AIM-PIbd-31-Alekseev-I-S/Lab_2/Lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-й Датасет: Mobile Phone Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/dewangmoghe/mobile-phone-price-prediction?resource=download\n",
    "\n",
    "* Из названия датасета(описания у Kaggle не было предсталвено) очевидно, что объектами иследования являются смартфоны.\n",
    "* Атрибуты объектов: id, name, spec_score, no_of_sim, RAM, battery, display, camera, external_memory, android_version, price, company, inbuilt_memory, fast_charging, screen_resolution, processor, processor_name\n",
    "* Очевидная цель этого датасета - научиться предсказывать цену смартфона."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 18\n",
      "колонки: Unnamed: 0, Name, Rating, Spec_score, No_of_sim, Ram, Battery, Display, Camera, External_Memory, Android_version, Price, company, Inbuilt_memory, fast_charging, Screen_resolution, Processor, Processor_name\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1370 entries, 0 to 1369\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         1370 non-null   int64  \n",
      " 1   Name               1370 non-null   object \n",
      " 2   Rating             1370 non-null   float64\n",
      " 3   Spec_score         1370 non-null   int64  \n",
      " 4   No_of_sim          1370 non-null   object \n",
      " 5   Ram                1370 non-null   object \n",
      " 6   Battery            1370 non-null   object \n",
      " 7   Display            1370 non-null   object \n",
      " 8   Camera             1370 non-null   object \n",
      " 9   External_Memory    1370 non-null   object \n",
      " 10  Android_version    927 non-null    object \n",
      " 11  Price              1370 non-null   object \n",
      " 12  company            1370 non-null   object \n",
      " 13  Inbuilt_memory     1351 non-null   object \n",
      " 14  fast_charging      1281 non-null   object \n",
      " 15  Screen_resolution  1368 non-null   object \n",
      " 16  Processor          1342 non-null   object \n",
      " 17  Processor_name     1370 non-null   object \n",
      "dtypes: float64(1), int64(2), object(15)\n",
      "memory usage: 192.8+ KB\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Spec_score</th>\n",
       "      <th>No_of_sim</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Display</th>\n",
       "      <th>Camera</th>\n",
       "      <th>External_Memory</th>\n",
       "      <th>Android_version</th>\n",
       "      <th>Price</th>\n",
       "      <th>company</th>\n",
       "      <th>Inbuilt_memory</th>\n",
       "      <th>fast_charging</th>\n",
       "      <th>Screen_resolution</th>\n",
       "      <th>Processor</th>\n",
       "      <th>Processor_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Samsung Galaxy F14 5G</td>\n",
       "      <td>4.65</td>\n",
       "      <td>68</td>\n",
       "      <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
       "      <td>4 GB RAM</td>\n",
       "      <td>6000 mAh Battery</td>\n",
       "      <td>6.6 inches</td>\n",
       "      <td>50 MP + 2 MP Dual Rear &amp;amp; 13 MP Front Camera</td>\n",
       "      <td>Memory Card Supported, upto 1 TB</td>\n",
       "      <td>13</td>\n",
       "      <td>9,999</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>128 GB inbuilt</td>\n",
       "      <td>25W Fast Charging</td>\n",
       "      <td>2408 x 1080 px Display with Water Drop Notch</td>\n",
       "      <td>Octa Core Processor</td>\n",
       "      <td>Exynos 1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Samsung Galaxy A11</td>\n",
       "      <td>4.20</td>\n",
       "      <td>63</td>\n",
       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
       "      <td>2 GB RAM</td>\n",
       "      <td>4000 mAh Battery</td>\n",
       "      <td>6.4 inches</td>\n",
       "      <td>13 MP + 5 MP + 2 MP Triple Rear &amp;amp; 8 MP Fro...</td>\n",
       "      <td>Memory Card Supported, upto 512 GB</td>\n",
       "      <td>10</td>\n",
       "      <td>9,990</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>32 GB inbuilt</td>\n",
       "      <td>15W Fast Charging</td>\n",
       "      <td>720 x 1560 px Display with Punch Hole</td>\n",
