AIM-PIbd-31-Potapov-N-S/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Вариант 2. Показатели сердечных заболеваний"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Этот датасет представляет собой данные, собранные в ходе ежегодного опроса CDC о состоянии здоровья более 400 тысяч взрослых в США. Он включает информацию о различных факторах риска сердечных заболеваний, таких как гипертония, высокий уровень холестерина, курение, диабет, ожирение, недостаток физической активности и злоупотребление алкоголем. Также содержатся данные о состоянии здоровья респондентов, наличии хронических заболеваний (например, диабет, артрит, астма), уровне физической активности, психологическом здоровье, а также о социальных и демографических характеристиках, таких как пол, возраст, этническая принадлежность и место проживания. Датасет предоставляет информацию, которая может быть использована для анализа и предсказания риска сердечных заболеваний, а также для разработки программ профилактики и улучшения общественного здоровья."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Бизнес-цели:\n",
    "- Предсказание риска сердечных заболеваний: создание модели для определения вероятности заболевания сердечными болезнями на основе факторов риска.\n",
    "- Идентификация ключевых факторов, влияющих на здоровье: выявление наиболее значимых факторов, влияющих на риск сердечных заболеваний, чтобы разработать программы профилактики.\n",
    "\n",
    "#### Цели технического проекта:\n",
    "- Предсказание риска сердечных заболеваний: разработка модели машинного обучения (например, логистической регрессии, случайного леса) для классификации респондентов по риску сердечных заболеваний (с использованием функции \"HadHeartAttack\").\n",
    "- Идентификация ключевых факторов: анализ факторов, влияющих на развитие сердечных заболеваний, чтобы выявить наиболее значимые признаки для предсказания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Краткое описание для колонок:\n",
    "1. **State** — штат проживания респондента.\n",
    "2. **Sex** — пол респондента.\n",
    "3. **GeneralHealth** — общее самочувствие респондента.\n",
    "4. **PhysicalHealthDays** — количество дней, когда респондент испытывал физические ограничения.\n",
    "5. **MentalHealthDays** — количество дней с психическими ограничениями.\n",
    "6. **LastCheckupTime** — время последнего медицинского осмотра.\n",
    "7. **PhysicalActivities** — уровень физической активности респондента.\n",
    "8. **SleepHours** — количество часов сна.\n",
    "9. **RemovedTeeth** — наличие отсутствующих зубов.\n",
    "10. **HadHeartAttack** — был ли у респондента сердечный приступ (целевая переменная).\n",
    "11. **HadAngina** — был ли у респондента стенокардия.\n",
    "12. **HadStroke** — был ли у респондента инсульт.\n",
    "13. **HadAsthma** — был ли у респондента астма.\n",
    "14. **HadSkinCancer** — был ли у респондента рак кожи.\n",
    "15. **HadCOPD** — был ли у респондента хронический обструктивный бронхит.\n",
    "16. **HadDepressiveDisorder** — был ли у респондента депрессивное расстройство.\n",
    "17. **HadKidneyDisease** — был ли у респондента заболевания почек.\n",
    "18. **HadArthritis** — был ли у респондента артрит.\n",
    "19. **HadDiabetes** — был ли у респондента диабет.\n",
    "20. **DeafOrHardOfHearing** — имеется ли у респондента проблемы со слухом.\n",
    "21. **BlindOrVisionDifficulty** — имеются ли у респондента проблемы со зрением.\n",
    "22. **DifficultyConcentrating** — имеется ли у респондента проблемы с концентрацией внимания.\n",
    "23. **DifficultyWalking** — имеются ли у респондента проблемы с ходьбой.\n",
    "24. **DifficultyDressingBathing** — имеются ли у респондента проблемы с одеванием и купанием.\n",
    "25. **DifficultyErrands** — имеются ли у респондента проблемы с выполнением повседневных дел.\n",
    "26. **SmokerStatus** — статус курения респондента.\n",
    "27. **ECigaretteUsage** — использование электронных сигарет.\n",
    "28. **ChestScan** — проходил ли респондент обследование грудной клетки.\n",
    "29. **RaceEthnicityCategory** — этническая принадлежность респондента.\n",
    "30. **AgeCategory** — возрастная категория респондента.\n",
    "31. **HeightInMeters** — рост респондента в метрах.\n",
    "32. **WeightInKilograms** — вес респондента в килограммах.\n",
    "33. **BMI** — индекс массы тела.\n",
    "34. **AlcoholDrinkers** — является ли респондент алкоголиком.\n",
    "35. **HIVTesting** — проходил ли респондент тест на ВИЧ.\n",
    "36. **FluVaxLast12** — получал ли респондент прививку от гриппа за последние 12 месяцев.\n",
    "37. **PneumoVaxEver** — получал ли респондент прививку от пневмококка.\n",
    "38. **TetanusLast10Tdap** — получал ли респондент прививку от столбняка за последние 10 лет.\n",
    "39. **HighRiskLastYear** — был ли респондент в группе высокого риска в прошлом году.\n",
    "40. **CovidPos** — был ли респондент заражен COVID-19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "from math import ceil\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузим данные из датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('csv\\\\heart_2022_no_nans.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Посмотрим общие сведения о датасете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246022 entries, 0 to 246021\n",
      "Data columns (total 40 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   State                      246022 non-null  object \n",
      " 1   Sex                        246022 non-null  object \n",
      " 2   GeneralHealth              246022 non-null  object \n",
      " 3   PhysicalHealthDays         246022 non-null  float64\n",
      " 4   MentalHealthDays           246022 non-null  float64\n",
      " 5   LastCheckupTime            246022 non-null  object \n",
      " 6   PhysicalActivities         246022 non-null  object \n",
      " 7   SleepHours                 246022 non-null  float64\n",
      " 8   RemovedTeeth               246022 non-null  object \n",
      " 9   HadHeartAttack             246022 non-null  object \n",
      " 10  HadAngina                  246022 non-null  object \n",
      " 11  HadStroke                  246022 non-null  object \n",
      " 12  HadAsthma                  246022 non-null  object \n",
      " 13  HadSkinCancer              246022 non-null  object \n",
      " 14  HadCOPD                    246022 non-null  object \n",
      " 15  HadDepressiveDisorder      246022 non-null  object \n",
      " 16  HadKidneyDisease           246022 non-null  object \n",
      " 17  HadArthritis               246022 non-null  object \n",
      " 18  HadDiabetes                246022 non-null  object \n",
      " 19  DeafOrHardOfHearing        246022 non-null  object \n",
      " 20  BlindOrVisionDifficulty    246022 non-null  object \n",
      " 21  DifficultyConcentrating    246022 non-null  object \n",
