouTYLFForbOUUo8CfwLOwNda671KqVeALVrrRRhFTd7AXPMV8imt9e1Xu6z3lxtvY9/TruD6C0eUmJiOr687rVrVYMSIcCZM6EhQkDSZLoQoeTZ9REZrvRRYmm/YixZ/dy+O5aw9FEODqt60Ca5cHLOzq9jYFJ55ZjnLlh1j797ReHu78dFHve0dlhCiHHP4ZylTM7JJzcymUwPH7ttZa8333+9i/PhlxMWl8uST7ZFqCCFEaeDwieLnLcYTuHX9Hbd+IiEhjf7957B69Qnat6/Fp5/2oXnzsv2YrxDCcTh8olh7yHjR7r4brqv1D7vQWqOUwtfXHX9/Lz7/vA8jR7aS7kiFEKWKwz8+88+xWNxcnHB2sJPrn38eoVWrz4mMTEQpxdy5d/LQQ60lSQghSh2HThTHLiSTkpHNDfWq2DsUq509m8Tdd8+jZ88fSUnJJDr6kr1DEkKIQjl00dOm4xcBGN4h2L6BWGnGjE0899xK0tOzePnlLkyc2BF3d4feBUKIcsChz1JzzBXZzWo5xvsFW7eepV27msyY0ZuGDR3nLkgIUb45bKJIychi+6l4wmr44u9dOtt3SkxM58UXVzF0aHNatw7k449vw93dWZrfEEI4FIdNFF+vPw7A3W1LX292Wmvmz9/PuHF/cPZsEkFBFWndOlCaABdCOCSHPXMdOJcEwL3tStdjscePx/Hoo7+zdOlhwsOr88svg2nX7pq72RBCCLtz2ERx+HwyoTV8S91jsT/+uJu1a0/y3nu38uijbaUBPyGEw3PIRGEyaQ6eT2JwROm4Ul+37iTp6dl0716PCRM68MAD4dSq5WvvsIQQolg45OXu8Vjj3YOKnq52jSMmJoURI37lxhtn8sorawBwd3eRJCGEKFMc8o7iwFmjfqJVUCW7LF9rzcyZO5gw4S8SEtKZOLEjL7xwo11iEaVbZmYmkZGRpKWl2TsUUU54eHhQq1YtXF2L70LaIRNFZrYJgMbVfeyy/KVLDzNixCI6dqzNp5/2oWnTqnaJQ5R+kZGR+Pj4EBwcLI9FC5vTWhMbG0tkZCR169Yttvk6ZNHT2QTj6szD1bnElpmSksmGDacA6N27Ib/+ejdr1w6XJCEKlZaWRpUqVSRJiBKhlKJKlSrFfgfrkIniRIxRR+HnVTJ1FL//fpimTT+mV68fiY9PQynF7bc3lgb8hFUkSYiSZIvjzSETxcbjsQB4udm25OzMmUTuvHMuvXvPwt3dhd9+G4Kfn4dNlymEEKWNQyYKN2cnAnxs22xHdPQlwsI+ZvHiQ7z66s3s3DmKm24KtukyhbAFZ2dnwsPDadq0KX379iU+Pj533N69e+natSuNGjWiYcOGTJkyBa117vjff/+diIgIQkNDCQkJ4amnnrLHKhRq+/btPPjgg3mG9evXj/bt2+cZ9sADDzBv3rw8w7y9vXP/PnToEL1796ZBgwaEhoYyePBgzp8/f12xXbx4kR49etCwYUN69OhBXFzcZdOsWrWK8PDw3H8eHh4sXLgQgM6dO+cODwwMpH///gAsXryYyZMnX1dsV0Vr7VD/WrdurW98c6Ue99M2bQuRkQm5f3/wwUZ95EisTZYjyod9+/bZOwRdoUKF3L/vv/9+/eqrr2qttU5JSdH16tXTf/75p9Za60uXLumePXvq6dOna6213r17t65Xr57ev3+/1lrrzMxMPWPGjGKNLTMz87rnMWjQIL1jx47cz3FxcbpWrVo6JCREHzt2LHf4sGHD9Ny5c/N8N2fbpKam6gYNGuhFixbljlu5cqXevXv3dcU2YcIEPW3aNK211tOmTdNPP/10odPHxsbqSpUq6UuXLl02bsCAAfrbb7/VWmttMpl0eHh4gdNpXfBxB2zR13jedcinnlIysnF3Kd6K7ISENJ5/fiWffbaVjRsfpFWrGjz2WLtiXYYo317+bS/7ohKLdZ5hgb5M7tvE6unbt2/Prl27AJg1axYdO3bklltuAcDLy4vp06fTpUsXxowZw5tvvsmkSZMICQkBwMXFhdGjR182z+TkZMaOHcuWLVtQSjF58mQGDhyIt7c3ycnJAMybN4/Fixczc+ZMHnjgASpXrsz27dsJDw9nwYIF7NixAz8/PwAaNGjAhg0bcHJyYtSoUZw6ZTxE8v7779OxY8c8y05KSmLXrl20aNEid9j8+fPp27cv1apVY/bs2Tz77LNFbpdZs2bRvn17+vbtmzvs5ptvtnq7Xsmvv/7K6tWrARg2bBhdunThjTfeuOL08+bNo1evXnh5eeUZnpSUxMqVK/nmm28Aox6iS5cuLF68mMGDB193nEVxuEShNcQkpVOxMoGG2QAADyxJREFUmCqytdbMnbuPxx//g3Pnknn00bbUr2+f9zOEsKXs7GxWrFjByJEjAaPYqXXr1nmmqV+/PsnJySQmJrJnzx7Gjx9f5HynTJlCxYoV2b17N0CBxSv5HTp0iOXLl+Ps7IzJZGLBggUMHz6cf//9l+DgYKpVq8Y999zDE088QadOnTh16hS33nor+/fvzzOfLVu20LRp0zzDfvrpJyZPnky1atUYNGiQVYliz549l22LgiQlJdG5c+cCx82aNYuwsLA8w86fP0+NGjUAqFGjBtHR0YXOf/bs2Tz55JOXDV+wYAHdunXD1/e/l3kjIiJYt26dJIqCpGVlAxBU2auIKYumtWbAgJ9ZuPAArVrVYNGiIUREBF73fIUoyNVc+Ren1NRUwsPDOXHiBK1bt6ZHjx7Af322F+RqnpxZvnw5s2fPzv1cqVLRF1p33nknzs5GqcBdd93FK6+8wvDhw5k9ezZ33XVX7nz37duX+53ExESSkpLw8fnv/amzZ88SEBCQ+/n8+fMcOXKETp06oZTCxcWFPXv20LRp0wLX6WqfEPLx8WHHjh1X9R1rnT17lt27d3PrrbdeNu6nn366rB6matWqREVF2SSW/ByuMjsjy3jZrvZ1JIrMTCPZKKXo1Kk2H37Yk02bHpQkIcokT09PduzYwcmTJ8nIyGDGjBkANGnShC1btuSZ9tixY3h7e+Pj40OTJk3YunVrkfO/UsKxHJb/uf4KFSrk/t2+fXuOHDnChQsXWLhwIQMGDADAZDLxzz//sGPHDnbs2MGZM2fyJImcdbOc95w5c4iLi6Nu3boEBwdz4sSJ3CRWpUqVPHc7Fy9exN/fP3dbWLOuSUlJeSqeLf9ZJrUc1apV4+zZs4CRCKpWvfJ7Vz///DN33PH/9u4/uKr6zOP4+7MUTFRIV1wYLVXomGJCDIjQpnYQEZfxRwpLxxHCj8qOrSOI1KI47ODMuiu1tAV0I9rIuk7i2gYWpwoDCotsLBX5IbQKCppSmgnZ6SrLsqyl8vvZP85Jck1ubk5i7r25N89r5s7ce35+88zNee75nnOe7+RWT1QfPXqUXbt2cfvtt39m+smTJ8nNzW23zV0h4xLFyfAgnz/g4naWjO+NN+ooLq5g7doPAHjwweu5//6v06tXxoXCuQ7Jy8ujvLycpUuXcubMGaZPn86bb77J66+/DgRnHvPmzePhhx8GYMGCBTz++OPU1tYCwYF7+fLlrbY7YcIEVqxY0fS58WA8cOBADhw40NS11BZJTJ48mfnz51NQUED//v3jbjfeL/mCggIOHjzY9Lm6upqNGzdSV1dHXV0de/bsaUoUN954I6tXr+b06dMAVFZWNl2HmDZtGm+99RYbNmxo2tbGjRubutMaNZ5RxHu17HYCmDhxIlVVVQBUVVUxadKkNuNQXV1NWVlZq+lr1qyhtLSUnJzP3ppfW1vbqtstWTLu6CiCXymXXNSnQ+sdOXKCu+56hXHjqjh16ix9k3x7rXPd0bXXXsvw4cNZtWoVubm5rF27lsWLFzN06FCuueYaRo8ezdy5cwEoLi7mySefpKysjIKCAoqKipp+Hcd65JFHOHbsGEVFRQwfPpyamhoAlixZQmlpKTfddFNTP31bpkyZwosvvtjU7QRQXl7O7t27KS4uprCwkIqKilbrXX311Rw/fpxPPvmEuro66uvrKSkpaZo/ZMgQ+vXrx86dOyktLWXMmDFcd911jBgxgm3btjVdWM7NzWX9+vU89dRT5OfnU1hYSGVlZcIzgCgWLlzI5s2byc/PZ/PmzSxcuBAIrq3EdiXV1dVx+PBhxo4d22obq1atiptAampqWp1lJIss5p7pTHD5VcOs79SlfLj41sjrVFfv4777XuVPfzrNggXXs2jRDVyYoqe6Xc924MABCgoK0t2MrPbEE0/Qt2/fVn342eyjjz5i2rRpbNmyJe78eN87SXvMbFRn9pdxZxSnzp5nQL+OnQ2cPXueoqIBvPPOvfzwh+M9STiXRWbPns0FF/SsHoL6+nqWLVuWsv1l3l1PZ84xuP9FCZc5ceI0jz22lSuuyGPOnNHMmFHMjBnFXnPHuSyUk5PDzJkz092MlBo9enRK95dxZxRnzxunzpxvc/769bUMG/YMP/7xNmprg5pQkjxJuLTJtO5dl9mS8X3LuDMKgILLWo9D0dDwf8yb9xovv/wBhYV/xdatsxgz5so0tM65Zjk5ORw9etRLjbuUsHA8ipZ3SH1eGZko8ge2ThSHDh1j06bf86MfjWf+/G/Qp0/qxqpwri2DBg2ioaGBI0eOpLsprodoHOGuK2VkorgsL8iWu3b9J9u3H+b73y/hhhuupL7+Afr3//xPbDvXVXr37t2lI405lw5JvUYh6RZJH0o6KGlhnPkXSFodzt8paXCU7fbr1Ys5czZQUvIcy5fv4MSJ4AEaTxLOOdf1kpYoJPUCngZuBQqBMkktH128GzhmZlcBTwBtl1WM8a2bX+DZZ/cwb97X2bdvNhd18OE755xz0SWz6+lrwEEzOwQgaRUwCYgtiDIJeDR8/xKwQpKsncv2gwb25dVXyhg5MvHTns455z6/ZCaKLwGHYz43AC0HeGhaxszOSjoO9Af+O3YhSfcA94QfT+35r3vei1ARuCe4lBax6sE8Fs08Fs08Fs2GdnbFZCaKePcCtjxTiLIMZrYSWAkgaXdnH0PPNh6LZh6LZh6LZh6LZpJ2t79UfMm8mN0AfDnm8yCgZfH0pmUkfQHIA/4niW1yzjnXQclMFG8D+ZKGSOoDTAXWtVhmHXBX+P4O4D/auz7hnHMutZLW9RRec5gLbAJ6Ac+b2fuS/pFgkO91wL8A/yrpIMGZxNQIm16ZrDZnII9FM49FM49FM49Fs07HIuPKjDvnnEutjCsK6JxzLrU8UTjnnEuo2yaKZJX/yEQRYjFf0n5JeyVtkZS1ZXPbi0XMcndIMklZe2tklFhIujP8brwv6RepbmOqRPgfuUJSjaTfhv8nt6Wjnckm6XlJH0t6r435klQexmmvpJGRNmxm3e5FcPH798BXgD7Au0Bhi2XmABXh+6nA6nS3O42xGAdcGL6f3ZNjES7XF9gK7ABGpbvdafxe5AO/Bf4y/Dwg3e1OYyxWArPD94VAXbrbnaRY3ACMBN5rY/5twGsEz7CVADujbLe7nlE0lf8ws9NAY/mPWJOAqvD9S8B4ZWfB/3ZjYWY1Zvbn8OMOgmdWslGU7wXAY8BPgJOpbFyKRYnF94CnzewYgJl9nOI2pkqUWBjQL3yfR+tnurKCmW0l8bNok4AXLLAD+KKkdmshdddEEa/8x5faWsbMzgKN5T+yTZRYxLqb4BdDNmo3FpKuBb5sZutT2bA0iPK9+CrwVUnbJO2QdEvKWpdaUWLxKDBDUgPwKnB/aprW7XT0eAJ03/Eouqz8RxaI/HdKmgGMAsYmtUXpkzAWkv6CoArxrFQ1KI2ifC++QND9dCPBWeavJRWZ2f8muW2pFiUWZUClmS2T9A2C57eKzKztcZWzU6eOm931jMLLfzSLEgsk3QwsAiaa2akUtS3V2otFX6AIeENSHUEf7LosvaAd9X9krZmdMbM/AB8SJI5sEyUWdwP/BmBm24EcgoKBPU2k40lL3TVRePmPZu3GIuxueZYgSWRrPzS0EwszO25ml5rZYDMbTHC9ZqKZdboYWjcW5X/kFYIbHZB0KUFX1KGUtjI1osSiHhgPIKmAIFH0xPFp1wHfCe9+KgGOm9kf21upW3Y9WfLKf2SciLH4KXAxsCa8nl9vZhPT1ugkiRiLHiFiLDYBEyTtB84BC8zsaPpanRwRY/Eg8M+SfkDQ1TIrG39YSqom6Gq8NLwe8/dAbwAzqyC4PnMbcBD4M/C3kbabhbFyzjnXhbpr15NzzrluwhOFc865hDxROOecS8gThXPOuYQ8UTjnnEvIE4XrdiSdk/ROzGtwgmUHt1Ups4P7fCOsPvpuWPJiaCe2ca+k74TvZ0m6PGbec5IKu7idb0saEWGdByRd+Hn37XouTxSuO/rUzEbEvOpStN/pZjacoNjkTzu6splVmNkL4cdZwOUx875rZvu7pJXN7XyGaO18APBE4TrNE4XLCOGZw68l/SZ8XR9nmWGSdoVnIXsl5YfTZ8RMf1ZSr3Z2txW4Klx3fDiGwb6w1v8F4fQlah4DZGk47VFJD0m6g6Dm1s/DfeaGZwKjJM2W9JOYNs+S9FQn27mdmIJukn4mabeCsSf+IZw2jyBh1UiqCadNkLQ9jOMaSRe3sx/Xw3micN1Rbky308vhtI+BvzazkcAUoDzOevcC/2RmIwgO1A1huYYpwDfD6eeA6e3s/1vAPkk5QCUwxcyuIahkMFvSJcBkYJiZFQOLY1c2s5eA3QS//EeY2acxs18Cvh3zeQqwupPtvIWgTEejRWY2CigGxkoqNrNyglo+48xsXFjK4xHg5jCWu4H57ezH9XDdsoSH6/E+DQ+WsXoDK8I++XMEdYta2g4skjQI+KWZ/U7SeOA64O2wvEkuQdKJ5+eSPgXqCMpQDwX+YGa14fwq4D5gBcFYF89J2gBELmluZkckHQrr7Pwu3Me2cLsdaedFBOUqYkcou1PSPQT/15cRDNCzt8W6JeH0beF++hDEzbk2eaJwmeIHwEfAcIIz4VaDEpnZLyTtBG4HNkn6LkFZ5Soz+7sI+5geW0BQUtzxTcLaQl8jKDI3FZgL3NSBv2U1cCfwAfCymZmCo3bkdhKM4rYEeBr4tqQhwEPAaDM7JqmSoPBdSwI2m1lZB9rrejjvenKZIg/4Yzh+wEyCX9OfIekrwKGwu2UdQRfMFuAOSQPCZS5R9DHFPwAGS7oq/DwT+FXYp59nZq8SXCiOd+fRJwRlz+P5JfA3BGMkrA6ndaidZnaGoAupJOy26gecAI5LGgjc2kZbdgDfbPybJF0oKd7ZmXNNPFG4TPEMcJekHQTdTifiLDMFeE/SO8DVBEM+7ic4oP67pL3AZoJumXaZ2UmC6pprJO0DzgMVBAfd9eH2fkVwttNSJVDReDG7xXaPAfuBK81sVzitw+0Mr30sAx4ys3cJxsd+H3ieoDur0UrgNUk1ZnaE4I6s6nA/Owhi5VybvHqsc865hPyMwjnnXEKeKJxzziXkicI551xCniicc84l5InCOedcQp4onHPOJeSJwjnnXEL/D9lWMQCoInvUAAAAAElFTkSuQmCC\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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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 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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 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\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/.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..3103496 --- /dev/null +++ b/.ipynb_checkpoints/BT2101_Default - EDIT-MASTER- Reon-checkpoint.ipynb @@ -0,0 +1,7047 @@ +{ + "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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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5 rows × 24 columns

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" + ], + "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": [ + "
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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
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" + ], + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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" + ], + "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": 37, + "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, 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 1987 Defaulters, the Decision Tree (GINI) identified 809\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5482 1280\n", + "1 1178 809" + ] + }, + "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", 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" + ] + }, + "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 0.81 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": [ + "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 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5509 1253\n", + "1 1156 831" + ] + }, + "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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" + ] + }, + "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 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": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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])" + ] + }, + { + "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": 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 0.86 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": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "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 0.89 6762\n", + " 1 0.65 0.36 0.46 1987\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, 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": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
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" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" + ] + }, + "execution_count": 56, + "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": 57, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "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: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784
Time: 19:35:32 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
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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: 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 -0.8737 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": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "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 0.88 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 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": 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: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\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: 0.445388\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: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756
Time: 19:35:33 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
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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: 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 -0.8984 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": 60, + "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": 61, + "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": 62, + "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": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21650864211883647\n" + ] + }, + { + "data": { + "image/png": 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nBvh5V6OFvzfV3V0JqVOdOl5uuLu64FJFobUmMvIzYg7FMXXqDTz6aGeqfn/HFcdpz0QRTeGOyYMw+i8QQginobXm8PlUYhIz2HsmidMJ6ZyIS2fLiYslzuNSRdGsnjf9W9fn+lBffL3c8XRzoa63O0oV2wU9AH/+eZrWrevi7e3O558PwtfXkwYNLqdH5uLZM1EsweikfB7QGUiS+gkhhDOITc5k6tL97DmTxMn49EvG+3m70ye8Ln7e1Qiq5UFwbU9aBPgQVMsDN5cqpSaD4sTHp/PMM6v4/PMdTJnSk5df7kW7dv7XanNslyiUUnOBXoCvUioaowvEqgBa648xOlkfgNHZeDpwv61iEUIIWzgVn87aw7Hsjk7iVHw62XkmdkUnYtkfXJ/weoT7e9MuuCbBtT0Jrl0dt6usM8inteabb3bx1FMrSUjIYPLkrkye3PWaLNuSLZ96GlXGeA08bKv1CyHE5dJak5Vr4mJaNimZuWTm5BGTmMGF1CxOxqdz6FwKeSbN0QupXEjJumT+ej7u9GrqRy1PN25qWZ+bW9a77LuDy/H006t4660/6dq1AR9/fAutW9ezyXocrj8KIYS4FnLzTOyNSWbdoQucjE8jKi6NnacTy5yvZYAPzet7E1LHk5YBNejboh6dG9XG1aV8GrrIyMghLS0HX19Pxo5tR1hYbcaObU+VKrZLSJIohBCVRlJGDnO3nGLV/vNsO5lQaFzboBoMjgiglqcbQbU88HRzpa63O64uCv8aHtT3qUYNz6p2itywfPlRHn74VyIi6rNw4QiaNfOlWTNfm69XEoUQwimlZeUSm5LFztMJrDt0gfVH4riYll0wvnl9b25oXpeeTf3o3Ki2TYuIrlZMTAqPP76cH3/cT7NmdXjkkY7lun5JFEIIh3cxLZtlu2P481g8J+LTOVDMC2k1PKoysI0/Q9oH0rWJL9Wqutgh0sv3xx9R3H77fLKz85g27QYmT+6Ku3v5nrolUQghHEJ2ronjcWlsOR7PgXMpZOWYWHf4AmlZuWTk5BVM5+vlzh0dG+BSRRHu70NwbU86htTGw80xEkO+nJw8qlZ1oW3b+gwYEMarr95IaGhtu8QiiUIIUSFtO3GRXdFJHD6XwrrDFziXnHnJNK0Da+Dn7U6HhrVoEeBDt1BfqpZTpbKtJCdn8eKLq/n77zNs2jQGX19P5s0bZteYJFEIIewqO9dEcmYOe88ksfbQBbafTGDPmcLNVzT2q073MF96NvWjQ8NaNKvvjaebc52+tNYsWLCfxx5bzrlzqUyY0JGsrDw8Pe2f+JxrTwshKqSLadkcj0tl64kE0rNySc7MZVd0IjtOFf84amO/6rQOrMHozg1pFejjdEmhqAsX0rj33p/57bejtGtXn8WL76Bjx0B7h1XAufe+EKLcmUya7/8+ycajceyLSSY6IeOSaTyqulC7uhudG9UmoKYHrQJrUEVBp0a1Ca/vY9N3AioiHx934uLSee+9m3n44U64XqM3t68VSRRCiKuSnJnDsdhUDp1LYeuJBBb+E10wztvdlSZ+1eke5kdkSC18vdyJbFir3F5Oq8jWrz/Ja69tYOHCEXh5ubF58wMVNkFKohBCWMVk0mw9cZE9Z5LYcTqR6IQMdhXzJrOnmwt3dgrm2QHhuFTQE589xcWlM3nySmbP3klISE1OnEikVau6FTZJgCQKIUQxTCbN/rPJ7IpOZOvxi8SnZbPhSFyhaep6uzOobQDe1Vzp3Kg2daq70zLAh1rV3ewUdcWmtearr3YyefJKkpOzePbZbrzwQg887fy2tzUkUQghACM5HDiXzPOL9hbb5lHfFvW4vkkdujTxpWEdT4d5Ya0i+e673bRo4cfHH99Cy5Z17R2O1SRRCFEJ5Zk055Mz2ReTzOHzKWyOii90x9DItzq3twvkhmZ1aVK3utM/dWQr6ek5vP76BsaNiyQoyIeFC0dQo0a1Cl3MVBz59oWoJDJz8vh43TH+OBDLwXPJ5OQV7lU4tK4XvZr6cUenYELretkpSufx669HePjhXzlxIpHAQG/Gj+9IrVoe9g7rikiiEMIJaa25kJrFin3n+etYPFtPXCTW3H9CWF0vRnZsQCNfL2p6VCXC3KGOo7/RXFFERyfz+OPLWbjwAOHhvqxbdx89ejS0d1hXRRKFEE4iMyePlfvPs2RXDCv3ny80zsvdlU4htRnaIZCRHYPtFGHl8Npr61m27Aivv34jkyZ1xc3B2pgqjtJalz1VBRIZGam3bdtm7zCEqBB2nk7kfysOEZOYwbELaYXG9Wzqx+3tAukdXhfvahX/yRpHtmXLGTw8XGnduh7x8ekkJWXRuHEte4dViFJqu9Y68krmlTsKIRzMxiNxfLExir+i4snMMRUMbx1Yg5ta1OPBHo3liaRykpSUyXPP/cFHH21j4MCmLFkyijp1PKlTx9PeoV1TkiiEqOAyc/L4cXs0+2OSmbvlVMHwVoE+9GzqR/9W/rQKrGHHCCsfrTXz5+/jiSd+JzY2jYkTOzFt2o32DstmJFEIUQHtj0nm/1Yf4fD5lEJFSm2DauDnXY0pg1rQoLZzXbU6ku++28099/xMZGQAS5eOokOHAHuHZFOSKISoAM4nZ/LHgViOxKawYHs0KZm5BeO6h/nSPcyXe7qESJGSHWVl5RIVlUB4uB8jRrQkN9fEPfe0xaUSPC0miUIIO8jJM/HJumOsOXSB2JRMTl/8t4XVcH8fWgf68FCPJvI+QwWxZs1xxo9fRnp6DkeOTMTd3ZX7729n77DKjSQKIcqB1pq/jsXzy+6zbDx6oVBiaOxbnUduCKVLkzq0C64pb0FXILGxaTz11Aq+/XY3jRvX4tNPB5V7f9UVQeXbYiHKidaaXdFJzPn7JEt3nyU92+jX2aOqC91CfekdXpf7uoaglGM151BZHD16kU6dPiM1NZvnn+/O8893x8Ojcj5mLIlCiGsoN8/EjtOJrDpwnjl/nypU1zC0fRAP9WhMs/redoxQlCU5OQsfH3eaNKnF2LHtGDOmHeHhfvYOy64kUQhxlTJz8vi/1UdYd/gCe88kFwxvGeDDbRGB9GtVn8CaHg7XEFxlk5aWzdSp6/jss3/YvXs8QUE+vPXWTfYOq0KQRCHEFdp1OpFx323nbFJmwbCm9bzo18qfAa3r07y+jx2jE5fjl18O8cgjv3HqVBJjx7ZziD4iypMkCiEuQ3p2Lr/siuHlJfvJyDHqHDo3qs2A1v6M6hSMWwXr61iULjfXxIgRP7Jo0UFatvRjw4b76dZN2sIqShKFEGX4Oyqe1387yJ7oREwWTaM1qO3BuyMiiAypbb/gxBXRWqOUwtW1Cv7+XrzxRm+eeKKLUzTgZwuSKIQohsmk2RuTxPebTzF/22kAqrooejerS+/mdRnQxh8faWjPIW3eHM3DD//KZ58Non17f2bNusXeIVV4kiiEsHD6Yjqz1hxl3tbTBcOua1yb/xvVHj9vdztGJq5WQkIGzz33B598sp2AAG8SEjLKnkkANk4USql+wPuAC/C51vqNIuODga+BmuZpntFa/2rLmIQoKifPxLytp5myeG9B0ZKvlxujOzekb4t60uCeE5g/fy+PPrqcuLh0Hn/8Ol55pRfekvitZrNEoZRyAWYBfYFoYKtSaonWer/FZC8AP2itP1JKtQB+BUJsFZMQ+TJz8vht71nOJGTw0z9niIpLI7CmByG+njx8QyhdGteRF+GcyMGDcYSE1GT58tG0a+dv73Acji3vKDoBR7XWUQBKqXnAYMAyUWgg/xnCGkCMDeMRgr+j4vli43E2Ho0reFO6eX1vPr8nkt7hdSU5OInMzFzefHMj7dv7M2hQM557rjsvvNCjUjTgZwu2TBSBwGmLz9FA5yLTvAysUEpNBKoDfYpbkFLqIeAhgOBgeXRNXJ6jsalMXbqfqAupRJvLpa9rXJt7uoTQLcxXKqWdzKpVUUyYsIwjRy4yaVIXBg1qRlVpdfeq2DJRFHdpVrTf1VHAbK31/5RSXYBvlVKttNamQjNp/SnwKRhdodokWuFUkjJy+HDtUTYdjSt4WzqolgcP39CEB7o1plZ1NztHKK618+dTefLJFcyZs4fQ0NqsWHEXffs2sXdYTsGWiSIaaGDxOYhLi5bGAv0AtNZ/KaWqAb5ArA3jEk7sZHwai3fG8M7KwwXDBrUNYPJNzQh2su4pRWErV0axYMF+XnqpB88+251q1eShzmvFlntyKxCmlGoEnAHuAO4sMs0poDcwWykVDlQDLtgwJuGkohPSeXflERb+Ew0Ydw/3dQ1hbLdGUu/gxHbtOseRIxcZNqwFo0e35vrrG9CoUS17h+V0bJYotNa5SqlHgN8xHn39Umu9Tyk1FdimtV4CTAI+U0o9gVEsdZ/WWoqWhNUupGQxbel+lu89R3aeiVta+zO2eyPaB8vJwpmlpmYzZcoa3n//b0JCanLbbc1xda0iScJGbHpvZn4n4tciw16y+Hs/cL0tYxDO6diFVCZ89w+HzqcAUN+nGnMe7ExjP+kRztn9/PNBJk78jejoZB56qD3Tp/fBVdrYsikpxBMOJSkjh3dXHmb2nycACKhRjVdvb8UNzeTR1spgz57z3H77fFq3rsv8+cPo2rVB2TOJqyaJQlR4Wmu+2HicRTvOsC/GeILJ18uNr+7rROsgeWva2eXk5LFhwyluvLERrVvXY9myO+nbt7E88lqOJFGICstk0rz/xxHmbjlFbEoWAON7NSHc34eBrf2lI6BK4M8/TzNu3FL27bvAoUOPEBpamwEDwuwdVqUjiUJUOOeTM/l0fRRLdsVwwZwg/tOjMU/e1BR3V7mKrAwuXszgmWdW8dln/9CggQ8//TSC0FBpzt1eJFGICuXT9cd4/deDBZ/fHNqa29sFSYdAlUhmZi4RER8TE5PCpEldePnlXnh5yQuS9iSJQthdenYuM5YfKqigBvh2bCe6hfpKBXUlEh2dTFCQD9WquTJt2g1ERNSnbdv69g5LIIlC2ElWbh5rD13gq03H2Rx1sWD4sA5BvHJrS6q7y6FZWWRk5DB9+kbefHMTCxYMZ9CgZtx7b4S9wxIWrPo1KqXcgGCt9VEbxyMqga0nLjL8478KPncL9eXWiACGdwiSO4hKZsWKY0yYsIxjxxK46642dOoUaO+QRDHKTBRKqVuAdwA3oJFSKgKYorW+3dbBCefx+YYoVuw/z5bj/949RDasxfuj2hFY08OOkQl7mTjxV2bO3EpYWG1Wrbqb3r0b2zskUQJr7iimYjQPvgZAa71TKRVq06iEU0jOzOGz9VF8sj6K7FyjQeDIhrUIq+fF/dc3omk9bztHKMpbXp5xHLi4VOG664Lw9fXk6ae7SQN+FZw1306O1jqxSJGAtMckSpSSmcMjc3aw7vC/7TsOaR/IK7e2xFv6fqi0/vnnLOPGLeXuu9swcWJnRo9uY++QhJWsSRQHlFIjgCrmlmAfAzbbNizhaLTW/LzzDPO3ni5UOf3m0NYMbR+Eq/QsVmmlpGTx0ktr+OCDLfj5eeLvL3eSjsaaRPEI8BJgAn7CaA32WVsGJRzH+sMXeOv3Q+w5k1QwrHuYL7e09mdkxwZSOV3JrVhxjDFjFhMTk8K4cZG8/npvatasZu+wxGWyJlHcrLV+Gng6f4BSaghG0hCV1P6YZJ78YScHzxmttzat58XgiEDu6NiAOl7udo5OVBRubi7UrVudhQtH0LlzkL3DEVdIldX9g1LqH611+yLDtmutO9g0shJERkbqbdu22WPVAiNBvLH8IOvN9Q+jOwfzWO8w6vrIVaIwGvB7552/SE7O4rXXegNGm13SLpf9mc/bkVcyb4l3FEqpmzG6KQ1USr1jMcoHoxhKVCLp2bm8sGgvP+04A0Cf8Ho8cmMoEQ1q2jkyUVFs3HiqoAG/4cNbFCQISRKOr7Sip1hgL5AJ7LMYngI8Y8ugRMWy90wS9321lbjULOp6uzN1cCv6tZKmFYQhPj6dp59exRdf7CA4uAa//DKKgQOb2jsscQ2VmCi01juAHUqp77XWmeUYk6gglu89y+w/TxQ8xTRjaBtGdJSOYkRh8fEZzJu3l//+tysvvdST6tWlAT9nY01ldqBS6jWgBVBQEIqCSY4AACAASURBVK21lksGJ5SbZ+Lzjcf5YetpouLSAOgUUptHe4fRLczXztGJiuLAgQv88MM+pkzpRdOmdTh16glq15Y37J2VNYliNvAq8DbQH7gfqaNwOlprPt9wnNd+PVAwrGuTOvxvRFv8a8gJQBjS03N47bX1vPXWn3h5uTF2bHuCgnwkSTg5axKFp9b6d6XU21rrY8ALSqkNtg5MlA+tNXO3nOa5RXsKho3uHMyLA1tQTbqaFBaWLz/KhAnLOH48kXvvbctbb/XFz6+6vcMS5cCaRJGljLemjimlxgFngLq2DUvYmtaarScSGDN7K6lZuQD0aOrHl/dGylvU4hKpqdncffci6tTxYM2ae+nVK8TeIYlyZE2ieALwAh4FXgNqAGNsGZSwnZTMHB6bt5PVB2MLho2MbMALA8OlHSZRSF6eiblz9zJqVCu8vNxYtepumjf3xV36Cql0yvzGtdZ/m/9MAe4GUErJK5YO5mhsCq/8sp8NR+IAaFjHkxGRDejcqDaRIdIXsShs+/YY/vOfpWzffhYPD1eGDm0hvc1VYqUmCqVURyAQ2Ki1jlNKtcRoyuNGQJKFg/htz1nGf/8PAEG1PHhpYAtuaik/enGppKRMXnxxDbNmbaVu3erMmzeUIUPC7R2WsLPS3syeDgwFdmFUYC/CaDn2TWBc+YQnrpTWmlUHYnnh5z2cT84C4K1hbRgeKe9BiJINHfoDq1cf5+GHO/LqqzdSo4Y0zSJKv6MYDLTVWmcopWoDMebPh8onNHGljsel8f6qw/y8MwYwGux7Z0QErQJr2DkyURFFRSXg5+eJt7c7r712I1WqKDp2lC5Jxb9KSxSZWusMAK31RaXUQUkSFVt2romnftzFkl1GghgZ2YDJ/ZrhK625imJkZ+fx9tt/Mm3aeh59tBNvvtlXWngVxSotUTRWSuU3Ja6AEIvPaK2H2DQycVnWHb7Ag19vIzvPRLvgmjw/IFwqqUWJ1q8/ybhxSzlwII5hw1rw6KOd7R2SqMBKSxRDi3yeactAxJXRWjN16X6+2nQCgLHdGvHiwBb2DUpUaO+++xdPPrmCkJCaLFt2JwMGhNk7JFHBldYo4B/lGYi4fCaTZtDMjeyLSaZTo9q8f0eENLchimUyadLSsvH2dueWW5py4UI6L7zQA09PeXdGlE1ewXVQR2NTuferLeyLSaZ/q/rMe/A6SRKiWPv2xdKz52zuu28xAE2b1uH113tLkhBWs2miUEr1U0odUkodVUoV24eFUmqEUmq/UmqfUmqOLeNxFhuPxDHg/Q1sOBLH+F5N+HB0e+kcRlwiPT2HZ59dRUTEJxw4cIGBA8Moq0dLIYpj9bv4Sil3rXXWZUzvAswC+gLRwFal1BKt9X6LacKAZ4HrtdYJSilpQ6oUyZk5PDl/J6sOGM1vLBjXRSqsRbF27DjLkCE/cOJEIvffH8GMGX3x9fW0d1jCQZWZKJRSnYAvMNp4ClZKtQUe0FpPLGPWTsBRrXWUeTnzMN7N2G8xzYPALK11AoDWOvaSpQgAvvnrBC8tNjoa9HRzYe6D19FWuiEVRWitUUoRHFyD4OAafP31bfTo0dDeYQkHZ80dxQfAQOBnAK31LqXUDVbMFwictvgcDRR9Bq8pgFJqE+ACvKy1Xm7FsiuNPJPmwW+2FTTiN6FXE566qZkUNYlCcnNNzJy5hSVLDrFy5d3UqePJunX32Tss4SSsSRRVtNYnjZbGC+RZMV9xZ7KiBaSuQBjQC6PtqA1KqVZa68RCC1LqIeAhgODgYCtW7Rze+v0gs9YcA4w2mj67J5Jwfx87RyUqmi1bzjBu3FJ27DhH//6hJCdnUauWPNggrh1rEsVpc/GTNtc7TAQOWzFfNGDZsFAQRjMgRafZrLXOAY4rpQ5hJI6tlhNprT8FPgWIjIx0+tq4uNQsnlm4h1UHzgPweJ8wHu8jPc+KwlJTs3n66ZV89NE2/P29+fHH4QwdGk6Rizohrpo1iWI8RvFTMHAeWGUeVpatQJhSqhFGZ0d3AHcWmeZnYBQwWynli1EUFWVd6M7pTGIGPWesIdek6R7my4ej20s/EaJYVatWYe3ak0yc2Ilp027Ex0eaahG2YU2iyNVa33G5C9Za5yqlHgF+x6h/+FJrvU8pNRXYprVeYh53k1JqP0Zx1mStdfzlrstZrNh3jv98t50qSklLr6JYR49eZOrUdcyaNQBvb3e2b3+IatWkIyFhW6qs56qVUseAQ8B84CetdUp5BFaSyMhIvW3bNnuGcM1prbl/9lbWHroAwJf3RXJj83p2jkpUJFlZucyYsYnXXtuAm5sLy5bdSffu8jSTsJ5SarvWOvJK5rWmh7smSqmuGEVHryildgLztNbzrmSForCzSRmM+OQvTl/MAOD7BzpzfaivnaMSFcmaNccZP34Zhw7FM3JkS95552YCArztHZaoRKy6Z9Va/wn8qZR6GXgP+B6QRHGVMnPy6PrGarSGyTc3Y3zPJvLYqyhEa81rr20gJ8fE8uWjufnmUHuHJCoha16488J4Ue4OIBxYDHS1cVxOLzMnj2Ef/4nWML5XEx6+QU4AwmAyab744h/69QulQYMafPvt7dSsWQ0PD3moQdiHNW097QWuA2ZorUO11pO01n/bOC6nlpieTdtXVrD3TDJD2gfydL/m9g5JVBC7d5+nW7cveeihpXz+udHPub+/tyQJYVfWFD011lqbbB5JJbHl+EXGzN5KVq6JOzo24I2hbewdkqgAUlOzeeWVtbz77mZq1fJg9uzB3HNPW3uHJQRQSqJQSv1Paz0JWKiUuuTRKOnh7vL9ceA8Y782nth6d2Rbbm8n3U4Kw8svr+V///uLBx5oxxtv9KFOHWnAT1Qcpd1RzDf/Lz3bXQN/HYtn7NfbcHOpwg/juhAhDfpVeqdPJ5GWlkPz5r4880w3brutOd26VZ4maoTjKLGOQmu9xfxnuNb6D8t/GJXawkrH49IY9dlmAH6Z2E2SRCWXm2vinXf+Ijx8Fv/5z1IAfH09JUmICsuayuwxxQwbe60DcVZaa576cReebi58O7YTzerL8++V2ebN0URGfsqkSSvo1SuEr7++zd4hCVGm0uooRmI8EttIKfWTxShvILH4uURR87aeZvvJBCb1bUr3MD97hyPsaNmywwwaNJeAAG9++mkEt93WXBrwEw6htDqKLUA8RquvsyyGpwA7bBmUs9gfk8yzP+2hY0gtxvdqYu9whB1orYmJSSEw0Ic+fRozdeoNPPZYZ7y9pQE/4ThKTBRa6+PAcYzWYsVlOnYhlQEfbADguQHhuLrYtHtyUQEdPhzPhAnLOHw4nv37H8bLy40XXuhh77CEuGylFT2t01r3VEolULjDIQVorbV01lyME3Fp3PHpZs4lZwLwyq0taRdcy85RifKUmZnLG29sZPr0jXh4uDJ9em88PKSFV+G4Sjt687s7lRbqrLTpaByjPzdeWm/kW53/jWhLe0kSlcq5c6n06PEVR45cZNSoVrzzzs3Ur+9l77CEuCqlFT3lv43dAIjRWmcrpboBbYDvgORyiM9h/LbnLOO/N5pc+OTuDtzcsr6dIxLlKScnj6pVXahXrzo9ejRk1qwB9O0r9VLCOVhTcP4zRjeoTYBvMN6hmGPTqBxMfpJwc6nCb491lyRRiZhMmo8/3kaTJh8QHZ2MUorPP79VkoRwKtYkCpO5T+shwHta64lAoG3Dchzfbj7JxLk7CKzpwS8TuxHu72PvkEQ52bXrHF27fsH48csIC6tDTk6evUMSwias6gpVKTUcuBvIfzuo0jdlaTJpZv95gqlL91OtahUWjO+Cfw0Pe4clyoHWmsmTV/Lee5upXduDb7+9ndGjW8s7EcJpWZMoxgATMJoZj1JKNQLm2jasiu3A2WT6v288+tonvB5vD29DTU83O0clyotSioSEDMaONRrwq1VLLhCEcyuzz2wApZQrkN+zzlGtda5NoyqFvfvMjknMoOsbqwGYeGMoT/ZtKleSlcDJk4k89thyXnqpJ+3b+2MyaemNUDiUq+kzu8w6CqVUd+Ao8AXwJXBYKXX9lazM0cUmZ3L7h5twc6nCa7e3YtJNzSRJOLmcnDxmzNhEixYfsnJlFIcOxQFIkhCVijVFT+8CA7TW+wGUUuHAt8AVZSZHlZieTafX/wDgs3si6duinp0jErb255+n+c9/lrJ3byyDBzfjgw/6Exxcw95hCVHurEkUbvlJAkBrfUApVakK5NcciuX+r7YCcF/XEEkSlcSqVVEkJWXy888jGTxYuqsVlVeZdRRKqdlAFsZdBMBowFNrfa9tQyteeddRfLj2KDOWHwLguQHNeaiHPB/vrLTWfPvtbvz8POnfP4ysrFxyckx4eVWq6yLhpK6mjsKaO4pxwKPAfzHaeVoP/N+VrMzRTP/tAJ+siyKwpgdzH7yOYOme0mkdPBjH+PHLWLv2BMOHt6B//zDc3V1xl0ZehSg9USilWgNNgEVa6xnlE1LFsOZgLJ+siwJgxRM9qO4ujbo5o4yMHF5/fQNvvrmJ6tXd+OSTgTzwQHt7hyVEhVLiU09Kqecwmu8YDaxUShXX051Tys418ei8HVR1Ufz17I2SJJzYL78c5tVXNzByZCsOHnyYhx7qIE80CVFEaWfA0UAbrXWaUsoP+BXj8Vin9/nGKFIyc3lnRFt529oJnTuXys6d5+jXL5Thw1sQEvIAnTpJqzRClKS09yiytNZpAFrrC2VM6zROX0xnxvJD+Hm7c2vbAHuHI66hvDwTH364lWbNZnL33YvIyMhBKSVJQogylHZH0diir2wFNLHsO1trPcSmkdlBbEom3WesAWDa4JbSK50T+eefs4wbt5StW2Po06cxH344AA+PSt9kmRBWKS1RDC3yeaYtA6kIXl92AID/9mtGv1b+do5GXCvHjyfQqdNn+Pp6MmfOEO64o5W8US/EZSit46I/yjMQezuXlMnPO2O4pY0/E3qFlj2DqNC01uzZE0ubNvVo1KgWX301mEGDmlGzZjV7hyaEw5GyFbNP1h8D4IFujewcibhax48nMHDgXNq1+4Tdu88DcPfdbSVJCHGFbJoolFL9lFKHlFJHlVLPlDLdMKWUVkrZpf2ov6Pi+WrTCXo186Od9HHtsLKz83jjjY20bPkh69ad4O23+9KihZ+9wxLC4Vn9goBSyl1rnXUZ07sAs4C+QDSwVSm1xLLdKPN03hhvfv9t7bKvpYzsPB6dtwNPNxdmDGtjjxDENZCXZ6Jr1y/Yvv0sQ4aE8957N9OggTTgJ8S1YE0z452UUnuAI+bPbZVS1jTh0Qmj74oorXU2MA8YXMx004AZQKb1YV87by4/yPnkLKYPaU1dbymacDTJyca1i4tLFcaMaccvv4xi4cIRkiSEuIasKXr6ABgIxANorXcBN1gxXyBw2uJzNEX62lZKtQMaaK2XlrYgpdRDSqltSqltFy5csGLV1skzab7dfJKWAT4MjpBn6R2J1prZs3fSuPH7LF58EIAJEzoycGBTO0cmhPOxJlFU0VqfLDLMml7ki3v+sKCpWqVUFYy+LiaVtSCt9ada60itdaSf37Urc16y6wx5Js2wDkHXbJnC9vbvv0CvXl9z//2Lad7clyZNats7JCGcmjV1FKeVUp0Aba53mAgctmK+aKCBxecgIMbiszfQClhrfqa9PrBEKXWr1rpc2hH/6Z8zAIzs2KCMKUVFMWPGJp5/fjU+Pu58/vkg7r+/nbTNJISNWZMoxmMUPwUD54FV5mFl2QqEKaUaAWeAO4A780dqrZMA3/zPSqm1wFPllSRMJs3WExfpHuaLp5s0+lfRaa1RSlG/vhejR7fmrbf64udX3d5hCVEplHmG1FrHYpzkL4vWOlcp9QjwO+ACfKm13qeUmgps01ovuexor6H1Ry6QmWOiZ1N5fLIii4lJ4bHHltO9ezCPPtqZe+5pyz33tLV3WEJUKmUmCqXUZ1jULeTTWj9U1rxa618xWp21HPZSCdP2Kmt519LcLafwqOrC6M4Ny3O1wkr5Dfg9//xqcnJMdO0q9UhC2Is1ZS6rLP6uBtxO4aeZHE5Wbh7rDl+gZ1M/PNxc7B2OKGLnznM88MAStm8/y003NeHDDwdIhbUQdmRN0dN8y89KqW+BlTaLqBxsOhpHZo6J29vJI7EVUVJSJjExKcyfP4zhw1tIA35C2NmV1OI2Ahy6vOajtcdwc61CD6mfqBC01vz4436OHInn+ed70LNnCFFRj1GtmjxkIERFYM2b2QlKqYvmf4kYdxPP2T4021hzMJatJxK4qUU9edqpAjh27CIDBsxh5MgFLF58iJwc4xUdSRJCVByl/hqVcc/fFuPxVgCT1vqSim1H8u1m493BFwe2sHMklVtWVi5vv/0nr766gapVq/D++/2YMKEjrq7SoLEQFU2piUJrrZVSi7TWHcorIFtbcyiW3s3rUs9H2nWyp9Onk5k2bT2DBjXjvfduJjDQx94hCSFKYM3l2xalVHubR1IOTl9MR2sIqOlh71AqpQsX0pg5cwsAoaG12b//YX78cbgkCSEquBLvKJRSrlrrXKAb8KBS6hiQhtGGk9ZaO1zy+GxDFAD3dHHouniHYzJpvvpqB//97ypSUrLo27cxzZr50rix9P0hhCMorehpC9AeuK2cYrEpk0nz656z3NDMj7B63vYOp9LYuzeW8eOXsXHjKbp3D+bjjwfSrJlv2TMKISqM0hKFAtBaHyunWGxq4T/RxKVmM6htgL1DqTSys/O46aZvyc7O48svb+W++yLknQghHFBpicJPKfVkSSO11u/YIB6b+X3fOQAGtPa3cyTOb/Xq4/Ts2RA3Nxd++GE4zZv74uvrae+whBBXqLTKbBfAC6M58OL+OYzUrFxWHYjlltb+VKsqTXbYSnR0MkOH/kDv3t/wzTe7AOjWLViShBAOrrQ7irNa66nlFokNff3nCQB6NJWycVvIzTUxc+YWXnxxDXl5JqZP783o0dL/uBDOosw6Cmfw0VqjmuU2advJJu6+exHz5u2lf/9QZs0aQKNG8jSTEM6ktETRu9yisKGLadmkZuXSJqgG7q5S7HStJCZm4upaBS8vNx5+uCNDh4YzdGi4VFYL4YRKrKPQWl8sz0Bs5be9ZwG4t0uIfQNxElpr5s3bS3j4LF58cTVg1EMMGyatvArhrJy+YZ3FO2NQCga2laedrtbRoxe5+ebvGDVqIUFBPtx1l9RDCFEZOHUTnVEXUtly/CL/6dFYip2u0pw5exgzZjHu7q7MnNmfceMicXFx+usMIQROniiW7jaKnYa0l240r1ROTh5Vq7oQGRnAsGEtmDGjLwEBDvV0tBDiKjl1olh14Dyebi40qy8ntssVG5vGpEkrSEvL5qefRtK0aR2++26IvcMSQtiB05Yd5Jk0u6OT6BgifS1fDpNJ8+mn22nWbCbz5++lZUs/8vJM9g5LCGFHTntHse5wLAA3NJPuTq0VFZXAXXf9xF9/RdOrVwgffXQLzZvLS4pCVHZOmygW74wBoLv0i221GjXcSUzM5Ouvb+Puu9vI465CCMCJi54Onk0hpI4nTfy87B1KhbZkySGGDJlPXp6JOnU82bt3Avfc01aShBCigFMmiuiEdA6dT5EmxUtx6lQSt902j8GD53H4cDxnz6YCUKWKJAghRGFOWfS0LyYZgB5S7HSJ3FwT7723mSlT1qK15s03+/DEE9dRVVrVFUKUwCkTxfaTCSgFzeWx2Evk5Zn4/PN/uPHGRvzf//UnJKSmvUMSQlRwTln0tOloHOH1ffCuVtXeoVQICQkZPP30SlJSsnB3d2XTpjEsWXKHJAkhhFWcLlGciEtjX0yyFDthNOD3/fe7ad58Fv/731+sWXMCgDp1PKWyWghhNacrelq2x2i2Y3BE5a7IPnw4ngkTlvHHH8fp1CmQ33+/i4iI+vYOSwjhgJwqUWitmfP3KRrU9qj09ROPP76cbdti+PDDATz0UAdpwE8IccWcKlEcOJvCmcQMnu3fvFIWraxceYzmzX1p0KAGH310C+7urtSvL++RCCGujk0vM5VS/ZRSh5RSR5VSzxQz/kml1H6l1G6l1B9KqYZXs76/ouIB6NWs7tUsxuGcO5fKnXcu5KabvuPNNzcB0LBhTUkSQohrwmaJQinlAswC+gMtgFFKqRZFJtsBRGqt2wALgBlXs86V+89Rp7oboXUrxwnSZNJ8/PE2mjefycKFB5gypSdvv32TvcMSQjgZW95RdAKOaq2jtNbZwDxgsOUEWus1Wut088fNwBV3HJGUnsPmqItc17gOLpXk7eLp0zcwfvwyOnQIYPfucbz8ci+qVXOq0kQhRAVgy7NKIHDa4nM00LmU6ccCvxU3Qin1EPAQQHBwcLEz74pOBOD6UOdu7TQlJYu4uHQaNarFuHGRNGpUi1GjWlXKOhkhRPmw5R1FcWcuXeyESt0FRAJvFTdea/2p1jpSax3p51f8+xH/nEoAoHuYcyYKrTWLFh2gRYsPGTlyAVpr6tTx5M47W0uSEELYlC0TRTTQwOJzEBBTdCKlVB/geeBWrXXWla5s12njjiKolseVLqLCOnkykVtvnceQIT9Qu7YHH3zQX5KDEKLc2LLoaSsQppRqBJwB7gDutJxAKdUO+ATop7WOvZqVpWTm4uft7nQn0L/+Ok2fPt8C8PbbfXnssetwdZV3IoQQ5cdmiUJrnauUegT4HXABvtRa71NKTQW2aa2XYBQ1eQE/mk/wp7TWt17J+o7HpTlVsVNychY+Pu60b+/PmDERTJ58PcHBNewdlhCiErLpIzJa61+BX4sMe8ni7z7XYj3nkjKJT8t2isdi4+PTeeaZVaxYEcW+fRPw8nLj//5vgL3DEkJUYk7xLOXinWc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" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "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": 68, + "metadata": {}, + "outputs": [], + "source": [ + "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": { + "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": 70, + "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": 70, + "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": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16469105377809068\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6432 330\n", + "1 1274 713" + ] + }, + "execution_count": 72, + "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": 73, + "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 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 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": 74, + "metadata": {}, + "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", + " 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": 75, + "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": 75, + "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": 76, + "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" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "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": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6492 270\n", + "1 1349 638" + ] + }, + "execution_count": 77, + "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": 78, + "metadata": {}, + "outputs": [ + { + "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": [ + "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": 79, + "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": 79, + "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": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15910713557498266\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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": 81, + "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 0.38 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": [ + "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": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
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" + ], + "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": [ + "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": 83, + "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": 83, + "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": 84, + "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" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "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 0.69 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": "markdown", + "metadata": {}, + "source": [ + "### 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": 93, + "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: 0.4787 - 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", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\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='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": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.25378615\n" + ] + }, + { + "data": { + "image/png": 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7ImL59K+DnItLyrTeoGSxwnStX46kVBtNKpekUaUSFPYSingXoqRvEeqW93fpSfdGLFlygEcfXUxoaHnmzOlPnTpB1KkT5PLtaqJQSnmkhOQ0Nh2LYvH2CH7451im84zoWAOARpVKEOjnQ4uQUi6rL3ClkydjeeKJJcyatYs6dQJ57LEWubp9TRRKqTwvMSWNI5EXiU1MZd/pWCYu2Ut0Qspl85QPKEr/sGDa1gyiZlk/Av183BRtzvr990PcffdMkpPTGDfuFsaMaYNPLheHaaJQSuUpxhjOxCaxcFsEy/ecYePRKBJS0q6Yr3Y5Px5qX52OtctQxt/HI+8UspKSkkbhwl40aVKebt1q8cYbnahZs7RbYtFEoZRyq8SUNHaciGb9kSh2RcSwcv9ZLsT/e7dQ1t+Hrg3KUa9CALXL+VHCtzAVSvhSsaSvG6N2nZiYJF566Q/++ecEq1YNJSioGDNm9HVrTJoolFK57kJ8MvM2n+C7tUeveEahcXAJ2tYsxh0Ny9Olfjl8vL3cFGXuMsYwe/YuHn98CadOxTFiRAuSktIoVsz9rw3SRKGUyhWJKWn8uvMUP206wV/7zqaPv6l6aZpVKUXXBuWpFlicEsXyxjMKuens2Ys88MA8fvnlAE2blmf+/Htp0aKSu8NKp4lCKZXjzl9M5oe1R4lLSmX/mTg2HDl/WVPVakHF6RVaiYE3VSEon1Q634iAAB/OnYvngw9u49FHW+Lt7f67CEeaKJRS1+34+XgWb48gLimVreHRnItNYu/pWNIcekUt4VsYm7EeYutcrxzdG1fINy2SbsSKFUcZP34lc+b0x8+vCGvXPpRnn9/QRKGUclqazfDPoUhW7D/H4XNx/LrzdPq0It6FSLMZGlQMoFGlErSqHkj3RhXy7MnPXc6di2fMmN+YMmULISElOXLkAg0bls3T35MmCqVUpowxnItLJjohhYNn41iw9SSLtl3ewXP1MsXp2aQSo26tme+ap+Y0YwzffLOFMWN+IyYmieeea8eLL3agmAfUyWiiUEpdZseJaEbN2MyhTHpMBejdtBLDO9agVtmc7y47v/v++23Ur1+GTz+9kwYNyro7HKdpolCqgDPGsP5IFNP+OcqK/ec4fzE5fVqPJhXpWLsMhQpBvQoB1Cnnr8nhGsTHp/DmmysZPjyM4OAA5szpT4kSRfN0MVNmNFEoVQAZY5i9MZzfdp1m6a7Tl01rUyOQxzrVpE0N13c2l58tXryfRx9dzJEjF6hUyZ9HHmlBqVKe+ZCgJgqlCgibzbBg60mmrjnC5mMX0sc3r1qKWmX9uLdlFRpVKoGXh13t5jXh4TE88cQS5szZTb16Qfz114N06FDV3WHdEE0USuVTKWk2zl9MZvOxC2w8ep4vVh6+bHrP0Io83bUOlUsXc1OE+dP48StYtGg/b77ZidGj21CkiOc/WS7Wi+Y8R1hYmNmwYYO7w1AqT4pPTmXZ7jNMWLybiOjEy6YVEuvdza/3akhAHnlDW36xbt0JfH29adSoHJGR8URHJ1G9eil3h3UZEdlojAm7nmX1jkIpD7f+yHlWH4jkxw3HOXEhIX18hRJFuSu0IvUrBNCwUglCAotrsVIOi45O5Pnnf+eTTzbQvXttFiwYQGBgMQID89ddmiYKpTxEms2w82Q0K/efY/OxKHadjOFkhruGSiV9+U/7avRoUlGffnYhYwwzZ+7kySd/5cyZi4wc2ZJx4zq5OyyX0UShVB52NjaJUdM3cyomkcPnLn+uoYh3IZpWKUnzKqW4u1kl6pYP0DuGXPL999sYPHgeYWEVWbhwAM2bV3R3SC6liUKpPOb8xWTe+20vf+49S3jUv0VJA1pWJqBoYbo2KE+zKiX1eYZclpSUyqFDUdSrV4b+/RuQmmpj8OAmeHnlrQ78XEEThVJ5wJnYRMbO3sbJC4nsPR2bPr5f82DqlPdnSNtqerfgRsuXH+aRRxYRH5/C/v0j8fHxZsiQpu4OK9doolDKDYwxLN5+ihnrj5GYksb6I1EAlC5ehFvqlKFNjSD+06G6m6NUZ85c5Omnl/Ldd9uoXr0Un3/eI9ffV50XFLw9VsqN0myGOZvCeXPx7vTXffr5eNO2ZiC31CnLQ+01OeQVBw6cp2XLL4iLS+aFF9rzwgvt8fUtmM2KNVEo5ULGGJJSbWw/Ec2sDcf5cUN4+rR2NYP48N5QbZ2Ux8TEJBEQ4EONGqUYNqwpQ4c2pV69Mu4Oy600USjlIvO3nGDcwt2ci0tKH+db2IteTSvxROdalAso6sboVEYXLybz+ut/8cUXm9i27RGCgwN4552u7g4rT9BEoVQOMcawKyKGRdsi+PjPg+nj72xUgZbVrPdCNwou4cYI1dX8/PNeHnvsF44di2bYsKYe8Y6I3KSJQqkbtC38As/O2c6uiJjLxpf19+GbIS1oUFGTQ16Vmmqjf/9ZzJ27hwYNyrBy5RDatavi7rDyHE0USl2nU9GJLN4ewesLdwHWnUPp4kW4K7QiTSuXxLsAtK/3VMYYRARv70JUqODHW2/dypNPts4XHfi5giYKpZyUZjOsPRTJh8v2s/tUDLGJqenTnuxcm8c713JjdMpZa9eG8+iji/niix40a1aByZPvdHdIeZ4mCqWyYYzhtZ93sXh7BGdi/62YfrBNCGEhpehcrxxFC+uVaF4XFZXA88//zmefbaRiRX+iHJ56V1lzaaIQkduBDwEv4EtjzFsZplcBpgIl7fM8a4xZ7MqYlLoWaw5GMuCLtenDo7vU5vaG5alVzt+NUalrNXPmDkaNWsK5c/E88cRNvPZaR/z9tVmys1yWKETEC5gMdAHCgfUissAYs8ththeBH40xn4hIfWAxEOKqmJTKjjGGP/ac4a99Z/l995n0brtbhJTih4duooi31jt4oj17zhESUpIlSwbStGkFd4fjcVx5R9ESOGCMOQQgIjOAnoBjojBAgP1zCeCkC+NRKlPxyal8ufIwfx84x7rD5y+b1rBSAK/2aEBYSGk3RaeuR2JiKm+//TfNmlWgR486PP98e158sUOB6MDPFVyZKCoBxx2Gw4FWGeZ5FVgqIiOB4kDnzFYkIg8DDwNUqaJN11TOWLn/LL/uPMWMdcdJtVlveixexIv7W1fl/lZVCS7lqz20eqBlyw4xYsQi9u8/z+jRrenRow6FtQ7phrgyUWT2F5bxvasDgCnGmP+JSGvgOxFpaIyxXbaQMZ8Dn4P1KlSXRKsKhOPn4/l61WH+3n+O/WfiAAgu5cvITjXp0yxYm7R6sNOn43jqqaVMm7admjVLs3Tp/XTpUsPdYeULrkwU4UBlh+FgrixaGgbcDmCMWSMiRYEg4IwL41IFTGxiCusOn+f9ZfvYccJ6KC6weBHuaFie0V1rU7OsVkznB7/9dojZs3fx8ssdeO659hQtqo06c4orv8n1QC0RqQacAO4F7sswzzHgVmCKiNQDigJnXRiTKiCMMUxdfYT3l+0nOiElfXzJYoV57Jaa2ktrPrF16yn27z9P3771GTiwEW3bVqZatVLuDivfcVmiMMakishjwK9YTV+/NsbsFJHXgQ3GmAXAaOALEXkSq1jqQWOMFi2p62azGWZvCuf5n7an1zvc1qAcbWsG0btZMH4F8F0C+VFcXDKvvLKcDz/8h5CQkvTqVRdv70KaJFzEpX819mciFmcY97LD511AW1fGoPI3m82w42Q0649EMW/zCbafiE6fVrucHz+NaKvJIZ+ZN28PI0f+Qnh4DA8/3IwJEzrjrc2WXUr/gpRH2nEimom/7mXFvitLKrvUL8f4uxtS1l+78c5vtm8/zd13z6RRo7LMnNmXNm0qZ7+QumGaKJTHOH4+nuV7z/Dn3rP8scdq7xDk50PXBuXo2zyY+hUCtCuNfCglJY2VK4/RqVM1GjUqx6JF99GlS3Vt8pqLNFGoPC0hOY21hyMZM2vbZS8ACqtaihfurEfTKlomnZ+tXn2c4cMXsnPnWfbufYyaNUvTrZt2vpjbNFGoPGvzsSju/nj1ZeOm/+cmWlYrjVchfRAuPzt/PoFnn13GF19sonLlAH76qT81a+rT8e6iiULlOVuPX6Dn5FXpw//tUJ2BrapSJbCYG6NSuSUxMZXQ0E85eTKW0aNb8+qrHfHzK+LusAo0TRQqT4hPTmXaP8d4Y9Hu9HFNgkswuHUIfZoHuzEylVvCw2MIDg6gaFFvxo27hdDQ8jRpUt7dYSk0USg3stkMn644yMEzF1m8PYKElDQAmlQuydt9GlG3fEA2a1D5QUJCChMm/M3bb69i9ux+9OhRhwceCHV3WMqBU4lCRIoAVYwxB1wcjyogYhNTaPTq0vThJsElGNa+Oj0aV9CO+AqQpUsPMmLEIg4ejOL++xvTsmUld4ekMpFtohCRO4H3gCJANREJBV4xxtzt6uBU/vTtmiO8PH9n+vDBN7tp5XQBNHLkYiZNWk+tWqVZtmwQt96q3arkVc7cUbyO1T34cgBjzBYRqenSqFS+NWXVYV792XolSed65fhicHO9gyhA0tKsjqG9vApx003BBAUVY+zYdtqBXx7nzK+TYoy5kOGPWftjUk5LSE5j2rpjjFv47zurNr/UhVLFtSVLQbJpUwTDhy9k0KDGjBzZioEDG7s7JOUkZxLFbhHpDxSy9wT7OLA2m2WUYv6WE0z75xj/OLw1rngRL+Y+2laTRAESG5vEyy8v56OP1lGmTDEqVNBu3T2NM4niMeBlwAb8hNUb7HOuDEp5ruRUG5OWH+DbNUe4EG917922ZiAtQkoz/OYa2sVGAbN06UGGDp3PyZOxDB8exptv3krJktoHl6dxJlHcZowZC4y9NEJEemMlDaUAiElMYcysrfy683T6uFvqlGHsHXW1mWsBVqSIF2XLFmfOnP60aqXPw3gqye71DyKyyRjTLMO4jcaY5i6N7CrCwsLMhg0b3LFplYlNx6IYOW0zJy4kAFCppC93N63EY51q6t1DAZSSksZ7760hJiaJ8eNvBaznZQppqza3s5+3w65n2aveUYjIbVivKa0kIu85TArAKoZSBdjBs3H0nLSKuKTU9HHfDGnBLXXKujEq5U5//30svQO/fv3qpycITRKeL6uipzPADiAR2OkwPhZ41pVBqbwrNc3G5ysPMXHJXgBEYObDrWlZTTtsK6giI+MZO3YZX321mSpVSvDzzwPo3r22u8NSOeiqicIYsxnYLCI/GGMSczEmlccYY1i4LYL3f9vHoXMX08c/0rEGY2+v68bIVF4QGZnAjBk7eOaZNrz88s0U1xZt+Y4zldmVRGQ8UB9Ib65gjNFLhgJgd0QM932xlih7C6amVUrSrWEFHmgTQhF9/WSBtXv3WX78cSevvNKR2rUDOXbsSUqX9nV3WMpFnEkUU4A3gHeBO4AhaB1FvmaMYd6WE/yw9hgbjkYB0KF2Gcb3akjl0trVd0EWH5/C+PEreOed1fj5FWHYsGYEBwdoksjnnEkUxYwxv4rIu8aYg8CLIrLS1YGp3LfqwDmGTllPUuq/1wFtagTSP6wyvZpqZ20F3ZIlBxgxYhGHD1/ggQea8M47XShTpri7w1K5wJlEkSRW/x0HRWQ4cALQpi35zLJdp3no23+bHY/qVJOuDcrTsFIJN0al8oq4uGQGDZpLYKAvy5c/QMeOIe4OSeUiZxLFk4AfMAoYD5QAhroyKJV7klLTmLz8IB/9vh+AOY+0pnlVbcGkrA78pk/fwYABDfHzK8KyZYOoWzcIHx/twK+gyfYXN8b8Y/8YCwwCEBF9xNLDGWOYtTGcZ2ZvSx83/u6GmiQUABs3nuS//13Ixo0R+Pp606dPfX3bXAGWZaIQkRZAJeBvY8w5EWmA1ZVHJ0CThYc6fj6eoVPWs/9MHAA9QysyuksdfSe1Ijo6kZdeWs7kyespW7Y4M2b0oXfveu4OS7lZVk9mTwD6AFuxKrDnYvUc+zYwPHfCUznBZjPsORXL4u0RzN96guPnre42apb149uhLalYUlusKEufPj/yxx+HefTRFrzxRidKlNAO/FTWdxQ9gSbGmAQRKQ2ctA/vzZ3Q1I0yxvDsnO3M3HD8svEVSxTlrT6N6VC7jJsiU3nJoUNRlClTDH9/H8aP70ShQp3qQFoAACAASURBVEKLFtrKTf0rq0SRaIxJADDGnBeRPZokPMe5uCQe/WFT+rsgHmpXjdsalqdFiNZBKEtychrvvruaceNWMGpUS95+u4v28KoylVWiqC4il7oSFyDEYRhjTG+XRqaumc1m+Gb1Ed5bupeLyWkAtAwpzeeDm1OymHaroP61YsVRhg9fyO7d5+jbtz6jRrVyd0gqD8sqUfTJMDzJlYGoG3MsMp4O7yxPH25YKYCnu9aho/bmqjJ4//01PPXUUkJCSrJo0X1061bL3SGpPC6rTgF/z81A1PVJTEnjf0v38sXKwwA0qBjAgsfa4aVdOysHNpvh4sVk/P19uPPO2pw9G8+LL3agWLHC7g5NeQB9csaDrTt8nv6frUkf/uCeUO1qQ11h584zDB++KP1Nc7VrB/Lmm7e6OyzlQVyaKETkduBDwAv40hjzVibz9AdeBQyw1Rhznytjyg/WHT7P1DVHWLQtAoDhN9fg6a618fbS3lzVv+LjUxg37i/efXcNJUr4MHRoKMYYrB55lHKe04lCRHyMMUnXML8XMBnoAoQD60VkgTFml8M8tYDngLbGmCgR0QL1LOw7HcvEJXtYtvtM+rgH24Tw7B36Tgh1uc2bI+jd+0eOHLnAkCGhTJzYhaAgfaBSXZ9sE4WItAS+wurjqYqINAEeMsaMzGbRlsABY8wh+3pmYD2bscthnv8Ak40xUQDGmDNXrEUBsHDbSR6btjl9+LNBzelSr5y+ZlJd5tIdQ5UqJahSpQRTp/aiQ4eq7g5LeThn7ig+AroD8wCMMVtF5BYnlqsEOD7pFQ5kbINXG0BEVmEVT71qjFnixLoLjHNxSfy0KZw3F+8B4IeHWtG2ZpCbo1J5TWqqjUmT1rFgwV5++20QgYHF+OuvB90dlsonnEkUhYwxRzOUa6Y5sVxml7omk+3XAjpi9R21UkQaGmMuXLYikYeBhwGqVKnixKbzh+V7zzDkm/Xpw0PbVtMkoa6wbt0Jhg9fyObNp7jjjprExCRRqpR2y6JyjjOJ4ri9+MnY6x1GAvucWC4cqOwwHIzVDUjGedYaY1KAwyKyFytxrHecyRjzOfA5QFhYWMZkk++kpNlYse8sj/ywCYCnutTmofbVKFZEG6mpf8XFJTN27G988skGKlTwZ9asfvTpU08rq1WOc+bM8whW8VMV4DSwzD4uO+uBWiJSDetlR/cCGVs0zQMGAFNEJAirKOqQc6HnTxuPnqfPJ/82ef3w3lB6hmqTV3WlwoUL8eefRxk5siXjxnUiIMDH3SGpfMqZRJFqjLn3WldsjEkVkceAX7HqH742xuwUkdeBDcaYBfZpXUVkF1Zx1hhjTOS1biu/2Hg0Kj1J9GsezIhbalItSF81qf514MB5Xn/9LyZP7oa/vw8bNz5M0aJ6p6lcS4zJuiRHRA4Ce4GZwE/GmNjcCOxqwsLCzIYNG7Kf0cOcjkmk1ZvWw/APtgnh1bsauDkilZckJaUyceIqxo9fSZEiXixadB/t22trJuU8EdlojAm7nmWdecNdDRFpg1V09JqIbAFmGGNmXM8G1ZViElPSk8S4Xg0ZdJOeANS/li8/zCOPLGLv3kjuuacB7713GxUr+rs7LFWAOPUorzFmtTFmFNAMiAF+cGlUBUhcUiqNX10KQIUSRbm/VcFp1aWyZ4xh/PiVpKTYWLJkIDNm9NUkoXKdMw/c+WE9KHcvUA+YD7RxcVwFRos3lgHg412I1c920hYrCpvN8NVXm7j99ppUrlyC7767m5Ili+Lrqx34KfdwphZsB/AzMNEYs9LF8RQICclpDPxyLUci40lISaNJ5ZL89EgbTRKKbdtOM3z4QtasCefllzvw2mu3UKGC3kEo93ImUVQ3xthcHkkBceBMHJ3f+yt9+L83V2dY22raLXgBFxeXzGuv/cn776+lVClfpkzpyeDBTdwdllJAFolCRP5njBkNzBGRK5pG6Rvurt3Ok9Hc+dHfAIRWLsncEXoXoSyvvvon//vfGh56qClvvdWZwEDtwE/lHVndUcy0/69vtrtBiSlp9PlkNTtPxgAwsU9j+reonM1SKr87fjyaixdTqFs3iGefbUevXnVp104bM6i856qtnowx6+wf6xljfnf8h1WprZywdOcp6r60hJ0nY/At7MX/+jXRJFHApabaeO+9NdSrN5n//nchAEFBxTRJqDzLmTqKoVx5VzEsk3Eqg0Ff/cPK/ecA60nrcb0aUrSwl5ujUu60dm04w4cvZOvW09x5Zy0mTerm7pCUylZWdRT3YDWJrSYiPzlM8gcuZL6UuuTl+TtYuf8cgcWL8N2wVtSvGODukJSbLVq0jx49plOxoj8//dSfXr3qah2V8ghZ3VGsAyKxen2d7DA+Ftic6RKK3RExTF19hBnrrVdx/PJ4e8oGFHVzVMpdjDGcPBlLpUoBdO5cnddfv4XHH2+Fv7924Kc8x1UThTHmMHAYq7dY5YTYxBS6/9/fpNkM1YOK8949oZokCrB9+yIZMWIR+/ZFsmvXo/j5FeHFFzu4OyylrllWRU9/GWNuFpEoLn/hkADGGFPa5dF5mJHTN5NmM4zuUpuRt9ZydzjKTRITU3nrrb+ZMOFvfH29mTDhVnx9tYdX5bmyOnovve5UX6mWjYNn43jwm3UcP59AQFFvTRIF2KlTcXTo8A37959nwICGvPfebZQv7+fusJS6IVkVPV16GrsycNIYkywi7YDGwPdYnQMWeHtPxXLbByvSh38f3dF9wSi3SUlJo3BhL8qVK06HDlWZPLkbXbrUcHdYSuUIZ3qPnYf1GtQawLdYz1BMc2lUHiIpNS09STzQuiqHJ3SjjFZSFig2m+HTTzdQo8ZHhIfHICJ8+eVdmiRUvuJMorDZ32ndG/jAGDMS0HdzAu3fXg7AvS0q81rPhtrUsYDZuvUUbdp8xSOPLKJWrUBSUtLcHZJSLuHUq1BFpB8wCOhlH1fg+zv+4Z+jnIlN4qbqpXmrT2N3h6NykTGGMWN+44MP1lK6tC/ffXc3Awc20gsFlW85+2T2CKxuxg+JSDVgumvDytsWbD3JC3N3ADB1aEs3R6Nym4gQFZXAsGFWB36lSvm6OySlXCrboidjzA5gFLBBROoCx40x410eWR724tztAHx6f3N8vLVLjoLg6NEL9Oo1g02bIgD44ou7+OyzHpokVIGQbaIQkfbAAeAr4Gtgn4i0dXVgedXEJXuISUxl0E1Vub1heXeHo1wsJSWNiRNXUb/+x/z22yH27rX67iqk7w9RBYgzRU/vA92MMbsARKQe8B0Q5srA8qKZ64/x8Z8H8fPx5sXu2oFufrd69XH++9+F7Nhxhp496/DRR3dQpUoJd4elVK5zJlEUuZQkAIwxu0WkiAtjypMSU9IYO8cqclryRHstcioAli07RHR0IvPm3UPPnnXdHY5SbuNMotgkIp9h3UUADKQAdgr41i97AJjYtzHBpfTtY/mRMYbvvttGmTLFuOOOWowd25annmqNn1+Buy5S6jLOPEcxHDgIPAOMBQ4B/3VlUHnNxqNRTFl9BIC+zYLdG4xyiT17ztGp07c88MA8vvlmCwA+Pt6aJJQimzsKEWkE1ADmGmMm5k5IeYvNZujzyWoAxt/dUCsx85mEhBTefHMlb7+9iuLFi/DZZ9156KFm7g5LqTzlqncUIvI8VvcdA4HfRGRorkWVh4z4YRMAPZpUZGCrqm6ORuW0n3/exxtvrOSeexqyZ8+jPPxwc70YUCqDrO4oBgKNjTEXRaQMsBireWyB8fvu0yzZeQqAj+4NdXM0KqecOhXHli2nuP32mvTrV5+QkIdo2VJ7pVHqarKqo0gyxlwEMMaczWbefGfd4fMMm7oBgP/1a6LdM+QDaWk2Pv54PXXqTGLQoLkkJKQgIpoklMpGVncU1R3elS1ADcd3Zxtjers0MjdasiOC4d9vIsivCO/0a8Itdcq6OyR1gzZtimD48IWsX3+Szp2r8/HH3fD1LfBdlinllKwSRZ8Mw5NcGUheEZuYwvDvrXqJN+9upEkiHzh8OIqWLb8gKKgY06b15t57tadfpa5FVi8u+j03A8krBn75DwDPd6tL1wbaRYenMsawffsZGjcuR7Vqpfjmm5706FGHkiX1HeZKXasCVe+Qna//Psy28GiCS/nyn/bV3R2Ouk6HD0fRvft0mjb9jG3bTgMwaFATTRJKXSeXJgoRuV1E9orIARF5Nov5+oqIERG39R81YfFuXl9o9VQy79G2WjThgZKT03jrrb9p0OBj/vrrCO++24X69cu4OyylPJ4zXXgAICI+xpika5jfC5gMdAHCgfUissCx3yj7fP5Y3Zj/4+y6c9qeUzF8tuIQAN8MaUGQn77O1NOkpdlo0+YrNm6MoHfvenzwwW1Urqwd+CmVE5zpZryliGwH9tuHm4jI/zmx7pbAAWPMIWNMMjAD6JnJfOOAiUCi82HnnJQ0G7d/sBKAzwc118prDxMTY127eHkVYujQpvz88wDmzOmvSUKpHORM0dNHQHcgEsAYsxW4xYnlKgHHHYbDyfCubRFpClQ2xizMakUi8rCIbBCRDWfPnnVi086bsd4KsX2tILrUL5ej61auY4xhypQtVK/+IfPnWx02jhjRgu7da7s5MqXyH2cSRSFjzNEM45x5i3xmhfwmfaJIIax3XYzObkXGmM+NMWHGmLAyZXK2zHmqvbO/T+5vrvUSHmLXrrN07DiVIUPmU7duEDVqlHZ3SErla87UURwXkZaAsdc7jAT2ObFcOFDZYTgYOOkw7A80BP60n6DLAwtE5C5jzAZngr8Rxhhu+2AFB87EEVi8CH4+TlfXKDeaOHEVL7zwBwEBPnz5ZQ+GDGmqfTMp5WLOnB0fwSp+qgKcBpbZx2VnPVBLRKoBJ4B7gfsuTTTGRANBl4ZF5E/g6dxIEgB/7TvLvtNxgNXKSeVtxhhEhPLl/Rg4sBHvvNOFMmWKuzsspQqEbBOFMeYM1kn+mhhjUkXkMeBXwAv42hizU0ReBzYYYxZcc7Q56P/+OADA2udupXwJbV+fV508Gcvjjy+hffsqjBrVisGDmzB4cBN3h6VUgZJtohCRL3CoW7jEGPNwdssaYxZj9TrrOO7lq8zbMbv15ZR9p2PZeDQK/6LemiTyqEsd+L3wwh+kpNho00ZfGKWUuzhT9LTM4XNR4G4ub83kUYwxjJpuvcn158fauTkalZktW07x0EML2Lgxgq5da/Dxx920wlopN3Km6Gmm47CIfAf85rKIXGzTsSj2nIqlXoUAQoK0jDsvio5O5OTJWGbO7Eu/fvW1NZpSbnY9TX2qAR77qrdP/7KewJ46pIWbI1GXGGOYNWsX+/dH8sILHbj55hAOHXqcokW1JZpSeYEzT2ZHich5+78LWHcTz7s+tJyXmmbjt11WJ3FlA7RuIi84ePA83bpN4557ZjN//l5SUqxHdDRJKJV3ZPnXKNY9fxOs5q0ANmPMFRXbnuKl+TsB+E/7am6ORCUlpfLuu6t5442VFC5ciA8/vJ0RI1rg7a0dGiuV12SZKIwxRkTmGmOa51ZArvL92qNMX3cMgBfurO/maNTx4zGMG7eCHj3q8MEHt1GpUoC7Q1JKXYUzl2/rRKSZyyNxoe3h0bw4bwcAnw3y+Jznsc6evcikSesAqFmzNLt2PcqsWf00SSiVx131jkJEvI0xqUA74D8ichC4iNWHkzHGeEzymLrmCABzHmlD86ql3BpLQWSzGb75ZjPPPLOM2NgkunSpTp06QVSvrr+FUp4gq6KndUAzoFcuxeIyszeGE+Tno0nCDXbsOMMjjyzi77+P0b59FT79tDt16gRlv6BSKs/IKlEIgDHmYC7F4hK/77ZaOd3TQp/szW3JyWl07fodyclpfP31XTz4YKg+E6GUB8oqUZQRkaeuNtEY854L4slxw6ZafQze3bRSNnOqnPLHH4e5+eaqFCnixY8/9qNu3SCCgoq5Oyyl1HXKqjLbC/DD6g48s395XlxSKgBehYSaZT0iZI8WHh5Dnz4/cuut3/Ltt1sBaNeuiiYJpTxcVncUEcaY13MtEhd4yd7SaVSnWm6OJH9LTbUxadI6XnppOWlpNiZMuJWBAxu7OyylVA7Jto7CU9lshrmbrecEH+lYw83R5G+DBs1lxowd3HFHTSZP7ka1atpoQKn8JKtEcWuuReECf+233q3du2kliujTvjnuwoVEvL0L4edXhEcfbUGfPvXo06eeVlYrlQ9d9QxqjDmfm4HktEvvwh5xS033BpLPGGOYMWMH9epN5qWX/gCseoi+fbWXV6Xyq3x7qX3wrPWa05pl/dwcSf5x4MB5brvtewYMmENwcAD336/1EEoVBPmyi8745FSOn09gWDvt/C+nTJu2naFD5+Pj482kSXcwfHgYXl759jpDKeUgXyaKd3/dB0CTyiXdHInnS0lJo3BhL8LCKtK3b30mTuxCxYra1FipgiTfJYrUNBtfrzoMQLeG5d0cjec6c+Yio0cv5eLFZH766R5q1w7k++97uzsspZQb5Luyg8U7TgHQt3kw3lo0cs1sNsPnn2+kTp1JzJy5gwYNypCWZnN3WEopN8p3dxTrDkcC8FSX2m6OxPMcOhTF/ff/xJo14XTsGMInn9xJ3bragZ9SBV2+SxQ/b40AoGJJXzdH4nlKlPDhwoVEpk7txaBBjbW5q1IKyGdFT4fOxhGdkEL1MsXdHYrHWLBgL717zyQtzUZgYDF27BjB4MFNNEkopdLlq0SxaJt1NzGioz5kl51jx6Lp1WsGPXvOYN++SCIirOdOChXSBKGUuly+KnrafSoGgD7NtEvxq0lNtfHBB2t55ZU/Mcbw9tudefLJmyhc2MvdoSml8qh8lShsNihexEuLTbKQlmbjyy830alTNf7v/+4gJESfNVFKZS1fFT3tPR2rldiZiIpKYOzY34iNTcLHx5tVq4ayYMG9miSUUk7JN4kiNjGFw+cuEuhXxN2h5BnGGH74YRt1607mf/9bw/LlRwAIDCymd11KKaflm6Kn7eHRAPQM1foJgH37IhkxYhG//36Yli0r8euv9xMaqk+qK6WuXb5JFPvPWK12GgeXcHMkecMTTyxhw4aTfPxxNx5+uLl24KeUum75JlEssXfdUakA11H89ttB6tYNonLlEnzyyZ34+HhTvrx2s66UujEuvcwUkdtFZK+IHBCRZzOZ/pSI7BKRbSLyu4hUvd5tbQ2/AEDJYgWvjuLUqTjuu28OXbt+z9tvrwKgatWSmiSUUjnCZYlCRLyAycAdQH1ggIjUzzDbZiDMGNMYmA1MvN7txSen0bp64PUu7pFsNsOnn26gbt1JzJmzm1deuZl33+3q7rCUUvmMK+8oWgIHjDGHjDHJwAygp+MMxpjlxph4++BaIPh6NpRmMwCUL1H0+qP1QBMmrOSRRxbRvHlFtm0bzquvdqRo0XxTmqiUyiNceVapBBx3GA4HWmUx/zDgl8wmiMjDwMMAVapUuWL6yQsJADSoGHB9kXqQ2Ngkzp2Lp1q1UgwfHka1aqUYMKChNndVSrmMK+8oMjtzmUxnFLkfCAPeyWy6MeZzY0yYMSasTJkyV0z/ds0RABoH598HyIwxzJ27m/r1P+aee2ZjjCEwsBj33ddIk4RSyqVcmSjCgcoOw8HAyYwziUhn4AXgLmNM0vVsaN4Wa7VhVUtdz+J53tGjF7jrrhn07v0jpUv78tFHd2hyUErlGlcWPa0HaolINeAEcC9wn+MMItIU+Ay43Rhz5no2kpiSxtnYJEICi+XLnk/XrDlO587fAfDuu114/PGb8PbWZyKUUrnHZYnCGJMqIo8BvwJewNfGmJ0i8jqwwRizAKuoyQ+YZb9CPmaMuetatrPzpNVj7Ihb8lfX4jExSQQE+NCsWQWGDg1lzJi2VKmiDxMqpXKfS5vIGGMWA4szjHvZ4XPnG93GuTirtKpCPmnxFBkZz7PPLmPp0kPs3DkCP78i/N//dXN3WEqpAszj21LujrDuKKqULubmSG6MMYbvvtvG6NFLiYpK4KmnWqPVEEqpvMDjE8XGo1EAVC7luYkiOjqRXr1m8uefR2jdOphPP+1O48bl3B2WUkoB+SBRnI1NoqqHVmQbYxARAgJ8CAoqxuefd2fYsGYeuS9KqfzLo5vPGGM4EnmRVtVKuzuUa/brrwdo1uxzwsNjEBFmzerHf/7TXJOEUirP8ehEcex8PIkpNmqX83d3KE6LiIjl3ntnc/vtPxAfn8KZMxfdHZJSSmXJo4ueVh2IBKBBRc9oNjp58jqef/4PkpJSee21jowd2xYfH4/+CZRSBYBHn6Uioq0+nkKCPKMie+PGCFq1qsTkyd2oVatg9XSrlPJcHp0oDp2zim3K+Pm4OZLMxcQk8fLLyxk0qDHNm1fk44/vxMfHS7vfUEp5FI9OFOsPn8fHuxDeeew1n8YY5szZzeOPLyEiIpYqVUrQvHlF7QJcKeWRPPbMlZpm40xsEjXKFHd3KJc5fDiKxx77hcWL9xMaWp6ffupPq1bX9ZoNpZTKEzw2USzcFgHAzbXLujmSy/3ww3ZWrDjK++/fxmOPtdQO/JRSHs9jE8X3a48C8J8O1dwcCaxceZSkpDQ6d67OmDFtePDBUIKD8/9LlJRSBYPHXu7GJaUCUM7ffZ0BnjsXz9Ch8+nQYQqvv/4XAD4+3poklFL5isfeUZy4kEDzqqXc8iSzMYYpU7YwZsxvREcnMXZsW156qUOux6HyvpSUFMLDw0lMTHR3KKqAKFq0KMHBwRQuXDjH1umRiSIyLonYxFRKFcu5L+JaLF68n6FDF9C2bWU+/bQ7DRvmrXoSlXeEh4fj7+9PSEiINotWLmeMITIykvDwcKpVy7lieY8serr0/ETbmkG5ts34+BRWrToGQLdutZg//15WrBiiSUJlKTExkcDAQE0SKleICIGBgTl+B+uRieKnTeEAtAjJnc4Af/llPw0bfswdd/zAhQuJiAh33VVHO/BTTtEkoXKTK443j0wUyakGwOWdAZ44EUO/frPo1m0aPj7e/PzzAEqWzB9v0lNKKWd5ZKIwxlC5tC9FXPiMwpkzF6lf/2MWLtzHG2/cwtatw7n55hCXbU8pV/Hy8iI0NJSGDRvSo0cPLly4kD5t586ddOrUidq1a1OrVi3GjRuHMSZ9+i+//EJYWBj16tWjbt26PP300+7YhSxt3ryZhx566LJxPXv2pHXr1peNe/DBB5k9e/Zl4/z8/NI/79u3j27dulGzZk3q1atH//79OX369A3Fdv78ebp06UKtWrXo0qULUVFRV8yzfPlyQkND0/8VLVqUefPmAdC+ffv08RUrVqRXr14ALFy4kFdeeeWGYrsmxhiP+te8eXPzyPcbTKd3lxtXCA+PTv/84YdrzYEDkS7ZjioYdu3a5e4QTPHixdM/Dx482LzxxhvGGGPi4+NN9erVza+//mqMMebixYvm9ttvN5MmTTLGGLN9+3ZTvXp1s3v3bmOMMSkpKWby5Mk5GltKSsoNr6Nv375my5Yt6cNRUVEmODjY1K1b1xw6dCh9/AMPPGBmzZp12bKXvpuEhARTs2ZNs2DBgvRpf/zxh9m+ffsNxTZmzBgzYcIEY4wxEyZMMM8880yW80dGRppSpUqZixcvXjGtd+/eZurUqcYYY2w2mwkNDc10PmMyP+6ADeY6z7se2epp18mYHL+biI5O5MUX/+Czzzaydu1DNGtWgVGjWuXoNlTB9trPO9l1MiZH11m/YgCv9Gjg9PytW7dm27ZtAEybNo22bdvStWtXAIoVK8akSZPo2LEjjz76KBMnTuSFF16gbt26AHh7ezNixIgr1hkXF8fIkSPZsGEDIsIrr7xCnz598PPzIy4uDoDZs2ezcOFCpkyZwoMPPkjp0qXZvHkzoaGhzJ07ly1btlCyZEkAatasyapVqyhUqBDDhw/n2DGrEckHH3xA27ZtL9t2bGws27Zto0mTJunj5syZQ48ePShXrhwzZszgueeey/Z7mTZtGq1bt6ZHjx7p42655Ranv9ermT9/Pn/++ScADzzwAB07duTtt9++6vyzZ8/mjjvuoFixy3vEjo2N5Y8//uCbb74BrHqIjh07snDhQvr373/DcWbHIxNFeFRCjtVPGGOYNWsXTzyxhFOn4njssZbUqFEqR9atVF6SlpbG77//zrBhwwCr2Kl58+aXzVOjRg3i4uKIiYlhx44djB49Otv1jhs3jhIlSrB9+3aATItXMtq3bx/Lli3Dy8sLm83G3LlzGTJkCP/88w8hISGUK1eO++67jyeffJJ27dpx7NgxbrvtNnbv3n3ZejZs2EDDhg0vGzd9+nReeeUVypUrR9++fZ1KFDt27Ljiu8hMbGws7du3z3TatGnTqF+//mXjTp8+TYUKFQCoUKECZ86cyXL9M2bM4Kmnnrpi/Ny5c7n11lsJCPj3Yd6wsDBWrlypiSIzyWk2Um2GehVu/OlnYwy9e//IvHl7aNasAgsWDCAsrGIORKnUla7lyj8nJSQkEBoaypEjR2jevDldunQB/n1ne2aupeXMsmXLmDFjRvpwqVLZX2j169cPLy8vAO655x5ef/11hgwZwowZM7jnnnvS17tr1670ZWJiYoiNjcXf/9+LxIiICMqUKZM+fPr0aQ4cOEC7du0QEby9vdmxYwcNGzbMdJ+utYWQv78/W7ZsuaZlnBUREcH27du57bbbrpg2ffr0K+phypYty8mTJ10SS0YeV5kdk5ACQPcmFa57HSkpaYB1kLRrV5mPPrqddese0iSh8iVfX1+2bNnC0aNHSU5OZvLkyQA0aNCADRs2XDbvoUOH8PPzw9/fnwYNGrBx48Zs13+1hOM4LmO7/uLF/+31uXXr1hw4cICzZ88yb948evfuDYDNZmPNmjVs2bKFLVu2cOLEicuSxKV9c1z3zJkziYqKolq1aoSEhHDkFE8O9QAADLlJREFUyJH0JBYYGHjZ3c758+cJCgpK/y6c2dfY2NjLKp4d/zkmtUvKlStHRITVgWlERARly179uasff/yRu++++4onqiMjI1m3bh133nnnZeMTExPx9fXNNuac4HGJwmZvkNG6+vW9Ie7PP4/QuPGnzJ+/B4DRo9swcmQrvPLYOy2UymklSpTgo48+4t133yUlJYWBAwfy999/s2zZMsC68xg1ahTPPPMMAGPGjOHNN99k3759gHXifu+9965Yb9euXZk0aVL68KWTcbly5di9e3d60dLViAh33303Tz31FPXq1SMwMDDT9WZ2JV+vXj0OHDiQPjx9+nSWLFnCkSNHOHLkCBs3bkxPFB07dmTmzJkkJycDMGXKlPR6iPvuu4/Vq1ezaNGi9HUtWbIkvTjtkkt3FJn9y1jsBHDXXXcxdepUAKZOnUrPnj2v+j1Mnz6dAQMGXDF+1qxZdO/enaJFL2+av2/fviuK3VzF486OKWk2AHyusTL77NmLPPDAPG65ZSpJSan4++fNt+L9f3v3HiNldcZx/PurCiwKeKEYFRWsK7Iuu4BgVw1SwRq1VFpjRQSURmsEKV6qCQ0mtVUj1qIWb0itQasC1WgliKGErmIIF9eqoHjHDWxjlFJKFa/A0z/OYXdcZmffXZnrPp9kk5n3+uyTmTnznvPOc5zLpkGDBlFdXc38+fMpKyvjmWee4eabb6Zfv34MGDCAoUOHMmXKFACqqqq46667GDt2LP3796eysrLx23GqG264ga1bt1JZWUl1dTW1tbUAzJgxg1GjRjFixIjGfvqWjBkzhkcffbSx2wlg1qxZ1NXVUVVVRUVFBbNnz95jv+OPP55t27bxySefUF9fz8aNG6mpqWlc37dvX7p3787q1asZNWoUw4YN48QTT2TgwIGsWLGicWC5rKyMRYsWcffdd1NeXk5FRQVz587NeAWQxLRp01i6dCnl5eUsXbqUadOmAWFsJbUrqb6+nk2bNjF8+PA9jjF//vy0DUhtbe0eVxnZIku5Z7oYHPq9Civ72e3Uz0ieoHnz1nHllYv59NOvuP76U5g+/TS65qlOlOtY3nzzTfr375/vMEranXfeSbdu3fbowy9lH330ERdddBHLli1Luz7d607Sy2Y2pD3nK7orih07jT6HdG19w9R9duyisrIXr756BbfcMtIbCedKyKRJk+jcuWP1EGzcuJGZM2fm7HxFd9fTlzt20fugzA3F9u1fcdNNyznqqB5MnjyU8eOrGD++ymvuOFeCunTpwoQJE/IdRk4NHTo0p+cruiuKnbuMvj1bnid70aJ3OOGE+7jtthW8884WIAyWeSPh8qXYunddccvG663orih2mdErzUB0Q8P/mDr1OZ5++i0qKr7L8uUTGTbs6DxE6FyTLl26sGXLFi817nLC4nwUze+Q+raKrqEAOGj/Tnss27BhK0uWvM+tt47k2mtPplOnffIQmXPf1Lt3bxoaGti8eXO+Q3EdxO4Z7vamorvrqfNh5bZmzUtUH3kga9b8i5UrN3HVVeF2uC1bPuOQNg50O+dcR1Cwdz1JOkvS25LekzQtzfrOkhbE9asl9Uly3F6d92Py5GepqXmQO+5Yxfbt4Qc03kg459zel7WGQtI+wL3A2UAFMFZS858uXgpsNbNjgTuBlssqphhUdT8PPPAyU6d+n3XrJrF/mq4o55xze0c2xyhOAt4zsw0AkuYDo4HUgiijgRvj4yeBeyTJMvSH2U7jyKN7sHjxOAYPbn+9J+ecc8lks6E4AtiU8rwBaD7BQ+M2ZrZD0jbgEODfqRtJuhy4PD79sm7z5a8nqAjcEfSkWa46MM9FE89FE89Fk37t3TGbDUW6ewGbXykk2QYzmwPMAZBU194BmVLjuWjiuWjiuWjiuWgiqa71rdLL5mB2A3BkyvPeQPPi6Y3bSNoX6AH8J4sxOeeca6NsNhQvAeWS+krqBFwILGy2zULgkvj4fOAfmcYnnHPO5V7Wup7imMMUYAmwD/CQmb0h6XeESb4XAn8G/iLpPcKVxIUJDj0nWzEXIc9FE89FE89FE89Fk3bnouh+cOeccy63iq4ooHPOudzyhsI551xGBdtQZKv8RzFKkItrJa2XtFbSMkklWza3tVykbHe+JJNUsrdGJsmFpAvia+MNSY/nOsZcSfAeOUpSraRX4vvknHzEmW2SHpL0saTXW1gvSbNintZKGpzowGZWcH+Ewe/3gWOATsBrQEWzbSYDs+PjC4EF+Y47j7k4HegaH0/qyLmI23UDlgOrgCH5jjuPr4ty4BXgoPi8V77jzmMu5gCT4uMKoD7fcWcpF6cBg4HXW1h/DvAc4TdsNcDqJMct1CuKxvIfZvYVsLv8R6rRwMPx8ZPASJVmwf9Wc2FmtWb2WXy6ivCblVKU5HUBcBPwe+CLXAaXY0ly8QvgXjPbCmBmH+c4xlxJkgsDusfHPdjzN10lwcyWk/m3aKOBRyxYBRwoqdVaSIXaUKQr/3FES9uY2Q5gd/mPUpMkF6kuJXxjKEWt5kLSIOBIM1uUy8DyIMnr4jjgOEkrJK2SdFbOosutJLm4ERgvqQFYDPwyN6EVnLZ+ngCFO3HRXiv/UQIS/5+SxgNDgOFZjSh/MuZC0ncIVYgn5iqgPEryutiX0P30A8JV5ouSKs3sv1mOLdeS5GIsMNfMZko6mfD7rUoz25X98ApKuz43C/WKwst/NEmSCySdAUwHzjWzL3MUW661lotuQCXwvKR6Qh/swhId0E76HnnGzL42sw+AtwkNR6lJkotLgb8CmNlKoAuhYGBHk+jzpLlCbSi8/EeTVnMRu1seIDQSpdoPDa3kwsy2mVlPM+tjZn0I4zXnmlm7i6EVsCTvkb8RbnRAUk9CV9SGnEaZG0lysREYCSCpP6Gh6Ijz0y4ELo53P9UA28zsw9Z2KsiuJ8te+Y+ikzAXtwMHAE/E8fyNZnZu3oLOkoS56BAS5mIJcKak9cBO4Hoz25K/qLMjYS5+BfxJ0jWErpaJpfjFUtI8Qldjzzge8xtgPwAzm00YnzkHeA/4DPh5ouOWYK6cc87tRYXa9eScc65AeEPhnHMuI28onHPOZeQNhXPOuYy8oXDOOZeRNxSu4EjaKenVlL8+Gbbt01KlzDae8/lYffS1WPKiXzuOcYWki+PjiZIOT1n3oKSKvRznS5IGJtjnakldv+25XcflDYUrRJ+b2cCUv/ocnXecmVUTik3e3tadzWy2mT0Sn04EDk9Zd5mZrd8rUTbFeR/J4rwa8IbCtZs3FK4oxCuHFyX9M/6dkmabEyStiVchayWVx+XjU5Y/IGmfVk63HDg27jsyzmGwLtb67xyXz1DTHCB/iMtulHSdpPMJNbcei+csi1cCQyRNkvT7lJgnSrq7nXGuJKWgm6T7JdUpzD3x27hsKqHBqpVUG5edKWllzOMTkg5o5Tyug/OGwhWispRup6fjso+BH5rZYGAMMCvNflcAfzSzgYQP6oZYrmEMcGpcvhMY18r5fwysk9QFmAuMMbMBhEoGkyQdDPwUOMHMqoCbU3c2syeBOsI3/4Fm9nnK6ieB81KejwEWtDPOswhlOnabbmZDgCpguKQqM5tFqOVzupmdHkt53ACcEXNZB1zbynlcB1eQJTxch/d5/LBMtR9wT+yT30moW9TcSmC6pN7AU2b2rqSRwInAS7G8SRmh0UnnMUmfA/WEMtT9gA/M7J24/mHgSuAewlwXD0p6Fkhc0tzMNkvaEOvsvBvPsSIety1x7k8oV5E6Q9kFki4nvK8PI0zQs7bZvjVx+Yp4nk6EvDnXIm8oXLG4BvgIqCZcCe8xKZGZPS5pNfAjYImkywhllR82s18nOMe41AKCktLObxJrC51EKDJ3ITAFGNGG/2UBcAHwFvC0mZnCp3biOAmzuM0A7gXOk9QXuA4YamZbJc0lFL5rTsBSMxvbhnhdB+ddT65Y9AA+jPMHTCB8m/4GSccAG2J3y0JCF8wy4HxJveI2Byv5nOJvAX0kHRufTwBeiH36PcxsMWGgON2dR58Qyp6n8xTwE8IcCQvisjbFaWZfE7qQamK3VXdgO7BN0qHA2S3Esgo4dff/JKmrpHRXZ8418obCFYv7gEskrSJ0O21Ps80Y4HVJrwLHE6Z8XE/4QP27pLXAUkK3TKvM7AtCdc0nJK0DdgGzCR+6i+LxXiBc7TQ3F5i9ezC72XG3AuuBo81sTVzW5jjj2MdM4Doze40wP/YbwEOE7qzd5gDPSao1s82EO7LmxfOsIuTKuRZ59VjnnHMZ+RWFc865jLyhcM45l5E3FM455zLyhsI551xG3lA455zLyBsK55xzGXlD4ZxzLqP/A0VKntbppOanAAAAAElFTkSuQmCC\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", + "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": 95, + "metadata": {}, + "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": [ + "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))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 96, + "metadata": {}, + "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=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" + ] + }, + { + "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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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
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" + ], + "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": [ + "### 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": 99, + "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(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": [ + "We will now try searching for the smoothing parameter to achieve a better ROC and recall on default." + ] + }, + { + "cell_type": "code", + "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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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
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" + ], + "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=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": 109, + "metadata": {}, + "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=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": 110, + "metadata": {}, + "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=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": 111, + "metadata": {}, + "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=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": 112, + "metadata": {}, + "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=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": 113, + "metadata": {}, + "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", 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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
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6Neural Network - adagrad0.3598390.763871
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" + ], + "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", + "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": [ + { + "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": [ + "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-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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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", 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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", - 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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": [ + "
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LIMIT_BALAGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
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45000037469904823349291283142895929547200020191200110010691000
550000578617567035835209401914619131200036681100009000689679
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" + ], + "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