       "      <td>1.8 GHz Processor</td>\n",
       "      <td>Octa Core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Samsung Galaxy A13</td>\n",
       "      <td>4.30</td>\n",
       "      <td>75</td>\n",
       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
       "      <td>4 GB RAM</td>\n",
       "      <td>5000 mAh Battery</td>\n",
       "      <td>6.6 inches</td>\n",
       "      <td>50 MP Quad Rear &amp;amp; 8 MP Front Camera</td>\n",
       "      <td>Memory Card Supported, upto 1 TB</td>\n",
       "      <td>12</td>\n",
       "      <td>11,999</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>64 GB inbuilt</td>\n",
       "      <td>25W Fast Charging</td>\n",
       "      <td>1080 x 2408 px Display with Water Drop Notch</td>\n",
       "      <td>2 GHz Processor</td>\n",
       "      <td>Octa Core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Samsung Galaxy F23</td>\n",
       "      <td>4.10</td>\n",
       "      <td>73</td>\n",
       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
       "      <td>4 GB RAM</td>\n",
       "      <td>6000 mAh Battery</td>\n",
       "      <td>6.4 inches</td>\n",
       "      <td>48 MP Quad Rear &amp;amp; 13 MP Front Camera</td>\n",
       "      <td>Memory Card Supported, upto 1 TB</td>\n",
       "      <td>12</td>\n",
       "      <td>11,999</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>64 GB inbuilt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720 x 1600 px</td>\n",
       "      <td>Octa Core</td>\n",
       "      <td>Helio G88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Samsung Galaxy A03s (4GB RAM + 64GB)</td>\n",
       "      <td>4.10</td>\n",
       "      <td>69</td>\n",
       "      <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
       "      <td>4 GB RAM</td>\n",
       "      <td>5000 mAh Battery</td>\n",
       "      <td>6.5 inches</td>\n",
       "      <td>13 MP + 2 MP + 2 MP Triple Rear &amp;amp; 5 MP Fro...</td>\n",
       "      <td>Memory Card Supported, upto 1 TB</td>\n",
       "      <td>11</td>\n",
       "      <td>11,999</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>64 GB inbuilt</td>\n",
       "      <td>15W Fast Charging</td>\n",
       "      <td>720 x 1600 px Display with Water Drop Notch</td>\n",
       "      <td>Octa Core</td>\n",
       "      <td>Helio P35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                  Name  Rating  Spec_score  \\\n",
       "0           0                 Samsung Galaxy F14 5G    4.65          68   \n",
       "1           1                    Samsung Galaxy A11    4.20          63   \n",
       "2           2                    Samsung Galaxy A13    4.30          75   \n",
       "3           3                    Samsung Galaxy F23    4.10          73   \n",
       "4           4  Samsung Galaxy A03s (4GB RAM + 64GB)    4.10          69   \n",
       "\n",
       "                       No_of_sim       Ram            Battery     Display  \\\n",
       "0  Dual Sim, 3G, 4G, 5G, VoLTE,   4 GB RAM  6000 mAh Battery   6.6 inches   \n",
       "1      Dual Sim, 3G, 4G, VoLTE,   2 GB RAM  4000 mAh Battery   6.4 inches   \n",
       "2      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.6 inches   \n",
       "3      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM   6000 mAh Battery  6.4 inches   \n",
       "4      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.5 inches   \n",
       "\n",
       "                                              Camera  \\\n",
       "0    50 MP + 2 MP Dual Rear &amp; 13 MP Front Camera   \n",
       "1  13 MP + 5 MP + 2 MP Triple Rear &amp; 8 MP Fro...   \n",
       "2            50 MP Quad Rear &amp; 8 MP Front Camera   \n",
       "3           48 MP Quad Rear &amp; 13 MP Front Camera   \n",
       "4  13 MP + 2 MP + 2 MP Triple Rear &amp; 5 MP Fro...   \n",
       "\n",
       "                      External_Memory Android_version   Price  company  \\\n",
       "0    Memory Card Supported, upto 1 TB              13   9,999  Samsung   \n",
       "1  Memory Card Supported, upto 512 GB              10   9,990  Samsung   \n",
       "2    Memory Card Supported, upto 1 TB              12  11,999  Samsung   \n",
       "3    Memory Card Supported, upto 1 TB              12  11,999  Samsung   \n",
