{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №2\n",
    "## Были выбраны следующие датасеты:\n",
    " - ### 11. Цены на бриллианты.\n",
    " - ### 18. Цены на мобильные устройства.\n",
    " - ### 19. Данные о миллионерах."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начнем анализировать датасет №11.\n",
    "\n",
    "Ссылка на исходные данные: https://www.kaggle.com/datasets/nancyalaswad90/diamonds-prices\n",
    "\n",
    "**Общее описание**: Данный датасет содержит цены и атрибуты для 53940 алмазов круглой огранки. Имеются 10 характеристик (карат, огранка, цвет, чистота, глубина, таблица, цена, x, y и z). Большинство переменных являются числовыми по своей природе, но переменные cut, color и clearity являются упорядоченными факторными переменными.\n",
    "\n",
    "**Проблемная область**: Финансовый анализ и прогнозирование цен акций.\n",
    "\n",
    "**Объекты наблюдения**: Данные о алмазах, включающие атрибуты: _Carat, Cut, Color, Clarity, Depth, Table, Price_.\n",
    "\n",
    "**Бизнес цели**:\n",
    "- ***Прогнозирование цен на алмазы***: Позволяет покупателям и продавцам лучше ориентироваться в рыночных ценах, а также помогает в принятии решений о покупке или продаже алмазов,\n",
    "- ***Анализ факторов, влияющих на стоимость***: Понимание, какие характеристики алмаза (например, качество огранки или цвет) оказывают наибольшее влияние на его цену, может помочь в разработке стратегий ценообразования и улучшении ассортимента.\n",
    "\n",
    "**Цели технического проекта**:\n",
    "1. ***Прогнозирование цен на алмазы***: Входные данные - атрибуты алмазов; целевой признак - _цена_,\n",
    "2. ***Анализ факторов влияния***: Входные данные -  атрибуты, описывающие качество и характеристики алмаза; целевой признак - влияние каждого атрибута на конечную цену, что может быть проанализировано с помощью методов регрессии и визуализации данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
      "       'price', 'x', 'y', 'z'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"./data/Diamonds-Prices.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты: \n",
    "- Неизвестный: 0, \n",
    "- Караты (carat), \n",
    "- Огранка (cut), \n",
    "- Цвет (color), \n",
    "- Чистота (clarity), \n",
    "- Глубина (depth), \n",
    "- Площадь огранки (table), \n",
    "- Цена (price), \n",
    "- Ширина (координата X), \n",
    "- Длина (координата Y), \n",
    "- Высота (координата Z). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем на выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"./data/Diamonds-Prices.csv\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"price\"], df[\"carat\"])\n",
    "plt.xlabel(\"Цена\")\n",
    "plt.ylabel(\"Карат\")\n",
    "plt.title(\"Диаграмма зависимости цены от карата\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выброс с наибольшим значением был замечен при ~175000\n",
    "Начнем использовать метод межквантильного размаха для удаления выбросов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пустые значения по столбцам:\n",
      "Unnamed: 0    0\n",
      "carat         0\n",
      "cut           0\n",
      "color         0\n",
      "clarity       0\n",
      "depth         0\n",
      "table         0\n",
      "price         0\n",
      "x             0\n",
      "y             0\n",
      "z             0\n",
      "dtype: int64\n",
      "\n",
      "Количество дубликатов: 0\n",
      "\n",
      "Статистический обзор данных:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "      <td>53943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26972.000000</td>\n",
       "      <td>0.797935</td>\n",
       "      <td>61.749322</td>\n",
       "      <td>57.457251</td>\n",
       "      <td>3932.734294</td>\n",
       "      <td>5.731158</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15572.147122</td>\n",
       "      <td>0.473999</td>\n",
       "      <td>1.432626</td>\n",
       "      <td>2.234549</td>\n",
       "      <td>3989.338447</td>\n",
       "      <td>1.121730</td>\n",
       "      <td>1.142103</td>\n",
       "      <td>0.705679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13486.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26972.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40457.500000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.000000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53943.000000</td>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         carat         depth         table         price  \\\n",
       "count  53943.000000  53943.000000  53943.000000  53943.000000  53943.000000   \n",
       "mean   26972.000000      0.797935     61.749322     57.457251   3932.734294   \n",
