{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ нескольких датасетов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Выбрать три набора данных, которые не соответствуют Вашему варианту задания\n",
    "### 2. Провести анализ сведений о каждом наборе данных со страницы загрузки в Kaggle. Какова проблемная область?\n",
    "\n",
    "Магазины, Цены на автомобиль, Инсульты"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Инсульты "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данный датасет используется для предсказания вероятности возникновения инсульта у пациента на основе различных параметров, таких как пол, возраст, наличие заболеваний и статус курения. Инсульт является второй по значимости причиной смерти в мире, по данным Всемирной организации здравоохранения (ВОЗ), и ответственен за около 11% всех случаев смерти.\n",
    "\n",
    "Информация о колонках\n",
    "\n",
    "- id: уникальный идентификатор пациента (int)\n",
    "- gender: пол пациента, возможные значения — \"Male\" (мужчина), \"Female\" (женщина) или \"Other\" (другое) (object, строковый)\n",
    "- age: возраст пациента (float)\n",
    "- hypertension: наличие гипертензии; 0 — если гипертензии нет, 1 — если гипертензия есть (int)\n",
    "- heart_disease: наличие сердечных заболеваний; 0 — если заболеваний нет, 1 — если есть (int)\n",
    "- ever_married: статус брака; \"No\" (нет) или \"Yes\" (да) (object, строковый)\n",
    "- work_type: тип работы; возможные значения — \"children\" (дети), \"Govt_job\" (государственная работа), \"Never_worked\" (никогда не работал), \"Private\" (частный сектор) или \"Self-employed\" (самозанятый) (object, строковый)\n",
    "- Residence_type: тип проживания; \"Rural\" (сельская местность) или \"Urban\" (городская местность) (object, строковый)\n",
    "- avg_glucose_level: средний уровень глюкозы в крови (float)\n",
    "- bmi: индекс массы тела (ИМТ) (float)\n",
    "- smoking_status: статус курения; возможные значения — \"formerly smoked\" (курил раньше), \"never smoked\" (никогда не курил), \"smokes\" (курит) или \"Unknown\" (неизвестно). Значение \"Unknown\" указывает на недоступность информации о статусе курения пациента (object, строковый) \n",
    "- stroke: наличие инсульта; 1 — если инсульт был, 0 — если не был (int)\n",
    "\n",
    "Каждая строка в датасете содержит соответствующую информацию о пациенте, что позволяет проводить анализ и строить модели для предсказания риска инсульта."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>18234</td>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>44873</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>37544</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0      9046    Male  67.0             0              1          Yes   \n",
       "1     51676  Female  61.0             0              0          Yes   \n",
       "2     31112    Male  80.0             0              1          Yes   \n",
       "3     60182  Female  49.0             0              0          Yes   \n",
       "4      1665  Female  79.0             1              0          Yes   \n",
       "...     ...     ...   ...           ...            ...          ...   \n",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0           Private          Urban             228.69  36.6  formerly smoked   \n",
       "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2           Private          Rural             105.92  32.5     never smoked   \n",
       "3           Private          Urban             171.23  34.4           smokes   \n",
       "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
       "...             ...            ...                ...   ...              ...   \n",
       "5105        Private          Urban              83.75   NaN     never smoked   \n",
       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  \n",
       "\n",
       "[5110 rows x 12 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "strokes = pd.read_csv(\"healthcare-dataset-stroke-data.csv\")\n",
    "\n",
    "strokes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     int64\n",
       "gender                object\n",
       "age                  float64\n",
       "hypertension           int64\n",
       "heart_disease          int64\n",
       "ever_married          object\n",
       "work_type             object\n",
       "Residence_type        object\n",
       "avg_glucose_level    float64\n",
       "bmi                  float64\n",
       "smoking_status        object\n",
       "stroke                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strokes.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Автомобили "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данный датасет используется для предсказания цены автомобиля на основе различных параметров, таких как производитель, модель, год выпуска и другие характеристики.\n",
    "\n",
    "Информация о колонках\n",
    "- ID: уникальный идентификатор автомобиля (int)\n",
    "- Price: цена автомобиля (целевой столбец) (int)\n",
