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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d8b7a37b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting fsspec\n",
+      "  Downloading fsspec-2025.7.0-py3-none-any.whl.metadata (12 kB)\n",
+      "Downloading fsspec-2025.7.0-py3-none-any.whl (199 kB)\n",
+      "Installing collected packages: fsspec\n",
+      "Successfully installed fsspec-2025.7.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 25.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install fsspec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee911a32",
+   "metadata": {},
+   "source": [
+    "# 1 датасет. NASA - Nearest Earth Objects\n",
+    "## В космосе находится бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Хотя мы можем думать, что расстояние в 70 000 км не может потенциально навредить нам, но в астрономических масштабах это очень малое расстояние и может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут оказаться вредными. Следовательно, разумно знать, что нас окружает и что из этого может навредить нам. Таким образом, этот набор данных составляет список сертифицированных NASA астероидов, которые классифицируются как ближайшие к Земле объекты.¶\n",
+    "## В данном датасете объектами исследования являются околоземные объекты. В нём содержатся данные атрибуты: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous. Цель создания данного датасета- научиться определять, опасен ли объект на орбите или же нет.¶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85ecb995",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "количество колонок: 10\n",
+      "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "df = pd.read_csv(\"C://AIM//static//csv//neo_v2.csv\")\n",
+    "print('количество колонок: ' + str(df.columns.size))  \n",
+    "print('колонки: ' + ', '.join(df.columns))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce58052f",
+   "metadata": {},
+   "source": [
+    "## Получим сведения о пропущенных данных. Из вывода видно, что пропущенные данные не обнаружены.¶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c42c0a0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id                    0\n",
+      "name                  0\n",
+      "est_diameter_min      0\n",
+      "est_diameter_max      0\n",
+      "relative_velocity     0\n",
+      "miss_distance         0\n",
+      "orbiting_body         0\n",
+      "sentry_object         0\n",
+      "absolute_magnitude    0\n",
+      "hazardous             0\n",
+      "dtype: int64\n",
+      "\n",
+      "id                    False\n",
+      "name                  False\n",
+      "est_diameter_min      False\n",
+      "est_diameter_max      False\n",
+      "relative_velocity     False\n",
+      "miss_distance         False\n",
+      "orbiting_body         False\n",
+      "sentry_object         False\n",
+      "absolute_magnitude    False\n",
+      "hazardous             False\n",
+      "dtype: bool\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6cba962",
+   "metadata": {},
+   "source": [
+    "## На сайте в карточке датасета видно, что в колонках orbiting_body и sentry_object в каждой строке одинаковое значение. Это значит, что эти колонки не являются информативными и их можно убрать."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "67143392",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "количество колонок: 8\n",
+      "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, absolute_magnitude, hazardous\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.drop(columns=['orbiting_body'])\n",
+    "df = df.drop(columns=['sentry_object'])\n",
+    "print('количество колонок: ' + str(df.columns.size))  \n",
+    "print('колонки: ' + ', '.join(df.columns))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94651a8d",
+   "metadata": {},
+   "source": [
+    "## При беглом осмотре данных, зашумлённости не обнаружила. Необходимо ознакомиться с данными о выбросах."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6e46753e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка est_diameter_min:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 8306\n",
+      "  Минимальное значение: 0.0006089126\n",
+      "  Максимальное значение: 0.32962154705\n",
+      "  1-й квантиль (Q1): 0.0192555078\n",
+      "  3-й квантиль (Q3): 0.1434019235\n",
+      "\n",
+      "Колонка est_diameter_max:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 8306\n",
+      "  Минимальное значение: 0.00136157\n",
+      "  Максимальное значение: 0.7370561859\n",
+      "  1-й квантиль (Q1): 0.0430566244\n",
+      "  3-й квантиль (Q3): 0.320656449\n",
+      "\n",
+      "Колонка relative_velocity:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 1574\n",
+      "  Минимальное значение: 203.34643253\n",
+      "  Максимальное значение: 114380.48061454494\n",
+      "  1-й квантиль (Q1): 28619.02064490995\n",
+      "  3-й квантиль (Q3): 62923.60463276395\n",
+      "\n",
+      "Колонка miss_distance:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 6745.532515957\n",
+      "  Максимальное значение: 74798651.4521972\n",
+      "  1-й квантиль (Q1): 17210820.23576468\n",
+      "  3-й квантиль (Q3): 56548996.45139917\n",
+      "\n",
+      "Колонка absolute_magnitude:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 101\n",
+      "  Минимальное значение: 14.8\n",
+      "  Максимальное значение: 32.239999999999995\n",
+      "  1-й квантиль (Q1): 21.34\n",
+      "  3-й квантиль (Q3): 25.7\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
+    "for column in numeric_columns:\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        q1 = df[column].quantile(0.25)  # Находим 1-й квантиль (Q1)\n",
+    "        q3 = df[column].quantile(0.75)  # Находим 3-й квантиль (Q3)\n",
+    "        iqr = q3 - q1  # Вычисляем межквантильный размах (IQR)\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
+    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
+    "\n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
+    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
+    "\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квантиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квантиль (Q3): {q3}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebe2f096",
+   "metadata": {},
+   "source": [
+    "## Необходимо построить диаграммы для поиска зависимостей опасности от других колонок."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "39419217",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Список числовых колонок, для которых мы будем строить графики\n",
+    "numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
+    "\n",
+    "# Создание диаграмм зависимости\n",
+    "for column in numeric_columns:\n",
+    "    plt.figure(figsize=(4, 8))  # Установка размера графика\n",
+    "    plt.scatter(df['hazardous'], df[column], alpha=0.5)  # Создаем диаграмму рассеяния\n",
+    "    plt.title(f'Зависимость {column} от hazardous')\n",
+    "    plt.xlabel('hazardous (0 = нет, 1 = да)')\n",
+    "    plt.ylabel(column)\n",
+    "    plt.xticks([0, 1])  # Установка меток по оси X\n",
+    "    plt.grid()  # Добавление сетки для удобства восприятия\n",
+    "    plt.show()  # Отображение графика"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "841d6b92",
+   "metadata": {},
+   "source": [
+    "## Далее создадим выборки. Разбивать данные на классы стоит по критерию опасности, потому что это то, что будет предсказывать модель."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d5aff35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting scikit-learn\n",
+      "  Downloading scikit_learn-1.7.1-cp312-cp312-win_amd64.whl.metadata (11 kB)\n",
+      "Requirement already satisfied: numpy>=1.22.0 in c:\\aim\\aimenv\\lib\\site-packages (from scikit-learn) (2.1.3)\n",
+      "Collecting scipy>=1.8.0 (from scikit-learn)\n",
+      "  Downloading scipy-1.16.1-cp312-cp312-win_amd64.whl.metadata (60 kB)\n",
