From 3829fecfe3b36dba4372baf7598c3e4f1a53d557 Mon Sep 17 00:00:00 2001
From: asoc1al <windows0804@yandex.ru>
Date: Sat, 19 Oct 2024 23:03:18 +0400
Subject: [PATCH] vrode all done

---
 lab2.ipynb     | 935 ++++++++++++++++++++++++++++++++++++++++++++++++-
 poetry.lock    | 183 +++++++++-
 pyproject.toml |   4 +
 3 files changed, 1106 insertions(+), 16 deletions(-)

diff --git a/lab2.ipynb b/lab2.ipynb
index f111cce..68b73a3 100644
--- a/lab2.ipynb
+++ b/lab2.ipynb
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -351,7 +351,7 @@
        "[5110 rows x 12 columns]"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -360,13 +360,12 @@
     "import pandas as pd\n",
     "\n",
     "var4 =  pd.read_csv(\"./datasets/var4/healthcare-dataset-stroke-data.csv\")\n",
-    "\n",
     "var4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -387,7 +386,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -779,7 +778,7 @@
        "[21613 rows x 21 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -791,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -821,7 +820,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1216,7 +1215,7 @@
        "[1370 rows x 18 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1228,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1255,7 +1254,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1268,13 +1267,919 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. Провести анализ содержимого каждого набора данных. Что является объектом/объектами наблюдения? Каковы атрибуты объектов? Есть ли связи между объектами?"
+    "### 3. Провести анализ содержимого каждого набора данных. Что является объектом/объектами наблюдения? Каковы атрибуты объектов? Есть ли связи между объектами?\n",
+    "\n",
+    "1. Датасет о риске инсульта\n",
+    "    - Объект наблюжения: Пациенты\n",
+    "2. Датасет о продажах недвижимости\n",
+    "    - Объект наблюдения: Сделки по проданным домам в King Country, США\n",
+    "3. Датасет о цене мобильных устройств\n",
+    "    - Объект наблюдения: Модели телефонов и их цены\n",
+    "\n",
+    "Все аттрибуты были перечислены выше."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "### 4. Привести примеры бизнес-целей, для достижения которых могут подойти выбранные наборы данных. Каков эффект для бизнеса?\n",
+    "1. Датасет о риске инсульта\n",
+    "    - Бизнес-цель: Определить факторы риска инсульта и предохранить пациентов от инсульта.\n",
+    "    - Эффект для бизнеса: Снижение количества случаев инсульта, снижение затрат на лечение и улучшение репутации клиники.\n",
+    "2. Датасет о продажах недвижимости\n",
+    "    - Бизнес-цель: Определить факторы, влияющие на продажи недвижимости\n",
+    "    - Эффект для бизнеса: Улучшение стратегии продаж, повышение эффективности подбора имущества для последующего извлесения прибыли\n",
+    "3. Датасет о цене мобильных устройств\n",
+    "    - Бизнес-цель: Определить факторы, влияющие на цену мобильных устройств\n",
+    "    - Эффект для бизнеса: Улучшение стратегии ценообразования, повышение эффективности продаж и прибыли."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Привести примеры целей технического проекта для каждой выделенной ранее бизнес-цели. Что поступает на вход, что является целевым признаком?\n",
+    "1. Датасет о риске инсульта\n",
+    "    - Бизнес-цель: Разработка системы раннего предупреждения инсульта.\n",
+    "    - Цель технического проекта: Создание модели машинного обучения для прогнозирования вероятности инсульта.\n",
+    "    - Входные данные:\n",
+    "        Пол Возраст Наличие гипертензии Наличие сердечных заболеваний Статус брака Тип работы Тип проживания Средний уровень глюкозы Индекс массы тела Статус курения и т.д.\n",
+    "    - Целевой признак: Наличие инсульта (stroke).\n",
+    "2. Датасет о продажах недвижимости\n",
+    "    - Бизнес-цель: Развитие системы рекомендации недвижимости рекомендованной к покупке для последующей перепродажи.\n",
+    "    - Цель технического проекта: Разработка модели машинного обучения для прогнозирования цены недвижимости.\n",
+    "    - Входные данные:\n",
+    "        Площадь Площадь комнат Площадь участка Тип дома Тип комнат и другие признаки.\n",
+    "    - Целевой признак: Цена недвижимости (Price).\n",
+    "3. Датасет о цене мобильных устройств\n",
+    "    - Бизнес-цель: Оптимизация ценообразования и улучшение стратегии продаж мобильных устройств.\n",
+    "    - Цель технического проекта: Построение модели для предсказания рекомендованной цены мобильного устройства на основе характеристик.\n",
+    "    - Входные данные:\n",
+    "        Имя Рейтинг Очки производительности Кол-во SIM-слотов Оперативная память Емкость аккумклятора Дисплей Камера Дополнительные слоты для карт памяти и остальное.\n",
