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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Датасет: Цены на акции\n",
+    "https://www.kaggle.com/datasets/nancyalaswad90/yamana-gold-inc-stock-Volume\n",
+    "##### О наборе данных: \n",
+    "Yamana Gold Inc. — это канадская компания, которая занимается разработкой и управлением золотыми, серебряными и медными рудниками, расположенными в Канаде, Чили, Бразилии и Аргентине. Головной офис компании находится в Торонто.\n",
+    "\n",
+    "Yamana Gold была основана в 1994 году и уже через год была зарегистрирована на фондовой бирже Торонто. В 2007 году она стала участником Нью-Йоркской фондовой биржи, а в 2020 году — Лондонской.\n",
+    "В 2003 году компания претерпела значительные изменения: была проведена реструктуризация, в результате которой Питер Марроне занял пост главного исполнительного директора. Кроме того, Yamana объединилась с бразильской компанией Santa Elina Mines Corporation. Благодаря этому слиянию Yamana получила доступ к капиталу, накопленному Santa Elina, что позволило ей начать разработку и эксплуатацию рудника Чапада. Затем компания объединилась с другими организациями, зарегистрированными на бирже TSX: RNC Gold, Desert Sun Mining, Viceroy Exploration, Northern Orion Resources, Meridian Gold, Osisko Mining и Extorre Gold Mines. Каждая из них внесла свой вклад в разработку месторождения или проект, который в итоге был успешно запущен.\n",
+    "##### Таким образом:\n",
+    "* Объект наблюдения - цены и объемы акций компании\n",
+    "* Атрибуты: 'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'\n",
+    "\n",
+    "##### Бизнес цели:\n",
+    "*  Прогнозирование будущей цены акций.\n",
+    "        Использование данных для создания модели, которая будет предсказывать цену акций компании в будущем.\n",
+    "*  Определение волатильности акций.\n",
+    "        Определение, колебаний цен акций, что поможет инвесторам понять риски.\n",
+    "\n",
+    "##### Технические цели:\n",
+    "*  Разработать модель машинного обучения для прогноза цены акций на основе имеющихся данных.\n",
+    "*  Разработать метрику и модель для оценки волатильности акций на основе исторических данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество колонок: 7\n",
+      "Колонки: Date, Open, High, Low, Close, Adj Close, Volume\n",
+      "\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5251 entries, 0 to 5250\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype         \n",
+      "---  ------     --------------  -----         \n",
+      " 0   Date       5251 non-null   datetime64[ns]\n",
+      " 1   Open       5251 non-null   float64       \n",
+      " 2   High       5251 non-null   float64       \n",
+      " 3   Low        5251 non-null   float64       \n",
+      " 4   Close      5251 non-null   float64       \n",
+      " 5   Adj Close  5251 non-null   float64       \n",
+      " 6   Volume     5251 non-null   int64         \n",
+      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
+      "memory usage: 287.3 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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2001-06-22</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>2.806002</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2001-06-25</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>3.428571</td>\n",
+       "      <td>2.806002</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2001-06-26</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.039837</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2001-06-27</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.039837</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2001-06-28</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.714286</td>\n",
+       "      <td>3.039837</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date      Open      High       Low     Close  Adj Close  Volume\n",
+       "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
+       "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
+       "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
+       "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
+       "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837       0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\".//static//csv//Stocks.csv\", sep=\",\")\n",
+    "print('Количество колонок: ' + str(df.columns.size))  \n",
+    "print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
+    "df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
+    "\n",
+    "df.info()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Подготовка данных:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1. Получение сведений о пропущенных данных\n",
+    "Типы пропущенных данных:\n",
+    "\n",
+    "- None - представление пустых данных в Python\n",
+    "- NaN - представление пустых данных в Pandas\n",
+    "- '' - пустая строка"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date         0\n",
+      "Open         0\n",
+      "High         0\n",
+      "Low          0\n",
+      "Close        0\n",
+      "Adj Close    0\n",
+      "Volume       0\n",
+      "dtype: int64\n",
+      "\n",
+      "Date         False\n",
+      "Open         False\n",
+      "High         False\n",
+      "Low          False\n",
+      "Close        False\n",
+      "Adj Close    False\n",
+      "Volume       False\n",
+      "dtype: bool\n",
+      "\n",
+      "Количество бесконечных значений в каждом столбце:\n",
+      "Date         0\n",
+      "Open         0\n",
+      "High         0\n",
+      "Low          0\n",
+      "Close        0\n",
+      "Adj Close    0\n",
+      "Volume       0\n",
+      "dtype: int64\n",
+      "Date процент пустых значений: %0.00\n",
+      "Open процент пустых значений: %0.00\n",
+      "High процент пустых значений: %0.00\n",
+      "Low процент пустых значений: %0.00\n",
+      "Close процент пустых значений: %0.00\n",
+      "Adj Close процент пустых значений: %0.00\n",
+      "Volume процент пустых значений: %0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "print()\n",
+    "\n",
+    "# Проверка на бесконечные значения\n",
+    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
+    "print(np.isinf(df).sum())\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Таким образом, пропущенных значений не найдено."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Проверка выбросов данных и устранение их при наличии:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "До устранения выбросов:\n",
