binomial_Logistic_regression_Small_diabetes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MasrukHabib/Practice_of_Machine_Learning/blob/main/binomial_Logistic_regression_Small_diabetes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#import library"
      ],
      "metadata": {
        "id": "M9lJbYuRwWt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model"
      ],
      "metadata": {
        "id": "oGVey7KNDCW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xf2GyJf3Ctny",
        "outputId": "f008ad86-5fca-4dff-e761-80c8b4f8e4dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "\n",
        "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']\n",
        "# load dataset\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "pima = pd.read_csv(\"/content/drive/My Drive/Machine Learning/dataset/diabetes.csv\", header=0, names=col_names)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pima.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YSPwAf2IC7wF",
        "outputId": "5951499a-48b0-4c4f-aaf2-a89b9485c559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pregnant  glucose  bp  skin  insulin   bmi  pedigree  age  label\n",
              "0         6      148  72    35        0  33.6     0.627   50      1\n",
              "1         1       85  66    29        0  26.6     0.351   31      0\n",
              "2         8      183  64     0        0  23.3     0.672   32      1\n",
              "3         1       89  66    23       94  28.1     0.167   21      0\n",
              "4         0      137  40    35      168  43.1     2.288   33      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b98828e2-a800-4589-a4d0-beb2cd3d376f\" 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>pregnant</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bp</th>\n",
              "      <th>skin</th>\n",
              "      <th>insulin</th>\n",
              "      <th>bmi</th>\n",
              "      <th>pedigree</th>\n",
              "      <th>age</th>\n",
              "      <th>label</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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b98828e2-a800-4589-a4d0-beb2cd3d376f')\"\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-b98828e2-a800-4589-a4d0-beb2cd3d376f 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-b98828e2-a800-4589-a4d0-beb2cd3d376f');\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-4e24620c-ceb5-4420-983d-35af2ed64c44\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e24620c-ceb5-4420-983d-35af2ed64c44')\"\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",
              "\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-4e24620c-ceb5-4420-983d-35af2ed64c44 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pima",
              "summary": "{\n  \"name\": \"pima\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"pregnant\",\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\": \"bp\",\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\": \"skin\",\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\": \"pedigree\",\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\": \"label\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Selecting Feature"
      ],
      "metadata": {
        "id": "tyIVyXn7wsCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split dataset in features and target variable\n",
        "feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']\n",
        "X = pima[feature_cols] # Features\n",
        "y = pima.label # Target variable"
      ],
      "metadata": {
        "id": "92vxhqoSDo2Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting Data"
      ],
      "metadata": {
        "id": "er9tUhQawvww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split X and y into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=16)"
      ],
      "metadata": {
        "id": "4cg_ypAGDxmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Development and Prediction"
      ],
      "metadata": {
        "id": "T031rUdmwS0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import the class\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# instantiate the model (using the default parameters)\n",
        "logreg = LogisticRegression(random_state=16, max_iter=500 )\n",
        "\n",
        "# fit the model with data\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_test)"
      ],
      "metadata": {
        "id": "Qs52nBEwD9lq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Evaluation using Confusion Matrix"
      ],
      "metadata": {
        "id": "DC7KDzVTw2wu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Test confusion matrix\n",
        "# import the metrics class\n",
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "daxWti1XEBUg",
        "outputId": "684468c9-b0b7-47f3-fdeb-fd70507e7e5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[115,  10],\n",
              "       [ 25,  42]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Understanding of Confusion Matrix:\n",
        "\n",
        " TN = 115, FP = 10, FN = 25, TP = 42\n",
        "\n",
        "*   TP (True Positives): The number of positive samples correctly classified as positive. This is the value in the second row and second column. Thus, TP = 42.\n",
        "\n",
        "*   TN (True Negatives): The number of negative samples correctly classified as negative. This is the value in the first row and first column. Thus, TN = 115.\n",