+    "    - Целевой признак: Очки производительности (Spec_score)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Определить проблемы выбранных наборов данных: зашумленность, смещение, актуальность, выбросы, просачивание данных.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. Привести примеры решения обнаруженных проблем для каждого набора данных¶\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Проверка на зашумленность ---- количество пропусков в процентах от общего кол-ва\n",
+    "def check_noise(dataframe):\n",
+    "    total_values = dataframe.size\n",
+    "    missing_values = dataframe.isnull().sum().sum()\n",
+    "    noise_percentage = (missing_values / total_values) * 100\n",
+    "    return f\"Зашумленность: {noise_percentage:.2f}%\"\n",
+    "\n",
+    "# 2. Проверка на смещение ----- объем уникальных значений внутри определнной колонки \n",
+    "def check_bias(dataframe, target_column):\n",
+    "    if target_column in dataframe.columns:\n",
+    "        unique_values = dataframe[target_column].nunique()\n",
+    "        total_values = len(dataframe)\n",
+    "        bias_percentage = (unique_values / total_values) * 100\n",
+    "        return f\"Смещение по {target_column}: {bias_percentage:.2f}% уникальных значений\"\n",
+    "    return \"Целевой признак не найден.\"\n",
+    "\n",
+    "# 3. Проверка на дубликаты\n",
+    "def check_duplicates(dataframe):\n",
+    "    duplicate_percentage = dataframe.duplicated().mean() * 100\n",
+    "    return f\"Количество дубликатов: {duplicate_percentage:.2f}%\"\n",
+    "\n",
+    "# 4. Проверка на выбросы\n",
+    "def check_outliers(dataframe, column):\n",
+    "    if column in dataframe.columns:\n",
+    "        Q1 = dataframe[column].quantile(0.25)\n",
+    "        Q3 = dataframe[column].quantile(0.75)\n",
+    "        IQR = Q3 - Q1\n",
+    "        lower_bound = Q1 - 1.5 * IQR\n",
+    "        upper_bound = Q3 + 1.5 * IQR\n",
+    "        outlier_count = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)].shape[0]\n",
+    "        total_count = dataframe.shape[0]\n",
+    "        outlier_percentage = (outlier_count / total_count) * 100\n",
+    "        return f\"Выбросы по {column}: {outlier_percentage:.2f}%\"\n",
+    "    return f\"Признак {column} не найден.\"\n",
+    "\n",
+    "# 5. Проверка на просачивание данных\n",
+    "def check_data_leakage(dataframe, target_column):\n",
+    "    if target_column in dataframe.columns:\n",
+    "        correlation_matrix = dataframe.select_dtypes(include=[np.number]).corr()\n",
+    "        leakage_info = correlation_matrix[target_column].abs().nlargest(10)\n",
+    "        leakage_report = \", \".join([f\"{feature}: {value:.2f}\" for feature, value in leakage_info.items() if feature != target_column])\n",
+    "        return f\"Признаки просачивания данных: {leakage_report}\"\n",
+    "    return \"Целевой признак не найден.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Датасет о риске инсульта:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Зашумленность: 0.33%\n",
+      "Смещение по avg_glucose_level: 77.87% уникальных значений\n",
+      "Количество дубликатов: 0.00%\n",
+      "Выбросы по avg_glucose_level: 12.27%\n",
+      "Признаки просачивания данных: age: 0.25, heart_disease: 0.13, avg_glucose_level: 0.13, hypertension: 0.13, bmi: 0.04, id: 0.01\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise_columns = check_noise(var4)\n",
+    "bias_info = check_bias(var4, 'avg_glucose_level') \n",
+    "duplicate_count = check_duplicates(var4)\n",
+    "outliers_data = check_outliers(var4, 'avg_glucose_level')  \n",
+    "leakage_info = check_data_leakage(var4, 'stroke') \n",
+    "\n",
+    "print(noise_columns)\n",
+    "print(bias_info)\n",
+    "print(duplicate_count)\n",
+    "print(outliers_data)\n",
+    "print(leakage_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Датасет о продажах недвижимости:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Зашумленность: 0.00%\n",
+      "Смещение по price: 18.64% уникальных значений\n",
+      "Количество дубликатов: 0.00%\n",
+      "Выбросы по yr_renovated: 4.23%\n",
+      "Признаки просачивания данных: yr_built: 0.36, floors: 0.26, sqft_basement: 0.17, sqft_above: 0.16, grade: 0.14, bathrooms: 0.12, long: 0.11, sqft_living15: 0.09, yr_renovated: 0.06\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise_columns = check_noise(var6)\n",
+    "bias_info = check_bias(var6, 'price') \n",
+    "duplicate_count = check_duplicates(var6)\n",
+    "outliers_data = check_outliers(var6, 'yr_renovated')  \n",
+    "leakage_info = check_data_leakage(var6, 'condition') \n",
+    "\n",
+    "print(noise_columns)\n",
+    "print(bias_info)\n",
+    "print(duplicate_count)\n",
+    "print(outliers_data)\n",
+    "print(leakage_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Датасет о цене мобильных устройств:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Зашумленность: 2.36%\n",
+      "Смещение по company: 1.90% уникальных значений\n",
+      "Количество дубликатов: 0.00%\n",