    "- Levy: налог или сбор, связанный с автомобилем (obect, строковый)\n",
    "- Manufacturer: производитель автомобиля (obect, строковый)\n",
    "- Model: модель автомобиля (obect, строковый)\n",
    "- Prod. year: год производства (int)\n",
    "- Category: категория автомобиля (obect, строковый)\n",
    "- Leather interior: наличие кожаного салона (да/нет) (obect, строковый) \n",
    "- Fuel type: тип топлива (бензин, дизель и т.д.) (obect, строковый)\n",
    "- Engine volume: рабочий объем двигателя (obect, строковый)\n",
    "- Mileage: пробег автомобиля (obect, строковый)\n",
    "- Cylinders: количество цилиндров в двигателе (float)\n",
    "- Gear box type: тип коробки передач (механическая, автоматическая и т.д.) (obect, строковый)\n",
    "- Drive wheels: тип привода (передний, задний, полный) (obect, строковый)\n",
    "- Doors: количество дверей (obect, строковый)\n",
    "- Wheel: расположение руля (левосторонний, правосторонний) (obect, строковый)\n",
    "- Color: цвет автомобиля (obect, строковый)\n",
    "- Airbags: наличие подушек безопасности (int)\n",
    "\n",
    "\n",
    "Каждая строка в датасете содержит соответствующую информацию о автомобиле, что позволяет проводить анализ и строить модели для предсказания его цены."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Price</th>\n",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>45798355</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0 Turbo</td>\n",
       "      <td>300000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>02-Mar</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>45778856</td>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>45804997</td>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>116365 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>45793526</td>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>51258 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>45813273</td>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Price  Levy   Manufacturer    Model  Prod. year   Category  \\\n",
       "0      45654403  13328  1399          LEXUS   RX 450        2010       Jeep   \n",
       "1      44731507  16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2      45774419   8467     -          HONDA      FIT        2006  Hatchback   \n",
       "3      45769185   3607   862           FORD   Escape        2011       Jeep   \n",
       "4      45809263  11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...         ...    ...   ...            ...      ...         ...        ...   \n",
       "19232  45798355   8467     -  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  45778856  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  45804997  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235  45793526   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236  45813273    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
       "0                  Yes    Hybrid           3.5  186005 km        6.0   \n",
       "1                   No    Petrol             3  192000 km        6.0   \n",
       "2                   No    Petrol           1.3  200000 km        4.0   \n",
       "3                  Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4                  Yes    Petrol           1.3   91901 km        4.0   \n",
       "...                ...       ...           ...        ...        ...   \n",
       "19232              Yes       CNG     2.0 Turbo  300000 km        4.0   \n",
       "19233              Yes    Petrol           2.4  161600 km        4.0   \n",
       "19234              Yes    Diesel             2  116365 km        4.0   \n",
       "19235              Yes    Diesel             2   51258 km        4.0   \n",
       "19236              Yes    Hybrid           2.4  186923 km        4.0   \n",
       "\n",
       "      Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0         Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1         Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2          Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3         Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4         Automatic        Front  04-May        Left wheel  Silver        4  \n",
       "...             ...          ...     ...               ...     ...      ...  \n",
       "19232        Manual         Rear  02-Mar        Left wheel  Silver        5  \n",
       "19233     Tiptronic        Front  04-May        Left wheel     Red        8  \n",
       "19234     Automatic        Front  04-May        Left wheel    Grey        4  \n",
       "19235     Automatic        Front  04-May        Left wheel   Black        4  \n",
       "19236     Automatic        Front  04-May        Left wheel   White       12  \n",
       "\n",
       "[19237 rows x 18 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto = pd.read_csv(\"car_price_prediction.csv\")\n",
    "\n",
    "auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    int64\n",
       "Price                 int64\n",
