Upload 2 files

Diabetes.ipynb

@@ -0,0 +1,1621 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LnPbntVRnfvV"
+      },
+      "source": [
+        "Importing the Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-71UtHzNVWjB"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn import svm\n",
+        "from sklearn.metrics import accuracy_score"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bmfOfG8joBBy"
+      },
+      "source": [
+        "Data Collection and Analysis\n",
+        "\n",
+        "PIMA Diabetes Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Xpw6Mj_pn_TL"
+      },
+      "source": [
+        "# loading the diabetes dataset to a pandas DataFrame\n",
+        "diabetes_dataset = pd.read_csv('/content/diabetes.csv')"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eupSUC7yoo9M"
+      },
+      "source": [
+        "pd.read_csv?"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "-tjO09ncovoh",
+        "outputId": "be1717b1-cd50-419d-a3fa-16b4e8fec9a6"
+      },
+      "source": [
+        "# printing the first 5 rows of the dataset\n",
+        "diabetes_dataset.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0            6      148             72             35        0  33.6   \n",
+              "1            1       85             66             29        0  26.6   \n",
+              "2            8      183             64              0        0  23.3   \n",
+              "3            1       89             66             23       94  28.1   \n",
+              "4            0      137             40             35      168  43.1   \n",
+              "\n",
+              "   DiabetesPedigreeFunction  Age  Outcome  \n",
+              "0                     0.627   50        1  \n",
+              "1                     0.351   31        0  \n",
+              "2                     0.672   32        1  \n",
+              "3                     0.167   21        0  \n",
+              "4                     2.288   33        1  "
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-1aec5124-92a3-4474-a6b7-64473ba6287f\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1aec5124-92a3-4474-a6b7-64473ba6287f')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a23e5510-a452-4d18-9834-1fb6ca8dc0e5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a23e5510-a452-4d18-9834-1fb6ca8dc0e5')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-a23e5510-a452-4d18-9834-1fb6ca8dc0e5 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-1aec5124-92a3-4474-a6b7-64473ba6287f button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-1aec5124-92a3-4474-a6b7-64473ba6287f');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lynParo6pEMB",
+        "outputId": "d3c5aba8-7253-4757-8b6f-c7e62ef5d34d"
+      },
+      "source": [
+        "# number of rows and Columns in this dataset\n",
+        "diabetes_dataset.shape"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(768, 9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "3NDJOlrEpmoL",
+        "outputId": "06871fbc-c80e-440d-89a2-12f40e0cb79e"
+      },
+      "source": [
+        "# getting the statistical measures of the data\n",
+        "diabetes_dataset.describe()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
+              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
+              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
+              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
+              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
+              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
+              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
+              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
+              "\n",
+              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
+              "count  768.000000                768.000000  768.000000  768.000000  \n",
+              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
+              "std      7.884160                  0.331329   11.760232    0.476951  \n",
+              "min      0.000000                  0.078000   21.000000    0.000000  \n",
+              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
+              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
+              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
+              "max     67.100000                  2.420000   81.000000    1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-25cb54c1-69f1-400d-8d06-6c08deb52ad1\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25cb54c1-69f1-400d-8d06-6c08deb52ad1')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f34ee77e-12c2-4314-a6b1-e077095db111\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f34ee77e-12c2-4314-a6b1-e077095db111')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-f34ee77e-12c2-4314-a6b1-e077095db111 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-25cb54c1-69f1-400d-8d06-6c08deb52ad1 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-25cb54c1-69f1-400d-8d06-6c08deb52ad1');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LrpHzaGpp5dQ",
+        "outputId": "e56f7356-cd9b-4cb0-e7ef-0676778b4da0"
+      },
+      "source": [
+        "diabetes_dataset['Outcome'].value_counts()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    500\n",
