From 39f65612d8dff2a28aacfb809bc6cacbfec84210 Mon Sep 17 00:00:00 2001
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 =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= <alexkryukov73@yandex.ru>
Date: Sat, 30 Nov 2024 13:16:02 +0400
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---
 Lab_3/lab3.ipynb | 1048 +++++++++++++++++++++-------------------------
 1 file changed, 481 insertions(+), 567 deletions(-)

diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb
index 4824c01..cb9478f 100644
--- a/Lab_3/lab3.ipynb
+++ b/Lab_3/lab3.ipynb
@@ -10,35 +10,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
-       "       'Day_of_week', 'Month', 'Year'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<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",
+      "None\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\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\n"
+     ]
     }
    ],
    "source": [
     "import pandas as pd\n",
     "\n",
-    "data  = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\")\n",
-    "\n",
-    "# Преобразование даты\n",
+    "ddata = pd.read_csv(\"../static/csv/Yamana_Gold_Inc._AUY.csv\")\n",
     "data['Date'] = pd.to_datetime(data['Date'])\n",
     "\n",
-    "# Преобразование данных: создание новых признаков\n",
-    "data['Day_of_week'] = data['Date'].dt.dayofweek\n",
-    "data['Month'] = data['Date'].dt.month\n",
-    "data['Year'] = data['Date'].dt.year\n",
-    "data .columns"
+    "# Первичный анализ данных\n",
+    "print(data.info())\n",
+    "print(data.head())"
    ]
   },
   {
@@ -65,42 +76,129 @@
     "#3 Проверим датасет на пропуски и удалим при необходимости строки с недостающими данными"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Обработка пропусков  \n",
+    "Пропуски в данных могут негативно влиять на обучение моделей. Сначала оцениваем количество пропусков по столбцам. Если пропуски присутствуют, удаляем строки с отсутствующими значениями.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Date           0\n",
-       "Open           0\n",
-       "High           0\n",
-       "Low            0\n",
-       "Close          0\n",
-       "Adj Close      0\n",
-       "Volume         0\n",
-       "Day_of_week    0\n",
-       "Month          0\n",
-       "Year           0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
+     "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",
+      "Данные после очистки: (5251, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3. Проверим датасет на наличие пропусков и удалим строки с недостающими данными\n",
+    "print(data.isnull().sum())  # Суммируем пропуски по каждому столбцу\n",
+    "data.dropna(inplace=True)  # Удаляем строки с пропусками\n",
+    "print(\"Данные после очистки:\", data.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Добавление признаков  \n",
+    "Для выполнения задач добавляем два новых признака:  \n",
+    "Daily_Change: разница между ценой закрытия и открытия торгов.  \n",
+    "Volatility: относительная волатильность, рассчитываемая как отношение разницы между максимальной и минимальной ценой к цене открытия."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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",
+      "Количество строк после удаления пропусков: 5251\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.isnull().sum())  # Вывод количества пропусков\n",
+    "data.dropna(inplace=True)  # Удаление строк с пропущенными значениями\n",
+    "print(f\"Количество строк после удаления пропусков: {data.shape[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Создание новых признаков "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Daily_Change  Volatility\n",
+      "0           0.0         0.0\n",
+      "1           0.0         0.0\n",
+      "2           0.0         0.0\n",
+      "3           0.0         0.0\n",
+      "4           0.0         0.0\n"
+     ]
     }
    ],
    "source": [
     "\n",
-    "# Проверим на пропущенные значения\n",
-    "data.isnull().sum()\n",
+    "data['Daily_Change'] = data['Close'] - data['Open']\n",
+    "data['Volatility'] = (data['High'] - data['Low']) / data['Open']\n",
+    "print(data[['Daily_Change', 'Volatility']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проведем масштабированние данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "# Заполним пропуски или удалим строки с пропусками\n",
-    "data = data.dropna()\n",
-    "\n",
-    "# Проверим, что данные очищены\n",
-    "data.isnull().sum()\n",
+    "scaler = StandardScaler()\n",
+    "scaled_columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
+    "data[scaled_columns] = scaler.fit_transform(data[scaled_columns])\n",
     "\n"
    ]
   },
@@ -108,251 +206,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Конструирование признаков"