      " 22  DifficultyWalking          246022 non-null  object \n",
      " 23  DifficultyDressingBathing  246022 non-null  object \n",
      " 24  DifficultyErrands          246022 non-null  object \n",
      " 25  SmokerStatus               246022 non-null  object \n",
      " 26  ECigaretteUsage            246022 non-null  object \n",
      " 27  ChestScan                  246022 non-null  object \n",
      " 28  RaceEthnicityCategory      246022 non-null  object \n",
      " 29  AgeCategory                246022 non-null  object \n",
      " 30  HeightInMeters             246022 non-null  float64\n",
      " 31  WeightInKilograms          246022 non-null  float64\n",
      " 32  BMI                        246022 non-null  float64\n",
      " 33  AlcoholDrinkers            246022 non-null  object \n",
      " 34  HIVTesting                 246022 non-null  object \n",
      " 35  FluVaxLast12               246022 non-null  object \n",
      " 36  PneumoVaxEver              246022 non-null  object \n",
      " 37  TetanusLast10Tdap          246022 non-null  object \n",
      " 38  HighRiskLastYear           246022 non-null  object \n",
      " 39  CovidPos                   246022 non-null  object \n",
      "dtypes: float64(6), object(34)\n",
      "memory usage: 75.1+ MB\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PhysicalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.119026</td>\n",
       "      <td>8.405844</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealthDays</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>4.167140</td>\n",
       "      <td>8.102687</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepHours</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>7.021331</td>\n",
       "      <td>1.440681</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeightInMeters</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>1.705150</td>\n",
       "      <td>0.106654</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeightInKilograms</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>83.615179</td>\n",
       "      <td>21.323156</td>\n",
       "      <td>28.12</td>\n",
       "      <td>68.04</td>\n",
       "      <td>81.65</td>\n",
       "      <td>95.25</td>\n",
       "      <td>292.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>28.668136</td>\n",
       "      <td>6.513973</td>\n",
       "      <td>12.02</td>\n",
       "      <td>24.27</td>\n",
       "      <td>27.46</td>\n",
       "      <td>31.89</td>\n",
       "      <td>97.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std    min    25%    50%  \\\n",
       "PhysicalHealthDays  246022.0   4.119026   8.405844   0.00   0.00   0.00   \n",
       "MentalHealthDays    246022.0   4.167140   8.102687   0.00   0.00   0.00   \n",
       "SleepHours          246022.0   7.021331   1.440681   1.00   6.00   7.00   \n",
       "HeightInMeters      246022.0   1.705150   0.106654   0.91   1.63   1.70   \n",
       "WeightInKilograms   246022.0  83.615179  21.323156  28.12  68.04  81.65   \n",
       "BMI                 246022.0  28.668136   6.513973  12.02  24.27  27.46   \n",
       "\n",
       "                      75%     max  \n",
       "PhysicalHealthDays   3.00   30.00  \n",
       "MentalHealthDays     4.00   30.00  \n",
       "SleepHours           8.00   24.00  \n",
       "HeightInMeters       1.78    2.41  \n",
       "WeightInKilograms   95.25  292.57  \n",
       "BMI                 31.89   97.65  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получим информацию о пустых значениях в колонках датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_null_columns_info(df: DataFrame) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Возвращает информацию о пропущенных значениях в колонках датасета\n",
    "    \"\"\"\n",
    "    w = []\n",
    "    df_len = len(df)\n",
    "\n",
    "    for column in df.columns:\n",
    "        column_nulls = df[column].isnull()\n",
    "        w.append([column, column_nulls.any(), column_nulls.sum() / df_len])\n",
    "\n",
    "    null_df = DataFrame(w).rename(columns={0: \"Column\", 1: \"Has Null\", 2: \"Null Percent\"})\n",
    "\n",
    "    return null_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>Has Null</th>\n",
       "      <th>Null Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GeneralHealth</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LastCheckupTime</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PhysicalActivities</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RemovedTeeth</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HadHeartAttack</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HadAngina</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HadStroke</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HadAsthma</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HadSkinCancer</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HadCOPD</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HadDepressiveDisorder</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HadKidneyDisease</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HadArthritis</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HadDiabetes</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DeafOrHardOfHearing</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BlindOrVisionDifficulty</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DifficultyConcentrating</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DifficultyWalking</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DifficultyDressingBathing</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DifficultyErrands</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SmokerStatus</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ECigaretteUsage</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ChestScan</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RaceEthnicityCategory</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AgeCategory</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>BMI</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>AlcoholDrinkers</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>HIVTesting</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>FluVaxLast12</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PneumoVaxEver</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TetanusLast10Tdap</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>HighRiskLastYear</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CovidPos</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Column  Has Null  Null Percent\n",
       "0                       State     False           0.0\n",
       "1                         Sex     False           0.0\n",
       "2               GeneralHealth     False           0.0\n",
       "3          PhysicalHealthDays     False           0.0\n",
