McCulloch_pitts_model.ipynb

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    "colab": {
      "name": "McCulloch-pitts model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Shibu Mohapatra\n",
        "\n",
        "Artificial Intelligence"
      ],
      "metadata": {
        "id": "IDGVUX9hlWk6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Implement McCulloch-pitts model of Neuron.**"
      ],
      "metadata": {
        "id": "vSlHf95nLVqk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.datasets\n",
        "\n",
        "breast_cancer= sklearn.datasets.load_breast_cancer()\n",
        "print(breast_cancer.data)"
      ],
      "metadata": {
        "id": "IcB4HRSwMXIx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a2cfe74-e2df-47ce-a111-f50fd97aa701"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBHM187tMdvp",
        "outputId": "8d8fd37c-9d11-4402-893c-e3a50d68100a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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 0 0 0 0 0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer.target_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11q9HaGAMgu4",
        "outputId": "627200c8-d8dc-4adb-955e-198af05ab42b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['malignant' 'benign']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(breast_cancer.feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7kIk1E9Mh59",
        "outputId": "85629fb1-d7d5-4b4a-f0c5-884107f0fcad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data= pd.DataFrame(breast_cancer.data,columns= breast_cancer.feature_names)\n",
        "data['class']= breast_cancer.target\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "2BSK5cdFMqWf",
        "outputId": "65c4fc62-ad06-4d03-b03a-22895d6cd8fd"
      },
      "execution_count": null,
      "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",
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              "      <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",
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              "  <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>0.30010</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>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</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>0.08690</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>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</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>0.19740</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>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</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>0.24140</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>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</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>0.19800</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>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</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",
              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d28d904-27c4-426e-99fe-1350a2f4a464')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d28d904-27c4-426e-99fe-1350a2f4a464 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d28d904-27c4-426e-99fe-1350a2f4a464');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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",
              "564        21.56         22.39  ...                  0.07115      0\n",
              "565        20.13         28.25  ...                  0.06637      0\n",
              "566        16.60         28.08  ...                  0.07820      0\n",
              "567        20.60         29.33  ...                  0.12400      0\n",
              "568         7.76         24.54  ...                  0.07039      1\n",
              "\n",
              "[569 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "i0TYtj73Ms-k",
        "outputId": "1d335d8d-b16a-4302-9620-0a755a5ac933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1d3763f2-0813-4d3e-bed2-c541de25e180\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d3763f2-0813-4d3e-bed2-c541de25e180')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d3763f2-0813-4d3e-bed2-c541de25e180 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d3763f2-0813-4d3e-bed2-c541de25e180');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data['class'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCj7vmywMvb_",
        "outputId": "2515f5c8-a50a-44e6-8808-c9519c39019f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    357\n",
            "0    212\n",
            "Name: class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('class').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "N9it0fYNMwtY",
        "outputId": "166bda5e-97ff-498d-af9d-2c533c7a78eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8c289db0-d2ef-4a5b-9be9-c0388b89d211\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c289db0-d2ef-4a5b-9be9-c0388b89d211')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c289db0-d2ef-4a5b-9be9-c0388b89d211 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c289db0-d2ef-4a5b-9be9-c0388b89d211');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train_Test_Split**"
      ],
      "metadata": {
        "id": "HyT1H7JFMzu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X= data.drop('class',axis =1) #Load everything except class column(axis = 1)\n",
        "Y=data['class']\n",
        "X_train, X_test, Y_train, Y_test= train_test_split(X, Y, test_size=.15, random_state=1, stratify= Y) \n",
        "#Split in 85-15 ratio\n",
        "#random_state to ensure same split in every run\n",
        "#stratify to avoid unequal distribution of class among test and train\n",
        "\n",
        "print(Y.mean(), Y_train.mean(), Y_test.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtLo029CMymL",
