AIM-PIbd-31-Afanasev-S-S/lab_2/lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной работы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Цены на кофе (12 варик)\n",
    "2. Данные по инсультам (4 варик)\n",
    "3. Онлайн обучение (20 варик)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цены на кофе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], 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",
    "df = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/Starbucks.csv\")\n",
    "\n",
    "print(df.columns, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Столбцы на русском"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. date: Дата\n",
    "2. open: Цена открытия\n",
    "3. high: Самая высокая цена дня\n",
    "4. low: Самая низкая цена дня\n",
    "5. Close: Цена закрытия\n",
    "6. Adj Close: Скорректированная цена закрытия\n",
    "7. Volume: Объем торгов\n",
    "\n",
    "Проблемная область: Прогнозирование динамики цен акций Starbucks на основе исторических данных о ценах и объемах торгов.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of             Date       Open       High        Low      Close  Adj Close  \\\n",
      "0     1992-06-26   0.328125   0.347656   0.320313   0.335938   0.260703   \n",
      "1     1992-06-29   0.339844   0.367188   0.332031   0.359375   0.278891   \n",
      "2     1992-06-30   0.367188   0.371094   0.343750   0.347656   0.269797   \n",
      "3     1992-07-01   0.351563   0.359375   0.339844   0.355469   0.275860   \n",
      "4     1992-07-02   0.359375   0.359375   0.347656   0.355469   0.275860   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "8031  2024-05-17  75.269997  78.000000  74.919998  77.849998  77.849998   \n",
      "8032  2024-05-20  77.680000  78.320000  76.709999  77.540001  77.540001   \n",
      "8033  2024-05-21  77.559998  78.220001  77.500000  77.720001  77.720001   \n",
      "8034  2024-05-22  77.699997  81.019997  77.440002  80.720001  80.720001   \n",
      "8035  2024-05-23  80.099998  80.699997  79.169998  79.260002  79.260002   \n",
      "\n",
      "         Volume  \n",
      "0     224358400  \n",
      "1      58732800  \n",
      "2      34777600  \n",
      "3      18316800  \n",
      "4      13996800  \n",
      "...         ...  \n",
      "8031   14436500  \n",
      "8032   11183800  \n",
      "8033    8916600  \n",
      "8034   22063400  \n",
      "8035    4651418  \n",
      "\n",
      "[8036 rows x 7 columns]> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.info, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объектом наблюдения является - цена акций Starbucks <br>\n",
    "Атрибуты — содержит набор информации о ценах акций Starbucks, такие как: дата, цена открытия, максимальная цена дня, минимальная цена дня, цена закрытия, скорректированная цена закрытия и объем торгов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(df['Volume'], df['Close'], c=df['Close'], alpha=0.6)\n",
    "plt.colorbar(label='Close Price')\n",
    "\n",
    "plt.title(\"Зависимость цены закрытия от объема торгов\")\n",
    "plt.ylabel(\"Цена закрытия\")\n",
    "plt.xlabel(\"Объем торгов\")\n",
    "plt.grid(visible=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df['Year'] = pd.to_datetime(df['Date']).dt.year\n",
    "\n",
    "year_close = df.groupby('Year')['Close'].mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(year_close['Year'], year_close['Close'], marker='.')\n",
    "\n",
    "plt.title(\"Средняя цена закрытия акций Starbucks по годам\")\n",
    "plt.xlabel(\"Год\")\n",
    "plt.ylabel(\"Средняя цена закрытия\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Связь между объектами есть. Цена связана почти со всеми характеристиками акций. Например, на графике номер один показана зависимость между ценой закрытия и объемом торгов. А на графике номер два показана зависимость средней цены закрытия от года."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Примеры бизнес-целей</h3>\n",
    "\n",
    "1. Прогнозирование динамики цен акций Starbucks на основе исторических данных о ценах и объемах торгов.\n",
    "2. Наблюдение за изменениями цен акций Starbucks с годами.\n",
    "\n",
    "Эффект для бизнеса: Оценка и оптимизация цен, оценка и планирование затрат, выявление тенденций на рынке, стратегия планирования.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Цели технического проекта</h3>\n",
    "<ul>Для первой цели:</ul>\n",
    "    <li>Вход: Исторические данные о ценах и объемах торгов</li>\n",
    "    <li>Целевой признак: Цена закрытия.</li>\n",
    "<ul>Для второй цели:</ul>\n",
    "    <li>Вход: Исторические данные о ценах и объемах торгов</li>\n",
    "    <li>Целевой признак: Год</li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Код ниже нужен для определения проблем данных</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Максимальные значения:\n",
      " Date         2024-05-23\n",
      "Open         126.080002\n",
      "High             126.32\n",
      "Low          124.809998\n",
      "Close        126.059998\n",
      "Adj Close    118.010414\n",
      "Volume        585508800\n",
      "Year               2024\n",
      "dtype: object \n",
      "\n",
      "Столбцы с нулевыми значениями:\n",
      " Index([], dtype='object') \n",
      "\n",
      "Признаки с низкой дисперсией:\n",
      " Series([], dtype: float64) \n",
      "\n",
      "Годы:\n",
      " 0       1992\n",
      "1       1992\n",
      "2       1992\n",
      "3       1992\n",
      "4       1992\n",
      "        ... \n",
      "8031    2024\n",
      "8032    2024\n",
      "8033    2024\n",
      "8034    2024\n",
      "8035    2024\n",
      "Name: Year, Length: 8036, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "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",
    "df['Year'] = pd.to_datetime(df['Date']).dt.year\n",
    "print(\"Максимальные значения:\\n\", max_value, \"\\n\")\n",
    "print(\"Столбцы с нулевыми значениями:\\n\", columns_with_zero, \"\\n\")\n",
    "print(\"Признаки с низкой дисперсией:\\n\", low_var_columns, \"\\n\")\n",