+    "## Разбиение данных на выборки  \n",
+    "Для предотвращения просачивания данных используем разбиение на три части:  \n",
+    "Обучающая выборка (60%): для тренировки модели.  \n",
+    "Валидационная выборка (20%): для подбора гиперпараметров модели и проверки ее производительности на новых данных.  \n",
+    "Тестовая выборка (20%): для оценки финальной производительности модели.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
-     "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",
-       "      <th>Day_of_week</th>\n",
-       "      <th>Month</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Price_Change</th>\n",
-       "      <th>SMA_5</th>\n",
-       "      <th>SMA_20</th>\n",
-       "      <th>STD_5</th>\n",
-       "      <th>STD_20</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",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.285715</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.156493</td>\n",
-       "      <td>NaN</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   \n",
-       "\n",
-       "   Day_of_week  Month  Year  Price_Change  SMA_5  SMA_20     STD_5  STD_20  \n",
-       "0            4      6  2001           NaN    NaN     NaN       NaN     NaN  \n",
-       "1            0      6  2001      0.000000    NaN     NaN       NaN     NaN  \n",
-       "2            1      6  2001      0.285715    NaN     NaN       NaN     NaN  \n",
-       "3            2      6  2001      0.000000    NaN     NaN       NaN     NaN  \n",
-       "4            3      6  2001      0.000000    3.6     NaN  0.156493     NaN  "
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Создаем новый признак - разницу между текущей и предыдущей ценой (Price_Change)\n",
-    "data['Price_Change'] = data['Close'].diff()\n",
-    "\n",
-    "# Создадим скользящие средние для 5 и 20 дней\n",
-    "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n",
-    "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n",
-    "\n",
-    "# Стандартное отклонение для 5 и 20 дней\n",
-    "data['STD_5'] = data['Close'].rolling(window=5).std()\n",
-    "data['STD_20'] = data['Close'].rolling(window=20).std()\n",
-    "\n",
-    "data.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " Разделение данных на обучающую, контрольную и тестовую выборки"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((3150, 10), (1050, 10), (1051, 10))"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (3150, 5)\n",
+      "Размер валидационной выборки: (1050, 5)\n",
+      "Размер тестовой выборки: (1051, 5)\n"
+     ]
     }
    ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "# Преобразуем колонку 'Date' в тип datetime для правильного сортирования\n",
-    "data['Date'] = pd.to_datetime(data['Date'])\n",
+    "X = data[scaled_columns]\n",
+    "y = data['Close']  # Заменить на целевую переменную, если другая\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
     "\n",
-    "# Сортируем данные по дате, чтобы не нарушить временную зависимость\n",
-    "data = data.sort_values(by='Date')\n",
-    "\n",
-    "# Отделяем целевую переменную (например, Price_Change) и признаки\n",
-    "X = data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n",
-    "y = data['Price_Change']\n",
-    "\n",
-    "# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\n",
-    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, shuffle=False)\n",
-    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, shuffle=False)\n",
-    "\n",
-    "# Проверка размеров выборок\n",
-    "(X_train.shape, X_val.shape, X_test.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Конструирование признаков для решения задач"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Признаки для задачи прогнозирования изменений цен\n",
-    "data['Price_Change'] = data['Close'].diff()\n",
-    "\n",
-    "# Скользящие средние и стандартное отклонение\n",
-    "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n",
-    "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n",
-    "data['STD_5'] = data['Close'].rolling(window=5).std()\n",
-    "data['STD_20'] = data['Close'].rolling(window=20).std()\n",
-    "\n",
-    "# Признаки для оценки волатильности\n",
-    "data['Volatility'] = data['Close'].rolling(window=5).std()\n",
+    "print(f\"Размер обучающей выборки: {X_train.shape}\")\n",
+    "print(f\"Размер валидационной выборки: {X_val.shape}\")\n",
+    "print(f\"Размер тестовой выборки: {X_test.shape}\")\n",
     "\n"
    ]
   },
@@ -360,349 +246,377 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Подготовка признаков: one-hot encoding, дискретизация, синтез признаков, масштабирование\n",
-    "One-hot encoding: Применим для категориальных признаков (например, день недели).\n",
-    "Масштабирование: Стандартизируем числовые признаки."
