{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной работы №2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цены на мобильные телефоны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Name', 'Rating', 'Spec_score', 'No_of_sim', 'Ram',\n",
      "       'Battery', 'Display', 'Camera', 'External_Memory', 'Android_version',\n",
      "       'Price', 'company', 'Inbuilt_memory', 'fast_charging',\n",
      "       'Screen_resolution', 'Processor', 'Processor_name'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"../static/csv/mobile phone price prediction.csv\",delimiter=',')\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблема область: Данные о мобильных телефонах, включая их характеристику.\\\n",
    "Объект наблюдения: Мобильные телефоны.\\\n",
    "Атрибуты: Имя, рейтинг, оценка, поддержка двух SIM-карт, оперативная память, аккумулятор, дисплей, камера, внешняя память, версия Android телефона, цена, компания производителя, разрешение экрана, харатеристика процессора, название процессора.\\\n",
    "Пример бизнес-цели: \n",
    "1. Анализ данных: Изучение и очистка данных для выявления закономерностей и корреляций между характеристиками мобильных телефонов и их ценами.\n",
    "2. Разработка модели: Создание и обучение модели машинного обучения, которая будет прогнозировать цены на мобильные телефоны на основе их характеристик.\n",
    "3. Внедрение: Интеграция модели в систему ценообразования компании для автоматического расчета цен на мобильные телефоны.\n",
    "\n",
    "\n",
    "Актуальность: Данный датасет является актуальным и ценным ресурсом для компаний, занимающихся продажей мобильных телефонов, а также для исследователей и инвесторов, поскольку он предоставляет обширную информацию о ценах и характеристиках мобильных телефонов на вторичном рынке. Эти данные могут быть использованы для разработки моделей прогнозирования цен, анализа рыночных тенденций и принятия обоснованных бизнес-решений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Spec_score', ylabel='Price'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "data = pd.read_csv(\"../static/csv/mobile phone price prediction.csv\",delimiter=',')\n",
    "data.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "data['Price'] = data['Price'].str.replace(',', '').astype(float)\n",
    "data.describe(include='all')\n",
    "f, ax = plt.subplots(figsize=(10,6))\n",
    "sns.despine(f)\n",
    "sns.scatterplot(data=data, x='Spec_score', y='Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При проверке на шум можно заметить выброс в 75 оценке. Цена там запредельная.\n",
    "\n",
    "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество строк до удаления выбросов: 1370\n",
      "Количество строк после удаления выбросов: 1256\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "df['Spec_score'] = df['Spec_score'].astype(int)\n",
    "df['Price'] = df['Price'].str.replace(',', '').astype(float)\n",
    "# Выбор столбцов для анализа\n",
    "column1 = 'Spec_score'\n",
    "column2 = 'Price'\n",
    "\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
    "\n",
    "# Удаление выбросов для каждого столбца\n",
    "df_cleaned = df.copy()\n",
    "for column in [column1, column2]:\n",
    "    df_cleaned = remove_outliers(df_cleaned, column)\n",
    "\n",
    "# Построение точечной диаграммы после удаления выбросов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_cleaned[column1], df_cleaned[column2], alpha=0.5)\n",
    "plt.xlabel(column1)\n",
    "plt.ylabel(column2)\n",
    "plt.title(f'Scatter Plot of {column1} vs {column2} (After Removing Outliers)')\n",
    "plt.show()\n",
    "\n",
    "# Вывод количества строк до и после удаления выбросов\n",
    "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n",
    "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь очистим датасет от пустых строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Общая информация о датасете:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1370 entries, 0 to 1369\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         1370 non-null   int64  \n",
      " 1   Name               1370 non-null   object \n",
      " 2   Rating             1370 non-null   float64\n",
      " 3   Spec_score         1370 non-null   int64  \n",
      " 4   No_of_sim          1370 non-null   object \n",
      " 5   Ram                1370 non-null   object \n",
      " 6   Battery            1370 non-null   object \n",
      " 7   Display            1370 non-null   object \n",
      " 8   Camera             1370 non-null   object \n",
      " 9   External_Memory    1370 non-null   object \n",
      " 10  Android_version    927 non-null    object \n",
      " 11  Price              1370 non-null   object \n",
      " 12  company            1370 non-null   object \n",
      " 13  Inbuilt_memory     1351 non-null   object \n",
      " 14  fast_charging      1281 non-null   object \n",
      " 15  Screen_resolution  1368 non-null   object \n",
      " 16  Processor          1342 non-null   object \n",
      " 17  Processor_name     1370 non-null   object \n",
      "dtypes: float64(1), int64(2), object(15)\n",
      "memory usage: 192.8+ KB\n",
      "None\n",
      "Общая информация о датасете:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1370 entries, 0 to 1369\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         1370 non-null   int64  \n",
      " 1   Name               1370 non-null   object \n",
      " 2   Rating             1370 non-null   float64\n",
      " 3   Spec_score         1370 non-null   int64  \n",
      " 4   No_of_sim          1370 non-null   object \n",
      " 5   Ram                1370 non-null   object \n",
      " 6   Battery            1370 non-null   object \n",
      " 7   Display            1370 non-null   object \n",
      " 8   Camera             1370 non-null   object \n",
      " 9   External_Memory    1370 non-null   object \n",
      " 10  Android_version    927 non-null    object \n",
      " 11  Price              1370 non-null   object \n",
      " 12  company            1370 non-null   object \n",
      " 13  Inbuilt_memory     1351 non-null   object \n",
      " 14  fast_charging      1281 non-null   object \n",
      " 15  Screen_resolution  1368 non-null   object \n",
      " 16  Processor          1342 non-null   object \n",
      " 17  Processor_name     1370 non-null   object \n",
      "dtypes: float64(1), int64(2), object(15)\n",
      "memory usage: 192.8+ KB\n",
      "None\n",
      "\n",
      "Таблица анализа пропущенных значений:\n",
      "                   Количество пропущенных значений  \\\n",
      "Unnamed: 0                                       0   \n",
      "Name                                             0   \n",
      "Rating                                           0   \n",
      "Spec_score                                       0   \n",
      "No_of_sim                                        0   \n",
      "Ram                                              0   \n",
      "Battery                                          0   \n",
      "Display                                          0   \n",
      "Camera                                           0   \n",
      "External_Memory                                  0   \n",
      "Android_version                                443   \n",
      "Price                                            0   \n",
      "company                                          0   \n",
      "Inbuilt_memory                                  19   \n",
      "fast_charging                                   89   \n",
      "Screen_resolution                                2   \n",
      "Processor                                       28   \n",
      "Processor_name                                   0   \n",
      "\n",
      "                   Процент пропущенных значений  \n",
      "Unnamed: 0                             0.000000  \n",
      "Name                                   0.000000  \n",
      "Rating                                 0.000000  \n",
      "Spec_score                             0.000000  \n",
      "No_of_sim                              0.000000  \n",
      "Ram                                    0.000000  \n",
      "Battery                                0.000000  \n",
      "Display                                0.000000  \n",
      "Camera                                 0.000000  \n",
      "External_Memory                        0.000000  \n",
      "Android_version                       32.335766  \n",
      "Price                                  0.000000  \n",
      "company                                0.000000  \n",
      "Inbuilt_memory                         1.386861  \n",
      "fast_charging                          6.496350  \n",
      "Screen_resolution                      0.145985  \n",
      "Processor                              2.043796  \n",
