Predicting_Diabetes_with_Multilayer_Perceptrons.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Predicting Diabetes with Multilayer Perceptrons.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jnkppdQzixg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from IPython.display import display\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ru8ppDx_1oUu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = 'xxxx'\n",
        "os.environ['KAGGLE_KEY'] = 'xxxx'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3OFj5fh8Kp3",
        "colab_type": "text"
      },
      "source": [
        "## **Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssDtmKSyz0hB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7aeaafb8-4c45-4649-dfde-f33303f2d33b"
      },
      "source": [
        "!kaggle datasets download -d uciml/pima-indians-diabetes-database"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pima-indians-diabetes-database.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2v5pGFtz9pK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8262d4ca-2e07-4279-8645-070372e3bd9d"
      },
      "source": [
        "!unzip pima-indians-diabetes-database.zip"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  pima-indians-diabetes-database.zip\n",
            "replace diabetes.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PMtSgrF8XCN",
        "colab_type": "text"
      },
      "source": [
        "## **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qkpiBwg1Via",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('./diabetes.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HH1MTIGb1h8j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a9212a16-2488-4816-d399-41e348beef5a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 33,
      "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72  ...                     0.627   50        1\n",
              "1            1       85             66  ...                     0.351   31        0\n",
              "2            8      183             64  ...                     0.672   32        1\n",
              "3            1       89             66  ...                     0.167   21        0\n",
              "4            0      137             40  ...                     2.288   33        1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7olaskr2Sqy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "bbc800e7-2b7a-4ecf-9e5b-e0d4205ed8e7"
      },
      "source": [
        "df.hist()\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VsKZLmhrDLiCsfHW3pFOAOcAxMe4RYDBhJaz3gJMrvL/jOI5TBSodTeQrYTmO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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWPWDTUY5Iz5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba355260-0dce-45b8-ca47-c1c805ebaaf1"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# create a subplot of 3 x 3\n",
        "plt.subplots(3,3,figsize=(15,15))\n",
        "\n",
        "# Plot a density plot for each variable\n",
        "for idx, col in enumerate(df.columns):\n",
        "    ax = plt.subplot(3,3,idx+1)\n",
        "    ax.yaxis.set_ticklabels([])\n",
        "    sns.distplot(df.loc[df.Outcome == 0][col], hist=False, axlabel= False, \n",
        "    kde_kws={'linestyle':'-',  \n",
        "    'color':'black', 'label':\"No Diabetes\"})\n",
        "    sns.distplot(df.loc[df.Outcome == 1][col], hist=False, axlabel= False, \n",
        "    kde_kws={'linestyle':'--', \n",
        "    'color':'black', 'label':\"Diabetes\"})\n",
        "    ax.set_title(col)\n",
        "\n",
        "# Hide the 9th subplot (bottom right) since there are only 8 plots\n",
        "plt.subplot(3,3,9).set_visible(False)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
            "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  FAC1 = 2*(np.pi*bw/RANGE)**2\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
            "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \"Adding an axes using the same arguments as a previous axes \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vXr0Me8oOHTqEm2++2SIxkwVJKZv9z+DBg2V7UFpaKu3s7OSrr75q8nuys7Ml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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTb6TVvy8p6N",
        "colab_type": "text"
      },
      "source": [
        "## **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3igopkr8sNO",
        "colab_type": "text"
      },
      "source": [
        "###*Handling missing values*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WiQZYXa5jaj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "be47b559-e455-48b6-b1c8-e7529c634da0"
      },
      "source": [
        "df.isnull().any()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 False\n",
              "Glucose                     False\n",
              "BloodPressure               False\n",
              "SkinThickness               False\n",
              "Insulin                     False\n",
              "BMI                         False\n",
              "DiabetesPedigreeFunction    False\n",
              "Age                         False\n",
              "Outcome                     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMCPWk7X5qPt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8b1bdbcc-db2d-40c4-d845-f1bc63db7054"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 37,
