ANN_Classifier.ipynb

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    "colab": {
      "name": "ANN Classifier",
      "provenance": [],
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/subhomoy-roy-choudhury/python-projects/blob/master/ANN_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mv52nuDcC0Hv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgoDgMwMGwIV",
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          "height": 35
        },
        "outputId": "d3ca8c96-4c21-4867-a36c-849e88e95efb"
      },
      "source": [
        " tf.__version__"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "bc03d081-c18d-4f61-9694-55dfa78d8023"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-445092b4-6d94-4c2d-9a7e-03a25d7513ec\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-445092b4-6d94-4c2d-9a7e-03a25d7513ec\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving datasets_180_408_data.csv to datasets_180_408_data (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3QtJaCFJB3k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "39e0390f-c78f-4981-b25c-1f783e624f33"
      },
      "source": [
        "dataset = pd.read_csv('datasets_180_408_data.csv') \n",
        "x = dataset.iloc[:,2:] \n",
        "y = dataset.iloc[:, 1].values\n",
        "x"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</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>0.30010</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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  texture_mean  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0          17.99         10.38  ...                  0.11890          NaN\n",
              "1          20.57         17.77  ...                  0.08902          NaN\n",
              "2          19.69         21.25  ...                  0.08758          NaN\n",
              "3          11.42         20.38  ...                  0.17300          NaN\n",
              "4          20.29         14.34  ...                  0.07678          NaN\n",
              "..           ...           ...  ...                      ...          ...\n",
              "564        21.56         22.39  ...                  0.07115          NaN\n",
              "565        20.13         28.25  ...                  0.06637          NaN\n",
              "566        16.60         28.08  ...                  0.07820          NaN\n",
              "567        20.60         29.33  ...                  0.12400          NaN\n",
              "568         7.76         24.54  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuRdsP9oixsG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "outputId": "697a4e5b-944b-40eb-d80b-d0dbc7b2ef20"
      },
      "source": [
        "y"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
              "       'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'M', 'M',\n",
              "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M',\n",
              "       'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B',\n",
              "       'B', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B',\n",
              "       'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'M',\n",
              "       'B', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'M', 'M',\n",
              "       'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M',\n",
              "       'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M',\n",
              "       'B', 'M', 'M', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'B', 'M',\n",
              "       'B', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'B', 'B', 'M', 'M', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M',\n",
              "       'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B',\n",
              "       'B', 'B', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
              "       'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M',\n",
              "       'B', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
              "       'B', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
              "       'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'M', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'M', 'B', 'B', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M',\n",
              "       'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
              "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'M', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M',\n",
              "       'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
              "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'M', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
              "       'B', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'B'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5F8fvUwPUYtD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "e352e71a-05e2-4191-9f70-a9c5d0ffe3af"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)\n",
        "y"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IncqzIRC6PRe",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Cr8_qr6Smqu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_ATzYoDsSZv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "cf7d29a2-5e37-4b8f-af53-83fb762e95ec"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:765: RuntimeWarning: invalid value encountered in true_divide\n",
            "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:706: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
            "  result = op(x, *args, **kwargs)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgsdAzi8X0-s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "7493a816-d11e-4743-8ab8-0f71530156ea"
      },
      "source": [
        "x_train"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.15036482, -0.39064196, -1.12855021, ..., -0.01614761,\n",
              "        -0.38503402,         nan],\n",
              "       [-0.93798972,  0.68051405, -0.94820146, ...,  0.09669004,\n",
              "        -0.38615797,         nan],\n",
              "       [ 0.574121  , -1.03333557,  0.51394098, ..., -0.20050207,\n",
              "        -0.75144254,         nan],\n",
              "       ...,\n",
              "       [-1.32422924, -0.20048168, -1.31754581, ..., -0.71542314,\n",
              "        -0.11978123,         nan],\n",
              "       [-1.24380987, -0.2245526 , -1.28007609, ..., -1.58157125,\n",
              "        -1.00601779,         nan],\n",
              "       [-0.73694129,  1.14989702, -0.71226578, ..., -1.25895095,\n",
              "         0.21515662,         nan]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmkX6sTK-wmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann = tf.keras.models.Sequential()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEA0AGbZzEUR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhnhX7Jj-5MI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "am7hXhOA-8ew",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann.add(tf.keras.layers.Dense(units=1))"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6b1aOBS_BgB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ann.compile(optimizer = 'adam', loss = 'mean_squared_error')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4BP93cz_ENh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b42020da-c89d-4719-e361-a38891ba3f11"
      },
      "source": [
        "ann.fit(x_train, y_train, batch_size = 32, epochs = 100)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.3581\n",
            "Epoch 2/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3480\n",
            "Epoch 3/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3386\n",
            "Epoch 4/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3298\n",
            "Epoch 5/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3214\n",
            "Epoch 6/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3135\n",
            "Epoch 7/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3062\n",
            "Epoch 8/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2992\n",
            "Epoch 9/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2931\n",
            "Epoch 10/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2874\n",
            "Epoch 11/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2820\n",
            "Epoch 12/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2773\n",
            "Epoch 13/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2726\n",
            "Epoch 14/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2686\n",
            "Epoch 15/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2646\n",
            "Epoch 16/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2611\n",
            "Epoch 17/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2578\n",
            "Epoch 18/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2549\n",
            "Epoch 19/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2521\n",
            "Epoch 20/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2498\n",
            "Epoch 21/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2476\n",
            "Epoch 22/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2457\n",
            "Epoch 23/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2441\n",
            "Epoch 24/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2424\n",
            "Epoch 25/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2410\n",
            "Epoch 26/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2399\n",
            "Epoch 27/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2387\n",
            "Epoch 28/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2377\n",
            "Epoch 29/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2369\n",
            "Epoch 30/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2362\n",
            "Epoch 31/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2356\n",
            "Epoch 32/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2350\n",
            "Epoch 33/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2345\n",
            "Epoch 34/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2341\n",
            "Epoch 35/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2335\n",
            "Epoch 36/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2331\n",
            "Epoch 37/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2329\n",
            "Epoch 38/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2325\n",
            "Epoch 39/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2323\n",
            "Epoch 40/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2320\n",
            "Epoch 41/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2319\n",
            "Epoch 42/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2318\n",
            "Epoch 43/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2317\n",
            "Epoch 44/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2316\n",
            "Epoch 45/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2315\n",
            "Epoch 46/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2314\n",