       "4    Memory Card Supported, upto 1 TB              11  11,999  Samsung   \n",
       "\n",
       "    Inbuilt_memory       fast_charging  \\\n",
       "0   128 GB inbuilt   25W Fast Charging   \n",
       "1    32 GB inbuilt   15W Fast Charging   \n",
       "2    64 GB inbuilt   25W Fast Charging   \n",
       "3    64 GB inbuilt                 NaN   \n",
       "4    64 GB inbuilt   15W Fast Charging   \n",
       "\n",
       "                               Screen_resolution             Processor  \\\n",
       "0   2408 x 1080 px Display with Water Drop Notch   Octa Core Processor   \n",
       "1          720 x 1560 px Display with Punch Hole     1.8 GHz Processor   \n",
       "2   1080 x 2408 px Display with Water Drop Notch       2 GHz Processor   \n",
       "3                                  720 x 1600 px             Octa Core   \n",
       "4    720 x 1600 px Display with Water Drop Notch             Octa Core   \n",
       "\n",
       "  Processor_name  \n",
       "0    Exynos 1330  \n",
       "1      Octa Core  \n",
       "2      Octa Core  \n",
       "3      Helio G88  \n",
       "4      Helio P35  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//mppp.csv\", sep=\",\")\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Получение сведений о пропущенных данных\n",
    "\n",
    "Типы пропущенных данных:\n",
    "\n",
    "* None - представление пустых данных в Python\n",
    "* NaN - представление пустых данных в Pandas\n",
    "* '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0             0\n",
      "Name                   0\n",
      "Rating                 0\n",
      "Spec_score             0\n",
      "No_of_sim              0\n",
      "Ram                    0\n",
      "Battery                0\n",
      "Display                0\n",
      "Camera                 0\n",
      "External_Memory        0\n",
      "Android_version      443\n",
      "Price                  0\n",
      "company                0\n",
      "Inbuilt_memory        19\n",
      "fast_charging         89\n",
      "Screen_resolution      2\n",
      "Processor             28\n",
      "Processor_name         0\n",
      "dtype: int64\n",
      "\n",
      "Unnamed: 0           False\n",
      "Name                 False\n",
      "Rating               False\n",
      "Spec_score           False\n",
      "No_of_sim            False\n",
      "Ram                  False\n",
      "Battery              False\n",
      "Display              False\n",
      "Camera               False\n",
      "External_Memory      False\n",
      "Android_version       True\n",
      "Price                False\n",
      "company              False\n",
      "Inbuilt_memory        True\n",
      "fast_charging         True\n",
      "Screen_resolution     True\n",
      "Processor             True\n",
      "Processor_name       False\n",
      "dtype: bool\n",
      "\n",
      "Android_version процент пустых значений: %32.34\n",
      "Inbuilt_memory процент пустых значений: %1.39\n",
      "fast_charging процент пустых значений: %6.50\n",
      "Screen_resolution процент пустых значений: %0.15\n",
      "Processor процент пустых значений: %2.04\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по статистике выше, пустые значения всё-таки присутствуют. Наиболее корректным решением будет заполнение пропущенных данных, потому что количество объектов с пропущенными значениями составляет практически половину объектов датасета. Так как все атрибуты имеют строковый тип данных, воспользуемся заполнением наиболее частым значением."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0           0\n",
      "Name                 0\n",
      "Rating               0\n",
      "Spec_score           0\n",
      "No_of_sim            0\n",
      "Ram                  0\n",
      "Battery              0\n",
      "Display              0\n",
      "Camera               0\n",
      "External_Memory      0\n",
      "Android_version      0\n",
      "Price                0\n",
      "company              0\n",
      "Inbuilt_memory       0\n",
      "fast_charging        0\n",
      "Screen_resolution    0\n",
      "Processor            0\n",
      "Processor_name       0\n",
