{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка данных из файла"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0\n",
      "Name\n",
      "Rating\n",
      "Spec_score\n",
      "No_of_sim\n",
      "Ram\n",
      "Battery\n",
      "Display\n",
      "Camera\n",
      "External_Memory\n",
      "Android_version\n",
      "Price\n",
      "company\n",
      "Inbuilt_memory\n",
      "fast_charging\n",
      "Screen_resolution\n",
      "Processor\n",
      "Processor_name\n",
      "   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",
      "5           5                 Samsung Galaxy M13 5G    4.40          75   \n",
      "6           6               Samsung Galaxy M21 2021    4.10          76   \n",
      "7           7                    Samsung Galaxy A12    4.10          71   \n",
      "8           8                 Samsung Galaxy A14 5G    4.05          75   \n",
      "9           9                    Samsung Galaxy M13    4.50          75   \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",
      "5  Dual Sim, 3G, 4G, 5G, VoLTE,   6 GB RAM  5000 mAh Battery   6.5 inches   \n",
      "6      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  6000 mAh Battery   6.4 inches   \n",
      "7      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.5 inches   \n",
      "8  Dual Sim, 3G, 4G, 5G, VoLTE,   4 GB RAM  5000 mAh Battery   6.6 inches   \n",
      "9      Dual Sim, 3G, 4G, VoLTE,   6 GB RAM  6000 mAh Battery   6.6 inches   \n",
      "\n",
      "                                              Camera  \\\n",
      "0    50 MP + 2 MP Dual Rear & 13 MP Front Camera   \n",
      "1  13 MP + 5 MP + 2 MP Triple Rear & 8 MP Fro...   \n",
      "2            50 MP Quad Rear & 8 MP Front Camera   \n",
      "3           48 MP Quad Rear & 13 MP Front Camera   \n",
      "4  13 MP + 2 MP + 2 MP Triple Rear & 5 MP Fro...   \n",
      "5     50 MP + 2 MP Dual Rear & 5 MP Front Camera   \n",
      "6  48 MP + 8 MP + 5 MP Triple Rear & 20 MP Fr...   \n",
      "7            48 MP Quad Rear & 8 MP Front Camera   \n",
      "8  50 MP + 2 MP + 2 MP Triple Rear & 13 MP Fr...   \n",
      "9  50 MP + 5 MP + 2 MP Triple Rear & 8 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",
      "5    Memory Card Supported, upto 1 TB              12  11,990  Samsung   \n",
      "6  Memory Card Supported, upto 512 GB              11  11,990  Samsung   \n",
      "7               Memory Card Supported              10  11,990  Samsung   \n",
      "8    Memory Card Supported, upto 1 TB              13  11,599  Samsung   \n",
      "9    Memory Card Supported, upto 1 TB              12  12,298  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",
      "5   128 GB inbuilt   15W Fast Charging   \n",
      "6    64 GB inbuilt   15W Fast Charging   \n",
      "7    64 GB inbuilt   15W Fast Charging   \n",
      "8    64 GB inbuilt   15W Fast Charging   \n",
      "9   128 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",
      "5                                  720 x 1600 px             Octa Core   \n",
      "6   1080 x 2340 px Display with Water Drop Notch             Octa Core   \n",
      "7    720 x 1560 px Display with Water Drop Notch             Octa Core   \n",
      "8                                 1080 x 2408 px             Octa Core   \n",
      "9   1080 x 2400 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  \n",
      "5  Dimensity 700  \n",
      "6    Exynos 9611  \n",
      "7      Helio P35  \n",
      "8    Exynos 1330  \n",
      "9    Exynos  850  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"../static/csv/mobile phone price prediction.csv\")\n",
    "\n",
    "attributes = df.columns\n",
    "for attribute in attributes:\n",
    "    print(attribute)\n",
    "    # Вывод первых 10 строк\n",
    "print(df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Бизнес-цели\n",
    "1. Классифицировать мобильные устройства по ценовым категориям (например, бюджетные, средний класс, флагманы).\n",
    "2. Определить, какие характеристики мобильных устройств наиболее сильно влияют на их рейтинг."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пропущенные данные по каждому столбцу:\n",
      "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"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_data = df.isnull().sum()\n",
    "print(\"Пропущенные данные по каждому столбцу:\")\n",
    "print(missing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При проверке на шум можно заметить выброс в 75 оценке. Цена там запредельная.\n",
    "\n",
    "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Name  Rating  Spec_score  \\\n",
      "0                 Samsung Galaxy F14 5G    4.65          68   \n",
      "1                    Samsung Galaxy A11    4.20          63   \n",
      "2                    Samsung Galaxy A13    4.30          75   \n",