      "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</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>0.243750</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>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</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>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</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": [
              "       Pregnancies     Glucose  ...         Age     Outcome\n",
              "count   768.000000  768.000000  ...  768.000000  768.000000\n",
              "mean      3.845052  120.894531  ...   33.240885    0.348958\n",
              "std       3.369578   31.972618  ...   11.760232    0.476951\n",
              "min       0.000000    0.000000  ...   21.000000    0.000000\n",
              "25%       1.000000   99.000000  ...   24.000000    0.000000\n",
              "50%       3.000000  117.000000  ...   29.000000    0.000000\n",
              "75%       6.000000  140.250000  ...   41.000000    1.000000\n",
              "max      17.000000  199.000000  ...   81.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "os1QDHAG5spu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "56916461-d8df-4708-a97f-980a976f1793"
      },
      "source": [
        "print(\"Number of rows with 0 values for each variable\")\n",
        "for col in df.columns:\n",
        "    missing_rows = df.loc[df[col]==0].shape[0]\n",
        "    print(col + \": \" + str(missing_rows))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows with 0 values for each variable\n",
            "Pregnancies: 111\n",
            "Glucose: 5\n",
            "BloodPressure: 35\n",
            "SkinThickness: 227\n",
            "Insulin: 374\n",
            "BMI: 11\n",
            "DiabetesPedigreeFunction: 0\n",
            "Age: 0\n",
            "Outcome: 500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqsMQDW857Xi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Glucose'] = df['Glucose'].replace(0, np.nan)\n",
        "df['BloodPressure'] = df['BloodPressure'].replace(0, np.nan)\n",
        "df['SkinThickness'] = df['SkinThickness'].replace(0, np.nan)\n",
        "df['Insulin'] = df['Insulin'].replace(0, np.nan)\n",
        "df['BMI'] = df['BMI'].replace(0, np.nan)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdIKg1nC6Bl5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "3183277b-38b8-4161-a283-5a016fa441e0"
      },
      "source": [
        "print(\"Number of rows with 0 values for each variable\")\n",
        "for col in df.columns:\n",
        "    missing_rows = df.loc[df[col]==0].shape[0]\n",
        "    print(col + \": \" + str(missing_rows))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows with 0 values for each variable\n",
            "Pregnancies: 111\n",
            "Glucose: 0\n",
            "BloodPressure: 0\n",
            "SkinThickness: 0\n",
            "Insulin: 0\n",
            "BMI: 0\n",
            "DiabetesPedigreeFunction: 0\n",
            "Age: 0\n",
            "Outcome: 500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60mzkpuE6ENe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Glucose'] = df['Glucose'].fillna(df['Glucose'].mean())\n",
        "df['BloodPressure'] = df['BloodPressure'].fillna(df['BloodPressure'].mean())\n",
        "df['SkinThickness'] = df['SkinThickness'].fillna(df['SkinThickness'].mean())\n",
        "df['Insulin'] = df['Insulin'].fillna(df['Insulin'].mean())\n",
        "df['BMI'] = df['BMI'].fillna(df['BMI'].mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nW_5FYuL86rt",
        "colab_type": "text"
      },
      "source": [
        "###*Data standardization*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rFMrMFHA6GoI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "df_scaled = preprocessing.scale(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HTV6C9s6H3C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_scaled = pd.DataFrame(df_scaled, columns=df.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMNY39iR6JuE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_scaled['Outcome'] = df['Outcome']\n",
        "df = df_scaled"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3gd7Scj6MGn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "60341966-1789-4040-906a-4498a328a627"
      },
      "source": [
        "df.describe().loc[['mean', 'std','max'],].round(2).abs()"
      ],
      "execution_count": 47,
      "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.91</td>\n",
              "      <td>2.54</td>\n",
              "      <td>4.1</td>\n",
              "      <td>7.95</td>\n",
              "      <td>8.13</td>\n",
              "      <td>5.04</td>\n",
              "      <td>5.88</td>\n",
              "      <td>4.06</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Pregnancies  Glucose  ...   Age  Outcome\n",
              "mean         0.00     0.00  ...  0.00     0.35\n",
              "std          1.00     1.00  ...  1.00     0.48\n",
              "max          3.91     2.54  ...  4.06     1.00\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCWRuVtD9K6g",
        "colab_type": "text"
      },
      "source": [