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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": [ + "
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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", 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" ] @@ -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", 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\n", 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" ] @@ -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", "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", 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" + ] + }, + "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": [ + "
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" + ], + "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 - 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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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", 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\n", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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" + ], + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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ModelRecall-1AUROC
0Decision Tree (GINI)0.4287830.616773
1Random Forest0.3684470.761964
2Gradient Boosted0.3649850.773571
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" + ], + "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
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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
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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" + } + ], + "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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" + ], + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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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 Project Submission.ipynb b/BT2101 Project Submission.ipynb new file mode 100644 index 0000000..c7cbbe1 --- /dev/null +++ b/BT2101 Project Submission.ipynb @@ -0,0 +1,6490 @@ +{ + "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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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 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" + ] + }, + { + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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0 rows × 47 columns

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" + ], + "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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" + ] + }, + "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": [ + "
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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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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
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" + ], + "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
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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
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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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" + ] + }, + { + "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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" + ] + }, + "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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" + ], + "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", 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" + ] + }, + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
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" + ], + "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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fIpKFde+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": [ + "
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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
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" + ], + "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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" + ] + }, + "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": [ + "
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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
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" + ], + "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": [ + "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 - 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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7385720.5219368.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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0 rows × 45 columns

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" + ], + "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": [ + "
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" + ], + "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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" + ], + "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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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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" + ] + }, + "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": [ + "
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ModelRecall-1AUROC
0Decision Tree0.4100480.610882
1Random Forest0.3631580.767925
2Gradient Boosted0.3492820.775518
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" + ], + "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
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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
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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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" + ], + "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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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 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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 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\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-MASTER- Reon.ipynb b/BT2101_Default - EDIT-MASTER- Reon.ipynb new file mode 100644 index 0000000..3103496 --- /dev/null +++ b/BT2101_Default - EDIT-MASTER- Reon.ipynb @@ -0,0 +1,7047 @@ +{ + "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": [ + "
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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": [ + "
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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
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" + ], + "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", 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TDbk3D0k6FnhFROxNrxcBH+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", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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" + ], + "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": 37, + "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, 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 1987 Defaulters, the Decision Tree (GINI) identified 809\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5482 1280\n", + "1 1178 809" + ] + }, + "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", 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" + ] + }, + "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 0.81 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": [ + "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 1987 Defaulters, the Decision Tree (Entropy) identified 831\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 5509 1253\n", + "1 1156 831" + ] + }, + "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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" + ] + }, + "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 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": [ + "print(classification_report(y_test, randf.predict(X_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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])" + ] + }, + { + "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": 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 0.86 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": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of 1987 Defaulters, the Decision Tree (Gradient Boosted Trees) identified 717\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ] + }, + "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 0.89 6762\n", + " 1 0.65 0.36 0.46 1987\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, 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": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
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" + ], + "text/plain": [ + " Model F1-1 AUROC\n", + "0 Decision Trees - Random Forest 0.461339 0.768458" + ] + }, + "execution_count": 56, + "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": 57, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "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: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1784
Time: 19:35:32 Log-Likelihood: -7781.7
converged: False LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
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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: 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 -0.8737 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": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glm = sm.Logit(y_train,X_train).fit()\n", + "glm.summary()" + ] + }, + { + "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 0.88 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 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": 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: 0.445386\n", + " Iterations 7\n", + "Optimization terminated successfully.\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: 0.445388\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: Thu, 21 Nov 2019 Pseudo R-squ.: 0.1756
Time: 19:35:33 Log-Likelihood: -7808.3
converged: True LL-Null: -9471.2
Covariance Type: nonrobust LLR p-value: 0.000
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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: 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 -0.8984 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": 60, + "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": 61, + "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": 62, + "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": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.21650864211883647\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.24907536768337235\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "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": 68, + "metadata": {}, + "outputs": [], + "source": [ + "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": { + "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": 70, + "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": 70, + "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": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.16469105377809068\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6432 330\n", + "1 1274 713" + ] + }, + "execution_count": 72, + "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": 73, + "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 0.66 0.68 8749\n", + "weighted avg 0.80 0.82 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": 74, + "metadata": {}, + "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", + " 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": 75, + "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": 75, + "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": 76, + "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" + } + ], + "source": [ + "auroc = get_roc(clf_reduced_tuned, y_test, X_test, \n", + " \"SVM reduced features and tuning linear kernel\")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "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": [ + "
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" + ], + "text/plain": [ + "Predicted 0 1\n", + "Actual \n", + "0 6492 270\n", + "1 1349 638" + ] + }, + "execution_count": 77, + "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": 78, + "metadata": {}, + "outputs": [ + { + "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": [ + "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": 79, + "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": 79, + "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": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.15910713557498266\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "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": 81, + "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 0.38 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": [ + "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": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
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" + ], + "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": [ + "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": 83, + "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": 83, + "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": 84, + "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" + } + ], + "source": [ + "auroc = get_roc(mlp, y_test, X_test, \"Neural Network (5x26)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "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 0.69 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": "markdown", + "metadata": {}, + "source": [ + "### 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": 93, + "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: 0.4787 - 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", + "from keras.layers import Dense\n", + "\n", + "# define the keras model\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='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": 94, + "metadata": {}, + "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", + "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": 95, + "metadata": {}, + "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": [ + "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))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 96, + "metadata": {}, + "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=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" + ] + }, + { + "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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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
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" + ], + "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": [ + "### 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": 99, + "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(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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dzFNPLeKzzzbx4otdeOmlrrRpU1AX9xfHYYlCRL4HugIBIhIJvAh4ABhjPgJ+B3pjdS6eDIxwVCxKKeVI6ZnZ7D2VQMTZZNYeOsuGIzFsOxaX57xXhVTh1jZBXF3fn/qBPpe1XWMMX3+9hSeeWEhMTApPPtmJJ5/sdFnrzIsjn3oaWsh0A4xx1PaVUqo4/Lwpksd+2ELuzkIHtgsiPTObDvX98fF0p3F1X0KqVsSrvNsV2/a4cYt4441VdOpUh48+upmWLatfsXXbc7n+KJRSylmOnk1m2b4zrNofTWZ2NusPxxCdZD159GDXBnSo50/Daj5U96uAh5tjXlNLSckgKSmDgABv7r23DY0a+XPvvW0vqqjqYmmiUEqpQsSnZjDiy3VsOBKTM652ZS/q+HvTuLovkwe2oo6/t8PjmD9/P2PG/E5YWA1mzx5MkyYBNGkS4PDtaqJQSqlcjDH8vu0kP22MZO3hsySkZuZMe+/2MLqFVsO3gkexxXP8eAL/+c98fvxxJ02aVOWhh64qtm2DJgqlVBmXkJrB3lOJnIpP5XhsCpuPxjJ3678PYHZuWBUvD3dGdA6hc0PHX73n9tdfB7n11pmkp2cxfvz1PPlkJzw9i/enWxOFUqpMiUvJYMrifaw5eJbD0Unn3S2cIwI3hFbnzUGtqOxd3glRQkZGFh4ebrRuXYPevRvx6qvdaNjQ3ymxaKJQSpV6ZxLSuO/r9UQnphEZk5IzvmXtSrQKqkQdf2/a1a1CFW8Pgv0rUt7deT0wxMen8fzzi/nnn2OsXDmSgABvZswY6LR4QBOFUqoUMsYQn5LJzPURbDwSy/wdJwEo71aO4R3r0ryWH4Pa1XHok0IXyxjDrFk7eeSR+Zw8mcjo0VeRlpaFt7fzuw3SRKGUclnGGJbti+LvPWfYGhlLORHWHj57wXxdGgcytH0wPVvUcEKUhTtzJom77/6FP/7YT5s2Nfj119u56qrazg4rhyYKpZRLOBGXwsKdp9h1Ip5/Dp0lO9twODr5vHm8y7vRsX5VkjOy6Nm8BpW8PLglrBY+xVz5e7H8/DyJikrm3XdvYsyY9rg7segrLyX76Cmlyrydx+MZM30jh6KSzhvfpLovQ9vXoWJ5d4Z3DCGoileJKkoqzLJlR5gwYTmzZw/Gx6c8a9bcV2Lj10ShlCqRMrOyueOzf1h7yCpKqubryVO9QrmxeQ0qlndDpGT+qBYmKiqZJ59cyLRpmwkJqczhw7G0aFGtxCYJ0EShlCphTsSlMGt9JG8t3Jszbvr/daBTg+J/h+FKMsbw5ZebefLJhcTHp/H009fw3HPX4e1dfC/uXSpNFEopp0nLzOKrVYdJSc9mzcFoUjOz2BQRmzP95pY1mXJHG5e9e8jt22+30qxZIB99dDPNm1dzdjhFpolCKVVs4lIymL/9BEt2n2HbsTiOxf77ToMIVPbyoH9YLXq1rEm30GoOa1ivuCQnZ/Daa8sZNSqcoCA/Zs8eTKVKF9fXREmgiUIp5VCpGVks3XOaiX/s5ojdU0q+nu6E1alMh/r+PHJDozx7bHNlv/++jzFjfufw4Vhq1/blwQevokoVL2eHdUlK1yejlCoRsrIN24/FMW72VnafTMgZ37J2JYa2D6Zv65rF2qhecYqMjOc//5nP7Nm7aNo0gL//vofrrqvr7LAuiyYKpdQVkZiWyfztJ5m14ShrDv770ltlbw/u6RTC7VcFU6NSBSdGWDwmTFjGvHn7eO21bjz+eCfKX8GOipxFTO5umUq48PBws379emeHoVSZZYxhw5EYFuw8xT8Ho3ErJ2w/Fk96VnbOPH4V3Lnz6rrc1LwGYXUqOzHa4rF27TG8vNxp2bI60dHJxMWlUb9+FWeHdR4R2WCMCb+UZfWOQimVr/WHz7LzRDxpGdnsOZXArhPx7DgenzO9hl8F0jKz6BZaDYOha5Nq9GlVeouVcouLS+WZZ/7iww/X06dPY+bMGUrVqt5Urer4ToyKkyYKpVSOU/GprD4Qzeajsfyy+RixyRkXzFOxvBuDwuswonMIdatWdEKUzmeMYebMHTz66J+cPp3E2LHtGT++m7PDchhNFEopdh6P567P/+Gsrf/nc66u78/jNzahaU0/PN3LufzjqlfKt99uZfjwXwgPr8XcuUNp166Ws0NyKE0USpVRK/ZF8f3aCDZGxHAiLhWAKt4evNq/JWHBlQn08XRqvwwlTVpaJgcPxtC0aSCDBzcnMzOb4cNb41YGkqcmCqXKmG2RcfSdsiJnuI6/Fx3q+TOqSwOuD3Wdt4WL05Ilh3jwwXkkJ2ewb99YPD3dGTGijbPDKjaaKJQq5aIT03ho+ibOJKaRmpGV08NbDb8KTBt5FaE1/JwcYcl1+nQSTzyxgG++2Ur9+lX45JO+xd5fdUlQ9vZYqTIgIyubyfN3s2xvFHtO/fvC24C2tenaxI2bmtfg2kaBToyw5Nu//yzt239KYmI6zz57Lc8+ey1eXmXjaa7cNFEo5eKMMRw9m8LJ+FQS0zL4atUR/t57Jmd6+3r+9GlVk6Htg7Uyugji49Pw8/OkQYMq3HtvG0aObEPTpmU7qWqiUMrFZGcbTiek8dwv2zkem8LOE/EXzFO7shejujbgrg7BpablVUdLSkrnlVf+5tNPN7J164MEBfnxxhs3OjusEkEThVIlXFJaJjuOx7Ni3xkizibzy+bj502/tU1t/Cq40ya4CnX8vajsXZ4GgT5OitY1/fbbHh566A8iIuK49942LtFHRHHSRKFUCROdmMbRmBRu/2Q17uXKkZiWed70tsGVCatThWsbBdC1SaDeMVyGzMxsBg/+kZ9/3k3z5oEsXz6Ca64JdnZYJY4mCqWcbM/JBH7aGMmK/VEcjkoiKT0rZ5pvhXI83K0hfl4etAmuQuugSrhrPcNlM8YgIri7l6NmTR9ef/0GHn20Y6lowM8RNFEo5QQn41L5c8dJ/jkUze/bTuaMrxdQkZa1K3F9aCA1K3lxdf2qToyydFqzJpIxY37n00/70rZtTaZOvdnZIZV4miiUcrC45AzmbDnGzhPxpKRnXVDH0LlhVR7r0Zg2daq4XM9nriQmJoVnnvmLjz/eQK1avsTEpBS+kAIcnChEpCfwHuAGfGaMeT3X9GDgK6CybZ6njDG/OzImpYrDrhPxfLXqMDtPxLM1Mi5nfK1KFWhUzQev8m482KUB1zYOxKcMvsBV3GbO3M7DD88nKiqZ//znal5+uSu+vp7ODstlOOwMFRE3YCrQA4gE1onIHGPMTrvZngN+MMZ8KCLNgN+BEEfFpJSjpGdms+pAFLM2RLJk9+nz6hlCa/gysF0QA9sFUdm7vBOjLLt2744iJKQy8+ffSZs2NZ0djstx5KVMe2C/MeYggIjMAPoB9onCAOfaD6gEnH9PrlQJdPBMIhuOxLDvdCK/bDrG6YS086Z7upejeS0/XuzbnPb1/J0UZdmWmprJpEkraNu2Jn37NuGZZ67lueeuKxMN+DmCIxNFbeCo3XAk0CHXPC8BC0RkLFAR6J7XikTkfuB+gOBgfXRNFa+0zCxmbYjkj20n2XE8jpg8+mgY3bUBAT6e9GhWnTr+pavTGlezaNFBRo+ex759Z3n88Y707dsEDw99mulyODJR5FUrl7vf1aHANGPMWyLSEfhGRFoYY7LPW8iYT4BPwOoK1SHRKmVna2Qs01Yd5qeNx84bX8Xbg65NAhl2dV3C6lSmkpeHPq5aQpw6lchjjy1g+vRtNGzoz4IFd9GjRwNnh1UqODJRRAJ17IaDuLBo6V6gJ4AxZrWIVAACgNMOjEupC6RlZjF5/h5WHYjm6NnkC15ye6Vfc25tU7vMdPHpihYuPMisWTt54YXrePrpa6lQQR8SuFIceSTXAY1EpB5wDLgduCPXPBHADcA0EWkKVADOoFQxSEnPYtqqwyzfd4ZVB6JzxofW8KVWZS8GtQuiZ4sa+uZzCbZly0n27TvLwIHNuPPOlnTuXId69ao4O6xSx2GJwhiTKSIPAX9iPfr6hTFmh4i8Aqw3xswBHgc+FZFHsYql7jHGaNGScpgj0UlMnr/nvF7dACp5eTDs6ro8cVMTJ0aniioxMZ0XX1zCe+/9Q0hIZfr3D8XdvZwmCQdx6L2Z7Z2I33ONe8Hu751AZ0fGoFRaZhZfrzrChN935YwL8CnPiM4h1AuoyF0d6uqLbi7kl192M3bsH0RGxnP//W2ZOLE77tplq0NpIZ4qtRbsOMn/Fu9n27F/X3gL8PHku/s60KSGrxMjU5dq27ZT3HrrTFq2rMbMmQPp1KlO4Qupy6aJQpU6myJiGPPdRo7bipZuCK1G81p+3N+lgb4F7YIyMrJYvjyCbt3q0bJldebNu4MePerrI6/FSL81qlQ4ejaZT5cf5OvVR3LGVfb2YNaoTjSspn0zuKpVq44yatRcduw4w549D9GwoT+9ezdydlhljiYK5bKMMWw6GsvnKw4xb+uJnPH1Aioy9Y62NKvlV8DSqiQ7ezaFp55axKefbqROHT9++mkwDRvqW+7OoolCuaSI6GRufPdvUjP+fTfz9QEtGXJVHX2c1cWlpmYSFvYRx48n8PjjHXnppa74+GgbWc6kiUK5lG/XHOH5X7dz7iHqq+v782Lf5jStqXcPri4yMp6gID8qVHBn/PjrCQurQevWNZwdlkIThXIBy/ae4bt/jnAmIY2NEbEAXN8kkLs7hdC1STUnR6cuV0pKBhMnrmDSpJXMmjWIvn2bcPfdYc4OS9kpUqIQkfJAsDFmv4PjUQqArGzDmoPRfLj0ACv2RwHWuw+D2gXxXJ9mVPLSpjRKgwULDjB69DwOHIjhrrta0b59bWeHpPJQaKIQkZuBt4HyQD0RCQNeNMbc6ujgVNmSmZXN2sNnWbDjFD+uP5rTp0OwvzdfjriKBoH69FJpMnbs70yZso5GjfxZtGgYN9xQ39khqXwU5Y7iFazmwZcAGGM2i0hDh0alypSoxDRe+33XBS21DmwXxF1X16V1UCWtoC4lsrKshw/c3Mpx9dVBBAR4M27cNdqAXwlXlE8nwxgTm+uLqu0xqUuWmpHF+3/t459DZ9lzMuG8llpHdA5hYLsgmtX00+RQymzceIJRo+YybFgrxo7twJ13tnJ2SKqIipIodonIYKCcrSXYR4A1jg1LlTapGVlsiohlzpZjzFx3lGzbpUagryddmgTSs3kNeraogYf27VDqJCSk8cILS3j//bUEBnpTs6Y2n+JqipIoHgJeALKBn7Bag33akUGp0iE72/DlqsP8uf0kaw+fPW/aHR2CmdC/hd41lHILFhxg5MhfOX48gVGjwnnttRuoXLmCs8NSF6koieImY8w4YNy5ESIyACtpKJWnXSfi6fXe8pxhT/dyDG0fzB0dgmlUzUcTRBlRvrwb1apVZPbswXToEOTscNQlksK6fxCRjcaYtrnGbTDGtHNoZPkIDw8369evd8amVRGcTUpn6Cdr2HMqIWfcnld74umuDbiVBRkZWbz99mri49OYMOEGwLqz1Gbcnc/2ux1+Kcvme0chIjdhdVNaW0Tetpvkh1UMpRQAB84kMn/7Sab/E8Gx2BQA3MoJ0+/rQIf6VZ0cnSouK1ZE5DTgN2hQs5wEoUnC9RVU9HQa2A6kAjvsxicATzkyKFXyZWZlsyUyjrHT/23OG6B2ZS8e7dGYge20mKGsiI5OZty4RXz++SaCgyvx229D6dOnsbPDUldQvonCGLMJ2CQi3xljUvObT5UtKelZfLHyEO8t2ke67Zl493LCtBHt6digKm569VjmREenMGPGdv7730688EIXKlbUBvxKm6JUZtcWkQlAMyDncQVjjF4ylBFpmVnc/P4KYpLSiU5Kzxk/JLwO93QOIbSGr1ZOlzG7dp3hhx928OKLXWncuCoREY/i7+/l7LCUgxQlUUwDXgXeBHoBI9A6ilIrNSOL79dG8Neu00TGJJNtIOJsMgAhVb3p27oWdfy9uf2qOlTU3uLKnOTkDCZMWMYbb6zCx6c8997blqAgP00SpVxRvunexpg/ReRNY8wB4DkRWV7oUsqlHI9N4Ykft7DhSAxpmdZ1QK1KFWhRuxKdG1alcXVfRnSu5+QolTPNn7+f0aPncehQLMCdypEAACAASURBVHff3Zo33uhBYGBFZ4elikFREkWaWOUKB0RkFHAM0LadS4nsbMNtH61ik635bh9Pd17o24ybmtcgwMfTydGpkiIxMZ1hw36malUvliy5m65dQ5wdkipGRUkUjwI+wMPABKASMNKRQanicSIuhY4TF+cMzx17DS1qV3JiRKokycrK5vvvtzN0aAt8fMqzaNEwQkMD8NQixzKn0E/cGPOP7c8EYBiAiOizjy4sOT2TMd9tZMmeMwD0bF6D/93RRttZUjk2bDjOAw/MZcOGE3h5uXPbbc20t7kyrMBEISJXAbWBFcaYKBFpjtWURzdAk4UL2XMygS9WHOJoTDKrDkTnjH+6VygPdGngxMhUSRIXl8rzzy9h6tR1VKtWkRkzbmPAgKbODks5WUFvZk8EbgO2YFVg/4zVcuwkYFTxhKcu1+GoJN5euJc5W47njAv09WRweBBP3NhEH2tV57ntth9YvPgQY8ZcxauvdqNSJW3ATxV8R9EPaG2MSRERf+C4bXhP8YSmLkd6ZjZvL9zLR38fAMDX0503B7fmpuZafKDOd/BgDIGB3vj6ejJhQjfKlROuukq7JFX/KihRpBpjUgCMMWdFZLcmiZIvPTObT5cf5I0///2oPh7WThOEukB6ehZvvrmK8eOX8fDD7Zk0qYe28KryVFCiqC8i55oSFyDEbhhjzACHRqYu2qaIGG79YFXO8LCr6/LwDY0I9NXHXNX5li07wqhRc9m1K4qBA5vx8MMdnB2SKsEKShS35Rqe4shA1OVZd/gsgz5aDcDQ9sG80KcZXuW1aW91oXfeWc1jjy0gJKQy8+bdQe/ejZwdkirhCmoU8K/iDERduv/9tY+3Fu4F4OVbmnN3pxDnBqRKnOxsQ1JSOr6+ntx8c2POnEnmueeuw9vbw9mhKRegb864sJ83RfLEj1vJsnVA/euYzrSuU9nJUamSZseO04waNS+np7nGjavy2ms3ODss5UIc+oaViPQUkT0isl9E8uzDQkQGi8hOEdkhItMdGU9pMn7uTh6duYWsbMPIzvXYP6GXJgl1nuTkDJ5+ehFhYR+za9cZ+vRpRGE9WiqVlyLfUYiIpzEm7SLmdwOmAj2ASGCdiMwxxuy0m6cR8DTQ2RgTIyLahlQhziSk8fRPW1m06zQAS5/oSkiANsymzrdp0wkGDPiBw4djGTEijMmTexAQ4O3ssJSLKjRRiEh74HOsNp6CRaQ1cJ8xZmwhi7YH9htjDtrWMwPr3YyddvP8HzDVGBMDYIw5ffG7UHYcPZvMtZOXABBaw5dPhoUTXFW//OpfxhhEhODgSgQHV+Krr/pz3XV1nR2WcnFFuaN4H+gD/AJgjNkiItcXYbnawFG74Ugg9zN4jQFEZCXgBrxkjJlfhHWXKakZWdz9xVr+OXQWgP/2bMLorg2dHJUqSTIzs5kyZS1z5uxh4cJhVK3qzd9/3+PssFQpUZREUc4YcyRXUw9ZRVgur7YhcheQugONgK5YbUctF5EWxpjY81Ykcj9wP0BwcHARNl16GGO449M1bIyIpUFgRZ7v04yuTbSETv1r7dpjjBo1l02bTtKrV0Pi49OoUkU7ElJXTlESxVFb8ZOx1TuMBfYWYblIoI7dcBBWMyC551ljjMkADonIHqzEsc5+JmPMJ8AnAOHh4WWqNu7xH7awMSKWW9vU5p0hYc4OR5UgiYnpjBu3kA8/XE/Nmr78+OMgbrutqbbfpa64oiSKB7GKn4KBU8Ai27jCrAMaiUg9rM6ObgfuyDXPL8BQYJqIBGAVRR0sWuilW1xyBq1fWZAzPHlgKydGo0oiD49yLF16hLFj2zN+fDf8/PQNfOUYRUkUmcaY2y92xcaYTBF5CPgTq/7hC2PMDhF5BVhvjJljm3ajiOzEKs560hgTnf9ay4bMrGy6vmlVWrcKqsR393XQviIUAPv3n+WVV/5m6tTe+Pp6smHD/VSooK9DKceSwp6rFpEDwB5gJvCTMSahOALLT3h4uFm/fr0zQ3CouOQMbpm6giPRyfRpVZMpd7R1dkiqBEhLy2Ty5JVMmLCc8uXdmDfvDq69Vp9mUkUnIhuMMeGXsmxRerhrICKdsIqOXhaRzcAMY8yMS9mgyltqRhb9p65k90krD9/TKYQX+zZzclSqJFiy5BAPPjiPPXuiGTKkOW+/fRO1avk6OyxVhhTpntUYswpYJSIvAe8C3wGaKK6QI9FJdHljac7we7eH0S9M+wNQ1lNvEyYsJyMjm/nz7+Smm/SxaFX8ivLCnQ/Wi3K3A02BX4FODo6rTMjMyua9v/bxv8X7AejVogYf3NlWn1op47KzDZ9/vpGePRtSp04lvvnmVipXroCXlzbgp5yjKHcU24HfgMnGmOUOjqfMyMzK5uqJi4lKtFpFeX9oG25pXcvJUSln27r1FKNGzWX16kheeOE6Xn75emrW1GIm5VxFSRT1jTHZDo+kDNl7KoEb31kGQMvalfhqZHv8K5Z3clTKmRIT03n55aW8884aqlTxYtq0fgwf3trZYSkFFJAoROQtY8zjwGwRueDRKO3h7uIZY3j5t51MW3UYAL8K7vw8uhPu+uhrmffSS0t5663V3HdfG15/vTtVtQ0vVYIUdEcx0/a/9mx3hQz/Yi3L90UBsOixLjSs5uPkiJQzHT0aR1JSBqGhATz11DX07x/KNdeUrSZqlGvI91LWGLPW9mdTY8xf9v+wKrXVRTgVn8ryfVGUE9jzak9NEmVYZmY2b7+9mqZNp/LAA3MBCAjw1iShSqyilHmMzGPcvVc6kNJs/+lE7v7Cyruf33MVnu7al3VZtWZNJOHhn/D44wvo2jWEr77q7+yQlCpUQXUUQ7Aeia0nIj/ZTfIFYvNeSuV2Ii6F7m//DUDDaj5cry2/llnz5u2lb9/vqVXLl59+Gkz//qH6KLRyCQXVUawForFafZ1qNz4B2OTIoEqTIR+vAeDR7o15pHsjJ0ejipsxhuPHE6hd24/u3evzyivX88gjHfD11Qb8lOvIN1EYYw4Bh7Bai1UXKS4lg9s/WUPE2WSCqnhpkiiD9u6NZvToeezdG83OnWPw8SnPc89d5+ywlLpoBRU9/W2M6SIiMZzf4ZAAxhjj7/DoXNSBM4nc8NbfOcOLHuvixGhUcUtNzeT111cwceIKvLzcmTjxBry8tIVX5boKOnvPdXcaUByBlBbpmdk5L9Pd3LImbw1uTQUPrbwuK06eTOS6675k376zDB3agrffvokaNfQJN+XaCip6Ovc2dh3guDEmXUSuAVoB3wLxxRCfy/lq1WGysg3dm1Zj6p3aRHhZkZGRhYeHG9WrV+S66+oydWpvevRo4OywlLoiivJ47C9Y3aA2AL7GeodiukOjcmHL91sv1H14VzsnR6KKQ3a24aOP1tOgwftERsYjInz22S2aJFSpUpREkW3r03oA8K4xZiygbWDnISvbsPZQNN2bVtMe6cqALVtO0qnT5zz44DwaNapKRkaWs0NSyiGK1BWqiAwChgHn3g7S9o7zMGn+blIzsrmxeQ1nh6IcyBjDk08u5N131+Dv78U339zKnXe21HciVKlVlEQxEhiN1cz4QRGpB3zv2LBcz4p9UXyy7CAAA9roDVdpJiLExKRw771WA35Vqng5OySlHKrQ8hFjzHbgYWC9iIQCR40xExwemYtZeygagM+Gh2trsKXQkSOx9O8/g40bTwDw6ae38PHHfTVJqDKh0F80EbkW2A98DnwB7BWRzo4OzJUkp2fy/uL91PH34oam2kRHaZKRkcXkyStp1uwDFi48yJ491sMK5cppMZMqO4pS9PQO0NsYsxNARJoC3wDhjgzMlfy25TgAd3aoq+XUpciqVUd54IG5bN9+mn79mvD++70IDq7k7LCUKnZFSRTlzyUJAGPMLhHR7tjsnOvzekTnEOcGoq6oRYsOEheXyi+/DKFfv1Bnh6OU0xQlUWwUkY+x7iIA7kQbBcyx6kAUkTEp+FVw1+bDXZwxhm++2UpgoDe9ejVi3LjOPPZYR3x89LpIlW1FqXUdBRwA/guMAw4CDzgyKFeRlW2449N/AHhnSJiTo1GXY/fuKLp1+5q77/6FL7/cDICnp7smCaUo5I5CRFoCDYCfjTGTiyck1xCXnMFVE6yGdYe2D+aGptWdHJG6FCkpGbz22nImTVpJxYrl+fjjPtx3nza9opS9fO8oROQZrOY77gQWikhePd2VSWeT0mn9ygLSs7JpH+LPa7e2cHZI6hL99tteXn11OUOGtGD37jHcf387faJJqVwKuqO4E2hljEkSkUDgd6zHY8u00wmptJ/wFwD3X1efZ3pr9+Gu5uTJRDZvPknPng0ZNKgZISH30b69viSpVH4KqqNIM8YkARhjzhQyb5kwf/vJnCRxZ4dgTRIuJisrmw8+WEeTJlMYNuxnUlIyEBFNEkoVoqA7ivp2fWUL0MC+72xjzACHRlaCJKVlMvGPXXy7JgKAV/u34K6r6zo5KnUxNm48wahRc1m37jjdu9fngw964+WlTZYpVRQFJYrbcg1PcWQgJVmXN5YQlZhOJS8P5o69hjr+3s4OSV2EQ4diaN/+UwICvJk+fQC3395CX4xU6iIU1HHRX8UZSEn14dIDRCWm0yCwIvP/c502H+4ijDFs23aaVq2qU69eFb78sh99+zahcuUKzg5NKZejv3oFGPPdRibN301lbw++va+DJgkXcehQDH36fE+bNh+zdespAIYNa61JQqlL5NBfPhHpKSJ7RGS/iDxVwHwDRcSISIlpP2rn8XjmbbNaCl38eFdqVtJWQku69PQsXn99Bc2bf8Dffx/mzTd70KxZoLPDUsrlFaUJDwBExNMYk3YR87sBU4EeQCSwTkTm2LcbZZvPF6sZ83+Kuu7i8OvmYwD88EBH/Cvq27klXVZWNp06fc6GDScYMKAp7757E3XqaAN+Sl0JRWlmvL2IbAP22YZbi8j/irDu9sB+Y8xBY0w6MAPol8d844HJQGrRw3a8TRGxAITXreLkSFRB4uOtaxc3t3KMHNmG334byuzZgzVJKHUFFaXo6X2gDxANYIzZAlxfhOVqA0fthiPJ1de2iLQB6hhj5ha0IhG5X0TWi8j6M2fOFGHTl+ftBXtYe/gst7UN0rd0SyhjDNOmbaZ+/ff49dfdAIwefRV9+jR2cmRKlT5FSRTljDFHco0rSi/yef3CmpyJIuWw+rp4vLAVGWM+McaEG2PCAwMdW+a8/Vgc79uaDX/2Zn2hriTaufMMXbt+xYgRvxIaGkCDBv7ODkmpUq0odRRHRaQ9YGz1DmOBvUVYLhKoYzccBBy3G/YFWgBLbc+01wDmiMgtxpj1RQn+SjPG8MA3GwD4cZTWTZREkyev5NlnF+Pn58lnn/VlxIg2etenlIMVJVE8iFX8FAycAhbZxhVmHdBIROoBx4DbgTvOTTTGxAEB54ZFZCnwhLOSBMCGIzEci03hgS71uSpEr1JLEmMMIkKNGj7ceWdL3nijB4GBFZ0dllJlQqGJwhhzGutH/qIYYzJF5CHgT8AN+MIYs0NEXgHWG2PmXHS0DvbqvF0A3HtNPSdHos45fjyBRx6Zz7XXBvPwwx0YPrw1w4e3dnZYSpUphSYKEfkUu7qFc4wx9xe2rDHmd6xWZ+3HvZDPvF0LW58jjfhyLZuPxlI/oCLVfPXFLGc714Dfs88uJiMjm06dgpwdklJlVlGKnhbZ/V0BuJXzn2ZyeafiU1my5wx1/L2Y+UBHZ4dT5m3efJL77pvDhg0nuPHGBnzwQW+tsFbKiYpS9DTTflhEvgEWOiwiJ/h69WEAnu3djEBfT6fGoiAuLpXjxxOYOXMggwY10wb8lHKyIr+ZbaceUGra2M7Myuab1dbTvz2aaXemzmCM4ccfd7JvXzTPPnsdXbqEcPDgI1SocCmnp1LqSivKm9kxInLW9i8W627iGceHVjxenbeL+NRMnu3dFDd9zLLYHThwlt69pzNkyCx+/XUPGRnWKzqaJJQqOQr8Nop1z98a6/FWgGxjzAUV267qvq/WsWjXaQB6tazh5GjKlrS0TN58cxWvvrocD49yvPdeT0aPvgp3d22hV6mSpsBEYYwxIvKzMaZdcQVUXP7eeyYnSfzxyLUEVdHOiIrT0aPxjB+/jL59m/DuuzdRu7afs0NSSuWjKJdva0WkrcMjKWbj51qN2C59oitNa+qPVHE4cyaJKVPWAtCwoT87d47hxx8HaZJQqoTL945CRNyNMZnANcD/icgBIAmrDSdjjHHZ5LEpIob9pxNpWtOPkAB9u9fRsrMNX365if/+dxEJCWn06FGfJk0CqF9fW+ZVyhUUVPS0FmgL9C+mWIrNxD+s1kbfGqRv+Dra9u2nefDBeaxYEcG11wbz0Ud9aNIkoPAFlVIlRkGJQgCMMQeKKZZic+B0IsH+3jSrpUUejpSensWNN35DenoWX3xxC/fcE6bvRCjlggpKFIEi8lh+E40xbzsgHof7a9cpopPS9Z0JB1q8+BBdutSlfHk3fvhhEKGhAQQE6MMCSrmqgiqz3QAfrObA8/rncjKysnlo+iZAG/5zhMjIeG677QduuOFrvv56CwDXXBOsSUIpF1fQHcUJY8wrxRZJMfh69RFSMrJ4qlcojaq7ZK4rkTIzs5kyZS3PP7+ErKxsJk68gTvvbOXssJRSV0ihdRSlRVpmVs4jsb1a6Mt1V9KwYT8zY8Z2evVqyNSpvalXT59mUqo0KShR3FBsURSDh7+3ipxGdA6hblV9JPZyxcam4u5eDh+f8owZcxW33daU225rqpXVSpVC+dZRGGPOFmcgjvTXrlP8ueMUzWv58fzNzZwdjkszxjBjxnaaNp3K888vBqx6iIEDtZVXpUqrMtGwzku/7QDg7cFh2r/yZdi//yw33fQtQ4fOJijIj7vu0noIpcqCUt9EZ3a24ejZFJpU96VJDa3AvlTTp29j5Mhf8fR0Z8qUXowaFY6bW5m4zlCqzCv1iWL1wWgABoVrV5qXIiMjCw8PN8LDazFwYDMmT+5BrVqacJUqS0p9onjZVuw0oK0miotx+nQSjz++gKSkdH76aQiNG1fl228HODsspZQTlOqyg90n49l7KpFrGwXgX7G8s8NxCdnZhk8+2UCTJlOYOXM7zZsHkpWV7eywlFJOVKrvKFbtt4qdHr6hkZMjcQ0HD8Zw110/sXp1JF27hvDhhzcTGqoN+ClV1pXqRHGufqJl7UpOjsQ1VKrkSWxsKl991Z9hw1rp465KKaCUFz0lpWXi6+lOBQ83Z4dSYs2Zs4cBA2aSlZVN1arebN8+muHDW2uSUErlKNWJYvuxODo31KKTvERExNG//wz69ZvB3r3RnDiRCKDvmSilLlBqi56ORCcRn5pJ6zqVnR1KiZKZmc27767hxReXYoxh0qTuPPro1XjoXZdSKh+lNlEs2xcFQMcGVZ0cScmSlZXNZ59tpFu3evzvf70ICdFEqpQqWKktenpv0V4CfDxppRXZxMSkMG7cQhIS0vD0dGflypHMmXO7JgmlVJGUykSxMSKGqMR0bm5Zo0yXuRtj+O67rYSGTuWtt1azZMlhAKpW9dbKaqVUkZXKoqftx+IAGBRex8mROM/evdGMHj2Pv/46RPv2tfnzz7sIC9N+OJRSF69UJoodx+LxdC9H05p+zg7Faf7zn/msX3+cDz7ozf33t9MG/JRSl6zUJYrk9Exmrj9Km+DKuJWxYqeFCw8QGhpAnTqV+PDDm/H0dKdGDR9nh6WUcnEOvcwUkZ4iskdE9ovIU3lMf0xEdorIVhH5S0TqXu42X5pjNQI4tH3w5a7KZZw8mcgdd8zmxhu/ZdKklQDUrVtZk4RS6opwWKIQETdgKtALaAYMFZHc3cttAsKNMa2AWcDky93ukj1nCPDxZFC70t9abHa24aOP1hMaOoXZs3fx4otdePPNG50dllKqlHFk0VN7YL8x5iCAiMwA+gE7z81gjFliN/8a4K7L3ah7OaF2Za8y8VTPxInLee65JXTrVo8PPuhNkyb6FrpS6spzZKKoDRy1G44EOhQw/73AH3lNEJH7gfsBgoPzL1JKy8ziRFwqt7apfdHBuoqEhDSiopKpV68Ko0aFU69eFYYObVEmEqNSyjkcWUeR1y+XyXNGkbuAcOCNvKYbYz4xxoQbY8IDAwPz3eDZpHQAqvl6XnSwJZ0xhp9/3kWzZh8wZMgsjDFUrerNHXe01CShlHIoRyaKSMD+RYYg4HjumUSkO/AscIsxJu1yNniuIrt+YOmqxD1yJJZbbpnBgAE/4O/vxfvv99LkoJQqNo4seloHNBKResAx4HbgDvsZRKQN8DHQ0xhz+nI3uPbQWQCua5z/XYerWb36KN27fwPAm2/24JFHrsbdXd+JUEoVH4clCmNMpog8BPwJuAFfGGN2iMgrwHpjzBysoiYf4EfbFXKEMeaWS9newTOJxCRnMLzjZT9hWyLEx6fh5+dJ27Y1GTkyjCef7ExwsLZbpZQqfg594c4Y8zvwe65xL9j93f1KbetwdBIA1zZy7buJ6OhknnpqEQsWHGTHjtH4+JTnf//r7eywlFJlWKl4M9sYw8hp6/FwE9rVreLscC6JMYZvvtnK448vICYmhcce64hWQyilSoJSkSjeWbgXgK5NquFfsbyTo7l4cXGp9O8/k6VLD9OxYxAffdSHVq2qOzsspZQCSkGiMMbw/uL9AEwc0NLJ0VwcYwwigp+fJwEB3nzySR/uvbdtmW4aXSlV8rj84zPxqZkADGwXRICP67w/8eef+2nb9hMiI+MREX78cRD/93/tNEkopUocl08UO4/HA9Am2DV6aztxIoHbb59Fz57fkZycwenTSc4OSSmlCuTyRU8z10UA0LF+ye8be+rUtTzzzGLS0jJ5+eWujBvXGU9Pl/8IlFKlnMv/Sv216zQebuISb2Nv2HCCDh1qM3Vqbxo1KvmJTSmlwMUTxeGoJBLSMukfVsvZoeQpPj6NF15YwrBhrWjXrhYffHAznp5u2vyGUsqluHSiOJ1gNQ3Vt3XJShTGGGbP3sUjj8znxIkEgoMr0a5dLSpUcOnDrZQqo1z6l2vbsTgAalbycnIk/zp0KIaHHvqD33/fR1hYDX76aTAdOpT+TpSUUqWXayeKyFi8PNxoUsPX2aHk+O67bSxbdoR33rmJhx5qrw34KaVcnksnio0RsYTW9MXNye8eLF9+hLS0LLp3r8+TT3binnvCCAryc2pMSil1pbjs5W5aZhYRZ5NpVtN5P8hRUcmMHPkr1103jVde+RsAT093TRJKqVLFZe8ovlx5GICqTmjbyRjDtGmbefLJhcTFpTFuXGeef/66Yo9DlXwZGRlERkaSmprq7FBUGVGhQgWCgoLw8PC4Yut02USxOSIWgDHdGhb7tn//fR8jR86hc+c6fPRRH1q0qFbsMSjXEBkZia+vLyEhIfpYtHI4YwzR0dFERkZSr169K7Zely16WrrX6hDP092tWLaXnJzBypXWW+C9ezfi119vZ9myEZokVIFSU1OpWrWqJglVLESEqlWrXvE7WJdNFAE+nvgWU/MXf/yxjxYtPqBXr++IjU1FRLjllibagJ8qEk0Sqjg54nxz2USRmpFNHwe/aHfsWDyDBv1I797T8fR057ffhlK5cgWHblMppUoal0wUKelZRCWm4V3eccVOp08n0azZB8ydu5dXX72eLVtG0aVLiMO2p5SjuLm5ERYWRosWLejbty+xsbE503bs2EG3bt1o3LgxjRo1Yvz48Rhjcqb/8ccfhIeH07RpU0JDQ3niiSecsQsF2rRpE/fdd9954/r160fHjh3PG3fPPfcwa9as88b5+PzbRtzevXvp3bs3DRs2pGnTpgwePJhTp05dVmxnz56lR48eNGrUiB49ehATE3PBPEuWLCEsLCznX4UKFfjll19yYq5Xr17OtM2bNwMwd+5cXnzxxcuK7aIYY1zqX7t27czrf+wydcfNNd+tOWKutMjIuJy/33tvjdm/P/qKb0OVHTt37nR2CKZixYo5fw8fPty8+uqrxhhjkpOTTf369c2ff/5pjDEmKSnJ9OzZ00yZMsUYY8y2bdtM/fr1za5du4wxxmRkZJipU6de0dgyMjIuex0DBw40mzdvzhmOiYkxQUFBJjQ01Bw8eDBn/N13321+/PHH85Y9d2xSUlJMw4YNzZw5c3KmLV682Gzbtu2yYnvyySfNxIkTjTHGTJw40fz3v/8tcP7o6GhTpUoVk5SUlG/MxhiTnZ1twsLCcubLLa/zDlhvLvF31yWfekpJzwLglivYGGBcXCrPPbeYjz/ewJo199G2bU0efrjDFVu/Ui//tiOn/5QrpVktP17s27zI83fs2JGtW7cCMH36dDp37syNN94IgLe3N1OmTKFr166MGTOGyZMn8+yzzxIaGgqAu7s7o0ePvmCdiYmJjB07lvXr1yMivPjii9x22234+PiQmJgIwKxZs5g7dy7Tpk3jnnvuwd/fn02bNhEWFsbPP//M5s2bqVzZ6lOmYcOGrFy5knLlyjFq1CgiIqyHSN599106d+583rYTEhLYunUrrVu3zhk3e/Zs+vbtS/Xq1ZkxYwZPP/10ocdl+vTpdOzYkb59++aMu/7664t8XPPz66+/snTpUgDuvvtuunbtyqRJk/Kdf9asWfTq1Qtvb+8C1ysidO3alblz5zJ48ODLjrMwLln0tCUyFv+K5fG5ApXZxhh++GEHTZtOZerUdYwaFU6