From 9fe3e445d06664c4d080b40ef6801b4a22d06068 Mon Sep 17 00:00:00 2001
From: Yourdax <tikhonenkov2015@gmail.com>
Date: Thu, 19 Dec 2024 17:02:15 +0400
Subject: [PATCH] .

---
 lab3.ipynb | 1113 +++++++++++++++++
 lab4.ipynb | 3436 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 4549 insertions(+)
 create mode 100644 lab3.ipynb
 create mode 100644 lab4.ipynb

diff --git a/lab3.ipynb b/lab3.ipynb
new file mode 100644
index 0000000..7d659e3
--- /dev/null
+++ b/lab3.ipynb
@@ -0,0 +1,1113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Вариант 4. Данные по инсультам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "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>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": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import featuretools as ft\n",
+    "import time\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "df = pd.read_csv(\"./data/healthcare-dataset-stroke-data.csv\")\n",
+    "\n",
+    "print(df.columns)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Бизнес цели и цели технического проекта.\n",
+    "## Бизнес цели:\n",
+    "### 1. Предсказание инсульта: Разработать систему, которая сможет предсказать вероятность инсульта у пациентов на основе их медицинских и социальных данных. Это может помочь медицинским учреждениям и специалистам в более раннем выявлении пациентов с высоким риском.\n",
+    "### 2. Снижение затрат на лечение: Предупреждение инсультов у пациентов позволит снизить затраты на лечение и реабилитацию. Это также поможет улучшить качество медицинских услуг и повысить удовлетворенность пациентов.\n",
+    "### 3. Повышение эффективности профилактики: Выявление факторов риска инсульта на ранней стадии может способствовать более эффективному проведению профилактических мероприятий.\n",
+    "## Цели технического проекта:\n",
+    "### 1. Создание и обучение модели машинного обучения: Разработка модели, способной предсказать вероятность инсульта на основе данных о пациентах (например, возраст, уровень глюкозы, наличие сердечно-сосудистых заболеваний, тип работы, индекс массы тела и т.д.).\n",
+    "### 2. Анализ и обработка данных: Провести предобработку данных (очистка, заполнение пропущенных значений, кодирование категориальных признаков), чтобы улучшить качество и надежность модели.\n",
+    "### 3. Оценка модели: Использовать метрики, такие как точность, полнота и F1-мера, чтобы оценить эффективность модели и минимизировать риск ложных положительных и ложных отрицательных предсказаний."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "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",
+      "bmi процент пустых значений: %3.93\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())\n",
+    "print()\n",
+    "\n",
+    "print(df.isnull().any())\n",
+    "print()\n",
+    "\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",
+   "metadata": {},
+   "source": [
+    "### Видим пустые значения в bmi, заменяем их"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество пустых значений в каждом столбце после замены:\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": [
+    "df[\"bmi\"] = df[\"bmi\"].fillna(df[\"bmi\"].median())\n",
+    "\n",
+    "missing_values = df.isnull().sum()\n",
+    "\n",
+    "print(\"Количество пустых значений в каждом столбце после замены:\")\n",
+    "print(missing_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['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": [
+    "df = df.drop('id', axis = 1)\n",
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Создаем выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки:  (2503, 10)\n",
+      "Размер контрольной выборки:  (1074, 10)\n",
+      "Размер тестовой выборки:  (1533, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Разделим данные на признак (X) и переменую (Y)\n",
+    "# Начнем со stroke\n",
+    "X = df.drop(columns=['stroke'])\n",
+    "y = df['stroke']\n",
+    "\n",
+    "# Разбиваем на обучающую и тестовую выборки\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
+    "\n",
+    "# Разбиваем на обучающую и контрольную выборки\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.3)\n",
+    "\n",
+    "print(\"Размер обучающей выборки: \", X_train.shape)\n",
+    "print(\"Размер контрольной выборки: \", X_val.shape)\n",
+    "print(\"Размер тестовой выборки: \", X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Оценим сбалансированность сборок"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение классов в обучающей выборке:\n",
+      "stroke\n",
+      "0    0.948861\n",
+      "1    0.051139\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в контрольной выборке:\n",
+      "stroke\n",
+      "0    0.947858\n",
+      "1    0.052142\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в тестовой выборке:\n",
+      "stroke\n",
+      "0    0.957599\n",
+      "1    0.042401\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from locale import normalize\n",
+    "\n",
+    "\n",
+    "def analyze_balance(y_train, y_val, y_test, y_name):\n",
+    "    print(\"Распределение классов в обучающей выборке:\")\n",
+    "    print(y_train.value_counts(normalize=True))\n",
+    "\n",
+    "    print(\"\\nРаспределение классов в контрольной выборке:\")\n",
+    "    print(y_val.value_counts(normalize=True))\n",
+    "\n",
+    "    print(\"\\nРаспределение классов в тестовой выборке:\")\n",
+    "    print(y_test.value_counts(normalize=True))\n",
+    "\n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(18,5), sharey=True)\n",
+    "    fig.suptitle('Распределение в различных выборках')\n",
+    "\n",
+    "    sns.barplot(x=y_train.value_counts().index, y=y_train.value_counts(normalize=True), ax=axes[0])\n",
+    "    axes[0].set_title('Обучающая выборка')\n",
+    "    axes[0].set_xlabel(y_name)\n",
+    "    axes[0].set_ylabel('Доля')\n",
+    "\n",
+    "    sns.barplot(x=y_val.value_counts().index, y=y_val.value_counts(normalize=True), ax=axes[1])\n",
+    "    axes[1].set_title('Контрольная выборка')\n",
+    "    axes[1].set_xlabel(y_name)\n",
+    "\n",
+    "    sns.barplot(x=y_test.value_counts().index, y=y_test.value_counts(normalize=True), ax=axes[2])\n",
+    "    axes[2].set_title('Тестовая выборка')\n",
+    "    axes[2].set_xlabel(y_name)\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "analyze_balance(y_train, y_val, y_test, 'stroke')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Заметим, что выборки не сбалансированы. Для балансировки будем использовать RandomOverSampler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение классов в обучающей выборке:\n",
+      "stroke\n",
+      "0    0.5\n",
+      "1    0.5\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в контрольной выборке:\n",
+      "stroke\n",
+      "0    0.5\n",
+      "1    0.5\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в тестовой выборке:\n",
+      "stroke\n",
+      "0    0.957599\n",
+      "1    0.042401\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "randoversamp = RandomOverSampler(random_state=42)\n",
+    "\n",
+    "# Применение RandomOverSampler для балансировки выборок\n",
+    "X_train_resampled, y_train_resampled = randoversamp.fit_resample(X_train, y_train)\n",
+    "X_val_resampled, y_val_resampled = randoversamp.fit_resample(X_val, y_val)\n",
+    "\n",
+    "# Проверка сбалансированности после RandomOverSampler\n",
+    "analyze_balance(y_train_resampled, y_val_resampled, y_test, \"stroke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Выборки сбалансированы"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Применим унитарное кодирование категориальных признаков (one-hot encoding), переведя их в бинарные вектора."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    age  hypertension  heart_disease  avg_glucose_level   bmi  gender_Male  \\\n",
+      "0  53.0             0              0             113.21  28.6         True   \n",
+      "1  62.0             0              0              88.63  24.5        False   \n",
+      "2  17.0             0              0              83.23  28.1        False   \n",
+      "3  77.0             1              0             176.71  33.2        False   \n",
+      "4   7.0             0              0              62.08  16.1         True   \n",
+      "\n",
+      "   gender_Other  ever_married_Yes  work_type_Never_worked  work_type_Private  \\\n",
+      "0         False              True                   False               True   \n",
+      "1         False              True                   False              False   \n",
+      "2         False             False                   False               True   \n",
+      "3         False              True                   False              False   \n",
+      "4         False             False                   False              False   \n",
+      "\n",
+      "   work_type_Self-employed  work_type_children  Residence_type_Urban  \\\n",
+      "0                    False               False                  True   \n",
+      "1                    False               False                  True   \n",
+      "2                    False               False                 False   \n",
+      "3                     True               False                 False   \n",
+      "4                    False                True                 False   \n",
+      "\n",
+      "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
+      "0                           False                        False   \n",
+      "1                           False                         True   \n",
+      "2                           False                         True   \n",
+      "3                           False                         True   \n",
+      "4                           False                        False   \n",
+      "\n",
+      "   smoking_status_smokes  \n",
+      "0                   True  \n",
+      "1                  False  \n",
+      "2                  False  \n",
+      "3                  False  \n",
+      "4                  False  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Определение категориальных признаков\n",
+    "categorical_features = [\n",
+    "    \"gender\",\n",
+    "    \"ever_married\",\n",
+    "    \"work_type\",\n",
+    "    \"Residence_type\",\n",
+    "    \"smoking_status\",\n",
+    "]\n",
+    "\n",
+    "# Применение one-hot encoding к обучающей выборке\n",
+    "X_train_encoded = pd.get_dummies(\n",
+    "    X_train_resampled, columns=categorical_features, drop_first=True\n",
+    ")\n",
+    "\n",
+    "# Применение one-hot encoding к контрольной выборке\n",
+    "X_val_encoded = pd.get_dummies(\n",
+    "    X_val_resampled, columns=categorical_features, drop_first=True\n",
+    ")\n",
+    "\n",
+    "# Применение one-hot encoding к тестовой выборке\n",
+    "X_test_encoded = pd.get_dummies(X_test, columns=categorical_features, drop_first=True)\n",
+    "\n",
+    "print(X_train_encoded.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Перейдем к числовым признакам, а именно к колонке age, применим дискретизацию (позволяет преобразовать данные из числового представления в категориальное):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   hypertension  heart_disease  avg_glucose_level   bmi  gender_Male  \\\n",
+      "0             0              0             113.21  28.6         True   \n",
+      "1             0              0              88.63  24.5        False   \n",
+      "2             0              0              83.23  28.1        False   \n",
+      "3             1              0             176.71  33.2        False   \n",
+      "4             0              0              62.08  16.1         True   \n",
+      "\n",
+      "   gender_Other  ever_married_Yes  work_type_Never_worked  work_type_Private  \\\n",
+      "0         False              True                   False               True   \n",
+      "1         False              True                   False              False   \n",
+      "2         False             False                   False               True   \n",
+      "3         False              True                   False              False   \n",
+      "4         False             False                   False              False   \n",
+      "\n",
+      "   work_type_Self-employed  work_type_children  Residence_type_Urban  \\\n",
+      "0                    False               False                  True   \n",
+      "1                    False               False                  True   \n",
+      "2                    False               False                 False   \n",
+      "3                     True               False                 False   \n",
+      "4                    False                True                 False   \n",
+      "\n",
+      "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
+      "0                           False                        False   \n",
+      "1                           False                         True   \n",
+      "2                           False                         True   \n",
+      "3                           False                         True   \n",
+      "4                           False                        False   \n",
+      "\n",
+      "   smoking_status_smokes      age_bin  \n",
+      "0                   True  middle-aged  \n",
+      "1                  False          old  \n",