        "outputId": "8943a0a8-c73d-481d-bd97-54db9162e2c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6274165202108963 0.6273291925465838 0.627906976744186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Binarization of input**"
      ],
      "metadata": {
        "id": "fYib9QmUM9VY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(X_train.T,'.')\n",
        "plt.xticks(rotation='vertical') #show xlabels vertically\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "F9gn2rS6M7wy",
        "outputId": "2031a0d5-2040-4278-c28a-23cee6ba7466"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert features values into Binary \n",
        "X_train= X_train.apply(pd.cut,bins=2,labels=[1,0])\n",
        "X_test= X_test.apply(pd.cut,bins=2,labels=[1,0])"
      ],
      "metadata": {
        "id": "GZonbm7fNB8t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train.T,'.')\n",
        "plt.xticks(rotation='vertical') #show xlabels vertically\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "lSd0tnz-NDPZ",
        "outputId": "dfe5796d-eb2c-4ac0-be71-10a2dc1c550b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pandas dataframe to Numpy arrays for faster calculations\n",
        "X_train= X_train.values\n",
        "X_test= X_test.values\n",
        "print(type(X_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EvzoAn3jNGmy",
        "outputId": "fa93bc65-30df-423a-8482-0c69b4143a1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model**"
      ],
      "metadata": {
        "id": "XE72e2KrNHlA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNdiNEv1NIgA",
        "outputId": "0afabce8-7c6a-4869-9ece-9754e0834ec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 1 1 ... 1 1 1]\n",
            " [1 1 1 ... 1 1 1]\n",
            " [1 1 1 ... 0 1 1]\n",
            " ...\n",
            " [1 1 1 ... 1 1 1]\n",
            " [1 1 1 ... 1 1 1]\n",
            " [0 1 0 ... 0 1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ff-XB2hhNLYl",
        "outputId": "7ba5bc2f-6e23-4bb1-aee0-3647f944edb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(483, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores={} #stores accuracy with value of b as key\n",
        "for b in range(X_train.shape[1] +1): #b can range between 0 - no. of features\n",
        "    y_pred_train=[]\n",
        "    for x in X_train:\n",
        "        y_pred= np.sum(x) >= b\n",
        "        y_pred_train.append(y_pred)\n",
        "    accuracy= accuracy_score(y_pred_train,Y_train)\n",
        "    scores[b]=accuracy\n",
        "\n",
        "print(scores)\n",
        "b=max(scores,key=scores.get) #highest accuracy key i.e value of b\n",
        "print(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jfiR-aTNNo9",
        "outputId": "58d98479-625b-459d-c6ea-cf0a0e89562b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 0.6273291925465838, 1: 0.6273291925465838, 2: 0.6273291925465838, 3: 0.6273291925465838, 4: 0.6273291925465838, 5: 0.6273291925465838, 6: 0.6273291925465838, 7: 0.6273291925465838, 8: 0.6273291925465838, 9: 0.6273291925465838, 10: 0.6273291925465838, 11: 0.6273291925465838, 12: 0.6273291925465838, 13: 0.629399585921325, 14: 0.6314699792960663, 15: 0.6335403726708074, 16: 0.6438923395445134, 17: 0.650103519668737, 18: 0.6583850931677019, 19: 0.6666666666666666, 20: 0.6749482401656315, 21: 0.6915113871635611, 22: 0.7018633540372671, 23: 0.7246376811594203, 24: 0.7556935817805382, 25: 0.7888198757763976, 26: 0.8198757763975155, 27: 0.8426501035196687, 28: 0.8426501035196687, 29: 0.8136645962732919, 30: 0.7515527950310559}\n",
            "27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test=[]\n",
        "for x in X_test:\n",
        "    y_pred= np.sum(x) >= b\n",
        "    y_pred_test.append(y_pred)\n",
        "accuracy=accuracy_score(y_pred_test,Y_test)\n",
        "print(\"b: \",b)\n",
        "print(\"Accuracy: \",accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFWGXlLZNP1u",
        "outputId": "4c7dade1-5574-4ea8-97e9-3c4cd9945dc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b:  27\n",
            "Accuracy:  0.8604651162790697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MP Neuron**"
      ],
      "metadata": {
        "id": "zA61O-xiNRlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MPNeuron:\n",
        "\n",
        "    def __init__(self):\n",
        "        self.b=None\n",
        "\n",
        "    def model(self,x):\n",
        "        return np.sum(x) >=self.b\n",
        "\n",
        "    def predict(self,X):\n",
        "        y=[]\n",
        "        for x in X:\n",
        "            y_pred= self.model(x)\n",
        "            y.append(y_pred)\n",
        "        return np.array(y)\n",
        "\n",
        "    def fit(self,X):\n",
        "        scores={}\n",
        "        for b in range(X.shape[1] +1):\n",
        "            self.b= b\n",
        "            Y_pred= self.predict(X)\n",
        "            scores[b]=accuracy_score(Y_train,Y_pred)\n",
        "        self.b= max(scores, key=scores.get)\n",
        "        print(\"Value of b: \",self.b)\n",
        "mp_neuron= MPNeuron()\n",
        "mp_neuron.fit(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXBB1H_1NSPV",
        "outputId": "fcb09aeb-e1b9-4329-c55f-ceaecbbfe0a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value of b:  27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred= mp_neuron.predict(X_test)\n",
        "accuracy= accuracy_score(Y_test,Y_pred)\n",
        "print(\"Accuracy: \",accuracy)\n",
        "#print(f\"Accuracy: {accuracy*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cx9q_ieMNWPk",
        "outputId": "e955bcf0-144b-4943-e735-b553bb0cc8a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8604651162790697\n"
          ]
        }
      ]
    }
  ]
}