Keras_Diabetes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Keras - Diabetes.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xq57hNKb8l6U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0KX7Vm7878L",
        "colab_type": "code",
        "outputId": "1def0684-6732-4a0f-f7e9-bd341c8ae48e",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-99d2845f-3e3e-457a-a35a-58d406b038ab\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-99d2845f-3e3e-457a-a35a-58d406b038ab\">\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": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-21dc3c638f66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     62\u001b[0m   result = _output.eval_js(\n\u001b[1;32m     63\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m---> 64\u001b[0;31m           input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m     65\u001b[0m   \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result)\u001b[0m\n\u001b[1;32m     37\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PODD7vco9Tp8",
        "colab_type": "code",
        "outputId": "1576364a-08e8-47bf-dc80-04b2a983c72b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "dataset = pd.read_csv(\"pima-indians-diabetes.data.csv\")\n",
        "dataset.head()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>6</th>\n",
              "      <th>148</th>\n",
              "      <th>72</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <th>33.6</th>\n",
              "      <th>0.627</th>\n",
              "      <th>50</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
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              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>35</td>\n",
              "      <td>168</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
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              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   6  148  72  35    0  33.6  0.627  50  1\n",
              "0  1   85  66  29    0  26.6  0.351  31  0\n",
              "1  8  183  64   0    0  23.3  0.672  32  1\n",
              "2  1   89  66  23   94  28.1  0.167  21  0\n",
              "3  0  137  40  35  168  43.1  2.288  33  1\n",
              "4  5  116  74   0    0  25.6  0.201  30  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pow3ZpnL9rVy",
        "colab_type": "text"
      },
      "source": [
        "There are 768 observations with 8 input variables and 1 output variable.\n",
        "\n",
        "The variable names are as follows:\n",
        "\n",
        "    Number of times pregnant.\n",
        "    Plasma glucose concentration a 2 hours in an oral glucose tolerance test.\n",
        "    Diastolic blood pressure (mm Hg).\n",
        "    Triceps skinfold thickness (mm).\n",
        "    2-Hour serum insulin (mu U/ml).\n",
        "    Body mass index (weight in kg/(height in m)²).\n",
        "    Diabetes pedigree function.\n",
        "    Age (years).\n",
        "    Class variable (0 or 1)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAKRTsDk9s7C",
        "colab_type": "code",
        "outputId": "a39985dd-bc5f-4ba8-cbe2-ac4699a9cdfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "dataset.describe(include='all')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>6</th>\n",
              "      <th>148</th>\n",
              "      <th>72</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <th>33.6</th>\n",
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              "      <th>50</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>767.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.842243</td>\n",
              "      <td>120.859192</td>\n",
              "      <td>69.101695</td>\n",
              "      <td>20.517601</td>\n",
              "      <td>79.903520</td>\n",
              "      <td>31.990482</td>\n",
              "      <td>0.471674</td>\n",
              "      <td>33.219035</td>\n",
              "      <td>0.348110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.370877</td>\n",
              "      <td>31.978468</td>\n",
              "      <td>19.368155</td>\n",
              "      <td>15.954059</td>\n",
              "      <td>115.283105</td>\n",
              "      <td>7.889091</td>\n",
              "      <td>0.331497</td>\n",
              "      <td>11.752296</td>\n",
