{
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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# Mind Pulse\n",
        "\n"
      ],
      "metadata": {
        "id": "uVBQ8eFYMJii"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sOKb4InlIWgE"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using drive to load our dataset\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zt5eI3jZI-HI",
        "outputId": "f396fb7a-04ab-4656-d1c7-634bc71e5916"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/dataset/bs.csv\")\n",
        "del df['id'],df['ever_married'],df['work_type'],df['Residence_type']\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "q17IF39-JA5c",
        "outputId": "7827647c-c8a7-48bb-8c61-86df6396fc0d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender   age  hypertension  heart_disease  avg_glucose_level   bmi  \\\n",
              "0       Male  67.0             0              1             228.69  36.6   \n",
              "1     Female  61.0             0              0             202.21   NaN   \n",
              "2       Male  80.0             0              1             105.92  32.5   \n",
              "3     Female  49.0             0              0             171.23  34.4   \n",
              "4     Female  79.0             1              0             174.12  24.0   \n",
              "...      ...   ...           ...            ...                ...   ...   \n",
              "5105  Female  80.0             1              0              83.75   NaN   \n",
              "5106  Female  81.0             0              0             125.20  40.0   \n",
              "5107  Female  35.0             0              0              82.99  30.6   \n",
              "5108    Male  51.0             0              0             166.29  25.6   \n",
              "5109  Female  44.0             0              0              85.28  26.2   \n",
              "\n",
              "       smoking_status  stroke  \n",
              "0     formerly smoked       1  \n",
              "1        never smoked       1  \n",
              "2        never smoked       1  \n",
              "3              smokes       1  \n",
              "4        never smoked       1  \n",
              "...               ...     ...  \n",
              "5105     never smoked       0  \n",
              "5106     never smoked       0  \n",
              "5107     never smoked       0  \n",
              "5108  formerly smoked       0  \n",
              "5109          Unknown       0  \n",
              "\n",
              "[5110 rows x 8 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Female</td>\n",
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              "      <td>0</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</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",
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              "    <tr>\n",
              "      <th>5105</th>\n",
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              "      <td>0</td>\n",
              "      <td>83.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>Female</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>Female</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>30.6</td>\n",
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              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>Male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.6</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>Female</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5110 rows × 8 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5110,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Male\",\n          \"Female\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.61264672311349,\n        \"min\": 0.08,\n        \"max\": 82.0,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          45.0,\n          24.0,\n          33.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_disease\",\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      \"column\": \"avg_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.28356015058198,\n        \"min\": 55.12,\n        \"max\": 271.74,\n        \"num_unique_values\": 3979,\n        \"samples\": [\n          178.29,\n          156.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.854066729680164,\n        \"min\": 10.3,\n        \"max\": 97.6,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          49.5,\n          18.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoking_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"never smoked\",\n          \"Unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stroke\",\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['gender']=(df['gender']=='Male').astype(int)\n",
        "df['smoking_status']=(df['smoking_status']=='smokes').astype(int)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "P9x3rK02KCaZ",
        "outputId": "75594c20-f7b6-4365-fa1b-edeb50d1b94c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender   age  hypertension  heart_disease  avg_glucose_level   bmi  \\\n",
              "0          0  67.0             0              1             228.69  36.6   \n",
              "1          0  61.0             0              0             202.21   0.0   \n",
              "2          0  80.0             0              1             105.92  32.5   \n",
              "3          0  49.0             0              0             171.23  34.4   \n",
              "4          0  79.0             1              0             174.12  24.0   \n",
              "...      ...   ...           ...            ...                ...   ...   \n",
              "5105       0  80.0             1              0              83.75   0.0   \n",
              "5106       0  81.0             0              0             125.20  40.0   \n",
              "5107       0  35.0             0              0              82.99  30.6   \n",
              "5108       0  51.0             0              0             166.29  25.6   \n",
              "5109       0  44.0             0              0              85.28  26.2   \n",
              "\n",
              "      smoking_status  stroke  \n",
              "0                  0       1  \n",
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              "2                  0       1  \n",
              "3                  1       1  \n",
              "4                  0       1  \n",
              "...              ...     ...  \n",
              "5105               0       0  \n",
              "5106               0       0  \n",
              "5107               0       0  \n",
              "5108               0       0  \n",
              "5109               0       0  \n",
              "\n",
              "[5110 rows x 8 columns]"
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>80.0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>49.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>79.0</td>\n",
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              "      <td>174.12</td>\n",
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              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5107</th>\n",
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              "      <td>35.0</td>\n",
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              "      <td>82.99</td>\n",
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              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>0</td>\n",
              "      <td>51.0</td>\n",
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              "      <td>166.29</td>\n",
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              "      <td>44.0</td>\n",
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              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
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              "</table>\n",
              "<p>5110 rows × 8 columns</p>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5110,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.61264672311349,\n        \"min\": 0.08,\n        \"max\": 82.0,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_disease\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_glucose_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.28356015058198,\n        \"min\": 55.12,\n        \"max\": 271.74,\n        \"num_unique_values\": 3979,\n        \"samples\": [\n          178.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.529497256055075,\n        \"min\": 0.0,\n        \"max\": 97.6,\n        \"num_unique_values\": 419,\n        \"samples\": [\n          36.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoking_status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stroke\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.fillna(0)"
      ],
      "metadata": {
        "id": "Z3VztZ8HLtmD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = df.drop(['stroke'], axis = 1)\n",
        "y = df.stroke.values"
      ],
      "metadata": {
        "id": "jvdxSOtN35up"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y, test_size = 0.2, random_state= 0)"
      ],
      "metadata": {
        "id": "nk7QPMxYLian"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()\n",
        "lr.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TB8qV9OnkH_5",
        "outputId": "b710672b-3a9b-4cf4-fd59-7bb0cd436e41"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr.predict(x_test)"
      ],
      "metadata": {
        "id": "M66dC8FOXNEt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_pred,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L06DnXKhXPzS",
        "outputId": "cd79637c-876e-4d65-c515-f58c8b145481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.56      0.53         9\n",
            "           1       0.92      0.91      0.91        53\n",
            "\n",
            "    accuracy                           0.85        62\n",
            "   macro avg       0.71      0.73      0.72        62\n",
            "weighted avg       0.86      0.85      0.86        62\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('mp.pkl','wb') as f:\n",
        "    pickle.dump(lr,f)\n",
        "\n",
        "# load\n",
        "with open('mp.pkl', 'rb') as f:\n",
        "    lr = pickle.load(f)"
      ],
      "metadata": {
        "id": "4IrkPQCLXhYw"
      },
      "execution_count": 18,
      "outputs": []
    }
  ]
}