        "\n",
        "\n",
        "*  FP (False Positives): The number of negative samples incorrectly classified as positive. This is the value in the first row and second column. Thus, FP = 10.\n",
        "\n",
        "*  FN (False Negatives): The number of positive samples incorrectly classified as negative. This is the value in the second row and first column. Thus, FN = 25.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zNTtJ0N0tdPV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import required modules\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib.text import Text # Import the Text object\n",
        "\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "Text(0.5,257.44,'Predicted label');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "B21buqH5GHl4",
        "outputId": "18d64971-f96e-441c-c9a9-cc1d1e302c08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Test Data\n",
        "#Confusion Matrix Evaluation Metrics\n",
        "from sklearn.metrics import classification_report\n",
        "target_names = ['without diabetes', 'with diabetes']\n",
        "print(classification_report(y_test, y_pred, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0o3zCS8XGPz2",
        "outputId": "59d9a05a-e3d1-41e1-c508-064ca650f615"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  precision    recall  f1-score   support\n",
            "\n",
            "without diabetes       0.82      0.92      0.87       125\n",
            "   with diabetes       0.81      0.63      0.71        67\n",
            "\n",
            "        accuracy                           0.82       192\n",
            "       macro avg       0.81      0.77      0.79       192\n",
            "    weighted avg       0.82      0.82      0.81       192\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Understanding of Classification report:\n",
        "\n",
        "\n",
        "Precision: The proportion of true positive results in what was predicted as positive.\n",
        "\n",
        "\n",
        "*  For \"without diabetes,\" precision is 0.82. This means that out of all the times the model predicted \"no diabetes,\" it was correct 82% of the time.\n",
        "\n",
        "\n",
        "*   For \"with diabetes,\" precision is 0.81. This means that out of all the times the model predicted \"diabetes,\" it was correct 81% of the time.\n",
        "\n",
        "\n",
        "Recall: The proportion of true positive results in what is actually positive.\n",
        "\n",
        "\n",
        "\n",
        "*   For \"without diabetes,\" recall is 0.92. This means that out of all the people who actually don't have diabetes, the model correctly identified 92% of them.\n",
        "\n",
        "*   For \"with diabetes,\" recall is 0.63. This means that out of all the people who actually have diabetes, the model correctly identified 63% of them.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "F1-Score: This is a balance between precision and recall. It gives a single score that tries to balance the two. Higher is better.\n",
        "\n",
        "\n",
        "*   For \"without diabetes,\" the F1-score is 0.87.\n",
        "*  For \"with diabetes,\" the F1-score is 0.71.\n",
        "\n",
        "Accuracy: The proportion of all predictions (both \"without diabetes\" and \"with diabetes\") that are correct. Here, it's 0.82 or 82%.\n",
        "\n",
        "Support: This is the number of instances in each category. There are 125 people without diabetes and 67 people with diabetes.\n",
        "\n"
      ],
      "metadata": {
        "id": "dnqhSV3VuZ8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ROC Curve\n",
        "y_pred_proba = logreg.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "UzjymaUdGia_",
        "outputId": "4731d853-54e5-46c7-b82d-bf87368ced45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: print confusion matrix for traning\n",
        "\n",
        "# Train confusion matrix\n",
        "y_train_pred = logreg.predict(X_train)\n",
        "cnf_matrix_train = metrics.confusion_matrix(y_train, y_train_pred)\n",
        "print(\"Confusion Matrix for Training Data:\")\n",
        "print(cnf_matrix_train)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9RCReowmFyT",
        "outputId": "7f08797e-0c54-4be4-ef3b-43f2393e5b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for Training Data:\n",
            "[[333  42]\n",
            " [ 88 113]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Train Data\n",
        "# Confusion Matrix Evaluation Metrics\n",
        "\n",
        "target_names = ['without diabetes', 'with diabetes']\n",
        "print(classification_report(y_train, y_train_pred, target_names=target_names))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLxUWtUInRYx",
        "outputId": "e2b497ed-c5c2-4fa8-c75c-7fee526315f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  precision    recall  f1-score   support\n",
            "\n",
            "without diabetes       0.79      0.89      0.84       375\n",
            "   with diabetes       0.73      0.56      0.63       201\n",
            "\n",
            "        accuracy                           0.77       576\n",
            "       macro avg       0.76      0.73      0.74       576\n",
            "    weighted avg       0.77      0.77      0.77       576\n",
            "\n"
          ]
        }
      ]
    }
  ]
}