HW2_SVM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOuC2k6xdJqjC3g+4OOlmoi",
      "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/negarhonarvar/Machine-Learning/blob/main/HW2_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialization"
      ],
      "metadata": {
        "id": "va3wUF2Tnj3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "id": "IcHt6fW3s3jS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "28OFAa0ttpyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62e2fad0-438b-4888-e354-ba7465046c4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/dataset.csv')\n",
        "data"
      ],
      "metadata": {
        "id": "oUgKy6yzkLbJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "5dc5ebf8-1f1b-42b7-b30c-32a6531b5c49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0      30669    Male   3.0             0              0           No   \n",
              "1      30468    Male  58.0             1              0          Yes   \n",
              "2      16523  Female   8.0             0              0           No   \n",
              "3      56543  Female  70.0             0              0          Yes   \n",
              "4      46136    Male  14.0             0              0           No   \n",
              "...      ...     ...   ...           ...            ...          ...   \n",
              "43395  56196  Female  10.0             0              0           No   \n",
              "43396   5450  Female  56.0             0              0          Yes   \n",
              "43397  28375  Female  82.0             1              0          Yes   \n",
              "43398  27973    Male  40.0             0              0          Yes   \n",
              "43399  36271  Female  82.0             0              0          Yes   \n",
              "\n",
              "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0          children          Rural              95.12  18.0              NaN   \n",
              "1           Private          Urban              87.96  39.2     never smoked   \n",
              "2           Private          Urban             110.89  17.6              NaN   \n",
              "3           Private          Rural              69.04  35.9  formerly smoked   \n",
              "4      Never_worked          Rural             161.28  19.1              NaN   \n",
              "...             ...            ...                ...   ...              ...   \n",
              "43395      children          Urban              58.64  20.4     never smoked   \n",
              "43396      Govt_job          Urban             213.61  55.4  formerly smoked   \n",
              "43397       Private          Urban              91.94  28.9  formerly smoked   \n",
              "43398       Private          Urban              99.16  33.2     never smoked   \n",
              "43399       Private          Urban              79.48  20.6     never smoked   \n",
              "\n",
              "       stroke  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  \n",
              "...       ...  \n",
              "43395       0  \n",
              "43396       0  \n",
              "43397       0  \n",
              "43398       0  \n",
              "43399       0  \n",
              "\n",
              "[43400 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac6bdd90-d878-4e44-93dd-e5a05b20d22a\" 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>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30669</td>\n",
              "      <td>Male</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>children</td>\n",
              "      <td>Rural</td>\n",
              "      <td>95.12</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30468</td>\n",
              "      <td>Male</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>87.96</td>\n",
              "      <td>39.2</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16523</td>\n",
              "      <td>Female</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>110.89</td>\n",
              "      <td>17.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56543</td>\n",
              "      <td>Female</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>69.04</td>\n",
              "      <td>35.9</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46136</td>\n",
              "      <td>Male</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Never_worked</td>\n",
              "      <td>Rural</td>\n",
              "      <td>161.28</td>\n",
              "      <td>19.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43395</th>\n",
              "      <td>56196</td>\n",
              "      <td>Female</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>children</td>\n",
              "      <td>Urban</td>\n",
              "      <td>58.64</td>\n",
              "      <td>20.4</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43396</th>\n",
              "      <td>5450</td>\n",
              "      <td>Female</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>213.61</td>\n",
              "      <td>55.4</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43397</th>\n",
              "      <td>28375</td>\n",
              "      <td>Female</td>\n",
              "      <td>82.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>91.94</td>\n",
              "      <td>28.9</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43398</th>\n",
              "      <td>27973</td>\n",
              "      <td>Male</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>99.16</td>\n",
              "      <td>33.2</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43399</th>\n",
              "      <td>36271</td>\n",
              "      <td>Female</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>79.48</td>\n",
              "      <td>20.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43400 rows × 12 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-ac6bdd90-d878-4e44-93dd-e5a05b20d22a')\"\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-ac6bdd90-d878-4e44-93dd-e5a05b20d22a 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-ac6bdd90-d878-4e44-93dd-e5a05b20d22a');\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-e4f3d24e-8561-49ed-9fbd-d2196d2fa689\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4f3d24e-8561-49ed-9fbd-d2196d2fa689')\"\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-e4f3d24e-8561-49ed-9fbd-d2196d2fa689 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I also checked the BMI,Age and avg colostrol level for their possible and also missing values."