+      "Выбросы по Spec_score: 1.24%\n",
+      "Признаки просачивания данных: Spec_score: 0.06, Unnamed: 0: 0.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "noise_columns = check_noise(var18)\n",
+    "bias_info = check_bias(var18, 'company') \n",
+    "duplicate_count = check_duplicates(var18)\n",
+    "outliers_data = check_outliers(var18, 'Spec_score')  \n",
+    "leakage_info = check_data_leakage(var18, 'Rating') \n",
+    "\n",
+    "print(noise_columns)\n",
+    "print(bias_info)\n",
+    "print(duplicate_count)\n",
+    "print(outliers_data)\n",
+    "print(leakage_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9. Устранить проблему пропущенных данных. Для каждого набора данных использовать разные методы: удаление, подстановка константного значения (0 или подобное), подстановка среднего значения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Инсульт\n",
+    "var4.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var4['bmi'] = var4['bmi'].fillna(var4['bmi'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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                  0\n",
+       "smoking_status       0\n",
+       "stroke               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var4.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id               0\n",
+       "date             0\n",
+       "price            0\n",
+       "bedrooms         0\n",
+       "bathrooms        0\n",
+       "sqft_living      0\n",
+       "sqft_lot         0\n",
+       "floors           0\n",
+       "waterfront       0\n",
+       "view             0\n",
+       "condition        0\n",
+       "grade            0\n",
+       "sqft_above       0\n",
+       "sqft_basement    0\n",
+       "yr_built         0\n",
+       "yr_renovated     0\n",
+       "zipcode          0\n",
+       "lat              0\n",
+       "long             0\n",
+       "sqft_living15    0\n",
+       "sqft_lot15       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Дома\n",
+    "var6.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0             0\n",
+       "Name                   0\n",
+       "Rating                 0\n",
+       "Spec_score             0\n",
+       "No_of_sim              0\n",
+       "Ram                    0\n",
+       "Battery                0\n",
+       "Display                0\n",
+       "Camera                 0\n",
+       "External_Memory        0\n",
+       "Android_version      443\n",
+       "Price                  0\n",
+       "company                0\n",
+       "Inbuilt_memory        19\n",
+       "fast_charging         89\n",
+       "Screen_resolution      2\n",
+       "Processor             28\n",
+       "Processor_name         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Мобильные устройства\n",
+    "var18.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var18['Android_version'] = var18['Android_version'].fillna('No info')\n",
+    "var18['Inbuilt_memory'] = var18['Android_version'].fillna('No info')\n",
+    "var18['fast_charging'] = var18['Android_version'].fillna('No info')\n",
+    "var18['Screen_resolution'] = var18['Android_version'].fillna('No info')\n",
+    "var18['Processor'] = var18['Android_version'].fillna('No info')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0           0\n",
+       "Name                 0\n",
+       "Rating               0\n",
+       "Spec_score           0\n",
+       "No_of_sim            0\n",
+       "Ram                  0\n",
+       "Battery              0\n",
+       "Display              0\n",
+       "Camera               0\n",
+       "External_Memory      0\n",
+       "Android_version      0\n",
+       "Price                0\n",
+       "company              0\n",
+       "Inbuilt_memory       0\n",
+       "fast_charging        0\n",
+       "Screen_resolution    0\n",
+       "Processor            0\n",
+       "Processor_name       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var18.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10. Выполнить разбиение каждого набора данных на обучающую, контрольную и тестовую выборки¶\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "var4 Dataset:\n",
+      "Train: 80.00%\n",
+      "Validation: 10.00%\n",
+      "Test: 10.00%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Разбиение var4 (Инсульт)\n",
+    "\n",
+    "original_var4_size = len(var4)\n",
+    "train_var4, temp_var4 = train_test_split(var4, test_size=0.2, random_state=42)\n",
+    "val_var4, test_var4 = train_test_split(temp_var4, test_size=0.5, random_state=42)\n",
+    "\n",
+    "print(\"var4 Dataset:\")\n",
+    "print(f\"Train: {len(train_var4)/original_var4_size*100:.2f}%\")\n",
+    "print(f\"Validation: {len(val_var4)/original_var4_size*100:.2f}%\")\n",
+    "print(f\"Test: {len(test_var4)/original_var4_size*100:.2f}%\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "var6 Dataset:\n",