       "std    15572.147122      0.473999      1.432626      2.234549   3989.338447   \n",
       "min        1.000000      0.200000     43.000000     43.000000    326.000000   \n",
       "25%    13486.500000      0.400000     61.000000     56.000000    950.000000   \n",
       "50%    26972.000000      0.700000     61.800000     57.000000   2401.000000   \n",
       "75%    40457.500000      1.040000     62.500000     59.000000   5324.000000   \n",
       "max    53943.000000      5.010000     79.000000     95.000000  18823.000000   \n",
       "\n",
       "                  x             y             z  \n",
       "count  53943.000000  53943.000000  53943.000000  \n",
       "mean       5.731158      5.734526      3.538730  \n",
       "std        1.121730      1.142103      0.705679  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        4.710000      4.720000      2.910000  \n",
       "50%        5.700000      5.710000      3.530000  \n",
       "75%        6.540000      6.540000      4.040000  \n",
       "max       10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values_diamond = df.isnull().sum()\n",
    "print(\"Пустые значения по столбцам:\")\n",
    "print(null_values_diamond)\n",
    "\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
    "\n",
    "print(\"\\nСтатистический обзор данных:\")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Коэффициент асимметрии для столбца 'Unnamed: 0': 0.0\n",
      "\n",
      "Коэффициент асимметрии для столбца 'carat': 1.1167052359880187\n",
      "\n",
      "Коэффициент асимметрии для столбца 'depth': -0.08218721424717913\n",
      "\n",
      "Коэффициент асимметрии для столбца 'table': 0.7968359775412807\n",
      "\n",
      "Коэффициент асимметрии для столбца 'price': 1.6184763222032386\n",
      "\n",
      "Коэффициент асимметрии для столбца 'x': 0.37868453466912216\n",
      "\n",
      "Коэффициент асимметрии для столбца 'y': 2.4342330799873775\n",
      "\n",
      "Коэффициент асимметрии для столбца 'z': 1.5224810204974413\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "for column in df.select_dtypes(include=[np.number]).columns:\n",
    "    asymmetry = df[column].skew()\n",
    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {asymmetry}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим выбросы. Очистим данные от шумов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"price\"], df[\"carat\"])\n",
    "plt.xlabel(\"Цена\")\n",
    "plt.ylabel(\"Карат\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Диаграмма рассеивания перед чисткой\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "column1 = \"carat\"\n",
    "column2 = \"price\"\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",
    "\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",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_cleaned[column1], df_cleaned[column2])\n",
    "plt.xlabel(\"Цена\")\n",
    "plt.ylabel(\"Карат\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Диаграмма рассеивания после чистки\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество строк до удаления выбросов: 53943\n",
      "Количество строк после удаления выбросов: 49517\n"
     ]
    }
   ],
   "source": [
    "# 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": [
    "Перейдем к созданию выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 32365\n",
      "Размер контрольной выборки: 10789\n",
      "Размер тестовой выборки: 10789\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\"./data/Diamonds-Prices.csv\")\n",
    "\n",
    "# Выбираем признаки и целевую переменную\n",
    "X = df.drop(\"price\", axis=1)  # Все столбцы, кроме цены\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(\n",
    "    X, y, test_size=0.4, random_state=42\n",
    ")\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(\n",
    "    X_temp, y_temp, test_size=0.5, random_state=42\n",
    ")\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(f\"Размер обучающей выборки: {X_train.shape[0]}\")\n",
    "print(f\"Размер контрольной выборки: {X_val.shape[0]}\")\n",
    "print(f\"Размер тестовой выборки: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Price в обучающей выборке:\n",
      "price\n",
      "327      1\n",
      "334      1\n",
      "336      1\n",
      "337      1\n",
      "338      1\n",
      "        ..\n",
      "18791    1\n",
      "18795    2\n",
      "18797    1\n",
      "18804    1\n",
      "18806    1\n",
      "Name: count, Length: 9476, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в контрольной выборке:\n",
      "price\n",
      "326      2\n",
      "340      1\n",
      "344      1\n",