      ],
      "metadata": {
        "id": "_g71L5_8bI0w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experimenting on Dataset"
      ],
      "metadata": {
        "id": "Ohkze401LuTc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaVxBfrUY1kK",
        "outputId": "1f9d7537-c8f0-42f2-f624-7c5e07bb05af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', 'Female', 'Other'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['hypertension'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WI25VoKMZdkQ",
        "outputId": "a137b263-13f6-4b7f-a705-5e0c667f9025"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['heart_disease'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKB3ChhuZ4F_",
        "outputId": "5f9f7a3d-f753-4345-ccf2-fef9cb070cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['ever_married'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3VM7XQaaD-R",
        "outputId": "ebe79990-b57e-4132-a006-79d513238b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['work_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJy3-tuLaDx6",
        "outputId": "3c03ec4d-2dc8-44fe-8cef-c532a5a8d711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['children', 'Private', 'Never_worked', 'Self-employed', 'Govt_job'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Residence_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X4Ne0fwZwQQ",
        "outputId": "af509695-a79b-4814-a9c8-ebdee4f2ca36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Rural', 'Urban'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['smoking_status'].unique()\n",
        "data['smoking_status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajHQXtkSgDzB",
        "outputId": "431118a2-a239-4c15-c232-a6154c47e213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "never smoked       16053\n",
              "formerly smoked     7493\n",
              "smokes              6562\n",
              "Name: smoking_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Handling missing values"
      ],
      "metadata": {
        "id": "WCfuG3HIL7sJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By checking diffrent columns of the dataset I found out that 'smoking_status' and 'bmi' columns have missing values shown as NaN"
      ],
      "metadata": {
        "id": "G6dj44CObVLb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For handling this missing data we have 2 major solutions :\n",
        "\n",
        "1- we can remove this samples from our dataset if our dataset is large enough\n",
        "\n",
        "2- we can use data imputation methods\n",
        "\n",
        "this dataset has over 43000 samples so we can safely remove such examples.we can check if the dataset size shrinks heavily to decide whether to continue with removing or using dataimputation"
      ],
      "metadata": {
        "id": "Y4SnOIxjd3lX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['bmi'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "782ec8f0-c937-4336-c3bb-bf3e8f8509cd",
        "id": "7k63qD13gPLA"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['smoking_status'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2713b87-5da2-417e-b826-8c7456e3210b",
        "id": "I9WpoSzfgOwz"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "in worst possible case we will almost 1/3 of our dataset if we remove samples with missing values. so we use data imputation methods to work with such datas\n",
        "we use Average method for bmi and for smoking status we replace the missing values with \"never smoked\" value , since its the most used value"
      ],
      "metadata": {
        "id": "Yde53MC3iAS3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "9e7oXonjL3Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['smoking_status'].fillna('never smoked',inplace = True)\n",
        "data['smoking_status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0874221-d138-4471-fede-d138ef989920",
        "id": "J8U2_dRynEb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "never smoked       29345\n",
              "formerly smoked     7493\n",
              "smokes              6562\n",
              "Name: smoking_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As mentioned earlier, the NaN values of bmi column gonna be replaces with mean of this column values. now we take a step further in feature engineering , for next step we will discrete the age to ranges (binning method) and normalize bmi and glucose level"
      ],
      "metadata": {
        "id": "ps04U0W0o2Wi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['bmi'].fillna(data['bmi'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "ky56r8qDlttr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "7H_g4FmbnxWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in data.iterrows():\n",
        "    if row['age'] < 20:\n",
        "        data.at[index, 'age'] = 'teen'\n",
        "    if row['age'] >= 20 and row['age'] < 40:\n",
        "        data.at[index , 'age'] = 'young adults'\n",
        "    if row['age'] >= 40 and row['age'] <60:\n",
        "        data.at[index, 'age'] = 'adults'\n",
        "    if row['age'] >= 60 :\n",
        "        data.at[index, 'age'] = 'middle age adults'\n",
        "data['age'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTgY5T2OyXkY",
        "outputId": "0261b0ff-98b7-4f5f-980b-ebf96a886e8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adults               13090\n",
              "middle age adults    11127\n",
              "young adults         10672\n",
              "teen                  8511\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In next step we turn the columns with 2 options to binary"
      ],
      "metadata": {
        "id": "5QP2EO6FzjEA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lblenc=LabelEncoder()\n",
        "zero_one_valued = ['ever_married','Residence_type']\n",
        "for col in zero_one_valued:\n",
        "  data[col] = lblenc.fit_transform(data[col])\n",
        "# for No and Rural its 0 and for yes and Urban is 1\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "C1-RW3GIziRj",
        "outputId": "62fbb352-2c71-42fe-90f9-14f99b36843a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  gender                age  hypertension  heart_disease  \\\n",
              "0  30669    Male               teen             0              0   \n",
              "1  30468    Male             adults             1              0   \n",
              "2  16523  Female               teen             0              0   \n",
              "3  56543  Female  middle age adults             0              0   \n",
              "4  46136    Male               teen             0              0   \n",
              "\n",
              "   ever_married     work_type  Residence_type  avg_glucose_level   bmi  \\\n",
              "0             0      children               0              95.12  18.0   \n",
              "1             1       Private               1              87.96  39.2   \n",
              "2             0       Private               1             110.89  17.6   \n",
              "3             1       Private               0              69.04  35.9   \n",
              "4             0  Never_worked               0             161.28  19.1   \n",
              "\n",
              "    smoking_status  stroke  \n",
              "0     never smoked       0  \n",
              "1     never smoked       0  \n",
              "2     never smoked       0  \n",
              "3  formerly smoked       0  \n",
              "4     never smoked       0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0381db35-ed7a-4592-9440-4c90dbe0a111\" 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>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30669</td>\n",
              "      <td>Male</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>children</td>\n",
              "      <td>0</td>\n",
              "      <td>95.12</td>\n",
              "      <td>18.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30468</td>\n",
              "      <td>Male</td>\n",
              "      <td>adults</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>87.96</td>\n",
              "      <td>39.2</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16523</td>\n",
              "      <td>Female</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>110.89</td>\n",
              "      <td>17.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56543</td>\n",
              "      <td>Female</td>\n",
              "      <td>middle age adults</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>0</td>\n",
              "      <td>69.04</td>\n",
              "      <td>35.9</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46136</td>\n",
              "      <td>Male</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Never_worked</td>\n",
              "      <td>0</td>\n",
              "      <td>161.28</td>\n",
              "      <td>19.1</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</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-0381db35-ed7a-4592-9440-4c90dbe0a111')\"\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-0381db35-ed7a-4592-9440-4c90dbe0a111 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-0381db35-ed7a-4592-9440-4c90dbe0a111');\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-a95ae67a-c7ba-4ab5-857a-f411f919c81b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a95ae67a-c7ba-4ab5-857a-f411f919c81b')\"\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-a95ae67a-c7ba-4ab5-857a-f411f919c81b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Last step of our feature engineering task is to OneHot the columns with multiple values , this would be gender , age , work_type ,avg_glucose_type, bmi and smoking_status but before that we drop the id column since it has no efect on possibility of stroke"
