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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лабораторная 3\n",
+    "\n",
+    "Датасет: Информация об онлайн обучении учеников"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Education Level', 'Institution Type', 'Gender', 'Age', 'Device',\n",
+      "       'IT Student', 'Location', 'Financial Condition', 'Internet Type',\n",
+      "       'Network Type', 'Flexibility Level'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import featuretools as ft\n",
+    "import time\n",
+    "import math\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from sklearn.model_selection import train_test_split\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",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "df = pd.read_csv(\"..\\\\static\\\\csv\\\\students_adaptability_level_online_education.csv\")\n",
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Столбцы:\n",
+    "\n",
+    "Education Level - уровень образования\\\n",
+    "Institution Type - тип учреждения\\\n",
+    "Gender - пол\\\n",
+    "Age - возраст\\\n",
+    "Device - устройство\\\n",
+    "IT Student - ученик IT направления или нет\\\n",
+    "Location - локация\\\n",
+    "Financial Condition - финансовое состояние\\\n",
+    "Internet Type - тип доступа к сети\\\n",
+    "Network Type - уровень сети\\\n",
+    "Flexibility Level - уровень приспособления"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1205 entries, 0 to 1204\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column               Non-Null Count  Dtype \n",
+      "---  ------               --------------  ----- \n",
+      " 0   Education Level      1205 non-null   object\n",
+      " 1   Institution Type     1205 non-null   object\n",
+      " 2   Gender               1205 non-null   object\n",
+      " 3   Age                  1205 non-null   int64 \n",
+      " 4   Device               1205 non-null   object\n",
+      " 5   IT Student           1205 non-null   object\n",
+      " 6   Location             1205 non-null   object\n",
+      " 7   Financial Condition  1205 non-null   object\n",
+      " 8   Internet Type        1205 non-null   object\n",
+      " 9   Network Type         1205 non-null   object\n",
+      " 10  Flexibility Level    1205 non-null   object\n",
+      "dtypes: int64(1), object(10)\n",
+      "memory usage: 103.7+ KB\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>Education Level</th>\n",
+       "      <th>Institution Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Device</th>\n",
+       "      <th>IT Student</th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Financial Condition</th>\n",
+       "      <th>Internet Type</th>\n",
+       "      <th>Network Type</th>\n",
+       "      <th>Flexibility Level</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>University</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Tab</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Town</td>\n",
+       "      <td>Mid</td>\n",
+       "      <td>Wifi</td>\n",
+       "      <td>4G</td>\n",
