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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Датасет №1 (Использование мобильных устройств и поведение пользователей)\n",
+    "Ссылка: https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset\n",
+    "\n",
+    "Проблемная область: прогнозирование пользовательского поведения и сегментация пользователей для улучшения работы приложений, оптимизации потребления энергии, анализа пользовательского опыта или рекламы.\n",
+    "\n",
+    "Объекты наблюдения: пользователи мобильных устройств, чьи данные об использовании собираются и анализируются."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['User ID', 'Device Model', 'Operating System',\n",
+      "       'App Usage Time (min/day)', 'Screen On Time (hours/day)',\n",
+      "       'Battery Drain (mAh/day)', 'Number of Apps Installed',\n",
+      "       'Data Usage (MB/day)', 'Age', 'Gender', 'User Behavior Class'],\n",
+      "      dtype='object')\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 700 entries, 0 to 699\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                      Non-Null Count  Dtype  \n",
+      "---  ------                      --------------  -----  \n",
+      " 0   User ID                     700 non-null    int64  \n",
+      " 1   Device Model                700 non-null    object \n",
+      " 2   Operating System            700 non-null    object \n",
+      " 3   App Usage Time (min/day)    700 non-null    int64  \n",
+      " 4   Screen On Time (hours/day)  700 non-null    float64\n",
+      " 5   Battery Drain (mAh/day)     700 non-null    int64  \n",
+      " 6   Number of Apps Installed    700 non-null    int64  \n",
+      " 7   Data Usage (MB/day)         700 non-null    int64  \n",
+      " 8   Age                         700 non-null    int64  \n",
+      " 9   Gender                      700 non-null    object \n",
+      " 10  User Behavior Class         700 non-null    int64  \n",
+      "dtypes: float64(1), int64(7), object(3)\n",
+      "memory usage: 60.3+ KB\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>User ID</th>\n",
+       "      <th>Device Model</th>\n",
+       "      <th>Operating System</th>\n",
+       "      <th>App Usage Time (min/day)</th>\n",
+       "      <th>Screen On Time (hours/day)</th>\n",
+       "      <th>Battery Drain (mAh/day)</th>\n",
+       "      <th>Number of Apps Installed</th>\n",
+       "      <th>Data Usage (MB/day)</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>User Behavior Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>393</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>1872</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1122</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>OnePlus 9</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>268</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>42</td>\n",
+       "      <td>944</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Xiaomi Mi 11</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>154</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>761</td>\n",
+       "      <td>32</td>\n",
+       "      <td>322</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Google Pixel 5</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>239</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>1676</td>\n",
+       "      <td>56</td>\n",
+       "      <td>871</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>iPhone 12</td>\n",
+       "      <td>iOS</td>\n",
+       "      <td>187</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>1367</td>\n",
+       "      <td>58</td>\n",
+       "      <td>988</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   User ID    Device Model Operating System  App Usage Time (min/day)  \\\n",
+       "0        1  Google Pixel 5          Android                       393   \n",
+       "1        2       OnePlus 9          Android                       268   \n",
+       "2        3    Xiaomi Mi 11          Android                       154   \n",
+       "3        4  Google Pixel 5          Android                       239   \n",
+       "4        5       iPhone 12              iOS                       187   \n",
+       "\n",
+       "   Screen On Time (hours/day)  Battery Drain (mAh/day)  \\\n",
+       "0                         6.4                     1872   \n",
+       "1                         4.7                     1331   \n",
+       "2                         4.0                      761   \n",
+       "3                         4.8                     1676   \n",
+       "4                         4.3                     1367   \n",
+       "\n",
+       "   Number of Apps Installed  Data Usage (MB/day)  Age  Gender  \\\n",
+       "0                        67                 1122   40    Male   \n",
+       "1                        42                  944   47  Female   \n",
+       "2                        32                  322   42    Male   \n",
+       "3                        56                  871   20    Male   \n",
+       "4                        58                  988   31  Female   \n",
+       "\n",
+       "   User Behavior Class  \n",
+       "0                    4  \n",
+       "1                    3  \n",
+       "2                    2  \n",
+       "3                    3  \n",
+       "4                    3  "
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "df_mobiles = pd.read_csv(\".//static//csv//user_behavior_dataset.csv\")\n",
+    "print(df_mobiles.columns)\n",
+    "df_mobiles.info()\n",
+    "df_mobiles.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Атрибуты объектов:\n",
+    "1. User ID — уникальный идентификатор пользователя.\n",
+    "2. Device Model — модель устройства.\n",
+    "3. Operating System — операционная система устройства.\n",
+    "4. App Usage Time (min/day) — время использования приложений в минутах в день.\n",
+    "5. Data Usage (MB/day) — время включенного экрана в часах в день.\n",
+    "6. Battery Drain (mAh/day) — потребление батареи в мАч в день.\n",
+    "7. Number of Apps Installed — количество установленных приложений.\n",
+    "8. Screen On Time (hours/day) — объем данных в мегабайтах в день.\n",
+    "9. Age — возраст пользователя.\n",
+    "10. Gender — пол пользователя.\n",
+    "11. User Behavior Class — класс поведения пользователя (категория для классификации).\n",
+    "\n",
+    "Связи между объектами:\n",
+    "Атрибуты, такие как модель устройства, ОС и время использования приложений, могут быть связаны с классом поведения, представляя зависимости между действиями пользователя и его характеристиками.\n",
+    "\n",
+    "Примеры бизнес-целей и эффекты для бизнеса:\n",
+    "1. Оптимизация энергопотребления устройств:\n",
+    "   - Бизнес-цель: Оптимизировать работу приложений для снижения расхода батареи, что увеличит время работы устройства и улучшит пользовательский опыт.\n",
+    "   - Эффект: Повышение удовлетворенности клиентов и снижение вероятности перехода на конкурентные приложения.\n",
+    "\n",
+    "2. Сегментация пользователей для рекламы:\n",
+    "   - Бизнес-цель: Создание таргетированной рекламы на основе поведения пользователей (классы поведения).\n",
+    "   - Эффект: Увеличение конверсий и доходов от рекламных кампаний за счет более точной сегментации.\n",
+    "\n",
+    "Примеры целей технического проекта:\n",
+    "1. Цель: Построение модели для прогнозирования расхода батареи.\n",
+    "   - Вход: Модель устройства, ОС, время использования приложений, количество приложений, возраст.\n",
+    "   - Целевой признак: Battery Drain (mAh/day).\n",
+    "\n",
+    "2. Цель: Сегментация пользователей для рекламных кампаний.\n",
+    "   - Вход: Время использования приложений, возраст, пол, объем данных.\n",
+    "   - Целевой признак: User Behavior Class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на пустые значения и дубликаты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "User ID                       0\n",
+      "Device Model                  0\n",
+      "Operating System              0\n",
+      "App Usage Time (min/day)      0\n",
+      "Screen On Time (hours/day)    0\n",
+      "Battery Drain (mAh/day)       0\n",
+      "Number of Apps Installed      0\n",
+      "Data Usage (MB/day)           0\n",
+      "Age                           0\n",
+      "Gender                        0\n",
+      "User Behavior Class           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>User ID</th>\n",
+       "      <th>App Usage Time (min/day)</th>\n",
+       "      <th>Screen On Time (hours/day)</th>\n",
+       "      <th>Battery Drain (mAh/day)</th>\n",
+       "      <th>Number of Apps Installed</th>\n",
+       "      <th>Data Usage (MB/day)</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>User Behavior Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>700.00000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>350.50000</td>\n",
+       "      <td>271.128571</td>\n",
+       "      <td>5.272714</td>\n",
+       "      <td>1525.158571</td>\n",
+       "      <td>50.681429</td>\n",
+       "      <td>929.742857</td>\n",
+       "      <td>38.482857</td>\n",
+       "      <td>2.990000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>202.21688</td>\n",
+       "      <td>177.199484</td>\n",
+       "      <td>3.068584</td>\n",
+       "      <td>819.136414</td>\n",
+       "      <td>26.943324</td>\n",
+       "      <td>640.451729</td>\n",
+       "      <td>12.012916</td>\n",
+       "      <td>1.401476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>302.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>175.75000</td>\n",