+    "## Оценка и балансировка классов  \n",
+    "Проверяем распределение классов в целевой переменной для каждого набора данных. Если данные несбалансированы (например, если цена часто растет, а иногда падает), применяем SMOTE (Synthetic Minority Over-sampling Technique) для генерации синтетических примеров из меньшинства.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   Day_of_week  Month\n",
-      "0            4      6\n",
-      "1            0      6\n",
-      "2            1      6\n",
-      "3            2      6\n",
-      "4            3      6\n"
+      "Проверка на пропуски после масштабирования:\n",
+      " Date            0\n",
+      "Open            0\n",
+      "High            0\n",
+      "Low             0\n",
+      "Close           0\n",
+      "Adj Close       0\n",
+      "Volume          0\n",
+      "Daily_Change    0\n",
+      "Volatility      0\n",
+      "dtype: int64\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>Close</th>\n",
-       "      <th>SMA_5</th>\n",
-       "      <th>SMA_20</th>\n",
-       "      <th>STD_5</th>\n",
-       "      <th>STD_20</th>\n",
-       "      <th>Day_of_week_1</th>\n",
-       "      <th>Day_of_week_2</th>\n",
-       "      <th>Day_of_week_3</th>\n",
-       "      <th>Day_of_week_4</th>\n",
-       "      <th>Month_2</th>\n",
-       "      <th>Month_3</th>\n",
-       "      <th>Month_4</th>\n",
-       "      <th>Month_5</th>\n",
-       "      <th>Month_6</th>\n",
-       "      <th>Month_7</th>\n",
-       "      <th>Month_8</th>\n",
-       "      <th>Month_9</th>\n",
-       "      <th>Month_10</th>\n",
-       "      <th>Month_11</th>\n",
-       "      <th>Month_12</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.721096</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.507666</td>\n",
-       "      <td>-0.507962</td>\n",
-       "      <td>-0.502320</td>\n",
-       "      <td>1.999048</td>\n",
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-       "      <td>-0.309491</td>\n",
-       "      <td>-0.300916</td>\n",
-       "      <td>-0.297137</td>\n",
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-       "      <td>-0.303543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.721096</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.507666</td>\n",
-       "      <td>-0.507962</td>\n",
-       "      <td>-0.502320</td>\n",
-       "      <td>-0.500238</td>\n",
-       "      <td>-0.28793</td>\n",
-       "      <td>-0.309491</td>\n",
-       "      <td>-0.300916</td>\n",
-       "      <td>-0.297137</td>\n",
-       "      <td>3.335719</td>\n",
-       "      <td>-0.30429</td>\n",
-       "      <td>-0.311702</td>\n",
-       "      <td>-0.296377</td>\n",
-       "      <td>-0.311335</td>\n",
-       "      <td>-0.298274</td>\n",
-       "      <td>-0.303543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.660890</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.969800</td>\n",
-       "      <td>-0.507962</td>\n",
-       "      <td>-0.502320</td>\n",
-       "      <td>-0.500238</td>\n",
-       "      <td>-0.28793</td>\n",
-       "      <td>-0.309491</td>\n",
-       "      <td>-0.300916</td>\n",
-       "      <td>-0.297137</td>\n",
-       "      <td>3.335719</td>\n",
-       "      <td>-0.30429</td>\n",
-       "      <td>-0.311702</td>\n",
-       "      <td>-0.296377</td>\n",
-       "      <td>-0.311335</td>\n",
-       "      <td>-0.298274</td>\n",
-       "      <td>-0.303543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.660890</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.507666</td>\n",
-       "      <td>1.968649</td>\n",
-       "      <td>-0.502320</td>\n",
-       "      <td>-0.500238</td>\n",
-       "      <td>-0.28793</td>\n",
-       "      <td>-0.309491</td>\n",
-       "      <td>-0.300916</td>\n",
-       "      <td>-0.297137</td>\n",
-       "      <td>3.335719</td>\n",
-       "      <td>-0.30429</td>\n",
-       "      <td>-0.311702</td>\n",
-       "      <td>-0.296377</td>\n",
-       "      <td>-0.311335</td>\n",
-       "      <td>-0.298274</td>\n",
-       "      <td>-0.303543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.660890</td>\n",
-       "      <td>-0.686033</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.269917</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.507666</td>\n",
-       "      <td>-0.507962</td>\n",
-       "      <td>1.990763</td>\n",
-       "      <td>-0.500238</td>\n",
-       "      <td>-0.28793</td>\n",
-       "      <td>-0.309491</td>\n",
-       "      <td>-0.300916</td>\n",
