{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Maazuddin1/Diabetes_prediction/blob/main/Diabetes_prediction_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmfPRLGyV_JO"
      },
      "source": [
        "# **Predictive modelling for Diabetes**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jh4WhkrODvKy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YDRBsHbCEIow"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "I8uKq3nIFLaT",
        "outputId": "ddce1aff-46f8-4265-afff-dc657021c58d"
      },
      "outputs": [
        {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
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              "      <th>BMI</th>\n",
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              "      <th>Outcome</th>\n",
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              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d01a70c3-4765-4880-99e4-59974694d595 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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  "
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/datascience/diabetes (1).csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IutjpRqFDSs1"
      },
      "source": [
        "# **DATA PREPROCESSING & EDA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DUM3XEcUGE1W",
        "outputId": "9678230b-5106-4ede-bad6-dd095edf186e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
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              "\n",
              "  <div id=\"df-605afd8a-2a11-46cd-99db-4e39aeb4e429\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "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",
              "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  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  \n",
              "..                        ...  ...      ...  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  \n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wObmQoogGBMC",
        "outputId": "415e4ed0-f346-4830-d9f6-9caa121ca94c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n",
            "shape of dataset : { (768, 9) }\n",
            "Pregnancies                   int64\n",
            "Glucose                       int64\n",
            "BloodPressure                 int64\n",
            "SkinThickness                 int64\n",
            "Insulin                       int64\n",
            "BMI                         float64\n",
            "DiabetesPedigreeFunction    float64\n",
            "Age                           int64\n",
            "Outcome                       int64\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(df.isnull().sum())\n",
        "print(\"shape of dataset : {\",df.shape,\"}\")\n",
        "print(df.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbssz2twXmdg",
        "outputId": "0bdc4ab1-359c-41d4-f4d4-3d380254d4ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       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  \n"
          ]
        }
      ],
      "source": [
        "print(df.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "XQRDkoCOC_2U",
        "outputId": "b53619dd-72db-4617-8a6a-a5d8c5328578"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(df.corr(),annot=True,cmap='coolwarm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "aUO6GmVABb9J",
        "outputId": "b539b19d-c7f1-4c86-d255-e1837e979ffe"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-123-cdc48b108383>:6: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  plt.subplot((int(length)//2), 3, j + 1)\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1500 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import itertools\n",
        "col = df.columns[:8]\n",
        "plt.subplots(figsize = (20, 15))\n",
        "length =int(len(col))\n",
        "for i, j in itertools.zip_longest(col, range(length)):\n",
        "    plt.subplot((int(length)//2), 3, j + 1)\n",
        "    plt.subplots_adjust(wspace = 0.1,hspace = 0.5)\n",
        "    df[i].hist(bins = 20)\n",
        "    plt.title(i)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "trHYk66OKkUj",
        "outputId": "b5b31bf0-374d-4017-eea9-91f89ddc0e88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total patients: 768\n",
            "patients cured: 500\n",
            "patients not cured: 268\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Outcome', ylabel='count'>"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"total patients:\",df.shape[0])\n",
        "print(\"patients cured:\",df['Outcome'].value_counts()[0])\n",
        "print(\"patients not cured:\",df['Outcome'].value_counts()[1])\n",
        "sns.countplot(x='Outcome',data=df,hue='Outcome')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GsfLWX6ZT3mf",
        "outputId": "b8c6821d-1380-4143-b80d-c50507ff6eb1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.8525053581062,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.10546875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684730531098,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.536458333333332,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.47695137724279896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "    <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",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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  "