      "354      1\n",
      "357      1\n",
      "        ..\n",
      "18781    1\n",
      "18784    1\n",
      "18791    1\n",
      "18803    1\n",
      "18823    1\n",
      "Name: count, Length: 5389, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в тестовой выборке:\n",
      "price\n",
      "335      1\n",
      "336      1\n",
      "337      1\n",
      "351      1\n",
      "353      1\n",
      "        ..\n",
      "18766    1\n",
      "18768    1\n",
      "18780    1\n",
      "18788    1\n",
      "18818    1\n",
      "Name: count, Length: 5308, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def analyze_distribution(data, title):\n",
    "    print(f\"Распределение Price в {title}:\")\n",
    "    distribution = data.value_counts().sort_index()\n",
    "    print(distribution)\n",
    "    total = len(data)\n",
    "    positive_count = (data > 0).sum()\n",
    "    negative_count = (data < 0).sum()\n",
    "    positive_percent = (positive_count / total) * 100\n",
    "    negative_percent = (negative_count / total) * 100\n",
    "    print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n",
    "    print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n",
    "    print(\"\\nНеобходима аугментация данных для балансировки классов.\\n\")\n",
    "\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train, \"обучающей выборке\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применяем методы приращения данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Price в обучающей выборке после oversampling:\n",
      "price\n",
      "327      85\n",
      "334      85\n",
      "336      85\n",
      "337      85\n",
      "338      85\n",
      "         ..\n",
      "18791    85\n",
      "18795    85\n",
      "18797    85\n",
      "18804    85\n",
      "18806    85\n",
      "Name: count, Length: 9476, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в контрольной выборке:\n",
      "price\n",
      "326      2\n",
      "340      1\n",
      "344      1\n",
      "354      1\n",
      "357      1\n",
      "        ..\n",
      "18781    1\n",
      "18784    1\n",
      "18791    1\n",
      "18803    1\n",
      "18823    1\n",
      "Name: count, Length: 5389, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в тестовой выборке:\n",
      "price\n",
      "335      1\n",
      "336      1\n",
      "337      1\n",
      "351      1\n",
      "353      1\n",
      "        ..\n",
      "18766    1\n",
      "18768    1\n",
      "18780    1\n",
      "18788    1\n",
      "18818    1\n",
      "Name: count, Length: 5308, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Применение oversampling к обучающей выборке\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train_resampled, \"обучающей выборке после oversampling\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начнем анализировать датасет №18.\n",
    "\n",
    "Ссылка на исходные данные: https://www.kaggle.com/datasets/dewangmoghe/mobile-phone-price-prediction\n",
    "\n",
    "**Общее описание**: Данный датасет содержит информацию о ценах и атрибутах для 1369 мобильных телефонов разных конфигураций и производителей. Имеются 17 характеристик (именование модели, оценка (мин - 0, макс - 5), оценка на основе характеристик (мин - 0, макс - 100), информация о поддержке 2 симок и сетевых технологий (3G, 4G, 5G, VoLTE), количество оперативной памяти, характеристики батареи, информация о дисплее, характеристики камеры, поддержка внешней памяти, версия системы Android, цена, компания-производитель, поддержка быстрой зарядки, разрешение экрана, тип процессора, название процессора).\n",
    "\n",
    "**Проблемная область**: Финансовый анализ и прогнозирование цен на мобильные телефоны.\n",
    "\n",
    "**Объекты наблюдения**: телефон, включающий атрибуты: _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",
    "\n",
    "**Бизнес цели**:\n",
    "- ***Прогнозирование цен мобильные телефоны на основе оценки характеристик***.\n",
    "- ***Прогнозирование оценки на основе фирмы и цены***.\n",
    "\n",
    "**Цели технического проекта**:\n",
    "1. ***Прогнозирование цен на телефоны***: Входные данные - _оценка характеристик_; целевой признак - _цена_,\n",
    "2. ***Анализ факторов влияния***: Входные данные -  _фирма и цена_; целевой признак - _оценка характеристик_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../data/mobile-phone-price-prediction.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../data/mobile-phone-price-prediction.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mcolumns)\n",