      "Processor_name                         0.000000  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
    "\n",
    "# Вывод общей информации о датасете\n",
    "print(\"Общая информация о датасете:\")\n",
    "print(df.info())\n",
    "\n",
    "# Вывод общей информации о датасете\n",
    "print(\"Общая информация о датасете:\")\n",
    "print(df.info())\n",
    "\n",
    "# Вывод таблицы анализа пропущенных значений\n",
    "missing_values = df.isnull().sum()\n",
    "missing_values_percentage = (missing_values / len(df)) * 100\n",
    "missing_data = pd.concat([missing_values, missing_values_percentage], axis=1, keys=['Количество пропущенных значений', 'Процент пропущенных значений'])\n",
    "\n",
    "print(\"\\nТаблица анализа пропущенных значений:\")\n",
    "print(missing_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(817, 18)\n",
      "Unnamed: 0           False\n",
      "Name                 False\n",
      "Rating               False\n",
      "Spec_score           False\n",
      "No_of_sim            False\n",
      "Ram                  False\n",
      "Battery              False\n",
      "Display              False\n",
      "Camera               False\n",
      "External_Memory      False\n",
      "Android_version      False\n",
      "Price                False\n",
      "company              False\n",
      "Inbuilt_memory       False\n",
      "fast_charging        False\n",
      "Screen_resolution    False\n",
      "Processor            False\n",
      "Processor_name       False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "\n",
    "print(df.shape)\n",
    "\n",
    "print(df.isnull().any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 822\n",
      "Размер контрольной выборки: 274\n",
      "Размер тестовой выборки: 274\n"
     ]
    }
   ],
   "source": [
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение company в обучающей выборке:\n",
      "company\n",
      "Vivo        118\n",
      "Realme      111\n",
      "Samsung      98\n",
      "Motorola     77\n",
      "Xiaomi       56\n",
      "Honor        54\n",
      "Poco         45\n",
      "Huawei       43\n",
      "OnePlus      43\n",
      "iQOO         29\n",
      "OPPO         24\n",
      "Oppo         19\n",
      "Lava         15\n",
      "TCL          13\n",
      "Google       13\n",
      "POCO         11\n",
      "Lenovo       10\n",
      "itel          9\n",
      "Asus          9\n",
      "Tecno         7\n",
      "LG            5\n",
      "Nothing       5\n",
      "Gionee        5\n",
      "Itel          2\n",
      "Coolpad       1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в контрольной выборке:\n",
      "company\n",
      "Samsung     44\n",
      "Vivo        36\n",
      "Realme      35\n",
      "Motorola    26\n",
      "Xiaomi      20\n",
      "OnePlus     17\n",
      "iQOO        17\n",
      "Honor       16\n",
      "Poco        13\n",
      "Huawei       9\n",
      "Google       6\n",
      "OPPO         5\n",
      "Nothing      5\n",
      "POCO         4\n",
      "Asus         4\n",
      "TCL          4\n",
      "itel         3\n",
      "Oppo         3\n",
      "Lenovo       2\n",
      "Lava         2\n",
      "Tecno        2\n",
      "IQOO         1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в тестовой выборке:\n",
      "company\n",
      "Realme      40\n",
      "Samsung     39\n",
      "Vivo        32\n",
      "Motorola    24\n",
      "Honor       18\n",
      "Poco        17\n",
      "OnePlus     15\n",
      "Xiaomi      14\n",
      "iQOO        11\n",
      "Huawei      10\n",
      "OPPO         9\n",
      "TCL          9\n",
      "Asus         8\n",
      "Oppo         5\n",
      "Nothing      5\n",
      "POCO         4\n",
      "Google       4\n",
      "Tecno        4\n",
      "Lenovo       2\n",
      "Lava         2\n",
      "LG           1\n",
      "Itel         1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def check_balance(df, name):\n",
    "    counts = df['company'].value_counts()\n",
    "    print(f\"Распределение company в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки не сбалансированы, и для улучшения качества модели рекомендуется провести аугментацию данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение company в обучающей выборке после oversampling:\n",
      "company\n",
      "TCL         118\n",
      "Vivo        118\n",
      "Realme      118\n",
      "Samsung     118\n",
      "Huawei      118\n",
      "LG          118\n",
      "POCO        118\n",
      "Xiaomi      118\n",
      "Motorola    118\n",
      "Honor       118\n",
      "Poco        118\n",
      "Lava        118\n",
      "OPPO        118\n",
      "Tecno       118\n",
      "OnePlus     118\n",
      "Oppo        118\n",
      "Asus        118\n",
      "iQOO        118\n",
      "Google      118\n",
      "itel        118\n",
      "Lenovo      118\n",
      "Nothing     118\n",
      "Gionee      118\n",
      "Itel        118\n",
      "Coolpad     118\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в контрольной выборке после oversampling:\n",
      "company\n",
      "Xiaomi      44\n",
      "Motorola    44\n",
      "Honor       44\n",
      "Samsung     44\n",
      "OnePlus     44\n",
      "Vivo        44\n",
      "iQOO        44\n",
      "Nothing     44\n",
      "Lenovo      44\n",
      "Realme      44\n",
      "Poco        44\n",
      "Oppo        44\n",
      "OPPO        44\n",
      "Huawei      44\n",
      "Google      44\n",
      "POCO        44\n",
      "Lava        44\n",
      "itel        44\n",
      "TCL         44\n",
      "Tecno       44\n",
      "Asus        44\n",
      "IQOO        44\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в тестовой выборке после oversampling:\n",
      "company\n",
      "iQOO        40\n",
      "OnePlus     40\n",
      "Asus        40\n",
      "Honor       40\n",
      "Vivo        40\n",
      "Samsung     40\n",
      "Xiaomi      40\n",
      "Motorola    40\n",
      "Realme      40\n",
      "Poco        40\n",
      "Lenovo      40\n",
      "TCL         40\n",
      "OPPO        40\n",
      "Oppo        40\n",
      "Huawei      40\n",
      "Lava        40\n",
      "Tecno       40\n",
      "Google      40\n",
      "POCO        40\n",
      "LG          40\n",
      "Itel        40\n",
      "Nothing     40\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в обучающей выборке после undersampling:\n",
      "company\n",
      "Asus        1\n",
      "Coolpad     1\n",
      "Gionee      1\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "Itel        1\n",
      "LG          1\n",
      "Lava        1\n",
      "Lenovo      1\n",
      "Motorola    1\n",
      "Nothing     1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Tecno       1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "itel        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в контрольной выборке после undersampling:\n",
      "company\n",
      "Asus        1\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "IQOO        1\n",
      "Lava        1\n",
      "Lenovo      1\n",
      "Motorola    1\n",
      "Nothing     1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Tecno       1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "itel        1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение company в тестовой выборке после undersampling:\n",
      "company\n",
      "Asus        1\n",
      "Google      1\n",
      "Honor       1\n",
      "Huawei      1\n",
      "Itel        1\n",
      "LG          1\n",
      "Lava        1\n",
      "Lenovo      1\n",
      "Motorola    1\n",
      "Nothing     1\n",
      "OPPO        1\n",
      "OnePlus     1\n",
      "Oppo        1\n",
      "POCO        1\n",
      "Poco        1\n",
      "Realme      1\n",
      "Samsung     1\n",
      "TCL         1\n",
      "Tecno       1\n",
      "Vivo        1\n",
      "Xiaomi      1\n",
      "iQOO        1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df['company'].value_counts()\n",
    "    print(f\"Распределение company в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop('company', axis=1)\n",
    "    y = df['company']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")\n",
    "\n",
    "def undersample(df):\n",
    "    X = df.drop('company', axis=1)\n",
    "    y = df['company']\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42) # type: ignore\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_undersampled = undersample(train_df)\n",
    "val_df_undersampled = undersample(val_df)\n",
    "test_df_undersampled = undersample(test_df)\n",
    "\n",
    "check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Цены на автомобили\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n",
      "       'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n",
      "       'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n",