      "3                    Samsung Galaxy F23    4.10          73   \n",
      "4  Samsung Galaxy A03s (4GB RAM + 64GB)    4.10          69   \n",
      "5                 Samsung Galaxy M13 5G    4.40          75   \n",
      "6               Samsung Galaxy M21 2021    4.10          76   \n",
      "7                    Samsung Galaxy A12    4.10          71   \n",
      "8                 Samsung Galaxy A14 5G    4.05          75   \n",
      "9                    Samsung Galaxy M13    4.50          75   \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",
      "5  Dual Sim, 3G, 4G, 5G, VoLTE,   6 GB RAM  5000 mAh Battery   6.5 inches   \n",
      "6      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  6000 mAh Battery   6.4 inches   \n",
      "7      Dual Sim, 3G, 4G, VoLTE,   4 GB RAM  5000 mAh Battery   6.5 inches   \n",
      "8  Dual Sim, 3G, 4G, 5G, VoLTE,   4 GB RAM  5000 mAh Battery   6.6 inches   \n",
      "9      Dual Sim, 3G, 4G, VoLTE,   6 GB RAM  6000 mAh Battery   6.6 inches   \n",
      "\n",
      "                                              Camera  \\\n",
      "0    50 MP + 2 MP Dual Rear & 13 MP Front Camera   \n",
      "1  13 MP + 5 MP + 2 MP Triple Rear & 8 MP Fro...   \n",
      "2            50 MP Quad Rear & 8 MP Front Camera   \n",
      "3           48 MP Quad Rear & 13 MP Front Camera   \n",
      "4  13 MP + 2 MP + 2 MP Triple Rear & 5 MP Fro...   \n",
      "5     50 MP + 2 MP Dual Rear & 5 MP Front Camera   \n",
      "6  48 MP + 8 MP + 5 MP Triple Rear & 20 MP Fr...   \n",
      "7            48 MP Quad Rear & 8 MP Front Camera   \n",
      "8  50 MP + 2 MP + 2 MP Triple Rear & 13 MP Fr...   \n",
      "9  50 MP + 5 MP + 2 MP Triple Rear & 8 MP Fro...   \n",
      "\n",
      "                      External_Memory Android_version    Price  company  \\\n",
      "0    Memory Card Supported, upto 1 TB              13   9999.0  Samsung   \n",
      "1  Memory Card Supported, upto 512 GB              10   9990.0  Samsung   \n",
      "2    Memory Card Supported, upto 1 TB              12  11999.0  Samsung   \n",
      "3    Memory Card Supported, upto 1 TB              12  11999.0  Samsung   \n",
      "4    Memory Card Supported, upto 1 TB              11  11999.0  Samsung   \n",
      "5    Memory Card Supported, upto 1 TB              12  11990.0  Samsung   \n",
      "6  Memory Card Supported, upto 512 GB              11  11990.0  Samsung   \n",
      "7               Memory Card Supported              10  11990.0  Samsung   \n",
      "8    Memory Card Supported, upto 1 TB              13  11599.0  Samsung   \n",
      "9    Memory Card Supported, upto 1 TB              12  12298.0  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",
      "5   128 GB inbuilt   15W Fast Charging   \n",
      "6    64 GB inbuilt   15W Fast Charging   \n",
      "7    64 GB inbuilt   15W Fast Charging   \n",
      "8    64 GB inbuilt   15W Fast Charging   \n",
      "9   128 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",
      "5                                  720 x 1600 px             Octa Core   \n",
      "6   1080 x 2340 px Display with Water Drop Notch             Octa Core   \n",
      "7    720 x 1560 px Display with Water Drop Notch             Octa Core   \n",
      "8                                 1080 x 2408 px             Octa Core   \n",
      "9   1080 x 2400 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  \n",
      "5  Dimensity 700  \n",
      "6    Exynos 9611  \n",
      "7      Helio P35  \n",
      "8    Exynos 1330  \n",
      "9    Exynos  850  \n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"../static/csv/mobile phone price prediction.csv\",delimiter=',')\n",
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "df.describe(include='all')\n",
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "sns.despine(f)\n",
    "sns.scatterplot(data=df, x='Spec_score', y='Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество строк до удаления выбросов: 1370\n",
      "Количество строк после удаления выбросов: 1256\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "df['Spec_score'] = df['Spec_score'].astype(int)\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "# Выбор столбцов для анализа\n",
    "column1 = 'Spec_score'\n",
    "column2 = 'Price'\n",
    "\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
    "\n",
    "# Удаление выбросов для каждого столбца\n",
    "df_cleaned = df.copy()\n",
    "for column in [column1, column2]:\n",
    "    df_cleaned = remove_outliers(df_cleaned, column)\n",
    "\n",
    "# Построение точечной диаграммы после удаления выбросов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_cleaned[column1], df_cleaned[column2], alpha=0.5)\n",
    "plt.xlabel(column1)\n",
    "plt.ylabel(column2)\n",
    "plt.title(f'Scatter Plot of {column1} vs {column2} (After Removing Outliers)')\n",