+      "Колонка Open:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.42\n",
+      "  1-й квартиль (Q1): 2.857143\n",
+      "  3-й квартиль (Q3): 10.65\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка Open:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.42\n",
+      "  1-й квартиль (Q1): 2.857143\n",
+      "  3-й квартиль (Q3): 10.65\n",
+      "\n",
+      "До устранения выбросов:\n",
+      "Колонка High:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.59\n",
+      "  1-й квартиль (Q1): 2.88\n",
+      "  3-й квартиль (Q3): 10.86\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка High:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.59\n",
+      "  1-й квартиль (Q1): 2.88\n",
+      "  3-й квартиль (Q3): 10.86\n",
+      "\n",
+      "До устранения выбросов:\n",
+      "Колонка Low:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.09\n",
+      "  1-й квартиль (Q1): 2.81\n",
+      "  3-й квартиль (Q3): 10.425\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка Low:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.09\n",
+      "  1-й квартиль (Q1): 2.81\n",
+      "  3-й квартиль (Q3): 10.425\n",
+      "\n",
+      "До устранения выбросов:\n",
+      "Колонка Close:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.389999\n",
+      "  1-й квартиль (Q1): 2.857143\n",
+      "  3-й квартиль (Q3): 10.64\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка Close:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 1.142857\n",
+      "  Максимальное значение: 20.389999\n",
+      "  1-й квартиль (Q1): 2.857143\n",
+      "  3-й квартиль (Q3): 10.64\n",
+      "\n",
+      "До устранения выбросов:\n",
+      "Колонка Adj Close:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 0.935334\n",
+      "  Максимальное значение: 17.543156\n",
+      "  1-й квартиль (Q1): 2.537094\n",
+      "  3-й квартиль (Q3): 8.951944999999998\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка Adj Close:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 0.935334\n",
+      "  Максимальное значение: 17.543156\n",
+      "  1-й квартиль (Q1): 2.537094\n",
+      "  3-й квартиль (Q3): 8.951944999999998\n",
+      "\n",
+      "До устранения выбросов:\n",
+      "Колонка Volume:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 95\n",
+      "  Минимальное значение: 0\n",
+      "  Максимальное значение: 76714000\n",
+      "  1-й квартиль (Q1): 2845900.0\n",
+      "  3-й квартиль (Q3): 13272450.0\n",
+      "\n",
+      "После устранения выбросов:\n",
+      "Колонка Volume:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 0.0\n",
+      "  Максимальное значение: 28912275.0\n",
+      "  1-й квартиль (Q1): 2845900.0\n",
+      "  3-й квартиль (Q3): 13272450.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_columns = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
+    "\n",
+    "for column in numeric_columns:\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
+    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
+    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
+    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
+    "\n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        print(\"До устранения выбросов:\")\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n",
+    "\n",
+    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
+    "        if outlier_count != 0:\n",
+    "            df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
+    "        \n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        print(\"После устранения выбросов:\")\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выбросы присутствовали, но мы их устранили."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Разбиение на выборки:\n",
+    "\n",
+    "Разобьем наш набор на обучающую, контрольную и тестовую выборки для устранения проблемы просачивания данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки:  4200\n",
+      "Размер контрольной выборки:  1051\n",
+      "Размер тестовой выборки:  1051\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
+    "X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
+    "X_train, X_val = train_test_split(df, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки: \", len(X_train))\n",
+    "print(\"Размер контрольной выборки: \", len(X_test))\n",
+    "print(\"Размер тестовой выборки: \", len(X_val))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Гистограмма распределения цены закрытия в обучающей выборке\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.histplot(X_train['Close'], bins=30, kde=False)\n",
+    "plt.title(\"Распределение классов (до балансировки)\")\n",
+    "plt.xlabel('Целевая переменная: Close')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "# Гистограмма распределения цены закрытия в контрольной выборке\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.histplot(X_val['Close'], bins=30, kde=False)\n",
+    "plt.title(\"Распределение классов (до балансировки)\")\n",
+    "plt.xlabel('Целевая переменная: Close')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "# Гистограмма распределения цены закрытия в тестовой выборке\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.histplot(X_test['Close'], bins=30, kde=False)\n",
+    "plt.title(\"Распределение классов (до балансировки)\")\n",
+    "plt.xlabel('Целевая переменная: Close')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Применим овер- и андерсемплинг к обучающей выборке:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Date   Open   High    Low      Close  Adj Close      Volume  \\\n",
+      "4789 2020-07-08   5.66   5.73   5.47   5.560000   5.341250  23355100.0   \n",
+      "3469 2015-04-10   3.86   3.93   3.81   3.880000   3.513961   7605300.0   \n",
+      "2503 2011-06-07  12.19  12.28  11.95  12.020000  10.138681   7243200.0   \n",
+      "1580 2007-10-08  11.77  11.84  11.53  11.570000   9.509553   3025900.0   \n",
+      "2759 2012-06-12  15.77  16.17  15.76  16.120001  13.771020   6113400.0   \n",
+      "\n",
+      "     closePrice_category  \n",
+      "4789                high  \n",
+      "3469              medium  \n",
+      "2503           very_high  \n",
+      "1580           very_high  \n",
+      "2759           very_high  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки до oversampling и undersampling:  4200\n",
+      "Размер обучающей выборки после oversampling и undersampling:  4232\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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-07-08</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>5.73</td>\n",