+              "1    268\n",
+              "Name: Outcome, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cB1qRaNcqeh5"
+      },
+      "source": [
+        "0 --> Non-Diabetic\n",
+        "\n",
+        "1 --> Diabetic"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "I6MWR0k_qSCK",
+        "outputId": "f48cb514-0a48-4c2c-f9e1-7c4e26b3d1c8"
+      },
+      "source": [
+        "diabetes_dataset.groupby('Outcome').mean()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "Outcome                                                                      \n",
+              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
+              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
+              "\n",
+              "               BMI  DiabetesPedigreeFunction        Age  \n",
+              "Outcome                                                  \n",
+              "0        30.304200                  0.429734  31.190000  \n",
+              "1        35.142537                  0.550500  37.067164  "
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-dbd165c1-51a1-4a8b-a88d-2013cb6502f4\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Outcome</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3.298000</td>\n",
+              "      <td>109.980000</td>\n",
+              "      <td>68.184000</td>\n",
+              "      <td>19.664000</td>\n",
+              "      <td>68.792000</td>\n",
+              "      <td>30.304200</td>\n",
+              "      <td>0.429734</td>\n",
+              "      <td>31.190000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.865672</td>\n",
+              "      <td>141.257463</td>\n",
+              "      <td>70.824627</td>\n",
+              "      <td>22.164179</td>\n",
+              "      <td>100.335821</td>\n",
+              "      <td>35.142537</td>\n",
+              "      <td>0.550500</td>\n",
+              "      <td>37.067164</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbd165c1-51a1-4a8b-a88d-2013cb6502f4')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-a9492bbf-84b3-4c23-bc99-b7589c4880b8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9492bbf-84b3-4c23-bc99-b7589c4880b8')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-a9492bbf-84b3-4c23-bc99-b7589c4880b8 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-dbd165c1-51a1-4a8b-a88d-2013cb6502f4 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-dbd165c1-51a1-4a8b-a88d-2013cb6502f4');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RoDW7l9mqqHZ"
+      },
+      "source": [
+        "# separating the data and labels\n",
+        "X = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
+        "Y = diabetes_dataset['Outcome']"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3eiRW9M9raMm",
+        "outputId": "307a82aa-e9ce-4fbc-b6ca-e45a0b3cae68"
+      },
+      "source": [
+        "print(X)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+            "0              6      148             72             35        0  33.6   \n",
+            "1              1       85             66             29        0  26.6   \n",
+            "2              8      183             64              0        0  23.3   \n",
+            "3              1       89             66             23       94  28.1   \n",
+            "4              0      137             40             35      168  43.1   \n",
+            "..           ...      ...            ...            ...      ...   ...   \n",
+            "763           10      101             76             48      180  32.9   \n",
+            "764            2      122             70             27        0  36.8   \n",
+            "765            5      121             72             23      112  26.2   \n",
+            "766            1      126             60              0        0  30.1   \n",
+            "767            1       93             70             31        0  30.4   \n",
+            "\n",
+            "     DiabetesPedigreeFunction  Age  \n",
+            "0                       0.627   50  \n",
+            "1                       0.351   31  \n",
+            "2                       0.672   32  \n",
+            "3                       0.167   21  \n",
+            "4                       2.288   33  \n",
+            "..                        ...  ...  \n",
+            "763                     0.171   63  \n",
+            "764                     0.340   27  \n",
+            "765                     0.245   30  \n",
+            "766                     0.349   47  \n",
+            "767                     0.315   23  \n",
+            "\n",
+            "[768 rows x 8 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AoxgTJAMrcCl",
+        "outputId": "81847448-8589-4af9-a8ff-f90d74fb62e1"
+      },
+      "source": [
+        "print(Y)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0      1\n",
+            "1      0\n",
+            "2      1\n",
+            "3      0\n",
+            "4      1\n",
+            "      ..\n",
+            "763    0\n",
+            "764    0\n",
+            "765    0\n",
+            "766    1\n",
+            "767    0\n",
+            "Name: Outcome, Length: 768, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "umAbo_kqrlzI"
