{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Продажи домов\n",
    "2. Данные о населении \n",
    "3. Набор данных для анализа и прогнозирования сердечного приступа"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Продажа домов</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
      "       'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
      "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
      "       'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"..//static//csv//House.csv\", index_col=\"id\")\n",
    "\n",
    "print(df.columns, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Столбцы для русских:<br>\n",
    "<br>\n",
    "<span style=\"color: #189FFF;\">id:</span> Идентификатор объекта<br>\n",
    "<span style=\"color: #189FFF;\">date:</span> Дата продажи<br>\n",
    "<span style=\"color: #189FFF;\">price:</span> Цена недвижимости<br>\n",
    "<span style=\"color: #189FFF;\">bedrooms:</span> Количество спален<br>\n",
    "<span style=\"color: #189FFF;\">bathrooms:</span> Количество ванных комнат<br>\n",
    "<span style=\"color: #189FFF;\">sqft_living:</span> Жилая площадь<br>\n",
    "<span style=\"color: #189FFF;\">sqft_lot:</span> Площадь участка<br>\n",
    "<span style=\"color: #189FFF;\">floors:</span> Количество этажей<br>\n",
    "<span style=\"color: #189FFF;\">waterfront:</span> Признак наличия вида на водоем<br>\n",
    "<span style=\"color: #189FFF;\">view:</span> Оценка вида<br>\n",
    "<span style=\"color: #189FFF;\">condition:</span> Состояние дома<br>\n",
    "<span style=\"color: #189FFF;\">grade:</span> Оценка конструкции<br>\n",
    "<span style=\"color: #189FFF;\">sqft_above:</span> Площадь надземных помещений<br>\n",
    "<span style=\"color: #189FFF;\">sqft_basement:</span> Площадь подвала<br>\n",
    "<span style=\"color: #189FFF;\">yr_built:</span> Год постройки<br>\n",
    "<span style=\"color: #189FFF;\">yr_renovated:</span> Год последнего ремонта<br>\n",
    "<span style=\"color: #189FFF;\">zipcode:</span> Почтовый индекс<br>\n",
    "<span style=\"color: #189FFF;\">lat:</span> Широта<br>\n",
    "<span style=\"color: #189FFF;\">long:</span> Долгота<br>\n",
    "<span style=\"color: #189FFF;\">sqft_living15:</span> Жилая площадь соседних домов<br>\n",
    "<span style=\"color: #189FFF;\">sqft_lot15:</span> Площадь участка соседних домов<br>\n",
    "<br>\n",
    "Проблемная область: Прогнозирование стоимости недвижимости в зависимости от характеристик дома.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of                        date     price  bedrooms  bathrooms  sqft_living  \\\n",
      "id                                                                        \n",
      "7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
      "6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
      "5631500400  20150225T000000  180000.0         2       1.00          770   \n",
      "2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
      "1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
      "...                     ...       ...       ...        ...          ...   \n",
      "263000018   20140521T000000  360000.0         3       2.50         1530   \n",
      "6600060120  20150223T000000  400000.0         4       2.50         2310   \n",
      "1523300141  20140623T000000  402101.0         2       0.75         1020   \n",
      "291310100   20150116T000000  400000.0         3       2.50         1600   \n",
      "1523300157  20141015T000000  325000.0         2       0.75         1020   \n",
      "\n",
      "            sqft_lot  floors  waterfront  view  condition  grade  sqft_above  \\\n",
      "id                                                                             \n",
      "7129300520      5650     1.0           0     0          3      7        1180   \n",
      "6414100192      7242     2.0           0     0          3      7        2170   \n",
      "5631500400     10000     1.0           0     0          3      6         770   \n",
      "2487200875      5000     1.0           0     0          5      7        1050   \n",
      "1954400510      8080     1.0           0     0          3      8        1680   \n",
      "...              ...     ...         ...   ...        ...    ...         ...   \n",
      "263000018       1131     3.0           0     0          3      8        1530   \n",
      "6600060120      5813     2.0           0     0          3      8        2310   \n",
      "1523300141      1350     2.0           0     0          3      7        1020   \n",
      "291310100       2388     2.0           0     0          3      8        1600   \n",
      "1523300157      1076     2.0           0     0          3      7        1020   \n",
      "\n",
      "            sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
      "id                                                                             \n",