+       "      <td>Moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>University</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Mobile</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Town</td>\n",
+       "      <td>Mid</td>\n",
+       "      <td>Mobile Data</td>\n",
+       "      <td>4G</td>\n",
+       "      <td>Moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>Public</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Mobile</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Town</td>\n",
+       "      <td>Mid</td>\n",
+       "      <td>Wifi</td>\n",
+       "      <td>4G</td>\n",
+       "      <td>Moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>School</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Mobile</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Town</td>\n",
+       "      <td>Mid</td>\n",
+       "      <td>Mobile Data</td>\n",
+       "      <td>4G</td>\n",
+       "      <td>Moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>School</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Mobile</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Town</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>Mobile Data</td>\n",
+       "      <td>3G</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Education Level Institution Type  Gender  Age  Device IT Student Location  \\\n",
+       "0      University          Private    Male   23     Tab         No     Town   \n",
+       "1      University          Private  Female   23  Mobile         No     Town   \n",
+       "2         College           Public  Female   18  Mobile         No     Town   \n",
+       "3          School          Private  Female   11  Mobile         No     Town   \n",
+       "4          School          Private  Female   18  Mobile         No     Town   \n",
+       "\n",
+       "  Financial Condition Internet Type Network Type Flexibility Level  \n",
+       "0                 Mid          Wifi           4G          Moderate  \n",
+       "1                 Mid   Mobile Data           4G          Moderate  \n",
+       "2                 Mid          Wifi           4G          Moderate  \n",
+       "3                 Mid   Mobile Data           4G          Moderate  \n",
+       "4                Poor   Mobile Data           3G               Low  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.info()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Примеры бизнес-целей для датасета:\n",
+    "1. Улучшение доступа к онлайн-образованию для учеников с низким уровнем финансового обеспечения.\n",
+    "2. Повышение удовлетворенности учеников онлайн-обучением на основе их устройств, типу соединения, местоположения."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Цели технического проекта:\n",
+    "\n",
+    "1. Провести анализ зависимости учеников от уровня интернет-соединения и устройств\n",
+    "2. Провести анализ влияния различных факторов (тип устройства, интернет-соединение, финансовое положение) на уровень приспособленности."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверяем на выбросы."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пустые значения по столбцам:\n",