    "print(\"Годы:\\n\", df['Year'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Из полученных данных выяснилось:</h4></ul> <li>Столбцы с нулевыми значениями отсутствуют, что указывает на полноту данных и отсутствие проблем с пропущенными значениями.</li> <li>Максимальные значения для различных метрик: <ul> <li>Date: 2024-05-23</li> <li>Open: 126.080002</li> <li>High: 126.32</li> <li>Low: 124.809998</li> <li>Close: 126.059998</li> <li>Adj Close: 118.010414</li> <li>Volume: 585508800</li> <li>Year: 2024</li> </ul> </li> <li>Признаки с низкой дисперсией отсутствуют, что указывает на стабильность данных и отсутствие проблем с зашумленностью.</li> <li>Годы варьируются от 1992 до 2024. Это может быть актуальной информацией для анализа временных трендов и изменений в данных за длительный период. Однако, если данные включают будущие даты (например, 2024 год), это может указывать на проблему с актуальностью данных или просачивание данных.</li> <li>Выбросы: Максимальные значения для некоторых метрик (например, Volume) могут указывать на наличие выбросов, которые могут искажать анализ и моделирование.</li> <li>Смещение: Отсутствие столбцов с нулевыми значениями и признаков с низкой дисперсией указывает на отсутствие явных проблем со смещением данных. Однако, для более точного анализа смещения необходимо провести дополнительные исследования, такие как сравнение распределений признаков в тренировочном и тестовом наборах данных.</li> <li>Просачивание данных: Наличие будущих дат (например, 2024 год) может указывать на проблему с просачиванием данных, если эти данные используются для прогнозирования будущих событий. Это может привести к некорректным результатам моделирования.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol><h3>Примеры решения проблем для набора данных</h3></ol>\n",
    "    <li>Удаление выбросов на основе значения или Volume</li>\n",
    "    <li>Удаление или обновить устаревшие даты, так как наличие будущих дат может указывать на проблему с актуальностью данных</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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Исходный размер строк: 8036 строк\n",
      "Размер обучающей выборки: 5625 строк\n",
      "Размер валидационной выборки: 1205 строк\n",
      "Размер тестовой выборки: 1206 строк\n"
     ]
    }
   ],
   "source": [
    "df_numeric = df.select_dtypes(include='number')\n",
    "\n",
    "x = df_numeric.drop(['Close'], axis=1)\n",
    "y = df_numeric['Close']\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\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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: 2.53\n",
      "Стандартное отклонение: 1.54\n",
      "Минимальное значение: -1.06\n",
      "Максимальное значение: 4.84\n",
      "Количество наблюдений: 5625\n",
      "\n",
      "Статистические показатели для валидационной выборки:\n",
      "Среднее значение: 2.44\n",
      "Стандартное отклонение: 1.58\n",
      "Минимальное значение: -1.02\n",
      "Максимальное значение: 4.79\n",
      "Количество наблюдений: 1205\n",
      "\n",
      "Статистические показатели для тестовой выборки:\n",
      "Среднее значение: 2.49\n",
      "Стандартное отклонение: 1.57\n",
      "Минимальное значение: -1.09\n",
      "Максимальное значение: 4.81\n",
      "Количество наблюдений: 1206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log'], axis=1)\n",
    "y = df['Close_log']\n",
    "\n",
    "# Выбор только числовых признаков\n",
    "X = X.select_dtypes(include='number')\n",
    "\n",
    "# Разделение данных на обучающую, валидационную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, 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",
    "# Функция для построения гистограммы распределения целевого признака\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 Close Price')\n",
    "    plt.ylabel('Count')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Построение гистограмм распределения целевого признака\n",
    "plot_distribution(y_train, 'Распределение логарифма цены закрытия в обучающей выборке')\n",
    "plot_distribution(y_val, 'Распределение логарифма цены закрытия в валидационной выборке')\n",
    "plot_distribution(y_test, 'Распределение логарифма цены закрытия в тестовой выборке')\n",
    "\n",
    "# Функция для вывода статистических показателей\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",
    "# Вывод статистических показателей для обучающей, валидационной и тестовой выборок\n",
    "get_statistics(y_train, \"обучающей\")\n",
    "get_statistics(y_val, \"валидационной\")\n",
    "get_statistics(y_test, \"тестовой\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Oversampling и undersampling</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов после SMOTE (oversampling):\n",
      "Close_category\n",
      "0    1157\n",
      "1    1157\n",
      "2    1157\n",
      "3    1157\n",
      "4    1157\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "Close_category\n",
      "0    1092\n",
      "1    1092\n",
      "2    1092\n",
      "3    1092\n",
      "4    1092\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Проверка наличия столбца 'Date' и создание столбца 'Year'\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",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Создание категорий для целевой переменной\n",
    "df['Close_category'] = pd.qcut(df['Close_log'], q=5, labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log', 'Close_category'], axis=1)\n",
    "y = df['Close_category']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение SMOTE для oversampling\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",
    "# Применение RandomUnderSampler для undersampling\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные в выборке обучающей являются категориальными.\n",
      "Проверка необходимости аугментации для валидационной выборки:\n",
      "Среднее значение: 2.44, Стандартное отклонение: 1.58\n",
      "25-й квантиль: 1.20\n",
      "50-й квантиль (медиана): 2.53\n",
      "75-й квантиль: 4.01\n",
      "Выборка валидационной несбалансирована, рекомендуется аугментация.\n",
      "\n",
      "Данные в выборке тестовой являются категориальными.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def check_augmentation_need(data, name):\n",
    "    \"\"\"Проверка необходимости аугментации данных\"\"\"\n",
    "    # Проверка на наличие числовых значений\n",
    "    if isinstance(data.dtype, pd.CategoricalDtype):\n",
    "        print(f\"Данные в выборке {name} являются категориальными.\")\n",
    "        return\n",
    "    elif not np.issubdtype(data.dtype, np.number):\n",
    "        print(f\"Данные в выборке {name} не являются числовыми.\")\n",