+      "2                  False        young  \n",
+      "3                  False          old  \n",
+      "4                  False        young  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Определение числовых признаков для дискретизации\n",
+    "numerical_features = [\"age\"]\n",
+    "\n",
+    "\n",
+    "# Функция для дискретизации числовых признаков\n",
+    "def discretize_features(df, features, bins, labels):\n",
+    "    for feature in features:\n",
+    "        df[f\"{feature}_bin\"] = pd.cut(df[feature], bins=bins, labels=labels)\n",
+    "        df.drop(columns=[feature], inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "# Заданные интервалы и метки\n",
+    "age_bins = [0, 25, 55, 100]\n",
+    "age_labels = [\"young\", \"middle-aged\", \"old\"]\n",
+    "\n",
+    "# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n",
+    "X_train_encoded = discretize_features(\n",
+    "    X_train_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
+    ")\n",
+    "X_val_encoded = discretize_features(\n",
+    "    X_val_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
+    ")\n",
+    "X_test_encoded = discretize_features(\n",
+    "    X_test_encoded, numerical_features, bins=age_bins, labels=age_labels\n",
+    ")\n",
+    "\n",
+    "print(X_train_encoded.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Применим ручной синтез признаков. Например, в этом случае создадим признак, в котором вычисляется отклонение уровня глюкозы от среднего для определенной возрастной группы. Вышеуказанный признак может быть полезен для определения пациентов с аномальными данными."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   hypertension  heart_disease  avg_glucose_level   bmi  gender_Male  \\\n",
+      "0             0              0             113.21  28.6         True   \n",
+      "1             0              0              88.63  24.5        False   \n",
+      "2             0              0              83.23  28.1        False   \n",
+      "3             1              0             176.71  33.2        False   \n",
+      "4             0              0              62.08  16.1         True   \n",
+      "\n",
+      "   gender_Other  ever_married_Yes  work_type_Never_worked  work_type_Private  \\\n",
+      "0         False              True                   False               True   \n",
+      "1         False              True                   False              False   \n",
+      "2         False             False                   False               True   \n",
+      "3         False              True                   False              False   \n",
+      "4         False             False                   False              False   \n",
+      "\n",
+      "   work_type_Self-employed  work_type_children  Residence_type_Urban  \\\n",
+      "0                    False               False                  True   \n",
+      "1                    False               False                  True   \n",
+      "2                    False               False                 False   \n",
+      "3                     True               False                 False   \n",
+      "4                    False                True                 False   \n",
+      "\n",
+      "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
+      "0                           False                        False   \n",
+      "1                           False                         True   \n",
+      "2                           False                         True   \n",
+      "3                           False                         True   \n",
+      "4                           False                        False   \n",
+      "\n",
+      "   smoking_status_smokes      age_bin  glucose_age_deviation  \n",
+      "0                   True  middle-aged              10.186796  \n",
+      "1                  False          old             -46.562537  \n",
+      "2                  False        young             -10.882496  \n",
+      "3                  False          old              41.517463  \n",
+      "4                  False        young             -32.032496  \n"
+     ]
+    }
+   ],
+   "source": [
+    "age_glucose_mean = X_train_encoded.groupby(\"age_bin\", observed=False)[\n",
+    "    \"avg_glucose_level\"\n",
+    "].transform(\"mean\")\n",
+    "X_train_encoded[\"glucose_age_deviation\"] = (\n",
+    "    X_train_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
+    ")\n",
+    "\n",
+    "age_glucose_mean = X_val_encoded.groupby(\"age_bin\", observed=False)[\n",
+    "    \"avg_glucose_level\"\n",
+    "].transform(\"mean\")\n",
+    "X_val_encoded[\"glucose_age_deviation\"] = (\n",
+    "    X_val_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
+    ")\n",
+    "\n",
+    "age_glucose_mean = X_test_encoded.groupby(\"age_bin\", observed=False)[\n",
+    "    \"avg_glucose_level\"\n",
+    "].transform(\"mean\")\n",
+    "X_test_encoded[\"glucose_age_deviation\"] = (\n",
+    "    X_test_encoded[\"avg_glucose_level\"] - age_glucose_mean\n",
+    ")\n",
+    "\n",
+    "print(X_train_encoded.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Используем масштабирование признаков, для приведения всех числовых признаков к одинаковым или очень похожим диапазонам значений/распределениям. \n",
+    "### Масштабирование признаков позволяет получить более качественную модель за счет снижения доминирования одних признаков над другими."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   hypertension  heart_disease  avg_glucose_level       bmi  gender_Male  \\\n",
+      "0             0              0          -0.120278 -0.174711         True   \n",
+      "1             0              0          -0.561737 -0.757960        False   \n",
+      "2             0              0          -0.658721 -0.245839        False   \n",
+      "3             1              0           1.020189  0.479666        False   \n",
+      "4             0              0          -1.038577 -1.952910         True   \n",
+      "\n",
+      "   gender_Other  ever_married_Yes  work_type_Never_worked  work_type_Private  \\\n",
+      "0         False              True                   False               True   \n",
+      "1         False              True                   False              False   \n",
+      "2         False             False                   False               True   \n",
+      "3         False              True                   False              False   \n",
+      "4         False             False                   False              False   \n",
+      "\n",
+      "   work_type_Self-employed  work_type_children  Residence_type_Urban  \\\n",
+      "0                    False               False                  True   \n",
+      "1                    False               False                  True   \n",
+      "2                    False               False                 False   \n",
+      "3                     True               False                 False   \n",
+      "4                    False                True                 False   \n",
+      "\n",
+      "   smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
+      "0                           False                        False   \n",
+      "1                           False                         True   \n",
+      "2                           False                         True   \n",
+      "3                           False                         True   \n",
+      "4                           False                        False   \n",
+      "\n",
+      "   smoking_status_smokes      age_bin  glucose_age_deviation  \n",
+      "0                   True  middle-aged               0.192712  \n",
+      "1                  False          old              -0.880860  \n",
+      "2                  False        young              -0.205873  \n",
+      "3                  False          old               0.785418  \n",
+      "4                  False        young              -0.605984  \n"
+     ]
+    }
+   ],
+   "source": [
+    "numerical_features = [\"avg_glucose_level\", \"bmi\", \"glucose_age_deviation\"]\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_encoded[numerical_features] = scaler.fit_transform(\n",
+    "    X_train_encoded[numerical_features]\n",
+    ")\n",
+    "X_val_encoded[numerical_features] = scaler.transform(X_val_encoded[numerical_features])\n",
+    "X_test_encoded[numerical_features] = scaler.transform(\n",
+    "    X_test_encoded[numerical_features]\n",
+    ")\n",
+    "\n",
+    "print(X_train_encoded.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сконструируем признаки, используя фреймворк Featuretools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       hypertension  heart_disease  avg_glucose_level       bmi  gender_Male  \\\n",
+      "index                                                                          \n",
+      "0                 0              0          -0.120278 -0.174711         True   \n",
+      "1                 0              0          -0.561737 -0.757960        False   \n",
+      "2                 0              0          -0.658721 -0.245839        False   \n",
+      "3                 1              0           1.020189  0.479666        False   \n",
+      "4                 0              0          -1.038577 -1.952910         True   \n",
+      "\n",
+      "       gender_Other  ever_married_Yes  work_type_Never_worked  \\\n",
+      "index                                                           \n",
+      "0             False              True                   False   \n",
+      "1             False              True                   False   \n",
+      "2             False             False                   False   \n",
+      "3             False              True                   False   \n",
+      "4             False             False                   False   \n",
+      "\n",
+      "       work_type_Private  work_type_Self-employed  work_type_children  \\\n",
+      "index                                                                   \n",
+      "0                   True                    False               False   \n",
+      "1                  False                    False               False   \n",
+      "2                   True                    False               False   \n",
+      "3                  False                     True               False   \n",
+      "4                  False                    False                True   \n",
+      "\n",
+      "       Residence_type_Urban  smoking_status_formerly smoked  \\\n",
+      "index                                                         \n",
+      "0                      True                           False   \n",
+      "1                      True                           False   \n",
+      "2                     False                           False   \n",
+      "3                     False                           False   \n",
+      "4                     False                           False   \n",
+      "\n",
+      "       smoking_status_never smoked  smoking_status_smokes  \\\n",
+      "index                                                       \n",
+      "0                            False                   True   \n",
+      "1                             True                  False   \n",
+      "2                             True                  False   \n",
+      "3                             True                  False   \n",
+      "4                            False                  False   \n",
+      "\n",
+      "       glucose_age_deviation  age_bin_middle-aged  age_bin_old  \n",
+      "index                                                           \n",
+      "0                   0.192712                 True        False  \n",
+      "1                  -0.880860                False         True  \n",
+      "2                  -0.205873                False        False  \n",
+      "3                   0.785418                False         True  \n",
+      "4                  -0.605984                False        False  \n"
+     ]
+    }
+   ],
+   "source": [
+    "data = X_train_encoded.copy()\n",
+    "\n",
+    "es = ft.EntitySet(id=\"patients\")\n",
+    "\n",
+    "es = es.add_dataframe(\n",
+    "    dataframe_name=\"strokes_data\", dataframe=data, index=\"index\", make_index=True\n",
+    ")\n",
+    "\n",
+    "feature_matrix, feature_defs = ft.dfs(\n",
+    "    entityset=es, target_dataframe_name=\"strokes_data\", max_depth=1\n",
+    ")\n",
+    "\n",
+    "print(feature_matrix.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Оценим качество набора признаков.\n",
+    "\n",
+    "1. Предсказательная способность (для задачи классификации)\n",
+    " - Метрики: Accuracy, Precision, Recall, F1-Score, ROC AUC\n",
+    " - Методы: Обучение модели на обучающей выборке и оценка на валидационной и тестовой выборках.\n",
+    "\n",
+    "2. Вычислительная эффективность\n",
+    " - Методы: Измерение времени, затраченного на генерацию признаков и обучение модели.\n",
+    "\n",
+    "3. Надежность\n",
+    " - Методы: Кросс-валидация и анализ чувствительности модели к изменениям в данных.\n",
+    "\n",
+    "4. Корреляция\n",
+    " - Методы: Анализ корреляционной матрицы признаков и исключение мультиколлинеарных признаков.\n",
