MPNeuron.ipynb

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  "metadata": {
    "colab": {
      "name": "MPNeuron.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMyVwNm1mLpVtjvzS31Ju8q",
      "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/MALLI7622/Deep-Learning-with-PyTorch/blob/master/MPNeuron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFA9rQVLuaqa",
        "colab_type": "text"
      },
      "source": [
        "# Loading Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj0gPiovuXe-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn.datasets\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtyfD56Jr0kK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "breast_cancer = sklearn.datasets.load_breast_cancer()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npODsG3jr0hH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = breast_cancer.data\n",
        "Y = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWMqseZcr0fq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "2ebfaab8-5201-428e-8ace-43cb42aa1859"
      },
      "source": [
        "print(\"Features: \", X,\n",
        "      \"\\n Target: \", Y)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features:  [[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]] \n",
            " Target:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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            " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
            " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
            " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
            " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-Fp9OV6r0c3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6dfc447c-5396-4a55-e0e2-4dd68d173ae2"
      },
      "source": [
        "print(X.shape, Y.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569, 30) (569,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zSPQ7m2r0Z7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLFS0QqXr0XE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fn33OIxHr0Ud",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['class'] = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5Kh6IUrr0RP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "48eccf9f-c769-4708-a448-3cef99b04b80"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  class\n",
              "0        17.99         10.38  ...                  0.11890      0\n",
              "1        20.57         17.77  ...                  0.08902      0\n",
              "2        19.69         21.25  ...                  0.08758      0\n",
              "3        11.42         20.38  ...                  0.17300      0\n",
              "4        20.29         14.34  ...                  0.07678      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCREjmASr0Nz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "0da80238-466a-4e4b-fe1e-f43716855e57"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.627417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.483918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  ...  worst fractal dimension       class\n",
              "count   569.000000    569.000000  ...               569.000000  569.000000\n",
              "mean     14.127292     19.289649  ...                 0.083946    0.627417\n",
              "std       3.524049      4.301036  ...                 0.018061    0.483918\n",
              "min       6.981000      9.710000  ...                 0.055040    0.000000\n",
              "25%      11.700000     16.170000  ...                 0.071460    0.000000\n",
              "50%      13.370000     18.840000  ...                 0.080040    1.000000\n",
              "75%      15.780000     21.800000  ...                 0.092080    1.000000\n",
              "max      28.110000     39.280000  ...                 0.207500    1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDkCLEair0KU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "a00375c9-9cba-4d6e-c389-4809dc3398aa"
      },
      "source": [
        "print(data['class'].value_counts())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    357\n",
            "0    212\n",
            "Name: class, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWEElQvzr0IL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db1bdcd3-d016-460a-e676-e05a4ec2acf9"
      },
      "source": [
        "print(breast_cancer.target_names)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['malignant' 'benign']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKdOtWw4r0E4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "94dc057b-58c6-49e1-dfeb-d5ce1b68c3ff"
      },
      "source": [
        "data.groupby('class').mean()"
      ],
      "execution_count": 15,
      "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>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>0.609083</td>\n",
              "      <td>1.210915</td>\n",
              "      <td>4.323929</td>\n",
              "      <td>72.672406</td>\n",
              "      <td>0.006780</td>\n",
              "      <td>0.032281</td>\n",
              "      <td>0.041824</td>\n",
              "      <td>0.015060</td>\n",
              "      <td>0.020472</td>\n",
              "      <td>0.004062</td>\n",
              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
              "      <td>141.370330</td>\n",
              "      <td>1422.286321</td>\n",
              "      <td>0.144845</td>\n",
              "      <td>0.374824</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.182237</td>\n",