+      },
+      "source": [
+        "Data Standardization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "njfM5X60rgnc"
+      },
+      "source": [
+        "scaler = StandardScaler()"
+      ],
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "g0ai5ARbr53p",
+        "outputId": "6b0bcd67-dca6-48e2-ef4c-5f643b696997"
+      },
+      "source": [
+        "scaler.fit(X)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "StandardScaler()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FHxNwPuZr-kD"
+      },
+      "source": [
+        "standardized_data = scaler.transform(X)"
+      ],
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fjMwZ5x6sPUJ",
+        "outputId": "a575517e-b432-48e7-980c-e0f94eb2c594"
+      },
+      "source": [
+        "print(standardized_data)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
+            "   1.4259954 ]\n",
+            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
+            "  -0.19067191]\n",
+            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
+            "  -0.10558415]\n",
+            " ...\n",
+            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
+            "  -0.27575966]\n",
+            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
+            "   1.17073215]\n",
+            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
+            "  -0.87137393]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZxWSl4SGsRjE"
+      },
+      "source": [
+        "X = standardized_data\n",
+        "Y = diabetes_dataset['Outcome']"
+      ],
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lhJF_7QjsjmP",
+        "outputId": "22698a97-5ba3-4afd-fd64-468f42da44ed"
+      },
+      "source": [
+        "print(X)\n",
+        "print(Y)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
+            "   1.4259954 ]\n",
+            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
+            "  -0.19067191]\n",
+            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
+            "  -0.10558415]\n",
+            " ...\n",
+            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
+            "  -0.27575966]\n",
+            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
+            "   1.17073215]\n",
+            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
+            "  -0.87137393]]\n",
+            "0      1\n",
+            "1      0\n",
+            "2      1\n",
+            "3      0\n",
+            "4      1\n",
+            "      ..\n",
+            "763    0\n",
+            "764    0\n",
+            "765    0\n",
+            "766    1\n",
+            "767    0\n",
+            "Name: Outcome, Length: 768, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gHciEFkxsoQP"
+      },
+      "source": [
+        "Train Test Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AEfKGj_yslvD"
+      },
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)"
+      ],
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DR05T-o0t3FQ",
+        "outputId": "ac141250-46de-4a22-ca5a-7ed8f934b802"
+      },
+      "source": [
+        "print(X.shape, X_train.shape, X_test.shape)"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(768, 8) (614, 8) (154, 8)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ElJ3tkOtuC_n"
+      },
+      "source": [
+        "Training the Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5szLWHlNt9xc"
+      },
+      "source": [
+        "classifier = svm.SVC(kernel='linear')"
+      ],
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "ncJWY_7suPAb",
+        "outputId": "6723e7bf-6435-465a-ab29-870e9791b070"
+      },
+      "source": [
+        "#training the support vector Machine Classifier\n",
+        "classifier.fit(X_train, Y_train)"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear')"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UV4-CAfquiyP"
+      },
+      "source": [
+        "Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yhAjGPJWunXa"
+      },
+      "source": [
+        "Accuracy Score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fJLEPQK7ueXp"
+      },
+      "source": [
+        "# accuracy score on the training data\n",
+        "X_train_prediction = classifier.predict(X_train)\n",
+        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
+      ],
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mmJ22qhVvNwj",
+        "outputId": "8feadaa7-5d57-4e1e-8c2a-b3a8082c694c"
+      },
+      "source": [
+        "print('Accuracy score of the training data : ', training_data_accuracy)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score of the training data :  0.7866449511400652\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G2CICFMEvcCl"
+      },
+      "source": [
+        "# accuracy score on the test data\n",
+        "X_test_prediction = classifier.predict(X_test)\n",
+        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
+      ],
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i2GcW_t_vz7C",
+        "outputId": "8cfe13de-d7a4-4524-d4b5-d05f862bd0d4"
+      },
+      "source": [
+        "print('Accuracy score of the test data : ', test_data_accuracy)"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score of the test data :  0.7727272727272727\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gq8ZX1xpwPF5"