      "       'Airbags'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: Данные о ценах на автомобили, включая их характеристики\n",
    "\n",
    "Объект наблюдения: автомобиль\n",
    "\n",
    "Атрибуты: идентификатор, цена, налог, производитель, модель, год производства, категория, наличие кожаного салона, тип топлива, объем двигателя, пробег автомобиля, количество цилиндров в двигателе, тип коробки передач, тип привода, количество дверей, расположение руля, цвет, количество подушек безопасностей.\n",
    "\n",
    "Пример бизнес-цели: \n",
    "1. Анализ данных: Изучение и очистка данных для выявления закономерностей и корреляций между характеристиками автомобилей и их ценами.\n",
    "2. Разработка модели: Создание и обучение модели машинного обучения, которая будет прогнозировать цены на автомобили на основе их характеристик.\n",
    "3. Внедрение: Интеграция модели в систему ценообразования компании для автоматического расчета цен на автомобили.\n",
    "\n",
    "\n",
    "Актуальность: Данный датасет является актуальным и ценным ресурсом для компаний, занимающихся продажей автомобилей, а также для исследователей и инвесторов, поскольку он предоставляет обширную информацию о ценах и характеристиках автомобилей на вторичном рынке. Эти данные могут быть использованы для разработки моделей прогнозирования цен, анализа рыночных тенденций и принятия обоснованных бизнес-решений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Преобразуем год производства в целочисленный тип\n",
    "df['Prod. year'] = df['Prod. year'].astype(int)\n",
    "\n",
    "# Визуализация данных\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Prod. year'], df['Price'])\n",
    "plt.xlabel('Production Year')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Scatter Plot of Price vs Production Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "             ID  Price  Levy Manufacturer     Model  Prod. year   Category  \\\n",
      "41     45797488  45734  1091      HYUNDAI        H1        2016  Universal   \n",
      "72     45797480  43952  1249      HYUNDAI        H1        2017  Universal   \n",
      "75     45624039  42337     -         FORD   Mustang        2016  Cabriolet   \n",
      "112    45731735  44752  1091      HYUNDAI        H1        2016  Universal   \n",
      "172    45802937  43880   891      HYUNDAI  Santa FE        2016       Jeep   \n",
      "...         ...    ...   ...          ...       ...         ...        ...   \n",
      "19000  45646433  43278  1514        LEXUS    GX 460        2011       Jeep   \n",
      "19056  45802290  44843  1091      HYUNDAI        H1        2016  Universal   \n",
      "19089  45810098  44611   891        HONDA     Civic        2016      Sedan   \n",
      "19136  45731793  41811  1249      HYUNDAI        H1        2017  Universal   \n",
      "19175  45804283  42883   900         JEEP   Compass        2015       Jeep   \n",
      "\n",
      "      Leather interior Fuel type Engine volume    Mileage  Cylinders  \\\n",
      "41                 Yes    Diesel           2.5   61057 km        4.0   \n",
      "72                 Yes    Diesel           2.5  111643 km        4.0   \n",
      "75                 Yes    Petrol     2.3 Turbo   75000 km        4.0   \n",
      "112                Yes    Diesel           2.5   86000 km        4.0   \n",
      "172                Yes    Diesel             2  113700 km        4.0   \n",
      "...                ...       ...           ...        ...        ...   \n",
      "19000              Yes    Petrol           4.6  160138 km        8.0   \n",
      "19056              Yes    Diesel           2.5  133687 km        4.0   \n",
      "19089              Yes    Petrol             2   44914 km        4.0   \n",
      "19136              Yes    Diesel           2.5  146644 km        4.0   \n",
      "19175              Yes    Petrol           2.4   62200 km        4.0   \n",
      "\n",
      "      Gear box type Drive wheels   Doors       Wheel   Color  Airbags  \n",
      "41        Automatic        Front  04-May  Left wheel   Black        4  \n",
      "72        Automatic        Front  04-May  Left wheel    Grey        4  \n",
      "75        Tiptronic         Rear  02-Mar  Left wheel  Silver        6  \n",
      "112       Automatic        Front  04-May  Left wheel    Grey        4  \n",
      "172       Automatic        Front  04-May  Left wheel  Silver        4  \n",
      "...             ...          ...     ...         ...     ...      ...  \n",
      "19000     Automatic          4x4  04-May  Left wheel   White        0  \n",
      "19056     Automatic        Front  04-May  Left wheel    Grey        4  \n",
      "19089     Automatic        Front  04-May  Left wheel  Silver        4  \n",
      "19136     Automatic        Front  04-May  Left wheel   Black        4  \n",
      "19175     Automatic        Front  04-May  Left wheel   White        4  \n",
      "\n",
      "[627 rows x 18 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Преобразуем год производства в целочисленный тип\n",
    "df['Prod. year'] = df['Prod. year'].astype(int)\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df['Price'].quantile(0.25)\n",
    "Q3 = df['Price'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df['Price'] < (Q1 - threshold)) | (df['Price'] > (Q3 + threshold))\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы заменим выбросы на медианное значение\n",
    "median_price = df['Price'].median()\n",
    "df.loc[outliers, 'Price'] = median_price\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Prod. year'], df['Price'])\n",
    "plt.xlabel('Production Year')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Scatter Plot of Price vs Production Year (After Handling Outliers)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Смртрем, есть ли пропущенные значения. Пропущенных данных  не обнаружено."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                  0\n",
      "Price               0\n",
      "Levy                0\n",
      "Manufacturer        0\n",
      "Model               0\n",
      "Prod. year          0\n",
      "Category            0\n",
      "Leather interior    0\n",
      "Fuel type           0\n",
      "Engine volume       0\n",
      "Mileage             0\n",
      "Cylinders           0\n",
      "Gear box type       0\n",
      "Drive wheels        0\n",
      "Doors               0\n",
      "Wheel               0\n",
      "Color               0\n",
      "Airbags             0\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим выборки.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 11542\n",
      "Размер контрольной выборки: 3847\n",
      "Размер тестовой выборки: 3848\n"
     ]
    }
   ],
   "source": [
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n",
    "\n",
    "# Разделение данных на обучающую и временную выборки\n",
    "train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разделение остатка на контрольную и тестовую выборки\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Сохранение выборок в файлы\n",
    "train_df.to_csv(\"..//static//csv//train_data.csv\", index=False)\n",
    "val_df.to_csv(\"..//static//csv//val_data.csv\", index=False)\n",
    "test_df.to_csv(\"..//static//csv//test_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Category в обучающей выборке:\n",
      "Category\n",
      "Sedan          5289\n",
      "Jeep           3246\n",
      "Hatchback      1684\n",
      "Minivan         396\n",
      "Coupe           318\n",
      "Universal       216\n",
      "Microbus        184\n",
      "Goods wagon     151\n",
      "Pickup           31\n",
      "Cabriolet        20\n",
      "Limousine         7\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 45.82%\n",
      "Процент автомобилей категории 'Джип': 28.12%\n",
      "\n",
      "Распределение Category в контрольной выборке:\n",
      "Category\n",
      "Sedan          1697\n",
      "Jeep           1109\n",
      "Hatchback       608\n",
      "Minivan         129\n",
      "Coupe           105\n",
      "Universal        73\n",
      "Microbus         57\n",
      "Goods wagon      42\n",
      "Pickup           17\n",
      "Cabriolet         9\n",
      "Limousine         1\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 44.11%\n",
      "Процент автомобилей категории 'Джип': 28.83%\n",
      "\n",
      "Распределение Category в тестовой выборке:\n",
      "Category\n",
      "Sedan          1750\n",
      "Jeep           1118\n",
      "Hatchback       555\n",
      "Minivan         122\n",
      "Coupe           109\n",
      "Universal        75\n",
      "Microbus         65\n",
      "Goods wagon      40\n",
      "Cabriolet         7\n",
      "Pickup            4\n",
      "Limousine         3\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 45.48%\n",