DBlWuQJRKlSxZWVn89ddf3HLLLYBV7NSuXbvz5mnQoAGJiYnEx8ezffv2C6bnZfz48VSqVIlt27axdetWunXrVugye/fuZdGiRbzzzjv069ePn3/+GYB//vmHkJAQqlevziOPPMKjjz7KunXrmD179gXFSwDr16+nRYsW5437/vvvGTp0KEOHDuX7778vNBagyPuakJBw0eG+TQAADpdJREFUXjGR/b+dO3deMP+pU6eoWbMmADVr1uT06dMFrn/GjBkMHTr0vHHPPvssrVq14tFHHyUtLS1nfHh4OMuXLy/K7l02l7yj8PF0x8Pt8mv2jTEMGPADv/yym7ZtazJnzlDCw0tWS7Sq9LiYK/8rKSUlhbCwMA4fPky7du3o0aMH8G+f7Xm5mCdnFi1axIwZM3KGq1Qp/EJr0KBBuLlZdYxDhgzhlVdeYcSIEcyYMYMhQ4bkrNf+xzc+Pp6EhAR8ff9t2+3EiRMEBgbmDJ86dYr9+/dzzTXXICK4u7uzfft2WrRokec+XewTQr6+vjn1BFfaiRMn2LZtGzfddFPOuIkTJ1KjRg3S09O5//77mTRpEi+88AIA1apV4/jx4w6JJTeXvKOITkynfsCld1SUkWEVXYkI11xTh/ff78natfdpklClkpeXF5s3b+bIkSOkp6czdepUAJo3b8769evPm/fgwYP4+Pjg6+tL8+bN2bBhQ6Hrzy/h2I/L/Vx/xYoVc/7u2LEj+/fv58yZM/zyyy8MGDAAgOzsbFavXs3mzZvZvHkzx44dOy9JnNs3+3XPnDmTmJgY6tWrx/+3d//BVdVnHsffn0UgwQJdw8LYIoSOFBKTgAhtagdRaRmrFErHESFQcew6gkgtisMOzKy7WktbQYvoImudpGuLqFOBAYVFGktFfghbBQWbUnonsMMoIkstld/P/nFObi7h5uYk5t7k3jyvmczce35+88zNeXK+33ueb2FhIbFYLJ7ECgoKzhtM/vjjj+nVq1c8FlF+1+beUfTp04dDhw4BQSLo3bvx565eeOEFJkyYcN4T1ZdeeimS6Nq1K7fffjvbt2+Prztx4gT5+ZmpnJ2ViWLPob+2uBDg66/HKCtbyqpV7wNw331Xc889X6VTp6wMhXOR9ezZk8WLF/Poo49y+vRpKioqeOONN3jttdeA4M5j1qxZPPDAAwDMmTOHRx55hJqaGiC4cC9atOiC444ZM4YlS5bE39ddjPv06cPevXs5d+5cvGspGUlMmDCB2bNnU1RUREFBQdLjJvtPvqioiH379sXfL1++nHXr1hGLxYjFYuzcuTOeKK699lpWrFjBqVOnAKisrIyPQ0yePJk333yTtWvXxo+1bt06du/efd756u4okv0UFxdf0L5x48ZRVVUFQFVVFePHj280DnVdZonqkoyZsXLlyvO62Wpqai7odkuXrLs6Hj95BoDePbo2a7/Dh49z220rue66Kk6ePEP37s3b37lccOWVVzJkyBCef/558vPzWbVqFQ8//DCDBg2itLSUESNGMHPmTADKysp4/PHHmTRpEkVFRZSUlMQvXInmz5/P0aNHKSkpYciQIVRXVwOwYMECxo4dy/XXXx/vp2/MxIkTee655+LdTgCLFy9mx44dlJWVUVxczNKlSy/Yb/DgwRw7doxPPvmEWCxGbW0t5eXl8fUDBgygR48ebNu2jbFjxzJy5Eiuuuoqhg4dyubNm+MDy/n5+axZs4YnnniCgQMHUlxcTGVlZco7gCjmzp3Lhg0bGDhwIBs2bGDu3LlAMLaSOOYSi8U4cOAAo0aNOm//iooKSktLKS0t5aOPPmL+/PnxddXV1dx0002fqX1RyRK+M50NCgeXGt9ZwG/vGxV5nuzly3dz992v8Le/nWLOnKuZN+8aunVrvYJZzjVm7969FBUVtXUzctpjjz1G9+7dkw5256oPPviAyZMns3HjxqTrk33uJO00s+EtOV/W3VGcOnOO7l0vYkCvi5veOHTmzDlKSnrz9tt38aMfjfYk4VwOmT59Ol27dqwegtraWhYuXJix82Xdt55OnTlHv4JuKb+tcPz4KR56aBP9+vVkxowRTJlSxpQpZV5zx7kclJeXx9SpU9u6GRk1YsSIjJ4v++4ozp6j3yWNP4yyZk0NV1zxFD/5yWZqao4AwWCZJwnXVrKte9dlt3R83rLzjiJJojh48K/MmvUqL7/8PsXF/8SmTdMYObJ/G7TQuXp5eXkcOXLES427jLBwPoq8vNYtXpp1icKAy5Ikiv37j7J+/Z/58Y9HM3v21+iSxoKBzkXVt29fDh48yOHDh9u6Ka6DqJvhrjVlXaIA6F8QJIrt2/+XLVsO8IMflHPNNf2prb2XgoLUNVKcy6TOnTu36kxjzrWFtI5RSLpB0h8l7ZM0N8n6rpJWhOu3SSqMctzPX9SJGTPWUl7+DIsWbeX48eABGk8SzjnX+tKWKCR1Ap4EvgUUA5MkNXx08Q7gqJldDjwGNF5WMcE3r36Wp5/eyaxZX2X37ulcfHGX1my6c865BOnsevoKsM/M9gNIeh4YDyQWRBkPPBi+fglYIkmWYtjezhqX9e/JK2srGDYs9dOezjnnPrt0JoovAgcS3h8EGk7wEN/GzM5IOgYUAB8lbiTpTuDO8O3JHYfvfDdCReCOoBcNYtWBeSzqeSzqeSzqDWrpjulMFMm+C9jwTiHKNpjZMmAZgKQdLX0MPdd4LOp5LOp5LOp5LOpJ2tH0VsmlczD7IHBZwvu+QMPi6fFtJF0E9AQ+TmObnHPONVM6E8VbwEBJAyR1AW4FVjfYZjVwW/j6ZuC3qcYnnHPOZV7aup7CMYeZwHqgE/Csmb0n6d8JJvleDfwC+C9J+wjuJG6NcOhl6WpzFvJY1PNY1PNY1PNY1GtxLLKuzLhzzrnMyrqigM455zLLE4VzzrmU2m2iSFf5j2wUIRazJe2RtEvSRkk5Wza3qVgkbHezJJOUs1+NjBILSbeEn433JP06023MlAh/I/0kVUv6Q/h3cmNbtDPdJD0r6UNJ7zayXpIWh3HaJWlYpAObWbv7IRj8/jPwJaAL8A5Q3GCbGcDS8PWtwIq2bncbxuI6oFv4enpHjkW4XXdgE7AVGN7W7W7Dz8VA4A/AP4bve7d1u9swFsuA6eHrYiDW1u1OUyyuAYYB7zay/kbgVYJn2MqBbVGO217vKOLlP8zsFFBX/iPReKAqfP0SMFq5WfC/yViYWbWZ/T18u5XgmZVcFOVzAfAQ8FPgRCYbl2FRYvHPwJNmdhTAzD7McBszJUosDOgRvu7Jhc905QQz20TqZ9HGA7+0wFbg85KarIXUXhNFsvIfX2xsGzM7A9SV/8g1UWKR6A6C/xhyUZOxkHQlcJmZrclkw9pAlM/Fl4EvS9osaaukGzLWusyKEosHgSmSDgKvAPdkpmntTnOvJ0D7nY+i1cp/5IDIv6ekKcBwYFRaW9R2UsZC0j8QVCGelqkGtaEon4uLCLqfriW4y/y9pBIz+780ty3TosRiElBpZgslfY3g+a0SMzuX/ua1Ky26brbXOwov/1EvSiyQ9A1gHjDOzE5mqG2Z1lQsugMlwOuSYgR9sKtzdEA76t/IKjM7bWZ/Af5IkDhyTZRY3AG8AGBmW4A8goKBHU2k60lD7TVRePmPek3GIuxueZogSeRqPzQ0EQszO2Zmvcys0MwKCcZrxplZi4uhtWNR/kZWEnzRAUm9CLqi9me0lZkRJRa1wGgASUUEiaIjzk+7Gvhe+O2ncuCYmR1qaqd22fVk6Sv/kXUixuJnwOeAF8Px/FozG9dmjU6TiLHoECLGYj0wRtIe4Cwwx8yOtF2r0yNiLO4D/lPSDwm6Wqbl4j+WkpYTdDX2Csdj/hXoDGBmSwnGZ24E9gF/B26PdNwcjJVzzrlW1F67npxzzrUTniicc86l5InCOedcSp4onHPOpeSJwjnnXEqeKFy7I+mspLcTfgpTbFvYWKXMZp7z9bD66DthyYtBLTjGXZK+F76eJukLCeuekVTcyu18S9LQCPvcK6nbZz2367g8Ubj26FMzG5rwE8vQeSvMbAhBscmfNXdnM1tqZr8M304DvpCw7vtmtqdVWlnfzqeI1s57AU8UrsU8UbisEN45/F7S/4Q/VyfZ5gpJ28O7kF2SBobLpyQsf1pSpyZOtwm4PNx3dDiHwe6w1n/XcPkC1c8B8mi47EFJ90u6maDm1q/Cc+aHdwLDJU2X9NOENk+T9EQL27mFhIJukv5D0g4Fc0/8W7hsFkHCqpZUHS4bI2lLGMcXJX2uifO4Ds4ThWuP8hO6nV4Ol30IfNPMhgETgcVJ9rsL+LmZDSW4UB8MyzVMBL4eLj8LVDRx/m8DuyXlAZXARDMrJahkMF3SJcAE4AozKwMeTtzZzF4CdhD85z/UzD5NWP0S8N2E9xOBFS1s5w0EZTrqzDOz4UAZMEpSmZktJqjlc52ZXReW8pgPfCOM5Q5gdhPncR1cuyzh4Tq8T8OLZaLOwJKwT/4sQd2ihrYA8yT1BX5jZn+SNBq4CngrLG+ST5B0kvmVpE+BGEEZ6kHAX8ysJlxfBdwNLCGY6+IZSWuByCXNzeywpP1hnZ0/hefYHB63Oe28mKBcReIMZbdIupPg7/pSggl6djXYtzxcvjk8TxeCuDnXKE8ULlv8EPgAGEJwJ3zBpERm9mtJ24CbgPWSvk9QVrnKzP4lwjkqEgsISko6v0lYW+grBEXmbgVmAtc343dZAdwCvA+8bGam4KoduZ0Es7gtAJ4EvitpAHA/MMLMjkqqJCh815CADWY2qRntdR2cdz25bNETOBTOHzCV4L/p80j6ErA/7G5ZTdAFsxG4WVLvcJtLFH1O8feBQkmXh++nAr8L+/R7mtkrBAPFyb559AlB2fNkfgN8h2COhBXhsma108xOE3QhlYfdVj2A48AxSX2AbzXSlq3A1+t+J0ndJCW7O3MuzhOFyxZPAbdJ2krQ7XQ8yTYTgXclvQ0MJpjycQ/BBfW/Je0CNhB0yzTJzE4QVNd8UdJu4BywlOCiuyY83u8I7nYaqgSW1g1mNzjuUWAP0N/MtofLmt3OcOxjIXC/mb1DMD/2e8CzBN1ZdZYBr0qqNrPDBN/IWh6eZytBrJxrlFePdc45l5LfUTjnnEvJE4VzzrmUPFE455xLyROFc865lDxROOecS8kThXPOuZQ8UTjnnEvp/wEPUrgXoUnBDAAAAABJRU5ErkJggg==\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": [ + "We will now try searching for the smoothing parameter to achieve a better ROC and recall on default." + ] + }, + { + "cell_type": "code", + "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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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
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" + ], + "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=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": 109, + "metadata": {}, + "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=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": 110, + "metadata": {}, + "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=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": 111, + "metadata": {}, + "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=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": 112, + "metadata": {}, + "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=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": 113, + "metadata": {}, + "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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LQqCTiFxn55i6IjJARLrav/c1IhJq/w4ZWPVVsZFYl67zylpApZO8XWl8gHX3Nlg78F7gZxHJwCoQne1x12HdUPAS1tnbSk6c1UzBOgrZgdUk9B+gZRmL/gTryPRjt1iKsAp8H6w765OxkkZoJVbpLqyjr/1YZwgfYyXAYmuBjva8nwYm2mdUVVmHv2HdWJGOVTj/6/H9LOBxEUkTkQcqsQ4YY7bb6zIP6+g2E+uGktIKwANYN7ytx7qr8x9UrDw8gHWmkIm1g8wvZ/wlWNfUdmM1C+ZycnPVi1gHWkuxCvG7WE2KYCWI9+3tcZUxZgPWPRn/xNree7GueVXUGGC7iGRh3fF/tTEm124KfRr40V7WYPeJ7Cbg0Vhl7SjW2c25FVzmHKymt1VYZTQX63eqEjvBPYt1407xsC+wfr959j64DetaeLGbsZLOMawbqcp7iUZ55bQyPsEqj+7K2m++A7YDR0Uk2W2alcAxY8wht88C/OI2ziSss9Z44Aus65rfViRIY8xMrDOlZXairaxSt5kxJh+YgFVWU7Guw7pv05exynwy8DOw2GPe12Dd63QM636H+ZS+X4NVP8dgHdztsOfp7k6sZuejWC2z75Wzbm9hXR6piuIXQh0TkU3235WqN8sq38aYZKzLEn/H2j4dse7Or5Cy6hRjzA6sJ5B+wkroPT3mXVJZfQnrXqgE4H3KOci065YLgKuxyu1Re12LT7iuAw7a6z0N60C32DVYB0BlKr5jXZVArJeNTDXGDHM6lsqyzxrTsJrVDzgdj1Lq9BCR+cBOY8yT5Y58+pb5A3CXOfFCHOUgEekJvG2MGVLeuDXytbaqakTkEruZqAHWfRO/Yt3pqpTyUnbzbXu7aXsM1vX2L6szBmPMME3wNYcx5teKJHjQJO9rxmM1+cRjNVtdbbSpRilv1wLriYIsrDv4b9OEqypKm+uVUkopH6Vn8koppZSP8slOGpzWtGlTEx0d7XQYSinlVTZu3JhsjGnmdBy+RJP8GRAdHc2GDRucDkMppbyKiJT19j1VBdpcr5RSSvkoTfJKKaWUj9Ikr5RSSvkoTfJKKaWUj9Ikr5RSSvkoTfJKKaWUj6r1SV5E5ohIoohsK+V7EZFXRWSviGwVkX7VHaNSSilVFbU+yWN1tTimjO8vwnoPfEfgFuCNaohJKaVqlSKXvmL9TKj1L8MxxqwSkegyRhkPfGB39PKziDQSkZbGGM8+spVSStkKi1wcSskhISOPzbFppOXks/ZACkEBdSgocpFfZMjNL2JXQiZ+Aprjz4xan+QrIBKIdfscZw87KcmLyC1YZ/pERUVVW3BKKVWdjDHEpR5nQ0wK2w5nkJCRy7GsfLLyCikoclFQ5KLQZYg5lvOHacMaBFBQ6KJHZCj1A/wIC6pLTnIOu7clYTLzHVgb36dJvnxSwrA/HHMaY94G3gbo37+/HpMqpbxaXmER6w+kciglh/i04/x2JINDKTnEpuaQW+ACILCuHy1D69OkQQBNgwOoW8fP/icMiA4juJ4/F3QLp3lIPVo1DiKwbp2TlvHCC2uY+8xarrqqOy+9cRmRkVOdWFWfpkm+fHFAa7fPrbD6a1dKKZ9QWORiT2IWW+PS2BqXzta4dHYezaCg6MT5SrtmDejQLJhzOjWjTZMgerVqRI/IUOr4lXQeVLqkpGzi4jLo27clt98+gF69whk9uv3pXiVl0yRfvgXAnSIyDxgEpOv1eKWUt8ovdLE3MYvdCZlssZP69vj038/OG9bzp2erUG4a1o62TYPo0iKEri1DCPA/tfu0XS7DnDm/8OCD39K8eQN27LiD+vXraoI/w2p9kheRT4CRQFMRiQOeBOoCGGPeBBYBY4G9QA5wgzORKqVUxWXnFZKdV8iR9Fx+PWwl8m2HM9h1NJP8ohPN7T0iQpk8sA29WoXSq1Uo0U0a4FfJs/Py/PprAtOmfcOaNbGcc04b3nhj3GlfhipZrU/yxphJ5XxvgDuqKRyllKqSIpfhWJZ1J/vy3xL54pfDvydzgEZBdekREcoNw6LpHhFKx+bBdGwejH+dM/sk9fr1hzn77Dk0ahTI3LnjmTKlNyKa4KtLrU/ySinlLXILikjIyOVoei5HM3LZHp/BhoMpHE3PJTEzj0K359AmDYyie0QITYMD6BEZSmSj+tWaXOPiMmjVKoSzzopg5sxzufnmfjRpElRty1cWTfJKKVWD5Be62J2QyY74DGJSsjmcepyM3EJSsvP59XD6SS+NqVtH6BvVmMHtm9AyNJAWIYGEhwTSLSKEVo2dSaixsencffdivv/+ALt23Ul4eDAPPzzMkViUJnmllHLU7oRMNh9KY+vhNH6NS+e3IyeumYtAZKP6NAqqS0hgXSYPjKJXq1BahtanRWg9IhrVJyigZlTjhYUuXn11LU888T0ul+HJJ8+hceP6TodV69WM0qGUUj7ueH4Rsak57EvMYl9SFvuSslmxK5HUnALAuqu9R2QoNwyNpmerUHpEhNKyUSD1/OuUM2fnZWfnM3ToHLZsSWDcuI78859jiY5u5HRYCk3ySil1xsSm5PDZxjiWbDvKroTMk76LCA2kR2QorcOCuH5INB2bB3vdHecFBUXUrVuHBg0CGDWqLU88cQ6XX95Fb6yrQTTJK6XUaVJY5GJzbBrf7Uzku52J7DxqJfa+UY24f3QnopoE0b5ZMG2bNqBBPe+tfo0xfPLJNh56aBmLFk2mZ89wXnjhQqfDUiXw3lKmlFIOMcbYz55nEHMsh1j7da877evp/n5C/+jGPDq2CyM7N6dTeEOnQz5t9uw5xu23L2LZsv0MGBDhda0PtY0meaWUqoD4tOOsO5DCmn3JfL7p8O93udetI7RqHESrxvWZ0C+SYR2bMqJTM0IC6zoc8ek3a9ZqZsxYSWCgP7Nnj+XWW8+izhl+zl6dGk3ySinlxuUyHErJ4ZfYVOLTctmflM3aA8eISz0OQMNAf/q3aUzfqMZMGtiaVo2DKv3+dm+VlZXPhAldefHFC2jZ0ndaJ3yZJnmlVK11OO04O49ksDshiz0JmexOzGRvYtbv73EHq3vUAdGNuXFoWwa1C6NLi5Bak9QTErK4//6lXHNNTy66qCMzZ56nzfNeRpO8UqpWKO4Hff3BFNYfTGHtgRT2J2X//n2LkEA6hgdzzaA2dAoPplN4Q7q2DPlD96i1gctleOedjTz88HKys/MZPLgVgCZ4L6RJXinlszJyC1iz9xjf70xk/obY34eHBPozIDqMP/VvTf/oxnRo3pDQ+r53Db0qtm5N4NZbF/Lzz3Gce240r78+ji5dmjodlqoiTfJKKZ9RWORiS1w6q/cksXpPMptj0yhyGRoE1GFg2zAGtQ1jbM+WdA5vqGelpVi7No59+1L48MPLueaanvrMu5cTq5M1dTr179/fbNiwwekwlKoVEjNyWfZbIqv3JPHj3mQycgsRgV6tGjGiY1OGd2xG36hG1NW7wEv11Vc7yc4uYPLknrhchoyMPBo1Cqz2OERkozGmf7Uv2IfpmbxSyislZeaxcGs8Ly7dTWZeIRGhgVzUoyXDOzVlaPumNG4Q4HSINV5MTBp3372YBQt2MWJEGyZN6oGfnziS4NWZoUleKeU1MnILWLLtKAu2xPPj3mRcBnpEhvD3Cb3oHhGiTcsVVFBQxMsv/8yMGSsBeO650dxzzyDdfj5Ik7xSqsbbHp/Oa8v38t2uRPILXbQOq89tI9tzae9IOrfQ57Ur66ef4njwwWVcemlnXnvtIqKiQp0OSZ0hmuSVUjWSMYaf9h3j802H+XxTHAA3DI3m0t4R9GndSM86Kykl5TgrVhxkwoSujBjRhnXrpjJgQKTTYakzTJO8UqrGiU87zuNfbuO7nYk0rOfPJb0juHl4W3q10u5LK8sYw0cfbeX++5eSmZnPoUPTadasgSb4WkKTvFKqRsgvdLF6TxJfb4lnyfYEAB4f15VrB7eplS+kOR127Urmttu+4fvvDzJ4cCvefHMczZo1cDosVY00ySulHJOWk88vh9JYuuMo/9t2lLScAkLr1+WyvhHcdk4HopoEOR2i10pNPU7//u/g7+/Hm2+O4+abz9J3A9RCmuSVUtXmeH4Ry35LYNXuJNYeSOFQSs7v343vE8GlvSMY3rEZAf76THtVbdlylN69W9C4cX3ee288w4dHER4e7HRYyiGa5JVSZ1R+oYtVu5NYsCWeZb8lkJNfRKOgurRp0oBJA6NoGhzAmB4taOiDXbNWpyNHMrnvvqXMm7eNZcuuY9Sodkyc2M3psJTDNMkrpU67Ipd1Z/zXW+L537YjZOQW0jioLpf1jeSSXhEMbBtWa3pyO9OKily89dZGHnlkObm5hcyYcQ5Dh0Y5HZaqITTJK6VOC2MMmw6l8vWWIyzceoTkrDwaBNThwu4tuKRPBMM6NNVXy54BF1/8CYsX72XUqLa8/vo4OnVq4nRIqgbRJK+UqjJjDDuOZLBgSzwLtxzhcNpxAvz9GNWlOZf2juDcLs31zvgzICsrn6Cguvj5Cddd14vrruvFpEk99N0B6g80ySulKm1/UhYLtsTz9ZZ49iVl4+8nDO/YlPsv6MTobuF6ff0MMcbwxRc7ufvu//HXv47g1lv7M3lyT6fDUjWYJnmlVIUcTjvOwi3xLNgSz/b4DERgUNswbhzWlot6tCRMO4Q5ow4eTOPOOxfxzTd76N07nD59WjgdkvICmuSVUqX67UgGa/cfY+HWI2yISQWgd+tGPD6uKxf3iqBFqPZWVh3mzPmFO+9chJ+f8OKLF3DXXYPw18cMVQVokldKAdYz7LsSMtmTkMn2+Aw+XnuI/CIXAJ3DG/KXCztzca+WtGmib0yrLsYYRITWrUO48MIOvPrqGFq31s5kVMVpkleqFjPG8Mm6WJ5fuouU7Pzfh9fz96Nnq1A6hQdz7eA2dI/QxFKdjh3L4cEHvyU8PJhnnhnF6NHtGT26vdNhKS+kSV6pWqigyMXy3xL56OcYftibTJsmQVw9oDVhDQI4t0tzops00OfYHWCM4f33t/DAA0tJT8/joYeGOh2S8nK1PsmLyBjgFaAO8C9jzN89vo8C3gca2eM8bIxZVO2BKnUaxKXmMH99LPPXx5KYmUeLkEAeG9uVG4e11aTusD17jjF16tesWhXD2We35s03x9GzZ7jTYSkvV6uTvIjUAWYDo4E4YL2ILDDG7HAb7XHgU2PMGyLSDVgERFd7sEpVUV5hEat2J/Px2hhW7E4CYGSnZjw9qA3ndm6Gv76gpkbIzy9i165k3nnnEm68sa92JqNOi1qd5IGBwF5jzH4AEZkHjAfck7wBQuy/Q4H4ao1QqSrIyC1gT0Imc348yHe/JXK8oIjmDetx57kd+NOA1rRqrL271QSLF+9l+fL9PPfcBXTv3pyYmOnUq1fbq2V1OtX20hQJxLp9jgMGeYwzA1gqIncBDYDzS5qRiNwC3AIQFaXvjVbVL+ZYNp9uiOXLX+I5nHYcgAYBdZjQL5LzujRnRKdm+lrZGiI+PpN7713Cp59up3PnJjz++AhCQwM1wavTrraXqJLaw4zH50nAXGPMCyIyBPhQRHoYY1wnTWTM28DbAP379/ech1JnRG5BEUu2H2X++ljW7DuGn8DIzs25bkgbopsEMahtExrrS2pqjKIiF6+/vp7HHvuO/PwiZs48l7/85WxN7uqMqe0lKw5o7fa5FX9sjr8JGANgjPlJRAKBpkBitUSoVAl+O5LB/PWxfPHLYdKPF9A6rD4PXNCJiWe11hfU1GApKcd54okVDBnSmtmzx9KhQ5jTISkfV9uT/Hqgo4i0BQ4DVwOTPcY5BIwC5opIVyAQSKrWKFWtV+Qy7DqayU/7j/HV5sNsjUsnoI4fF/ZowdUDWjOkXRO9UauGSk/P5Z13NnHffUNo1qwBmzbdQnR0I+1MRlWLWp3kjTGFInInsATr8bg5xpjtIvIUsMEYswC4H3hHRO7Fasr/szFGm+NVtSgocvHN1iO89t0e9iVlA9A0uB5PXNyNy/tGalN8DWaM4T//2cE99yzm6NEsBg9uxbBhUbRt29jp0FQtUquTPID9zPsij2FPuP29A9A3UqhqlZqdz8frDvHBTwdJyMijc3hDnt/NQwoAACAASURBVJvYizZNGtC7dSj1/LX71pps//5U7rhjEYsX76Vv3xZ89dXVDBgQ6XRYqhaq9UleqZoiNTufn/cf4+3V+/nlUBoA7Zo24N3re3Ju5+baHO8ljDGMHz+PmJg0XnllDLffPkA7k1GO0SSvlMOO5xfx0c8xvLJ8D1l5hUSFBfGXCzszqG0YvVs30sfevMQPPxyiX7+WBAXVZe7c8bRoEUxkZEj5Eyp1BmmSV8ohm2PTWLztKP/ZGEdyVh5DOzTh2kFtOL9buCZ2L5KUlM2DDy5j7tzNPP30eTz66HDOOivC6bCUAjTJK1XtNsem8cLSXazekwzAsA5Nuef8fgyI1sepvInLZXjvvV948MFlZGTk8cgjw5g+fbDTYSl1Ek3ySlWD9OMFLNwaz/NLdpGaU0BYgwDuG92J8X0itH92L3XffUt45ZW1DBsWxZtvjqN79+ZOh6TUH2iSV+oMScnO598/x7A+JpWf9iVTUGQICfRn0sDWPDauG8H6ljOvk5NTQG5uIWFh9bn55n707h3O9df30ZsiVY2ltYxSp9mxrDyeW7KLr7fEk51fROOgulw/JJqLe0fQu1WovgTFSy1atIc77ljE4MGt+OSTK+jevbmevasaz2eSvIgEAFHGmL1Ox6JqH2MMB4/lsHJXIm+u3M/RjFzG9WzJTcPb0iMilAB9hMprxcVlMH36Yj7//De6dm3Kbbf1dzokpSrMJ5K8iIwDXgQCgLYi0gd40hhzubORKV92OO04m2JSycor5J1V+9mfbL2RrmvLEN6Z0p+erUIdjlCdqiVL9jJx4mcUFrp45pnzuP/+swkI0BcRKe/hE0keeAqri9jvAYwxm0Wkg7MhKV+zMSaVuNQckjLzWLPvGN/tPNFHUftmDZg5vjsjOjXTG+l8QEFBEXXr1qFPnxaMHduRWbNG0a6dvo5WeR9fSfIFxpg0j2ud+n55dcryCov48KcYvt4Sz5a49N+HRzcJYsqQNozp3oLmIfVo06SBPtvuA9LTc3n00eVs2ZLAqlU3EB4ezPz5E50OS6kq85Uk/5uIXAX42T3K3QP87HBMysttjUvj/k+3sCcxi+4RIUw/vyMXdGtBs4b1aNawntPhqdPIGMP8+du5994lJCZmc+edA8jPLyIw0FeqSFVb+UoJvhN4AnAB/8XqVe4RRyNSXscYwze/HuFoei7f7Uzk5/3HaNawHq9N6sslvfUNZr4qISGLKVO+ZOnSffTvH8HChZP0jXXKZ/hKkr/QGPMQ8FDxABGZgJXwlSpTYZGL1XuTeW35HjbZHcMAjOrSnBf/1IfQ+nUdjE6daSEh9UhOzuG11y7ittv6U0cvuygfIr7QNbqIbDLG9PMYttEYc5YT8fTv399s2LDBiUWrCjLGsDk2jde+28sPe5LJL3LRpEEAkwZGMWVIGxoFBehjbz7s++8P8Oyza/j886sICqqLy2X0hTY1gF1v6zOKp5FXn8mLyIXAGCBSRF50+yoEq+leKQAycguYt+4QB4/lcDA5mx1HMkjLKaBRUF2uHdyGQe3CGNm5mfbT7uMSE7N54IGlfPjhVtq1a0xMTBpduzbTBK98llcneSAR2AbkAtvdhmcCDzsSkapRcguK2BiTymNf/MrBYzmENQigdVgQF/VoSa9WoVzSO0JfL1sLuFyGd9/dxEMPLSMrK5/HHx/Oo48Op75eilE+zqtrN2PML8AvIvJvY0yu0/GomqG4Kf65JbtYfzCFgiJDeEg9Pps2RHt6q8U++GArvXqF88Yb4+jatZnT4ShVLbw6ybuJFJGngW5AYPFAY0wn50JS1Sknv5D3fjzIyt1J7E/KIjkrn+B6/twwtC1ntWnMkPZNCAnUs7baJDs7n2eeWc1ddw2iRYtgvvrqaho3DtS+A1St4itJfi7wf8DzwEXADeg1eZ936FgOK3cnsuy3RFbtScIYaNe0ASM6NWNw2yZc2L0FoUGa2GujBQt2cddd/+PQoXTatm3M1Kn9CAur73RYSlU7X0nyQcaYJSLyvDFmH/C4iKx2Oih1+hW5DEu3H+W17/ay40gGAJGN6jPtnPYMahvGOZ2a6ZlaLRYbm87ddy/myy930qNHc3744QaGDo1yOiylHOMrST5PrJp9n4hMAw4D2gekj8ktKGL0SyuJTTkOWK+W/efkfnSPCNHErgCYMWMFS5bs5R//OJ977x1M3br6tISq3XzlOflBwA6gMfA0EAr8wxjzoxPx6HPyp09eYRHbDmcwd81Bvt4SD8D5XZsza0IvfbWsAuDnn+No2DCA7t2bk5iYTU5OAdHRjZwOS1WBPid/+vnEmbwxZq39ZyZwHYCItHIuInWqtsSmsXpPEnPXHCQ5Kx+A8JB6TDunPVOGRFNHn2uu9VJTj/PII8t5++2NXH55Vz7//CqaN9ceAJVy5/VJXkQGAJHAD8aYZBHpjvV62/MATfReJjU7n+vfW8dWu8e3ri1DeOCCznRtGULv1np2pqxHJD/++Ffuu28pyck5TJ8+mL/9baTTYSlVI3l1kheRWcAVwBasm+2+wOqB7h/ANCdjU5W3OTaNy2ZbV1iGdmjC9PM76XPt6g/mzPmFqVO/ZsCACBYvvoa+fVs6HZJSNZZXJ3lgPNDbGHNcRMKAePvzLofjUpWQlpPP2FdWE59uvc/oyUu6ccPQtg5HpWqS3NxCYmLS6Ny5KZMn98TPT5gypbd2JqNUObw9yecaY44DGGNSRGSnJnjvsT8pi5kLd/D9riQA+kU14oELOnN2h6YOR6ZqkmXL9nP77d9QWOhi5847qV+/Ljfc0NfpsJTyCt6e5NuJSHF3sgJEu33GGDPBmbBUWQqKXLz23V5eXb4HgNHdwrl+SDTDOmpyVyckJGRx331L+fjjX+nQIYy33rqYgAB9JE6pyvD2JH+Fx+d/OhKFqrD04wXc+fEmVu9J5oJu4fz14m60DgtyOixVw+zcmczgwf/i+PFCnnhiBI88MpzAQG+vrpSqfl691xhjljsdg6qYgiIXq/ckMWPBDmJTc7hvdCfuHtXR6bBUDZORkUdISD06dWrCjTf25dZbz6JzZ23hUaqqvDrJq5rPGMNXm+N5edluDh7LoU2TID65eTCD2zVxOjRVg2Rl5fPkk9/z/vtb2Lbtdlq0CObFFy90OiylvF6tvzVVRMaIyC4R2SsiJfZBLyJXicgOEdkuIh9Xd4ze7JN1sUyfvxkR4ZWr+7D4nhGa4NXvjDF8+eVOunadzYsv/swVV3SlXj297q7U6eJTZ/IiUs8Yk1eJ8esAs4HRQBywXkQWGGN2uI3TEXgEGGqMSRURfSd+ObLzCvn7/3ayISaV345k0C+qEZ9NO1vfUqdOkp9fxJVXfsaCBbvo2bM5n346kSFDWjsdllI+xSeSvIgMBN7Femd9lIj0BqYaY+4qZ9KBwF5jzH57PvOwnr3f4TbOzcBsY0wqgDEm8XTH7yviUnN4dvEuFtjvmAd44IJOTBoYpQle/c4Yg4gQEFCHFi0a8Pzzo7n77kHamYxSZ4BPJHngVeBi4EsAY8wWETm3AtNFArFun+OAQR7jdAIQkR+BOsAMY8ziU47Yh7hchk/WH+KxL7YBcFGPFozs3IzL+7YiwL/WXxFSbtasieWuu/7H3Lnj6dkznLfeusTpkJTyab6S5P2MMTEe3Y0WVWC6kk4vPbvl8wc6AiOx3oW/WkR6GGPSTpqRyC3ALQBRUbWn/+q41Bwuee0HUnMKAHjm8p5MHlR71l9VTErKcR5+eBnvvLOJ1q1DSLG7C1ZKnVm+kuRj7SZ7Y19nvwvYXYHp4gD3i4CtsF6N6znOz8aYAuCAiOzCSvrr3UcyxrwNvA1WV7NVWgsvkVtQxGcbYvluZyJr9h0jr9DFxb1a8vyVvQnUJlfl4eOPf2X69MWkpBzngQeG8OSTIwkODnA6LKVqBV9J8rdhNdlHAQnAMntYedYDHUWkLXAYuBqY7DHOl8AkYK6INMVqvt9/muL2Kll5hcxa9Btf/nKY7HyroWRC30gmDYrSjmRUqXbsSKJ9+zC+/XYcvXu3cDocpWoVMcb7TzpFJMwYk1LFaccCL2Ndb59jjHlaRJ4CNhhjFoh1DeAFYAzWJYCnjTHzyppn//79zYYNG6oSTo1kjGHOjwd5a+U+EjPzmNA3kivOasWgtmH4awchysPx4wU888xqhg6NYsyYDuTnF+Hv74ef3nypyiEiG40x/Z2Ow5f4ypn8ersZfT7wX2NMZkUnNMYsAhZ5DHvC7W8D3Gf/q3XSjxdw1sxvKXQZ6vgJT1/eg2sGtXE6LFVDLV26j9tv/4Z9+1J5+OGhjBnTQd83r5SDfCLJG2Pai8jZWM3tfxORzcC88s64VdlW70niunfXAVDHT9j8xGgaBtZ1OCpVEx05ksm99y5h/vztdOrUhOXLp3DeedpdsFJO85m2VmPMGmPM3UA/IAP4t8MhebWsvEJunGvdW3jPqI7se2asJnhVqkWL9vDllzv5299GsnXrNE3wStUQPnEmLyLBWC+xuRroCnwFnO1oUF7u6y3xFBQZPrppkHYBq0q0adMRYmLSuPzyrtxwQ19GjWpHdHQjp8NSSrnxiSQPbAO+Bp41xqx2Ohhvl5lbwNPf/EbzhvUY2kHfM69OlpGRxxNPfM9rr62jY8cwLr20M3Xq+GmCV6oG8pUk384Y43I6CG+XX+ji6y3x3P/ZFkTgqfHd8XjBkKrFjDF8/vlv3HPPYo4cyWTatP4888wo6ugTFkrVWF6d5EXkBWPM/cDnIvKHZwGNMRMcCMtrXT9nHT/tPwbA1GFtmdCvlcMRqZpk06YjXHnlZ/Tp04L//vcqBg3S8qFUTefVSR7rkTmAfzoahQ/4YU/y7wl+3zNjtUMZBVg9xa1ZE8vIkdGcdVYECxdO4sILO+CvfRIo5RW8ek81xqyz/+xqjFnu/g/rBjxVAWk5+Vz77loAFk8frgleAbB6dQz9+r3F6NEfEhNjddUwblwnTfBKeRFf2VtvLGHYTdUehZe68OVVgNW5TJcWIQ5Ho5yWnJzDTTd9xYgRc8nKyueLL/5EmzZ6U51S3sirm+tF5E9Yj821FZH/un3VEEgreSrl7p1V+0nIyGNQ2zAmDWxd/gTKp+XkFNCr1xskJeXw4INn88QT59CggXYmo5S38uokD6wDjmH1HjfbbXgm8IsjEXmZj9bGAPDCVb31TvpaLC4ug1atQggKqsvMmecycGAkPXuGOx2WUuoUeXWSN8YcAA5g9TqnKunzjXHEHMuhVeP6tGoc5HQ4ygE5OQU8/fQqnntuDQsWTGLMmA7cdFM/p8NSSp0mXp3kRWSlMeYcEUkF3B+hE6y+ZbT/01Jk5hbw0OdbAfji9qEOR6OcsHjxXm6//RsOHEhjypTe9OvX0umQlFKnmVcneeBc+39972olHM8v4t75myl0Gf4+oSfNGtZzOiRVzW699WvefnsTnTs34fvvr2fkyGinQ1JKnQFeneTd3nLXGog3xuSLyDCgF/ARVkc1ymaMYcn2o0z7aBMA1w1uw9UDoxyOSlWXoiIXIoKfnzBkSGuiokJ54IGzqVfPq6sBpVQZfGXv/hIYICLtgQ+Ab4CPgYsdjaqGufmDDSz7LRGARy7qws3D2zkckaouGzbEM23aQqZO7ce0af3585/7OB2SUqoa+EqSdxljCkRkAvCyMeZVEdG7623pOQW8vnIvy35LpEdkCB/eOIjG+lhUrZCensvjj3/H7NnrCQ8PJjy8gdMhKaWqka8k+UIRuRK4DrjMHqadnwM5+YVc9vqPHEjOZlyvlvx9Qk/tF76WWLRoD1OnLuDo0SzuuGMA//d/5xEaGuh0WEqpauQrSf5G4Hasrmb3i0hb4BOHY3JcbkERN83dwIHkbO4e1ZH7RndyOiRVjfz9/WjZsiELFkyif/8Ip8NRSjlAjPlD521eSUT8gQ72x73GmEKnYunfv7/ZsGGDU4sHrEfkpr6/gXUHU5hxSXemDGmjL7vxcXl5hTz//BoKClzMmDESAJfL4Kd9ESgvISIbjTH9nY7Dl/jEmbyIDAc+BA5jPSPfQkSuM8b86Gxkzvl0QxxrD6Tw3MReXNlfX1fr61auPMi0ad+wc2cykyb1wBjz+530SqnayyeSPPASMNYYswNARLpiJf1aeUS4PT6d177bQ4uQQCaepX1++7Lk5BweeGAp77+/hbZtG/HNN5MZO7aj02EppWoIX0nyAcUJHsAY85uI1Mrbx40xjP/njxS6DO/f2Feb6H1cQkIWn322g0cfHcZjj40gKEhvqlRKneArSX6TiLyFdfYOcA21sIOawiIX9326hUKX4Y5z23NOp2ZOh6TOgG3bEvnyy508/vgIundvTmzsvYSF1Xc6LKVUDeQrSX4acDfwINY1+VXAa45GVM1Ss/MZ8dz3ZOYWMrBtGPeer3fS+5rs7HxmzlzFCy/8REhIPaZO7UeLFsGa4JVSpfL6JC8iPYH2wBfGmGedjscJW2LTuHHuejJzC5k8KIpnLu/pdEjqNPvmm93cccciYmLSueGGPjz77GiaNtWeA5VSZfPqJC8ijwI3AZuwXmv7lDFmjsNhVavEjFwe+e+vHMvOZ+4NAxjZubnTIanTLD09l+uu+4IWLYJZufLPjBjRxumQlFJewquTPNa1917GmGwRaQYsAmpNks8tKGLsq6tJyyngjWv6aYL3IYWFLubN28bkyT0JDQ1k+fIpdO/enICAOk6HppTyIt6e5POMMdkAxpgkEfFzOqDqNPv7vSRn5fPcxF5c1FP7AvcV69YdZtq0hfzyy1FCQ+txySWd6dtXf1+lVOV5e5JvJyL/tf8WoL3bZ4wxE5wJ68zbl5TFa9/tpV9UI33ZjY9IS8vlsceW88YbG2jZsiGffXYlF1+sN1AqparO25P8FR6f/+lIFNUsv9DFLR9Yr82drnfR+4xLL/2EH3+M5a67BjJz5nmEhNRzOiSllJfz6iRvjFnudAxOGPPyKvYnZ9OlRUNG6LPwXm3v3hQiIhoSFFSXWbNGERjoz1lnaWcySqnTo1Zdw/YFK3cnsT85mz6tG7F4+ginw1FVlJdXyFNPraRHj9eZNWs1AEOHRmmCV0qdVrU+yYvIGBHZJSJ7ReThMsabKCJGRBx9H/7Ly3YD8O71tfK1/D7h++8P0KvXmzz55Aouu6wLt902wOmQlFI+yqeSvIhU6iKmiNQBZgMXAd2ASSLSrYTxGmK9UW/t6YizqmJTctgen8HA6DCaBOv1Wm/07LM/ct55H1BY6GLx4muYN28iERENnQ5LKeWjfCLJi8hAEfkV2GN/7i0iFXmt7UCsvuf3G2PygXnA+BLGmwk8C+SerpgrKy0nn+HPfg8GZlza3akwVBW4XIasrHwAxo3ryOOPD2fbttu48MIODkemlPJ1PpHkgVeBi4FjAMaYLcC5FZguEoh1+xxnD/udiPQFWhtjFpY1IxG5RUQ2iMiGpKSkysReIdPnbwZg2jnt6BYRctrnr86MrVsTGDZsDjff/DUA3bs3Z+bM86hfX3uLU0qdeb6S5P2MMTEew4oqMF1J/bCa37+0Xq7zEnB/eTMyxrxtjOlvjOnfrNnpveP92x0JrNiVxI1D23LfBZ1P67zVmZGdnc9f/rKUfv3eYs+eFC66SM/alVLVz6sfoXMTKyIDAWNfZ78L2F2B6eIA9zfJtALi3T43BHoAK+x+2VsAC0TkUmPMhtMSeTnScwp4auF2Avz9mD66Y3UsUp2i9esPM3HiZxw6lM7UqX35xz9Ga09xSilH+EqSvw2ryT4KSACW2cPKsx7oKCJtgcPA1cDk4i+NMelA0+LPIrICeKC6Erwxhr9+tY3Dqcf54MZBhARqE29NZoxBRIiKCqVNm1A+/ngCQ4dGOR2WUqoW84kkb4xJxErQlZ2uUETuBJYAdYA5xpjtIvIUsMEYs+A0h1phx/OLuP3fG/l+VxIPXNCJYR2blj+RckRBQRGvvrqWxYv3sWTJtYSHB7Nq1Q1Oh6WUUr6R5EXkHdyupRczxtxS3rTGmEVYvde5D3uilHFHVjHEShs/+wd2J2QxeVAUt43U67k11c8/x3HrrQvZujWBceM6kpmZR2hooNNhKaUU4CNJHqt5vlggcDkn3zXvVQ4kZ7M7IYtBbcN45vKeToejSpCZmcdf/vItb7+9kcjIEP7736u47LIu2PduKKVUjeATSd4YM9/9s4h8CHzrUDinpMhluO2jjQA8M0ETfE3l7+/HihUHuffewcyYMZKGDfXlREqpmscnknwJ2gJtnA6iKhZvO8rOo5mM6NSM9s2CnQ5Hudm9+xhPP72aN94YR1BQXTZvnkZgoK/uQkopX+ATz8mLSKqIpNj/0rDO4h91Oq7KKnIZ7vxkExGhgfxrir6bvqbIzS1kxowV9Oz5Bl99tZOtWxMANMErpWo8r6+lxLoI2hvrETgAlzHmDzfheYPYlByMgXO7NCfA3yeOv7zet9/u4/bbF7F3bwqTJ/fkhRcuoEULbWFRSnkHr0/yxhgjIl8YY85yOpZT9dP+YwBc1jeynDFVdTDG8NRTqwBYuvRaRo9u73BESilVOV6f5G3rRKSfMWaT04GcindW7QegR0Sow5HUXi6X4Z13NnLppZ1p2bIh8+ZdQZMmQdo0r5TySl5dc4mIvzGmEBgG3Cwi+4BsrHfSG2NMP0cDrIS0nHxiUnIY3yeC+gF1nA6nVtq8+SjTpi1k7drDJCfn8NhjI4iM1M6AlFLey6uTPLAO6Adc5nQgp+qLXw5T5DJcO9grHwrwapmZeTz55ApeeWUtTZsG8dFHlzN5sj6+qJTyft6e5AXAGLPP6UBO1cdrD9EjMoT+bRo7HUqt89hj3/Haa+u49dazmDVrFI0ba2cySinf4O1JvpmI3Ffal8aYF6szmKraEZ/BnsQs/npxN31jWjWJiUkjL6+ITp2a8Nhjw5k0qQdDhrQuf0KllPIi3v6cVh0gGKtL2JL+eYXPNlpv4L2we7jDkfi+goIinn32R7p1e5077rC6LAgPD9YEr5TySd5+Jn/EGPOU00GciiKX4dsdCfRp3YhWjYOcDsenrVkTy623LmTbtkTGj+/Mq69e5HRISil1Rnl7kvf6tu1Ve5KISz3OrefoM9hn0ldf7eSyy+bTunUIX375J8aP7+J0SEopdcZ5e5If5XQAp+qXQ2kADGob5nAkvscYQ3x8JpGRIVxwQXtmzjyX6dMHExwc4HRoSilVLbz6mrwxJsXpGE7Vol+PANCmiTbVn047dyZz3nkfMGzYe+TkFFC/fl0ef3yEJnilVK3i1Une2207nM7exCw6NA+mnr++AOd0OH68gL/+9Tt69XqDzZuP8sgjw/RtdUqpWktrP4cYY3hq4Q4AXrqqj8PR+IbDhzM455y57NuXyrXX9uKFFy6gefMGToellFKO0STvkLjU46w7kEKDgDr0bKXvqj8VBQVF1K1bh5YtGzJ8eBvefvsSzjuvrdNhKaWU47S53iGJmbkAvDa5r8OReK+iIhf//Oc6OnR4jaNHs/DzE957b7wmeKWUsumZvEP2JWYD0K6p9k1eFZs2HeHWWxeyYUM855/fjvz8IqdDUkqpGkeTvEP2JGZSt47QOkzvqq8Ml8tw//1LePXVdTRrFsTHH0/g6qt76OuAlVKqBJrkHZCdV8g7qw/QKTyYOn6anCrDz084duw406adxdNPj6JRo0CnQ1JKqRpLr8k74GiGdT3+3M7NHY7EOxw4kMr48fP49dcEAObOvYzZs8dpgldKqXJoknfAgSTrevxAfctdmfLzi5g1azXdu7/O8uX72bkzGbDO5pVSSpVPm+sdEJOSA0DXliEOR1JzrV4dw7Rp37BjRxITJnTl5ZcvpHVrfdRQKaUqQ5O8A3YeyaBpcAAtQ7W5uTRLluwjOzufr7+exMUXd3I6HKWU8kraXO+AxMw8IhrV1zvC3RhjmDt3M99+uw+Axx4bzvbtt2uCV0qpU6BJ3gFJmXk0Da7ndBg1xo4dSYwc+T433PAVc+duAaB+/bo0aKCdySil1KnQJO+AhIxcwkM0yefkFPDoo8vp3ftNtm1L5F//uoQPP7zc6bCUUspn6DX5anY0PZdj2fl0bN7Q6VAc9/nnO5g16weuv743zz03mmbNtDMZpZQ6nTTJV7PNsWkA9Ilq5HAkzjh8OIPt25O44IL2XHNNLzp3bsrAgZFOh6WUUj6p1jfXi8gYEdklIntF5OESvr9PRHaIyFYRWS4ibU5leT/ts571bl/L3llfVOTi1VfX0rXrbK6//kvy8grx8xNN8EopdQbV6iQvInWA2cBFQDdgkoh08xjtF6C/MaYX8B/g2aouzxjDN78eoWlwAKFBdas6G6+zYUM8gwb9i3vuWcyQIa354YcbqFdPG5GUUupMq9VJHhgI7DXG7DfG5APzgPHuIxhjvjfG5NgffwZaVXVhmw6lkpyVz5Qh0VWdhdfZvfsYgwb9i/j4TObPn8jixdfQvr2+6U8ppapDbT+digRi3T7HAYPKGP8m4H8lfSEitwC3AERFRZU48avL9+IncN3gU2rxr/GMMWzblkjPnuF06tSEOXMu5bLLuhCqL/9RSqlqVdvP5Et6G40pcUSRa4H+wHMlfW+MedsY098Y079Zs2YlLswAofXr0tiHn//ety+FsWM/pl+/t/nttyQArr++jyZ4pZRyQG0/k48DWrt9bgXEe44kIucDjwHnGGPyqrqwg8nZDO3QtKqT12h5eYU8//wa/u//VlO3rh8vvHABnTo1cTospZSq1Wp7kl8PdBSRtsBh4GpgsvsIItIXeAsYY4xJrOqC8gtdxKXmML5PxKnEWyMVFroYNOhfbNmSwMSJ3Xj55QuJjNTOd5RSymm1OskbYwpF5E5gCVAHmGOM2S4iTwEbjDELsJrng4HP7HfNHzLGXFrZZcUcy8ZlILqJ77zwJSMjj5CQevj7nQ56CAAAFqtJREFU+3HjjX3p0CGMsWM7Oh2WUkopW61O8gDGmEXAIo9hT7j9ff7pWM7XW48AMCDa++8sd7kMc+b8wkMPLePf/57AmDEduPvusu5XVEop5YRan+SrgzGG+esPcV6X5kQ1CXI6nFOybVsi06Yt5McfYxk+PIo2bbSPd6WUqqlq+9311WJfUjYJGXlc0C3c6VBOyaxZq+nb9y127kzmvffGs3Lln+nateQnCZRSSjlPz+SrQWyq9S6dDs2981W2xhhEhObNGzBlSi/+8Y/RNG3q3S0SSilVG+iZfDX498+HAIhsXN/hSConLi6DK674lLfe2gjATTf14913x2uCV0opL6FJ/gwzxpCYmUtAHT9ahnpHki8sdPHSSz/Rtets/ve/PRQUFDkdklJKqSrQ5vozLC2ngK1x6dx7fienQ6mQjRvjmTr1azZvPspFF3Vg9uyxtG3b2OmwlFJKVYEm+TMsO78QgJZe8lrX1NRckpKy+c9/rmTChK7Y7wZQSinlhTTJn2Fb49IBaBhYMze1MYb587dz6FA6Dz44lPPPb8f/t3fn0VFVeQLHvz8CGJYYZYtgbAMETEiACOhIYzcRBRQEBJGwNIuigtA6imCPhz4njjitTbu0DIgyPR6g2wCyiriNbIo0oKGJYYnSESIdxYAhmrAEQvKbP95LkYRAKpCqgsrvc847p959r9771T1V9at33617MzMfI/QSjdcYY4z37J68j2UczAfgl20vvTHrMzOP0Lfv3xgxYjnvvPM1p0+XAFiCN8aYIGFJ3sdy8gtpEXYF4Q3rBToUj5MnT/Pss58QH/8a27Z9x+zZd/Hpp+OoW9feDsYYE0zsks3HcvJP0uLKKwIdRjn79//Ec899ypAhsbz8cl9atQoLdEjGGGN8wJK8jx04cpwOLQM/I1tOzlGWL89g0qSbiIlpRkbGZNq2vfzH0TfGGHNu1j7rQ/mFRfzryHGiAjh4TEmJMm/edmJi5vD44x+yb18egCV4Y4ypBexK3ofeTz/I6RKlb9w1ATl/enoOEyeuYcuWbBITo5g7tz9t2th/3s3ZioqKyM7OprCwMNChmFogNDSUyMhI6tW7dPoqBStL8j70r7zjhNQROl7r/5naCgtP07v3XykpURYsuIfRozvZf97NOWVnZxMWFkZUVJS9T4xPqSq5ublkZ2fTunXrQIcT9CzJ+9CRY0Vc3bC+X78016/fT2JiFKGhdVm69D7i41vQpMnlMZyuCZzCwkJL8MYvRISmTZty+PDhQIdSK9g9eR85ebqYjV8f4ppw//SsP3DgZ+65ZzG3376QlJSdAPz619dbgjdeswRv/MXea/5jV/I+suf7fA7+XMiwbtf59DxFRcW8+uo2kpM3AjBz5h0kJcX59JzGGGMuD3Yl7yPfHD4GQL+OLX16nuHDlzNt2sf06tWaPXsmMW1aD+rVC/HpOY3xhZCQEBISEoiPj2fAgAH89NNPnm27d++mV69etG/fnnbt2jFjxgxU1bP9gw8+oFu3bsTGxhITE8PUqVMD8RLOa8eOHTz44IPlygYNGkT37t3LlY0bN45ly5aVK2vcuLHn8d69e+nXrx/R0dHExsYybNgwcnJyLiq2I0eO0Lt3b9q1a0fv3r3Jy8s7a58NGzaQkJDgWUJDQ1m1ahXg3GefPn067du3JzY2llmzZgGwZs0akpOTLyo2c5FU1ZYaXrp27aqz1/9Tr//dGi0sOq017ciR43rs2ClVVd2wYb+uWLFHS0pKavw8pvbYs2dPoEPQRo0aeR6PGTNGn3vuOVVVPX78uLZp00Y/+ugjVVU9duyY3nnnnTp79mxVVd25c6e2adNGMzIyVFW1qKhI58yZU6OxFRUVXfQxhg4dqmlpaZ71vLw8jYyM1JiYGN23b5+nfOzYsbp06dJyzy2tmxMnTmh0dLSuXr3as239+vW6c+fOi4pt2rRp+vzzz6uq6vPPP69PPfXUeffPzc3Vq6++Wo8dO6aqqm+++aaOHj1ai4uLVVU1JydHVVVLSko0ISHBs19Zlb3ngFS9BL7Dg2mx5nof+ebQUQDqh9RcY4mq8tZbO5ky5SMeeOBGXnjhDhITo2rs+MYA/Oe7u9nzfX6NHrNDqytJHuD9baTu3buTnp4OQEpKCj169KBPnz4ANGzYkNmzZ5OYmMjkyZOZOXMm06dPJyYmBoC6desyadKks4559OhRHn30UVJTUxERkpOTuffee2ncuDFHjzqf12XLlrFmzRrmz5/PuHHjaNKkCTt27CAhIYGVK1eSlpbGVVddBUB0dDSbN2+mTp06TJw4kQMHDgDw5z//mR49epQ7d0FBAenp6XTu3NlTtnz5cgYMGEBERASLFy/m6aefrrJeUlJS6N69OwMGDPCU3XbbbV7X67m88847bNy4EYCxY8eSmJjIH//4x3Puv2zZMu666y4aNnTGAJk7dy4pKSnUqeN837Vo0QJw7r0nJiayZs0ahg0bdtFxmuqzJO8jpQ2JNdXB5Ouvf2TSpPdZv34/N998LcOHx9fIcY251BQXF7Nu3TrGjx8POE31Xbt2LbdP27ZtOXr0KPn5+ezatYsnn3yyyuPOmDGD8PBwdu50OqZW1iRd0d69e1m7di0hISGUlJSwcuVK7r//frZt20ZUVBQRERGMHDmSJ554gltvvZUDBw7Qt29fMjIyyh0nNTWV+Pjyn9lFixaRnJxMREQEQ4cO9SrJ79q166y6qExBQQG/+tWvKt2WkpJChw4dypXl5OTQsqVza7Fly5YcOnTovMdfvHgxU6ZM8ax/8803LFmyhJUrV9K8eXNmzZpFu3btAOjWrRubNm2yJB8gluR95OM9OTU2nO3ChV/y0EPv0qBBXebO7c9DD3UhpAZbCIwpqzpX3DXpxIkTJCQkkJWVRdeuXenduzfgtGCd68dydX5Er127lsWLF3vWr7666oGh7rvvPkJCnD4uSUlJPPvss9x///0sXryYpKQkz3H37NnjeU5+fj4FBQWEhZ2ZE+LgwYM0b97cs56Tk0NmZia33norIkLdunXZtWsX8fHxlb6m6l4shIWFkZaWVq3neOvgwYPs3LmTvn37espOnjxJaGgoqamprFixggceeIBNmzYBzlX9999/75NYTNUsU/hAcYly9ORpftXu4qaXLSoqBqBr15YkJcXx1Ve/ZeLEbpbgTVBq0KABaWlpfPvtt5w6dYo5c+YAEBcXR2pqarl99+3bR+PGjQkLCyMuLo7t27dXefxz/VgoW1ZxxL9GjRp5Hnfv3p3MzEwOHz7MqlWrGDJkCAAlJSVs2bKFtLQ00tLS+O6778ol+NLXVvbYS5YsIS8vj9atWxMVFUVWVpbnB0jTpk3LtTIcOXKEZs2aeerCm9daUFBQrpNc2aXsD5JSERERHDx4EHCSeGlze2XefvttBg8eXG60usjISO69914ABg8e7LnVAk6dNmhgf+UNFMsWPnC6xGmsv+GaC5vd7YcfjjJy5HJGj14JQFxcCxYuHMw11zSu4pnGXP7Cw8OZNWsWL774IkVFRYwaNYrPPvuMtWvXAs4V/2OPPcZTTz0FwLRp0/jDH/7A3r17ASfpvvzyy2cdt0+fPsyePduzXppIIyIiyMjI8DTHn4uIMHjwYKZMmUJsbCxNmzat9LiVXUHHxsaSmZnpWV+0aBEffvghWVlZZGVlsX37dk+ST0xMZMmSJZw6dQqA+fPne+67jxw5kr///e+89957nmN9+OGHnlsQpUqv5CtbKjbVAwwcOJAFCxYAsGDBAgYNGnTOeli0aBEjRowoV3bPPfewfv16AD755BPat2/v2bZ3796zblUYPwp0z79gXGI7Juj1v1ujG75yeph66/TpYn3ttc81PPx5rV9/hiYnb9DiYus1b3zvUutdr6p6991368KFC1VVNT09XXv27Knt27fXtm3b6jPPPFPuHyXvvvuudunSRWNiYjQ2NlanTp161vELCgp0zJgxGhcXp506ddLly5erqurSpUu1TZs22rNnT508ebKOHTtWVSvv5f7FF18ooPPnz/eUHT58WIcNG6YdO3bU2NhYnTBhQqWvLz4+XvPz83X//v3aqlWrs/4Rc+ONN+rWrVtVVfWZZ57R+Ph47dy5sw4ZMkQPHTrk2S8jI0P79u2r0dHRGhsbq0lJSfrDDz+ct26r8uOPP2qvXr00Ojpae/Xqpbm5uZ7XO378eM9+pbGX9qIvlZeXp/369dP4+Hi95ZZbyv2LoH///pqenn7WOa13vX8WcerV1KSYjgla2P+/eHtCd25u7d1sb5mZRxg1agWff/4dvXq1Zu7c/rRv39THkRrjyMjIIDY2NtBhBLVXXnmFsLCws/4rH8xycnIYOXIk69atO2tbZe85Edmuqt38FV9tYM31PlDi/nBqUI1Baa688gry80/yt78NZu3a0ZbgjQkyjzzyCFdc4Z9hri8VBw4c4KWXXgp0GLWa9a73gVOnSwC4quG5p1FUVVat+oqUlF0sWTKUFi0asXv3JOrUsTGdjQlGoaGhjB49OtBh+NVNN90U6BBqPbuS94FjJ4uJatqQ65o0rHT7t9/+xMCBixky5G327s3l0CFnCFxL8CaQ7Nad8Rd7r/mPJXkfKFElvGH9s8qLioqZOXMzHTq8xoYN+3nxxd5s3/6w9Zo3ARcaGkpubq59+RqfU3Xmkw8NDQ10KLWCNdf7QFFxCc0bn53ki4uVefO206dPW1599U5+8YvwAERnzNkiIyPJzs62Ob6NX4SGhhIZGRnoMGoFS/I+cPJ0Cc3DnA42ubnHmTlzM8nJiTRsWI9t2x6kadPKm/GNCZR69erRunXrQIdhjKlhtb65XkTuFJGvRSRTRP6jku1XiMgSd/s2EYny5rj1QuqwcOGXxMTM4aWXtrBxYxaAJXhjjDF+U6uTvIiEAHOAu4AOwAgRqTgc1HggT1WjgVeAc0/NVMZ7i3Yzduwq2rVrwj/+MYF+/drVZOjGGGNMlWp1kgduBjJVdZ+qngIWAxXHcxwELHAfLwNuFy9mi/g2LYc33ribzz57gE6dImo0aGOMMcYbtf2e/LXAv8qsZwP/dq59VPW0iPwMNAV+LLuTiDwMPOyunsz94d93TZgAEyb4JO7LSTMq1FUtZnVxhtXFGVYXZ9wQ6ACCTW1P8pVdkVf8D5E3+6Cq84B5ACKSakMzOqwuzrC6OMPq4gyrizNEJLXqvUx11Pbm+mzgujLrkUDFiY89+4hIXSAcOOKX6IwxxpiLUNuT/BdAOxFpLSL1geHA6gr7rAbGuo+HAuvVRgwxxhhzGajVzfXuPfbfAh8BIcCbqrpbRJ7FmfJwNfC/wF9FJBPnCn64F4ee57OgLz9WF2dYXZxhdXGG1cUZVhc1zKaaNcYYY4JUbW+uN8YYY4KWJXljjDEmSFmSvwi+GhL3cuRFXUwRkT0iki4i60Tk+kDE6Q9V1UWZ/YaKiIpI0P59ypu6EJFh7ntjt4ik+DtGf/HiM/ILEdkgIjvcz0m/QMTpayLypogcEpFd59guIjLLrad0Eeni7xiDiqracgELTke9b4A2QH3gS6BDhX0mAa+7j4cDSwIddwDr4jagofv4kdpcF+5+YcCnwFagW6DjDuD7oh2wA7jaXW8R6LgDWBfzgEfcxx2ArEDH7aO6+DXQBdh1ju39gA9wxii5BdgW6Jgv58Wu5C+cz4bEvQxVWRequkFVj7urW3HGJAhG3rwvAGYAM4FCfwbnZ97UxUPAHFXNA1DVQ36O0V+8qQsFrnQfh3P2mB1BQVU/5fxjjQwCFqpjK3CViLT0T3TBx5L8hatsSNxrz7WPqp4GSofEDTbe1EVZ43F+qQejKutCRG4ErlPVNf4MLAC8eV+0B9qLyGYR2Soid/otOv/ypi6eAX4jItnA+8Cj/gntklPd7xNzHrX6f/IXqcaGxA0CXr9OEfkN0A3o6dOIAue8dSEidXBmMxznr4ACyJv3RV2cJvtEnNadTSISr6o/+Tg2f/OmLkYA81X1JRHpjjM+R7yqlvg+vEtKbfne9Au7kr9wNiTuGd7UBSJyBzAdGKiqJ/0Um79VVRdhQDywUUSycO45rg7SznfefkbeUdUiVd0PfI2T9IONN3UxHngbQFW3AKE4k9fUNl59nxjvWJK/cDYk7hlV1oXbRP0GToIP1vuuUEVdqOrPqtpMVaNUNQqnf8JAVQ3GiTm8+YyswumUiYg0w2m+3+fXKP3Dm7o4ANwOICKxOEn+sF+jvDSsBsa4vexvAX5W1YOBDupyZc31F0h9NyTuZcfLuvgT0BhY6vY9PKCqAwMWtI94WRe1gpd18RHQR0T2AMXANFXNDVzUvuFlXTwJ/I+IPIHTPD0uGC8KRGQRzu2ZZm7/g2SgHoCqvo7TH6EfkAkcB+4PTKTBwYa1NcYYY4KUNdcbY4wxQcqSvDHGGBOkLMkbY4wxQcqSvDHGGBOkLMkbY4wxQcqSvDEXSESKRSStzBJ1nn2jzjXrVjXPudGdyexLdyjYGy7gGBNFZIz7eJyItCqz7S8i0qGG4/xCRBK8eM7jItLwYs9tjDnDkrwxF+6EqiaUWbL8dN5RqtoZZ/KjP1X3yar6uqoudFfHAa3KbHtQVffUSJRn4nwN7+J8HLAkb0wNsiRvTA1yr9g3icg/3OWXlewTJyKfu1f/6SLSzi3/TZnyN0QkpIrTfQpEu8+93Z2HfKc7X/cVbvkL7lzt6SLyolv2jIhMFZGhOPMIvOWes4F7Bd5NRB4RkZllYh4nIv99gXFuocwEIyIyV0RSxZk//j/dssdwfmxsEJENblkfEdni1uNSEWlcxXmMMRVYkjfmwjUo01S/0i07BPRW1S5AEjCrkudNBF5V1QScJJvtDmOaBPRwy4uBUVWcfwCwU0RCgflAkqp2xBnJ8hERaQIMBuJUtRPwXNknq+oyIBXnijtBVU+U2bwMGFJmPQlYcoFx3okzfG2p6araDegE9BSRTqo6C2d88ttU9TZ3iNvfA3e4dZkKTKniPMaYCmxYW2Mu3Ak30ZVVD5jt3oMuxhmLvaItwHQRiQRWqOo/ReR2oCvwhTvsbwOcHwyVeUtETgBZONOR3gDsV9W97vYFwGRgNs589X8RkfcAr6e2VdXDIrLPHTv8n+45NrvHrU6cjXCGce1SpnyYiDyM8/3TEugApFd47i1u+Wb3PPVx6s0YUw2W5I2pWU8AOUBnnJaywoo7qGqKiGwD+gMficiDONNrLlDVp704x6iyE9qISNPKdnLHS78ZZ9KT4cBvgV7VeC1LgGHAV8BKVVVxMq7XcQJfAi8Ac4AhItIamArcpKp5IjIfZyKWigT4WFVHVCNeY0wF1lxvTM0KBw66c4CPxrmKLUdE2gD73Cbq1TjN1uuAoSLSwt2niYhc7+U5vwKiRCTaXR8NfOLeww5X1fdxOrVV1sO9AGf628qsAO7Bmed8iVtWrThVtQin2f0Wt6n/SuAY8LOIRAB3nSOWrUCP0tckIg1FpLJWEWPMeViSN6ZmvQaMFZGtOE31xyrZJwnYJSJpQAyw0O3R/nvg/0QkHfgYpym7SqpaiDNT11IR2QmUAK/jJMw17vE+wWllqGg+8Hppx7sKx80D9gDXq+rnblm143Tv9b8ETFXVL4EdwG7gTZxbAKXmAR+IyAZVPYzT83+Re56tOHVljKkGm4XOGGOMCVJ2JW+MMcYEKUvyxhhjTJCyJG+MMcYEKUvyxhhjTJCyJG+MMcYEKUvyxhhjTJCyJG+MMcYEqf8HSE27vYaWxfoAAAAASUVORK5CYII=\n", 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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
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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", + "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": [ + { + "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": [ + "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.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", 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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", - 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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": [ + "
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LIMIT_BALAGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6
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45000037469904823349291283142895929547200020191200110010691000
550000578617567035835209401914619131200036681100009000689679
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" + ], + "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