       "4            MentalHealthDays     False           0.0\n",
       "5             LastCheckupTime     False           0.0\n",
       "6          PhysicalActivities     False           0.0\n",
       "7                  SleepHours     False           0.0\n",
       "8                RemovedTeeth     False           0.0\n",
       "9              HadHeartAttack     False           0.0\n",
       "10                  HadAngina     False           0.0\n",
       "11                  HadStroke     False           0.0\n",
       "12                  HadAsthma     False           0.0\n",
       "13              HadSkinCancer     False           0.0\n",
       "14                    HadCOPD     False           0.0\n",
       "15      HadDepressiveDisorder     False           0.0\n",
       "16           HadKidneyDisease     False           0.0\n",
       "17               HadArthritis     False           0.0\n",
       "18                HadDiabetes     False           0.0\n",
       "19        DeafOrHardOfHearing     False           0.0\n",
       "20    BlindOrVisionDifficulty     False           0.0\n",
       "21    DifficultyConcentrating     False           0.0\n",
       "22          DifficultyWalking     False           0.0\n",
       "23  DifficultyDressingBathing     False           0.0\n",
       "24          DifficultyErrands     False           0.0\n",
       "25               SmokerStatus     False           0.0\n",
       "26            ECigaretteUsage     False           0.0\n",
       "27                  ChestScan     False           0.0\n",
       "28      RaceEthnicityCategory     False           0.0\n",
       "29                AgeCategory     False           0.0\n",
       "30             HeightInMeters     False           0.0\n",
       "31          WeightInKilograms     False           0.0\n",
       "32                        BMI     False           0.0\n",
       "33            AlcoholDrinkers     False           0.0\n",
       "34                 HIVTesting     False           0.0\n",
       "35               FluVaxLast12     False           0.0\n",
       "36              PneumoVaxEver     False           0.0\n",
       "37          TetanusLast10Tdap     False           0.0\n",
       "38           HighRiskLastYear     False           0.0\n",
       "39                   CovidPos     False           0.0"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_null_columns_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получим информацию о выбросах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_numeric_columns(df: DataFrame) -> list[str]:\n",
    "    \"\"\"\n",
    "    Возвращает список числовых колонок\n",
    "    \"\"\"\n",
    "    return list(filter(lambda column: pd.api.types.is_numeric_dtype(df[column]), df.columns))\n",
    "\n",
    "def get_filtered_columns(df: DataFrame, no_numeric=False, no_text=False) -> list[str]:\n",
    "    \"\"\"\n",
    "    Возвращает список колонок по фильтру\n",
    "    \"\"\"\n",
    "    w = []\n",
    "    for column in df.columns:\n",
    "        if no_numeric and pd.api.types.is_numeric_dtype(df[column]):\n",
    "            continue\n",
    "        if no_text and not pd.api.types.is_numeric_dtype(df[column]):\n",
    "            continue\n",
    "        w.append(column)\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_outliers_info(df: DataFrame) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Возаращает информацию о выбросах в числовых колонках датасета\n",
    "    \"\"\"\n",
    "    data = {\n",
    "        \"Column\": [],\n",
    "        \"Has Outliers\": [],\n",
    "        \"Outliers Count\": [],\n",
    "        \"Min Value\": [],\n",
    "        \"Max Value\": [],\n",
    "        \"Q1\": [],\n",
    "        \"Q3\": []\n",
    "    }\n",
    "\n",
    "    info = DataFrame(data)\n",
    "\n",
    "    for column in get_numeric_columns(df):\n",
    "        Q1: float = df[column].quantile(0.25)\n",
    "        Q3: float = df[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        outliers: DataFrame = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count: int = outliers.shape[0]\n",
    "\n",
    "        info.loc[len(info)] = [column, outlier_count > 0, outlier_count, df[column].min(), df[column].max(), Q1, Q3]\n",
    "\n",
    "    return info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим данные по выбросам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Column</th>\n",
       "      <th>Has Outliers</th>\n",
       "      <th>Outliers Count</th>\n",
       "      <th>Min Value</th>\n",
       "      <th>Max Value</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>True</td>\n",
       "      <td>38810</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>True</td>\n",
       "      <td>32714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>True</td>\n",
       "      <td>3488</td>\n",
       "      <td>1.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>True</td>\n",
       "      <td>830</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2.41</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>True</td>\n",
       "      <td>5940</td>\n",
       "      <td>28.12</td>\n",
       "      <td>292.57</td>\n",
       "      <td>68.04</td>\n",
       "      <td>95.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMI</td>\n",
       "      <td>True</td>\n",
       "      <td>7563</td>\n",
       "      <td>12.02</td>\n",
       "      <td>97.65</td>\n",
       "      <td>24.27</td>\n",
       "      <td>31.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column  Has Outliers  Outliers Count  Min Value  Max Value  \\\n",
       "0  PhysicalHealthDays          True           38810       0.00      30.00   \n",
       "1    MentalHealthDays          True           32714       0.00      30.00   \n",
       "2          SleepHours          True            3488       1.00      24.00   \n",
       "3      HeightInMeters          True             830       0.91       2.41   \n",
       "4   WeightInKilograms          True            5940      28.12     292.57   \n",
       "5                 BMI          True            7563      12.02      97.65   \n",
       "\n",
       "      Q1     Q3  \n",
       "0   0.00   3.00  \n",
       "1   0.00   4.00  \n",
       "2   6.00   8.00  \n",
       "3   1.63   1.78  \n",
       "4  68.04  95.25  \n",
       "5  24.27  31.89  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_info = get_outliers_info(df)\n",
    "outliers_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_outliers(df: DataFrame) -> None:\n",
    "    \"\"\"\n",
    "    Генерирует диаграммы BoxPlot для числовых колонок датасета\n",
    "    \"\"\"\n",
    "    columns = get_numeric_columns(df)\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    rows: int = ceil(len(columns) / 3)\n",
    "    for index, column in enumerate(columns, 1):\n",