+       "      <td>113.250000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>722.250000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>373.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>350.50000</td>\n",
+       "      <td>227.500000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>1502.500000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>823.500000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>525.25000</td>\n",
+       "      <td>434.250000</td>\n",
+       "      <td>7.400000</td>\n",
+       "      <td>2229.500000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1341.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>700.00000</td>\n",
+       "      <td>598.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>2993.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>2497.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         User ID  App Usage Time (min/day)  Screen On Time (hours/day)  \\\n",
+       "count  700.00000                700.000000                  700.000000   \n",
+       "mean   350.50000                271.128571                    5.272714   \n",
+       "std    202.21688                177.199484                    3.068584   \n",
+       "min      1.00000                 30.000000                    1.000000   \n",
+       "25%    175.75000                113.250000                    2.500000   \n",
+       "50%    350.50000                227.500000                    4.900000   \n",
+       "75%    525.25000                434.250000                    7.400000   \n",
+       "max    700.00000                598.000000                   12.000000   \n",
+       "\n",
+       "       Battery Drain (mAh/day)  Number of Apps Installed  Data Usage (MB/day)  \\\n",
+       "count               700.000000                700.000000           700.000000   \n",
+       "mean               1525.158571                 50.681429           929.742857   \n",
+       "std                 819.136414                 26.943324           640.451729   \n",
+       "min                 302.000000                 10.000000           102.000000   \n",
+       "25%                 722.250000                 26.000000           373.000000   \n",
+       "50%                1502.500000                 49.000000           823.500000   \n",
+       "75%                2229.500000                 74.000000          1341.000000   \n",
+       "max                2993.000000                 99.000000          2497.000000   \n",
+       "\n",
+       "              Age  User Behavior Class  \n",
+       "count  700.000000           700.000000  \n",
+       "mean    38.482857             2.990000  \n",
+       "std     12.012916             1.401476  \n",
+       "min     18.000000             1.000000  \n",
+       "25%     28.000000             2.000000  \n",
+       "50%     38.000000             3.000000  \n",
+       "75%     49.000000             4.000000  \n",
+       "max     59.000000             5.000000  "
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_mobiles.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_mobiles.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_mobiles.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Пустых значений и дубликатов нет, проверим на выбросы:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество выбросов в столбце 'App Usage Time (min/day)': 0\n",
+      "Количество выбросов в столбце 'Screen On Time (hours/day)': 0\n",
+      "Количество выбросов в столбце 'Battery Drain (mAh/day)': 0\n",
+      "Количество выбросов в столбце 'Number of Apps Installed': 0\n",
+      "Количество выбросов в столбце 'Data Usage (MB/day)': 0\n",
+      "Количество выбросов в столбце 'User Behavior Class': 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Выбираем столбцы для анализа\n",
+    "columns_to_check = ['App Usage Time (min/day)', 'Screen On Time (hours/day)', 'Battery Drain (mAh/day)', 'Number of Apps Installed', 'Data Usage (MB/day)', 'User Behavior Class']\n",
+    "\n",
+    "# Функция для подсчета выбросов\n",
+    "def count_outliers(data, columns):\n",
+    "    outliers_count = {}\n",
+    "    for col in columns:\n",
+    "        Q1 = data[col].quantile(0.25)\n",
+    "        Q3 = data[col].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "        lower_bound = Q1 - 1.5 * IQR\n",
+    "        upper_bound = Q3 + 1.5 * IQR\n",
+    "        \n",
+    "        # Считаем количество выбросов\n",
+    "        outliers = data[(data[col] < lower_bound) | (data[col] > upper_bound)]\n",
+    "        outliers_count[col] = len(outliers)\n",
+    "    \n",
+    "    return outliers_count\n",
+    "\n",
+    "# Подсчитываем выбросы\n",
+    "outliers_count = count_outliers(df_mobiles, columns_to_check)\n",
+    "\n",
+    "# Выводим количество выбросов для каждого столбца\n",
+    "for col, count in outliers_count.items():\n",
+    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
+    "\n",
+    "# Создаем диаграммы размахов\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_check, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.boxplot(x=df_mobiles[col])\n",
+    "    plt.title(f'Box Plot of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выбросов нет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 420\n",
+      "Размер контрольной выборки: 140\n",
+      "Размер тестовой выборки: 140\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df, test_df = train_test_split(df_mobiles, 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))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n",
+      "User Behavior Class\n",
+      "2    88\n",
+      "5    88\n",
+      "4    86\n",
+      "3    84\n",
+      "1    74\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n",
+      "User Behavior Class\n",
+      "1    35\n",
+      "2    29\n",
+      "4    26\n",
+      "5    25\n",
+      "3    25\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n",
+      "User Behavior Class\n",
+      "3    34\n",
+      "2    29\n",
+      "4    27\n",
+      "1    27\n",
+      "5    23\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_balance(df, name):\n",
+    "    counts = df['User Behavior Class'].value_counts()\n",
+    "    print(f\"Распределение \\\"Класс поведения пользователя\\\" в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оверсемплинг и андерсемплинг"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n",
+      "User Behavior Class\n",
+      "1    88\n",
+      "2    88\n",
+      "5    88\n",
+      "4    88\n",
+      "3    88\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n",
+      "User Behavior Class\n",
+      "5    35\n",
+      "3    35\n",
+      "1    35\n",
+      "2    35\n",
+      "4    35\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n",
+      "User Behavior Class\n",
+      "4    34\n",
+      "1    34\n",
+      "2    34\n",
+      "3    34\n",
+      "5    34\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n",
+      "User Behavior Class\n",
+      "1    74\n",
+      "2    74\n",
+      "3    74\n",
+      "4    74\n",
+      "5    74\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n",
+      "User Behavior Class\n",
+      "1    25\n",
+      "2    25\n",
+      "3    25\n",
+      "4    25\n",
+      "5    25\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n",
+      "User Behavior Class\n",
+      "1    23\n",
+      "2    23\n",
+      "3    23\n",
+      "4    23\n",
+      "5    23\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "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) # type: ignore\n",
+    "    \n",
+    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1) \n",
+    "    return resampled_df\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) # type: ignore\n",
+    "    \n",
+    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n",
+    "    return resampled_df\n",
+    "\n",
+    "train_df_oversampled = oversample(train_df, 'User Behavior Class')\n",
+    "val_df_oversampled = oversample(val_df, 'User Behavior Class')\n",
+    "test_df_oversampled = oversample(test_df, 'User Behavior Class')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'User Behavior Class')\n",
+    "val_df_undersampled = undersample(val_df, 'User Behavior Class')\n",
+    "test_df_undersampled = undersample(test_df, 'User Behavior Class')\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": [
+    "## Датасет №2 (Характеристики автомобиля: данные об экономии топлива)\n",
+    "Ссылка: https://www.kaggle.com/datasets/arslaan5/explore-car-performance-fuel-efficiency-data\n",
+    "\n",
+    "Проблемная область: производительность и экономичность транспортных средств.\n",
+    "\n",
+    "Объекты наблюдения: автомобили, представленные набором характеристик."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['city_mpg', 'class', 'combination_mpg', 'cylinders', 'displacement',\n",
+      "       'drive', 'fuel_type', 'highway_mpg', 'make', 'model', 'transmission',\n",
+      "       'year'],\n",
+      "      dtype='object')\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 550 entries, 0 to 549\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   city_mpg         550 non-null    int64  \n",
+      " 1   class            550 non-null    object \n",
+      " 2   combination_mpg  550 non-null    int64  \n",
+      " 3   cylinders        548 non-null    float64\n",
+      " 4   displacement     548 non-null    float64\n",
+      " 5   drive            550 non-null    object \n",
+      " 6   fuel_type        550 non-null    object \n",
+      " 7   highway_mpg      550 non-null    int64  \n",