        "###*Splitting the data into training, testing, and validation sets*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfWmdLFZ6Qsz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.loc[:, df.columns != 'Outcome']\n",
        "y = df.loc[:, 'Outcome']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGejR6WL6TIl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q188U746V3L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3-RDbd69VoG",
        "colab_type": "text"
      },
      "source": [
        "## **Model Building in Python Using Keras**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwT9H3kW9azQ",
        "colab_type": "text"
      },
      "source": [
        "###*Model building*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2gotkOX6iGg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "a64fcdd9-db8a-4bd9-ed78-cd85f3f4ffca"
      },
      "source": [
        "from keras.models import Sequential\n",
        "\n",
        "model = Sequential()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbkniGp_6kKZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a54bdfd9-36be-4a5d-fc30-e8fb4d2b54e6"
      },
      "source": [
        "from keras.layers import Dense\n",
        "# Add the first hidden layer\n",
        "model.add(Dense(32, activation='relu', input_dim=8))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMDn_ETP6nEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add the second hidden layer\n",
        "model.add(Dense(16, activation='relu'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kEV9Zyo6oq5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add the output layer\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpRPGF749k1o",
        "colab_type": "text"
      },
      "source": [
        "###*Model compilation*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ClOoXXX6rAP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "a61f220f-4b77-4efa-d543-8db8452388c8"
      },
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCZR7P-l6siR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "480d6849-c16a-4e55-d174-5546ed512027"
      },
      "source": [
        "# Train the model for 200 epochs\n",
        "model.fit(X_train, y_train, epochs=200)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "Epoch 1/200\n",
            "491/491 [==============================] - 1s 2ms/step - loss: 0.6710 - acc: 0.6090\n",
            "Epoch 2/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.6200 - acc: 0.6965\n",
            "Epoch 3/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.5773 - acc: 0.6965\n",
            "Epoch 4/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.5431 - acc: 0.7169\n",
            "Epoch 5/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.5153 - acc: 0.7332\n",
            "Epoch 6/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.4915 - acc: 0.7495\n",
            "Epoch 7/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.4752 - acc: 0.7617\n",
            "Epoch 8/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.4588 - acc: 0.7699\n",
            "Epoch 9/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.4481 - acc: 0.7882\n",
            "Epoch 10/200\n",
            "491/491 [==============================] - 0s 68us/step - loss: 0.4376 - acc: 0.7841\n",
            "Epoch 11/200\n",
            "491/491 [==============================] - 0s 62us/step - loss: 0.4310 - acc: 0.7821\n",
            "Epoch 12/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.4232 - acc: 0.7862\n",
            "Epoch 13/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.4188 - acc: 0.7882\n",
            "Epoch 14/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.4139 - acc: 0.7943\n",
            "Epoch 15/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.4100 - acc: 0.7943\n",
            "Epoch 16/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.4050 - acc: 0.8004\n",
            "Epoch 17/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.4004 - acc: 0.7984\n",
            "Epoch 18/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3976 - acc: 0.8045\n",
            "Epoch 19/200\n",
            "491/491 [==============================] - 0s 71us/step - loss: 0.3937 - acc: 0.8126\n",
            "Epoch 20/200\n",
            "491/491 [==============================] - 0s 59us/step - loss: 0.3918 - acc: 0.8147\n",
            "Epoch 21/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.3887 - acc: 0.8086\n",
            "Epoch 22/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.3849 - acc: 0.8167\n",
            "Epoch 23/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.3812 - acc: 0.8228\n",
            "Epoch 24/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3786 - acc: 0.8248\n",
            "Epoch 25/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3771 - acc: 0.8208\n",
            "Epoch 26/200\n",
            "491/491 [==============================] - 0s 58us/step - loss: 0.3736 - acc: 0.8289\n",
            "Epoch 27/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.3699 - acc: 0.8269\n",
            "Epoch 28/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.3668 - acc: 0.8228\n",
            "Epoch 29/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.3641 - acc: 0.8310\n",
            "Epoch 30/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.3618 - acc: 0.8330\n",
