AIM-PIbd-32-Chubykina-P-P/lab_4/lab4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
      "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
      "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
      "       'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yes</td>\n",
       "      <td>28.87</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Fair</td>\n",
       "      <td>12.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>21.63</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>70-74</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>31.64</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>26.45</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No, borderline diabetes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fair</td>\n",
       "      <td>5.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>40.69</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>10.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "5          Yes  28.87     Yes              No     No             6.0   \n",
       "6           No  21.63      No              No     No            15.0   \n",
       "7           No  31.64     Yes              No     No             5.0   \n",
       "8           No  26.45      No              No     No             0.0   \n",
       "9           No  40.69      No              No     No             0.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race  \\\n",
       "0          30.0          No  Female        55-59  White   \n",
       "1           0.0          No  Female  80 or older  White   \n",
       "2          30.0          No    Male        65-69  White   \n",
       "3           0.0          No  Female        75-79  White   \n",
       "4           0.0         Yes  Female        40-44  White   \n",
       "5           0.0         Yes  Female        75-79  Black   \n",
       "6           0.0          No  Female        70-74  White   \n",
       "7           0.0         Yes  Female  80 or older  White   \n",
       "8           0.0          No  Female  80 or older  White   \n",
       "9           0.0         Yes    Male        65-69  White   \n",
       "\n",
       "                  Diabetic PhysicalActivity  GenHealth  SleepTime Asthma  \\\n",
       "0                      Yes              Yes  Very good        5.0    Yes   \n",
       "1                       No              Yes  Very good        7.0     No   \n",
       "2                      Yes              Yes       Fair        8.0    Yes   \n",
       "3                       No               No       Good        6.0     No   \n",
       "4                       No              Yes  Very good        8.0     No   \n",
       "5                       No               No       Fair       12.0     No   \n",
       "6                       No              Yes       Fair        4.0    Yes   \n",
       "7                      Yes               No       Good        9.0    Yes   \n",
       "8  No, borderline diabetes               No       Fair        5.0     No   \n",
       "9                       No              Yes       Good       10.0     No   \n",
       "\n",
       "  KidneyDisease SkinCancer  \n",
       "0            No        Yes  \n",
       "1            No         No  \n",
       "2            No         No  \n",
       "3            No        Yes  \n",
       "4            No         No  \n",
       "5            No         No  \n",
       "6            No        Yes  \n",
       "7            No         No  \n",
       "8           Yes         No  \n",
       "9            No         No  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "print(df.columns)\n",
    "\n",
    "display(df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Регрессия"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цель: Разработать модель регрессии, которая будет предсказывать количество часов сна, которое человек получает в сутки, на основе его демографических данных, образа жизни и состояния здоровья.\n",
    "\n",
    "Применение:\n",
    "\n",
    "Медицинские учреждения: Модель может помочь врачам оценить качество сна пациента и разработать индивидуальные планы лечения и профилактики нарушений сна.\n",
    "\n",
    "Компании, разрабатывающие приложения для отслеживания сна: Модель может использоваться для улучшения своих продуктов и предоставления более точных рекомендаций.\n",
    "\n",
    "Исследования в области сна: Модель может помочь в изучении факторов, влияющих на качество сна."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сначала подготовим данные для работы - удалим выбросы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер данных до удаления выбросов: (929, 18)\n",