+      " 8   make             550 non-null    object \n",
+      " 9   model            550 non-null    object \n",
+      " 10  transmission     550 non-null    object \n",
+      " 11  year             550 non-null    int64  \n",
+      "dtypes: float64(2), int64(4), object(6)\n",
+      "memory usage: 51.7+ KB\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>city_mpg</th>\n",
+       "      <th>class</th>\n",
+       "      <th>combination_mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>drive</th>\n",
+       "      <th>fuel_type</th>\n",
+       "      <th>highway_mpg</th>\n",
+       "      <th>make</th>\n",
+       "      <th>model</th>\n",
+       "      <th>transmission</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25</td>\n",
+       "      <td>midsize car</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>36</td>\n",
+       "      <td>mazda</td>\n",
+       "      <td>6</td>\n",
+       "      <td>m</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>26</td>\n",
+       "      <td>midsize car</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>37</td>\n",
+       "      <td>mazda</td>\n",
+       "      <td>6</td>\n",
+       "      <td>a</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>small sport utility vehicle</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>31</td>\n",
+       "      <td>mazda</td>\n",
+       "      <td>cx-5 2wd</td>\n",
+       "      <td>a</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26</td>\n",
+       "      <td>small sport utility vehicle</td>\n",
+       "      <td>29</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>34</td>\n",
+       "      <td>mazda</td>\n",
+       "      <td>cx-5 2wd</td>\n",
+       "      <td>m</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>26</td>\n",
+       "      <td>small sport utility vehicle</td>\n",
+       "      <td>28</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>fwd</td>\n",
+       "      <td>gas</td>\n",
+       "      <td>32</td>\n",
+       "      <td>mazda</td>\n",
+       "      <td>cx-5 2wd</td>\n",
+       "      <td>a</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   city_mpg                        class  combination_mpg  cylinders  \\\n",
+       "0        25                  midsize car               29        4.0   \n",
+       "1        26                  midsize car               30        4.0   \n",
+       "2        25  small sport utility vehicle               27        4.0   \n",
+       "3        26  small sport utility vehicle               29        4.0   \n",
+       "4        26  small sport utility vehicle               28        4.0   \n",
+       "\n",
+       "   displacement drive fuel_type  highway_mpg   make     model transmission  \\\n",
+       "0           2.5   fwd       gas           36  mazda         6            m   \n",
+       "1           2.5   fwd       gas           37  mazda         6            a   \n",
+       "2           2.5   fwd       gas           31  mazda  cx-5 2wd            a   \n",
+       "3           2.0   fwd       gas           34  mazda  cx-5 2wd            m   \n",
+       "4           2.0   fwd       gas           32  mazda  cx-5 2wd            a   \n",
+       "\n",
+       "   year  \n",
+       "0  2014  \n",
+       "1  2014  \n",
+       "2  2014  \n",
+       "3  2014  \n",
+       "4  2014  "
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cars = pd.read_csv(\".//static//csv//car_data.csv\")\n",
+    "print(df_cars.columns)\n",
+    "df_cars.info()\n",
+    "df_cars.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Атрибуты объектов:\n",
+    "\n",
+    "1. city_mpg — расход топлива в городе (миль на галлон).\n",
+    "2. class — класс автомобиля (например, седан среднего размера, малый внедорожник).\n",
+    "3. combination_mpg — комбинированный расход топлива (миль на галлон).\n",
+    "4. cylinders — количество цилиндров.\n",
+    "5. displacement — объем двигателя (в литрах).\n",
+    "6. drive — тип привода (например, передний, полный).\n",
+    "7. fuel_type — тип топлива (бензин, дизель и др.).\n",
+    "8. highway_mpg — расход топлива на шоссе (миль на галлон).\n",
+    "9. make — марка автомобиля.\n",
+    "10. model — модель автомобиля.\n",
+    "11. transmission — тип трансмиссии (автоматическая, механическая).\n",
+    "12. year — год выпуска автомобиля.\n",
+    "\n",
+    "Связи между объектами:\n",
+    "Атрибуты, такие как объем двигателя, тип топлива, количество цилиндров и класс автомобиля, могут быть связаны с комбинированным расходом топлива (combination_mpg). Это позволяет выявлять зависимости между характеристиками автомобиля и его экономичностью.\n",
+    "\n",
+    "Примеры бизнес-целей и эффекты для бизнеса:\n",
+    "\n",
+    "1. Оптимизация ассортимента автомобилей:\n",
+    "   - Бизнес-цель: Анализировать топливную экономичность различных моделей для оптимизации ассортимента, предлагать более популярные и экономичные модели.\n",
+    "   - Эффект: Снижение затрат на производство низкоэффективных моделей и увеличение продаж популярных, экономичных автомобилей.\n",
+    "\n",
+    "2. Снижение углеродного следа:\n",
+    "   - Бизнес-цель: Определение моделей с высоким расходом топлива для улучшения их эффективности и снижения выбросов.\n",
+    "   - Эффект: Соответствие экологическим стандартам, улучшение репутации компании и соблюдение требований законодательства.\n",
+    "\n",
+    "Примеры целей технического проекта:\n",
+    "\n",
+    "1. Цель: Создание модели для прогнозирования топливной эффективности.\n",
+    "   - Вход: Объем двигателя, тип топлива, количество цилиндров, класс, тип трансмиссии.\n",
+    "   - Целевой признак: combination_mpg.\n",
+    "\n",
+    "2. Цель: Модель для предсказания углеродного следа автомобиля.\n",
+    "   - Вход: Тип топлива, объем двигателя, класс автомобиля, тип привода.\n",
+    "   - Целевой признак: combination_mpg."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на пустые значения и дубликаты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "city_mpg           0\n",
+      "class              0\n",
+      "combination_mpg    0\n",
+      "cylinders          2\n",
+      "displacement       2\n",
+      "drive              0\n",
+      "fuel_type          0\n",
+      "highway_mpg        0\n",
+      "make               0\n",
+      "model              0\n",
+      "transmission       0\n",
+      "year               0\n",
+      "dtype: int64\n",
+      "\n",
+      "Количество дубликатов: 2\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>city_mpg</th>\n",
+       "      <th>combination_mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>highway_mpg</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>548.000000</td>\n",
+       "      <td>548.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>21.460000</td>\n",
+       "      <td>24.069091</td>\n",
+       "      <td>5.315693</td>\n",
+       "      <td>2.931752</td>\n",
+       "      <td>28.609091</td>\n",
+       "      <td>2019.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.147392</td>\n",
+       "      <td>7.478369</td>\n",
+       "      <td>1.759999</td>\n",
+       "      <td>1.248419</td>\n",
+       "      <td>6.832228</td>\n",
+       "      <td>3.165156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.200000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>2019.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>126.000000</td>\n",
+       "      <td>112.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>6.800000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>2024.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         city_mpg  combination_mpg   cylinders  displacement  highway_mpg  \\\n",
+       "count  550.000000       550.000000  548.000000    548.000000   550.000000   \n",
+       "mean    21.460000        24.069091    5.315693      2.931752    28.609091   \n",
+       "std      8.147392         7.478369    1.759999      1.248419     6.832228   \n",
+       "min     11.000000        14.000000    3.000000      1.200000    18.000000   \n",
+       "25%     17.000000        20.000000    4.000000      2.000000    24.000000   \n",
+       "50%     20.000000        23.000000    4.000000      2.500000    28.000000   \n",
+       "75%     24.000000        27.000000    6.000000      3.500000    32.000000   \n",
+       "max    126.000000       112.000000   12.000000      6.800000   102.000000   \n",
+       "\n",
+       "              year  \n",
+       "count   550.000000  \n",
+       "mean   2019.000000  \n",
+       "std       3.165156  \n",
+       "min    2014.000000  \n",
+       "25%    2016.000000  \n",
+       "50%    2019.000000  \n",
+       "75%    2022.000000  \n",
+       "max    2024.000000  "
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_cars.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_cars.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_cars.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что есть пустые данные, и дубликаты, удаляем их:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "В наборе данных 'Cars' было удалено 2 строк с пустыми значениями.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_cars = df_cars.drop_duplicates()\n",
+    "\n",
+    "def drop_missing_values(dataframe, name):\n",
+    "    before_shape = dataframe.shape \n",
+    "    cleaned_dataframe = dataframe.dropna()  \n",
+    "    after_shape = cleaned_dataframe.shape \n",
+    "    print(f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\")\n",
+    "    return cleaned_dataframe\n",
+    "\n",