    "plt.show()\n",
    "\n",
    "# Вывод количества строк до и после удаления выбросов\n",
    "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n",
    "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь очистим датасет отпустых строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(817, 18)\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"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "\n",
    "print(df.shape)\n",
    "\n",
    "print(df.isnull().any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разбиение данных на обучающую, контрольную и тестовую выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: 490 записей\n",
      "Контрольная выборка: 163 записей\n",
      "Тестовая выборка: 164 записей\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Разделение признаков (features) и целевой переменной (target)\n",
    "X = df.drop(columns=['company'])  # Признаки (все столбцы, кроме 'сompany')\n",
    "y = df['company']  # Целевая переменная (сompany)\n",
    "\n",
    "# Разбиение на обучающую (60%), валидационную (20%) и тестовую (20%) выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\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",
    "# Проверка размеров выборок\n",
    "print(f\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} записей\")\n",
    "print(f\"Контрольная выборка: {X_val.shape[0]} записей\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} записей\")\n",
    "\n",
    "# Визуализация распределения марок в каждой выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.xticks(rotation=45)  \n",
    "plt.hist(y_train, bins=20, color='blue', alpha=0.7)\n",
    "plt.title('Обучающая выборка')\n",
    "plt.xlabel('Марка')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные не сбалансированы, так как существует большая разница в количестве наблюдений для разных марок. Это может привести к тому, что модель будет хуже предсказывать цены для марок, численность которых  в выборке меньше, а для других - лучше. Применим методы приращения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение company в обучающей выборке после oversampling:\n",
      "company\n",
      "POCO        82\n",
      "Vivo        82\n",
      "OPPO        82\n",
      "LG          82\n",
      "Realme      82\n",
      "Motorola    82\n",
      "Samsung     82\n",
      "Xiaomi      82\n",
      "Lava        82\n",
      "itel        82\n",
      "iQOO        82\n",
      "Poco        82\n",
      "Honor       82\n",
      "OnePlus     82\n",
      "Huawei      82\n",
      "TCL         82\n",
      "Google      82\n",
      "Nothing     82\n",
      "Asus        82\n",
      "Coolpad     82\n",
      "Itel        82\n",
      "Oppo        82\n",
      "Lenovo      82\n",
      "IQOO        82\n",
      "Gionee      82\n",
      "Tecno       82\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в контрольной выборке после oversampling:\n",
      "company\n",
      "Motorola    37\n",
      "Samsung     37\n",
      "TCL         37\n",
      "Poco        37\n",
      "itel        37\n",
      "Realme      37\n",
      "Vivo        37\n",
      "Xiaomi      37\n",
      "Oppo        37\n",
      "iQOO        37\n",
      "OPPO        37\n",
      "LG          37\n",
      "POCO        37\n",
      "Honor       37\n",
      "OnePlus     37\n",
      "Huawei      37\n",
      "Lava        37\n",
      "Google      37\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в тестовой выборке после oversampling:\n",
      "company\n",
      "Realme      30\n",
      "Samsung     30\n",
      "OPPO        30\n",
      "TCL         30\n",
      "Xiaomi      30\n",
      "iQOO        30\n",
      "Motorola    30\n",
      "Lenovo      30\n",
      "Vivo        30\n",
      "Honor       30\n",
      "Poco        30\n",
      "Huawei      30\n",
      "Oppo        30\n",
      "OnePlus     30\n",
      "Google      30\n",
      "Lava        30\n",
      "itel        30\n",
      "POCO        30\n",
      "Tecno       30\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в обучающей выборке после undersampling:\n",
      "company\n",
      "Asus        1\n",
      "Coolpad     1\n",
      "Gionee      1\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "IQOO        1\n",
      "Itel        1\n",
      "LG          1\n",
      "Lava        1\n",
      "Lenovo      1\n",
      "Motorola    1\n",
      "Nothing     1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Tecno       1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "itel        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в контрольной выборке после undersampling:\n",
      "company\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "LG          1\n",
      "Lava        1\n",
      "Motorola    1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "itel        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в тестовой выборке после undersampling:\n",