      "7129300520              0      1955             0    98178  47.5112 -122.257   \n",
      "6414100192            400      1951          1991    98125  47.7210 -122.319   \n",
      "5631500400              0      1933             0    98028  47.7379 -122.233   \n",
      "2487200875            910      1965             0    98136  47.5208 -122.393   \n",
      "1954400510              0      1987             0    98074  47.6168 -122.045   \n",
      "...                   ...       ...           ...      ...      ...      ...   \n",
      "263000018               0      2009             0    98103  47.6993 -122.346   \n",
      "6600060120              0      2014             0    98146  47.5107 -122.362   \n",
      "1523300141              0      2009             0    98144  47.5944 -122.299   \n",
      "291310100               0      2004             0    98027  47.5345 -122.069   \n",
      "1523300157              0      2008             0    98144  47.5941 -122.299   \n",
      "\n",
      "            sqft_living15  sqft_lot15  \n",
      "id                                     \n",
      "7129300520           1340        5650  \n",
      "6414100192           1690        7639  \n",
      "5631500400           2720        8062  \n",
      "2487200875           1360        5000  \n",
      "1954400510           1800        7503  \n",
      "...                   ...         ...  \n",
      "263000018            1530        1509  \n",
      "6600060120           1830        7200  \n",
      "1523300141           1020        2007  \n",
      "291310100            1410        1287  \n",
      "1523300157           1020        1357  \n",
      "\n",
      "[21613 rows x 20 columns]> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.info, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объектом наблюдения является - Недвижимость<br>\n",
    "Атрибуты - содержит набор информации о продаже дома, такие как:<br>\n",
    "цену продажи, дата продажи, количество спален, ванных комнат, общую площадь дома, площадь участка, местоположение.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(df['sqft_living'], df['price'], c=df['price'], alpha=0.6)\n",
    "plt.colorbar(label='Price')\n",
    "\n",
    "plt.title(\"Номер 1\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Living Area\")\n",
    "plt.grid(visible='true')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_condition = df.groupby('yr_built')['condition'].mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(year_condition['yr_built'], year_condition['condition'], marker='.')\n",
    "\n",
    "plt.title(\"Номер 2\")\n",
    "plt.xlabel(\"Year Built\")\n",
    "plt.ylabel(\"Condition\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Связь между объектами есть. Цена связана почти со всеми характиристиками дома.<br> Например на графике номер один показана зависимоость между ценой и размером дома.<br> А на графике номер 2 показа зависимость состояния домов с годами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Примеры бизнес целей</h3>\n",
    "\n",
    "1. Прогнозирование стоимости недвижимости на основе характиристик дома.\n",
    "2. Наблюдение за изменениями характиристик дома с годами.\n",
    "\n",
    "Эффект для бизнеса: Оценка и оптимизация цен, Оценка и планирование затрат, выявление тенденции на рынке, стратегия планирования."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Цели технического проекта</h3>\n",
    "<ul>Для первой цели:</ul>\n",
    "        <li>Вход: Характеристики недвижимости</li>\n",
    "        <li>Целевой признак: Цена.</li>\n",
    "<ul>Для второй цели:</ul>\n",
    "        <li>Вход: оценка конструкции, Состояние дома</li>\n",
    "        <li>Целевой признак: Год постройки</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Код ниже нужен для определения проблем данных</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date             20150527T000000\n",
      "price                  7700000.0\n",
      "bedrooms                      33\n",
      "bathrooms                    8.0\n",
      "sqft_living                13540\n",
      "sqft_lot                 1651359\n",
      "floors                       3.5\n",
      "waterfront                     1\n",
      "view                           4\n",
      "condition                      5\n",
      "grade                         13\n",
      "sqft_above                  9410\n",
      "sqft_basement               4820\n",
      "yr_built                    2015\n",
      "yr_renovated                2015\n",
      "zipcode                    98199\n",
      "lat                      47.7776\n",
      "long                    -121.315\n",
      "sqft_living15               6210\n",
      "sqft_lot15                871200\n",
      "dtype: object \n",
      "\n",
      "Index(['bedrooms', 'bathrooms', 'waterfront', 'view', 'sqft_basement',\n",
      "       'yr_renovated'],\n",