            "Epoch 31/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.3584 - acc: 0.8350\n",
            "Epoch 32/200\n",
            "491/491 [==============================] - 0s 59us/step - loss: 0.3556 - acc: 0.8391\n",
            "Epoch 33/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3512 - acc: 0.8432\n",
            "Epoch 34/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.3499 - acc: 0.8432\n",
            "Epoch 35/200\n",
            "491/491 [==============================] - 0s 62us/step - loss: 0.3489 - acc: 0.8411\n",
            "Epoch 36/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.3452 - acc: 0.8574\n",
            "Epoch 37/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.3406 - acc: 0.8473\n",
            "Epoch 38/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.3408 - acc: 0.8473\n",
            "Epoch 39/200\n",
            "491/491 [==============================] - 0s 64us/step - loss: 0.3361 - acc: 0.8554\n",
            "Epoch 40/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.3345 - acc: 0.8473\n",
            "Epoch 41/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.3304 - acc: 0.8574\n",
            "Epoch 42/200\n",
            "491/491 [==============================] - 0s 59us/step - loss: 0.3274 - acc: 0.8513\n",
            "Epoch 43/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.3262 - acc: 0.8574\n",
            "Epoch 44/200\n",
            "491/491 [==============================] - 0s 64us/step - loss: 0.3244 - acc: 0.8554\n",
            "Epoch 45/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3223 - acc: 0.8534\n",
            "Epoch 46/200\n",
            "491/491 [==============================] - 0s 59us/step - loss: 0.3210 - acc: 0.8554\n",
            "Epoch 47/200\n",
            "491/491 [==============================] - 0s 73us/step - loss: 0.3193 - acc: 0.8697\n",
            "Epoch 48/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.3194 - acc: 0.8574\n",
            "Epoch 49/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.3139 - acc: 0.8697\n",
            "Epoch 50/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.3134 - acc: 0.8676\n",
            "Epoch 51/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.3131 - acc: 0.8574\n",
            "Epoch 52/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.3110 - acc: 0.8758\n",
            "Epoch 53/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.3061 - acc: 0.8676\n",
            "Epoch 54/200\n",
            "491/491 [==============================] - 0s 58us/step - loss: 0.3039 - acc: 0.8717\n",
            "Epoch 55/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.3029 - acc: 0.8697\n",
            "Epoch 56/200\n",
            "491/491 [==============================] - 0s 64us/step - loss: 0.3023 - acc: 0.8778\n",
            "Epoch 57/200\n",
            "491/491 [==============================] - 0s 60us/step - loss: 0.2983 - acc: 0.8717\n",
            "Epoch 58/200\n",
            "491/491 [==============================] - 0s 62us/step - loss: 0.2983 - acc: 0.8697\n",
            "Epoch 59/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2971 - acc: 0.8798\n",
            "Epoch 60/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.2953 - acc: 0.8717\n",
            "Epoch 61/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.2918 - acc: 0.8819\n",
            "Epoch 62/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2904 - acc: 0.8778\n",
            "Epoch 63/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2925 - acc: 0.8697\n",
            "Epoch 64/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.2884 - acc: 0.8737\n",
            "Epoch 65/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2850 - acc: 0.8758\n",
            "Epoch 66/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.2822 - acc: 0.8798\n",
            "Epoch 67/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.2825 - acc: 0.8859\n",
            "Epoch 68/200\n",
            "491/491 [==============================] - 0s 59us/step - loss: 0.2797 - acc: 0.8737\n",
            "Epoch 69/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.2786 - acc: 0.8737\n",
            "Epoch 70/200\n",
            "491/491 [==============================] - 0s 70us/step - loss: 0.2770 - acc: 0.8819\n",
            "Epoch 71/200\n",
            "491/491 [==============================] - 0s 61us/step - loss: 0.2757 - acc: 0.8737\n",
            "Epoch 72/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2747 - acc: 0.8778\n",
            "Epoch 73/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2716 - acc: 0.8819\n",
            "Epoch 74/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.2704 - acc: 0.8880\n",
            "Epoch 75/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.2691 - acc: 0.8798\n",
            "Epoch 76/200\n",
            "491/491 [==============================] - 0s 60us/step - loss: 0.2698 - acc: 0.8758\n",
            "Epoch 77/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.2644 - acc: 0.8839\n",
            "Epoch 78/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.2609 - acc: 0.8839\n",
            "Epoch 79/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2645 - acc: 0.8798\n",