-       "      <td>-0.297137</td>\n",
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-       "      <td>-0.30429</td>\n",
-       "      <td>-0.311702</td>\n",
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-       "      <td>-0.298274</td>\n",
-       "      <td>-0.303543</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
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",
       "text/plain": [
-       "      Close     SMA_5  SMA_20     STD_5  STD_20  Day_of_week_1  Day_of_week_2  \\\n",
-       "0 -0.721096       NaN     NaN       NaN     NaN      -0.507666      -0.507962   \n",
-       "1 -0.721096       NaN     NaN       NaN     NaN      -0.507666      -0.507962   \n",
-       "2 -0.660890       NaN     NaN       NaN     NaN       1.969800      -0.507962   \n",
-       "3 -0.660890       NaN     NaN       NaN     NaN      -0.507666       1.968649   \n",
-       "4 -0.660890 -0.686033     NaN -0.269917     NaN      -0.507666      -0.507962   \n",
-       "\n",
-       "   Day_of_week_3  Day_of_week_4  Month_2   Month_3   Month_4   Month_5  \\\n",
-       "0      -0.502320       1.999048 -0.28793 -0.309491 -0.300916 -0.297137   \n",
-       "1      -0.502320      -0.500238 -0.28793 -0.309491 -0.300916 -0.297137   \n",
-       "2      -0.502320      -0.500238 -0.28793 -0.309491 -0.300916 -0.297137   \n",
-       "3      -0.502320      -0.500238 -0.28793 -0.309491 -0.300916 -0.297137   \n",
-       "4       1.990763      -0.500238 -0.28793 -0.309491 -0.300916 -0.297137   \n",
-       "\n",
-       "    Month_6  Month_7   Month_8   Month_9  Month_10  Month_11  Month_12  \n",
-       "0  3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543  \n",
-       "1  3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543  \n",
-       "2  3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543  \n",
-       "3  3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543  \n",
-       "4  3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543  "
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "execution_count": 110,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "import pandas as pd\n",
+    "from collections import Counter\n",
+    "from imblearn.over_sampling import SMOTE\n",
     "\n",
-    "# Преобразуем дату, если это еще не сделано\n",
-    "data['Date'] = pd.to_datetime(data['Date'])\n",
+    "# Проверка на наличие пропусков после масштабирования\n",
+    "print(\"Проверка на пропуски после масштабирования:\\n\", data.isnull().sum())\n",
     "\n",
-    "# Добавим дополнительные признаки (день недели и месяц)\n",
-    "data['Day_of_week'] = data['Date'].dt.dayofweek\n",
-    "data['Month'] = data['Date'].dt.month\n",
+    "# Проверка распределения целевой переменной\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "# Проверим, что эти столбцы добавлены\n",
-    "print(data[['Day_of_week', 'Month']].head())\n",
-    "\n",
-    "# Выбираем признаки и целевую переменную\n",
-    "X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20', 'Day_of_week', 'Month']]\n",
-    "y = data['Price_Change']\n",
-    "\n",
-    "# Применяем one-hot encoding для категориальных признаков (Day_of_week и Month)\n",
-    "X = pd.get_dummies(X, columns=['Day_of_week', 'Month'], drop_first=True)\n",
-    "\n",
-    "# Масштабирование числовых признаков (Close, SMA, STD)\n",
-    "scaler = StandardScaler()\n",
-    "X_scaled = scaler.fit_transform(X)\n",
-    "\n",
-    "# Преобразуем обратно в DataFrame для удобства\n",
-    "X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
-    "\n",
-    "# Проверим результат\n",
-    "X_scaled_df.head()\n"
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.hist(data['Close'], bins=30, edgecolor='k', alpha=0.7)\n",
+    "plt.title(\"Распределение целевой переменной 'Close'\")\n",
+    "plt.xlabel(\"Цена закрытия\")\n",
+    "plt.ylabel(\"Частота\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Создадим модель для прогназирования "
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Прогнозирование цены акции ---\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Модель для прогнозирования цены акции\n",
+    "print(\"\\n--- Прогнозирование цены акции ---\")\n",
+    "price_model = LinearRegression()\n",
+    "price_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Предсказание на тестовой выборке\n",
+    "y_pred_price = price_model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оценка модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE): 0.0000\n",
+      "R^2 Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка модели\n",