      "      dtype='object') \n",
      "\n",
      "Признаки с низкой дисперсией:\n",
      " waterfront    0.007485\n",
      "lat           0.019200\n",
      "long          0.019833\n",
      "dtype: float64 \n",
      "\n",
      "id\n",
      "7129300520    1955\n",
      "6414100192    1951\n",
      "5631500400    1933\n",
      "2487200875    1965\n",
      "1954400510    1987\n",
      "              ... \n",
      "263000018     2009\n",
      "6600060120    2014\n",
      "1523300141    2009\n",
      "291310100     2004\n",
      "1523300157    2008\n",
      "Name: yr_built, Length: 21613, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "max_value = df.max(axis=0)\n",
    "\n",
    "columns_with_zero = df.columns[(df == 0).any()]\n",
    "\n",
    "numeric_data = df.select_dtypes(include='number')\n",
    "shum = numeric_data.var()\n",
    "low_dispers = 0.1\n",
    "low_var_columns = shum[shum < low_dispers]\n",
    "\n",
    "\n",
    "year = df['yr_built']\n",
    "\n",
    "print(max_value, \"\\n\")\n",
    "print(columns_with_zero, \"\\n\")\n",
    "print(\"Признаки с низкой дисперсией:\\n\", low_var_columns, \"\\n\")\n",
    "print(year)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul><h4>Из полученных данных выяснилось:</h4></ul>\n",
    "    <li>признаки bedrooms, bathrooms, waterfront, view, sqft_basement и yr_renovated имеют в себе нулевые поля, что может создать смещение если искать по этим полям</li>\n",
    "    <li>признаки bedrooms, bathrooms и price имеют аномально высокие значения, и это указывает на наличие выбросов</li>\n",
    "    <li>признаки waterfront, view, condition имеют низкие значения дисперсии, что может привести к снижению значимости этих признаков</li>\n",
    "    <li>признак yr_built варьируется от 1900 до 2015. Это может быть актуальной информацией для анализа старых зданий, но актуальность данных по ремонту и реконструкции (это призгак yr_renovated) может быть ниже, так как 0 указывает на отсутствие ремонта</li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol><h3>Примеры решения проблем для набора данных</h3></ol>\n",
    "    <li>Удаление выбросов на основе значения или bathrooms > 5</li>\n",
    "    <li>Замена 0 на год постройки (это признак yr_built), если дом не подвергался ремонту</li>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Оценка качества данных</h3>\n",
    "1. Информативность. Набор данных предоставляет достаточную информацию для анализа цен на недвижимость.\n",
    "2. Степень покрытия. Набор данных затрагивает только один райно, не включая информацию о других райнов.\n",
    "3. Соответствие реальным данным. Данные вполне кажутся реальными, не считая некоторых редких выбросов.\n",
    "4. Согласованность меток. Метки состояние и оценка вида, имеют четкие значения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Разбиение данных на обучающую, контрольную и тестовую выборки</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Исходный размер строк: 21613 строк\n",
      "Размер обучающей выборки: 15129 строк\n",
      "Размер валидационной выборки: 3242 строк\n",
      "Размер тестовой выборки: 3242 строк\n"
     ]
    }
   ],
   "source": [
    "df_numeric = df.select_dtypes(include='number')\n",
    "\n",
    "x = df_numeric.drop(['price'], axis=1)\n",
    "y = df_numeric['price']\n",
    "\n",
    "x_train, x_temp, y_train, y_temp = train_test_split(x, y, test_size=0.3, random_state=14)\n",
    "\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=14)\n",
    "\n",
    "print(f\"Исходный размер строк: {df_numeric.shape[0]} строк\")\n",
    "print(f\"Размер обучающей выборки: {x_train.shape[0]} строк\")\n",
    "print(f\"Размер валидационной выборки: {x_val.shape[0]} строк\")\n",
    "print(f\"Размер тестовой выборки: {x_test.shape[0]} строк\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
      "Статистические показатели для обучающей выборки:\n",
      "Среднее значение: 13.05\n",
      "Стандартное отклонение: 0.52\n",
      "Минимальное значение: 11.23\n",
      "Максимальное значение: 15.86\n",
      "Количество наблюдений: 15129\n",
      "\n",
      "Статистические показатели для валидационной выборки:\n",
      "Среднее значение: 13.05\n",
      "Стандартное отклонение: 0.53\n",
      "Минимальное значение: 11.26\n",
      "Максимальное значение: 15.49\n",
      "Количество наблюдений: 3242\n",
      "\n",
      "Статистические показатели для тестовой выборки:\n",
      "Среднее значение: 13.06\n",
      "Стандартное отклонение: 0.54\n",
      "Минимальное значение: 11.35\n",
      "Максимальное значение: 15.53\n",
      "Количество наблюдений: 3242\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df['price_log'] = np.log(df['price'])\n",
    "\n",
    "X = df.drop(['price', 'price_log'], axis=1)\n",
    "y = df['price_log']\n",
    "\n",
    "X = X.select_dtypes(include='number')\n",
    "\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "def plot_distribution(data, title):\n",