      ],
      "metadata": {
        "id": "xsThYkJA0wga"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns='id',inplace=True)"
      ],
      "metadata": {
        "id": "8vO4Iddd1OvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp=data"
      ],
      "metadata": {
        "id": "rHDFpKNX728O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "HLfvfjla7547",
        "outputId": "64d79cd9-a900-4802-c94f-9ebb394a010e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender                age  hypertension  heart_disease  ever_married  \\\n",
              "0    Male               teen             0              0             0   \n",
              "1    Male             adults             1              0             1   \n",
              "2  Female               teen             0              0             0   \n",
              "3  Female  middle age adults             0              0             1   \n",
              "4    Male               teen             0              0             0   \n",
              "\n",
              "      work_type  Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0      children               0              95.12  18.0     never smoked   \n",
              "1       Private               1              87.96  39.2     never smoked   \n",
              "2       Private               1             110.89  17.6     never smoked   \n",
              "3       Private               0              69.04  35.9  formerly smoked   \n",
              "4  Never_worked               0             161.28  19.1     never smoked   \n",
              "\n",
              "   stroke  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85b4b4c9-1a4f-44ac-84d5-b8baebb0ea39\" 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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>children</td>\n",
              "      <td>0</td>\n",
              "      <td>95.12</td>\n",
              "      <td>18.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>adults</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>87.96</td>\n",
              "      <td>39.2</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Private</td>\n",
              "      <td>1</td>\n",
              "      <td>110.89</td>\n",
              "      <td>17.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>middle age adults</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "      <td>0</td>\n",
              "      <td>69.04</td>\n",
              "      <td>35.9</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>teen</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Never_worked</td>\n",
              "      <td>0</td>\n",
              "      <td>161.28</td>\n",
              "      <td>19.1</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</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-85b4b4c9-1a4f-44ac-84d5-b8baebb0ea39')\"\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-85b4b4c9-1a4f-44ac-84d5-b8baebb0ea39 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-85b4b4c9-1a4f-44ac-84d5-b8baebb0ea39');\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-3ad2a974-1b16-4eec-9338-e72b71a0f09b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ad2a974-1b16-4eec-9338-e72b71a0f09b')\"\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-3ad2a974-1b16-4eec-9338-e72b71a0f09b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "enc = OneHotEncoder(sparse=False)\n",
        "age = enc.fit_transform(data[['age']])\n",
        "enc_data1 = pd.DataFrame(age, columns=enc.get_feature_names_out(['age']))\n",
        "temp.drop(columns='age', inplace=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaIOQ6Ha1mfH",
        "outputId": "a8ec0015-3e6c-4305-971a-2a763f74ad14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender = enc.fit_transform(data[['gender']])\n",
        "enc_data2 = pd.DataFrame(gender, columns=enc.get_feature_names_out(['gender']))\n",
        "temp.drop(columns='gender', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WNYHhye6pfH",
        "outputId": "4add914e-eb7b-4eb2-ca8c-7c855d742097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "work = enc.fit_transform(data[['work_type']])\n",
        "enc_data3 = pd.DataFrame(work, columns=enc.get_feature_names_out(['work_type']))\n",
        "temp.drop(columns='work_type', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmsMvM4s6zGK",
        "outputId": "b602b5b2-ee6d-47d0-ab4f-d19ae82102e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smoking_status = enc.fit_transform(data[['smoking_status']])\n",
        "enc_data6 = pd.DataFrame(smoking_status, columns=enc.get_feature_names_out(['smoking_status']))\n",
        "temp.drop(columns='smoking_status', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qigiq2I6qUB",
        "outputId": "80f5aa9d-bf8c-4bcb-d871-6fa77afbf021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "OneHotData = temp.join(enc_data1)\n",
        "OneHotData = OneHotData.join(enc_data2)\n",
        "OneHotData = OneHotData.join(enc_data3)\n",
        "OneHotData = OneHotData.join(enc_data6)\n",
        "OneHotData.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "AvKina759EwE",
        "outputId": "37a9cc0b-312d-4ce1-9227-f41ed5713e60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "0             0              0             0               0   \n",
              "1             1              0             1               1   \n",
              "2             0              0             0               1   \n",
              "3             0              0             1               0   \n",
              "4             0              0             0               0   \n",
              "\n",
              "   avg_glucose_level   bmi  stroke  age_adults  age_middle age adults  \\\n",
              "0              95.12  18.0       0         0.0                    0.0   \n",
              "1              87.96  39.2       0         1.0                    0.0   \n",
              "2             110.89  17.6       0         0.0                    0.0   \n",
              "3              69.04  35.9       0         0.0                    1.0   \n",
              "4             161.28  19.1       0         0.0                    0.0   \n",
              "\n",
              "   age_teen  ...  gender_Male  gender_Other  work_type_Govt_job  \\\n",
              "0       1.0  ...          1.0           0.0                 0.0   \n",
              "1       0.0  ...          1.0           0.0                 0.0   \n",
              "2       1.0  ...          0.0           0.0                 0.0   \n",
              "3       0.0  ...          0.0           0.0                 0.0   \n",
              "4       1.0  ...          1.0           0.0                 0.0   \n",
              "\n",
              "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "0                     0.0                0.0                      0.0   \n",
              "1                     0.0                1.0                      0.0   \n",
              "2                     0.0                1.0                      0.0   \n",
              "3                     0.0                1.0                      0.0   \n",
              "4                     1.0                0.0                      0.0   \n",
              "\n",
              "   work_type_children  smoking_status_formerly smoked  \\\n",
              "0                 1.0                             0.0   \n",
              "1                 0.0                             0.0   \n",
              "2                 0.0                             0.0   \n",
              "3                 0.0                             1.0   \n",
              "4                 0.0                             0.0   \n",
              "\n",
              "   smoking_status_never smoked  smoking_status_smokes  \n",
              "0                          1.0                    0.0  \n",
              "1                          1.0                    0.0  \n",
              "2                          1.0                    0.0  \n",
              "3                          0.0                    0.0  \n",
              "4                          1.0                    0.0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33b01c1f-dd09-47fe-a6ed-92135b724d89\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>95.12</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>87.96</td>\n",
              "      <td>39.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.89</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>69.04</td>\n",
              "      <td>35.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161.28</td>\n",
              "      <td>19.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 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-33b01c1f-dd09-47fe-a6ed-92135b724d89')\"\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-33b01c1f-dd09-47fe-a6ed-92135b724d89 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-33b01c1f-dd09-47fe-a6ed-92135b724d89');\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-de074793-145c-43f5-a113-b0af6a7b59c8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de074793-145c-43f5-a113-b0af6a7b59c8')\"\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-de074793-145c-43f5-a113-b0af6a7b59c8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we should normalize bmi and glucose level"
      ],
      "metadata": {
        "id": "9MK_STGHOMiA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "first_column = OneHotData.pop('stroke')\n",