+      "Collecting joblib>=1.2.0 (from scikit-learn)\n",
+      "  Downloading joblib-1.5.1-py3-none-any.whl.metadata (5.6 kB)\n",
+      "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
+      "  Downloading threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n",
+      "Downloading scikit_learn-1.7.1-cp312-cp312-win_amd64.whl (8.7 MB)\n",
+      "   ---------------------------------------- 0.0/8.7 MB ? eta -:--:--\n",
+      "   -- ------------------------------------- 0.5/8.7 MB 5.6 MB/s eta 0:00:02\n",
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+      "   ---------------------------------------- 8.7/8.7 MB 6.5 MB/s eta 0:00:00\n",
+      "Downloading joblib-1.5.1-py3-none-any.whl (307 kB)\n",
+      "Downloading scipy-1.16.1-cp312-cp312-win_amd64.whl (38.5 MB)\n",
+      "   ---------------------------------------- 0.0/38.5 MB ? eta -:--:--\n",
+      "   - -------------------------------------- 1.0/38.5 MB 6.3 MB/s eta 0:00:06\n",
+      "   -- ------------------------------------- 2.6/38.5 MB 6.3 MB/s eta 0:00:06\n",
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+      "   ---------------------------------------- 38.5/38.5 MB 6.8 MB/s eta 0:00:00\n",
+      "Downloading threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
+      "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
+      "Successfully installed joblib-1.5.1 scikit-learn-1.7.1 scipy-1.16.1 threadpoolctl-3.6.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 25.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cac2586e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
+    "    following fractional ratios provided by the user, where each subset is\n",
+    "    stratified by the values in a specific column (that is, each subset has\n",
+    "    the same relative frequency of the values in the column). It performs this\n",
+    "    splitting by running train_test_split() twice.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df_input : Pandas dataframe\n",
+    "        Input dataframe to be split.\n",
+    "    stratify_colname : str\n",
+    "        The name of the column that will be used for stratification. Usually\n",
+    "        this column would be for the label.\n",
+    "    frac_train : float\n",
+    "    frac_val   : float\n",
+    "    frac_test  : float\n",
+    "        The ratios with which the dataframe will be split into train, val, and\n",
+    "        test data. The values should be expressed as float fractions and should\n",
+    "        sum to 1.0.\n",
+    "    random_state : int, None, or RandomStateInstance\n",
+    "        Value to be passed to train_test_split().\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df_train, df_val, df_test :\n",
+    "        Dataframes containing the three splits.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "\n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a0954a1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hazardous\n",
+      "False    81996\n",
+      "True      8840\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Обучающая выборка:  (54501, 6)\n",
+      "hazardous\n",
+      "False    49197\n",
+      "True      5304\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Контрольная выборка:  (18167, 6)\n",
+      "hazardous\n",
+      "False    16399\n",
+      "True      1768\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тестовая выборка:  (18168, 6)\n",
+      "hazardous\n",
+      "False    16400\n",
+      "True      1768\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print(df.hazardous.value_counts())\n",
+    "print()\n",
+    "\n",
+    "\n",
+    "data = df[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   data, stratify_colname=\"hazardous\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.hazardous.value_counts())\n",
+    "hazardous_counts = df_train['hazardous'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))# Установка размера графика\n",
+    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
+    "plt.title('Распределение классов hazardous в обучающей выборке')# Добавление заголовка\n",
+    "plt.show()# Отображение графика\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.hazardous.value_counts())\n",
+    "hazardous_counts = df_val['hazardous'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов hazardous в контрольной выборке')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.hazardous.value_counts())\n",
+    "hazardous_counts = df_test['hazardous'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов hazardous в тестовой выборке')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6671791",
+   "metadata": {},
+   "source": [
+    "## По диаграммам видно, что выборка плохая, слишком большое смещение классов. Проведём аугментацию данных оверсемплингом. Этот метод позволяет увеличить количество примеров меньшинства."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ac90445",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting imblearnNote: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "  Downloading imblearn-0.0-py2.py3-none-any.whl.metadata (355 bytes)\n",
+      "Collecting imbalanced-learn (from imblearn)\n",
+      "  Downloading imbalanced_learn-0.14.0-py3-none-any.whl.metadata (8.8 kB)\n",
+      "Requirement already satisfied: numpy<3,>=1.25.2 in c:\\aim\\aimenv\\lib\\site-packages (from imbalanced-learn->imblearn) (2.1.3)\n",
+      "Requirement already satisfied: scipy<2,>=1.11.4 in c:\\aim\\aimenv\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.1)\n",
+      "Requirement already satisfied: scikit-learn<2,>=1.4.2 in c:\\aim\\aimenv\\lib\\site-packages (from imbalanced-learn->imblearn) (1.7.1)\n",
+      "Requirement already satisfied: joblib<2,>=1.2.0 in c:\\aim\\aimenv\\lib\\site-packages (from imbalanced-learn->imblearn) (1.5.1)\n",
+      "Requirement already satisfied: threadpoolctl<4,>=2.0.0 in c:\\aim\\aimenv\\lib\\site-packages (from imbalanced-learn->imblearn) (3.6.0)\n",
+      "Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
+      "Downloading imbalanced_learn-0.14.0-py3-none-any.whl (239 kB)\n",
+      "Installing collected packages: imbalanced-learn, imblearn\n",
+      "Successfully installed imbalanced-learn-0.14.0 imblearn-0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 25.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cd4d8ccf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после oversampling:  (100367, 6)\n",
+      "hazardous\n",
+      "True     51170\n",
+      "False    49197\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import ADASYN\n",
+    "\n",
+    "# Создание экземпляра ADASYN\n",
+    "ada = ADASYN()\n",
+    "\n",
+    "# Применение ADASYN\n",
+    "X_resampled, y_resampled = ada.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
+    "\n",
+    "# Создание нового DataFrame\n",
+    "df_train_adasyn = pd.DataFrame(X_resampled)\n",
+    "df_train_adasyn['hazardous'] = y_resampled  # Добавление целевой переменной\n",
+    "\n",
+    "# Вывод информации о новой выборке\n",
+    "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
+    "print(df_train_adasyn['hazardous'].value_counts())\n",
+    "hazardous_counts = df_train_adasyn['hazardous'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов hazardous в тренировачной выборке после ADASYN')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95ede2c8",
+   "metadata": {},
+   "source": [
+    "## Проведём балансировку методом андерсемплинга для уменьшения примеров большинства."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc22c45a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после undersampling:  (10608, 6)\n",
+      "hazardous\n",
+      "False    5304\n",
+      "True     5304\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
+    "\n",
+    "# Применение RandomUnderSampler\n",
+    "X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
+    "\n",
+    "# Создание нового DataFrame\n",
+    "df_train_undersampled = pd.DataFrame(X_resampled)\n",