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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": [ + "
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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", 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" ] @@ -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", 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H5/bRPLd5gLe/QzlontuM15V/wquGhgYaGhoK2FsR/oIgCIJQEOon1dO2qI3AlgA72ndQW1OL/1g/XpeXoJT0FQRBEITRidflZf7xpZ/wSmz+giAIglBmiPAXBEEQhAyYxdqXEvn0T4S/IAiCIFhQVVXF/v37S3YCoLVm//79VFVV5XSe2PwFQRAEwYIJEyawZ88e3nvvvUFdp7u7O2cBbZeqqiomTJiQ0zki/AVBEATBgsrKSo488shBX2fTpk186lOfKkCPCoOo/QVBEAShzBDhLwiCIAhlhgh/QRAEQSgzRPgLgiAIQpkhwl8QBEEQygwR/oIgCIJQZojwFwRBEIQyQ4S/IAiCIJQZIvwFQRAEocwYNuGvlLpdKfWuUmpLyr4apdSjSqnWxPZgi3PnJdq0KqXmDV2vBUEQBGHkM5wr/zuAs9L2LQEe11rXAY8nXvdDKVUDXA2cDHwauNpqkiAIgiAIwkCGTfhrrZ8C2tN2fxlYlfh/FfAVk1NnAo9qrdu11h8AjzJwEiEIgiAIggWlZvM/TGu9DyCxPdSkzXhgd8rrPYl9giAIgiDYQA1njWKl1GTgIa31sYnXH2qtD0o5/oHW+uC0c/4NcGutr0m8vgro1FpfZ3L9i4GLAQ477LAT1qxZk1c/Ozo68Hq9eZ1bSoyW+4DRcy9yH6XFaLkPGD33IveRP6eeeurLWusTzY6VWknfd5RSh2ut9ymlDgfeNWmzB5iR8noCsMnsYlrr24DbAE488UQ9Y8YMs2ZZ2bRpE/meW0qMlvuA0XMvch+lxWi5Dxg99yL3URxKTe3/ByDpvT8PeMCkzUbgTKXUwQlHvzMT+wRBEARBsMFwhvrdAzwLTFFK7VFKzQeWAWcopVqBMxKvUUqdqJRqAtBatwO/AF5M/P08sU8QBEEQBBsMm9pfa32hxaHTTNq+BHwn5fXtwO1F6pogCIIgjGpKzeYvCEIZEgwHCWwN0Lq/lbqxdfin+fG5fcPdLUEYtYjwFwRhWGnZ1ULD6gbiOk4oGsJT6aFxYyPNc5upn1Q/3N0ThFFJqTn8CYJQRgTDQRpWNxCMBAlFQwCEoiGCEWN/R6RjmHsoCKMTEf6CIAwbga0B4jpueiyu4wS2BIa4R4JQHojwFwRh2Gjd39q74k8nFA2xo33HEPdIEMoDEf6CIAwbdWPr8FR6TI95Kj3U1tQOcY8EoTwQ4S8IwrDhn+bHocyHIYdy4D/WP8Q9EoTyQIS/IAjDhs/to3luMz6Xr1cD4Kn04HMZ+72ukZ/TXRBKEQn1EwRhWKmfVE/bojYCWwLsaN9BbU0t/mP9IvgFoYiI8BcEYdjxurzMP37+cHdDEMoGUfsLgiAIQpkhwl8QBEEQygwR/oIgCIJQZojwFwRBEIQyQ4S/IAiCIJQZIvwFQRAEocwQ4S8IgiAIZYYIf0EQBEEoM0T4C4IgCEKZIcJfEARBEMoMSe8rCIJQ6gSDEAhAayvU1YHfDz7fcPdKGMGI8BcEQShlWlqgoQHicQiFwOOBxkZobob6+uHunTBCEbW/IAhCqRIMGoI/GDQEPxjb5P6OjuHtnzBiEeEvCIJQqgQCxorfjHjcOC4IeSBqf0EAguEgga0BWve3Uje2Dv80Pz632FSFYaa1tW/Fn04oBDt2DG1/hFGDCH+h7GnZ1ULD6gbiOk4oGsJT6aFxYyPNc5upnyQ2VWEYqaszbPxmEwCPB2prh75PwqhA1P5CWRMMB2lY3UAwEiQUNQbYUDREMGLs74iITVUYRvx+cFgM0w6HcVwQ8kCEv1DWBLYGiGtzm2pcxwlsEZuqMIz4fIZXv89nrPTB2Cb3e73D2z9hxCJqf6Gsad3f2rviTycUDbGjXWyqwjBTXw9tbYZz344dhqrf7xfBLwwKWfkLZU3d2Do8lR7TY55KD7U1YlMVSgCvl+A3LqDp/I+zeNLrNL2+hmA4ONy9EkYwsvIXyhr/ND+NGxtNjzmUA/+xYlMVhh9xShUKjaz8hbLG5/bRPLcZn8vXqwHwVHrwuYz9XpeoVoXhRZxShWIgK3+h7KmfVE/bojYCWwLsaN9BbU0t/mP9IviFksCOU+r84+cPca+EkY4If0EAvC6vDKAliCRfEqdUoTiI8BcEoSQRO7dB0inVbAIgTqlCvojNXxCEkkPs3H34p/lxKPOhWpxShXwR4S8IZUgwHKTplSYWP7qYpleaSi5sTJIv9SFOqUIxELW/IJQZI0GdLnbu/ohTqlBoRPgLQhmRqk5PkhSyDasbaFvUVhICRezcAxGnVKGQiNpfEMqIkaJOFzu3MFIpdZNaEln5C8IowG5I3EhRpyft3OnmCYdyiJ1bKFkymdRKDRH+gjDcBING0ZbWVqN+u99vVG2zSS42/JGkThc7tzCSyGZS+/1nfj9cXTNFhL8gDCcdHTB+PMTjEAoZ5VobG41yrfXZne9yteGPtFoGYue2RhIglRbZTGrtXe1D3KPMiPAXyoaSGyyDQWO1H0yxCYYSK/KGBqOMa5ayrbmmfhV1+uhgJERslBvZTGrhWHiIe5QZEf5CWVCSg2Ugg3NdPG4cn5951ZuPDV/U6SObkRKxUW5kM6m5ne5h6JU1IvyFUUtypb/13a3c/NLN/WbeJTFYtrbCoYeaHwuFYEd257t8bfiiTh+5SKGf0iSbSa1mTM0Q9ygzEuonjEpadrUw/vrxLNywkBufv9FS5Tas4W11deCw+Al6PFDbJ7itwockJK78GCkRG+VGtkyMVr/T4UJW/sKow0wtasWwDpZ+PzQ1mR9zOIzjZDdZiA2/vBhJERvlRiaT2qY3Nw139/ohwl8Y0Zg58WVSi6YzrIOlz2es/n2+/t7+Dofh7e/12rLvig2/vBhpERvlxkgxqZWU8FdKTQFSdbBHAT/RWt+Y0mYG8ADwVmLXfVrrnw9ZJ4WSwWpFfM6UcyzVoukM+2Dp9Rpe/YGAYeOvrTVW/Akvf7v23ZEy4AiDZ6RHbJRc1E2ZUlLCX2u9HTgOQCnlBPYC95s0fVprffZQ9k0oLeI6brkiXrdtnaVaNElJDZZer6VXv9h3BTNGqranJKNuypSSEv5pnAa8obX++3B3RCg92rvaLVfETuUkpmOmx6oqqrjkxEuY+rGpI2KwFPuuYMVI0/ZIiGJpUVruh/35GnCPxbFTlFKblVIPK6WmDWWnhNIgHAtbrog7ezqZfcxsU6/bR7/5KNfNvK5XVV7qiDe/MFoYKUWlygWltR7uPgxAKeUC2oBpWut30o4dAMS11h1KqQZguda6zuI6FwMXAxx22GEnrFmzJq/+dHR04M2SaW0kMFruA+CDf3zAzs6dpoOJQzmYeMBEasbU0N7VTjgWxu10UzOmxlSQJlNvZmtXDOx8Jx2RDlrbWwGjr8m+1dXUlcwEZrQ8W6PlPqD07mVvcC9vd7xteXycdxzjfeMH7C+1+8iX4biPU0899WWt9Ylmx0pV+H8ZWKC1PtNG253AiVrr9zO1O/HEE/VLL72UV382bdrEjBkz8jq3lBgt9wHwxJNP8JXnvmIazudz+WyrENNtkG6nG601Cz+zkCs/f2XRHZHsficdkY6Stu+OlmdrtNwHlM69JB381m5by6adm0xzbngqPSw/a7mpGaNU7mOwDMd9KKUshX+p2vwvxELlr5QaB7yjtdZKqU9jmC72D2XnhOEn6aw3GI9nMxtkcmC69plrWfniSh7+xsMl4Yhky76bVh0weF4Dgb83i1e1MGykT66tEBPW0FNywl8pVQ2cAXwvZd/3AbTWtwBzgB8opXqALuBruhTVF0LRqZ9UT9vF2wnctYQd722n9qAp+L+xDO/Yw22dny0fQEe0Y+Q4IrW0GMWAEvkCWo6uouHN7xIfU0Uo3i1e1cKQYyfZVklF3ZQZJSf8tdadwNi0fbek/L8CWDHU/RJKkJYWvA0NzO9NkLMFrrrfdjncTGF0SUZErvRg0BD8ieqAQRc0zO4m6Abi3YB4VQtDT6bJtcvp4rTJpzF76uySM2GVC6Xs7S8I1sTjfQIvWQY3FOoThB0dWS+RDKPLxIiIpQ8EjM8j+fJYsNJnjGavaqv6B8LwkGlyHYlFmD5u+oiJuhmNlNzKXxBs0d7eT+D1w2Y53ExpUpOMiFj61ta+CRDQWgMhi+qhI2IykweSPKb0GIocFZItMH9E+Asjk3C4n8Drh81yuMk0qbNWz6IjYq4pKEVHpAED3scn4vN4ej+PiR8BGlDm5084YMKQ9XUokOQxpUmxaxDIhG9wiPAXRiZut1EEx2wCkFYONxP1k+rZt2gf1zx1Ddc/ez1KKSKxSMk6IpkOeCiaJ8apf83mRSwmBSMVqW9feAqxoi5mDQKZ8A0eEf7CyKSmxqh+Z0ZKOdwBpIXD4ffj9flYdvoyrvz8lSUdS59xwJtbTdsNXrxhze4DQxkF/J6P9hS7q0OK1D8oLIVcURerBoFM+AaPCH9hZJIse5sS3pZeDncAaeFweDzQ2NgbHZAplr4UbIsZBzyHIvDQMuZvq6LurXV41BOEtHkylZL3YciRfG3LZt9puZNxgnn7abQdcR3eC+cZZahtUowaBDLhGzwi/IWRS319xnK4/UgLhwP6TAYNDcZ1LFJvFsO2mBQ8Y4JjaHqlydZkIuuAF9oD85fiD19A4/XjITJQ+JeiD8Ngyce2bPWd3n3C3cXubkmTcYIZiRD430XM//EVtsNpi4UUvBo8EuonjGiClZqmT2kWfzFO06c0wUqLfE9p4XD9SEYHmF0/ZSWUHGhC0RDBiLHfylEwEy27Whh//XgWbljI2x1vs3DDQsZfP56WXS0Zz8sUmpg64CVtrWaFjXKytQaD0NQEixcb22Bphs7ler+ZvtPW9ta8vtPRQsYJpht2eCI5hdMWCyl4NXhk5S+MWFp2tTBr9SyisWhvUZ7LN17Ow3NNUvKmhcP1I0N0QKFti4NxVMplhTtoW2sWE0mpkcv9ZsvsWM724owr6jDUtide2AynLRbFdCYsF0T4CyOSuI4z866ZdEY7e/eFY2HCsTBn/u5Mrj3jWnZ/tLvPPl9Xl1d0QKFti4OZTOQ64OVtax2EiWTAddKcK3OxFeeK3fvN9J3Gdbys7cUZJ5iAf2vihc1w2mJSLGfCckGEvzAi2d+1v5/gT6Wrp4tFGxcRiUf67PPnraU+j+iAQtsWBzuZGJIBz46JJNuKr4Q1B5m+U4dylLW9uN8EMxIhRBhP2BD8zavBG0k0zCGctpgUw5mwXBDhL4xIPuz+MOPxSNwYpXpV6vfPoe0Pa/GeO8d+dACFT1RSiMlELgOelUd7xsiFPE0kfW9aIM1BkciW2bEc7MWZold6J5gvrWLHNZdT+3YU/9YUwQ+Zw2mFEYEIf6EsiOs4gQN2M99udECCQtsWi531LBUzj/YfbfgRWmscymEduZCniaSXQmgOikim77Supm7Uq43tRK94XV7mf3YBXDHdmLBVxiGSNmHW2nAEHSKzjlBYRPgLI5KDqg7KqX2vSt3rzVnwFFLVni54oDhlTTM5FqZi6mzo9xsqejPsrPgGqzkYAqy+05eeeWm4u1ZUcnY4tQqnffVVGD++JM06gj1E+AsjklyF/2Bjf9NV7ckKcvkk/UkVPFV7q1h+1vKC2+2zebSn08/Z0OfLPYFSKoPVHAwR5WgvzsvhNH3CXOJmHcEeIvyFEclH3R9R5ayiO9Ztq/1gVeqpNlIUrHxhJRqdd9KfpODZ9I9NzDh+Rt79siKTY6EZA5wNc0mglM5gNQdC0ShI9EqJm3UEe4jwF0Yk4Vg4o+CvdFQSjUcLolJPt5GmU4oFRerawROBkMtee1PNSB4mEmDwmgOhaBQkemUEmHWE7IjwF0YkbqfbchCrrqjm/Gnnc7j38EGHwpnZSK0omYIiwSD+S1bS+D37pxQ8K9pgNAdC0SiIw+kIMesImcmY3lcpdbZS6nGl1F+VUgGl1OdN2pyslIoVr4uCMJCaMTWW6T2dDidLT1vKx2s+zuv7X2fNljUEw/mlps3Fdl4yBUUCAXxhbcRlhwGLjMeQZ9pfuyQ1B0uXGlsR/MNOQVI/+/35VdQUSgrLlb9S6gzgAeA54I/AKcCTSqkbgR9rrTMMKYJQXJKqfLNwrZ/O+ClHLj+SWDxGj8oAKUkAACAASURBVO6hWlfQ+NBlNF/wAPWfODOn98nFdl4yBUUSatn6ECx9DBadCZHKgc0qVAVzps5hRcOKkjBVCEPDoKNXxKwzKsik9r8auFNr/a/JHUqpi4BfA0cppS7UWtvzthKEImA2iI2tHst5gfP6tetUPaB7aLhzJm1nbsQ7w/4EIJONNJ10temgywDnmx43RS27+0BzwQ/Qo3s43Ht4QcMLh7vs8YiliKmQrb6XQZmnxKwz4skk/I/FmAD0orW+XSm1GXgIeEIpdXYxOyeUMemDYUODsapIvj7qKKB/uFYwHORjvxxreckYELjiXOY/8r7tQSpjNjgNKPM4/XQnQZfTxSXrL+HyUy7nyn+5MrtQHEx63BRv+7p2oyBLyD2wWSE1FR2RDsZfP76gZY/LhiKmQi5GOepe8nUIFUqCTMK/GxhQP1Rr/bJS6nPARuAZ4KfF6ZpQtrS0EPzyLAJHR2j1RqjbVIF/QQ8+RxV0dxuD4y9+QdDRQ+CAXb0rmq17/kw4HgVlftlON+w4MJZTKJLP7aP5vLU03DmTOIYQ9YSNt1jwAihXJbVX/RL/CfN6Bb+Zk2AkZuRGvfZP17LyxZXmlQeTDDaOOkUt638zRiPmNRAK5eQXDBulcPOpVFj2FDFmPq7jeVeQFEY/mYT/X4BZwB/SD2it30xMAJqBO4rTNaEsCQZp+e6ZNHyvK0XY9tB4GjSv7qZ+FxAK0VERZ/wjM4l7DZV8dWW1UejHQvADVMSg9p2enEOR6lt20XZzNYEjO9lRY5Q17c117nHBtio4pW8QzeYk2BHp6B184zpuJAvat5W6He342w7G9/YHELPwobUbR51Qy/oCAZrfeJIGtY54hbMopU8DWwOWx0omAqJUKWLMfHtXe0HLUQuji0zCfx1whVKqRmvdnn5Qa/2uUuoLwP3A6cXqoFBeBO9ZRcN5XQRT1NRJlXXDXGi7ztC2t9ZAsB1IrGSsKvz1Q4P/rercQ5FaW/F+2Mn8P5scM4lrtuMkGNdxrnnqGg57/zCuarmCULwbTwQandD8tJPpPTECxxv3WdcO/i3gi5i/nyUJtWw982mL3FK0SoCt+1s5VB9qeqxkIiBKlSLGzIdj4YKWoxZGF5bCX2t9K3BrppO11iEgN/dpQchAYOdDWK2Z40BgGugMq/tMfPVv4I05bYUi9XOSOuJt/AdV4/vQZIJhEtdsx0kwFA1x/bPXs7R2KaG44TebTMhz5oUxnNqY5CTNDI0zjZKq9fvzi6MuZirburF1BPebh1KWTAREqVLEmPlMuTDkexEyxvkLwlDTOtbcOQ2M/dsOgbXHQDzHJ7eiB87c67YVitSyq4Xx149n4YaFXPvMtSwMrWX8DzppmWTS2CSu2T/Nb5mDIInb6UYp81lMVyV0uPs+h5Abgm5D89HhViUXR50sE2xGwZMHjTaKGDOfKReGfC+CCH+hpKg75Ww8EfNjzhj8+mR4YnLu161yVuJfvzOr93Sqs15yxRSKdvYJ34OqjYYeT59jXdpkIplIxVtpPcnQWvc6AQ7AQrMRVxC4eUHJhVP53D7qauoGlzimXEk+Qz6f8UxBxmcrF5J+HfK9CGZIel+hpPCfMI/GJxZDbKCKPebM75pOnDx80RN4a8ZlbZux6pnXQ+Bnc5i/7/Cscc31k+rZ9+N9XPPHa7jh+RtQKMKxcK+z3Q9O/AE3vXhTTvcRcsGOmjxtHkXG6/IWrOxx2VHEmPlClqMWRhci/IWhwWYSE5/bR/O3NtJw1yxi0QidWKyObeJyurh+5vW2Y5qzVj2bejj8cOmAY1aJVJadsYwrv3DlgMFXa83NL91s3olE/oB0St1OW44lcgtGEWPmy/17keRT5ojwF4pPjklM6ifVs/2y15kdmM1ze59DZ0pOn8CpnMT0wPA4t9PNvOnzbHc1n6pnlolUzltLfcsuvK2tzK+rA/8V/SY8zXObefGZF/E4qnq9/ZU2/Bk6TbLyiZ1WEHKjqEmORji2hL9S6gngEq31aybHjgZu0Vp/sdCdE0YBeSQxSf5gu6JdtgS/p9LDpSddysqXVg7I85+rbTPXqmdmCX16E6ncOZO2m6vxfthpOuGpn1RP5I0Iy49ewY53tlHbuh//vrG8ekQlDR/eRIw4ndFOKh2VOB1O7vzKndzx6h089PpDAJx99NnMmz5PVjHCqCef1XvG36YkObK98p8BHGBx7ABgQLU/QQAyJzGJROD882H27F4zQC4ldJM4lIMrv3ClqXo91x930lnPrGCQ2UQik49AdwUsmNHJimbwWUx4HMrRp5KdZWzqgbVvfJFz7zmXClVBNB5FacV59/avWbDxjY0sfmwxG7+xsexXMcLoJd/U0Rn9dyTJUU5q/wFLMKWUC/gi8HbBeiSMLjIlMQmHYcMGePrp3lVxoPo12yV0nTipdlX3E8qF+DHn4iSVyUcg6oS7j4X7j0nE6O/CVta2YDjInHvnEI6Fe/dFtLnvQ2e0k1mrZ7Fv0b7irGKKWHBGELKRNXX0xdvx3r/e9PnM6r9T5kmOMpX0vRr4SeKlBp6ziksG/rvA/RJGC5mSmABBFwSmhIxMdotPZ+uS79guofuN6d8oWjlau05S2RL69FRAsKIvO6E3LWtbb3rfFHVmtvTA6URj0eKsYopYcKbkSE5yxoyBpqbhmeTIRGsAGVNH90QJnHMk8/9SYfp85uO/U05kWvk3A+9j+B3/GrgO2JnWJgK8prV+uii9E0Y+KRXm0mmZZAjF3hz+kTA9r9xKVUUV3T2Zq0U7lKMk6tBnrPqXQtgJn5kPE0IOzh73d+aFg2x+ZzOb39nMVc9e1U+dec7R59ieAIGRxrXgq5giFpwpOVInOT/7GVx99dBPckpsolUqHvIZU0fHu9lRDYQSGrK05zNX/51yI1N63xeBFwGUUkFgvdb6/aHqmDBKSKkw1zuwYaz4G+bSP4e/C6AHenosL1ddUY3T4aSupm7YBT/09xHo7ukmGo+atotUwNbDYCtxNn54D//+379HORQ/P/LnKcmEjO19r91HdUU1nT026hUAFaqCCQdMKMwNJSliwZlCUDDhVAqTnFLoQwql5CGfMXV0xCiyNYDE8+mbPz8n/51yw1aGP631KhH8Qt4kk5gsXw5nnQUuF4FjsczhX+WswuVwDdjvVE4uO/mykvPSTfoInD/1fBw2k2Z2xbosixE5lMM0bNGKHt3DkseW0LKrxfY5WSliwZnBMiD98oaFjL9+fH73b2eSU2xKoQ8JzDNchghGjP0dkY4h6wtkSR2tjeqaA0h5PpO/zeVnLWfJ55aw/KzltC1qEwdZbAp/pVSlUurHSqlnlFK7lFLvpv8Vu6PCCCeZxOTee8HtprXGOod/d6zbNMQvpmPc9OJN3PHqHewN7qXplSaCYftRAcXk1bdf5Q/b/0Dcckpjn85oJ3OmzsHn8lFdUW3rnI5oR2EH56SvhhmDLDgzGAounEphklMKfUhgx0N+KLFMHa2qaF5XZZTVTift+Uz67yw9fSnzj59fUguH4cRubv8bgKXAO8DvgJtM/gQhOz4fwcCdbD4Mk/gRA5dz4Ko/SUekg0WPLOLtjrcHt+IrIEmB1BEtjOD1VHo4dfKptC1q49ezfs3cf55LVUUV1ZWZJwIFHZyLWHDGNsGg4Xy3eLGxDQYLL5xKYZJTCn1IUIoe8snU0f1W7wvepH6fSSYsGLrnc4RjN9TvfGCJ1vq6YnZGGP207Gph1otfo6MW6wI20Qg9GaalyYI4pZCwIxgO8r2HvkcoYt9BLxtJZ6TkimX+8fO5JXILgS0BfvPKb3h+7/Om5xV0cDbz1fB4jIF1kAVnbGHhANe63NoZMq/7z+CQOmRCJFMflIKuLmMClGcEQC7+EaXqIW8afTOcz+cowK7wV8BfitkRYRSTCGEKtm5hpudmOnXEUvCj4dAOaPNh3SaN4UrY0bKrhZl3zbS03eeKlTNS6uA9ZewUXt33KuF4eMD5Vc6qwg7ORSw4k5EMDnB1TevwfKmAwil9kgNDL0SsJlpaQywGS5bkHQGQi/NeMByku6fbstpkyXnID9fzOUqwK/x/A1wIPFrEvgijkZQV3K2fDNF5JpmFuoI2b5Y2aQyHOjIYDjJr9aysgr/KWUVcx4nErQsUuZ1uvnjkF5l9zOwByYTSB+/qympTwQ+Gr8SXjv5SfjdkRRELzliSwQHOv81J40xzZ8i8hVOqEKmqMhxTh1qIpAuyCRMMod+RYkrKMQIgruO209umPmfpESsl7SE/HM/nKMGu8H8HmKuUehJjAvBh2nGttbYoUSaULSkruJZJsPh07Al1u54oCYZDHRnYGrBcIaVS6azk9cte51v3f4tH3zSfO4djYT4x9hNoNL/44y96VbPAgME702SjqqKK9a+vH/kpSzM4wPk+7KS5ey4Nvj8UNnwrKUQ2bYIZM/Lu+qBIFWRNTcbK3wyboZbtXe2W/hHhWJjz7z2f2VNn01DbYJlS2+V08cvTf8m84+Zl/WxLJTdAJkZCH4cKu8L/xsR2EvAFk+MaEOEv9CexgkvG9MedxXmb4VBHtu5vzSr8FYq1F6xlnHccF0y7gGd2P2Oqrq5yVnHzyzfjVM5+qtkfnPSDnDL9dfd0s+29bTnfy3BhORBnygrp8VD/8VNp++Yto7tGfQEiAMKxsKV/RCQWYcMbG3h619Ncpi+zdL6tdFRSVVFlaYZKfm+b39lcMrkBrCil/AWlgC3hr7XOcS0mCPQOYIHjIZaDGj8bVRVVwNCqI9MHvIkHTrQsI5xEoZhz7xya5zbjn+bnhw//0LRdd6x/NsPkgH3DszdYJg2y4sbnbqTCWcGV/3JlSa9oMg7ENpzwRn2N+iwTIDsRAG6nO2PqaSBrJsl0k5rZ93b5xsuJxWN09XQNuG42Z9yhWolLhb+B5FLYRxByIzGAtdaE6LSO3suZ7p5uDq46mOVnLR+SFZ/ZgKds2C/ixI348zvOYHvoIpTHfuIeAKUUbqe7X4EfO+957Z+uZeWLK3l47sOmTl1FHWxt5Ke3NRCXuyd3AaIQasbU4FCDW7elmtQyfW9WZHLGHfC7wk3jHxbQ/LGF1H/ryt7nxqz+Ra7PrFT4G4ht4a+UOhRYBJwITATO01pvVUr9CHhBa/1skfoojFQSA9jEj8AZh1gB9Ucfdn/IxcdePCQr/lwHvHTi4W6WbF+JmgbkMAmKxCIZcx5koiPSkdGpqyhqT5v56W0NxPXzy9uTe5ChlsFwkPauds6Zcg7rtq3DqZy200WnkmpSy7XgFFg745r+rgiDgoa919I2eSXeBx6mZRKm9S+sohWsJralmL9guLGb4e/TQCswG6O4z8eBZH62wzEmBYLQH5+PlruX8R9n5Kj2t7A/pjMU2cbyGfDSCblg+yHJ2gX28VR6uPzky/tlN8uF1KQ3RU/bGo/3heclVdWhUJ/TZ4rXuu2BOOkAt3SpsS0XwZ8kNS32kiXGtq0ta5hfMv3x7n/s5u6/3k2Fo4I4cU4+4mTcTvO0mlUVVb1mAkhk0XP5+pnUMn1vVlg542acAAKBf+og+OVZNNw1q3eyCtbPbMuuFo64/ggubb6Ua5+5lksf/AFHLB1Ly02LIRjszV+QSx9HO3ZX/jcATwJfxZgw/GvKsReArxeyU0qpnUAQiAE9WusT044rYDnQAHQC39Zav1LIPgiDICWuv8FzKx05CD0nik8cOpWt75kl7e5Do/Oareeq9s5nwBuAhokfwp/HQcQiKZkZDuXgyi9cyZVfuJLAlgDb3tvGTS/eZNsMkCpIMw224ViYVa+uYsGnF9jvXDrt7bYLAQ11IplS9/DO2L8cQ9lSJ3nJ7zv5OW99fyuVjkrT56fSYUSlrH99vaUTZV27UUwnl0mslTNuxgmgG3bUQMAdId5jvmro1RDVXUDwnlXMbLucTtVXECxMD2EFM/ddyzuTV+Jft45GCxNIyeUvGCLsCv/jgS9rreMJwZvKfsC85uLgODVDMaFZQF3i72SMSIOTi9AHIVdSVL+BKSFiZ5GTqjuGpr3LrFRXfxw4chYS+ai9MwmqXHjwE0ZlPzPGVIzB6XCitbYMXUvaI8875jwaVjcQ07GsOQZSBWmmwTYSi9D4SCPTx03PX/0fDtv2Th/KUqul7uFd6P5lmuRprbn005ey8sWVpmGS47zjrO3ewSD+S1bS+D3zw9UVY3AoJxrrZziVjBPAsFGt7/WxEUIWWsBQNMSOvzwJMy5n1T930XmGeSXQzgpY9fEOFpw7h+YX1tJw/xyp8JfArvD/CPiYxbGjMPIADCVfBu7UWmvgOaXUQUqpw7XW+4a4H0IqaZnZWmvI3dFPw76O7F+jRuckJPL19s0kqGyjIGyVhlw5eOSbj3DcuOMsQ9eSK8Ot726lvaudb37ym3REOwhHw9z/2v2WCYRSBWm2SUwkFhmc17Pbbds7PbUMcjEH4uD+NhruOIOg7oumKCUP72J4oGczqSgUbYvacg+TDATwhTXNqxNhuxgrdE/YUAU3f+yHHPedK21fN+MEEKNa35rjXXgGrDUNPJXV1N62FoJhHjoS6/whCtbXwYJtcer/tDu/ex+l2BX+DwA/U0o9C/w9sU8rpQ4BfgzcV+B+aeARpZQGbtVa35Z2fDywO+X1nsQ+Ef7DSVpmtrp2qIxBNJf4fpu+AQePOTinH20+3r5JoTtYpyk0pvfldrqZcMCE3hVeJo/oaDxKd0+fEKtyVlHprOTBrz/IE28+wQ3P34BCEY6FTQWpnUnMoLyea2pyKgSULLVatIG4pYXA4tOJzwibap5KwcO7GB7odkwqdsIkB5giWrfgC4WoD0HbdRCYZqjma9sNQe1tVJBD+GXvBHDVmcS7uvpPJFaDNwL+1100nmN+fiQapsvhIGh3cZHQPo36ENEcUNoqi1RqI6UOBh4HpgIvA6cALwK1wFsYKvqC1VZVSh2htW5LRBg8ClymtX4q5fh6YKnWuiXx+nHg37XWL6dd52LgYoDDDjvshDVr1uTVn46ODryjwNmo6Pexdy+8/Xbvy7iCV8eBLmCMPxgr2snVkzn4gIPtdy24l7c73rY8Ps47jvG+8b2vOyIdtLa3AsZA7FAONBq3091PCA+WI6uPpObAGtNjcR1n8zubMzocOpSD6YdNB4yMbuFYGLfTbRrm1RHp4PX9r5uWS06S/jnYpaOjAy8YIX5gTAKTk4G6uqF11ovHYfNm9nrjvJ3hbc3udSh/67k+k3ZIfWYmuCewJ7yn91jyWckW/mf27KM1de3g7TZ5dhwOmDgRDjkkY78GPJ8a4n/dTLsrTtgJ7hjUdIEj+RZ1dXRUOegMdbI3vHfA78CReHlIJ7yb4Sub9CF8rDt7H4vNcMiRU0899eV0n7kkdpP8fKCU+gzwTeA0IAS0A00Y6nf7gcj23q8tsX1XKXU/8GngqZQmezDCDZNMANpMrnMbcBvAiSeeqGfkmbZz06ZN5HtuKVH0+2hqgquv7lX9rjwJFjUkjhVwAuBz+fj9Z36f0700vdLE1RuutlwRLT9rOTOON64XDAcZf/1403SnhaRCVfA/U/+Hp7qf4thDjx3giNb0SlNviJMVnkoPy49ebns1s+KFFfz4kR+bOn2lfw650PtsdXQMf3heUxNcdRVNU0JcPdNQT6dToSr4+j9/nRWnrOj3mQ/lbz2XZzIXXLtcNKxu4GdH/owfv/7jfpqgbH4EmZ59X9hY9XvTrUw+X8ZaA2Z+DQ7loPmAH1D//26yNhX98pfgdvPEmCree+9FGoNriZgkvfKGIeyEqIk0GxOBd38FXnfmPg4FpSZHbMf5a60jwG8Tf0VDKeUBHFrrYOL/M4GfpzX7A3CpUmoNhqPfR2LvH3oGqAbPa8CXSEzS5oUFDZgLfQs1uBXJRDepg1jPm+YOPlbk4mRWiPA+O/ToHkLREMufX06V003jQ5extutsdo2rprW2hs0f/C3nDGzZmDd9Hlc8foWp8FdK0dXTxeJHF+fvFT/IQisF8cxPZJb0b4HGmeZNenQP6/62jvtfuz9v57rB9rVYjo9Jk8qGxzaw5HNLcjKpZDRFjKkicIJm/l8qbOcdyOjX8N6NtEUjmJ4ZCsHll4PLheNnP8O9dh2VX4wSMVHzd7igIpk/KzG2VPRAVQweXuvG63aVT3KoHMg5w59SqgITK5rWujB1TeEw4P5EUEEFcLfWeoNS6vuJ97kFaMYI89uBEer3rxbXEoqEqZeyaqT57mVMn7eYObND2A7Yz4C30suyM5ax56M9/QaxTW9uyuk6uTiZFSS8Lx0Nrpi1x393LEw3MNO9lup3ofNDcKnsP88BoXFZMuxZfQ4aTSweY8ljS4bNK75gnu+JzJK+UKjXQa1HQVfaqDUY57pC9LWYjo9el5dDqg9h6Yylts8JhoOs3bbW2mEw3s2OqxqhbWqfZqehAdavhwcfNH3eMk4mlCJwvIv5z1nUyIhGjT+g1RuxDjFU0FPR9z+AU0PrnvMYt/hL5ZUcKgdsCX+l1AHAf2HE+R+K+bqtIGVbtNZvAtNN9t+S8r8GBhGULAyGTLP5M15dhL48TjiWQfDnsOpf51/HmR8/M9+u9sPSySysDVVxQmDWTZ1YkPC+dI57B/483kmUDGl+VV+ERERn1270WyHazLCX/jlMOGACSx5bQkdPX9KUofaKL6jne0pq3PpdsDYAX/o6lhqnXJ3rCtnXojs+2iQ5mclUrMpT6aH2sKkwK/E5tbTAlCkZn7eM0QeE2XGwvcQXde2GQ6CZCceMimoP6xd+SZz7MmB35X8rcDaGjX8bkL2WqTBqyTSbTy9SMxjGOMew+6Pd1g1s5JFPZ4C3r4nA9LsVjZcPXmvRD2Wo1TMKfis0VMYTURMJAVYVhUpXVd8KMS3MEshY/z31c2h6pcnSCXCovOIL6vmekho36Iwxx9/ZtzI0IVfTSaG99AfjgZ4eBnrwmIN7/UdyuYZVSd9U+k00bT5vWaMPvn0ptKzs/f0FD6omUBem9cAYde3g32K0zWTCMaNcU/bmgl3hPxO4XGvdVMzOCCODoqjFTeiKdVn/gDs6YPz4rKvcjFgMYL4QNK8eQ8M3vcSIZ06mk5SZWbQZnghMOX4mW/Y/nftnp+D0N+CY92B/NYzthKnvg3/mJXiTKua0MMt+ZKn/Xgp5zwveh0Rq3MDtC4i33wNYV0fMNatgKXxekDkMtHFjI3efcLet62TzcXE5Xbid7v6miJTnLeiCVdPhoaONQ2fvDDPvnlX4vrsgo19DJBaha/IEgju347u/mZY3nqShah1x7SREDE/YEPh3u8AXoS/HQJWbkA7j6oGIE8vf3nN7nqPplaaSy+hYKtgV/iEMD3tBKFjWu2xUqArzQTkYNFb7Nle5lmQQmPW7Haw98Luc3b7C8nRnj/EDCtv4FcXHjGHZ12/n/hVTsjdOwxOG2X+D+X9O3emBBVP7XudQ/92sPHGm1dmEAyYMuqpaNoqS8tfrpfWYcYSeyVwW2dK5zkKzNNTpic3ItFrvjnXTHeumtb2VjkhHVhNCtsn8aZNP494L7kVr3fccvLkZfzTE5klw5jegq5JeIbyxNsLivQvZuMvIGJnu15AkGo+y5LElXPH4Fay9YC1z3v0DwUhKQqaEir+1xnDqq98Fbbd6CTy0jB2hPTzauoGX333Vst+b/r6JF9teLKmMjqWEXeF/HXCJUuoRrYfADVooaQqS9c4GTofTfFAOZCjok2WVm0qwdQuBKSFaa+hVMfoSBq1gNMTs91YQVdaCI+bsizXOhnKoXgev0+88nXBP2LbvQzLjWf+daYlzbNZ/typPbKX212iWPLakX9rWYgymxfJ8zzZRraqoMneuy+A/4T9p6NITW2E3IsWOCSLbZGb21Nm8+var/Z8bt5vLFxkFu9IdKVHQSQ+zVs9i36J9vX4Nq15dReMjjf38CpLvee495+J0mLuNaQULzlYsfRyaZxzClj+u5YOjDqf1wzez3n8pZXQsNewWWR2P4YS3XSl1m1Lq2rS/Xxaxj0KJkRRiqdXmXI78Ss9m4g8X/sH8x9raaq3iTlnlBsNBml5pYvGji2l6pYlguG+V1LKrhfGeW1l4FlxbDwtnwvhF0DLJOB443kVEZRlclf3shQpFYEuA+kn1XHLSJZkFf0IOe8Lgi1fQfP8YvMmKZB5Pn007Vbvh92fNsGdV2a8j2oHWGq/L26+qm9flRWtNR7SjOJUAUzB7pswqy9kh9Xvv7ulGWXzYbqebt3701sBJTKo5yKRCoS+qCtbXfLFjeovruC0ThH+a3zLxj0M5aKhrGPjcEKbDnVjxWxCNRXurSnpdXtwVbiod5ieEY2FL85pWcPc0zfiFmgXH7GR5eBN3bruHf0T+kfXekqRWuBQM7K7852Ckc64AzjA5roHFheqUUPqkeyn//cO/c8/We/K7mAZ3j5H/vjIGzjg88PsxnHlatVE8Op26uv4q/1QSq9xMoVjTD5tuDGa6uzdoNalinDUXlj4GK0+M2PNqtZmzINUWfNTBR2VsWxGDHz4PU4Mu/Bddj/fFedkT59io/x54pclytehQDpadtoyqiqper/Ouni6WPLbEtH0xHAEL4flu9r1rNGMqxqCUojPaSaWjEqfDyQPn3sO4NQ/1V+uDLf+J+vnzh9VL347pzaHsFb/KFnK4vnW9tZYhw7MfjoX7TT62vLslb1NhT2KS3VsVM8ekYaFoiN+88hujJoj4AAD2M/wdWeyOCCOPpJdyMBzk/P87P/cLaEPQrb0X3vek5QqPdFnb7/1+IzTPDIeD4HkNNNw6xTIU679O+y/LwazDBQvPMlT6trA5CHl0pT1bsIbLnofrHgV8brhwnv3EOcn67xYThWyOag9sf4B1F6zrHRgXP7p4yB3bBuv5bhWCN6ZiDD2xHipUBdF4lErtYM4959G8ror617v71Pp3323bf2I488TbNb3ZNUFkQ7PoHQAAIABJREFUmng9uP3BvIS2y+miraONxY8uBgW3vHRL9pOKyPN7n2fLu1vEByBBzkl+BCGV5ErLbo35VNw98J+PwzOTDJv7FU/32dwBa/u9z2es1Hw+01VuYKf1SiWu46x/fb31YKZyEPxJtJGXPFyBpSbAkeK/kDF8UYHbWQEeN3z2s3DaafCJT8DSpXDEEdn7kmGikG21+OibjzLuunFs/MZG6ifVF9WxrSCZ/NLIZAfv6unq97pTh8ENDbO7jZS1SWHf2mrkgLdZoXC4SF2tWxV9qqupy0kTYTWZydfBNxKLsHbr2vyKYRUJ8QHow7bwV0odBfwbUA/UYOT2fxr4VSIxj1Bm2I0PTqe6Ygx0daE1XH1qX2nQxplGOE/9rkTDUIjgjq0EzDzNvV7LVW7ro9YrleT+gkYrKPjimzD9HUP23/RpY9uvUtm3HugdaOo8E/FEMM1Y5olA7aTjIPQSbNxo7HzhBbjzTrjpJrjkkry7aWe12Bnt7HXUKpYTXqFr2CfJJwQ1jlGhrl8khVI5VSgcLlJX69ve38b+zv2MHTOWqR+biv9YPy8981JB3sc/zU/jhsvzOreUBH8qpVDVcbixm+HvBOBJoBt4CHgHIw3vbGCuUupUrfUrReulUJIEtgaI6dyS1igU/3rcRTS9fBth3edJn7S5N8ztKx7SUuemYczNxDc4BwgJwHKVm23FevbRZ/On3X/Kqd+ZSA/Fu/JpQ6Bs/RjsP6CCsbO+ymudu5geDuJz+4yEJRY5hBwa/HdYDNoLFsBXvwrjxuXVz+Rq8YzfnZGxMmHSUWv+8fNpntvMrLtmEYlHiMQiuJwuXA5X3o5txahhnySfFWrIbZibeonHYc+erP4TpUK/1XoyNPGJ1+GVNXBUZt+SjCSvtXUrvvZ21oZqOfcTrxJLpNJ1RROpqk20XE7lxKEcRE2K8BSVFA2cqwd6KhRxiygWSQJk39v/V8Cfgcla64u01v+htb4IODKx/1fF6qBQujy588nMCXBM0GhueummfoI/lS4nLJhlFAZqmBMmqLtNPc0zhTll816ed9y8Ad7ag8EBNLxVQdNnXCw+HdZMdzIx5KTpFBfrPuXmht33snDDQsZfP56WXS34tu2gebVRJS0ZKugJG6+TtcwtWWLugGeX+kn1fP+E72dsk+6ohaLXY14ZL/LGTna8fMn0vVvhCRt+Jr04HIYWKek/sXy58ZkvX268tptAaqhpaTGSXi1cCNdea2w3bzb253utyy6DG2+kZdOdzDn6VSq0IfgrY8YjMCZD9ulCC35HDEOdlinxpoIv/t3BkucrWfnPi1k+69eWv++hysdQythV+38auCC9eI/WulMp9StAYijKjGA4yLpt6wp+3Z4KuOeTcO+xgNMJJulwk7XBrbBTMCVVZfqbV37D83uft93HaucYOmNdeCLGIHjMuzDhhz0obfgLOIkTQxt9jxqSvHd1e+dM2m6PUR80NBwbboQlTxsCqKEV1h8NDx49MO9AL9u32+4nmNvWpx06DZfTZZnH3e10U1tT27tKTw3pC8fChGPhvFfpxcyOl/q9d/d02xJApjkUkmr9HCsUFsOPwd4bW6TajcdzS3plcq2gy9DGBVNy6kedgNMol+sNG6F4IZeRM6G7pxuHcuSsEcxG3IYfjqfHwewDTmb+L9fB4YcTDAe54vErTNsOVT6GUsau8O8Cxlocq8EwBwhlRGBrAKcqSC2nAUSdyfh58wEkFA1ldTC0EzaWVJlqNH9956+27JNup5vph36S+MsvQFzz/Hh4YQL9VsOxDMuTeFcngVrDROCNwCGdsPRxI7/AlMsgnhhI3T1GSeSFz8GVT6VMAqbYzxBoZVtfe/5aXA5r4V/prMR/rJ81W9ZYrtIjsQirXl3Fgk/nVl+rbmwd1RXVpp91IVZjye99QfMC7v7L3fRYFEdyqQrc3T00r6vCG+nuU+vX1eWl1i+WH4MtAgGCzhiB4+mXsAqAeJzgPasInOC2NylJC3MMHGv4RZjh0LDsUaiqcLPtK5/lpq6njNwXQ63uT/YnFse/ejPcOQWam/HVD8wuaLtqYh51Q0YadoX/emCZUupNrXWvHkkpVQ8sBR4sRueE0qV1f+uwOfN4Kj24ndnLe9kNxcolY2E4FuaFfS8QO0LbjvFPJeRKszFjvrpKpgy+9nOw8iR4OOkIuWyZrYEpk219zv/NYZ1/HV9Z85UBXvDVldU8PPdhvC5vxlV6OBbm8o2XM33c9JyE26QDJlk+N3Edz2s1ZrbiXjFrBff/7X5TZ1SX08X1Z17PvNrZeD++vr/D6Eu5O8kV04/BDi1vPEnDDzqJ09959m4XtIwN0fD3HxJvqyREOPukJC3MsbXGupJeyA17xlaw9CkHTfyJilNjhG1W3SskFT0wJpYwmX2YeLYSGo+8ckfYrI450rEr/BuBB4A/KqXew3D4OzTx9wywqDjdE0oVw7mqmlCONv9C4FAODqw6sCD55pOC45yjz+G+1+5DoQYIxHR6V/Z52L4H2JjJvLpCQYc74Qg5/jq8O3bYGpiy2dZ3f7Sbd//tXVa9uor1retBw5emfIl50+f1RSVkcaCLxqO9ws0OwXCQ2ffOttXWLplW3FarvrUXrGXXR7v4xcs3UPepOvzfuGJQ6vlCV/nLhWA4SMOY+wimKJtSc+L/ZC50VMYBQ1OW/C5nrZ7F0tOWsvuj3f1/P2lpojOV0vXgovYjBV1dbDnQfrndQlIZg6//FVY8nOYrkxImnFM+hhyrY45k7Cb52Q/UK6XOAk4CDgf2Ac9rrR8pYv+EEiQYDtK9fSuRrs6iZ4qocFTgdrr7Dd7LTl/Glje2cNVzVw1KxdonOGKEop1U6wpiCvxHf5Xfv7E+r9wF2YhWQFeFsdpPqvIzra6SxD3VBD7lZv5Z9gYmO7Z1r8vLgk8vMFXdB8NBunu6M9Z3hz7h9nHTVIz9CWwNEIlbXy9XQWlnxZ2+6pt44ETm3DunoOr54azyF9gaIOZUYGLd0AoiFv6PHZEOFj2yiEgsQnVFNZc2X8rsqbM5deop+N0KX+J2MpXSdaBo2B5j8emw4mTy0oQNFqXhxDYTP8C0Yla2GUR1zJFGTkO31noDsKFIfRFKjFR16sQDJwLw7J5nWbdtHc6uMFEXRf/Bf/+E73PcuON6B++GugamrJjC1ZOv7hcFADZVrAmVebB1Cw2eW40Uvwk6lTGCNv/1PtZ88idc+LdriOk4UaexwrCbxz8TEScsOR2uON1QU0JidWUR958k1NPJjmcf6jcwBV2G1qC1Buo6wvgTZVRhcFXyUlfT2ey3SeH28Yrswr91f2vGycSAKIM00tX73T3dtlbcyclEMBxk/PXjM04W8qFQyZDycRh88q0n6bTQVGmVkg7XhOR3kTTD3P3Xu3ngtQdovFzTvHoM9Ts1vu5u1gbg3Ash5jDS7FYrN06Hk2Xrwxz9/RgdLoZc6CelfaSi/++pN0dIvsmYcqiOOdLJSfgrpc7E8PxPXfk/WoyOCcNLujp1AElBVcQffZWziuPGHddvJdiUIT991pVjii0vMCVE/Cz67iOFqAO+tuXnODV0uwybosYIN7LjdZyN1JwGv3c68O/00jgmDjFrE4qn0kPtfozERy645vNw42eMjz9cAZ5whMa9jTQnyqjmkqAnfZL3H4//h+2iPb3CzUaNlbqxdbaiDMwwU+9HY1FLTUIoGmLdtnVcMO2CXgFqRz1vR4ORTiGSIeXjMBgMB7nvtfssr6k0VEYhmmECkE7yt376113Mr/osb3z0Fk9U7EYBPSpOpaOSuILVa+N8qyFGxzCo+tMXHGY5QvJOxmSzOuZowFZQrFLqCKXU8xir/kuBf0lsNyqlXlBKjS9iH4Uhxqz623CQ9DoPhoOseGEFZ911Fv/51H/mp2JNq9TWWmO90u6uNIoMdSaO91QYf/FcQsgzxSMniLsqaT/iYHxbXqf54EvxautR2qEc+Cd/iZajqzhikeEIGKnocwwMuSGoIr2hebaq5AWDtNy0mPFLD2Hhg5dy7TPXsuiRRTlV61NK2XbS80/zZ6z+mPy+07GqRpjJhADwxM4nenMrQPHU84OtSGh1f8FIkFmrZ1l+H4Gtgax5DXLLetBHOB5hZecmNlb+naiK91a4jMajdMfCXHhOt0UszvARx6jGaVr10i42qmOOFuyu/G/DWO3Xa62fSe5USn0OuAe4FTi78N0ThgO7tcKLhSMxZM3/1Hye2f0M5wXOs51MaMKBE8wPpNny6tqhKmoI+gFYmDJ6V1F2tB0KnKiMYX8hFSXs1DBlCvXxOPuiUa75YgXXn9iDqqwgov8/e+ceH0V97v/3d/aa7C5W8AIGqAIBTdqDpWr1Z06PFwQJVo+CbHuw5fyKx18pWLm0B1Q8pz3aSqkg9AD21Pj7FRVLNN4lXLy2plrwUmgJCsEb0nANrW42yV5mvr8/Znezm8zMzoYNubDv1yuvJLuzM9/ZnZ3n+zzf5/k88YzSJOkbQeXHt1p6W+nRD8tM57o6QtdNovL/NGdUGWRb4++IqqlsP7jd1rYBT4CNN21kwiMTLKsMOtLV67GjJoGt8Lz9LrEZ5JpVnh5tOdh80LQuvjnazD2/v4cl45d0eq6hqcHye3FqK1z7Pjzx5a6dkxWqgLYe8PqFBtLENoc9sHfqlfDi411PyrPRHbO/YNf4XwF8N93wA0gp/yCEWAQ8mPeRFegxuqKRnk+0RO77iq0rWLF1RW4vNrO1DQ2EYuFULfTpzdBmdvWbGPeY237o36lC3GHt/vtcxXiO/C2VwOcHlmyKs/gVqP6qg713/YBRZ5aljEjVu1VoRV7QzGU1OnqwhpnOiShIdWmzeZWBTVrjrVSuq+SZi5+xtX3F8IqsVQYdyXY9uhQXQgjTiUtyQmQnPH88evgd3+tQJGRYkdIxxO8UTlM9AoDlby5n8dcXd3p/LCczUT2htCipjJfn5bmYA93V7mpooStImLJbsHGkNO+LMe7KDAPdJeGlLN0x+wt2jf8hdKEfI1qBo/kZToHeQOmgUopdxTlL9/YGNuzZwHfGfqfTF7xuOFQuIFUL7bGQJrXC7pp/XAFPTF8+MEOJawxs63xX9kdh2p9Vqje8yZ5JGusTPcgbmhoIWxh+sJlgloiC2KkysEM2xcWOWFUZGJHNY//5VT/nhd0vsOkD41zk5ITIjvLj8ZBuaBCwZtsaJDJTYGlaDVMfn5qRdGhl+EFfWknPZUkeZ+fhnaYRA8XjZWDgDAaefR6I947rvAzpgcx+bwy+1qjwbKnJOUsIXncn1H4NKiqOT3gpR3XHvohd4/8z4L+EEO9IKfcnHxRCDAX+E/hpdwyuQM8w/JTh3Wv4u/HGkVznTf+ChyIhKj9bYyiikzN2xy6sj+F3+6n9/Fqifo2qDspsOwZD5fQ4mniT8Ntv4lO8zN88n1kXzMrauMZWglkio9mqhht0j9qOWls2xcXjlb3N5rHPGDsDj8PD6/tez5px3xXRFzvjz5Ygm3zs2t9ei1PJ7eKLqlH2HtwFVVW6oE/RU2hOhXCsBa/DC7RL66ZPZuIfxhk5+vuw8dacjmeLE2j4hQRvVJ9433GFRkzQHuETaZ0z14E/FIHKSkIf7+5R4aW+gN2rcAK6vO8HQoh3gcPoAj/jEn+PF0KMT2wrpZT9JyviJCMUCTGlOr9CLCeS9HXe3XN28+R7T/Krt3+VPzGiHG563kSVgFPqyYVuhxupSa4ceSXnDjqX5+vfY/BguGtiuzLbvIn6emprWlgzrLVBtI2VW1fiFOZfWb/bb8+DTWQ0B3eGTWu4fS4fcc1eeMRKcTEfsrd2PPZcMu6zib7Y8eA7TS5ttrZWNTVn/Qif4mXUPasJveegclaLLuiTmJO1qXokSErJ/EvmU3Za+zLRax++ltNxeiPnOE7jdIppS30fMgW23HH4+Ysw489pIj+aRvWji3pMeKmvYNf4nwY0JH4ABqDr+SdzAE7P87gK9BDV9dXdIm6TgQCHFHgVN2EZsSz/6iotsRaG3T8s7w1GcqHNBfP/AGVHoX7yhbw3chAvf/Qyr3z0Cpv26iHq+8RVKc875YGbpApE1AjSISlyFqEIhXAsrE8oEjd+o3VhQ4JBmD+fQFT3liqnty+H+KKg+PzMuvD7rH5rta1rQREKA4sGdno8n7K32Tz2gCfAkvFLmF3beSlhyfglSCltKUI2R5spWV6S1YNPH38uCYlxGbcdUUmitLYRfAfWWyhBRtQILuHqZNA+/ezTrPv3RBPLUye6Vt8GP7vuv/nrzr+aPu/SwKt2UPcLh2k4spuws2eEl/oKdhX+Lu/ugRToOdI9ndc+fu3ENOZQFK4tu4HB/sGMHDiShS8tzGuSYU8a/SSeuG74h0U8zHb+icgHNhMNLG7CUTWKx+Hh0mGX4lScXDP6Gmacb54sZ0haRnNFk0bjsjDV49zsPVUy6l/nE/znxdz9u7uzfh4dQ8wdybfsrZXHHoqEWPSScbvjH264jYUb5yOdDsKxFtPoQygSouFYgy0PPn38uSTIFjuLTa/NImcRDsWBlO1RBiWuUvsk+KNtWXM07t96P4v/KXMCaJUv4YnDFR/C6WF4vJw077p3cGPZjUx/ajpLRy813caoVwY+H6Wnj8HXvPO4hZf6M90szlqgt5NVzKebUKXKk+8/icfhoXZ6LZtu2mRYAlbkLCKuxXusU5gpNtb+NeBPZ8LNX8tvJCWiRnjpo5fwuXz84dM/5NxcB8jIaPbv3cvMDhnNVkbD7XBz5dlXMqVsimWI+UTK3lpNNFqJg4ynQuVm0YfqevudydPHn60HQjoSaVibX+wqZvNNmzl/8PkZ0Y3K6nfY4H+A58fDQb8e5o6a3LU1TePGx2/UP5dyfZkjWB5k3uZ5ZoPhoWdhxvW9x/AP9A7kopKLqNtXx3O7n0tV/pjhUjv3ykBRCN60hPn/87Tha6zyYnqsLXMPYNv4CyHOAr4BlADejs9LKf89j+MqcALIZa2yO4iqUaJqNHUT7lgCduWIK/E4PazZtob3mrohY/l4yBYiTTT9W/01G9savNbOa447gckio9lqDd3j8PDQdQ+xoWEDd//ubkoHlTJCjui0nV3Z23zccC29b5P3smP0oaGpgTPkGbaOlz5+y66Qic/SFwXhLUITwjCZVkHh/MHnZ0Q3tnywhXOKv4c6WZfVLYro8tBmxGWcTR9s4uWPXubWjbdSfUE1Ow7tQNWMIw0RJ4y8DVpzUADsViRc/cUref6jTTTH7AlNOSQEPyoGWjLq8QMDh+Rc2dGjbZl7AFvGXwjxTWAt+tfoCNBxgVYCBePfx+hpMZ8k6TfhZAlYT0Uk8km8i3G1orgeNYg4sTUJ6I4EJqskuyXjlzBm1ZiMx+8ecTfufe6Mm6SdJLzON9xi5r9wK7WtN1Ax8nLbfdRz8b6TdIw+lA4qJdRkbyKc7j1mvld6kyhfRP/oZm/Tf486Bq1+jR9NMP5A4zKe8Rlu2buFiesS2ZgJg9+anhNicV3EtBgxLUbDsQbueuQu8y6VycRSG2qUJwQBRz7amdM9af0N6/Gf3WxYj59LZUdPt2XuCezenn4KPAl8T0rZRQ2sAr2NnhbzSdLxJtzTEYnj5nhuphJ+8Ee4bSuMuM1EgbADyfcv3yFLo5tnsrFSx5ukJrVON8lsWfpSSoMbru4VV4rHaPzRM/ht9lG34313pOPa7/ABw/mL/Ivlccy8x9R79cBs9m56jFGH4gTrMxPR5k1WaVON8z7a4m3sOroL0K//66qvsxyHXbK1pwZ6TaKfNwYOtY2ww949ySVcHJXNlvX4dtv59mRb5p7CrvEfBDxUMPz9i9JBpfjwEKabs/uz0PEm3FsiEl3meG6mQn/5kGZ48ZFEJr7bRVjELI2YRGZkqecrZNnx5plrY6WK4RW882/vMOPZGXz8t485+9SzWXvdWkpPK7XeF1B9Tgsz/4StPuoZE41olDCRlPetifY+Demke++hSIipT0zlP8/+T8P9z/vaPDxOj6X36Hf7mdk4GDYZG/hjrril197U0gQk2vSahOrB/PV9HZcGk0+7hNdbDttySmIyxq4juwyfy3Ui3JNtmXsKu+KMTwGXdeM4CvQAwfIgiuz5mF/HBJzeEpHoCXyR9gSmin3Q+EAxK49ewPxtTlNVQhGPs3rbasPGMMlGPzkRCkFVFSxcqP8OtXvmud4k17y1htGrR/Pm/jc5ED7Am/vfZPTq0Ux4ZAI1u2rM9+VJy+JO9lHPQtL7XnnVMhZtdbFyExxYBpsfhUBEf2/BuOmO1YTT5/IxcuBIRg4cyZ6mPazfuZ5QxCQqlewKZ8CpMYel4R5UNAjQ3+Nel+DajXji+udT+3QRM75zX9ZmRek0tTZ1eqxuXx0ly0uYu2kuS99YytxNczMaPBmRXDYyor9WB9j1/OcADwkhqoBXgL933EBKWZvPgRXofgKeAEu+MJXZf3/shHoTbk0QVSQ+3Cgud6cQqtX6rdfpJRq31gS4uORi3jnwjqXee29FAMH69v/9qoOZ7ouh9k2u39mhJj+hbDbLcT6rnTsN92cWsjT1jBINf9aeF+WFs6NwUOGah2dzw39UUzvwKDsO7cDj8BjW/3e8STZ+3mhYdw/w4ocvWnb488RhaDLOmEMfdb/bz8z/NRvuGKtHDFwaFfvCND5QTPV5Kntvmcqof7i8k/eebVIzb/M83A539qhKQkPBiC/9zYnX4UoJ82Scr+Kh7PQyoG/La+eKKw7LXnUz4z03/mc3gkGinhWDigdl/N/4eSNXPXIVbfH299jO2n0+2jL3Newa/9HARcA5wHcNnpek0lIK9BVCkRCLWp474WHE0YclvhiMORplSZ1gyNeB4e3PW30RXYqLe666B/GJ8aBdiovRp43mqeBT1DbUsvfYXiSS/97637TE83sz9Tg8CCFAYnhDzxmptw3ePhgqmtI6ib3/Pvh8uhFbBtXlulc86pie6Xz34lMJN9v3xk2zmq+vgX/7ZyZ+r5WWlOiLxuZzotxad70uIWtxnh1vkre/fLvl6Vq15I04YNF4GHso8V4MHQpVVYQadlJ91t9oGDWQ0iHl5uHcDs1Z/KNGMdOiOYuV5wftSXSQxZhYdIUL/rSG+W9ONXwPI1qEYacMA7LkL+RKmgxub8Lr8OLSoFadQsX/vtw4Ue+Ze6jZ8AtEqck+nF7KTitL/V+3r47xD483FaayWrvv7r4PvRG7xv//oTe7nAzspXO2f4E+SHV9NdqJTvWV0HCansm+8wx4uixC7c0TqHj7cOrLn+2LOPbMsVTtqzLcfUyLsW7HOqp3VvPct55jZuk06h6+hxWxxA0hjzfBK865goeufYgxq8YYG8VcexgIaHFB5f9201jyc/zfmqG/J2PHprxJfxR9HTxJwEHpJZPxvZZd1x6ss5onVV8LN0Q6r48nzsHM8Kc+m+tr8D+8Xu8dUFrK+/F6w+1tIaDZo0c6Gtdo+Bctou6sOJVT2tAOQ/jvpHoemOY15NCcJVge5LZNt+U0xE7GJBTSJxsNDfCzn4EQsH9/Kgs94PdTM+hhJj51veH+pj4+lcYFje3X/2+uQou06R3sjqcfRi8z/KDrHeyZ/zE+l4+q+moa3rw7IwLlj0hm3ryGaRGNqgnG+3Aproycjcp1lZaKlNnW7rvS96Evk4vnf4OUcnN3DqZA96JJLUPitP5w/YlfW09reJNUK6u8oZXG367F/2/tIeJsX8TSgaUE3AFUqXYKj6qoqKrKxEcn8lCtm9kTol1v5GOCz+VjynlT8Ecl33Ccx2NyW6ebrEc4iZB7+0DN7aL6q15mJr3UQIDQczVU33ktDaeolB6KE/ywmIDqgNpagl8dy/zf3WG4r47euNXadlTGbXcthEyxn3MaT6fioqkZ3u65lRG2ldvbl4JiKOiiCag+N860P7VSOYWM5kzJngf5KsWSOea/ZBiTujrTHvChC8dSXb+ehh2vcnDj4xSXGicgJicT08qn8f7R97k5Wk5T/TsEIvDQV40rP7K1A+4Rwy/BHYOoxaTFoThY8eYK1ry9xjhJdcv7oGkEonrDq0CkgwS1x2s7ZyOJnbV7u9UB/QG7t8RtZARmC/Q16vbVsePQDu56865UPXU8FsUrHbSJEySFa3Ij0IDqTzYwk8z14eQXMbk+nRSUCZbrk4DGBfqa8mN/fsz0BjhzUvcEqeJanC1b1zH76ZuJODA8L8Xpwq94aYm2ZFUqSyccC7P31RoonQaBRP/3N6eiTXYSjkXwSRfzFY3aaU9TcW4FAbAdsmw4YD7hiwo1p8W7qBrlvNPPQ7a1cuDgXqpKQwR3QgAgHObejfBwmfF7k45DOEwlb8Nu2HuqpNpC1z4fpVjV9dU5JZqBbngbQ42EmhoJVFZmJEYS1t/jun+bQOW3HWhohGMtOMeY6z+EY2EefPdBvr/h+wghiLgiFJfrE6CKT6Dui3rb2hZ30gB6GD34S7xz4J2unna3IDRQBJQdgl1nGm/TEmvhF2/+IsNgZyyn/H0m/sR76I/Seblr4vfxp0V77CQJ99e1+65i92qfD8wRQtwkhDhLCFHc8ac7B1ng+EiGxNITaMKxFiLEaeMEauCbGIGwB2r8n7LwxYVUvVuVkUltlrnbHG3G7/Yz2Dc4u+eTR+8ntSsJjx9+1VKIRxEKP/6nH6MouRkVXwRG1bwCJSWEXtuSCtOnPjsRIyTbqHx6aiqTv2J4Bbvn7GbKeVP4WsnXmHLeFHbP2Z0ZDq+ro/RnD+AzmQ+5HW6cOVwODuHgl1t/yW0vLuCgTzJ3IpQsgLqEm3BWM6x+KbturFUfBp90MepQ3FLXPtdSrFAkxKptq7j60au5+tGrWbVtFe8eeDfnKFhcxnnyvScpWT2SuiGds/NDbph0fSuhWHNKvyCb8NPWv24lqkVT4esWt+7xvzRKN6qqgOk7YOVGaFzhpOXtEX5TAAAgAElEQVSzozmN+UQgHfqYPxgIRRZzb8uS0bOOZVRNJJe77n0ZZu7x4R9VlvGabDkbXqe3367ddxW7nn9yarnWYptCwl8vpVfUzUtwqya65BJeie9h0xs7KXYVc+vGW7nh3Bu4ZNgl3P7y7RmlaskbdMOxBpqbDsCfdxzfemiup5E4VkTLro0QjoX5zfbf4Ha4bbfHBX1GHnw3CtEo1XdeizbZ+Gua7vF2TOLbeXgnT7//dGo9PNTUSPXC8ewMRFBN3iu34ga3g2bVhjAM7UY7eWapZZzpsPu/YcNo+KQoyn2xy3n3K0PYfnA7e47tye29UBwEP3KxXuiqeUYTgGJXse1SrLp9dZ16SGz+oOurmcnr8aob4cMVMCRt/nDP16E5j5r5SYW/mjL4xYuwfWCY3aFwr73zRpzg6IJvEY6F2Vt6mr5sYoSi6AmCaWSTo/7oto8Y7B+c+2D6MXaN/3fpPSKQBXKkK7rn+cYpHLg11ThTVJAqyUuu3z+28zEer3/c3KuXkrX/fA6rvx6BE9mUJMf3a+cR4xK8DJL674nSvdp17cpwDaeohGPGE410ZT8radKaaTVMXXcd2mURwm7wRvVjeuO6h5ZaQ71pIwATH5lAi4EynFmJX0diQlcndGiJNVrxBsoevaRz+8HtLNiywLQEM9neOT2JUK6cQpsDYiZGriXWksqUtxJ3CUVCTHp0kj3Vuxxpc8I5c+GlR3R9hpAbVlxMt3y/Ik4YNk9fBpG91PADIEDmFvQCEmvzg8vaqyaSk4C0PIqOVRvZkoTTDX8oEmLtjrW8sOcFAL0z5tgZ/baBjxl2W/r+ppvHUaAbyRYSOxEEv/xNvlf2HSofvxYtHifsUPHgIGKx7GAVzteQPDImQri3NCU5DjxxXQO+7CidJGFLD8XxyYTCXweSCUxWkR1Vqlz722uJyPZJUrKDW1zABfv14y45dyZDEksEh350mLU71rJh9wYQMLl0MkMHDGXqE1NtnU/HDnFhGYFohMp1ldw87mZL7YUrz76SsYPHphI8tx/cTsk8Da01ETUyifJMfXyqPsl5fKqpymF1fbVleeFxISDigknT4d6X4IXR+lp9dx1L7Q1GX4JDKKgW+SyKBGcMojl8T1Nr826/Xq65aRMsWkRoxFCqyyUN4ecpfff9TmWedrL16/bVMfHRiRlJwps/2My/v/jvbPn2ln7ZwMeMnHKgE539LgEGAseAN6WUjd0xsAL5o3JUJXO0OT12fJ/Lx+VnX07FuRNoXHQ09eV89eNX2frXrV3ap5Dw9ln0yjIm20hQELw0aC4Vr/8K2vRSupAbqr8EDQNhWNiBUBTDuFvyJnn37+42jey0xFpwCuOvedwBbw+F986Apz0PUbvvm1QMr8Dv9jP7wtnMvlBPwAxFQpQsL7EfrjdL7JQaTS1N5v3lHR4kkpEDRzKtfFq79r/a0h7dMfm8VU3luvXXWYq7NDQ1dLvoU7MbFkzIzdj1WQRMKZvKE7ueQJoEhuMOcGWLGScjXy4fAsHMr8xk6uP6RPOa0ddQNqiMulmTqVxXifqqSktcv6bn1M7huW89x4SR7bWAVtn6oUiISesmGYontcZbmfToJA788MBJkxdgKygjhHAIIdYAnwBPAP+T+P2JEGK1EDmmyRY4YdTtq+PcVWPQTBqKnAjSs2yTX847/vEOth/c3uV9SgFqX73qpB4SXzjgar505pd5f2wJCyvaqBoHW0boSXNzJ8LSClh0mYqqCIpdxanojc/lw+/2M+uCWdz9u7s52HyQYpdxzq1LcZlHUBKGNOxGTyA0kQJeu2OtrXB/x/12JBwLM6hokGlWfUSNsOmDTamkzntev8d2rkpLvMVUDz+ZG1E6qBS3o5vXiMRJYvgTjDh1BD+69Eemz/siMP9N8EcwXTj2xGH+Wy7mnH8LqlRZsXUFmz/YzOYPNnPrxlvZcWgHEx6ZQCgaSgl1xWWciBph4qMT2fLBFltjra6vJqaayyZHtSjVO7PLSPcX7Hr+P0Ff978DqAYOAWcCQeC/gCbgP7pjgAW6TigSovLhibrn1EOYZdmu3bH2+JMQ+6rXL2DWH+GacYN46/BO7tq9mHAFFEcS9d9p5xX2AGobflnEkquXsv+z/Ugkq7etZvVbqwnHwhQ7i03VCx2KA5fiNFzD74hRyVzdvjoWbF5gK1xe7CxOJQGayf+Wn1GeVb41+djyN5ZbV3Kk4RROUz38ZG7E5edc3ufknnsFWboiTiufxpq31hhOHBVg8TvFLH69hXsuU7j/Ig2BnreQnuMy9nMXZ120hlbZ+brRpGaZp3Hd+us48qMjWT32hqYGy0lsVI32ywY+Ztj1nb4DLJZS/kJKuU9KGUn8/gVwF/Cv3TbCAl2m+p21aK09qw/+/Qu+32kdLWlQ+mTzEpn2cxz7ePBCmOB5QjeAia6KLR5MJzSRWCt/+OB3DCoaxPI3l9Mca04ZyXTDn/Rsk81rng0+i0O1N1iz1srZDL9TcTKwaCC/nPRL1k9db3qDTUaAUg14rl7J1SOvNvXG4zKOS7HnRjsUh2VjlqEDhjKleor1TgopzTmhSpXK0kpA/567FTceh16S4HF4cCtuZl06F7l0Kf75i1iiTKBpKazeAIteh5Wb9Pr9in1QPaKFWA5VIBnj0FRbHnvpoNLU+IxwO9z9soGPGXY9/zOAP5s89+fE8wV6GQ1vvqBLg/YQPpcv1awkmYVdf7ieB95+oPsSr04ExxtxSKkc2n8PYg747e4n+O3uJyy3UzUVl+JizkVzWPz1xfjdfmpbb6BSPJZSSLMqjUxXuVu7Y60tT/mWcbfwxeIvcmH5hZQsLzHdrmZaTco7Sy7/7Gnaw6YPNpm+xnSCmFwnjoLi81MTfDK1TtyRuBanNd5qec25hIuKT1QYY7rJSUtZdAD7HM3EFUGb0r60EovH+OKKL+JUnChCIapFU/klqlSJa3FW73iQB4RC7axaKraMxP/668z8U+doT8OZTiKia8Y/psVseezB8iDzNs8znZy6VUGweie8W6WXEgb6d/a/Xc9/D/BNk+e+CezOz3AK5JPSJijuQRub9PTShXpWbF2R2/pxb6Mrhv8EepSqVIlpMda8tSb1WMXIy2lcU8TKTTD/D5i2BgZY/fZqmqPN1O2rY/7m+bY+qzVvr2HHoR2Wa/Q+l49PP/u00+PDThmGQ5inrrsUFx6cKWEiX0RfP15Yl/AeX3Gze9BP2PfZPr4x+ht4nd5O3p0QgjtfvtNyIhOTMbxfuRBR8P478UFxBFlUhOrMNBcq+rXWGm9NRaGSyzTJ5NCM1tLXTzat3S/9zIHHosujFXZb7gY8ATZO32iYH1MUhY2PCfxLV8DcuVBSoks292Psev73AOuFEMOBGvQ1/zOAG4HLMZ8YFOhBgmdfw62f9kw7Br/LT+302vaM7ahJ//OTgR7ITUiGQqeVT6O6rI2GiyOUHoEf/w6cEpZeajwuKSVrt6/l9pdvz2lZRpMa9795f9Z193Tq9tVx+8u3Wyr8xbQY82MXULbx7XZp17RyyLrhUcYcuB1tk4twLEyRo6hTA6L0CgAzXIoLeeqpyHx+VhLccT05NWYiAd0XiKiR456wR9Uoaz94itkWHQ/nvTmFSNR4guZ1mHeUzEW2t2J4BYd+eKi9lFWNM7nqd8zYFsUfTew/IStMZaVeamjSCbKvY7fO/3EhxN/RE/9WAi4ghq78d7WU8sXuG2KBrhL41gy+cctcnjj3BEr4oq/3/fiyH/P+0fe55/