            "Epoch 80/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.2645 - acc: 0.8798\n",
            "Epoch 81/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.2571 - acc: 0.8859\n",
            "Epoch 82/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.2568 - acc: 0.8921\n",
            "Epoch 83/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.2545 - acc: 0.8921\n",
            "Epoch 84/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.2509 - acc: 0.8839\n",
            "Epoch 85/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2540 - acc: 0.8880\n",
            "Epoch 86/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.2573 - acc: 0.8798\n",
            "Epoch 87/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2475 - acc: 0.8941\n",
            "Epoch 88/200\n",
            "491/491 [==============================] - 0s 61us/step - loss: 0.2463 - acc: 0.8982\n",
            "Epoch 89/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.2420 - acc: 0.8961\n",
            "Epoch 90/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.2465 - acc: 0.8859\n",
            "Epoch 91/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.2507 - acc: 0.8921\n",
            "Epoch 92/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2376 - acc: 0.8982\n",
            "Epoch 93/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.2348 - acc: 0.8941\n",
            "Epoch 94/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.2352 - acc: 0.8921\n",
            "Epoch 95/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.2318 - acc: 0.9043\n",
            "Epoch 96/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.2366 - acc: 0.8982\n",
            "Epoch 97/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.2301 - acc: 0.8982\n",
            "Epoch 98/200\n",
            "491/491 [==============================] - 0s 62us/step - loss: 0.2301 - acc: 0.9043\n",
            "Epoch 99/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2266 - acc: 0.9043\n",
            "Epoch 100/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.2229 - acc: 0.9063\n",
            "Epoch 101/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.2224 - acc: 0.9063\n",
            "Epoch 102/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.2184 - acc: 0.9084\n",
            "Epoch 103/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.2166 - acc: 0.9002\n",
            "Epoch 104/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.2166 - acc: 0.9043\n",
            "Epoch 105/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.2129 - acc: 0.9104\n",
            "Epoch 106/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.2105 - acc: 0.9124\n",
            "Epoch 107/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.2112 - acc: 0.9145\n",
            "Epoch 108/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2120 - acc: 0.9124\n",
            "Epoch 109/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2111 - acc: 0.9063\n",
            "Epoch 110/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.2057 - acc: 0.9063\n",
            "Epoch 111/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2032 - acc: 0.9104\n",
            "Epoch 112/200\n",
            "491/491 [==============================] - 0s 50us/step - loss: 0.2009 - acc: 0.9206\n",
            "Epoch 113/200\n",
            "491/491 [==============================] - 0s 41us/step - loss: 0.1997 - acc: 0.9145\n",
            "Epoch 114/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1966 - acc: 0.9145\n",
            "Epoch 115/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.1972 - acc: 0.9206\n",
            "Epoch 116/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.1959 - acc: 0.9246\n",
            "Epoch 117/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.1922 - acc: 0.9287\n",
            "Epoch 118/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.1893 - acc: 0.9267\n",
            "Epoch 119/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.1881 - acc: 0.9206\n",
            "Epoch 120/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1864 - acc: 0.9308\n",
            "Epoch 121/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.1852 - acc: 0.9226\n",
            "Epoch 122/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1892 - acc: 0.9185\n",
            "Epoch 123/200\n",
            "491/491 [==============================] - 0s 57us/step - loss: 0.1956 - acc: 0.9165\n",
            "Epoch 124/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1867 - acc: 0.9246\n",
            "Epoch 125/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.1790 - acc: 0.9206\n",
            "Epoch 126/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.1827 - acc: 0.9246\n",
            "Epoch 127/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.1775 - acc: 0.9287\n",
            "Epoch 128/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.1743 - acc: 0.9328\n",
            "Epoch 129/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1717 - acc: 0.9328\n",
            "Epoch 130/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.1705 - acc: 0.9348\n",
            "Epoch 131/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1756 - acc: 0.9348\n",