      "File \u001b[1;32md:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32md:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[1;32md:\\Users\\Leo\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\mai-S9i2J6c7-py3.12\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/mobile-phone-price-prediction.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../data/mobile-phone-price-prediction.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты: \n",
    "- Неизвестный: 0, \n",
    "- Наименование телефона (Name), \n",
    "- Рейтинг (Rating),\n",
    "- Рейтинг на основе характеристик (Spec_score),\n",
    "- Поддержка различных технологий (No_of_sim),\n",
    "- Количество оперативной памяти (Ram),\n",
    "- Инфо о батарее (Battery),\n",
    "- Инфо о дисплее (Display),\n",
    "- Инфо о камере (Camera),\n",
    "- Инфо о внешней памяти (External_Memory),\n",
    "- Версия Android (Android_version),\n",
    "- Цена (Price),\n",
    "- Компания-производитель (company),\n",
    "- Инфо о внутренней памяти (Inbuilt_memory),\n",
    "- Быстрая зарядка (fast_charging),\n",
    "- Разрешение экрана (Screen_resolution),\n",
    "- Тип процессора (Processor),\n",
    "- Наименование процессора (Processor_name)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "\n",
    "plt.scatter(df[\"company\"].str.lower(), df[\"Spec_score\"])\n",
    "plt.xlabel(\"Фирма\")\n",
    "plt.ylabel(\"Оценка характеристик\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Диаграмма 1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Между атрибутами присутствует связь. Пример, на диаграмме 1 - связь между фирмой и оценкой характеристик"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перейдем к проверке на выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_values = df.isnull().sum()\n",
    "print(\"Пустые значения по столбцам:\")\n",
    "print(null_values)\n",
    "\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
    "\n",
    "print(\"\\nСтатистический обзор данных:\")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что есть пустые данные, но нет дубликатов. Удаляем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_missing_values(dataframe, name):\n",
    "    before_shape = dataframe.shape\n",
    "    cleaned_dataframe = dataframe.dropna()\n",
    "    after_shape = cleaned_dataframe.shape\n",
    "    print(\n",
    "        f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\"\n",
    "    )\n",
    "    return cleaned_dataframe\n",
    "\n",
    "\n",
    "cleaned_df = drop_missing_values(df, \"Phones\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассчитаем коэффициент ассиметрии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "for column in df.select_dtypes(include=[np.number]).columns:\n",
    "    asymmetry = df[column].skew()\n",
    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {asymmetry}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросы незначительные.\n",
    "\n",
    "Очистим данные от шумов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(cleaned_df[\"company\"].str.lower(), cleaned_df[\"Spec_score\"])\n",
    "plt.xlabel(\"Фирма\")\n",
    "plt.ylabel(\"Оценка характеристик\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Диаграмма рассеивания перед чисткой\")\n",
    "plt.show()\n",
    "\n",
    "Q1 = cleaned_df[\"Spec_score\"].quantile(0.25)\n",
    "Q3 = cleaned_df[\"Spec_score\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "threshold = 1.5 * IQR\n",
    "lower_bound = Q1 - threshold\n",
    "upper_bound = Q3 + threshold\n",
    "\n",
    "outliers = (cleaned_df[\"Spec_score\"] < lower_bound) | (\n",
    "    cleaned_df[\"Spec_score\"] > upper_bound\n",
    ")\n",
    "\n",
    "print(\"Выбросы в датасете:\")\n",
    "print(cleaned_df[outliers])\n",
    "\n",
    "median_score = cleaned_df[\"Spec_score\"].median()\n",
    "cleaned_df.loc[outliers, \"Spec_score\"] = median_score\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(cleaned_df[\"company\"].str.lower(), cleaned_df[\"Spec_score\"])\n",
    "plt.xlabel(\"Фирма\")\n",
    "plt.ylabel(\"Оценка характеристик\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Диаграмма рассеивания после чистки\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиваем на выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2, random_state=42)\n",
    "\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\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",
    "print()\n",
    "\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df[\"Spec_score\"].value_counts()\n",
    "    print(f\"Распределение оценки характеристик в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оверсемплинг и андерсемплинг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "def oversample(df, target_column):\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\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",