+       "      <td>5.47</td>\n",
+       "      <td>5.56</td>\n",
+       "      <td>5.341250</td>\n",
+       "      <td>23355100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>2021-01-19</td>\n",
+       "      <td>5.15</td>\n",
+       "      <td>5.15</td>\n",
+       "      <td>5.02</td>\n",
+       "      <td>5.13</td>\n",
+       "      <td>4.966732</td>\n",
+       "      <td>15906300.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>2010-04-08</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>10.48</td>\n",
+       "      <td>10.52</td>\n",
+       "      <td>8.794909</td>\n",
+       "      <td>10456400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>2020-12-07</td>\n",
+       "      <td>5.47</td>\n",
+       "      <td>5.80</td>\n",
+       "      <td>5.47</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>5.541336</td>\n",
+       "      <td>12929600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>6.15</td>\n",
+       "      <td>6.16</td>\n",
+       "      <td>5.98</td>\n",
+       "      <td>6.04</td>\n",
+       "      <td>5.847770</td>\n",
+       "      <td>15080900.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date   Open   High    Low  Close  Adj Close      Volume\n",
+       "0  2020-07-08   5.66   5.73   5.47   5.56   5.341250  23355100.0\n",
+       "20 2021-01-19   5.15   5.15   5.02   5.13   4.966732  15906300.0\n",
+       "21 2010-04-08  10.60  10.65  10.48  10.52   8.794909  10456400.0\n",
+       "24 2020-12-07   5.47   5.80   5.47   5.75   5.541336  12929600.0\n",
+       "28 2021-01-05   6.15   6.16   5.98   6.04   5.847770  15080900.0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
+    "X_train['closePrice_category'] = pd.qcut(X_train['Close'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n",
+    "print(X_train.head())\n",
+    "\n",
+    "# Визуализация распределения цен после преобразования в категории\n",
+    "sns.countplot(x=X_train['closePrice_category'])\n",
+    "plt.title('Распределение категорий закрывающей цены в обучающей выборке')\n",
+    "plt.xlabel('Категория закрывающей цены')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n",
+    "ros = RandomOverSampler(random_state=42)\n",
+    "y_train = X_train['closePrice_category']\n",
+    "X_train = X_train.drop(columns=['closePrice_category'])\n",
+    "\n",
+    "\n",
+    "# Применяем oversampling. Здесь важно, что мы используем X_train как DataFrame и y_train_categories как целевую переменную\n",
+    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
+    "\n",
+    "# Визуализация распределения цен после oversampling\n",
+    "sns.countplot(x=y_resampled)\n",
+    "plt.title('Распределение категорий закрывающей цены после oversampling')\n",
+    "plt.xlabel('Категория закрывающей цены')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "# Применение RandomUnderSampler для уменьшения большего класса\n",
+    "rus = RandomUnderSampler(random_state=42)\n",
+    "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
+    "\n",
+    "# Визуализация распределения цен после undersampling\n",
+    "sns.countplot(x=y_resampled)\n",
+    "plt.title('Распределение категорий закрывающей цены после undersampling')\n",
+    "plt.xlabel('Категория закрывающей цены')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "print(\"Размер обучающей выборки до oversampling и undersampling: \", len(X_train))\n",
+    "\n",
+    "\n",
+    "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))\n",
+    "X_resampled.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "По сути, балансировка так то  не требовалась, но все же мы ее провели, добавив в обучающую выборку 5 значений (ーー;)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Конструирование признаков\n",
+    "1. **Унитарное кодирование категориальных признаков. Преобразование категориальных признаков в бинарные векторы.**\n",
+    "* В данном датасете категориальные признаки отсутствуют, так что пропустим этот пункт.\n",
+    "2. **Дискретизация числовых признаков. Преобразование непрерывных числовых значений в дискретные категории или интервалы (бины).**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Названия столбцов в датасете:\n",
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
+      "Статистические параметры:\n",
+      "                                Date         Open         High          Low  \\\n",
+      "count                           5251  5251.000000  5251.000000  5251.000000   \n",
+      "mean   2011-12-01 11:59:51.772995840     6.863639     6.986071     6.720615   \n",
+      "min              2001-06-22 00:00:00     1.142857     1.142857     1.142857   \n",
+      "25%              2006-09-13 12:00:00     2.857143     2.880000     2.810000   \n",
+      "50%              2011-11-29 00:00:00     4.600000     4.710000     4.490000   \n",
+      "75%              2017-02-16 12:00:00    10.650000    10.860000    10.425000   \n",
+      "max              2022-05-05 00:00:00    20.420000    20.590000    20.090000   \n",
+      "std                              NaN     4.753836     4.832010     4.662891   \n",
+      "\n",
+      "             Close    Adj Close        Volume  \n",
+      "count  5251.000000  5251.000000  5.251000e+03  \n",
+      "mean      6.850606     5.895644  8.976705e+06  \n",
+      "min       1.142857     0.935334  0.000000e+00  \n",
+      "25%       2.857143     2.537094  2.845900e+06  \n",
+      "50%       4.600000     4.337419  8.216200e+06  \n",
+      "75%      10.640000     8.951945  1.327245e+07  \n",
+      "max      20.389999    17.543156  2.891228e+07  \n",
+      "std       4.746055     3.941634  7.251098e+06  \n",
+      "После дискретизации 'Close':\n",
+      "        Date      Open      High       Low     Close  Adj Close  Volume  \\\n",
+      "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
+      "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
+      "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
+      "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
+      "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
+      "\n",
+      "  Close_Disc  \n",
+      "0        2-4  \n",
+      "1        2-4  \n",
+      "2        2-4  \n",
+      "3        2-4  \n",
+      "4        2-4  \n",
+      "           Date       Open       High    Low      Close  Adj Close     Volume  \\\n",
+      "2623 2011-11-25  14.730000  15.050000  14.65  14.650000  12.429751  2433000.0   \n",