    "    \"\"\"Построение гистограммы распределения целевого признака\"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.histplot(data, kde=True, bins=30, color='skyblue')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Logarithm of Price')\n",
    "    plt.ylabel('Count')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "plot_distribution(y_train, 'Распределение логарифма цены в обучающей выборке')\n",
    "plot_distribution(y_val, 'Распределение логарифма цены в валидационной выборке')\n",
    "plot_distribution(y_test, 'Распределение логарифма цены в тестовой выборке')\n",
    "\n",
    "def get_statistics(df, name):\n",
    "    print(f\"Статистические показатели для {name} выборки:\")\n",
    "    print(f\"Среднее значение: {df.mean():.2f}\")\n",
    "    print(f\"Стандартное отклонение: {df.std():.2f}\")\n",
    "    print(f\"Минимальное значение: {df.min():.2f}\")\n",
    "    print(f\"Максимальное значение: {df.max():.2f}\")\n",
    "    print(f\"Количество наблюдений: {df.count()}\\n\")\n",
    "\n",
    "get_statistics(y_train, \"обучающей\")\n",
    "\n",
    "get_statistics(y_val, \"валидационной\")\n",
    "\n",
    "get_statistics(y_test, \"тестовой\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Oversampling и undersampling</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов после SMOTE (oversampling):\n",
      "price_category\n",
      "0    3054\n",
      "1    3054\n",
      "2    3054\n",
      "3    3054\n",
      "4    3054\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "price_category\n",
      "0    2993\n",
      "1    2993\n",
      "2    2993\n",
      "3    2993\n",
      "4    2993\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "\n",
    "if 'date' in df.columns:\n",
    "    df['year'] = pd.to_datetime(df['date'], errors='coerce').dt.year\n",
    "    df = df.drop(['date'], axis=1)\n",
    "\n",
    "df['price_log'] = np.log(df['price'])\n",
    "\n",
    "df['price_category'] = pd.qcut(df['price_log'], q=5, labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "X = df.drop(['price', 'price_log', 'price_category'], axis=1)\n",
    "y = df['price_category']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после SMOTE (oversampling):\")\n",
    "print(pd.Series(y_train_smote).value_counts())\n",
    "\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после RandomUnderSampler (undersampling):\")\n",
    "print(pd.Series(y_train_under).value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Оценка сбалансированности выборок</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка необходимости аугментации данных\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[91], line 18\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     16\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mВыборка \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m сбалансирована, аугментация не требуется.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 18\u001b[0m \u001b[43mcheck_augmentation_need\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mобучающей\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     19\u001b[0m check_augmentation_need(y_val, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mвалидационной\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     20\u001b[0m check_augmentation_need(y_test, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mтестовой\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[91], line 3\u001b[0m, in \u001b[0;36mcheck_augmentation_need\u001b[1;34m(data, name)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck_augmentation_need\u001b[39m(data, name):\n\u001b[0;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Проверка необходимости аугментации данных\"\"\"\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m     quantiles \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.25\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.75\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m     mean \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m      5\u001b[0m     std \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mstd()\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\series.py:2887\u001b[0m, in \u001b[0;36mSeries.quantile\u001b[1;34m(self, q, interpolation)\u001b[0m\n\u001b[0;32m   2883\u001b[0m \u001b[38;5;66;03m# We dispatch to DataFrame so that core.internals only has to worry\u001b[39;00m\n\u001b[0;32m   2884\u001b[0m \u001b[38;5;66;03m#  about 2D cases.