    "\n",
    "def undersample(df, target_column):\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "\n",
    "    undersampler = RandomUnderSampler(random_state=42)\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_oversampled = oversample(train_df, \"Spec_score\")\n",
    "val_df_oversampled = oversample(val_df, \"Spec_score\")\n",
    "test_df_oversampled = oversample(test_df, \"Spec_score\")\n",
    "\n",
    "train_df_undersampled = undersample(train_df, \"Spec_score\")\n",
    "val_df_undersampled = undersample(val_df, \"Spec_score\")\n",
    "test_df_undersampled = undersample(test_df, \"Spec_score\")\n",
    "\n",
    "print(\"Оверсэмплинг:\")\n",
    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
    "\n",
    "print(\"Андерсэмплинг:\")\n",
    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начнем анализировать датасет №19.\n",
    "\n",
    "Ссылка на исходные данные: https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed\n",
    "\n",
    "**Общее описание**: «Миллиардеры мира» — это ежегодный рейтинг документально подтвержденного состояния богатейших миллиардеров мира, который составляется и публикуется ежегодно в марте американским деловым журналом Forbes. Список был впервые опубликован в марте 1987 года. Общий собственный капитал каждого человека в списке оценивается и указывается в долларах США на основе их документально подтвержденных активов, а также с учетом долга и других факторов. Члены королевской семьи и диктаторы, чье богатство обусловлено их положением, исключены из этих списков. Этот рейтинг представляет собой индекс самых богатых задокументированных людей, исключая любой рейтинг тех, кто обладает богатством, которое невозможно полностью установить.\n",
    "\n",
    "**Проблемная область**: Анализ состояния, возраста и источников богатства самых богатых людей в мире.\n",
    "\n",
    "**Объекты наблюдения**: Богатейшие люди мира, представленные в датасете.\n",
    "\n",
    "**Связи между объектами**: можно выявить следующие связи:\n",
    "- Между возрастом и состоянием\n",
    "- Между страной проживания и источником дохода\n",
    "- Между отраслью бизнеса и уровнем благосостояния.\n",
    "\n",
    "**Бизнес цели**:\n",
    "- ***Понять факторы успеха:***: Исследовать, какие факторы (возраст, страна, источник дохода) влияют на высокие состояния. Это может помочь новым предпринимателям и стартапам учиться на опыте успешных людей.\n",
    "- ***Анализ тенденций богатства***: Понимание как источники богатства меняются со временем и как это связано с экономическими условиями в разных странах. Это непременно поможет инвесторам и аналитикам определить, какие секторы могут быть наиболее перспективными для инвестиций в будущем.  \n",
    "\n",
    "**Цели технического проекта**:\n",
    "1. ***Исследование факторов успеха***: Входные данные - данные о богатейших людях (возраст, чистая стоимость, индустрия); целевой признак - выявление факторов, способствующих накоплению состояния.\n",
    "2. ***Анализ тенденций богатства***: Входные данные - данные о богатейших людях (возраст, страна, источник богатства); целевой признак - наличие зависимости между источником богатства и страной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../data/Forbes Billionaires.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты:\n",
    "- Ранг (Rank),\n",
    "- Имя (Name),\n",
    "- Общая стоимость (Networth),\n",
    "- Возраст (Age),\n",
    "- Страна (Country),\n",
    "- Источник дохода(Source),\n",
    "- Индустрия (Industry)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на связи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Связь между возрастом и состоянием\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.scatterplot(data=df, x=\"Age\", y=\"Networth\")\n",
    "plt.title(\"Связь между возрастом и состоянием\")\n",
    "plt.xlabel(\"Возраст\")\n",
    "plt.ylabel(\"Состояние (млрд)\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Связь между страной проживания и состоянием (топ-10 стран)\n",
    "plt.subplot(2, 2, 2)\n",
    "top_countries = df[\"Country\"].value_counts().index[:10]\n",
    "sns.boxplot(data=df[df[\"Country\"].isin(top_countries)], x=\"Country\", y=\"Networth\")\n",
    "plt.title(\"Связь между страной проживания и состоянием\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Страна\")\n",
    "plt.ylabel(\"Состояние (млрд)\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Связь между источником дохода и состоянием (топ-10 источников дохода)\n",
    "plt.subplot(2, 2, 3)\n",
    "top_sources = df[\"Source\"].value_counts().index[:10]\n",