+    "\n",
+    "5. Логическая согласованность\n",
+    " - Методы: Проверка логической связи признаков с целевой переменной и интерпретация результатов модели."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Время обучения модели: 0.43 секунд\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train_encoded = pd.get_dummies(X_train_encoded, drop_first=True)\n",
+    "X_val_encoded = pd.get_dummies(X_val_encoded, drop_first=True)\n",
+    "X_test_encoded = pd.get_dummies(X_test_encoded, drop_first=True)\n",
+    "\n",
+    "all_columns = X_train_encoded.columns\n",
+    "X_train_encoded = X_train_encoded.reindex(columns=all_columns, fill_value=0)\n",
+    "X_val_encoded = X_val_encoded.reindex(columns=all_columns, fill_value=0)\n",
+    "X_test_encoded = X_test_encoded.reindex(columns=all_columns, fill_value=0)\n",
+    "\n",
+    "# Выбор модели\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "\n",
+    "# Начинаем отсчет времени\n",
+    "start_time = time.time()\n",
+    "model.fit(X_train_encoded, y_train_resampled)\n",
+    "\n",
+    "# Время обучения модели\n",
+    "train_time = time.time() - start_time\n",
+    "\n",
+    "print(f\"Время обучения модели: {train_time:.2f} секунд\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature Importance:\n",
+      "                           feature  importance\n",
+      "2                avg_glucose_level    0.195252\n",
+      "3                              bmi    0.184431\n",
+      "15           glucose_age_deviation    0.181013\n",
+      "17                     age_bin_old    0.168510\n",
+      "16             age_bin_middle-aged    0.031286\n",
+      "0                     hypertension    0.026751\n",
+      "6                 ever_married_Yes    0.026492\n",
+      "11            Residence_type_Urban    0.025740\n",
+      "4                      gender_Male    0.024989\n",
+      "9          work_type_Self-employed    0.022764\n",
+      "1                    heart_disease    0.021314\n",
+      "8                work_type_Private    0.020773\n",
+      "13     smoking_status_never smoked    0.019126\n",
+      "12  smoking_status_formerly smoked    0.017622\n",
+      "10              work_type_children    0.017389\n",
+      "14           smoking_status_smokes    0.016418\n",
+      "7           work_type_Never_worked    0.000127\n",
+      "5                     gender_Other    0.000003\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Получение важности признаков\n",
+    "importances = model.feature_importances_\n",
+    "feature_names = X_train_encoded.columns\n",
+    "\n",
+    "# Сортировка признаков по важности\n",
+    "feature_importance = pd.DataFrame({\"feature\": feature_names, \"importance\": importances})\n",
+    "feature_importance = feature_importance.sort_values(by=\"importance\", ascending=False)\n",
+    "\n",
+    "print(\"Feature Importance:\")\n",
+    "print(feature_importance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.949119373776908\n",
+      "Precision: 0.15789473684210525\n",
+      "Recall: 0.046153846153846156\n",
+      "F1 Score: 0.07142857142857142\n",
+      "ROC AUC: 0.5176273317962691\n",
+      "Cross-validated Accuracy: 0.991578947368421\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy: 1.0\n",
+      "Train Precision: 1.0\n",
+      "Train Recall: 1.0\n",
+      "Train F1 Score: 1.0\n",
+      "Train ROC AUC: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Предсказание и оценка\n",
+    "y_pred = model.predict(X_test_encoded)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "precision = precision_score(y_test, y_pred)\n",
+    "recall = recall_score(y_test, y_pred)\n",
+    "f1 = f1_score(y_test, y_pred)\n",
+    "roc_auc = roc_auc_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy}\")\n",
+    "print(f\"Precision: {precision}\")\n",
+    "print(f\"Recall: {recall}\")\n",
+    "print(f\"F1 Score: {f1}\")\n",
+    "print(f\"ROC AUC: {roc_auc}\")\n",
+    "\n",
+    "# Кросс-валидация\n",
+    "scores = cross_val_score(\n",
+    "    model, X_train_encoded, y_train_resampled, cv=5, scoring=\"accuracy\"\n",
+    ")\n",
+    "accuracy_cv = scores.mean()\n",
+    "print(f\"Cross-validated Accuracy: {accuracy_cv}\")\n",
+    "\n",
+    "# Анализ важности признаков\n",
+    "feature_importances = model.feature_importances_\n",
+    "feature_names = X_train_encoded.columns\n",
+    "\n",
+    "importance_df = pd.DataFrame(\n",
+    "    {\"Feature\": feature_names, \"Importance\": feature_importances}\n",
+    ")\n",
+    "importance_df = importance_df.sort_values(by=\"Importance\", ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x=\"Importance\", y=\"Feature\", data=importance_df)\n",
+    "plt.title(\"Feature Importance\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Проверка на переобучение\n",
+    "y_train_pred = model.predict(X_train_encoded)\n",
+    "\n",
+    "accuracy_train = accuracy_score(y_train_resampled, y_train_pred)\n",
+    "precision_train = precision_score(y_train_resampled, y_train_pred)\n",
+    "recall_train = recall_score(y_train_resampled, y_train_pred)\n",
+    "f1_train = f1_score(y_train_resampled, y_train_pred)\n",
+    "roc_auc_train = roc_auc_score(y_train_resampled, y_train_pred)\n",
+    "\n",
+    "print(f\"Train Accuracy: {accuracy_train}\")\n",
+    "print(f\"Train Precision: {precision_train}\")\n",
+    "print(f\"Train Recall: {recall_train}\")\n",
+    "print(f\"Train F1 Score: {f1_train}\")\n",
+    "print(f\"Train ROC AUC: {roc_auc_train}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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
+}
diff --git a/lab4.ipynb b/lab4.ipynb
new file mode 100644
index 0000000..ab9d447
--- /dev/null
+++ b/lab4.ipynb
@@ -0,0 +1,3436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Бизнес-цели\n",
+    "Цель: разработать модель классификации, которая сможет предсказать возможность возникновения инсульта у человека на основе социально-демографических факторов, состояния здоровья и образа жизни.\n",
+    "Применение:\n",
+    "1. Медицинские учреждения: модель может использоваться для раннего выявления пациентов с высоким риском инсульта, что позволит предпринять профилактические меры и уменьшить вероятность серьезных последствий.\n",
+    "2. Системы поддержки принятия медицинских решений: модель может быть встроена в электронные медицинские карты для автоматического предупреждения врачей о пациентах, находящихся в зоне повышенного риска.\n",
+    "3. Образовательные программы: модель может помочь повысить осведомленность населения о факторах риска инсульта и способах их снижения, что также может улучшить профилактику заболеваний.\n",
+    "\n",
+    "### Регрессия\n",
+    "Цель: разработать модель регрессии для прогнозирования уровня глюкозы в крови человека на основе социально-демографических факторов, состояния здоровья и образа жизни. Модель позволит определить тенденцию к повышению или снижению уровня глюкозы и, в дальнейшем, оценить возможные риски, связанные с состоянием пациента.\n",
+    "Применение:\n",
+    "1. Медицинские учреждения: помощь в раннем выявлении пациентов с потенциально высоким уровнем глюкозы для контроля и назначения профилактических мер, снижающих риск диабета и других осложнений.\n",
+    "2. Системы поддержки принятия медицинских решений: интеграция модели в медицинские записи позволит врачам получать оценку уровня глюкозы, что упростит мониторинг и ведение пациентов, особенно при отсутствии лабораторных данных в реальном времени.\n",
+    "3. Образовательные программы и общественное здравоохранение: с помощью модели можно повысить осведомленность населения о факторах, влияющих на уровень глюкозы, и предлагать рекомендации по улучшению образа жизни для поддержания нормального уровня глюкозы."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка набора данных "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>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",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9046</th>\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>51676</th>\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>31112</th>\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>60182</th>\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>1665</th>\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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18234</th>\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>NaN</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44873</th>\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>19723</th>\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>37544</th>\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>44679</th>\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",
+       "<p>5110 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+       "id                                                                             \n",
+       "9046     Male  67.0             0              1          Yes        Private   \n",
+       "51676  Female  61.0             0              0          Yes  Self-employed   \n",
+       "31112    Male  80.0             0              1          Yes        Private   \n",
+       "60182  Female  49.0             0              0          Yes        Private   \n",
+       "1665   Female  79.0             1              0          Yes  Self-employed   \n",
+       "...       ...   ...           ...            ...          ...            ...   \n",
+       "18234  Female  80.0             1              0          Yes        Private   \n",
+       "44873  Female  81.0             0              0          Yes  Self-employed   \n",
+       "19723  Female  35.0             0              0          Yes  Self-employed   \n",
+       "37544    Male  51.0             0              0          Yes        Private   \n",
+       "44679  Female  44.0             0              0          Yes       Govt_job   \n",
+       "\n",
+       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
+       "id                                                                      \n",
+       "9046           Urban             228.69  36.6  formerly smoked       1  \n",
+       "51676          Rural             202.21   NaN     never smoked       1  \n",
+       "31112          Rural             105.92  32.5     never smoked       1  \n",
+       "60182          Urban             171.23  34.4           smokes       1  \n",
+       "1665           Rural             174.12  24.0     never smoked       1  \n",
+       "...              ...                ...   ...              ...     ...  \n",
+       "18234          Urban              83.75   NaN     never smoked       0  \n",
+       "44873          Urban             125.20  40.0     never smoked       0  \n",
+       "19723          Rural              82.99  30.6     never smoked       0  \n",
+       "37544          Rural             166.29  25.6  formerly smoked       0  \n",
+       "44679          Urban              85.28  26.2          Unknown       0  \n",
+       "\n",
+       "[5110 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn import set_config\n",
+    "set_config(transform_output=\"pandas\")\n",
+    "random_state = 9\n",
+    "df = pd.read_csv(\"../data/healthcare-dataset-stroke-data.csv\", index_col=\"id\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Классификация\n",
+    "Разделим набор данных на на обучающую и тестовые выборки (80/20). Целевой признак - stroke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Tuple\n",
+    "from pandas import DataFrame\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\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",
+    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\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",
+    "    if frac_val <= 0:\n",
+    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
+    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\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",
+    "    return df_train, df_val, df_test, y_train, y_val, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'X_train'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>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",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22159</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>97.06</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8920</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>76.35</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65507</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>55.72</td>\n",
+       "      <td>38.2</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43196</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>59.54</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59745</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>76.74</td>\n",
+       "      <td>53.9</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66546</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>80.08</td>\n",
+       "      <td>25.1</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68798</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>59.86</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61409</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>58.24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69259</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>100.85</td>\n",