       "Levy                 object\n",
       "Manufacturer         object\n",
       "Model                object\n",
       "Prod. year            int64\n",
       "Category             object\n",
       "Leather interior     object\n",
       "Fuel type            object\n",
       "Engine volume        object\n",
       "Mileage              object\n",
       "Cylinders           float64\n",
       "Gear box type        object\n",
       "Drive wheels         object\n",
       "Doors                object\n",
       "Wheel                object\n",
       "Color                object\n",
       "Airbags               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Магазины "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Информация о колонках\n",
    "- Store ID: уникальный идентификатор конкретного магазина (индекс) (int)\n",
    "- Store_Area: физическая площадь магазина в квадратных ярдах (int)\n",
    "- Items_Available: количество различных товаров, доступных в соответствующем магазине (int)\n",
    "- Daily_Customer_Count: среднее количество клиентов, посещающих магазины за месяц (int)\n",
    "- Store_Sales: объем продаж (в долларах США), полученный магазинами (int)\n",
    "\n",
    "Каждая строка в датасете содержит соответствующую информацию о магазине, что позволяет проводить анализ и строить модели для оценки его работы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Store ID</th>\n",
       "      <th>Store_Area</th>\n",
       "      <th>Items_Available</th>\n",
       "      <th>Daily_Customer_Count</th>\n",
       "      <th>Store_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1659</td>\n",
       "      <td>1961</td>\n",
       "      <td>530</td>\n",
       "      <td>66490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1461</td>\n",
       "      <td>1752</td>\n",
       "      <td>210</td>\n",
       "      <td>39820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>1609</td>\n",
       "      <td>720</td>\n",
       "      <td>54010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1451</td>\n",
       "      <td>1748</td>\n",
       "      <td>620</td>\n",
       "      <td>53730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1770</td>\n",
       "      <td>2111</td>\n",
       "      <td>450</td>\n",
       "      <td>46620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>892</td>\n",
       "      <td>1582</td>\n",
       "      <td>1910</td>\n",
       "      <td>1080</td>\n",
       "      <td>66390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>893</td>\n",
       "      <td>1387</td>\n",
       "      <td>1663</td>\n",
       "      <td>850</td>\n",
       "      <td>82080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>894</td>\n",
       "      <td>1200</td>\n",
       "      <td>1436</td>\n",
       "      <td>1060</td>\n",
       "      <td>76440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>895</td>\n",
       "      <td>1299</td>\n",
       "      <td>1560</td>\n",
       "      <td>770</td>\n",
       "      <td>96610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>896</td>\n",
       "      <td>1174</td>\n",
       "      <td>1429</td>\n",
       "      <td>1110</td>\n",
       "      <td>54340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>896 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
       "0            1        1659             1961                   530        66490\n",
       "1            2        1461             1752                   210        39820\n",
       "2            3        1340             1609                   720        54010\n",
       "3            4        1451             1748                   620        53730\n",
       "4            5        1770             2111                   450        46620\n",
       "..         ...         ...              ...                   ...          ...\n",
       "891        892        1582             1910                  1080        66390\n",
       "892        893        1387             1663                   850        82080\n",
       "893        894        1200             1436                  1060        76440\n",
       "894        895        1299             1560                   770        96610\n",
       "895        896        1174             1429                  1110        54340\n",
       "\n",
       "[896 rows x 5 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop = pd.read_csv(\"Stores.csv\")\n",
    "\n",
    "shop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store ID                int64\n",
       "Store_Area              int64\n",
       "Items_Available         int64\n",
       "Daily_Customer_Count    int64\n",
       "Store_Sales             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Провести анализ содержимого каждого набора данных. Что является объектом/объектами наблюдения? Каковы атрибуты объектов? Есть ли связи между объектами?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Датасет о риске инсульта\n",
    "\n",
    "Объект наблюдения: Пациенты. \n",