      "Размер данных после удаления выбросов: (929, 18)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "data = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\").head(1000)\n",
    "\n",
    "numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n",
    "\n",
    "z_scores = stats.zscore(data[numeric_features])\n",
    "\n",
    "threshold = 3\n",
    "\n",
    "data_cleaned = data[(z_scores < threshold).all(axis=1)]\n",
    "data = data_cleaned\n",
    "print(\"Размер данных до удаления выбросов:\", data.shape)\n",
    "print(\"Размер данных после удаления выбросов:\", data_cleaned.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь перейдем к делению на выборки и созданию ориентира"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (255836, 16)\n",
      "Размер тестовой выборки: (63959, 16)\n",
      "Baseline MAE: 1.0154101277944922\n",
      "Baseline MSE: 2.085820163563156\n",
      "Baseline R²: -7.204157852269688e-05\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "features = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "target = 'SleepTime'\n",
    "\n",
    "global X_train, X_test, y_train, y_test\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape)\n",
    "\n",
    "baseline_predictions = [y_train.mean()] * len(y_test)\n",
    "\n",
    "print('Baseline MAE:', mean_absolute_error(y_test, baseline_predictions))\n",
    "print('Baseline MSE:', mean_squared_error(y_test, baseline_predictions))\n",
    "print('Baseline R²:', r2_score(y_test, baseline_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создание конвейера и обучение моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Linear Regression trained.\n",
      "Model: Decision Tree trained.\n",
      "Model: Gradient Boosting trained.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "categorical_features = ['Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth']\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numeric_features),\n",
    "        ('cat', OneHotEncoder(), categorical_features)])\n",
    "\n",
    "pipeline_linear_regression = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', LinearRegression())])\n",
    "\n",
    "pipeline_decision_tree = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', DecisionTreeRegressor(random_state=42))])\n",
    "\n",
    "pipeline_gradient_boosting = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', GradientBoostingRegressor(random_state=42))])\n",
    "\n",
    "pipelines = [\n",
    "    ('Linear Regression', pipeline_linear_regression),\n",
    "    ('Decision Tree', pipeline_decision_tree),\n",
    "    ('Gradient Boosting', pipeline_gradient_boosting)\n",
    "]\n",
    "\n",
    "for name, pipeline in pipelines:\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    print(f\"Model: {name} trained.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка качества моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Linear Regression\n",
      "MAE: 0.999721882988516\n",
      "MSE: 2.007024248723743\n",
      "R²: 0.03770762552704621\n",
      "\n",
      "Model: Decision Tree\n",
      "MAE: 1.405790088390023\n",
      "MSE: 4.053338792508978\n",
      "R²: -0.9434229624615185\n",
      "\n",
      "Model: Gradient Boosting\n",
      "MAE: 0.9962143800804221\n",
      "MSE: 1.9983219431838193\n",
      "R²: 0.041880052575063775\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "for name, pipeline in pipelines:\n",
    "    y_pred = pipeline.predict(X_test)\n",
    "    print(f\"Model: {name}\")\n",
    "    print('MAE:', mean_absolute_error(y_test, y_pred))\n",
    "    print('MSE:', mean_squared_error(y_test, y_pred))\n",
    "    print('R²:', r2_score(y_test, y_pred))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Линейная регрессия имеет низкое смещение, так как MAE и MSE близки к 1. Однако, R² близок к 0, что указывает на то, что модель не очень хорошо объясняет дисперсию в данных. Это может быть связано с тем, что линейная модель не может хорошо аппроксимировать сложные зависимости в данных.\n",
    "\n",
    "Дерево решений имеет высокое смещение и дисперсию. Отрицательный R² указывает на то, что модель работает хуже, чем простое предсказание среднего значения. Это свидетельствует о переобучении и высокой дисперсии.\n",
    "\n",
    "Градиентный бустинг имеет низкое смещение, так как MAE и MSE близки к 1. R² также близок к 0, что указывает на то, что модель не очень хорошо объясняет дисперсию в данных. Однако, это лучший результат среди всех моделей, что указывает на то, что градиентный бустинг лучше справляется с данными, чем линейная регрессия.\n",
    "\n",
    "Линейная регрессия и Градиентный бустинг имеют низкое смещение, но низкий R², что указывает на то, что они не могут хорошо объяснить дисперсию в данных.\n",
    "\n",