\u001b[39;00m\n\u001b[0;32m   2885\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m-> 2887\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   2888\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m   2889\u001b[0m     result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\frame.py:12191\u001b[0m, in \u001b[0;36mDataFrame.quantile\u001b[1;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[0;32m  12187\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m  12188\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid method: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Method must be in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalid_method\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m  12189\u001b[0m     )\n\u001b[0;32m  12190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m> 12191\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m  12192\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtable\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  12193\u001b[0m     valid_interpolation \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhigher\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1549\u001b[0m, in \u001b[0;36mBlockManager.quantile\u001b[1;34m(self, qs, interpolation)\u001b[0m\n\u001b[0;32m   1545\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m   1546\u001b[0m new_axes[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m Index(qs, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[0;32m   1548\u001b[0m blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m-> 1549\u001b[0m     \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mqs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m   1550\u001b[0m ]\n\u001b[0;32m   1552\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(blocks, new_axes)\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1891\u001b[0m, in \u001b[0;36mBlock.quantile\u001b[1;34m(self, qs, interpolation)\u001b[0m\n\u001b[0;32m   1888\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m   1889\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m is_list_like(qs)  \u001b[38;5;66;03m# caller is responsible for this\u001b[39;00m\n\u001b[1;32m-> 1891\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mquantile_compat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1892\u001b[0m \u001b[38;5;66;03m# ensure_block_shape needed for cases where we start with EA and result\u001b[39;00m\n\u001b[0;32m   1893\u001b[0m \u001b[38;5;66;03m#  is ndarray, e.g. IntegerArray, SparseArray\u001b[39;00m\n\u001b[0;32m   1894\u001b[0m result \u001b[38;5;241m=\u001b[39m ensure_block_shape(result, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\array_algos\\quantile.py:41\u001b[0m, in \u001b[0;36mquantile_compat\u001b[1;34m(values, qs, interpolation)\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m quantile_with_mask(values, mask, fill_value, qs, interpolation)\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_quantile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\arrays\\_mixins.py:519\u001b[0m, in \u001b[0;36mNDArrayBackedExtensionArray._quantile\u001b[1;34m(self, qs, interpolation)\u001b[0m\n\u001b[0;32m    515\u001b[0m fill_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_fill_value\n\u001b[0;32m    517\u001b[0m res_values \u001b[38;5;241m=\u001b[39m quantile_with_mask(arr, mask, fill_value, qs, interpolation)\n\u001b[1;32m--> 519\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cast_quantile_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mres_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_backing_data(res_values)\n",
      "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\arrays\\categorical.py:2480\u001b[0m, in \u001b[0;36mCategorical._cast_quantile_result\u001b[1;34m(self, res_values)\u001b[0m\n\u001b[0;32m   2478\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_cast_quantile_result\u001b[39m(\u001b[38;5;28mself\u001b[39m, res_values: np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m   2479\u001b[0m     \u001b[38;5;66;03m# make sure we have correct itemsize for resulting codes\u001b[39;00m\n\u001b[1;32m-> 2480\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m res_values\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ndarray\u001b[38;5;241m.\u001b[39mdtype\n\u001b[0;32m   2481\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def check_augmentation_need(data, name):\n",
    "    \"\"\"Проверка необходимости аугментации данных\"\"\"\n",
    "    quantiles = data.quantile([0.25, 0.5, 0.75])\n",
    "    mean = data.mean()\n",
    "    std = data.std()\n",
    "    \n",
    "    print(f\"Проверка необходимости аугментации для {name} выборки:\")\n",
    "    print(f\"Среднее значение: {mean:.2f}, Стандартное отклонение: {std:.2f}\")\n",
    "    print(f\"25-й квантиль: {quantiles[0.25]:.2f}\")\n",
    "    print(f\"50-й квантиль (медиана): {quantiles[0.5]:.2f}\")\n",
    "    print(f\"75-й квантиль: {quantiles[0.75]:.2f}\")\n",
    "    \n",
    "    if std > mean * 0.5:\n",
    "        print(f\"Выборка {name} несбалансирована, рекомендуется аугментация.\\n\")\n",
    "    else:\n",
    "        print(f\"Выборка {name} сбалансирована, аугментация не требуется.\\n\")\n",
    "\n",
    "check_augmentation_need(y_train, \"обучающей\")\n",
    "check_augmentation_need(y_val, \"валидационной\")\n",