+    "df_cars = drop_missing_values(df_cars, \"Cars\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на выбросы:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество выбросов в столбце 'combination_mpg': 8\n",
+      "Количество выбросов в столбце 'cylinders': 10\n",
+      "Количество выбросов в столбце 'displacement': 21\n",
+      "Количество выбросов в столбце 'highway_mpg': 3\n",
+      "Количество выбросов в столбце 'city_mpg': 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0hQoVMuHh4VZb3PvGx8fH/P3331b7zz//bCSZAQMGWG0VKlQwhQoVMhcuXLDa1qxZYySZkJCQZNcMABkd1wL20upaICIiwri5uSU4b1wfF3csVq9ebU1r0aJFgv3QsmXLjCSzdOlSp/aKFSs69YXJEXf+bz8+kydPNpJM/vz5zfnz5632QYMGxTuW4eHhRpIZN26c1Xbt2jXr/Fy/ft0YY8y4ceOMJLNgwQJrvitXrpiwsLB4+w0AAACkBI92QZr54IMPtGLFCq1YsUIzZ85UgwYN1L17d3311VfWPN9++63c3d2tu5bjvPjiizLGaOnSpVbbsGHDVK5cOUVGRqpXr14KDw+Pt5ydrl27Kk+ePAoODlaLFi106dIlzZgxw+nZrbc7duyYtm7dqs6dOyswMNBqr1ixopo0aaJvv/022du+3a+//qoTJ06oV69eTs8obdGihcLCwrRkyZK7Wm+cnj17Or2uW7euTp06pfPnzye6zHfffadatWqpcuXKVltgYGCid5qVLVvW6S7yPHnyqHTp0vrzzz+dtnv8+HHt2bNH0q270OrVq6e6detq/fr1km7dpW6McVqXj4+P9f+XLl3SyZMnVbt2bRljnB7lEhERoaNHjzrdYTZr1iz5+PjoscceS3RfE9OtWzc5HA7rdc2aNWWMUbdu3aw2d3d3VatWzWk/4zz66KNOd5bXqFFDNWvWtN4nR48e1fbt2xURESE/Pz9rvvDwcFWoUCHF9QJAZsC1QMLS4logNjZWCxYsUKtWrRLcn9v7uORq3LixgoODNWvWLKttx44d2rZtm9OnCpKrUaNGTo8yq1mzpiTpscceU44cOeK139nfZsuWTc8++6z12tPTU88++6xOnDih3377TdKta5qCBQvqkUcesebz9vbWM888k+J6AQAAgNsRpCPN1KhRQ40bN1bjxo3VqVMnLVmyRGXLllWfPn2s53QeOnRIwcHBToMnSSpTpow1PY6np6c++eQTRUdH68KFC9ZzspNryJAhWrFihb7//ntt27ZNR48e1dNPP53o/HHbLl26dLxpZcqU0cmTJ3Xp0qVkbz856w0LC3Pa57tRpEgRp9e5cuWSdOvj0HY1lShRIl57Qm0JbSNuO7dvIy4cX79+vS5duqQtW7aobt26qlevnhWkr1+/Xv7+/qpUqZK13OHDh63Aws/PT3ny5FF4eLikW48IiNOkSRMVKFDAGtzHxsZqzpw5at26dbz3U3LcuU85c+aUJBUuXDhee0LHsmTJkvHaSpUqZT3jNe68puQ4A0Bmx7VAytd7t9cC//77r86fP6/y5cuneNnEuLm5qVOnTlqwYIH1fSuzZs2St7e32rdvn+L1paSvleJfuwQHB8vX19eprVSpUpLk1N8WL1483vuCvhYAAAD3iiAd6cbNzU0NGjTQsWPHtG/fvrtax7JlyyRJV69eTfE6KlSooMaNG6tBgwaqUKFCvC86yyoSe0asue0LMtNjG8HBwSpatKjWrVunjRs3yhijWrVqqW7duvrrr7906NAhrV+/XrVr15ab261fRTExMWrSpImWLFmiV155RQsWLNCKFSsUFRUl6VZYfnsNTz75pL788ktdvXpVq1ev1tGjR+/qDjm7fUqoPTWPJQDcT7gWyHwiIiJ08eJFLViwQMYYzZ49Wy1btrTC7pRISV8r0d8CAAAgYyFIR7q6efOmJOnixYuSpJCQEB09elQXLlxwmm/37t3W9Djbtm3TiBEj1KVLF1WpUkXdu3d3ukM5tcVtO+7RJHfWlzt3buuuqJTcDWe33j179jjtc3oJCQnR/v3747Un1JYScY9xWb9+vSpXrqwcOXKoUqVKypkzp7777jtt3rxZ9erVs+bfvn279u7dq3HjxumVV15R69atrY+VJyQiIkLnz5/XokWLNGvWLOXJk0dNmza9p5rvVkJhzt69e62PsMed17Q4zgCQmXAtkDbXAnny5JG/v7927NiR4mXtai9fvryqVKmiWbNmaf369Tp8+LDtXfxp6ejRo/E+AbB3715JcupvDxw4EC+Ep68FAADAvSJIR7q5ceOGli9fLk9PT+vj2s2bN1dMTIzef/99p3nHjx8vh8OhZs2aWct27txZwcHBmjBhgqKionT8+HENGDAgzeotUKCAKleurBkzZujs2bNW+44dO7R8+XI1b97caosbRN8+X2KqVaumvHnzatKkSbp27ZrVvnTpUu3atUstWrRItX1IrqZNm2rjxo3aunWr1Xb69GmnZ6Lejbp16+rgwYP6/PPPrUe9uLm5qXbt2nrnnXd048YNp+ejx92Rdvvg1xijCRMmJLj+ihUrqmLFipo2bZq+/PJLPfHEEy67u3DBggU6cuSI9fqXX37Rzz//bL2Hg4ODVb58eX366adWeCRJa9eu1fbt29O9XgBwBa4FbkmLawE3Nzc9+uijWrRokX799dd40+3u7vb19bX9g8TTTz+t5cuX691331VQUJB1TtLbzZs3NXnyZOv19evXNXnyZOXJk0dVq1aVdOua5siRI/rmm2+s+a5evaqpU6eme70AAADIWvg8K9LM0qVLrbvJTpw4odmzZ2vfvn169dVX5e/vL0lq1aqVGjRooMGDB+vgwYOqVKmSli9froULF6p///4qXry4JGnkyJHaunWrVq1apRw5cqhixYoaMmSI/vvf/6pdu3ZOA9nUNHbsWDVr1ky1atVSt27ddOXKFU2cOFE5c+bUsGHDrPniBm+DBw/WE088IQ8PD7Vq1SreczwlycPDQ2+++aa6dOmi8PBwdezYUcePH9eECRMUGhqapoFAYgYOHKiZM2eqSZMmev755+Xr66tp06apSJEiOn369F19QZn0f89J37Nnj0aPHm2116tXT0uXLpWXl5eqV69utYeFhal48eJ66aWXdOTIEfn7++vLL7+0fb57RESEXnrpJUm668e6pIYSJUrowQcf1HPPPadr165ZYcPAgQOteUaPHq3WrVurTp066tKli86cOaP3339f5cuXdwrXASCr4Fogfa8FRo8ereXLlys8PFw9evRQmTJldOzYMc2bN08//PCDAgICElyuatWq+vzzz/XCCy+oevXq8vPzU6tWrazpTz75pAYOHKivv/5azz33nDw8PO6qvnsVHBysN998UwcPHlSpUqX0+eefa+vWrZoyZYpV07PPPqv3339fHTt2VL9+/azvU4n7Ute7vaYBAAAAZIBUNn36dCPJ6Z+3t7epXLmy+eijj0xsbKzT/BcuXDADBgwwwcHBxsPDw5QsWdKMHTvWmu+3334z2bJlM88//7zTcjdv3jTVq1c3wcHB5syZM4nWs3r1aiPJzJs3z7bu6OhoI8lMnz7dqX3lypWmTp06xsfHx/j7+5tWrVqZnTt3xlv+jTfeMAULFjRubm5GkomOjrbd3ueff26qVKlivLy8TGBgoOnUqZP5+++/neaJO5abNm2yXZcxxgwdOtRIMv/++2+C67i9npCQEBMZGek035YtW0zdunWNl5eXKVSokBkzZox57733jCTzzz//OC3bokWLeNsPDw834eHh8drz5s1rJJnjx49bbT/88IORZOrWrRtv/p07d5rGjRsbPz8/kzt3bvPMM8+Y33//PcFzY4wxx44dM+7u7qZUqVKJHBl7iR3jxI5nZGSk8fX1tV7HvW/Gjh1rxo0bZwoXLmy8vLxM3bp1ze+//x5ve3PnzjVhYWHGy8vLlC9f3nzzzTfmscceM2FhYXdVPwBkRFwLuOZawBhjDh06ZCIiIkyePHmMl5eXKVasmOndu7e5du2aMeb/jsXq1autZS5evGiefPJJExAQYCSZkJCQeOtt3ry5kWR+/PHHZNVxJ0mmd+/eTm2396G3S+h8hYeHm3Llyplff/3V1KpVy3h7e5uQkBDz/vvvx9vWn3/+aVq0aGF8fHxMnjx5zIsvvmi+/PJLI8n89NNPd1U/AAAA4DCGb/EBkLD+/ftr8uTJunjxYqJfBOZqJ0+eVIECBTRkyBC9/vrr6b79gwcPqmjRoho7dqx1Z3xKVa5cWXny5NGKFStSuToAAFJHmzZttH37dpc9a7x+/fo6efLkXT0DXpLeffddDRgwQH///bcKFiyYytUBAADgfsAz0gFIkq5cueL0+tSpU/rss8/04IMPZtgQXZKioqIUExPjsi8+S4kbN25YX7IXZ82aNfr9999Vv3591xQFAEASjh07piVLlmSKvlaKf01z9epVTZ48WSVLliREBwAAwF3jGekAJEm1atVS/fr1VaZMGR0/flwff/yxzp8/75K7vJPj+++/186dOzVq1Cg9+uijCg0NdZp+5coV2y9Ok6TAwEB5enqmYZXOjhw5osaNG+upp55ScHCwdu/erUmTJil//vzq2bNnutUBAEByREdHa8OGDZo2bZo8PDz07LPPxpvnn3/+sV2Hj4+PcubMmVYlJqht27YqUqSIKleurHPnzmnmzJnavXv3PX+JOgAAAO5vBOkAJEnNmzfX/PnzNWXKFDkcDj3wwAP6+OOPVa9ePVeXlqARI0boxx9/VJ06dTRx4sR40z///HN16dLFdh2rV69O1zvBc+XKpapVq2ratGn6999/5evrqxYtWuh///ufgoKC0q0OAACSY+3aterSpYuKFCmiGTNmKH/+/PHmKVCggO06IiMjFRUVlUYVJqxp06aaNm2aZs2apZiYGJUtW1Zz587V448/nq51AAAAIGvhGekAsqRjx47pjz/+sJ2natWqypUrVzpVBABA1rNy5Urb6cHBwSpbtmw6VQMAAACkHYJ0AAAAAAAAAABs8GWjAAAAAAAAAADYuOtnpMfG/r/27j1K6vI+/Phn2WVhFXYFEhdQrhUR0RWjiVJbIMFTa9Si8RATTYzVSFHo8VIrtYpIowdjaqPGWzVeSgNiiUWq1npJBE6OeEMIxniIUhQtIO1Ruahc3P3+/siPicvlYVlmd4aZ1+scjs7Md2efZ57deb68mZ1tilWrVkXXrl2joqIin2MCACIiy7LYsGFD9O7dOzp02Pt/+7Z3A0DbyvfeDQAUj1aH9FWrVkWfPn3yORYAYCfefffdOPjgg/f6fuzdANA+8rV3AwDFo9UhvWvXrhHx+xOE2travA0IAPi99evXR58+fXJ77t6ydwNA28r33g0AFI9Wh/RtPxJeW1vrL+MA0Iby9TYs9m4AaB/eQg0ASo83bQMAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgAQhHQAAAAAAEoR0AAAAAABIENIBAAAAACBBSAcAAAAAgISqQg+Avff+++/HunXrCj0MPqeuri7q6+sLPQwA8syeuyN7HgAAUA6E9H3c+++/H9/57rmxdcvmQg+Fz+lY3Sl+9q/ThQWAEmLP3Tl7HgAAUA6E9H3cunXrYuuWzfHpwJHR1Lmu0MPZKx0+/ShqViyITweMiKaaAwo9nFbrsGldxH/Pj3Xr1okKACWkUHtuMe+P9jwAAKBcCOkloqlzXTTt/4VCDyMvmmoOKJm5AFB6CrXn2h8BAAAKxy8bBQAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAAShHQAAAAAAEgQ0gEAAAAAIEFIBwAAAACABCEdAAAAAAASiiqkb9q0KX73u9/Fpk2bCj0UgKLluZJi4usR2o7vLwAAKB5FFdJXrlwZ48aNi5UrVxZ6KABFy3MlxcTXI7Qd318AAFA8iiqkAwAAAABAsRHSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASBDSAQAAAAAgQUgHAAAAAIAEIR0AAAAAABKEdAAAAAAASKgq9AAAaDuNjY2xdOnS+OCDD6J79+7R0NAQlZWVhR5WqxTrXIp1XABtacuWLTF37txYtWpV9O7dO8aMGRPV1dWtPq4QWvr87Xm+dfL9uOXz/qwpANAaQjpAiVqwYEHceeedsWbNmtx1PXv2jIsvvjhGjBhRwJHtuWKdS7GOC6At3X333TF79uxobGxsdt3YsWNj/Pjxe3xcIbT0+dvzfOvk+3HL5/1ZUwCgtby1C0AJWrBgQUyZMiUGDhwYd9xxR/znf/5n3HHHHTFw4MCYMmVKLFiwoNBDbLFinUuxjgugLd19990xa9asqK2tjSuuuCIeeeSRuOKKK6K2tjZmzZoVd9999x4dVwgtff72PN86+X7c8nl/1hQA2BtCOkCJaWxsjDvvvDOGDx8e119/fQwdOjT222+/GDp0aFx//fUxfPjwuOuuu5q9QrBYFetcinVcAG1py5YtMXv27OjWrVvMnj07Tj311OjRo0eceuqpza7fuHFji47bsmVLu8+hpc/fW7Zs8TzfCvneH/N5f/ZuAGBvtfitXTZv3hybN2/OXV6/fn2bDCgi4p133mmz+y41HqviZW1oK7v72lq6dGmsWbMmJk+eHB06NP/30g4dOsQ555wTEyZMiKVLl8bRRx/dlkPda8U6l2Id1/bs3flVDnNsLY9N2yi2x3Xu3LnR2NgYF1xwQVRVNf9rRFVVVZx//vlx8803x7Rp01p03Ny5c2Ps2LHtOYUWP3/PnTt3n3ieLzb53h/zeX/7yt4NABSvFof0adOmxdSpU9tyLDk33HBDu3weaEu+jimUDz74ICIiBgwYsNPbt12/7bhiVqxzKdZxbc/eTXux/uVh1apVERExfPjwnd6+7fo9Pa49tfT5e9vYiv15vtjke3/M5/3tK3s3AFC8WhzSr7rqqrj88stzl9evXx99+vRpk0FdffXV0a9fvza571Lzzjvv+MtrkfJ1TFvZ3fd99+7dIyJixYoVMXTo0B1uX7FiRbPjilmxzqVYx7U9e3d+2XN3rRzWvxCK7Wuud+/eERGxcOHCOPXUU3e4feHChbnjVqxY0aLj2ltLn7+3ja3Yn+eLTb73x3ze376ydwMAxavFIb1Tp07RqVOnthxLTr9+/eLQQw9tl88FbcXXMYXS0NAQPXv2jBkzZsT111/f7MeXm5qaYsaMGdGrV69oaGgo4ChbpljnUqzj2p69m/Zi/cvDmDFj4u6774777rsv/vzP/7zZ27Z89tlncf/990dlZWVcddVVMWbMmN0eN2bMmHafQ0ufv8eMGROPPPJI0T/PF5t874/5vL99Ze8GAIqXXzYKUGIqKyvj4osvjoULF8Y111wTr7/+enzyySfx+uuvxzXXXBMLFy6Miy66KCorKws91N0q1rkU67gA2lJ1dXWMHTs2Pvzwwxg7dmw89thj8X//93/x2GOPNbu+S5cuLTquurq63efQ0ufv6upqz/OtkO/9MZ/3Z+8GAPZWi1+RDsC+Y8SIETF16tS48847Y8KECbnre/XqFVOnTo0RI0YUcHR7pljnUqzjAmhL48ePj4iI2bNnx80335y7vrKyMr71rW/lbm/pcYXQ0udvz/Otk+/HLZ/3Z00BgL0hpAOUqBEjRsQJJ5wQS5cujQ8++CC6d+8eDQ0N++QrrYp1LsU6LoC2NH78+Dj//PNj7ty5sWrVqujdu3eMGTNmh1eYt/S4Qmjp87fn+dbJ9+OWz/uzpgBAawnpACWssrIyjj766EIPIy+KdS7FOi6AtrTtbV7ydVwhtPT52/N86+T7ccvn/VlTAKA1vEc6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAAAAJAgpAMAAAAAQIKQDgAAAAAACUI6AAAAAAAkCOkAAAA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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Выбираем столбцы для анализа\n",
+    "columns_to_check = ['combination_mpg', 'cylinders', 'displacement', 'highway_mpg', 'city_mpg']\n",
+    "\n",
+    "# Подсчитываем выбросы\n",
+    "outliers_count = count_outliers(df_cars, columns_to_check)\n",
+    "\n",
+    "# Выводим количество выбросов для каждого столбца\n",
+    "for col, count in outliers_count.items():\n",
+    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
+    "\n",
+    "# Создаем диаграммы размахов\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_check, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.boxplot(x=df_cars[col])\n",
+    "    plt.title(f'Box Plot of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В каждом из выбранных столбцов присутствуют выбросы. Очистим их."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество удаленных строк: 36\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1500x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Выбираем столбцы для очистки\n",
+    "columns_to_clean = ['combination_mpg', 'cylinders', 'displacement', 'highway_mpg', 'city_mpg']\n",
+    "\n",
+    "# Функция для удаления выбросов\n",
+    "def remove_outliers(df, columns):\n",
+    "    for col in columns:\n",
+    "        Q1 = df[col].quantile(0.25)\n",
+    "        Q3 = df[col].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "        lower_bound = Q1 - 1.5 * IQR\n",
+    "        upper_bound = Q3 + 1.5 * IQR\n",