      "company\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "Lava        1\n",
      "Lenovo      1\n",
      "Motorola    1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Tecno       1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "itel        1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df['company'].value_counts()\n",
    "    print(f\"Распределение company в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop('company', axis=1)\n",
    "    y = df['company']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")\n",
    "\n",
    "def undersample(df):\n",
    "    X = df.drop('company', axis=1)\n",
    "    y = df['company']\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42) # type: ignore\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_undersampled = undersample(train_df)\n",
    "val_df_undersampled = undersample(val_df)\n",
    "test_df_undersampled = undersample(test_df)\n",
    "\n",
    "check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные были сбалансированы. Теперь можно перейти к конструированию признаков. Поставлены следующие задачи:\n",
    "1. Классифицировать мобильные устройства по ценовым категориям (например, бюджетные, средний класс, флагманы).\n",
    "2. Определить, какие характеристики мобильных устройств наиболее сильно влияют на их рейтинг."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "\n",
    "# Определение категориальных признаков\n",
    "categorical_features = [\n",
    "    'Rating', 'Ram',\n",
    "       'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n",
    "       'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n",
    "       'Screen_resolution', 'Processor'\n",
    "]\n",
    "\n",
    "# Применение one-hot encoding к обучающей выборке\n",
    "train_df_resampled_encoded = pd.get_dummies(train_df_undersampled, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к контрольной выборке\n",
    "val_df_encoded = pd.get_dummies(val_df_undersampled, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к тестовой выборке\n",
    "test_df_encoded = pd.get_dummies(test_df_undersampled, columns=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дискретизация числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после балансировки: (5600, 22)\n",
      "Размер контрольной выборки: (288, 22)\n",
      "Размер тестовой выборки: (411, 22)\n",
      "Unnamed: 0\n",
      "Name\n",
      "Rating\n",
      "Spec_score\n",
      "No_of_sim\n",
      "Ram\n",
      "Battery\n",
      "Display\n",
      "Camera\n",
      "External_Memory\n",
      "Android_version\n",
      "Price\n",
      "company\n",
      "Inbuilt_memory\n",
      "fast_charging\n",
      "Screen_resolution\n",
      "Processor\n",
      "Processor_name\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "import re\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "# Извлечение числовых значений из столбца Battery\n",
    "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n",
    "df['Ram'] = df['Ram'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n",
    "df['Camera'] = df['Camera'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n",
    "\n",
    "# Удаление запятых из столбца Price и преобразование в числовой формат\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение upsampling к обучающей выборке (если это необходимо)\n",
    "X_train = train_df.drop('Price', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['Price']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Определение числовых признаков для дискретизации\n",
    "numerical_features = ['Spec_score', 'Battery', 'Ram', 'Camera' ]\n",
    "\n",
    "# Функция для дискретизации числовых признаков\n",
    "def discretize_features(df, features, bins=5, labels=False):\n",
    "    for feature in features:\n",
    "        try:\n",
    "            df[f'{feature}_bin'] = pd.cut(df[feature], bins=bins, labels=labels)\n",
    "        except Exception as e:\n",
    "            print(f\"Ошибка при дискретизации признака {feature}: {e}\")\n",
    "    return df\n",
    "\n",
    "# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n",
    "train_df_resampled = discretize_features(train_df_resampled, numerical_features)\n",
    "val_df = discretize_features(val_df, numerical_features)\n",
    "test_df = discretize_features(test_df, numerical_features)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n",
    "print(\"Размер контрольной выборки:\", val_df.shape)\n",
    "print(\"Размер тестовой выборки:\", test_df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ручной синтез. Создание новых признаков на основе экспертных знаний и логики предметной области."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после балансировки: (5600, 19)\n",
      "Размер контрольной выборки: (288, 19)\n",