+      "2624 2011-11-28  15.150000  15.370000  15.04  15.200000  12.896397  4348600.0   \n",
+      "2625 2011-11-29  15.270000  15.710000  15.21  15.600000  13.235776  4576500.0   \n",
+      "2626 2011-11-30  16.120001  16.850000  16.07  16.830000  14.279361  9537100.0   \n",
+      "2627 2011-12-01  16.770000  16.940001  16.58  16.809999  14.262395  5111500.0   \n",
+      "\n",
+      "     Close_Disc  \n",
+      "2623      14-16  \n",
+      "2624      14-16  \n",
+      "2625      14-16  \n",
+      "2626        16+  \n",
+      "2627        16+  \n",
+      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
+      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
+      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
+      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
+      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
+      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Пример дискретизации по цене закрытия\n",
+    "# Проверка на наличие числовых признаков\n",
+    "print(\"Названия столбцов в датасете:\")\n",
+    "print(df.columns)\n",
+    "\n",
+    "# Выводим основные статистические параметры для количественных признаков\n",
+    "print(\"Статистические параметры:\")\n",
+    "print(df.describe())\n",
+    "\n",
+    "# Дискретизация столбца 'Close' на группы\n",
+    "bins = [0, 2, 4, 6, 8, 10, 12, 14, 16, 30]  # Определяем границы корзин\n",
+    "labels = ['0-2', '2-4', '4-6', '6-8', '8-10', '10-12', '12-14', '14-16', '16+']  # Названия категорий\n",
+    "\n",
+    "# Создание нового столбца 'Close_Disc' на основе дискретизации\n",
+    "df['Close_Disc'] = pd.cut(df['Close'], bins=bins, labels=labels, include_lowest=True)\n",
+    "\n",
+    "# Проверка результата\n",
+    "print(\"После дискретизации 'Close':\")\n",
+    "print(df.head())\n",
+    "n = len(df)\n",
+    "middle_index = n // 2\n",
+    "print(df.iloc[middle_index - 2: middle_index + 3])\n",
+    "print(df.tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Конструирование новых признаков:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Исходный датасет: \n",
+      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
+      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
+      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
+      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
+      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
+      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n",
+      "\n",
+      "Обучающая выборка: \n",
+      "           Date       Open   High        Low      Close  Adj Close      Volume\n",
+      "2435 2011-04-14  12.530000  12.84  12.480000  12.750000  10.754427  10527200.0\n",
+      "1756 2013-05-30  11.510000  11.76  11.480000  11.720000  10.166282   9028100.0\n",
+      "3296 2009-11-20  13.100000  13.28  12.870000  13.220000  11.031483  17024900.0\n",
+      "1243 2012-09-17  18.870001  19.00  18.469999  18.870001  16.178450   6652400.0\n",
+      "343  2006-12-12  12.920000  13.00  12.580000  12.800000  10.487218   3981100.0\n",
+      "\n",
+      "Тестовая выборка: \n",
+      "           Date       Open       High        Low      Close  Adj Close  \\\n",
+      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
+      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
+      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
+      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
+      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
+      "\n",
+      "          Volume  \n",
+      "3095   5861400.0  \n",
+      "859     211300.0  \n",
+      "3134   5335400.0  \n",
+      "2577  14524400.0  \n",
+      "378          0.0  \n",
+      "\n",
+      "Контрольная выборка: \n",
+      "           Date       Open       High        Low      Close  Adj Close  \\\n",
+      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
+      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
+      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
+      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
+      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
+      "\n",
+      "          Volume  \n",
+      "3095   5861400.0  \n",
+      "859     211300.0  \n",
+      "3134   5335400.0  \n",
+      "2577  14524400.0  \n",
+      "378          0.0  \n",
+      "\n",
+      "Новые признаки в обучающей выборке:\n",
+      "      Volume_Change\n",
+      "2435       0.977868\n",
+      "1756      -0.142403\n",
+      "3296       0.885768\n",
+      "1243      -0.609255\n",
+      "343       -0.401554\n",
+      "\n",
+      "Новые признаки в тестовой выборке:\n",
+      "      Volume_Change\n",
+      "3095            inf\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n",
+      "\n",
+      "Новые признаки в контрольной выборке:\n",
+      "      Volume_Change\n",
+      "3095            inf\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n",
+      "\n",
+      "Новые признаки в датасете:\n",
+      "      Volume_Change\n",
+      "5246      -0.218393\n",
+      "5247       0.631626\n",
+      "5248      -0.302232\n",
+      "5249       0.085465\n",
+      "5250      -0.031733\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nИсходный датасет: ')\n",
+    "print(df.tail())\n",
+    "print('\\nОбучающая выборка: ')\n",
+    "print(X_resampled.tail())\n",
+    "print('\\nТестовая выборка: ')\n",
+    "print(X_test.tail())\n",
+    "print('\\nКонтрольная выборка: ')\n",
+    "print(X_val.tail())\n",
+    "\n",
+    "#Объем изменений\n",
+    "df['Volume_Change'] = df['Volume'].pct_change()\n",
+    "X_resampled['Volume_Change'] = X_resampled['Volume'].pct_change()\n",
+    "X_test['Volume_Change'] = X_test['Volume'].pct_change()\n",
+    "X_val['Volume_Change'] = X_val['Volume'].pct_change()\n",
+    "\n",
+    "# Проверка создания новых признаков\n",
+    "print(\"\\nНовые признаки в обучающей выборке:\")\n",
+    "print(X_resampled[['Volume_Change']].tail())\n",
+    "\n",
+    "print(\"\\nНовые признаки в тестовой выборке:\")\n",
+    "print(X_test[['Volume_Change']].tail())\n",
+    "\n",
+    "print(\"\\nНовые признаки в контрольной выборке:\")\n",
+    "print(X_val[['Volume_Change']].tail())\n",
+    "\n",
+    "print(\"\\nНовые признаки в датасете:\")\n",
+    "print(df[['Volume_Change']].tail())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Проверим новые признаки:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Исходный датасет: \n",
+      "Volume_Change    501\n",