    "        return\n",
    "\n",
    "    # Проверка на наличие пустых значений\n",
    "    if data.isnull().any():\n",
    "        print(f\"Выборка {name} содержит пустые значения.\")\n",
    "        return\n",
    "\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",
    "# Пример использования функции\n",
    "# y_train, y_val, y_test должны быть определены заранее\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 выборка валидационной несбалансирована и демонстрирует значительный разброс значений, что подтверждается квантилями и стандартным отклонением, применение методов аугментации рекомендуется для улучшения сбалансированности и качества модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'Close_category' в обучающей выборке:\n",
      " Close_category\n",
      "2    1157\n",
      "4    1134\n",
      "1    1126\n",
      "3    1116\n",
      "0    1092\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: 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=category_counts.index, y=category_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 'Close_category' в валидационной выборке:\n",
      " Close_category\n",
      "0    263\n",
      "1    242\n",
      "3    238\n",
      "4    238\n",
      "2    224\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: 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=category_counts.index, y=category_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": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: 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=category_counts.index, y=category_counts.values, palette='viridis')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'Close_category' в тестовой выборке:\n",
      " Close_category\n",
      "0    253\n",
      "3    252\n",
      "1    241\n",
      "4    235\n",
      "2    225\n",
      "Name: count, dtype: int64\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 'Close_category' в обучающей выборке:\n",
      "Минимальное количество наблюдений в классе: 1092\n",
      "Максимальное количество наблюдений в классе: 1157\n",
      "Выборка 'обучающей' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'Close_category' в валидационной выборке:\n",
      "Минимальное количество наблюдений в классе: 224\n",
      "Максимальное количество наблюдений в классе: 263\n",
      "Выборка 'валидационной' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'Close_category' в тестовой выборке:\n",
      "Минимальное количество наблюдений в классе: 225\n",
      "Максимальное количество наблюдений в классе: 253\n",
      "Выборка 'тестовой' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Распределение классов после SMOTE (oversampling):\n",
      "Close_category\n",
      "0    1157\n",
      "1    1157\n",
      "2    1157\n",
      "3    1157\n",
      "4    1157\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "Close_category\n",
      "0    1092\n",
      "1    1092\n",
      "2    1092\n",
      "3    1092\n",
      "4    1092\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Создание категорий для целевой переменной\n",
    "df['Close_category'] = pd.qcut(df['Close_log'], q=5, labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log', 'Close_category'], axis=1)\n",
    "y = df['Close_category']\n",
    "\n",
    "# Разделение данных на обучающую, валидационную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, 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_close_category_distribution(data, name):\n",
    "    \"\"\"Проверка и визуализация распределения признака 'Close_category'\"\"\"\n",
    "    category_counts = data.value_counts()\n",
    "    print(f\"Распределение 'Close_category' в {name} выборке:\\n\", category_counts)\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.barplot(x=category_counts.index, y=category_counts.values, palette='viridis')\n",
    "    plt.title(f\"Распределение признака 'Close_category' в {name} выборке\")\n",
    "    plt.xlabel('Close Category')\n",
    "    plt.ylabel('Count')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "analyze_close_category_distribution(y_train, 'обучающей')\n",
    "analyze_close_category_distribution(y_val, 'валидационной')\n",
    "analyze_close_category_distribution(y_test, 'тестовой')\n",
    "\n",
    "def check_close_category_augmentation(data, name):\n",
    "    print(f\"Проверка необходимости аугментации для признака 'Close_category' 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_close_category_augmentation(y_train, 'обучающей')\n",
    "check_close_category_augmentation(y_val, 'валидационной')\n",
    "check_close_category_augmentation(y_test, 'тестовой')\n",
    "\n",
    "# Применение SMOTE для oversampling\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",
    "# Применение RandomUnderSampler для undersampling\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 сбалансированы, поэтому аугментация не требуется ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Данные по инсультам</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
      "0   9046    Male  67.0             0              1          Yes   \n",
      "1  51676  Female  61.0             0              0          Yes   \n",
      "2  31112    Male  80.0             0              1          Yes   \n",
      "3  60182  Female  49.0             0              0          Yes   \n",
      "4   1665  Female  79.0             1              0          Yes   \n",
      "\n",
      "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
      "0        Private          Urban             228.69  36.6  formerly smoked   \n",
      "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
      "2        Private          Rural             105.92  32.5     never smoked   \n",
      "3        Private          Urban             171.23  34.4           smokes   \n",
      "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
      "\n",
      "   stroke  \n",
      "0       1  \n",
      "1       1  \n",
      "2       1  \n",
      "3       1  \n",
      "4       1   \n",
      "\n",
      "id\n",
      "gender\n",
      "age\n",
      "hypertension\n",
      "heart_disease\n",
      "ever_married\n",
      "work_type\n",
      "Residence_type\n",
      "avg_glucose_level\n",
      "bmi\n",
      "smoking_status\n",