+    "        \n",
+    "        # Удаляем строки, содержащие выбросы\n",
+    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Удаляем выбросы\n",
+    "df_cars_clean = remove_outliers(df_cars, columns_to_clean)\n",
+    "\n",
+    "# Выводим количество удаленных строк\n",
+    "print(f\"Количество удаленных строк: {len(df_cars) - len(df_cars_clean)}\")\n",
+    "\n",
+    "# Создаем диаграммы размаха для очищенных данных\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "\n",
+    "# Создаем диаграммы размахов\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_clean, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.boxplot(x=df_cars_clean[col])\n",
+    "    plt.title(f'Box Plot of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "df_cars = df_cars_clean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 306\n",
+      "Размер контрольной выборки: 102\n",
+      "Размер тестовой выборки: 102\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df, test_df = train_test_split(df_cars, 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))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение \"Комбинированный расход топлива\" в обучающей выборке:\n",
+      "combination_mpg\n",
+      "23    32\n",
+      "22    29\n",
+      "24    23\n",
+      "25    22\n",
+      "27    22\n",
+      "18    21\n",
+      "19    19\n",
+      "29    18\n",
+      "21    18\n",
+      "26    17\n",
+      "31    16\n",
+      "28    14\n",
+      "20    13\n",
+      "32    12\n",
+      "17    11\n",
+      "30    10\n",
+      "16     3\n",
+      "34     3\n",
+      "36     1\n",
+      "33     1\n",
+      "14     1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в контрольной выборке:\n",
+      "combination_mpg\n",
+      "20    17\n",
+      "19    15\n",
+      "21    13\n",
+      "26     9\n",
+      "27     7\n",
+      "22     6\n",
+      "30     5\n",
+      "23     5\n",
+      "18     4\n",
+      "17     3\n",
+      "24     3\n",
+      "28     3\n",
+      "29     3\n",
+      "25     2\n",
+      "34     2\n",
+      "33     2\n",
+      "32     1\n",
+      "14     1\n",
+      "31     1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в тестовой выборке:\n",
+      "combination_mpg\n",
+      "21    14\n",
+      "18    13\n",
+      "22    12\n",
+      "27    12\n",
+      "23    10\n",
+      "31     5\n",
+      "20     5\n",
+      "26     5\n",
+      "24     4\n",
+      "29     4\n",
+      "28     4\n",
+      "19     4\n",
+      "25     3\n",
+      "32     3\n",
+      "17     3\n",
+      "30     1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_balance(df, name):\n",
+    "    counts = df['combination_mpg'].value_counts()\n",
+    "    print(f\"Распределение \\\"Комбинированный расход топлива\\\" в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\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": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение \"Комбинированный расход топлива\" в обучающей выборке:\n",
+      "combination_mpg\n",
+      "21    32\n",
+      "22    32\n",
+      "25    32\n",
+      "19    32\n",
+      "29    32\n",
+      "23    32\n",
+      "28    32\n",
+      "18    32\n",
+      "27    32\n",
+      "20    32\n",
+      "16    32\n",
+      "30    32\n",
+      "32    32\n",
+      "31    32\n",
+      "24    32\n",
+      "26    32\n",
+      "17    32\n",
+      "36    32\n",
+      "34    32\n",
+      "33    32\n",
+      "14    32\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в контрольной выборке:\n",
+      "combination_mpg\n",
+      "20    17\n",
+      "19    17\n",
+      "17    17\n",
+      "27    17\n",
+      "22    17\n",
+      "26    17\n",
+      "24    17\n",
+      "32    17\n",
+      "21    17\n",
+      "18    17\n",
+      "30    17\n",
+      "23    17\n",
+      "29    17\n",
+      "28    17\n",
+      "34    17\n",
+      "25    17\n",
+      "14    17\n",
+      "33    17\n",
+      "31    17\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в тестовой выборке:\n",
+      "combination_mpg\n",
+      "28    14\n",
+      "32    14\n",
+      "30    14\n",
+      "23    14\n",
+      "20    14\n",
+      "26    14\n",
+      "21    14\n",
+      "18    14\n",
+      "27    14\n",
+      "25    14\n",
+      "22    14\n",
+      "19    14\n",
+      "29    14\n",
+      "24    14\n",
+      "31    14\n",
+      "17    14\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение \"Комбинированный расход топлива\" в обучающей выборке:\n",
+      "combination_mpg\n",
+      "14    1\n",
+      "16    1\n",
+      "17    1\n",
+      "18    1\n",
+      "19    1\n",
+      "20    1\n",
+      "21    1\n",
+      "22    1\n",
+      "23    1\n",
+      "24    1\n",
+      "25    1\n",
+      "26    1\n",
+      "27    1\n",
+      "28    1\n",
+      "29    1\n",
+      "30    1\n",
+      "31    1\n",
+      "32    1\n",
+      "33    1\n",
+      "34    1\n",
+      "36    1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в контрольной выборке:\n",
+      "combination_mpg\n",
+      "14    1\n",
+      "17    1\n",
+      "18    1\n",
+      "19    1\n",
+      "20    1\n",
+      "21    1\n",
+      "22    1\n",
+      "23    1\n",
+      "24    1\n",
+      "25    1\n",
+      "26    1\n",
+      "27    1\n",
+      "28    1\n",
+      "29    1\n",
+      "30    1\n",
+      "31    1\n",
+      "32    1\n",
+      "33    1\n",
+      "34    1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Комбинированный расход топлива\" в тестовой выборке:\n",
+      "combination_mpg\n",
+      "17    1\n",
+      "18    1\n",
+      "19    1\n",
+      "20    1\n",
+      "21    1\n",
+      "22    1\n",
+      "23    1\n",
+      "24    1\n",
+      "25    1\n",
+      "26    1\n",
+      "27    1\n",
+      "28    1\n",
+      "29    1\n",
+      "30    1\n",
+      "31    1\n",
+      "32    1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df_oversampled = oversample(train_df, 'combination_mpg')\n",
+    "val_df_oversampled = oversample(val_df, 'combination_mpg')\n",
+    "test_df_oversampled = oversample(test_df, 'combination_mpg')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'combination_mpg')\n",
+    "val_df_undersampled = undersample(val_df, 'combination_mpg')\n",
+    "test_df_undersampled = undersample(test_df, 'combination_mpg')\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": [
+    "## Датасет №3 (Экономика стран)\n",
+    "Ссылка: https://www.kaggle.com/datasets/pratik453609/economic-data-9-countries-19802020\n",
+    "\n",
+    "Проблемная область: экономический анализ и прогнозирование макроэкономических показателей.\n",
+    "\n",
+    "Объекты наблюдения: экономические индексы по странам за определённые годы."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n",
+      "       'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
+      "       'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
+      "       'tradebalance', 'USTreasury'],\n",
+      "      dtype='object')\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 369 entries, 0 to 368\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   stock index          369 non-null    object \n",
+      " 1   country              369 non-null    object \n",
+      " 2   year                 369 non-null    float64\n",
+      " 3   index price          317 non-null    float64\n",
+      " 4   log_indexprice       369 non-null    float64\n",
+      " 5   inflationrate        326 non-null    float64\n",
+      " 6   oil prices           369 non-null    float64\n",
+      " 7   exchange_rate        367 non-null    float64\n",
+      " 8   gdppercent           350 non-null    float64\n",
+      " 9   percapitaincome      368 non-null    float64\n",
+      " 10  unemploymentrate     348 non-null    float64\n",
+      " 11  manufacturingoutput  278 non-null    float64\n",
+      " 12  tradebalance         365 non-null    float64\n",
+      " 13  USTreasury           369 non-null    float64\n",
+      "dtypes: float64(12), object(2)\n",
+      "memory usage: 40.5+ KB\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>stock index</th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>index price</th>\n",
+       "      <th>log_indexprice</th>\n",
+       "      <th>inflationrate</th>\n",
+       "      <th>oil prices</th>\n",
+       "      <th>exchange_rate</th>\n",
+       "      <th>gdppercent</th>\n",
+       "      <th>percapitaincome</th>\n",
+       "      <th>unemploymentrate</th>\n",
+       "      <th>manufacturingoutput</th>\n",
+       "      <th>tradebalance</th>\n",
+       "      <th>USTreasury</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NASDAQ</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>168.61</td>\n",
+       "      <td>2.23</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>21.59</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>12575.0</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-13.06</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NASDAQ</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>1981.0</td>\n",
+       "      <td>203.15</td>\n",
+       "      <td>2.31</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>31.77</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>13976.0</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-12.52</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NASDAQ</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>1982.0</td>\n",
+       "      <td>188.98</td>\n",