+      "dtype: int64\n",
+      "\n",
+      "Обучающая выборка: \n",
+      "Volume_Change    102\n",
+      "dtype: int64\n",
+      "\n",
+      "Тестовая выборка: \n",
+      "Volume_Change    16\n",
+      "dtype: int64\n",
+      "\n",
+      "Контрольная выборка: \n",
+      "Volume_Change    16\n",
+      "dtype: int64\n",
+      "\n",
+      "Есть ли пустые значения признаков: \n",
+      "\n",
+      "Исходный датасет: \n",
+      "Volume_Change    True\n",
+      "dtype: bool\n",
+      "\n",
+      "Обучающая выорка: \n",
+      "Volume_Change    True\n",
+      "dtype: bool\n",
+      "\n",
+      "Тестовая выборка: \n",
+      "Volume_Change    True\n",
+      "dtype: bool\n",
+      "\n",
+      "Контрольная выборка: \n",
+      "Volume_Change    True\n",
+      "dtype: bool\n",
+      "\n",
+      "Количество бесконечных значений в каждом столбце:\n",
+      "\n",
+      "Исходный датасет: \n",
+      "Volume_Change    32\n",
+      "dtype: int64\n",
+      "\n",
+      "Обучающая выборка: \n",
+      "Volume_Change    310\n",
+      "dtype: int64\n",
+      "\n",
+      "Тестовая выборка: \n",
+      "Volume_Change    107\n",
+      "dtype: int64\n",
+      "\n",
+      "Контрольная выборка: \n",
+      "Volume_Change    107\n",
+      "dtype: int64\n",
+      "Volume_Change процент пустых значений в датасете: %9.54\n",
+      "Volume_Change процент пустых значений в обучающей выборке: %2.41\n",
+      "Volume_Change процент пустых значений в тестовой выборке: %1.52\n",
+      "Volume_Change процент пустых значений в контрольной выборке: %1.52\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nИсходный датасет: ')\n",
+    "print(df[['Volume_Change']].isnull().sum())\n",
+    "print('\\nОбучающая выборка: ')\n",
+    "print(X_resampled[['Volume_Change']].isnull().sum())\n",
+    "print('\\nТестовая выборка: ')\n",
+    "print(X_test[['Volume_Change']].isnull().sum())\n",
+    "print('\\nКонтрольная выборка: ')\n",
+    "print(X_val[['Volume_Change']].isnull().sum())\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print('Есть ли пустые значения признаков: ')\n",
+    "print('\\nИсходный датасет: ')\n",
+    "print(df[['Volume_Change']].isnull().any())\n",
+    "print('\\nОбучающая выорка: ')\n",
+    "print(X_resampled[['Volume_Change']].isnull().any())\n",
+    "print('\\nТестовая выборка: ')\n",
+    "print(X_test[['Volume_Change']].isnull().any())\n",
+    "print('\\nКонтрольная выборка: ')\n",
+    "print(X_val[['Volume_Change']].isnull().any())\n",
+    "print()\n",
+    "\n",
+    "# Проверка на бесконечные значения\n",
+    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
+    "print('\\nИсходный датасет: ')\n",
+    "print(np.isinf(df[['Volume_Change']]).sum())\n",
+    "print('\\nОбучающая выборка: ')\n",
+    "print(np.isinf(X_resampled[['Volume_Change']]).sum())\n",
+    "print('\\nТестовая выборка: ')\n",
+    "print(np.isinf(X_test[['Volume_Change']]).sum())\n",
+    "print('\\nКонтрольная выборка: ')\n",
+    "print(np.isinf(X_val[['Volume_Change']]).sum())\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df[['Volume_Change']].columns:\n",
+    "    null_rate = df[['Volume_Change']][i].isnull().sum() / len(df[['Volume_Change']]) * 100\n",
+    "    print(f\"{i} процент пустых значений в датасете: %{null_rate:.2f}\")\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in X_resampled[['Volume_Change']].columns:\n",
+    "    null_rate = X_resampled[['Volume_Change']][i].isnull().sum() / len(X_resampled[['Volume_Change']]) * 100\n",
+    "    print(f\"{i} процент пустых значений в обучающей выборке: %{null_rate:.2f}\")\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in X_test[['Volume_Change']].columns:\n",
+    "    null_rate = X_test[['Volume_Change']][i].isnull().sum() / len(X_test[['Volume_Change']]) * 100\n",
+    "    print(f\"{i} процент пустых значений в тестовой выборке: %{null_rate:.2f}\")\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in X_val[['Volume_Change']].columns:\n",
+    "    null_rate = X_val[['Volume_Change']][i].isnull().sum() / len(X_val[['Volume_Change']]) * 100\n",
+    "    print(f\"{i} процент пустых значений в контрольной выборке: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Заполним пустые данные"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5251, 1)\n",
+      "(4232, 1)\n",
+      "(1051, 1)\n",
+      "(1051, 1)\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "      Volume_Change\n",
+      "5246      -0.218393\n",
+      "5247       0.631626\n",
+      "5248      -0.302232\n",
+      "5249       0.085465\n",
+      "5250      -0.031733\n",
+      "      Volume_Change\n",
+      "2435       0.977868\n",
+      "1756      -0.142403\n",
+      "3296       0.885768\n",
+      "1243      -0.609255\n",
+      "343       -0.401554\n",
+      "      Volume_Change\n",
+      "3095       0.000000\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n",
+      "      Volume_Change\n",
+      "3095       0.000000\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_21516\\2904461267.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
+      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_21516\\2904461267.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
+      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_21516\\2904461267.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
+      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_21516\\2904461267.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
+      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
+      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
+      "\n",
+      "df[\"col\"][row_indexer] = value\n",
+      "\n",
+      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "  X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Заменяем бесконечные значения на NaN\n",
+    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "X_resampled.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "X_test.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "X_val.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "\n",
+    "fillna_df = df[['Volume_Change']].fillna(0)\n",
+    "fillna_X_resampled = X_resampled[['Volume_Change']].fillna(0)\n",
+    "fillna_X_test = X_test[['Volume_Change']].fillna(0)\n",
+    "fillna_X_val = X_val[['Volume_Change']].fillna(0)\n",
+    "\n",
+    "\n",
+    "print(fillna_df.shape)\n",
+    "print(fillna_X_resampled.shape)\n",
+    "print(fillna_X_test.shape)\n",
+    "print(fillna_X_val.shape)\n",