+      },
+      "source": [
+        "Making a Predictive System"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "U-ULRe4yv5tH"
+      },
+      "source": [
+        "def predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age):\n",
+        "  #input_data = (5,166,72,19,175,25.8,0.587,51)\n",
+        "  input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age)\n",
+        "\n",
+        "\n",
+        "  # changing the input_data to numpy array\n",
+        "  input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "  # reshape the array as we are predicting for one instance\n",
+        "  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "  # standardize the input data\n",
+        "  std_data = scaler.transform(input_data_reshaped)\n",
+        "  print(std_data)\n",
+        "\n",
+        "  prediction = classifier.predict(std_data)\n",
+        "  #print(prediction)\n",
+        "\n",
+        "  if (prediction[0] == 0):\n",
+        "    print('The person is not diabetic')\n",
+        "  else:\n",
+        "    print('The person is diabetic')\n",
+        "  return prediction"
+      ],
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ex2A_pr4yCpm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "138bc5ec-bdb3-4a7f-a229-2614b47da4df"
+      },
+      "source": [
+        "predict(4,136,64,20,175,25.6,0.597,50)"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 0.04601433  0.47275805 -0.26394125 -0.03365099  0.82661621 -0.81134119\n",
+            "   0.37788842  1.4259954 ]]\n",
+            "The person is not diabetic\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "id": "NBOIKhAAxeyx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "\n",
+        "\n",
+        "def dibetis_predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age):\n",
+        "  #input_data = (5,166,72,19,175,25.8,0.587,51)\n",
+        "  input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age)\n",
+        "\n",
+        "\n",
+        "  # changing the input_data to numpy array\n",
+        "  input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "  # reshape the array as we are predicting for one instance\n",
+        "  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "  # standardize the input data\n",
+        "  std_data = scaler.transform(input_data_reshaped)\n",
+        "  print(std_data)\n",
+        "\n",
+        "  prediction = classifier.predict(std_data)\n",
+        "\n",
+        "  if (prediction[0] == 0):\n",
+        "    print('The person is not diabetic')\n",
+        "    return 'The person is not diabetic'\n",
+        "  else:\n",
+        "    print('The person is diabetic')\n",
+        "    return 'The person is diabetic'\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "demo = gr.Interface(\n",
+        "    fn=dibetis_predict,\n",
+        "    inputs = [\n",
+        "        gr.Slider(1, 20, value=4, label=\"Pregnancies\", info=\"Choose between 1 and 20\"),\n",
+        "        gr.Slider(1, 200, value=136, label=\"Glucose\", info=\"Choose between 1 and 200\"),\n",
+        "        gr.Slider(1, 100, value=64, label=\"BloodPressure\", info=\"Choose between 1 and 100\"),\n",
+        "        gr.Slider(1, 50, value=20, label=\"SkinThickness\", info=\"Choose between 1 and 50\"),\n",
+        "        gr.Slider(1, 200, value=175, label=\"Insulin\", info=\"Choose between 1 and 200\"),\n",
+        "        gr.Slider(1, 100, value=25.5, label=\"BMI\", info=\"Choose between 1 and 100\"),\n",
+        "        gr.Slider(0, 1.0, value=0.549, label=\"DiabetesPedigreeFunction\", info=\"Choose between 0.0 and 1.0\"),\n",
+        "        gr.Slider(1, 100, value=50, label=\"Age\", info=\"Choose between 1 and 100\"),\n",
+        "    ],\n",
+        "    outputs = \"text\",\n",
+        ")\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    demo.launch(share=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 611
+        },
+        "id": "1_9DwNyd7dso",
+        "outputId": "d10a4f57-1450-4c46-921f-887a7d3bdbc9"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "Running on public URL: https://1eacd78d38dbb14861.gradio.live\n",
+            "\n",
+            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://1eacd78d38dbb14861.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "4,136,62,20,175,25.6,0.597,50\n",
+        "0\t6\t148\t72\t35\t0\t33.6\t0.627\t50"
+      ],
+      "metadata": {
+        "id": "x81jZvv8Vw6n"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

diabetes.csv

@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
+7,150,66,42,342,34.7,0.718,42,0
+1,73,50,10,0,23,0.248,21,0
+7,187,68,39,304,37.7,0.254,41,1
+0,100,88,60,110,46.8,0.962,31,0
+0,146,82,0,0,40.5,1.781,44,0
+0,105,64,41,142,41.5,0.173,22,0
+2,84,0,0,0,0,0.304,21,0
+8,133,72,0,0,32.9,0.27,39,1
+5,44,62,0,0,25,0.587,36,0
+2,141,58,34,128,25.4,0.699,24,0
+7,114,66,0,0,32.8,0.258,42,1
+5,99,74,27,0,29,0.203,32,0
+0,109,88,30,0,32.5,0.855,38,1
+2,109,92,0,0,42.7,0.845,54,0
+1,95,66,13,38,19.6,0.334,25,0
+4,146,85,27,100,28.9,0.189,27,0
+2,100,66,20,90,32.9,0.867,28,1
+5,139,64,35,140,28.6,0.411,26,0
+13,126,90,0,0,43.4,0.583,42,1
+4,129,86,20,270,35.1,0.231,23,0
+1,79,75,30,0,32,0.396,22,0
+1,0,48,20,0,24.7,0.14,22,0
+7,62,78,0,0,32.6,0.391,41,0