+      "Education Level        0\n",
+      "Institution Type       0\n",
+      "Gender                 0\n",
+      "Age                    0\n",
+      "Device                 0\n",
+      "IT Student             0\n",
+      "Location               0\n",
+      "Financial Condition    0\n",
+      "Internet Type          0\n",
+      "Network Type           0\n",
+      "Flexibility Level      0\n",
+      "dtype: int64\n",
+      "\n",
+      "Количество дубликатов: 980\n",
+      "\n",
+      "Статистический обзор данных:\n",
+      "\n",
+      "Коэффициент асимметрии для столбца 'Age': 0.024342017300169792\n"
+     ]
+    }
+   ],
+   "source": [
+    "null_values = df.isnull().sum()\n",
+    "print(\"Пустые значения по столбцам:\")\n",
+    "print(null_values)\n",
+    "\n",
+    "duplicates = df.duplicated().sum()\n",
+    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
+    "\n",
+    "print(\"\\nСтатистический обзор данных:\")\n",
+    "df.describe()\n",
+    "\n",
+    "for column in df.select_dtypes(include=[np.number]).columns:\n",
+    "    skewness = df[column].skew()\n",
+    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {skewness}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выбросы незначительны, дубликаты есть. Удаляем дубликаты и очищаем от шумов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Шумы в датасете:\n",
+      "Empty DataFrame\n",
+      "Columns: [Education Level, Institution Type, Gender, Age, Device, IT Student, Location, Financial Condition, Internet Type, Network Type, Flexibility Level]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df = df.drop_duplicates()\n",
+    "\n",
+    "Q1 = df[\"Age\"].quantile(0.25)\n",
+    "Q3 = df[\"Age\"].quantile(0.75)\n",
+    "\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "threshold = 1.5 * IQR\n",
+    "lower_bound = Q1 - threshold\n",
+    "upper_bound = Q3 + threshold\n",
+    "\n",
+    "outliers = (df[\"Age\"] < lower_bound) | (df[\"Age\"] > upper_bound)\n",
+    "\n",
+    "print(\"Шумы в датасете:\")\n",
+    "print(df[outliers])\n",
+    "\n",
+    "median_score = df[\"Age\"].median()\n",
+    "df.loc[outliers, \"Age\"] = median_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Преобразуем строковые значение в столбце \"Уровень приспособления\" в числовые значения. Это понадобится для расчёта качества набора признаков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_flexibility_to_int = {'Low': 0, 'Moderate': 1, 'High': 2}\n",
+    "\n",
+    "df['Flexibility Level'] = df['Flexibility Level'].map(map_flexibility_to_int).astype('int32')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Шумов в датасете нет. Разбиваем датасет на три выборки: обучающую, контрольную и тестовую."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (723, 10)\n",
+      "Размер контрольной выборки: (241, 10)\n",
+      "Размер тестовой выборки: (241, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=['Flexibility Level'])\n",
+    "Y = df['Flexibility Level']\n",
+    "\n",