f35DXJyu1wo6on9nwyOIFqgsdDS7yFVz9+lXmb5+l5BZdoeub1RPjGbiwz8jfs2dClZEwhhKkQULGrmMbmRha+uJBhpwyjLd7GnS/fQTTLBMPn8lH2hTJm7nmv/aacIOTW1QRDIgox/RprtalO2JGYFmP8iPEo+3IsIbFqmSt0w7/tf+DiW3QdgJOViBph3uZ5jJ3xChVprZbTOx5uHLGxUw0+6E2fXjz1B7Ts/4jrvE+jKoKYFutyy92MUtaqKvjTG2A06dA0fZw2O0P2NWzX+UsptwBbEmV9pwFHpcyvZqwQYhjwMDAYXcDq11LKlR22uQx4Fvgo8dBTUsr/yuc4+gshN+y/6Fz4rP6EGqyIGuH2l2/H7XDnpWugUzj5+fifc6TlCEMHDGXBlgV5GGWOSPDFIKaYSBQfB95oQhgnjxMLBw6efO/JzLr3RDT8yfPAFYOYgTHyuXxIZJc+t6gaNU3ea4m1UFNfY1qVYIYiFCqnLabq8WoaivUOb8GdEIjqWgj5EtLxOrx4nV6iOeRhODQQKsQtjHrMAV/7P3D+AXi7JDHeXjp5PKPoDCJahM8in3XL/mNarL0Do4FB7eSVC5jsOJfzDkHFnashHObIF4qpPk9l7y3TGfUPlx9/y92Ghk6TyhThsD5B6adY3saEEF8G/ial3J98LGHwDyeeLwEGSin/kqfxxIEFUsp3hRAB4B0hxItSyl0dtntdSnlNno7Zv0j0FK/74FUqi56ilXiP3GxiWixv2fw3eM5nwAtb+HSgysP+F3ssZyAmQOmOFgkCZv/RvG1r13YpccRUw8/eqr2xIhQmj57M6/tez3kC4HP5mHPhnIw2rcWu4pQnl6vhB/jxZT9mzG++ina1IKzpCX7zJ0Ltk14aBquE3fm5xtrUNvZ/vp9LB15KwB2gOdpsKlyTRBVkd5+EPlncNlT/u7dWFLjj0BIJ6Z0du5FsHRgzvPJQCEpKeO0//zNloP1/b2Hmm8DO56DxV7oK4PFQWqovPRhNAHw+PTLRTzGNcQkhJqC38v2CxetPBbYKIa7Lx2CklAeklO8m/g4B7wHm6cMFMqmrg5ISGu/4AeMdjxGSbbbrpHsrigrPNr/NrcpmVoRfYtvBHmpfmhBv6Shdmw8UCSEv3LALPDG93h/aNfhzNhgSbngPQ0lgwLTTYTJPY8bYGTm3twVdrXDxPy1OlfEtunQRU8umUuzsWtNPl3Bx+0u36x0NNT2CEXZDyAOTpkuGLby3y/vuSLIcsC3exs3jbuZMv0kv2nRy8eJFh9+9CGdcj2I0a62WS3RFzqLjPlY4Fqb+cL3p8xkdF1dfwqp/aDOO7iRD8sdLMJhTA6H+hNU3fC7w/6SUpp1JEs89BHwv3wMTQpwNfAUw0n+9RAixQwixUQhRnu9j90lCIaispO7UECNvabX07voSmqJ7qimPuBfePI+XFjc89iV4bKyejKcpetvWVRvhvs16N0QjnMKJx+FJ1be7FBfeOGx+BK5q0Nf47eJxeFhy1RIqhlekmqQE3IHUvoudxXgcHm449wbKTy9HQKr9ry+iR0RqH1Hxb9uul/GVTuPej0cy+O33u+Txg56Bb1ae1ywjvH5oW5f33ZG2WBvzNs9j32f7uP+P93Os5Vhe9tvbEZpeFWTW6RH0ayPgDrDl21uY+7W5WffpVtw4FfMb0APvPEDdvs6Z9HX76hi8bDC3brxVV/iL1HPr+Bg7Bre3iU6Rr5B8IKA3CgoE2lsI+3ztj/fTZD8AkV7Xm/GEEMeAb0spN1juQIjJwMNSykF5G5QQfuB3wE+llE91eG4AoEkpm4UQlcBKKWWpyX5uAW4BOPPMM7+6fv36Lo2nubkZf2+/CI4eRdv/KTtO19BMpnRDPUPZH9lv/GQfo7+ci9l5KBqMPQQHAnDQ4tITiJSX6mmNMbDxb6BpNBXDpwPIKXt9sH8wJYH2QJsmNY61HiOiRnTRFoebD/72Qeq5ZFncmc0w4JShBPbv172lc86BDz8E4GiR5NNTurHJTZ7pL9cV5O9cTvGcwohTR6AIBU2NsePgn03vMYpQOOcL56SuEzMUoTD2zLGpCJMmNXYc2mGYaDrUM5TG1v2MPaRHyfQdKDBsGJx22vGcWjuaBseOQSQCHg8MHGgeEegiPWFHLr/88neklBcYPWflHxYDn9vY/+eJbfOCEMIFPAms62j4AaSUn6f9XSuEWCOEOE1KedRg218Dvwa44IIL5GWXXdalMb322mt09bUnjIULqXppKYsnJpTiDLhv9H38cM8PT+y4uon+ci5m5+GJw+Ufwamt8GSZdZJhwB3Qk6juupu69UupnE6moA9kjZj4XD5WXr2Sy8ZdZvh8srmPUclmIALPnHYfl/3wh+B2Z2ROh9x6r4KQubBa1+lCgmSx4kWNtplGxrp0XUm9b71q0XWwJ8jHdyR5XVwx7gr9gaoq3L9YTOWUttQ15onrpz3vbSeLb1jB+tFHWfzOYssSy2JXMb8c/cvU2n/Vu1Xc/ofbDfN57ht9H3e890PW1MLMPyUeDAT6XBleb7MjVsZ/P3Ae8HqWfZQBf83HYIQQAn0Z4T0p5XKTbQYDh6SUUghxEfrSRVM+jt+nKS2l4S9OWqwUXHojvehmecJInLNV8mDECZtKsbXeH4m2cstzt3Dm6Ud44NsdSspsvrfZaqWt2gZrwLHEcnCIKGsv1FvaAlyzB2qqYWpQ9/47Kk76IvrjMYfeAS4nunDdxKPZa/5zxZswfoqaqOdPn3D1lus7fUwatvMVBILWeCsLX1xI6aBSgg07qdjTRuMyqC6ng/ZCHM7fT8PZWlZthZaYXoqaNP5Zdfdd+rGSugD9PSR/IrAy/i8AC4QQ66SUhmm/ifD8POD5PI3nUuDbwF+EEMmWb3cAwwGklL8CpgKzhBBxoBX4pjRbuziZCAYp/X9zQIvb123saSScd1hvKdsrbpAnCqGvtZqFTjtum40ocX5b/1v9ny7kengcnqy10g1NDabZ/2EPRBz6uuzEm6DF1T7uzaOgKAbPrIdPT4G9ky9m6DduAgH7j37IqN9uIVi9k9+MhR9OtK5E6EQXDKsmujDJyDKGNmeHcYj253rDde2Kwy1vQ9VX9U6Yds7f5/KhSQ0NjYUvLmxvo1sMz5V5mLAr0u6Fp16kZ8eXDpKmbZvTqdlVw6+u+RV+tz+lu28qvYuTURdfBd+aQuj6Sqo/3kDDi8/rE5LyIAFP/5bi7Q6sbj8/A/zAG0KISiFEKnAnhHALISahRwX8wL35GIyUsk5KKaSU/yClPD/xUyul/FXC8COlXCWlLJdSjpVSXiylfCMfx+7zBAIEf/pcr7jZ2EbotfO2xtzPpneyuyZoXfj8Z180u1PzpY6UDio1b5wTAbcGk6Z37kqIgFY3TAlCcLeTe8++mdkX6aVc905axswhlfijMGOHeWKjKV04V4dsT1TMC1YedC/4Lioa/NPH8OsL9IhQNsPvEA4mjZrEz8f/HIfioCXWktlGlzgTb4ywZYTRwRSorCT4dhtaNHu2qUM4Ug15guVBXA7zOle3y0vwPx6n7qoxlPzPGOZumsvSN5am2j8bJRAWsMb0FiSlPAxcga7k9wIQEkL8VQixHwgBG9Dr8q9IbFughwlcNqFnDmxRjias6uIluDQss9LdMXqNB9Wt9ODkxk7nvGB50LT8T0H3/JstyiCjCqwdK6k6Tw8hV71bRSgSStVZB6J6a1ervu/5eI8iTk4q/X5NgZdGQsxmREWVKv/3uv+Lx+lB00y+vAIqb4JVl7oIuWnPjl+yBMaMgYULEbHsy48t8ZaU5LOV7r4iFDbetDFDKjwZVcjoHWDQUriAOZb+h5RydyJT8DLgv4Dn0EP8dwNfl1JeKKXc0+2jLGCbIlWceENi4f1YZpsL+Gqj+UXojsN1u7vgEfZFkmuxPcDqbas73ThDkRBV71alDDVAzY01eJ3e1GShOKon+9VUw2EflhO0qAvmT5DM3TI/4bHdpntsFcNTWdUV++DAMr1pjyuuf/6gh6BdOPIyASyOwpUfJbz/k2USkOP7tuKPK2hoarAso1QVmHeVRsntbuqWzoHdu2HRIgiFqB7RYmuC5XV6MxryJBX+VlWuYtLISUwaNYlVk1Yx9syxVAyvsM47SYgHFbCPXW3/3wO/7+axFMgDRU4vrXRN37xbsKofjsHnXl1r/qnz9DKeFneiTauEJS/BbVfneY02X3RHNKKHcjU0NNZuX4vH6aGhqQEErNm2JiXz63a4+d4L38OhOHAIR0q5scUFw/8GD46DS7IdREJUaEQTM5xwQvVvfM21fPTUeobc8B3QNPzhMHe+U8zQaJwNN34FTj2Va0Zfg3zmaRbxckqiuKu0uOD3wxPXlFWEoS9EmrppnMvfXM7yictxCqelSFgclZBQqfxsDY3PD8WfiBQ0DMTW59QWb2Py6MkZj2Uo/CV47bXX9P1a5Z0YNI4qYE0/kYIpkCTmcuoLNX2AiBNqztNLE4ujoHrcTI+N4pIXd9HmgLkTe6nhh75hHGzSEmth7ua5eBwew5trUvWtU0MlAfsGwr5T4eJs74fJ8xE1wrA3prLgt7eyuHEUOxpep7LoSTQhCB/bSvHnRbz68atce8qXUHON6iaWlWIO/fpKJiKmSmH7+mfYTeMXQiAQOBQHcTV7+F6TGtUfb2BmQiK39Fji/c6ihul1eNmwZ4Op1G86oUiIg80HTSck2dr6hiIhquuraWhqKCQJJigY/37GF7xfIBTr5va5XfU4Eq8zuhG3uAEZ5UnnbqqvhrjSxWMU6BJxLZ7qwZ4zx/k5qVJl6dsrWC28aO42WtMiIC1xPYr1hPqOfrOSemldmwtcydI6Exwq3L8Z9g+AxkD7RDMrfem6s6njkAtRNcr+z/fz3LeeY+KjE7NuH46F2TuIlEZ+cCfcOin7cdrUNvYe3KV31mto0PM/gkE9fyCN5mgzJctLUKVqGomwKlWt21dH5brKVL8Jn8vH/M3zqZ1emzXRtT/TV4rCCtjEKmO2x0mUuJ13GIpMJMTbpKp7+33pBtxVTpY1Z5uEZRutFu5IPFFSpwqY/wf4Vr3AYxblkvDDN2H2W3DH63DIZ9Pw9yZkh99GiPwnMCa96AkjJ9iS8/W5fIy65JpU7kYgqveWyPo6xcuoe1bD3LmwdKn+u6RE71GSIBQJ0XCsgVA01KnVL+iy0wF3wLRUNRQJmScJPjrppE4SLBj/fsbfI3/v/oMch2GWCrxTAq1mN+KTwegnOZnO1S423pOYA/48GAY2S1NdAG8cFv/RRd1wXWHwtXPyO8xuJWHMHZr+t2Jl3GX+y0bTvej/uvy/8LusxXQUoRC8YEaGRv7lH+sRPsvXtbYRfCfS3lEvHE71KKFZN8rV9eZJfC7FxY3lN9K4oNHUg7dMEgw3U/3MPe0PhEJ6FGLhQv13qJsjqD1Mwfj3M9xKN7Sds4MdLyVJPzR6QtJj2fonHUIvX1txCabXUpsTfnl+jEnTdWnhPtXoKnFOaiICZikGlYfvksehz8R9Ll8nLzrgCbDxpo0E3AG8Dm/G67xOb+b2FRW65O7KlQQnzMPh8XY6VhI/Hmqf9OI3miCkdexraGowNd4xLcYQ/5Cui1O5Ye9vlusTjURHVKsoRH/D9CuRaJpjGyll7fEPp8DxcvYXzuZg+OCJP3CiVM0hEzetk4yvur7I2/FPenoYJxdZykjvHH/CRtJn8Tt9LJnwc/Z/tp9RA0cR/FKwkzGtGF5B44JGqndWs+vILppamxhUPIiy08o6b+/3w8yZBIDafTdkrLV7HB4kknlfm8filyL496wwHlRax77SQaWEmow9cKskv2SC345DO3DjJIpBkmAERv1NwNq1cPvtmZ5+MhpRWdnnegjYJZu8r93ULgmchLf83oemad1bqmS1bwVOhpL8jjiFk7PP/grbd3+St+oEt+I2bWdrF0Uopl5TJ9ISx4TWjQqEJ5p+GGXqKg4UPFFNL6eN6GHf2vUaFf80FsbPtnyt3+23lZWfTvqkYe+xvZmTi0+qUgmCnUjIBIMuLlW1p8pw/2ZJfh0T/MzuWQoQfDcKp27Qow1GJKMQM3M7976AlfHvS6tkBRKoqN17wyvcTDsRl3Fq9j6T3+mv0Nc0kzX1XcG24U8cL4nMoSGNR/EQ0bJLufYIhWs1hdfh4b7NEm842qEZT2u3erd+t59p5dOorq9mT9Me1u9cr5fZBYMwf77xixRFz/pHX3YoHVhKwB3IyNZXhGKY5Jee4JeiQ6+F1MRnHfhdPpDSeBICGVEII/pyCaGp8ZdSFmKYfZDRg0bzTuM7hRtfb+A4IjDJ2voeIYcxW4nA5I2+IrrTVU7A+bWpEabsLmLwEYMn8+zdphtEgDVvr0FK2bnMrrZWn3homm5kTTr2+d1+8whCB6wS/DwqXPEhTHkvOfEBAgpMngyvv541CtGRvl5CmFMajBDCid5hr1Mmh5RyV74GVaDrjBsyjt/+5bc9PYwCYH1D7ycGTZUn40JPHjlB14FXOtgwtJWZRsY/i3drhpHXu+PQjsyQe8dDJR6btG4S9155L5+uv5nSvU0EDwwiMKpc9/jTDH8oEuJoy1Hu/t3dlA4q5Y5/vMPSs7ZK8Is4YewxJzP/FNeNuicx0Rg7Fu64o/P5uaF6bJyGIfWUvluV4dUbRRiSx61cV0njgkbLRMTegC3jL4RwAb8EZgBmRVqFNf9ewJHwkX5hVAr0PVzCSaw7IgH9+HpWJBQJN2G6N9LTJlT2numk0R/n9vHw/iA4twnufQnOkrp3m0sI28jrnbd5Hqqm0hrPLi/eHG1mwZYFRNWo7jEPUKi9qpaKNMOfPMZPzvkJS3cvteVZJ7tPGk0AXIqLxisuIHT+xZ0nGh2iEHWjvVROaUMrEoTfur/Tse30Gcg1R+JEYzet5z+Aa4CZ6F/FOcD/Bl4GPga+0R2DK5A7pYNKe77kzKLLXwFy8vYE+WvU5BDdOz8f/4Fkzvm3UH56uT7uAll59MpVqF1VVsyBYmcRnwYkJQvg4bGwbZj+u2QBrPlKjLpLh1GyvMRWq1wz4ZzmaLMtw58kubRl1Jkv/RhJI2ung59V98mYFqMmuoOSAVXUXTUmM8chrUwxtGgelYkS0bDWlnHsSesmsWrbKh5858E+32fArvGfBvwYeDzx/zYp5cNSyglAHVdyOlcAACAASURBVHBdN4ytQBcIlgd7Xr3Bqsd5gZxU2WQeZ1GqVLvNKAsJ5zYJfr39IeqP1Od13P0RBYVlvuv5P5vmQDwxW+/GtyymxllXrmZ+NxN/zx4f5eonb7DdKtfK6z0e0jvzdbWDX8Cj6w4E3AHD9sAt8RZC0RBXPXIV8zbNa28tDakyxeoby9CcxhPlZMRiW+M20/PI1megt2DXTAwD9kgpVaANODXtuXXAlHwPrEDX2HFoR08PoYANcrrP59Fed5dRlsD9F8SJnpTFnrnz7vWb+PGxpwl5oC2py5WelZ5nzhpwluXzZh67kaG1Wlc/HsKxMHufehCqqmg4UN9lzzpZYji1bCpOYbyy3RZvY8XWFcx6YRan/+J0tnywJfVctvPLloxr1WegN2E34e8A8IXE3x8BXwdeSvw/Mt+DKtA1kqGyAr0fRQWtP2XJFKI9tln29Z/x1q//k9gXsm+bD3wuH+GotbHWTNYKjQxtqW8YvqiukNeJ40hg9EVh1MatsGcnpWPj+K72psLuGdt18KzNchUG+wZnrUaJyzhxNc7ERyey+abNTBg5gdJBpRS7ig17CViO36IEsTdi1/i/Bvwj8DzwIHCfEGIUEAGCQCG9vBfQXeG4AvlHc1LIizgJcajg+Y+fUD+smLbTTTbK8yRKEQqn+07naOvRnF9rFMIO7oT53XDtKlIvwSMaJvg2zP8nDNPL0z1rs3K7mhtrLFsAG3Hd+us48qMjDD9leM6G/+KSi7l53M2mJYi9Ebth/zuBhwGklCuAHwFfBMYC/w38oFtGVyAnuiscV6CbKHjKJx2qA+ZdFuEvzr91++RPIFLa+989/7uW25r1BDEKYQc++JTadRCI6II5AMURcvb6Uz0FouCPwKy34O6vQ9U4/fnaJ70EhDeVwNex94BVx76J6ybyxK4nctKhUDWVtdvXMvXxqfZPIjGum8fdzMxxM/uM4Qebnr+U8iBwMO3/+4H7u2tQBbqGVZlLgQIFegcxJ7x6NvmZ/JkYXKdwMrBoII0LGpFSsv3jP+LQQDVw9zxxeG7a40x94dspD9rtcCOlZNaFs5CywyyltJSKJh+Ny8JUl8PegdDohyfLTZYCDPC7/Cy5agn7n30EuXUrqy+C1RdC2KNPKOZPhNp1bTS2zmfTgGEsunRRJ3GfbJHOXCoPQK8GeGHPCzlHT/vKGn9HcsoLF0J8QQhRIYS4UQhxqRDiBK1aFbCDVZlLgT5KYWmgf5IHw+/QoMhE/bnIVcTwU4az/eB2SpaXsOjVxbrhTyvDdaq6x/3SY04m/H4/jQsamXPhHNwONwJBTIuxetvqziV/wSAoCv4ozPwT3PsyDA5bG/5k4l3Se99400ZmXzibO4bfxJqLoNmjG37Qf4c8UDkdGDGC04pP497x93byrHONdLoUF4qFyfO5fPrxLfbpUlyp7Yy6IPYl7Ir8OIGfArOB9PqJFiHEGuBOKWXXRcgL5IVkmUvlukq9fKUQVu77CBBqotFOrp9nWrOeAr0Lyza9dpCw9GWFiw46qJwWQxO68XULJ1IIZl04C1Wqpjr3rjjcvxlm7AB/NA7z5yPLS1nz9pqMbHZD1bpAoJMoTmmzG180ajgB8Ll8TC2byhD/kM7e+5dAMxGSjzpg7bltlJsUkOQa6YxpMYLlQarrjcsEFaEwefRkXt/3uuE+fS4fP7/q53gd3qwyw30Bu5fgcuA24GdAGXBa4ve96Ov9y7pldAVyJlnmUqB/UBSFCw/orZK7RDbDX4gs5B8b76lTBbfi6vIhPCrc8pZGxQcxGpfBnK3gjoOIxRMe+yr+cvgvxFRjn8ytgjee0LcHiEapvvNa+7X1aaI4LFpE8LvLUXzGRlARCqsqVxl77+FPTSMGESfMe+32Lgn6GOFz+bhqxFVsvmkzXqcXV+L9T/fgZ4ydYbpPRSjMGDuDmeNmGp5LX8Nutv+3gTuklMvTHjsG/FQI0QYsppD012voyxdkgUxaXfD2kK55ii5VX1+2pBAV6BGcLjfP/svz3FB9Q5dydJQ0Gy2BNRdBNO2zDsdakFLSpnYulQM9tL53YOZjDaeohGPGHRrDsTB7X62B0mm65w8pURyAAFC7b2ynzPtspW/ZvPeYFqPhWAPN0eaMfSTL+74x+hs89f5TKCi0xFsolk5ahHGSX3Jt3u/2c+RHR0wbBSWjp7mcR1/ErvHXgHqT53ZS8B96HV9wD+Dvsc97ehgFjhfRBT0ACV85CH8a0i0jKpAtq91GS+Rn/+V5JoycwJLxS5i3aZ55VrpZL3oJ1eX6mnv1l3JX9PZF9La+6ZQeiuOTLsKic7TAF4FRm16Bn5ToIf+Kztr6yaijne57SYLlQeZvNmntm0a6Vn7H8r5i4UGNtjB9j4PL98YZFvEw9boIWpGuE2BkvP1uv6n2flfOoy9i1/g/AtwMbDZ47t+AR/M2ogJ5YfyoCdTsqil4dvmij3Xh+8RP7xxzbxxTrtgZv9E2GjiF4IlBs7jk17VUnbWORzz11uVoJsdK99wbBrYny9lFIVFTn0bww2LmKxoYpG/FFagfEKWqNErwukkEPjmQqY2fwMqoGpHMU7py7ZVENWPlPE1qPPjug0gklaMqO+UxtMgIuOC5USq/ehb80QiNe6H6q5K9d81n1JllVJZWsqFhA8/vfj5r06KunEdfxK7x/wSYIoSoB54DDgNnoGv6B4BlQojvJ7aVUsoH8j7SAjmx8uqV1LxX09PD6D/0JYMl4FiA3hmP60vvYz6RgAKeqGT6oTVIJyiHs5TGST28bxT58UXbPffSY7pnbjQB8EgHqiJASuKoFEf1/JHadWnr/QkCqoPaaU9T+fRUtGiUMBG8UWhLpCbc/7/AGYc5spnnfnMXE+bkp9q7YngFyyYuS3X5M2LrX7ey8/BO5mhzEML4ItJoj4b4ozDzz05oLKOufAxjVo3pJARk1R3wZMDuSuIyoAQ4D1iY+H8hcG7i8eXAqrSfAj3MWQPO6p03/wInjpPV0PZGEp9F2A0tLmh126uJN8v1UJxOgp/4weMhuNP8Rh4RKm6HizgqLhU0ATXVULEvbSO3W/+ZNYuKkktoXNDIysjlzHuDlGxzJDEBiDv1vyceXZGhh3+8zBg7IyX6Y0Y4FiaiRmiL28xjCIcJ7a03FQJKNi0KRUJUvVvFwhcXZjb66efYMv5SSiWHn/6kWF6gQIECPYNRvwSp1+bXnrkA/ycHYNkyAtJlrLiXoCUhdhNz6F781CA0p088NA2iUVi9GkpK8G/bzrSzJ9M0wEHczEIIXQ43X8YzvRtfso4+VzrlMfh8VA9psqxguOd399huZWxEX5442A37F+iDiPy1gi9gRr7WsAs1+QVs4FFhyeseKjYu1tfcZ8+GsWOpqKyk8QGV6nNa2Humk0Y/KF/OHh4HIJ7IOQjrnnHdv02g8tsOWs9TiVu4cqqmcs/v72HNW2vyElJPT7R78N0H2frXrTm9vlMeg6LQMGoQ4bfMuwPev/X+7LoGJpj1Fegrywmmxl8IUQZ8IKWMJP62REq5K68jK3DcDIg7+cwRLxiU7iRP760rbqMsr8BJT8QJ+2dOzUy2S9Tc+6urmbl3L4waxcIh9WifGU/9jcr8koTcUHl9K6EYkCWGG9NiLHtjWUbCYi7G04hkop1EsvPwTsMSQK/DCwIcwqEbXcWL0tpG7ZNe/NE28PlAUaC2ltLi901LCZNKhkYkdQ3Mkv7S+wp09dzNuhGeKKxuNzuBi4FtWJfzJQtbCuH+XoYrphViO30BCbGu670UOInwOYsZNfoSqKqChgYoLdXldgOBVM09QOm7VYQ+Nw5BG5X5Jcm1bNCsUiGb8cyGVQmgy+Fiz6172LBnQ3sp3tmT8Y/cAInJD8Eg+P0EI2NN9yOlNK0wMGplnE51fTVa3FhASYvHsp57b4gaWJmGy4FdaX8X6EPU7avjmFfrH6VV/Z3C59NvKHYW0xLPrR1sLiixOMFvLIKI1MP0Ph/Mn9+p9j5YHqRqT5XxPuhc5pekK2WDRmQzngCEQlBdTahhJ9Vn/Y2GUQMpHVKe8oCTYjvpXf2S9fqD/YM7G9eZnY1t+n46ivbMunAWq7etNpXy7djKOJ2GA/WENZPEQ62NvYd2dfLsR8gR+mnnIWqQD0yNv5Tyd0Z/F+j9JC8urWBUupfCxKpAAo90MPuS2yg7vYwNezbw9O6n87PjxDXmUMGlQc0TKv5jaXK3iXV6Kit1uV0pobqaQEMDpRf8AwGXHw3Z3qlPVZm1/wwkBwwPZ1U2mAvZjCd1dVBZSd2QGJVT2tAOQ/jv4FO8GR5w44JGNr20ybCrn13MRHuklDzwlnFVerZOfaV7j+GLGlds+KIgd+2iZHtJxoTj7hF3497n5v2j72eVUT4RGgO2sv2FEFcKIf7V5Ll/FUIUIgO9iGytLgvkiYLhLwAg4bnTfsCyicuYOW5mftdtE9eY6gAhYeoUlbrhnTcLOVSq7r2Rhd8cRNVDswmtWIp//2Fd9/+sf27v1IfKqpFNnPHvMP0GqBqnr/MnsSobzAVL4xkKQaXefKxyShshT7sRDWttGWV4frfftKtfLiRzCdL3Y1RhYLdTX7DxVBSThXAhYXXo5U7lhZrUqFxXya4ju0zljG1FTPKE3c/5p8CZJs+dht7wp0AvIddWlwW6SKGU4uTC5PMuisGnXx2ZKvuqP2KmhH58tLrbW902u3WjXTVON+JnzG7hNjax9KIYcy+PUrIAmp0asrmZNXvWEVWjRFS9/q+FKG1OeOwf4LaJULKA1IQiEIXaGg8B4U0ZxGJnsdmQKHIW4Xf7czOe1dWgaZb5BZ0aCXUTyajAyqtXsujSRay8eiWNCxqzrrsHSr9E7ZPejPJKX0Qvt/z+n5xIxdi0alKjqaXJtJwxa8Qkj9hNBysH7jR57k/AXfkZToF8kFOry0LoukABe5h8T1rdUL33Wea9dnsqzNudaMA9/6g381GBlg4h+mTIfs8geOwaUC2+38nXXvkdWLYZbngP3g9EuDl2AU1fLmNQ0SDKzyhn2CnDmPr4VMNmN+cPPt9aBz+xtp9KUKyvh3DYMr8gHAuz9+CJKSDrkpRvMEjF/Pk0Lmujulyvnhh1TM+luHu8ICzNGyQNKhqEohnPJBVNWi435BO7xj8OmBSHMChPYymQJ+w2y0hRmAB0jcJ7ViDBix++eMKOFfbA/ZdkdvEzQgq9nt9OY6ioE+ZOhFsr9Va/bc638b33XsrAZ2t2M618GtX11exp2sP6nevby9YSa/uoKrS0gMul5yV4vZQeazPNL/BFYdQ9q2HA9V14h04AgQDU1uKvrGTmHq09+dKjMGz6d3F/+IChVLHP5aN8wEhq10kqr9cncmGPHjVQgNqnJf65gA31x+PFrvGvA34khHhWSpk6IyGEG1gAvN4dgyvQNYwyXN2ODldTYuJ5Sit8Zh7VK1CgQC/DbdEHqCO5dIRUE9YgqeVvlIFu5CGblq1dX0NF5VTd808SS5THxeNU7oFbJ5mMRUDlnyP6xOGZZ+yfRDdgWo+f0FegujpVYlh36TBuf3qKaY8CRSgEd0r8nyo0LqNT1MDvUvT9GVQu5Bu7xv9O9AnAXiFENXAAGAJMA04B+nf7oz6I0Ux96N+GcrV2NZv3bkbRJKoDQl4Knn+BAt2IQCDzmCCiOh2oqHnbXzaMMtCTBnHn4Z38zzv/k6G3n5o0PH4djYrAaOW/brieu5AieQ9K+z3mB1DzbJTPj+5j4YsL8yeE03EZIqmTYDTObPX4fn/KUIciISqXl9AcbTbcV6qt8OrnIRzGT5rKYpJoWJ9InABsGX8p5Z+FEBcCPwa+jR7qbwJeBn4ipdzTbSMs0GU6ztRfefUV/rDvD0h0ww/mjUMKFChw/DiEA1Xmx1A7hAOvw0tMxlDV3PfpUlzENGNhGivCsTA1u2qYVj6NgCfQySCaoWkq1SNinQxcyK0b/lB6uF9k/m5zQxsw8cYIy9VjLH1jaX6EcJLLEJpmqZMAudfjW1VZeRwehg4Yqo+79H39uGGD987n00WKTgC2b/1Syt1Sym9JKQdLKV2J39MLhr/vcKz1WKEEsECBE4iUEreSnwVch+IgLuMImXuYrthZzL98+V+Y/uXpKF0o5nvlo1c4/RenE3wiyPiHx2eUsZkRFjH2ntZ5rDmpCArQElGTjt34ciZRYkgo1G54w+H2x5sz92llzI2qEayqrCJqpH0pIBjU5YeNUBT9+RNAwe87iYiokUIJYIGTk1yj7ja3dynWusxCCHsT7sTxvDH9b4eBY58s14toxpnkVjgUB6sqV/HoDY+yctLKzjlAWYhq+rEf3/V4qmQwGz5XMaM+65x0cLwqgl0uA0yUGBrvVNOfT8PKmBvV45cOKjUti/S5fO0tixPJggQCuqcP+u/k4/7uV/eDHIy/EGKqEOIxIcTvhRDbOv505yAL5AePw2PeLrNQs16gvyLhlBbyeo0rCJwi+6qpKlVb6/0ODa5qgPu2wIH7YPp7zqwTi44UO4tZeOnCrKI1M8bOaDdEBtg5LzsowkHwxzWdHk+qCHaVLgvhNDQYh9pBf3xvZ2OeSz3+8AHDTaWdFaEwsCitYC6ZLLhyJSxapP9ubOy09NCd2FX4+zHwOHAe8ClQb/BToJczsGhgSie7E4WEvwL9FQGf+cjtGs+yrYYkLuO21tCzrvlLXcHvjeFw+3jYO6yIwZXTcl6fdygOFn99cUq0ZrB/sKFojZGynUtx4XV6eSb4DONHjM/puEZ4nV59wjHpOti8GTwecOqTiuCHRccVcu6yEE5pabun3Wmnndfag+VB0/tlRwXDUCTE1Cemmh66ZlpN530lkwXvvVf/fYI8/iR2p3gzgSVSyju6czAFupdktqmdZJ0CBQrkj45taN0Od/sacGKikQyFV37bwc/KL8F34FnjlrZOL1JKnIqzk+BO0rufOW4mr33+GpeNu8xwPFZ1+0dajvD6vt8TjnW9QdH3L/h++4RjwgQ4ejRVEhcYNYraS4dR+XS7aJDX6aUt3obX4aVNbbNskJRNd9+UYFBP7jNCCGhthYULUxUAgYB5U6COCoZW+QE+l49PP/uUkYzMfczdiF3jH0DP7C/Qx0l+6dduX8ttm27LWyZygQIFzGlT25h/8XzKTi9j77G9NDY3UlNfY2jgNCQCYep1uhQX797yLnf//m52N+1mzKAxLBm/hCGBIbbG0rFu/Y5/vCOjfC74+XDmN7dAF9flfS4fZaeXZT6YVhIHUAGdJh+TR0/OaNObVBXM6OqHoHbALPx33Z21TK8TyTX1jtn+UuoiRIsWdaoAqKiwFjdKYic/YKSzbxr/9cDVFCYA/QK/24/H6UEUYv0FTnbSl+O78evgcXgoO70sVXq78MWFpp5tOBZm/+f7Tb3OJeOXMO7X41KP7zy8k6fff9pWCVzWuvVQiMC1U6k9VS/HM5IPzoZdz9xINKjj/xld/Y5JgrNW44+szlqmZ0pHYZ6hQ3Wj32zeKVG6JBKJJjUkEilzSx5JLVN8ntPLuh27xv9l4OdCiNOAF4G/d9xASlmbjwEJIa4GVgIOoEpKuaTD8x7gYeCr6FoDQSnlx/k4dq6YKj/1ARqaGojLHKTCChTojwhwqvocQM1BDS9XVE3NMIhW/TeSxsIoNF9ZWsmYVWMMa88nrZvEvVfey6effZrRPz6JVd36FWuvYPnE5czYDgFNo2IfKQW6V8+GJ8vAIfXue+mTkEUvLcoaEj8eUl39/n979x4eVXUufvz7zjVkJtoCCsiliqAWbWkRFW1OC4oI8dYWJG2xpUesraJVgVa09tdqW8W79Ij2wvGoFSWI9xKuKsfmaEW0VUGFAFqERFGwkguZ6/r9sWfCZDJ7MplMMpPk/TxPHjIze2avPUzm3Xutd73rxOtg8GCoS7OccRtj5i2+r786kvILr4MHH6TihCDVfisRsXyTtbgRANEoVQ/9lrLP7rU/WYq97r0b77XdryCUn1DOxpc2duStyLlMg398DsSRwMwUjxusYN0hIuIEFgFnAruAV0XkGWNM4goPs4BPjTEjROQ7wC1A10yMTNDmGXSBi09Lsbv6UKq3CDuhOAiOMDhiw7ZFwYNlbnPRIxA2YR564yEuO+kywEomu3LVlSm3NRxc3MUfMMx63VBXfYCKI17kP99cals6tj5Yz9w1cwlGgi3Wj49/H6Ublw5FQ8xbM4/rwobKfkFKG8AftCrQzfoH/GFFrBTt2eMYce7FzV3fPxj9gza7xNsr+aJquBme2TS9NCVxU31f/3TlTwmHApiJUcIuKA7AnLOgcgmU7oS6UANlH95OXcK8y1RFfio2V6TtDZh98uycnQzlUqbB/6hObcVBJwPbjDE7AERkKXA+kBj8z8eqNAiwHLhHRMS0ty+mA9pb+akQtXvxH6V6MDGwYB0MGArzX3Iyos7FPy4qY9HuJ3O2j9mVs/n2F7/NQP9AANuA0Xx/rBpd1aAQZVObiOyBxlZ9ri3FTwwS14+Pfx+1tdR3IBIgIFZ3f80dVvCP8wdh1lYfzL4YErrms1oRL41UQfo3w3+DZ/tGStsxTS/xBGLooUO59rlrWxQGan4fhOYoGB/eiB9/xQkQjURSXtYmljxu630t1OHVTMv7/quzGxIzGGsqYdwu4BS7bYwxYRH5DKvc8Cdd0kIyq/yUyz+IzpC4+E8kcIBG0SEA1Xs1eGHXIXB8I9z8ohvee4/FNX/Ft2dNTmfFzF83nwe++QAVmyvSTiOr2Pggs8qutS40piaVwm2HxO+jTJf6jsZWA2xVdz7H1eeSr/DLRpSlvKiKmihlfZ5gy2F9WDH0ANV9k7rok6bpJZ9AtJhZkYEI8OBoePYYa5gjlcRaA5kM4RQisTv7FJFiY0xj/Pe2Xii+bYcaI3IBcJYx5uLY7e8DJxtjrkjYZnNsm12x29tj2+xN8XqXAJcADBgw4MSlS5dm1a76+nr8CeNJu+t282H9h7bbD/QPZHDJ4Kz21ZmSjwOsL4d9H+/k34172e+xlgHtDoZ4h7ArsCvfzegwPY7CMbAODj10CP6aGhg6lGi/vrzx0Rs5LYntc/s4rv9xfLD/A/Y07LHd7hDpQ3F9E0ExfNonu7/L+P9J/PsoaqIZH8/ABmFwnVhd6vFStCNH5mwuen2wnup91YD1HeQQh9XjIa17RIZ4h7A7sBuMQYy1Hkl8eGbkPvCHHTB6NDgc7TrGdMTEckFt3ncRYdghw+hf3D/tPh3iYPSA0TjEkfL7t7NNmDDhNWPM2FSPpbvyrxORU40xG4B62q6PlYt0mV3A0ITbQ4Aam212iYgLa1XBfalezBjzJ+BPAGPHjjXjx4/PqlHr168n8bmLX1/Mr1b9yvZMb+Hkhbbza/Mp+Tia1dVRd+QRDP5xfdZXGF3t9mNuZ97WefluRofpceRIDlam9Afg/mG387fKeYw8ZQplP1/M6ldWc9tLt+VsVb6Zo2dywvATuOQvl7RYCS+ZxzgJSiSj47JbPOj2Y27nV+/9qsX3kWenh7IlZS1rzSfxuX0snHAL498ual6qlvLynAX+ukAdg+8c3OIKP510n62SANRMWo1//OmA9d38y5d/2bHemgw/S7Vza5uHcOLva6rkx3jOhe33b56kC/4XAdtjv/9nF7QF4FVgpIgcBewGvgN8L2mbZ7CSDl8GpgHPd+V4P6QfL8+6AEU+lZRQ8vRKKn80ibJvHaDOg1b8U91LDj6v9R744BC4tRSKWMeP7hpMkbOo7cDfjhOPq065ilPvPzVt4AeswE9mrxs1UdsTgOTvo8Q6H3PWzEl5AuAQB+UnzoRTO+cqNd2waXtF/T4qDvmgeU35tsbfc6XIWcSKrSuah3fTFU0qVLbB3xjzIICIuIFtwHvGmOSr8JyKjeFfDqzG6km43xizWURuBDYaY54B/hv4i4hsw7ri/05ntimVxPHyzpzm0qVKSyl9dQ81jz7Id3fexV+d2/UEQPUucrB7vQmrtG5TJH2Qjj8vE1ePu5px/z0u44VxMmUwrQJ/uu8jv8fP7JNnM3rg6Lx8h+UyQCfX+W8rr8Hr9KZ//zM8kWuKNLVaXyDXyY+dLZOEvwjwPFBG6y74nIvVC6hMuu//JfzeBFzQ2e1oS3c802uT34//R7N5JPADDl1waM66OpXqrfrh45zQUVx/xHcY89qCnAf+VNwON7eceQtfrP9i2mnH+foOSxegk8sg+9w+RKS57G8yH15GFA9pvp2uV9bv9rPgzAXs+mwXBsOiDYswGBpCDbhx4ghHGLgf/tU35dNb7reAE/ky1WbwN8ZERaQaGNAF7elWutuZXqZKvCWsmrGKs5acle+mKNWtHfppAw8s3MTicTcSPT0I7VtJNysOcYDBfhGvBPn4DksXoN1ON1uv2NqizO+wfw/D/Z47ZfB3BAKUnzsfnh4NpaVt9somngxd//Xrm098zAsvsMi8Qu0hmR1DtxzeTZLpPP9fYFX4e8sY81ZnNkgVhkkjJrH6wtWc+8i5hKIh7QVQPVsOEgZTKY5d6Ff7g7bTxnItEAlwxcor+Mtxf6TumqcoGXlC+2rgd7K2AvRA/8AWJyTr16+3tn94CtGGeqvKYMBakrZyCfj31beo8pdpj0b8xKcuUMfgl+6mPoOvuG4/vJsg0+B/PdZc+n+KyG7gI5Ky/40xJ+e4bSrPJh09ib3X7G3+I6qpq2HJGw8R0VwA1dN0xmfawH/+0/o1voZ9QxfNpjEYPozu5wjnQlbeVkRpe2vgdzK70sUrqlfw7JZnW5VLHz1gNDcVn8uKLRVIOMrZ1TDzjYRCRElV/trTo1GxuYKoywE2Kyg7I+DyeJk2ahoTjpzQ/Yd3YzIN/puBTZ3ZEFWYEv+I6gJ1LN30KJF2rjOuVG/kisAlr1u/l2+ySse2l10Wf6bqvTDxgibeW9jEoAxr4HeVxO+Wqp1VHHvPsSnLpdcHstA9mwAAIABJREFU6xl852CigSYahkfxBaDqCzD6I6sML5Cyyl+mrARE+zI1kw4fx7JL1vaIgJ8o0wp/P+zkdqhuoMRbwlXjrubWl27Nd1OUKmwGVjxy8Mq0JGh1UZfNgJDX3TyboC3pAr9DHBlNmQu4YPiVsPaxEKUVFdRdOL2gFiRLVy59ysNTuHH4jdZjsd6ZhqQyvP6EKn/ZLLbWVoW+qadd3OMCP7QR/EWkD1aW/5FALfCcMeajLmiXKlDXf/167n31XupD9W1vrFS+xQcnu3ioyh2FlSNg5+cOlqEt3Qk1f/RT8dcFvL1/By/vepkNuzYQIbMre7fDTSih1y1qopmVrhVrkaKyqU0s376GaXdeXVALkqWb998YarRdAyFKQhlih4Oqrw2l7M7B7T62Hle3JUO2wV9EhgPrsAJ/3H4RmW6MWdPZDVOFqcRbwsoLV7ZI1lGqU3UkGS/+vE5K6LMTcsLdp4EvGFsp7vEiSmvd+J+uZNapB5eCbU+lu6iJ4hJXi6W421WzXuA8zxMEggefXwgLkqWb9x8lapts3OCF5Sc42DrQydCvnMy1S8+hXg6eHGV6bD2ybksG0l3534p1cvUfwGtYK/vdC/yRrlvlTxWg5GSddz95l6e2PJXvZqmeIFWQzkXQbuM1XOKij7sPIz4/ou1CMO0Qz/AvmwE1l2/F33dg82Opgk46BoPT4STDjoJWGj3gElIWas/ngmQj+420ncefloHnhwurjg7hijyH3dJkTeEmZlfO5p4p99gOAfTIui1tSDcR9FTgemPM/xljmowx7wA/BoaJyKCuaZ4qVPFknZsn3syB8IF8N0epDpnwua9SM7eGQ4sOZd0P1uFz+XL6+lGXk4r3V7S6Px50Fk5eyFcHfDX9a5hou670k7nF2aLXIFFypbyuVDairP2BH0AOlkEOO7E9wQtFQzzy5iMMvnMwVTurbF8u8Ttt1phZPTrwQ/rgPwjYkXTfdqy3eGDrzZVSKnuOKHkpKe0NQfnSN5uT80qHlXLecefldB8tgmtdHSxeDNdcA4sX4w8Yju1/LO/sfSfta7jEhceZfbEAp9NNsSv1Aq35qFhXF6hj8euLueiZi3A73J26r7AJUxe0Egvrg5qvBG1n+2tlF9Wmc445h9XbV+e7Gaobc0SsZVQRuvwEwBOF8nec1jzxo48GYPu+7W08q32ag2tVlVWQJhq1pqf5fNRdczVlV0fbvPotchVldeXvcXrwODw8Pv1xpj02LeU2XZ3YVrWzKi95Q/kc3ig0bdV/XC0ie+I/WBn/AM8l3h97TPVSM0fPpNid+opCqWZpLiWiDoi46JrAH2tHccBaErZyCfj/3dhinvjRfY/O6S4d4qD8C2VW4K+rswI/QEMDFUfWEz1gP88coNhdzMoLV/KVgV/JeJ8SezMFAYFiTzGVMyop8ZTgc1vDGj63jxJPSVaJbfEr92vWXsPi1xdTF8gscTFxal9XJwznc3ij0KS78r+hy1qhurUSbwmrL1zNlCVTCEVCBCIBXDgIx6fvaEVABak/B12chQ9QFIbLNsCoT6B8c8t54mBdlT7z7jM525/f47eC65MrrCv+JNV9SVv698zhZ/JE+RP4PX4+3+fzGe83niUfiAQIRALNWe8dTWyrC9Tx27/9lrtevgsRIRgJtmvKYC6X9LVl87nqCQvy5Eq6JX01+KuMlQ4rpXZu7cEvlef/ydm/X8X3vwXrjkZPAFRqkvRvZzPw9KMwKTmbyeGA8nJCG15myl+m0BTOIAHNgNNAJE3/qcfpYcEZC6yAWP3swSv+BOlK//rcPsqPPxicOzLEltjlnW23d9XOKqYsmdJq3Dx+BX/mX85kx093MKjEPie8o0v6ep1eDCb9EIjN56knz9tvr7aXfVIqQy2yZYdPZSA+nliWUH9bqTwrdnj5YIDXutIHa8y9r5/Ff76UGWt+zFsfvZVZ4AcQOP19ae5eTyUYCbJr/y7rxsiRB/eboHyT/RdxcrDqyBBbR7u849316RLmmsJNHLXwqLRZ9fGKetnwODz87vTfpU0QLHJ6WVS2KGfDGz2VBn/VOcrLweGgJAgrl4ArjKaPqrxrNAG2/XI2LFwI8+dTdevlDJ4LV26/h0feeqRdq1f6AjCowYEHp+02HqfnYDdz7G8iWUkQKp/sg9/tx+u0Lv+9Ti8+t49LT7qU3/zvb5rH1ONDbNkEz452eWfaXR8fYrA7SSg/vrzN5YaLXEUpA7zL6eL12tfT9hxcdtJsLjvpsuYplPO/Np+FkxdSM7cmb1UMC5EGf9U5SkqsVcRKSijd62PnXbFsbqXyyOf2MWLAKJg1i7pfX0fZZ/dSF6qnMc3CLnYcQN+6CAHb8jJgjDl45Z7wN5HY80BJCdx2u7V97OQjYiI0hBr4/Su/59aXbuWqVVc1z1MvHVbKh/M+5JrTrsHlyHRtto53ebenuz4+xJBKibeE5dOX40gTfux6XxpDjTzx7hNppyyOOmwU0Pvm7beXBn/VeUpLrVXEFi5k0BXz+dmhWSxrpnqOAjj5SwyA7U48i7XflzBL4Pj9Hnxiv07vmEFjWLpp6cFM+IS/CebPh4ULqXt/C2Vvzac+VN88jh2OWicU8SDYEGpoMU/d7/Hzi6//giJnUdpjhdx1ebenu74h1MDjbz+eciZA1c4qpi2bhstpf+LidXoRST2c4hCH7YJHOqafucxPG5XKht/fvMb29YE67r61P8GoJgGoruUUJ26nm3OPOZelm5ZSfnx55leyBjwRuPplK49sxD4o2worjoVN/aNE0pTcfWX3K2zas6llJnzC3wR1dVTcfznRQFNGSY/RYJCKjQ8y67TZVGyusB2m8Ll9fL7o88z/2vy0Gf3tWQUv3QI4qTz//vOs3L6yxUyA0QNGt1rBLxVjjO33RGOokRlfmsEzW57pVbX4c02Dv+oyJd4Snv3es5z1sPYA9Eq5yOjPcmpgxETwGA+PbHqEp7c8zZzVc7j0pEttl3JN5nG6OWe3i9IPHFT1a+DYn0JUoMETpij2NVrkKkrZXW27wEys4E916QEaTrEfOmjxWgTY9tur4brRVB+wP3lpCDXgdrq5eeLNtq+VXGgnOUhXbK5g055NfHrgU/weP/XBeiZ/4QyefecpHFFrrYB0/x/x9RHibZz40ER+e/pv0/a2eJ1ePE4Pl469lEWvLrJdZnfCkRP4wzl/6FW1+HNNg7/qUpOOnsTEoyay7r11+W6K6o46sErfgYi1BkU8oNy74V7bruXkfdYTouz7Xrb0u4Gy2mupk4NXpfHKfMYYJhw5gRf/9WLKbulooImKOZOY5TrZqilw7bVQX8/Ij+yn+iXzBWDEhyEoK2PkqpvSrkMfTx5MJbHQTlz8dSb9ZRJOh5NwJJyy6qDXWCsEzngDhuyHRSdb/x0NXvDgImiTAxGIBJi/br5tlz3A6UedzrILlmGM4b6N96XcJt61Hx/TV9nRMX/V5R785oP5boLq7nLQi2AwzB47mxIpwheL5WKwzU2IYpg/4A0CNpdMEROxDfwADRJi27svW+P9V18N9VY2fNlWCGf4TezAKkxENEr5ZrHNmneIg759+tq+Trp8hwPhA9QH623LDQfc1s/Tx1nB/0evwdS34eqX4PTI0LTtTxf4fW4fU784Fb/H37ziYYmnJOe5C8qiwV91uSMOOYI7vv67gkgAUz1Qhp+rhlADIkLN7B0sfN7L/L9ZiXx2JxYNoQbe+ugt2+Iy4Wg4fXALWPkC1sbW1XHVMDjuioRdxtruMQ4wUBQ6+NzmUsRBoKGBkh270pbrTTedrqOFdgDqPTB3Etx1Gjw+ChafCF8+9Nis5/AnJ+vFVzwceshQna7XCbTbX+XFIW9uoU8IDmS/SJlSKTmiELWfet8sPu/d328Qs25bB5MmsXxq+mp7/z7w7+zbReyqPabOA2UzoC5xX7GzAIc42HbYb1j/lxvY5gsyYl9CKWJoLkecbh369TvW27YlnrnfoRMAgWBsKn78/bq36W8ZDaW4HW48Tk+byXp+j5/+xf25ebx97oLKjgZ/lRfVH7+rgV8dlMMa/1EnuCMQauMEoMWVZmkp7NlD38oVOGqdpErfdwRDhMLZBX9HOOGqPabiBLBLfXO6vaz/yqHMmu+FuhQ9DbFyxEBWY9/tzdzPlBEoHfa1tCWIvU4vd5x1B0XOIk3WyyMN/iovRh52HL49G9IuaKJ6kRzW9neH0l/5xyvHXXrSpRiTMEbg9+M4fACVF69vmQUftJp36YYg/z0mCFlU1406YcTelvdV97VP8msINbCtcZdVFChpCWAcDut+f/bBMj6mnpztLwhRolkVPYq3+82P3ky7jcEwc/RMDfZ5pmP+Ki/KL7wZh475q04QcsUW20n4fPncPjwODy6HC4c4CEVDLNqwqLlqXqLSYaVsuXwLU0eezyk1wn/8C4yBRSfB3mLscwra+DzPn9jy9sh9NCcaJmsuxZuiKBA1Ndb9HRQfMoiXwL1l4i3cPPFmvjn8bIrC4A0lHVfsX7e40r4HtfW1Ng9arj7lag38BUCv/FVelPQ7gsrTFlH20uzYfGnwhCHoRFcAVNmJDx0krRTodri5YfwN/Hr9r6kP1ROOTUWzm39f9e4aypadTzQcouEI03pIwu7zme5zK7ClX8u7yqs9zPmWCyKtr7JbDEn4/dRdOD1WjGcrI7cuTVuMJxW7Yj7xIYPkOf/FUWs63/S3oE/YSjas84I/4uDTY4fwWJ/3CaWKHm387frdfq7/xvUZt1t1Hg3+Km9Kz7mMmtO+TcXD89n28RaG9B/O/P1PUh89kO+mqe7IJvB4nB7e3POmbTW8xKVuo/X7KXvofCsJLz50kOHJqCDWMEKq7Q0M/zThdlERJVvfozK4rVXXe3LyW7piPJlkvrf1/FRz/htjw3Erj4GaO6xchaphUDYjStQRC/zxt1NoM2dDK/AVHg3+Kq/8fQcy66cPNN8evbOKMx48Q0sA91aJQcRYiXtOA032K7i2qSHUwJZPtqSthrdt3zaoq2Pf7m22SXhtMdgE/pgTa2k5Zj9wIKUMtM3Wh/TFeFpVDEwh3fOnPDyF2nm1PPjGg7YL6QQFKo6H6ZvtZyaIsRL97IwbPI6Lx1ysSX0FRoO/Kiilw0r53Rm/42drf5bvpqg88ETgmE9g6H44eyt8+1049gpIHZoy43a4Obb/sWzas8m2Gl7/4v7MvO8sTjxiakaV9lJxhSFs940q8PHEcXDxxVaWfkKyXrps/XTFeBJ7LOyke359qJ6f/PUnVGyqIGxsqvJ5YM1wK7jbnRSZNFf+PrePi8dcrJX4CpAGf1VwPm74ON9NUHkSdME51XDzcwfvq1wCUy60ispkw+lwsuCMBTz59hMpHw9FQ8xbOw8MfNk5Netph04D3iApZ7D43D5GnHsxtDMIpivG09xjkSQ+vt+nrg/Ldy1PO5d/yVtL2mzDE1+EwXVtlB+2eb90lb3Cpdn+quCM7DeSYncW86lUt+eMwrPHwD0nQY0fFo+xbp+7zYGHDCr3pPD0d55m0BvbqfxLhJKAVcQHrEx7nxQdrNiXlCjYHp4wPPMotjNYsg2C6ZbRbZ4RkKBqZxWD7xzMVauu4sP6D3nh/Rfavc9WHLC3z8H3LRNairfw6ZW/KjidVYBEFb6IAzYPgCvKrB9v2KojL9FoVtWgjzz0SCYdfip8dTCldQeoucMaw97W16pL/9CYIBsG2Tw53gOQ/G8STwjuXA2TdkDl40WUzYCoy5mTpWbT/S0kn1CkGt+3K0XcHmEH9As5cditW5xk+OeGc91/XKdj/AVOg78qOPECJFMenkJ9sN42ezqXVeFUgYn9vwZiiX4myz7KK065AioqrCI5WFnrs/4Rz1yHenea9L7kngCbz5o3CjNjdW1Ka93UXL6VivdX5KR6nV0xnlQnFOnG9zui2NWH4z+NUrkkQtkMaHTH6ijYOLbfsTrG3w1o8FcFqXRYKbXzavntIz/hzu1LEKzx4KIQNLlAEudzK5WC1+nlkhMvgad+Y1XHi0lZUz8LvqDVzV+5BPxuH3itLH5/34HM6pu74GdXv98Yw+LXFzfP3bdLaOwop8NF+U3L8Z83jZp7w/z4jAM88iVs//7OPvbsnLdB5Z4Gf1Ww/B4/C374MNfvu52Kh+fz9sdvs8j9DyCsCwKq1mI9QU6EIncxqy5cZV0ZjxxpTbGLnQBUnGAVsOmIUYeNYs5XL6N8E/i/twtGjGiVxZ/IrsiOnVTbJ15Np5q7HzERipxFtkvxehweRIRAJLPB+2JXMU6H0+phGFYKy5fjP/98/rDaxVPHhZtrAbR4jruYmaNnZvT6Kr80+KuCF68FsPj1xbhWXUUglHpakurFYoHfHQGnx8MT5U8cLIBTXg5zrHHzOg8s/yIpA1d7bN+3nfITZ+I/te3u/EyL9MQD/gvvv8Djbz+OU5w0hhtbbV+zv4Yz/3Jmi7n5mVzxuyNRTvOOZG3knba3dbi5ddKtB2vw19XBtGnQ1EQJsPphmDLDWjwp4AKv04Pb6WHljJU6zt9NaLa/6jZysQa5yj9XmDbr4Ldb7Eo+5ISmSIBpy6ZZ+SIAJSVQWUnVcX0YPBdeOLLju3OKk4pNFW1ul5iEF//sNoQaqAta98fbGM/Sv3LVlTzy1iMEIgEaw42ttl+zfQ3Dfz/ctiiPy5Hmei4YZtDrWynOIAfQ4/RQ5Cw6GMgT8iYASndC7R2waAXM/7uLRYd8l9q5tRlVHFSFQYO/6jbSTXtS3YSBaOwKvcUJgCGnJwTxAjhxdSeNpuz7Tuq8B9eg74jGcGPKOfYHd1gHixdTccMFRIOpo228jYknCOlW04tEI5y/9Py03fbhqH2vWIMX+u2P4MzgfW5VQ6C6ukXeBBxMnrx5VZhZtYP0ir+b0eCvuo3y48txiH5kuzWxlrcNuWi1WI4nDBO2gzOzGWVpJQevis0VZDpZ0CVtj4ammmPfrKoKBg+Gq66i+tXVNJA6WMfbmGmWfmO4kXAk/ZCXE6dtHqwvAMd/bCUolgSs99tOq+OL502k3Nhn5TyobkW/SVW3EZ/2VOIpOXgSoJl/PYY7CjM2wfoHwR+w5vgDWf0fJwevTIaMil3FOMTBCz98gSfLn8Qp9kWFbIv21NVBWZn1b0ODtWyvzYV6vI2ZDme5xGVbhjcuYiK2b5cDKN8Moz+Em9bB13fan2i1Or7ycmtNgpQbO6zHVbeiwV91K6MHjOamM27C7/EzxXu8tViK6hEavFbxnbjmIJYuMz/Dinrphow8Tg9Tjp7C76f8ntEDRgPwgyd/gNfZei5gm5XrksbGyzfZf8nG25jpcJbT4Wy7V8KmJoY/YF3x/3MgDJ4L88+EdcPB7XSDgSLc6Y8vljdBScnBHgCf7+D9NrMcVOHSbH/VbSRmTd9w1A28GH2P8GGxuf85GMdV+eULWFX3ymZAfYZz8N2xK1cHVta5z1GEw+Vm+TkPsfS+2VR/uJmRdR7KXKOY0zf1Za7X6WXZ9GX4PX6ef+H5VlXymvflcHPLmbcczIBPJWlsvCRoBd2yGdbCOA3e1svbtlXRMj7lbvn05Zz36Hltdv0nc0VhwTrrin/w3Jb1DZoIWScMLidzxl7BqMNG2RclKi2FmhrrBGfbtjanN6rCpsFfdQuplyZtBDfa9d9DOLD+K9tTo87pctNECA8u3CJcfuqVnN5wONMe/RZRgYYi8AnM4WUWrHIxfzxE+xTREG1KWSlv74G9tgl1rTLgU0mqKQBWZnzNHVAxxsO2aWcwYsLUFgHWropfxESYOuJ8JnzopbymL/4XdvKTL89i4T/ubcc7BGEn7PqcUHGCIWrTi+IUJ6MOG9V2ZT6/H2Zp9b6eQIO/6hbSJUUVuYogEgZjaJIcZIuprmWszPHKJdYiPhktqRub199ECIAgYTCw6JV7WNTY0KLnIP5688eH2fp7WPFlw7ZfzmHEgJZXuVU7q/jgsw9s6+HbraLXQkJNgUT+IMza7IW1y1JeKaes4rd/KP7zplnDCA0N4PPh/UYQTm777UnkCsOIY05l6+cjNLheyf7YVI+iwV91C+mSopoiTcwZN4dRh41i7Y61PP72482JURKxSrBGHGiGSwHr1wh/HgNf2ufGFwylXBa3BZsr2GA4gN2EkIjANWfCwECEkdWfMP306c2BP96z9Ksjf2W7y3iCXtpqffEx8LKyFkEbh6PNsXF/wDDrdQPVURh6AK6dSl2wnooToLovjNzXwNCPrbLCbb4/CcJOONt9PKZ/AF/DW1aPmc2xqd5Dg7/qFuJJUalOAHxuX3OX5awxs6gP1lPx6wvYtmEVI/ZZGc4vDYGzvo+uB1CIBP71eXjoc4CEOvRSQcK232qNHnjkBAi7wvj2PcqcO59srppXsbmCUDT9vh3iYOihQxl85+D01fpsxsbr3IaKhFr8LU4a1qyB886DSATCYfB4qBoYbJEr4IpYJzDt/QgXhWHF//6Z8geKmXNpI6ToWcl2yWHVfWnwV91Ce5Y29Xv8zBo+FX7/t+ax152fA0/EWhxIFajkqJbFqo0eXEg4TMDm/zkcu79BQhAMUbakjJq5NWzes9m2ah5YyX7LL1jOtGXTkvJOrM9X/HWa8wGSxsbTlvh9txHOOos6D1R82brKH/pZkGsntkx8DMdmHpp2rmjZ5LZmUZT8o/Fg8qHfl5Mlh1X3VTBfhSJyG3AuEAS2A/9pjPl3iu3eB+qACBA2xoztynaq/EhOioLWWdMtJI29VvfVwN9hXb2Echb78ri8EAzblNVpLWIiVGyqYN+BfWm3u2DUBezcv9M27yQaaKLivtnMuugeq+s/QepkVeuk4cz7x7PjbsP2YVat/KDDqkDoDEPErsxAe0+IQjAidnilO6HmvmIqbpjGtlGDOrzksOq+CunrcC1wrTEmLCK3ANcC19hsO8EY80nXNU0VgsSkqKLdRSycvND+iys+9jplCtTXNxdbySiZTKVWaEMmBtziJESkxYkgb75J2UuzrWx/jzUdMGQTSBtDjbzw/gv0L+6fdlcD/QPT5p00SIhtqx6BXz5pfe5Gj7a6/aurqTjiQ6ImdSJqExGOutw6r0osOxzJ4TezEWvoK87/70Zm1Q6Cn96cu52obqdgUqCMMWuMaS5f9XdgSD7bowqT3+Nn1phZDC4ZzKwxs9JfsZSWQm0tXHMN5VvdBRe7VJYSur0d4Qgeh5tLxlzCTWfcxLNbnuXdIzxs+Wk1CwfMZH7gJL7bOJwiY/9Vt/zt5YzoO4IiZ1HKx4ucRQz//HA+rP/QtshOcQBqvGGuOaWOxT87g7ovDIKrroJbb6V69SMpk+zAOoaAuxN7pQzMedmabdBMy/EqCuvKP9FFgN2SWQZYIyIG+KMx5k9d1yzV7fj9sGABJddfz2V/msat+1cX3hWsylzS0EPABURD3PXKXRS7ig8ugRvrBZg1rJQ129bw0JKzbF/SKU4EsardpSAizH9uPuFo2La8bqMHHj/e6mnwBYLMmRCkcgmUNsDIj8IUB9tYRriTPpP+IFz/t6Q7tRyvAsSYrquQIiLrgIEpHvqFMebp2Da/AMYC3zYpGiciRxhjakTkcKyhgiuMMS/a7O8S4BKAAQMGnLh06dKs2l1fX4+/B1Sx6inHAdkdy+663XxY/2EntSg7Q7xD2BXYle9mdFghHodDHJxw+Als2rOpzYVzBvoHcqj3UBobGtkd2E3URFssIpXJwjvJxMCXPgKnscrqmk486ZTY2YMAUQwOhMHeIRTX7rau+qPRg7X5R47sVlX5esr3Vj6OY8KECa/Z5cV1afBvi4jMBH4CnGGMsV/b8uD2vwbqjTG3t7Xt2LFjzcaNG7Nq1/r16xk/fnxWzy0kPeU4ILtjWfz6Yq5adVVGi6h0lduPuZ15W+fluxkd1nwcXZ0UmEaxq5hpo6bx+DuPp/0/97l9LJy8kOnHT2fVc6u4f9/9CMLZx5yNwTB/3fz0nxm7YzbWNLu1f4H7xsIjX+7wIaUVr3URLxJ01GdHcfpJJ3f7crw95XsrH8chIrbBv2C6/UVkMlaC3zfsAr+I+ACHMaYu9vsk4MYubKbqxtqqoa46ptjVh8bwgXw3o1ljuJGNNRvbPNlLnL9/w1E3sGr7KnxuH1UfVHHusee2fbJod7Ij1jS7shnWKnrLRh2caphribUu4tavX6/leJWtgkn4A+4BSoC1IvJPEfkDWN38IlIZ22YAUCUibwAbgBXGmFX5aa7qbhKXBI6vouZz+/C7/XzruG9ZXaeF0xHW7bgcLoo6VqMn57bu3Zr2ca/Ty/LpB+fvx7v3G0IN1AXrWPpWdkOFiYJOCDihqBMrT2uRHtVeBXPlb4xJmX5qjKkBymK/7wBGd2W7VM+SsoZ6bLpg/X/dwYN/mcecs7QmQDb6fRbiEwfYl8rJEQPeiJXs11YinUj6E7qbzriJnR9tJRpMXc8/2q5lhlILuODaiXB3ZCJXm+cJEs16aKTIVYQxBpfDpUV6VIfoV5zqdeLTBVvdv2sPs1+F0R9ZXbVNLvv54aq1k95t4v3+sCE+Sbezxv4FTt9h/T+NaPSy+ooyHtv6ZMpN2yrZ+/GW19n5eAUNY9u3TG57hVwwP7KOH70Ci8al37bEU5JySWGwKg1uvWIrK7auaHXyqlR7aPBXKi62HGvpzgZq7oDZZfDwlyFaSINjBWzZV2K/dHLCny8AU3d4rFXyKisxxe9S+d6alGPzHqcHQVIu0+tzF2OWLeOPY8JdkqgYBl4/AiQKacoOpC0zvHz6cgb6B7a99K5SbdCvNaXiysubp0P5g3BPJRQX2Bh2QRO6JNPf4fFQftGd1uI5paWUH1/eYlpeIo/DYz9/PxRh0VdDNLnpknYf8MKGwekDvxOxPRaf28cHn33QSa1TvY0Gf6Xi4iWBS0rA56MkCCsfL8ITRhMB88FYK9n5YsPxPrePEk8JlRfiUJ+jAAAbd0lEQVQ9h/9Hs5unrNklcpZ4Slh54UpumXhLypc/LzScrp7pbFuvP8YhrpS9FGAlIW7bt60TWqV6I+32VypR0nKspSNG8P74Exn60FeJFMj89V5D4D8+EIaOO4stzk85tv+xLDhjAYNKBrXa1C6R0xhD2ZKylC//mHsree/YiQ03FAfB2aeYS8ddzqINi2yXrh7RV8vyqtzQ4K9UsqS50YOAuUd+j1v/9UjBFLDpLV48Uija+zcaQg1s2rOJJ9950loGd1hpq21TJXIufn0xoUjqEG9E8IawXf63SwiMcw/n4klzKD9xJsYY7nv1vpSb6nQ+lUva7a9UBq7/3h/wONMVZ1edIUK0+So4Pve+bEkZ9cH6jJ6/ac8mmiKpE+jCJkzUnd/rH5/bx8WTr2PWqbPxe/xphzB0Op/KJb3yVyoDJd4Snv3es5z1sP0CMaprRE2Uik0VthnvdYE6KjZXUL23mg27NqR9rTPcx/J/ga0ETJigo+sTO1JdzaerRaFUrmjwVypDk46exOoLV3Peo+cRjoaJ2KzRrjomvmyu3Qp66RLfqnZWUbakjKixegzcjtSZ/nGBrW+z5THDIye6mTehEzIA0kwhLHIV2V7N29WiUCpXtNtfqXaYdPQkPvn5JyycvNB2SpbqmInDJ+J02KfF+/Ay4vl/wuLFUHewGE5dwBoSqAvWNQ8VtFXk54UvGIZfCbs6qy6xTWeC1+nlvSvfS5m7oFRX0G8vpdrJ7/HjdXnTX1Xq1MCsrd2x1na6G8CBSIClH6zingcuo+7II6CqCoCKzRXtX3pXoMkDd4+jU5I5PVHwu/2txu/X/WAdA/2pVjdXqmtot79SWajeW502QGFiP3p63W5ph1MMRJ2wbgSsOzrEz78RYs3FkyjduIfqvdXZL9fcSbM43J4iFkxcQJGrSMfvVUHR4K9UFkb2G4nP7UsdbAx88WN4Z0DXt6vHk5a/H/DAlG8fYOt3z+XD0R/jcAvRbLpdOhD805XrbYg2sWv/Lm6eeHP2O1CqE+h1iVJZSFdS1h+CHX27uEG9WJMTjhq9ngqzObvA3xEGrvz7wSqEybQwjypUGvyVyoLtfGy3n8tec+LUMf8uE3ZBwA2Bzi7DECs3DOCOQFEIVj/q5MZXfTjcqXeuhXlUodJuf6WyZDcf+zeHzaKxZlm+m9d7dMGKfABFYbh9Dew6BEbsg/LN4D9jEry2jMp9/2wxxdDn9uEQhxbmUQVLg79SHZByPvZRR0JNPlqTZ50RhDN5za4ouWzg6Udh0o6E+3w+mDoV/H5K/VqYR3UvGvyVyqG6QB33brg3383Ij84IwgWylkJxED44NOlOh8NaBjpGC/Oo7kSDv1I5VLG5AqOT/FtwO9w4xIHL4Wp7Kl4XdeG3V6MXtsWTOIuKwO22ln/265W96p40+CuVQx2aa95dZXCu8/5V77Ni6wr+/PqfeWX3K/YbFmDgB/CFhBHHnQqjx8GoUdYVvwZ+1Y1p8Fcqh9LO/+9hHFEr+/2yV8FxTPptt+3bxqwxszAYXqt5zbZuv6089wg4in2U/7/VoGP4qofQqX5K5VC6+f89bTTA7fbg8Xj50omTiaYJzKFoiNMfPJ3vPf49/lH7j+x3mI/3z0CRw0PlhSs1eU/1KBr8lcqhlPP/8eIPwtUvg7MHLQQYiASpNwFmeVe1uW0oGuLRTY9y78Z7s7/qz8eVv8BlJ1+uC/CoHke7/ZXKsVbz/4uHUH7ufPz76vnIB4+Mtn+uIJx/zPk8tfWprmtwL+cUJx6HhwORA60e87l9jDpsVB5apVTn0uCvVCdoNe3r6dFQVsaE2gBPfzFIg001up+f9nM+2P9B1zSyO+jksX6Xw8WK765g2mPTIEWvjFboUz2Vdvsr1RVKS6GmhvKL7rQtBet3+7ly3JU88e4TXdy4AtbZXf1Rw2nDTmseqonna8SX3tUKfaqn0uCvVFfx+yn50WwqL3qu9ZoAnhJWXriSFdUr7BMGVc5JOELFaw82D9UMPWQo8782n4WTF1Izt0bH+lWPpd3+SnWxxJyAot1FLJy8sLkU7LNbnqUx1JjvJvYaIRdse2kFnDobv8dP/+L+3Dxel99VPZ8Gf6XyIJ4TsH7/esaPGd98/9BDh+J1eglEAvlrXKYKtBpfe7jCMOLTfLdCqa6n/YtKFYiqnVVcu+7a7hH4odsHfgCngfIvnJ3vZijV5fTKX6kCUBeoo2xJGfWh+nw3JS1HFKugT3cK/HY9FAaeeboI/8szu7pFSuWdXvkrVQAqNlcQiRZ+BaDuFvi9Yau4UkkAfEHrPncEisKwenkfJt23Vmv0q15Jr/yVKgDVe6tpDGuiX655InDjK324caOTirt/xLY97zLiU6ur3//3mRr4Va+lwV+pAjCy30hc4mp/6duu1l2u+g34g1C550z8d5RDeTmzNNAr1Uy7/ZUqAOXHl+N0OLtuhz1hkSFj/XiNs7lmggcXbuPgmpLJ1M6rpXTxGpg1S6/wlUqiV/5KFYASbwnPfPcZznr4LPuN4gFbkn5vrx4wRQ8Dp+2Ex//aB//vbqPixCJrHYW+I5prJiil7GnwV6pATDp6EqsvXM15j55HOBomYiJ4HB5EhGnHfJNTdxrY9Ba7XAcYsvkDiER4cRg8djyYdibiuSIQ7sKOhpwT+PpOGNjkgu/O1C59pdpJg79SBaTYXYzb4cZgiEQiIOB2uPnJuMspnZ5QaraqCsrKmP12lLtXNXDU1RDI9K/ZdPPAj5W5P6LRC5WV2qWvVBY0+CtVIFLN9Q9GggQjQcqWlFEzt+Zgd3ZsoSAqKhi0bRvrBhnK/r2IKIaGUEP6HfWATB+H10v5X9+HvgPz3RSluiUN/koViIrNFURNNOVjUROlYlNFy2WC/X4rmQ0oBWqC11OxqYKKTRWsfW9txvuVCBgHhZ0HEMtT8AXB0aeYyh+sxq+BX6msafBXqkBU7622vWpvCDWwbd+2tM+Prxcw/fjpDLh9AAfCBzLar3GCMwKRAh4KmBj5AmM/N4oRp51N+YkzNaFPqQ7S4K9UgRjZbyQ+ty/lCYDP7WNE3xEZvU6Jt4Q131/DlIenEIxawwZpRQs78Pdx9eHJazdpwFcqh3rA6J9SPUP58eU4JPWfpEMclJ9QnvFrlQ4rpXZeLfeW3cvkoyfjcXrsNxarZn/BMeBzFrPm+2s08CuVYxr8lSoQJd4SKmdUUuIpaS5a43P7KPFY97c3AMaHAZZdsMz2pAIAgWgBfhOccqAfH/78I0qHlba9sVKqXbTbX6kCUjqslJq5NVRsqshZ0ZoSbwnfPu7bPLLpkRy2tHMVB+FHg8/RK36lOokGf6UKTPyKPZcmHDWBp959qtssHuQ0UH7hgnw3Q6keqwA7+5RSudblawdkK74gz2mLdCqfUp1Ig79SvUCJt4SLx1yc72a04owVF3BGBZfBWpBnbi2l51yW55Yp1bNpt79SvUDN/hru+vtd+W5GC31cfbjtzNvYtX+XLsijVBcrmOAvIr8GfgR8HLvrOmNMZYrtJgMLASew2BijA4NKteHa567NdxOaecPg7uNn5YUrNZNfqTwpmOAfc5cx5na7B0XECSwCzgR2Aa+KyDPGmLe7qoFKdUfvfvJuvpuAJwxn/MvB1HN+Rvk3r9erfKXyqNCCf1tOBrYZY3YAiMhS4HxAg79SaRzX/zg21GzIaxu8bi/L7n5fE/mUKgBijMl3G4Dmbv8fAvuBjcBcY8ynSdtMAyYbYy6O3f4+cIox5nKb17wEuARgwIABJy5dujSrttXX1+PvAcuG9pTjgJ5zLF11HKFoiDc/erPTXn+Idwi7ArtsH3eIg5F9Rxb81X5P+VxBzzkWPY7sTZgw4TVjzNhUj3Xplb+IrANSnfb/ArgP+A3W+l2/Ae4ALkp+iRTPtT17Mcb8CfgTwNixY8348ePb32hg/fr1ZPvcQtJTjgN6zrF05XFUv1rN7BWzrRs5XsHv9mNuZ97WeSkfu3rc1dw44caCD/zQcz5X0HOORY+jc3Rp8DfGTMxkOxH5M/DXFA/tAoYm3B4C1OSgaUr1eJeddBnffu0A89ddw7JjIxxIU+4/Wy5xETZh3A43ToeTp7/zNJOOnpT7HSmlOqRgxvxFZJAxpjZ281vAphSbvQqMFJGjgN3Ad4DvdVETler2Bs64hAd+fgMn7K7jZ2eRcQ+A1+klEAlQ7C6mMZS6SqBO3VOq+yiY4A/cKiJfwerGfx/4MYCIHIE1pa/MGBMWkcuB1VhT/e43xmzOV4OV6nZKSqCykh+fP4XrJ9YTyOAbwO/2s+DMBez6zArqQw8dytRlUwlFQgQiAbxOLw5xsOb7a3TqnlLdRMEEf2PM923urwHKEm5XAq3m/yulMlRaSsm/aln30G+ZvOcOAhIlTBQnTiJEKHIV0RRuwuf24RAHlTMqWwX12rm1LRYfOuqzozTwK9WNFEzwV0p1rrpAHRWbK6jeW83IfiMpn/ULPpTrWwTxs485mxVbV7S5omDy4kPr16/vwiNRSnWUBn+leoGqnVWULSkjYiI0hhpxipOf/PUnXHHyFdw44UZKvCXN2+Z6RUGlVOHRhX2U6uHqAnWULSmjLljXnKwXMREiJsLdr9zN4bcdTtXOqjy3UinVlTT4K9XDVWyuIGIito83RZqY8vAU6oP1XdgqpVQ+afBXqoer3lttOz0vLhgNUrGpootapJTKNw3+SvVwI/uNxCnOtNsEI0G27dvWRS1SSuWbBn+lerjy48vb3MYpTkb0HdEFrVFKFQIN/kr1cCXeEq44+Yq027gcLspPaPskQSnVM2jwV6oXuHHCjRQ5i2wff+a7z2gpXqV6EQ3+SvUCJd4S1v5gLX63H4/DWtHHgQOPw8PqC1fr4jtK9TJa5EepXqJ0WCm181qW5dXFd5TqnTT4K9WLJJflVUr1Ttrtr5RSSvUyGvyVUkqpXkaDv1JKKdXLaPBXSimlehkN/koppVQvo8FfKaWU6mU0+CullFK9jAZ/pZRSqpfR4K+UUkr1Mhr8lVJKqV5Gg79SSinVy2jwV0oppXoZDf5KKaVUL6PBXymllOplNPgrpZRSvYwGf6WUUqqX0eCvlFJK9TJijMl3G7qEiHwM/CvLp/cHPslhc/KlpxwH9Jxj0eMoLD3lOKDnHIseR/a+YIw5LNUDvSb4d4SIbDTGjM13OzqqpxwH9Jxj0eMoLD3lOKDnHIseR+fQbn+llFKql9Hgr5RSSvUyGvwz86d8NyBHespxQM85Fj2OwtJTjgN6zrHocXQCHfNXSimlehm98ldKKaV6GQ3+7SAiV4jIFhHZLCK35rs9HSEi80TEiEj/fLclGyJym4i8KyJvisiTIvK5fLepPURkcuyztE1E5ue7PdkSkaEi8oKIvBP7u7gy323qCBFxisg/ROSv+W5LtkTkcyKyPPb38Y6InJrvNmVDRK6OfaY2icijIlKU7zZlSkTuF5E9IrIp4b6+IrJWRKpj/34+n23U4J8hEZkAnA982RhzPHB7npuUNREZCpwJ7Mx3WzpgLXCCMebLwFbg2jy3J2Mi4gQWAVOAUcB3RWRUfluVtTAw1xjzRWAcMLsbHwvAlcA7+W5EBy0EVhljjgNG0w2PR0QGAz8FxhpjTgCcwHfy26p2eQCYnHTffOA5Y8xI4LnY7bzR4J+5S4EFxpgAgDFmT57b0xF3AT8Hum3ChzFmjTEmHLv5d2BIPtvTTicD24wxO4wxQWAp1ollt2OMqTXGvB77vQ4r0AzOb6uyIyJDgLOBxfluS7ZE5BDg68B/AxhjgsaYf+e3VVlzAX1ExAUUAzV5bk/GjDEvAvuS7j4feDD2+4PAN7u0UUk0+GfuGOA/ROQVEflfETkp3w3KhoicB+w2xryR77bk0EXAynw3oh0GAx8k3N5FNw2YiUTkSOCrwCv5bUnW7sY6KY7muyEdMBz4GPif2PDFYhHx5btR7WWM2Y3Vu7oTqAU+M8asyW+rOmyAMaYWrJNm4PB8NsaVz50XGhFZBwxM8dAvsN6rz2N1bZ4ELBOR4aYAp0u0cRzXAZO6tkXZSXccxpinY9v8AqvreUlXtq2DJMV9Bfc5ag8R8QOPA1cZY/bnuz3tJSLnAHuMMa+JyPh8t6cDXMAY4ApjzCsishCre/mX+W1W+8TGw88HjgL+DTwmIhcaYx7Ob8t6Dg3+CYwxE+0eE5FLgSdiwX6DiESxajV/3FXty5TdcYjIl7D+mN4QEbC6yl8XkZONMR92YRMzku7/A0BEZgLnAGcU4klYGruAoQm3h9CNujSTiYgbK/AvMcY8ke/2ZOlrwHkiUgYUAYeIyMPGmAvz3K722gXsMsbEe1+Wk+ex5SxNBN4zxnwMICJPAKcB3Tn4fyQig4wxtSIyCMjr0LF2+2fuKeB0ABE5BvDQzRabMMa8ZYw53BhzpDHmSKwvijGFGPjbIiKTgWuA84wxjfluTzu9CowUkaNExIOVyPRMntuUFbHOIv8beMcYc2e+25MtY8y1xpghsb+L7wDPd8PAT+xv+QMROTZ21xnA23lsUrZ2AuNEpDj2GTuDbpi4mOQZYGbs95nA03lsi175t8P9wP2xqRtBYGY3u9rsae4BvMDaWC/G340xP8lvkzJjjAmLyOXAaqws5vuNMZvz3KxsfQ34PvCWiPwzdt91xpjKPLapt7sCWBI7sdwB/Gee29NusSGL5cDrWMN6/6DAKuSlIyKPAuOB/iKyC/gVsABruHgW1snNBflroVb4U0oppXod7fZXSimlehkN/koppVQvo8FfKaWU6mU0+CullFK9jAZ/pZRSqpfR4K9UAhH5dWy1w/hPjYg8LiJHZ/DcH8ae489xm8bHXveEXL5u7LWPjL32ORlsO0BE7haR7SISEJFPRWSliJyV63b1RCJysoj8OsNtx4rIA7GVH6Mi8kDntk71Nhr8lWrtM+DU2M884CvAcxnUSF8Re06uiw69Hnvd7Tl+3YzFisb8A2vhm9uxSkT/AHgfeEZERuerbd3IyVjzvTPxNaAUqyBUtyvCpQqfFvlRqrWwMebvsd//LiI7gb8BZcBjyRvHluh1xkqR5rzcc6xW/t/b3LBzLcFapey0pNr9z4rIfVj111Xu/JcxZiGAiGzMd2NUz6NX/kq17bXYv0cCxLpjN4rIN0VkM9AEnJLc7Z/QpT5dRP4oIp+JyC4RuUFEWvzticiXReRZEfm3iNSLyAYROTP2WKtu/9jtOSKyUET2xZ73X7GqbvFtBonI/SKyQ0QOiMhWEflt4jaZEJGvAycC16ZatMcY86YxZmfC9tNF5K3Y0MAHIvI7sZZljT8ef5/GiMh6EWkUkX/GbvtE5H9i79UOEfluUlvWi8hyEblERN6PHdcKsdZ/T9yuv4g8KCJ7Y6+/XkTGJm3zvojcLiJXx/5fPhWRpSLyuaTt+sb+/z4SkSYReUlETknaxojIlSJyk4h8LCJ7RGSRiHjjxwz8V8K2RkTW273nxpjuvLKg6gY0+CvVtiNj/36YdN+twM1YPQLvpXn+rUA9MA1rYZL/F/sdABE5Dvg/YBDwE+BbwJO0XPwnlblYiwLNAH4LXAL8LuHx/lhX63OAycBtWKVe/6uN1032DSACrGtrQxGZBFRgDVWcH9vXPKxyzMkeBB4FpmKtdLgca52AGqz35xXgIREZkvS8U7FK2M4BZgFfxlp7I9FTwFmxfZdjfde9ICIjkrabjlU3/hKstSLOAW5KOB5v7LjPBH6GtQb7x8A6EUlecXIucARwIdZ7/WPgythjK4A7Etp/KnBZivdEqa5hjNEf/dGf2A/wa6wFm1yxn2OAF4D9wKDYNg9gLcH7laTn/jB2vz92+8jY7YeStvsnsDTh9qNYiyz1sWnT+NjrnJBwnwHeBRwJ9/0CK9+gr83ruIDvYfVUeJLaeE6a9+QPQG2G79/fgReS7vs51snDkKT3aWbCNmWx++5PuO9QIARcmnDf+th9X0i472ux506O3Z4cu/2NhG18WEH7jwn3vY+VR+FKuO9u4MOE27Ow1vIYmfQ+bgduS/r/eDHpuJ/CWnMifvty6yu33Z/JjcAD+f7b0J+e9aNX/kq11g8rwISALcBwoNwYU5uwzW5jzD9TPTmFNUm338a6Yo87HagwxhxoZzufNi27h58A+gAngLXinohcJSJvi8gBrONZgrUg0rB27qvNRUBiuQ9jaJ0XUYF15X1q0v3PJfy+Lfbv8807NOYzrIDdoksfeN0Y86+E7f4Pa3nUk2N3nQx8bIz534RtGoC/YiXRJXrBGBNOuP02cHjC0MhErGGf90TElTB88b9Ai2EE2v5/VqpgaMKfUq19hvWlb7C6+muMMcnB76N2vF5yMlwQa834uH5ALe2XvB54/Pag2L9XYWXmL8AKVp8CJwGLkvbflt3AYSJSZIxpSrNdf8BN6/cmfrtv0v2J70swxX3x+5Pbmmod9D0cPO5BKdoQb0e6NsT3J1hLdgexjmkc1olTsuTZF5m0XamCoMFfqdbCxpi2MqxzuRzmXg4GrvY43OZ2/ETiAuAxY8wv4huIyKgs9rMeuBFrbHxFmu0+wQqSye0aEPt3Xxb7TiX59eP3xY+71mabAVm0YR9Wt/ulKR4LtPO1lCoY2u2vVP49B0wXkfZeJZ6fNGvg28ABYFPsdh9aB6gZ7W2cMeZvWF3fN4lISfLjIvIlERlqjInEtktep3w6EAVebu++bYwRkeZhCxH5Glaw3xC76xWsrvuvJ2xTjFWjoKqd+3oOGAHsNMZsTPp5q52vFYy1RXsDVN7plb9S+XcDVjGXF0XkDqyegK8Ce40x96d5XgnwmIj8GTgeaxbBPcaY+NXtWuCnIvIKVhf1DKxAlo0ZWImPG0XkLqzx7EOwMup/BJwCfIBVxGa1iPwPsBT4EvAb4M/GmF1Z7jvZHuCvYlXLKwJuwcoDWAVgjFktIv8HVIjIfKz3cx7WydBt7dzXQ1gzMNaLyO3ADqxhmpOxEgPvasdrvRv790oReR7Yb4zZkmpDETkMa5YFwOeBL4jINABjzPJ2HoNSrWjwVyrPjDFbRKQUa2x+cezut4Hr2njqHVjJiI9i9eItTnrOjcBhWNMAwUoI/CnwbJZtHANci5W9PxhrZsEG4HvGmDdi260Rke8A12OdMOyJtTPTynaZeBlr+t3dWMe3HmuqXqJvxfZ7N9YJwgbgdGPMNtrBGNMkIhOw3ssbsIYO9sRe75l2tvtvWCcfV2JNEX0RayZHKsfTMnFyeMK20s79KtWKtM5jUkoVOhExwBXGmFTz53usWGGcT4wx09raVillT8f8lVJKqV5Gg79SSinVy2i3v1JKKdXL6JW/Ukop1cto8FdKKaV6GQ3+SimlVC+jwV8ppZTqZTT4K6WUUr2MBn+llFKql/n/VqW3kPnOL6wAAAAASUVORK5CYII=\n", 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" ] @@ -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", "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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/jN3rcayroL/FKpr9Des5IsaYZcAdwJtYd2mLOHfleBvWwbMR65nrdOwisGxMxaq88JVbLKlAX6yakLuwrk4/wCrqzasHsa5IdwJ/2PP/yG34P0A9e95jgRuMMWlF3Pldhxewio5isU6EMzINfwUYLVYRzMh8rAPGmA32unyNdeV7Gus5W2I2k4zEqrS2HKtIdRx52x9GYj0OOY110p2Wy/hzsU7aW7GKABPIWDT1BtYB/SvWBcOHWBVMwHoW9an9e9xkjFmBVcfiHazfeztZvOmQg57ABhE5A0zAeraXYD+2GAv8aS/rcveJjDGnsSpW9cUqQtsGdM3jMj/CesVmMdY+moC1nfLqFFapyF6sk/t44D5jTHqNfWNMHOeS+5dZzcQeL94Y85sxJj4fy3ef/nus/eRr+1hfD/Syhx3DqvTzKtYjoHpYtZTTpp2Hta+sxapsNCvT7G/FurPcjLXfPmxPl+02N8YkAf3s7pNYxbGZj6mc1mcfcC3W73sUa78cBZSyt/kIrH3zJNY+P9Nt2s1Y56Sd9j5THWs7r8F6HvsruRwbeTh/Zbm/5nX93OR2jnOX9lGl4yKSVvLzDFaJ1Umsc9hXWU1YAMZgPcPehZVHppP9+es8xph1WDnG/YbpHbHfXMDaPqONMT9faID2TWV3oJeIvFhAOch9/huB17EuWg8DTXE7jvIqraa5yoKIDMGq/NPR6Vjyy747jMEqMt/ldDxKKZUfInIf1sVMTqWgKgvF8pOy6sKISF8RKSPWc7jXsO7IdzsblVJK5U5EqolIB/uR06VY9UG+dzouT6SJ3btci1VZ6wBWUegAo0UySinPUBrrzaLTWB9r+hF419GIPJQWxSullFJeRO/YlVJKKS+iDREUsEqVKpmIiAinw1BKKY/y77//HjPGVM59TJUbTewFLCIighUrVjgdhlJKeRQRyc9XA1UOtCheKaWU8iKa2JVSSikvooldKaWU8iKa2JVSSikvooldKaWU8iKa2JVSSikvUmITu4h8JCJHRGR9NsNFRN4Wke0islZEWhZ1jEoppVR+ldjEDnyC1SxidnphfW+9HnAPMKkIYlJKqRIjITk1/Z8qOCX2AzXGmMUiEpHDKNcCn9mNqPwtIuVEpJox5mCRBKiUUl5owm/b2H70DCv3nGR/TLzT4XilEpvY86AGsM+tO9rud15iF5F7sO7qCQ8PL5LglFLKEySmpHI2MZVtR87w8pxNrN4XA0DlAD84ncTJf4/QsWNN9LNzBUcTe/Yki35ZNoVnjHkPeA8gKipKm8tTSpVoKaku3lmwnbd+25bl8NFd63FPr6k0aFCJzyb3oXPnWsjrRRykF9PEnr1ooKZbdxhWO+dKKaXcGGP4dkU0246c5p9dJ1gbHZs+LKi0D/1bhRFevgynD5zhkQHNAAibM4grr6xN6dI+ToXttTSxZ28mMFxEvgbaArH6fF0pVdKlugz7T8aTkJLK5kOn2Xs8jtd+3ZphnApBpakS7M+0e9sRGujHsmX7GTZsFqtWHeLqplVp3LgKPXvWdWgNvF+JTewiMhW4AqgkItHAc4AfgDFmMjAH6A1sB84CdzgTqVJKOe9QbAITF2zn87+zfhpep3IQXw69nKqhAen9YmMTGD5qHu++u5xq1YL59tsbadRIW2YtbCU2sRtjbslluAEeKKJwlFKq2Br17Rq+/Tc6vbtx9RDu7FAbEWhcPZSQQF+qhQZmmCY5OZVWrd5j164YRoxoy5gxXQkJ8S/q0EukEpvYlVJKZS8pxcXsdQd4ZNqa9H4D24bzdO+GBPlnnzqio09Ro0Ywfn4+jBnTlUsvrUirVtWLImRl08SulFIqg4VbjjDk4+UZ+q157mpCA/2ynSYxMYVx4/7k5ZeX8Nln13PTTY0ZOLBpYYeqsqCJXSmlFMYYEpJd9H57CbuOxQEQVas8r/ZvSt0qwTlO+/vvu7jvvtls3Xqcm29uTMeO+j0PJ2liV0qpEswYw4yV+3ns2zUZ+k8Y0Jxrm9fIdfqRI3/l9deXEhlZnp9/HqS13YsBTexKKVUCbT9yhpV7T/LUjHWkuKzvakVULEOfZtW5u1MkoWWyL3Z3uQwul8HXtxTt2oXx9NOdePrpTgTmUFSvio4mdqWUKgEOxMTz+HdrWb03htOJKecNn/VgR5rUCM11PuvWHWbYsNn07VufJ57oSP/+jejfv1FhhKwukCZ2pZTyQj+s2s9Paw6Q4jK4jGHJtmMAlPYtRfXQAC6tGkyvptVoHVGBWhXKUKpUVl/RPicuLokXXljEG28spXz5QMLDc78IUM7QxK6UUh5s5d6TfL9yP8EB1un83YU7zhuncfUQ6lYpS6NqIUwY0ByRnJN4ZgsW7GLIkB/ZuzeWoUNb8Oqr3ahYsUyBxK8KniZ2pZTyMNEnz/LJn7v54I9dGfr7+ZxL2P+5rDr3domkcfWLv7MuU8aP0FB/liy5Q2u8ewBN7Eop5SGMMcxZd4gHvlqZoX9ea7DnVUqKi7ff/of9+0/x+us9aNs2jNWrh+VaXK+KB03sSilVDKW6DIu3HuXwqQQWbjnKLxsOZRjetEYoPz3YscCX+/ff0QwbNos1aw7Tt299UlJc+PqW0qTuQTSxK6VUMRB7NpkXZ29k1d6T7Dgal+U4DaoG0yK8PEPaR3Bp1Zw/GpNfMTEJPPnkb0yZ8i/VqwczY8ZNXHddg3w/j1fO08SulFJFyOUyJKa42HL4NF/+vYdUl2HBliOcPJucPo5vKaFN7Qo0rRHKVQ0vIax8IJXK+lPat1ShxRUTk8CXX67jkUcu5/nnryA4WBts8VSa2JVSqpAZY1i1L4a3ftvG4q1HzxteOdifqiEBNKoewoe3RxXZXfLWrcf5/PM1jBnTlYiIcuze/TAVKgTmPqEq1jSxK6VUIYlPSmXOuoO8PGcTx+OS0vt3rFuJ5jXL0aZ2BaIiylOmdNGeihMSUnj11T945ZU/CAz05Y47WhAZWV6TupfQxK6UUgUkKcXFqr0n+eSv3fy8/tB5w9+/LYou9SsXapF6bn77bSf33z+bbdtOMHBgU15//WqqVi3rWDyq4GliV0qpi3D8TCJJqS6mLNrJJ3/tzjCscrA/N7QKo0+zajSsGuJ4zfL4+GRuu+17goJK8+uvg+nevY6j8ajCoYldKaXyadPBU8zfdJg35m3Fbj8lXf+WYfRvVYO2tSviUwxeEXO5DF99tY6bb25MYKAfc+cOpl69igQE6OnfW+mWVUqpPJi38TBnk1L4bdMRflpzIMOw5/o2wqeUcEX9KoQXo0+trl59iGHDZvHPP/sBGDy4GU2bXuJwVKqwaWJXSqkcrN8fy4ipq9h5LOO75c/1bUS/lmGEFsOmSs+cSeK55xYwYcI/VKgQyOefX8+gQU2dDksVEU3sSimVybroWNbuj2HvibNMWbQzvf9Xd7flkpAAKpQpTfmg0g5GmLNbbvmOWbO2cu+9rXjllasoX15ru5ckYozJfSyVZ1FRUWbFihVOh6GUyoO4xBTmrDvIzDUH2Hr4NJWD/YmNT2bfifgM4w1qG87Y64v3He+ePTGEhgZQrlwAK1ceJDExhXbtajodVp6JyL/GmCin4/AGeseulCoxjDH8u+ckC7YcYfbag+w+fjbD8JAAP+pVCaZGuUD6tQyjY91KBAf4EhxQ/Irb0yQnp/Lmm3/zwguLuOuuFrz9di9atqzmdFjKQZrYlVJe7ejpRJ7+fh1bD58+L5E3rRFK/UuCub9rHepU9rx3uf/8cy/Dhs1m/foj/Oc/lzJyZHunQ1LFgCZ2pZRXSnUZpi3fx1Pfr0vvd03TasQnp3JjqzC6XFq5yL/4VpAmTVrO/ffPoWbNEH744WauvbaB0yGpYsJz92qllMrkdEIyT3y3joVbjhCXlJre/5qm1Rh7fRPKlSm+Fd7ywhjD6dNJhIT406tXPf773/Y880wXypb17PVSBUsTu1LKoy3eepTPlu4hOdXFIrcGVqqFBtCqVnke7V6fSA8sZs9s8+Zj3HffbAICfJkzZyAREeUYN66702GpYkgTu1LKo5yIS2LZruOs2hvDlMU7MwxrXD0EgFkPdvSadsTj45N5+eUljBv3J0FBpRk3rpvTIaliThO7UsojHDmVwC8bDvHsjxvOGzZ5cCt6NqnqQFSFa/36I1x33dfs2HGSwYOb8dpr3bnkEs8vfVCFSxO7UqrY+mX9Qf5v7hZ2HM341bcbWoVxd6dI6lQOwtfHuZbSCosxBhGhZs0QwsJCmDKlD1ddFel0WMpDaGJXShULZ5NSmLFyP9sOn8ZlYPG2o+xxez2tWVgoN7euSaNqIbQIL+9gpIUnNdXF5MkrmDp1PQsW3E5oaAALFw5xOizlYTSxK6WKXKrLsGTbUZ74bh2VgktzMi6Z/TEZv/YWbLc+9tPwjjQNC3UizCK1cuVBhg2bxfLlB+jWLZKYmAQqVw5yOizlgUp0YheRnsAEwAf4wBjzaqbh4cCnQDl7nCeMMXOKPFClPJQxhvjkVLYcOo3LGDYfOs3T36/PMM6hUwlc2aAKVUMDaFQthHs6R1IlxB9/Xx+Hoi5a8fHJPPnkfP73v2VUrlyGr77qx4ABTbym8p8qeiU2sYuIDzAR6A5EA8tFZKYxZqPbaKOBb4wxk0SkETAHiCjyYJXyECmpLmauOcCJuCQAXpq9KdtxH7qqHo2rh3B1Y++r9JYfvr6lWLx4D8OGtWLs2KsoVy7A6ZCUhyuxiR1oA2w3xuwEEJGvgWsB98RugBD771AgYyPMSpVwv208zLYjZ9h59Ay/bDjE6YSU88YJ9vfljg4RREVUwAB1KgcRVr74tFnuhF27TvLccwuZMKEn5csHsnTpXfj7l+TTsSpIJXlPqgHsc+uOBtpmGud54FcReRAIArJ8gVRE7gHuAQgPDy/wQJUqLuZtPMyWQ6eYsXI/0THxJKW4MgwvV8aPzvUqc3/XOlQvF0gpEcpqwkqXlJTKG28sZcyYRZQqJdx++2VcdVWkJnVVoEry3pTVA6zMbdjeAnxijHldRNoBn4tIE2NMhrOZMeY94D2wmm0tlGiVcsgHS3by3cr9bDp46rxh7SIrcmu7WnS9tAp+PuKVr54VlCVL9jBs2Gw2bjxKv34NeeutHtSs6f2VAlXRK8mJPRpwb6w4jPOL2u8CegIYY5aKSABQCThSJBEq5aCDsfG0e+X3DP1a1SrPuP5NqVUxCD9N4vkyfvxfxMUl8dNPt9CnT32nw1FerCQn9uVAPRGpDewHBgADM42zF7gK+EREGgIBwFGU8lIpqS6+WRGdoUU0gBWju1GprL9DUXkmYwyffrqGzp1rERlZng8+6EvZsqUJCtIGW1ThKrGJ3RiTIiLDgblYr7J9ZIzZICJjgBXGmJnAY8D7IvIIVjH9EGOMFrUrr7R6XwzXTfwzvbusvy9DO9Xm4W56d5lfGzce5b77ZrN48R5GjWrP+PH6KVhVdEpsYgew30mfk6nfs25/bwQ6FHVcShWV6JNn+fjP3SzeepRtR84AEFkpiM+HtqVGuUCHo/M8Z88m89JLi/m///uLkBB/PvigL3fc0cLpsFQJU6ITu1IljctlmLp8L+8u2HHel94ARvW4lAe61nUgMu/wyitLeOWVP7j99sv4v//rrl+OU47QxK5UCZCQnErrsb9leM+8UtnShJUvw/UtanBjVBhlSuvp4EIcOHCaEyfiadKkCiNHtqdbt0i6dIlwOixVgumRrJQXe23uFt5ZsD1DvyHtI7i7c6QWtV+k1FQXEycuZ/To32nQoBL//DOU0NAATerKcZrYlfJCZxJTaPLc3PTuy8JC6Vy/MsO61CFIP4Zy0VasOMC9985i5cqDXH11Hd59t7d+210VG3qEK+UlklNdrNkXw9roWMbMOvdl5HmPdKbeJcEORuZd5s/fSffun3PJJWWZNu0GbryxkSZ1VaxoYlfKQyWmpLJ0x3HW7Ivlzd+2njc8slIQcx/prB+SKQDGGPbvP01YWAidO9fixRe7Mnx4G0JDtcEWVfxoYleqmIs5m5Re6e1ATDy7j8fxy/pDLNiS8VtJLcLLUbdyWXo3rUb9qsH6DL2A7Nx5kgcemMOqVQfZvHk45coF8PTTnZ0OS6lseUViF3hNqnAAACAASURBVJHSQLgxZnuuIytVzO07cZZbP/yHuKRUjp5OzHHcJjVCeOX6ZlQJ8eeSEL17LEhJSam89tpfvPjiYvz8SvHSS1cSHKxfjVPFn8cndhG5BngDKA3UFpHmwHPGmOudjUyp/ElITuXbFft45scN6f1qlAukbWQFGlYNoVwZPwxQoUxpGtcIoVqo3pEXluPHz9Kp08ds2nSMG25oxFtv9aBGjZDcJ1SqGPD4xA6MwWpudQGAMWa1iOgXNpRHMMbw4R+7eGn2pgz9W9Uqz/Rh7bRSVhFLTk7Fz8+HChUC6dKlFq+9djW9e9dzOiyl8sUbEnuyMSYm0wlQv+euiiVjDN+t3M+CLUc4eiqRZbtPZBh+T+dIbmgVRn2txV6kXC7Dxx+v4vnnF7Fo0RAiI8szaVIfp8NS6oJ4Q2LfJCI3AaXsltoeAv52OCalstT25fkcsZ+bp12LVg8N4PsHOugzcoesX3+E++6bzR9/7KVTp3BcLr0vUJ7NGxL7cOBZwAXMwGqt7UlHI1LKzbJdJ5gwfyt/bj+e3u+3R7tQt4q29uUkYwyjR//O+PF/ERrqz0cf/YchQ5rr4w/l8bwhsfcwxjwOPJ7WQ0T6YSV5pRz13uIdvDxnc3p3rYpl+OnBjoQE+DkYlQIQEWJjE7nttmaMG9edSpXKOB2SUgVCPL15cRFZaYxpmanfv8aYVk7EExUVZVasWOHEolUxkOoy/LH9GMYYFmw+wqdL9wDwSr+m3NIm3OHoVHT0KR5++BcefbQd7dvXxOUylCqld+jFgX3ejnI6Dm/gsXfsItID6AnUEJE33AaFYBXLK1WkDsTE0/7V38/r/2q/pgzQpO6olBQX//vfPzz77EJSU1307Vuf9u1ralJXXsljEztwBFgPJAAb3PqfBp5wJCJVIp1NSmHUt2uZve5ger/v7muPCERUDKJCkH7UxEnLlu3n3ntnsXr1IXr1qsvEib2pXbu802EpVWg8NrEbY1YBq0TkS2NMgtPxqJJn5poDjPlpI8fOnPs63MPd6vHQVfW0AlYxsnjxHo4ciePbb2+kf/+Gum2U1/OGZ+x1gLFAIyD9fSFjTH0n4tFn7CXDrLUHGP7VqvTuXk2qMuKqejSspl8nc5oxhq+/Xk9goB/XXdeA5ORU4uNTCAnxdzo0lQN9xl5wPPaO3c0nwEvAa0Av4A70GbsqJCmpLh6atprZa61i96/ubkv7OpUcjkql2b79BPffP5t583bSt299rruuAX5+Pvj5+TgdmlJFxhsSexljzFwRec0YswMYLSJLnA5KeRdjDCkuQ72nf07vN6rHpZrUi4nExBTGjfuTl19egr+/L++804thw/TmT5VM3pDYE8V6aLZDRIYB+4EqDsekvERKqotlu04w8IN/MvRf/0IPyvp7w+HjHX79dQfPPbeQAQOa8MYbV1Otmn6SV5Vc3nBmegQoC4zAetYeCtzpaETKK+w7cZZO4xekdzeqFsI1zapxd6dISvuWcjAyBXDkSBzLlu2nT5/69OlTn2XLhtK6dQ2nw1LKcR6f2I0xabdSp4FbAUQkzLmIlKc7GBvP6O/XM3/zEQAiKpbh9Zsuo1WtCg5HpsBqsOWDD1by+OO/YYxh375HCA7216SulM2jE7uItAZqAH8YY46JSGOsT8teCWhyV/lyNimFRs/OzdDvljY1eaVfM4ciUpmtXXuYYcNmsXRpNF261GLSpGsIDtba7kq589jELiKvAP2BNVgV5r7HatltHDDMydiUZ0lOdXHbh8tYuvNcIy2P92zA0E618fPRIvfi4sCB07Ru/T4hIf58+ul13HprM30nXakseGxiB64FLjPGxItIBeCA3b3F4biUBzDGcDA2gbGzN2X4YtxNUWG82q+Zfmq0GFm79jDNml1C9erBfPzxtfToUYeKFbXBFqWy48mJPcEYEw9gjDkhIps1qau8+GvHMQa+n7GWe/s6FXm1XzPCNWEUG3v3xjJixM/8+OOW9IpxAwc2dTospYo9T07skSKS1jSrABFu3Rhj+jkTlirOjDEZkvqEAc3p0bgqAfoBk2IjOTmVt9/+h+eeW4jLZRg3rhvNm1d1OiylPIYnJ/b+mbrfcSQK5VG+W7kfgHaRFZl6z+UOR6Myc7kMXbp8wtKl0fTpU5///a8XERHlnA5LKY/isYndGDPf6RiUZzlyOoGR364BYFx/relenJw6lUhwcGlKlRLuuKM5o0a157rrGmjlOKUugFb5VSXC49PX0masdS3YpX5lfZZeTBhj+OKLtdSr9z+++cZqffnuu1tx/fXaCptSF6rEJnYR6SkiW0Rku4hk2X67iNwkIhtFZIOIfFXUMaqLl5TiIuKJ2UxbsQ+Ap3o34JM7WjsclQLYsuUY3bp9zq23fk9ERDkuvVS/u69UQfDYovjMRMTfGJOY+5ggIj7ARKA7EA0sF5GZxpiNbuPUA54EOhhjToqIfn/ew+w4eoarXl+U3j1zeAeahenz2uLg7bf/YdSoeQQG+jJp0jXcfXdLfPSbAUoVCI9P7CLSBvgQ6xvx4SJyGTDUGPNgDpO1AbYbY3ba8/ga6734jW7j3A1MNMacBDDGHCmM+FXBS0xJpfeEJew4GgdAaZ9SrHvhavx9tea704wxiAjVqpXlhhsa8frrV1O1almnw1LKq3jDJfLbQB/gOIAxZg3QNZdpagD73Lqj7X7u6gP1ReRPEflbRHoWULyqkBhj2H7kNJeO/iU9qY+9vgkbx/TQpO6wQ4fOMGjQDMaN+xOAG29szJdf9tOkrlQh8Pg7dqCUMWZPpoo2qblMk1WtHJOp2xeoB1yB9d35JSLSxBgTc97MRO4B7gEIDw/PY9iqIP2++TB3frIiQ7+NY3pQprQ37OKey+UyTJmygiefnE98fApNm+oTLaUKmzec9fbZxfHGfnb+ILA1l2migZpu3WFYn6TNPM7fxphkYJeIbMFK9Mszz8wY8x7wHkBUVFTmCwRViP7eeZznftzAlsOnAahU1p8nezWgZ5OqmtQdtn79EYYOnck//+znyitr8+67vbWCnFJFwBvOfPdhFceHA4eB3+x+OVkO1BOR2sB+YAAwMNM4PwC3AJ+ISCWsovmdBRi3ukBr9sUwe91B3lt8bnP4+Qhjr2vKTa1r5jClKkpxcUns2RPL559fz6BBTfX1NaWKiDck9hRjzID8TGCMSRGR4cBcwAf4yBizQUTGACuMMTPtYVeLyEasov1Rxpjj2c9VFbbZaw/y8LRVJKeeKxTpWLcSD15Zl7aRFR2MTIFVx+GHHzazevUhXnihK23bhrFr10MEBHjDaUYpzyHGeHbJsYjsALYA04AZxpjTTsYTFRVlVqxYkfuIKkvJqS5W7D7J+LmbiU9KpbRvKdZGx5433jsDW9CxbiXKlSntQJQqsz17Yhg+/GdmzdpK8+ZVWbr0Lk3oKl9E5F9jTJTTcXgDjz/yjDF1RKQ9VnH6CyKyGvjaGPO1w6GpfMqqAlynepXoUr8yyakualcK4o4OEdSpXFaLdYuJ5ORU3nzzb154YREi8Npr3Xnoocvx9fWGF26U8kwef8fuzm6X/S1gkDHGkfeb9I49/2LPJnPfl//y1w7rSUdooB+TBrWkde0K+OlHS4q1vXtjadhwIldfXYcJE3oSHh7qdEjKQ+kde8Hx+Dt2ESmL9XGZAUBD4EegvaNBqTxbuOUIQz4+96LB6GsaMrRTpIMRqdwcP36Wzz9fy0MPtSU8PJT16++jdu3yToellLJ5fGIH1gM/AeONMUucDkblzfxNh7nr03MlG81rluOHBzo4GJHKjTGGzz5bw8iR8zh5Mp4rr6xNs2aXaFJXqpjxhsQeaYxxOR2EyptXft7ElEXnXlMLDvBl7PVN+c9l1R2MSuVm06aj3HffbBYt2kO7dmFMntyHZs0ucTospVQWPDaxi8jrxpjHgO9E5LyKAsaYfg6EpTJJdRle/XkTy3afBGNYY9dwr1EukJf7NaVL/coOR6hyk5Lionfvr4iJSWDKlD4MHdqSUqW08qJSxZXHJnas19sA3nE0CpUlYwzT/41m1PS1Gfq3jijPK/2aUbeKfiO8uFu4cDcdOtTEz8+Hr77qR506FahSJcjpsJRSufDYxG6MWWb/2dAYkyG52x+fmV/0USmAX9YfYtgX/6Z3d720MpMGtyLATxti8QQHD57mkUfmMm3aBiZO7M3997emXTv9op9SnsJjE7ubOzn/rv2uLPqpQvbotNWs3R/L9iNnAKu4/bUbL6NdHf0qnCdITXUxadIKnn76dxITUxgz5gruuquF02EppfLJYxO7iNyM9YpbbRGZ4TYoGDivBTZVuB6fvpYZq/YDcFnNcgxqE67fbfcwQ4f+xCefrKZbt0jefbc39erpBZlSnshjEzuwDKsN9jBgolv/08AqRyIqoWauOcC0FVbz9r8/1oXIyvr83FOcOpWIMYbQ0ADuuy+Kq6+OZMCAJvplP6U8mMcmdmPMLmAXVmtuykEjplrXUV/d3VaTuocwxvDdd5t46KFf6Nu3PpMn96FNmxq0aVPD6dCUUhfJY7/XKSKL7P9PisgJt38nReSE0/GVBEkpLi574VcAIisF0b6OtrXtCXbtOkmfPlO58cZvqVIliDvuaO50SEqpAuSxd+xAV/t/zSYOePbH9Xy2dE9697fD2jkYjcqrGTM2MXjwDHx8SvHmmz0YPryNNtiilJfx2MTu9rW5msABY0ySiHQEmgFfAKccC86LJae66PHWYnYejQOgT7NqvNq/GWX9PXZXKhGSk1Px8/OhZctqXHddA8aP705YWIjTYSmlCoHHt+5mN9PaGggH5gGzgdrGmD5OxONtrbttPHCK6f9GE+Tvwz+7TrBs17mnHN/d155WtfQ74cXZsWNnefzxeRw4cIY5cwZqpThVbGnrbgXHG26zXMaYZBHpB7xljHlbRLRWfAE4lZBM77fPb1enWmgAfz1xpSaJYswYwyefrGbUqHnExiby2GPtSE01+PrqNlPK23lDYk8RkRuBW4Hr7H5+Dsbj0Vwuw8QF2/l+9f704vYGVYP55eHODkem8mrv3lgGD57BkiV76dChJpMn96FJkypOh6WUKiLekNjvBO7HarZ1p4jUBqY6HJNHen/xTsbO2ZTeXbtSEA2qBvPuoJYORqXyKzTUnxMn4vngg77ccUcLbbBFqRLG45+xA4iIL1DX7txujElxKhZPfcZ+5HQCbcZan9ePqFiGD26Pom6VYIejUnk1Z842Jk1awYwZN+Hn54PLZTShK4+iz9gLjsffsYtIJ+BzYD8gQFURudUY86ezkXmGuMQUnvlxPTNWWp+DfaBrHUb1aOBwVCqv9u8/xcMPz2X69I00aFCJ/ftPExFRTpO6UiWYxyd24E2gtzFmI4CINMRK9Hrll4tTCck0e/7X9O6BbcM1qXuI1FQXEycuZ/To30lOdjF27JWMHNme0qW1BT2lSjpvSOyl05I6gDFmk4iUdjKg4mzn0TOM+2Uz/+45ybEzSYDVCtuS/3bVuzwP4nIZPvxwFR06hDNxYm8iI/W1Q6WUxRsS+0oRmYJ1lw4wCG0E5jzGGC574VdOJZyrfhAS4Mv1LWrw9DWNNKl7gNjYBF5+eQlPPNGR8uUDWbDgdsqXD9DXDpVSGXhDYh8GjAD+i/WMfTHwP0cjKkbiElP4a8dx7v7sXIW+dwe1pFeTqpoQPIQxhm++2cDDD8/l8OEztGpVnZtuakyFCoFOh6aUKoY8OrGLSFOgDvC9MWa80/EUN3PWHeT+L1dm6Lfqme6UD9InFZ5ix44TPPDAHObO3UHLltX46adbiIqq7nRYSqlizGMTu4g8BdwFrARai8gYY8xHDodVLCQkpzJ3wyEe+no1AHd2qE3PJlVpU7uCw5Gp/Hr88d/46699TJjQkwceaI2PjzbYopTKmccmdqxn6c2MMXEiUhmYA5ToxG6M4ef1hzLcpXe9tDLP9m3kYFQqvxYt2k3NmqFERpbnrbd6IgI1amiDLUqpvPHkxJ5ojIkDMMYcFZESfyvz8pxNvL9kF2B9BvadgS2oXamsw1GpvDp6NI5Ro+bx6adruPPO5nz44bXaAptSKt88ObFHisgM+28B6rh1Y4zp50xYRc8YQ++3/2DTQaul2h8e6EDzmuUcjkrllctl+OijVfz3v/M4cyaJp57qyNNP67f5lVIXxpMTe/9M3e84EoWDEpJT6fO/P9h+5Ex6vw9vj9Kk7mHeeutvHnvsVzp1Cmfy5D40alTZ6ZCUUh7MYxO7MWa+0zE4afexOK54bWF6d6NqIcy4vz0BfvrlMU8QF5fEwYNnqFu3Anfd1YLKlcsweHAzfQVRKXXRPDaxl1TfLN/Hf79bm6Hf7levcSgadSFmzdrK8OFzCAnxZ/XqYYSGBnDrrZc5HZZSykuU6ApnItJTRLaIyHYReSKH8W4QESMijn1/PjnVxX/e+SM9qYcE+PJ4zwbsfLm3UyGpfIqOPkW/ftPo23cqQUGlmTixt37xTylV4Lzmjl1E/I0xifkY3weYCHQHooHlIjLT/bvz9njBWF+2+6cg482vek//nP73/Me6UKey1nb3JCtXHqRLl09ITXXxyitX8eij7bTBFqVUofD4O3YRaSMi64BtdvdlIpKXT8q2wWq7facxJgn4Grg2i/FeBMYDCQUVc34t2no0/e+tL/XSpO5BTp2yrjWbNq3CnXc2Z8OG+3niiY6a1JVShcbjEzvwNtAHOA5gjFkDdM3DdDWAfW7d0Xa/dCLSAqhpjJmV04xE5B4RWSEiK44ePZrTqPky+od19Hv3T27/aBkAb958GaV9vWGTeb+YmATuv382DRq8Q0xMAn5+PkyY0IvatbUVNqVU4fKGovhSxpg9mWoTp+Zhuqwebpr0gdYHb94EhuQ2I2PMe8B7AFFRUSaX0fPk1w2H+OLvvQBcFhbKwLbhXN8irCBmrQqRMYavv17PI4/M5ejRs4wY0QYfH32OrpQqOt6Q2PeJSBvA2M/NHwS25mG6aKCmW3cYcMCtOxhoAiy0LxqqAjNF5D/GmBUUomd+WM/nf+8B4PUbL6N/K03onuDMmST69ZvGvHk7iYqqzpw5g2jZsprTYSmlShhvSOz3YRXHhwOHgd/sfrlZDtQTkdrAfmAAMDBtoDEmFqiU1i0iC4GRhZ3U1++PTU/q4/s306TuAYwxiAhBQX5UqlSGd97pxbBhUdpgi1LKER6f2I0xR7CScn6nSxGR4cBcwAf4yBizQUTGACuMMTMLONRcHTmVQJ///QHA2OubcFPrmrlMoZz2+++7ePTRucyYcTORkeX56qvMH0RUSqmi5fGJXUTex+3ZeBpjzD25TWuMmYPVKpx7v2ezGfeKCwwxT96ct5UJ87eldw9sE16Yi1MX6fDhM4wcOY8vvlhLZGR5jh07S2SkVoxTSjnP4xM7VtF7mgDgejLWdvcIaUn9yV4NuKdzpH5atBj74IOVjBo1j7i4JJ55pjNPPtmRwEA/p8NSSinACxK7MWaae7eIfA7McyicC7Ll0GkAWoaX494udRyORuVm1aqDNG9elUmTrqFBg0q5T6CUUkXI4xN7FmoDtZwOIj9+Xn8QQJN6MXXmTBIvvLCQ669vSPv2NXnjjR6ULu2jpSpKqWLJ4xO7iJzk3DP2UsAJINvvvhc3f2w7xlu/WcXwXeprc53FzY8/bubBB39m375TlC8fSPv2NfH39/jDRinlxTz6DCXWLdNlWK+rAbiMMQXygZiiMvhD6xP0j3Srr02uFiN79sQwYsQvzJy5hSZNqjB1an86dNAKjUqp4s+jE7sxxojI98aYVk7HciH+2nEs/e+HutVzMBKV2fTpG/ntt52MH9+Nhx++HD+96FJKeQiPTuy2ZSLS0hiz0ulA8uNkXBID37fu1ueM6ORwNArg77+jiYlJoGfPuowY0ZYbb2xMeHio02EppVS+eOynsUQk7aKkI1Zy3yIiK0VklYgU+yQ/dbn1HXh/31I0qh7icDQl28mT8QwbNov27T/k2WcXYIzBz89Hk7pSyiN58h37MqAlcJ3TgeRXSqqL8b9sAWDNc1c7HE3JZYzhyy/X8eijczlxIp5HHrmcF17oqrXdlVIezZMTuwAYY3Y4HUh+NXz2l/S/tcKccxYs2M2tt35P27Y1+PXXW2nevKrTISml1EXz5MReWUQezW6gMeaNogwmr9ZFx5KcalXc3za2l8PRlDwJCSksX76fTp1q0bVrBD/+OIBrrqmnDbYopbyGJ5/NfICyWM2rZvWvWJq1zmoZdvQ1DfHTZFKk5s3bQdOmk+jR4wuOHo1DRPjPfy7VpK6U8iqefMd+0Bgzxukg8uvzpVaTrLe1i3A2kBLk0KEzPProXKZOXU+9ehWYOfMWKlcOcjospZQqFJ6c2D2uhtOxM4mcTUoFoLSv3iUWhZMn42nc+F3OnEniuee68MQTHQkI8OTdXimlcubJZ7irnA4gv776x3rF7eXrmzociffbv/8UNWqEUL58IC++2JVu3SKpX7+i02EppVSh89jbRmPMCadjyK/4ZOtuvV/LGg5H4r1On07k0UfnEhExgb/+slrvvf/+1prUlVIlhiffsXuc9xfvBKyP0qiCZYzh++83M2LEzxw4cJp7721Fw4bapKpSquTRxF6EUlzWa276AZSCN3DgDL7+ej3Nml3C9Ok3cfnlYU6HpJRSjtDEXkTOJKYAcE2zag5H4j2Sk1Px9S2FiNChQ02ioqrx0EOX46slIkqpEkzPgEXkz+1WS24tapZzOBLv8Mcfe2nRYgrffLMBgOHD2/DYY+01qSulSjw9CxaRyYusL9/eGFXT4Ug82/HjZxk6dCadOn3MqVOJhIYGOB2SUkoVK1oUX0RSUg2Vg/0JDfRzOhSP9d13Gxk2bDYnT8YzalR7nn22C2XLlnY6LKWUKlY0sReRdftj6dlYGxm5GMZAvXoVmDy5D82aXeJ0OEopVSxpYi8Ch08lAOCnz3/zJT4+mbFjl1ChQiCPPtqO/v0b0q9fQ0qV0rcKlFIqO5ppisCmg6cA6N5I7zLzau7c7TRpMomxY5ewdetxwHpNUJO6UkrlTO/Yi8DUZdanZOtVKetwJMXfwYOnefjhuXzzzQbq16/I/Pm3ceWVtZ0OSymlPIYm9iKw7fAZRKBhtRCnQyn29u6N5aeftjBmzBX8978d8PfXXVQppfJDz5pFYOexOFqG6/vr2Vm58iC//76LkSPb07ZtGPv2PULFimWcDksppTySPmMvAiJQvVyg02EUO6dOJfLww7/QuvX7vPHGUmJjrUqGmtSVUurCaWIvZKcSkq3XtKoEOx1KsWGMYfr0jTRsOJG33/6HYcNasXHjA/qxGaWUKgBaFF/I1u6LBSA4QH/qNIcPx3H77T9Qv35Fvv/+Ztq00WZslVKqoGi2KWRnk6zGX5qFhTocibOSklL59tsNDBzYlKpVy7Jo0RCaN6+q33ZXSqkCVmLPqiLSU0S2iMh2EXkii+GPishGEVkrIvNFpNaFLGfnsTgAypUpuZ8+XbJkDy1aTGHw4O9ZssR69S8qqromdaWUKgQl8swqIj7ARKAX0Ai4RUQaZRptFRBljGkGTAfGX8iyjNUEO5XL+l9ouB7r2LGz3Hnnj3Tu/AlxcUn89NMtdO58QddHSiml8qikFsW3AbYbY3YCiMjXwLXAxrQRjDEL3Mb/Gxh8IQs6FBsPlLxn7MYYrrrqMzZuPMoTT3Rg9OjOBAWV3FILpZQqKiUr25xTA9jn1h0NtM1h/LuAn7MbKCL3APcAhIeHZxh27EwSQIn5FOrmzceIjCxP6dI+vPlmD6pUCaJJkypOh6WUUiVGiSyKB7LKsibLEUUGA1HA/2U3M2PMe8aYKGNMVOXKlTMM8yklVA3x/te4zp5N5qmn5tO06STefvsfAK68srYmdaWUKmIl9Y49Gqjp1h0GHMg8koh0A54GuhhjEi9kQXtOnPX6NtjnzNnGAw/MYffuGIYMac6QIc2dDkkppUqsknrHvhyoJyK1RaQ0MACY6T6CiLQApgD/McYcudAFxSWmcCoh+aKCLc5Gj/6da675ioAAXxYuvJ2PP76WSpX0y3FKKeWUEnnHboxJEZHhwFzAB/jIGLNBRMYAK4wxM7GK3ssC34oIwF5jzH/yu6ztR87Qo7F3NdeakuIiMTGFoKDS9O1bn8BAX0aN6kDp0j5Oh6aUUiVeiUzsAMaYOcCcTP2edfu728UuIyXVBUBwgPcUxS9fvp9hw2bTunV1Jk/uQ9u2YbRtG+Z0WEoppWwltSi+SKzeFwNAhBc0ahIbm8Dw4XNo2/YDDh48zVVXaRvpSilVHJXYO/ai8Ptm69F85/qVcxmzeFu8eA8DBkzn8OE4hg9vw0svXUlISMn74I5SSnkCTeyFKCHZKopvUDXE4UgujDEGEaFWrVDq1avIzJm3EBVV3emwlFJK5UATeyFKTnVRvowfpT3sm+iJiSn83//9xb//HmTGjJuoVascixYNcTospZRSeeBZGcfDfP73Ho9L6gsX7qZ58yk888wC/PxKER+f4nRISiml8sGzso4HcbmsD9kdPnVB37UpcidOxHP77T/QteunJCamMGfOQL755kbKlPGeGv1KKVUSaFF8IflprfUhu2Fd6jgcSd74+AiLFu3mqac68vTTnTWhK6WUh9LEXkgWbjkKwB0dIpwNJAfr1x/hjTeWMmVKH0JDA9i8eTgBJawVOqWU8jZaFF9Ivl+1H4BLimEDMHFxSTz++DxatJjCzJlb2Lz5GIAmdaWU8gJ6Ji8Eh2ITAKhZIdDhSM43a9ZWhg+fw549sdx5Z3PGj+9ORS/4gI5SSimLJvZC8Oa8rQA82r2+w5FklJrqYvTo3wkKKs3ixUPo1KmW0yEppZQqYJrYC8G0FfsA6N20msORWA22TJ68gkGDmlK+fCAzZ95C1apltcEWpZTyUprYr20iKAAAE/9JREFUC1iK/Zpb4+oh+Ps6mzz/+Seae++dxZo1hxGBBx5oQ3h4qKMxKaWUKlya2AtYst2i24DWNR2LISYmgaeems/kySuoVi2Y6dNvpF+/ho7Fo4qv5ORkoqOjSUhIcDoUVUIEBAQQFhaGn5++UltYNLEXsET7+/BhFZyrkPbQQ7/wxRdreeihtowZ05XgYG2wRWUtOjqa4OBgIiIiEBGnw1FezhjD8ePHiY6OpnZtbSGysGhiL2BJqS5KAbUrBhXpcrdtO46fnw8REeUYM+YKHnqoLS1bOv+MXxVvCQkJmtRVkRERKlasyNGjR50Oxavpe+wFzBjrGXvV0KJ5fz0xMYUxYxbRtOkkRo78FYBatcppUld5pkldFSXd3wqf3rEXMAP4lhIC/Aq/4tz8+Tu5//45bN16nAEDmvDGG1cX+jKVUkoVb3rHXsCMAV+fwr8i/eyzNXTr9jmpqS7mzh3M1Kn9qVYtuNCXq1RB8/HxoXnz5jRp0oS+ffsSExOTPmzDhg1ceeWV1K9fn3r16vHiiy+ml4oB/Pzzz0RFRdGwYUMaNGjAyJEjnViFHK1atYqhQ4dm6HfttdfSrl27DP2GDBnC9OnTM/QrW7Zs+t9bt26ld+/e1K1bl4YNG3LTTTdx+PDhi4rtxIkTdO/enXr16tG9e3dOnjx53jgLFiygefPm6f8CAgL44YcfABg0aBCXXnopTZo04c477yQ5ORmAWbNm8dxzz11UbOoiGGP0XwH+uySykWn4zM+mMKSmusz+/aeMMcbExMSbsWMXm7NnkwplWapk2Lhxo9MhmKCgoPS/b7vtNvPSSy8ZY4w5e/asiYyMNHPnzjXGGBMXF2d69uxp3nnnHWOMMevWrTORkZFm06ZNxhhjkpOTzcSJEws0tuTk5Iuexw033GBWr16d3n3y5Mn/b+/eg6OqsgUO/xaJGpAYeUgGJqMRoiYkkAjoFUGJoOALFUSCoAKXUXmM1ohiXcepAnTK14hP4CLjtQI6SRAYFMHRmaCOiuAQJIZIEHMxRjQCIlcSBYnpdf84h04ndEg3pruhs76qrureZ59zVq/q9O6zz87empSUpKmpqbp9+3Zv+fjx43Xp0qUN9j2Um/3792tKSoquXLnSu+2tt97SzZs3/6LYZsyYoQ8//LCqqj788MN67733HrH+nj17tEOHDvrDDz+oqurq1avV4/Gox+PRMWPG6Pz581VV1ePxaFZWlrdeY/4+d0CRHgPf4dHwsK74FlZb5+Gnnz0tftyPP/6GyZNXU139E5s23U5CQhx/+MNFLX4e03rNfu0Ttny9r0WP2bPbKcwcnh5w/f79+1NSUgJAXl4eAwYMYOhQ5xZTu3btmDt3LtnZ2UybNo3HHnuM+++/n9TUVABiY2OZOnXqYcesqanhjjvuoKioCBFh5syZXH/99bRv356amhoAli1bxqpVq8jNzWXChAl07NiRTZs2kZWVxYoVKyguLubUU08FICUlhbVr19KmTRsmT55MZWUlAE899RQDBgxocO7q6mpKSkrIzMz0li1fvpzhw4eTmJhIQUEB9913X7N5ycvLo3///gwfPtxbdskllwSc16a8+uqrvPPOOwCMHz+e7OxsHn300SbrL1u2jCuuuIJ27Zz/+rnyyiu9284//3x27NgBOPfRs7OzWbVqFaNHj/7FcZrgWMPewkSg26ktN3CupuYgs2a9w1NPradjx7bMmTOU2Fi7g2KiT11dHWvWrGHSpEmA0w3ft2/fBnV69OhBTU0N+/bto7S0lLvvvrvZ4z744IMkJCSwefNmAL/dzY1t27aNwsJCYmJi8Hg8rFixgokTJ/Lhhx+SnJxMYmIiY8eO5a677mLgwIFUVlYybNgwysrKGhynqKiIjIyMBmX5+fnMnDmTxMRERo0aFVDDXlpaelgu/Kmuruaii/z/4M/Ly6Nnz54Nynbu3EnXrs5A265du7Jr164jHr+goIDp06cfVl5bW8uLL77I008/7S3r168f7733njXsEWANewurPvAzifEt07B/9tkehgxZzJdf7uPWW/vwyCOX0vEYXFjGRIdgrqxb0v79+8nKyqKiooK+ffty2WWXAc5twqZGUAczsrqwsJCCggLv6w4dOjS7zw033EBMjDMANicnhwceeICJEydSUFBATk6O97hbtmzx7rNv3z6qq6uJj68f61JVVcVpp53mfb1z507Ky8sZOHAgIkJsbCylpaVkZGT4fU/BjiCPj4+nuLg4qH0CVVVVxebNmxk2bNhh26ZOncrFF1/c4EdFly5d+Prrr0MSizkyu/Q7BtXW1gGQnHwqAweeztq1/8nChcOtUTdRqW3bthQXF/PFF19w8OBB5s2bB0B6ejpFRUUN6m7fvp327dsTHx9Peno6GzdubPb4Tf1A8C1rPPPeySfXz0PRv39/ysvL2b17N6+88gojR44EwOPxsG7dOoqLiykuLuarr75q0Kgfem++x16yZAl79+7lzDPPJDk5mYqKCu+Pjk6dOjXoTfjuu+/o3LmzNxeBvNfq6uoGA918H74/Qg5JTEykqqoKcBruLl26NHnsl19+mREjRhw2Y9zs2bPZvXs3TzzxRIPyAwcO0LatfWdFgjXsITCqb9JR7VdbW8fjj39Aauo89u7dzwknxJCXdz0XXhi56WmNCZeEhASeeeYZHn/8cWpraxk3bhzvv/8+hYWFgHNlf+edd3LvvfcCMGPGDB566CG2bXNWU/R4PIc1LgBDhw5l7ty53teHGs/ExETKysq8Xe1NERFGjBjB9OnTSUtLo1OnTn6P6+9KOS0tjfLycu/r/Px83njjDSoqKqioqGDjxo3ehj07O5slS5Zw8OBBAHJzc7330ceOHcsHH3zA6tWrvcd64403vLcXDjl0xe7v0bgbHuCaa65h0aJFACxatIhrr722yTzk5+dz4403Nih7/vnnefPNN8nPz6dNm4bNybZt2w67DWHCJNKj96LtceKvUnT5xi81WGvXVmqvXvMVZunw4XlaVVUd9DGMCdaxNipeVfXqq6/WxYsXq6pqSUmJDho0SM8++2zt0aOHzpo1Sz0ej7fua6+9pn369NHU1FRNS0vTe+6557DjV1dX6y233KLp6enau3dvXb58uaqqLl26VLt3766DBg3SadOm6fjx41XV/+j0DRs2KKC5ubnest27d+vo0aO1V69empaWprfffrvf95eRkaH79u3Tzz//XLt169YgflXVc889V9evX6+qqrNmzdKMjAzNzMzUkSNH6q5du7z1ysrKdNiwYZqSkqJpaWmak5Oj33zzzRFz25xvv/1WBw8erCkpKTp48GDds2eP9/1OmjTJW+9Q7HV1dQ32j4mJ0e7du2tmZqZmZmbq7NmzvduuuuoqLSkp8XteGxUf2oc4+TQt5aSuZ+lHG4tI7xbYKmoHD9Zxxx2vs3DhRyQlncKzz17BddelhjhKYxxlZWWkpdkCQaH05JNPEh8ff9j/skeznTt3MnbsWNasWeN3u7/PnYhsVNV+4Ygv2llXfAicGBN4Wk84oQ27dv3I9OkXUFY2zRp1Y6LMlClTOOmk1rUQU2VlJXPmzIl0GK2WjYoPgZg2Rx7J+umn3zJ9+j949tkr6N69A8uXj6ZNM/sYY45PcXFx3HzzzZEOI6zOO++8SIfQqtkVewh0TfA/EvTAgZ+ZOfNtevdewNq1lWzd+i2ANeomoux2nAkn+7yFnl2xh8BJfiaQKSzczpQpqykv/45x43oxZ85QEhPb+9nbmPCJi4tjz549dOrUyVbdMiGn6qzHHhcXntUvWytr2EPA3xX4q69uRQQKC29myJDuEYjKmMMlJSWxY8cOWx/bhE1cXBxJSUf3L8EmMDYqvoUlnJ6q31dupa7Ow3PPbeTcc39F//6/oabmILGxbYiLs99SxhjTmI2Kbzmt+h67iFwuIp+KSLmI/Jef7SeJyBJ3+4ciktzcMWPbCJs2VXHhhS8wbdrrvPSSs6BF+/YnWqNujDEm5FptSyMiMcA84DJgB7BBRFaqqu+8i5OAvaqaIiJjgEeBnCMdt/bAz/Tr9xc6d27HSy+NYOzYXqF6C8YYY8xhWvMV+/lAuapuV9WDQAHQeD7Fa4FF7vNlwBBpZoTRjzUHue22PmzdOo1x43rbgCRjjDFh1Wqv2IFfA1/6vN4B/EdTdVT1ZxH5HugEfOtbSURuA25zX/60YMHw0gULQhLz8aYzjXLVilku6lku6lku6p0T6QCiRWtu2P1dSjceSRhIHVR1IbAQQESKbACIw3JRz3JRz3JRz3JRT0SKmq9lAtGau+J3AL7LpiUBjRcP9tYRkVggAfguLNEZY4wxR6E1N+wbgLNE5EwROREYA6xsVGclMN59Pgp4S+3/A40xxhzDWm1XvHvP/HfAm0AM8IKqfiIiD+AsH7gS+B/gRREpx7lSHxPAoReGLOjjj+WinuWinuWinuWinuWihdgENcYYY0wUac1d8cYYY0zUsYbdGGOMiSLWsB+FUExFe7wKIBfTRWSLiJSIyBoROSMScYZDc7nwqTdKRFREovbfnALJhYiMdj8bn4hIXrhjDJcA/kZOF5G3RWST+3dyZSTiDAcReUFEdolIaRPbRUSecXNVIiJ9wh1jVFBVewTxwBlo979Ad+BE4GOgZ6M6U4EF7vMxwJJIxx3BXFwCtHOfT2nNuXDrxQPvAuuBfpGOO4Kfi7OATUAH93WXSMcdwVwsBKa4z3sCFZGOO4T5uBjoA5Q2sf1K4O84c4hcAHwY6ZiPx4ddsQcvJFPRHqeazYWqvq2qP7ov1+PMFxCNAvlcADwIPAYcCGdwYRZILm4F5qnqXgBV3RXmGMMlkFwocIr7PIHD59OIGqr6LkeeC+RaYLE61gOnikjX8EQXPaxhD56/qWh/3VQdVf0ZODQVbbQJJBe+JuH8Go9GzeZCRM4FfqOqq8IZWAQE8rk4GzhbRNaKyHoRuTxs0YVXILmYBdwkIjuA14E7whPaMSnY7xTjR6v9P/ZfoMWmoo0CAb9PEbkJ6AcMCmlEkXPEXIhIG+BJYEK4AoqgQD4XsTjd8dk4vTjviUiGqv5fiGMLt0BycSOQq6pzRKQ/ztwZGarqCX14x5zW8t0ZUnbFHjybirZeILlARC4F7geuUdWfwhRbuDWXi3ggA3hHRCpw7h+ujNIBdIH+jbyqqrWq+jnwKU5DH20CycUk4GUAVV0HxOEsDtMaBfSdYo7MGvbg2VS09ZrNhdv9/BxOox6t91GhmVyo6veq2llVk1U1GWe8wTWqGo0LXwTyN/IKzsBKRKQzTtf89rBGGR6B5KISGAIgImk4DfvusEZ57FgJ3OKOjr8A+F5VqyId1PHGuuKDpKGbiva4E2Au/gy0B5a64wcrVfWaiAUdIgHmolUIMBdvAkNFZAtQB8xQ1T2Rizo0AszF3cBfROQunG7nCVF6IYCI5OPcfunsjimYCZwAoKoLcMYYXAmUAz8CEyMT6fHNppQ1xhhjooh1xRtjjDFRxBp2Y4wxJopYw26MMcZEEWvYjTHGmChiDbsxxhgTRaxhNyZIIlInIsU+j+Qj1E1uaiWrIM/5jrtC2MfuNKznHMUxJovILe7zCSLSzWfb8yLSs4Xj3CAiWQHs83sRafdLz22McVjDbkzw9qtqls+jIkznHaeqmTgLDP052J1VdYGqLnZfTgC6+Wz7rapuaZEo6+OcT2Bx/h6wht2YFmINuzEtwL0yf09EPnIfF/qpky4i/3av8ktE5Cy3/Caf8udEJKaZ070LpLj7DnHX8d7srnV9klv+iLvWeYmIPO6WzRKRe0RkFM68/X91z9nWvdLuJyJTROQxn5gniMizRxnnOnwW8BCR/xaRInHWX5/tlt2J8wPjbRF52y0bKiLr3DwuFZH2zZzHGOPDGnZjgtfWpxt+hVu2C7hMVfsAOcAzfvabDDytqlk4DesOdwrRHGCAW14HjGvm/MOBzSISB+QCOaraC2cmySki0hEYAaSram/gT747q+oyoAjnyjpLVff7bF4GjPR5nQMsOco4L8eZOvaQ+1W1H9AbGCQivVX1GZy5wC9R1Uvc6WX/CFzq5rIImN7MeYwxPmxKWWOCt99t3HydAMx17ynX4cx93tg64H4RSQL+pqqficgQoC+wwZ1yty3OjwR//ioi+4EKnKU9zwE+V9Vt7vZFwDRgLs5678+LyGog4GViVXW3iGx35+n+zD3HWve4wcR5Ms4Uqn18ykeLyG043ztdgZ5ASaN9L3DL17rnOREnb8aYAFnDbkzLuAvYCWTi9IQdaFxBVfNE5EPgKuBNEfktzjKVi1T1vgDOMc530RgR6eSvkjs/+fk4C4uMAX4HDA7ivSwBRgNbgRWqquK0sgHHCXwMPALMA0aKyJnAPcB5qrpXRHJxFjtpTIB/quqNQcRrjPFhXfHGtIwEoMpdQ/tmnKvVBkSkO7Dd7X5eidMlvQYYJSJd3DodReSMAM+5FUgWkRT39c3Av9x70gmq+jrOwDR/I9OrcZaS9edvwHU464QvccuCilNVa3G61C9wu/FPAX4AvheRROCKJmJZDww49J5EpJ2I+Ov9MMY0wRp2Y1rGfGC8iKzH6Yb/wU+dHKBURIqBVGCxOxL9j8A/RKQE+CdON3WzVPUAzupXS0VkM+ABFuA0kqvc4/0LpzehsVxgwaHBc42OuxfYApyhqv92y4KO0713Pwe4R1U/BjYBnwAv4HTvH7IQ+LuIvK2qu3FG7Oe751mPkytjTIBsdTdjjDEmitgVuzHGGBNFrGE3xhhjoog17MYYY0wUsYbdGGOMiSLWsBtjjDFRxBp2Y4wxJopYw26MMcZEkf8HqSUO9nWyqqIAAAAASUVORK5CYII=\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", 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" + ], + "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, 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": [ + "
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LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6Y
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\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": [ + "
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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
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" + ], + "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", 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\n", 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" + ] + }, + "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": [ + "
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" + ], + "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": [ + "
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std116558.6165300.4879940.7381750.5226398.8320280.7657300.8148780.8114910.7863140.743923...4835.0819064751.2632875271.1981000.4209540.8798760.8834720.8952640.8861150.8777890.900723
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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
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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" + ], + "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": [ + "
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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
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0 rows × 47 columns