    "\n",
    "Атрибуты перечисленны выше. \n",
    "\n",
    "2. Датасет с ценами автомобилей\n",
    "\n",
    "Объект наблюдения: Автомобили. \n",
    "\n",
    "Атрибуты перечисленны выше. \n",
    "\n",
    "3. Датасет супермакета\n",
    "\n",
    "Объект наблюдения: Магазины супермаркета.\n",
    "\n",
    "Атрибуты перечисленны выше. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Привести примеры бизнес-целей, для достижения которых могут подойти выбранные наборы данных. Каков эффект для бизнеса?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Датасет о риске инсульта\n",
    "\n",
    "Бизнес-цель: Разработка системы раннего предупреждения инсульта на основе анализа данных пациентов.\n",
    "\n",
    "Эффект для бизнеса:\n",
    "\n",
    "Улучшение здоровья пациентов: Снижение числа инсультов за счет раннего выявления рисков.\n",
    "\n",
    "Снижение затрат: Уменьшение расходов на лечение инсульта и реабилитацию.\n",
    "\n",
    "2. Датасет для прогнозирования цен на автомобили\n",
    "\n",
    "Бизнес-цель: Оптимизация ценообразования и улучшение стратегии продаж автомобилей.\n",
    "\n",
    "Эффект для бизнеса:\n",
    "\n",
    "Увеличение прибыли: Установка конкурентоспособных цен на автомобили на основе анализа данных.\n",
    "\n",
    "Лучшее планирование запасов: Снижение излишков и оптимизация поставок.\n",
    "\n",
    "3. Датасет супермаркета\n",
    "\n",
    "Бизнес-цель: Оптимизация ассортимента и улучшение обслуживания клиентов на основе анализа посещаемости и продаж.\n",
    "\n",
    "Эффект для бизнеса:\n",
    "\n",
    "Увеличение объема продаж: Подбор товаров, наиболее популярных среди клиентов.\n",
    "Снижение затрат: Оптимизация площади магазина и распределения товаров.\n",
    "Повышение клиентской удовлетворенности: Улучшение опыта покупок за счет более эффективной организации товаров и обслуживания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Привести примеры целей технического проекта для каждой выделенной ранее бизнес-цели. Что поступает на вход, что является целевым признаком?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Датасет о риске инсульта\n",
    "\n",
    "Бизнес-цель: Разработка системы раннего предупреждения инсульта.\n",
    "\n",
    "Цель технического проекта: Создание модели машинного обучения для прогнозирования вероятности инсульта.\n",
    "\n",
    "Входные данные:\n",
    "\n",
    "Пол\n",
    "Возраст\n",
    "Наличие гипертензии\n",
    "Наличие сердечных заболеваний\n",
    "Статус брака\n",
    "Тип работы\n",
    "Тип проживания\n",
    "Средний уровень глюкозы\n",
    "Индекс массы тела\n",
    "Статус курения\n",
    "и так далее\n",
    "\n",
    "Целевой признак: Наличие инсульта (stroke).\n",
    "\n",
    "2. Датасет для прогнозирования цен на автомобили\n",
    "\n",
    "Бизнес-цель: Оптимизация ценообразования и улучшение стратегии продаж автомобилей.\n",
    "\n",
    "Цель технического проекта: Построение модели для предсказания цены автомобиля на основе характеристик.\n",
    "\n",
    "Входные данные:\n",
    "\n",
    "Производитель\n",
    "Модель\n",
    "Год производства\n",
    "Категория\n",
    "Налог\n",
    "Наличие кожаного салона\n",
    "Тип топлива\n",
    "Рабочий объем двигателя\n",
    "Пробег\n",
    "Количество цилиндров\n",
    "Тип коробки передач\n",
    "и так далее\n",
    "\n",
    "Целевой признак: Цена автомобиля (Price).\n",
    "\n",
    "3. Датасет супермаркета\n",
    "\n",
    "Бизнес-цель: Оптимизация ассортимента и улучшение обслуживания клиентов.\n",
    "\n",
    "Цель технического проекта: Разработка аналитической платформы для анализа посещаемости и продаж.\n",
    "\n",
    "Входные данные:\n",
    "\n",
    "Физическая площадь магазина\n",
    "Количество доступных товаров\n",
    "Среднее количество клиентов\n",
    "Объем продаж\n",
    "и так далее\n",
    "\n",
    "Целевой признак: Объем продаж (Store_Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Определить проблемы выбранных наборов данных: зашумленность, смещение, актуальность, выбросы, просачивание данных.\n",
    "### 7. Привести примеры решения обнаруженных проблем для каждого набора данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. Проверка на зашумленность ---- количество пропусков в процентах от общего кол-ва\n",
    "def check_noise(dataframe):\n",
    "    total_values = dataframe.size\n",
    "    missing_values = dataframe.isnull().sum().sum()\n",
    "    noise_percentage = (missing_values / total_values) * 100\n",
    "    return f\"Зашумленность: {noise_percentage:.2f}%\"\n",
    "\n",
    "# 2. Проверка на смещение ----- объем уникальных значений внутри определнной колонки \n",
    "def check_bias(dataframe, target_column):\n",
    "    if target_column in dataframe.columns:\n",
    "        unique_values = dataframe[target_column].nunique()\n",
    "        total_values = len(dataframe)\n",
    "        bias_percentage = (unique_values / total_values) * 100\n",
    "        return f\"Смещение по {target_column}: {bias_percentage:.2f}% уникальных значений\"\n",