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "zgcPA5LUUTf-",
        "outputId": "5128a423-61e0-4a91-8de4-27a7072cd34c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"mean_glucose = df['Glucose'].mean()\\ndf['Glucose'] = df['Glucose'].replace(0,mean_glucose)\\nmean_bp = df['BloodPressure'].mean()\\ndf['BloodPressure'] = df['BloodPressure'].replace(0,mean_bp)\\nmean_skin = df['SkinThickness'].mean()\\ndf['SkinThickness'] = df['SkinThickness'].replace(0,mean_skin)\\nmean_bmi = df['BMI'].mean()\\ndf['BMI'] = df['BMI'].replace(0,mean_bmi)\\ndf['Insulin'] = df['Insulin'].replace(0,df['Insulin'].mean())\""
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''mean_glucose = df['Glucose'].mean()\n",
        "df['Glucose'] = df['Glucose'].replace(0,mean_glucose)\n",
        "mean_bp = df['BloodPressure'].mean()\n",
        "df['BloodPressure'] = df['BloodPressure'].replace(0,mean_bp)\n",
        "mean_skin = df['SkinThickness'].mean()\n",
        "df['SkinThickness'] = df['SkinThickness'].replace(0,mean_skin)\n",
        "mean_bmi = df['BMI'].mean()\n",
        "df['BMI'] = df['BMI'].replace(0,mean_bmi)\n",
        "df['Insulin'] = df['Insulin'].replace(0,df['Insulin'].mean())'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "heFpz7LnMVuT",
        "outputId": "9918c59c-7d1b-4106-b46f-5919bc2aa834"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th>Pregnancies</th>\n",
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              "      <th>DiabetesPedigreeFunction</th>\n",
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              "      <th>Outcome</th>\n",
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              "      <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",
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              "      <td>0.429734</td>\n",
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              "      <th>1</th>\n",
              "      <td>4.865672</td>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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  "
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.groupby('Outcome').mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IAjvmnW-N838"
      },
      "outputs": [],
      "source": [
        "# lets split data into input and output\n",
        "X = df.drop(columns='Outcome',axis=1)\n",
        "Y = df['Outcome']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Hjs8ungwTGk1",
        "outputId": "b74e6e82-286b-4332-9c02-e18913bf065a"
      },
      "outputs": [
        {
          "data": {
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              "      <th>1</th>\n",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7f5c5ce-3f2e-49c4-a657-d818f9aebddf')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c7f5c5ce-3f2e-49c4-a657-d818f9aebddf 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-c7f5c5ce-3f2e-49c4-a657-d818f9aebddf');\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",
              "\n",
              "\n",
              "<div id=\"df-0b6f284e-9c72-4578-ac0f-60ca31826378\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0b6f284e-9c72-4578-ac0f-60ca31826378')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b6f284e-9c72-4578-ac0f-60ca31826378 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_85023db9-1ec7-49e2-98fb-3d9e9386f986\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_85023db9-1ec7-49e2-98fb-3d9e9386f986 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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",
              "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]"
            ]
          },
          "execution_count": 129,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVo0hIBJTpBV",
        "outputId": "86074766-bdfd-4a39-dea9-f01252a99ff7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ldH2ZHmTpyc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aceWsw5EWsJG"
      },
      "source": [
        "# **data standardization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OPL2gcUWyG2",
        "outputId": "9f92b4c3-8c67-4ea1-8da0-6574da661a22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "scaler= StandardScaler()\n",
        "scaler.fit(X)\n",
        "standardized_data = scaler.transform(X)\n",
        "print(standardized_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x_gFAglxXp92"
      },
      "outputs": [],
      "source": [
        "# save scaled data into X,Y varibales\n",
        "X = standardized_data\n",
        "Y= df['Outcome']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-W0mdSSYoRX",
        "outputId": "f8ff13a4-b941-4fcf-f731-a85004106f94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "print(X)\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOJ_uN6BZHH3"
      },
      "source": [
        "# **train test split**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ax0hMd8KZNnF"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icLZgMTWQI24",