      "stroke\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(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/healthcare.csv\")\n",
    "\n",
    "print(df2.head(), \"\\n\")\n",
    "print(*list(df2.columns), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объектом наблюдения являются пациенты и информация о их состоянии здоровья.\n",
    "\n",
    "Атрибуты объекта:\n",
    "\n",
    "- id (Идентификатор)\n",
    "- gender (Пол)\n",
    "- age (Возраст)\n",
    "- hypertension (Гипертония)\n",
    "- heart_disease (Сердечное заболевание)\n",
    "- ever_married (Был ли когда-либо в браке)\n",
    "- work_type (Тип работы)\n",
    "- Residence_type (Тип проживания)\n",
    "- avg_glucose_level (Средний уровень глюкозы)\n",
    "- bmi (Индекс массы тела)\n",
    "- smoking_status (Статус курения)\n",
    "- stroke (Инсульт)\n",
    "\n",
    "Связь между объектами:\n",
    "Имеется связь между атрибутами, например между индексом массы тела (bmi) и риском инсульта (stroke), а также между средним уровнем глюкозы (avg_glucose_level) и гипертонией (hypertension)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\2664058835.py:4: 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['bmi_category'] = pd.cut(df_clean['bmi'], bins=range(0, 100, 10))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df2['bmi'] = pd.to_numeric(df2['bmi'], errors='coerce')\n",
    "df_clean = df2.dropna(subset=['bmi', 'avg_glucose_level'])\n",
    "df_clean['bmi_category'] = pd.cut(df_clean['bmi'], bins=range(0, 100, 10))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=df_clean, x='bmi_category', y='avg_glucose_level')\n",
    "plt.title('Распределение среднего уровня глюкозы по категориям индекса массы тела')\n",
    "plt.xlabel('Категория индекса массы тела')\n",
    "plt.ylabel('Средний уровень глюкозы')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В регионах с низким индексом массы тела (BMI) средний уровень глюкозы значительно варьируется, в то время как в регионах с индексом массы тела более 40 единиц наблюдается более стабильный и высокий уровень глюкозы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Бизнес-цели</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Бизнес-цель**: Определение наилучших регионов для внедрения программ профилактики инсультов.<br>\n",
    "    Эффект для бизнеса: Возможность выбора регионов с высоким уровнем риска инсультов для запуска маркетинговых кампаний и открытия новых медицинских центров.\n",
    "- **Цели технического проекта**:\n",
    "  - Построить модель для определения регионов с высоким потенциалом развития программ профилактики инсультов.\n",
    "  - **Входные признаки**: Индекс массы тела (BMI), Средний уровень глюкозы, Возраст, Пол.\n",
    "  - **Целевой признак**: Регион с высоким или низким потенциалом для внедрения программ профилактики инсультов.\n",
    "2. **Бизнес-цель**: Оптимизация стратегий здравоохранения для снижения риска инсультов.\n",
    "    Эффект для бизнеса: Компании, предоставляющие медицинские услуги, могут использовать эти данные для выбора регионов, где их услуги будут наиболее востребованы.\n",
    "- **Цели технического проекта**:\n",
    "  - Построить модель, определяющую регионы с наибольшим риском инсультов и прогнозировать влияние профилактических мер на здоровье населения.\n",
    "  - **Входные признаки**: Гипертония, Сердечное заболевание, Курение, Индекс массы тела (BMI).\n",
    "  - **Целевой признак**: Уровень риска инсультов.\n",
    "3. **Бизнес-цель**: Определение экономического потенциала регионов на основе демографических данных и уровня здоровья населения.\n",
    "    Эффект для бизнеса: Компании могут определить регионы с высоким уровнем здоровья населения для открытия новых офисов, производств или филиалов.\n",
    "- **Цели технического проекта**:\n",
    "  - Создать модель для ранжирования регионов по их экономическому потенциалу на основе демографических данных и уровня здоровья населения.\n",
    "  - **Входные признаки**: Индекс массы тела (BMI), Средний уровень глюкозы, Возраст, Пол, Гипертония, Сердечное заболевание.\n",
    "  - **Целевой признак**: Оценка экономического потенциала."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<h3>Поиск проблем</h3>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных ячеек: \n",
      " id                     0\n",
      "gender                 0\n",
      "age                    0\n",
      "hypertension           0\n",
      "heart_disease          0\n",
      "ever_married           0\n",
      "work_type              0\n",
      "Residence_type         0\n",
      "avg_glucose_level      0\n",
      "bmi                  201\n",
      "smoking_status         0\n",
      "stroke                 0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов (по Z-оценке): 118\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "missing_val = df2.isnull().sum()\n",
    "print(\"Количество пропущенных ячеек: \\n\", missing_val)\n",
    "\n",
    "df2['bmi'] = pd.to_numeric(df2['bmi'], errors='coerce')\n",
    "df2['avg_glucose_level'] = pd.to_numeric(df2['avg_glucose_level'], errors='coerce')\n",
    "df2['age'] = pd.to_numeric(df2['age'], errors='coerce')\n",
    "df2['hypertension'] = df2['hypertension'].astype(int)\n",
    "df2['heart_disease'] = df2['heart_disease'].astype(int)\n",
    "df2['smoking_status'] = df2['smoking_status'].astype('category').cat.codes\n",
    "\n",
    "# Удаление пропусков для корректного анализа\n",
    "data = df2.dropna()\n",
    "\n",
    "# 1. Визуализация распределения данных (помогает выявить выбросы)\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=data[['bmi', 'avg_glucose_level', 'age']])\n",
    "plt.title('Поиск выбросов с помощью Boxplot')\n",
    "plt.show()\n",
    "\n",
    "# Вычисление Z-оценки для выявления выбросов\n",
    "z_score = np.abs(stats.zscore(data[['bmi', 'avg_glucose_level', 'age']]))\n",
    "outliers = np.where(z_score > 3)\n",
    "print(f\"Количество выбросов (по Z-оценке): {len(outliers[0])}\")\n",
    "\n",
    "# Построение корреляционной матрицы для поиска зашумленности\n",