+      "Train: 80.00%\n",
+      "Validation: 10.00%\n",
+      "Test: 10.00%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Разбиение var6 (Дома)\n",
+    "original_var6_size = len(var6)\n",
+    "train_var6, temp_var6 = train_test_split(var6, test_size=0.2, random_state=42)\n",
+    "val_var6, test_var6 = train_test_split(temp_var6, test_size=0.5, random_state=42)\n",
+    "\n",
+    "print(\"var6 Dataset:\")\n",
+    "print(f\"Train: {len(train_var6)/original_var6_size*100:.2f}%\")\n",
+    "print(f\"Validation: {len(val_var6)/original_var6_size*100:.2f}%\")\n",
+    "print(f\"Test: {len(test_var6)/original_var6_size*100:.2f}%\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "var18 Dataset:\n",
+      "Train: 80.00%\n",
+      "Validation: 10.00%\n",
+      "Test: 10.00%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Разбиение var18 (Мобильные устройства)\n",
+    "original_var18_size = len(var18)\n",
+    "train_var18, temp_var18 = train_test_split(var18, test_size=0.2, random_state=42)\n",
+    "val_var18, test_var18 = train_test_split(temp_var18, test_size=0.5, random_state=42)\n",
+    "\n",
+    "print(\"var18 Dataset:\")\n",
+    "print(f\"Train: {len(train_var18)/original_var18_size*100:.2f}%\")\n",
+    "print(f\"Validation: {len(val_var18)/original_var18_size*100:.2f}%\")\n",
+    "print(f\"Test: {len(test_var18)/original_var18_size*100:.2f}%\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 11. Оценить сбалансированность выборок для каждого набора данных. Оценить необходимость использования методов приращения (аугментации) данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 12. Выполнить приращение данных методами выборки с избытком (oversampling) и выборки с недостатком (undersampling). Должны быть представлены примеры реализации обоих методов для выборок каждого набора данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_sample_balance(y, sample_name):\n",
+    "    plt.figure(figsize=(8, 5))\n",
+    "    sns.histplot(y, bins=30, kde=True)\n",
+    "    plt.title(f'Распределение целевой переменной для {sample_name}')\n",
+    "    plt.xlabel(sample_name)\n",
+    "    plt.ylabel('Частота')\n",
+    "    plt.show()\n",
+    "\n",
+    "# Оценка сбалансированности выборок\n",
+    "plot_sample_balance(train_var6['price'], 'Train var6')\n",
+    "plot_sample_balance(val_var6['price'], 'Validation var6')\n",
+    "plot_sample_balance(test_var6['price'], 'Test var6')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Распределения выборок у данного датасета выглядят схоже. Это говорит о сбалансированности выборок."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sample_balance(train_var4['stroke'], 'Train var4')\n",
+    "plot_sample_balance(val_var4['stroke'], 'Validation var4')\n",
+    "plot_sample_balance(test_var4['stroke'], 'Test var4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выборки выглядят схоже, но у всех трех имеется явный дисбаланс классов. Это проблема, т.к в дальнейшем не сможем обучить какую-либо модель."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sample_balance(train_var18['Spec_score'], 'Train var18')\n",
+    "plot_sample_balance(val_var18['Spec_score'], 'Validation var18')\n",
+    "plot_sample_balance(test_var18['Spec_score'], 'Test var18')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Распределения выборок у данного датасета выглядят схоже. Это говорит о сбалансированности выборок. Однако в тренировочной выборке значительно больший размах значений"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 12. Выполнить приращение данных методами выборки с избытком (oversampling) и выборки с недостатком (undersampling). Должны быть представлены примеры реализации обоих методов для выборок каждого набора данных\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Инсульт"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "После oversampling (var4): stroke\n",
+      "1    4861\n",
+      "0    4861\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "\n",
+    "X_var4 = var4.drop('stroke', axis=1)\n",
+    "y_var4 = var4['stroke']\n",
+    "\n",
+    "# Кодирование категориальных признаков\n",
+    "for column in X_var4.select_dtypes(include=['object']).columns:\n",
+    "    X_var4[column] = X_var4[column].astype('category').cat.codes\n",
+    "\n",
+    "# Теперь применяем SMOTE\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_resampled_var4, y_resampled_var4 = smote.fit_resample(X_var4, y_var4)\n",
+    "\n",
+    "# Получаем результаты\n",
+    "print(f'После oversampling (var4): {pd.Series(y_resampled_var4).value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "После undersampling (var4): stroke\n",
+      "0    249\n",
+      "1    249\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "# Undersampling для var4\n",
+    "undersample = RandomUnderSampler(random_state=42)\n",
+    "X_under_var4, y_under_var4 = undersample.fit_resample(X_var4, y_var4)\n",