      "Процент автомобилей категории 'Джип': 29.05%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "Необходима аугментация данных для балансировки классов.\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n",
    "val_df = pd.read_csv(\"..//static//csv//val_data.csv\")\n",
    "test_df = pd.read_csv(\"..//static//csv//test_data.csv\")\n",
    "\n",
    "# Оценка сбалансированности\n",
    "def check_balance(df, name):\n",
    "    counts = df['Category'].value_counts()\n",
    "    print(f\"Распределение Category в {name}:\")\n",
    "    print(counts)\n",
    "    print(f\"Процент автомобилей категории 'Седан': {counts['Sedan'] / len(df) * 100:.2f}%\")\n",
    "    print(f\"Процент автомобилей категории 'Джип': {counts['Jeep'] / len(df) * 100:.2f}%\")\n",
    "    print()\n",
    "\n",
    "# Определение необходимости аугментации данных\n",
    "def need_augmentation(df):\n",
    "    counts = df['Category'].value_counts()\n",
    "    ratio = counts['Sedan'] / counts['Jeep']\n",
    "    if ratio > 1.5 or ratio < 0.67:\n",
    "        print(\"Необходима аугментация данных для балансировки классов.\")\n",
    "    else:\n",
    "        print(\"Аугментация данных не требуется.\")\n",
    "        \n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")\n",
    "\n",
    "need_augmentation(train_df)\n",
    "need_augmentation(val_df)\n",
    "need_augmentation(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По результатам анализа требуется приращение."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оверсэмплинг:\n",
      "Распределение Category в обучающей выборке:\n",
      "Category\n",
      "Jeep           5289\n",
      "Hatchback      5289\n",
      "Sedan          5289\n",
      "Goods wagon    5289\n",
      "Cabriolet      5289\n",
      "Universal      5289\n",
      "Minivan        5289\n",
      "Microbus       5289\n",
      "Coupe          5289\n",
      "Pickup         5289\n",
      "Limousine      5289\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n",
      "Распределение Category в контрольной выборке:\n",
      "Category\n",
      "Jeep           1697\n",
      "Sedan          1697\n",
      "Minivan        1697\n",
      "Coupe          1697\n",
      "Hatchback      1697\n",
      "Goods wagon    1697\n",
      "Universal      1697\n",
      "Microbus       1697\n",
      "Pickup         1697\n",
      "Cabriolet      1697\n",
      "Limousine      1697\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n",
      "Распределение Category в тестовой выборке:\n",
      "Category\n",
      "Jeep           1750\n",
      "Hatchback      1750\n",
      "Sedan          1750\n",
      "Coupe          1750\n",
      "Minivan        1750\n",
      "Goods wagon    1750\n",
      "Microbus       1750\n",
      "Universal      1750\n",
      "Cabriolet      1750\n",
      "Pickup         1750\n",
      "Limousine      1750\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n",
      "Андерсэмплинг:\n",
      "Распределение Category в обучающей выборке:\n",
      "Category\n",
      "Cabriolet      7\n",
      "Coupe          7\n",
      "Goods wagon    7\n",
      "Hatchback      7\n",
      "Jeep           7\n",
      "Limousine      7\n",
      "Microbus       7\n",
      "Minivan        7\n",
      "Pickup         7\n",
      "Sedan          7\n",
      "Universal      7\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n",
      "Распределение Category в контрольной выборке:\n",
      "Category\n",
      "Cabriolet      1\n",
      "Coupe          1\n",
      "Goods wagon    1\n",
      "Hatchback      1\n",
      "Jeep           1\n",
      "Limousine      1\n",
      "Microbus       1\n",
      "Minivan        1\n",
      "Pickup         1\n",
      "Sedan          1\n",
      "Universal      1\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n",
      "Распределение Category в тестовой выборке:\n",
      "Category\n",
      "Cabriolet      3\n",
      "Coupe          3\n",
      "Goods wagon    3\n",
      "Hatchback      3\n",
      "Jeep           3\n",
      "Limousine      3\n",
      "Microbus       3\n",
      "Minivan        3\n",
      "Pickup         3\n",
      "Sedan          3\n",
      "Universal      3\n",
      "Name: count, dtype: int64\n",
      "Процент автомобилей категории 'Седан': 9.09%\n",
      "Процент автомобилей категории 'Джип': 9.09%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Загрузка данных\n",
    "train_df = pd.read_csv(\"..//static//csv//train_data.csv\")\n",
    "val_df = pd.read_csv(\"..//static//csv//val_data.csv\")\n",
    "test_df = pd.read_csv(\"..//static//csv//test_data.csv\")\n",
    "\n",
    "# Преобразование категориальных признаков в числовые\n",
    "def encode(df):\n",
    "    label_encoders = {}\n",
    "    for column in df.select_dtypes(include=['object']).columns:\n",
    "        if column != 'Category':  # Пропускаем целевую переменную\n",
    "            le = LabelEncoder()\n",
    "            df[column] = le.fit_transform(df[column])\n",
    "            label_encoders[column] = le\n",
    "    return label_encoders\n",
    "\n",
    "# Преобразование целевой переменной в числовые значения\n",
    "def encode_target(df):\n",
    "    le = LabelEncoder()\n",
    "    df['Category'] = le.fit_transform(df['Category'])\n",
    "    return le\n",
    "\n",
    "# Применение кодирования\n",
    "label_encoders = encode(train_df)\n",
    "encode(val_df)\n",
    "encode(test_df)\n",
    "\n",
    "# Кодирование целевой переменной\n",
    "le_target = encode_target(train_df)\n",
    "encode_target(val_df)\n",
    "encode_target(test_df)\n",
    "\n",
    "# Проверка типов данных\n",
    "def check_data_types(df):\n",
    "    for column in df.columns:\n",
    "        if df[column].dtype == 'object':\n",
    "            print(f\"Столбец '{column}' содержит строковые данные.\")\n",
    "\n",
    "check_data_types(train_df)\n",
    "check_data_types(val_df)\n",
    "check_data_types(test_df)\n",
    "\n",
    "# Функция для выполнения oversampling\n",
    "def oversample(df):\n",
    "    if 'Category' not in df.columns:\n",
    "        print(\"Столбец 'Category' отсутствует.\")\n",
    "        return df\n",
    "    \n",
    "    X = df.drop('Category', axis=1)\n",
    "    y = df['Category']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "# Функция для выполнения undersampling\n",
    "def undersample(df):\n",
    "    if 'Category' not in df.columns:\n",
    "        print(\"Столбец 'Category' отсутствует.\")\n",
    "        return df\n",
    "    \n",
    "    X = df.drop('Category', axis=1)\n",
    "    y = df['Category']\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42)\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "# Применение oversampling и undersampling к каждой выборке\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "train_df_undersampled = undersample(train_df)\n",
    "val_df_undersampled = undersample(val_df)\n",
    "test_df_undersampled = undersample(test_df)\n",
    "\n",
    "# Обратное преобразование целевой переменной в строковые метки\n",
    "def decode_target(df, le_target):\n",
    "    df['Category'] = le_target.inverse_transform(df['Category'])\n",
    "\n",
    "decode_target(train_df_oversampled, le_target)\n",
    "decode_target(val_df_oversampled, le_target)\n",
    "decode_target(test_df_oversampled, le_target)\n",
    "\n",
    "decode_target(train_df_undersampled, le_target)\n",
    "decode_target(val_df_undersampled, le_target)\n",
    "decode_target(test_df_undersampled, le_target)\n",
    "\n",
    "# Проверка результатов\n",
    "def check_balance(df, name):\n",
    "    if 'Category' not in df.columns:\n",
    "        print(f\"Столбец 'Category' отсутствует в {name}.\")\n",
    "        return\n",
    "    \n",
    "    counts = df['Category'].value_counts()\n",
    "    print(f\"Распределение Category в {name}:\")\n",
    "    print(counts)\n",
    "    \n",
    "    if 'Sedan' in counts and 'Jeep' in counts:\n",
    "        print(f\"Процент автомобилей категории 'Седан': {counts['Sedan'] / len(df) * 100:.2f}%\")\n",
    "        print(f\"Процент автомобилей категории 'Джип': {counts['Jeep'] / len(df) * 100:.2f}%\")\n",
    "    else:\n",
    "        print(\"Отсутствуют одна или обе категории (Седан/Внедорожник).\")\n",
    "    print()\n",
    "\n",
    "# Проверка сбалансированности после oversampling\n",
    "print(\"Оверсэмплинг:\")\n",
    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
    "\n",
    "# Проверка сбалансированности после undersampling\n",
    "print(\"Андерсэмплинг:\")\n",
    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Цены на кофе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: Данные о ценах на акции кофе Starbucks Corporation.\n",
    "\n",
    "Объект наблюдения: цены на акции кофе.\n",
    "\n",
    "Атрибуты:  дата , цена открытия , самая высокая цена дня, самая низкая цена дня, цена закрытия , скорректированная цена закрытия и объем торгов.\n",
    "\n",
    "Пример бизнес-цели: \n",