    "corr_matrix = data[['bmi', 'avg_glucose_level', 'age', 'hypertension', 'heart_disease', 'smoking_status']].corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Корреляционная матрица')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из матрицы корреляции можно сделать выводы относительно зависимости между атрибутами и выявить наиболее бесполезные из них. Признаки с низкими корреляциями, такие как сердечные заболевания (heart_disease), уровень глюкозы (avg_glucose_level), и статус курения (smoking_status), могут содержать шум или не являться значимыми для текущей задачи медицинского анализа. Это не обязательно означает, что эти переменные всегда шумны, но в контексте данного анализа они могут оказаться несущественными."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проведем анализа остаточных ошибок, чтобы убедиться в том, что выше упомянутые атрибуты действиельно бесполезны в данном контексе."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По итогу вышло больше количество шумов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее значение возраста: 42.865373803218574\n",
      "Медиана возраста: 44.0\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(data=data['age'], kde=True, bins=30)\n",
    "plt.title('Распределение возраста по выборке')\n",
    "plt.xlabel('Возраст')\n",
    "plt.ylabel('Количество людей')\n",
    "plt.show()\n",
    "\n",
    "mean_age = data['age'].mean()\n",
    "median_age = data['age'].median()\n",
    "print(f\"Среднее значение возраста: {mean_age}\")\n",
    "print(f\"Медиана возраста: {median_age}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определяем смещение. Если разница между средним и медианным значениями существенна, это может указывать на смещение. В данном случаем смещение нет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Просачивания данных в столбце 'avg_glucose_level' не обнаружено.\n",
      "Просачивания данных в столбце 'bmi' не обнаружено.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\3655327482.py:2: 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",
      "  data['avg_glucose_level'] = pd.to_numeric(data['avg_glucose_level'].astype(str).str.replace(',', ''), errors='coerce')\n",
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\3655327482.py:3: 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",
      "  data['bmi'] = pd.to_numeric(data['bmi'].astype(str).str.replace(',', ''), errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "# Приведение столбцов к числовым значениям\n",
    "data['avg_glucose_level'] = pd.to_numeric(data['avg_glucose_level'].astype(str).str.replace(',', ''), errors='coerce')\n",
    "data['bmi'] = pd.to_numeric(data['bmi'].astype(str).str.replace(',', ''), errors='coerce')\n",
    "\n",
    "# Проверка на аномалии в данных\n",
    "invalid_avg_glucose = data[data['avg_glucose_level'] < 0]  # Проверка на отрицательные значения\n",
    "invalid_bmi = data[data['bmi'] < 0]  # Проверка на отрицательные значения\n",
    "\n",
    "if not invalid_avg_glucose.empty:\n",
    "    print(\"Просачивание данных: Неверные значения среднего уровня глюкозы в следующих строках:\")\n",
    "    print(invalid_avg_glucose)\n",
    "else:\n",
    "    print(\"Просачивания данных в столбце 'avg_glucose_level' не обнаружено.\")\n",
    "\n",
    "if not invalid_bmi.empty:\n",
    "    print(\"Просачивание данных: Неверные значения индекса массы тела в следующих строках:\")\n",
    "    print(invalid_bmi)\n",
    "else:\n",
    "    print(\"Просачивания данных в столбце 'bmi' не обнаружено.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также я хочу выявить наличие \"просачивания данных\" путем проверки аномалий. Для этого хочу сравнить уровень глюкозы и индекс массы тела с неверными значениями. Логично если они окажутся отрицательнами, это будет свидетельствовать о нарушении логики данных. В данном случае, после проверки, данные не показали никаких аномалий, и \"просачивания данных\" не обнаружено."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер данных после удаления выбросов: (4792, 12)\n",
      "Обрабатываем столбец: avg_glucose_level, медианное значение: 91.68\n",
      "Обрабатываем столбец: bmi, медианное значение: 28.1\n",
      "Обрабатываем столбец: age, медианное значение: 44.0\n",
      "Обрабатываем столбец: heart_disease, медианное значение: 0.0\n",
      "Обрабатываем столбец: hypertension, медианное значение: 0.0\n",
      "Выбросы заменены медианными значениями.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\1082538639.py:15: 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",
      "  data[column] = np.where(col_z_score > 3, median_value, data[column])\n",
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\1082538639.py:15: 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",
      "  data[column] = np.where(col_z_score > 3, median_value, data[column])\n",
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\1082538639.py:15: 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",
      "  data[column] = np.where(col_z_score > 3, median_value, data[column])\n",
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\1082538639.py:15: 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",
      "  data[column] = np.where(col_z_score > 3, median_value, data[column])\n",
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_22948\\1082538639.py:15: 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",
      "  data[column] = np.where(col_z_score > 3, median_value, data[column])\n"
     ]
    }
   ],
   "source": [
    "data_no_outliers = data[(z_score < 3).all(axis=1)]\n",
    "print(f\"Размер данных после удаления выбросов: {data_no_outliers.shape}\")\n",
    "\n",
    "# Столбцы для проверки и замены выбросов\n",
    "columns_to_check = ['avg_glucose_level', 'bmi', 'age', 'heart_disease', 'hypertension']\n",
    "\n",
    "for column in columns_to_check:\n",
    "    col_z_score = np.abs(stats.zscore(data[column]))\n",
    "    \n",
    "    median_value = data[column].median()\n",
    "    \n",
    "    print(f\"Обрабатываем столбец: {column}, медианное значение: {median_value}\")\n",
    "    \n",
    "    # Замена выбросов медианными значениями\n",
    "    data[column] = np.where(col_z_score > 3, median_value, data[column])\n",
    "\n",
    "print(\"Выбросы заменены медианными значениями.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Решили проблему с выбрасами "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Оценка качества набора данных:</h3>\n",
    "\n",
    "**Информативность:** Набор данных содержит информацию о пациентах с инсультами, включая демографические и медицинские показатели, такие как пол, возраст, наличие гипертонии, сердечных заболеваний, индекс массы тела (BMI), уровень глюкозы в крови и статус курения. Эти колонки обеспечивают важные метрики для анализа факторов риска инсульта.\n",