              "      <td>0.323468</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "      <td>0.284082</td>\n",
              "      <td>1.220380</td>\n",
              "      <td>2.000321</td>\n",
              "      <td>21.135148</td>\n",
              "      <td>0.007196</td>\n",
              "      <td>0.021438</td>\n",
              "      <td>0.025997</td>\n",
              "      <td>0.009858</td>\n",
              "      <td>0.020584</td>\n",
              "      <td>0.003636</td>\n",
              "      <td>13.379801</td>\n",
              "      <td>23.515070</td>\n",
              "      <td>87.005938</td>\n",
              "      <td>558.899440</td>\n",
              "      <td>0.124959</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.166238</td>\n",
              "      <td>0.074444</td>\n",
              "      <td>0.270246</td>\n",
              "      <td>0.079442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "class                             ...                                         \n",
              "0        17.462830     21.604906  ...        0.323468                 0.091530\n",
              "1        12.146524     17.914762  ...        0.270246                 0.079442\n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdisf5OquiAJ",
        "colab_type": "text"
      },
      "source": [
        "# Test train split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Yn-E2Qntxse",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMgc1XhqtxqZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.drop('class',axis = 1)\n",
        "\n",
        "Y = data['class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-0guTRUtxn0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ec609c85-56ca-4e11-d340-fc5b6288db2c"
      },
      "source": [
        "type(X)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XclC0fewtxk4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jZdQB4Itxiv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf951c9b-a71a-4b48-e12d-06bd664c5191"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569, 30) (512, 30) (57, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mm102CStxfa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuA-tdTEtxd0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c4f8545a-e401-46b9-8602-08f7166b332f"
      },
      "source": [
        "print(Y.mean(), Y_train.mean(), Y_test.mean())"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6274165202108963 0.626953125 0.631578947368421\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWXWegN7txak",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.1, stratify = Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2swmGJ8r0AA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "397f6f07-2d09-4f29-f02d-b6ac6872a785"
      },
      "source": [
        "print(X.mean(), X_train.mean(), X_test.mean())"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean radius                 14.127292\n",
            "mean texture                19.289649\n",
            "mean perimeter              91.969033\n",
            "mean area                  654.889104\n",
            "mean smoothness              0.096360\n",
            "mean compactness             0.104341\n",
            "mean concavity               0.088799\n",
            "mean concave points          0.048919\n",
            "mean symmetry                0.181162\n",
            "mean fractal dimension       0.062798\n",
            "radius error                 0.405172\n",
            "texture error                1.216853\n",
            "perimeter error              2.866059\n",
            "area error                  40.337079\n",
            "smoothness error             0.007041\n",
            "compactness error            0.025478\n",
            "concavity error              0.031894\n",
            "concave points error         0.011796\n",
            "symmetry error               0.020542\n",
            "fractal dimension error      0.003795\n",
            "worst radius                16.269190\n",
            "worst texture               25.677223\n",
            "worst perimeter            107.261213\n",
            "worst area                 880.583128\n",
            "worst smoothness             0.132369\n",
            "worst compactness            0.254265\n",
            "worst concavity              0.272188\n",
            "worst concave points         0.114606\n",
            "worst symmetry               0.290076\n",
            "worst fractal dimension      0.083946\n",
            "dtype: float64 mean radius                 14.121510\n",
            "mean texture                19.283789\n",
            "mean perimeter              91.940039\n",
            "mean area                  653.713867\n",
            "mean smoothness              0.096325\n",
            "mean compactness             0.104699\n",
            "mean concavity               0.088910\n",
            "mean concave points          0.048947\n",
            "mean symmetry                0.181255\n",
            "mean fractal dimension       0.062822\n",
            "radius error                 0.400226\n",
            "texture error                1.212727\n",
            "perimeter error              2.835779\n",
            "area error                  39.537277\n",
            "smoothness error             0.007014\n",
            "compactness error            0.025341\n",
            "concavity error              0.031225\n",
            "concave points error         0.011686\n",
            "symmetry error               0.020490\n",