+    "df_train_undersampled['hazardous'] = y_resampled  # Добавление целевой переменной\n",
+    "\n",
+    "# Вывод информации о новой выборке\n",
+    "print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
+    "print(df_train_undersampled['hazardous'].value_counts())\n",
+    "\n",
+    "# Визуализация распределения классов\n",
+    "hazardous_counts = df_train_undersampled['hazardous'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов hazardous в тренировочной выборке после Undersampling')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "567a81ff",
+   "metadata": {},
+   "source": [
+    "# 2 датасет. База данных по Диабету индейцев Пима\n",
+    "## Этот набор данных был получен из Национального института диабета, болезней органов пищеварения и почек. Цель набора данных - диагностически предсказать, есть ли у пациента сахарный диабет, на основе определенных диагностических измерений, включенных в набор данных. На выбор этих образцов из более обширной базы данных было наложено несколько ограничений. В частности, все пациенты были женщинами в возрасте не менее 21 года, родом из племени пима.\n",
+    "##### Таким образом:\n",
+    "* Объект наблюдения - женщины племени пима, возрастом от 21 года.\n",
+    "* Атрибуты: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome.\n",
+    "* Проблемная область: Предсказание диабета у пациента на основе измерений."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2eaac2a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество колонок: 9\n",
+      "Колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n",
+      "\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 768 entries, 0 to 767\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column                    Non-Null Count  Dtype  \n",
+      "---  ------                    --------------  -----  \n",
+      " 0   Pregnancies               768 non-null    int64  \n",
+      " 1   Glucose                   768 non-null    int64  \n",
+      " 2   BloodPressure             768 non-null    int64  \n",
+      " 3   SkinThickness             768 non-null    int64  \n",
+      " 4   Insulin                   768 non-null    int64  \n",
+      " 5   BMI                       768 non-null    float64\n",
+      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
+      " 7   Age                       768 non-null    int64  \n",
+      " 8   Outcome                   768 non-null    int64  \n",
+      "dtypes: float64(2), int64(7)\n",
+      "memory usage: 54.1 KB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Pregnancies",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Glucose",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "BloodPressure",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "SkinThickness",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Insulin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "BMI",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DiabetesPedigreeFunction",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Age",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Outcome",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "4b186d28-2475-4e5e-8f21-09f64cd38e49",
+       "rows": [
+        [
+         "0",
+         "6",
+         "148",
+         "72",
+         "35",
+         "0",
+         "33.6",
+         "0.627",
+         "50",
+         "1"
+        ],
+        [
+         "1",
+         "1",
+         "85",
+         "66",
+         "29",
+         "0",
+         "26.6",
+         "0.351",
+         "31",
+         "0"
+        ],
+        [
+         "2",
+         "8",
+         "183",
+         "64",
+         "0",
+         "0",
+         "23.3",
+         "0.672",
+         "32",
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+        ],
+        [
+         "3",
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+         "43.1",
+         "2.288",
+         "33",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "0            6      148             72             35        0  33.6   \n",
+       "1            1       85             66             29        0  26.6   \n",
+       "2            8      183             64              0        0  23.3   \n",
+       "3            1       89             66             23       94  28.1   \n",
+       "4            0      137             40             35      168  43.1   \n",
+       "\n",
+       "   DiabetesPedigreeFunction  Age  Outcome  \n",
+       "0                     0.627   50        1  \n",
+       "1                     0.351   31        0  \n",
+       "2                     0.672   32        1  \n",
+       "3                     0.167   21        0  \n",
+       "4                     2.288   33        1  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"C://AIM//static//csv//diabetes.csv\", sep=\",\")\n",
+    "print('Количество колонок: ' + str(df.columns.size))  \n",
+    "print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
+    "\n",
+    "df.info()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93935db9",
+   "metadata": {},
+   "source": [
+    "## Получение сведений о пропущенных данных\n",
+    "Типы пропущенных данных:\n",
+    "\n",
+    "- None - представление пустых данных в Python\n",
+    "- NaN - представление пустых данных в Pandas\n",
+    "- '' - пустая строка"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b64d77df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pregnancies                 0\n",
+      "Glucose                     0\n",
+      "BloodPressure               0\n",
+      "SkinThickness               0\n",
+      "Insulin                     0\n",
+      "BMI                         0\n",
+      "DiabetesPedigreeFunction    0\n",
+      "Age                         0\n",
+      "Outcome                     0\n",
+      "dtype: int64\n",
+      "\n",
+      "Pregnancies                 False\n",
+      "Glucose                     False\n",
+      "BloodPressure               False\n",
+      "SkinThickness               False\n",
+      "Insulin                     False\n",
+      "BMI                         False\n",
+      "DiabetesPedigreeFunction    False\n",
+      "Age                         False\n",
+      "Outcome                     False\n",
+      "dtype: bool\n",
+      "\n",
+      "Pregnancies процент пустых значений: %0.00\n",
+      "Glucose процент пустых значений: %0.00\n",
+      "BloodPressure процент пустых значений: %0.00\n",
+      "SkinThickness процент пустых значений: %0.00\n",
+      "Insulin процент пустых значений: %0.00\n",
+      "BMI процент пустых значений: %0.00\n",
+      "DiabetesPedigreeFunction процент пустых значений: %0.00\n",
+      "Age процент пустых значений: %0.00\n",
+      "Outcome процент пустых значений: %0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b16ae07",
+   "metadata": {},
+   "source": [
+    "## Проверим выбросы и устраним их:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fc05042a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка Pregnancies:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 4\n",
+      "  Минимальное значение: 0.0\n",
+      "  Максимальное значение: 13.5\n",
+      "  1-й квартиль (Q1): 1.0\n",
+      "  3-й квартиль (Q3): 6.0\n",
+      "\n",
+      "Колонка Glucose:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 5\n",
+      "  Минимальное значение: 37.125\n",
+      "  Максимальное значение: 199.0\n",
+      "  1-й квартиль (Q1): 99.0\n",
+      "  3-й квартиль (Q3): 140.25\n",
+      "\n",
+      "Колонка BloodPressure:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 45\n",
+      "  Минимальное значение: 35.0\n",
+      "  Максимальное значение: 107.0\n",
+      "  1-й квартиль (Q1): 62.0\n",
+      "  3-й квартиль (Q3): 80.0\n",
+      "\n",
+      "Колонка SkinThickness:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 1\n",
+      "  Минимальное значение: 0.0\n",
+      "  Максимальное значение: 80.0\n",
+      "  1-й квартиль (Q1): 0.0\n",