+       "      <td>29.5</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17231</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>90.42</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4088 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+       "id                                                                             \n",
+       "22159  Female  54.0             1              0           No        Private   \n",
+       "8920   Female  51.0             0              0          Yes  Self-employed   \n",
+       "65507    Male  33.0             0              0          Yes        Private   \n",
+       "43196  Female  52.0             0              0          Yes  Self-employed   \n",
+       "59745  Female  27.0             0              0          Yes        Private   \n",
+       "...       ...   ...           ...            ...          ...            ...   \n",
+       "66546  Female  20.0             0              0           No        Private   \n",
+       "68798  Female  58.0             0              0          Yes        Private   \n",
+       "61409    Male  32.0             1              0           No       Govt_job   \n",
+       "69259  Female  77.0             0              0          Yes        Private   \n",
+       "17231  Female  24.0             0              0           No        Private   \n",
+       "\n",
+       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
+       "id                                                                      \n",
+       "22159          Urban              97.06  28.5  formerly smoked       0  \n",
+       "8920           Rural              76.35  33.5  formerly smoked       0  \n",
+       "65507          Rural              55.72  38.2     never smoked       0  \n",
+       "43196          Urban              59.54  42.2          Unknown       0  \n",
+       "59745          Urban              76.74  53.9          Unknown       0  \n",
+       "...              ...                ...   ...              ...     ...  \n",
+       "66546          Urban              80.08  25.1     never smoked       0  \n",
+       "68798          Rural              59.86  28.0  formerly smoked       1  \n",
+       "61409          Urban              58.24   NaN  formerly smoked       0  \n",
+       "69259          Rural             100.85  29.5           smokes       0  \n",
+       "17231          Urban              90.42  24.3     never smoked       0  \n",
+       "\n",
+       "[4088 rows x 11 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_train'"
+      ]
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+       "      <th>68798</th>\n",
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+       "      <th>61409</th>\n",
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+      ],
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+       "       stroke\n",
+       "id           \n",
+       "22159       0\n",
+       "8920        0\n",
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+       "43196       0\n",
+       "59745       0\n",
+       "...       ...\n",
+       "66546       0\n",
+       "68798       1\n",
+       "61409       0\n",
+       "69259       0\n",
+       "17231       0\n",
+       "\n",
+       "[4088 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'X_test'"
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+       "      <th></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",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18072</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>107.47</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67063</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>130.56</td>\n",
+       "      <td>36.1</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40387</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>77.46</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18032</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>90.61</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5478</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>203.04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57710</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>112.25</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63043</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>61.80</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63986</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>153.48</td>\n",
+       "      <td>37.3</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28461</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Never_worked</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>79.59</td>\n",
+       "      <td>28.4</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54975</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>64.06</td>\n",
+       "      <td>18.9</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1022 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+       "id                                                                             \n",
+       "18072  Female  39.0             0              0          Yes       Govt_job   \n",
+       "67063    Male  62.0             0              0          Yes  Self-employed   \n",
+       "40387  Female  17.0             0              0           No        Private   \n",
+       "18032    Male  62.0             0              1          Yes        Private   \n",
+       "5478   Female  60.0             0              0          Yes  Self-employed   \n",
+       "...       ...   ...           ...            ...          ...            ...   \n",
+       "57710  Female  50.0             0              0          Yes        Private   \n",
+       "63043  Female  27.0             0              0           No        Private   \n",
+       "63986    Male  60.0             0              0          Yes        Private   \n",
+       "28461    Male  15.0             0              0           No   Never_worked   \n",
+       "54975    Male   7.0             0              0           No  Self-employed   \n",
+       "\n",
+       "      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
+       "id                                                                      \n",
+       "18072          Urban             107.47  21.3          Unknown       0  \n",
+       "67063          Urban             130.56  36.1          Unknown       0  \n",
+       "40387          Rural              77.46  24.0          Unknown       0  \n",
+       "18032          Rural              90.61  25.8           smokes       0  \n",
+       "5478           Urban             203.04   NaN           smokes       0  \n",
+       "...              ...                ...   ...              ...     ...  \n",
+       "57710          Rural             112.25  21.6          Unknown       0  \n",
+       "63043          Urban              61.80  26.8  formerly smoked       0  \n",
+       "63986          Rural             153.48  37.3     never smoked       0  \n",
+       "28461          Rural              79.59  28.4          Unknown       0  \n",
+       "54975          Rural              64.06  18.9          Unknown       0  \n",
+       "\n",
+       "[1022 rows x 11 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_test'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
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+       "      <th></th>\n",
+       "      <th>stroke</th>\n",
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+       "    <tr>\n",
+       "      <th>id</th>\n",
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+       "      <th>67063</th>\n",
+       "      <td>0</td>\n",
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+       "      <th>40387</th>\n",
+       "      <td>0</td>\n",
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+       "      <th>18032</th>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>5478</th>\n",
+       "      <td>0</td>\n",
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+       "      <th>...</th>\n",
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+       "      <th>57710</th>\n",
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+       "      <th>63043</th>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>63986</th>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>28461</th>\n",
+       "      <td>0</td>\n",
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+       "      <th>54975</th>\n",
+       "      <td>0</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1022 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       stroke\n",
+       "id           \n",
+       "18072       0\n",
+       "67063       0\n",
+       "40387       0\n",
+       "18032       0\n",
+       "5478        0\n",
+       "...       ...\n",
+       "57710       0\n",
+       "63043       0\n",
+       "63986       0\n",
+       "28461       0\n",
+       "54975       0\n",
+       "\n",
+       "[1022 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
+    "    df,\n",
+    "    stratify_colname=\"stroke\",\n",
+    "    frac_train=0.80,\n",
+    "    frac_val=0,\n",
+    "    frac_test=0.20,\n",
+    "    random_state=random_state,\n",
+    ")\n",
+    "\n",
+    "display(\"X_train\", X_train)\n",
+    "display(\"y_train\", y_train)\n",
+    "\n",
+    "display(\"X_test\", X_test)\n",
+    "display(\"y_test\", y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Выберем ориентир для задачи классификации. \n",
+    "Для этого применим алгоритм случайного предсказания, т.е. в каждом случае в качестве предсказания выберем случайный класс."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline Accuracy: 0.5058708414872799\n",
+      "Baseline Precision: 0.05304518664047151\n",
+      "Baseline Recall: 0.54\n",
+      "Baseline F1 Score: 0.09660107334525939\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score\n",
+    "\n",
+    "# Получаем уникальные классы для целевого признака из тренировочного набора данных\n",
+    "unique_classes = np.unique(y_train)\n",
+    "\n",
+    "# Генерируем случайные предсказания, выбирая случайное значение из области значений целевого признака\n",
+    "random_predictions = np.random.choice(unique_classes, size=len(y_test))\n",
+    "\n",
+    "# Вычисление метрик для ориентира\n",
+    "baseline_accuracy = accuracy_score(y_test, random_predictions)\n",
+    "baseline_precision = precision_score(y_test, random_predictions)\n",
+    "baseline_recall = recall_score(y_test, random_predictions)\n",
+    "baseline_f1 = f1_score(y_test, random_predictions)\n",
+    "\n",
+    "print(\"Baseline Accuracy:\", baseline_accuracy)\n",
+    "print(\"Baseline Precision:\", baseline_precision)\n",
+    "print(\"Baseline Recall:\", baseline_recall)\n",
+    "print(\"Baseline F1 Score:\", baseline_f1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Сформируем конвейер для классификации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.discriminant_analysis import StandardScaler\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "columns_to_drop = [\"work_type\", \"stroke\"]\n",
+    "columns_not_to_modify = [\"hypertension\", \"heart_disease\"]\n",
+    "\n",
+    "num_columns = [\n",
+    "    column\n",
+    "    for column in df.columns\n",
+    "    if column not in columns_to_drop\n",
+    "    and column not in columns_not_to_modify\n",
+    "    and df[column].dtype != \"object\"\n",
+    "]\n",
+    "\n",
+    "cat_columns = [\n",
+    "    column\n",
+    "    for column in df.columns\n",
+    "    if column not in columns_to_drop\n",
+    "    and column not in columns_not_to_modify\n",
+    "    and df[column].dtype == \"object\"\n",
+    "]\n",
+    "\n",
+    "num_imputer = SimpleImputer(strategy=\"median\")\n",
+    "num_scaler = StandardScaler()\n",
+    "preprocessing_num = Pipeline(\n",
+    "    [\n",
+    "        (\"imputer\", num_imputer),\n",
+    "        (\"scaler\", num_scaler),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
+    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
+    "preprocessing_cat = Pipeline(\n",
+    "    [\n",
+    "        (\"imputer\", cat_imputer),\n",
+    "        (\"encoder\", cat_encoder),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "features_preprocessing = ColumnTransformer(\n",
+    "    verbose_feature_names_out=False,\n",
+    "    transformers=[\n",
+    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
+    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
+    "    ],\n",
+    "    remainder=\"passthrough\",\n",
+    ")\n",
+    "\n",
+    "drop_columns = ColumnTransformer(\n",
+    "    verbose_feature_names_out=False,\n",
+    "    transformers=[\n",
+    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
+    "    ],\n",
+    "    remainder=\"passthrough\",\n",
+    ")\n",
+    "\n",
+    "pipeline_end = Pipeline(\n",
+    "    [\n",
+    "        (\"features_preprocessing\", features_preprocessing),\n",
+    "        (\"drop_columns\", drop_columns),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>age</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>gender_Other</th>\n",
+       "      <th>ever_married_Yes</th>\n",
+       "      <th>Residence_type_Urban</th>\n",
+       "      <th>smoking_status_formerly smoked</th>\n",
+       "      <th>smoking_status_never smoked</th>\n",
+       "      <th>smoking_status_smokes</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22159</th>\n",