              "      <td>0.476682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.371000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.500000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                6         148          72  ...       0.627          50           1\n",
              "count  767.000000  767.000000  767.000000  ...  767.000000  767.000000  767.000000\n",
              "mean     3.842243  120.859192   69.101695  ...    0.471674   33.219035    0.348110\n",
              "std      3.370877   31.978468   19.368155  ...    0.331497   11.752296    0.476682\n",
              "min      0.000000    0.000000    0.000000  ...    0.078000   21.000000    0.000000\n",
              "25%      1.000000   99.000000   62.000000  ...    0.243500   24.000000    0.000000\n",
              "50%      3.000000  117.000000   72.000000  ...    0.371000   29.000000    0.000000\n",
              "75%      6.000000  140.000000   80.000000  ...    0.625000   41.000000    1.000000\n",
              "max     17.000000  199.000000  122.000000  ...    2.420000   81.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJEES3ss9wai",
        "colab_type": "code",
        "outputId": "2758da1a-510b-42fd-e296-a978d979b924",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "sns.heatmap(dataset.corr(), annot=True)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f885c0007b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LlkFpaEBTjxZc97uiV96SpsYYFNOOWZuYl6KqSw3CAkNylL178y6OFR2wK2uL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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R13ym-c1-FnW",
        "colab_type": "text"
      },
      "source": [
        "We see that all feature have some relationship with Class so we keep all of them. Plasma glucose has the strongest relationship with Class(a person having diabetes or not). Age and Body Mass Index are also strong influencers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GdgxoeF-ILv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X= dataset.iloc[:,0:8]\n",
        "y= dataset.iloc[:,8]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RBkrjOH-LKR",
        "colab_type": "code",
        "outputId": "96c7297c-d120-4b47-fad9-dc39edae0c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "source": [
        "X.head(2)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>6</th>\n",
              "      <th>148</th>\n",
              "      <th>72</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <th>33.6</th>\n",
              "      <th>0.627</th>\n",
              "      <th>50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   6  148  72  35  0  33.6  0.627  50\n",
              "0  1   85  66  29  0  26.6  0.351  31\n",
              "1  8  183  64   0  0  23.3  0.672  32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kQBBPUp-MtH",
        "colab_type": "code",
        "outputId": "e0be0178-d48b-4c4b-f57a-c15d74ae6793",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)\n",
        "X"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.84372629, -1.12208597, -0.16024856, ..., -0.68372895,\n",
              "        -0.36426474, -0.18894038],\n",
              "       [ 1.23423997,  1.94447577, -0.26357823, ..., -1.10230105,\n",
              "         0.60470064, -0.1037951 ],\n",
              "       [-0.84372629, -0.99692019, -0.16024856, ..., -0.49346891,\n",
              "        -0.91968415, -1.0403932 ],\n",
              "       ...,\n",
              "       [ 0.343683  ,  0.0044061 ,  0.14974046, ..., -0.73446496,\n",
              "        -0.68423462, -0.27408566],\n",
              "       [-0.84372629,  0.16086333, -0.47023757, ..., -0.23978884,\n",
              "        -0.37030191,  1.17338414],\n",
              "       [-0.84372629, -0.8717544 ,  0.04641078, ..., -0.20173684,\n",
              "        -0.47293375, -0.87010264]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTRiiUYl-PC0",
        "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=0.3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HP0H93pR-RyY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Activation\n",
        "from keras.layers.normalization import BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo8ZUUmn-YSg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(8,kernel_initializer=\"uniform\",input_dim=8))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "#Second Hidden Layer\n",
        "model.add(Dense(6,kernel_initializer='uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "#Third Hidden Layer\n",