+    "\n",
+    "print(fillna_df.isnull().any())\n",
+    "print(fillna_X_resampled.isnull().any())\n",
+    "print(fillna_X_test.isnull().any())\n",
+    "print(fillna_X_val.isnull().any())\n",
+    "\n",
+    "# Замена пустых данных на 0\n",
+    "df[\"Volume_Change\"] = df[\"Volume_Change\"].fillna(0)\n",
+    "X_resampled[\"Volume_Change\"] = X_resampled[\"Volume_Change\"].fillna(0)\n",
+    "X_test[\"Volume_Change\"] = X_test[\"Volume_Change\"].fillna(0)\n",
+    "X_val[\"Volume_Change\"] = X_val[\"Volume_Change\"].fillna(0)\n",
+    "\n",
+    "# Вычисляем медиану для колонки \"Volume_Change\"\n",
+    "median_Volume_Change_df = df[\"Volume_Change\"].median()\n",
+    "median_Volume_Change_train = X_resampled[\"Volume_Change\"].median()\n",
+    "median_Volume_Change_test = X_test[\"Volume_Change\"].median()\n",
+    "median_Volume_Change_val = X_val[\"Volume_Change\"].median()\n",
+    "\n",
+    "# Заменяем значения 0 на медиану\n",
+    "df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
+    "X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
+    "X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
+    "X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n",
+    "\n",
+    "print(df[['Volume_Change']].tail())\n",
+    "print(X_resampled[['Volume_Change']].tail())\n",
+    "print(X_test[['Volume_Change']].tail())\n",
+    "print(X_val[['Volume_Change']].tail())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удалим наблюдения с пропусками"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5251, 1)\n",
+      "(4232, 1)\n",
+      "(1051, 1)\n",
+      "(1051, 1)\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "      Volume_Change\n",
+      "5246      -0.218393\n",
+      "5247       0.631626\n",
+      "5248      -0.302232\n",
+      "5249       0.085465\n",
+      "5250      -0.031733\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "      Volume_Change\n",
+      "2435       0.977868\n",
+      "1756      -0.142403\n",
+      "3296       0.885768\n",
+      "1243      -0.609255\n",
+      "343       -0.401554\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "      Volume_Change\n",
+      "3095       0.000000\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n",
+      "Volume_Change    False\n",
+      "dtype: bool\n",
+      "      Volume_Change\n",
+      "3095       0.000000\n",
+      "859       -0.963951\n",
+      "3134      24.250355\n",
+      "2577       1.722270\n",
+      "378       -1.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropna_df = df[['Volume_Change']].dropna()\n",
+    "dropna_X_resampled = X_resampled[['Volume_Change']].dropna()\n",
+    "dropna_X_test = X_test[['Volume_Change']].dropna()\n",
+    "dropna_X_val = X_val[['Volume_Change']].dropna()\n",
+    "\n",
+    "print(dropna_df.shape)\n",
+    "print(dropna_X_resampled.shape)\n",
+    "print(dropna_X_test.shape)\n",
+    "print(dropna_X_val.shape)\n",
+    "\n",
+    "print(dropna_df.isnull().any())\n",
+    "print(df[['Volume_Change']].tail())\n",
+    "print(dropna_X_resampled.isnull().any())\n",
+    "print(X_resampled[['Volume_Change']].tail())\n",
+    "print(dropna_X_test.isnull().any())\n",
+    "print(X_test[['Volume_Change']].tail())\n",
+    "print(dropna_X_val.isnull().any())\n",
+    "print(X_val[['Volume_Change']].tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Масштабируем новые признаки:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Результаты после масштабирования:\n",
+      "\n",
+      " Датафрейм:\n",
+      "      Volume_Change\n",
+      "5246      -0.176620\n",
+      "5247       0.224373\n",
+      "5248      -0.216171\n",
+      "5249      -0.033276\n",
+      "5250      -0.088564\n",
+      "\n",
+      " Обучающая:\n",
+      "      Volume_Change\n",
+      "2435      -0.033736\n",
+      "1756      -0.033805\n",
+      "3296      -0.033742\n",
+      "1243      -0.033834\n",
+      "343       -0.033821\n",
+      "\n",
+      " Тестовая:\n",
+      "      Volume_Change\n",
+      "3095      -0.033796\n",
+      "859       -0.033856\n",
+      "3134      -0.032301\n",
+      "2577      -0.033690\n",
+      "378       -0.033858\n",
+      "\n",
+      " Контрольная:\n",
+      "      Volume_Change\n",
+      "3095      -0.033796\n",
+      "859       -0.033856\n",
+      "3134      -0.032301\n",
+      "2577      -0.033690\n",
+      "378       -0.033858\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "\n",
+    "# Пример масштабирования числовых признаков\n",
+    "numerical_features = ['Volume_Change']\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "df[numerical_features] = scaler.fit_transform(df[numerical_features])\n",
+    "X_resampled[numerical_features] = scaler.fit_transform(X_resampled[numerical_features])\n",
+    "X_val[numerical_features] = scaler.transform(X_val[numerical_features])\n",
+    "X_test[numerical_features] = scaler.transform(X_test[numerical_features])\n",
+    "\n",
+    "# Вывод результатов после масштабирования\n",
+    "print(\"Результаты после масштабирования:\")\n",
+    "print(\"\\n Датафрейм:\")\n",
+    "print(df[numerical_features].tail())\n",
+    "print(\"\\n Обучающая:\")\n",
+    "print(X_resampled[numerical_features].tail())\n",
+    "print(\"\\n Тестовая:\")\n",
+    "print(X_val[numerical_features].tail())\n",
+    "print(\"\\n Контрольная:\")\n",
+    "print(X_test[numerical_features].tail())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Данные признаки предоставляют важную информацию о текущем тренде и возможных изменениях в будущих ценах. Положительные значения Price_Change и Percentage_Change, наряду с высоким Volume_Change, могут поддерживать гипотезу о росте цен на акции.\n",
+    "\n",
+    "Также, эти признаки помогают понять уровень рискованности инвестиций. Высокие значения Price_Range и резкие изменения в Volume_Change могут указывать на склонность к большим колебаниям, что требует внимательного управления рисками."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Применим featuretools для конструирования признаков:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  pd.to_datetime(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Date, Open, High, Low, Close, Adj Close, Volume, Volume_Change, id]\n",
+      "Index: []\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Open      High       Low     Close  Adj Close  Volume Close_Disc  \\\n",
+      "id                                                                         \n",
+      "0   3.428571  3.428571  3.428571  3.428571   2.806002     0.0        2-4   \n",