    "        plt.subplot(rows, 3, index)\n",
    "        plt.boxplot(df[column], vert=True, patch_artist=True)\n",
    "        plt.title(f\"Диаграмма размахов\\n\\\"{column}\\\"\")\n",
    "        plt.xlabel(column)\n",
    "            \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем выбросы с помощью диаграмм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(df: DataFrame, columns: list[str]) -> DataFrame:\n",
    "    \"\"\"\n",
    "    Устраняет выбросы в заданных колонках:\n",
    "    задает значениям выше максимального значение максимума, ниже минимального - значение минимума\n",
    "    \"\"\"\n",
    "    for column in columns:\n",
    "        Q1: float = df[column].quantile(0.25)\n",
    "        Q3: float = df[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаляем выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_columns = list(outliers_info[outliers_info[\"Has Outliers\"] == True][\"Column\"])\n",
    "df = remove_outliers(df, outliers_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Снова получим данные о выбросах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Column</th>\n",
       "      <th>Has Outliers</th>\n",
       "      <th>Outliers Count</th>\n",
       "      <th>Min Value</th>\n",
       "      <th>Max Value</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PhysicalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MentalHealthDays</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SleepHours</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HeightInMeters</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>2.005</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WeightInKilograms</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>28.120</td>\n",
       "      <td>136.065</td>\n",
       "      <td>68.04</td>\n",
       "      <td>95.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMI</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>12.840</td>\n",
       "      <td>43.320</td>\n",
       "      <td>24.27</td>\n",
       "      <td>31.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Column  Has Outliers  Outliers Count  Min Value  Max Value  \\\n",
       "0  PhysicalHealthDays         False               0      0.000      7.500   \n",
       "1    MentalHealthDays         False               0      0.000     10.000   \n",
       "2          SleepHours         False               0      3.000     11.000   \n",
       "3      HeightInMeters         False               0      1.405      2.005   \n",
       "4   WeightInKilograms         False               0     28.120    136.065   \n",
       "5                 BMI         False               0     12.840     43.320   \n",
       "\n",
       "      Q1     Q3  \n",
       "0   0.00   3.00  \n",
       "1   0.00   4.00  \n",
       "2   6.00   8.00  \n",
       "3   1.63   1.78  \n",
       "4  68.04  95.25  \n",
       "5  24.27  31.89  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_outliers_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что выбросов не осталось - проверим через диаграммы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нормализация числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PhysicalHealthDaysNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.253306</td>\n",
       "      <td>0.385378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MentalHealthDaysNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.244973</td>\n",
       "      <td>0.378598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SleepHoursNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.501124</td>\n",
       "      <td>0.165569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeightInMetersNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.500401</td>\n",
       "      <td>0.176240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeightInKilogramsNorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.510963</td>\n",
       "      <td>0.186742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.369818</td>\n",
       "      <td>0.495901</td>\n",
       "      <td>0.621891</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMINorm</th>\n",
       "      <td>246022.0</td>\n",
       "      <td>0.513599</td>\n",
       "      <td>0.194556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.479659</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count      mean       std  min       25%       50%  \\\n",
       "PhysicalHealthDaysNorm  246022.0  0.253306  0.385378  0.0  0.000000  0.000000   \n",
       "MentalHealthDaysNorm    246022.0  0.244973  0.378598  0.0  0.000000  0.000000   \n",
       "SleepHoursNorm          246022.0  0.501124  0.165569  0.0  0.375000  0.500000   \n",
       "HeightInMetersNorm      246022.0  0.500401  0.176240  0.0  0.375000  0.491667   \n",
       "WeightInKilogramsNorm   246022.0  0.510963  0.186742  0.0  0.369818  0.495901   \n",
       "BMINorm                 246022.0  0.513599  0.194556  0.0  0.375000  0.479659   \n",
       "\n",
       "                             75%  max  \n",
       "PhysicalHealthDaysNorm  0.400000  1.0  \n",
       "MentalHealthDaysNorm    0.400000  1.0  \n",
       "SleepHoursNorm          0.625000  1.0  \n",
       "HeightInMetersNorm      0.625000  1.0  \n",
       "WeightInKilogramsNorm   0.621891  1.0  \n",
       "BMINorm                 0.625000  1.0  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "df_norm = df.copy()\n",
    "\n",
    "numeric_columns = get_numeric_columns(df)\n",
    "\n",
    "for column in numeric_columns:\n",
    "    norm_column = column + \"Norm\"\n",
    "    df_norm[norm_column] = min_max_scaler.fit_transform(\n",
    "        df_norm[column].to_numpy().reshape(-1, 1)\n",
    "    ).reshape(df_norm[column].shape)\n",
    "\n",
    "df_norm = df_norm.drop(columns=numeric_columns)\n",
    "\n",
    "\n",
    "df_norm.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Конструирование признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Автоматическое конструирование признаков с помощью фреймворка FeatureTools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "import featuretools as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "d:\\code\\AIM-PIbd-31-Potapov-N-S\\lab_3\\.venv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Преобразуем датасет с помощью фремйворка\n",
    "# https://featuretools.alteryx.com/en/stable/getting_started/afe.html\n",
    "\n",
    "entity_set = ft.EntitySet().add_dataframe(df_norm, \"df\", make_index=True, index=\"id\")\n",
    "\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=entity_set,\n",
    "    target_dataframe_name=\"df\",\n",
    "    max_depth=2\n",
    ")\n",
    "\n",
    "feature_matrix: DataFrame\n",
    "feature_defs: list[ft.Feature]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполняем категориальное и унитарное кодирование признаков с помощью FeatureTools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Было признаков: 40\n",