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" + ], + "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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" + ] + }, + "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": [ + "
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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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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
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" + ], + "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
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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
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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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nBvh5V6OFvzfV3V0JqVOdOl5uuLu64FJFobUmMvIzYg7FMXXqDTz6aGeqfn/HFcdpz0QRTeGOyYMw+i8QQginobXm8PlUYhIz2HsmidMJ6ZyIS2fLiYslzuNSRdGsnjf9W9fn+lBffL3c8XRzoa63O0oV2wU9AH/+eZrWrevi7e3O558PwtfXkwYNLqdH5uLZM1EsweikfB7QGUiS+gkhhDOITc5k6tL97DmTxMn49EvG+3m70ye8Ln7e1Qiq5UFwbU9aBPgQVMsDN5cqpSaD4sTHp/PMM6v4/PMdTJnSk5df7kW7dv7XanNslyiUUnOBXoCvUioaowvEqgBa648xOlkfgNHZeDpwv61iEUIIWzgVn87aw7Hsjk7iVHw62XkmdkUnYtkfXJ/weoT7e9MuuCbBtT0Jrl0dt6usM8inteabb3bx1FMrSUjIYPLkrkye3PWaLNuSLZ96GlXGeA08bKv1CyHE5dJak5Vr4mJaNimZuWTm5BGTmMGF1CxOxqdz6FwKeSbN0QupXEjJumT+ej7u9GrqRy1PN25qWZ+bW9a77LuDy/H006t4660/6dq1AR9/fAutW9ezyXocrj8KIYS4FnLzTOyNSWbdoQucjE8jKi6NnacTy5yvZYAPzet7E1LHk5YBNejboh6dG9XG1aV8GrrIyMghLS0HX19Pxo5tR1hYbcaObU+VKrZLSJIohBCVRlJGDnO3nGLV/vNsO5lQaFzboBoMjgiglqcbQbU88HRzpa63O64uCv8aHtT3qUYNz6p2itywfPlRHn74VyIi6rNw4QiaNfOlWTNfm69XEoUQwimlZeUSm5LFztMJrDt0gfVH4riYll0wvnl9b25oXpeeTf3o3Ki2TYuIrlZMTAqPP76cH3/cT7NmdXjkkY7lun5JFEIIh3cxLZtlu2P481g8J+LTOVDMC2k1PKoysI0/Q9oH0rWJL9Wqutgh0sv3xx9R3H77fLKz85g27QYmT+6Ku3v5nrolUQghHEJ2ronjcWlsOR7PgXMpZOWYWHf4AmlZuWTk5BVM5+vlzh0dG+BSRRHu70NwbU86htTGw80xEkO+nJw8qlZ1oW3b+gwYEMarr95IaGhtu8QiiUIIUSFtO3GRXdFJHD6XwrrDFziXnHnJNK0Da+Dn7U6HhrVoEeBDt1BfqpZTpbKtJCdn8eKLq/n77zNs2jQGX19P5s0bZteYJFEIIewqO9dEcmYOe88ksfbQBbafTGDPmcLNVzT2q073MF96NvWjQ8NaNKvvjaebc52+tNYsWLCfxx5bzrlzqUyY0JGsrDw8Pe2f+JxrTwshKqSLadkcj0tl64kE0rNySc7MZVd0IjtOFf84amO/6rQOrMHozg1pFejjdEmhqAsX0rj33p/57bejtGtXn8WL76Bjx0B7h1XAufe+EKLcmUya7/8+ycajceyLSSY6IeOSaTyqulC7uhudG9UmoKYHrQJrUEVBp0a1Ca/vY9N3AioiHx934uLSee+9m3n44U64XqM3t68VSRRCiKuSnJnDsdhUDp1LYeuJBBb+E10wztvdlSZ+1eke5kdkSC18vdyJbFir3F5Oq8jWrz/Ja69tYOHCEXh5ubF58wMVNkFKohBCWMVk0mw9cZE9Z5LYcTqR6IQMdhXzJrOnmwt3dgrm2QHhuFTQE589xcWlM3nySmbP3klISE1OnEikVau6FTZJgCQKIUQxTCbN/rPJ7IpOZOvxi8SnZbPhSFyhaep6uzOobQDe1Vzp3Kg2daq70zLAh1rV3ewUdcWmtearr3YyefJKkpOzePbZbrzwQg887fy2tzUkUQghACM5HDiXzPOL9hbb5lHfFvW4vkkdujTxpWEdT4d5Ya0i+e673bRo4cfHH99Cy5Z17R2O1SRRCFEJ5Zk055Mz2ReTzOHzKWyOii90x9DItzq3twvkhmZ1aVK3utM/dWQr6ek5vP76BsaNiyQoyIeFC0dQo0a1Cl3MVBz59oWoJDJz8vh43TH+OBDLwXPJ5OQV7lU4tK4XvZr6cUenYELretkpSufx669HePjhXzlxIpHAQG/Gj+9IrVoe9g7rikiiEMIJaa25kJrFin3n+etYPFtPXCTW3H9CWF0vRnZsQCNfL2p6VCXC3KGOo7/RXFFERyfz+OPLWbjwAOHhvqxbdx89ejS0d1hXRRKFEE4iMyePlfvPs2RXDCv3ny80zsvdlU4htRnaIZCRHYPtFGHl8Npr61m27Aivv34jkyZ1xc3B2pgqjtJalz1VBRIZGam3bdtm7zCEqBB2nk7kfysOEZOYwbELaYXG9Wzqx+3tAukdXhfvahX/yRpHtmXLGTw8XGnduh7x8ekkJWXRuHEte4dViFJqu9Y68krmlTsKIRzMxiNxfLExir+i4snMMRUMbx1Yg5ta1OPBHo3liaRykpSUyXPP/cFHH21j4MCmLFkyijp1PKlTx9PeoV1TkiiEqOAyc/L4cXs0+2OSmbvlVMHwVoE+9GzqR/9W/rQKrGHHCCsfrTXz5+/jiSd+JzY2jYkTOzFt2o32DstmJFEIUQHtj0nm/1Yf4fD5lEJFSm2DauDnXY0pg1rQoLZzXbU6ku++28099/xMZGQAS5eOokOHAHuHZFOSKISoAM4nZ/LHgViOxKawYHs0KZm5BeO6h/nSPcyXe7qESJGSHWVl5RIVlUB4uB8jRrQkN9fEPfe0xaUSPC0miUIIO8jJM/HJumOsOXSB2JRMTl/8t4XVcH8fWgf68FCPJvI+QwWxZs1xxo9fRnp6DkeOTMTd3ZX7729n77DKjSQKIcqB1pq/jsXzy+6zbDx6oVBiaOxbnUduCKVLkzq0C64pb0FXILGxaTz11Aq+/XY3jRvX4tNPB5V7f9UVQeXbYiHKidaaXdFJzPn7JEt3nyU92+jX2aOqC91CfekdXpf7uoaglGM151BZHD16kU6dPiM1NZvnn+/O8893x8Ojcj5mLIlCiGsoN8/EjtOJrDpwnjl/nypU1zC0fRAP9WhMs/redoxQlCU5OQsfH3eaNKnF2LHtGDOmHeHhfvYOy64kUQhxlTJz8vi/1UdYd/gCe88kFwxvGeDDbRGB9GtVn8CaHg7XEFxlk5aWzdSp6/jss3/YvXs8QUE+vPXWTfYOq0KQRCHEFdp1OpFx323nbFJmwbCm9bzo18qfAa3r07y+jx2jE5fjl18O8cgjv3HqVBJjx7ZziD4iypMkCiEuQ3p2Lr/siuHlJfvJyDHqHDo3qs2A1v6M6hSMWwXr61iULjfXxIgRP7Jo0UFatvRjw4b76dZN2sIqShKFEGX4Oyqe1387yJ7oREwWTaM1qO3BuyMiiAypbb/gxBXRWqOUwtW1Cv7+XrzxRm+eeKKLUzTgZwuSKIQohsmk2RuTxPebTzF/22kAqrooejerS+/mdRnQxh8faWjPIW3eHM3DD//KZ58Non17f2bNusXeIVV4kiiEsHD6Yjqz1hxl3tbTBcOua1yb/xvVHj9vdztGJq5WQkIGzz33B598sp2AAG8SEjLKnkkANk4USql+wPuAC/C51vqNIuODga+BmuZpntFa/2rLmIQoKifPxLytp5myeG9B0ZKvlxujOzekb4t60uCeE5g/fy+PPrqcuLh0Hn/8Ol55pRfekvitZrNEoZRyAWYBfYFoYKtSaonWer/FZC8AP2itP1JKtQB+BUJsFZMQ+TJz8vht71nOJGTw0z9niIpLI7CmByG+njx8QyhdGteRF+GcyMGDcYSE1GT58tG0a+dv73Acji3vKDoBR7XWUQBKqXnAYMAyUWgg/xnCGkCMDeMRgr+j4vli43E2Ho0reFO6eX1vPr8nkt7hdSU5OInMzFzefHMj7dv7M2hQM557rjsvvNCjUjTgZwu2TBSBwGmLz9FA5yLTvAysUEpNBKoDfYpbkFLqIeAhgOBgeXRNXJ6jsalMXbqfqAupRJvLpa9rXJt7uoTQLcxXKqWdzKpVUUyYsIwjRy4yaVIXBg1qRlVpdfeq2DJRFHdpVrTf1VHAbK31/5RSXYBvlVKttNamQjNp/SnwKRhdodokWuFUkjJy+HDtUTYdjSt4WzqolgcP39CEB7o1plZ1NztHKK618+dTefLJFcyZs4fQ0NqsWHEXffs2sXdYTsGWiSIaaGDxOYhLi5bGAv0AtNZ/KaWqAb5ArA3jEk7sZHwai3fG8M7KwwXDBrUNYPJNzQh2su4pRWErV0axYMF+XnqpB88+251q1eShzmvFlntyKxCmlGoEnAHuAO4sMs0poDcwWykVDlQDLtgwJuGkohPSeXflERb+Ew0Ydw/3dQ1hbLdGUu/gxHbtOseRIxcZNqwFo0e35vrrG9CoUS17h+V0bJYotNa5SqlHgN8xHn39Umu9Tyk1FdimtV4CTAI+U0o9gVEsdZ/WWoqWhNUupGQxbel+lu89R3aeiVta+zO2eyPaB8vJwpmlpmYzZcoa3n//b0JCanLbbc1xda0iScJGbHpvZn4n4tciw16y+Hs/cL0tYxDO6diFVCZ89w+HzqcAUN+nGnMe7ExjP+kRztn9/PNBJk78jejoZB56qD3Tp/fBVdrYsikpxBMOJSkjh3dXHmb2nycACKhRjVdvb8UNzeTR1spgz57z3H77fFq3rsv8+cPo2rVB2TOJqyaJQlR4Wmu+2HicRTvOsC/GeILJ18uNr+7rROsgeWva2eXk5LFhwyluvLERrVvXY9myO+nbt7E88lqOJFGICstk0rz/xxHmbjlFbEoWAON7NSHc34eBrf2lI6BK4M8/TzNu3FL27bvAoUOPEBpamwEDwuwdVqUjiUJUOOeTM/l0fRRLdsVwwZwg/tOjMU/e1BR3V7mKrAwuXszgmWdW8dln/9CggQ8//TSC0FBpzt1eJFGICuXT9cd4/deDBZ/fHNqa29sFSYdAlUhmZi4RER8TE5PCpEldePnlXnh5yQuS9iSJQthdenYuM5YfKqigBvh2bCe6hfpKBXUlEh2dTFCQD9WquTJt2g1ERNSnbdv69g5LIIlC2ElWbh5rD13gq03H2Rx1sWD4sA5BvHJrS6q7y6FZWWRk5DB9+kbefHMTCxYMZ9CgZtx7b4S9wxIWrPo1KqXcgGCt9VEbxyMqga0nLjL8478KPncL9eXWiACGdwiSO4hKZsWKY0yYsIxjxxK46642dOoUaO+QRDHKTBRKqVuAdwA3oJFSKgKYorW+3dbBCefx+YYoVuw/z5bj/949RDasxfuj2hFY08OOkQl7mTjxV2bO3EpYWG1Wrbqb3r0b2zskUQJr7iimYjQPvgZAa71TKRVq06iEU0jOzOGz9VF8sj6K7FyjQeDIhrUIq+fF/dc3omk9bztHKMpbXp5xHLi4VOG664Lw9fXk6ae7SQN+FZw1306O1jqxSJGAtMckSpSSmcMjc3aw7vC/7TsOaR/IK7e2xFv6fqi0/vnnLOPGLeXuu9swcWJnRo9uY++QhJWsSRQHlFIjgCrmlmAfAzbbNizhaLTW/LzzDPO3ni5UOf3m0NYMbR+Eq/QsVmmlpGTx0ktr+OCDLfj5eeLvL3eSjsaaRPEI8BJgAn7CaA32WVsGJRzH+sMXeOv3Q+w5k1QwrHuYL7e09mdkxwZSOV3JrVhxjDFjFhMTk8K4cZG8/npvatasZu+wxGWyJlHcrLV+Gng6f4BSaghG0hCV1P6YZJ78YScHzxmttzat58XgiEDu6NiAOl7udo5OVBRubi7UrVudhQtH0LlzkL3DEVdIldX9g1LqH611+yLDtmutO9g0shJERkbqbdu22WPVAiNBvLH8IOvN9Q+jOwfzWO8w6vrIVaIwGvB7552/SE7O4rXXegNGm13SLpf9mc/bkVcyb4l3FEqpmzG6KQ1USr1jMcoHoxhKVCLp2bm8sGgvP+04A0Cf8Ho8cmMoEQ1q2jkyUVFs3HiqoAG/4cNbFCQISRKOr7Sip1hgL5AJ7LMYngI8Y8ugRMWy90wS9321lbjULOp6uzN1cCv6tZKmFYQhPj6dp59exRdf7CA4uAa//DKKgQOb2jsscQ2VmCi01juAHUqp77XWmeUYk6gglu89y+w/TxQ8xTRjaBtGdJSOYkRh8fEZzJu3l//+tysvvdST6tWlAT9nY01ldqBS6jWgBVBQEIqCSY4AACAASURBVK21lksGJ5SbZ+Lzjcf5YetpouLSAOgUUptHe4fRLczXztGJiuLAgQv88MM+pkzpRdOmdTh16glq15Y37J2VNYliNvAq8DbQH7gfqaNwOlprPt9wnNd+PVAwrGuTOvxvRFv8a8gJQBjS03N47bX1vPXWn3h5uTF2bHuCgnwkSTg5axKFp9b6d6XU21rrY8ALSqkNtg5MlA+tNXO3nOa5RXsKho3uHMyLA1tQTbqaFBaWLz/KhAnLOH48kXvvbctbb/XFz6+6vcMS5cCaRJGljLemjimlxgFngLq2DUvYmtaarScSGDN7K6lZuQD0aOrHl/dGylvU4hKpqdncffci6tTxYM2ae+nVK8TeIYlyZE2ieALwAh4FXgNqAGNsGZSwnZTMHB6bt5PVB2MLho2MbMALA8OlHSZRSF6eiblz9zJqVCu8vNxYtepumjf3xV36Cql0yvzGtdZ/m/9MAe4GUErJK5YO5mhsCq/8sp8NR+IAaFjHkxGRDejcqDaRIdIXsShs+/YY/vOfpWzffhYPD1eGDm0hvc1VYqUmCqVURyAQ2Ki1jlNKtcRoyuNGQJKFg/htz1nGf/8PAEG1PHhpYAtuaik/enGppKRMXnxxDbNmbaVu3erMmzeUIUPC7R2WsLPS3syeDgwFdmFUYC/CaDn2TWBc+YQnrpTWmlUHYnnh5z2cT84C4K1hbRgeKe9BiJINHfoDq1cf5+GHO/LqqzdSo4Y0zSJKv6MYDLTVWmcopWoDMebPh8onNHGljsel8f6qw/y8MwYwGux7Z0QErQJr2DkyURFFRSXg5+eJt7c7r712I1WqKDp2lC5Jxb9KSxSZWusMAK31RaXUQUkSFVt2romnftzFkl1GghgZ2YDJ/ZrhK625imJkZ+fx9tt/Mm3aeh59tBNvvtlXWngVxSotUTRWSuU3Ja6AEIvPaK2H2DQycVnWHb7Ag19vIzvPRLvgmjw/IFwqqUWJ1q8/ybhxSzlwII5hw1rw6KOd7R2SqMBKSxRDi3yeactAxJXRWjN16X6+2nQCgLHdGvHiwBb2DUpUaO+++xdPPrmCkJCaLFt2JwMGhNk7JFHBldYo4B/lGYi4fCaTZtDMjeyLSaZTo9q8f0eENLchimUyadLSsvH2dueWW5py4UI6L7zQA09PeXdGlE1ewXVQR2NTuferLeyLSaZ/q/rMe/A6SRKiWPv2xdKz52zuu28xAE2b1uH113tLkhBWs2miUEr1U0odUkodVUoV24eFUmqEUmq/UmqfUmqOLeNxFhuPxDHg/Q1sOBLH+F5N+HB0e+kcRlwiPT2HZ59dRUTEJxw4cIGBA8Moq0dLIYpj9bv4Sil3rXXWZUzvAswC+gLRwFal1BKt9X6LacKAZ4HrtdYJSilpQ6oUyZk5PDl/J6sOGM1vLBjXRSqsRbF27DjLkCE/cOJEIvffH8GMGX3x9fW0d1jCQZWZKJRSnYAvMNp4ClZKtQUe0FpPLGPWTsBRrXWUeTnzMN7N2G8xzYPALK11AoDWOvaSpQgAvvnrBC8tNjoa9HRzYe6D19FWuiEVRWitUUoRHFyD4OAafP31bfTo0dDeYQkHZ80dxQfAQOBnAK31LqXUDVbMFwictvgcDRR9Bq8pgFJqE+ACvKy1Xm7FsiuNPJPmwW+2FTTiN6FXE566qZkUNYlCcnNNzJy5hSVLDrFy5d3UqePJunX32Tss4SSsSRRVtNYnjZbGC+RZMV9xZ7KiBaSuQBjQC6PtqA1KqVZa68RCC1LqIeAhgODgYCtW7Rze+v0gs9YcA4w2mj67J5Jwfx87RyUqmi1bzjBu3FJ27DhH//6hJCdnUauWPNggrh1rEsVpc/GTNtc7TAQOWzFfNGDZsFAQRjMgRafZrLXOAY4rpQ5hJI6tlhNprT8FPgWIjIx0+tq4uNQsnlm4h1UHzgPweJ8wHu8jPc+KwlJTs3n66ZV89NE2/P29+fHH4QwdGk6Rizohrpo1iWI8RvFTMHAeWGUeVpatQJhSqhFGZ0d3AHcWmeZnYBQwWynli1EUFWVd6M7pTGIGPWesIdek6R7my4ej20s/EaJYVatWYe3ak0yc2Ilp027Ex0eaahG2YU2iyNVa33G5C9Za5yqlHgF+x6h/+FJrvU8pNRXYprVeYh53k1JqP0Zx1mStdfzlrstZrNh3jv98t50qSklLr6JYR49eZOrUdcyaNQBvb3e2b3+IatWkIyFhW6qs56qVUseAQ8B84CetdUp5BFaSyMhIvW3bNnuGcM1prbl/9lbWHroAwJf3RXJj83p2jkpUJFlZucyYsYnXXtuAm5sLy5bdSffu8jSTsJ5SarvWOvJK5rWmh7smSqmuGEVHryildgLztNbzrmSForCzSRmM+OQvTl/MAOD7BzpzfaivnaMSFcmaNccZP34Zhw7FM3JkS95552YCArztHZaoRKy6Z9Va/wn8qZR6GXgP+B6QRHGVMnPy6PrGarSGyTc3Y3zPJvLYqyhEa81rr20gJ8fE8uWjufnmUHuHJCoha16488J4Ue4OIBxYDHS1cVxOLzMnj2Ef/4nWML5XEx6+QU4AwmAyab744h/69QulQYMafPvt7dSsWQ0PD3moQdiHNW097QWuA2ZorUO11pO01n/bOC6nlpieTdtXVrD3TDJD2gfydL/m9g5JVBC7d5+nW7cveeihpXz+udHPub+/tyQJYVfWFD011lqbbB5JJbHl+EXGzN5KVq6JOzo24I2hbewdkqgAUlOzeeWVtbz77mZq1fJg9uzB3HNPW3uHJQRQSqJQSv1Paz0JWKiUuuTRKOnh7vL9ceA8Y782nth6d2Rbbm8n3U4Kw8svr+V///uLBx5oxxtv9KFOHWnAT1Qcpd1RzDf/Lz3bXQN/HYtn7NfbcHOpwg/juhAhDfpVeqdPJ5GWlkPz5r4880w3brutOd26VZ4maoTjKLGOQmu9xfxnuNb6D8t/GJXawkrH49IY9dlmAH6Z2E2SRCWXm2vinXf+Ijx8Fv/5z1IAfH09JUmICsuayuwxxQwbe60DcVZaa576cReebi58O7YTzerL8++V2ebN0URGfsqkSSvo1SuEr7++zd4hCVGm0uooRmI8EttIKfWTxShvILH4uURR87aeZvvJBCb1bUr3MD97hyPsaNmywwwaNJeAAG9++mkEt93WXBrwEw6htDqKLUA8RquvsyyGpwA7bBmUs9gfk8yzP+2hY0gtxvdqYu9whB1orYmJSSEw0Ic+fRozdeoNPPZYZ7y9pQE/4ThKTBRa6+PAcYzWYsVlOnYhlQEfbADguQHhuLrYtHtyUQEdPhzPhAnLOHw4nv37H8bLy40XXuhh77CEuGylFT2t01r3VEolULjDIQVorbV01lyME3Fp3PHpZs4lZwLwyq0taRdcy85RifKUmZnLG29sZPr0jXh4uDJ9em88PKSFV+G4Sjt687s7lRbqrLTpaByjPzdeWm/kW53/jWhLe0kSlcq5c6n06PEVR45cZNSoVrzzzs3Ur+9l77CEuCqlFT3lv43dAIjRWmcrpboBbYDvgORyiM9h/LbnLOO/N5pc+OTuDtzcsr6dIxLlKScnj6pVXahXrzo9ejRk1qwB9O0r9VLCOVhTcP4zRjeoTYBvMN6hmGPTqBxMfpJwc6nCb491lyRRiZhMmo8/3kaTJh8QHZ2MUorPP79VkoRwKtYkCpO5T+shwHta64lAoG3Dchzfbj7JxLk7CKzpwS8TuxHu72PvkEQ52bXrHF27fsH48csIC6tDTk6evUMSwias6gpVKTUcuBvIfzuo0jdlaTJpZv95gqlL91OtahUWjO+Cfw0Pe4clyoHWmsmTV/Lee5upXduDb7+9ndGjW8s7EcJpWZMoxgATMJoZj1JKNQLm2jasiu3A2WT6v288+tonvB5vD29DTU83O0clyotSioSEDMaONRrwq1VLLhCEcyuzz2wApZQrkN+zzlGtda5NoyqFvfvMjknMoOsbqwGYeGMoT/ZtKleSlcDJk4k89thyXnqpJ+3b+2MyaemNUDiUq+kzu8w6CqVUd+Ao8AXwJXBYKXX9lazM0cUmZ3L7h5twc6nCa7e3YtJNzSRJOLmcnDxmzNhEixYfsnJlFIcOxQFIkhCVijVFT+8CA7TW+wGUUuHAt8AVZSZHlZieTafX/wDgs3si6duinp0jErb255+n+c9/lrJ3byyDBzfjgw/6Exxcw95hCVHurEkUbvlJAkBrfUApVakK5NcciuX+r7YCcF/XEEkSlcSqVVEkJWXy888jGTxYuqsVlVeZdRRKqdlAFsZdBMBowFNrfa9tQyteeddRfLj2KDOWHwLguQHNeaiHPB/vrLTWfPvtbvz8POnfP4ysrFxyckx4eVWq6yLhpK6mjsKaO4pxwKPAfzHaeVoP/N+VrMzRTP/tAJ+siyKwpgdzH7yOYOme0mkdPBjH+PHLWLv2BMOHt6B//zDc3V1xl0ZehSg9USilWgNNgEVa6xnlE1LFsOZgLJ+siwJgxRM9qO4ujbo5o4yMHF5/fQNvvrmJ6tXd+OSTgTzwQHt7hyVEhVLiU09Kqecwmu8YDaxUShXX051Tys418ei8HVR1Ufz17I2SJJzYL78c5tVXNzByZCsOHnyYhx7qIE80CVFEaWfA0UAbrXWaUsoP+BXj8Vin9/nGKFIyc3lnRFt529oJnTuXys6d5+jXL5Thw1sQEvIAnTpJqzRClKS09yiytNZpAFrrC2VM6zROX0xnxvJD+Hm7c2vbAHuHI66hvDwTH364lWbNZnL33YvIyMhBKSVJQogylHZH0diir2wFNLHsO1trPcSmkdlBbEom3WesAWDa4JbSK50T+eefs4wbt5StW2Po06cxH344AA+PSt9kmRBWKS1RDC3yeaYtA6kIXl92AID/9mtGv1b+do5GXCvHjyfQqdNn+Pp6MmfOEO64o5W8US/EZSit46I/yjMQezuXlMnPO2O4pY0/E3qFlj2DqNC01uzZE0ubNvVo1KgWX301mEGDmlGzZjV7hyaEw5GyFbNP1h8D4IFujewcibhax48nMHDgXNq1+4Tdu88DcPfdbSVJCHGFbJoolFL9lFKHlFJHlVLPlDLdMKWUVkrZpf2ov6Pi+WrTCXo186Od9HHtsLKz83jjjY20bPkh69ad4O23+9KihZ+9wxLC4Vn9goBSyl1rnXUZ07sAs4C+QDSwVSm1xLLdKPN03hhvfv9t7bKvpYzsPB6dtwNPNxdmDGtjjxDENZCXZ6Jr1y/Yvv0sQ4aE8957N9OggTTgJ8S1YE0z452UUnuAI+bPbZVS1jTh0Qmj74oorXU2MA8YXMx004AZQKb1YV87by4/yPnkLKYPaU1dbymacDTJyca1i4tLFcaMaccvv4xi4cIRkiSEuIasKXr6ABgIxANorXcBN1gxXyBw2uJzNEX62lZKtQMaaK2XlrYgpdRDSqltSqltFy5csGLV1skzab7dfJKWAT4MjpBn6R2J1prZs3fSuPH7LF58EIAJEzoycGBTO0cmhPOxJlFU0VqfLDLMml7ki3v+sKCpWqVUFYy+LiaVtSCt9ada60itdaSf37Urc16y6wx5Js2wDkHXbJnC9vbvv0CvXl9z//2Lad7clyZNats7JCGcmjV1FKeVUp0Aba53mAgctmK+aKCBxecgIMbiszfQClhrfqa9PrBEKXWr1rpc2hH/6Z8zAIzs2KCMKUVFMWPGJp5/fjU+Pu58/vkg7r+/nbTNJISNWZMoxmMUPwUD54FV5mFl2QqEKaUaAWeAO4A780dqrZMA3/zPSqm1wFPllSRMJs3WExfpHuaLp5s0+lfRaa1RSlG/vhejR7fmrbf64udX3d5hCVEplHmG1FrHYpzkL4vWOlcp9QjwO+ACfKm13qeUmgps01ovuexor6H1Ry6QmWOiZ1N5fLIii4lJ4bHHltO9ezCPPtqZe+5pyz33tLV3WEJUKmUmCqXUZ1jULeTTWj9U1rxa618xWp21HPZSCdP2Kmt519LcLafwqOrC6M4Ny3O1wkr5Dfg9//xqcnJMdO0q9UhC2Is1ZS6rLP6uBtxO4aeZHE5Wbh7rDl+gZ1M/PNxc7B2OKGLnznM88MAStm8/y003NeHDDwdIhbUQdmRN0dN8y89KqW+BlTaLqBxsOhpHZo6J29vJI7EVUVJSJjExKcyfP4zhw1tIA35C2NmV1OI2Ahy6vOajtcdwc61CD6mfqBC01vz4436OHInn+ed70LNnCFFRj1GtmjxkIERFYM2b2QlKqYvmf4kYdxPP2T4021hzMJatJxK4qUU9edqpAjh27CIDBsxh5MgFLF58iJwc4xUdSRJCVByl/hqVcc/fFuPxVgCT1vqSim1H8u1m493BFwe2sHMklVtWVi5vv/0nr766gapVq/D++/2YMKEjrq7SoLEQFU2piUJrrZVSi7TWHcorIFtbcyiW3s3rUs9H2nWyp9Onk5k2bT2DBjXjvfduJjDQx94hCSFKYM3l2xalVHubR1IOTl9MR2sIqOlh71AqpQsX0pg5cwsAoaG12b//YX78cbgkCSEquBLvKJRSrlrrXKAb8KBS6hiQhtGGk9ZaO1zy+GxDFAD3dHHouniHYzJpvvpqB//97ypSUrLo27cxzZr50rix9P0hhCMorehpC9AeuK2cYrEpk0nz656z3NDMj7B63vYOp9LYuzeW8eOXsXHjKbp3D+bjjwfSrJlv2TMKISqM0hKFAtBaHyunWGxq4T/RxKVmM6htgL1DqTSys/O46aZvyc7O48svb+W++yLknQghHFBpicJPKfVkSSO11u/YIB6b+X3fOQAGtPa3cyTOb/Xq4/Ts2RA3Nxd++GE4zZv74uvrae+whBBXqLTKbBfAC6M58OL+OYzUrFxWHYjlltb+VKsqTXbYSnR0MkOH/kDv3t/wzTe7AOjWLViShBAOrrQ7irNa66nlFokNff3nCQB6NJWycVvIzTUxc+YWXnxxDXl5JqZP783o0dL/uBDOosw6Cmfw0VqjmuU2advJJu6+exHz5u2lf/9QZs0aQKNG8jSTEM6ktETRu9yisKGLadmkZuXSJqgG7q5S7HStJCZm4upaBS8vNx5+uCNDh4YzdGi4VFYL4YRKrKPQWl8sz0Bs5be9ZwG4t0uIfQNxElpr5s3bS3j4LF58cTVg1EMMGyatvArhrJy+YZ3FO2NQCga2laedrtbRoxe5+ebvGDVqIUFBPtx1l9RDCFEZOHUTnVEXUtly/CL/6dFYip2u0pw5exgzZjHu7q7MnNmfceMicXFx+usMIQROniiW7jaKnYa0l240r1ROTh5Vq7oQGRnAsGEtmDGjLwEBDvV0tBDiKjl1olh14Dyebi40qy8ntssVG5vGpEkrSEvL5qefRtK0aR2++26IvcMSQtiB05Yd5Jk0u6OT6BgifS1fDpNJ8+mn22nWbCbz5++lZUs/8vJM9g5LCGFHTntHse5wLAA3NJPuTq0VFZXAXXf9xF9/RdOrVwgffXQLzZvLS4pCVHZOmygW74wBoLv0i221GjXcSUzM5Ouvb+Puu9vI465CCMCJi54Onk0hpI4nTfy87B1KhbZkySGGDJlPXp6JOnU82bt3Avfc01aShBCigFMmiuiEdA6dT5EmxUtx6lQSt902j8GD53H4cDxnz6YCUKWKJAghRGFOWfS0LyYZgB5S7HSJ3FwT7723mSlT1qK15s03+/DEE9dRVVrVFUKUwCkTxfaTCSgFzeWx2Evk5Zn4/PN/uPHGRvzf//UnJKSmvUMSQlRwTln0tOloHOH1ffCuVtXeoVQICQkZPP30SlJSsnB3d2XTpjEsWXKHJAkhhFWcLlGciEtjX0yyFDthNOD3/fe7ad58Fv/731+sWXMCgDp1PKWyWghhNacrelq2x2i2Y3BE5a7IPnw4ngkTlvHHH8fp1CmQ33+/i4iI+vYOSwjhgJwqUWitmfP3KRrU9qj09ROPP76cbdti+PDDATz0UAdpwE8IccWcKlEcOJvCmcQMnu3fvFIWraxceYzmzX1p0KAGH310C+7urtSvL++RCCGujk0vM5VS/ZRSh5RSR5VSzxQz/kml1H6l1G6l1B9KqYZXs76/ouIB6NWs7tUsxuGcO5fKnXcu5KabvuPNNzcB0LBhTUkSQohrwmaJQinlAswC+gMtgFFKqRZFJtsBRGqt2wALgBlXs86V+89Rp7oboXUrxwnSZNJ8/PE2mjefycKFB5gypSdvv32TvcMSQjgZW95RdAKOaq2jtNbZwDxgsOUEWus1Wut088fNwBV3HJGUnsPmqItc17gOLpXk7eLp0zcwfvwyOnQIYPfucbz8ci+qVXOq0kQhRAVgy7NKIHDa4nM00LmU6ccCvxU3Qin1EPAQQHBwcLEz74pOBOD6UOdu7TQlJYu4uHQaNarFuHGRNGpUi1GjWlXKOhkhRPmw5R1FcWcuXeyESt0FRAJvFTdea/2p1jpSax3p51f8+xH/nEoAoHuYcyYKrTWLFh2gRYsPGTlyAVpr6tTx5M47W0uSEELYlC0TRTTQwOJzEBBTdCKlVB/geeBWrXXWla5s12njjiKolseVLqLCOnkykVtvnceQIT9Qu7YHH3zQX5KDEKLc2LLoaSsQppRqBJwB7gDutJxAKdUO+ATop7WOvZqVpWTm4uft7nQn0L/+Ok2fPt8C8PbbfXnssetwdZV3IoQQ5cdmiUJrnauUegT4HXABvtRa71NKTQW2aa2XYBQ1eQE/mk/wp7TWt17J+o7HpTlVsVNychY+Pu60b+/PmDERTJ58PcHBNewdlhCiErLpIzJa61+BX4sMe8ni7z7XYj3nkjKJT8t2isdi4+PTeeaZVaxYEcW+fRPw8nLj//5vgL3DEkJUYk7xLOXinWc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" + ] + }, + { + "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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" + ] + }, + "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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" + ], + "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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TVva4y7EqTbyKdfW4kONXVaOxDsQNWLdrvsR6NlmcaVi1OT9ziSUXuAjrGdwOrCvkD7Fqp5bVHVhXANuxarx+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", 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" + ] + }, + "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": [ + "
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" + ], + "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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" + ] + }, + "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": [ + "
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ModelF1-1AUROC
0Decision Trees - Random Forest0.4613390.768458
1Logistic Regression (Optimal Cutoff)0.5276650.765244
2SVM-RBF (Grid Search)0.4822470.748465
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" + ], + "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": [ + "
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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
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" + ], + "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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Edp9IJjUzJ2+eWtWrMrpnEP1D6tGrpX+h6/niiy08+ugS4uPTmDixBxMn9ijxeJ351NPIYqYbYIKztq+UUq6WmJrF1qMJHI5L5e+oOGpUq8K2o0nssqtb8HJ3o5pbFQK83Lm9exDdmvnSyKcmPh7VzrlrKMikSUt57bU/6dGjCe+/fxXt29dzyn6Uu/4olFKqrNoWnciGQ3Es2x3Dqn2nC5ynuX8t2jXypk+rAEZ0aUIz/1rntY20tCxSUrLw9/dg7NhOtGzpy9ixnalSpeikcjE0USil1AVISs9i8fYTbDgUz9qoWBLTsohPzTpnnlu7BRLSwJuwxj4E1/WkZvWqF7XNX3+NZMKERYSF1WfevBtp3dqf1q0LLpYqSZoolFKqEDm5hqS0LCJPnSE1M4ej8WkcjE1h5d5T7D6RnDdfi4Ba1KlVnUGh9bm5axOC/GpRy92N6m4l0/jFsWPJPPjgr8yZs5PWrf24995LSmS9jtJEoZRSBfhx81EemL250OlBfh7c3bsFw8MaUsvdeT+ly5ZFce2135KZmcPUqZczcWIP3J24vYJoolBKVXrpWTlExpxh6a6TJKZlsXx3DAdjUwG4o3tTgut50aquJ5413AjwcqeuVw2nx5SVlUO1alXp2LE+Q4a05IUX+hEc7Ov07RZEE4VSqtI6lpDGte+u4WRSxjnj3d2q0LGJD2/e2JHmAZ6lGlNSUgbPPPM7f/99lDVrxuDv78Hs2SNKNYb8NFEopSqFxLQsFm49zqnkDLYdTWDprpi8ab61qjOuT3M6BdahS2Adpz5BVBhjDHPn7uSBB37lxIkz3HPPJWRk5ODh4fpugzRRKKUqpMS0LFbtO8WRuDRe+XX3v6ZXEQhr4sO4Pi0YGFqv2HcWnOnUqRTuuOMHfvklkk6d6vPjjzdzySWNXBZPfpoolFIVRnZOLsnp2YyeuZ7NRxLOmdaqnidjejZjcLv6eLq74VbV9VfqZ3l7u3P6dCpvvXUFEyZ0xa2EnpYqKZoolFLl3tqoWB6bu5XDcal54zzd3Zg0uDV9W9fFz7M6HtXL1s/dypWHmDZtFfPm3YinZ3XWrr3LJUVejihbR04ppRyUlplDxKE47vxsPTm5Vu8Dw8Ma0r5Rbep612BYhwYuLU4qzOnTqUycuISZMzcTFOTDwYMJtGtXt8wmCdBEoZQqB3JyDesPxrE1OoHNRxJYtO3Ev+ZZcG9POjT2cUF0jjHG8Nlnm5k4cQlJSRk88UQvnn66Nx4e1VwdWrE0USilyqTk9Cx+3x3Dsl0xLN8dQ3LGP720+Xu60zygFtd3bsTlretS19v57zWUhK++2kpoaADvv38VbdvWdXU4DtNEoZQqE+JTMvn7QCyRMWf4YGUUyen/JIbm/rV4eFArwpv60rq+V4k1jeFsqalZvPjiKsaNC6dxY2/mzbuR2rVrlOlipoJoolBKuURaZg5Rp8/w3foj/Lk/ln0xZ86ZfmlzX/q0qstV7RsQ6Ofhoigv3KJF+5gwYREHDybQqJEX48dfQp06NV0d1gXRRKGUKhUxyel88EcUP205RlxKJtm2CuizujStQ++WAQwPa0iAl7tT209ypujoJB588FfmzdtFmzb+/PHHnfTu3dTVYV2U8vlJKKXKjdgzGTz+/TaW7DyZN65L0zr0bRWAe7UqdGzsQ5emdcrUew0XY9q0lSxcuI8XX+zHI4/0oPpFNi1eFojV0Vz5ER4ebiIiIlwdhlLKAc8t2MHMPw/mDT86qBX/6d0cd7fy/+Npb926o9Ss6Ub79vWIjU0lMTGD5s3ruDqsc4jIBmNM+IUsq3cUSqkSlZaZw/Tl+/hm3RHiUjIBmDQ4hLG9mpWbSmhHJSam8+STy3jvvQiGDm3FggUj8fPzwK8c1qkURROFUqpEbD+ayIPfbibSrlJ6QJu6TL2mHQ1ql89K3MIYY/j22x089NBiYmJSuO++rkyd2s/VYTmNJgql1AU5EpfKij0x/Lk/lj/3W12BArSs68nA0Ho8MKBlhStiOuurr7Zy++0/EB7ekJ9/HkmXLg1dHZJTaaJQSjnkRGI6P24+ysp9p1gTGXvOtHre7gT5+zB5WCidA8tW2XxJycjIJioqnjZtArjxxrZkZ+dy++0dqVpBKuGLoolCKVWk1MxsZq87wpSfd+aNa1C7BqENvLn10kC6NPWlds2y3wzFxVi+/ADjxy8kNTWLffvuw93djdGjO7k6rFKjiUIp9S8JqZlM+Wkn+2LOsO1oYt74m8KbMOWathW2SCm/mJgUHn30N778civNm9fhww+HlXp/1WVB5dtjpVShdhxL5Np3/yQzOzdvXNdmvlwW7M/4vi0qzLsOjoiMjKNr1484cyaTp566jKeeuoyaFfzOqTCaKJSqxIwxzN90lKW7TrJ0V0xegvByd2Pi4Nbc0jWwUiUHsPqs9vZ2p0WLOowd24kxYzrRpk2Aq8NyKU0USlVSkTFnGPXJ3xxPTM8bd1X7Btzduzkdm5Td5rqdJSUlkylT/uCjjzaydet4Gjf25rXXBrk6rDJBE4VSlUTsmQyOxKexeMcJ5kREc/pMBgCXtfTn/du6lNu2lUrCTz/t4d57f+Hw4UTGju1ULvqIKE2V98xQqoI7mZTOT1uO8cfeU6yOPE3+1noGhdZjZNdALg8pP/0ilLTs7FxuvHEO8+fvpm3bAFatGk2vXoGuDqvM0UShVAWSkZ3D538e5K2l+0jNzMkb7+/pTrtG3gzt0JCGPjXo0rROpXlyqSDGGEQEN7cqNGjgycsv9+ehh7pXiAb8nEEThVIVQFJ6Fg/N3syqyNN5FdJ9Wwcwoktj+ofUo6b+AOZZuzaaCRMW8dFHw+jcuQEzZlzl6pDKPE0USpVDxhiOJabz6eoDrIk8ze4TyQB0DfJlSPv6DAitR+M6FathuosVH5/Gk08u44MPNtCwoRfx8WmuDqnccGqiEJHBwNtAVeBjY8zL+aYHAp8DPrZ5HjfGLHJmTEqVZ0t2nuTdFZFsOpxwzvhm/rW4tVsgd13W3EWRlW3ffrud++//ldOnU3nwwUt5/vm+eHm5uzqscsNpiUJEqgIzgIFANLBeRBYYY3bazfY08J0x5j0RCQUWAUHOikmp8uRUcgYRB+NYsuskubmGtVFxnEiyHmVt29Cbrs18uap9A8KDfF0cadm3e/dpgoJ8+PXXW+nUqYGrwyl3nHlH0RWINMZEAYjIbGA4YJ8oDOBt+7s2cMyJ8ShVZuXmGnYcS2LN/tNsOZLAL9tPnDNdBFrX86JtQ2+eHRZKU79aLoq0fEhPz+aVV1bTuXMDhg1rzZNPXsbTT/euFA34OYMzE0Uj4IjdcDTQLd88zwG/ich9QC1gQEErEpG7gbsBAgP10TVVMWTn5LJ0Vwwfr4oi4lD8OdN6tPAjyL8WYU186BXsT0OfitWfgzMtXRrFPfcsZN++OB55pDvDhrWmWjWtzL8YzkwUUsC4/P2ujgRmGmP+JyLdgS9FpJ0xJvechYz5EPgQrK5QnRKtUk5mjCEhNYuo02d4/qedbI3+p7G95gG16BXsz9AODekU6EM1vfI9bydPnuHhh39j1qxtBAf78ttvtzFwYAtXh1UhODNRRANN7IYb8++ipbHAYABjzF8iUgPwB2KcGJdSpSY5PYtPVh9g57Ekftt58l/T/9u7Off2C8arhr4JfLGWLIli7tydPPtsb5544jJq1NCHOkuKM4/keqCliDQDjgI3A7fkm+cw0B+YKSJtgBrAKSfGpJRTGGPYczKZrUcS2XY0kbSsHP7aH8vRhH8ewQxt4I0I3NkjiJD63rRvXNuFEVcMW7acYN++OEaMCOXWW9vTs2cTmjWrmB0nuZLTEoUxJltE7gUWYz36+qkxZoeITAEijDELgEeAj0TkIaxiqTuNyd/QgFJlT2Z2LicS0/lh81GW7Dx5Tp8NZ7Ws60loA2/6t6nLjeFNaOKr7zWUlDNnMpk8eTlvv/03QUE+XHNNCG5uVTRJOIlT781s70QsyjfuWbu/dwI9nRmDUiUhIzuHtVFxHIlLZePheL7fePSc6cF1Pekc6MN1nRvTPKAWAZ7uiBRUTacu1g8/7Oa++34hOjqJu+/uzEsvDcDNTet0nEkL8ZQqRHZOLiv3nWLexqMs3Hr8nGluVYQRXRrTp1UA/drUrdTtJpWmbdtOcu2139K+fV2+/XYEPXo0KX4hddE0USiVz8mkdDYdTmDCrI3k5FoloY18anLzJU3o36YejX1r4q2Vz6UmKyuHVasO069fM9q3r8fChbcwcGBzfeS1FGmiUJVaTHI6i7efYG1UHCv3niI5I/uc6X1aBTB5WCjNAzxdFGHl9uefRxg37md27DjFnj33Ehzsy5AhLV0dVqWjiUJVOtuPJjJt4S4iDsWRlfPPsxNdm/niVkUIqe/NJUF16NjER190c5G4uDQef3wpH320kSZNvPn++xsJDtamSlxFE4Wq8NKzcohJyiDyVDJvLNnL9qNJAHjXcGNI+7r0C6nLgDb1KnUPb2VJeno2YWHvc+xYMo880p3nnuuLp2d1V4dVqek3Q1VIObmGzUcSeGvpXtYdiCMj+5+X/d2qCG/eFMawjg1dGKHKLzo6icaNvalRw42pUy8nLKw+HTvWd3VYCk0UqgI5lpBGTHIGf+2P5ZVfd+eNr1mtKs8ODaWhTw1a1vOihdY3lClpaVm89NJqXnllDXPn3sCwYa25444wV4el7DiUKESkOhBojIl0cjxKOSQ31/BXVCzfbzzKij0xxKZknjPdq4YbA0PrMaZnM9o29NZ3Gsqo337bzz33LGT//nhuu60DXbs2cnVIqgDFJgoRuQp4A6gONBORMGCyMeZaZwen1FlZObnEp2Tyw+ajLNhyLK+e4ay2Db3p0cKPzoF1qO1RjfCmvlTXl7DKtPvuW8T06etp2dKXpUtH0b+/drpUVjlyRzEFq3nw5QDGmM0iEuzUqJSyWbTtODOWR7Lj2LmJwa2KMLhdfR4Z1JogPw+9YygncnKsuqKqVatw6aWN8ff3YNKkXtqAXxnnyKeTZYxJyPdF1PaYlFNkZuey/Vgis9cd5lRyBsv3WG1E9mkVQNdmvjTyqcmwjg2pWkUTQ3mzceNxxo37mVGjOnDffd249dYOrg5JOciRRLFLRG4Eqthagn0AWOvcsFRlEp+SyakzGczfdJT3Vuw/Z9plLf15aGArOgdqY2/lVXJyBs8+u5x33llHQIAHDRp4uTokdZ4cSRT3As8CucD3WK3BPuHMoFTFFZeSyfcbo9l9Ipm5G6ILnGdohwb857LmdGziU8rRqZL222/7GTPmR44dS2bcuHBefLE/Pj41XB2WOk+OJIorjDGTgElnR4jIdVhJQ6libTgUx+uL9/JXVOy/prVvVJtLm/sSXNeTAC93Lgny1U58KpDq1atSt24t5s27kW7dGrs6HHWBpLjuH0RkozGmc75xG4wxXZwaWSHCw8NNRESEKzatzoMxhrkbonnmx+2kZ1kVmA1r16BP6wAuaxnAwNB62t1nBZSVlcMbb/xFUlIG06b1B6xHmatonZLL2X63wy9k2ULvKETkCqxuShuJyBt2k7yxiqGUOkduruFEUjqvL97D95v+6a+hfaPaPH5lCD2D/V0YnXK21asP5zXgd8MNoXkJQpNE+VdU0VMMsB1IB3bYjU8GHndmUKr82RqdwNXT15wz7rpOjZhyTTs8tQ2lCi02NpVJk5byySebCAyszU8/jWTo0FauDkuVoEK/wcaYTcAmEfnaGJNeijGpMi4rJ5dtRxN5Z9k+1h2IQ4CUzBwAmvp5MKFvMMM6NqRmde0voDKIjU1j9uztPPZYD559tg+1amkDfhWNI5d6jURkGhAK5D2uYIzRS4ZKJCUjm9GfrWfdwbhzxtfzdie4rieXNvNjWMeGBPnXclGEqjTt2nWK777bweTJfWnVyo/Dhx/C11ebZK+oHEkUM4EXgNeBK4HRaB1FpZGelcPNH65l85GEvHHDwxoSUt9qMqND49r6VnQlkpqaxbRpK3nttT/x9KzO2LGdadzYW5NEBedIovAwxiwWkdeNMfuBp0VklbMDU66XnpVD28mLyck1hNT34uZLmnBHjyBNDJXUr79Gcs89CzlwIHgWAHoAACAASURBVIE77ujIa68NJCBA7yArA0cSRYZYvwz7RWQccBSo69ywlCsZY/h0zUHe+G0PObmGDo1rs+DeXq4OS7nQmTOZjBo1Hz+/mixffgd9+wa5OiRVihxJFA8BnsD9wDSgNjDGmUEp1zl4OoX7Z29ia3QiAFOHt+W2S5u6OCrlCjk5uXzzzXZGjmyHp2d1li4dRUiIP+76FFulU+wnboz52/ZnMjAKQET0FcsKZsOheOZEHGH2+iMA9G4VwMw7L9Fn4CupDRuO8d///syGDcepWdON668P1d7mKrEiE4WIXAI0AlYbY06LSFuspjz6AZosyjljDI/N3crcjdHYv6B/f79gHhrYSusiKqHExHSeeWY5M2asp27dWsyefT3XXdfG1WEpFyvqzeyXgOuBLVgV2POxWo59BRhXOuEpZ8nJNQx68w/2n0oBYGyvZtx0SRNa1dOWPSuz66//jt9/P8CECZfwwgv9qF1bG/BTRd9RDAc6GmPSRMQXOGYb3lM6oamSlpSexc5jSTz9w3YiY84AMKBNXabf0pka1fTluMoqKiqegAAPvLzcmTatH1WqCJdcol2Sqn8UlSjSjTFpAMaYOBHZrUmifJr++z7e/yOKMxnZeeN8a1XnwQEtua1bU62HqKQyM3N4/fU/mTp1Jfff35VXXhmoLbyqAhWVKJqLyNmmxAUIshvGGHOdUyNTFyUhNZO3lu7jaEIaS3aeBOC/fZpT16sGPVr4EVLfS+sgKrGVKw8xbtzP7Np1mhEjQrn//m6uDkmVYUUliuvzDU93ZiCq5Hy6+gBTft6ZN9w1yJdHBrWiW3M/F0alyoo33/yLhx/+jaAgHxYuvIUhQ1q6OiRVxhXVKOCy0gxEXbjcXMNPW48xd0M0q/adzhv/3LBQ7uzZzIWRqbIiN9eQkpKJl5c7V13VilOnUnn66d54eGgnUap4+uZMObdy7ylu/3Rd3nAdj2p0a+bH9Fs64aYdAylgx44Yxo1bmNfTXKtWfrz4Yn9Xh6XKEacmChEZDLwNVAU+Nsa8XMA8NwLPAQbYYoy5xZkxVRTGGG756O+87kWv7dSIe/sF0yLA08WRqbIiNTWLqVP/4PXX/6J2bXfGjAnDGKN1U+q8OZwoRMTdGJNxHvNXBWYAA4FoYL2ILDDG7LSbpyXwBNDTGBMvItqGlAMOxabw4Leb2XTYatF1wb096dDYx8VRqbJk06bjXHfddxw8mMDo0WG8+upA/P09XB2WKqeKTRQi0hX4BKuNp0AR6QjcZYy5r5hFuwKRxpgo23pmY72bsdNunv8AM4wx8QDGmJjz34XKJSY5nT6vrQCgVT1P5o7vgXcNLWdWlrN3DIGBtQkMrM3nn19D797aVpe6OI7cUbwDDAV+ADDGbBGRyx1YrhFwxG44Gsj/DF4rABFZg1U89Zwx5lcH1l3p5OYaxn6+nuV7TgFWMxsPD2rt4qhUWZGdncv06etYsGAPS5aMws/Pgz/+uNPVYakKwpFEUcUYcyhfuWaOA8sVVBBq8g27AS2BvlhtR60SkXbGmAT7mUTkbuBugMDAQAc2XbGkZ+Vw+esrOJ5o9UirLboqe+vWHWXcuJ/ZtOkEV14ZTFJSBnXqaEdCquQ4kiiO2IqfjK3e4T5grwPLRQNN7IYbYzUDkn+etcaYLOCAiOzBShzr7WcyxnwIfAgQHh6eP9lUaGujYrn5w7UAtAioxbzxPfDx0D6JldVHxKRJS3jvvQgaNPBizpwbuP76NlpZrUqcI4liPFbxUyBwElhqG1ec9UBLEWmG1dnRzUD+J5p+AEYCM0XEH6soKsqx0Cu2xNQshk1fzeG4VAA6NvFh/vge2tyGylOtWhVWrDjEffd1ZerUfnh7u7s6JFVBOZIoso0xN5/vio0x2SJyL7AYq/7hU2PMDhGZAkQYYxbYpg0SkZ1YxVkTjTGx57utisb+zWofj2rMHded4LraqquCyMg4pkz5gxkzhuDl5c6GDXdTo4a+DqWcS4wpuiRHRPYDe4Bvge+NMcmlEVhhwsPDTUREhCtDcJrsnFye+XEH36w7DMDEK1oz4fJgF0elyoKMjGxefXUN06atonr1qixceAuXXab1VMpxIrLBGBN+Ics60sNdCxHpgVV09LyIbAZmG2NmX8gGVcFW7zvNbZ9YnQnW9XJn3vgeNPHV594VLF9+gPHjF7JnTyw33dSWN964goYN9Q5TlR6H7lmNMX8Cf4rIc8BbwNeAJooSsu5AXF6SuL9/SyZc3gJ3N+0fQlnvRUybtoqsrFx+/fVWrrhC7zBV6XPkhTtPrBflbgbaAD8CPZwcV6Vy4wd/AfB/IzsxrGNDF0ejXC031/DJJxsZPDiYJk1q8+WX1+LjU4OaNfXFSuUajrQatx24FHjVGBNsjHnEGPO3k+OqNP7Ya71A175RbU0Siq1bT9Kr16fcfffPfPzxRgAaNPDSJKFcypGip+bGmFynR1JJTVtoPd302ehLXByJcqUzZzJ5/vkVvPnmWurUqcnMmcO5/faOrg5LKaCIRCEi/zPGPALME5F/PRqlPdxdnNxcw80frmXvyTN0bOKDv6c+A1+ZPffcCv73v7+4665OvPzyAPz89EEGVXYUdUfxre1/7dmuhBljGPXp36w7GEerep58fZd2Q1kZHTmSSEpKFiEh/jz+eC+uuSaEXr0qXxM1quwrtI7CGHO2N5w2xphl9v+wKrXVBfh1+wm6vriMNZGxNK5Tk8UP9sbTXV+Yqkyys3N5442/aNNmBv/9788A+Pt7aJJQZZYjv1Bj+PddxdgCxqkiLN5xgu/WH2HZbqsl9a5Bvnw+pqu2y1PJrF0bzbhxP7Nly0muuqol06cPcXVIShWrqDqKm7AeiW0mIt/bTfICEgpeShXk5V928/4f+/OGtaOhymnhwr0MG/YNDRt68f33N3LNNSF6oaDKhaLuKNYBsVitvs6wG58MbHJmUBXJ5B+38/lfhxCBvx7vTz1vd/1xqESMMRw7lkyjRt4MGNCcKVMu54EHuuHlpQ8vqPKj0ERhjDkAHMBqLVZdgEOxKXz+1yEA3rm5E/Vr13BxRKo07d0byz33LGTv3lh27pyAp2d1nn66t6vDUuq8FVX09Icxpo+IxHNuh0MCGGOMr9OjK+du/dh6L3HWXd3oEezv4mhUaUlPz+bll1fz0kurqVnTjZde6k/NmvrAgiq/ijp7z3Z3qr9w5ykuJZMR7/1JdHwawXU9NUlUIidOnKF378/Yty+OkSPb8cYbV1C/vqerw1LqohRV9HT2bewmwDFjTKaI9AI6AF8BSaUQX7n06JwtRJ1O4dLm1pNNquLLysqhWrWq1KtXi969mzJjxhAGDmzh6rCUKhGOtPX0A1Y3qC2AL7DeoZjl1KjKsRcX7eL33TGE1Pdi9t3dtRXYCi431/D++xG0aPEO0dFJiAgff3y1JglVoTiSKHJtfVpfB7xljLkPaOTcsMqniINxfLjS6sl1xq2dXRyNcrYtW07Qo8cnjB+/kJYt/cjKynF1SEo5hUNdoYrIDcAo4BrbOG3KMh9jTF5z4csf7Usz/1oujkg5izGGiROX8NZba/H1rcmXX17Lrbe218eeVYXl6JvZ92A1Mx4lIs2Ab5wbVvmSk2u4ZNpScg00rlNTk0QFJyLEx6cxdqzVgF+dOjVdHZJSTlVs0ZMxZjtwPxAhIiHAEWPMNKdHVo58sHI/cSmZACx9uI+Lo1HOcOhQAtdcM5uNG48D8NFHV/PBB8M0SahKodhEISKXAZHAJ8CnwF4R6enswMqT33acBGDPC4OpUU0rryuSrKwcXn11DaGh77JkSRR79pwGoEoVLWZSlYcjRU9vAkOMMTsBRKQN8CUQ7szAyouZaw6w+UgCg0Lr6RNOFcyffx7hv//9me3bYxg+vDXvvHMlgYG1XR2WUqXOkURR/WySADDG7BKR6k6MqdyIjk/luZ+sQzNleDsXR6NK2tKlUSQmpvPDDzcxfHiIq8NRymUcSRQbReQDrLsIgFvRRgEBuP69PwF4ZmiotuNUARhj+PLLrQQEeHDllS2ZNKknDz/cHU9PvS5SlZsj71GMA/YDjwGTgCjgv84Mqjx4Z9k+TiZl4O/pzthezVwdjrpIu3efpl+/L7jjjh/47LPNALi7u2mSUIpi7ihEpD3QAphvjHm1dEIqHz5ZfQCARQ/0cnEk6mKkpWXx4oureOWVNdSqVZ0PPhjKXXfpy5JK2Sv0jkJEnsRqvuNWYImIjCm1qMq4I3GpJKZlMbJrIHW9tMipPPvpp7288MIqbrqpHbt3T+Duu7voE01K5VPUHcWtQAdjTIqIBACLsB6PrdQOnk6h7+srAOjbOsC1wagLcuLEGTZvPsHgwcHccEMoQUF30bWrtkqjVGGKqqPIMMakABhjThUzb6WwLTqRWz5aC8CdPYIYFFrPxRGp85GTk8u7766ndevpjBo1n7S0LEREk4RSxSjqjqK5XV/ZArSw7zvbGHOdUyMrYxLTshg2fTVgJYnnrm7r4ojU+di48Tjjxv3M+vXHGDCgOe++O4SaNbXJMqUcUVSiuD7f8HRnBlKWncnIptuLVo+wDw1oxQMDWro4InU+DhyIp2vXj/D392DWrOu4+eZ22oCfUuehqI6LlpVmIGVVdk4u7SYvBqBnsB/39w92cUTKEcYYtm2LoUOHejRrVofPPhvOsGGt8fHRhw+UOl+Vvt6hOD1e/h0ArxpufH3XpXolWg4cOBDP0KHf0KnTB2zdarXDNWpUR00SSl0gpyYKERksIntEJFJEHi9ivhEiYkSkTLUf9djcLcQkZ+DjUY2tkwe5OhxVjMzMHF5+eTVt277LH38c5PXXBxIaqk+mKXWxHGnCAwARcTfGZJzH/FWBGcBAIBpYLyIL7NuNss3nhdWM+d+Orrs0fBdxhO8iogFY/GBvvZMo43JycunR4xM2bDjOdde14a23rqBJE23AT6mS4Egz411FZBuwzzbcUUT+z4F1dwUijTFRxphMYDYwvID5pgKvAumOh+1cW44k8NjcrQB8ckc49by1yKKsSkqyrl2qVq3CmDGd+Omnkcybd6MmCaVKkCNFT+8AQ4FYAGPMFuByB5ZrBByxG44mX1/bItIJaGKM+bmoFYnI3SISISIRp06dcmDTF+dskvjlgcvo30bflSiLjDHMnLmZ5s3f5scfdwNwzz2XMHRoKxdHplTF40iiqGKMOZRvnCO9yBdUVmPyJopUwerr4pHiVmSM+dAYE26MCQ8IcG6Zc0xSOntOJgPQpoG3U7elLszOnafo2/dzRo/+kZAQf1q08HV1SEpVaI7UURwRka6AsdU73AfsdWC5aKCJ3XBj4JjdsBfQDlhhK/+vDywQkauNMRGOBO8M//1qAwBPX9XGVSGoIrz66hqeeup3vL3d+fjjYYwe3UnbZlLKyRxJFOOxip8CgZPAUtu44qwHWopIM+AocDNwy9mJxphEwP/ssIisAB51ZZJYE3maTYcT8K1Vnbsua+6qMFQBjDGICPXre3Lrre157bWBBATUcnVYSlUKxSYKY0wM1o/8eTHGZIvIvcBioCrwqTFmh4hMASKMMQvOO1onm7fBespp1n+6uTgSddaxY8k88MCvXHZZIPff343bb+/I7bd3dHVYSlUqxSYKEfkIu7qFs4wxdxe3rDFmEVars/bjni1k3r7Frc+Zth9N5PtNR+nRwo+Q+lo34WpnG/B76qnfycrKpUePxq4OSalKy5Gip6V2f9cAruXcp5kqhPu/sXp3ffHa9i6ORG3efIK77lrAhg3HGTSoBe++O0QrrJVyIUeKnr61HxaRL4ElTovIBbYcSSDqdAqXNvclyF/LvV0tMTGdY8eS+fbbEdxwQ6i+7KiUizn8ZradZkDTkg7EVYwx3P1lBNWqCs9f3c7V4VRKxhjmzNnJvn2xPPVUb/r0CSIq6gFq1LiQ01MpVdIceTM7XkTibP8SsO4mnnR+aKXjq7WHOJmUwbg+LWhd38vV4VQ6+/fHMWTILG66aS4//riHrCzrFR1NEkqVHUV+G8W65++I9XgrQK4x5l8V2+XV4h0neObHHQDc3Vsfhy1NGRnZvP76n7zwwiqqVavC228P5p57LsHNTRs0VqqsKTJRGGOMiMw3xnQprYBK0yerDwAwb3x3vGpob2el6ciRJKZOXcmwYa15660raNRInzRTqqxy5PJtnYh0dnokpWzZrpOsOxBHz2A/ujTVJ2pKw6lTKUyfvg6A4GBfdu6cwJw5N2iSUKqMK/SOQkTcjDHZQC/gPyKyH0jBasPJGGPKbfLYcSyRsZ9bL4A/PFAbkXO23FzDZ59t4rHHlpKcnMHAgc1p3dqf5s3ruDo0pZQDiip6Wgd0Bq4ppVhKzbUz/gRg8rBQvZtwsu3bYxg/fiGrVx/msssCef/9obRu7V/8gkqpMqOoRCEAxpj9pRRLqVi26ySZObkAjO7ZzMXRVGyZmTkMGvQlmZk5fPrp1dx5Z5i+E6FUOVRUoggQkYcLm2iMecMJ8Tjdl2utFtP/eqKfiyOpuH7//QB9+jSlevWqfPfdDYSE+OPv7+HqsJRSF6ioyuyqgCdWc+AF/St30rNyWLHnFAPa1KVB7ZquDqfCiY5O4vrrv6N//y/44ostAPTqFahJQqlyrqg7iuPGmCmlFkkpeHOJ1Y1Gp0CtRC1J2dm5TJ++jmeeWU5OTi4vvdSfW2/t4OqwlFIlpNg6iorCGMMHK6NwqyKM79PC1eFUKKNGzWf27O1ceWUwM2YMoVkzTcRKVSRFJYr+pRZFKVi47ThgvYGtPaJdvISEdNzcquDpWZ0JEy7h+uvbcP31bbSyWqkKqNA6CmNMXGkG4kzGGP5vWSQAt11aYdozdAljDLNnb6dNmxk888zvgFUPMWKEtvKqVEVVKRrWmbfxKHtOJjO+bwsa+mgl9oWKjIzjiiu+YuTIeTRu7M1tt2k9hFKVQaVoovPROdYTOOP7at3EhZo1axtjxvyIu7sb06dfybhx4VStWimuM5Sq9Cp8ojiVnAHAVR0a4K0N/523rKwcqlWrSnh4Q0aMCOXVVwfSsGG5fDpaKXWBKnyieOmXXQB0auLj4kjKl5iYFB555DdSUjL5/vubaNXKj6++us7VYSmlXKBClx2kZ+Xw8xbraac7ewS5NphyIjfX8OGHG2jdejrffrudtm0DyLE1eaKUqpwq9B3FV2sPkZmTy7NDQ3HT8vRiRUXFc9tt3/PXX9H07RvEe+9dRUiINuCnVGVXoRPFItu7E7d310diHVG7tjsJCel8/vk1jBrVQR93VUoBFbzoaefxJMKa+OjdRBEWLNjDddd9S05OLn5+Hmzffg+3395Rk4RSKk+F/QWNT8kkPSuX/iF1XR1KmXT4cCLXXDOb4cNns3dvLMePnwHQt9aVUv9SYYueVu47BYCfp7uLIylbsrNzeeuttUyevAJjDK+8MoCHHrqUatWqujo0pVQZVWETxabDCQBc26mRiyMpW3Jycvn4443069eM//u/KwkK0seGlVJFq5BFT8YYlu0+SZsG3tSsrlfK8fFpTJq0hOTkDNzd3VizZgwLFtysSUIp5ZAKmSg2HIrnSFwaQ9rVd3UoLmWM4euvtxISMoP//e8vli8/CICfn4dWViulHFYhi57mREQDcHklrsjeuzeWe+5ZyLJlB+jatRGLF99GWFjlTpxKqQtTIRPF6sjT1KxWlXaNars6FJd58MFfiYg4xrvvDuHuu7toA35KqQtW4RJFUnoWRxPSGNK+8l09L1myn5AQf5o0qc17712Fu7sb9et7ujospVQ559TLTBEZLCJ7RCRSRB4vYPrDIrJTRLaKyDIRuehXqN/4zeoX+4q2lSdRnDhxhltumcegQV/xyitrAGja1EeThFKqRDgtUYhIVWAGcCUQCowUkdB8s20Cwo0xHYC5wKsXu92Zfx4EYHAlqMjOzTW8/34EISHTmTdvF5Mn9+H11we5OiylVAXjzDuKrkCkMSbKGJMJzAaG289gjFlujEm1Da4FGl/MBtMycwAY1rEh7m4V/7HYl15axfjxC+nSpSFbt47juef6UqNGhStNVEq5mDN/VRoBR+yGo4FuRcw/FviloAkicjdwN0BgYGChK0jNzAagQwWuxE5OzuD06VSaNavDuHHhNGtWh5Ej2+njrkopp3HmHUVBv1ymwBlFbgPCgdcKmm6M+dAYE26MCQ8ICCh0gyeS0gGo5V7xrqqNMcyfv4vQ0He56aa5GGPw8/Pgllvaa5JQSjmVMxNFNNDEbrgxcCz/TCIyAHgKuNoYk3ExGzz7/kSQn8fFrKbMOXQogauvns11132Hr29N3nnnSk0OSqlS48xL7/VASxFpBhwFbgZusZ9BRDoBHwCDjTExF7MxY0xeRfalzf0uZlVlyl9/HWHAgC8BeP31gTzwwKW4uek7EUqp0uO0RGGMyRaRe4HFQFXgU2PMDhGZAkQYYxZgFTV5AnNsV8iHjTFXX8j2th1NBKBzoE+FaCo7KSkDb293OnduwJgxYUyc2JPAwIpb96KUKrucWphvjFkELMo37lm7vweU1Lb+2h8LwONXtimpVbpEbGwqjz++lN9+i2LHjnvw9KzO//3fEFeHpZSqxCpMre+i7ScA6NC4fF51G2P48sutPPLIb8THp/Hww93RagilVFlQIRKFMYYtRxJoXc+LGuWwA57ExHSuueZbVqw4SPfujXn//aF06FDP1WEppRRQQRLFRlsnRf3blK/WYo0xiAje3u74+3vw4YdDGTu2c4WoY1FKVRwV4vGZN5dY7TsNDC0/V+GLF0fSufOHREcnISLMmXMD//lPF00SSqkyp9wnitgzGayOPI2PRzU6BdZxdTjFOn48mZtvnsvgwV+TmppFTEyKq0NSSqkilfuip+V7TgFwW7eLbnjW6WbMWMeTT/5ORkY2zz/fl0mTeuJeAd8iV0pVLOX+V2pbtFU/MbJb4W1AlRUbNhynW7dGzJgxhJYtK85LgUqpiq3cJ4p5G48CUN+7hosj+bekpAyefXY5o0Z1oEuXhrz77lW4u1fV5jeUUuVKuU4U6Vk5nMnIZnhYQ6qWoUpgYwzz5u3igQd+5fjxZAIDa9OlS0NtAlwpVS6V61+uyJgzAHRt5uviSP5x4EA89977C4sW7SMsrD7ff38j3bpdVDcbSinlUuU6UXz992EAegX7uziSf3z99TZWrjzEm29ewb33dtUG/JRS5V65TRQ5uYbvN0ZT37sGTf1quTSWVasOkZGRw4ABzZk4sQd33hlG48beLo1JKaVKSrm93D0an0ZGdq5L+8Y+fTqVMWN+pHfvmUyZ8gcA7u5umiSUUhVKub2jWLP/NADhQaX/kp0xhpkzNzNx4hISEzOYNKknzzzTu9TjUOVDVlYW0dHRpKenuzoUVQnUqFGDxo0bU61atRJbZ7lNFDuPJQFweevSb99p0aJ9jBmzgJ49m/D++0Np1658tTGlSld0dDReXl4EBQXpo9HKqYwxxMbGEh0dTbNmzUpsveW26OmHTUcJ9PUotf6xU1OzWLPGqjwfMqQlP/54MytXjtYkoYqVnp6On5+fJgnldCKCn59fid+9lttEkZaVQ0pGdqls65df9tGu3btceeXXJCSkIyJcfXVrbcBPOUyThCotzjjXymWiSM/KITvXcEN4E6du5+jRJG64YQ5DhszC3d2Nn34aiY9P2XsDXCmlnKlcJoqzL9rV8Si5ypr8YmJSCA19l59/3ssLL1zOli3j6NMnyGnbU8pZqlatSlhYGO3atWPYsGEkJCTkTduxYwf9+vWjVatWtGzZkqlTp2KMyZv+yy+/EB4eTps2bQgJCeHRRx91xS4UadOmTdx1113njBs+fDjdu3c/Z9ydd97J3Llzzxnn6emZ9/fevXsZMmQIwcHBtGnThhtvvJGTJ09eVGxxcXEMHDiQli1bMnDgQOLj4/81z/LlywkLC8v7V6NGDX744Ydz5rnvvvvOiXX69Ol89tlnFxXbeTHGlKt/Xbp0Mc8v2GGaTvrZrD8Qa0padHRi3t9vv73WREaW/DZU5bJz506Xbr9WrVp5f99+++3mhRdeMMYYk5qaapo3b24WL15sjDEmJSXFDB482EyfPt0YY8y2bdtM8+bNza5du4wxxmRlZZkZM2aUaGxZWVkXvY4RI0aYzZs35w3Hx8ebxo0bm5CQEBMVFZU3/o477jBz5sw5Z9mzxyYtLc0EBwebBQsW5E37/fffzbZt2y4qtokTJ5qXXnrJGGPMSy+9ZB577LEi54+NjTV16tQxKSkpeePWr19vbrvttnM+x5SUFBMWFlboego654AIc4G/u+XyqadjCWkAhDXxKbF1Jiam8/TTv/PBBxtYu/YuOnduwP33dyux9SsF8PxPO/Ke2CspoQ29mTysrUPzdu/ena1btwIwa9YsevbsyaBBgwDw8PBg+vTp9O3blwkTJvDqq6/y1FNPERISAoCbmxv33HPPv9Z55swZ7rvvPiIiIhARJk+ezPXXX4+npydnzlh3/3PnzuXnn39m5syZ3Hnnnfj6+rJp0ybCwsKYP38+mzdvxsfH+j4HBwezZs0aqlSpwrhx4zh82HqI5K233qJnz57nbDs5OZmtW7fSsWPHvHHz5s1j2LBh1KtXj9mzZ/PEE08Ue1xmzZpF9+7dGTZsWN64yy+/3KFjWpQff/yRFStWAHDHHXfQt29fXnnllULnnzt3LldeeSUeHh4A5OTkMHHiRGbNmsX8+fPz5vPw8CAoKIh169bRtWvXi46zOOUyUWw6Ek9dL3fcql58yZkxhjlzdvLgg79y4sQZ7r23Ky1alP0OkJQ6Xzk5OSxbtoyxY8cCVrFTly5dzpmnRYsWnDlzhqSkJLZv384jjzxS7HqnTp1K7dq12bZtG0CBxSv57d27l6VLl1K1alVyc3OZP38+o0eP5u+//yYoKIh69epxyy23aPPJNwAADs5JREFU8NBDD9GrVy8OHz7MFVdcwa5du85ZT0REBO3atTtn3DfffMPkyZOpV68eI0aMcChRbN++/V/HoiDJyclcdtllBU6bNWsWoaGh54w7efIkDRo0AKBBgwbExMQUuf7Zs2fz8MMP5w1Pnz6dq6++Om8d9sLDw1m1apUmisJ416hWYkniuuu+44cfdtO5cwMWLBhJeHjDEohQqYI5euVfktLS0ggLC+PgwYN06dKFgQMHAv/02V6Q83lyZunSpcyePTtvuE6d4i+0brjhBqpWrQrATTfdxJQpUxg9ejSzZ8/mpptuylvvzp0785ZJSkoiOTkZLy+vvHHHjx8nICAgb/jkyZNERkbSq1cvRAQ3Nze2b99Ou3btCtyn831CyMvLi82bN5/XMo46fvw427Zt44orrgDg2LFjzJkzJ++OJL+6deuye/dup8SSX7mszE7LyqFNfa/iZyxEVlYOYJ0kvXo14Z13BrNu3V2aJFSFVLNmTTZv3syhQ4fIzMxkxowZALRt25aIiIhz5o2KisLT0xMvLy/atm3Lhg0bil1/YQnHflz+5/pr1fqnfbbu3bsTGRnJqVOn+P/27j+4qvLO4/j7swokKNAtLM5a0KQhBUIMyI+alkWK0I4o1co4ImsoOHYdQRasCyM7OLPuYreULujijyJ1Hai2iDoVMcrQTE1LJwJKTfghiKWYAVxmRciyFAQkfPePc3JzCTc3JzH33vz4vmbuzD3nnh/P/ebe++R5nnO+z7p165g8eTIA58+fZ/PmzVRVVVFVVcXHH398QSVR997ij7127VpqamrIzc0lJyeH6urqWCXWu3fvC1o7x44do0+fPrFYRHmvJ06cuGDgOf4RX6nVueKKKzh8+DAQVAR9+zZ+39VLL73EbbfdFrujurKykn379jFgwABycnI4deoUAwYMiG1/+vRpsrOzmyxzq2jp4EamHiNGjLCrHyq1R9bvanQgJ5ny8o9s0KAnbd26PS3a37nmakuD2e+9957179/fzp49a6dOnbLc3FwrKyszs2Bw++abb7bly5ebmdn27dstLy/P9u7da2ZmtbW1tnTp0ouO/9BDD9ncuXNjy8eOHTMzs7y8PNu9e7fV1tba5MmTbfr06WaWeFB53rx5VlJSYhMnToytmzp1qi1ZsiS2XFlZedG59+zZY6NHj44tFxcX29tvvx1b3r9/v+Xl5ZmZ2euvv27jx4+3M2fOmJnZ0qVL7e67746997y8PCstLY3tu2HDBtuxY8dF52yOefPmXTCYPX/+/Ea3ve666+ytt95q9PX4v6OZ2ezZs23NmjUJt23twex216I4HbYG+lzerVn7HTlykunT1zFu3GrOnDlHjx7N29+5juDaa69l6NChvPjii2RnZ/Paa6/x6KOPMnDgQK655hpGjRrF7NmzASgqKuLxxx9n6tSpDB48mMLCwth/x/EefvhhampqKCwsZOjQoZSXlwOwePFiJk2axA033JCwjz3elClTeOGFF2LdTgDLly9n27ZtFBUVUVBQwIoVKy7ab9CgQRw/fpwTJ05QXV3NgQMHKC4ujr2em5tLz5492bp1K5MmTWLMmDGMGDGCYcOGUVFRERtYzs7OprS0lCeeeIL8/HwKCgpYtWpV0hZAFAsWLKCsrIz8/HzKyspYsGABEIytxF/SW11dzcGDBxk7dmzkY1dUVDBhwoQvVL6oZHHXTLcH+UOG2uff/XfemPN3DLmyV6R91qzZyf33v8lf/nKW+fO/ycKF19M9hfdgOBdvz549DB48ONPF6LAee+wxevTocdG9FB1ZZWUly5Yt4/nnn0/4eqLPnKQ/mtnIlpyv3bUozpw7D0BOM+agOHfuPIWFfamquo8f/Wi8VxLOdSAzZ86kW7fO1UPw6aefsmjRorSdr91d9XT23Hmu6tEtaTLAkyfPsmjRJq66qhezZo2ipKSIkpIiz7fjXAeUlZXFtGnTMl2MtKq7ci1d2mGLopbcPo23JkpLP2TIkKf5yU8q+PDDo0Bw9YVXEi6T2lsXr2u/UvFZa5ctikQVxaFD/8ecORt49dUPKCj4GzZtmsGYMVdnoITOXSgrK4ujR496qnGXchbOR5GV1brJS9tdRXHuvJGToKLYv7+GjRv/zI9/PJ4HH/wGXbtekoHSOXexfv36cejQIY4cOZLporhOoG6Gu9bU7ioKINaieOedj9m8+SBz5xZz/fVXc+DAA/Tu3T3DpXPuQl26dGnV2cacS7eUjlFIulHSXkn7JC1I8Ho3SWvD17dKyoly3N5dL2XWrDcoLn6WZcu2cPLk2WC9VxLOOdfqUlZRSLoEeAqYCBQAUyUVNNjsHqDGzAYAjwGNp1WMM+lbq3nmmT8yZ8517Nw5k8su69qaRXfOORcnlV1PXwf2mdl+AEkvArcC8QlRbgUeCZ+/AjwpSZZk2N5qjf79evLm63/P8OHJ7/Z0zjn3xaWyovgKcDBu+RDQcIKH2DZmdk7ScaA38Gn8RpLuBe4NF89sO3LvrggZgTuDPjSIVSfmsajnsajnsag3sKU7prKiSHQdYMOWQpRtMLOVwEoASdtaeht6R+OxqOexqOexqOexqCdpW9NbJZbKwexDQP+45X7Afze2jaRLgV7AsRSWyTnnXDOlsqJ4F8iXlCupK3AnsL7BNuuB6eHz24G3ko1POOecS7+UdT2FYw6zgY3AJcBzZva+pH8jyIu+Hvgv4HlJ+whaEndGOPTKVJW5HfJY1PNY1PNY1PNY1GtxLNpdmnHnnHPp1e6SAjrnnEsvryicc84l1WYrilSl/2iPIsTiQUm7Je2Q9FtJHTZtblOxiNvudkkmqcNeGhklFpLuCD8b70v6VbrLmC4RviNXSSqXVBl+T27KRDlTTdJzkj6RtKuR1yVpeRinHZKGRzpwSyfbTuWDYPD7z8BXga7AdqCgwTazgBXh8zuBtZkudwZjMQ7oHj6f2ZljEW7XA9gEbAFGZrrcGfxc5AOVwF+Hy30zXe4MxmIlMDN8XgBUZ7rcKYrF9cBwYFcjr98EbCC4h60Y2BrluG21RRFL/2FmZ4G69B/xbgVWh89fAcarYyb7bzIWZlZuZqfCxS0E96x0RFE+FwCLgCXA6XQWLs2ixOIfgKfMrAbAzD5JcxnTJUosDOgZPu/Fxfd0dQhmtonk96LdCvzCAluAL0lqMhdSW60oEqX/+Epj25jZOaAu/UdHEyUW8e4h+I+hI2oyFpKuBfqbWWk6C5YBUT4XXwO+JqlC0hZJN6atdOkVJRaPACWSDgFvAv+YnqK1Oc39PQHa7nwUrZb+owOI/D4llQAjgbEpLVHmJI2FpL8iyEI8I10FyqAon4tLCbqfvkXQyvyDpEIz+98Uly3dosRiKrDKzJZK+gbB/VuFZnY+9cVrU1r0u9lWWxSe/qNelFggaQKwELjFzM6kqWzp1lQsegCFwO8kVRP0wa7voAPaUb8jr5nZ52b2EbCXoOLoaKLE4h7gJQAz2wxkESQM7Gwi/Z401FYrCk//Ua/JWITdLc8QVBIdtR8amoiFmR03sz5mlmNmOQTjNbeYWYuTobVhUb4j6wgudEBSH4KuqP1pLWV6RInFAWA8gKTBBBVFZ5ybdj3w/fDqp2LguJkdbmqnNtn1ZKlL/9HuRIzFT4HLgZfD8fwDZnZLxgqdIhFj0SlEjMVG4DuSdgO1wHwzO5q5UqdGxFj8E/BzST8k6GqZ0RH/sZS0hqCrsU84HvMvQBcAM1tBMD5zE7APOAXcHem4HTBWzjnnWlFb7XpyzjnXRnhF4ZxzLimvKJxzziXlFYVzzrmkvKJwzjmXlFcUrs2RVCupKu6Rk2TbnMYyZTbznL8Ls49uD1NeDGzBMe6T9P3w+QxJV8a99qykglYu57uShkXY5wFJ3b/ouV3n5RWFa4s+M7NhcY/qNJ33LjMbSpBs8qfN3dnMVpjZL8LFGcCVca/9wMx2t0op68v5NNHK+QDgFYVrMa8oXLsQthz+IOm98PHNBNsMkfRO2ArZISk/XF8St/4ZSZc0cbpNwIBw3/HhHAY7w1z/3cL1i1U/B8h/hOsekTRP0u0EObd+GZ4zO2wJjJQ0U9KSuDLPkPREC8u5mbiEbpJ+Jmmbgrkn/jVcN4egwiqXVB6u+46kzWEcX5Z0eRPncZ2cVxSuLcqO63Z6NVz3CfBtMxsOTAGWJ9jvPuA/zWwYwQ/1oTBdwxRgdLi+FririfN/F9gpKQtYBUwxs2sIMhnMlPRl4DZgiJkVAY/G72xmrwDbCP7zH2Zmn8W9/AowOW55CrC2heW8kSBNR52FZjYSKALGSioys+UEuXzGmdm4MJXHw8CEMJbbgAebOI/r5NpkCg/X6X0W/ljG6wI8GfbJ1xLkLWpoM7BQUj/g12b2J0njgRHAu2F6k2yCSieRX0r6DKgmSEM9EPjIzD4MX18N3A88STDXxbOS3gAipzQ3syOS9od5dv4UnqMiPG5zynkZQbqK+BnK7pB0L8H3+m8JJujZ0WDf4nB9RXiergRxc65RXlG49uKHwP8AQwlawhdNSmRmv5K0FbgZ2CjpBwRplVeb2T9HOMdd8QkEJSWc3yTMLfR1giRzdwKzgRua8V7WAncAHwCvmpkp+NWOXE6CWdwWA08BkyXlAvOAUWZWI2kVQeK7hgSUmdnUZpTXdXLe9eTai17A4XD+gGkE/01fQNJXgf1hd8t6gi6Y3wK3S+obbvNlRZ9T/AMgR9KAcHka8PuwT7+Xmb1JMFCc6MqjEwRpzxP5NfA9gjkS1obrmlVOM/ucoAupOOy26gmcBI5LugKY2EhZtgCj696TpO6SErXOnIvxisK1F08D0yVtIeh2OplgmynALklVwCCCKR93E/yg/kbSDqCMoFumSWZ2miC75suSdgLngRUEP7ql4fF+T9DaaWgVsKJuMLvBcWuA3cDVZvZOuK7Z5QzHPpYC88xsO8H82O8DzxF0Z9VZCWyQVG5mRwiuyFoTnmcLQayca5Rnj3XOOZeUtyicc84l5RWFc865pLyicM45l5RXFM4555LyisI551xSXlE455xLyisK55xzSf0/8J7VjnFa9qUAAAAASUVORK5CYII=\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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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
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" + ], + "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
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+ + +
+
              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
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+
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
+
+ +
+
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+ +
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+ + +
+ +
Out[107]:
+ + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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
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+ +
+ +
+
+ +
+
+
+
+