      "dtype: int64\n",
      "\n",
      "Unnamed: 0           False\n",
      "Name                 False\n",
      "Rating               False\n",
      "Spec_score           False\n",
      "No_of_sim            False\n",
      "Ram                  False\n",
      "Battery              False\n",
      "Display              False\n",
      "Camera               False\n",
      "External_Memory      False\n",
      "Android_version      False\n",
      "Price                False\n",
      "company              False\n",
      "Inbuilt_memory       False\n",
      "fast_charging        False\n",
      "Screen_resolution    False\n",
      "Processor            False\n",
      "Processor_name       False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df['Inbuilt_memory'].fillna(df['Inbuilt_memory'].mode()[0], inplace=True)\n",
    "df['Processor'].fillna(df['Processor'].mode()[0], inplace=True)\n",
    "df['Android_version'].fillna(df['Android_version'].mode()[0], inplace=True)\n",
    "df['fast_charging'].fillna(df['fast_charging'].mode()[0], inplace=True)\n",
    "df['Screen_resolution'].fillna(df['Screen_resolution'].mode()[0], inplace=True)\n",
    "\n",
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим выбросы и устраним их:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка Rating:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 3.75\n",
      "  Максимальное значение: 4.75\n",
      "  1-й квартиль (Q1): 4.15\n",
      "  3-й квартиль (Q3): 4.55\n",
      "\n",
      "Колонка Spec_score:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 58.5\n",
      "  Максимальное значение: 98.0\n",
      "  1-й квартиль (Q1): 75.0\n",
      "  3-й квартиль (Q3): 86.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = ['Name', 'Rating', 'Spec_score', 'No_of_sim', 'Ram', 'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version', 'Price', 'company', 'Inbuilt_memory', 'fast_charging', 'Screen_resolution', 'Processor', 'Processor_name']\n",
    "for column in numeric_columns:\n",
    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\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",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Постараемся выявить зависимости Outcome от остальных колонок:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Сначала Label Encoding для упорядоченных категорий (если нужно)\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == 'object':  # Если тип данных - строка\n",
    "        if df[column].nunique() <= 10:  # Если небольшое количество уникальных значений, применяем One-Hot Encoding\n",
    "            df = pd.get_dummies(df, columns=[column], drop_first=True)\n",
    "        else:\n",
    "            df[column] = label_encoder.fit_transform(df[column])\n",
    "\n",
    "# Удаление строки \"Price\" из признаков (она является целевой переменной)\n",
    "X = df.drop('Price', axis=1)  # Все признаки, кроме цены\n",
    "y = df['Price']\n",
    "\n",
    "# Создание модели RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Определение важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "features = X.columns\n",
    "\n",
    "# Визуализация важности признаков\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.barh(features, feature_importances)\n",
    "plt.title('Feature Importance for Price Prediction')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (822, 32)\n",
      "Размер контрольной выборки: (274, 32)\n",
      "Размер тестовой выборки: (274, 32)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Price', axis=1)  # Все признаки, кроме цены\n",
    "y = df['Price']  # Целевая переменная (цена)\n",
    "\n",
    "# Сначала разбиваем данные на обучающую и промежуточную выборки (80% обучающих данных и 20% тестовых)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Теперь делим временные данные (X_temp и y_temp) на контрольную (валидационную) и тестовую выборки (по 20% каждой)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Выводим размеры выборок для проверки\n",
    "print(f'Размер обучающей выборки: {X_train.shape}')\n",
    "print(f'Размер контрольной выборки: {X_val.shape}')\n",
    "print(f'Размер тестовой выборки: {X_test.shape}')"
   ]
  }
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