    "\n",
    "**Степень покрытия:** В наборе данных представлено множество пациентов, что обеспечивает достаточно широкий охват для анализа влияния различных факторов на риск инсульта. Этот объем данных позволяет проводить статистические и предсказательные анализы.\n",
    "\n",
    "**Соответствие реальным данным:** Данные являются актуальными и могут быть основаны на реальных медицинских обследованиях и исследованиях, что делает их значимыми для клинической практики и научных исследований.\n",
    "\n",
    "**Согласованность меток:** Имена колонок в наборе данных четкие и интуитивно понятные, такие как \"age\" (возраст), \"avg_glucose_level\" (средний уровень глюкозы) и \"stroke\" (инсульт). Однако необходимо обратить внимание на формат данных, поскольку некоторые числовые значения могут быть представлены как строки, что требует предобработки для корректного анализа."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Устранение проблемы пропущенных данных</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные после заполнения средним значением: 0 пропущенных значений осталось.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_filled_mean = data.copy()\n",
    "data_filled_mean[\"avg_glucose_level\"] = pd.to_numeric(data_filled_mean[\"avg_glucose_level\"], errors='coerce')\n",
    "\n",
    "mean_value = data_filled_mean[\"avg_glucose_level\"].mean()\n",
    "data_filled_mean[\"avg_glucose_level\"] = data_filled_mean[\"avg_glucose_level\"].fillna(mean_value)\n",
    "\n",
    "print(f\"Данные после заполнения средним значением: {data_filled_mean['avg_glucose_level'].isnull().sum()} пропущенных значений осталось.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка: 2945 строк\n",
      "Валидационная выборка: 982 строк\n",
      "Тестовая выборка: 982 строк\n"
     ]
    }
   ],
   "source": [
    "columns_to_drop = [\"id\", \"stroke\"]\n",
    "columns_to_drop = [col for col in columns_to_drop if col in data_filled_mean.columns]\n",
    "\n",
    "X = data_filled_mean.drop(columns=columns_to_drop)\n",
    "y = data_filled_mean[\"stroke\"] \n",
    "\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} строк\")\n",
    "print(f\"Валидационная выборка: {X_val.shape[0]} строк\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} строк\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение в обучающей выборке (инсульт):\n",
      " count    2945.000000\n",
      "mean        0.039389\n",
      "std         0.194551\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: stroke, dtype: float64\n",
      "\n",
      "Распределение в валидационной выборке (инсульт):\n",
      " count    982.000000\n",
      "mean       0.046843\n",
      "std        0.211411\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: stroke, dtype: float64\n",
      "\n",
      "Распределение в тестовой выборке (инсульт):\n",
      " count    982.000000\n",
      "mean       0.047862\n",
      "std        0.213582\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: stroke, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Проверка распределения целевой переменной (инсульт) в обучающей, валидационной и тестовой выборках\n",
    "print(\"Распределение в обучающей выборке (инсульт):\\n\", y_train.describe())\n",
    "print(\"\\nРаспределение в валидационной выборке (инсульт):\\n\", y_val.describe())\n",
    "print(\"\\nРаспределение в тестовой выборке (инсульт):\\n\", y_test.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дисбаланс данных:\n",
    "\n",
    "- В обучающей выборке среднее значение инсульта составляет около 3.9%, однако максимальное значение достигает 1, что указывает на наличие случаев инсульта у отдельных пациентов.\n",
    "\n",
    "- Стандартное отклонение в обучающей выборке составляет 0.195, что свидетельствует о наличии значительного числа пациентов, не перенесших инсульт (поскольку 0 составляет 75% выборки), наряду с небольшим числом случаев инсульта (около 4%).\n",
    "\n",
    "- В валидационной выборке среднее значение инсульта немного выше, около 4.7%, и также имеет стандартное отклонение 0.211. В тестовой выборке среднее значение инсульта составляет 4.8% с аналогичным разбросом. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Методы приращения данных<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после Oversampling: 5658 строк\n",
      "Распределение целевой переменной после Oversampling:\n",
      "stroke\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "Размер обучающей выборки после Undersampling: 232 строк\n",
      "Распределение целевой переменной после Undersampling:\n",
      "stroke\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Oversampling (увеличение выборки) для обучающих данных\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train_res, y_train_res = ros.fit_resample(X_train, y_train)\n",
    "print(f\"Размер обучающей выборки после Oversampling: {X_train_res.shape[0]} строк\")\n",
    "print(f\"Распределение целевой переменной после Oversampling:\\n{y_train_res.value_counts(normalize=True)}\")\n",
    "\n",
    "# Undersampling (уменьшение выборки) для обучающих данных\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_train_res_under, y_train_res_under = rus.fit_resample(X_train, y_train)\n",
    "print(f\"Размер обучающей выборки после Undersampling: {X_train_res_under.shape[0]} строк\")\n",
    "print(f\"Распределение целевой переменной после Undersampling:\\n{y_train_res_under.value_counts(normalize=True)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Онлайн обучение<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Education Level', 'Institution Type', 'Gender', 'Age', 'Device',\n",
      "       'IT Student', 'Location', 'Financial Condition', 'Internet Type',\n",
      "       'Network Type', 'Flexibility Level'],\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",
    "df3 = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/students_adaptability_level_online_education.csv\")\n",
    "\n",
    "print(df3.columns, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основные столбцы:</br>\n",
    "**Education Level** — Уровень образования</br>\n",
    "**Institution Type** — Тип учебного заведения</br>\n",
    "**Gender** — Пол</br>\n",
    "**Age** — Возраст</br>\n",
    "**Device** — Устройство</br>\n",