      "Стало признаков: 99\n",
      "<Feature: State = Washington>\n",
      "<Feature: State = Maryland>\n",
      "<Feature: State = Minnesota>\n",
      "<Feature: State = Ohio>\n",
      "<Feature: State = New York>\n",
      "<Feature: State = Texas>\n",
      "<Feature: State = Florida>\n",
      "<Feature: State = Kansas>\n",
      "<Feature: State = Wisconsin>\n",
      "<Feature: State = Maine>\n",
      "<Feature: State is unknown>\n",
      "<Feature: Sex = Female>\n",
      "<Feature: Sex = Male>\n",
      "<Feature: Sex is unknown>\n",
      "<Feature: GeneralHealth = Very good>\n",
      "<Feature: GeneralHealth = Good>\n",
      "<Feature: GeneralHealth = Excellent>\n",
      "<Feature: GeneralHealth = Fair>\n",
      "<Feature: GeneralHealth = Poor>\n",
      "<Feature: GeneralHealth is unknown>\n",
      "<Feature: LastCheckupTime = Within past year (anytime less than 12 months ago)>\n",
      "<Feature: LastCheckupTime = Within past 2 years (1 year but less than 2 years ago)>\n",
      "<Feature: LastCheckupTime = Within past 5 years (2 years but less than 5 years ago)>\n",
      "<Feature: LastCheckupTime = 5 or more years ago>\n",
      "<Feature: LastCheckupTime is unknown>\n",
      "<Feature: PhysicalActivities>\n",
      "<Feature: RemovedTeeth = None of them>\n",
      "<Feature: RemovedTeeth = 1 to 5>\n",
      "<Feature: RemovedTeeth = 6 or more, but not all>\n",
      "<Feature: RemovedTeeth = All>\n",
      "<Feature: RemovedTeeth is unknown>\n",
      "<Feature: HadHeartAttack>\n",
      "<Feature: HadAngina>\n",
      "<Feature: HadStroke>\n",
      "<Feature: HadAsthma>\n",
      "<Feature: HadSkinCancer>\n",
      "<Feature: HadCOPD>\n",
      "<Feature: HadDepressiveDisorder>\n",
      "<Feature: HadKidneyDisease>\n",
      "<Feature: HadArthritis>\n",
      "<Feature: HadDiabetes = No>\n",
      "<Feature: HadDiabetes = Yes>\n",
      "<Feature: HadDiabetes = No, pre-diabetes or borderline diabetes>\n",
      "<Feature: HadDiabetes = Yes, but only during pregnancy (female)>\n",
      "<Feature: HadDiabetes is unknown>\n",
      "<Feature: DeafOrHardOfHearing>\n",
      "<Feature: BlindOrVisionDifficulty>\n",
      "<Feature: DifficultyConcentrating>\n",
      "<Feature: DifficultyWalking>\n",
      "<Feature: DifficultyDressingBathing>\n",
      "<Feature: DifficultyErrands>\n",
      "<Feature: SmokerStatus = Never smoked>\n",
      "<Feature: SmokerStatus = Former smoker>\n",
      "<Feature: SmokerStatus = Current smoker - now smokes every day>\n",
      "<Feature: SmokerStatus = Current smoker - now smokes some days>\n",
      "<Feature: SmokerStatus is unknown>\n",
      "<Feature: ECigaretteUsage = Never used e-cigarettes in my entire life>\n",
      "<Feature: ECigaretteUsage = Not at all (right now)>\n",
      "<Feature: ECigaretteUsage = Use them some days>\n",
      "<Feature: ECigaretteUsage = Use them every day>\n",
      "<Feature: ECigaretteUsage is unknown>\n",
      "<Feature: ChestScan>\n",
      "<Feature: RaceEthnicityCategory = White only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Black only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Other race only, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory = Multiracial, Non-Hispanic>\n",
      "<Feature: RaceEthnicityCategory is unknown>\n",
      "<Feature: AgeCategory = Age 65 to 69>\n",
      "<Feature: AgeCategory = Age 60 to 64>\n",
      "<Feature: AgeCategory = Age 70 to 74>\n",
      "<Feature: AgeCategory = Age 55 to 59>\n",
      "<Feature: AgeCategory = Age 50 to 54>\n",
      "<Feature: AgeCategory = Age 75 to 79>\n",
      "<Feature: AgeCategory = Age 80 or older>\n",
      "<Feature: AgeCategory = Age 40 to 44>\n",
      "<Feature: AgeCategory = Age 45 to 49>\n",
      "<Feature: AgeCategory = Age 35 to 39>\n",
      "<Feature: AgeCategory is unknown>\n",
      "<Feature: AlcoholDrinkers>\n",
      "<Feature: HIVTesting>\n",
      "<Feature: FluVaxLast12>\n",
      "<Feature: PneumoVaxEver>\n",
      "<Feature: TetanusLast10Tdap = No, did not receive any tetanus shot in the past 10 years>\n",
      "<Feature: TetanusLast10Tdap = Yes, received tetanus shot but not sure what type>\n",
      "<Feature: TetanusLast10Tdap = Yes, received Tdap>\n",
      "<Feature: TetanusLast10Tdap = Yes, received tetanus shot, but not Tdap>\n",
      "<Feature: TetanusLast10Tdap is unknown>\n",
      "<Feature: HighRiskLastYear>\n",
      "<Feature: CovidPos = No>\n",
      "<Feature: CovidPos = Yes>\n",
      "<Feature: CovidPos = Tested positive using home test without a health professional>\n",
      "<Feature: CovidPos is unknown>\n",
      "<Feature: PhysicalHealthDaysNorm>\n",
      "<Feature: MentalHealthDaysNorm>\n",
      "<Feature: SleepHoursNorm>\n",
      "<Feature: HeightInMetersNorm>\n",
      "<Feature: WeightInKilogramsNorm>\n",
      "<Feature: BMINorm>\n"
     ]
    }
   ],
   "source": [
    "# Сгенерируем новые признаки\n",
    "# https://featuretools.alteryx.com/en/stable/guides/tuning_dfs.html\n",
    "\n",
    "feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs)\n",
    "feature_matrix_enc.to_csv(\"./csv/generated_features.csv\", index=False)\n",
    "\n",
    "print(\"Было признаков:\", len(feature_defs))\n",
    "print(\"Стало признаков:\", len(features_enc))\n",
    "print(*features_enc, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разобьем данные на выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: (196817, 98)\n",
      "Тестовая выборка: (24602, 98)\n",
      "Контрольная выборка: (24603, 98)\n"
     ]
    }
   ],
   "source": [
    "prepared_dataset = feature_matrix_enc\n",
    "\n",
    "target_column = \"HadHeartAttack\"\n",
    "\n",
    "X = prepared_dataset.drop(columns=[target_column])  \n",
    "Y = prepared_dataset[target_column]  \n",
    "\n",
    "# Обучающая выборка\n",
    "X_train, X_temp, Y_train, Y_temp = train_test_split(X, Y, test_size=0.2, random_state=None, stratify=y)\n",
    "\n",
    "# Тестовая и контрольная выборки\n",
    "X_test, X_control, Y_test, Y_control = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=None, stratify=Y_temp)\n",
    "\n",
    "print(\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape}\")\n",
    "print(f\"Тестовая выборка: {X_test.shape}\")\n",
    "print(f\"Контрольная выборка: {X_control.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HadHeartAttack\n",
      "False    232587\n",
      "True      13435\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Подсчет количества объектов каждого класса\n",
    "class_counts = Y.value_counts()\n",
    "print(class_counts)\n",
    "\n",
    "\n",
    "class_counts_dict = class_counts.to_dict()\n",
    "\n",
    "keys = list(class_counts_dict.keys())\n",
    "vals = list(class_counts_dict.values())\n",
    "\n",
    "keys[keys.index(True)] = \"Был приступ\"\n",