+5,95,72,33,0,37.7,0.37,27,0
+0,131,0,0,0,43.2,0.27,26,1
+2,112,66,22,0,25,0.307,24,0
+3,113,44,13,0,22.4,0.14,22,0
+2,74,0,0,0,0,0.102,22,0
+7,83,78,26,71,29.3,0.767,36,0
+0,101,65,28,0,24.6,0.237,22,0
+5,137,108,0,0,48.8,0.227,37,1
+2,110,74,29,125,32.4,0.698,27,0
+13,106,72,54,0,36.6,0.178,45,0
+2,100,68,25,71,38.5,0.324,26,0
+15,136,70,32,110,37.1,0.153,43,1
+1,107,68,19,0,26.5,0.165,24,0
+1,80,55,0,0,19.1,0.258,21,0
+4,123,80,15,176,32,0.443,34,0
+7,81,78,40,48,46.7,0.261,42,0
+4,134,72,0,0,23.8,0.277,60,1
+2,142,82,18,64,24.7,0.761,21,0
+6,144,72,27,228,33.9,0.255,40,0
+2,92,62,28,0,31.6,0.13,24,0
+1,71,48,18,76,20.4,0.323,22,0
+6,93,50,30,64,28.7,0.356,23,0
+1,122,90,51,220,49.7,0.325,31,1
+1,163,72,0,0,39,1.222,33,1
+1,151,60,0,0,26.1,0.179,22,0
+0,125,96,0,0,22.5,0.262,21,0
+1,81,72,18,40,26.6,0.283,24,0
+2,85,65,0,0,39.6,0.93,27,0
+1,126,56,29,152,28.7,0.801,21,0
+1,96,122,0,0,22.4,0.207,27,0
+4,144,58,28,140,29.5,0.287,37,0
+3,83,58,31,18,34.3,0.336,25,0
+0,95,85,25,36,37.4,0.247,24,1
+3,171,72,33,135,33.3,0.199,24,1
+8,155,62,26,495,34,0.543,46,1
+1,89,76,34,37,31.2,0.192,23,0
+4,76,62,0,0,34,0.391,25,0
+7,160,54,32,175,30.5,0.588,39,1
+4,146,92,0,0,31.2,0.539,61,1
+5,124,74,0,0,34,0.22,38,1
+5,78,48,0,0,33.7,0.654,25,0
+4,97,60,23,0,28.2,0.443,22,0
+4,99,76,15,51,23.2,0.223,21,0
+0,162,76,56,100,53.2,0.759,25,1
+6,111,64,39,0,34.2,0.26,24,0
+2,107,74,30,100,33.6,0.404,23,0
+5,132,80,0,0,26.8,0.186,69,0
+0,113,76,0,0,33.3,0.278,23,1
+1,88,30,42,99,55,0.496,26,1
+3,120,70,30,135,42.9,0.452,30,0
+1,118,58,36,94,33.3,0.261,23,0
+1,117,88,24,145,34.5,0.403,40,1
+0,105,84,0,0,27.9,0.741,62,1
+4,173,70,14,168,29.7,0.361,33,1
+9,122,56,0,0,33.3,1.114,33,1
+3,170,64,37,225,34.5,0.356,30,1
+8,84,74,31,0,38.3,0.457,39,0
+2,96,68,13,49,21.1,0.647,26,0
+2,125,60,20,140,33.8,0.088,31,0
+0,100,70,26,50,30.8,0.597,21,0
+0,93,60,25,92,28.7,0.532,22,0
+0,129,80,0,0,31.2,0.703,29,0
+5,105,72,29,325,36.9,0.159,28,0
+3,128,78,0,0,21.1,0.268,55,0
+5,106,82,30,0,39.5,0.286,38,0
+2,108,52,26,63,32.5,0.318,22,0
+10,108,66,0,0,32.4,0.272,42,1
+4,154,62,31,284,32.8,0.237,23,0
+0,102,75,23,0,0,0.572,21,0
+9,57,80,37,0,32.8,0.096,41,0
+2,106,64,35,119,30.5,1.4,34,0
+5,147,78,0,0,33.7,0.218,65,0
+2,90,70,17,0,27.3,0.085,22,0
+1,136,74,50,204,37.4,0.399,24,0
+4,114,65,0,0,21.9,0.432,37,0
+9,156,86,28,155,34.3,1.189,42,1
+1,153,82,42,485,40.6,0.687,23,0
+8,188,78,0,0,47.9,0.137,43,1
+7,152,88,44,0,50,0.337,36,1
+2,99,52,15,94,24.6,0.637,21,0
+1,109,56,21,135,25.2,0.833,23,0
+2,88,74,19,53,29,0.229,22,0
+17,163,72,41,114,40.9,0.817,47,1
+4,151,90,38,0,29.7,0.294,36,0
+7,102,74,40,105,37.2,0.204,45,0
+0,114,80,34,285,44.2,0.167,27,0
+2,100,64,23,0,29.7,0.368,21,0
+0,131,88,0,0,31.6,0.743,32,1
+6,104,74,18,156,29.9,0.722,41,1
+3,148,66,25,0,32.5,0.256,22,0
+4,120,68,0,0,29.6,0.709,34,0
+4,110,66,0,0,31.9,0.471,29,0
+3,111,90,12,78,28.4,0.495,29,0
+6,102,82,0,0,30.8,0.18,36,1
+6,134,70,23,130,35.4,0.542,29,1
+2,87,0,23,0,28.9,0.773,25,0
+1,79,60,42,48,43.5,0.678,23,0
+2,75,64,24,55,29.7,0.37,33,0
+8,179,72,42,130,32.7,0.719,36,1
+6,85,78,0,0,31.2,0.382,42,0
+0,129,110,46,130,67.1,0.319,26,1
+5,143,78,0,0,45,0.19,47,0
+5,130,82,0,0,39.1,0.956,37,1
+6,87,80,0,0,23.2,0.084,32,0
+0,119,64,18,92,34.9,0.725,23,0
+1,0,74,20,23,27.7,0.299,21,0
+5,73,60,0,0,26.8,0.268,27,0
+4,141,74,0,0,27.6,0.244,40,0
+7,194,68,28,0,35.9,0.745,41,1
+8,181,68,36,495,30.1,0.615,60,1
+1,128,98,41,58,32,1.321,33,1
+8,109,76,39,114,27.9,0.64,31,1
+5,139,80,35,160,31.6,0.361,25,1
+3,111,62,0,0,22.6,0.142,21,0
+9,123,70,44,94,33.1,0.374,40,0
+7,159,66,0,0,30.4,0.383,36,1
+11,135,0,0,0,52.3,0.578,40,1
+8,85,55,20,0,24.4,0.136,42,0
+5,158,84,41,210,39.4,0.395,29,1
+1,105,58,0,0,24.3,0.187,21,0
+3,107,62,13,48,22.9,0.678,23,1
+4,109,64,44,99,34.8,0.905,26,1
+4,148,60,27,318,30.9,0.15,29,1
+0,113,80,16,0,31,0.874,21,0
+1,138,82,0,0,40.1,0.236,28,0
+0,108,68,20,0,27.3,0.787,32,0
+2,99,70,16,44,20.4,0.235,27,0
+6,103,72,32,190,37.7,0.324,55,0
+5,111,72,28,0,23.9,0.407,27,0
+8,196,76,29,280,37.5,0.605,57,1
+5,162,104,0,0,37.7,0.151,52,1
+1,96,64,27,87,33.2,0.289,21,0
+7,184,84,33,0,35.5,0.355,41,1
+2,81,60,22,0,27.7,0.29,25,0
+0,147,85,54,0,42.8,0.375,24,0
+7,179,95,31,0,34.2,0.164,60,0
+0,140,65,26,130,42.6,0.431,24,1
+9,112,82,32,175,34.2,0.26,36,1
+12,151,70,40,271,41.8,0.742,38,1
+5,109,62,41,129,35.8,0.514,25,1
+6,125,68,30,120,30,0.464,32,0
+5,85,74,22,0,29,1.224,32,1
+5,112,66,0,0,37.8,0.261,41,1
+0,177,60,29,478,34.6,1.072,21,1
+2,158,90,0,0,31.6,0.805,66,1
+7,119,0,0,0,25.2,0.209,37,0
+7,142,60,33,190,28.8,0.687,61,0
+1,100,66,15,56,23.6,0.666,26,0
+1,87,78,27,32,34.6,0.101,22,0
+0,101,76,0,0,35.7,0.198,26,0
+3,162,52,38,0,37.2,0.652,24,1
+4,197,70,39,744,36.7,2.329,31,0
+0,117,80,31,53,45.2,0.089,24,0
+4,142,86,0,0,44,0.645,22,1
+6,134,80,37,370,46.2,0.238,46,1
+1,79,80,25,37,25.4,0.583,22,0
+4,122,68,0,0,35,0.394,29,0
+3,74,68,28,45,29.7,0.293,23,0
+4,171,72,0,0,43.6,0.479,26,1
+7,181,84,21,192,35.9,0.586,51,1
+0,179,90,27,0,44.1,0.686,23,1
+9,164,84,21,0,30.8,0.831,32,1
+0,104,76,0,0,18.4,0.582,27,0
+1,91,64,24,0,29.2,0.192,21,0
+4,91,70,32,88,33.1,0.446,22,0
+3,139,54,0,0,25.6,0.402,22,1
+6,119,50,22,176,27.1,1.318,33,1
+2,146,76,35,194,38.2,0.329,29,0
+9,184,85,15,0,30,1.213,49,1
+10,122,68,0,0,31.2,0.258,41,0
+0,165,90,33,680,52.3,0.427,23,0
+9,124,70,33,402,35.4,0.282,34,0