            "Epoch 132/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.1734 - acc: 0.9328\n",
            "Epoch 133/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1692 - acc: 0.9308\n",
            "Epoch 134/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.1743 - acc: 0.9369\n",
            "Epoch 135/200\n",
            "491/491 [==============================] - 0s 54us/step - loss: 0.1760 - acc: 0.9104\n",
            "Epoch 136/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1694 - acc: 0.9287\n",
            "Epoch 137/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1588 - acc: 0.9491\n",
            "Epoch 138/200\n",
            "491/491 [==============================] - 0s 55us/step - loss: 0.1594 - acc: 0.9450\n",
            "Epoch 139/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1575 - acc: 0.9389\n",
            "Epoch 140/200\n",
            "491/491 [==============================] - 0s 62us/step - loss: 0.1530 - acc: 0.9450\n",
            "Epoch 141/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.1500 - acc: 0.9470\n",
            "Epoch 142/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1546 - acc: 0.9389\n",
            "Epoch 143/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1527 - acc: 0.9532\n",
            "Epoch 144/200\n",
            "491/491 [==============================] - 0s 41us/step - loss: 0.1473 - acc: 0.9470\n",
            "Epoch 145/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1450 - acc: 0.9572\n",
            "Epoch 146/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.1426 - acc: 0.9552\n",
            "Epoch 147/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.1407 - acc: 0.9511\n",
            "Epoch 148/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1415 - acc: 0.9450\n",
            "Epoch 149/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.1473 - acc: 0.9511\n",
            "Epoch 150/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.1427 - acc: 0.9450\n",
            "Epoch 151/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.1391 - acc: 0.9593\n",
            "Epoch 152/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.1338 - acc: 0.9491\n",
            "Epoch 153/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.1340 - acc: 0.9593\n",
            "Epoch 154/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.1323 - acc: 0.9511\n",
            "Epoch 155/200\n",
            "491/491 [==============================] - 0s 64us/step - loss: 0.1300 - acc: 0.9511\n",
            "Epoch 156/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1257 - acc: 0.9552\n",
            "Epoch 157/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.1249 - acc: 0.9674\n",
            "Epoch 158/200\n",
            "491/491 [==============================] - 0s 60us/step - loss: 0.1252 - acc: 0.9613\n",
            "Epoch 159/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.1252 - acc: 0.9572\n",
            "Epoch 160/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1242 - acc: 0.9654\n",
            "Epoch 161/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.1190 - acc: 0.9695\n",
            "Epoch 162/200\n",
            "491/491 [==============================] - 0s 41us/step - loss: 0.1186 - acc: 0.9572\n",
            "Epoch 163/200\n",
            "491/491 [==============================] - 0s 41us/step - loss: 0.1166 - acc: 0.9674\n",
            "Epoch 164/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.1149 - acc: 0.9654\n",
            "Epoch 165/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.1131 - acc: 0.9633\n",
            "Epoch 166/200\n",
            "491/491 [==============================] - 0s 48us/step - loss: 0.1124 - acc: 0.9633\n",
            "Epoch 167/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.1096 - acc: 0.9695\n",
            "Epoch 168/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.1077 - acc: 0.9633\n",
            "Epoch 169/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.1107 - acc: 0.9715\n",
            "Epoch 170/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.1103 - acc: 0.9654\n",
            "Epoch 171/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.1044 - acc: 0.9674\n",
            "Epoch 172/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.1027 - acc: 0.9715\n",
            "Epoch 173/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.1071 - acc: 0.9674\n",
            "Epoch 174/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.1062 - acc: 0.9695\n",
            "Epoch 175/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0987 - acc: 0.9695\n",
            "Epoch 176/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.0965 - acc: 0.9735\n",
            "Epoch 177/200\n",
            "491/491 [==============================] - 0s 51us/step - loss: 0.0946 - acc: 0.9735\n",
            "Epoch 178/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.0983 - acc: 0.9735\n",
            "Epoch 179/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.0939 - acc: 0.9817\n",
            "Epoch 180/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.0929 - acc: 0.9756\n",
            "Epoch 181/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.0906 - acc: 0.9796\n",
            "Epoch 182/200\n",