        "OneHotData.insert(21, 'stroke', first_column)\n",
        "OneHotData"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "LIuPUEX2SYr5",
        "outputId": "ee4630cb-12ee-4e2b-f7e0-35c8194e1d42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "0                 0              0             0               0   \n",
              "1                 1              0             1               1   \n",
              "2                 0              0             0               1   \n",
              "3                 0              0             1               0   \n",
              "4                 0              0             0               0   \n",
              "...             ...            ...           ...             ...   \n",
              "43395             0              0             0               1   \n",
              "43396             0              0             1               1   \n",
              "43397             1              0             1               1   \n",
              "43398             0              0             1               1   \n",
              "43399             0              0             1               1   \n",
              "\n",
              "       avg_glucose_level   bmi  age_adults  age_middle age adults  age_teen  \\\n",
              "0                  95.12  18.0         0.0                    0.0       1.0   \n",
              "1                  87.96  39.2         1.0                    0.0       0.0   \n",
              "2                 110.89  17.6         0.0                    0.0       1.0   \n",
              "3                  69.04  35.9         0.0                    1.0       0.0   \n",
              "4                 161.28  19.1         0.0                    0.0       1.0   \n",
              "...                  ...   ...         ...                    ...       ...   \n",
              "43395              58.64  20.4         0.0                    0.0       1.0   \n",
              "43396             213.61  55.4         1.0                    0.0       0.0   \n",
              "43397              91.94  28.9         0.0                    1.0       0.0   \n",
              "43398              99.16  33.2         1.0                    0.0       0.0   \n",
              "43399              79.48  20.6         0.0                    1.0       0.0   \n",
              "\n",
              "       age_young adults  ...  gender_Other  work_type_Govt_job  \\\n",
              "0                   0.0  ...           0.0                 0.0   \n",
              "1                   0.0  ...           0.0                 0.0   \n",
              "2                   0.0  ...           0.0                 0.0   \n",
              "3                   0.0  ...           0.0                 0.0   \n",
              "4                   0.0  ...           0.0                 0.0   \n",
              "...                 ...  ...           ...                 ...   \n",
              "43395               0.0  ...           0.0                 0.0   \n",
              "43396               0.0  ...           0.0                 1.0   \n",
              "43397               0.0  ...           0.0                 0.0   \n",
              "43398               0.0  ...           0.0                 0.0   \n",
              "43399               0.0  ...           0.0                 0.0   \n",
              "\n",
              "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "0                         0.0                0.0                      0.0   \n",
              "1                         0.0                1.0                      0.0   \n",
              "2                         0.0                1.0                      0.0   \n",
              "3                         0.0                1.0                      0.0   \n",
              "4                         1.0                0.0                      0.0   \n",
              "...                       ...                ...                      ...   \n",
              "43395                     0.0                0.0                      0.0   \n",
              "43396                     0.0                0.0                      0.0   \n",
              "43397                     0.0                1.0                      0.0   \n",
              "43398                     0.0                1.0                      0.0   \n",
              "43399                     0.0                1.0                      0.0   \n",
              "\n",
              "       work_type_children  smoking_status_formerly smoked  \\\n",
              "0                     1.0                             0.0   \n",
              "1                     0.0                             0.0   \n",
              "2                     0.0                             0.0   \n",
              "3                     0.0                             1.0   \n",
              "4                     0.0                             0.0   \n",
              "...                   ...                             ...   \n",
              "43395                 1.0                             0.0   \n",
              "43396                 0.0                             1.0   \n",
              "43397                 0.0                             1.0   \n",
              "43398                 0.0                             0.0   \n",
              "43399                 0.0                             0.0   \n",
              "\n",
              "       smoking_status_never smoked  smoking_status_smokes  stroke  \n",
              "0                              1.0                    0.0       0  \n",
              "1                              1.0                    0.0       0  \n",
              "2                              1.0                    0.0       0  \n",
              "3                              0.0                    0.0       0  \n",
              "4                              1.0                    0.0       0  \n",
              "...                            ...                    ...     ...  \n",
              "43395                          1.0                    0.0       0  \n",
              "43396                          0.0                    0.0       0  \n",
              "43397                          0.0                    0.0       0  \n",
              "43398                          1.0                    0.0       0  \n",
              "43399                          1.0                    0.0       0  \n",
              "\n",
              "[43400 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1d9efe51-b9f3-4704-8f46-6dee8437910b\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>age_young adults</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>95.12</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>87.96</td>\n",
              "      <td>39.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.89</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>69.04</td>\n",
              "      <td>35.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161.28</td>\n",
              "      <td>19.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43395</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.64</td>\n",
              "      <td>20.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43396</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>213.61</td>\n",
              "      <td>55.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43397</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>91.94</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43398</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>99.16</td>\n",
              "      <td>33.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43399</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79.48</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43400 rows × 22 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-1d9efe51-b9f3-4704-8f46-6dee8437910b')\"\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-1d9efe51-b9f3-4704-8f46-6dee8437910b 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-1d9efe51-b9f3-4704-8f46-6dee8437910b');\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-cdb08418-99df-4cdf-b9c6-6ff2e01676f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cdb08418-99df-4cdf-b9c6-6ff2e01676f0')\"\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-cdb08418-99df-4cdf-b9c6-6ff2e01676f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = preprocessing.normalize(OneHotData.iloc[:,0:21])\n",
        "scaled_df = pd.DataFrame(d, columns=OneHotData.iloc[:,0:21].columns)\n",
        "scaled_df.insert(21, 'stroke', first_column)\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "RvRqa2d_OUc5",
        "outputId": "7cfac6a8-1c28-4784-8bbb-37dd623224e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "0          0.000000            0.0      0.000000        0.000000   \n",
              "1          0.010380            0.0      0.010380        0.010380   \n",
              "2          0.000000            0.0      0.000000        0.008905   \n",
              "3          0.000000            0.0      0.012846        0.000000   \n",
              "4          0.000000            0.0      0.000000        0.000000   \n",
              "...             ...            ...           ...             ...   \n",
              "43395      0.000000            0.0      0.000000        0.016096   \n",
              "43396      0.000000            0.0      0.004531        0.004531   \n",
              "43397      0.010372            0.0      0.010372        0.010372   \n",
              "43398      0.000000            0.0      0.009560        0.009560   \n",