+      "  3-й квартиль (Q3): 32.0\n",
+      "\n",
+      "Колонка Insulin:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 34\n",
+      "  Минимальное значение: 0.0\n",
+      "  Максимальное значение: 318.125\n",
+      "  1-й квартиль (Q1): 0.0\n",
+      "  3-й квартиль (Q3): 127.25\n",
+      "\n",
+      "Колонка BMI:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 19\n",
+      "  Минимальное значение: 13.35\n",
+      "  Максимальное значение: 50.550000000000004\n",
+      "  1-й квартиль (Q1): 27.3\n",
+      "  3-й квартиль (Q3): 36.6\n",
+      "\n",
+      "Колонка DiabetesPedigreeFunction:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 29\n",
+      "  Минимальное значение: 0.078\n",
+      "  Максимальное значение: 1.2\n",
+      "  1-й квартиль (Q1): 0.24375\n",
+      "  3-й квартиль (Q3): 0.62625\n",
+      "\n",
+      "Колонка Age:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 9\n",
+      "  Минимальное значение: 21.0\n",
+      "  Максимальное значение: 66.5\n",
+      "  1-й квартиль (Q1): 24.0\n",
+      "  3-й квартиль (Q3): 41.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
+    "for column in numeric_columns:\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
+    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
+    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
+    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
+    "\n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
+    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
+    "\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "651c095b",
+   "metadata": {},
+   "source": [
+    "## Постараемся выявить зависимости Outcome от остальных колонок:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25472054",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# Создание диаграмм зависимости\n",
+    "for column in numeric_columns:\n",
+    "    plt.figure(figsize=(4, 6))  # Установка размера графика\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
+    "        if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
+    "            counts = df[column].value_counts() \n",
+    "            counts.plot(kind='bar', width=0.4)  # Создаем столбчатую диаграмму\n",
+    "            plt.title(f'Количество значений для {column}')\n",
+    "            plt.xlabel(column)\n",
+    "            plt.ylabel('Количество повторений')\n",
+    "        else:\n",
+    "            grouped_data = df.groupby('Outcome')[column].mean()\n",
+    "\n",
+    "            # Создаем столбчатую диаграмму\n",
+    "            plt.bar(grouped_data.index, grouped_data.values, alpha=0.5, width=0.4)\n",
+    "            plt.title(f'Среднее значение {column} по Outcome')\n",
+    "            plt.xlabel('Outcome (0 = нет, 1 = да)')\n",
+    "            plt.ylabel(f'Среднее значение {column}')\n",
+    "            plt.xticks([0, 1])  # Установка меток по оси X\n",
+    "            plt.grid(axis='y')\n",
+    "    else:\n",
+    "        # Если колонка не числовая, строим столбчатую диаграмму\n",
+    "        counts = df[column].value_counts()  # Считаем количество повторений каждого значения\n",
+    "        counts.plot(kind='bar', width=0.4)  # Создаем столбчатую диаграмму\n",
+    "        plt.title(f'Количество значений для {column}')\n",
+    "        plt.xlabel(column)\n",
+    "        plt.ylabel('Количество повторений')\n",
+    "\n",
+    "    plt.show()  # Отображение графика"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e06c442c",
+   "metadata": {},
+   "source": [
+    "## Разобьем наш набор на выборки относительно параметра Outcome:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b34d3d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # содержит все столбцы\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # датафрейм с колонкой, относительно которой разбиваем\n",
+    "\n",
+    "    # Разделяем датафрейм на обучающую выборку и временную\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # разделяем временную на тестовую и контрольную\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "    # проверяем, что в сумме все три выборки дают то же количество значений, что и было в изначальной выборке\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0831880b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outcome\n",
+      "0    500\n",
+      "1    268\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Обучающая выборка:  (460, 9)\n",
+      "Outcome\n",
+      "0    299\n",
+      "1    161\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Контрольная выборка:  (154, 9)\n",
+      "Outcome\n",
+      "0    101\n",
+      "1     53\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тестовая выборка:  (154, 9)\n",
+      "Outcome\n",
+      "0    100\n",
+      "1     54\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print(df.Outcome.value_counts())\n",
+    "print()\n",
+    "\n",
+    "data = df.copy()\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   data, stratify_colname=\"Outcome\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.Outcome.value_counts())\n",
+    "counts = df_train['Outcome'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))# Установка размера графика\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
+    "plt.title('Распределение классов Outcome в обучающей выборке')# Добавление заголовка\n",
+    "plt.show()# Отображение графика\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.Outcome.value_counts())\n",
+    "counts = df_val['Outcome'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов Outcome в контрольной выборке')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.Outcome.value_counts())\n",
+    "counts = df_test['Outcome'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов Outcome в тестовой выборке')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7b31edf",
+   "metadata": {},
+   "source": [
+    "## Сбалансируем распределение:\n",
+    "1. Балансировка данных оверсемплингом. Это метод, увеличивающий число наблюдений в меньшинственном классе для достижения более равномерного распределения классов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5a2a7104",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка:  (460, 9)\n",
+      "Outcome\n",
+      "0    299\n",
+      "1    161\n",
+      "Name: count, dtype: int64\n",
+      "Обучающая выборка после oversampling:  (587, 9)\n",
+      "Outcome\n",
+      "0    299\n",
+      "1    288\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Pregnancies",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Glucose",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "BloodPressure",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "SkinThickness",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Insulin",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "BMI",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "DiabetesPedigreeFunction",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Age",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Outcome",
+         "rawType": "int64",
+         "type": "integer"
+        }
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+       "shape": {
+        "columns": 9,
+        "rows": 587
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>37.0</td>\n",
+       "      <td>225.000000</td>\n",
+       "      <td>34.500000</td>\n",
+       "      <td>0.356000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>70.000000</td>\n",
+       "      <td>33.0</td>\n",
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+       "      <td>25.500000</td>\n",
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+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>582</th>\n",
+       "      <td>6.367937</td>\n",
+       "      <td>116.452825</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.269079</td>\n",
+       "      <td>0.217415</td>\n",
+       "      <td>27.820762</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>583</th>\n",