    "Дерево решений имеет высокую дисперсию и переобучение, что приводит к отрицательному R²."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Классификация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цель: Разработать модель, которая сможет предсказывать вероятность развития сердечно-сосудистых заболеваний (HeartDisease) у пациентов на основе их демографических данных, образа жизни и состояния здоровья.\n",
    "\n",
    "Применение: Модель может использоваться в медицинских учреждениях для раннего выявления пациентов с высоким риском сердечных заболеваний, что позволит назначить профилактические меры и улучшить результаты лечения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проведем деление на выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HeartDisease\n",
      "0    796\n",
      "1    796\n",
      "Name: count, dtype: int64\n",
      "Размер обучающей выборки: (1273, 49)\n",
      "Размер тестовой выборки: (319, 49)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "features = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "target = 'HeartDisease'\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "data[target] = label_encoder.fit_transform(data[target])\n",
    "\n",
    "categorical_features = ['Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth']\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ])\n",
    "\n",
    "X = preprocessor.fit_transform(data[features])\n",
    "y = data[target]\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
    "\n",
    "print(pd.Series(y_resampled).value_counts())\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшие гиперпараметры для логистической регрессии:\n",
      "{'classifier__C': np.float64(0.7272998688284025), 'classifier__penalty': 'l1', 'classifier__solver': 'liblinear'}\n",
      "Accuracy: 0.7398\n",
      "Precision: 0.7239\n",
      "Recall: 0.7564\n",
      "F1-Score: 0.7398\n",
      "ROC-AUC: 0.8338\n",
      "Лучшие гиперпараметры для случайного леса:\n",
      "{'classifier__bootstrap': True, 'classifier__max_depth': np.int64(25), 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 6, 'classifier__n_estimators': 317}\n",
      "Accuracy: 0.9122\n",
      "Precision: 0.9571\n",
      "Recall: 0.8590\n",
      "F1-Score: 0.9054\n",
      "ROC-AUC: 0.9773\n",
      "Лучшие гиперпараметры для градиентного бустинга:\n",
      "{'classifier__learning_rate': np.float64(0.17269984907963387), 'classifier__max_depth': np.int64(52), 'classifier__min_samples_leaf': 8, 'classifier__min_samples_split': 8, 'classifier__n_estimators': 294, 'classifier__subsample': np.float64(0.8288064461501716)}\n",
      "Accuracy: 0.9185\n",
      "Precision: 0.9577\n",
      "Recall: 0.8718\n",
      "F1-Score: 0.9128\n",
      "ROC-AUC: 0.9745\n",
      "\n",
      "Результаты моделей:\n",
      "\n",
      "Logistic Regression:\n",
      "Accuracy: 0.7398\n",
      "Precision: 0.7239\n",
      "Recall: 0.7564\n",
      "F1: 0.7398\n",
      "Roc_auc: 0.8338\n",
      "\n",
      "Random Forest:\n",
      "Accuracy: 0.9122\n",
      "Precision: 0.9571\n",
      "Recall: 0.8590\n",
      "F1: 0.9054\n",
      "Roc_auc: 0.9773\n",
      "\n",
      "Gradient Boosting:\n",
      "Accuracy: 0.9185\n",
      "Precision: 0.9577\n",
      "Recall: 0.8718\n",
      "F1: 0.9128\n",
      "Roc_auc: 0.9745\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from scipy.stats import uniform, randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "def evaluate_model(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    \n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, pos_label=1) \n",
    "    recall = recall_score(y_test, y_pred, pos_label=1)  \n",
    "    f1 = f1_score(y_test, y_pred, pos_label=1)  \n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "    \n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1-Score: {f1:.4f}\")\n",
    "    print(f\"ROC-AUC: {roc_auc:.4f}\")\n",
    "    \n",
    "    return {\n",
    "        'accuracy': accuracy,\n",
    "        'precision': precision,\n",
    "        'recall': recall,\n",
    "        'f1': f1,\n",
    "        'roc_auc': roc_auc\n",
    "    }\n",
    "\n",
    "logreg_param_dist = {\n",
    "    'classifier__C': uniform(loc=0, scale=4),\n",
    "    'classifier__penalty': ['l1', 'l2'],\n",
    "    'classifier__solver': ['liblinear', 'saga']\n",
    "}\n",
    "\n",
    "logreg_pipeline = Pipeline([\n",
    "    ('classifier', LogisticRegression(max_iter=1000, random_state=42))\n",
    "])\n",
    "\n",
    "logreg_random_search = RandomizedSearchCV(logreg_pipeline, param_distributions=logreg_param_dist, n_iter=50, cv=5, random_state=42, n_jobs=-1)\n",
    "logreg_random_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры для логистической регрессии:\")\n",