        "model.add(Dense(4,kernel_initializer='uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "#Output Layer\n",
        "model.add(Dense(1,kernel_initializer='uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aMUZmN3_J5i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJf0OMRt_XQ5",
        "colab_type": "code",
        "outputId": "9566f64c-d179-4c21-b297-a53a2c98e989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        }
      },
      "source": [
        "history = model.fit(X_train, y_train,\n",
        "          batch_size=20,\n",
        "          epochs=20,\n",
        "          verbose=2,\n",
        "validation_data=(X_test, y_test))\n",
        "\n",
        "score = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "print('\\nTest loss:', score[0] * 100)\n",
        "print('\\n\\nTest accuracy:', score[1] * 100)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 536 samples, validate on 231 samples\n",
            "Epoch 1/20\n",
            " - 0s - loss: 0.5769 - acc: 0.7276 - val_loss: 0.4895 - val_acc: 0.7662\n",
            "Epoch 2/20\n",
            " - 0s - loss: 0.5418 - acc: 0.7463 - val_loss: 0.4892 - val_acc: 0.7792\n",
            "Epoch 3/20\n",
            " - 0s - loss: 0.5559 - acc: 0.7257 - val_loss: 0.4927 - val_acc: 0.7706\n",
            "Epoch 4/20\n",
            " - 0s - loss: 0.5756 - acc: 0.7090 - val_loss: 0.4914 - val_acc: 0.7835\n",
            "Epoch 5/20\n",
            " - 0s - loss: 0.5601 - acc: 0.7183 - val_loss: 0.4848 - val_acc: 0.7879\n",
            "Epoch 6/20\n",
            " - 0s - loss: 0.5763 - acc: 0.7146 - val_loss: 0.4830 - val_acc: 0.8009\n",
            "Epoch 7/20\n",
            " - 0s - loss: 0.5590 - acc: 0.7183 - val_loss: 0.4865 - val_acc: 0.7965\n",
            "Epoch 8/20\n",
            " - 0s - loss: 0.5410 - acc: 0.7407 - val_loss: 0.4877 - val_acc: 0.7922\n",
            "Epoch 9/20\n",
            " - 0s - loss: 0.5613 - acc: 0.7090 - val_loss: 0.4869 - val_acc: 0.7922\n",
            "Epoch 10/20\n",
            " - 0s - loss: 0.5642 - acc: 0.7052 - val_loss: 0.4901 - val_acc: 0.7879\n",
            "Epoch 11/20\n",
            " - 0s - loss: 0.5543 - acc: 0.7313 - val_loss: 0.4904 - val_acc: 0.7922\n",
            "Epoch 12/20\n",
            " - 0s - loss: 0.5830 - acc: 0.6866 - val_loss: 0.4876 - val_acc: 0.7922\n",
            "Epoch 13/20\n",
            " - 0s - loss: 0.5647 - acc: 0.7425 - val_loss: 0.4931 - val_acc: 0.7792\n",
            "Epoch 14/20\n",
            " - 0s - loss: 0.5571 - acc: 0.7201 - val_loss: 0.4944 - val_acc: 0.7749\n",
            "Epoch 15/20\n",
            " - 0s - loss: 0.5473 - acc: 0.7500 - val_loss: 0.4967 - val_acc: 0.7922\n",
            "Epoch 16/20\n",
            " - 0s - loss: 0.5585 - acc: 0.7052 - val_loss: 0.4945 - val_acc: 0.7879\n",
            "Epoch 17/20\n",
            " - 0s - loss: 0.5682 - acc: 0.7276 - val_loss: 0.4904 - val_acc: 0.7965\n",
            "Epoch 18/20\n",
            " - 0s - loss: 0.5544 - acc: 0.7257 - val_loss: 0.4933 - val_acc: 0.7879\n",
            "Epoch 19/20\n",
            " - 0s - loss: 0.5719 - acc: 0.7164 - val_loss: 0.4929 - val_acc: 0.7965\n",
            "Epoch 20/20\n",
            " - 0s - loss: 0.5621 - acc: 0.7127 - val_loss: 0.4945 - val_acc: 0.8052\n",
            "231/231 [==============================] - 0s 96us/step\n",
            "\n",
            "Test loss: 49.44874159185401\n",
            "\n",
            "\n",
            "Test accuracy: 80.51948059688915\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5p19KqyW_poH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=model.predict(X_test)\n",
        "y_pred =(y_pred>0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlYy5jOG_2k1",
        "colab_type": "code",
        "outputId": "b013f7d8-8d05-4bc7-ab19-4bce2d453c67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Error vs Iteration')\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss over Epochs')\n",
        "plt.legend(['Train Set','Test Set'],loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LqzlljoWjQD8fZo2N0H4LL1q8eDGvvPIKZWXWCZDl5eUcOXKE0lLrsOUrr7ySNWvWsGPH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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq1ICSVc_zbY",
        "colab_type": "code",
        "outputId": "f9b78d04-ac93-498d-cc82-a125aef2bfc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[149   8]\n",
            " [ 43  31]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMnMhBaXgd6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}