        "outputId": "a1922138-d704-4700-c31e-e6942ecaa381"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(768, 8) (614, 8) (154, 8)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i3OlGu879zxn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFGiWPKG94G2"
      },
      "source": [
        "# **ALGORITHM SELECTION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "35NpJ2__bHjH"
      },
      "outputs": [],
      "source": [
        "models = {\n",
        "    'Logistic Regression': LogisticRegression(),\n",
        "    'Decision Tree': DecisionTreeClassifier(),\n",
        "    'Random Forest': RandomForestClassifier(),\n",
        "    'Gradient Boosting': GradientBoostingClassifier(),\n",
        "    'Support Vector Machine': SVC(),\n",
        "    'k-Nearest Neighbors': KNeighborsClassifier()\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4qgGA9IsrY3"
      },
      "source": [
        "**using single train_test_split data**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "possibly biased out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-uIuy4J9024",
        "outputId": "c70d883a-5d3d-4321-c9e0-4a90939180da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression: 0.7597\n",
            "Decision Tree: 0.6753\n",
            "Random Forest: 0.7468\n",
            "Gradient Boosting: 0.7078\n",
            "Support Vector Machine: 0.7273\n",
            "k-Nearest Neighbors: 0.7208\n",
            "\n",
            "Best model: Logistic Regression with accuracy: 0.7597\n"
          ]
        }
      ],
      "source": [
        "results = {}\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, Y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    results[name] = accuracy_score(Y_test, y_pred)\n",
        "    print(f'{name}: {results[name]:.4f}')\n",
        "\n",
        "# Print the best model\n",
        "best_model_name = max(results, key=results.get)\n",
        "print(f'\\nBest model: {best_model_name} with accuracy: {results[best_model_name]:.4f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "or3n5f1kralG"
      },
      "source": [
        "**using cross validation**\n",
        "genralized accuracy (preferable)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQWDewExqSMo",
        "outputId": "16111655-afd0-48c5-d65e-7a604a38f7aa"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'dir(cross_val_score)\\nhelp(cross_val_score)'"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''dir(cross_val_score)\n",
        "help(cross_val_score)'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0d2q6CHE907S",
        "outputId": "582b9393-f5ab-4455-fcff-adc817cdc97d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression: 0.7709\n",
            "Decision Tree: 0.7124\n",
            "Random Forest: 0.7696\n",
            "Gradient Boosting: 0.7566\n",
            "Support Vector Machine: 0.7709\n",
            "k-Nearest Neighbors: 0.7331\n",
            "\n",
            "Best model: Support Vector Machine with accuracy: 0.7709\n"
          ]
        }
      ],
      "source": [
        "# Evaluate each model using cross-validation\n",
        "results = {}\n",
        "for name, model in models.items():\n",
        "    scores = cross_val_score(model, X, Y, cv=5)\n",
        "    results[name] = scores.mean()\n",
        "    print(f'{name}: {scores.mean():.4f}')\n",
        "\n",
        "# Print the best model\n",
        "best_model_name = max(results, key=results.get)\n",
        "print(f'\\nBest model: {best_model_name} with accuracy: {results[best_model_name]:.4f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VC5XZl2wgzE6"
      },
      "source": [
        "# **SELECTING THE BEST CLASSIFICATION ALGORITHM**\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "W8r84zBnkmcQ"
      },
      "outputs": [],
      "source": [
        "#dir(classifier)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jy7zOvsXbKFP"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "**Training the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f7MDqkdaB-Ta"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "auusAbz9zKft"
      },
      "source": [
        "# Gridsearch on 2 best score algo - 1)logistic regg &  2)SVM\n",
        "---\n",
        "**to find best hyperparameters for both algos**\n",
        "---\n",
        "\n",
        "**1)LOGISTIC REGG**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApPfWQOm5oom",
        "outputId": "c73c0d49-56e6-47e8-a649-abbad95a400b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best hyperparameters for Logistic Regression: {'C': 1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "Best cross-validation score for Logistic Regression: 0.7708853238265002\n"
          ]
        }
      ],
      "source": [
        "param_grid = {\n",
        "    'penalty': ['l1', 'l2'],\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
        "    'solver': ['liblinear', 'saga']\n",
        "}\n",
        "\n",
        "logistic_reg = LogisticRegression()\n",
        "\n",
        "grid_search = GridSearchCV(logistic_reg, param_grid, cv=5, scoring='accuracy')\n",
        "grid_search.fit(X, Y)\n",
        "\n",