              "43399      0.000000            0.0      0.012174        0.012174   \n",
              "\n",
              "       avg_glucose_level       bmi  age_adults  age_middle age adults  \\\n",
              "0               0.982352  0.185895    0.000000               0.000000   \n",
              "1               0.913055  0.406910    0.010380               0.000000   \n",
              "2               0.987442  0.156723    0.000000               0.000000   \n",
              "3               0.886855  0.461154    0.000000               0.012846   \n",
              "4               0.992985  0.117597    0.000000               0.000000   \n",
              "...                  ...       ...         ...                    ...   \n",
              "43395           0.943867  0.328358    0.000000               0.000000   \n",
              "43396           0.967916  0.251030    0.004531               0.000000   \n",
              "43397           0.953621  0.299757    0.000000               0.010372   \n",
              "43398           0.948002  0.317403    0.009560               0.000000   \n",
              "43399           0.967584  0.250783    0.000000               0.012174   \n",
              "\n",
              "       age_teen  age_young adults  ...  gender_Other  work_type_Govt_job  \\\n",
              "0      0.010328               0.0  ...           0.0            0.000000   \n",
              "1      0.000000               0.0  ...           0.0            0.000000   \n",
              "2      0.008905               0.0  ...           0.0            0.000000   \n",
              "3      0.000000               0.0  ...           0.0            0.000000   \n",
              "4      0.006157               0.0  ...           0.0            0.000000   \n",
              "...         ...               ...  ...           ...                 ...   \n",
              "43395  0.016096               0.0  ...           0.0            0.000000   \n",
              "43396  0.000000               0.0  ...           0.0            0.004531   \n",
              "43397  0.000000               0.0  ...           0.0            0.000000   \n",
              "43398  0.000000               0.0  ...           0.0            0.000000   \n",
              "43399  0.000000               0.0  ...           0.0            0.000000   \n",
              "\n",
              "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "0                    0.000000           0.000000                      0.0   \n",
              "1                    0.000000           0.010380                      0.0   \n",
              "2                    0.000000           0.008905                      0.0   \n",
              "3                    0.000000           0.012846                      0.0   \n",
              "4                    0.006157           0.000000                      0.0   \n",
              "...                       ...                ...                      ...   \n",
              "43395                0.000000           0.000000                      0.0   \n",
              "43396                0.000000           0.000000                      0.0   \n",
              "43397                0.000000           0.010372                      0.0   \n",
              "43398                0.000000           0.009560                      0.0   \n",
              "43399                0.000000           0.012174                      0.0   \n",
              "\n",
              "       work_type_children  smoking_status_formerly smoked  \\\n",
              "0                0.010328                        0.000000   \n",
              "1                0.000000                        0.000000   \n",
              "2                0.000000                        0.000000   \n",
              "3                0.000000                        0.012846   \n",
              "4                0.000000                        0.000000   \n",
              "...                   ...                             ...   \n",
              "43395            0.016096                        0.000000   \n",
              "43396            0.000000                        0.004531   \n",
              "43397            0.000000                        0.010372   \n",
              "43398            0.000000                        0.000000   \n",
              "43399            0.000000                        0.000000   \n",
              "\n",
              "       smoking_status_never smoked  smoking_status_smokes  stroke  \n",
              "0                         0.010328                    0.0       0  \n",
              "1                         0.010380                    0.0       0  \n",
              "2                         0.008905                    0.0       0  \n",
              "3                         0.000000                    0.0       0  \n",
              "4                         0.006157                    0.0       0  \n",
              "...                            ...                    ...     ...  \n",
              "43395                     0.016096                    0.0       0  \n",
              "43396                     0.000000                    0.0       0  \n",
              "43397                     0.000000                    0.0       0  \n",
              "43398                     0.009560                    0.0       0  \n",
              "43399                     0.012174                    0.0       0  \n",
              "\n",
              "[43400 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3eefce5-0061-42c3-876f-559a240549b5\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>age_young adults</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.982352</td>\n",
              "      <td>0.185895</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010328</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010328</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010328</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.913055</td>\n",
              "      <td>0.406910</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010380</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008905</td>\n",
              "      <td>0.987442</td>\n",
              "      <td>0.156723</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.012846</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.886855</td>\n",
              "      <td>0.461154</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012846</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012846</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012846</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.992985</td>\n",
              "      <td>0.117597</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006157</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43395</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016096</td>\n",
              "      <td>0.943867</td>\n",
              "      <td>0.328358</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.016096</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43396</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>0.967916</td>\n",
              "      <td>0.251030</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004531</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43397</th>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.953621</td>\n",
              "      <td>0.299757</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010372</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43398</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.948002</td>\n",
              "      <td>0.317403</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009560</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43399</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.967584</td>\n",
              "      <td>0.250783</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43400 rows × 22 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-e3eefce5-0061-42c3-876f-559a240549b5')\"\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-e3eefce5-0061-42c3-876f-559a240549b5 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-e3eefce5-0061-42c3-876f-559a240549b5');\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-8c407903-590f-49d4-9443-3257e6863e79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c407903-590f-49d4-9443-3257e6863e79')\"\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-8c407903-590f-49d4-9443-3257e6863e79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In next step we want to split data into test and train . but before that we need to make sure that we don't face overfitting. we can use a number of tricks to avoid this problem. we shall do so starting with Data Augmentation"
      ],
      "metadata": {
        "id": "kV-eKiV1AQOv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['stroke'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZ-txfdl9HKS",
        "outputId": "51b40f3e-9b6f-4853-de36-fdd9e5fba145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    42617\n",
              "1      783\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of possible strokes is so less it can be considered outlier.If we want our model to weok well we need to have equal number of samples for both classes so we should make sure that our training data includes enough number of positive samples to be capable of currect prediction , we shall overcome the problem of imbalaceness of our dataset as follows :\n",
        "\n",
        "1- we can remove samples from majority class which can lead to losing valuable data\n",
        "\n",
        "2- we can add samples to minority class\n",
