AIM-PIbd-31-Bakalskaya-E-D/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество колонок: 12\n",
      "Колонки: Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
      "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
      "       'smoking_status', 'stroke'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "data = pd.read_csv('./csv/option4.csv')\n",
    "\n",
    "# Обзор данных\n",
    "print(\"Количество колонок:\", data.columns.size)\n",
    "print(\"Колонки:\", data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Наличие пропущенных значений:\n",
      "id                     0\n",
      "gender                 0\n",
      "age                    0\n",
      "hypertension           0\n",
      "heart_disease          0\n",
      "ever_married           0\n",
      "work_type              0\n",
      "Residence_type         0\n",
      "avg_glucose_level      0\n",
      "bmi                  201\n",
      "smoking_status         0\n",
      "stroke                 0\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "<bound method NDFrame.describe of          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",
      "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",
      "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",
      "5105        Private          Urban              83.75   NaN     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",
      "0          1  \n",
      "1          1  \n",
      "2          1  \n",
      "3          1  \n",
      "4          1  \n",
      "...      ...  \n",
      "5105       0  \n",
      "5106       0  \n",
      "5107       0  \n",
      "5108       0  \n",
      "5109       0  \n",
      "\n",
      "[5110 rows x 12 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nНаличие пропущенных значений:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "print(data.describe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Возьмем и заменим нулевые значения в столбце bmi на средние значения по столбцу </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Наличие пропущенных значений:\n",
      "id                   0\n",
      "gender               0\n",
      "age                  0\n",
      "hypertension         0\n",
      "heart_disease        0\n",
      "ever_married         0\n",
      "work_type            0\n",
      "Residence_type       0\n",
      "avg_glucose_level    0\n",
      "bmi                  0\n",
      "smoking_status       0\n",
      "stroke               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data['bmi'] = data['bmi'].fillna(data['bmi'].median())\n",
    "print(\"\\nНаличие пропущенных значений:\")\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Взглянем на выбросы: </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_numeric_boxplots(dataframe, columns):\n",
    "    # Фильтрация числовых столбцов\n",
    "    numeric_columns = ['age', 'avg_glucose_level', 'bmi']\n",
    "    \n",
    "    # Построение графиков\n",
    "    if numeric_columns:\n",
    "        plt.figure(figsize=(15, 5))\n",
    "        \n",