+       "      <td>7.996288</td>\n",
+       "      <td>125.019489</td>\n",
+       "      <td>95.996288</td>\n",
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+       "      <td>13.366566</td>\n",
+       "      <td>0.232285</td>\n",
+       "      <td>54.006496</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.276615</td>\n",
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+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>3.058667</td>\n",
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+       "      <td>35.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>29.317799</td>\n",
+       "      <td>0.359554</td>\n",
+       "      <td>34.588001</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
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+       "      <td>116.557384</td>\n",
+       "      <td>75.467459</td>\n",
+       "      <td>25.0</td>\n",
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+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>587 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+       "0       3.000000  173.000000      82.000000           48.0  318.125000   \n",
+       "1       1.000000   87.000000      60.000000           37.0   75.000000   \n",
+       "2       0.000000  179.000000      50.000000           36.0  159.000000   \n",
+       "3       3.000000  170.000000      64.000000           37.0  225.000000   \n",
+       "4       7.000000  124.000000      70.000000           33.0  215.000000   \n",
+       "..           ...         ...            ...            ...         ...   \n",
+       "582     6.367937  116.452825      35.000000            0.0    0.000000   \n",
+       "583     7.996288  125.019489      95.996288            0.0    0.000000   \n",
+       "584     4.034240  133.922961      72.205439            0.0    0.000000   \n",
+       "585     3.058667  179.234670      35.000000            0.0    0.000000   \n",
+       "586     2.306508  116.557384      75.467459           25.0  186.532541   \n",
+       "\n",
+       "           BMI  DiabetesPedigreeFunction        Age  Outcome  \n",
+       "0    38.400000                  1.200000  25.000000        1  \n",
+       "1    37.200000                  0.509000  22.000000        0  \n",
+       "2    37.800000                  0.455000  22.000000        1  \n",
+       "3    34.500000                  0.356000  30.000000        1  \n",
+       "4    25.500000                  0.161000  37.000000        0  \n",
+       "..         ...                       ...        ...      ...  \n",
+       "582  20.269079                  0.217415  27.820762        1  \n",
+       "583  13.366566                  0.232285  54.006496        1  \n",
+       "584  23.710549                  0.276615  59.948640        1  \n",
+       "585  29.317799                  0.359554  34.588001        1  \n",
+       "586  33.301539                  0.439428  32.547935        1  \n",
+       "\n",
+       "[587 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import ADASYN\n",
+    "\n",
+    "ada = ADASYN()\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.Outcome.value_counts())\n",
+    "\n",
+    "X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Outcome\"])\n",
+    "df_train_adasyn = pd.DataFrame(X_resampled)\n",
+    "\n",
+    "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
+    "print(df_train_adasyn.Outcome.value_counts())\n",
+    "\n",
+    "counts = df_train_adasyn['Outcome'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов Outcome в тренировочной выборке после ADASYN')\n",
+    "plt.show()\n",
+    "\n",
+    "df_train_adasyn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1a7f523",
+   "metadata": {},
+   "source": [
+    "2. Балансировка данных андерсемплингом. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3eb5ba63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после undersampling:  (322, 9)\n",
+      "Outcome\n",
+      "0    161\n",
+      "1    161\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
+    "\n",
+    "# Применение RandomUnderSampler\n",
+    "X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['Outcome']), df_train['Outcome'])\n",
+    "\n",
+    "# Создание нового DataFrame\n",
+    "df_train_undersampled = pd.DataFrame(X_resampled)\n",
+    "df_train_undersampled['Outcome'] = y_resampled  # Добавление целевой переменной\n",
+    "\n",
+    "# Вывод информации о новой выборке\n",
+    "print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
+    "print(df_train_undersampled['Outcome'].value_counts())\n",
+    "\n",
+    "# Визуализация распределения классов\n",
+    "counts = df_train_undersampled['Outcome'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов Outcome в тренировочной выборке после Undersampling')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65ea7b3a",
+   "metadata": {},
+   "source": [
+    "# 3 датасет. Данные по инсультам\n",
+    "## О наборе данных: \n",
+    "По данным Всемирной организации здравоохранения (ВОЗ), инсульт является второй по значимости причиной смертности во всем мире, на его долю приходится примерно 11% от общего числа смертей.\n",
+    "Этот набор данных используется для прогнозирования вероятности инсульта у пациента на основе входных параметров, таких как пол, возраст, различные заболевания и статус курильщика. Каждая строка в данных содержит соответствующую информацию о пациенте.\n",
+    "\n",
+    "Атрибуты:\n",
+    "1) id: уникальный идентификатор\n",
+    "2) gender: \"Male\", \"Female\" или \"Other\"\n",
+    "3) age: возраст пациента\n",
+    "4) hypertension: 0, если у пациента нет артериальной гипертензии, 1, если у пациента есть артериальная гипертензия\n",
+    "5) heart_disease: 0, если у пациента нет сердечных заболеваний, 1, если у пациента есть сердечные заболевания\n",
+    "6) ever_married: \"No\" или \"Yes\"\n",
+    "7) work_type: \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" or \"Self-employed\"\n",
+    "8) Residence_type: \"Rural\" or \"Urban\"\n",
+    "9) avg_glucose_level: средний уровень глюкозы в крови\n",
+    "10) bmi: индекс массы тела\n",
+    "11) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" или \"Unknown\"*\n",
+    "12) stroke: 1, если у пациента был инсульт, или 0, если нет.\n",
+    "##### Таким образом:\n",
+    "* Объект наблюдения - Реальные пациенты.\n",
+    "* Атрибуты: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke.\n",
+    "* Проблемная область: Прогнозирование вероятности инсульта у пациента."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9af4c21a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество колонок: 12\n",
+      "Колонки: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n",
+      "\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5110 entries, 0 to 5109\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   id                 5110 non-null   int64  \n",
+      " 1   gender             5110 non-null   object \n",
+      " 2   age                5110 non-null   float64\n",
+      " 3   hypertension       5110 non-null   int64  \n",
+      " 4   heart_disease      5110 non-null   int64  \n",
+      " 5   ever_married       5110 non-null   object \n",
+      " 6   work_type          5110 non-null   object \n",
+      " 7   Residence_type     5110 non-null   object \n",
+      " 8   avg_glucose_level  5110 non-null   float64\n",
+      " 9   bmi                4909 non-null   float64\n",
+      " 10  smoking_status     5110 non-null   object \n",
+      " 11  stroke             5110 non-null   int64  \n",
+      "dtypes: float64(3), int64(4), object(5)\n",