    "    return \"Целевой признак не найден.\"\n",
    "\n",
    "# 3. Проверка на дубликаты\n",
    "def check_duplicates(dataframe):\n",
    "    duplicate_percentage = dataframe.duplicated().mean() * 100\n",
    "    return f\"Количество дубликатов: {duplicate_percentage:.2f}%\"\n",
    "\n",
    "# 4. Проверка на выбросы\n",
    "def check_outliers(dataframe, column):\n",
    "    if column in dataframe.columns:\n",
    "        Q1 = dataframe[column].quantile(0.25)\n",
    "        Q3 = dataframe[column].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        outlier_count = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)].shape[0]\n",
    "        total_count = dataframe.shape[0]\n",
    "        outlier_percentage = (outlier_count / total_count) * 100\n",
    "        return f\"Выбросы по {column}: {outlier_percentage:.2f}%\"\n",
    "    return f\"Признак {column} не найден.\"\n",
    "\n",
    "# 5. Проверка на просачивание данных\n",
    "def check_data_leakage(dataframe, target_column):\n",
    "    if target_column in dataframe.columns:\n",
    "        correlation_matrix = dataframe.select_dtypes(include=[np.number]).corr()\n",
    "        leakage_info = correlation_matrix[target_column].abs().nlargest(10)\n",
    "        leakage_report = \", \".join([f\"{feature}: {value:.2f}\" for feature, value in leakage_info.items() if feature != target_column])\n",
    "        return f\"Признаки просачивания данных: {leakage_report}\"\n",
    "    return \"Целевой признак не найден.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Зашумленность: 0.33%\n",
      "Смещение по avg_glucose_level: 77.87% уникальных значений\n",
      "Количество дубликатов: 0.00%\n",
      "Выбросы по avg_glucose_level: 12.27%\n",
      "Признаки просачивания данных: age: 0.25, heart_disease: 0.13, avg_glucose_level: 0.13, hypertension: 0.13, bmi: 0.04, id: 0.01\n"
     ]
    }
   ],
   "source": [
    "noise_columns = check_noise(strokes)\n",
    "bias_info = check_bias(strokes, 'avg_glucose_level') \n",
    "duplicate_count = check_duplicates(strokes)\n",
    "outliers_data = check_outliers(strokes, 'avg_glucose_level')  \n",
    "leakage_info = check_data_leakage(strokes, 'stroke') \n",
    "\n",
    "print(noise_columns)\n",
    "print(bias_info)\n",
    "print(duplicate_count)\n",
    "print(outliers_data)\n",
    "print(leakage_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Зашумленность: 0.00%\n",
      "Смещение по Price: 12.03% уникальных значений\n",
      "Количество дубликатов: 1.63%\n",
      "Выбросы по Prod. year: 5.10%\n",
      "Признаки просачивания данных: Prod. year: 0.24, Cylinders: 0.18, ID: 0.02, Price: 0.01\n"
     ]
    }
   ],
   "source": [
    "##Машины\n",
    "noise_columns = check_noise(auto)\n",
    "bias_info = check_bias(auto, 'Price') \n",
    "duplicate_count = check_duplicates(auto)\n",
    "outliers_data = check_outliers(auto, 'Prod. year')  \n",
    "leakage_info = check_data_leakage(auto, 'Airbags') \n",
    "\n",
    "print(noise_columns)\n",
    "print(bias_info)\n",
    "print(duplicate_count)\n",
    "print(outliers_data)\n",
    "print(leakage_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Зашумленность: 0.00%\n",
      "Смещение по Items_Available: 68.75% уникальных значений\n",
      "Количество дубликатов: 0.00%\n",
      "Выбросы по Store_Sales: 0.11%\n",
      "Признаки просачивания данных: Store_Area: 0.04, Items_Available: 0.04, Store ID : 0.01, Store_Sales: 0.01\n"
     ]
    }
   ],
   "source": [
    "noise_columns = check_noise(shop)\n",
    "bias_info = check_bias(shop, 'Items_Available') \n",
    "duplicate_count = check_duplicates(shop)\n",
    "outliers_data = check_outliers(shop, 'Store_Sales')  \n",
    "leakage_info = check_data_leakage(shop, 'Daily_Customer_Count') \n",
    "\n",
    "print(noise_columns)\n",
    "print(bias_info)\n",
    "print(duplicate_count)\n",
    "print(outliers_data)\n",
    "print(leakage_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Устранить проблему пропущенных данных. Для каждого набора данных использовать разные методы: удаление, подстановка константного значения (0 или подобное), подстановка среднего значения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Инсульт\n",
    "\n",
    "strokes.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "strokes['bmi'] = strokes['bmi'].fillna(strokes['bmi'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strokes.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "Price               0\n",
       "Levy                0\n",
       "Manufacturer        0\n",
       "Model               0\n",
       "Prod. year          0\n",
       "Category            0\n",
       "Leather interior    0\n",
       "Fuel type           0\n",
       "Engine volume       0\n",
       "Mileage             0\n",
       "Cylinders           0\n",
       "Gear box type       0\n",
       "Drive wheels        0\n",
       "Doors               0\n",
       "Wheel               0\n",
       "Color               0\n",