        "print(\"Best hyperparameters for Logistic Regression:\", grid_search.best_params_)\n",
        "print(\"Best cross-validation score for Logistic Regression:\", grid_search.best_score_)\n",
        "#gcv_df=pd.DataFrame(grid_search.cv_results_)\n",
        "#gcv_df[['param_C','param_penalty','mean_test_score']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dl0x49IL0z0W"
      },
      "source": [
        "**2)SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xm9D40-iNoz",
        "outputId": "ba3a6360-b649-4e1b-fd00-99a0730d17f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters for SVM: {'C': 1, 'kernel': 'linear'}\n",
            "Best cross-validation score for SVM: 0.7734827264239028\n"
          ]
        }
      ],
      "source": [
        "classifier_svc=SVC()\n",
        "\n",
        "param_grid_svc = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}\n",
        "grid_search_svc = GridSearchCV(classifier_svc, param_grid_svc, cv=5, scoring='accuracy')\n",
        "grid_search_svc.fit(X, Y)\n",
        "\n",
        "print(\"Best parameters for SVM:\", grid_search_svc.best_params_)\n",
        "print(\"Best cross-validation score for SVM:\", grid_search_svc.best_score_)\n",
        "grid_search_svc_df = pd.DataFrame(grid_search_svc.cv_results_)\n",
        "#grid_search_svc_df[['param_C', 'param_kernel', 'mean_test_score']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6P74cmj2qFj"
      },
      "source": [
        "**conclusion**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PqL2BUVn-O3",
        "outputId": "9ea372ce-7ae3-4b5f-adf3-56453493bc7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "best parameter for logistic regression {'C': 1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "score : 0.7708853238265002\n",
            "\n",
            "Best parameters for SVM: {'C': 1, 'kernel': 'linear'}\n",
            "Best cross-validation score for SVM: 0.7734827264239028\n"
          ]
        }
      ],
      "source": [
        "print(\"best parameter for logistic regression\",grid_search.best_params_)\n",
        "print(\"score :\",grid_search.best_score_)\n",
        "print(\"\\nBest parameters for SVM:\", grid_search_svc.best_params_)\n",
        "print(\"Best cross-validation score for SVM:\", grid_search_svc.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krf_bnXa57BZ"
      },
      "source": [
        "**Best parameters for SVM: {'C': 1, 'kernel': 'linear'}**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Best cross-validation score for SVM: 0.7734827264239028**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmEMZaZeR0ex"
      },
      "source": [
        "#**Train the final SVM model with the best hyperparameters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mHU3Y70n9Gv",
        "outputId": "f2acd065-e1e7-481d-dfa3-93eaf5b73061"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final accuracy of the SVM model: 0.7727\n"
          ]
        }
      ],
      "source": [
        "best_svc = SVC(C=1, kernel='linear')\n",
        "best_svc.fit(X_train, Y_train)\n",
        "\n",
        "y_pred = best_svc.predict(X_test)\n",
        "\n",
        "final_accuracy = accuracy_score(Y_test, y_pred)\n",
        "print(f'Final accuracy of the SVM model: {final_accuracy:.4f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mKL5EI8Hl9Y2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoQGL2t8XSYC",
        "outputId": "ceabb1e7-3927-4d05-8da5-6d6758da37e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 0.3429808   1.41167241  0.14964075 -0.09637905  0.82661621 -0.78595734\n",
            "   0.34768723  1.51108316]]\n",
            "[1]\n",
            "person is diabetic\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "#reshappe array for predicting one istance\n",
        "input_data_reshaped= input_data_as_numpy_array.reshape(1,-1)         #(1 row,-1 till last col)\n",
        "\n",
        "std_data=scaler.transform(input_data_reshaped)\n",
        "print(std_data)\n",
        "classifier_svc.fit(X_train,Y_train)\n",
        "prediction=classifier_svc.predict(std_data)\n",
        "print(prediction)\n",
        "\n",
        "if prediction[0]==0: #prediction is list so use 0th element as output\n",
        "    print('person is not diabetic')\n",
        "else:\n",
        "    print('person is diabetic')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VX42kAr4aJpf",
        "outputId": "9b116d47-e3c2-4595-fa95-bede447d443b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.91      0.84       100\n",
            "           1       0.76      0.52      0.62        54\n",
            "\n",
            "    accuracy                           0.77       154\n",
            "   macro avg       0.77      0.71      0.73       154\n",
            "weighted avg       0.77      0.77      0.76       154\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPHI_WvUamLM"
      },
      "source": [
        "**using svm**\n",
        "\n",
        "**accuracy 77.27%**\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyPJKK4eqKym/2KAkTgYfQay",
      "include_colab_link": true,
      "mount_file_id": "1_DzATjkNqJwnRO7TaioUaShWd-AVDou1",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}