+       "      <td>0.472344</td>\n",
+       "      <td>-0.194427</td>\n",
+       "      <td>-0.059214</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8920</th>\n",
+       "      <td>0.339807</td>\n",
+       "      <td>-0.653763</td>\n",
+       "      <td>0.587887</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65507</th>\n",
+       "      <td>-0.455418</td>\n",
+       "      <td>-1.111325</td>\n",
+       "      <td>1.196162</td>\n",
+       "      <td>1.0</td>\n",
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+       "    <tr>\n",
+       "      <th>43196</th>\n",
+       "      <td>0.383986</td>\n",
+       "      <td>-1.026600</td>\n",
+       "      <td>1.713843</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59745</th>\n",
+       "      <td>-0.720492</td>\n",
+       "      <td>-0.645113</td>\n",
+       "      <td>3.228060</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66546</th>\n",
+       "      <td>-1.029746</td>\n",
+       "      <td>-0.571034</td>\n",
+       "      <td>-0.499243</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68798</th>\n",
+       "      <td>0.649060</td>\n",
+       "      <td>-1.019502</td>\n",
+       "      <td>-0.123924</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61409</th>\n",
+       "      <td>-0.499597</td>\n",
+       "      <td>-1.055433</td>\n",
+       "      <td>-0.098040</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69259</th>\n",
+       "      <td>1.488464</td>\n",
+       "      <td>-0.110367</td>\n",
+       "      <td>0.070206</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17231</th>\n",
+       "      <td>-0.853030</td>\n",
+       "      <td>-0.341699</td>\n",
+       "      <td>-0.602779</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4088 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            age  avg_glucose_level       bmi  gender_Male  gender_Other  \\\n",
+       "id                                                                        \n",
+       "22159  0.472344          -0.194427 -0.059214          0.0           0.0   \n",
+       "8920   0.339807          -0.653763  0.587887          0.0           0.0   \n",
+       "65507 -0.455418          -1.111325  1.196162          1.0           0.0   \n",
+       "43196  0.383986          -1.026600  1.713843          0.0           0.0   \n",
+       "59745 -0.720492          -0.645113  3.228060          0.0           0.0   \n",
+       "...         ...                ...       ...          ...           ...   \n",
+       "66546 -1.029746          -0.571034 -0.499243          0.0           0.0   \n",
+       "68798  0.649060          -1.019502 -0.123924          0.0           0.0   \n",
+       "61409 -0.499597          -1.055433 -0.098040          1.0           0.0   \n",
+       "69259  1.488464          -0.110367  0.070206          0.0           0.0   \n",
+       "17231 -0.853030          -0.341699 -0.602779          0.0           0.0   \n",
+       "\n",
+       "       ever_married_Yes  Residence_type_Urban  smoking_status_formerly smoked  \\\n",
+       "id                                                                              \n",
+       "22159               0.0                   1.0                             1.0   \n",
+       "8920                1.0                   0.0                             1.0   \n",
+       "65507               1.0                   0.0                             0.0   \n",
+       "43196               1.0                   1.0                             0.0   \n",
+       "59745               1.0                   1.0                             0.0   \n",
+       "...                 ...                   ...                             ...   \n",
+       "66546               0.0                   1.0                             0.0   \n",
+       "68798               1.0                   0.0                             1.0   \n",
+       "61409               0.0                   1.0                             1.0   \n",
+       "69259               1.0                   0.0                             0.0   \n",
+       "17231               0.0                   1.0                             0.0   \n",
+       "\n",
+       "       smoking_status_never smoked  smoking_status_smokes  hypertension  \\\n",
+       "id                                                                        \n",
+       "22159                          0.0                    0.0             1   \n",
+       "8920                           0.0                    0.0             0   \n",
+       "65507                          1.0                    0.0             0   \n",
+       "43196                          0.0                    0.0             0   \n",
+       "59745                          0.0                    0.0             0   \n",
+       "...                            ...                    ...           ...   \n",
+       "66546                          1.0                    0.0             0   \n",
+       "68798                          0.0                    0.0             0   \n",
+       "61409                          0.0                    0.0             1   \n",
+       "69259                          0.0                    1.0             0   \n",
+       "17231                          1.0                    0.0             0   \n",
+       "\n",
+       "       heart_disease  \n",
+       "id                    \n",
+       "22159              0  \n",
+       "8920               0  \n",
+       "65507              0  \n",
+       "43196              0  \n",
+       "59745              0  \n",
+       "...              ...  \n",
+       "66546              0  \n",
+       "68798              0  \n",
+       "61409              0  \n",
+       "69259              0  \n",
+       "17231              0  \n",
+       "\n",
+       "[4088 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessing_result = pipeline_end.fit_transform(X_train)\n",
+    "preprocessed_df = pd.DataFrame(\n",
+    "    preprocessing_result,\n",
+    "    columns=pipeline_end.get_feature_names_out(),\n",
+    ")\n",
+    "\n",
+    "preprocessed_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Подберем оптимальные гиперпараметры для каждой из выбранных моделей методом поиска по сетке и сформируем их набор.\n",
+    "- knn - k-ближайших соседей\n",
+    "- random_forest - метод случайного леса (набор деревьев решений)\n",
+    "- mlp - многослойный персептрон (многослойная нейронная сеть)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>gender_Other</th>\n",
+       "      <th>ever_married_Yes</th>\n",
+       "      <th>work_type_Never_worked</th>\n",
+       "      <th>work_type_Private</th>\n",
+       "      <th>work_type_Self-employed</th>\n",
+       "      <th>work_type_children</th>\n",
+       "      <th>Residence_type_Urban</th>\n",
+       "      <th>smoking_status_formerly smoked</th>\n",
+       "      <th>smoking_status_never smoked</th>\n",
+       "      <th>smoking_status_smokes</th>\n",
+       "      <th>hypertension</th>\n",
+       "      <th>heart_disease</th>\n",
+       "      <th>avg_glucose_level</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
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+       "      <td>-0.455418</td>\n",
+       "      <td>1.196162</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
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+       "      <td>-0.720492</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>76.74</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66546</th>\n",
+       "      <td>-1.029746</td>\n",
+       "      <td>-0.499243</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.08</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68798</th>\n",
+       "      <td>0.649060</td>\n",
+       "      <td>-0.123924</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59.86</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61409</th>\n",
+       "      <td>-0.499597</td>\n",
+       "      <td>-0.098040</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58.24</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69259</th>\n",
+       "      <td>1.488464</td>\n",
+       "      <td>0.070206</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.85</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17231</th>\n",
+       "      <td>-0.853030</td>\n",
+       "      <td>-0.602779</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90.42</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4088 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            age       bmi  gender_Male  gender_Other  ever_married_Yes  \\\n",
+       "id                                                                       \n",
+       "22159  0.472344 -0.059214          0.0           0.0               0.0   \n",
+       "8920   0.339807  0.587887          0.0           0.0               1.0   \n",
+       "65507 -0.455418  1.196162          1.0           0.0               1.0   \n",
+       "43196  0.383986  1.713843          0.0           0.0               1.0   \n",
+       "59745 -0.720492  3.228060          0.0           0.0               1.0   \n",
+       "...         ...       ...          ...           ...               ...   \n",
+       "66546 -1.029746 -0.499243          0.0           0.0               0.0   \n",
+       "68798  0.649060 -0.123924          0.0           0.0               1.0   \n",
+       "61409 -0.499597 -0.098040          1.0           0.0               0.0   \n",
+       "69259  1.488464  0.070206          0.0           0.0               1.0   \n",
+       "17231 -0.853030 -0.602779          0.0           0.0               0.0   \n",
+       "\n",
+       "       work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
+       "id                                                                          \n",
+       "22159                     0.0                1.0                      0.0   \n",
+       "8920                      0.0                0.0                      1.0   \n",
+       "65507                     0.0                1.0                      0.0   \n",
+       "43196                     0.0                0.0                      1.0   \n",
+       "59745                     0.0                1.0                      0.0   \n",
+       "...                       ...                ...                      ...   \n",
+       "66546                     0.0                1.0                      0.0   \n",
+       "68798                     0.0                1.0                      0.0   \n",
+       "61409                     0.0                0.0                      0.0   \n",
+       "69259                     0.0                1.0                      0.0   \n",
+       "17231                     0.0                1.0                      0.0   \n",
+       "\n",
+       "       work_type_children  Residence_type_Urban  \\\n",
+       "id                                                \n",
+       "22159                 0.0                   1.0   \n",
+       "8920                  0.0                   0.0   \n",
+       "65507                 0.0                   0.0   \n",
+       "43196                 0.0                   1.0   \n",
+       "59745                 0.0                   1.0   \n",
+       "...                   ...                   ...   \n",
+       "66546                 0.0                   1.0   \n",
+       "68798                 0.0                   0.0   \n",
+       "61409                 0.0                   1.0   \n",
+       "69259                 0.0                   0.0   \n",
+       "17231                 0.0                   1.0   \n",
+       "\n",
+       "       smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
+       "id                                                                   \n",
+       "22159                             1.0                          0.0   \n",
+       "8920                              1.0                          0.0   \n",
+       "65507                             0.0                          1.0   \n",
+       "43196                             0.0                          0.0   \n",
+       "59745                             0.0                          0.0   \n",
+       "...                               ...                          ...   \n",
+       "66546                             0.0                          1.0   \n",
+       "68798                             1.0                          0.0   \n",
+       "61409                             1.0                          0.0   \n",
+       "69259                             0.0                          0.0   \n",
+       "17231                             0.0                          1.0   \n",
+       "\n",
+       "       smoking_status_smokes  hypertension  heart_disease  avg_glucose_level  \\\n",
+       "id                                                                             \n",
+       "22159                    0.0             1              0              97.06   \n",
+       "8920                     0.0             0              0              76.35   \n",
+       "65507                    0.0             0              0              55.72   \n",
+       "43196                    0.0             0              0              59.54   \n",