+       "      <td>2.28</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>28.52</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>14434.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-19.97</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NASDAQ</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>1983.0</td>\n",
+       "      <td>285.43</td>\n",
+       "      <td>2.46</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>15544.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-51.64</td>\n",
+       "      <td>0.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NASDAQ</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>1984.0</td>\n",
+       "      <td>248.89</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>25.88</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>17121.0</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-102.73</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  stock index                   country    year  index price  log_indexprice  \\\n",
+       "0      NASDAQ  United States of America  1980.0       168.61            2.23   \n",
+       "1      NASDAQ  United States of America  1981.0       203.15            2.31   \n",
+       "2      NASDAQ  United States of America  1982.0       188.98            2.28   \n",
+       "3      NASDAQ  United States of America  1983.0       285.43            2.46   \n",
+       "4      NASDAQ  United States of America  1984.0       248.89            2.40   \n",
+       "\n",
+       "   inflationrate  oil prices  exchange_rate  gdppercent  percapitaincome  \\\n",
+       "0           0.14       21.59            1.0        0.09          12575.0   \n",
+       "1           0.10       31.77            1.0        0.12          13976.0   \n",
+       "2           0.06       28.52            1.0        0.04          14434.0   \n",
+       "3           0.03       26.19            1.0        0.09          15544.0   \n",
+       "4           0.04       25.88            1.0        0.11          17121.0   \n",
+       "\n",
+       "   unemploymentrate  manufacturingoutput  tradebalance  USTreasury  \n",
+       "0              0.07                  NaN        -13.06        0.11  \n",
+       "1              0.08                  NaN        -12.52        0.14  \n",
+       "2              0.10                  NaN        -19.97        0.13  \n",
+       "3              0.10                  NaN        -51.64        0.11  \n",
+       "4              0.08                  NaN       -102.73        0.12  "
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_countries = pd.read_csv(\".//static//csv//Economic Data - 9 Countries (1980-2020).csv\")\n",
+    "print(df_countries.columns)\n",
+    "df_countries.info()\n",
+    "df_countries.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Атрибуты объектов:\n",
+    "1. stock index — индекс акций.\n",
+    "2. country — страна.\n",
+    "3. year — год.\n",
+    "4. index price — цена индекса.\n",
+    "5. log_indexprice — логарифм цены индекса.\n",
+    "6. inflationrate — уровень инфляции.\n",
+    "7. oil prices — цены на нефть.\n",
+    "8. exchange_rate — валютный курс.\n",
+    "9. gdppercent — процент роста ВВП.\n",
+    "10. percapitaincome — доход на душу населения.\n",
+    "11. unemploymentrate — уровень безработицы.\n",
+    "12. manufacturingoutput — объём производства.\n",
+    "13. tradebalance — торговый баланс.\n",
+    "14. USTreasury — доходность казначейских облигаций США.\n",
+    "\n",
+    "Связи между объектами:\n",
+    "Некоторые атрибуты могут быть связаны друг с другом, например, уровень инфляции и процент роста ВВП могут коррелировать с ценами на нефть, уровнем безработицы и торговым балансом.\n",
+    "\n",
+    "Примеры бизнес-целей и эффект:\n",
+    "1. Прогнозирование экономического роста и планирование инвестиций:\n",
+    "   - Бизнес-цель: Создать модель прогнозирования роста экономики для стран, чтобы принять стратегические инвестиционные решения.\n",
+    "   - Эффект: Повышение точности экономических прогнозов и улучшение прибыльности инвестиционных стратегий.\n",
+    "\n",
+    "2. Анализ и оптимизация торговой политики:\n",
+    "   - Бизнес-цель: Изучение влияния изменений торгового баланса и валютных курсов на экономику стран.\n",
+    "   - Эффект: Улучшение торговых соглашений и политики, что приведёт к более устойчивому экономическому росту.\n",
+    "\n",
+    "Примеры целей технического проекта:\n",
+    "1. Цель: Построение модели для прогнозирования уровня инфляции.\n",
+    "   - Вход: Уровень безработицы, ВВП, доход на душу населения, валютный курс, цены на нефть.\n",
+    "   - Целевой признак: inflationrate.\n",
+    "\n",
+    "2. Цель: Построение модели для оценки экономического роста.\n",
+    "   - Вход: Торговый баланс, доход на душу населения, валютный курс, инфляция.\n",
+    "   - Целевой признак: gdppercent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на пустые значения и дубликаты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "stock index             0\n",
+      "country                 0\n",
+      "year                    0\n",
+      "index price            52\n",
+      "log_indexprice          0\n",
+      "inflationrate          43\n",
+      "oil prices              0\n",
+      "exchange_rate           2\n",
+      "gdppercent             19\n",
+      "percapitaincome         1\n",
+      "unemploymentrate       21\n",
+      "manufacturingoutput    91\n",
+      "tradebalance            4\n",
+      "USTreasury              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>year</th>\n",
+       "      <th>index price</th>\n",
+       "      <th>log_indexprice</th>\n",
+       "      <th>inflationrate</th>\n",
+       "      <th>oil prices</th>\n",
+       "      <th>exchange_rate</th>\n",
+       "      <th>gdppercent</th>\n",
+       "      <th>percapitaincome</th>\n",
+       "      <th>unemploymentrate</th>\n",
+       "      <th>manufacturingoutput</th>\n",
+       "      <th>tradebalance</th>\n",
+       "      <th>USTreasury</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>369.000000</td>\n",
+       "      <td>317.000000</td>\n",
+       "      <td>369.000000</td>\n",
+       "      <td>326.000000</td>\n",
+       "      <td>369.000000</td>\n",
+       "      <td>367.000000</td>\n",
+       "      <td>350.000000</td>\n",
+       "      <td>368.000000</td>\n",
+       "      <td>348.000000</td>\n",
+       "      <td>278.000000</td>\n",
+       "      <td>365.000000</td>\n",
+       "      <td>369.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>7898.648297</td>\n",
+       "      <td>3.610542</td>\n",
+       "      <td>0.041748</td>\n",
+       "      <td>39.743171</td>\n",
+       "      <td>27.897548</td>\n",
+       "      <td>0.037114</td>\n",
+       "      <td>20719.964674</td>\n",
+       "      <td>0.068908</td>\n",
+       "      <td>328.084820</td>\n",
+       "      <td>-15.996384</td>\n",
+       "      <td>0.059024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.848225</td>\n",
+       "      <td>7811.336862</td>\n",
+       "      <td>0.482481</td>\n",
+       "      <td>0.039579</td>\n",
+       "      <td>25.452654</td>\n",
+       "      <td>49.620521</td>\n",
+       "      <td>0.037850</td>\n",
+       "      <td>17435.037783</td>\n",
+       "      <td>0.043207</td>\n",
+       "      <td>622.395923</td>\n",
+       "      <td>154.557170</td>\n",
+       "      <td>0.033086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1980.000000</td>\n",
+       "      <td>168.610000</td>\n",
+       "      <td>2.230000</td>\n",
+       "      <td>-0.040000</td>\n",
+       "      <td>11.350000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>-0.110000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>0.590000</td>\n",
+       "      <td>-770.930000</td>\n",
+       "      <td>0.010000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1990.000000</td>\n",
+       "      <td>2407.100000</td>\n",
+       "      <td>3.320000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>19.410000</td>\n",
+       "      <td>1.330000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>2090.250000</td>\n",
+       "      <td>0.040000</td>\n",
+       "      <td>80.380000</td>\n",
+       "      <td>-25.370000</td>\n",
+       "      <td>0.030000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>5160.100000</td>\n",
+       "      <td>3.600000</td>\n",
+       "      <td>0.030000</td>\n",
+       "      <td>28.520000</td>\n",
+       "      <td>5.440000</td>\n",
+       "      <td>0.030000</td>\n",
+       "      <td>19969.500000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>188.160000</td>\n",
+       "      <td>-0.140000</td>\n",
+       "      <td>0.050000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>10279.500000</td>\n",
+       "      <td>3.980000</td>\n",
+       "      <td>0.057500</td>\n",
+       "      <td>57.880000</td>\n",
+       "      <td>15.055000</td>\n",
+       "      <td>0.060000</td>\n",
+       "      <td>36384.000000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>271.977500</td>\n",
+       "      <td>19.080000</td>\n",
+       "      <td>0.080000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>47751.330000</td>\n",
+       "      <td>4.680000</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>98.560000</td>\n",
+       "      <td>249.050000</td>\n",
+       "      <td>0.150000</td>\n",
+       "      <td>65280.000000</td>\n",
+       "      <td>0.260000</td>\n",
+       "      <td>3868.460000</td>\n",
+       "      <td>366.140000</td>\n",
+       "      <td>0.140000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year   index price  log_indexprice  inflationrate  oil prices  \\\n",
+       "count   369.000000    317.000000      369.000000     326.000000  369.000000   \n",