    "        for i, col in enumerate(numeric_columns):\n",
    "            if col != 'id':\n",
    "                  plt.subplot(1, len(numeric_columns), i + 1)\n",
    "                  sns.boxplot(y=dataframe[col])\n",
    "                  plt.title(f'{col}')\n",
    "                  plt.ylabel('')\n",
    "                  plt.xlabel(col)\n",
    "                          \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"Нет подходящих числовых столбцов для построения графиков.\")\n",
    "\n",
    "plot_numeric_boxplots(data, data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Видим выбросы в столбцах со средним уровнем глюкозы и в столбце bmi (индекс массы тела). устраним выбросы - поставим верхние и нижние границы</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def remove_outliers(df):\n",
    "\n",
    "    numeric_columns = ['age', 'avg_glucose_level', 'bmi']\n",
    "    for column in numeric_columns:\n",
    "            Q1 = df[column].quantile(0.25)\n",
    "            Q3 = df[column].quantile(0.75)\n",
    "            IQR = Q3 - Q1\n",
    "            lower_bound = Q1 - 1.5 * IQR\n",
    "            upper_bound = Q3 + 1.5 * IQR\n",
    "            df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "    return df\n",
    "    \n",
    "data = remove_outliers(data)\n",
    "plot_numeric_boxplots(data, data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Так, от выбросов избавились, теперь разобьем на выборки</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Размеры выборок:\n",
      "Обучающая выборка: (4088, 10)\n",
      "Тестовая выборка: (511, 10)\n",
      "Контрольная выборка: (511, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Определение признаков и целевой переменной\n",
    "X = data.drop(columns=['id', 'stroke'])  \n",
    "y = data['stroke']  \n",
    "\n",
    "# Обучающая выборка\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Тестовая и контрольная выборки\n",
    "X_test, X_control, y_test, y_control = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
    "\n",
    "print(\"\\nРазмеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape}\")\n",
    "print(f\"Тестовая выборка: {X_test.shape}\")\n",
    "print(f\"Контрольная выборка: {X_control.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stroke\n",
      "0    4861\n",
      "1     249\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Подсчет количества объектов каждого класса\n",
    "class_counts = y.value_counts()\n",
    "print(class_counts)\n",
    "\n",
    "# Визуализация\n",
    "sns.barplot(x=class_counts.index, y=class_counts.values)\n",
    "plt.title(\"Распределение классов (stroke)\")\n",
    "plt.xlabel(\"Класс\")\n",
    "plt.ylabel(\"Количество\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Напишем функцию и сделаем аугментацию данных</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные ДО аугментации в ОБУЧАЮЩЕЙ ВЫБОРКЕ  (60-80% данных)\n",
      "\n",
      "stroke\n",
      "0    3889\n",
      "1     199\n",
      "Name: count, dtype: int64\n",
      "\n",
      "После оверсемплинга\n",