+       "59745                    0.0             0              0              76.74   \n",
+       "...                      ...           ...            ...                ...   \n",
+       "66546                    0.0             0              0              80.08   \n",
+       "68798                    0.0             0              0              59.86   \n",
+       "61409                    0.0             1              0              58.24   \n",
+       "69259                    1.0             0              0             100.85   \n",
+       "17231                    0.0             0              0              90.42   \n",
+       "\n",
+       "       stroke  \n",
+       "id             \n",
+       "22159       0  \n",
+       "8920        0  \n",
+       "65507       0  \n",
+       "43196       0  \n",
+       "59745       0  \n",
+       "...       ...  \n",
+       "66546       0  \n",
+       "68798       1  \n",
+       "61409       0  \n",
+       "69259       0  \n",
+       "17231       0  \n",
+       "\n",
+       "[4088 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessing_result = pipeline_end_reg.fit_transform(X_train)\n",
+    "preprocessed_df = pd.DataFrame(\n",
+    "    preprocessing_result,\n",
+    "    columns=pipeline_end_reg.get_feature_names_out(),\n",
+    ")\n",
+    "\n",
+    "preprocessed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Лучшие параметры для knn: {'n_neighbors': 1, 'weights': 'uniform'}\n",
+      "Лучшие параметры для random_forest: {'class_weight': 'balanced_subsample', 'criterion': 'entropy', 'max_depth': 7, 'max_features': 'sqrt', 'n_estimators': 50, 'random_state': 9}\n",
+      "Лучшие параметры для mlp: {'alpha': np.float64(0.1), 'early_stopping': True, 'hidden_layer_sizes': np.int64(14), 'max_iter': 1000, 'random_state': 9, 'solver': 'adam'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn import neighbors, ensemble, neural_network\n",
+    "\n",
+    "# Словарь с вариантами гиперпараметров для каждой модели\n",
+    "param_grids = {\n",
+    "    \"knn\": {\n",
+    "        \"n_neighbors\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], \n",
+    "        \"weights\": ['uniform', 'distance']\n",
+    "    },\n",
+    "    \"random_forest\": {\n",
+    "        \"n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
+    "        \"max_features\": [\"sqrt\", \"log2\", 2],\n",
+    "        \"max_depth\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "        \"criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
+    "        \"random_state\": [random_state],\n",
+    "        \"class_weight\": [\"balanced\", \"balanced_subsample\"]\n",
+    "    },\n",
+    "    \"mlp\": {\n",
+    "        \"solver\": ['adam'], \n",
+    "        \"max_iter\": [1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000], \n",
+    "        \"alpha\": 10.0 ** -np.arange(1, 10), \n",
+    "        \"hidden_layer_sizes\":np.arange(10, 15), \n",
+    "        \"early_stopping\": [True, False],\n",
+    "        \"random_state\": [random_state]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Создаем экземпляры моделей\n",
+    "models = {\n",
+    "    \"knn\": neighbors.KNeighborsClassifier(),\n",
+    "    \"random_forest\": ensemble.RandomForestClassifier(),\n",
+    "    \"mlp\": neural_network.MLPClassifier()\n",
+    "}\n",
+    "\n",
+    "# Словарь для хранения моделей с их лучшими параметрами\n",
+    "class_models = {}\n",
+    "\n",
+    "# Выполнение поиска по сетке для каждой модели\n",
+    "for model_name, model in models.items():\n",
+    "    # Создаем GridSearchCV для текущей модели\n",
+    "    gs_optimizer = GridSearchCV(estimator=model, param_grid=param_grids[model_name], scoring=\"f1\", n_jobs=-1)\n",
+    "    \n",
+    "    # Обучаем GridSearchCV\n",
+    "    gs_optimizer.fit(preprocessed_df, y_train.values.ravel())\n",
+    "    \n",
+    "    # Получаем лучшие параметры\n",
+    "    best_params = gs_optimizer.best_params_\n",
+    "    print(f\"Лучшие параметры для {model_name}: {best_params}\")\n",
+    "    \n",
+    "    class_models[model_name] = {\n",
+    "        \"model\": model.set_params(**best_params)  # Настраиваем модель с лучшими параметрами\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: knn\n",
+      "Model: random_forest\n",
+      "Model: mlp\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "for model_name in class_models.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model = class_models[model_name][\"model\"]\n",
+    "\n",
+    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
+    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train)\n",
+    "    y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
+    "    y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
+    "\n",
+    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
+    "    class_models[model_name][\"probs\"] = y_test_probs\n",
+    "    class_models[model_name][\"preds\"] = y_test_predict\n",
+    "\n",
+    "    class_models[model_name][\"Precision_train\"] = precision_score(\n",
+    "        y_train, y_train_predict\n",
+    "    )\n",
+    "    class_models[model_name][\"Precision_test\"] = precision_score(\n",
+    "        y_test, y_test_predict\n",
+    "    )\n",
+    "    class_models[model_name][\"Recall_train\"] = recall_score(\n",
+    "        y_train, y_train_predict\n",
+    "    )\n",
+    "    class_models[model_name][\"Recall_test\"] = recall_score(\n",
+    "        y_test, y_test_predict\n",
+    "    )\n",
+    "    class_models[model_name][\"Accuracy_train\"] = accuracy_score(\n",
+    "        y_train, y_train_predict\n",
+    "    )\n",
+    "    class_models[model_name][\"Accuracy_test\"] = accuracy_score(\n",
+    "        y_test, y_test_predict\n",
+    "    ) \n",
+    "    class_models[model_name][\"F1_train\"] = f1_score(y_train, y_train_predict)\n",
+    "    class_models[model_name][\"F1_test\"] = f1_score(y_test, y_test_predict)\n",
+    "    class_models[model_name][\"Confusion_matrix\"] = confusion_matrix(\n",
+    "        y_test, y_test_predict\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Посмотрим на матрицы неточностей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "\n",
+    "for index, (key, model_info) in enumerate(class_models.items()):\n",
+    "    c_matrix = model_info[\"Confusion_matrix\"]\n",
+    "    \n",
+    "    disp = ConfusionMatrixDisplay(\n",
+    "        confusion_matrix=c_matrix, display_labels=[\"Not stroke\", \"Stroke\"]\n",
+    "    ).plot(ax=ax.flat[index])\n",
+    "    \n",
+    "    disp.ax_.set_title(key)\n",
+    "\n",
+    "if len(class_models) < len(ax.flat):\n",
+    "    for i in range(len(class_models), len(ax.flat)):\n",
+    "        fig.delaxes(ax.flat[i])\n",
+    "\n",
+    "plt.subplots_adjust(top=0.9, bottom=0.1, hspace=0.4, wspace=0.3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Precision, Recall, Accuracy, F1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
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+       "  background-color: #1f988b;\n",
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+       "  background-color: #a8db34;\n",
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+       "  color: #f1f1f1;\n",
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+       "  background-color: #da5a6a;\n",
+       "  color: #f1f1f1;\n",
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+       "  background-color: #4e02a2;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
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+       "  background-color: #1f968b;\n",
+       "  color: #f1f1f1;\n",
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+       "  background-color: #be3885;\n",
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+       "  background-color: #9c179e;\n",
+       "  color: #f1f1f1;\n",
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+       "#T_a7559_row2_col2 {\n",
+       "  background-color: #86d549;\n",
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+       "}\n",
+       "</style>\n",
+       "<table id=\"T_a7559\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_a7559_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
+       "      <th id=\"T_a7559_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
+       "      <th id=\"T_a7559_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
+       "      <th id=\"T_a7559_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
+       "      <th id=\"T_a7559_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
+       "      <th id=\"T_a7559_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
+       "      <th id=\"T_a7559_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
+       "      <th id=\"T_a7559_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7559_level0_row0\" class=\"row_heading level0 row0\" >mlp</th>\n",
+       "      <td id=\"T_a7559_row0_col0\" class=\"data row0 col0\" >0.400000</td>\n",
+       "      <td id=\"T_a7559_row0_col1\" class=\"data row0 col1\" >0.200000</td>\n",
+       "      <td id=\"T_a7559_row0_col2\" class=\"data row0 col2\" >0.020101</td>\n",
+       "      <td id=\"T_a7559_row0_col3\" class=\"data row0 col3\" >0.020000</td>\n",
+       "      <td id=\"T_a7559_row0_col4\" class=\"data row0 col4\" >0.950832</td>\n",
+       "      <td id=\"T_a7559_row0_col5\" class=\"data row0 col5\" >0.948141</td>\n",
+       "      <td id=\"T_a7559_row0_col6\" class=\"data row0 col6\" >0.038278</td>\n",
+       "      <td id=\"T_a7559_row0_col7\" class=\"data row0 col7\" >0.036364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7559_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
+       "      <td id=\"T_a7559_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
+       "      <td id=\"T_a7559_row1_col1\" class=\"data row1 col1\" >0.117647</td>\n",
+       "      <td id=\"T_a7559_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
+       "      <td id=\"T_a7559_row1_col3\" class=\"data row1 col3\" >0.120000</td>\n",
+       "      <td id=\"T_a7559_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
+       "      <td id=\"T_a7559_row1_col5\" class=\"data row1 col5\" >0.912916</td>\n",
+       "      <td id=\"T_a7559_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
+       "      <td id=\"T_a7559_row1_col7\" class=\"data row1 col7\" >0.118812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7559_level0_row2\" class=\"row_heading level0 row2\" >random_forest</th>\n",
+       "      <td id=\"T_a7559_row2_col0\" class=\"data row2 col0\" >0.228869</td>\n",
+       "      <td id=\"T_a7559_row2_col1\" class=\"data row2 col1\" >0.135135</td>\n",
+       "      <td id=\"T_a7559_row2_col2\" class=\"data row2 col2\" >0.884422</td>\n",
+       "      <td id=\"T_a7559_row2_col3\" class=\"data row2 col3\" >0.500000</td>\n",
+       "      <td id=\"T_a7559_row2_col4\" class=\"data row2 col4\" >0.849315</td>\n",
+       "      <td id=\"T_a7559_row2_col5\" class=\"data row2 col5\" >0.818982</td>\n",
+       "      <td id=\"T_a7559_row2_col6\" class=\"data row2 col6\" >0.363636</td>\n",
+       "      <td id=\"T_a7559_row2_col7\" class=\"data row2 col7\" >0.212766</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x23c21f18560>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
+    "    [\n",
+    "        \"Precision_train\",\n",
+    "        \"Precision_test\",\n",
+    "        \"Recall_train\",\n",
+    "        \"Recall_test\",\n",
+    "        \"Accuracy_train\",\n",
+    "        \"Accuracy_test\",\n",
+    "        \"F1_train\",\n",
+    "        \"F1_test\",\n",
+    "    ]\n",
+    "]\n",
+    "class_metrics.sort_values(\n",
+    "    by=\"Accuracy_test\", ascending=False\n",
+    ").style.background_gradient(\n",
+    "    cmap=\"plasma\",\n",
+    "    low=0.3,\n",
+    "    high=1,\n",
+    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
+    ").background_gradient(\n",
+    "    cmap=\"viridis\",\n",
+    "    low=1,\n",
+    "    high=0.3,\n",
+    "    subset=[\n",
+    "        \"Precision_train\",\n",
+    "        \"Precision_test\",\n",
+    "        \"Recall_train\",\n",
+    "        \"Recall_test\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Краткий анализ метрик:\n",
+    "\n",
+    "1. MLP (многослойный перцептрон)\n",
+    "\n",
+    "   - Precision (точность) на обучении: 0.40, на тесте: 0.20\n",
+    "\n",
+    "   - Recall (полнота) на обучении: 0.02, на тесте: 0.02\n",
+    "\n",
+    "   -  Accuracy (точность) на обучении: 0.95, на тесте: 0.95\n",
+    "\n",
+    "   - F1-метрика на обучении: 0.038, на тесте: 0.037\n",
+    "\n",
+    "   - Вывод: высокая точность на обучении и тесте указывает на хорошую способность модели правильно определять общий класс. Однако низкие значения precision, recall и F1-метрики говорят о сильном смещении: модель плохо справляется с выявлением положительных примеров.\n",
+    "\n",
+    "2. KNN (Метод K-ближайших соседей)\n",
+    "\n",
+    "   - Precision на обучении: 1.0, на тесте: 0.118\n",
+    "\n",
+    "   - Recall на обучении: 1.0, на тесте: 0.12\n",
+    "\n",
+    "   - Accuracy на обучении: 1.0, на тесте: 0.91\n",
+    "\n",
+    "   - F1-метрика на обучении: 1.0, на тесте: 0.119\n",
+    "\n",
+    "   - Вывод: модель показывает явное переобучение. Она идеально предсказывает на обучающем наборе, но значительно теряет точность на тестовых данных.\n",