       "Airbags             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store ID                0\n",
       "Store_Area              0\n",
       "Items_Available         0\n",
       "Daily_Customer_Count    0\n",
       "Store_Sales             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# удалить\n",
    "shop = shop.dropna()\n",
    "\n",
    "# заполнить значением\n",
    "shop['Items_Avialable'] = shop['Items_Avialable'].fillna(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Выполнить разбиение каждого набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shop Dataset:\n",
      "Train: 79.91%\n",
      "Validation: 10.04%\n",
      "Test: 10.04%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Разбиение shop\n",
    "original_shop_size = len(shop)\n",
    "train_shop, temp_shop = train_test_split(shop, test_size=0.2, random_state=42)\n",
    "val_shop, test_shop = train_test_split(temp_shop, test_size=0.5, random_state=42)\n",
    "\n",
    "print(\"Shop Dataset:\")\n",
    "print(f\"Train: {len(train_shop)/original_shop_size*100:.2f}%\")\n",
    "print(f\"Validation: {len(val_shop)/original_shop_size*100:.2f}%\")\n",
    "print(f\"Test: {len(test_shop)/original_shop_size*100:.2f}%\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strokes Dataset:\n",
      "Train: 80.00%\n",
      "Validation: 10.00%\n",
      "Test: 10.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Разбиение strokes\n",
    "original_strokes_size = len(strokes)\n",
    "train_strokes, temp_strokes = train_test_split(strokes, test_size=0.2, random_state=42)\n",
    "val_strokes, test_strokes = train_test_split(temp_strokes, test_size=0.5, random_state=42)\n",
    "\n",
    "print(\"Strokes Dataset:\")\n",
    "print(f\"Train: {len(train_strokes)/original_strokes_size*100:.2f}%\")\n",
    "print(f\"Validation: {len(val_strokes)/original_strokes_size*100:.2f}%\")\n",
    "print(f\"Test: {len(test_strokes)/original_strokes_size*100:.2f}%\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Auto Dataset:\n",
      "Train: 80.00%\n",
      "Validation: 10.00%\n",
      "Test: 10.00%\n"
     ]
    }
   ],
   "source": [
    "# Разбиение auto\n",
    "original_auto_size = len(auto)\n",
    "train_auto, temp_auto = train_test_split(auto, test_size=0.2, random_state=42)\n",
    "val_auto, test_auto = train_test_split(temp_auto, test_size=0.5, random_state=42)\n",
    "\n",
    "print(\"Auto Dataset:\")\n",
    "print(f\"Train: {len(train_auto)/original_auto_size*100:.2f}%\")\n",
    "print(f\"Validation: {len(val_auto)/original_auto_size*100:.2f}%\")\n",
    "print(f\"Test: {len(test_auto)/original_auto_size*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Оценить сбалансированность выборок для каждого набора данных. Оценить необходимость использования методов приращения (аугментации) данных.\n",
    "### 12. Выполнить приращение данных методами выборки с избытком (oversampling) и выборки с недостатком (undersampling). Должны быть представлены примеры реализации обоих методов для выборок каждого набора данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_sample_balance(y, sample_name):\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    sns.histplot(y, bins=30, kde=True)\n",
    "    plt.title(f'Распределение целевой переменной для {sample_name}')\n",
    "    plt.xlabel(sample_name)\n",
    "    plt.ylabel('Частота')\n",
    "    plt.show()\n",
    "\n",
    "# Оценка сбалансированности выборок\n",
    "plot_sample_balance(train_shop['Store_Sales'], 'Train Shop')\n",
    "plot_sample_balance(val_shop['Store_Sales'], 'Validation Shop')\n",
    "plot_sample_balance(test_shop['Store_Sales'], 'Test Shop')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Распределения выборок у данного датасета выглядят схоже. Это говорит о сбалансированности выборок. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample_balance(train_strokes['stroke'], 'Train Strokes')\n",
    "plot_sample_balance(val_strokes['stroke'], 'Validation Strokes')\n",
    "plot_sample_balance(test_strokes['stroke'], 'Test Strokes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки выглядят схоже, но у всех трех имеется явный дисбаланс классов. Это проблема, т.к в дальнейшем не сможем обучить какую-либо модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample_balance(train_auto['Price'], 'Train Auto')\n",
    "plot_sample_balance(val_auto['Price'], 'Validation Auto')\n",
    "plot_sample_balance(test_auto['Price'], 'Test Auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Распределения выборок у данного датасета выглядят схоже. Это говорит о сбалансированности выборок. Однако в тренировочной выборке значительно больший размах значений "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Выполнить приращение данных методами выборки с избытком (oversampling) и выборки с недостатком (undersampling). Должны быть представлены примеры реализации обоих методов для выборок каждого набора данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Инсультики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После oversampling (strokes): stroke\n",