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.

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+

Training the Naive bayes model

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In [97]:
+
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+
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()
+
+ +
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+ +
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+
In [98]:
+
+
+
#training naive bayes model
+gnb.fit(X_train, y_train)
+
+ +
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+ +
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+ +
Out[98]:
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+
GaussianNB(priors=None, var_smoothing=1e-09)
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In [99]:
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+
#classifying values
+predicted = gnb.predict(X_train)
+expected = y_train
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+ +
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+
In [100]:
+
+
+
#plot roc for naive Bayes
+auroc = get_roc(gnb, y_test, X_test, "Naive Bayes")
+
+ +
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+ +
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+ + +
+ +
+ + +
+
Optimal Threshold: 0.9999935527715175
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In [101]:
+
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+
#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
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+

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.

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In [102]:
+
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+
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)))
+
+ +
+
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+ +
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+ +
+ + +
+
Optimal Threshold: 0.038218795916133315
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+
              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
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+

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:

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+
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)))
+
+ +
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+ +
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+ + +
+ +
+ + +
+
Optimal Threshold: 0.038218795916133315
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+
              precision    recall  f1-score   support
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+           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
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+
In [104]:
+
+
+
evaluation.loc[5] = (["Naive Bayes" , 
+                      classification_report(y_test, gnb.predict(X_test), output_dict = True)["1"]["f1-score"],
+                      auroc])
+
+ +
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In [105]:
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+
evaluation.sort_values(["AUROC"], ascending = False)
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Out[105]:
+ + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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
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In [106]:
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raise(Exception("Stop Running"))
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+ + +
+ +
+ + +
+
+---------------------------------------------------------------------------
+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

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+
In [ ]:
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+
from sklearn.neural_network import MLPClassifier
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In [ ]:
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mlp = MLPClassifier(hidden_layer_sizes=(26,26,26,26,26), activation = "logistic")
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In [ ]:
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mlp.fit(X_train,y_train)
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In [ ]:
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predictions = mlp.predict(X_test)
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+
In [ ]:
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+
confusion(y_test,predictions,"Neural Network (5x26)")
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In [ ]:
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+
auroc = get_roc(mlp, y_test, X_test, "Neural Network (5x26)")
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In [ ]:
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+
print(classification_report(y_test,predictions))
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+
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

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+
In [ ]:
+
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+
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

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+
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+
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

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