+    "\n",
+    "print(f'После undersampling (var4): {pd.Series(y_under_var4).value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Дома"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"['Price'] not found in axis\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[65], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_var6 \u001b[38;5;241m=\u001b[39m \u001b[43mvar6\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m y_var6 \u001b[38;5;241m=\u001b[39m var6[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrice\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Кодирование категориальных признаков\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:5581\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5433\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5434\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5435\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5442\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5443\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5444\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5445\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5446\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5579\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5580\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5582\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5583\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5584\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5585\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5586\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5587\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5588\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5589\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:4788\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4786\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4787\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4788\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4790\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4791\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:4830\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4828\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4829\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4830\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4831\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4833\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4834\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:7070\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   7068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   7069\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 7070\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   7071\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   7072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
+      "\u001b[1;31mKeyError\u001b[0m: \"['Price'] not found in axis\""
+     ]
+    }
+   ],
+   "source": [
+    "X_var6 = var6.drop('Price', axis=1)\n",
+    "y_var6 = var6['Price']\n",
+    "\n",
+    "# Кодирование категориальных признаков\n",
+    "for column in X_var6.select_dtypes(include=['object']).columns:\n",
+    "    X_var6[column] = X_var6[column].astype('category').cat.codes\n",
+    "\n",
+    "# Теперь применяем SMOTE\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_resampled_var6, y_resampled_var6 = smote.fit_resample(X_var6, y_var6)\n",
+    "\n",
+    "# Получаем результаты\n",
+    "print(f'После oversampling (var6): {pd.Series(y_resampled_var6).value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[69], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# Теперь применяем SMOTE\u001b[39;00m\n\u001b[0;32m      9\u001b[0m smote \u001b[38;5;241m=\u001b[39m SMOTE(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m X_resampled_var18, y_resampled_var18 \u001b[38;5;241m=\u001b[39m \u001b[43msmote\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_var18\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_var18\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# Получаем результаты\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mПосле oversampling (var18): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpd\u001b[38;5;241m.