      "1    4861\n",
      "0    4861\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "X_strokes = strokes.drop('stroke', axis=1)\n",
    "y_strokes = strokes['stroke']\n",
    "\n",
    "# Кодирование категориальных признаков\n",
    "for column in X_strokes.select_dtypes(include=['object']).columns:\n",
    "    X_strokes[column] = X_strokes[column].astype('category').cat.codes\n",
    "\n",
    "# Теперь применяем SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled_strokes, y_resampled_strokes = smote.fit_resample(X_strokes, y_strokes)\n",
    "\n",
    "# Получаем результаты\n",
    "print(f'После oversampling (strokes): {pd.Series(y_resampled_strokes).value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После undersampling (strokes): stroke\n",
      "0    249\n",
      "1    249\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Undersampling для strokes\n",
    "undersample = RandomUnderSampler(random_state=42)\n",
    "X_under_strokes, y_under_strokes = undersample.fit_resample(X_strokes, y_strokes)\n",
    "\n",
    "print(f'После undersampling (strokes): {pd.Series(y_under_strokes).value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Машины"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После oversampling (strokes): stroke\n",
      "1    4861\n",
      "0    4861\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "X_strokes = strokes.drop('stroke', axis=1)\n",
    "y_strokes = strokes['stroke']\n",
    "\n",
    "# Кодирование категориальных признаков\n",
    "for column in X_strokes.select_dtypes(include=['object']).columns:\n",
    "    X_strokes[column] = X_strokes[column].astype('category').cat.codes\n",
    "\n",
    "# Теперь применяем SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled_strokes, y_resampled_strokes = smote.fit_resample(X_strokes, y_strokes)\n",
    "\n",
    "# Получаем результаты\n",
    "print(f'После oversampling (strokes): {pd.Series(y_resampled_strokes).value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После undersampling (strokes): stroke\n",
      "0    249\n",
      "1    249\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Undersampling для strokes\n",
    "undersample = RandomUnderSampler(random_state=42)\n",
    "X_under_strokes, y_under_strokes = undersample.fit_resample(X_strokes, y_strokes)\n",
    "\n",
    "print(f'После undersampling (strokes): {pd.Series(y_under_strokes).value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Магазины"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Матевос\\Desktop\\ИИ.Дырночкин\\ai\\lab2\\lab2.ipynb Cell 55\u001b[0m line \u001b[0;36m1\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/%D0%9C%D0%B0%D1%82%D0%B5%D0%B2%D0%BE%D1%81/Desktop/%D0%98%D0%98.%D0%94%D1%8B%D1%80%D0%BD%D0%BE%D1%87%D0%BA%D0%B8%D0%BD/ai/lab2/lab2.ipynb#Y151sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39m# Теперь применяем SMOTE\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/%D0%9C%D0%B0%D1%82%D0%B5%D0%B2%D0%BE%D1%81/Desktop/%D0%98%D0%98.%D0%94%D1%8B%D1%80%D0%BD%D0%BE%D1%87%D0%BA%D0%B8%D0%BD/ai/lab2/lab2.ipynb#Y151sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m smote \u001b[39m=\u001b[39m SMOTE(random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/%D0%9C%D0%B0%D1%82%D0%B5%D0%B2%D0%BE%D1%81/Desktop/%D0%98%D0%98.%D0%94%D1%8B%D1%80%D0%BD%D0%BE%D1%87%D0%BA%D0%B8%D0%BD/ai/lab2/lab2.ipynb#Y151sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m X_resampled_shop, y_resampled_shop \u001b[39m=\u001b[39m smote\u001b[39m.\u001b[39;49mfit_resample(X_shop, y_shop)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/%D0%9C%D0%B0%D1%82%D0%B5%D0%B2%D0%BE%D1%81/Desktop/%D0%98%D0%98.%D0%94%D1%8B%D1%80%D0%BD%D0%BE%D1%87%D0%BA%D0%B8%D0%BD/ai/lab2/lab2.ipynb#Y151sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m# Получаем результаты\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/%D0%9C%D0%B0%D1%82%D0%B5%D0%B2%D0%BE%D1%81/Desktop/%D0%98%D0%98.%D0%94%D1%8B%D1%80%D0%BD%D0%BE%D1%87%D0%BA%D0%B8%D0%BD/ai/lab2/lab2.ipynb#Y151sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mПосле oversampling (strokes): \u001b[39m\u001b[39m{\u001b[39;00mpd\u001b[39m.\u001b[39mSeries(y_resampled_shop)\u001b[39m.\u001b[39mvalue_counts()\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Матевос\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\base.py:208\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m    188\u001b[0m \n\u001b[0;32m    189\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[39m    The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m--> 208\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit_resample(X, y)\n",