            "491/491 [==============================] - 0s 52us/step - loss: 0.0880 - acc: 0.9857\n",
            "Epoch 183/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.0855 - acc: 0.9878\n",
            "Epoch 184/200\n",
            "491/491 [==============================] - 0s 56us/step - loss: 0.0875 - acc: 0.9776\n",
            "Epoch 185/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0842 - acc: 0.9776\n",
            "Epoch 186/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.0837 - acc: 0.9898\n",
            "Epoch 187/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0803 - acc: 0.9857\n",
            "Epoch 188/200\n",
            "491/491 [==============================] - 0s 44us/step - loss: 0.0812 - acc: 0.9817\n",
            "Epoch 189/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.0800 - acc: 0.9878\n",
            "Epoch 190/200\n",
            "491/491 [==============================] - 0s 47us/step - loss: 0.0801 - acc: 0.9817\n",
            "Epoch 191/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.0755 - acc: 0.9817\n",
            "Epoch 192/200\n",
            "491/491 [==============================] - 0s 53us/step - loss: 0.0746 - acc: 0.9857\n",
            "Epoch 193/200\n",
            "491/491 [==============================] - 0s 45us/step - loss: 0.0755 - acc: 0.9857\n",
            "Epoch 194/200\n",
            "491/491 [==============================] - 0s 43us/step - loss: 0.0710 - acc: 0.9898\n",
            "Epoch 195/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0714 - acc: 0.9919\n",
            "Epoch 196/200\n",
            "491/491 [==============================] - 0s 42us/step - loss: 0.0697 - acc: 0.9898\n",
            "Epoch 197/200\n",
            "491/491 [==============================] - 0s 49us/step - loss: 0.0665 - acc: 0.9939\n",
            "Epoch 198/200\n",
            "491/491 [==============================] - 0s 41us/step - loss: 0.0674 - acc: 0.9939\n",
            "Epoch 199/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0667 - acc: 0.9898\n",
            "Epoch 200/200\n",
            "491/491 [==============================] - 0s 46us/step - loss: 0.0655 - acc: 0.9878\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0f3aeb50b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjwTENF49sl0",
        "colab_type": "text"
      },
      "source": [
        "## **Results Analysis** ##"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qINm4BnD-Aii",
        "colab_type": "text"
      },
      "source": [
        "###*Testing accuracy*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcCKBmrG6yZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "816b46f8-a8a6-42d5-a92a-053fe4d11373"
      },
      "source": [
        "scores = model.evaluate(X_train, y_train)\n",
        "print(\"Training Accuracy: %.2f%%\\n\" % (scores[1]*100))\n",
        "\n",
        "scores = model.evaluate(X_test, y_test)\n",
        "print(\"Testing Accuracy: %.2f%%\\n\" % (scores[1]*100))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "491/491 [==============================] - 0s 115us/step\n",
            "Training Accuracy: 99.59%\n",
            "\n",
            "154/154 [==============================] - 0s 41us/step\n",
            "Testing Accuracy: 74.03%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSO8FMUj-E84",
        "colab_type": "text"
      },
      "source": [
        "###*Confusion matrix*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8tGQ_y862gI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "2863f7cd-2555-465b-8172-ac8f7998c002"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "y_test_pred = model.predict_classes(X_test)\n",
        "c_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "ax = sns.heatmap(c_matrix, annot=True, \n",
        "                 xticklabels=['No Diabetes','Diabetes'],\n",
        "                 yticklabels=['No Diabetes','Diabetes'], \n",
        "                 cbar=False, cmap='Blues')\n",
        "ax.set_xlabel(\"Prediction\")\n",
        "ax.set_ylabel(\"Actual\")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6DRjIHvscAMDwffbn5d//traNy0Dq8J8v6T6K8L9X0tbAhsR1Zi0iuPzSqdTX13PKaae/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCbqnsLf-QQ_",
        "colab_type": "text"
      },
      "source": [
        "###*ROC curve*###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqe3-0F-66uo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "y_test_pred_probs = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDhvgh5J678h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FPR, TPR, _ = roc_curve(y_test, y_test_pred_probs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrrDZ29i69Qg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "d83a2b65-ab08-4c15-d8a2-2525c0a4c0a4"
      },
      "source": [
        "plt.plot(FPR, TPR)\n",
        "plt.plot([0,1],[0,1],'--', color='black') #diagonal line\n",
        "plt.title('ROC Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'True Positive Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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01N/KLmC3detWxowZw4ABA1TATqQEOJAyF9WBE4s7ECkdVq9eTdWqVSlTpgxP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\nSgoiIpLj/wM517/i7KHnXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}