\u001b[39mSeries(y_resampled_var18)\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\imblearn\\base.py:208\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m    188\u001b[0m \n\u001b[0;32m    189\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;124;03m    The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m--> 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\imblearn\\base.py:112\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    106\u001b[0m X, y, binarize_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_X_y(X, y)\n\u001b[0;32m    108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy_ \u001b[38;5;241m=\u001b[39m check_sampling_strategy(\n\u001b[0;32m    109\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy, y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampling_type\n\u001b[0;32m    110\u001b[0m )\n\u001b[1;32m--> 112\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    114\u001b[0m y_ \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    115\u001b[0m     label_binarize(output[\u001b[38;5;241m1\u001b[39m], classes\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39munique(y)) \u001b[38;5;28;01mif\u001b[39;00m binarize_y \u001b[38;5;28;01melse\u001b[39;00m output[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    116\u001b[0m )\n\u001b[0;32m    118\u001b[0m X_, y_ \u001b[38;5;241m=\u001b[39m arrays_transformer\u001b[38;5;241m.\u001b[39mtransform(output[\u001b[38;5;241m0\u001b[39m], y_)\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:389\u001b[0m, in \u001b[0;36mSMOTE._fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    386\u001b[0m X_class \u001b[38;5;241m=\u001b[39m _safe_indexing(X, target_class_indices)\n\u001b[0;32m    388\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnn_k_\u001b[38;5;241m.\u001b[39mfit(X_class)\n\u001b[1;32m--> 389\u001b[0m nns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn_k_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkneighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_class\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[:, \u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m    390\u001b[0m X_new, y_new \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_samples(\n\u001b[0;32m    391\u001b[0m     X_class, y\u001b[38;5;241m.\u001b[39mdtype, class_sample, X_class, nns, n_samples, \u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m    392\u001b[0m )\n\u001b[0;32m    393\u001b[0m X_resampled\u001b[38;5;241m.\u001b[39mappend(X_new)\n",
+      "File \u001b[1;32mc:\\Users\\HomePC\\Desktop\\MII_Lab1\\.venv\\Lib\\site-packages\\sklearn\\neighbors\\_base.py:834\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[1;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[0;32m    832\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    833\u001b[0m         inequality_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_neighbors <= n_samples_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 834\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    835\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minequality_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    836\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_neighbors = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_neighbors\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, n_samples_fit = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_samples_fit\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    837\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mX\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# include n_samples for common tests\u001b[39;00m\n\u001b[0;32m    838\u001b[0m     )\n\u001b[0;32m    840\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m effective_n_jobs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n\u001b[0;32m    841\u001b[0m chunked_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[1;31mValueError\u001b[0m: Expected n_neighbors <= n_samples_fit, but n_neighbors = 6, n_samples_fit = 1, n_samples = 1"
+     ]
+    }
+   ],
+   "source": [
+    "X_var18 = var18.drop('Price', axis=1)\n",
+    "y_var18 = var18['Price']\n",
+    "\n",
+    "# Кодирование категориальных признаков\n",
+    "for column in X_var18.select_dtypes(include=['object']).columns:\n",
+    "    X_var18[column] = X_var18[column].astype('category').cat.codes\n",
+    "\n",
+    "# Теперь применяем SMOTE\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_resampled_var18, y_resampled_var18 = smote.fit_resample(X_var18, y_var18)\n",
+    "\n",
+    "# Получаем результаты\n",
+    "print(f'После oversampling (var18): {pd.Series(y_resampled_var18).value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "После undersampling (var4): stroke\n",
+      "0    249\n",
+      "1    249\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "# Undersampling для var4\n",
+    "undersample = RandomUnderSampler(random_state=42)\n",
+    "X_under_var4, y_under_var4 = undersample.fit_resample(X_var4, y_var4)\n",
+    "\n",
+    "print(f'После undersampling (var4): {pd.Series(y_under_var4).value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В данном случае у нас есть только один датасет, предназначенный для решения задачи классификации (инсульт). Проблему дисбаланса в нем мы решили применив undersampling & oversampling.\n",
+    "\n",
+    "Два остальных датасета не содержат классов, т.к предназначены для решения задачи регрессии (предсказания цен на недвижимость или цены мобильного устройства), поэтому выполнять приращение данных не требуется."
