diff --git a/MLCC_NeuralNetwork(Single_Layer).ipynb b/MLCC_NeuralNetwork(Single_Layer).ipynb
new file mode 100644
index 0000000..8ee22d7
--- /dev/null
+++ b/MLCC_NeuralNetwork(Single_Layer).ipynb
@@ -0,0 +1,496 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "MLCC NeuralNetwork(Single Layer).ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/ArnabG99/Assignment-1/blob/ArnabG99/MLCC_NeuralNetwork(Single_Layer).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RdfxQ2a3qrD1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "from pandas import DataFrame as df\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wmOBi6uvqtzZ",
+        "colab_type": "code",
+        "outputId": "9045f72a-6eb7-4403-8624-88ae3fccc532",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "iris = datasets.load_iris()\n",
+        "x = iris.data\n",
+        "y = iris.target\n",
+        "x = np.insert(x,x.shape[1],y,axis=1)\n",
+        "data = pd.DataFrame(x)\n",
+        "\n",
+        "data = data.reindex(np.random.permutation(data.index))\n",
+        "data.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>77</th>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>5.7</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>107</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       0    1    2    3    4\n",
+              "77   6.7  3.0  5.0  1.7  1.0\n",
+              "70   5.9  3.2  4.8  1.8  1.0\n",
+              "15   5.7  4.4  1.5  0.4  0.0\n",
+              "40   5.0  3.5  1.3  0.3  0.0\n",
+              "107  7.3  2.9  6.3  1.8  2.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9rj7RDGJssVD",
+        "colab_type": "code",
+        "outputId": "c788248b-a8e8-4129-f306-6e6f428d91c1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 2584
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "features = data.iloc[:,:4]\n",
+        "target = data.iloc[:,4]\n",
+        "\n",
+        "target = target.values.reshape((target.shape[0],1))\n",
+        "target_class = np.zeros(shape=(target.shape[0],3))\n",
+        "\n",
+        "for i in range(target.shape[0]):\n",
+        "  if target[i] == 0:\n",
+        "    target_class[i][0] = 1\n",
+        "  elif target[i] == 1:\n",
+        "    target_class[i][1] = 1\n",
+        "  else:\n",
+        "    target_class[i][2] = 1\n",
+        "    \n",
+        "print(target_class.shape)\n",
+        "print(target_class)\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(150, 3)\n",
+            "[[0. 1. 0.]\n",
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+            " [0. 0. 1.]\n",
+            " [0. 1. 0.]\n",
+            " [1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]\n",
+            " [0. 0. 1.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 1. 0.]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yBv3zOuYsArP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def sigmoid(x):\n",
+        "  return 1/(1+np.exp(-x))\n",
+        "\n",
+        "def der_sigmoid(x):\n",
+        "  return (sigmoid(x)*(1-sigmoid(x)))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9WbZu7ihvSSp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "input_nodes = features.shape[1]\n",
+        "output_nodes = 3;\n",
+        "weight_matrix = np.random.uniform(size=(input_nodes,output_nodes))\n",
+        "\n",
+        "epoch = 2000 #increase it to increase accuracy\n",
+        "lr = 0.015  #learning rate decrease it to increase accuracy "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NJzxuP_X0IIn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "for i in range(epoch):\n",
+        "  output_node_input = features.dot(weight_matrix)\n",
+        "  output = sigmoid(output_node_input)\n",