+    "\n",
+    "3. Random Forest (Случайный лес)\n",
+    "\n",
+    "   - Precision на обучении: 0.229, на тесте: 0.135\n",
+    "\n",
+    "   - Recall на обучении: 0.88, на тесте: 0.50\n",
+    "\n",
+    "   - Accuracy на обучении: 0.85, на тесте: 0.82\n",
+    "\n",
+    "   - F1-метрика на обучении: 0.364, на тесте: 0.213\n",
+    "\n",
+    "   - Вывод: модель по сравнению с остальными вариантами показывает сбалансированные значения метрик, но их сложно назвать хорошими. Так, precision остается достаточно низким, что указывает на необходимость улучшения способности к идентификации положительных примеров.\n",
+    "\n",
+    "4. Сравнение с ориентиром.\n",
+    "\n",
+    "   - Baseline Accuracy: 0.52\n",
+    "   - Baseline Precision: 0.058\n",
+    "   - Baseline Recall: 0.58\n",
+    "   - Baseline F1 Score: 0.106\n",
+    "\n",
+    "- Accuracy: все модели (особенно MLP и KNN) значительно превосходят базовую модель по точности. Random Forest также превосходит базовую модель, но не так явно.\n",
+    "\n",
+    "- Precision: все модели лучше, чем базовая модель, хотя точность остается низкой. Особенно низкие значения у KNN и Random Forest.\n",
+    "\n",
+    "- Recall: базовая модель показывает лучший recall, чем MLP и KNN. Это указывает на то, что обе модели (особенно MLP) с трудом находят положительные примеры. Random Forest лучше справляется с этой задачей.\n",
+    "\n",
+    "- F1 Score: Random Forest показывает наилучшую F1-метрику, указывая на баланс между precision и recall, но она все еще значительно ниже желаемого уровня.\n",
+    "\n",
+    "Выводы о смещении и дисперсии:\n",
+    "\n",
+    "- MLP: модель сильно смещена, поскольку плохо распознает положительные примеры, несмотря на высокую общую точность.\n",
+    "\n",
+    "- KNN: высокая дисперсия, модель сильно переобучена на обучающем наборе и плохо обобщает на тестовом.\n",
+    "\n",
+    "- Random Forest: наиболее сбалансированная модель с умеренным смещением и дисперсией. Она показывает лучший баланс между precision и recall, хотя precision остается невысоким.\n",
+    "\n",
+    "## Заключение:\n",
+    "### Самой качественной моделью в данном случае можно назвать Random Forest, так как она показывает лучший баланс между различными метриками, но при этом и данная модель далека от идеала."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Регрессия\n",
+    "Разделим набор данных на на обучающую и тестовые выборки (80/20). Целевой признак - avg_glucose_level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'X_train'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>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>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13276</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>22.6</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21346</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>children</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59178</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>children</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1534</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>26.1</td>\n",
+       "      <td>smokes</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30463</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41935</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>33.9</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68483</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>41.2</td>\n",
+       "      <td>formerly smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38617</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>29.9</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46527</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>41.9</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4088 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+       "id                                                                             \n",
+       "13276  Female  38.0             0              0          Yes        Private   \n",
+       "21346  Female  12.0             0              0           No       children   \n",
+       "59178  Female   7.0             0              0           No       children   \n",
+       "1679     Male  35.0             0              0          Yes        Private   \n",
+       "1534   Female  61.0             0              0          Yes        Private   \n",
+       "...       ...   ...           ...            ...          ...            ...   \n",
+       "30463    Male  29.0             0              0           No        Private   \n",
+       "41935    Male  34.0             0              0           No        Private   \n",
+       "68483  Female  60.0             0              0          Yes        Private   \n",
+       "38617    Male  28.0             0              0          Yes  Self-employed   \n",
+       "46527    Male  53.0             1              1          Yes       Govt_job   \n",
+       "\n",
+       "      Residence_type   bmi   smoking_status  stroke  \n",
+       "id                                                   \n",
+       "13276          Urban  22.6          Unknown       0  \n",
+       "21346          Rural  17.8          Unknown       0  \n",
+       "59178          Urban  22.3          Unknown       0  \n",
+       "1679           Rural   NaN  formerly smoked       0  \n",
+       "1534           Rural  26.1           smokes       0  \n",
+       "...              ...   ...              ...     ...  \n",
+       "30463          Urban  29.4  formerly smoked       0  \n",
+       "41935          Rural  33.9     never smoked       0  \n",
+       "68483          Urban  41.2  formerly smoked       0  \n",
+       "38617          Urban  29.9     never smoked       0  \n",
+       "46527          Rural  41.9     never smoked       0  \n",
+       "\n",
+       "[4088 rows x 10 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_train'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "id\n",
+       "13276     71.06\n",
+       "21346     70.13\n",
+       "59178     86.75\n",
+       "1679      77.48\n",
+       "1534      99.35\n",
+       "          ...  \n",
+       "30463     82.93\n",
+       "41935    125.29\n",
+       "68483     65.38\n",
+       "38617     73.98\n",
+       "46527    109.51\n",
+       "Name: avg_glucose_level, Length: 4088, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'X_test'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>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>bmi</th>\n",
+       "      <th>smoking_status</th>\n",
+       "      <th>stroke</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8385</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>35.9</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>937</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>children</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3494</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>26.7</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23850</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>33.1</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31156</th>\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>29.8</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71010</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39518</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7780</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Self-employed</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>never smoked</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56137</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>36.3</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33175</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Govt_job</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>28.5</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1022 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
+       "id                                                                             \n",
+       "8385     Male  37.0             0              0          Yes        Private   \n",
+       "937      Male   7.0             0              0           No       children   \n",
+       "3494   Female  80.0             0              0          Yes        Private   \n",
+       "23850    Male  66.0             0              0          Yes        Private   \n",
+       "31156  Female  49.0             0              0          Yes        Private   \n",
+       "...       ...   ...           ...            ...          ...            ...   \n",
+       "71010  Female  80.0             0              0           No  Self-employed   \n",
+       "39518  Female  20.0             0              0           No        Private   \n",
+       "7780     Male  51.0             0              0          Yes  Self-employed   \n",
+       "56137  Female  62.0             0              0          Yes        Private   \n",
+       "33175  Female  57.0             0              0          Yes       Govt_job   \n",
+       "\n",
+       "      Residence_type   bmi smoking_status  stroke  \n",
+       "id                                                 \n",
+       "8385           Urban  35.9        Unknown       0  \n",
+       "937            Urban   NaN        Unknown       0  \n",
+       "3494           Rural  26.7        Unknown       0  \n",
+       "23850          Urban  33.1   never smoked       0  \n",
+       "31156          Urban  29.8   never smoked       0  \n",
+       "...              ...   ...            ...     ...  \n",
+       "71010          Urban  22.8   never smoked       0  \n",
+       "39518          Rural  20.7   never smoked       0  \n",
+       "7780           Urban  30.7   never smoked       0  \n",
+       "56137          Urban  36.3        Unknown       0  \n",
+       "33175          Urban  28.5        Unknown       1  \n",
+       "\n",
+       "[1022 rows x 10 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_test'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "id\n",
+       "8385      90.78\n",
+       "937       87.94\n",
+       "3494     102.90\n",
+       "23850    103.01\n",
+       "31156    105.99\n",
+       "          ...  \n",
+       "71010     57.57\n",
+       "39518     78.94\n",
+       "7780      75.73\n",
+       "56137     88.32\n",
+       "33175    110.52\n",
+       "Name: avg_glucose_level, Length: 1022, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "features = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'bmi', 'smoking_status', 'stroke']\n",
+    "target = 'avg_glucose_level'\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=random_state)\n",
+    "\n",
+    "display(\"X_train\", X_train)\n",
+    "display(\"y_train\", y_train)\n",
+    "\n",
+    "display(\"X_test\", X_test)\n",
+    "display(\"y_test\", y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выберем ориентир для задачи регрессии. Для этого применим алгоритм правила нуля, т.е. в каждом случае в качестве предсказания выберем среднее значение из области значений целевого признака."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline RMSE: 44.12711275645952\n",
+      "Baseline RMAE: 5.662154850745081\n",
+      "Baseline R2: -0.0010729515309222393\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "\n",
+    "# Базовое предсказание: среднее значение по y_train\n",
+    "baseline_predictions = [y_train.mean()] * len(y_test)\n",
+    "\n",
+    "# Вычисление метрик качества для ориентира\n",
+    "baseline_rmse = math.sqrt(\n",
+    "        mean_squared_error(y_test, baseline_predictions)\n",
+    "    )\n",
+    "baseline_rmae = math.sqrt(\n",
+    "        mean_absolute_error(y_test, baseline_predictions)\n",
+    "    )\n",
+    "baseline_r2 = r2_score(y_test, baseline_predictions)\n",
+    "\n",
+    "print('Baseline RMSE:', baseline_rmse)\n",
+    "print('Baseline RMAE:', baseline_rmae)\n",
+    "print('Baseline R2:', baseline_r2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Были использованы следующие метрики:\n",
+    "\n",
+    "- RMSE: корень из MSE. MSE (Mean Squared Error) — среднеквадратическая ошибка, квадрат отклонения между предсказанными и истинными значениями. MSE чувствительна к большим ошибкам, так как отклонения возводятся в квадрат. RMSE также штрафует за большие ошибки, но в отличие от MSE, масштаб ошибки аналогичен исходным данным, что облегчает интерпретацию. Это делает RMSE хорошим выбором для многих практических задач, где важна интерпретируемость результата.\n",
+    "- RMAE: корень из MAE. MAE (Mean Absolute Error) — средняя абсолютная ошибка. Она показывает среднее отклонение предсказаний от истинных значений. MAE менее чувствительна к выбросам по сравнению с MSE и RMSE. Это делает её предпочтительным вариантом, когда выбросы присутствуют в данных, но не должны сильно влиять на общую производительность модели.\n",
+    "- R2 (коэффициент детерминации) : R2 измеряет, какая доля вариативности зависимой переменной объясняется независимыми переменными в модели. Это хороший способ оценить адекватность модели: близость к 1 говорит о хорошем объяснении данных моделью. R2 лучше всего подходит для сравнения моделей с одинаковыми данными.\n",
+    "\n",
+    "Таким образом, результаты этих метрик для базового ориентира позволят оценить, насколько лучше (или хуже) модель по сравнению с простым предсказанием среднего значения.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Сформируем конвейер для регрессии"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_drop = []\n",