      "Размер тестовой выборки: (411, 19)\n"
     ]
    }
   ],
   "source": [
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "# Преобразование столбца Battery в числовой формат\n",
    "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n",
    "\n",
    "# Преобразование столбцов Camera и Display в числовой формат\n",
    "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n",
    "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n",
    "\n",
    "# Удаление запятых из столбца Price и преобразование в числовой формат\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение upsampling к обучающей выборке (если это необходимо)\n",
    "X_train = train_df.drop('Price', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['Price']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Создание нового признака \"Camera_to_Display_Ratio\" на основе признаков \"Camera\" и \"Display\"\n",
    "train_df_resampled['Camera_to_Display_Ratio'] = train_df_resampled['Camera'] / train_df_resampled['Display']\n",
    "val_df['Camera_to_Display_Ratio'] = val_df['Camera'] / val_df['Display']\n",
    "test_df['Camera_to_Display_Ratio'] = test_df['Camera'] / test_df['Display']\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n",
    "print(\"Размер контрольной выборки:\", val_df.shape)\n",
    "print(\"Размер тестовой выборки:\", test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после балансировки: (5600, 19)\n",
      "Размер контрольной выборки: (288, 19)\n",
      "Размер тестовой выборки: (411, 19)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1137: RuntimeWarning: invalid value encountered in divide\n",
      "  updated_mean = (last_sum + new_sum) / updated_sample_count\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1142: RuntimeWarning: invalid value encountered in divide\n",
      "  T = new_sum / new_sample_count\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\utils\\extmath.py:1162: RuntimeWarning: invalid value encountered in divide\n",
      "  new_unnormalized_variance -= correction**2 / new_sample_count\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Определение числовых признаков для масштабирования\n",
    "numerical_features_to_scale = ['Spec_score', 'No_of_sim', 'Ram', 'Battery', 'Display', 'Camera', 'Inbuilt_memory', 'Screen_resolution', 'Camera_to_Display_Ratio']\n",
    "\n",
    "# Удаление строковых значений из числовых признаков\n",
    "for feature in numerical_features_to_scale:\n",
    "    train_df_resampled[feature] = pd.to_numeric(train_df_resampled[feature], errors='coerce')\n",
    "    val_df[feature] = pd.to_numeric(val_df[feature], errors='coerce')\n",
    "    test_df[feature] = pd.to_numeric(test_df[feature], errors='coerce')\n",
    "\n",
    "# Инициализация StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Масштабирование числовых признаков в обучающей выборке\n",
    "train_df_resampled[numerical_features_to_scale] = scaler.fit_transform(train_df_resampled[numerical_features_to_scale])\n",
    "\n",
    "# Масштабирование числовых признаков в контрольной и тестовой выборках\n",
    "val_df[numerical_features_to_scale] = scaler.transform(val_df[numerical_features_to_scale])\n",
    "test_df[numerical_features_to_scale] = scaler.transform(test_df[numerical_features_to_scale])\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки после балансировки:\", train_df_resampled.shape)\n",
    "print(\"Размер контрольной выборки:\", val_df.shape)\n",
    "print(\"Размер тестовой выборки:\", test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка после конструирования признаков:\n",
      "    Unnamed: 0  Rating  Spec_score                      No_of_sim        Ram  \\\n",
      "id                                                                             \n",
      "0          305    4.70          86  Dual Sim, 3G, 4G, 5G, VoLTE,   12 GB RAM   \n",
      "1          941    4.45          71      Dual Sim, 3G, 4G, VoLTE,    4 GB RAM   \n",
      "2          800    4.20          68      Dual Sim, 3G, 4G, VoLTE,    4 GB RAM   \n",
      "3           97    4.25          69      Dual Sim, 3G, 4G, VoLTE,    4 GB RAM   \n",
      "4         1339    4.30          74      Dual Sim, 3G, 4G, VoLTE,    6 GB RAM   \n",
      "\n",
      "    Battery                     External_Memory Android_version    Price  \\\n",
      "id                                                                         \n",
      "0      5000                         Android v12             NaN  30999.0   \n",
      "1      5000    Memory Card Supported, upto 1 TB              12   6999.0   \n",
      "2      5000               Memory Card Supported              12   8999.0   \n",
      "3      5000               Memory Card Supported              12   9999.0   \n",
      "4      5000  Memory Card Supported, upto 256 GB              12   8499.0   \n",