    "sns.boxplot(data=df[df[\"Source\"].isin(top_sources)], x=\"Source\", y=\"Networth\")\n",
    "plt.title(\"Связь между источником дохода и состоянием\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Источник дохода\")\n",
    "plt.ylabel(\"Состояние (млрд)\")\n",
    "plt.show()\n",
    "\n",
    "# Связь между отраслью и состоянием (топ-10 отраслей)\n",
    "plt.subplot(2, 2, 4)\n",
    "top_industries = df[\"Industry\"].value_counts().index[:10]\n",
    "sns.boxplot(data=df[df[\"Industry\"].isin(top_industries)], x=\"Industry\", y=\"Networth\")\n",
    "plt.title(\"Связь между отраслью и состоянием\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Отрасль\")\n",
    "plt.ylabel(\"Состояние (млрд)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перейдем к выявлению выбросов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = df.isnull().sum()\n",
    "print(\"Пропущенные значения в данных:\\n\", missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных данных не найдено.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "sns.boxplot(data=df, x='Networth', ax=axs[0])\n",
    "axs[0].set_title(\"Выбросы по состоянию\")\n",
    "\n",
    "sns.boxplot(data=df, x=\"Age\", ax=axs[1])\n",
    "axs[1].set_title(\"Выбросы по возрасту\")\n",
    "\n",
    "plt.show()\n",
    "print(\"Размер данных до удаления выбросов: \", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросов в данном случае не видно, данные в районе допустимых значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Гистограмма распределения чистой стоимости\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(df['Networth'], bins=10, kde=True)\n",
    "plt.title(\"Гистограмма распределения чистой стоимости\")\n",
    "plt.xlabel(\"Чистая стоимость (в миллиардах долларов)\")\n",
    "plt.ylabel(\"Частота\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Распределение чистой стоимости имеет ярко выраженное смещение: большая часть значений сосредоточена в нижнем диапазоне, с небольшим количеством высоких значений. Это указывает на преобладание людей с относительно низкой чистой стоимостью, тогда как у немногих (например, миллиардеров) чистая стоимость крайне высока."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Столбчатая диаграмма по странам\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(data=df, x=\"Country\", order=df[\"Country\"].value_counts().index)\n",
    "plt.title(\"Количество людей по странам\")\n",
    "plt.xlabel(\"Страна\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# 2. Столбчатая диаграмма по отраслям\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(data=df, x=\"Industry\", order=df[\"Industry\"].value_counts().index)\n",
    "plt.title(\"Количество людей по отраслям\")\n",
    "plt.xlabel(\"Отрасль\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# 3. Гистограмма для анализа возраста\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(df[\"Age\"], bins=30, kde=True)\n",
    "plt.title(\"Распределение возраста\")\n",
    "plt.xlabel(\"Возраст\")\n",
    "plt.ylabel(\"Частота\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Графики демонстрируют разнообразие стран и отраслей, представленных в наборе данных, что указывает на охват данных по множеству регионов и различных сфер деятельности.\n",
    "\n",
    "Разбиваем набор данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделим набор данных на признаки (X) и целевой признак (y)\n",
    "X = df.drop(columns=[\"Networth\"])\n",
    "y = df[\"Networth\"]\n",
    "\n",
    "# Разделение на обучающую, контрольную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(\n",
    "    X, y, test_size=0.4, random_state=42\n",
    ")\n",
    "X_val, X_test, y_val, y_test = train_test_split(\n",
    "    X_temp, y_temp, test_size=0.5, random_state=42\n",
    ")\n",
    "\n",
    "# Проверка размера выборок\n",
    "(X_train.shape, X_val.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Проверка распределения целевого признака по выборкам\n",
    "train_dist = y_train.describe()\n",
    "val_dist = y_val.describe()\n",
    "test_dist = y_test.describe()\n",
    "\n",
    "train_dist, val_dist, test_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "oversampler = RandomOverSampler(random_state=12)\n",
    "X_train_over, y_train_over = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "undersampler = RandomUnderSampler(random_state=12)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Размеры после oversampling:\", X_train_over.shape, y_train_over.shape)\n",
    "print(\"Размеры после undersampling:\", X_train_under.shape, y_train_under.shape)"
   ]
  }
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