    "1. Анализ данных: Изучение и очистка данных для выявления закономерностей и корреляций между объёмом торгов и цены на акции кофе Starbucks Corporation.\n",
    "2. Разработка модели: Создание и обучение модели машинного обучения, которая будет прогнозировать цены на акции кофе Starbucks Corporation.\n",
    "3. Внедрение: Интеграция модели в систему ценообразования компании для автоматического расчета цен на акции кофе.\n",
    "\n",
    "\n",
    "Актуальность:Эти данные бесценны для проведения исторического анализа , прогнозирования будущей динамики акций и понимания рыночных тенденций, связанных с акциями Starbucks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yf2/nr7Rp00Z9+vQpU/uqgiVLlig1NVWzZ8/W7Nmzi9w+c+ZM59wuX2vVqpWkgnktxZ0/X3nhhRd02223ad68efrhhx907733avLkyVq7dq0aNmwou90ui8Wi+fPnKygoqMj9C/8eFRYWFqZBgwZp7ty5euONN3To0CGtWrVKzzzzjMt+Y8eO1cCBA/Xll1/q+++/1xNPPKHJkydryZIlat++fYnHuPrqq2Wz2TRr1izdcsstmjVrloKCgnTzzTd7/4K4cW7v7bny8/PdvibutpW03fjfHLzyvtYAqp9qE4L69++v/v37F3t7Tk6OHn/8cX388cc6efKkWrdurSlTpjirz2zcuFH5+fl6+umnnUMcHnzwQV177bXKy8vzaL0LAL7nGAKWmpoqSWrevLnsdru2bdtW7EXssmXLdOzYMX3xxRcu5X3dVVcr7iKsuO2OCf8xMTGVJqg0adJEkrRjx44it/3666+qXbu2syx3cc+rtMd1VKhz2LFjh/N2b82cOVN169bV66+/XuS2L774QnPnztWbb76piIgINWnSxNn7Vvj4ZdG/f38FBQVpxowZZSqOcO5rcu7wr9zcXKWkpBT5nWjTpo3atGmjf/7zn1q9erW6deumN998U08//bSaN28uwzCUmJjo7Lnx1pAhQ/TBBx9o8eLF2r59uwzDcA6FO1fz5s31j3/8Q//4xz+0c+dOtWvXTi+88IJmzJhR4uOHhYXpxhtv1IcffqhDhw7p008/1RVXXKH4+HhJBUMTa9SoUezvntVqLdJLc66aNWu6XbNq7969Lq9vefnitQZQvVSb4XClufvuu7VmzRrNnj1bW7Zs0U033aR+/fo5P1w7dOjgrPCUn5+v9PR0ffTRR+rTpw8BCPCDpUuXuq2c5pjP4hhuM2jQIFmtVj311FNFenQc93d803vu4+Xm5uqNN94o8viRkZFuh8c5QkPhC7QOHTqoefPm+ve//61Tp04Vud+RI0eKfY4VJSEhQe3atdMHH3zg0t6tW7fqhx9+0FVXXeXcVtzzcqdjx46qW7eu3nzzTZdyw/Pnz9f27ds1YMAAr9t65swZffHFF7r66qt14403Fvm5++67lZmZ6ZwHdtVVV2nt2rVav3698zGOHDlSbDnt0jRq1Eh//etf9cMPP+jVV18tcrvdbtcLL7xQ7GKpffr0UWhoqF555RWX36/33ntP6enpztckIyPDOR/FoU2bNrJarc7X8vrrr1dQUJAmTpxY5HffMAwdO3as1OfTp08fxcXFac6cOZozZ446d+7sMvzy9OnTys7OdrlP8+bNFR0d7baEtDvDhg1TXl6e/va3v+nIkSMu87GCgoL0l7/8RfPmzXMZPn7o0CHNmjVL3bt3dw5BdKd58+Zau3atcnNzndu++eYbn5et9sVrDaB6qTY9QSXZt2+fpk2bpn379ql+/fqSCnp5FixYoGnTpumZZ55RYmKifvjhBw0ePFh/+9vflJ+fr65du5Z5QjEA79xzzz06ffq0rrvuOrVq1Uq5ublavXq15syZo6ZNm2rkyJGSpPPOO0+PP/64Jk2apB49euj6669XWFiYNmzYoPr162vy5Mm69NJLVbNmTY0YMUL33nuvLBaLPvroI7chq0OHDpozZ44eeOABderUSVFRURo4cKCaN2+u2NhYvfnmm4qOjlZkZKS6dOmixMREvfvuu+rfv78uvPBCjRw5Ug0aNNCBAwe0dOlSxcTE6Ouvv/b3y6fnn39e/fv3V9euXTVq1ChniWybzaYJEya4PF+poDz4zTffrJCQEA0cONDtAq4hISGaMmWKRo4cqV69emno0KHOEtlNmzbV/fff73U7v/rqK2VmZuqaa65xe/sll1yiOnXqaObMmRoyZIgefvhhffTRR+rXr5/uu+8+Z4nsJk2aaMuWLV4fXyoYprZ7927de++9zkBWs2ZN7du3T59++ql+/fXXYod71alTR+PGjdPEiRPVr18/XXPNNdqxY4feeOMNderUyTnvbMmSJbr77rt10003qWXLljp79qw++ugjBQUF6YYbbpBUEACefvppjRs3Tnv27NGgQYMUHR2tlJQUzZ07V3feeacefPDBEp9LSEiIrr/+es2ePVtZWVn697//7XL7b7/9pt69e2vw4MFKSkpScHCw5s6dq0OHDnk8pK1Xr15q2LCh5s2bp4iIiCJDzp9++mktXLhQ3bt319///ncFBwfrrbfeUk5Ojp577rkSH/uOO+7QZ599pn79+mnw4MHavXu3ZsyY4VGJdW/44rUGUM34vR6dH0gy5s6d6/y/o5xrZGSky09wcLAxePBgwzAKyoq2aNHCeOihh4zk5GRj+fLlRq9evYzevXu7lMEFUDHmz59v3H777UarVq2MqKgoIzQ01DjvvPOMe+65xzh06FCR/d9//32jffv2RlhYmFGzZk2jV69eznLLhmEYq1atMi655BIjIiLCqF+/vrPktgqV6D116pRxyy23GLGxsYYkl7K68+bNM5KSkozg4OAiJak3bdpkXH/99UatWrWMsLAwo0mTJsbgwYONxYsXO/dxlMg+cuSIR6+BowxwSWWVDcN9iWzDMIxFixYZ3bp1MyIiIoyYmBhj4MCBxrZt24rcf9KkSUaDBg0Mq9XqUbnsOXPmOF/ruLg4Y9iwYcYff/zhso+nJbIHDhxohIeHG1lZWcXuc9tttxkhISHG0aNHDcMwjC1bthi9evUywsPDjQYNGhiTJk0y3nvvvTKVyHY4e/as8e677xo9evQwbDabERISYjRp0sQYOXKkS/nswiWyHV577TWjVatWRkhIiFGvXj1j9OjRxokTJ5y3//7778btt99uNG/e3AgPDzfi4uKMyy+/3Fi0aFGRtnz++edG9+7dnZ9NrVq1MsaMGeMsQV2ahQsXGpIMi8Vi7N+/3+W2o0ePGmPGjDFatWplREZGGjabzejSpYvxySefePTYDg899JAhyfmZWVhycrJx5ZVXGlFRUUaNGjWMyy+/3Fi9erXLPu5KZBuGYbzwwgtGgwYNjLCwMKNbt27Gjz/+WGyJ7MJ/G8X93hX3t1fe1xpA9WExjDKs3FfJWSwWzZ07V4MGDZJUUC1n2LBh+uWXX4pMiIyKilJ8fLyeeOIJLViwQBs2bHDe9scff6hRo0Zas2aNLrnkEn8+BQCAl3r06KGwsDAtWrQo0E0BAFRyphgO1759e+Xn5+vw4cPOld8LO336dJE1RxyBqTyVpAAA/pGamlrsWkoAAJyr2hRGOHXqlDZv3qzNmzdLKqgCtXnzZu3bt08tW7bUsGHDdOutt+qLL75QSkqK1q9fr8mTJzsXthswYIA2bNigp556Sjt37lRycrJGjhypJk2alFpCFAAQOKtXr9aDDz6o3bt3q3fv3oFuDgCgCqg2w+GWLVvmXHn8XCNGjND06dOVl5enp59+Wh9++KEOHDig2rVr65JLLtHEiRPVpk0bSdLs2bP13HPP6bffflONGjXUtWtXTZkyxbmuBACg8hk5cqTmz5+voUOH6vnnn1dwsCkGOQAAyqHahCAAAAAA8ES1GQ4HAAAAAJ4gBAEAAAAwlSo9cNput+vgwYOKjo6WxWIJdHMAAAAABIhhGMrMzFT9+vWLVH0urEqHoIMHD6pRo0aBbgYAAACASmL//v1q2LBhiftU6RAUHR0tqeCJxsTEBLg1AAAAAAIlIyNDjRo1cmaEklTpEOQYAhcTE0MIAgAAAODRNBkKIwAAAAAwFUIQAAAAAFMhBAEAAAAwlYCHoAMHDmj48OGqVauWIiIi1KZNG/3444+BbhYAAACAaiqghRFOnDihbt266fLLL9f8+fNVp04d7dy5UzVr1gxkswAAAABUYwENQVOmTFGjRo00bdo057bExMQAtggAAABAdRfQ4XBfffWVOnbsqJtuukl169ZV+/bt9c477xS7f05OjjIyMlx+AAAAAMAbAQ1Bv//+u6ZOnaoWLVro+++/1+jRo3Xvvffqgw8+cLv/5MmTZbPZnD+NGjXyc4sBAAAAVHUWwzCMQB08NDRUHTt21OrVq53b7r33Xm3YsEFr1qwpsn9OTo5ycnKc/3esCpuens5iqQAAAICJZWRkyGazeZQNAtoTlJCQoKSkJJdtF1xwgfbt2+d2/7CwMMXExLj8AAAAAIA3AhqCunXrph07drhs++2339SkSZMAtQgAAABAdRfQEHT//fdr7dq1euaZZ7Rr1y7NmjVLb7/9tsaMGRPIZgEAAACoxgIagjp16qS5c+fq448/VuvWrTVp0iS99NJLGjZsWCCbBQAAAKAaC2hhhPLyZvJTZZRvN7Q+5bgOZ2arbnS4OifGKchqCXSzAAAAgCrHm2wQ0MVSzWzB1lRN/HqbUtOzndsSbOEaPzBJ/VonBLBlAAAAQPUW0OFwZrVga6pGz0h2CUCSlJaerdEzkrVga2qAWgYAAABUf4QgP8u3G5r49Ta5G4Po2Dbx623Kt1fZUYoAAABApUYI8rP1KceL9ACdy5CUmp6t9SnH/dcoAAAAwEQIQX52OLP4AFSW/QAAAAB4hxDkZ3Wjw326HwAAAADvEIL8rHNinBJs4SquELZFBVXiOifG+bNZAAAAgGkQgvwsyGrR+IFJklQkCDn+P35gEusFAQAAABWEEBQA/VonaOrwixVvcx3yFm8L19ThF7NOEAAAAFCBWCw1QPq1TlDfpHitTzmuw5nZqhtdMASOHiAAAACgYhGCAijIalHX5rUC3QwAAADAVBgOBwAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATCWgIWjChAmyWCwuP61atQpkkwAAAABUc8GBbsCFF16oRYsWOf8fHBzwJgEAAACoxgKeOIKDgxUfHx/oZgAAAAAwiYDPCdq5c6fq16+vZs2aadiwYdq3b1+x++bk5CgjI8PlBwAAAAC8EdAQ1KVLF02fPl0LFizQ1KlTlZKSoh49eigzM9Pt/pMnT5bNZnP+NGrUyM8tBgAAAFDVWQzDMALdCIeTJ0+qSZMm+s9//qNRo0YVuT0nJ0c5OTnO/2dkZKhRo0ZKT09XTEyMP5sKAAAAoBLJyMiQzWbzKBsEfE7QuWJjY9WyZUvt2rXL7e1hYWEKCwvzc6sAAAAAVCcBnxN0rlOnTmn37t1KSEgIdFMAAAAAVFMBDUEPPvigli9frj179mj16tW67rrrFBQUpKFDhwayWQAAAACqsYAOh/vjjz80dOhQHTt2THXq1FH37t21du1a1alTJ5DNAgAAAFCNBTQEzZ49O5CHBwAAAGBClWpOEAAAAABUNEIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwlUoTgp599llZLBaNHTs20E0BAAAAUI1VihC0YcMGvfXWW2rbtm2gmwIAAACgmgt4CDp16pSGDRumd955RzVr1gx0cwAAAABUcwEPQWPGjNGAAQPUp0+fUvfNyclRRkaGyw8AAAAAeCM4kAefPXu2kpOTtWHDBo/2nzx5siZOnFjBrQIAAABQnQWsJ2j//v267777NHPmTIWHh3t0n3Hjxik9Pd35s3///gpuJQAAAIDqxmIYhhGIA3/55Ze67rrrFBQU5NyWn58vi8Uiq9WqnJwcl9vcycjIkM1mU3p6umJiYiq6yQAAAAAqKW+yQcCGw/Xu3Vs///yzy7aRI0eqVatWeuSRR0oNQAAAAABQFgELQdHR0WrdurXLtsjISNWqVavIdgAAAADwlYBXhwMAAAAAfwpodbjCli1bFugmAAAAAKjm6AkCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCplCkH//e9/NXz4cHXt2lUHDhyQJH300UdauXKlTxsHAAAAAL7mdQj6/PPPdeWVVyoiIkKbNm1STk6OJCk9PV3PPPOMzxsIAAAAAL7kdQh6+umn9eabb+qdd95RSEiIc3u3bt2UnJzs08YBAAAAgK95HYJ27Nihnj17Ftlus9l08uRJX7QJAAAAACqM1yEoPj5eu3btKrJ95cqVatasmU8aBQAAAAAVxesQ9Ne//lX33Xef1q1bJ4vFooMHD2rmzJl68MEHNXr06IpoIwAAAAD4TLC3d3j00Udlt9vVu3dvnT59Wj179lRYWJgefPBB3XPPPRXRRgAAAADwGYthGEZZ7pibm6tdu3bp1KlTSkpKUlRUlK/bVqqMjAzZbDalp6crJibG78cHAAAAUDl4kw287glyCA0NVVJSUlnvDgAAAAAB4XUIys7O1quvvqqlS5fq8OHDstvtLrdTJhsAAABAZeZ1CBo1apR++OEH3XjjjercubMsFktFtAsAAAAAKoTXIeibb77Rd999p27dulVEewAAAACgQnldIrtBgwaKjo6uiLYAAAAAQIXzOgS98MILeuSRR7R3796KaA8AAAAAVCivQ1DHjh2VnZ2tZs2aKTo6WnFxcS4/3pg6daratm2rmJgYxcTEqGvXrpo/f763TQIAAAAAj3k9J2jo0KE6cOCAnnnmGdWrV69chREaNmyoZ599Vi1atJBhGPrggw907bXXatOmTbrwwgvL/LgAAAAAUByvF0utUaOG1qxZo4suuqhCGhQXF6fnn39eo0aNKnXf6rhYar7d0PqU4zqcma260eHqnBinICsV+AAAAICSVOhiqa1atdKZM2fK3Lji5Ofn69NPP1VWVpa6du3qdp+cnBzl5OQ4/5+RkeHzdgTSgq2pmvj1NqWmZzu3JdjCNX5gkvq1TghgywAAAIDqw+s5Qc8++6z+8Y9/aNmyZTp27JgyMjJcfrz1888/KyoqSmFhYbrrrrs0d+5cJSUlud138uTJstlszp9GjRp5fbzKasHWVI2ekewSgCQpLT1bo2cka8HW1AC1DAAAAKhevB4OZ7UW5KbCc4EMw5DFYlF+fr5XDcjNzdW+ffuUnp6uzz77TO+++66WL1/uNgi56wlq1KhRlR8Ol2831H3KkiIByMEiKd4WrpWPXMHQOAAAAMCNCh0Ot3Tp0jI3zJ3Q0FCdd955kqQOHTpow4YNevnll/XWW28V2TcsLExhYWE+PX5lsD7leLEBSJIMSanp2Vqfclxdm9fyX8MAAACAasjrENSrV6+KaIeT3W536e0xg8OZxQegsuwHAAAAoHheh6AVK1aUeHvPnj09fqxx48apf//+aty4sTIzMzVr1iwtW7ZM33//vbfNqtLqRof7dD8AAAAAxfM6BF122WVFtp07P8ibOUGHDx/WrbfeqtTUVNlsNrVt21bff/+9+vbt622zqrTOiXFKsIUrLT1b7iZoOeYEdU70bjFaAAAAAEV5HYJOnDjh8v+8vDxt2rRJTzzxhP71r3959Vjvvfeet4evloKsFo0fmKTRM5JlkVyCkCNejh+YRFEEAAAAwAe8DkE2m63Itr59+yo0NFQPPPCANm7c6JOGmU2/1gmaOvziIusExbNOEAAAAOBTXoeg4tSrV087duzw1cOZUr/WCeqbFK/1Kcd1ODNbdaMLhsDRAwQAAAD4jtchaMuWLS7/NwxDqampevbZZ9WuXTtftcu0gqwWymADAAAAFcjrENSuXTtZLBYVXmP1kksu0fvvv++zhgEAAABARfA6BKWkpLj832q1qk6dOgoPp3wzAAAAgMrP6xDUpEmTimgHAAAAAPiFRyHolVde8fgB77333jI3BgAAAAAqmsUoPLnHjcTERM8ezGLR77//Xu5GeSojI0M2m03p6emKiYnx23EBAAAAVC7eZAOPeoIKzwMCAAAAgKrKWp47G4ZRpEocAAAAAFRmZQpBH374odq0aaOIiAhFRESobdu2+uijj3zdNgAAAADwOa+rw/3nP//RE088obvvvlvdunWTJK1cuVJ33XWXjh49qvvvv9/njQQAAAAAX/GoMMK5EhMTNXHiRN16660u2z/44ANNmDDBr/OHKIwAAAAAQPIuG3g9HC41NVWXXnppke2XXnqpUlNTvX04AAAAAPArr0PQeeedp08++aTI9jlz5qhFixY+aRQAAAAAVBSP5wRt3bpVrVu31lNPPaXBgwdrxYoVzjlBq1at0uLFi92GIwAAAACoTDzuCWrbtq26dOmio0ePasmSJapdu7a+/PJLffnll6pdu7bWr1+v6667riLbCgAAAADl5nFP0PLlyzVt2jQ9+OCDstvtuuGGG/Tiiy+qZ8+eFdk+AAAAAPApj3uCevTooffff1+pqal69dVXtWfPHl1++eVq2bKlpkyZorS0tIpsJwAAAAD4hNeFESIjIzVy5EgtX75cO3bs0E033aTXX39djRs31jXXXFMRbQQAAAAAn/F6naDCsrKyNHPmTI0bN04nT55Ufn6+r9pWKtYJAgAAACB5lw08nhNU2IoVK/T+++/r888/l9Vq1eDBgzVq1KiyPhwAAAAA+IVXIejgwYOaPn26pk+frl27dunSSy/VK6+8osGDBysyMrKi2ggAAAAAPuNxCOrfv78WLVqk2rVr69Zbb9Xtt9+u888/vyLbBgAAAAA+53EICgkJ0Weffaarr75aQUFBFdkmAAAAAKgwHoegr776qiLbAQAAAAB+4XWJbAAAAACoyghBAAAAAEyFEAQAAADAVAhBAAAAAEyFEAQAAADAVAhBAAAAAEyFEAQAAADAVAhBAAAAAEyFEAQAAADAVAhBAAAAAEyFEAQAAADAVAhBAAAAAEwlONANqI7y7YbWpxzX4cxs1Y0OV+fEOAVZLYFuFgAAAAARgnxuwdZUTfx6m1LTs53bEmzhGj8wSf1aJwSwZQAAAAAkhsP51IKtqRo9I9klAElSWnq2Rs9I1oKtqQFqGQAAAAAHQpCP5NsNTfx6mww3tzm2Tfx6m/Lt7vYAAAAA4C+EIB/ItxuaviqlSA/QuQxJqenZWp9y3H8NAwAAAFAEc4LKyd0coJIczvRsPwAAAAAVgxBUDo45QN4McKsbHV5h7QEAAABQOkJQGZU0B8gdi6R4W0G5bAAAAACBw5ygMlqfctzjIXCOFYLGD0xivSAAAAAgwOgJKiNv5vbEs04QAAAAUGkQgsrI07k9Twy4QLd1S6QHCAAAAKgkGA5XRp0T45RgC1dx0cYiKcEWTgACAAAAKhlCUBkFWS0aPzBJkooEIeYAAQAAAJUXIagc+rVO0NThFyve5jo0Lt4WrqnDL2YOEAAAAFAJMSeonPq1TlDfpHitTzmuw5nZqhtdUAabHiAAAACgciIE+UCQ1aKuzWsFuhkAAAAAPMBwOAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmQggCAAAAYCqEIAAAAACmEtAQNHnyZHXq1EnR0dGqW7euBg0apB07dgSySQAAAACquYCGoOXLl2vMmDFau3atFi5cqLy8PP3lL39RVlZWIJsFAAAAoBqzGIZhBLoRDkeOHFHdunW1fPly9ezZs9T9MzIyZLPZlJ6erpiYGD+0EAAAAEBl5E02CPZTmzySnp4uSYqLi3N7e05OjnJycpz/z8jI8Eu7AAAAAFQflaYwgt1u19ixY9WtWze1bt3a7T6TJ0+WzWZz/jRq1MjPrQQAAABQ1VWa4XCjR4/W/PnztXLlSjVs2NDtPu56gho1asRwOAAAAMDkqtxwuLvvvlvffPONVqxYUWwAkqSwsDCFhYX5sWUAAAAAqpuAhiDDMHTPPfdo7ty5WrZsmRITEwPZHAAAAAAmENAQNGbMGM2aNUvz5s1TdHS00tLSJEk2m00RERGBbBoAAACAaiqgc4IsFovb7dOmTdNtt91W6v0pkQ0AAABAqkJzgipJTQYAAAAAJlJpSmQDAAAAgD8QggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYSnCgG2BW+XZD61OO63BmtupGh6tzYpyCrJZANwsAAACo9ghBAbBga6omfr1NqenZzm0JtnCNH5ikfq0TAtgyAAAAoPpjOJyfLdiaqtEzkl0CkCSlpWdr9IxkLdia6tPj5dsNrdl9TPM2H9Ca3ceUbzd8+vgAAABAVUNPkB/l2w1N/Hqb3MUQQ5JF0sSvt6lvUrxPhsbR4wQAAAAURU+QH61POV6kB+hchqTU9GytTzle7mP5u8cJAAAAqCoIQX50OLP4AFSW/YpTWo+TVNDjxNA4AAAAmBEhyI/qRof7dL/i+LPHCQAAAKhqCEF+1DkxTgm2cBU328eigjk7nRPjynUcf/U4AQAAAFURIciPgqwWjR+YJElFgpDj/+MHJpW7KIK/epwAAACAqogQ5Gf9Wido6vCLFW9zDSDxtnBNHX6xT6q2dWhSU3GRocXe7qseJwAAAKAqokR2APRrnaC+SfFan3JchzOzVTe6IJD4siz28axct7f7sscJAAAAqIoIQQESZLWoa/NayrcbWp9yXN9sOVjuMOQoi11Szbd41gkCAACAyRGCAsiXi5mWVBbbIS4yRMsfulyhwYyCBAAAgHlxNRwgvl7MtLSy2JJ0PCtPG/ee8LqtAAAAQHVCCAqAiljMlLLYAAAAgGcIQQFQEYuZUhYbAAAA8AwhKAAqotfGXwuxAgAAAFUdISgAKqLXxtcLsebbDa3ZfUzzNh/Qmt3HvBqaBwAAAFRmVIcLAEevTVp6drHV3GJrhHjda+NYiLVwxTlvy2L7smodAAAAUNlYDMOosl/xZ2RkyGazKT09XTExMYFujlcWbE3VXTOSS9znzeEXlyl0ONYeKstCrMWtNeS499QytgkAAACoSN5kA4bDBUjfpHjF1ggp9naLvK8Q5+BYiPXadg3UtXktr4bA+bpqHQAAAFDZEIICZH3KcZ08nVfs7WWpEOcNd3N+KqJqHQAAAFDZMCcoQAK5rk9xc376t44PWJsAAAAAfyEEBUig1vUpbs5PWnq23l+1JyBtAgAAAPyJ4XABEoh1fUqb82ORVNL0IdYaAgAAQHVACAoQX6/r4wlP5vw4ah74q00AAACAvxGCAsixrk+8zXV4WbwtvEJKUXs6l2dUt6Z+axMAAADgb8wJCrB+rRPUNym+zOv6eMPTuTx9kuL12IAkv7QJAAAA8DdCUCXgWNenojnmIaWlZ7udF2RRQY+PI/D4o00AAACAvzEczkT8PQ/J3VpEAAAAQKDRE2QyjnlIhdcJireFa/zAJJ/N+SluLSJfHgMAAAAoC4thGFX26/mMjAzZbDalp6crJiYm0M3xmXy7UeHzcSryGMWtReR4dAosAAAAwNe8yQb0BFUy/upBqag5P56sRTTx623qmxRPoQUAAAAEBHOCKhFHD0rhtXzS0rM1ekayFmxNDVDLPOfJWkSp6dlan3Lcf40CAAAAzkEIqiRK60GRCnpQKntxAU/XIvJ0PwAAAMDXCEGVRHXpQfF0LSJP9wMAAAB8jRBUSVSXHhTHWkTFzfaxqGCOU+fEOH82CwAAAHAiBFUS1aUHxd9rEQEAAADeIgRVEtWpB8WxFlG8zTWwxdvCKY8NAACAgKNEto+Vdf0dRw/K6BnJskguBRKqYg9Kv9YJ6psUX+HrHQEAAADeYrFUH/LFGj/+WicIAAAAqE68yQYBDUErVqzQ888/r40bNyo1NVVz587VoEGDPL5/ZQpBjjV+3L2YFsmrYWBl7U0CAAAAzMqbbBDQOUFZWVm66KKL9PrrrweyGeVW0ho/UsHQtnFf/FxkjZ98u6E1u49p3uYDWrP7mPP2IKtFXZvX0rXtGqhr81oEIAAAAMCHAjonqH///urfv38gm+ATpa3xI0knTufptSU7dV+flpIY9gYAAAAESpWqDpeTk6OMjAyXn8rA07V73l7xu/LthnPoXOHglJaerdEzkrVga2pFNBMAAACAqlgImjx5smw2m/OnUaNGgW6SJM/X7snKzdcri38rduicY9vEr7cVGToHAAAAwDeqVAgaN26c0tPTnT/79+8PdJMkFazxExka5NG+7674vcShc4ak1PRsrU857qPWAQAAADhXlVonKCwsTGFhYYFuhlsWD2sXZOXZPdrP0yF2AAAAALxTpXqCKqv1Kcd1Kiffp4/p6RA7AAAAAN4JaE/QqVOntGvXLuf/U1JStHnzZsXFxalx48YBbJl3fNlrY5EUbytYG6gisAYRAAAAzC6gIejHH3/U5Zdf7vz/Aw88IEkaMWKEpk+fHqBWec/XvTbjByZVSDChLDcAAAAgWQzDqLJlyLxZFbYi5dsNdXh6oU6ezivX48TWCNGz17epkEDiKMtd+GQ7otbU4RcThAAAAFBleZMNmBPkAwu3pZU7AEnSq0PaV0gQybcblOUGAAAA/ocQVE6OgFGSsGDPhrZt3HfCF00qYn3KccpyAwAAAP9TpUpkV0alBQxJyjnrWQ/LS4t3qlVCtPq1TvBpAQNPCzdQlhsAAABmQAgqJ18Hhwlf/aJfUzM1ffUenTzz5xC78hQw8LRwg6f7UWEOAAAAVRkhqJw8DQ4Wi+RJCYq0jBy9tHhn0e3p2Ro9I7lMBQw6J8YpwRautPRst/OCvCnL7a7CXFxkiK5r10B9kuIJRAAAAKj0mBNUTp0T4xQXGVrqfuWtwWf87+fRz3/Wql1HvSpiEGS1aPzAJEl/VoNzcPzfk7LcjgpzhYf/Hc/K03ur9mjoO2vVfcoSLdia6nHbAAAAAH8jBJVTkNWiQe3qe7Rv8zqR5T7eyTN5GvbuOq/DRr/WCZo6/GLF21x7ruJt4R71LpVUYe5cjh4rghAAAAAqK9YJ8oE1u49p6Dtr/XrMsq7vU9b5PN48R8fwupWPXMHQOAAAAPiFN9mAOUE+4JhzU1qVOF8yVBA2Jn69TX2T4j0OG0FWi7o2r+X18bwpAHFuye2yHMsdijEAAADAVwhBPuCYczN6RnKpw8V8qSLCRnE8LQBxLl9VznNXjKE81fIAAABgbswJ8pF+rRN0R49EWQLQOeGP9X06J8YpPibMq/uUJTgVVlwxBuYeAQAAoKwIQT4y+btteue/KeWuAlcWvggbpVm4LU3ZZ+0e7WtRQU+NJyW3S1JSMQbHtolfb/OqUh4AAADAcDgf+G7LQb21IsXvx3UUIOjQpKbW7D7ms/kyheffnMjK0ZhZmzwe6mdIemJA6SW3S7M+5XiJ86z8ORwQAMyKOZkAqiNCUDnl2w39c97WgB3/mosS1Ov5pT6bL+Nu/o3VIq/nOk36dpusVpVrzo6nw/z8MRwQAMyIOZkAqiuGw5XT+pTjOp6VV67HCAny/hs1q0Xqk1RXb69I8dl8meLm35RltJkv5ux4OszPH8MBAcBsmJMJoDojBJVTWkb5eyHy8g21bWjz6j52Q1q47bDP5st4uhiqp3wxZ8dRery4iOiruUdAZZBvN7Rm9zHN23xAa3YfY64bAoo5mQCqO4bDldPxUzk+eZwdqRk+eRwHb+fLlDb/xh9tKOzc0uMWuQ7JcwSj8QPLP/cICDSGHKGyYU4mgOqOnqByiosM9cnj5ORXzLdplWFeTXkeu1/rBN3Zs2jpcYtFurNnIheIqPIYcoTKqDJ8dgBARSIElVO8LSLQTSjRzkOZpQ6tybcbOprpmx4td8ozZ2fB1lS9vSKlyLwkuyG9vSKFC0RUaQw5QmXFnEwA1R0hqJxOZFVcePCF15bu1tB31qr7lCVuA8OCranqPmWJJn273efHLu+cHU/mKfnqApH5GAgEb4YcAf7EnEwA1R1zgsphwdZUjZm1KdDNKDJfxh3H0Jqpwy92DiFzDMOpyMv98szZ8deYdE/nY5RnrQzW2YA7DDlCZcWcTADVHSGojHxdTa2srm6boI17T5Ra1MBQwQfXxK+3qW9SvPS/f1dU++MiQ/TMdW0q/TpBxQXBwqGxPBPXmfSO4ng6lOhoZo7y7QYXnPCrfq0TNHX4xUXev+J5/wJQDRCCyqgiqql5K7ZGiF6+ub2zPat2HdVrS3cVu/+5PSd2w6iw9lss0lMDLyz3B2RFj0kvbT6GIzTa7dKYWaUHJXc8DVkwJ8eQo7T07BK/kJj07Xa9uzKFC0/4Xb/WCeqbFE9PNoBqhxBURpVheMo1FyVowldbJVnUtFYNNasd6dH9PlyzR6t3H6uwdhmGdM/szbJaraoZGVrmD87SLhAtKvhGsqxj0j0dbvfPeVtLDUp9k+KLPDdPQ5a7+8IcShpyVJivgzNDNOGpIKuFMtgAqh1CUBlVhoo4H67Z5/J/Ty9f5m9N831jCjEk3f1xsktVN2+HgFX0mHRPg+zxrNxib3MEpbW7j8lqtbhcULLOBjxR3JCjwnwZnBmiCQAwO6rDlZGjl6IyCfT8pMIKF1gry7onjgvE+EKvdbwtvNzfiPsyyI6Zlayh76zVfbM3O6vxLdzmWdisDL2KCKx+rRO08pEr9MSAC0rczxfV4liXCAAAeoLKzNFLcdeM5EA3pcoo6zfZFTUm3ZPhdjUjQ3Q8K6/Uxzp5xnWftPRsvb9qj0ftqAy9igi8IKtFtaPDPNq3rMGZIZoAABSgJ6gc+rVOULtGMYFuRpVS1m+yHWPSr23XQF2b1/LJBZojyEpFhxI6/v/0ta1LXCujOI4LypKayTobKKyii4FUlXWJWLcLAFDR6Akqh3y7ob3HzgS6GVXSwm1plWIejCclYK1Wi0cT1wszVFAkQiq6lhPrbMCdii4GUhXWJWK+EgDAHwhB5bA+5bhOnC59qBSKen/VHnVOjPPZYqTlUdpwu+KCUmxESJFhcO6M6tZU321NY50NlKqii4FUdE9TeVFSHgDgL4SgckhLpxeorBxzD65oVU8b957Qwm1p+nLzQZdKbPEx4RraubGa1q5R4aGotBKw7oKS3TA07N11pT52n6R4PTYgiXLE8EhFLlBZ0T1N5cF8JQCAPxGCyuHoqZxAN6HKcsw9uGTyomILD6RlZOvFRb85/x/oITGFg1K+3fD4gpJ1NuCNiioGUtE9TeVBSXkAgD9RGKEcPBkKhZJ5UnnNobKV8PWksAJzfnzLTBPmK6IYiFSxZefLoyrMVwIAVB/0BJWDxeuaYSgPx+WuL4bE+Gr+UUUOXYIrJsz7TkX1NJVHZZ+vBACoXiyGYVTZr1IzMjJks9mUnp6umBj/l6peteuoR3NC4Hv392mh+/q0LNN9K+JiOlBFHfwpkM+xuAnzjqMzYb7qy7cb6j5lSanDS1c+ckW1+9sCAPiGN9mAnqByuKRZLQVbpLNVNkZWXS8u2qnz46M9uvA99+J9z9HTemnRbz6vPlXd5/wEsheGCfPmUJnnKwEAqh/mBJVDkNWiKy+sG+hmmNbEr7eVOidkwdZUdXt2sYa+s1b3zd6sF90EIMl1qF11nmfirXy7oZcX7dRdM5KLTFr31xytyrzAZ1Wao1QV2lpZ5ysBAKofeoLKYcHWVH279XCgm2FaqenZmr4qRbWjw9wOz1qwNVV3zUj2+PHMXn2q8HC3E1m5euqbX5SW4b4Kor96YSrrhPmqNEepKrW1Ms5XAgBUP4SgMsq3G5rw1S+BbobpTfp2u/Pf517U5dsNPfrFz2V6zIq4mHYEjLSMbB0/laO4yFDF2yKc67E4LvhqR4VJhnQ0K8evF3/uLpI94Y/gWBknzJdlUU9/zKlyd4yF29Kq3AKk7srRr9l9jFBUjZhhHiWAyo0QVEYFF7SsE1SZnHtRFx0WopOny1bC/GhmjuZtPuD2g9kZZtLP6HhWruKiwhQfU/IHeEkBI7ZGiCQV21Z/fFtf3AW9NyqyF6ayLfBZljlKFdUTc+6F5O9HTunDNXt14pzfpfiYcGWfzfe4rZ5cmPr74tXT146L6qqjKvVMAqi+qA5XRvM2H9B9szf79ZjwTIItXBc1smnB1kNe37fwhOxzP5i/23JQ/5y31e3aRsV9gJc3YFR09TNHRS5ve4AK+/ivlxTpCfLlRanjdZTcT5j3Z2+Gp1UhHa9JRVW2K2vvXXFtTT+TW+qFqb8vXj197dy1Ky4yVE9f21pXteWiujKh0iOKwxcZ8AVvsgEhqIzW7D6moe+s9esx4bnwEKuy8+zlfhzH22+fpLpauK3k+V8WuX6A+ypgVGRpYF/8HteKDNWacb0VGvxnnZWKuFiuDN8eL9iaqkc//9mjhZJfvrmdrm5bv8TfgbKeW1/03p2rf+t6mu/mS4NzL0wl+fXitbS/H8dr98SAJI2ZVfxr8beeiRp3VZLP2uUrZrzg8/ScUga97Krq71VleH9H9UCJbD/onBinmjWCdeL02UA3BW74IgBJf/Y6lBaAHPs+PnerrmhVT6HB1lKrmnnThoqad+OLYWzHsnLV6/mlzg+rssyX8USgJ8x7GzzqRod7XNlu7e5j6taitsfD0YobjldW7gKQo32S/jf/0VLisLrH527Vmdx851w3T87Luc+38Hw4u93w6LX757ytJb4Wb61I0UUNY3VV2/qV5gLRH8Mjy/L8Kvr18abSoxmL00jlOwdVNUh4+plRWf5+UX0QgsooyGrRNRfV1wdr9gW6KahEjmXlqsPTP2hwh0aKCPXtn1dFzLvxVTEBx4fV67e016Rvt5c6B+WKVvW0ce8Jrz/MArUekzfB49w5St9sOejR44+ZlawhnRrqq59SS72A8VW49kZp8x8NFfzu3//JT5L+bHdJobW04XxRYUEete14Vm6p+zz0+Rb9duiUZm/Y5/JcAnGBWFFfEpT3Arisc686NKnp8d9yZa306KmKvggvzzmsqN8rXynutfN0jqXdLk36tuoFPFRuDIcrh8fnbtHMdfv9flyYk7t5N+WRbze09vdjuvOjH5WVk1/ux7NIqhkZ4nbOVGFxkaEuF6+V5cOsuA9qb4cN3t+npe6+4jyt/f2YR/OHiuNuqFlVmI/omFsXW8O1QInjPEvuh9b5m7/noVTUcDBv59kU/j0/eipH93y8yW17zr2/u4t0q0U6d8mpkv6WPf07Kst7XWUOKJ4+flmHm1b2YYYlvXbR4SFlfo9kHhncYTicnxw8eSbQTYAJlLf6mbuLg++3pv2vyEPp36J7ypA8CkBS0W/vK8O3lSV9UOec9W545YuLftO01Skq73dMjns/Nvdn5zDLPUezyvWY/uBod+Gqh2np2bprRrJqhAaVOwB5E7qLU9a1rsp6wV0Rw8G8rVboTUEN12/iDY2ZtanIcQqvuVvS33JFVXosa0Dx9DxWVC/LuUsnTPrml1IX8i6uB70yDzMs6bVzvBeUlb/WqkP1RQgqh20HMwLdBJjE+IFJZXqDd3dxUCM0SKdzy9/z40uB/jAr7SJnbJ8WXj9mWUu0u3M8K0+XTF6sp69trY/Xl38IbliQRTn5/u+HcRyxvL9/jt+Op69trX/O+6VcYb60C0T3iwhvU1rGn39T8THhmnBN6T0Cng7zmr81VZI8ClfeXACnn8n1ugfOcf9HvvjZo/uV9LccZLVo/MAkjZ6RXKQSp2Mvb9/ryhpQvBn+521JfE/b7U11x9T0bLWf9INLr723X9L4apihp+GxtNdOKv97AfPIUB6EoHI4k1e5LiRR/cTWCNGz17cp8xwBdxcHlS0AOQTqw8yTi5yP1+9TfEy4DmW4/wbbH45n5ervs5J98liBCEC+FO9ysWrxyeuSln5Ga3Yfc1nQeN/x0/p4/b5S50SlZRR8q/1mKYvkHs30bG25D9fs1Ydr9irhf9XvakaGFruYsqcXtmnpZzTxm7IX1MjM9rwI0Ll/y50T41wumPsmxWvq8IuLBIB4D+YfFVlcOjJME77yPqB4E5wqopelrNUdCw9bdvSm9G9dz6P7L/31sHYeOqWuzWvpkma1fPbFWnG9bv6cv1hZ55GhciMElUMVnk6FKuL1oRerW4vaXt8v96xdj80tuWpWZeXvDzNPLnLSMnI0tncLvbx4Z5FvsM0mJMiivACGqNE9mykuKlSrdh3Tql1H1a5hrJrVqaHfj5wu1+NO+nZ7uYeHPvjpFpcKeQu3pZU6h6YkqenZJQa8BFu4bu7UyKPH+uTHfT7tnfTED7+k6oFPNru9YF75yBUl9ia4u9gubXHpwtwFFE96Jx79/GdFh4fokma1lJbu2bB3T9+3fFnd0fEYxVV3LOzLzQWFWl5busvtF2yl9fB42+vmz/dyXxX5gbkQgsqB4aeoSFaLlF7MejQlfVgt2Jqqx+b+XK65EoHkzw+zfLuhVbuOerTv9NV7dGfPxCIV3MwmkAFIkqau+N3l/x/JNxU6fTE/7lTOWWeFvMJFIRw8DUCeSEvP1ouLdnq075rfT/juwB6atnpvkW3uLpgLl0pfn3JMLy/eVeS+ZQ1x516Me9I7cfJMnoa9u06xNUJ01sPfd0/ft9buPlYp3j9Ons5z6b0srYenLMMC/fFeXt45sw6U3zYnQlAZ5dsNpWf7Zi0awB27UVA6earV9ds1dx9WNWuEaETXpsrIztP7q/YEoLW+YbVIJ7LcDxlyVLNbs/uYJENdm9XWJc1LH9JR3Iebt2PyT57J09srUvT6Le2Vmp6tSd9u9/bpwUT80etSFXskC18wu+st87VzL8a96Z3w5ByWdBHubj7ZY3N/9vj4/lBS0YvUcwJrdHiI18MCOyfGKT4m3GX+XEU4dx5ZWUq4V9X1lVB+hKAyWp9yPNBNgAkYKlrZyd1whBOn8/TSYs++Ea7M7Ib091mb9KbV4vLh892WVD38+RadyvlzXsJrS3erRmiQ/tazme6+ooVzzYnCFx3u1pa45qIEvb0ipUwXkZO+3a6H+7Uqz9Os9iLDgnxSdh3Vk+OC+e5ZGz0eylUWjoBitxuat/mA6kaHK65GqM+P466Yw4KtqZrw1S+lzicLtNT0bD3+ZfFDpw1J//j0J3law+3ckLlwW5rO5FXsgvK9L6jj/KwoSwn30ob4vX5Le9WMDPOoh6gq9CZVhTb6E+sEldHc5D+cwx6AijZzVBfZ7YbumrXRFBeXsTVCtPGffRVktWjyd9v01oqUEve3RQTr5k6NPBqq5os5PY9fdYH+9R09Qe5cEB+tT++6VN2fW+L3OShAYUEW6dwRbb6c0xcXGaJnrmvjMqxv7e5jmrluj76rwHBXmTnWeFqwNVV3zfBNIZfSfHR7Z2Vm57ntzSrMcf5v79ZUvVvV0z8+/anEnipP18Gq6DLtvmCWHi9vsgEhqIzeWfE7F0Hwm8pY1rqi3d+npVrUjdTfZxVdxDHQaoRYdTqP4bCAmV1YP1rdz6utYKtVaRnZ+u7nVJ0x+ftCpyaxuvvyFrpvziadPFOxvUDn8lfBGsdxxvZuobN2Q5KhIKtVryzeWepCt4UDz7HMHD359VaX+buFQ4mvQlJ5FuOtaghBfvD897/q9aW7/XpMwExsEcGyWiw6QW8CAKCKigwL0qhLm+qTjX94NDzSooJQIsknPTf5dkPdpywpdpSEY9joykeu8DpgVcbhdd5kA+YElVWVjY5A1ZDux28RAQCoCFk5+XrFiy/NDUljZ29WtptFcFP/tzbUyze3U93o8GLXDzs3iFTEWleS++F1cZEhevra1rqqbX2PHyeQCEFl5FivAAAAAPAVdwHoXPfN3lzsbY7eor5J8Vqfclzzt6Z6dMxF29LKveDv8aw8/X3WJv3tj5Mad1WSR48VSISgMqrNwlwAAACoRNL+11tU3FplxXlv1R51SowrdaidJwv+vrUiRRc1rKmr2lbueUbWQDegqoqPIQQBFckWwXc0AAB4wxFOylKdc+LX25RfyorOniw4LEkPf76l1McKNEJQGXVoUjPQTQCqtdu7JQa6CQAAmIZjblBJPF1w+FTOWa39/ZgvmlVhCEFltIHFUoEKc88VzXX3FS2UYKPHFQAAfykt5NT1YjrImt2EoFK9/vrratq0qcLDw9WlSxetX78+0E0q1Zrfjwa6CUC1VCM0SGP7nK8gq0XjBybJoj/XMgAAABWntJDTOTFOkaFBHj4aw+FKNGfOHD3wwAMaP368kpOTddFFF+nKK6/U4cOHA920UnBZBlSE/wy+yFnes1/rBE0dfrHi6RFCOVzc2Fbi7X/tkai4yFA/tQYAKqcEW0GJ7ZIEWS26o4dnw9W7Nqvti2ZVmIAvltqlSxd16tRJr732miTJbrerUaNGuueee/Too4+WeN9ALpa6atdRDXt3nV+PCe+EWC3Kq+ST8uDqjVvau11fwLEg28JtaXp/1R7/NwwVLjIsSFk5+aXuN7BtvPq3TtCkb7e7XUTwilb19NGaPdp7/LSaxNXQ/3VtqtBgqxZsTdWEr35xWawwPiZME665sMhq7nERofr1UKb2Hc/SWbtdXyQfVE4pJWsBoCpzLNLqyUKs+XZDbSZ8r9O5xb9nx9YI0cZ/9vX74qneZIOAhqDc3FzVqFFDn332mQYNGuTcPmLECJ08eVLz5s1z2T8nJ0c5OX9+gGVkZKhRo0YBCUH5dkPNH/vOr8esCHGRIRrVtalaN4zVi4t/0+b96YFukkfCQ6zq2ryWlv56xO3tjj/mK1rV07RVKfrkx/3afSSrzMeLjQjWSRbvrFCvDm2vgReVvsCauwXawoOtCg6y6lQO56gqeuOWi3Vl64I1LdLSz2jlrqNa8EuaSyiqFRmqSde2dpZcLctK5WVd3by4NTEcbmjfQA1qRuiDNXuVfubPiky2iGD1vaCeup1XW/G2CJ3IytWDn23W6VwCFYCKUbNGiE6czpNFng9Gc3yJ5EkAcliwNVV3zUgu9vY3PQxUvlZlQtDBgwfVoEEDrV69Wl27dnVuf/jhh7V8+XKtW+fa0zJhwgRNnDixyOMEIgRJ0vSVOzXhm9/8ftyyqhFq1Tu3dtLRU+5XFZak77ak6uHPt/j1YvLy8+voju7N9NHaFC3bcbTURcKkP/+4vtuSqn/O26rjWbnO24r7Y3Z38exgkXR79ya6rGU9fZH8h07lnFXdmDBd3DhO9WMj1DkxTgu3pRW5f3HfXjvefGwRwUo/JzyFB1vUs0VtZZ819OPeEy7fotSsEayuzWqpWZ1odW1eS+mn8/TY3J918oxrmUurCkq0d0qsqevbN1RwsFWHM7K1ctdRfbMltcg31uEhVnVuGqeTp3O180iWzpTwzU2g/K1nolcLq7m7mJXkvIg+nJmjXw6c1Ia9J3QoI0fedgiGB1vUo2UdLdzm22G5QRYpv5p1To7t3UKJdSJVOzJMssj5/tKuUaxmrdur3UdP6UhGjnLO2rVp/0llZv/591DSB29ZA0tFcff+Ubj9nrQ5327ovtmb9O2W1AofLd+uYbQSYmtoya9HirwvBFmlfC+yWJsGMfr5QIaPWwjAV3q3qqM7ejQv9nolwRauJwYkqWZkqNIysnX8VI7iIkMVb4so8/traT3sgVBtQ1Bl6glyaProt349XmxEsEZ2a6b0M7n6cvNBl4v/mPAgZWQXf4HraSrPtxt6bckuvb8qxeVbzbgaoTqvbqRia4SqY5OauiA+Rkt2HNK01Xu9eg7hIVYl1opUx6Y19dhVSYo4Z4Kd4yIiLf2MVu06qh+2HVJGKRdN3lwsOfY9eOK0Nv9xUpJFTWv9OWTGk9em8LGKe7M5d8Vmd23z9IJp7e5j/yvEYVHX5rV0SbNaJT6/kvZ3vr4Z2Tqama3U9DOam3xAp3PzZTcM1YoMVusGNfXSzRcrIjSo4LU6eUbr9xxV8p6TysjOU1hwkPLOntXRrDzllXARlVirhg6mZ5c4jCgy1Krnb7zI7RA4X8m3G1q966g+37hf+0+cUXiIVRc1rKlu59VWp8Q4rfv9mD5P/kN/nDijBrHhuvHiRrq0RW0FWS3KPWvXtFW//y8MGepzQT1dmGDT8TO5qhsdrg5Namrj3hM6eOK0kvcXBK7svHwlJcTodO5ZGbLIapHaNarpDNP5dkMfrdmjPceylG83lHk6VzuOnFJUaLDiIkNVzxahZrUjdUuXJkree0Jrfj+q3Hy71v1+TL8dytTZfCk6zKoW9WJ0Nt+uLQcyAjbss7jhi8WpbMHGW75sf+5Zu8uwvSGdGuvj9fu0PuWoTufadVHDWHVtXktWq0VHT+Uorkaofk0rGKIn/fk71a5RrGas3au1Kce0+/ApRYZa1a5xTf1zwIXO99Z8u6G1vx/7X6UmQ12b1dYlzWu5fe8qLK5GiJ4e1EZXtXX/pVNpQoIsal67hnq0qKOs3HwdOZWjyNBgWS3SvM0HPfpS4KIGMWrTMFZ2w9DWA+naeiBDvu5LCwmyKK+YxsRGhKj7ebW17LcjRb4gDLZKQVary/tc4W/ga0aEKDIsSCeycmWxWnRh/Whd3rKe0jKz9dVPqS6vZ2xEiEZ2a6o7ezZ3folwOCNHhzOytT0ts9g2VjdWFbyG5ni2xasRYlWCLUL7jp8u9n2+cC+5g7/ebyvb+3qVCUHeDocrLJBzgs6V+Oi35fpDjQoL0h3dm6llvWhN+tb9B5ItPFi3d0/U3Ve0KPEieuG2NE34apvSMor/ttJTnv5iF/cN6bgrz9eGfSeUcvS0IkKs+ktSPTWMi/TqD6Sy/XEVp6q009fy7YaWbT+kp77dprT0bIUEWdS/TbyeuratIkKDnIF62qoUlx4txwf9ub/PKBvXYJujE6dzZbEUvMZxNUJ18kye4qLCFB/j2mN2ODNbKUdOafrqPS5DPc89N5LcfiFS1vcUVD6F37scwb6497Jzv6g6npWruKgw1Y0OkwzpaFZOkR7B0r6UWr7jiP6zcIfSz+SqTlSYbuzYSNtTC3qcmtaKdPsFlSPUrdp5VEt+PaTfj5zSuSMM4yJDdO1F9VU/toZO/u/voWuz2rq4SU2X3sl6MeFKrF1wjCCrxfk61I768/mc+xyK+5JJklevYUmvv6f71o4Kkz3f0NqUYzp48ozOnM3Xut+P68Q5C2RGhgVpVLem6tKstg5nZLs/X1FhstsLHueP46eVb9i1Iy1Tuw+fdgmaFkn1okOVEBuhk6dztf/EGZ37/VZEiFWdmsbp6rYJ+umPkzqUnq0/TmZr77EsnSnm2zKrpWAo8+lzbq8RatWdPZrrnt4F7z/nhvcuTWvJGlTwpcDvR7L04eo9OuHyRW2IJl7bWnE1QrVy9xH9vD9dEaFB6pxYSyMuLTjHK387orf/+7sOnDgtu2EoK+eszuTZFR5sldViKDffkCwW1QixKi/fULDVqrjoUJ1XO0pWa0E0q18zQrERoTp5Ok+p6WeUbzd0NPOMjmTm6kzeWcliVd2oUFksFmXlntW+49k6k/fnF9QhQRb9rWcz3dXrPD07f5tW7DyqgyddX8+wYKuevb6Nrru4ofPcO14Lu2FXzRphqh395/s6n6N/qjIhSCoojNC5c2e9+uqrkgoKIzRu3Fh33313pS6MUNiutFPq89Jyt7eFBEmXJNZS09qRat+opurFhBf7IeHuA8bbX/JAXJCbNQTAM/x+VF6e9khy/lBZ8ftZwJevQ2mP5emxCge3wuFSUpnbXFXOO++x/lWlQtCcOXM0YsQIvfXWW+rcubNeeuklffLJJ/r1119Vr169Eu9bmULQufhlBgAAAPzLm2wQ7Kc2FWvIkCE6cuSInnzySaWlpaldu3ZasGBBqQGoMguyFnSTAwAAAKh8At4TVB6VtScIAAAAgH95kw1KL4cFAAAAANUIIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqRCCAAAAAJgKIQgAAACAqQQHugHlYRiGJCkjIyPALQEAAAAQSI5M4MgIJanSISgzM1OS1KhRowC3BAAAAEBlkJmZKZvNVuI+FsOTqFRJ2e12HTx4UNHR0bJYLAFrR0ZGhho1aqT9+/crJiYmYO2A73FuqzfOb/XG+a2+OLfVG+e3eqvI82sYhjIzM1W/fn1ZrSXP+qnSPUFWq1UNGzYMdDOcYmJi+GOtpji31Rvnt3rj/FZfnNvqjfNbvVXU+S2tB8iBwggAAAAATIUQBAAAAMBUCEE+EBYWpvHjxyssLCzQTYGPcW6rN85v9cb5rb44t9Ub57d6qyznt0oXRgAAAAAAb9ETBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQVE6vv/66mjZtqvDwcHXp0kXr168PdJNQBpMnT1anTp0UHR2tunXratCgQdqxY4fLPtnZ2RozZoxq1aqlqKgo3XDDDTp06FCAWoyyevbZZ2WxWDR27FjnNs5t1XbgwAENHz5ctWrVUkREhNq0aaMff/zRebthGHryySeVkJCgiIgI9enTRzt37gxgi+Gp/Px8PfHEE0pMTFRERISaN2+uSZMm6dyaTpzfqmPFihUaOHCg6tevL4vFoi+//NLldk/O5fHjxzVs2DDFxMQoNjZWo0aN0qlTp/z4LOBOSec2Ly9PjzzyiNq0aaPIyEjVr19ft956qw4ePOjyGP4+t4SgcpgzZ44eeOABjR8/XsnJybrooot05ZVX6vDhw4FuGry0fPlyjRkzRmvXrtXChQuVl5env/zlL8rKynLuc//99+vrr7/Wp59+quXLl+vgwYO6/vrrA9hqeGvDhg1666231LZtW5ftnNuq68SJE+rWrZtCQkI0f/58bdu2TS+88IJq1qzp3Oe5557TK6+8ojfffFPr1q1TZGSkrrzySmVnZwew5fDElClTNHXqVL322mvavn27pkyZoueee06vvvqqcx/Ob9WRlZWliy66SK+//rrb2z05l8OGDdMvv/yihQsX6ptvvtGKFSt05513+uspoBglndvTp08rOTlZTzzxhJKTk/XFF19ox44duuaaa1z28/u5NVBmnTt3NsaMGeP8f35+vlG/fn1j8uTJAWwVfOHw4cOGJGP58uWGYRjGyZMnjZCQEOPTTz917rN9+3ZDkrFmzZpANRNeyMzMNFq0aGEsXLjQ6NWrl3HfffcZhsG5reoeeeQRo3v37sXebrfbjfj4eOP55593bjt58qQRFhZmfPzxx/5oIsphwIABxu233+6y7frrrzeGDRtmGAbntyqTZMydO9f5f0/O5bZt2wxJxoYNG5z7zJ8/37BYLMaBAwf81naUrPC5dWf9+vWGJGPv3r2GYQTm3NITVEa5ubnauHGj+vTp49xmtVrVp08frVmzJoAtgy+kp6dLkuLi4iRJGzduVF5ensv5btWqlRo3bsz5riLGjBmjAQMGuJxDiXNb1X311Vfq2LGjbrrpJtWtW1ft27fXO++847w9JSVFaWlpLufXZrOpS5cunN8q4NJLL9XixYv122+/SZJ++uknrVy5Uv3795fE+a1OPDmXa9asUWxsrDp27Ojcp0+fPrJarVq3bp3f24yyS09Pl8ViUWxsrKTAnNvgCnlUEzh69Kjy8/NVr149l+316tXTr7/+GqBWwRfsdrvGjh2rbt26qXXr1pKktLQ0hYaGOv9YHerVq6e0tLQAtBLemD17tpKTk7Vhw4Yit3Fuq7bff/9dU6dO1QMPPKDHHntMGzZs0L333qvQ0FCNGDHCeQ7dvVdzfiu/Rx99VBkZGWrVqpWCgoKUn5+vf/3rXxo2bJgkcX6rEU/OZVpamurWretye3BwsOLi4jjfVUh2drYeeeQRDR06VDExMZICc24JQUAhY8aM0datW7Vy5cpANwU+sH//ft13331auHChwsPDA90c+JjdblfHjh31zDPPSJLat2+vrVu36s0339SIESMC3DqU1yeffKKZM2dq1qxZuvDCC7V582aNHTtW9evX5/wCVVBeXp4GDx4swzA0derUgLaF4XBlVLt2bQUFBRWpIHXo0CHFx8cHqFUor7vvvlvffPONli5dqoYNGzq3x8fHKzc3VydPnnTZn/Nd+W3cuFGHDx/WxRdfrODgYAUHB2v58uV65ZVXFBwcrHr16nFuq7CEhAQlJSW5bLvgggu0b98+SXKeQ96rq6aHHnpIjz76qG6++Wa1adNG//d//6f7779fkydPlsT5rU48OZfx8fFFik+dPXtWx48f53xXAY4AtHfvXi1cuNDZCyQF5twSgsooNDRUHTp00OLFi53b7Ha7Fi9erK5duwawZSgLwzB09913a+7cuVqyZIkSExNdbu/QoYNCQkJczveOHTu0b98+zncl17t3b/3888/avHmz86djx44aNmyY89+c26qrW7duRcrZ//bbb2rSpIkkKTExUfHx8S7nNyMjQ+vWreP8VgGnT5+W1ep6qRIUFCS73S6J81udeHIuu3btqpMnT2rjxo3OfZYsWSK73a4uXbr4vc3wnCMA7dy5U4sWLVKtWrVcbg/Iua2QcgsmMXv2bCMsLMyYPn26sW3bNuPOO+80YmNjjbS0tEA3DV4aPXq0YbPZjGXLlhmpqanOn9OnTzv3ueuuu4zGjRsbS5YsMX788Ueja9euRteuXQPYapTVudXhDINzW5WtX7/eCA4ONv71r38ZO3fuNGbOnGnUqFHDmDFjhnOfZ5991oiNjTXmzZtnbNmyxbj22muNxMRE48yZMwFsOTwxYsQIo0GDBsY333xjpKSkGF988YVRu3Zt4+GHH3buw/mtOjIzM41NmzYZmzZtMiQZ//nPf4xNmzY5K4R5ci779etntG/f3li3bp2xcuVKo0WLFsbQoUMD9ZTwPyWd29zcXOOaa64xGjZsaGzevNnlOisnJ8f5GP4+t4Sgcnr11VeNxo0bG6GhoUbnzp2NtWvXBrpJKANJbn+mTZvm3OfMmTPG3//+d6NmzZpGjRo1jOuuu85ITU0NXKNRZoVDEOe2avv666+N1q1bG2FhYUarVq2Mt99+2+V2u91uPPHEE0a9evWMsLAwo3fv3saOHTsC1Fp4IyMjw7jvvvuMxo0bG+Hh4UazZs2Mxx9/3OXCifNbdSxdutTtZ+2IESMMw/DsXB47dswYOnSoERUVZcTExBgjR440MjMzA/BscK6Szm1KSkqx11lLly51Poa/z63FMM5ZdhkAAAAAqjnmBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAAAwFUIQAAAAAFMhBAEAAm7ChAlq165dsf/31eMCACARggAAFWDNmjUKCgrSgAEDynT/Bx98UIsXLy51v88//1yXXXaZbDaboqKi1LZtWz311FM6fvx4mY4LADAHQhAAwOfee+893XPPPVqxYoUOHjzo9f2joqJUq1atEvd5/PHHNWTIEHXq1Enz58/X1q1b9cILL+inn37SRx99VNamAwBMgBAEAPCpU6dOac6cORo9erQGDBig6dOnF9nn2WefVb169RQdHa1Ro0YpOzvb5fbShrGtX79ezzzzjF544QU9//zzuvTSS9W0aVP17dtXn3/+uUaMGOH2fna7XU899ZQaNmyosLAwtWvXTgsWLHDenpubq7vvvlsJCQkKDw9XkyZNNHnyZOftJ0+e1B133KE6deooJiZGV1xxhX766SfvXiAAQMARggAAPvXJJ5+oVatWOv/88zV8+HC9//77MgzD5fYJEybomWee0Y8//qiEhAS98cYbXh1j5syZioqK0t///ne3t8fGxrrd/vLLL+uFF17Qv//9b23ZskVXXnmlrrnmGu3cuVOS9Morr+irr77SJ598oh07dmjmzJlq2rSp8/433XSTDh8+rPnz52vjxo26+OKL1bt3b4bfAUAVQwgCAPjUe++9p+HDh0uS+vXrp/T0dC1fvtx5+0svvaRRo0Zp1KhROv/88/X0008rKSnJq2Ps3LlTzZo1U0hIiFf3+/e//61HHnlEN998s84//3xNmTJF7dq100svvSRJ2rdvn1q0aKHu3burSZMm6t69u4YOHSpJWrlypdavX69PP/1UHTt2VIsWLfTvf/9bsbGx+uyzz7xqBwAgsAhBAACf2bFjh9avX+8MDsHBwRoyZIjee+895z7bt29Xly5dXO7XtWtXr45zbs+SpzIyMnTw4EF169bNZXu3bt20fft2SdJtt92mzZs36/zzz9e9996rH374wbnfTz/9pFOnTqlWrVqKiopy/qSkpGj37t1etwcAEDjBgW4AAKD6eO+993T27FnVr1/fuc0wDIWFhem1116TzWbzyXFatmyplStXKi8vz+veoJJcfPHFSklJ0fz587Vo0SINHjxYffr00WeffaZTp04pISFBy5YtK3K/4obfAQAqJ3qCAAA+cfbsWX344Yd64YUXtHnzZufPTz/9pPr16+vjjz+WJF1wwQVat26dy33Xrl3r1bFuueUWnTp1qti5RCdPniyyLSYmRvXr19eqVatctq9atcplOF5MTIyGDBmid955R3PmzNHnn3+u48eP6+KLL1ZaWpqCg4N13nnnufzUrl3bq/YDAAKLniAAgE988803OnHihEaNGlWkx+eGG27Qe++9p7vuukv33XefbrvtNnXs2FHdunXTzJkz9csvv6hZs2YeH6tLly56+OGH9Y9//EMHDhzQddddp/r162vXrl1688031b17d913331F7vfQQw9p/Pjxat68udq1a6dp06Zp8+bNmjlzpiTpP//5jxISEtS+fXtZrVZ9+umnio+PV2xsrPr06aOuXbtq0KBBeu6559SyZUsdPHhQ3377ra677jp17NixfC8gAMBvCEEAAJ9477331KdPH7dD3m644QY999xz2rJli4YMGaLdu3fr4YcfVnZ2tm644QaNHj1a33//vVfHmzJlijp06KDXX39db775pux2u5o3b64bb7yx2BLZ9957r9LT0/WPf/xDhw8fVlJSkr766iu1aNFCkhQdHa3nnntOO3fuVFBQkDp16qTvvvtOVmvBwInvvvtOjz/+uEaOHKkjR44oPj5ePXv2VL169bx8tQAAgWQxyjK7FACACjRu3Dj997//1cqVKwPdFABANcScIABApWEYhnbv3q3FixfrwgsvDHRzAADVFCEIAFBppKenKykpSaGhoXrssccC3RwAQDXFcDgAAAAApkJPEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMJX/BygfR+Vui1wqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Adj Close'], df['Volume'])\n",
    "plt.xlabel('Adj Close')\n",
    "plt.ylabel('Volume')\n",
    "plt.title('Scatter Plot of Adj Close vs Volume')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выброс присутствует. Сделаем очистку данных.\n",
    "\n",
    "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "# Функция для удаления выбросов с использованием IQR\n",
    "def remove_outliers_iqr(df, column):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
    "\n",
    "# Удаление выбросов для столбцов 'Adj Close' и 'Volume'\n",
    "df_cleaned = remove_outliers_iqr(df, 'Adj Close')\n",
    "df_cleaned = remove_outliers_iqr(df_cleaned, 'Volume')\n",
    "\n",
    "# Построение графика для очищенных данных\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_cleaned['Adj Close'], df_cleaned['Volume'])\n",
    "plt.xlabel('Adj Close')\n",
    "plt.ylabel('Volume')\n",
    "plt.title('Scatter Plot of Adj Close vs Volume (Cleaned)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь посмотрим, если пустые значения. Пустых значений не оказалось."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 4821\n",
      "Размер контрольной выборки: 1607\n",
      "Размер тестовой выборки: 1608\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop('Volume', axis=1)  # Признаки (все столбцы, кроме 'volume')\n",
    "y = df['Volume']  # Целевая переменная ('volume')\n",
    "\n",
    "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\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",
    "# Вывод размеров выборок\n",
    "print(f\"Размер обучающей выборки: {X_train.shape[0]}\")\n",
    "print(f\"Размер контрольной выборки: {X_val.shape[0]}\")\n",
    "print(f\"Размер тестовой выборки: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Price в обучающей выборке:\n",
      "Volume\n",
      "1847800      1\n",
      "1875200      1\n",
      "1910400      1\n",
      "1949200      1\n",
      "2019200      1\n",
      "            ..\n",
      "143464800    1\n",
      "152007800    1\n",
      "230883200    1\n",
      "295411200    1\n",
      "585508800    1\n",
      "Name: count, Length: 4697, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в контрольной выборке:\n",
      "Volume\n",
      "2380800      1\n",
      "2407200      1\n",
      "2412800      1\n",
      "2547200      1\n",
      "2659200      1\n",
      "            ..\n",
      "85356800     1\n",
      "87072000     1\n",
      "111773600    1\n",
      "114960000    1\n",
      "155107200    1\n",
      "Name: count, Length: 1593, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в тестовой выборке:\n",
      "Volume\n",
      "1504000      1\n",
      "2011200      1\n",
      "2073600      1\n",
      "2169700      1\n",
      "2432000      1\n",
      "            ..\n",
      "67067400     1\n",
      "75863200     1\n",
      "81587200     1\n",
      "131420600    1\n",
      "224358400    1\n",
      "Name: count, Length: 1593, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Функция для анализа распределения и вывода результатов\n",
    "def analyze_distribution(data, title):\n",
    "    print(f\"Распределение Price в {title}:\")\n",
    "    distribution = data.value_counts().sort_index()\n",
    "    print(distribution)\n",
    "    total = len(data)\n",
    "    positive_count = (data > 0).sum()\n",
    "    negative_count = (data < 0).sum()\n",
    "    positive_percent = (positive_count / total) * 100\n",
    "    negative_percent = (negative_count / total) * 100\n",
    "    print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n",
    "    print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n",
    "    print(\"\\nНеобходима аугментация данных для балансировки классов.\\n\")\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train, \"обучающей выборке\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборка недостаточно сбалансирована. Выполним аугментацию данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Volume в обучающей выборке после oversampling:\n",
      "Volume\n",
      "1847800      3\n",
      "1875200      3\n",
      "1910400      3\n",
      "1949200      3\n",
      "2019200      3\n",
      "            ..\n",
      "143464800    3\n",
      "152007800    3\n",
      "230883200    3\n",
      "295411200    3\n",
      "585508800    3\n",
      "Name: count, Length: 4697, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "Распределение Volume в контрольной выборке:\n",
      "Volume\n",
      "2380800      1\n",
      "2407200      1\n",
      "2412800      1\n",
      "2547200      1\n",
      "2659200      1\n",
      "            ..\n",
      "85356800     1\n",
      "87072000     1\n",
      "111773600    1\n",
      "114960000    1\n",
      "155107200    1\n",
      "Name: count, Length: 1593, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "Распределение Volume в тестовой выборке:\n",
      "Volume\n",
      "1504000      1\n",
      "2011200      1\n",
      "2073600      1\n",
      "2169700      1\n",
      "2432000      1\n",
      "            ..\n",
      "67067400     1\n",
      "75863200     1\n",
      "81587200     1\n",
      "131420600    1\n",
      "224358400    1\n",
      "Name: count, Length: 1593, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n"
     ]
    }
   ],
   "source": [
    "# Применение oversampling к обучающей выборке\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "# Функция для анализа распределения и вывода результатов\n",
    "def analyze_distribution(data, title):\n",
    "    print(f\"Распределение Volume в {title}:\")\n",
    "    distribution = data.value_counts().sort_index()\n",
    "    print(distribution)\n",
    "    total = len(data)\n",
    "    positive_count = (data > 0).sum()\n",
    "    negative_count = (data < 0).sum()\n",
    "    positive_percent = (positive_count / total) * 100\n",
    "    negative_percent = (negative_count / total) * 100\n",
    "    print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n",
    "    print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train_resampled, \"обучающей выборке после oversampling\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  }
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