      "\n",
      "     company   Inbuilt_memory       fast_charging  \\\n",
      "id                                                  \n",
      "0     Realme   256 GB inbuilt   65W Fast Charging   \n",
      "1   Motorola    64 GB inbuilt   10W Fast Charging   \n",
      "2       Vivo    64 GB inbuilt   10W Fast Charging   \n",
      "3       Vivo   128 GB inbuilt   10W Fast Charging   \n",
      "4       Lava   128 GB inbuilt                 NaN   \n",
      "\n",
      "                               Screen_resolution   Processor  \n",
      "id                                                            \n",
      "0                                 1080 x 2400 px   Octa Core  \n",
      "1                                  720 x 1600 px   Octa Core  \n",
      "2    720 x 1600 px Display with Water Drop Notch   Octa Core  \n",
      "3    720 x 1600 px Display with Water Drop Notch   Octa Core  \n",
      "4                                  1600 x 720 px   Octa Core  \n",
      "Контрольная выборка после конструирования признаков:\n",
      "      Unnamed: 0  Rating  Spec_score                      No_of_sim       Ram  \\\n",
      "id                                                                              \n",
      "1028        <NA>     NaN        <NA>                            NaN       NaN   \n",
      "825         <NA>     NaN        <NA>                            NaN       NaN   \n",
      "900         <NA>     NaN        <NA>                            NaN       NaN   \n",
      "702         <NA>     NaN        <NA>                            NaN       NaN   \n",
      "230         1050    4.05          90  Dual Sim, 3G, 4G, 5G, VoLTE,   8 GB RAM   \n",
      "\n",
      "      Battery External_Memory Android_version    Price   company  \\\n",
      "id                                                                 \n",
      "1028     <NA>             NaN             NaN      NaN       NaN   \n",
      "825      <NA>             NaN             NaN      NaN       NaN   \n",
      "900      <NA>             NaN             NaN      NaN       NaN   \n",
      "702      <NA>             NaN             NaN      NaN       NaN   \n",
      "230      4500     Android v12             NaN  62990.0  Motorola   \n",
      "\n",
      "       Inbuilt_memory        fast_charging Screen_resolution   Processor  \n",
      "id                                                                        \n",
      "1028              NaN                  NaN               NaN         NaN  \n",
      "825               NaN                  NaN               NaN         NaN  \n",
      "900               NaN                  NaN               NaN         NaN  \n",
      "702               NaN                  NaN               NaN         NaN  \n",
      "230    128 GB inbuilt   125W Fast Charging    1080 x 2400 px   Octa Core  \n",
      "Тестовая выборка после конструирования признаков:\n",
      "      Unnamed: 0  Rating  Spec_score                      No_of_sim  \\\n",
      "id                                                                    \n",
      "427          187    4.40          91  Dual Sim, 3G, 4G, 5G, VoLTE,    \n",
      "1088        <NA>     NaN        <NA>                            NaN   \n",
      "668          592    4.45          91  Dual Sim, 3G, 4G, 5G, VoLTE,    \n",
      "572         1130    4.60          75      Dual Sim, 3G, 4G, VoLTE,    \n",
      "115          117    4.60          72      Dual Sim, 3G, 4G, VoLTE,    \n",
      "\n",
      "            Ram  Battery                   External_Memory Android_version  \\\n",
      "id                                                                           \n",
      "427   12 GB RAM     5000         Memory Card Not Supported              14   \n",
      "1088        NaN     <NA>                               NaN             NaN   \n",
      "668   12 GB RAM     4500                       Android v12             NaN   \n",
      "572    6 GB RAM     5000  Memory Card Supported, upto 1 TB              13   \n",
      "115    4 GB RAM     5000  Memory Card Supported, upto 1 TB              12   \n",
      "\n",
      "        Price company   Inbuilt_memory        fast_charging  \\\n",
      "id                                                            \n",
      "427   63999.0    Vivo   256 GB inbuilt   120W Fast Charging   \n",
      "1088      NaN     NaN              NaN                  NaN   \n",
      "668   54990.0   Honor   256 GB inbuilt   100W Fast Charging   \n",
      "572    8499.0  Xiaomi   128 GB inbuilt    18W Fast Charging   \n",
      "115   11580.0    Vivo    64 GB inbuilt    18W Fast Charging   \n",
      "\n",
      "                                 Screen_resolution   Processor  \n",
      "id                                                              \n",