            "Epoch 47/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2315\n",
            "Epoch 48/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2313\n",
            "Epoch 49/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2313\n",
            "Epoch 50/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2313\n",
            "Epoch 51/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 52/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2313\n",
            "Epoch 53/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 54/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 55/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 56/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 57/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 58/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 59/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 60/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 61/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 62/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 63/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 64/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 65/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 66/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2311\n",
            "Epoch 67/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 68/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2311\n",
            "Epoch 69/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2311\n",
            "Epoch 70/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 71/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 72/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 73/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 74/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 75/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 76/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 77/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 78/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 79/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 80/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 81/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 82/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 83/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 84/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 85/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 86/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 87/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 88/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2311\n",
            "Epoch 89/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2311\n",
            "Epoch 90/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 91/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.2312\n",
            "Epoch 92/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 93/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 94/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 95/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 96/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2311\n",
            "Epoch 97/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 98/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 99/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n",
            "Epoch 100/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2312\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f063c1cf080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rw_z66rvC0DH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e994c635-5d45-4c3d-bbb1-44c66464bfea"
      },
      "source": [
        "ann.fit(x_test, y_test, batch_size = 32, epochs = 100)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2443\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2443\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2442\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2441\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2440\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2439\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2438\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2438\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2436\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2436\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2435\n",
            "Epoch 12/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2434\n",
            "Epoch 13/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2434\n",
            "Epoch 14/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2433\n",
            "Epoch 15/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2433\n",
            "Epoch 16/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2432\n",
            "Epoch 17/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2431\n",
            "Epoch 18/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.2431\n",
            "Epoch 19/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2430\n",
            "Epoch 20/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2430\n",
            "Epoch 21/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2429\n",
            "Epoch 22/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2429\n",
            "Epoch 23/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2428\n",
            "Epoch 24/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2428\n",
            "Epoch 25/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2427\n",
            "Epoch 26/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2427\n",
            "Epoch 27/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2427\n",
            "Epoch 28/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2427\n",
            "Epoch 29/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2426\n",
            "Epoch 30/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2426\n",
            "Epoch 31/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2426\n",
            "Epoch 32/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2426\n",
            "Epoch 33/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 34/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 35/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2425\n",
            "Epoch 36/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 37/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 38/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 39/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 40/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 41/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2425\n",
            "Epoch 42/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 43/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 44/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 45/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 46/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2424\n",
            "Epoch 47/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 48/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 49/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 50/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 51/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 52/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 53/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 54/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2424\n",
            "Epoch 55/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 56/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 57/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 58/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 59/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 60/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 61/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 62/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2424\n",
            "Epoch 63/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2424\n",
            "Epoch 64/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 65/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 66/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 67/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.2423\n",
            "Epoch 68/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2423\n",
            "Epoch 69/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2424\n",
            "Epoch 70/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 71/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 72/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2423\n",
            "Epoch 73/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 74/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 75/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 76/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2423\n",
            "Epoch 77/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 78/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 79/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 80/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 81/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 82/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 83/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 84/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 85/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2423\n",
            "Epoch 86/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 87/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 88/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 89/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 90/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 91/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 92/100\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2423\n",
            "Epoch 93/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 94/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 95/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 96/100\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2423\n",
            "Epoch 97/100\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2423\n",
            "Epoch 98/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 99/100\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2423\n",
            "Epoch 100/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.2424\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f063c10d278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rl_x9qqPKvAM",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Yw8mSFSZFpN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=ann.predict(x_test)\n",
        "y_pred=(y_test>0.5)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eiz3gk9b_EMW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "97d1a599-16de-4e7e-8ec7-b62fba352f46"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[67  0]\n",
            " [ 0 47]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        67\n",
            "           1       1.00      1.00      1.00        47\n",
            "\n",
            "    accuracy                           1.00       114\n",
            "   macro avg       1.00      1.00      1.00       114\n",
            "weighted avg       1.00      1.00      1.00       114\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}