+    "columns_not_to_modify = [\"hypertension\", \"heart_disease\", \"stroke\", \"avg_glucose_level\"]\n",
+    "\n",
+    "num_columns = [\n",
+    "    column\n",
+    "    for column in df.columns\n",
+    "    if column not in columns_to_drop\n",
+    "    and column not in columns_not_to_modify\n",
+    "    and df[column].dtype != \"object\"\n",
+    "]\n",
+    "\n",
+    "cat_columns = [\n",
+    "    column\n",
+    "    for column in df.columns\n",
+    "    if column not in columns_to_drop\n",
+    "    and column not in columns_not_to_modify\n",
+    "    and df[column].dtype == \"object\"\n",
+    "]\n",
+    "\n",
+    "num_imputer = SimpleImputer(strategy=\"median\")\n",
+    "num_scaler = StandardScaler()\n",
+    "preprocessing_num = Pipeline(\n",
+    "    [\n",
+    "        (\"imputer\", num_imputer),\n",
+    "        (\"scaler\", num_scaler),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
+    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
+    "preprocessing_cat = Pipeline(\n",
+    "    [\n",
+    "        (\"imputer\", cat_imputer),\n",
+    "        (\"encoder\", cat_encoder),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "features_preprocessing = ColumnTransformer(\n",
+    "    verbose_feature_names_out=False,\n",
+    "    transformers=[\n",
+    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
+    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
+    "    ],\n",
+    "    remainder=\"passthrough\"\n",
+    ")\n",
+    "\n",
+    "drop_columns = ColumnTransformer(\n",
+    "    verbose_feature_names_out=False,\n",
+    "    transformers=[\n",
+    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
+    "    ],\n",
+    "    remainder=\"passthrough\",\n",
+    ")\n",
+    "\n",
+    "pipeline_end_reg = Pipeline(\n",
+    "    [\n",
+    "        (\"features_preprocessing\", features_preprocessing),\n",
+    "        (\"drop_columns\", drop_columns),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь проверим работу конвейера:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessing_result = pipeline_end_reg.fit_transform(X_train)\n",
+    "preprocessed_df = pd.DataFrame(\n",
+    "    preprocessing_result,\n",
+    "    columns=pipeline_end_reg.get_feature_names_out(),\n",
+    ")\n",
+    "\n",
+    "preprocessed_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Подберем оптимальные гиперпараметры для каждой из выбранных моделей методом поиска по сетке и сформируем их набор.\n",
+    "- knn - k-ближайших соседей\n",
+    "- random_forest - метод случайного леса (набор деревьев решений)\n",
+    "- mlp - многослойный персептрон"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Лучшие параметры для knn: {'n_jobs': -1, 'n_neighbors': 30, 'weights': 'uniform'}\n",
+      "Лучшие параметры для random_forest: {'criterion': 'squared_error', 'max_depth': 7, 'max_features': 'sqrt', 'n_estimators': 250, 'n_jobs': -1, 'random_state': 9}\n",
+      "Лучшие параметры для mlp: {'alpha': np.float64(1e-06), 'early_stopping': False, 'hidden_layer_sizes': np.int64(13), 'max_iter': 1000, 'random_state': 9, 'solver': 'adam'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Словарь с вариантами гиперпараметров для каждой модели\n",
+    "param_grids = {\n",
+    "    \"knn\": {\n",
+    "        \"n_neighbors\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], \n",
+    "        \"weights\": ['uniform', 'distance'],\n",
+    "        \"n_jobs\": [-1]\n",
+    "    },\n",
+    "    \"random_forest\": {\n",
+    "        \"n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
+    "        \"max_features\": [\"sqrt\", \"log2\", 2],\n",
+    "        \"max_depth\": [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "        \"criterion\": [\"squared_error\", \"absolute_error\", \"poisson\"],\n",
+    "        \"random_state\": [random_state],\n",
+    "        \"n_jobs\": [-1]\n",
+    "    },\n",
+    "    \"mlp\": {\n",
+    "        \"solver\": ['adam'], \n",
+    "        \"max_iter\": [1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000], \n",
+    "        \"alpha\": 10.0 ** -np.arange(1, 10), \n",
+    "        \"hidden_layer_sizes\":np.arange(10, 15), \n",
+    "        \"early_stopping\": [True, False],\n",
+    "        \"random_state\": [random_state]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Создаем экземпляры моделей\n",
+    "models = {\n",
+    "    \"knn\": neighbors.KNeighborsRegressor(),\n",
+    "    \"random_forest\": ensemble.RandomForestRegressor(),\n",
+    "    \"mlp\": neural_network.MLPRegressor()\n",
+    "}\n",
+    "\n",
+    "# Словарь для хранения моделей с их лучшими параметрами\n",
+    "class_models = {}\n",
+    "\n",
+    "# Выполнение поиска по сетке для каждой модели\n",
+    "for model_name, model in models.items():\n",
+    "    # Создаем GridSearchCV для текущей модели\n",
+    "    gs_optimizer = GridSearchCV(estimator=model, param_grid=param_grids[model_name], scoring='neg_mean_squared_error', n_jobs=-1)\n",
+    "    \n",
+    "    # Обучаем GridSearchCV\n",
+    "    gs_optimizer.fit(preprocessed_df, y_train.values.ravel())\n",
+    "    \n",
+    "    # Получаем лучшие параметры\n",
+    "    best_params = gs_optimizer.best_params_\n",
+    "    print(f\"Лучшие параметры для {model_name}: {best_params}\")\n",
+    "    \n",
+    "    class_models[model_name] = {\n",
+    "        \"model\": model.set_params(**best_params)  # Настраиваем модель с лучшими параметрами\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Далее обучим модели и оценим их качество."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: knn\n",
+      "Model: random_forest\n",
+      "Model: mlp\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model_name in class_models.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    \n",
+    "    model = class_models[model_name][\"model\"]\n",
+    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end_reg), (\"model\", model)])\n",
+    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
+    "\n",
+    "    y_train_pred = model_pipeline.predict(X_train)\n",
+    "    y_test_pred = model_pipeline.predict(X_test)\n",
+    "\n",
+    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
+    "    class_models[model_name][\"train_preds\"] = y_train_pred\n",
+    "    class_models[model_name][\"preds\"] = y_test_pred\n",
+    "    \n",
+    "    class_models[model_name][\"RMSE_train\"] = math.sqrt(\n",
+    "        mean_squared_error(y_train, y_train_pred)\n",
+    "    )\n",
+    "    class_models[model_name][\"RMSE_test\"] = math.sqrt(\n",
+    "        mean_squared_error(y_test, y_test_pred)\n",
+    "    )\n",
+    "    class_models[model_name][\"RMAE_test\"] = math.sqrt(\n",
+    "        mean_absolute_error(y_test, y_test_pred)\n",
+    "    )\n",
+    "    class_models[model_name][\"R2_test\"] = r2_score(y_test, y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "RMSE, RMAE, R2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_0650d_row0_col0, #T_0650d_row2_col1 {\n",
+       "  background-color: #a8db34;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_0650d_row0_col1, #T_0650d_row1_col0 {\n",
+       "  background-color: #26818e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row0_col2, #T_0650d_row2_col3 {\n",
+       "  background-color: #4e02a2;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row0_col3, #T_0650d_row2_col2 {\n",
+       "  background-color: #da5a6a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row1_col1 {\n",
+       "  background-color: #20938c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row1_col2 {\n",
+       "  background-color: #b42e8d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row1_col3 {\n",
+       "  background-color: #c8437b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_0650d_row2_col0 {\n",
+       "  background-color: #73d056;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_0650d\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_0650d_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
+       "      <th id=\"T_0650d_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
+       "      <th id=\"T_0650d_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
+       "      <th id=\"T_0650d_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0650d_level0_row0\" class=\"row_heading level0 row0\" >mlp</th>\n",
+       "      <td id=\"T_0650d_row0_col0\" class=\"data row0 col0\" >42.583378</td>\n",
+       "      <td id=\"T_0650d_row0_col1\" class=\"data row0 col1\" >40.922194</td>\n",
+       "      <td id=\"T_0650d_row0_col2\" class=\"data row0 col2\" >5.533579</td>\n",
+       "      <td id=\"T_0650d_row0_col3\" class=\"data row0 col3\" >0.139061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0650d_level0_row1\" class=\"row_heading level0 row1\" >random_forest</th>\n",
+       "      <td id=\"T_0650d_row1_col0\" class=\"data row1 col0\" >40.324186</td>\n",
+       "      <td id=\"T_0650d_row1_col1\" class=\"data row1 col1\" >41.085298</td>\n",
+       "      <td id=\"T_0650d_row1_col2\" class=\"data row1 col2\" >5.544678</td>\n",
+       "      <td id=\"T_0650d_row1_col3\" class=\"data row1 col3\" >0.132184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0650d_level0_row2\" class=\"row_heading level0 row2\" >knn</th>\n",
+       "      <td id=\"T_0650d_row2_col0\" class=\"data row2 col0\" >42.164413</td>\n",
+       "      <td id=\"T_0650d_row2_col1\" class=\"data row2 col1\" >41.826505</td>\n",
+       "      <td id=\"T_0650d_row2_col2\" class=\"data row2 col2\" >5.550755</td>\n",
+       "      <td id=\"T_0650d_row2_col3\" class=\"data row2 col3\" >0.100590</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x23c2371daf0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reg_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
+    "    [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n",
+    "]\n",
+    "reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n",
+    "    cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n",
+    ").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MLP\n",
+    "- Разница между тренировочной и тестовой ошибкой небольшая, что говорит о хорошей обобщающей способности модели.\n",
+    "- MLP объясняет только 13.9% дисперсии целевой переменной. Это говорит о том, что модель работает, но не идеально.\n",
+    "### Random_Forest\n",
+    "- Тренировочная ошибка меньше тестовой, что типично для Random Forest. Однако ошибка на тестовой выборке немного выше, что говорит о небольшой тенденции к переобучению.\n",
+    "- Random Forest объясняет примерно столько же дисперсии, как MLP (13.2%), но чуть хуже справляется на тестовой выборке.\n",
+    "### KNN\n",
+    "- Ошибка на тренировочной и тестовой выборке близка, что говорит о том, что модель не переобучилась.\n",
+    "- KNN объясняет наименьшую долю дисперсии целевой переменной (10.1%), что указывает на то, что модель хуже подходит для этой задачи по сравнению с MLP и Random Forest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Графики:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: knn\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: random_forest\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: mlp\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создаем графики для всех моделей\n",
+    "for model_name, model_data in class_models.items():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    y_pred = model_data[\"preds\"]\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.scatter(y_test, y_pred, alpha=0.5)\n",
+    "    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
+    "    plt.xlabel('Фактический уровень глюкозы')\n",
+    "    plt.ylabel('Прогнозируемый уровень глюкозы')\n",
+    "    plt.title(f\"Model: {model_name}\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "На представленных графиках можно заметить, что модели в целом не демонстрируют высокого качества. Визуализация их предсказаний показывает сильное рассеивание вокруг идеальной линии y = x, что указывает на значительные отклонения предсказаний от фактических значений.\n",
+    "\n",
+    "Тем не менее ориентир, хоть возможно и не столь значительно, каждая из моделей превосходит по всем показателям. Особенно заметное улучшение в R2, которая переходит из отрицательного значения в положительное, что говорит о том, что модели хотя бы частично объясняют дисперсию данных.\n",
+    "\n",
+    "Кроме того, можно сказать, что все модели имеет умеренную дисперсию и не сильно подвержены переобучению, потому что разница между RMSE на обучении и тесте незначительна.\n",
+    "\n",
+    "### Итоговые выводы:\n",
+    "\n",
+    "- Наиболее качественная модель: MLP, так как она показывает наименьшее значение RMSE и наибольшее значение R2, что указывает на лучшую точность и объяснение дисперсии целевой переменной.\n",
+    "\n",
+    "-  Random Forest: Близок по производительности к MLP, с чуть большим RMSE, но является более устойчивой моделью с небольшими отклонениями между обучением и тестом.\n",
+    "\n",
+    "-   KNN: Худшая модель, демонстрирующая наибольшие ошибки и низкое R2, что указывает на необходимость улучшения или использования другой модели для данной задачи.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}