        "\n",
        "I choose the second approach"
      ],
      "metadata": {
        "id": "6aFVjO-0Bz9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x= scaled_df.iloc[:,0:21]\n",
        "y= scaled_df.iloc[:,21:22]\n",
        "# choose the test size based on scikit learn documantation\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "nPr0cs_mKKWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = x_train.join(y_train)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "azOrsTzIOwbn",
        "outputId": "3b3854d1-591e-437a-9853-677cdb249991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "10092      0.000000            0.0      0.000000        0.009391   \n",
              "2613       0.000000            0.0      0.008623        0.000000   \n",
              "34426      0.011732            0.0      0.011732        0.011732   \n",
              "32207      0.000000            0.0      0.000000        0.000000   \n",
              "30105      0.000000            0.0      0.008175        0.000000   \n",
              "...             ...            ...           ...             ...   \n",
              "35433      0.000000            0.0      0.000000        0.000000   \n",
              "28250      0.000000            0.0      0.014053        0.000000   \n",
              "24923      0.000000            0.0      0.000000        0.000000   \n",
              "26875      0.000000            0.0      0.009151        0.009151   \n",
              "736        0.000000            0.0      0.000000        0.011011   \n",
              "\n",
              "       avg_glucose_level       bmi  age_adults  age_middle age adults  \\\n",
              "10092           0.963017  0.268622    0.000000               0.000000   \n",
              "2613            0.980292  0.196610    0.000000               0.008623   \n",
              "34426           0.954062  0.297998    0.011732               0.000000   \n",
              "32207           0.919630  0.392370    0.000000               0.000000   \n",
              "30105           0.985409  0.169221    0.008175               0.000000   \n",
              "...                  ...       ...         ...                    ...   \n",
              "35433           0.991449  0.129335    0.000000               0.000000   \n",
              "28250           0.934129  0.355551    0.000000               0.000000   \n",
              "24923           0.927160  0.373727    0.000000               0.000000   \n",
              "26875           0.984024  0.176617    0.009151               0.000000   \n",
              "736             0.968790  0.246657    0.000000               0.000000   \n",
              "\n",
              "       age_teen  age_young adults  ...  gender_Other  work_type_Govt_job  \\\n",
              "10092  0.009391          0.000000  ...           0.0                 0.0   \n",
              "2613   0.000000          0.000000  ...           0.0                 0.0   \n",
              "34426  0.000000          0.000000  ...           0.0                 0.0   \n",
              "32207  0.000000          0.009041  ...           0.0                 0.0   \n",
              "30105  0.000000          0.000000  ...           0.0                 0.0   \n",
              "...         ...               ...  ...           ...                 ...   \n",
              "35433  0.008680          0.000000  ...           0.0                 0.0   \n",
              "28250  0.000000          0.014053  ...           0.0                 0.0   \n",
              "24923  0.000000          0.013253  ...           0.0                 0.0   \n",
              "26875  0.000000          0.000000  ...           0.0                 0.0   \n",
              "736    0.011011          0.000000  ...           0.0                 0.0   \n",
              "\n",
              "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "10092                     0.0           0.009391                      0.0   \n",
              "2613                      0.0           0.008623                      0.0   \n",
              "34426                     0.0           0.011732                      0.0   \n",
              "32207                     0.0           0.009041                      0.0   \n",
              "30105                     0.0           0.008175                      0.0   \n",
              "...                       ...                ...                      ...   \n",
              "35433                     0.0           0.000000                      0.0   \n",
              "28250                     0.0           0.014053                      0.0   \n",
              "24923                     0.0           0.013253                      0.0   \n",
              "26875                     0.0           0.009151                      0.0   \n",
              "736                       0.0           0.011011                      0.0   \n",
              "\n",
              "       work_type_children  smoking_status_formerly smoked  \\\n",
              "10092             0.00000                        0.009391   \n",
              "2613              0.00000                        0.000000   \n",
              "34426             0.00000                        0.000000   \n",
              "32207             0.00000                        0.009041   \n",
              "30105             0.00000                        0.000000   \n",
              "...                   ...                             ...   \n",
              "35433             0.00868                        0.000000   \n",
              "28250             0.00000                        0.000000   \n",
              "24923             0.00000                        0.013253   \n",
              "26875             0.00000                        0.000000   \n",
              "736               0.00000                        0.000000   \n",
              "\n",
              "       smoking_status_never smoked  smoking_status_smokes  stroke  \n",
              "10092                     0.000000               0.000000       0  \n",
              "2613                      0.008623               0.000000       0  \n",
              "34426                     0.011732               0.000000       0  \n",
              "32207                     0.000000               0.000000       0  \n",
              "30105                     0.000000               0.008175       0  \n",
              "...                            ...                    ...     ...  \n",
              "35433                     0.008680               0.000000       0  \n",
              "28250                     0.014053               0.000000       0  \n",
              "24923                     0.000000               0.000000       0  \n",
              "26875                     0.009151               0.000000       0  \n",
              "736                       0.011011               0.000000       0  \n",
              "\n",
              "[34720 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b553c948-4aa9-4431-ba99-0a5ada590e0c\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>age_young adults</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10092</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009391</td>\n",
              "      <td>0.963017</td>\n",
              "      <td>0.268622</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009391</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009391</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.009391</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.980292</td>\n",
              "      <td>0.196610</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34426</th>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.954062</td>\n",
              "      <td>0.297998</td>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32207</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.919630</td>\n",
              "      <td>0.392370</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009041</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.009041</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30105</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008175</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.985409</td>\n",
              "      <td>0.169221</td>\n",
              "      <td>0.008175</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008175</td>\n",
              "      <td>0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35433</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.991449</td>\n",
              "      <td>0.129335</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008680</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008680</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28250</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.014053</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.934129</td>\n",
              "      <td>0.355551</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014053</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.014053</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014053</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24923</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.927160</td>\n",
              "      <td>0.373727</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013253</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.013253</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.013253</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26875</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009151</td>\n",
              "      <td>0.009151</td>\n",
              "      <td>0.984024</td>\n",