      "427                                 1260 x 2800 px   Octa Core  \n",
      "1088                                           NaN         NaN  \n",
      "668                                 1200 x 2652 px   Octa Core  \n",
      "572                                  720 x 1600 px   Octa Core  \n",
      "115    720 x 1612 px Display with Water Drop Notch   Octa Core  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import featuretools as ft\n",
    "import re\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='mobile_data')\n",
    "es = es.add_dataframe(dataframe_name='train', dataframe=train_df, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='train', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n",
    "\n",
    "# Вывод первых нескольких строк для проверки\n",
    "print(\"Обучающая выборка после конструирования признаков:\")\n",
    "print(feature_matrix.head())\n",
    "print(\"Контрольная выборка после конструирования признаков:\")\n",
    "print(val_feature_matrix.head())\n",
    "print(\"Тестовая выборка после конструирования признаков:\")\n",
    "print(test_feature_matrix.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importance:\n",
      "      feature  importance\n",
      "4       Price    0.999443\n",
      "2  Spec_score    0.000227\n",
      "3     Battery    0.000146\n",
      "0  Unnamed: 0    0.000146\n",
      "1      Rating    0.000039\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "import featuretools as ft\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import re\n",
    "\n",
    "\n",
    "# Оценка важности признаков\n",
    "X = feature_matrix\n",
    "y = train_df_resampled['Price']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Получение важности признаков\n",
    "importances = model.feature_importances_\n",
    "feature_names = feature_matrix.columns\n",
    "\n",
    "# Сортировка признаков по важности\n",
    "feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importances})\n",
    "feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n",
    "\n",
    "print(\"Feature Importance:\")\n",
    "print(feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 671\n",
      "Размер контрольной выборки: 288\n",
      "Размер тестовой выборки: 411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\Алина\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 53834536.21488374\n",
      "R2 Score: 0.9445638071244045\n",
      "Cross-validated Mean Squared Error: 311290473.964474\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Mean Squared Error: 40281623.425488226\n",
      "Train R2 Score: 0.9581963040734582\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "# Преобразование столбца Battery в числовой формат\n",
    "df['Battery'] = df['Battery'].apply(lambda x: int(re.search(r'\\d+', x).group()) if re.search(r'\\d+', x) else None)\n",
    "\n",
    "# Преобразование столбца Display в числовой формат\n",
    "df['Camera'] = pd.to_numeric(df['Camera'], errors='coerce')\n",
    "df['Display'] = pd.to_numeric(df['Display'], errors='coerce')\n",
    "df['Inbuilt_memory'] = pd.to_numeric(df['Inbuilt_memory'], errors='coerce')\n",
    "df['fast_charging'] = pd.to_numeric(df['fast_charging'], errors='coerce')\n",
    "\n",
    "# Удаление запятых из столбца Price и преобразование в числовой формат\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "\n",
    "# Удаление столбцов с текстовыми значениями, которые не могут быть преобразованы в числа\n",
    "df = df.drop(columns=['Name', 'company', 'Android_version', 'Processor_name', 'External_Memory', 'No_of_sim', 'Ram', 'Screen_resolution', 'Processor'  ])\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='mobile_data')\n",
    "es = es.add_dataframe(dataframe_name='mobile', dataframe=train_df, index='id')\n",
    "\n",
    "# Генерация признаков с уменьшенной глубиной\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='mobile', max_depth=1)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train = feature_matrix.drop('Price', axis=1)\n",
    "y_train = feature_matrix['Price']\n",
    "X_val = val_feature_matrix.drop('Price', axis=1)\n",
    "y_val = val_feature_matrix['Price']\n",
    "X_test = test_feature_matrix.drop('Price', axis=1)\n",
    "y_test = test_feature_matrix['Price']\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "print(f\"R2 Score: {r2}\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "mse_cv = -scores.mean()\n",
    "print(f\"Cross-validated Mean Squared Error: {mse_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "mse_train = mean_squared_error(y_train, y_train_pred)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train Mean Squared Error: {mse_train}\")\n",
    "print(f\"Train R2 Score: {r2_train}\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price')\n",
    "plt.title('Actual vs Predicted Price')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}