    "print(logreg_random_search.best_params_)\n",
    "\n",
    "logreg_best_model = logreg_random_search.best_estimator_\n",
    "logreg_results = evaluate_model(logreg_best_model, X_test, y_test)\n",
    "\n",
    "rf_param_dist = {\n",
    "    'classifier__n_estimators': randint(100, 1000),\n",
    "    'classifier__max_depth': [None] + list(randint(10, 100).rvs(10)),\n",
    "    'classifier__min_samples_split': randint(2, 20),\n",
    "    'classifier__min_samples_leaf': randint(1, 20),\n",
    "    'classifier__bootstrap': [True, False]\n",
    "}\n",
    "\n",
    "rf_pipeline = Pipeline([\n",
    "    ('classifier', RandomForestClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "rf_random_search = RandomizedSearchCV(rf_pipeline, param_distributions=rf_param_dist, n_iter=50, cv=5, random_state=42, n_jobs=-1)\n",
    "rf_random_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры для случайного леса:\")\n",
    "print(rf_random_search.best_params_)\n",
    "\n",
    "rf_best_model = rf_random_search.best_estimator_\n",
    "rf_results = evaluate_model(rf_best_model, X_test, y_test)\n",
    "\n",
    "gb_param_dist = {\n",
    "    'classifier__n_estimators': randint(100, 1000),\n",
    "    'classifier__learning_rate': uniform(0.01, 0.5),\n",
    "    'classifier__max_depth': [None] + list(randint(10, 100).rvs(10)),\n",
    "    'classifier__min_samples_split': randint(2, 20),\n",
    "    'classifier__min_samples_leaf': randint(1, 20),\n",
    "    'classifier__subsample': uniform(0.5, 0.5)\n",
    "}\n",
    "\n",
    "gb_pipeline = Pipeline([\n",
    "    ('classifier', GradientBoostingClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "gb_random_search = RandomizedSearchCV(gb_pipeline, param_distributions=gb_param_dist, n_iter=50, cv=5, random_state=42, n_jobs=-1)\n",
    "gb_random_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"Лучшие гиперпараметры для градиентного бустинга:\")\n",
    "print(gb_random_search.best_params_)\n",
    "\n",
    "gb_best_model = gb_random_search.best_estimator_\n",
    "gb_results = evaluate_model(gb_best_model, X_test, y_test)\n",
    "\n",
    "print(\"\\nРезультаты моделей:\")\n",
    "print(\"\\nLogistic Regression:\")\n",
    "for metric, value in logreg_results.items():\n",
    "    print(f\"{metric.capitalize()}: {value:.4f}\")\n",
    "\n",
    "print(\"\\nRandom Forest:\")\n",
    "for metric, value in rf_results.items():\n",
    "    print(f\"{metric.capitalize()}: {value:.4f}\")\n",
    "\n",
    "print(\"\\nGradient Boosting:\")\n",
    "for metric, value in gb_results.items():\n",
    "    print(f\"{metric.capitalize()}: {value:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Metrics:\n",
      "Accuracy: 0.7398\n",
      "Precision: 0.7239\n",
      "Recall: 0.7564\n",
      "F1-Score: 0.7398\n",
      "ROC-AUC: 0.8338\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
      "Random Forest Metrics:\n",
      "Accuracy: 0.9122\n",
      "Precision: 0.9571\n",
      "Recall: 0.8590\n",
      "F1-Score: 0.9054\n",
      "ROC-AUC: 0.9773\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
      "Gradient Boosting Metrics:\n",
      "Accuracy: 0.9185\n",
      "Precision: 0.9577\n",
      "Recall: 0.8718\n",
      "F1-Score: 0.9128\n",
      "ROC-AUC: 0.9745\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, title):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Предсказанные значения')\n",
    "    plt.ylabel('Истинные значения')\n",
    "    plt.show()\n",
    "\n",
    "def plot_roc_curve(y_true, y_pred_proba, title):\n",
    "    fpr, tpr, _ = roc_curve(y_true, y_pred_proba)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title(title)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "def evaluate_and_plot_model(model, X_test, y_test, model_name):\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "    \n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "    recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "    f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "    \n",
    "    print(f\"{model_name} Metrics:\")\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1-Score: {f1:.4f}\")\n",
    "    print(f\"ROC-AUC: {roc_auc:.4f}\")\n",
    "    \n",
    "    plot_confusion_matrix(y_test, y_pred, f'Confusion Matrix for {model_name}')\n",
    "    plot_roc_curve(y_test, y_pred_proba, f'ROC Curve for {model_name}')\n",
    "\n",
    "evaluate_and_plot_model(logreg_best_model, X_test, y_test, 'Logistic Regression')\n",
    "evaluate_and_plot_model(rf_best_model, X_test, y_test, 'Random Forest')\n",
    "evaluate_and_plot_model(gb_best_model, X_test, y_test, 'Gradient Boosting')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}