      "File \u001b[1;32mc:\\Users\\Матевос\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\base.py:112\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    106\u001b[0m X, y, binarize_y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_X_y(X, y)\n\u001b[0;32m    108\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msampling_strategy_ \u001b[39m=\u001b[39m check_sampling_strategy(\n\u001b[0;32m    109\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msampling_strategy, y, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampling_type\n\u001b[0;32m    110\u001b[0m )\n\u001b[1;32m--> 112\u001b[0m output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit_resample(X, y)\n\u001b[0;32m    114\u001b[0m y_ \u001b[39m=\u001b[39m (\n\u001b[0;32m    115\u001b[0m     label_binarize(output[\u001b[39m1\u001b[39m], classes\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39munique(y)) \u001b[39mif\u001b[39;00m binarize_y \u001b[39melse\u001b[39;00m output[\u001b[39m1\u001b[39m]\n\u001b[0;32m    116\u001b[0m )\n\u001b[0;32m    118\u001b[0m X_, y_ \u001b[39m=\u001b[39m arrays_transformer\u001b[39m.\u001b[39mtransform(output[\u001b[39m0\u001b[39m], y_)\n",
      "File \u001b[1;32mc:\\Users\\Матевос\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:389\u001b[0m, in \u001b[0;36mSMOTE._fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    386\u001b[0m X_class \u001b[39m=\u001b[39m _safe_indexing(X, target_class_indices)\n\u001b[0;32m    388\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnn_k_\u001b[39m.\u001b[39mfit(X_class)\n\u001b[1;32m--> 389\u001b[0m nns \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnn_k_\u001b[39m.\u001b[39;49mkneighbors(X_class, return_distance\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)[:, \u001b[39m1\u001b[39m:]\n\u001b[0;32m    390\u001b[0m X_new, y_new \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_samples(\n\u001b[0;32m    391\u001b[0m     X_class, y\u001b[39m.\u001b[39mdtype, class_sample, X_class, nns, n_samples, \u001b[39m1.0\u001b[39m\n\u001b[0;32m    392\u001b[0m )\n\u001b[0;32m    393\u001b[0m X_resampled\u001b[39m.\u001b[39mappend(X_new)\n",
      "File \u001b[1;32mc:\\Users\\Матевос\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neighbors\\_base.py:834\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[1;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[0;32m    832\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    833\u001b[0m         inequality_str \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mn_neighbors <= n_samples_fit\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m--> 834\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    835\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExpected \u001b[39m\u001b[39m{\u001b[39;00minequality_str\u001b[39m}\u001b[39;00m\u001b[39m, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    836\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mn_neighbors = \u001b[39m\u001b[39m{\u001b[39;00mn_neighbors\u001b[39m}\u001b[39;00m\u001b[39m, n_samples_fit = \u001b[39m\u001b[39m{\u001b[39;00mn_samples_fit\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    837\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mn_samples = \u001b[39m\u001b[39m{\u001b[39;00mX\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m  \u001b[39m# include n_samples for common tests\u001b[39;00m\n\u001b[0;32m    838\u001b[0m     )\n\u001b[0;32m    840\u001b[0m n_jobs \u001b[39m=\u001b[39m effective_n_jobs(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs)\n\u001b[0;32m    841\u001b[0m chunked_results \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1"
     ]
    }
   ],
   "source": [
    "X_shop = shop.drop('Store_Sales', axis=1)\n",
    "y_shop = shop['Store_Sales']\n",
    "\n",
    "# Кодирование категориальных признаков\n",
    "for column in X_shop.select_dtypes(include=['object']).columns:\n",
    "    X_shop[column] = X_shop[column].astype('category').cat.codes\n",
    "\n",
    "# Теперь применяем SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled_shop, y_resampled_shop = smote.fit_resample(X_shop, y_shop)\n",
    "\n",
    "# Получаем результаты\n",
    "print(f'После oversampling (strokes): {pd.Series(y_resampled_shop).value_counts()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После undersampling (strokes): stroke\n",
      "0    249\n",
      "1    249\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Undersampling для strokes\n",
    "undersample = RandomUnderSampler(random_state=42)\n",
    "X_under_strokes, y_under_strokes = undersample.fit_resample(X_strokes, y_strokes)\n",
    "\n",
    "print(f'После undersampling (strokes): {pd.Series(y_under_strokes).value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном случае у нас есть только один датасет, предназначенный для решения задачи классификации (инсульт). Проблему дисбаланса в нем мы решили применив undersampling & oversampling.\n",
    "\n",
    "Два остальных датасета не содержат классов, т.к предназначены для решения задачи регрессии (предсказания цен на автомобили или на чек в супермаркете), поэтому выполнять приращение данных не требуется."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
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 },
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 "nbformat_minor": 2
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