+    "X_train_df, X_test_df, Y_train_df, Y_test_df = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "X_train_df, X_val_df, Y_train_df, Y_val_df = train_test_split(X_train_df, Y_train_df, test_size=0.25, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", X_train_df.shape)\n",
+    "print(\"Размер контрольной выборки:\",X_val_df.shape)\n",
+    "print(\"Размер тестовой выборки:\", X_test_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка сбалансированности данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение классов в обучающей выборке:\n",
+      "Flexibility Level\n",
+      "1    0.531120\n",
+      "0    0.385892\n",
+      "2    0.082988\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в контрольной выборке:\n",
+      "Flexibility Level\n",
+      "1    0.522822\n",
+      "0    0.406639\n",
+      "2    0.070539\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в тестовой выборке:\n",
+      "Flexibility Level\n",
+      "1    0.477178\n",
+      "0    0.427386\n",
+      "2    0.095436\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": [
+    "\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_df, Y_val_df, Y_test_df, 'Flexibility Level')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выполним оверсемплинг для балансировки."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение классов в обучающей выборке:\n",
+      "Flexibility Level\n",
+      "2    0.333333\n",
+      "0    0.333333\n",
+      "1    0.333333\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в контрольной выборке:\n",
+      "Flexibility Level\n",
+      "1    0.333333\n",
+      "0    0.333333\n",
+      "2    0.333333\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Распределение классов в тестовой выборке:\n",
+      "Flexibility Level\n",
+      "1    0.477178\n",
+      "0    0.427386\n",
+      "2    0.095436\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5OVnbtm1TXFycnnzySc2cOdNhzJEjRzINlrPywgsvKDIyUi1btnT60UlJmjJlijp06KDGjRtfc1sdOnRw+kHKl156yf58uVKfPn3UuXNnpaena/v27Ro5cqQ6duyorVu3Zrn9Y8eOad26dZKkbdu2yWazOXxQlZ02bdpo6NChki6foT5mzBi1aNFC69atczjj+XrqunI9V1m1apVee+01rV27VoMHD1bbtm0dvnGwfPlytW/fXk2bNtV7772nUqVKqWDBgoqPj9dnn33mtL0buY8BAADyG4JuAABw25o3b55atGjhdFbqyZMnHcKjChUqaPXq1bp48WKe/KBihowztjMYY/TXX3+pVq1a9v1KUuHChRUREXHN7f3xxx+6ePFituF+xnaNMSpfvrz9zNHsbNu2TW5ubpmeoXrlNhcvXqwmTZrkKBAMCAhwOqbsfjBy2LBhqlOnjh555JEs9//HH3+oVatWuZ5O5o477nCoqXnz5ipdurTi4+Oz/EHRDH///bdSUlIczur+888/JUmhoaGSLj/f7rzzTn311VcONWacUXy1+vXrS5Luu+8+lSxZUj169NCLL76oatWqSbo81cpff/2ltm3b5uj45s+fr4SEhEynScnQoEED7dmzR5s2bdKZM2ckSZ988ok+/fRTp7Fly5Z1egzHjx+fadBdqVIl+9jIyEidO3dOL774YrY/9Nm/f3+dOXNGcXFxGjZsmMaPH68hQ4bk6FhLlSrlUFuVKlXUuHFjzZ8/X926dctVXRln3e/cudPhDGybzaZdu3apbt26kv53RvvOnTvtU+dk2Llzp9MHEzabTXv27HH4v3j1cydDr169NHz4cP3999+qXr26Bg8e7PDYfPnll/L29tZPP/3kMKVNfHx8pvfTjdzHAAAA+Q0f1wMAgNuWh4eH07y8c+fO1eHDhx2WPfTQQ0pOTtakSZOctpHTeX0z88knn9jDROlyEPrPP//ovvvukySFhYWpQoUKeuutt3T27Fmn9Y8ePepUu4eHh9q1a5ftfjt16iQPDw+NGjXKqX5jjI4dO2a/funSJX355Zdq0KBBtlNkdOnSRenp6XrllVecbrt06ZJ9+pPcSEhI0DfffKM33ngjyxC7S5cuOnz4sKZPn+502/nz55WSknLd+z1//rwkOUz/kJVLly5p6tSp9utpaWmaOnWqSpQoobCwMEmynz1/5X2+evVqJSQkXHP7ycnJTrV88803On/+vFOYmpn09HQNHz5cjz76aJbfDsjg4+Ojhg0bKiIiQhEREU7TauSFjG8rZPaNAuny/4U5c+bojTfe0H//+1917dpVL730kj0Avl45fSyzq6tVq1by8vLSu+++6/CNj1mzZikxMdH+/65evXoqWbKkpkyZ4rC/H3/8Udu3b1dUVJTTtq/822KM0aRJk1SwYEGnqUTCw8MlXZ4qZ8yYMZo5c6YWLlxov93Dw0Nubm4O0zDt27cv0w+R8vo+BgAAcDXO6AYAALetdu3aafTo0YqJiVHjxo21efNmzZo1yynY69Gjhz755BMNGTJEa9asUXh4uFJSUrR48WL169dPHTp0yNX+ixUrpnvvvVcxMTFKTEzU+PHjVbFiRfXu3VvS5bl7P/jgA913332qUaOGYmJiVKZMGR0+fFhLly5V4cKF9e233yolJUWTJ0/Wu+++q8qVK2vZsmX2fWQE5Js2bVJCQoIaNWqkChUq6NVXX9WwYcO0b98+dezYUYUKFdLevXv19ddfq0+fPnruuee0ePFivfzyy9q0aZO+/fbbbI+lWbNm6tu3r+Li4rRx40a1adNGBQsW1K5duzR37lxNmDBBDz/8cK7up4ULF6p169bZntX+2GOP6YsvvtCTTz6ppUuXqkmTJkpPT9eOHTv0xRdf6Keffrrmme6JiYn2qUGSk5M1depUFShQ4JofHEj/Cx737dunypUra86cOdq4caOmTZtm/xZAu3bt9NVXX+nBBx9UVFSU9u7dqylTpqh69eoOH2SMHj1ahw8fVs2aNeXl5aUNGzYoPj5etWrVUq1atXTu3DnFxsbqvffeU+PGjdWmTZtr1nfo0CF5enraf+j0Vtu5c6cWLFggm82mbdu2aezYsapfv779h2GvlJSUpKeeekotWrSwT40yadIkLV26VD179tSKFSuuOb3Gnj177I/l4cOHNWnSJBUuXNgpOL6euooVK6aXXnpJL7/8siIjI9WhQwft2bNHkyZNUu3atfXEE09IujzH/ZgxYxQTE6NmzZqpW7duSkxM1IQJExQaGqrBgwc7bNfb21sLFixQdHS0GjZsqB9//FHff/+9hg8fnu089n369NFnn32mJ598Ulu2bJGvr6+ioqI0btw4tW3bVo8++qiSkpI0efJkVaxYUZs2bcrT+xgAACDfMQAAAP8y8fHxRpJZu3ZttuMuXLhgnn32WVOqVCnj4+NjmjRpYhISEkyzZs1Ms2bNHMaeO3fOvPjii6Z8+fKmYMGCJigoyDz88MNm9+7dxhhj9u7daySZsWPHOu2nRo0aDttbunSpkWQ+//xzM2zYMFOyZEnj4+NjoqKizP79+53W//33302nTp1M8eLFjZeXlwkJCTFdunQxS5Yscdj3tS7R0dEO2/3yyy/Nvffea/z8/Iyfn5+pWrWq6d+/v9m5c6cxxpinn37aNG3a1CxYsMCpptjYWJPZS8lp06aZsLAw4+PjYwoVKmTuuusu8/zzz5u///7bPiYkJMRERUU5rdu/f3+nbUoybm5uZv369Q7LM3uM0tLSzJgxY0yNGjWMl5eXueOOO0xYWJgZNWqUOXXqlNP+rt7elfdV0aJFTZMmTcwPP/yQ7XoZ69aoUcOsW7fONGrUyHh7e5uQkBAzadIkh3E2m828/vrrJiQkxHh5eZm6deua7777zkRHR5uQkBD7uHnz5pn69eubwoULGx8fH1OxYkXz7LPPmqNHjxpjjDl06JAJDg42gwYNyvS4JJnY2Fj79ejoaCPJPPPMMw7jMv6f7N27N9vjy+yxlmT69+/vNDYqKsrhWK5+brq7u5uyZcua6Ohoc+jQoUy336lTJ1OoUCGzb98+h21/8803RpIZM2ZMtvWGhIQ47DMgIMC0adPGJCQk3FBdGSZPnmyqVq1qChYsaAIDA03fvn3NsWPHnMbNmTPH1K1b13h5eZlixYqZ7t2727edITo62vj5+Zndu3ebNm3aGF9fXxMYGGhiY2NNenq6U73x8fEO6+/cudN4e3ubwYMH25d9+OGHplKlSsbLy8tUrVrVxMfH5/l9DAAAkB+5GXMD37cFAADAdVu2bJlatGihuXPn5vos5yvt27dP5cuX1969e53m9M0wcuRI7du3L9MfIMSNad68uZKTk7VlyxZXlwKL6dmzp+bNm5fp1EQAAAC4PnwfDQAAAAAAAABgaczRDQAAYHH+/v7q3r17tj8WWatWLZUuXfoWVgUAAAAAtw5BNwAAgMUFBATYf3gvK506dbpF1QAAAADArccc3QAAAAAAAAAAS2OObgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm5AUkpKig4ePKgTJ064uhTkIR5XAACsyxij48ePa9euXa4uBQCAfyWbzabk5GTt2bPH1aUAeYKgG7etuXPnqlWrVipUqJD8/f1Vrlw5vfnmm64uCzeIxxUAAOs6c+aMXnrpJVWpUkWenp4qXry4KleurJ07d7q6NAAA/hWOHDmiQYMGKSQkRJ6enipRooSqV6+u06dPu7o04IYVcHUBQF7YunWr4uLitHTpUiUnJ6t48eJq0aKFhg8frho1ajiN/+9//6sxY8aoQ4cOmj59ugICAuTm5qbKlSu7oHrkFR5XAHC9jz76SDExMVq7dq3q1avncNv06dPVp08fdejQQV9++aU8PDxcVCXyo2PHjqlZs2Y6cOCAnn76aTVp0kSenp4qWLCgQkNDXV0eAOAqbm5uORq3dOlSNW/e/OYWgxz566+/1KJFC128eFEDBw7U3XffrQIFCsjHx0d+fn6uLg+4YQTdsLyvvvpK3bp1U7FixfT444+rfPny2rdvnz788EPNmzdPs2fP1oMPPmgf/8svv2jMmDGKi4vTf//7XxdWjrzE4woA+dvXX3+tp556SuHh4Zo9ezYhN5wMHTpU//zzjxISEjI9UQEAkL98+umnDtc/+eQTLVq0yGl5tWrVbmVZyEbfvn3l6empVatWqUyZMq4uB8hzbsYY4+oigNzavXu3atWqpXLlyunXX39ViRIl7LclJycrPDxcBw8e1KZNm3TnnXdKkh544AEdP35cK1eudFXZuAl4XAEgf8jsjO5ly5apbdu2qly5spYvX64iRYq4uErkN0