      "\n",
      "stroke\n",
      "0    3889\n",
      "1    1944\n",
      "Name: count, dtype: int64\n",
      "\n",
      "После балансировки данных (андерсемплинга)\n",
      "\n",
      "stroke\n",
      "0    1944\n",
      "1    1944\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные ДО аугментации в ТЕСТОВОЙ ВЫБОРКЕ  (10-20% данных)\n",
      "\n",
      "stroke\n",
      "0    486\n",
      "1     25\n",
      "Name: count, dtype: int64\n",
      "\n",
      "После оверсемплинга\n",
      "\n",
      "stroke\n",
      "0    486\n",
      "1    243\n",
      "Name: count, dtype: int64\n",
      "\n",
      "После балансировки данных (андерсемплинга)\n",
      "\n",
      "stroke\n",
      "0    243\n",
      "1    243\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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5+drn33zzDU6ePIlhw4YBKD37oVmzZnjzzTdRUFBQbvvTp0+Xy24yma56psSoUaNgMpnw0ksvlcsvhEBubq72udvtxty5c9GtW7crTi2MGTMGHo8Hr7zySrmvud3uGj1haf369fjuu+/wt7/97bI/zGPGjMHx48fxr3/9q9zXiouLUVhYeNXjtGzZEomJiQBKT6/0er144oknfK5T2fvDZDJBURSfKcmMjIxyxTdy5EioqoqXX3653Oi07HszdOhQxMTE4PXXXy+37lJ2nS5duiAhIQEfffSRz1TVokWLsHfvXp+znSridDrhdruvevpuz549IYTA5s2bfS6/+DEDABEREWjbti2EEHC5XABKSwNApR8LJpMJQ4cOxXfffadNqQJAVlYW/vOf/6BPnz6IjY0tt91zzz2HP/zhD5g8eTI+/PBD7fKqPkY2b94MRVHQs2fPSuXVM44ULhgxYgRefvllTJw4Eb169cLOnTsxe/Zsn0UtoHRBeObMmXjyySexceNGXH/99SgsLMSyZcvw8MMP49Zbb63W8evWrYs+ffpg4sSJyMrKwjvvvIPmzZvjgQceAACoqopPPvkEw4YNQ7t27TBx4kQ0aNAAx48fx4oVKxAbG4sFCxagsLAQ77//Pt599120bNkSK1eu1I5RViY7duzA+vXr0bNnTzRr1gyvvvoqJk+ejIyMDIwcORIxMTFIT0/HvHnz8OCDD+IPf/gDli1bhueeew47duzAggULrnhb+vXrh4ceegivv/46tm3bhqFDh8JisSAtLQ1z5szB1KlTMXr06GrdT0uXLsWQIUOuOFq655578PXXX+M3v/kNVqxYgd69e8Pj8WDfvn34+uuvsWTJkquOoC6WlJSEKVOmYNKkSRg/fjxuuummKt0fw4cPx1tvvYUbb7wR48aNQ3Z2Nt5//300b94cO3bs0K7XvHlzPPvss3jllVdw/fXXY9SoUbBardi0aROSk5Px+uuvIzY2Fm+//TYmTZqErl27Yty4cahTpw62b9+OoqIifPbZZ7BYLHjjjTcwceJE9OvXD3fddZd2SmqTJk3w+9//3idfYWGhz/TRrFmz4HA4cNttt13xdvXp0wdxcXFYtmyZz4hk6NChSEpKQu/evZGYmIi9e/di2rRpGD58uHYyRWpqKgDg2WefxZ133gmLxYKbb75ZK4uKvPrqq/jxxx/Rp08fPPzwwzCbzfj4449RUlKCv//975fdbsqUKcjLy8MjjzyCmJgYjB8/vsqPkR9//BG9e/f2WV80LCnnPAXRxaenXYnD4RBPPfWUqF+/voiMjBS9e/cW69ev9zk9sUxRUZF49tlnRUpKirBYLCIpKUmMHj1aO1WuOqekfvHFF2Ly5MkiISFBREZGiuHDh4sjR46U237r1q1i1KhRIi4uTlitVtG4cWMxZswYsXz5cp9jX+1jwoQJPvudO3eu6NOnj4iOjhbR0dGidevW4pFHHhH79+8XQgjx2GOPib59+4rFixeXy1TRKZhClJ7amJqaKiIjI0VMTIzo0KGDeOaZZ8SJEye061T1lFRFUcTmzZt9Lq/oe+R0OsUbb7wh2rVrJ6xWq6hTp45ITU0VL730ksjLyyt3vKvtTwghBg4cKK655hqRn59f5fvj3//+t2jRooWwWq2idevWYvr06Ze93z799FNx7bXXarn79esnfvzxR5/r/Pe//xW9evUSkZGRIjY2VnTr1k188cUXPtf56quvtP3UrVtX3H333dop2GUmTJjg87iw2+3iuuuuE7NmzbrifVTm8ccfF82bN/e57OOPPxZ9+/bVHqPNmjUTTz/9dLn7/ZVXXhENGjQQqqr6nJ4KQDzyyCMVHm/Lli3ihhtuEHa7XURFRYkBAwaIdevW+Vynop95j8cj7rrrLmE2m8X8+fOFEJV/jJw7d05ERESITz75pFL3id4pQtRgzoNqbOXKlRgwYADmzJlT7b+eL5aRkYGUlBSkp6ejSZMmFV7nxRdfREZGRtg8bZ8C5/Dhw2jdujUWLVqEQYMGyY4TEO+88w7+/ve/49ChQ35ZAA91XFMgompr2rQp/u///g9/+9vfZEcJCJfLhbfeegt/+ctfwqIQAK4pGI7dbsfdd999xYXPjh07ai/bQVRTFy/gGo3FYsHRo0dlxwgqloLBxMfHa4uGlzNq1KggpSEiveGaAhERabimQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKRhKRARkYalQEREGpYCERFpWApERKQxyw5AFAhu4UaRtwgFogCF3kIUegtRLIrhER54L/5PlP4rIKBc+E+FClVRUfafAgURSgSi1CjYVTuilWhEq9GIVCKhKIrsm0rkVywF0hWv8CLfm49CUaj9si/wFqBIFJX+e6EISkRJwLOoUBGlRCFaLS2JsrK4+HO7akeUGhXwLET+ogghhOwQRBXxCi9yPbnI8mQh25ONbHc2cjw58MAjO1qVRClRSDAlIMGcgERTIhLMCbCrdtmxiCrEUqCQYJQCqKwoJQoJ5gQkmFgUFFpYCiTFWc9ZnHCfKC0BgxdAZV1aFA0sDWBVrLJjUZhhKVBQeIUXJ9wncNh1GOmudJzznpMdKeSpUNHA3ABNLU3R1NIUsaZY2ZEoDLAUKGBKRAmOuI7gsOswjriOwCEcsiPpWpwpTiuIRFMiz3yigGApkF+d95zHYddhHHYdxnH3cXjhlR3JkKKUKKRYUtDU0hTXWK6BWeGJhOQfLAWqESEEsjxZSHel47DrMHI8ObIjhR0zzGhkaYSmlqZIsaQgWo2WHYl0jKVA1eLwOrDHuQc7S3ZyfSCEKFCQYklBR2tHXGO+hlNMVGUsBaqSLHcWdpTswAHnAbjhlh2HrqCWWgsdrR3RNqItbKpNdhzSCZYCXZVbuHHAeQA7SnYgy5MlOw5VkRlmtIhogU7WTkg0J8qOQyGOpUCXdc5zDjtLdmKPcw/PHDKIBFMCOlo7olVEKy5OU4VYCuRDCIF0Vzp2lOzAEfcR2XEoQGyKDW0j2qKDtQNqm2rLjkMhhKVAAEqniHaU7MC2km3I9+bLjkNB1NjcGF1tXdHA0kB2FAoBLIUw5xVe7HHuwYbiDSgQBbLjkESNzY3RO7I36pnryY5CErEUwliaMw3ri9fjrPes7CgUQlpFtEJPW0/UMtWSHYUkYCmEoUxXJtYWr+WZRHRZKlR0sHZAN1s3vh9EmGEphJFsdzbWFq/FUfdR2VFIJyyw4FrbtUi1pSJCiZAdh4KApRAGznnOYX3xehxwHZAdhXQqUolEF1sXdLR25KmsBsdSMLBCbyE2FG/AbuduvjAd+UWMGoMeth5oE9GGL6FhUCwFA/IKLzaXbMbG4o18KQoKiDg1DoOjByPJnCQ7CvkZS8Fgcj25+LHwRy4iU8ApUHCd9Tr0iOzBKSUDYSkYhFd4sdmxGRscG8L+bS0puOqqdTEkeghHDQbBUjAAjg5INo4ajIOloGMcHVCo4ahB/1gKOsXRAYUqjhr0jaWgMxwdkF5w1KBPquwAevH++++jSZMmsNls6N69OzZu3Bj0DLmeXHyd/zXWOdaxECjknfGewdf5X2NN0Rq4BU+N1guWQiV89dVXePLJJ/HCCy9gy5Yt6NSpE2644QZkZ2cHLcN2x3Z8cf4LTheRrggIbC7ZjC/Of4EznjOy41AlcPqoErp3746uXbti2rRpAACv14tGjRrhsccew5/+9KeAHtsjPFhRtAK7nbsDehyiQItQIjAsehiaWJrIjkJXwJHCVTidTmzevBmDBw/WLlNVFYMHD8b69esDeuwibxG+LfiWhUCG4BRO/Lfgv9js2Cw7Cl0BS+EqcnJy4PF4kJjo+4bniYmJOHXqVMCOe9p9Gl/mf4kT7hMBOwZRsAkIrClegyWFS7jOEKJ4vlgISnOmYWnhUr5uERnWPuc+nPWcxQj7CNhVu+w4dBGOFK4iPj4eJpMJWVm+C7xZWVlISvLvqXZCCKwvXo8fCn9gIZDhZXmy8OX5L3HKHbgRN1UdS+EqIiIikJqaiuXLl2uXeb1eLF++HD179vTbcZzCiYWFC7HREfxTXYlkKRSF+Cb/G+wt2Ss7Cl3A6aNKePLJJzFhwgR06dIF3bp1wzvvvIPCwkJMnDjRL/vP8+RhQcEC5Hpz/bI/Ij3xwIOlRUuR48lBn8g+fJ8GyVgKlTB27FicPn0azz//PE6dOoXOnTtj8eLF5Rafq+OY6xgWFi6EQzj8kJRIv7aUbEGuJxfD7MNgVayy44QtPk9Bol0lu7CiaAXfFY3oInXUOrjVfitqmWrJjhKWWAqSbHFsweri1bJjEIUku2LHqJhRqGOqIztK2GEpSLCxeCPWOwL7xDcivYtSojAqZhTiTHGyo4QVlkKQrS9ezzOMiCopUonEbfbbUM9cT3aUsMFSCKI1RWuwuYRP8SeqCqtixUj7SL4Ed5DweQpB8nPRzywEomooESWYlz8P+d582VHCAkshCFYVrcK2km2yYxDp1rW2axGjxsiOERZYCgG2rngdtpZslR2DSLe627qjR2QP2THCBkshgDYUb8AmxybZMYh0i4UQfCyFANns2IxfHL/IjkGkWywEOVgKAbDdsR1ritfIjkGkW5UtBOHlqwH4G0vBzw45D2Fl8UrZMYh0q7KF4MnNRcGHH8J9/HgQUoUPloIf5XhysKRwiewYRLpVlUIo/OwzeHNyUPT55/AE8F0Qww1LwU+KvcVYULAALrhkRyHSpaoWgsgvfd6CcDhQOGsWPNnZgY4YFlgKfuAVXvxQ+APOe8/LjkKkS9UthDKiqAiFM2fCk8v3JKkploIf/Fz8M465j8mOQaRLNS2EMqKwsHRK6exZf0cMKyyFGtpZshM7SnbIjkGkS/4qhDIiPx+Fs2bBW1Tkr4hhh6VQA8ddx7GyaKXsGES65O9CKOM9exZFc+bwdNVqYilU03nPeSwsXMh3TSOqhkAVgrZdRgYcixdXN15YYylUg0u4sKBwAYpFsewoRLoT6EIo49y0Cc7NfGXiqmIpVJEQAksKlyDHkyM7CpHuBKsQyhT/8APcR47UaB/hhqVQRRscG3DIdUh2DCLdCXYhAAC8XhR9/TW8587VfF9hgqVQBUdcR7DBsUF2DCLdkVIIF4iiIhR++SWE0+m3fRoZS6GSSkQJlhUukx2DSHdkFkIZb1YWir77Dnz34atjKVTS6qLVKBAFsmMQ6UooFEIZ9549KFm1KmD7NwqWQiUccR3Bbudu2TGIdCWUCqFMycqVcO3dG/Dj6BlL4So4bURUdaFYCGWK5s2DJysraMfTG5bCVXDaiKhqQrkQAAAuV+nCc0lJcI+rEyyFK+C0EVHVhHwhXCDOnUPxEr73SUVYCpfBaSOiqtFLIZRxbd0K18GDUjOEIpbCZXDaiKjy9FYIZYoXLOA00iVYChXgtBFR5em1EABAnD/PaaRLsBQuwWkjosrTcyGU4TSSL5bCJThtRFQ5RiiEMpxG+h+WwkWOu49z2oioEoxUCACnkS7GUrjI2qK1siMQhTyjFUIZTiOVYilccNh5GCc9J2XHIAppRi2EMpxGYikAKH3jnHXF62THIAppRi8EgNNIAEsBALDXuRe53lzZMYhCVjgUQplwn0YK+1JwCzd+cfwiOwZRyAqnQihTvGABhMslO4YUYV8KO0p2IN+r/wcxUSCEYyEApdNIJb+E5x+LYV0KJaIEvzp+lR2DKCSFayGUKVm7Ft7iYtkxgs4sO4BMWxxbUCz0+01f9LdFWPJ330WxhBYJ+POGPwMAXA4XvnvuO2z5dgvcTjdaD2iNO968AzEJMZfdpxACi15fhF9m/YLivGKkdE/BHW/egXrN6gEA3CVufPnEl9j5w07EJsZi9JTRaNW/lbb9T+/+hLPHz+L2N24PwC2mYAn3QgAAlJSgZPVqRA4dKjtJUIXtSKHQW4itjq2yY9RYUuskvLz3Ze3j8R8e174279l52LV4F+6bfh8eW/AY8k7l4dN7P73i/pa/uxyr/rkKd/zjDvz+x98jIioCH43+CC5H6fzqus/WIXNbJn635HfoeW9PzHpwlva+t7lHcrF+1noMf3Z44G4wBRwL4X+cmzbBe/687BhBFbalsNGxES7ofyFJNauITYzVPuxxdgBA8flibPh8A0a+OhIt+7ZEo86NMG7aOKRvTEfGpowK9yWEwKqPVmHoU0PR4aYOSG6XjLs/vBt5p/Kwc+FOAEDWgSy0H9Ye9dvUR59JfVCQU4DC3EIAwJyn5uDmF26GLdYWlNtO/sdCuITbDcfKlbJTBFVYlkKeJw+7SnbJjuEXOYdz8Hzb5/HKta9g1oOzcPbYWQBA5rZMeFwetOzfUrtuYstE1GlY57KlkHskF+ezzvtsExkbicapjbVtktsn4/Avh+EsdmLfT/sQmxSL6Lho/DrnV5htZnQc0TFgt5UCi4VQMde2bfCcPi07RtCE5ZrCesd6eOGVHaPGGqc2xrhp45DQIgF5p/Kw5O9L8O5N7+KPa/+I/Ox8mCJMiKoV5bNNTEIMzmdXPBzOzyr9IY+p57vmEFPvf9v0uLsHTu4+ib/1/Bui46Jx36f3oehcERa9vgiP/vdRLHxtIbZ+uxVxTeJw13t3oXZybf/fcPI7FsIVCAHHTz8heuxY2UmCIuxKIc+ThwPOA7Jj+EXbIW21/09ul4zGXRrj5Y4vY9v8bbBEWgJyTJPFhNFTRvtc9p9H/oO+D/bF8Z3HsXPhTjy96mn89O5P+PZP3+L+mfcHJAf5Dwvh6tz79sF97BjMDRvKjhJwYTd9tLNkJwSE7BgBEVUrCvWa18Pp9NOISYiBx+lBUV6Rz3Xys/MRmxBb4fYxiaUjhPzTvj/0+acvv03a6jSc2n8K1z9wPdLWpKHtkLawRlvReWRnHFwbvs8K1QsWQuU5li+XHSEowqoU3MKNPc49smMETElBCXLTcxGbGItGnRvBZDEh7ec07etZaVk4e+wsmnRtUuH2cY3jEJsY67ON47wDRzYfqXAbl8OFb57+BmPeGgPVpEJ4BDwuDwDA4/bA69H/FJ2RsRCqxpORERYvfxFWpZDmTNP18xIu9d1z3+Hg2oPIPZqL9A3p+Pc9/4ZiUpB6eyoiYyPRfXx3zP/LfKStTkPmtkx88egXaNK1ic8v+L92/yt2fL8DAKAoCvr+pi+W/mMpdi3ahRN7TuDzhz9HraRa6DC8Q7njL31zKdoOaYuGHUuH1CndU7Dj+x04sfsE1vxrDZp2bxqU+4GqjoVQPY7ly7VTsI0qrNYUdpTskB3Br86dOIeZD8xE4ZlC2OPsaNqjKX6/9Pewx5eelnrba7dBVVVMnzC99MlrA1uXWw/ITstG8fn/FeWgxwfBWejEV7//CsV5xWjaoykemvMQLDbfNYqTe05i6/ytePrnp7XLOt3aCQfXHsS7N72LhBYJuOef9wTw1lN1sRCqz3vqFFy7diGiQ/k/koxCEUavvQuy3dn4Iv8L2TGIpGIh1JwaFwf7I49AURTZUQIibKaPjDZKIKoqFoJ/eHNz4UlPlx0jYMKiFEpECfY798uOQSQNC8G/SjZtkh0hYMKiFPaU7IEbbtkxiKRgIfife/9+w74mUliUws6SnbIjEEnBQggQIeDcvFl2ioAwfCkcdR3FWe9Z2TGIgo6FEFjOLVsgPB7ZMfzO8KXABWYKRyyEwBMFBXDt2yc7ht8ZuhQKvAVIdxn3LAGiirAQgsdpwAVnQ5fCnpI9hng1VKLKYiEEl+fIEcO9rLahS+Gw67DsCERBw0KQw2ijBcOWQqG3EFmeLNkxiIKChSCPc8cOCKdTdgy/MWwpcJRA4YKFIFlJCZw7jHNCC0uBSMdYCKHB+euvsiP4jSFLwSVcyHRlyo5BFFAshNDhzcqCO9MYv3MMWQpHXUfhgfGeVEJUhoUQelx798qO4BeGLAVOHZGRsRBCk/uAMd773XClIITgE9bIsFgIocubmwtPTo7sGDVmuFI45TllqLfcJCrDQgh97v36f4l+w5UCp47IiFgI+uAywBSS8UrByVIgY2Eh6IcnMxPeoiLZMWrEUKVwznMOZ7xnZMcg8hsWgs4IAXdamuwUNWKoUuDUERkJC0GfXDpfVzBUKWS6jfHkESIWgn65Dx2CcOv37X8NVQrZ7mzZEYhqjIWgc04n3BkZslNUm2FKocBbgCKh7wUeIhaCMej51FTDlAJHCaR3LATj0POpqYYpBb53AukZC8FYxPnz8Jw8KTtGtRimFDhSIL1iIRiT6+BB2RGqxTil4GEpkP6wEIzLc+KE7AjVYohS4CIz6RELwdhYChJx6oj0hoVgfOL8eXgLC2XHqDJDlAIXmUlPWAjhQ4+LzYYoBY4USC9YCOFFj1NIxigFLjKTDrAQwg9HChJwkZn0gIUQnjhSkIBTRxTqWAjhS4+LzbovhRyP/t8TlYyLhUB6m0LSfSkUeAtkRyCqEAuBAP1NIem+FAqFvoZmFB5YCFSGI4UgK