            "fractal dimension error      0.003738\n",
            "worst radius                16.278699\n",
            "worst texture               25.709629\n",
            "worst perimeter            107.365020\n",
            "worst area                 881.691016\n",
            "worst smoothness             0.132650\n",
            "worst compactness            0.256596\n",
            "worst concavity              0.274613\n",
            "worst concave points         0.115092\n",
            "worst symmetry               0.290974\n",
            "worst fractal dimension      0.084217\n",
            "dtype: float64 mean radius                 14.179228\n",
            "mean texture                19.342281\n",
            "mean perimeter              92.229474\n",
            "mean area                  665.445614\n",
            "mean smoothness              0.096674\n",
            "mean compactness             0.101124\n",
            "mean concavity               0.087802\n",
            "mean concave points          0.048671\n",
            "mean symmetry                0.180321\n",
            "mean fractal dimension       0.062582\n",
            "radius error                 0.449604\n",
            "texture error                1.253916\n",
            "perimeter error              3.138049\n",
            "area error                  47.521263\n",
            "smoothness error             0.007284\n",
            "compactness error            0.026710\n",
            "concavity error              0.037899\n",
            "concave points error         0.012782\n",
            "symmetry error               0.021016\n",
            "fractal dimension error      0.004305\n",
            "worst radius                16.183772\n",
            "worst texture               25.386140\n",
            "worst perimeter            106.328772\n",
            "worst area                 870.631579\n",
            "worst smoothness             0.129841\n",
            "worst compactness            0.233326\n",
            "worst concavity              0.250406\n",
            "worst concave points         0.110244\n",
            "worst symmetry               0.282004\n",
            "worst fractal dimension      0.081511\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUk9ZxL6y6Vx",
        "colab_type": "text"
      },
      "source": [
        "# Binarisation of input\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXzDy7iyyBkv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6G2hhWReyBfI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "8c3ed949-bf5c-4eb8-da8a-d1cbdaa85beb"
      },
      "source": [
        "plt.plot(X_train.T, '*')\n",
        "plt.xticks(rotation= 'vertical')\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4oOFVdMmbjF+cdvp+qaBr3iQuaHilx8qygxpj+qq6Eljpfi8VkTlAK+BMYKBr9iwwCfi9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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEjlgNh5yBWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_3_train = X_train['mean area'].map(lambda x: 0 if x < 1000 else 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-ESzL3TyBOw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "be852a95-244f-487d-f457-5aeb3c37916c"
      },
      "source": [
        "plt.plot(X_binarised_3_train, '8')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f65b20d8208>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SHZKulrRC0nrbKwrNNkt6JiJ+StInJX20sgoLHjx6Uht2HCDMAQycs7nYOn0utGHHAT14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\nkAgCHQASQaADQCL+D0LZ5hrcXE4GAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2odpMUayBJZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_train = X_train.apply(pd.cut, bins = 2, labels= [1,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e0_yYi_yBBY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "5286eeeb-45f2-4f67-ee2f-4158c8065816"
      },
      "source": [
        "plt.plot(X_binarised_train,'8')\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RtJDKKZhdHazTzMzaaBvoETEK3ABsBLYB90fEVkm3S7oy67YR+LGkp4FHgI9FxI+nq2gz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2UL8QAjyA5e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_test = X_test.apply(pd.cut, bins = 2, labels= [1,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58oazlPq1DXM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_test = X_binarised_test.values\n",
        "X_binarised_train = X_binarised_train.values\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwIFcxt41DTz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "21aa8f8b-9268-4888-e0e4-26740b06f1d9"
      },
      "source": [
        "type(X_binarised_train)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWu2DC9M1WU6",
        "colab_type": "text"
      },
      "source": [
        "#MP neuron model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qz9RbEP_3Hxr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5mIlKqH1DSO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "553e36d8-9937-484c-af03-a25d6e27fb09"
      },
      "source": [
        "b = 3\n",
        "\n",
        "i = randint(0, X_binarised_train.shape[0])\n",
        "\n",
        "print('For row ',i)\n",
        "if (np.sum(X_binarised_train[100, :]) >= b):\n",
        "  print('MP Neuron inference is malignant')\n",
        "else:\n",
        "  print('MP Neuron inference is benign')\n",
        "\n",
        "if (Y_train[i] == 1):\n",
        "  print(\"MALI\")\n",
        "else:\n",
        "  print(\"begign\")"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For row  293\n",
            "MP Neuron inference is benign\n",
            "MALI\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCYo9OWN1DO7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "174321e1-a6b4-493b-8511-eabde9d74c45"
      },
      "source": [
        "b = 3\n",
        "\n",
        "Y_pred_train = []\n",
        "accuracy_rows = 0\n",
        "for x,y in zip(X_binarised_train,Y_train):\n",
        "  Y_pred = (np.sum(x) >= b)\n",
        "  Y_pred_train.append(Y_pred)\n",
        "  accuracy_rows += (y==Y_pred)\n",
        "\n",
        "print(accuracy_rows, accuracy_rows/X_binarised_train.shape[0])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "77 0.150390625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-ikJFEd1DNZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "outputId": "2c643477-5778-4240-c6b1-60f5cecbcefb"
      },
      "source": [
        "for b in range(X_binarised_train.shape[1]+1):\n",
        "  Y_pred_train = []\n",
        "  accuracy_rows = 0\n",
        "  for x,y in zip(X_binarised_train,Y_train):\n",
        "    Y_pred = (np.sum(x) >= b)\n",
        "    Y_pred_train.append(Y_pred)\n",
        "    accuracy_rows += (y==Y_pred)\n",
        "\n",
        "  print(b, accuracy_rows/X_binarised_train.shape[0])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.626953125\n",
            "1 0.626953125\n",
            "2 0.626953125\n",
            "3 0.626953125\n",
            "4 0.626953125\n",
            "5 0.626953125\n",
            "6 0.626953125\n",
            "7 0.626953125\n",
            "8 0.626953125\n",
            "9 0.626953125\n",
            "10 0.626953125\n",
            "11 0.626953125\n",
            "12 0.62890625\n",
            "13 0.634765625\n",
            "14 0.634765625\n",
            "15 0.638671875\n",
            "16 0.646484375\n",
            "17 0.65625\n",
            "18 0.66015625\n",
            "19 0.66796875\n",
            "20 0.67578125\n",
            "21 0.69140625\n",
            "22 0.71484375\n",
            "23 0.736328125\n",
            "24 0.763671875\n",
            "25 0.791015625\n",
            "26 0.82421875\n",
            "27 0.849609375\n",
            "28 0.849609375\n",
            "29 0.8125\n",
            "30 0.7421875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWOkGqYC7HBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Py8bMDw1DKH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "71e7c388-71f8-4946-a029-1f32a4fc7c90"
      },
      "source": [
        "b = 21\n",
        "Y_pred_test = []\n",
        "accuracy_rows = 0\n",
        "for x in X_binarised_test:\n",
        "  Y_pred = (np.sum(x) >= b)\n",
        "  Y_pred_test.append(Y_pred)\n",
        "    \n",
        "accuracy = accuracy_score(Y_pred_test, Y_test)\n",
        "print(b, accuracy)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "21 0.8421052631578947\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S2-Cbt68H4o",
        "colab_type": "text"
      },
      "source": [
        "#MP Neuron class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvuThGQX645P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MPNeuron:\n",
        "  def __init__(self):\n",
        "    self.b = None\n",
        "\n",
        "  def model(self, x):\n",
        "    return(sum(x) >= self.b)\n",
        "\n",
        "  def predict(self, X):\n",
        "    Y = []\n",
        "    for x in X:\n",
        "      result = self.model(x)\n",
        "      Y.append(result)\n",
        "    return np.array(Y)\n",
        "  def fit(self, X,Y):\n",
        "    accuracy = {}\n",
        "    for b in range(X.shape[1] + 1):\n",
        "      self.b = b\n",
        "      y_pred = self.predict(X)\n",
        "      accuracy[b] = accuracy_score(y_pred, Y)\n",
        "\n",
        "    best_b = max(accuracy, key = accuracy.get)\n",
        "    self.b = best_b\n",
        "    print('Optimal value of b is', best_b)\n",
        "    print('Highest accuracy is', accuracy[best_b])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NZZAuOa641i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b0ba0251-a6f8-44a2-8160-05e514f8f60a"
      },
      "source": [
        "mp_neuron = MPNeuron()\n",
        "\n",
        "mp_neuron.fit(X_binarised_train, Y_train)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal value of b is 27\n",
            "Highest accuracy is 0.849609375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_gNBwJq64yw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cb940c5d-58e2-4843-dd8b-353b84b968cc"
      },
      "source": [
        "type(Y_train)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9eJjQvp64wZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_test_pred = mp_neuron.predict(X_binarised_test)\n",
        "\n",
        "accuracy_test = accuracy_score(Y_test_pred, Y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouZxueHG64uG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "af7ece77-09d6-4269-eff0-9e58ec744689"
      },
      "source": [
        "print(accuracy_test)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7894736842105263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpldZ6hB64sN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XedZHTVO64o-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFwQwFxQ64li",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-s2d_9eZ64iB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3YiI80y1DG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbxJoIso1DFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqYjnN831DAh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}