+1,111,86,19,0,30.1,0.143,23,0
+9,106,52,0,0,31.2,0.38,42,0
+2,129,84,0,0,28,0.284,27,0
+2,90,80,14,55,24.4,0.249,24,0
+0,86,68,32,0,35.8,0.238,25,0
+12,92,62,7,258,27.6,0.926,44,1
+1,113,64,35,0,33.6,0.543,21,1
+3,111,56,39,0,30.1,0.557,30,0
+2,114,68,22,0,28.7,0.092,25,0
+1,193,50,16,375,25.9,0.655,24,0
+11,155,76,28,150,33.3,1.353,51,1
+3,191,68,15,130,30.9,0.299,34,0
+3,141,0,0,0,30,0.761,27,1
+4,95,70,32,0,32.1,0.612,24,0
+3,142,80,15,0,32.4,0.2,63,0
+4,123,62,0,0,32,0.226,35,1
+5,96,74,18,67,33.6,0.997,43,0
+0,138,0,0,0,36.3,0.933,25,1
+2,128,64,42,0,40,1.101,24,0
+0,102,52,0,0,25.1,0.078,21,0
+2,146,0,0,0,27.5,0.24,28,1
+10,101,86,37,0,45.6,1.136,38,1
+2,108,62,32,56,25.2,0.128,21,0
+3,122,78,0,0,23,0.254,40,0
+1,71,78,50,45,33.2,0.422,21,0
+13,106,70,0,0,34.2,0.251,52,0
+2,100,70,52,57,40.5,0.677,25,0
+7,106,60,24,0,26.5,0.296,29,1
+0,104,64,23,116,27.8,0.454,23,0
+5,114,74,0,0,24.9,0.744,57,0
+2,108,62,10,278,25.3,0.881,22,0
+0,146,70,0,0,37.9,0.334,28,1
+10,129,76,28,122,35.9,0.28,39,0
+7,133,88,15,155,32.4,0.262,37,0
+7,161,86,0,0,30.4,0.165,47,1
+2,108,80,0,0,27,0.259,52,1
+7,136,74,26,135,26,0.647,51,0
+5,155,84,44,545,38.7,0.619,34,0
+1,119,86,39,220,45.6,0.808,29,1
+4,96,56,17,49,20.8,0.34,26,0
+5,108,72,43,75,36.1,0.263,33,0
+0,78,88,29,40,36.9,0.434,21,0
+0,107,62,30,74,36.6,0.757,25,1
+2,128,78,37,182,43.3,1.224,31,1
+1,128,48,45,194,40.5,0.613,24,1
+0,161,50,0,0,21.9,0.254,65,0
+6,151,62,31,120,35.5,0.692,28,0
+2,146,70,38,360,28,0.337,29,1
+0,126,84,29,215,30.7,0.52,24,0
+14,100,78,25,184,36.6,0.412,46,1
+8,112,72,0,0,23.6,0.84,58,0
+0,167,0,0,0,32.3,0.839,30,1
+2,144,58,33,135,31.6,0.422,25,1
+5,77,82,41,42,35.8,0.156,35,0
+5,115,98,0,0,52.9,0.209,28,1
+3,150,76,0,0,21,0.207,37,0
+2,120,76,37,105,39.7,0.215,29,0
+10,161,68,23,132,25.5,0.326,47,1
+0,137,68,14,148,24.8,0.143,21,0
+0,128,68,19,180,30.5,1.391,25,1
+2,124,68,28,205,32.9,0.875,30,1
+6,80,66,30,0,26.2,0.313,41,0
+0,106,70,37,148,39.4,0.605,22,0
+2,155,74,17,96,26.6,0.433,27,1
+3,113,50,10,85,29.5,0.626,25,0
+7,109,80,31,0,35.9,1.127,43,1
+2,112,68,22,94,34.1,0.315,26,0
+3,99,80,11,64,19.3,0.284,30,0
+3,182,74,0,0,30.5,0.345,29,1
+3,115,66,39,140,38.1,0.15,28,0
+6,194,78,0,0,23.5,0.129,59,1
+4,129,60,12,231,27.5,0.527,31,0
+3,112,74,30,0,31.6,0.197,25,1
+0,124,70,20,0,27.4,0.254,36,1
+13,152,90,33,29,26.8,0.731,43,1
+2,112,75,32,0,35.7,0.148,21,0
+1,157,72,21,168,25.6,0.123,24,0
+1,122,64,32,156,35.1,0.692,30,1
+10,179,70,0,0,35.1,0.2,37,0
+2,102,86,36,120,45.5,0.127,23,1
+6,105,70,32,68,30.8,0.122,37,0
+8,118,72,19,0,23.1,1.476,46,0
+2,87,58,16,52,32.7,0.166,25,0
+1,180,0,0,0,43.3,0.282,41,1
+12,106,80,0,0,23.6,0.137,44,0
+1,95,60,18,58,23.9,0.26,22,0
+0,165,76,43,255,47.9,0.259,26,0
+0,117,0,0,0,33.8,0.932,44,0
+5,115,76,0,0,31.2,0.343,44,1
+9,152,78,34,171,34.2,0.893,33,1
+7,178,84,0,0,39.9,0.331,41,1
+1,130,70,13,105,25.9,0.472,22,0
+1,95,74,21,73,25.9,0.673,36,0
+1,0,68,35,0,32,0.389,22,0
+5,122,86,0,0,34.7,0.29,33,0
+8,95,72,0,0,36.8,0.485,57,0
+8,126,88,36,108,38.5,0.349,49,0
+1,139,46,19,83,28.7,0.654,22,0
+3,116,0,0,0,23.5,0.187,23,0
+3,99,62,19,74,21.8,0.279,26,0
+5,0,80,32,0,41,0.346,37,1
+4,92,80,0,0,42.2,0.237,29,0
+4,137,84,0,0,31.2,0.252,30,0
+3,61,82,28,0,34.4,0.243,46,0
+1,90,62,12,43,27.2,0.58,24,0
+3,90,78,0,0,42.7,0.559,21,0
+9,165,88,0,0,30.4,0.302,49,1
+1,125,50,40,167,33.3,0.962,28,1
+13,129,0,30,0,39.9,0.569,44,1
+12,88,74,40,54,35.3,0.378,48,0
+1,196,76,36,249,36.5,0.875,29,1
+5,189,64,33,325,31.2,0.583,29,1
+5,158,70,0,0,29.8,0.207,63,0
+5,103,108,37,0,39.2,0.305,65,0
+4,146,78,0,0,38.5,0.52,67,1
+4,147,74,25,293,34.9,0.385,30,0
+5,99,54,28,83,34,0.499,30,0
+6,124,72,0,0,27.6,0.368,29,1
+0,101,64,17,0,21,0.252,21,0
+3,81,86,16,66,27.5,0.306,22,0
+1,133,102,28,140,32.8,0.234,45,1
+3,173,82,48,465,38.4,2.137,25,1
+0,118,64,23,89,0,1.731,21,0
+0,84,64,22,66,35.8,0.545,21,0
+2,105,58,40,94,34.9,0.225,25,0
+2,122,52,43,158,36.2,0.816,28,0
+12,140,82,43,325,39.2,0.528,58,1
+0,98,82,15,84,25.2,0.299,22,0
+1,87,60,37,75,37.2,0.509,22,0
+4,156,75,0,0,48.3,0.238,32,1
+0,93,100,39,72,43.4,1.021,35,0
+1,107,72,30,82,30.8,0.821,24,0
+0,105,68,22,0,20,0.236,22,0
+1,109,60,8,182,25.4,0.947,21,0
+1,90,62,18,59,25.1,1.268,25,0
+1,125,70,24,110,24.3,0.221,25,0
+1,119,54,13,50,22.3,0.205,24,0
+5,116,74,29,0,32.3,0.66,35,1
+8,105,100,36,0,43.3,0.239,45,1
+5,144,82,26,285,32,0.452,58,1
+3,100,68,23,81,31.6,0.949,28,0
+1,100,66,29,196,32,0.444,42,0
+5,166,76,0,0,45.7,0.34,27,1
+1,131,64,14,415,23.7,0.389,21,0
+4,116,72,12,87,22.1,0.463,37,0
+4,158,78,0,0,32.9,0.803,31,1
+2,127,58,24,275,27.7,1.6,25,0
+3,96,56,34,115,24.7,0.944,39,0
+0,131,66,40,0,34.3,0.196,22,1
+3,82,70,0,0,21.1,0.389,25,0
+3,193,70,31,0,34.9,0.241,25,1
+4,95,64,0,0,32,0.161,31,1
+6,137,61,0,0,24.2,0.151,55,0
+5,136,84,41,88,35,0.286,35,1
+9,72,78,25,0,31.6,0.28,38,0
+5,168,64,0,0,32.9,0.135,41,1
+2,123,48,32,165,42.1,0.52,26,0
+4,115,72,0,0,28.9,0.376,46,1
+0,101,62,0,0,21.9,0.336,25,0
+8,197,74,0,0,25.9,1.191,39,1
+1,172,68,49,579,42.4,0.702,28,1
+6,102,90,39,0,35.7,0.674,28,0
+1,112,72,30,176,34.4,0.528,25,0
+1,143,84,23,310,42.4,1.076,22,0
+1,143,74,22,61,26.2,0.256,21,0
+0,138,60,35,167,34.6,0.534,21,1
+3,173,84,33,474,35.7,0.258,22,1
+1,97,68,21,0,27.2,1.095,22,0
+4,144,82,32,0,38.5,0.554,37,1
+1,83,68,0,0,18.2,0.624,27,0
+3,129,64,29,115,26.4,0.219,28,1
+1,119,88,41,170,45.3,0.507,26,0
+2,94,68,18,76,26,0.561,21,0