              "      <td>0.176617</td>\n",
              "      <td>0.009151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>736</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011011</td>\n",
              "      <td>0.968790</td>\n",
              "      <td>0.246657</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011011</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011011</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011011</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34720 rows × 22 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-b553c948-4aa9-4431-ba99-0a5ada590e0c')\"\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-b553c948-4aa9-4431-ba99-0a5ada590e0c 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-b553c948-4aa9-4431-ba99-0a5ada590e0c');\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-4d26f7e8-0106-43a0-9aba-c822ee9f2701\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d26f7e8-0106-43a0-9aba-c822ee9f2701')\"\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-4d26f7e8-0106-43a0-9aba-c822ee9f2701 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_0 = x[x['stroke'] == 0]\n",
        "class_1 = x[x['stroke']  == 1]\n",
        "class_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "spYArcmJBzS7",
        "outputId": "3f68567a-531e-41d4-892b-071755ef6611"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "39705           0.0       0.000000      0.011557        0.000000   \n",
              "31699           0.0       0.013081      0.000000        0.013081   \n",
              "37119           0.0       0.000000      0.004620        0.004620   \n",
              "14791           0.0       0.000000      0.010802        0.000000   \n",
              "13283           0.0       0.000000      0.012802        0.000000   \n",
              "...             ...            ...           ...             ...   \n",
              "24199           0.0       0.000000      0.000000        0.009609   \n",
              "8161            0.0       0.000000      0.004757        0.004757   \n",
              "39764           0.0       0.000000      0.000000        0.011029   \n",
              "20682           0.0       0.000000      0.011170        0.011170   \n",
              "22991           0.0       0.006361      0.006361        0.006361   \n",
              "\n",
              "       avg_glucose_level       bmi  age_adults  age_middle age adults  \\\n",
              "39705           0.931024  0.364042    0.000000               0.011557   \n",
              "31699           0.951883  0.304781    0.000000               0.013081   \n",
              "37119           0.992640  0.120575    0.000000               0.004620   \n",
              "14791           0.917744  0.396436    0.010802               0.000000   \n",
              "13283           0.931120  0.363589    0.000000               0.012802   \n",
              "...                  ...       ...         ...                    ...   \n",
              "24199           0.961246  0.274855    0.009609               0.000000   \n",
              "8161            0.986053  0.166023    0.000000               0.004757   \n",
              "39764           0.960923  0.275715    0.011029               0.000000   \n",
              "20682           0.959503  0.280367    0.011170               0.000000   \n",
              "22991           0.980351  0.196541    0.000000               0.006361   \n",
              "\n",
              "       age_teen  age_young adults  ...  gender_Other  work_type_Govt_job  \\\n",
              "39705       0.0               0.0  ...           0.0            0.000000   \n",
              "31699       0.0               0.0  ...           0.0            0.013081   \n",
              "37119       0.0               0.0  ...           0.0            0.000000   \n",
              "14791       0.0               0.0  ...           0.0            0.000000   \n",
              "13283       0.0               0.0  ...           0.0            0.000000   \n",
              "...         ...               ...  ...           ...                 ...   \n",
              "24199       0.0               0.0  ...           0.0            0.000000   \n",
              "8161        0.0               0.0  ...           0.0            0.000000   \n",
              "39764       0.0               0.0  ...           0.0            0.000000   \n",
              "20682       0.0               0.0  ...           0.0            0.000000   \n",
              "22991       0.0               0.0  ...           0.0            0.000000   \n",
              "\n",
              "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "39705                     0.0           0.000000                 0.011557   \n",
              "31699                     0.0           0.000000                 0.000000   \n",
              "37119                     0.0           0.004620                 0.000000   \n",
              "14791                     0.0           0.010802                 0.000000   \n",
              "13283                     0.0           0.012802                 0.000000   \n",
              "...                       ...                ...                      ...   \n",
              "24199                     0.0           0.009609                 0.000000   \n",
              "8161                      0.0           0.004757                 0.000000   \n",
              "39764                     0.0           0.000000                 0.011029   \n",
              "20682                     0.0           0.011170                 0.000000   \n",
              "22991                     0.0           0.000000                 0.006361   \n",
              "\n",
              "       work_type_children  smoking_status_formerly smoked  \\\n",
              "39705                 0.0                         0.00000   \n",
              "31699                 0.0                         0.00000   \n",
              "37119                 0.0                         0.00462   \n",
              "14791                 0.0                         0.00000   \n",
              "13283                 0.0                         0.00000   \n",
              "...                   ...                             ...   \n",
              "24199                 0.0                         0.00000   \n",
              "8161                  0.0                         0.00000   \n",
              "39764                 0.0                         0.00000   \n",
              "20682                 0.0                         0.01117   \n",
              "22991                 0.0                         0.00000   \n",
              "\n",
              "       smoking_status_never smoked  smoking_status_smokes  stroke  \n",
              "39705                     0.011557               0.000000       1  \n",
              "31699                     0.013081               0.000000       1  \n",
              "37119                     0.000000               0.000000       1  \n",
              "14791                     0.010802               0.000000       1  \n",
              "13283                     0.012802               0.000000       1  \n",
              "...                            ...                    ...     ...  \n",
              "24199                     0.000000               0.009609       1  \n",
              "8161                      0.004757               0.000000       1  \n",
              "39764                     0.000000               0.011029       1  \n",
              "20682                     0.000000               0.000000       1  \n",
              "22991                     0.000000               0.006361       1  \n",
              "\n",
              "[634 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a6aa482-90fd-41a4-94d2-2ec47889c82d\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>age_young adults</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39705</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011557</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.931024</td>\n",
              "      <td>0.364042</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.011557</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.013081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013081</td>\n",
              "      <td>0.951883</td>\n",
              "      <td>0.304781</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.013081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.013081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37119</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004620</td>\n",
              "      <td>0.004620</td>\n",
              "      <td>0.992640</td>\n",
              "      <td>0.120575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004620</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004620</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14791</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.917744</td>\n",
              "      <td>0.396436</td>\n",
              "      <td>0.010802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.010802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13283</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.931120</td>\n",
              "      <td>0.363589</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.012802</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.012802</td>\n",
              "      <td>0.000000</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24199</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009609</td>\n",
              "      <td>0.961246</td>\n",
              "      <td>0.274855</td>\n",