+       "mean   2000.000000   7898.648297        3.610542       0.041748   39.743171   \n",
+       "std      11.848225   7811.336862        0.482481       0.039579   25.452654   \n",
+       "min    1980.000000    168.610000        2.230000      -0.040000   11.350000   \n",
+       "25%    1990.000000   2407.100000        3.320000       0.020000   19.410000   \n",
+       "50%    2000.000000   5160.100000        3.600000       0.030000   28.520000   \n",
+       "75%    2010.000000  10279.500000        3.980000       0.057500   57.880000   \n",
+       "max    2020.000000  47751.330000        4.680000       0.240000   98.560000   \n",
+       "\n",
+       "       exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
+       "count     367.000000  350.000000       368.000000        348.000000   \n",
+       "mean       27.897548    0.037114     20719.964674          0.068908   \n",
+       "std        49.620521    0.037850     17435.037783          0.043207   \n",
+       "min         0.900000   -0.110000        27.000000          0.020000   \n",
+       "25%         1.330000    0.020000      2090.250000          0.040000   \n",
+       "50%         5.440000    0.030000     19969.500000          0.060000   \n",
+       "75%        15.055000    0.060000     36384.000000          0.090000   \n",
+       "max       249.050000    0.150000     65280.000000          0.260000   \n",
+       "\n",
+       "       manufacturingoutput  tradebalance  USTreasury  \n",
+       "count           278.000000    365.000000  369.000000  \n",
+       "mean            328.084820    -15.996384    0.059024  \n",
+       "std             622.395923    154.557170    0.033086  \n",
+       "min               0.590000   -770.930000    0.010000  \n",
+       "25%              80.380000    -25.370000    0.030000  \n",
+       "50%             188.160000     -0.140000    0.050000  \n",
+       "75%             271.977500     19.080000    0.080000  \n",
+       "max            3868.460000    366.140000    0.140000  "
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_values = df_countries.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df_countries.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df_countries.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Видим, что есть пустые данные, но нет дубликатов. Удаляем их"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "В наборе данных 'Countries' было удалено 150 строк с пустыми значениями.\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_countries = drop_missing_values(df_countries, \"Countries\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на выбросы:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество выбросов в столбце 'year': 0\n",
+      "Количество выбросов в столбце 'index price': 17\n",
+      "Количество выбросов в столбце 'log_indexprice': 1\n",
+      "Количество выбросов в столбце 'inflationrate': 35\n",
+      "Количество выбросов в столбце 'oil prices': 0\n",
+      "Количество выбросов в столбце 'exchange_rate': 53\n",
+      "Количество выбросов в столбце 'gdppercent': 13\n",
+      "Количество выбросов в столбце 'percapitaincome': 0\n",
+      "Количество выбросов в столбце 'unemploymentrate': 9\n",
+      "Количество выбросов в столбце 'manufacturingoutput': 29\n",
+      "Количество выбросов в столбце 'tradebalance': 47\n",
+      "Количество выбросов в столбце 'USTreasury': 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Выбираем столбцы для анализа\n",
+    "columns_to_check = ['year', 'index price', 'log_indexprice',\n",
+    "       'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
+    "       'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
+    "       'tradebalance', 'USTreasury']\n",
+    "\n",
+    "# Подсчитываем выбросы\n",
+    "outliers_count = count_outliers(df_countries, columns_to_check)\n",
+    "\n",
+    "# Выводим количество выбросов для каждого столбца\n",
+    "for col, count in outliers_count.items():\n",
+    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
+    "\n",
+    "# Создаем диаграммы размахов\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_check, 1):\n",
+    "    plt.subplot(3, 4, i)\n",
+    "    sns.boxplot(x=df_countries[col])\n",
+    "    plt.title(f'Box Plot of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В большинстве из выбранных столбцов присутствуют выбросы. Очистим их."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество удаленных строк: 136\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1500x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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g/8josmW1a9e2jxc2m03vvfee7rnnHhUqVEiVK1fWwoULHebPzBj4f//3fxo6dKiKFi2qkiVLasaMGTpz5owee+wxFSlSRJUqVdKSJUvsj1m1apVsNpsWL16s6Oho+fn56dZbb9XWrVuv256pU6eqYsWK8vHxUdWqVfXhhx/a7+vdu7c6dOjgMP/FixcVGhqqmTNnZrvWzPbBkCFD9Oyzz6pYsWIqVaqUw5h8rePrtMzgvffeU/ny5eXn5ydJ+vbbb3X77bcrODhYxYsXV4cOHRz2H8qXLy9JqlOnjmw2m+644w77fdfbtwDyDQPgul555RUTGRlpvv32W7N3714TGxtrfH19zapVq8xff/1lihYtat555x1jjDFdu3Y1DRs2NBcvXjTGGLNo0SLj6elpRo0aZbZv327i4+PNa6+9Zl92RESEKVasmJk8ebLZvXu3GTt2rPHw8DA7d+60z/Pyyy+bdevWmcTERLNw4UJTsmRJM378ePv9MTExpnDhwubee+81W7ZsMWvWrDGlSpUyzz//vH2evn37moiICLN8+XKzZcsWc88995giRYqYJ5980mGeJk2amDVr1pg9e/aYN954w/j6+ppdu3bdsI9iY2ONt7e3adKkiVm3bp3ZuXOnOXPmjFmxYoX58MMPzY4dO8z27dtNnz59TMmSJc3JkyeNMcYkJSUZSSY2NtYcPHjQJCUlGWOMWbNmjQkMDDSzZ882e/fuNd99950pV66cGT16dDa2IAAA13ajMbJdu3amVq1aJi4uzvz666+mSZMmxt/f30yYMMG+DEmmePHiZsaMGSYhIcG88MILxtPT02zfvt0YY0xiYqKRZEqXLm0+//xzs337dtO3b19TpEgRc+TIEWOMMcePHzclSpQwI0aMMDt27DAbN240d911l2nRooV9Pc2bNzeFCxc2w4YNMzt37jQ7d+40f/31lylWrJi59957zS+//GISEhLMrFmz7PsSH330kQkLCzNffPGF+f33380XX3xhihUrZmbPnu1QW2RkpFm0aJFJSEgw999/v4mIiDAXL14058+fN++8844JDAw0Bw8eNAcPHjSnTp3KgS0DAMgrIiIiHMZFY4ypVauWiYmJMcYY+xj48ccfm927d5shQ4aYwoULm6NHjxpjMj8GFilSxLz88stm165d5uWXXzaenp6mXbt2Zvr06WbXrl1mwIABpnjx4ubMmTPGGGNWrlxpJJlq1aqZ7777zmzevNl06NDBlCtXzly4cMEYc/lYNigoyL6e+fPnG29vbzN58mSTkJBg3nrrLePp6Wm+//57Y4wx69atM56enubvv/92eExAQIB9fMxOrZntg8DAQDN69Giza9cu8/777xubzWa+++47Y8y1j69jYmJMQECAadu2rdm4caP57bffjDHGfP755+aLL74wu3fvNps2bTIdO3Y0NWvWNCkpKcYYY9avX28kmeXLl5uDBw/at9eN9i2A/ILQHLiOf//91xQqVMj8+OOPDtP79OljHn74YWOMMZ999pnx8/Mzzz33nAkICHAImRs3bmy6det2zeVHRESYRx991H47NTXVhIaGmqlTp17zMW+88YapV6+e/XZMTIwpVKiQPYg2xphhw4aZRo0aGWOMOXnypPH29jbz5s2z33/ixAlTqFAheyCwb98+4+npaQ4cOOCwrpYtW5oRI0Zcs5Y0sbGxRpKJj4+/7nwpKSmmSJEi5uuvv7ZPk2S+/PLLdOu98sMFY4z58MMPTVhY2A1rAQAgs240RiYkJBhJZv369fb7d+zYYSSlC8379+/vsOxGjRqZAQMGGGP+F0yPGzfOfv/FixdN6dKl7R+Ev/zyy6Z169YOy/jzzz+NJJOQkGCMuXywXKdOHYd5RowYYcqXL28/+L9axYoVzccff+ww7eWXXzaNGzd2qO29996z379t2zYjyezYscMYkz5QAADgSpkJzV944QX7fadPnzaSzJIlS4wxmR8Db7/9dvv9ly5dMgEBAaZ79+72aQcPHjSSTFxcnDHmf6H53Llz7fMcPXrU+Pv7m08//dQYk36Ma9KkienXr59DLV27djV33323/Xb16tUdTmTr2LGj6dWrl/12dmrNTh8YY0yDBg3M8OHD7bczOr6OiYkx3t7e9hD9Wv755x8jyWzZssUY8799hE2bNjnMd6N9CyC/8MqhE9qBPGnPnj06e/as7rrrLofpFy5cUJ06dSRJXbt21Zdffqlx48Zp6tSpqly5sn2++Ph49evX77rriI6Otv/fZrOpVKlSSkpKsk/79NNP9d///ld79+7V6dOndenSJQUGBjoso1y5cipSpIj9dlhYmH0Zv//+uy5evKiGDRva7w8KClLVqlXtt7ds2aKUlBRVqVLFYbnnz59X8eLFr1t/Gh8fH4e2SNLhw4f1wgsvaNWqVUpKSlJKSorOnj2r/fv3X3dZv/32m9atW6dXX33VPi0lJUX//vuvzp49q0KFCmWqJgAArudGY+SOHTvk5eWlevXq2e+PjIzM8EcxGzdunO721T+cdeU8Xl5eql+/vnbs2CHp8ti3cuVKFS5cON2y9+7dax+jr6xFuryv0bRpU3l7e6d73JkzZ7R371716dPHYX/k0qVLCgoKcpj3yjE8LCxM0uVL00RGRqZbLgAAWXXlOBMQEKDAwED7MWtmx8Arl+Hp6anixYurZs2a9mklS5aUJIfjaclx/C1WrJiqVq1qH3+vtmPHDj3++OMO02677TZNnDjRfrtv376aPn26nn32WR0+fFhLlizR999/f832ZqbW7PSB5Hjsfz0REREqUaKEw7Tdu3dr1KhR+vnnn3XkyBH775Lt379fNWrUyHA5Wdm3API6QnPgOk6fPi1JWrx4sW655RaH+3x9fSVdvib5hg0b5Onpqd27dzvM4+/vf8N1XH2Qa7PZ7INVXFycunXrpjFjxqhNmzYKCgrS3Llz9dZbb2V6GZlx+vRpeXp62ttxpYwG7Yz4+/vLZrM5TOvZs6eOHj2qiRMnKiIiQr6+vmrcuPENf3jk9OnTGjNmjO69995096Vdfw0AgPzk9OnT6tixo8aPH5/uvrQQW7ocNFzpevsaafsxM2bMUKNGjRzuu3q8v3JfIm0850e9AQCZ4eHhYf+drzRX/g6XdP1j1syOgRktwx3jV48ePfTcc88pLi5OP/74o8qXL6+mTZs6zJPVWm+mDzLT3qv3HySpY8eOioiI0IwZMxQeHq7U1FTVqFHjusfrWdm3API6QnPgOq78UcvmzZtnOM/TTz8tDw8PLVmyRHfffbfat2+vO++8U9LlT4FXrFihxx57LFvr//HHHxUREaGRI0fap+3bty9Ly6hQoYK8vb31yy+/qGzZspKk5ORk7dq1S82aNZN0+Yc9UlJSlJSUlG6wvxnr1q3TlClTdPfdd0uS/vzzT4cfMpEuD/opKSkO0+rWrauEhARVqlTJabUAAHC1G42RkZGRunTpkjZs2KAGDRpIkhISEnTixIl0y/rpp5/Uo0cPh9tp30q7clra2Ju23MGDB0u6PPZ98cUXKleunLy8Mr+LHh0drffff18XL15MdyBdsmRJhYeH6/fff1e3bt0yvcyr+fj4pBurAQBIU6JECR08eNB+++TJk0pMTMz047M7BmbWTz/9ZB/njx8/rl27dqlatWoZzlutWjWtW7dOPXv2tE9bt26dqlevbr9dvHhxdenSRbGxsYqLi8v28f6VnNUHGR1fZ+To0aNKSEjQjBkz7BnADz/84DCPj4+PJDksz1n7FkBeQGgOXEeRIkX0zDPP6KmnnlJqaqpuv/12JScna926dQoMDFRISIhmzZqluLg41a1bV8OGDVPPnj21efNmFS1aVDExMWrZsqUqVqyohx56SJcuXdI333yj4cOHZ2r9lStX1v79+zV37lw1aNBAixcv1pdffpnlNvTs2VPDhg1TsWLFFBoaqpiYGHl4eNg/3a5SpYq6deumHj166K233lKdOnX0zz//aMWKFYqOjlb79u2z3Hdp9X/44YeqX7++Tp48qWHDhqU7I65cuXJasWKFbrvtNvn6+qpo0aIaNWqUOnTooLJly+r++++Xh4eHfvvtN23dulWvvPJKtmoBAOBqNxojq1atqrZt2+qJJ57Q1KlT5eXlpaFDh2Z4dve8efNUv3593X777ZozZ47Wr1+vmTNnOswzefJkVa5cWdWqVdOECRN0/Phx9e7dW5I0aNAgzZgxQw8//LCeffZZFStWTHv27NHcuXP13nvvXfPsrcGDB2vSpEl66KGHNGLECAUFBemnn35Sw4YNVbVqVY0ZM0ZDhgxRUFCQ2rZtq/Pnz+vXX3/V8ePH9Z///CdT/VSuXDmdPn1aK1asUK1atVSoUCEulQYAsLvzzjs1e/ZsdezYUcHBwRo1alSWzjrO7hiYWS+99JKKFy+ukiVLauTIkQoJCVGXLl0ynHfYsGF64IEHVKdOHbVq1Upff/215s+fr+XLlzvM17dvX3Xo0EEpKSkOAXt2OasPMjq+zkjRokVVvHhxTZ8+XWFhYdq/f7+ee+45h3lCQ0Pl7++vb7/9VqVLl5afn5+CgoKcsm8B5AUe7i4AyO1efvllvfjiixo7dqyqVaumtm3bavHixSpXrpz69Omj0aNHq27dupKkMWPGqGTJkurfv78k6Y477tC8efO0cOFC1a5dW3feeafWr1+f6XV36tRJTz31lAYPHqzatWvrxx9/1IsvvpjlNrz99ttq3LixOnTooFatWum2225TtWrVHC51Ehsbqx49eujpp59W1apV1aVLF4cz77Jj5syZOn78uOrWravu3btryJAhCg0NdZjnrbfe0rJly1SmTBn7GXlt2rTRokWL9N1336lBgwa69dZbNWHCBEVERGS7FgAAMnKjMTI2Nlbh4eFq3ry57r33Xj3++OPpxjLp8j7A3LlzFR0drQ8++ECffPKJw1lpkjRu3DiNGzdOtWrV0g8//KCFCxcqJCREkhQeHq5169Y