+0,102,64,46,78,40.6,0.496,21,0
+2,115,64,22,0,30.8,0.421,21,0
+8,151,78,32,210,42.9,0.516,36,1
+4,184,78,39,277,37,0.264,31,1
+0,94,0,0,0,0,0.256,25,0
+1,181,64,30,180,34.1,0.328,38,1
+0,135,94,46,145,40.6,0.284,26,0
+1,95,82,25,180,35,0.233,43,1
+2,99,0,0,0,22.2,0.108,23,0
+3,89,74,16,85,30.4,0.551,38,0
+1,80,74,11,60,30,0.527,22,0
+2,139,75,0,0,25.6,0.167,29,0
+1,90,68,8,0,24.5,1.138,36,0
+0,141,0,0,0,42.4,0.205,29,1
+12,140,85,33,0,37.4,0.244,41,0
+5,147,75,0,0,29.9,0.434,28,0
+1,97,70,15,0,18.2,0.147,21,0
+6,107,88,0,0,36.8,0.727,31,0
+0,189,104,25,0,34.3,0.435,41,1
+2,83,66,23,50,32.2,0.497,22,0
+4,117,64,27,120,33.2,0.23,24,0
+8,108,70,0,0,30.5,0.955,33,1
+4,117,62,12,0,29.7,0.38,30,1
+0,180,78,63,14,59.4,2.42,25,1
+1,100,72,12,70,25.3,0.658,28,0
+0,95,80,45,92,36.5,0.33,26,0
+0,104,64,37,64,33.6,0.51,22,1
+0,120,74,18,63,30.5,0.285,26,0
+1,82,64,13,95,21.2,0.415,23,0
+2,134,70,0,0,28.9,0.542,23,1
+0,91,68,32,210,39.9,0.381,25,0
+2,119,0,0,0,19.6,0.832,72,0
+2,100,54,28,105,37.8,0.498,24,0
+14,175,62,30,0,33.6,0.212,38,1
+1,135,54,0,0,26.7,0.687,62,0
+5,86,68,28,71,30.2,0.364,24,0
+10,148,84,48,237,37.6,1.001,51,1
+9,134,74,33,60,25.9,0.46,81,0
+9,120,72,22,56,20.8,0.733,48,0
+1,71,62,0,0,21.8,0.416,26,0
+8,74,70,40,49,35.3,0.705,39,0
+5,88,78,30,0,27.6,0.258,37,0
+10,115,98,0,0,24,1.022,34,0
+0,124,56,13,105,21.8,0.452,21,0
+0,74,52,10,36,27.8,0.269,22,0
+0,97,64,36,100,36.8,0.6,25,0
+8,120,0,0,0,30,0.183,38,1
+6,154,78,41,140,46.1,0.571,27,0
+1,144,82,40,0,41.3,0.607,28,0
+0,137,70,38,0,33.2,0.17,22,0
+0,119,66,27,0,38.8,0.259,22,0
+7,136,90,0,0,29.9,0.21,50,0
+4,114,64,0,0,28.9,0.126,24,0
+0,137,84,27,0,27.3,0.231,59,0
+2,105,80,45,191,33.7,0.711,29,1
+7,114,76,17,110,23.8,0.466,31,0
+8,126,74,38,75,25.9,0.162,39,0
+4,132,86,31,0,28,0.419,63,0
+3,158,70,30,328,35.5,0.344,35,1
+0,123,88,37,0,35.2,0.197,29,0
+4,85,58,22,49,27.8,0.306,28,0
+0,84,82,31,125,38.2,0.233,23,0
+0,145,0,0,0,44.2,0.63,31,1
+0,135,68,42,250,42.3,0.365,24,1
+1,139,62,41,480,40.7,0.536,21,0
+0,173,78,32,265,46.5,1.159,58,0
+4,99,72,17,0,25.6,0.294,28,0
+8,194,80,0,0,26.1,0.551,67,0
+2,83,65,28,66,36.8,0.629,24,0
+2,89,90,30,0,33.5,0.292,42,0
+4,99,68,38,0,32.8,0.145,33,0
+4,125,70,18,122,28.9,1.144,45,1
+3,80,0,0,0,0,0.174,22,0
+6,166,74,0,0,26.6,0.304,66,0
+5,110,68,0,0,26,0.292,30,0
+2,81,72,15,76,30.1,0.547,25,0
+7,195,70,33,145,25.1,0.163,55,1
+6,154,74,32,193,29.3,0.839,39,0
+2,117,90,19,71,25.2,0.313,21,0
+3,84,72,32,0,37.2,0.267,28,0
+6,0,68,41,0,39,0.727,41,1
+7,94,64,25,79,33.3,0.738,41,0
+3,96,78,39,0,37.3,0.238,40,0
+10,75,82,0,0,33.3,0.263,38,0
+0,180,90,26,90,36.5,0.314,35,1
+1,130,60,23,170,28.6,0.692,21,0
+2,84,50,23,76,30.4,0.968,21,0
+8,120,78,0,0,25,0.409,64,0
+12,84,72,31,0,29.7,0.297,46,1
+0,139,62,17,210,22.1,0.207,21,0
+9,91,68,0,0,24.2,0.2,58,0
+2,91,62,0,0,27.3,0.525,22,0
+3,99,54,19,86,25.6,0.154,24,0
+3,163,70,18,105,31.6,0.268,28,1
+9,145,88,34,165,30.3,0.771,53,1
+7,125,86,0,0,37.6,0.304,51,0
+13,76,60,0,0,32.8,0.18,41,0
+6,129,90,7,326,19.6,0.582,60,0
+2,68,70,32,66,25,0.187,25,0
+3,124,80,33,130,33.2,0.305,26,0
+6,114,0,0,0,0,0.189,26,0
+9,130,70,0,0,34.2,0.652,45,1
+3,125,58,0,0,31.6,0.151,24,0
+3,87,60,18,0,21.8,0.444,21,0
+1,97,64,19,82,18.2,0.299,21,0
+3,116,74,15,105,26.3,0.107,24,0
+0,117,66,31,188,30.8,0.493,22,0
+0,111,65,0,0,24.6,0.66,31,0
+2,122,60,18,106,29.8,0.717,22,0
+0,107,76,0,0,45.3,0.686,24,0
+1,86,66,52,65,41.3,0.917,29,0
+6,91,0,0,0,29.8,0.501,31,0
+1,77,56,30,56,33.3,1.251,24,0
+4,132,0,0,0,32.9,0.302,23,1
+0,105,90,0,0,29.6,0.197,46,0
+0,57,60,0,0,21.7,0.735,67,0
+0,127,80,37,210,36.3,0.804,23,0
+3,129,92,49,155,36.4,0.968,32,1
+8,100,74,40,215,39.4,0.661,43,1
+3,128,72,25,190,32.4,0.549,27,1
+10,90,85,32,0,34.9,0.825,56,1
+4,84,90,23,56,39.5,0.159,25,0
+1,88,78,29,76,32,0.365,29,0
+8,186,90,35,225,34.5,0.423,37,1
+5,187,76,27,207,43.6,1.034,53,1
+4,131,68,21,166,33.1,0.16,28,0
+1,164,82,43,67,32.8,0.341,50,0
+4,189,110,31,0,28.5,0.68,37,0
+1,116,70,28,0,27.4,0.204,21,0
+3,84,68,30,106,31.9,0.591,25,0
+6,114,88,0,0,27.8,0.247,66,0
+1,88,62,24,44,29.9,0.422,23,0
+1,84,64,23,115,36.9,0.471,28,0
+7,124,70,33,215,25.5,0.161,37,0
+1,97,70,40,0,38.1,0.218,30,0
+8,110,76,0,0,27.8,0.237,58,0
+11,103,68,40,0,46.2,0.126,42,0
+11,85,74,0,0,30.1,0.3,35,0
+6,125,76,0,0,33.8,0.121,54,1
+0,198,66,32,274,41.3,0.502,28,1
+1,87,68,34,77,37.6,0.401,24,0
+6,99,60,19,54,26.9,0.497,32,0
+0,91,80,0,0,32.4,0.601,27,0
+2,95,54,14,88,26.1,0.748,22,0
+1,99,72,30,18,38.6,0.412,21,0
+6,92,62,32,126,32,0.085,46,0
+4,154,72,29,126,31.3,0.338,37,0
+0,121,66,30,165,34.3,0.203,33,1
+3,78,70,0,0,32.5,0.27,39,0
+2,130,96,0,0,22.6,0.268,21,0
+3,111,58,31,44,29.5,0.43,22,0
+2,98,60,17,120,34.7,0.198,22,0
+1,143,86,30,330,30.1,0.892,23,0
+1,119,44,47,63,35.5,0.28,25,0
+6,108,44,20,130,24,0.813,35,0
+2,118,80,0,0,42.9,0.693,21,1
+10,133,68,0,0,27,0.245,36,0
+2,197,70,99,0,34.7,0.575,62,1
+0,151,90,46,0,42.1,0.371,21,1
+6,109,60,27,0,25,0.206,27,0
+12,121,78,17,0,26.5,0.259,62,0
+8,100,76,0,0,38.7,0.19,42,0
+8,124,76,24,600,28.7,0.687,52,1
+1,93,56,11,0,22.5,0.417,22,0
+8,143,66,0,0,34.9,0.129,41,1
+6,103,66,0,0,24.3,0.249,29,0
+3,176,86,27,156,33.3,1.154,52,1
+0,73,0,0,0,21.1,0.342,25,0
+11,111,84,40,0,46.8,0.925,45,1
+2,112,78,50,140,39.4,0.175,24,0
+3,132,80,0,0,34.4,0.402,44,1
+2,82,52,22,115,28.5,1.699,25,0
+6,123,72,45,230,33.6,0.733,34,0
+0,188,82,14,185,32,0.682,22,1
+0,67,76,0,0,45.3,0.194,46,0
+1,89,24,19,25,27.8,0.559,21,0
+1,173,74,0,0,36.8,0.088,38,1
+1,109,38,18,120,23.1,0.407,26,0
+1,108,88,19,0,27.1,0.4,24,0
+6,96,0,0,0,23.7,0.19,28,0
+1,124,74,36,0,27.8,0.1,30,0
+7,150,78,29,126,35.2,0.692,54,1
+4,183,0,0,0,28.4,0.212,36,1
+1,124,60,32,0,35.8,0.514,21,0
+1,181,78,42,293,40,1.258,22,1
+1,92,62,25,41,19.5,0.482,25,0
+0,152,82,39,272,41.5,0.27,27,0
+1,111,62,13,182,24,0.138,23,0
+3,106,54,21,158,30.9,0.292,24,0
+3,174,58,22,194,32.9,0.593,36,1
+7,168,88,42,321,38.2,0.787,40,1
+6,105,80,28,0,32.5,0.878,26,0
+11,138,74,26,144,36.1,0.557,50,1
+3,106,72,0,0,25.8,0.207,27,0
+6,117,96,0,0,28.7,0.157,30,0
+2,68,62,13,15,20.1,0.257,23,0
+9,112,82,24,0,28.2,1.282,50,1
+0,119,0,0,0,32.4,0.141,24,1
+2,112,86,42,160,38.4,0.246,28,0
+2,92,76,20,0,24.2,1.698,28,0
+6,183,94,0,0,40.8,1.461,45,0
+0,94,70,27,115,43.5,0.347,21,0
+2,108,64,0,0,30.8,0.158,21,0
+4,90,88,47,54,37.7,0.362,29,0
+0,125,68,0,0,24.7,0.206,21,0
+0,132,78,0,0,32.4,0.393,21,0
+5,128,80,0,0,34.6,0.144,45,0
+4,94,65,22,0,24.7,0.148,21,0
+7,114,64,0,0,27.4,0.732,34,1
+0,102,78,40,90,34.5,0.238,24,0
+2,111,60,0,0,26.2,0.343,23,0
+1,128,82,17,183,27.5,0.115,22,0
+10,92,62,0,0,25.9,0.167,31,0
+13,104,72,0,0,31.2,0.465,38,1
+5,104,74,0,0,28.8,0.153,48,0
+2,94,76,18,66,31.6,0.649,23,0
+7,97,76,32,91,40.9,0.871,32,1
+1,100,74,12,46,19.5,0.149,28,0
+0,102,86,17,105,29.3,0.695,27,0
+4,128,70,0,0,34.3,0.303,24,0
+6,147,80,0,0,29.5,0.178,50,1
+4,90,0,0,0,28,0.61,31,0
+3,103,72,30,152,27.6,0.73,27,0
+2,157,74,35,440,39.4,0.134,30,0
+1,167,74,17,144,23.4,0.447,33,1
+0,179,50,36,159,37.8,0.455,22,1
+11,136,84,35,130,28.3,0.26,42,1
+0,107,60,25,0,26.4,0.133,23,0
+1,91,54,25,100,25.2,0.234,23,0
+1,117,60,23,106,33.8,0.466,27,0
+5,123,74,40,77,34.1,0.269,28,0
+2,120,54,0,0,26.8,0.455,27,0
+1,106,70,28,135,34.2,0.142,22,0
+2,155,52,27,540,38.7,0.24,25,1
+2,101,58,35,90,21.8,0.155,22,0
+1,120,80,48,200,38.9,1.162,41,0
+11,127,106,0,0,39,0.19,51,0
+3,80,82,31,70,34.2,1.292,27,1
+10,162,84,0,0,27.7,0.182,54,0
+1,199,76,43,0,42.9,1.394,22,1
+8,167,106,46,231,37.6,0.165,43,1
+9,145,80,46,130,37.9,0.637,40,1
+6,115,60,39,0,33.7,0.245,40,1
+1,112,80,45,132,34.8,0.217,24,0
+4,145,82,18,0,32.5,0.235,70,1
+10,111,70,27,0,27.5,0.141,40,1
+6,98,58,33,190,34,0.43,43,0
+9,154,78,30,100,30.9,0.164,45,0
+6,165,68,26,168,33.6,0.631,49,0
+1,99,58,10,0,25.4,0.551,21,0
+10,68,106,23,49,35.5,0.285,47,0
+3,123,100,35,240,57.3,0.88,22,0
+8,91,82,0,0,35.6,0.587,68,0
+6,195,70,0,0,30.9,0.328,31,1
+9,156,86,0,0,24.8,0.23,53,1
+0,93,60,0,0,35.3,0.263,25,0
+3,121,52,0,0,36,0.127,25,1
+2,101,58,17,265,24.2,0.614,23,0
+2,56,56,28,45,24.2,0.332,22,0
+0,162,76,36,0,49.6,0.364,26,1
+0,95,64,39,105,44.6,0.366,22,0
+4,125,80,0,0,32.3,0.536,27,1
+5,136,82,0,0,0,0.64,69,0
+2,129,74,26,205,33.2,0.591,25,0
+3,130,64,0,0,23.1,0.314,22,0
+1,107,50,19,0,28.3,0.181,29,0
+1,140,74,26,180,24.1,0.828,23,0
+1,144,82,46,180,46.1,0.335,46,1
+8,107,80,0,0,24.6,0.856,34,0
+13,158,114,0,0,42.3,0.257,44,1
+2,121,70,32,95,39.1,0.886,23,0
+7,129,68,49,125,38.5,0.439,43,1
+2,90,60,0,0,23.5,0.191,25,0
+7,142,90,24,480,30.4,0.128,43,1
+3,169,74,19,125,29.9,0.268,31,1
+0,99,0,0,0,25,0.253,22,0
+4,127,88,11,155,34.5,0.598,28,0
+4,118,70,0,0,44.5,0.904,26,0
+2,122,76,27,200,35.9,0.483,26,0
+6,125,78,31,0,27.6,0.565,49,1
+1,168,88,29,0,35,0.905,52,1
+2,129,0,0,0,38.5,0.304,41,0
+4,110,76,20,100,28.4,0.118,27,0
+6,80,80,36,0,39.8,0.177,28,0
+10,115,0,0,0,0,0.261,30,1
+2,127,46,21,335,34.4,0.176,22,0
+9,164,78,0,0,32.8,0.148,45,1
+2,93,64,32,160,38,0.674,23,1
+3,158,64,13,387,31.2,0.295,24,0
+5,126,78,27,22,29.6,0.439,40,0
+10,129,62,36,0,41.2,0.441,38,1
+0,134,58,20,291,26.4,0.352,21,0
+3,102,74,0,0,29.5,0.121,32,0
+7,187,50,33,392,33.9,0.826,34,1
+3,173,78,39,185,33.8,0.97,31,1
+10,94,72,18,0,23.1,0.595,56,0
+1,108,60,46,178,35.5,0.415,24,0
+5,97,76,27,0,35.6,0.378,52,1
+4,83,86,19,0,29.3,0.317,34,0
+1,114,66,36,200,38.1,0.289,21,0
+1,149,68,29,127,29.3,0.349,42,1
+5,117,86,30,105,39.1,0.251,42,0
+1,111,94,0,0,32.8,0.265,45,0
+4,112,78,40,0,39.4,0.236,38,0
+1,116,78,29,180,36.1,0.496,25,0
+0,141,84,26,0,32.4,0.433,22,0
+2,175,88,0,0,22.9,0.326,22,0
+2,92,52,0,0,30.1,0.141,22,0
+3,130,78,23,79,28.4,0.323,34,1
+8,120,86,0,0,28.4,0.259,22,1
+2,174,88,37,120,44.5,0.646,24,1
+2,106,56,27,165,29,0.426,22,0
+2,105,75,0,0,23.3,0.56,53,0
+4,95,60,32,0,35.4,0.284,28,0
+0,126,86,27,120,27.4,0.515,21,0
+8,65,72,23,0,32,0.6,42,0
+2,99,60,17,160,36.6,0.453,21,0
+1,102,74,0,0,39.5,0.293,42,1
+11,120,80,37,150,42.3,0.785,48,1
+3,102,44,20,94,30.8,0.4,26,0
+1,109,58,18,116,28.5,0.219,22,0
+9,140,94,0,0,32.7,0.734,45,1
+13,153,88,37,140,40.6,1.174,39,0
+12,100,84,33,105,30,0.488,46,0
+1,147,94,41,0,49.3,0.358,27,1
+1,81,74,41,57,46.3,1.096,32,0
+3,187,70,22,200,36.4,0.408,36,1
+6,162,62,0,0,24.3,0.178,50,1
+4,136,70,0,0,31.2,1.182,22,1
+1,121,78,39,74,39,0.261,28,0
+3,108,62,24,0,26,0.223,25,0
+0,181,88,44,510,43.3,0.222,26,1
+8,154,78,32,0,32.4,0.443,45,1
+1,128,88,39,110,36.5,1.057,37,1
+7,137,90,41,0,32,0.391,39,0
+0,123,72,0,0,36.3,0.258,52,1
+1,106,76,0,0,37.5,0.197,26,0
+6,190,92,0,0,35.5,0.278,66,1
+2,88,58,26,16,28.4,0.766,22,0
+9,170,74,31,0,44,0.403,43,1
+9,89,62,0,0,22.5,0.142,33,0
+10,101,76,48,180,32.9,0.171,63,0
+2,122,70,27,0,36.8,0.34,27,0
+5,121,72,23,112,26.2,0.245,30,0
+1,126,60,0,0,30.1,0.349,47,1
+1,93,70,31,0,30.4,0.315,23,0