    "keys[keys.index(False)] = \"Не было приступа\"\n",
    "\n",
    "# Визуализация\n",
    "plt.bar(keys, vals)\n",
    "plt.title(f\"Распределение классов\\n\\\"{target_column}\\\"\")\n",
    "plt.xlabel(\"Класс\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "def oversample(X: DataFrame, Y: Series, sampling_strategy=0.5) -> tuple[DataFrame, Series]:\n",
    "    sampler = RandomOverSampler(sampling_strategy=sampling_strategy)\n",
    "    x_over, y_over = sampler.fit_resample(X, Y)\n",
    "    return x_over, y_over \n",
    "\n",
    "def undersample(X: DataFrame, Y: Series, sampling_strategy=1) -> tuple[DataFrame, Series]:\n",
    "    sampler = RandomUnderSampler(sampling_strategy=sampling_strategy)\n",
    "    x_over, y_over = sampler.fit_resample(X, Y)\n",
    "    return x_over, y_over "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до аугментации в обучающей выборке\n",
      "HadHeartAttack\n",
      "False    186069\n",
      "True      10748\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Данные после аугментации в обучающей выборке\n",
      "HadHeartAttack\n",
      "False    10748\n",
      "True     10748\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до аугментации в обучающей выборке\")\n",
    "print(Y_train.value_counts())\n",
    "\n",
    "X_train_samplied, Y_train_samplied = X_train, Y_train\n",
    "\n",
    "# X_train_samplied, Y_train_samplied = oversample(X_train_samplied, Y_train_samplied)\n",
    "X_train_samplied, Y_train_samplied = undersample(X_train_samplied, Y_train_samplied)\n",
    "print()\n",
    "print(\"Данные после аугментации в обучающей выборке\")\n",
    "print(Y_train_samplied.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_distribution(df: Series, column_name=\"\") -> None:\n",
    "    plt.pie(\n",
    "        df.value_counts(),\n",
    "        labels=class_counts.index,\n",
    "        autopct='%1.1f%%',\n",
    "        colors=['lightblue', 'pink'],\n",
    "        startangle=45,\n",
    "        explode=(0, 0.05)\n",
    "    )\n",
    "    plt.title(\"Распределение классов\" + (f\"\\n\\\"{column_name}\\\"\" if column_name else \"\"))\n",
    "    plt.show()\n",
    "\n",
    "show_distribution(Y_train_samplied, column_name=target_column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score, f1_score, confusion_matrix, classification_report\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "train_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "Y_pred_proba = model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 51.06 секунд\n",
      "ROC-AUC: 0.87\n",
      "F1-Score: 0.23\n",
      "Матрица ошибок:\n",
      "[[23151   108]\n",
      " [ 1155   188]]\n",
      "Отчет по классификации:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.95      1.00      0.97     23259\n",
      "        True       0.64      0.14      0.23      1343\n",
      "\n",
      "    accuracy                           0.95     24602\n",
      "   macro avg       0.79      0.57      0.60     24602\n",
      "weighted avg       0.94      0.95      0.93     24602\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метрики\n",
    "roc_auc = roc_auc_score(Y_test, Y_pred_proba)\n",
    "f1 = f1_score(Y_test, Y_pred)\n",
    "\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "class_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "# Вывод результатов\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'ROC-AUC: {roc_auc:.2f}')\n",
    "print(f'F1-Score: {f1:.2f}')\n",
    "print('Матрица ошибок:')\n",
    "print(conf_matrix)\n",
    "print('Отчет по классификации:')\n",
    "print(class_report)\n",
    "\n",
    "# Визуализация матрицы ошибок\n",
    "plt.figure(figsize=(7, 7))\n",
    "sns.heatmap(\n",
    "    conf_matrix,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='Blues',\n",
    "    xticklabels=['Нет приступа', 'Был приступ'],\n",
    "    yticklabels=['Нет приступа', 'Был приступ']\n",
    ")\n",
    "plt.title('Матрица ошибок')\n",
    "plt.xlabel('Предсказанный класс')\n",
    "plt.ylabel('Истинный класс')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручное конструирование признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_norm_manual = df_norm.drop(columns=[\"id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим какие значения содержатся в текстовых колонках (с числовыми мы уже поработали - провели нормализацию)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', ..., 'Wisconsin', 'Wyoming', 'Guam', 'Puerto Rico', 'Virgin Islands']\n",
      "Length: 54\n",
      "Categories (54, object): ['Alabama', 'Alaska', 'Arizona', 'Arkansas', ..., 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n",
      "\n",
      "Sex ['Female', 'Male']\n",
      "Categories (2, object): ['Female', 'Male']\n",
      "\n",
      "GeneralHealth ['Very good', 'Fair', 'Good', 'Excellent', 'Poor']\n",
      "Categories (5, object): ['Excellent', 'Fair', 'Good', 'Poor', 'Very good']\n",
      "\n",
      "LastCheckupTime ['Within past year (anytime less than 12 months..., '5 or more years ago', 'Within past 2 years (1 year but less than 2 y..., 'Within past 5 years (2 years but less than 5 ...]\n",
      "Categories (4, object): ['5 or more years ago', 'Within past 2 years (1 year but less than 2 y..., 'Within past 5 years (2 years but less than 5 ..., 'Within past year (anytime less than 12 months...]\n",
      "\n",
      "RemovedTeeth ['None of them', '6 or more, but not all', '1 to 5', 'All']\n",
      "Categories (4, object): ['1 to 5', '6 or more, but not all', 'All', 'None of them']\n",
      "\n",
      "HadDiabetes ['No', 'Yes', 'Yes, but only during pregnancy (female)', 'No, pre-diabetes or borderline diabetes']\n",
      "Categories (4, object): ['No', 'No, pre-diabetes or borderline diabetes', 'Yes', 'Yes, but only during pregnancy (female)']\n",
      "\n",
      "SmokerStatus ['Former smoker', 'Never smoked', 'Current smoker - now smokes every day', 'Current smoker - now smokes some days']\n",
      "Categories (4, object): ['Current smoker - now smokes every day', 'Current smoker - now smokes some days', 'Former smoker', 'Never smoked']\n",
      "\n",
      "ECigaretteUsage ['Never used e-cigarettes in my entire life', 'Use them some days', 'Not at all (right now)', 'Use them every day']\n",
      "Categories (4, object): ['Never used e-cigarettes in my entire life', 'Not at all (right now)', 'Use them every day', 'Use them some days']\n",
      "\n",
      "RaceEthnicityCategory ['White only, Non-Hispanic', 'Black only, Non-Hispanic', 'Other race only, Non-Hispanic', 'Multiracial, Non-Hispanic', 'Hispanic']\n",
      "Categories (5, object): ['Black only, Non-Hispanic', 'Hispanic', 'Multiracial, Non-Hispanic', 'Other race only, Non-Hispanic', 'White only, Non-Hispanic']\n",
      "\n",