+      "memory usage: 479.2+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "id",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "age",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "hypertension",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "heart_disease",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "ever_married",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "work_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Residence_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "avg_glucose_level",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "bmi",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "smoking_status",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "stroke",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "da9b7f48-ba9c-4c4b-9735-6c49421c719b",
+       "rows": [
+        [
+         "0",
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+         "Male",
+         "67.0",
+         "0",
+         "1",
+         "Yes",
+         "Private",
+         "Urban",
+         "228.69",
+         "36.6",
+         "formerly smoked",
+         "1"
+        ],
+        [
+         "1",
+         "51676",
+         "Female",
+         "61.0",
+         "0",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Rural",
+         "202.21",
+         null,
+         "never smoked",
+         "1"
+        ],
+        [
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+         "Male",
+         "80.0",
+         "0",
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+         "Private",
+         "Rural",
+         "105.92",
+         "32.5",
+         "never smoked",
+         "1"
+        ],
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+         "49.0",
+         "0",
+         "0",
+         "Yes",
+         "Private",
+         "Urban",
+         "171.23",
+         "34.4",
+         "smokes",
+         "1"
+        ],
+        [
+         "4",
+         "1665",
+         "Female",
+         "79.0",
+         "1",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Rural",
+         "174.12",
+         "24.0",
+         "never smoked",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 12,
+        "rows": 5
+       }
+      },
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "      <th>ever_married</th>\n",
+       "      <th>work_type</th>\n",
+       "      <th>Residence_type</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9046</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>228.69</td>\n",
+       "      <td>36.6</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>51676</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>202.21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31112</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>105.92</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>60182</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>171.23</td>\n",
+       "      <td>34.4</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1665</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>174.12</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      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  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"C://AIM//static//csv//stroke.csv\", sep=\",\")\n",
+    "print('Количество колонок: ' + str(df.columns.size))  \n",
+    "print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
+    "\n",
+    "df.info()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43629837",
+   "metadata": {},
+   "source": [
+    "## Получение сведений о пропущенных данных\n",
+    "Типы пропущенных данных:\n",
+    "\n",
+    "- None - представление пустых данных в Python\n",
+    "- NaN - представление пустых данных в Pandas\n",
+    "- '' - пустая строка"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bec311ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "\n",
+      "id                   False\n",
+      "gender               False\n",
+      "age                  False\n",
+      "hypertension         False\n",
+      "heart_disease        False\n",
+      "ever_married         False\n",
+      "work_type            False\n",
+      "Residence_type       False\n",
+      "avg_glucose_level    False\n",
+      "bmi                   True\n",
+      "smoking_status       False\n",
+      "stroke               False\n",
+      "dtype: bool\n",
+      "\n",
+      "id процент пустых значений: %0.00\n",
+      "gender процент пустых значений: %0.00\n",
+      "age процент пустых значений: %0.00\n",
+      "hypertension процент пустых значений: %0.00\n",
+      "heart_disease процент пустых значений: %0.00\n",
+      "ever_married процент пустых значений: %0.00\n",
+      "work_type процент пустых значений: %0.00\n",
+      "Residence_type процент пустых значений: %0.00\n",
+      "avg_glucose_level процент пустых значений: %0.00\n",
+      "bmi процент пустых значений: %3.93\n",
+      "smoking_status процент пустых значений: %0.00\n",
+      "stroke процент пустых значений: %0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a04bb643",
+   "metadata": {},
+   "source": [
+    "## Пропущенные данные существуют. Необходимо заполнить пропуски медианными значениями.\n",
+    "Заполним пропущенные данные:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e2362cec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5110, 12)\n",
+      "id                   False\n",
+      "gender               False\n",
+      "age                  False\n",
+      "hypertension         False\n",
+      "heart_disease        False\n",
+      "ever_married         False\n",
+      "work_type            False\n",
+      "Residence_type       False\n",
+      "avg_glucose_level    False\n",
+      "bmi                  False\n",
+      "smoking_status       False\n",
+      "stroke               False\n",
+      "dtype: bool\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "id",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "age",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "hypertension",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "heart_disease",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "ever_married",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "work_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Residence_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "avg_glucose_level",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "bmi",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "smoking_status",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "stroke",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "8ec39593-86e0-4d99-8488-1963c0d3eebe",
+       "rows": [
+        [
+         "5105",
+         "18234",
+         "Female",
+         "80.0",
+         "1",
+         "0",
+         "Yes",
+         "Private",
+         "Urban",
+         "83.75",
+         "27.7",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5106",
+         "44873",
+         "Female",
+         "81.0",
+         "0",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Urban",
+         "125.2",
+         "40.0",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5107",
+         "19723",
+         "Female",
+         "35.0",
+         "0",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Rural",
+         "82.99",
+         "30.6",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5108",
+         "37544",
+         "Male",
+         "51.0",
+         "0",
+         "0",
+         "Yes",
+         "Private",
+         "Rural",
+         "166.29",
+         "25.6",
+         "formerly smoked",
+         "0"
+        ],
+        [
+         "5109",
+         "44679",
+         "Female",
+         "44.0",
+         "0",
+         "0",
+         "Yes",
+         "Govt_job",
+         "Urban",
+         "85.28",
+         "26.2",
+         "Unknown",
+         "0"
+        ]
+       ],
+       "shape": {
+        "columns": 12,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "      <th>ever_married</th>\n",
+       "      <th>work_type</th>\n",
+       "      <th>Residence_type</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5105</th>\n",
+       "      <td>18234</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>83.75</td>\n",
+       "      <td>27.7</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5106</th>\n",