              "      <td>0.009609</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009609</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009609</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8161</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.986053</td>\n",
              "      <td>0.166023</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39764</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011029</td>\n",
              "      <td>0.960923</td>\n",
              "      <td>0.275715</td>\n",
              "      <td>0.011029</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011029</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20682</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011170</td>\n",
              "      <td>0.011170</td>\n",
              "      <td>0.959503</td>\n",
              "      <td>0.280367</td>\n",
              "      <td>0.011170</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011170</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01117</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22991</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.980351</td>\n",
              "      <td>0.196541</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006361</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>634 rows × 22 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-0a6aa482-90fd-41a4-94d2-2ec47889c82d')\"\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-0a6aa482-90fd-41a4-94d2-2ec47889c82d 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-0a6aa482-90fd-41a4-94d2-2ec47889c82d');\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-0e51292e-d345-4d71-9e6c-7a15d5d658bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0e51292e-d345-4d71-9e6c-7a15d5d658bc')\"\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-0e51292e-d345-4d71-9e6c-7a15d5d658bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_1_over = class_1.sample(len(class_0.index), replace=True)\n",
        "balanced_data = pd.concat([class_1_over, class_0], axis=0)\n",
        "balanced_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "8pqXGj-PIKTB",
        "outputId": "ed6056c4-be05-45e1-96b7-e7adf60efdf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       hypertension  heart_disease  ever_married  Residence_type  \\\n",
              "6803       0.012362       0.000000      0.012362        0.000000   \n",
              "42105      0.000000       0.000000      0.009284        0.000000   \n",
              "16303      0.010371       0.010371      0.010371        0.000000   \n",
              "19828      0.003878       0.000000      0.003878        0.003878   \n",
              "1315       0.000000       0.004743      0.004743        0.000000   \n",
              "\n",
              "       avg_glucose_level       bmi  age_adults  age_middle age adults  \\\n",
              "6803            0.940848  0.337474         0.0               0.012362   \n",
              "42105           0.957742  0.286877         0.0               0.009284   \n",
              "16303           0.967065  0.253044         0.0               0.010371   \n",
              "19828           0.994733  0.101986         0.0               0.003878   \n",
              "1315            0.987979  0.154149         0.0               0.004743   \n",
              "\n",
              "       age_teen  age_young adults  ...  gender_Other  work_type_Govt_job  \\\n",
              "6803        0.0               0.0  ...           0.0                 0.0   \n",
              "42105       0.0               0.0  ...           0.0                 0.0   \n",
              "16303       0.0               0.0  ...           0.0                 0.0   \n",
              "19828       0.0               0.0  ...           0.0                 0.0   \n",
              "1315        0.0               0.0  ...           0.0                 0.0   \n",
              "\n",
              "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
              "6803                      0.0           0.000000                 0.012362   \n",
              "42105                     0.0           0.009284                 0.000000   \n",
              "16303                     0.0           0.010371                 0.000000   \n",
              "19828                     0.0           0.003878                 0.000000   \n",
              "1315                      0.0           0.004743                 0.000000   \n",
              "\n",
              "       work_type_children  smoking_status_formerly smoked  \\\n",
              "6803                  0.0                        0.000000   \n",
              "42105                 0.0                        0.000000   \n",
              "16303                 0.0                        0.010371   \n",
              "19828                 0.0                        0.000000   \n",
              "1315                  0.0                        0.000000   \n",
              "\n",
              "       smoking_status_never smoked  smoking_status_smokes  stroke  \n",
              "6803                      0.000000               0.012362       1  \n",
              "42105                     0.009284               0.000000       1  \n",
              "16303                     0.000000               0.000000       1  \n",
              "19828                     0.003878               0.000000       1  \n",
              "1315                      0.004743               0.000000       1  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b60d2a8f-64ca-427b-a290-8e0c14d89d1c\" 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>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age_adults</th>\n",
              "      <th>age_middle age adults</th>\n",
              "      <th>age_teen</th>\n",
              "      <th>age_young adults</th>\n",
              "      <th>...</th>\n",
              "      <th>gender_Other</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6803</th>\n",
              "      <td>0.012362</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012362</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.940848</td>\n",
              "      <td>0.337474</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.012362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012362</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42105</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009284</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.957742</td>\n",
              "      <td>0.286877</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009284</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009284</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009284</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16303</th>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.967065</td>\n",
              "      <td>0.253044</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.010371</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19828</th>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.994733</td>\n",
              "      <td>0.101986</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004743</td>\n",
              "      <td>0.004743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.987979</td>\n",
              "      <td>0.154149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004743</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004743</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 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-b60d2a8f-64ca-427b-a290-8e0c14d89d1c')\"\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-b60d2a8f-64ca-427b-a290-8e0c14d89d1c 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-b60d2a8f-64ca-427b-a290-8e0c14d89d1c');\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-ac5ff4c6-4313-41a9-9ded-ae028cf25b8c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac5ff4c6-4313-41a9-9ded-ae028cf25b8c')\"\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-ac5ff4c6-4313-41a9-9ded-ae028cf25b8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = balanced_data.iloc[:,0:21]\n",
        "y_train = balanced_data.iloc[:,21:22]"
      ],
      "metadata": {
        "id": "cMXznVVdKIUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "oPvzR4hhn_xR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = svm.SVC()\n",
        "clf.fit(x_train, y_train)\n",
        "prediction = clf.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RXAqEvbK3UQ",
        "outputId": "3ec10a04-0d05-497e-d8e8-18f69e7a1faf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, prediction)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "876CoaSkN5ju",
        "outputId": "85db1486-ebdf-40a7-affe-1ab111f6f9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.713479262672811"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "oFTFf-hkooog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix = metrics.confusion_matrix(y_test, prediction)\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "gEdoq7PRod2N",
        "outputId": "30ddc691-51d4-4612-d33e-35ed8d1a0a65"
      },
      "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": [
        "from sklearn.metrics import roc_curve, auc"
      ],
      "metadata": {
        "id": "MD4qwYBMVgA6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, threshholds = roc_curve(y_test, prediction)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zAKNgxQvVxeE",
        "outputId": "08bcbf07-d25d-4811-a2a0-34841ae6952a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we could also handle the unbalanced data problem with svm fit function by setting the class_weight in fit method to 1"
      ],
      "metadata": {
        "id": "Bi42ydhfMJc_"
      }
    }
  ]
}