+        "  \n",
+        "  error = target_class - output\n",
+        "  drv = der_sigmoid(output_node_input)\n",
+        "  delta_weight = error*drv\n",
+        " \n",
+        "  weight_matrix = weight_matrix + lr*features.T.dot(delta_weight)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "6yfIk5ky1EJt",
+        "colab_type": "code",
+        "outputId": "32cb0d02-4dd7-41db-92fe-e1fa8957ba6b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "final_output = np.array(output)\n",
+        "#print(final_output) #test here\n",
+        "output_class = np.zeros(shape=final_output.shape[0])\n",
+        "\n",
+        "for i in range(final_output.shape[0]):\n",
+        "  output_class[i] = np.argmax(final_output[i])\n",
+        "\n",
+        "print(output_class) #test here\n",
+        "class_diff = output_class - target[:,0]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[1. 2. 0. 0. 1. 0. 0. 1. 1. 0. 2. 2. 1. 0. 1. 2. 0. 0. 0. 1. 0. 0. 0. 1.\n",
+            " 1. 1. 2. 1. 0. 2. 1. 2. 2. 0. 1. 0. 2. 2. 0. 1. 0. 0. 0. 0. 0. 0. 1. 2.\n",
+            " 2. 1. 0. 0. 2. 2. 0. 1. 2. 2. 0. 2. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 2. 1.\n",
+            " 1. 2. 2. 2. 2. 1. 1. 2. 1. 2. 2. 2. 0. 1. 1. 2. 1. 2. 2. 0. 1. 2. 1. 2.\n",
+            " 0. 0. 0. 1. 2. 2. 0. 1. 0. 0. 2. 2. 1. 1. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1.\n",
+            " 1. 0. 0. 2. 1. 1. 1. 1. 2. 0. 2. 1. 2. 1. 2. 2. 0. 2. 1. 0. 1. 0. 1. 2.\n",
+            " 2. 1. 2. 1. 2. 1.]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q9xP2z-dEqQC",
+        "colab_type": "code",
+        "outputId": "397bad9d-7060-4797-b494-e9584b5df4f1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 323
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print('Actual Class')\n",
+        "print(target[:,0])\n",
+        "print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>')\n",
+        "print('Predicted Class')\n",
+        "print(output_class)\n",
+        "\n",
+        "wrong_prediction = np.count_nonzero(class_diff)\n",
+        "N = len(class_diff)\n",
+        "#print(class_diff)\n",
+        "simple_accuracy = 100 * (N-wrong_prediction)/N\n",
+        "print('Accuracy : ', (simple_accuracy), '%')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Actual Class\n",
+            "[1. 1. 0. 0. 2. 0. 0. 2. 1. 0. 2. 2. 1. 0. 1. 2. 0. 0. 0. 1. 0. 0. 0. 1.\n",
+            " 1. 1. 2. 1. 0. 2. 2. 2. 2. 0. 1. 0. 2. 2. 0. 2. 0. 0. 0. 0. 0. 0. 1. 2.\n",
+            " 2. 1. 0. 0. 2. 2. 0. 2. 2. 2. 0. 2. 0. 0. 1. 2. 0. 0. 0. 0. 1. 1. 2. 1.\n",
+            " 2. 2. 2. 2. 2. 1. 1. 2. 1. 2. 2. 2. 0. 1. 1. 2. 1. 2. 2. 0. 1. 1. 1. 2.\n",
+            " 0. 0. 0. 1. 2. 2. 0. 1. 0. 0. 2. 2. 1. 1. 0. 0. 1. 0. 1. 1. 0. 1. 2. 1.\n",
+            " 1. 0. 0. 2. 1. 1. 1. 1. 2. 0. 2. 1. 2. 1. 2. 1. 0. 2. 1. 0. 1. 0. 1. 2.\n",
+            " 2. 1. 2. 1. 1. 1.]\n",
+            ">>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
+            "Predicted Class\n",
+            "[1. 2. 0. 0. 1. 0. 0. 1. 1. 0. 2. 2. 1. 0. 1. 2. 0. 0. 0. 1. 0. 0. 0. 1.\n",
+            " 1. 1. 2. 1. 0. 2. 1. 2. 2. 0. 1. 0. 2. 2. 0. 1. 0. 0. 0. 0. 0. 0. 1. 2.\n",
+            " 2. 1. 0. 0. 2. 2. 0. 1. 2. 2. 0. 2. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 2. 1.\n",
+            " 1. 2. 2. 2. 2. 1. 1. 2. 1. 2. 2. 2. 0. 1. 1. 2. 1. 2. 2. 0. 1. 2. 1. 2.\n",
+            " 0. 0. 0. 1. 2. 2. 0. 1. 0. 0. 2. 2. 1. 1. 0. 0. 1. 0. 1. 1. 0. 1. 1. 1.\n",
+            " 1. 0. 0. 2. 1. 1. 1. 1. 2. 0. 2. 1. 2. 1. 2. 2. 0. 2. 1. 0. 1. 0. 1. 2.\n",
+            " 2. 1. 2. 1. 2. 1.]\n",
+            "Accuracy :  92.0 %\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file