+       "      <td>44873</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>125.20</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5107</th>\n",
+       "      <td>19723</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>82.99</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5108</th>\n",
+       "      <td>37544</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>166.29</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5109</th>\n",
+       "      <td>44679</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>85.28</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
+       "5105  18234  Female  80.0             1              0          Yes   \n",
+       "5106  44873  Female  81.0             0              0          Yes   \n",
+       "5107  19723  Female  35.0             0              0          Yes   \n",
+       "5108  37544    Male  51.0             0              0          Yes   \n",
+       "5109  44679  Female  44.0             0              0          Yes   \n",
+       "\n",
+       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
+       "5105        Private          Urban              83.75  27.7     never smoked   \n",
+       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
+       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
+       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
+       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
+       "\n",
+       "      stroke  \n",
+       "5105       0  \n",
+       "5106       0  \n",
+       "5107       0  \n",
+       "5108       0  \n",
+       "5109       0  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fillna_df = df.fillna(0)\n",
+    "\n",
+    "print(fillna_df.shape)\n",
+    "\n",
+    "print(fillna_df.isnull().any())\n",
+    "\n",
+    "# Замена пустых данных на 0\n",
+    "df[\"bmi\"] = df[\"bmi\"].fillna(0)\n",
+    "\n",
+    "# Вычисляем медиану для колонки \"bmi\"\n",
+    "median_bmi = df[\"bmi\"].median()\n",
+    "\n",
+    "# Заменяем значения 0 на медиану\n",
+    "df.loc[df[\"bmi\"] == 0, \"bmi\"] = median_bmi\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a93b4ccd",
+   "metadata": {},
+   "source": [
+    "## Удалим наблюдения с пропусками:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ebdb9b67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5110, 12)\n",
+      "id                   False\n",
+      "gender               False\n",
+      "age                  False\n",
+      "hypertension         False\n",
+      "heart_disease        False\n",
+      "ever_married         False\n",
+      "work_type            False\n",
+      "Residence_type       False\n",
+      "avg_glucose_level    False\n",
+      "bmi                  False\n",
+      "smoking_status       False\n",
+      "stroke               False\n",
+      "dtype: bool\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "id",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "age",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "hypertension",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "heart_disease",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "ever_married",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "work_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Residence_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "avg_glucose_level",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "bmi",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "smoking_status",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "stroke",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "d73bfb6d-c6ae-4d89-9503-fe3d2b3a7f5d",
+       "rows": [
+        [
+         "5105",
+         "18234",
+         "Female",
+         "80.0",
+         "1",
+         "0",
+         "Yes",
+         "Private",
+         "Urban",
+         "83.75",
+         "27.7",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5106",
+         "44873",
+         "Female",
+         "81.0",
+         "0",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Urban",
+         "125.2",
+         "40.0",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5107",
+         "19723",
+         "Female",
+         "35.0",
+         "0",
+         "0",
+         "Yes",
+         "Self-employed",
+         "Rural",
+         "82.99",
+         "30.6",
+         "never smoked",
+         "0"
+        ],
+        [
+         "5108",
+         "37544",
+         "Male",
+         "51.0",
+         "0",
+         "0",
+         "Yes",
+         "Private",
+         "Rural",
+         "166.29",
+         "25.6",
+         "formerly smoked",
+         "0"
+        ],
+        [
+         "5109",
+         "44679",
+         "Female",
+         "44.0",
+         "0",
+         "0",
+         "Yes",
+         "Govt_job",
+         "Urban",
+         "85.28",
+         "26.2",
+         "Unknown",
+         "0"
+        ]
+       ],
+       "shape": {
+        "columns": 12,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "      <th>ever_married</th>\n",
+       "      <th>work_type</th>\n",
+       "      <th>Residence_type</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5105</th>\n",
+       "      <td>18234</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>83.75</td>\n",
+       "      <td>27.7</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5106</th>\n",
+       "      <td>44873</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>125.20</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5107</th>\n",
+       "      <td>19723</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>82.99</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5108</th>\n",
+       "      <td>37544</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>166.29</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5109</th>\n",
+       "      <td>44679</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>85.28</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
+       "5105  18234  Female  80.0             1              0          Yes   \n",
+       "5106  44873  Female  81.0             0              0          Yes   \n",
+       "5107  19723  Female  35.0             0              0          Yes   \n",
+       "5108  37544    Male  51.0             0              0          Yes   \n",
+       "5109  44679  Female  44.0             0              0          Yes   \n",
+       "\n",
+       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
+       "5105        Private          Urban              83.75  27.7     never smoked   \n",
+       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
+       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
+       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
+       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
+       "\n",
+       "      stroke  \n",
+       "5105       0  \n",
+       "5106       0  \n",
+       "5107       0  \n",
+       "5108       0  \n",
+       "5109       0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dropna_df = df.dropna()\n",
+    "\n",
+    "print(dropna_df.shape)\n",
+    "\n",
+    "print(fillna_df.isnull().any())\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3bb22e0",
+   "metadata": {},
+   "source": [
+    "## Проверим выбросы и усредним их:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "20ba157f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка age:\n",
+      "  Есть выбросы: Нет\n",
+      "  Количество выбросов: 0\n",
+      "  Минимальное значение: 0.08\n",
+      "  Максимальное значение: 82.0\n",
+      "  1-й квартиль (Q1): 25.0\n",
+      "  3-й квартиль (Q3): 61.0\n",
+      "\n",
+      "Колонка avg_glucose_level:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 627\n",
+      "  Минимальное значение: 55.12\n",
+      "  Максимальное значение: 271.74\n",
+      "  1-й квартиль (Q1): 77.245\n",
+      "  3-й квартиль (Q3): 114.09\n",
+      "\n",
+      "Колонка bmi:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 126\n",
+      "  Минимальное значение: 10.3\n",