    "check_augmentation_need(y_test, \"тестовой\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поскольку все выборки демонстрируют одинаковое распределение целевого признака и сбалансированное распределение значений, применение методов аугментации не требуется."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'condition' в обучающей выборке:\n",
      " condition\n",
      "3    9837\n",
      "4    3958\n",
      "5    1189\n",
      "2     121\n",
      "1      24\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\salih\\AppData\\Local\\Temp\\ipykernel_8140\\3337968062.py:11: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=condition_counts.index, y=condition_counts.values, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'condition' в валидационной выборке:\n",
      " condition\n",
      "3    2125\n",
      "4     830\n",
      "5     256\n",
      "2      27\n",
      "1       4\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\salih\\AppData\\Local\\Temp\\ipykernel_8140\\3337968062.py:11: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=condition_counts.index, y=condition_counts.values, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'condition' в тестовой выборке:\n",
      " condition\n",
      "3    2069\n",
      "4     891\n",
      "5     256\n",
      "2      24\n",
      "1       2\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\salih\\AppData\\Local\\Temp\\ipykernel_8140\\3337968062.py:11: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=condition_counts.index, y=condition_counts.values, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Проверка необходимости аугментации для признака 'condition' в обучающей выборке:\n",
      "Минимальное количество наблюдений в классе: 24\n",
      "Максимальное количество наблюдений в классе: 9837\n",
      "Выборка 'обучающей' несбалансирована, рекомендуется аугментация.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'condition' в валидационной выборке:\n",
      "Минимальное количество наблюдений в классе: 4\n",
      "Максимальное количество наблюдений в классе: 2125\n",
      "Выборка 'валидационной' несбалансирована, рекомендуется аугментация.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'condition' в тестовой выборке:\n",
      "Минимальное количество наблюдений в классе: 2\n",
      "Максимальное количество наблюдений в классе: 2069\n",
      "Выборка 'тестовой' несбалансирована, рекомендуется аугментация.\n",
      "\n",
      "Распределение классов после SMOTE (oversampling):\n",
      "condition\n",
      "3    9837\n",
      "5    9837\n",
      "4    9837\n",
      "2    9837\n",
      "1    9837\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "condition\n",
      "1    24\n",
      "2    24\n",
      "3    24\n",
      "4    24\n",
      "5    24\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "if 'condition' in df.columns:\n",
    "    X_train, X_temp, y_train, y_temp = train_test_split(df.drop(['price'], axis=1), df['condition'], test_size=0.3, 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",
    "    def analyze_condition_distribution(data, name):\n",
    "        \"\"\"Проверка и визуализация распределения признака 'condition'\"\"\"\n",
    "        condition_counts = data.value_counts()\n",
    "        print(f\"Распределение 'condition' в {name} выборке:\\n\", condition_counts)\n",
    "        \n",
    "        plt.figure(figsize=(8, 6))\n",
    "        sns.barplot(x=condition_counts.index, y=condition_counts.values, palette='viridis')\n",
    "        plt.title(f\"Распределение признака 'condition' в {name} выборке\")\n",
    "        plt.xlabel('Condition')\n",
    "        plt.ylabel('Count')\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "\n",
    "    analyze_condition_distribution(y_train, 'обучающей')\n",
    "    analyze_condition_distribution(y_val, 'валидационной')\n",
    "    analyze_condition_distribution(y_test, 'тестовой')\n",
    "\n",
    "    def check_condition_augmentation(data, name):\n",
    "        print(f\"Проверка необходимости аугментации для признака 'condition' в {name} выборке:\")\n",
    "        min_count = data.value_counts().min()\n",
    "        max_count = data.value_counts().max()\n",
    "        print(f\"Минимальное количество наблюдений в классе: {min_count}\")\n",
    "        print(f\"Максимальное количество наблюдений в классе: {max_count}\")\n",
    "        \n",
    "        if max_count > min_count * 1.5:\n",
    "            print(f\"Выборка '{name}' несбалансирована, рекомендуется аугментация.\\n\")\n",
    "        else:\n",
    "            print(f\"Выборка '{name}' сбалансирована, аугментация не требуется.\\n\")\n",
    "\n",
    "    check_condition_augmentation(y_train, 'обучающей')\n",
    "    check_condition_augmentation(y_val, 'валидационной')\n",
    "    check_condition_augmentation(y_test, 'тестовой')\n",
    "else:\n",