/SyFCi0sBDoYhwpBBlLgUIJC4EupbfFZl2XghCCp6NSyGAh0OV4Tp2SHaHSdF0KxaIYXnhlxyBiIdAVibw82REqTdelwKkjCgUsBLoar46+5/ouBZ55RJKxEKgy9PR913Up8DkKJBMLgSrLW6Cf31W6LoUiLxeZSQ4WAlWFnr7/ui6FAqGf9iXjYCFQVXFNIUg4UqBgYyFQdYjCQgghZMeoFF2XAtcUKJhYCFRtXi+ETp7AputS4CmpFCwsBKopoZPFZl2Xggsu2REoDLAQyB/0sq6g61LQyxwd6RcLgfxFL48NXZeCBx7ZEcjAWAjkT3p5roKuS0GAIwUKDBYC+ZteHiO6LgW+GB4FAguBAoEjhQDzChYC+R8LgQJFFBfLjlAp+i0FjhLIz1gIFFBeffzO0m0pEPkTC4ECjqUQWAoU2RHIIFgIFAyCpRBYqn6jUwhhIVDQsBQCS1EUjhaoRlgIFFQshcDjaIGqi4VAQcdSCDyWAlUHC4GkYCkEnqJw+oiqhoVA0qj6+HWrj5SXYYJJdgTSERYCScVSCLwoJUp2BNIJFgJJx1IIvGg1WnYE0gEWAoUChaUQeCwFuhoWAoUMlkLgsRToSlgIFFJYCoEXrbAUqGIsBAo5Jn2cGKPvUuBIgSrAQqBQpERGyo5QKSwFMhQWAoUqNSZGdoRK0XcpcPqILsJCoFCm2O2yI1SKvkuBIwW6gIVAoY4jhSAwK2ZYFavsGCQZC4H0QGEpBIdd0ceQjAKDhUB6wZFCkESpfKmLcMVCID3hmkKQ2FV93NHkXywE0hWTCWqUPv6A1X0p8EXxwg8LgfRGL6MEwAClUMdUR3YECiIWAumRXtYTAAOUQoIpQXYEChIWAumVXs48AgxQCnGmOL7ZThhgIZCeqZw+Ch5VURFvipcdgwKIhUB6x5FCkCWYOYVkVCwEMgKOFIKM6wrGxEIgo1Dr6OeEGEOUQqIpUXYE8jMWAhmJKSlJdoRKM0QpcLHZWFgIZCRq3bpQbDbZMSrNEKXAxWbjYCGQ0ZiSk2VHqBJDlALAxWYjYCGQEZnq15cdoUqMUwpcbNY1FgIZFUtBEi426xcLgYyMpSAJF5v1iYVARqa3RWbAQKXAxWb9YSGQ0eltkRkwUCkAQLJZf9+AcMVCoHCgt6kjwGClkGJJkR2BKoGFQOGCpSBZA3MDWBWr7Bh0BSwECicsBclURUVjc2PZMegyWAgUTvS4yAwYrBQAoGlEU9kRqAIsBAo3elxkBgxYCk0sTaAa72bpGguBwpGpQQPZEarFcL89rYqVZyGFEBYChStzixayI1SL4UoBAJpaOIUUClgIFK7UuDiY4uJkx6gWlgIFBAuBwpm5VSvZEarNkKVQy1QLcao+W9oIWAgU7iwshdDDs5DkYCFQuFOiomBq2FB2jGozbilwCinoWAhEpQvMiqrfX636TX4ViaZERClRsmOEDRYCUSlLy5ayI9SIYUtBURS+FlKQsBCILjCZYG7eXHaKGjFsKQBAqwj9LvboBQuB6H/MKSlQIiJkx6gRQ5dCI0sj1FHryI5hWCwEIl9mnU8dAQYvBQDoaO0oO4IhsRCIytPzqahlDF8KbaxtYIFFdgxDYSEQlacmJUGNjZUdo8YMXwpWxcq1BT9iIRBVzAijBCAMSgHgFJK/sBCILs/Stq3sCH4RFqVQz1wP9U36ewekUMJCILo80zXXwJSQIDuGX4RFKQAcLdQEC4HoyiK6dJEdwW/CphRaRLRApBIpO4busBCIrkyJjjbM1BEQRqVgUkxoZ20nO4ausBCIri7iuuugmEyyY/hN2JQCAHSI6AAFiuwYusBCIKoERUFEaqrsFH4VVqUQa4pFE0sT2TFCHguBqHLMLVtCrVVLdgy/CqtSALjgfDUsBKLKi+jaVXYEvzPLDhBsjc2NUUetg7Pes7KjhBwWgn69vmIF3vj5Z5/LWsTFYdNjjwEAHC4X/rJ0Kebu2gWn242BzZvjH8OHI8Fuv+w+hRD464oVmLllC/IcDnRv1AhvjRiBZhfee7jE7cZj//0vFu3bhwS7Hf8YPhz9mzXTtn937Vpk5uVhyk03BeAWy6fWrQtzU+O9b0vYjRQURUHPyJ6yY4QcFoL+talXD/ufekr7WHz//drX/rxkCRbv348Zd9yBhRMn4lR+Pu756qsr7m/q2rX4eMMGvDViBJZNmoSoiAiMmjULDpcLADBj82ZsP3ECSydNwn2pqZg0dy6EEACAjLNn8dnmzXhu4MDA3WDJIrp0gaIYb40y7EoBKD09NdGUKDtGyGAhGINJVZEYE6N9xEVHAwDyHA7M2rIFr91wA/o1bYrOycl4/9ZbsSEzE5syMyvclxACH/7yC57u2xfDW7dG+6QkfHTbbTiVn4+F+/YBAA6cPo1hrVqhTUICJnXrhpyiIuQWFQEAnvr+e7w4ZAhibbbg3PhgM5sR0bmz7BQBEZalAAC9I3vLjhASWAjGcfjMGbR+8010eucdPDB3LjLPnQMAbDtxAi6vF/0umupoWa8eGtaqhY3HjlW4ryNnzyKroMBnm1o2G1IbNtS2aZ+UhF+OHkWxy4XlBw8iyW5HXFQUvt6xA1azGTe3aRO4GyuZpX17KJHGfN5T2K0plGlkaYRrzNfgqPuo7CjSsBCMo0vDhvhg5Eg0j4tDVkEB3li5EsOmT8f6hx9GdkEBIkwm1L7kl1hCdDSyCwoq3F/WhcsvXXO4eJvx116L3VlZ6P7++4iLisL0O+7AueJi/HXFCnx/3314dflyzN21Cyl162Larbci2QCvIFrGasAF5jJhWwpA6WjhaH54lgILwViGtGih/X97AKkNGqDjO+9g3u7diDQH5sfcYjLhzeHDfS57eP58PNS9O3acPImF+/ZhzW9/i6lr1+KPixZh1tixAckRbKYGDWBKTpYdI2DCdvoIABLMCWhp0f87JVUVC8H4akdGollcHNLPnEGC3Q6nx4NzxcU+18kuLLzs2UeJFy6/dCRxpW1WpadjX3Y2HuzWDWsyMjCkRQtER0TgtnbtsCYjo+Y3KkRYBwyQHSGgwroUAKBnZE+oYXQ3sBDCQ0FJCdLPnEGi3Y7OycmwqCp+Tk/Xvp6Wk4NjeXno1rBhhds3rlMHiXa7zzbnHQ5sPnaswm0cLheeXrgQb998M0yqCo8QcHm9AACXxwPPhf/XO1NKCiwXnXZrROHz2/Ayaptqh81rIrEQjOsvS5ZgTUYGjpw9iw1Hj2L8V1/BpKoY3aEDatlsuOe66/DskiVYlZ6ObSdO4JH589GtYUN0bdRI20fX997Dgr17AZSeuv3bHj3w5qpV+GHfPuzOysJv5s1DUkwMhrduXe74U1atwpAWLdCpfulL1Pdo1AgL9u7FrlOn8K+NG9HjmmuCc0cEmG3wYNkRAi6s1xTKdLd1x96SvXDDLTtKwLAQjO3E+fOY9M03OFNcjPioKPS45hosmzQJ8RdOS/3rDTdAVRTc+9VXcHo8GNisGf5xyXpAWm4uzjsc2udP9O6NQqcTv1uwAHkOB3pccw3mjh8Pm8X37W33ZGVh3u7dWP2b32iX3dq2LdZkZOCm6dPRPC4On9x+ewBvfXCY27aF2cBrCWUUUfZskzC3rngdNjk2yY4RECwEohpSVdgffhimC8/mNrKwnz4qk2pLhU0x3hNtWAhENWfp3DksCgFgKWisihVdbMZ59ySAhUDkF2YzbP37y04RNCyFi3SydkJdta7sGH7BQiDyj4hu3aDGxMiOETQshYuYFTOGRA/R/RvxsBCI/MRmg61PH9kpgoqlcIkkcxKus14nO0a1sRCI/MfWu7dhX+PoclgKFegR2UOX00gsBCL/UWJiENG9u+wYQcdSqIAep5FYCET+ZevXD8olz8kIByyFy9DTNBILgci/1IQEWK69VnYMKVgKV9AjsgfqqHVkx7giFgKRn6kqokaOhKKG56/H8LzVlWRWzBgaPTRkp5FYCET+Z+3dG6YLr+EUjlgKVxGq00gsBCL/UxMSYO3XT3YMqVgKlRBq00gsBKIAKJs2MplkJ5GKpVAJoTSNxEIgCoxwnzYqw1KopFCYRmIhEAUGp43+h6VQBT0ieyDRlCjl2CwEogDhtJEPlkIVmBUzRthHIFqJDupxWQhEgcNpI18shSqyq3aMsI+ACcH5q4KFQBQ4nDYqj6VQDUnmJAyKGhTw47AQiAKI00YVYilUUxtrG1xrDdzT4FkIRIHFaaOKsRRqoE9kHzQ2N/b7flkIRIFlSk7mtNFlsBRqQFVUDLMP8+sT21gIRIGl2O2IGjuW00aXwVKoIatixc32m2FVrDXeFwuBKMBMJkSNHQs1NlZ2kpDFUvCDOqY6uDH6xho945mFQBR4kSNGwNywoewYIY2l4CdNLE3QO7J3tbZlIRAFXkSPHojo3Fl2jJDHUvCjVFsq2kS0qdI2LASiwDM3awbbkCGyY+gCS8HPBkYNRLI5uVLXZSEQBZ5aty6iRo8O2zfNqSreS35mVsy41X4rkkxJV7weC4EoCKxWRN11FxSbTXYS3WApBECEEoGR9pGoZ6pX4ddZCERBoCiIuv12mOLjZSfRFZZCgFhVK26z34Y4Nc7nchYCUXDYBg2CpUUL2TF0h6UQQJFqJG6LuU17chsLgSg4LB07wtq7emcDhjtFCCFkhzC6Am8BDjoPorOt81Wvy0IgqhlTgwaIvu8+KGaz7Ci6xFIIISwEoppRExNhnzABSmSk7Ci6xemjEMFCIKoZtV49RN97LwuhhlgKIYCFQFQzat26iL73XqhRUbKj6B5LQTIWAlHNKLVrI3rCBKh2u+wohsBSkIiFQFQzSmws7BMm8FVP/YjL8xIpFguUiAhwpZ+o6pRatUoLoXZt2VEMhWcfSeYtKEDhzJnwnj4tOwqRbmhrCLVqyY5iOCyFEOAtKkLhrFnwnjolOwpRyFPj40sLISZGdhRDYimECFFcjMLPP4fnxAnZUYhClpqQUFoI0dGyoxgWSyGEiJISFM6eDU9mpuwoRCFHrV8f0ePH87TTAOPZRyFEsVoRfc89MLdtKzsKUUgxt2hRuqjsh0JYtWoVbr75ZiQnJ0NRFMyfP7/mAQ2EpRBiFIsFUaNHw9q/v+woRCHB2rt36XsiWK1+2V9hYSE6deqE999/3y/7MxpOH4Uw1759KJo3D3A6ZUchCj6zGZG33IKIDh0CdghFUTBv3jyMHDkyYMfQG44UQpildWvY778fCs/DpjCjxMQgeuLEgBYCVYylEOJMiYmwP/AATE2ayI5CFBSmBg1gf+ABmJMr917n5F8sBR1Qo6IQfc89iOjSRXYUooCydOqE6Pvu43MQJOLLXOiEoqqIHD4camIiHIsWAV6v7EhE/qMosA0eDGuvXrKThD2Wgs5Yu3SBKT4eRXPmQBQVyY5DVHM2G6Juvx2W5s1lJyFw+kiXzE2awP7AA1ATEmRHIaoRNS4O9v/7v6AWQkFBAbZt24Zt27YBANLT07Ft2zYcPXo0aBlCGU9J1THhdKJ48WK4tm6VHYWoyizt2yNy+HAoNltQj7ty5UoMGDCg3OUTJkzAjBkzgpolFLEUDMB18CCKFyyAOH9edhSiq1KioxE5YgQsrVvLjkIVYCkYhHA4ULxkCVwXhsREocjSoQNsw4ZB5fsohyyWgsFw1EChiKMD/WApGBBHDRRKODrQF5aCgXHUQDIpdjsihw/n6EBnWAoGJ0pKSkcNPEOJgoijA/1iKYQJjhooGDg60D+WQhgRJSVwrFwJ56ZNgMcjOw4Ziaoi4rrrYB04kKMDnWMphCFvXh4cK1fCtX07wG8/1ZClfXtYBwyAqW5d2VHID1gKYcyTnQ3HTz/BvX+/7CikQ+bmzWEbNAimpCTZUciPWAoEd2YmHMuWwcPXfqFKMDVoANvgwTDzPT4MiaVAGldaGhzLl8OblSU7CoUgNT4etoEDYWnTRnYUCiCWAvkQQsC1cyccK1ZAnDsnOw6FACU2Frb+/WHp1AmKyhdWNjqWAlVIeDxw/vorSlavhigslB2HJFAiI2Ht0wcR3bpBMfOtV8IFS4GuSDidcG7fDuemTfCePi07DgWBGheHiC5dENG5c9Bf1prkYylQpbmPHIFz0ya49u7l24EajaLA3Lo1rF26wNy0qew0JBFLgarMW1AA5+bNcG7ZwmdI65xityPiuusQkZoKNTZWdhwKASwFqjbh9cK9fz+cv/4K9+HDsuNQFZgaN0ZE166wtG4NxWSSHYdCCEuB/MKTmwvnpk1wbt8OOByy41BFrFZEdOyIiK5dYapXT3YaClEsBfIr4XLBtXMnnNu3w5OZyZfRkE1RYGrQAJZOnRDRsSOUiAjZiSjEsRQoYLxFRXAfOADXgQNwHzoEOJ2yI4UHiwXmpk1hadkS5pYtodrtshORjrAUKCiE2w13Rgbc+/fDdeAAF6j9TLHbYW7ZEpZWrWBu2pTPK6BqYymQFJ6TJ+Havx+u/fvhPXVKdhxdUhMTS0cDrVrBlJwMRVFkRyIDYCmQdN7z5+Havx/uAwfgPnIEcLlkRwpNZjPM11yjjQjU2rVlJyIDYilQSBFCwJuTA8+JE/CcPFn676lT4VcUZjNMSUkw1a8PU3IyTPXrQ61Xj689RAHHUqCQV64oLnwYpiguLYDkZKjx8SwAkoKlQLp0aVF4T5+GNz8fIj8fIlSfJ2GzQbXbocTEwBQfzwKgkMRSIMMRbjdEfj68BQWl/14oC+3zC/+K4mK/HE+x2aDExECNiYFit/v+e9HlisXil+MRBRJLgcKWcLshCgshXK7SF/jzeiG8XsDj0T4HAKiq9qGYTP/7f7O59Jc9T/8kA2EpEBGRhhOZRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaVgKRESkYSkQEZGGpUBERBqWAhERaf4fX0VG5J6J36sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "def over_under_sampling(x_selection, y_selection):\n",
    "\n",
    "    # сначала увеличение меньшинства\n",
    "\n",
    "    oversampler = RandomOverSampler(sampling_strategy=0.5, random_state=42) \n",
    "    x_over, y_over = oversampler.fit_resample(x_selection, y_selection) \n",
    "\n",
    "    print(\"\\nПосле оверсемплинга\\n\")\n",
    "    print(y_over.value_counts())\n",
    "\n",
    "    # потом уменьшение большинства\n",
    "\n",
    "    undersampler = RandomUnderSampler(sampling_strategy=1.0, random_state=42)\n",
    "    x_balanced, y_balanced = undersampler.fit_resample(x_over, y_over)\n",
    "\n",
    "    print(\"\\nПосле балансировки данных (андерсемплинга)\\n\")\n",
    "    print(y_balanced.value_counts())\n",
    "\n",
    "    plt.pie(\n",
    "    y_balanced.value_counts(), \n",
    "    labels=class_counts.index,  # Метки классов (0 и 1)\n",
    "    autopct='%1.1f%%',  # Отображение процентов\n",
    "    colors=['lightgreen', 'lightcoral'],  # Цвета для классов\n",
    "    startangle=45,  # Поворот диаграммы\n",
    "    explode=(0, 0.05)  # Небольшое смещение для класса 1\n",
    "    )\n",
    "    plt.title(\"Распределение классов (stroke)\")\n",
    "    plt.show()\n",
    "\n",
    "print(\"Данные ДО аугментации в ОБУЧАЮЩЕЙ ВЫБОРКЕ  (60-80% данных)\\n\")\n",
    "print(y_train.value_counts())\n",
    "over_under_sampling(X_train, y_train)\n",
    "\n",
    "print(\"Данные ДО аугментации в ТЕСТОВОЙ ВЫБОРКЕ  (10-20% данных)\\n\")\n",
    "print(y_test.value_counts())\n",
    "over_under_sampling(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Теперь можно и к конструированию признаков приступить) данные ведь сбалансированы (в выборках)</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"margin: 30px;\">Унитарное кодирование категориальных признаков <br/> <br/>Применяем к категориальным (НЕ числовым) признакам: 'gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status'</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot Encoding\n",
    "categorical_columns = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']\n",
    "X_encoded = pd.get_dummies(X_train, columns=categorical_columns, drop_first=True)\n",
    "\n",
    "print(\"Данные после унитарного кодирования:\")\n",
    "print(X_encoded.head())\n"
   ]
  }
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