+      "1   3.428571  3.428571  3.428571  3.428571   2.806002     0.0        2-4   \n",
+      "2   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
+      "3   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
+      "4   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
+      "\n",
+      "    Volume_Change DAY(Date) MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
+      "id                                                                \n",
+      "0       -0.073594        22           6             4       2001  \n",
+      "1       -0.073594        25           6             0       2001  \n",
+      "2       -0.073594        26           6             1       2001  \n",
+      "3       -0.073594        27           6             2       2001  \n",
+      "4       -0.073594        28           6             3       2001  \n",
+      "     Open   High    Low  Close  Adj Close      Volume  Volume_Change  \\\n",
+      "id                                                                     \n",
+      "0    5.66   5.73   5.47   5.56   5.341250  23355100.0      -0.033796   \n",
+      "20   5.15   5.15   5.02   5.13   4.966732  15906300.0      -0.033816   \n",
+      "21  10.60  10.65  10.48  10.52   8.794909  10456400.0      -0.033817   \n",
+      "24   5.47   5.80   5.47   5.75   5.541336  12929600.0      -0.033782   \n",
+      "28   6.15   6.16   5.98   6.04   5.847770  15080900.0      -0.033786   \n",
+      "\n",
+      "   DAY(Date) MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
+      "id                                                 \n",
+      "0          8           7             2       2020  \n",
+      "20        19           1             1       2021  \n",
+      "21         8           4             3       2010  \n",
+      "24         7          12             0       2020  \n",
+      "28         5           1             1       2021  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, default_df], sort=True)\n",
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, default_df], sort=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import featuretools as ft\n",
+    "\n",
+    "df['id'] = df.index \n",
+    "X_resampled['id'] = X_resampled.index\n",
+    "X_val['id'] = X_val.index\n",
+    "X_test['id'] = X_test.index\n",
+    " # Добавляем уникальный идентификатор\n",
+    "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n",
+    "# Удаление дубликатов по идентификатору\n",
+    "df = df.drop_duplicates(subset='id')\n",
+    "duplicates = X_resampled[X_resampled['id'].duplicated(keep=False)]\n",
+    "\n",
+    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
+    "df = df.drop_duplicates(subset='id', keep='first')\n",
+    "\n",
+    "print(duplicates)\n",
+    "\n",
+    "\n",
+    "# Создание EntitySet\n",
+    "es = ft.EntitySet(id='stock_data')\n",
+    "\n",
+    "# Добавление датафрейма с домами\n",
+    "es = es.add_dataframe(dataframe_name='stocks', dataframe=df, index='id')\n",
+    "\n",
+    "# Генерация признаков с помощью глубокой синтезы признаков\n",
+    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
+    "\n",
+    "# Выводим первые 5 строк сгенерированного набора признаков\n",
+    "print(feature_matrix.head())\n",
+    "\n",
+    "X_resampled = X_resampled.drop_duplicates(subset='id')\n",
+    "X_resampled = X_resampled.drop_duplicates(subset='id', keep='first')  # or keep='last'\n",
+    "\n",
+    "# Определение сущностей (Создание EntitySet)\n",
+    "es = ft.EntitySet(id='stock_data')\n",
+    "\n",
+    "es = es.add_dataframe(dataframe_name='stocks', dataframe=X_resampled, index='id')\n",
+    "\n",
+    "# Генерация признаков\n",
+    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
+    "\n",
+    "# Преобразование признаков для контрольной и тестовой выборок\n",
+    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_val.index)\n",
+    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_test.index)\n",
+    "\n",
+    "print(feature_matrix.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Система сгенерировала следующие признаки:\n",
+    "1. **Open, High, Low, Close, Adj Close**: Это стандартные финансовые параметры акций, отражающие цены открытия, максимальные, минимальные и закрытия за определенный период.\n",
+    "**Volume**: Объем торгов акциями, который показывает, сколько акций было куплено/продано за определенный период.\n",
+    "\n",
+    "2. Сложные признаки:\n",
+    "**Close_Disc**: Это диапазон цены закрытия.\n",
+    "**Price_Change**: Изменение цены, т.е. разница между ценой закрытия и ценой открытия акций.\n",
+    "**Percentage_Change**: Процентное изменение цен, которое позволяет оценить относительное изменение стоимости акций.\n",
+    "**Average_Price**: Средняя цена акций за указанный период. Этот показатель может быть использован для оценки общей тенденции рынка.\n",
+    "\n",
+    "3. Также произошло разбиение даты на месяц, день недели и год, что может помочь в анализе сезонных и временных закономерностей."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Оценим качество каждого набора признаков:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер feature_matrix:  4232\n",
+      "Размер y_train_categories:  4232\n",
+      "Коэффициент детерминации R²: 1.00\n",
+      "Время обучения модели: 45.54 секунд\n",
+      "Среднеквадратичная ошибка: 0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
+    "y = feature_matrix['Close']\n",
+    "X = feature_matrix.drop('Close', axis=1)\n",
+    "\n",
+    "plt.hist(y, bins=30, edgecolor='k')\n",
+    "plt.title('Распределение целевой переменной')\n",
+    "plt.xlabel('Close Price')\n",
+    "plt.ylabel('Количество')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Размер feature_matrix: \", feature_matrix.shape[0])\n",
+    "print(\"Размер y_train_categories: \", y.shape[0])\n",
+    "\n",
+    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\n",
+    "X = pd.get_dummies(X, drop_first=True)\n",
+    "\n",
+    "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n",
+    "X.fillna(X.median(), inplace=True)\n",
+    "\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Обучение модели\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Начинаем отсчет времени\n",
+    "start_time = time.time()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Время обучения модели\n",
+    "train_time = time.time() - start_time\n",
+    "\n",
+    "# Предсказания и оценка модели и вычисляем среднеквадратичную ошибку\n",