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26a+//rqRZBYsWHDNtqeJiIhw6PO01+7KlSvTzQugYGP/4fpctf9wvRquN+adPXs2XX01atQwd955p31afHy8kWQGDhzoMO8jjzySbtzo06ePCQsLM0eOHHGY96GHHjJBQUH29aVtl1tuucWcPHnSPt9nn31mJJmJEydes0ZjjBk7dqyx2Wxm37599mkZjZkZPbZNmzamQoUKDtMiIiKMJLNmzRr7tKSkJOPr62uefvpp+7RRo0YZSWb+/Pnplpu2r/Lhhx8aDw8Ps3btWof7p02bZiSZdevWpXssAORWjOvX5+pc4J9//snw/quP/Q4dOmRKlChhJJnIyEjTv39/8/HHH5sTJ05cdz0ZHUNea78hs2N8Zo//n3zySRMYGGguXbp0zfqycjycNpZ/++23DvOeOHHC+Pn5meHDhztMHzJkiAkICDCnT5++5voBZ+DyLPnY5MmTtWzZMi1btkwfffSRWrRoob59+2r+/Pn2eb755ht5enpqyJAhDo99+umnZYxx+FXt0aNHKyoqSj179tTAgQPVvHnzdI+7nt69e6tEiRIKDw9X+/bt7ZfquPL6alc6ePCg4uPj1atXLxUrVsw+PTo6WnfddZe++eabTK/7Sr/++quSkpI0cOBAh686tW/fXpGRkVq8eHG2lpumb9++9v97enqqfv36MsaoT58+9unBwcGqWrWqw6+Ee3p6ysfHR9LlS50cO3ZMly5dUv369bVx48Z063nwwQdVtGhR++2mTZtKktN+eTwrNX3xxRey2WwZ/oioK37Bu3nz5lm6/nr//v0dbjdt2lRHjx7VyZMnJcn+lbWBAwc6zPd///d/DrdTUlL03XffqUuXLqpQoYJ9elhYmB555BH98MMP9mU60+OPP+7w7YEBAwbIy8sr268BALge9h8y5ur9h2u51ph35XU+jx8/ruTkZDVt2tRhfE5r69X9ffVZ48YYffHFF+rYsaOMMTpy5Ij9r02bNkpOTk63L9KjRw8VKVLEfvv+++9XWFiYQ/9eWeOZM2d05MgRNWnSRMYYbdq06brtvvKxaWdJNm/eXL///ruSk5Md5q1evbp9P0i6fFbh1ftZX3zxhWrVqqV77rkn3brS9lXmzZunatWqKTIy0qEP0i4dkNHX0wEgt2Ncz5i7xvWrlSxZUr/99pv69++v48ePa9q0aXrkkUcUGhqql19+2X55nMy6er8hK2N8Zo//g4ODdebMGS1btswJPXBZ+fLl011uJSgoSJ07d9Ynn3xi74eUlBR9+umn6tKliwICApy2fiAjXJ4lH2vYsKHDwPPwww+rTp06Gjx4sDp06CAfHx/t27dP4eHhDgc90v8u17Bv3z77NB8fH82aNUsNGjSQn5+fYmNjsxSIjho1Sk2bNpWnp6dCQkJUrVq1dNdRu1LauqtWrZruvmrVqmnp0qU6c+ZMlt8or7fcyMhI/fDDD1la3tXKli3rcDsoKEh+fn72r4ddOf3o0aMO095//3299dZb2rlzpy5evGifXr58+RuuJy1Av/pa1zcrMzXt3btX4eHhDjsxrpRRf1zP9foqMDBQ+/btk4eHR7rlVqpUyeH2P//8o7Nnz17zOZmamqo///zT/lVwZ6lcubLD7cKFCyssLOyG1+cDgOxg/yHry3XG/sO1XGvMW7RokV555RXFx8c7XG/7yr5NG9+u/hr51W34559/dOLECU2fPl3Tp0/PcH1pP4KW5uqxyWazqVKlSg5j0/79+zVq1CgtXLgw3f7J1cH31datW6eYmBjFxcWl+x2S5ORkBQUF2W9fPc5Ll8f6K9e5d+9e3Xfffddd5+7du7Vjx45rfpX/6j4AgLyAcT3ry3XluC6lP7EsLCxMU6dO1ZQpU7R7924tXbpU48eP16hRoxQWFuZwYt6NXL3fkNUxPjPH/wMHDtRnn32mdu3a6ZZbblHr1q31wAMPqG3btpmu80Z1p+nRo4c+/fRTrV27Vs2aNdPy5ct1+PBhde/ePdvrAjKL0LwA8fDwUIsWLTRx4kTt3r07W8He0qVLJUn//vuvdu/enaXwsmbNmmrVqlWW15nXZHQdt2td2+3KT40/+ugj9erVS126dNGwYcMUGhoqT09PjR071n6dt6wu02azZfjJdGZ/oCWrNWXXtXayrlVnVn9FOzN95WxZbZOr5PT6AOQ/7D+4V0Zj3tq1a9WpUyc1a9ZMU6ZMUVhYmLy9vRUbG6uPP/44y+tITU2VJD366KPXvJ5qdHR0lpaZkpKiu+66S8eOHdPw4cMVGRmpgIAAHThwQL169bKvMyN79+5Vy5YtFRkZqbfffltlypSRj4+PvvnmG02YMCHdY501zqempqpmzZr26/RerUyZMllaHgDkRozrrpV21vq1fvT67Nmz1/xxT5vNpipVqqhKlSpq3769KleurDlz5mQpNL96vyErY3xmj/9DQ0MVHx+vpUuXasmSJVqyZIliY2PVo0cPvf/++/a2ZCSrx/ht2rRRyZIl9dFHH6lZs2b66KOPVKpUqXz9HELuQWhewFy6dEnS5R9Dki7/6MTy5ct16tQph0+Vd+7cab8/zebNm/XSSy/pscceU3x8vPr27astW7Y4nOnjTGnrTkhISHffzp07FRISYv80OSufbF+53LSv26ZJSEhwaHNO+vzzz1WhQgXNnz/foT0ZXfIks4oWLZrh5VquPFPAGTVVrFhRS5cu1bFjx657tvm1tlPRokV14sSJbNd5syIiIpSamqrExESHM+f27NnjMF+JEiVUqFChaz4nPTw87AfUaWeznzhxQsHBwfb5MmrTjZ6/u3fvVosWLey3T58+rYMHD+ruu++2T8uoDy9cuKCDBw9maV0AkBH2H1y3/5Cd9+UvvvhCfn5+Wrp0qXx9fe3TY2NjHeZLG9/27t3rcCbd1X1TokQJFSlSRCkpKZk+CN29e7fDbWOM9uzZYz/w3rJli3bt2qX3339fPXr0sM+Xma9yf/311zp//rwWLlzocBb5zVwepWLFitq6desN5/ntt9/UsmVLxksA+RrjuuvG9SuXe/WHrWfPntWff/6p1q1b33A5FSpUUNGiRdMdz2VVVsb4rGQSPj4+6tixozp27KjU1FQNHDhQ7777rl588UVVqlQpS8fD1+Pp6alHHnlEs2fP1vjx4/XVV1+pX79+2frRWSCruKZ5AXLx4kV999138vHxsX/N6u6771ZKSor+3//7fw7zTpgwQTabTe3atbM/tlevXgoPD9fEiRM1e/ZsHT58WE899ZTL6g0LC1Pt2rX1/vvvO4SBW7du1XfffecQGKYNkhkFr1erX7++QkNDNW3aNIevMy9ZskQ7duxQ+/btndaGrEh707/yrKiff/5ZcXFx2V5mxYoVtXPnTv3zzz/2ab/99pvWrVvn1Jruu+8+GWM0ZsyYdMu48rEBAQEZbqOKFSsqOTlZmzdvtk87ePCgvvzyy0zVebPSrp02ZcoUh+mTJk1yuO3p6anWrVtrwYIFDl8/P3z4sD7++GPdfvvtCgwMlCT7V+HXrFljny/ten1Xu1a/pJk+fbrDV+OmTp2qS5cu2V+faeu7cl1pj7v6k/ysvFYAQGL/IY2r9h+y877s6ekpm83m8B7/xx9/6KuvvnKYL207/Pe//3WY/s4776Rb3n333acvvvgiw2D5yv2INB988IFOnTplv/3555/r4MGD9nVmtA9hjNHEiRMz1b6rH5ucnJzuQ4GsuO+++/Tbb79luG+Rtp4HHnhABw4c0IwZM9LNc+7cOZ05cybb6weA3IJx/TJXjestW7aUj4+Ppk6dmu6bUdOnT093HPfzzz9nOL6sX79eR48ezfDyMVmRlTE+s8f/V19m1sPDw/6heVpfZuV4+Ea6d++u48eP64knntDp06f16KOPZnkZQHZwpnk+tmTJEvsnw0lJSfr444+1e/duPffcc/Zgr2PHjmrRooVGjhypP/74Q7Vq1dJ3332nBQsWaOjQofY3urRrZq5YsUJFihRRdHS0Ro0apRdeeEH333+/w0DlTG+88YbatWunxo0bq0+fPjp37pwmTZqkoKAgjR492j5fvXr1JEkjR47UQw89JG9vb3Xs2DHD65p5e3tr/Pjxeuyxx9S8eXM9/PDDOnz4sCZOnKhy5cq5dMC/ng4dOmj+/Pm655571L59eyUmJmratGmqXr26/QyArOrdu7fefvtttWnTRn369FFSUpKmTZumqKioTP1gZWZratGihbp3767//ve/2r17t9q2bavU1FStXbtWLVq00ODBgyVd3k7Lly/X22+/rfDwcJUvX16NGjXSQw89pOHDh+uee+7RkCFDdPbsWU2dOlVVqlTJ8EdQna1evXq677779M477+jo0aO69dZbtXr1au3atUuS4xkLr7zyipYtW6bbb79dAwcOlJeXl959912dP39er7/+un2+1q1bq2zZsurTp4+GDRsmT09PzZo1SyVKlND+/fvTrX/q1Kl65ZVXVKlSJYWGhjqc7XDhwgW1bNlSDzzwgBISEjRlyhTdfvvt6tSpk32evn37qn///rrvvvt011136bffftPSpUvTXUu/du3a8vT01Pjx45WcnCxfX1/deeedCg0NdWqfAsi72H/I2f2HrNSQpn379nr77bfVtm1bPfLII0pKStLkyZNVqVIlhw+ga9eurYcfflhTpkxRcnKymjRpohUrVqT7JpUkjRs3TitXrlSjRo3Ur18/Va9eXceOHdPGjRu1fPlyHTt2zGH+YsWK6fbbb9djjz2mw4cP65133lGlSpXUr18/SZevB1uxYkU988wzOnDggAIDA/XFF19k6rdXWrdubT+DLe0AecaMGQoNDc32GXfDhg3T559/rq5du6p3796qV6+ejh07poULF2ratGmqVauWunfvrs8++0z9+/fXypUrddtttyklJUU7d+7UZ599pqVLl17zh+oAILdiXM/ZcT00NNTeJ82aNVOnTp1UqFAh/fjjj/rkk0/UunVrdezY0T7/hx9+qDl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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Выбираем столбцы для очистки\n",
+    "columns_to_clean = ['index price', 'log_indexprice',\n",
+    "       'inflationrate', 'exchange_rate', 'gdppercent', 'unemploymentrate', 'manufacturingoutput',\n",
+    "       'tradebalance', 'USTreasury']\n",
+    "\n",
+    "# Удаляем выбросы\n",
+    "df_countries_clean = remove_outliers(df_countries, columns_to_clean)\n",
+    "\n",
+    "# Выводим количество удаленных строк\n",
+    "print(f\"Количество удаленных строк: {len(df_countries) - len(df_countries_clean)}\")\n",
+    "\n",
+    "# Создаем диаграммы размаха для очищенных данных\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "\n",
+    "# Создаем диаграммы размахов\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_clean, 1):\n",
+    "    plt.subplot(3, 3, i)\n",
+    "    sns.boxplot(x=df_countries_clean[col])\n",
+    "    plt.title(f'Box Plot of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "df_countries = df_countries_clean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 49\n",
+      "Размер контрольной выборки: 17\n",
+      "Размер тестовой выборки: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df, test_df = train_test_split(df_countries, 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))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение \"Уровень инфляции\" в обучающей выборке:\n",
+      "inflationrate\n",
+      "0.02    25\n",
+      "0.03    11\n",
+      "0.01     9\n",
+      "0.04     4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в контрольной выборке:\n",
+      "inflationrate\n",
+      "0.03    6\n",
+      "0.01    6\n",
+      "0.02    5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в тестовой выборке:\n",
+      "inflationrate\n",
+      "0.02    6\n",
+      "0.03    6\n",
+      "0.01    4\n",
+      "0.04    1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_balance(df, name):\n",
+    "    counts = df['inflationrate'].value_counts()\n",
+    "    print(f\"Распределение \\\"Уровень инфляции\\\" в {name}:\")\n",
+    "    print(counts)\n",
+    "    print()\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": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Оверсэмплинг:\n",
+      "Распределение \"Уровень инфляции\" в обучающей выборке:\n",
+      "inflationrate\n",
+      "0.03    26\n",
+      "0.02    25\n",
+      "0.01     9\n",
+      "0.04     8\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в контрольной выборке:\n",
+      "inflationrate\n",
+      "0.03    11\n",
+      "0.01     6\n",
+      "0.02     5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в тестовой выборке:\n",
+      "inflationrate\n",
+      "0.03    8\n",
+      "0.02    6\n",
+      "0.01    4\n",
+      "0.04    2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Андерсэмплинг:\n",
+      "Распределение \"Уровень инфляции\" в обучающей выборке:\n",
+      "inflationrate\n",
+      "0.03    11\n",
+      "0.02    10\n",
+      "0.01     5\n",
+      "0.04     4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в контрольной выборке:\n",
+      "inflationrate\n",
+      "0.03    6\n",
+      "0.01    4\n",
+      "0.02    2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Распределение \"Уровень инфляции\" в тестовой выборке:\n",
+      "inflationrate\n",
+      "0.03    6\n",
+      "0.02    5\n",
+      "0.01    2\n",
+      "0.04    1\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def binning(target, bins):\n",
+    "    return pd.qcut(target, q=bins, labels=False)\n",
+    "\n",
+    "train_df['inflationrate_binned'] = binning(train_df['inflationrate'], bins=2)\n",
+    "val_df['inflationrate_binned'] = binning(val_df['inflationrate'], bins=2)\n",
+    "test_df['inflationrate_binned'] = binning(test_df['inflationrate'], bins=2)\n",
+    "\n",
+    "train_df_oversampled = oversample(train_df, 'inflationrate_binned')\n",
+    "val_df_oversampled = oversample(val_df, 'inflationrate_binned')\n",
+    "test_df_oversampled = oversample(test_df, 'inflationrate_binned')\n",
+    "\n",
+    "train_df_undersampled = undersample(train_df, 'inflationrate_binned')\n",
+    "val_df_undersampled = undersample(val_df, 'inflationrate_binned')\n",
+    "test_df_undersampled = undersample(test_df, 'inflationrate_binned')\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\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimvenv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab_2/requirements.txt b/lab_2/requirements.txt
new file mode 100644
index 0000000..649b7fd
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