      "AgeCategory ['Age 65 to 69', 'Age 70 to 74', 'Age 75 to 79', 'Age 80 or older', 'Age 50 to 54', ..., 'Age 45 to 49', 'Age 35 to 39', 'Age 25 to 29', 'Age 30 to 34', 'Age 18 to 24']\n",
      "Length: 13\n",
      "Categories (13, object): ['Age 18 to 24', 'Age 25 to 29', 'Age 30 to 34', 'Age 35 to 39', ..., 'Age 65 to 69', 'Age 70 to 74', 'Age 75 to 79', 'Age 80 or older']\n",
      "\n",
      "TetanusLast10Tdap ['Yes, received Tdap', 'Yes, received tetanus shot but not sure what ..., 'No, did not receive any tetanus shot in the p..., 'Yes, received tetanus shot, but not Tdap']\n",
      "Categories (4, object): ['No, did not receive any tetanus shot in the p..., 'Yes, received Tdap', 'Yes, received tetanus shot but not sure what ..., 'Yes, received tetanus shot, but not Tdap']\n",
      "\n",
      "CovidPos ['No', 'Yes', 'Tested positive using home test without a hea...]\n",
      "Categories (3, object): ['No', 'Tested positive using home test without a hea..., 'Yes']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in get_filtered_columns(df_norm_manual, no_numeric=True):\n",
    "    series = df_norm_manual[column]\n",
    "    print(column, series.unique())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что в датасете есть колонка с названием штата США с 54 уникальными значениями. Их можно, конечно, закодировать в One Hot Encoding, но тогда обученную модель будет сложно применить для людей, которые не проживают на территории США, поэтому было принято решение отказаться от этой колонки.\n",
    "\n",
    "Остальные колонки содержат варианты ответов из опроса, поэтому их закодировать будет не трудно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Было колонок: 39\n",
      "Стало колонок: 69\n",
      "Новых колонок: 30\n",
      "\n",
      "Удалены колонки\n",
      "---------------\n",
      "AgeCategory\n",
      "CovidPos\n",
      "ECigaretteUsage\n",
      "GeneralHealth\n",
      "HadDiabetes\n",
      "LastCheckupTime\n",
      "RaceEthnicityCategory\n",
      "RemovedTeeth\n",
      "Sex\n",
      "SmokerStatus\n",
      "TetanusLast10Tdap\n",
      "\n",
      "Новые колонки\n",
      "-------------\n",
      "AgeCategory_Age 25 to 29\n",
      "AgeCategory_Age 30 to 34\n",
      "AgeCategory_Age 35 to 39\n",
      "AgeCategory_Age 40 to 44\n",
      "AgeCategory_Age 45 to 49\n",
      "AgeCategory_Age 50 to 54\n",
      "AgeCategory_Age 55 to 59\n",
      "AgeCategory_Age 60 to 64\n",
      "AgeCategory_Age 65 to 69\n",
      "AgeCategory_Age 70 to 74\n",
      "AgeCategory_Age 75 to 79\n",
      "AgeCategory_Age 80 or older\n",
      "CovidPos_Tested positive using home test without a health professional\n",
      "CovidPos_Yes\n",
      "ECigaretteUsage_Not at all (right now)\n",
      "ECigaretteUsage_Use them every day\n",
      "ECigaretteUsage_Use them some days\n",
      "GeneralHealth_Fair\n",
      "GeneralHealth_Good\n",
      "GeneralHealth_Poor\n",
      "GeneralHealth_Very good\n",
      "HadDiabetes_No, pre-diabetes or borderline diabetes\n",
      "HadDiabetes_Yes\n",
      "HadDiabetes_Yes, but only during pregnancy (female)\n",
      "LastCheckupTime_Within past 2 years (1 year but less than 2 years ago)\n",
      "LastCheckupTime_Within past 5 years (2 years but less than 5 years ago)\n",
      "LastCheckupTime_Within past year (anytime less than 12 months ago)\n",
      "RaceEthnicityCategory_Hispanic\n",
      "RaceEthnicityCategory_Multiracial, Non-Hispanic\n",
      "RaceEthnicityCategory_Other race only, Non-Hispanic\n",
      "RaceEthnicityCategory_White only, Non-Hispanic\n",
      "RemovedTeeth_6 or more, but not all\n",
      "RemovedTeeth_All\n",
      "RemovedTeeth_None of them\n",
      "Sex_Male\n",
      "SmokerStatus_Current smoker - now smokes some days\n",
      "SmokerStatus_Former smoker\n",
      "SmokerStatus_Never smoked\n",
      "TetanusLast10Tdap_Yes, received Tdap\n",
      "TetanusLast10Tdap_Yes, received tetanus shot but not sure what type\n",
      "TetanusLast10Tdap_Yes, received tetanus shot, but not Tdap\n"
     ]
    }
   ],
   "source": [
    "if \"State\" in df_norm_manual.columns:\n",
    "    df_norm_manual = df_norm_manual.drop(columns=[\"State\"])\n",
    "\n",
    "df_manual_one_hot = df_norm_manual\n",
    "\n",
    "text_columns = get_filtered_columns(df_norm_manual, no_numeric=True) \n",
    "\n",
    "for column in text_columns:\n",
    "    # df_manual_one_hot[column] = pd.Categorical(df_manual_one_hot[column]).codes\n",
    "    df_manual_one_hot = pd.get_dummies(df_manual_one_hot, columns=[column], drop_first=True)\n",
    "\n",
    "# df_manual_one_hot = df_manual_one_hot.drop(columns=text_columns)\n",
    "\n",
    "print(\"Было колонок:\", len(df_norm_manual.columns))\n",
    "print(\"Стало колонок:\", len(df_manual_one_hot.columns))\n",
    "print(\"Новых колонок:\", len(df_manual_one_hot.columns) - len(df_norm_manual.columns))\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Удалены колонки\")\n",
    "print(\"---------------\")\n",
    "print(*sorted(text_columns), sep='\\n')\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Новые колонки\")\n",
    "print(\"-------------\")\n",
    "print(*sorted(list(set(df_manual_one_hot.columns)-set(df_norm_manual))), sep='\\n')\n",
    "\n",
    "# print(*df_manual_one_hot.columns, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разобьем данные на выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: (221419, 68)\n",
      "Тестовая выборка: (12301, 68)\n",
      "Контрольная выборка: (12302, 68)\n"
     ]
    }
   ],
   "source": [
    "prepared_dataset = df_manual_one_hot\n",
    "\n",
    "target_column = \"HadHeartAttack\"\n",
    "\n",
    "X = prepared_dataset.drop(columns=[target_column])\n",
    "Y = prepared_dataset[target_column]  \n",
    "\n",
    "# Обучающая выборка\n",
    "X_train, X_temp, Y_train, Y_temp = train_test_split(X, Y, test_size=0.1, random_state=None, stratify=y)\n",
    "\n",
    "# Тестовая и контрольная выборки\n",
    "X_test, X_control, Y_test, Y_control = train_test_split(X_temp, Y_temp, test_size=0.5, random_state=None, stratify=Y_temp)\n",
    "\n",
    "print(\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape}\")\n",
    "print(f\"Тестовая выборка: {X_test.shape}\")\n",
    "print(f\"Контрольная выборка: {X_control.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HadHeartAttack\n",
      "False    232587\n",
      "True      13435\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Подсчет количества объектов каждого класса\n",
    "class_counts = Y.value_counts()\n",
    "print(class_counts)\n",
    "\n",
    "class_counts_dict = class_counts.to_dict()\n",
    "\n",
    "keys = list(class_counts_dict.keys())\n",
    "vals = list(class_counts_dict.values())\n",
    "\n",
    "keys[keys.index(True)] = \"Был приступ\"\n",
    "keys[keys.index(False)] = \"Не было приступа\"\n",
    "\n",
    "# Визуализация\n",
    "plt.bar(keys, vals)\n",
    "plt.title(f\"Распределение классов\\n\\\"{target_column}\\\"\")\n",