+    "predictions = model.predict(X_val)\n",
+    "mse = mean_squared_error(y_val, predictions)\n",
+    "\n",
+    "r2 = r2_score(y_val, predictions)\n",
+    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
+    "\n",
+    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
+    "print(f'Среднеквадратичная ошибка: {mse:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В данном случае среднеквадратичные ошибки как в случае с контрольной выборкой, так и в случае с тестовой достаточно малы, что может значит о том, что предсказания модели близки к реальным значениям."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "RMSE: 0.09582857422264315\n",
+      "R²: 0.9995934815979668\n",
+      "MAE: 0.05673237514757995 \n",
+      "\n",
+      "Кросс-валидация RMSE: 0.10266281621290554 \n",
+      "\n",
+      "Train RMSE: 0.03608662827625366\n",
+      "Train R²: 0.9999422411727147\n",
+      "Train MAE: 0.022020514230428674\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Корреляция признаков с 'Close':\n",
+      "Close            1.000000\n",
+      "Low              0.999572\n",
+      "High             0.999527\n",
+      "Open             0.998976\n",
+      "Adj Close        0.997764\n",
+      "Volume           0.062913\n",
+      "WEEKDAY(Date)    0.009135\n",
+      "DAY(Date)       -0.011068\n",
+      "Volume_Change   -0.030591\n",
+      "MONTH(Date)     -0.034877\n",
+      "YEAR(Date)      -0.100269\n",
+      "Name: Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "\n",
+    "# Удаление строк с NaN\n",
+    "feature_matrix = feature_matrix.dropna()\n",
+    "val_feature_matrix = val_feature_matrix.dropna()\n",
+    "test_feature_matrix = test_feature_matrix.dropna()\n",
+    "\n",
+    "# Разделение данных на обучающую и тестовую выборки\n",
+    "y_train = feature_matrix['Close']\n",
+    "X_train = feature_matrix.drop('Close', axis=1)\n",
+    "y_val = val_feature_matrix['Close']\n",
+    "X_val = val_feature_matrix.drop('Close', axis=1)\n",
+    "y_test = test_feature_matrix['Close']\n",
+    "X_test = test_feature_matrix.drop('Close', axis=1)\n",
+    "\n",
+    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)  \n",
+    "\n",
+    "# Кодирования категориальных переменных с использованием одноразового кодирования\n",
+    "X = pd.get_dummies(X, drop_first=True)\n",
+    "\n",
+    "# Разобьём тренировочный тест и примерку модели\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор модели\n",
+    "model = RandomForestRegressor(random_state=42)\n",
+    "\n",
+    "# Обучение модели\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Предсказание и оценка\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "\n",
+    "print()\n",
+    "print(f\"RMSE: {rmse}\")\n",
+    "print(f\"R²: {r2}\")\n",
+    "print(f\"MAE: {mae} \\n\")\n",
+    "\n",
+    "# Кросс-валидация\n",
+    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
+    "rmse_cv = (-scores.mean())**0.5\n",
+    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
+    "\n",
+    "# Анализ важности признаков\n",
+    "feature_importances = model.feature_importances_\n",
+    "feature_names = X_train.columns\n",
+    "\n",
+    "# Проверка на переобучение\n",
+    "y_train_pred = model.predict(X_train)\n",
+    "\n",
+    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
+    "r2_train = r2_score(y_train, y_train_pred)\n",
+    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
+    "\n",
+    "print(f\"Train RMSE: {rmse_train}\")\n",
+    "print(f\"Train R²: {r2_train}\")\n",
+    "print(f\"Train MAE: {mae_train}\")\n",
+    "print()\n",
+    "\n",
+    "# Визуализация результатов\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
+    "plt.xlabel('Фактическая цена')\n",
+    "plt.ylabel('Прогнозируемая цена')\n",
+    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
+    "plt.show()\n",
+    "\n",
+    "correlation_matrix = feature_matrix.corr()\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True)\n",
+    "plt.title('Корреляционная матрица')\n",
+    "plt.show()\n",
+    "\n",
+    "# Рассмотрим корреляцию с целевой переменной 'Close'\n",
+    "correlation_with_close = correlation_matrix['Close'].sort_values(ascending=False)\n",
+    "print(\"Корреляция признаков с 'Close':\")\n",
+    "print(correlation_with_close)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "На основании представленных данных о корреляции признаков с целевой переменной 'Close', а также значений Mean Squared Error (MSE), можно сделать несколько важных выводов:\n",
+    "\n",
+    "**Эффективность модели**\n",
+    "Эффективность модели можно оценивать по нескольким критериям:\n",
+    "\n",
+    "* Точность предсказаний: График сравнения фактических и прогнозируемых цен показывает, что точки близки к линии равенства, это указывает на высокую точность модели. Высокая точность означает, что ваша модель хорошо прогнозирует цены, что критически важно для принятия обоснованных инвестиционных решений.\n",
+    "* Метрики оценки: Использование таких метрик, как средняя абсолютная ошибка (MAE), средняя квадратичная ошибка (MSE) или коэффициент детерминации (R²), позволит оценить, насколько близки прогнозы к реальным значениям. Эти меры позволяют количественно оценить уровень ошибки модели. В данном случае среднеквадратичные ошибки достаточно малы(~1.5e-10), что может значит о том, что предсказания модели близки к реальным значениям.\n",
+    "\n",
+    "**Высокая корреляция признаков**\n",
+    "\n",
+    "* Показатели High, Low, Open и Average Price имеют крайне высокую положительную корреляцию с целевой переменной Close:\n",
+    "Это говорит о том, что данные переменные практически линейно зависимы от значения Close. Таким образом, знание значений этих признаков позволяет с высокой степенью уверенности предсказывать значение Close.\n",
+    "\n",
+    "Year имеет наибольшую отрицательную корреляцию (-0.09) с Close, что говорит об их наименьшей зависимости друг от друга.\n",
+    "\n",
+    "**Переобучение**\n",
+    "Переобучение (overfitting) — это распространенная проблема в моделях машинного обучения:\n",
+    "\n",
+    "* Признаки переобучения: Если модель показывает отличные результаты на обучающей выборке, но значительно хуже на тестовой, это свидетельствует о том, что модель слишком сложна и запоминает данные вместо того, чтобы их обобщать.\n",
+    "В данном случае модель показала одинаково хорошие результаты на "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv",
+   "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
+}