+   ]
   }
  ],
  "metadata": {
diff --git a/poetry.lock b/poetry.lock
index 71a29fe..0ab2c91 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -366,6 +366,44 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "imbalanced-learn"
+version = "0.12.4"
+description = "Toolbox for imbalanced dataset in machine learning."
+optional = false
+python-versions = "*"
+files = [
+    {file = "imbalanced-learn-0.12.4.tar.gz", hash = "sha256:8153ba385d296b07d97e0901a2624a86c06b48c94c2f92da3a5354827697b7a3"},
+    {file = "imbalanced_learn-0.12.4-py3-none-any.whl", hash = "sha256:d47fc599160d3ea882e712a3a6b02bdd353c1a6436d8d68d41b1922e6ee4a703"},
+]
+
+[package.dependencies]
+joblib = ">=1.1.1"
+numpy = ">=1.17.3"
+scikit-learn = ">=1.0.2"
+scipy = ">=1.5.0"
+threadpoolctl = ">=2.0.0"
+
+[package.extras]
+docs = ["keras (>=2.4.3)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.5.0)", "pandas (>=1.0.5)", "pydata-sphinx-theme (>=0.13.3)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.13.0)", "sphinxcontrib-bibtex (>=2.4.1)", "tensorflow (>=2.4.3)"]
+examples = ["keras (>=2.4.3)", "matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)", "tensorflow (>=2.4.3)"]
+optional = ["keras (>=2.4.3)", "pandas (>=1.0.5)", "tensorflow (>=2.4.3)"]
+tests = ["black (>=23.3.0)", "flake8 (>=3.8.2)", "keras (>=2.4.3)", "mypy (>=1.3.0)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "tensorflow (>=2.4.3)"]
+
+[[package]]
+name = "imblearn"
+version = "0.0"
+description = "Toolbox for imbalanced dataset in machine learning."
+optional = false
+python-versions = "*"
+files = [
+    {file = "imblearn-0.0-py2.py3-none-any.whl", hash = "sha256:d42c2d709d22c00d2b9a91e638d57240a8b79b4014122d92181fcd2549a2f79a"},
+    {file = "imblearn-0.0.tar.gz", hash = "sha256:d8fbb662919c1b16f438ad91a8256220e53bcf6815c9ad5502c518b798de34f2"},
+]
+
+[package.dependencies]
+imbalanced-learn = "*"
+
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -454,6 +492,17 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+]
+
 [[package]]
 name = "jupyter-client"
 version = "8.6.3"
@@ -1284,6 +1333,127 @@ files = [
 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
+[[package]]
+name = "scikit-learn"
+version = "1.5.2"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "scikit_learn-1.5.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:2d4cad1119c77930b235579ad0dc25e65c917e756fe80cab96aa3b9428bd3fb0"},
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+
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+description = "Statistical data visualization"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1314,6 +1484,17 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
+]
+
 [[package]]
 name = "tornado"
 version = "6.4.1"
@@ -1374,4 +1555,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "c64b09f7679bf7188ea5bf1c9bdfaf15eca7d0ba61aadb336111b13ef1633f13"
+content-hash = "0873cc703854cb4dbaf70e8bdffeb557c2216e6414bb7fe5ebe42e93349ebdb1"
diff --git a/pyproject.toml b/pyproject.toml
index 265fa58..a70ab91 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,10 @@ readme = "README.md"
 python = "^3.12"
 pandas = "^2.2.3"
 matplotlib = "^3.9.2"
+seaborn = "^0.13.2"
+numpy = "^2.1.2"
+scikit-learn = "^1.5.2"
+imblearn = "^0.0"
 
 
 [tool.poetry.group.dev.dependencies]