+      "  Максимальное значение: 97.6\n",
+      "  1-й квартиль (Q1): 23.8\n",
+      "  3-й квартиль (Q3): 32.8\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_columns = ['age', 'avg_glucose_level', 'bmi']\n",
+    "for column in numeric_columns:\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
+    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
+    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
+    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
+    "\n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n",
+    "\n",
+    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
+    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "272bec5b",
+   "metadata": {},
+   "source": [
+    "## Постараемся выявить зависимости Stroke от остальных колонок:\n",
+    "## Разобьем наш набор на выборки относительно параметра Stroke:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c9421848",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# Создание диаграмм зависимости\n",
+    "for column in numeric_columns:\n",
+    "    plt.figure(figsize=(4, 6))  # Установка размера графика\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
+    "        if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
+    "            counts = df[column].value_counts() \n",
+    "            counts.plot(kind='bar', width=0.4)  # Создаем столбчатую диаграмму\n",
+    "            plt.title(f'Количество значений для {column}')\n",
+    "            plt.xlabel(column)\n",
+    "            plt.ylabel('Количество повторений')\n",
+    "        else:\n",
+    "            grouped_data = df.groupby('stroke')[column].mean()\n",
+    "\n",
+    "            # Создаем столбчатую диаграмму\n",
+    "            plt.bar(grouped_data.index, grouped_data.values, alpha=0.5, width=0.4)\n",
+    "            plt.title(f'Среднее значение {column} по Stroke')\n",
+    "            plt.xlabel('stroke (0 = нет, 1 = да)')\n",
+    "            plt.ylabel(f'Среднее значение {column}')\n",
+    "            plt.xticks([0, 1])  # Установка меток по оси X\n",
+    "            plt.grid(axis='y')\n",
+    "    else:\n",
+    "        # Если колонка не числовая, строим столбчатую диаграмму\n",
+    "        counts = df[column].value_counts()  # Считаем количество повторений каждого значения\n",
+    "        counts.plot(kind='bar', width=0.4)  # Создаем столбчатую диаграмму\n",
+    "        plt.title(f'Количество значений для {column}')\n",
+    "        plt.xlabel(column)\n",
+    "        plt.ylabel('Количество повторений')\n",
+    "\n",
+    "    plt.show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6632d5cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "\n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "08072eb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stroke\n",
+      "0    4861\n",
+      "1     249\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Обучающая выборка:  (3066, 12)\n",
+      "stroke\n",
+      "0    2917\n",
+      "1     149\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Контрольная выборка:  (1022, 12)\n",
+      "stroke\n",
+      "0    972\n",
+      "1     50\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тестовая выборка:  (1022, 12)\n",
+      "stroke\n",
+      "0    972\n",
+      "1     50\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print(df.stroke.value_counts())\n",
+    "print()\n",
+    "\n",
+    "data = df.copy()\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   data, stratify_colname=\"stroke\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.stroke.value_counts())\n",
+    "counts = df_train['stroke'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))# Установка размера графика\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
+    "plt.title('Распределение классов stroke в обучающей выборке')# Добавление заголовка\n",
+    "plt.show()# Отображение графика\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.stroke.value_counts())\n",
+    "counts = df_val['stroke'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов stroke в контрольной выборке')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.stroke.value_counts())\n",
+    "counts = df_test['stroke'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов stroke в тестовой выборке')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b8a9ab0",
+   "metadata": {},
+   "source": [
+    "## Сбалансируем распределение:\n",
+    "1. Балансировка данных оверсемплингом. Это метод, увеличивающий число наблюдений в меньшинственном классе для достижения более равномерного распределения классов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2097bdc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после oversampling:  (5827, 18)\n",
+      "stroke\n",
+      "0    2917\n",
+      "1    2910\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import ADASYN\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "categorical_features = ['gender', 'ever_married', 'work_type', 'Residence_type']  # Ваши категориальные признаки\n",
+    "numeric_features = ['age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi']  # Ваши числовые признаки\n",
+    "\n",
+    "# Создание пайплайна для обработки категориальных данных\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('cat', OneHotEncoder(), categorical_features),  # OneHotEncoder для категориальных данных\n",
+    "        ('num', 'passthrough', numeric_features)  # Оставляем числовые колонки без изменений\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Создание экземпляра ADASYN\n",
+    "ada = ADASYN()\n",
+    "\n",
+    "# Преобразование данных с помощью пайплайна\n",
+    "X = preprocessor.fit_transform(df_train.drop(columns=['stroke']))\n",
+    "y = df_train['stroke']\n",
+    "\n",
+    "# Применение ADASYN\n",
+    "X_resampled, y_resampled = ada.fit_resample(X, y)\n",
+    "\n",
+    "# Создание нового DataFrame\n",
+    "df_train_adasyn = pd.DataFrame(X_resampled)\n",
+    "# Восстанавливаем названия столбцов для DataFrame\n",
+    "ohe_columns = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
+    "new_column_names = list(ohe_columns) + numeric_features\n",
+    "df_train_adasyn.columns = new_column_names\n",
+    "\n",
+    "# Добавление целевой переменной\n",
+    "df_train_adasyn['stroke'] = y_resampled\n",
+    "\n",
+    "# Вывод информации о новой выборке\n",
+    "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
+    "print(df_train_adasyn['stroke'].value_counts())\n",
+    "\n",
+    "# Визуализация\n",
+    "counts = df_train_adasyn['stroke'].value_counts()\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов Stroke в тренировочной выборке после ADASYN')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397dad15",
+   "metadata": {},
+   "source": [
+    "2. Балансировка данных андерсемплингом. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3f5f765d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка после undersampling:  (298, 12)\n",
+      "stroke\n",
+      "0    149\n",
+      "1    149\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
+    "\n",
+    "# Применение RandomUnderSampler\n",
+    "X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['stroke']), df_train['stroke'])\n",
+    "\n",
+    "# Создание нового DataFrame\n",
+    "df_train_undersampled = pd.DataFrame(X_resampled)\n",
+    "df_train_undersampled['stroke'] = y_resampled  # Добавление целевой переменной\n",
+    "\n",
+    "# Вывод информации о новой выборке\n",
+    "print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
+    "print(df_train_undersampled['stroke'].value_counts())\n",
+    "\n",
+    "# Визуализация распределения классов\n",
+    "counts = df_train_undersampled['stroke'].value_counts()\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Распределение классов stroke в тренировочной выборке после Undersampling')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv (3.12.5)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}