    "**IT Student** — Студент IT</br>\n",
    "**Location** — Местоположение</br>\n",
    "**Financial Condition** — Финансовое состояние</br>\n",
    "**Internet Type** — Тип интернета</br>\n",
    "**Network Type** — Тип сети</br>\n",
    "**Flexibility Level** — Уровень гибкости</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Проблемная область<h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данный набор данных относится к анализу онлайн образования, его распределению по возрастам, странам и типам учебных заведений. Проблемная область связана с доступностью онлайн-обучения и с изучением факторов, влияющих на качество образовательного опыта, выявлением тенденций в использовании различных устройств, а также влиянием финансовых условий, интернет-соединения и гибкости учебных программ на эффективность и доступность онлайн обучения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Анализ содержимого<h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Объекты наблюдения:** Студенты, участвующие в онлайн обучении</br>\n",
    "**Атрибуты объектов:** Уровень образования, тип учебного заведения, пол, возраст, устройство, является ли студент IT-специальности, местоположение, финансовое состояние, тип интернета, тип сети, уровень гибкости обучения</br>\n",
    "**Связи между объектами:** Можно выявить связи между возрастом и использованием устройства для обучения, местоположением и типом интернет-соединения, а также финансовым состоянием и гибкостью образовательной программы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Связь между возрастом и использованием устройства для обучения\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.scatterplot(data=df3, x='Age', y='Device')\n",
    "plt.title('Связь между возрастом и использованием устройства')\n",
    "plt.xlabel('Возраст')\n",
    "plt.ylabel('Устройство')\n",
    "\n",
    "# Связь между местоположением и типом интернет-соединения\n",
    "plt.subplot(2, 2, 2)\n",
    "top_locations = df3['Location'].value_counts().index[:10]\n",
    "sns.boxplot(data=df3[df3['Location'].isin(top_locations)], x='Location', y='Internet Type')\n",
    "plt.title('Связь между местоположением и типом интернет-соединения')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Местоположение')\n",
    "plt.ylabel('Тип интернета')\n",
    "\n",
    "# Связь между финансовым состоянием и гибкостью обучения\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.boxplot(data=df3, x='Financial Condition', y='Flexibility Level')\n",
    "plt.title('Связь между финансовым состоянием и гибкостью обучения')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Финансовое состояние')\n",
    "plt.ylabel('Уровень гибкости')\n",
    "\n",
    "# Связь между типом учебного заведения и использованием устройства\n",
    "plt.subplot(2, 2, 4)\n",
    "top_institutions = df3['Institution Type'].value_counts().index[:10]\n",
    "sns.boxplot(data=df3[df3['Institution Type'].isin(top_institutions)], x='Institution Type', y='Device')\n",
    "plt.title('Связь между типом учебного заведения и устройством для обучения')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Тип учебного заведения')\n",
    "plt.ylabel('Устройство')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Примеры бизнес-целей<h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Образовательные платформы могут использовать эти данные для анализа, какие устройства и типы интернет-соединений студенты используют чаще всего, чтобы оптимизировать свои курсы под предпочтительные форматы обучения.\n",
    "2. Учебные заведения могут использовать эти данные для анализа возрастных моделей использования онлайн-обучения, что поможет лучше планировать программы и курсы для различных возрастных групп.\n",
    "3. Компании в сфере EdTech могут использовать данные для выявления стран и регионов, где онлайн-обучение наиболее востребовано, и нацелить свои маркетинговые и инвестиционные усилия на эти рынки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Цели технического проекта<h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Вход: Данные об устройствах и типе интернет-соединения.<br>\n",
    "Целевой признак: Устройство, тип интернета.\n",
    "\n",
    "2. Вход: Данные о возрасте и устройстве, используемом для онлайн-обучения.<br>\n",
    "Целевой признак: Возраст, устройство.\n",
    "\n",
    "3. Вход: Данные о местоположении и типе интернет-соединения.<br>\n",
    "Целевой признак: Местоположение, тип интернета."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Выявление и решение проблем<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер данных до удаления выбросов:  (1205, 11)\n",
      "Размер данных после удаления выбросов:  (1205, 11)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "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",
    "df3 = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/students_adaptability_level_online_education.csv\")\n",
    "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n",
    "\n",
    "sns.boxplot(data=df3, x='Age', ax=axs)\n",
    "axs.set_title(\"Выбросы по возрасту\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"Размер данных до удаления выбросов: \", df3.shape)\n",
    "\n",
    "# Функция для удаления выбросов с помощью IQR только для числовых данных\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",
    "    return df[~((df[column] < (Q1 - 1.5 * IQR)) | (df[column] > (Q3 + 1.5 * IQR)))]\n",
    "\n",
    "# Удаление выбросов по возрасту\n",
    "df3_cleaned = remove_outliers(df3, 'Age')\n",
    "\n",
    "print(\"Размер данных после удаления выбросов: \", df3_cleaned.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_15252\\1872101371.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=location_dist.values, y=location_dist.index, palette='coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_15252\\1872101371.py:16: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=institution_dist.values, y=institution_dist.index, palette='coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(Location\n",
       " Town     77.593361\n",
       " Rural    22.406639\n",
       " Name: proportion, dtype: float64,\n",
       " Institution Type\n",
       " Private    68.298755\n",
       " Public     31.701245\n",
       " Name: proportion, dtype: float64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Визуализация распределения студентов по местоположению\n",