+    "mse_price = mean_squared_error(y_test, y_pred_price)\n",
+    "r2_price = r2_score(y_test, y_pred_price)\n",
+    "print(f\"Mean Squared Error (MSE): {mse_price:.4f}\")\n",
+    "print(f\"R^2 Score: {r2_price:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MSE = 0.0000 и R² = 1.0000, это говорит о том, что ваша модель предсказывает данные абсолютно точно. (йоу)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Визуализируем полученный результат "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Визуализация результатов прогноза\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(y_test.values, label='Истинные значения', color='blue')\n",
+    "plt.plot(y_pred_price, label='Предсказанные значения', color='red')\n",
+    "plt.title(\"Прогнозирование цены акции\")\n",
+    "plt.xlabel(\"Наблюдения\")\n",
+    "plt.ylabel(\"Цена закрытия\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В целом, на основе этой диаграммы можно сделать вывод, что текущая модель не точно предсказывает цену акции"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь сделаем все то же самое для волотильности "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Оценка волатильности ---\n",
+      "Mean Squared Error (MSE) для волатильности: 0.0003\n",
+      "R^2 Score для волатильности: 0.6214\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Модель для оценки волатильности\n",
+    "print(\"\\n--- Оценка волатильности ---\")\n",
+    "X_volatility = data[['High', 'Low', 'Open']]  # Переменные для оценки волатильности\n",
+    "y_volatility = data['Volatility']\n",
+    "\n",
+    "# Разделение на выборки для модели волатильности\n",
+    "X_train_vol, X_test_vol, y_train_vol, y_test_vol = train_test_split(X_volatility, y_volatility, test_size=0.2, random_state=42)\n",
+    "\n",
+    "volatility_model = LinearRegression()\n",
+    "volatility_model.fit(X_train_vol, y_train_vol)\n",
+    "\n",
+    "# Предсказание на тестовой выборке\n",
+    "y_pred_volatility = volatility_model.predict(X_test_vol)\n",
+    "\n",
+    "# Оценка модели волатильности\n",
+    "mse_volatility = mean_squared_error(y_test_vol, y_pred_volatility)\n",
+    "r2_volatility = r2_score(y_test_vol, y_pred_volatility)\n",
+    "print(f\"Mean Squared Error (MSE) для волатильности: {mse_volatility:.4f}\")\n",
+    "print(f\"R^2 Score для волатильности: {r2_volatility:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MSE = 0.0003 и R² = 0.6214 говорят о том, что модель делает предсказания с малой ошибкой, но есть еще пространство для улучшения, поскольку она объясняет менее 70% вариации в данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Визуализируем "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Визуализация результатов прогноза волатильности\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(y_test_vol.values, label='Истинные значения волатильности', color='green')\n",
+    "plt.plot(y_pred_volatility, label='Предсказанные значения волатильности', color='orange')\n",
+    "plt.title(\"Прогнозирование волатильности\")\n",
+    "plt.xlabel(\"Наблюдения\")\n",
+    "plt.ylabel(\"Волатильность\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "По этой модели же можно сказать, что она более точно предсказывает, то есть она получилась более правильной "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь воспользуемся фреймворком Featuretools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated feature columns: Index(['open', 'high', 'low', 'close', 'Adj Close', 'volume', 'DAY(datetime)',\n",
+      "       'MONTH(datetime)', 'YEAR(datetime)'],\n",
+      "      dtype='object')\n",
+      "Размер обучающей выборки: (3150, 5)\n",
+      "Размер валидационной выборки: (1050, 5)\n",
+      "Размер тестовой выборки: (1051, 5)\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
-      "  warnings.warn(\n"
+      "  warnings.warn(\n",
+      "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
+      "  agg_primitives: ['mean', 'sum']\n",
+      "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
+      "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
      ]
     }
    ],
    "source": [
+    "# Импорт библиотек для работы с Featuretools\n",
     "import featuretools as ft\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import woodwork as ww\n",
     "\n",
-    "# Создаем сущности для Featuretools\n",
-    "es = ft.EntitySet(id=\"stock_prices\")\n",
-    "es = es.add_dataframe(dataframe_name=\"stock_data\", dataframe=data, index=\"Date\")\n",
     "\n",
-    "# Автоматическое создание признаков\n",