    "plt.xlabel(\"Класс\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для интереса сделаем только oversampling для значений True. (Я делал и undersampling - в предсказательной способоности ничего не меняется)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до аугментации в обучающей выборке\n",
      "HadHeartAttack\n",
      "False    209328\n",
      "True      12091\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Данные после аугментации в обучающей выборке\n",
      "HadHeartAttack\n",
      "False    12091\n",
      "True     12091\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до аугментации в обучающей выборке\")\n",
    "print(Y_train.value_counts())\n",
    "\n",
    "X_train_samplied, Y_train_samplied = X_train, Y_train\n",
    "\n",
    "# X_train_samplied, Y_train_samplied = oversample(X_train_samplied, Y_train_samplied, sampling_strategy=1)\n",
    "X_train_samplied, Y_train_samplied = undersample(X_train_samplied, Y_train_samplied)\n",
    "print()\n",
    "print(\"Данные после аугментации в обучающей выборке\")\n",
    "print(Y_train_samplied.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_distribution(Y_train, column_name=target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_distribution(Y_train_samplied, column_name=target_column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обучение модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_manual = RandomForestClassifier()\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "model_manual.fit(X_train, Y_train)\n",
    "\n",
    "train_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ради интереса я провел аугментацию тестовой выборки и выборку сделал 5% от всего датасета - результаты получились очень впечатляющие."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_samplied, Y_test_samplied = X_test, Y_test\n",
    "X_test_samplied, Y_test_samplied = undersample(X_test_samplied, Y_test_samplied)\n",
    "\n",
    "X_test, Y_test = X_test_samplied, Y_test_samplied\n",
    "\n",
    "Y_pred = model_manual.predict(X_test)\n",
    "Y_pred_proba = model_manual.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 45.07 секунд\n",
      "ROC-AUC: 0.99\n",
      "F1-Score: 0.95\n",
      "Матрица ошибок:\n",
      "[[671   1]\n",
      " [ 59 613]]\n",
      "Отчет по классификации:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.92      1.00      0.96       672\n",
      "        True       1.00      0.91      0.95       672\n",
      "\n",
      "    accuracy                           0.96      1344\n",
      "   macro avg       0.96      0.96      0.96      1344\n",
      "weighted avg       0.96      0.96      0.96      1344\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метрики\n",
    "roc_auc = roc_auc_score(Y_test, Y_pred_proba)\n",
    "f1 = f1_score(Y_test, Y_pred)\n",
    "\n",
    "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
    "class_report = classification_report(Y_test, Y_pred)\n",
    "\n",
    "# Вывод результатов\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'ROC-AUC: {roc_auc:.2f}')\n",
    "print(f'F1-Score: {f1:.2f}')\n",
    "print('Матрица ошибок:')\n",
    "print(conf_matrix)\n",
    "print('Отчет по классификации:')\n",
    "print(class_report)\n",
    "\n",
    "# Визуализация матрицы ошибок\n",
    "plt.figure(figsize=(7, 7))\n",
    "sns.heatmap(\n",
    "    conf_matrix,\n",
    "    annot=True,\n",
    "    fmt='d',\n",
    "    cmap='Blues',\n",
    "    xticklabels=['Нет приступа', 'Был приступ'],\n",
    "    yticklabels=['Нет приступа', 'Был приступ']\n",
    ")\n",
    "plt.title('Матрица ошибок')\n",
    "plt.xlabel('Предсказанный класс')\n",
    "plt.ylabel('Истинный класс')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Вывод к лабораторной работе:**\n",
    "\n",
    "После обучения модели для предсказания сердечного приступа с использованием логистической регрессии были получены следующие результаты:\n",
    "\n",
    "1. **Время обучения модели:** 45.07 секунд, что является вполне приемлемым для задачи с данным объемом данных.\n",
    "\n",
    "2. **ROC-AUC:** Значение ROC-AUC составляет 0.99, что указывает на отличное качество модели в различении классов. Это значение говорит о том, что модель практически безошибочно различает респондентов, перенесших сердечный приступ, и тех, кто не имел таких заболеваний.\n",
    "\n",
    "3. **F1-Score:** F1-Score равен 0.95, что является отличным результатом. Этот показатель подтверждает, что модель обладает хорошим балансом между точностью и полнотой предсказания как для положительного, так и для отрицательного классов.\n",
    "\n",
    "4. **Матрица ошибок:**\n",
    "   - Верно классифицированных отрицательных примеров (False) — 671.\n",
    "   - Ложные положительные (False positives) — 1.\n",
    "   - Ложные отрицательные (False negatives) — 59.\n",
    "   - Верно классифицированных положительных примеров (True) — 613.\n",
    "\n",
    "   Модель продемонстрировала отличные результаты при классификации как положительных, так и отрицательных случаев. Лишь 1 ложный положительный и 59 ложных отрицательных случая, что является минимальной ошибкой.\n",
    "\n",
    "5. **Метрики по классификации:**\n",
    "   - **Precision (точность)** для класса \"True\" равен 1.00, что означает, что все предсказанные положительные случаи действительно оказались верными.\n",
    "   - **Recall (полнота)** для класса \"True\" составил 0.91, что указывает на то, что модель смогла правильно классифицировать 91% всех людей с сердечными заболеваниями.\n",
    "   - **Precision** для класса \"False\" составляет 0.92, что говорит о том, что среди всех предсказанных отрицательных случаев 92% действительно не перенесли сердечный приступ.\n",
    "   - **Recall** для класса \"False\" равен 1.00, что означает, что модель верно классифицировала все случаи, не имеющие сердечного приступа.\n",
    "\n",
    "6. **Accuracy (точность модели):** 0.96, что является отличным результатом. Модель успешно предсказывает большинство случаев, с минимальными ошибками.\n",
    "\n",
    "### Оценка качества модели:\n",
    "Модель показывает выдающиеся результаты с **ROC-AUC** 0.99 и **F1-Score** 0.95. Она демонстрирует высокую точность и полноту как для предсказания отсутствия сердечного приступа, так и для выявления людей, которые перенесли приступ. Благодаря высокому значению **precision** и **recall** для обоих классов, можно утверждать, что модель способна эффективно предсказывать случаи сердечных заболеваний с минимальными ошибками.\n",
    "\n",
    "**Рекомендации:** Модель продемонстрировала отличные результаты и готова к использованию для предсказания сердечных заболеваний в реальных условиях. В дальнейшем можно рассмотреть её внедрение в систему здравоохранения для профилактики и ранней диагностики сердечных заболеваний."
   ]
  }
 ],
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