    "plt.figure(figsize=(10, 6))\n",
    "location_dist = df3['Location'].value_counts()\n",
    "sns.barplot(x=location_dist.values, y=location_dist.index, palette='coolwarm')\n",
    "plt.title('Распределение студентов по местоположению')\n",
    "plt.xlabel('Количество студентов')\n",
    "plt.ylabel('Местоположение')\n",
    "plt.show()\n",
    "\n",
    "# Визуализация распределения студентов по типу учебных заведений\n",
    "plt.figure(figsize=(10, 6))\n",
    "institution_dist = df3['Institution Type'].value_counts()\n",
    "sns.barplot(x=institution_dist.values, y=institution_dist.index, palette='coolwarm')\n",
    "plt.title('Распределение студентов по типам учебных заведений')\n",
    "plt.xlabel('Количество студентов')\n",
    "plt.ylabel('Тип учебного заведения')\n",
    "plt.show()\n",
    "\n",
    "# Процентное распределение по местоположению и типам учебных заведений (для наглядности)\n",
    "location_percentage = df3['Location'].value_counts(normalize=True) * 100\n",
    "institution_percentage = df3['Institution Type'].value_counts(normalize=True) * 100\n",
    "\n",
    "location_percentage.head(10), institution_percentage.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Имеется смещение по местоположению или типам учебных заведений. Но думаю это особенность данного датасета, а не самих данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 2, np.int64(980))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Проверим уникальность по ключевым столбцам\n",
    "unique_devices = df3['Device'].nunique()\n",
    "unique_locations = df3['Location'].nunique()\n",
    "unique_institution_types = df3['Institution Type'].nunique()\n",
    "\n",
    "# 2. Проверка дубликатов\n",
    "duplicates_count = df3.duplicated().sum() \n",
    "\n",
    "unique_devices, unique_locations, unique_institution_types, duplicates_count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, можно сказать, что наш набор данных демонстрирует высокую информативность, что позволяет проводить различные виды анализов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_15252\\2241712000.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(data=df3, y='Institution Type', order=df3['Institution Type'].value_counts().index, palette='coolwarm')\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Проверка на согласованность категорий\n",
    "unique_devices = df3['Device'].unique()  # Уникальные устройства\n",
    "unique_institution_types = df3['Institution Type'].unique()  # Уникальные типы учебных заведений\n",
    "\n",
    "# Пример для визуального анализа распределения по типам учебных заведений\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(data=df3, y='Institution Type', order=df3['Institution Type'].value_counts().index, palette='coolwarm')\n",
    "plt.title('Распределение по типам учебных заведений')\n",
    "plt.xlabel('Количество студентов')\n",
    "plt.ylabel('Тип учебного заведения')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для оценки покрытия мы смотрим на то, насколько отличаются данные по типу учебного заведения и количеству студентов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Устранение проблемы пропущенных данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_15252\\2536815725.py:13: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df_fillna_mean[column].fillna(df_fillna_mean[column].mean(), inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Education Level        0\n",
       " Institution Type       0\n",
       " Gender                 0\n",
       " Age                    0\n",
       " Device                 0\n",
       " IT Student             0\n",
       " Location               0\n",
       " Financial Condition    0\n",
       " Internet Type          0\n",
       " Network Type           0\n",
       " Flexibility Level      0\n",
       " dtype: int64,\n",
       " (1205, 11),\n",
       " (1205, 11),\n",
       " (1205, 11))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values = df3.isnull().sum()\n",
    "df_dropna = df3.dropna()\n",
    "df_fillna_const = df3.fillna(0)\n",
    "df_fillna_mean = df3.copy()\n",
    "for column in df_fillna_mean.select_dtypes(include=['float64', 'int64']):\n",
    "    df_fillna_mean[column].fillna(df_fillna_mean[column].mean(), inplace=True)\n",
    "missing_values, df_dropna.shape, df_fillna_const.shape, df_fillna_mean.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((723, 10), (241, 10), (241, 10))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделим набор данных на признаки (X) и целевой признак (y)\n",
    "X = df3.drop(columns=['Financial Condition'])\n",
    "y = df3['Financial Condition']\n",
    "\n",
    "# Разделение на обучающую, контрольную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размера выборок\n",
    "(X_train.shape, X_val.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка сбалансированности выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(count     723\n",
       " unique      3\n",
       " top       Mid\n",
       " freq      527\n",
       " Name: Financial Condition, dtype: object,\n",
       " count     241\n",
       " unique      3\n",
       " top       Mid\n",
       " freq      182\n",
       " Name: Financial Condition, dtype: object,\n",
       " count     241\n",
       " unique      3\n",
       " top       Mid\n",
       " freq      169\n",
       " Name: Financial Condition, dtype: object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка распределения целевого признака по выборкам\n",
    "train_dist = y_train.describe()\n",
    "val_dist = y_val.describe()\n",
    "test_dist = y_test.describe()\n",
    "\n",
    "train_dist, val_dist, test_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры после oversampling: (1581, 10) (1581,)\n",
      "Размеры после undersampling: (156, 10) (156,)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "oversampler = RandomOverSampler(random_state=12)\n",
    "X_train_over, y_train_over = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "undersampler = RandomUnderSampler(random_state=12)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Размеры после oversampling:\", X_train_over.shape, y_train_over.shape)\n",
    "print(\"Размеры после undersampling:\", X_train_under.shape, y_train_under.shape)"
   ]
  }
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