-    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"stock_data\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#Оценка качества признаков\n",
-    "Оценка признаков на основе предсказательной способности модели и других критериев."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5251\n",
-      "3150\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.05230198011754029), 0.5415652186272203)"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Проверим размерности данных после удаления NaN\n",
-    "print(X_scaled_df.shape[0])  # Количество строк в X_scaled_df\n",
-    "print(y_train.shape[0])       # Количество строк в y_train\n",
+    "# Проверим и удалим дублирующиеся столбцы\n",
+    "if not data.columns.is_unique:\n",
+    "    print(\"Обнаружены дублирующиеся столбцы. Переименуем их.\")\n",
+    "    data = data.loc[:, ~data.columns.duplicated()] \n",
     "\n",
-    "# Если данные имеют разные размеры, синхронизируем их\n",
-    "df = pd.concat([X_scaled_df, y_train], axis=1).dropna()\n",
-    "X_scaled_df = df.drop(columns=y_train.name)\n",
-    "y_train = df[y_train.name]\n",
+    "# Переименование столбцов для совместимости с Featuretools\n",
+    "data = data.rename(columns={'Date': 'datetime', 'Open': 'open', 'High': 'high', \n",
+    "                             'Low': 'low', 'Close': 'close', 'Volume': 'volume'})\n",
     "\n",
-    "# Приводим индексы к одному виду\n",
-    "y_train = y_train.reset_index(drop=True)\n",
-    "X_scaled_df = X_scaled_df.reset_index(drop=True)\n",
+    "# Создание EntitySet\n",
+    "es = ft.EntitySet(id=\"stocks\")\n",
     "\n",
-    "# После этого продолжаем обучение модели\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_scaled_df, y_train)\n",
+    "# Добавление данных в EntitySet с правильными логическими типами\n",
+    "es = es.add_dataframe(\n",
+    "    dataframe_name=\"stock_data\",\n",
+    "    dataframe=data,\n",
+    "    index=\"datetime\",  \n",
+    "    # Убираем time_index, так как datetime уже используется как индекс\n",
+    "    logical_types={\n",
+    "        \"open\": ww.logical_types.Double,  \n",
+    "        \"high\": ww.logical_types.Double,\n",
+    "        \"low\": ww.logical_types.Double,\n",
+    "        \"close\": ww.logical_types.Double,\n",
+    "        \"volume\": ww.logical_types.Double,\n",
+    "    },\n",
+    ")\n",
     "\n",
-    "# Прогнозирование и оценка качества\n",
-    "y_pred = model.predict(X_scaled_df)\n",
+    "# Автоматическое создание признаков с использованием Featuretools\n",
+    "feature_matrix, feature_defs = ft.dfs(\n",
+    "    entityset=es,\n",
+    "    target_dataframe_name=\"stock_data\",\n",
+    "    agg_primitives=[\"mean\", \"sum\"], \n",
+    "    trans_primitives=[\"day\", \"month\", \"year\"]  \n",
+    ")\n",
     "\n",
-    "# Оценка качества модели\n",
-    "mse = mean_squared_error(y_train, y_pred)  # Используем y_train, потому что данные для теста не созданы\n",
-    "r2 = r2_score(y_train, y_pred)\n",
+    "# Выводим имена столбцов в feature_matrix, чтобы убедиться, какие признаки были сгенерированы\n",
+    "print(\"Generated feature columns:\", feature_matrix.columns)\n",
     "\n",
-    "mse, r2\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MSE = 0.0523: Модель в среднем делает ошибку около 0.0523 при прогнозировании значений.\n",
-    "R² = 0.5416: Модель объясняет примерно 54.16% изменчивости целевой переменной\n",
+    "# Объединяем новые признаки с исходными данными, добавив суффиксы для дублирующихся столбцов\n",
+    "data_featuretools = data.join(feature_matrix, lsuffix='_orig', rsuffix='_feature')\n",
     "\n",
-    "Визуализируем"
+    "# Масштабирование данных\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Используем правильные имена столбцов для масштабирования\n",
+    "scaled_columns = ['open_orig', 'high_orig', 'low_orig', 'close_orig', 'volume_orig']\n",
+    "\n",
+    "data_featuretools[scaled_columns] = scaler.fit_transform(data_featuretools[scaled_columns])\n",
+    "\n",
+    "# Разделение данных на выборки\n",
+    "\n",
+    "feature_columns = [col for col in feature_matrix.columns if 'feature' in col]\n",
+    "X = data_featuretools[scaled_columns + feature_columns]\n",
+    "y = data_featuretools['close_orig']  # Целевая переменная\n",
+    "\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
+    "\n",
+    "print(f\"Размер обучающей выборки: {X_train.shape}\")\n",
+    "print(f\"Размер валидационной выборки: {X_val.shape}\")\n",
+    "print(f\"Размер тестовой выборки: {X_test.shape}\")"
    ]
   }
  ],