    "    print(\"Признак 'condition' отсутствует в данных.\")\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после SMOTE (oversampling):\")\n",
    "print(pd.Series(y_train_smote).value_counts())\n",
    "\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после RandomUnderSampler (undersampling):\")\n",
    "print(pd.Series(y_train_under).value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом исследование данные не сбалансированы, поэтому требуется аугментация."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Данные о населении</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Country (or dependency) Population 2020 Yearly Change  Net Change  \\\n",
      "no                                                                     \n",
      "1                    China   1,439,323,776         0.39%   5,540,090   \n",
      "2                    India   1,380,004,385         0.99%  13,586,631   \n",
      "3            United States     331,002,651         0.59%   1,937,734   \n",
      "4                Indonesia     273,523,615         1.07%   2,898,047   \n",
      "5                 Pakistan     220,892,340         2.00%   4,327,022   \n",
      "\n",
      "   Density  (P/Km²) Land Area (Km²) Migrants (net) Fert. Rate Med. Age  \\\n",
      "no                                                                       \n",
      "1               153       9,388,211       -348,399        1.7       38   \n",
      "2               464       2,973,190       -532,687        2.2       28   \n",
      "3                36       9,147,420        954,806        1.8       38   \n",
      "4               151       1,811,570        -98,955        2.3       30   \n",
      "5               287         770,880       -233,379        3.6       23   \n",
      "\n",
      "   Urban Pop % World Share  \n",
      "no                          \n",
      "1          61%      18.47%  \n",
      "2          35%      17.70%  \n",
      "3          83%       4.25%  \n",
      "4          56%       3.51%  \n",
      "5          35%       2.83%   \n",
      "\n",
      "Country (or dependency)\n",
      "Population 2020\n",
      "Yearly Change\n",
      "Net Change\n",
      "Density  (P/Km²)\n",
      "Land Area (Km²)\n",
      "Migrants (net)\n",
      "Fert. Rate\n",
      "Med. Age\n",
      "Urban Pop %\n",
      "World Share\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df2 = pd.read_csv(\"..//static//csv//WorldPopulation.csv\", index_col=\"no\")\n",
    "\n",
    "print(df2.head(), \"\\n\")\n",
    "print(*list(df2.columns), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объектом наблюдения является страны и информация о их наслении.<br>\n",
    "Атрибуты объекта: Страна, Население, Годовое изменение, NetChange, Плотность, Площадь суши, Мигранты, Fert.Rate, Средний возраст, UrbanPop%, Доля в мире;<br>\n",
    "Связь между объектами: имеется связь между атрибутами, например между Коэффициент фертильности и Плотностю населения.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\salih\\AppData\\Local\\Temp\\ipykernel_15148\\3959616127.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean['Density  (P/Km²)'] = pd.cut(df2['Density  (P/Km²)'], bins=range(0, 1000, 100))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Fert. Rate'] = pd.to_numeric(df2['Fert. Rate'], errors='coerce')\n",
    "\n",
    "\n",
    "df2['Fert. Rate'] = pd.to_numeric(df2['Fert. Rate'], errors='coerce')\n",
    "\n",
    "df_clean = df2.dropna(subset=['Fert. Rate', 'Density  (P/Km²)'])\n",
    "    \n",
    "## correlation = df_clean[['Density  (P/Km²)', 'Fert. Rate']].corr().iloc[0, 1] ## использовать только один раз потом удалить\n",
    "\n",
    "df_clean['Density  (P/Km²)'] = pd.cut(df2['Density  (P/Km²)'], bins=range(0, 1000, 100))\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=df_clean, x='Density  (P/Km²)', y='Fert. Rate')\n",
    "plt.title('Распределение уровня рождаемости по плотности населения')\n",
    "plt.xlabel('Плотность (чел./км²)')\n",
    "plt.ylabel('Уровень рождаемости')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Med. Age'] = pd.to_numeric(df2['Med. Age'], errors='coerce')\n",
    "\n",
    "\n",
    "df2['Med. Age'] = pd.to_numeric(df2['Med. Age'], errors='coerce')\n",
    "\n",
    "df_clean = df2.dropna(subset=['Fert. Rate', 'Density  (P/Km²)'])\n",
    "    \n",
    "## correlation = df_clean[['Density  (P/Km²)', 'Fert. Rate']].corr().iloc[0, 1] ## использовать только один раз потом удалить\n",
    "\n",
    "df_clean['Density  (P/Km²)'] = pd.cut(df2['Density  (P/Km²)'], bins=range(0, 1000, 100))\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=df_clean, x='Density  (P/Km²)', y='Fert. Rate')\n",
    "plt.title('Распределение уровня рождаемости по плотности населения')\n",
    "plt.xlabel('Плотность (чел./км²)')\n",
    "plt.ylabel('Уровень рождаемости')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}