lJSSpVqpSmTJmi3r17u7ocAEAuDBgwQJMnTxYxU/60fv161atXTwsXLlTr1q1dXQ5wUzBHNyxt7NixOnfunKZNm+YQcktSQECApk6dqpSUFIc5mletWqWaNWuqa9euKlasmHx8fFS/fn3Nnz/fPubs2bPy8/PTM88847TPQ4cOycPDQ3FxcZKknj17Zvp1Wjc3N40cOdJ+ff/+/erXr5+qVKkiHx8fFS9eXJ07d9a+ffsc1lu2bJnc3Ny0bNky+7K1a9eqdevWKlSokPz8/NS8eXMtX77cYb2PPvpIbm5uWrdunX1ZcnKyUx2S1K5dO6ealy9frs6dO6tcuXLy8vJScHCwBg8erPPnzzsd27x581SvXj0VKlRIbm5u9stbb73lNDazGjMuvr6+uuuuu/TBBx84jOvZs6f8/f2z3dbVx5WTxzVDUlKSHn/8cQUGBsrb21u1a9fWxx9/7DBm37599mN65513FBISIh8fHzVr1kxbtmxxqvfq+3PmzJlyd3fXG2+8YV+2adMm9ezZU3feeae8vb0VFBSkXr166dixY9keKwBY2caNG9WhQweVKlVKP/30U6Yh99y5cxUWFiYfHx8FBAToP//5jw4fPuwwJqveMG/ePIe+2bx5c4dek9klg5ubmwYMGKBZs2apSpUq8vb2VlhYmH799Ven/fz++++67777VLhwYfn7+6tVq1ZatWpVpsecVQ0fffSRw5iaNWte8/7LqPFqmfXyt956S40bN1bx4sXl4+OjsLAwzZs3z2nds2fP6tlnn9Wdd96pggULOtSYnJycbT1XH1tAQICioqKcemNWdWfIeE2Q8Tpo7dq1stlsSktLU7169eTt7a3ixYurW7duOnDggNP6P//8s8LDw+Xn56eiRYuqQ4cO2r59u8OYkSNHys3NTTt27FCXLl1UuHBhFS9eXM8884wuXLjgVO+VrysuXbqk+++/X8WKFdO2bdvsy+Pj49WyZUuVLFlSXl5eql69ut5///1s7zMAwGWpqamKjY1VxYoV7e85n3/+eaWmpjqNnTlzpho0aCBfX1/dcccdatq0qRYuXChJCg0NzbbPX9kfU1JS9Oyzzyo4OFheXl6qUqWK3nrrLacw/sr1PTw8VKZMGfXp00cnT560j0lLS9OIESMUFhamIkWKyM/PT+Hh4Vq6dKlT/RnvOcuVKycPDw/7tq/1Pvfq43N3d1dQUJAeeeQRh3545fvVrGT0wQyrVq2St7e3du/erRo1asjLy0tBQUHq27evjh8/7rT+9bw+27NnjyIjI+Xn56fSpUtr9OjRDvdxRr1XvhY6c+aMwsLCVL58ef3zzz/25Tl9PQNkhqlLYGnffvutQkNDFR4enuntTZs2VWhoqL7//nv7smPHjmnatGny9/fXwIEDVaJECc2cOVOdOnXSrFmz1K1bN/n7++vBBx/UnDlzNG7cOIevV3/++ecyxqh79+7XVevatWv122+/qWvXripbtqz27dun999/X82bN9e2bdvk6+ub6Xp//fWXmjdvLl9fXw0dOlS+vr6aPn26IiIitGjRIjVt2vS66sjK3Llzde7cOT311FMqXry41qxZo4kTJ+rQoUOaO3eufVxCQoK6dOmi2rVr64033lCRIkWUnJyswYMH53hf77zzjgICAnT69GnNmDFDvXv3VmhoqCIiInJdf04eV0k6f/68mjdvrr/++ksDBgxQ+fLlNXfuXPXs2VMnT550+nDjk08+0ZkzZ9S/f39duHBBEyZMUMuWLbV582YFBgZmWsvChQvVq1cvDRgwwGEalUWLFmnPnj2KiYlRUFCQtm7dqmnTpmnr1q1atWpVjue4AwCr2L17t9q2bSsvLy/99NNPKlWqlNOYjDPA69evr7i4OCUmJmrChAlauXKlfv/9dxUtWvS69vniiy/qiSeekCR7f+rTp0+WrxV++eUXzZkzRwMHDpSXl5fee+89tW3bVmvWrLEH0Vu3blV4eLgKFy6s559/XgULFtTUqVPVvHlz/fLLL2rYsKHTdqtWraoXX3zRoY6bbcKECWrfvr26d++utLQ0zZ49W507d9Z3332nqKgo+7ihQ4dqypQpevzxx9WkSRMVLFhQX331lb7++usc7Sfj2Iwx2r17t8aNG6f7778/00A6pzI+9B0wYIDCwsL0xhtv6OjRo3r33Xe1YsUK/f777woICJAkLV68WPfdd5/uvPNOjRw5UufPn9fEiRPVpEkTbdiwwekDgC5duig0NFRxcXFatWqV3n33XZ04cUKffPJJlvU88cQTWrZsmRYtWqTq1avbl7///vuqUaOG2rdvrwIFCujbb79Vv379ZLPZ1L9//1wfPwD829lsNrVv314rVqxQnz59VK1aNW3evFnvvPOO/vzzT4cTlEaNGqWRI0eqcePGGj16tDw9PbV69Wr9/PPPatOmjcaPH6+zZ89KkrZv367XX39dw4cPt0+RkhEmG2PUvn17LV26VI8//rjq1Kmjn376SUOHDtXhw4f1zjvvONT44IMPqlOnTrp06ZISEhI0bdo0nT9/3j4Vy+nTp/XBBx+oW7du6t27t86cOaMPP/xQkZGRWrNmjerUqWPfVnR0tBYvXqynn35atWvXloeHh6ZNm6YNGzbk6P4KDw9Xnz59ZLPZtGXLFo0fP15///230wlv1+PYsWO6cOGCnnrqKbVs2VJPPvmkdu/ercmTJ2v16tVavXq1vLy8JF3f67P09HS1bdtW99xzj958800tWLBAsbGxunTpkkaPHp1pLRcvXtRDDz2kAwcOaOXKlQ6vEXP6egbIlAEs6uTJk0aS6dChQ7bj2rdvbySZ06dPG2OMkWQkmWXLltnHnDt3zlSrVs0EBQWZtLQ0Y4wxP/30k5FkfvzxR4ft1apVyzRr1sx+PSYmxpQrV85pv5JMbGyswz6ulpCQYCSZTz75xL5s6dKlRpJZunSpMcaYhx56yHh4eJgtW7bYxyQnJ5vixYubsLAw+7L4+Hgjyaxdu9a+7OjRo051GGNMVFSUCQkJcViWWX1xcXHGzc3N7N+/375s2LBhRpL5559/7Mv27t1rJJmxY8c6beNKGTXu3bvXvuzPP/80ksybb75pXxYdHW38/Pyy3dbVx5XTx3X8+PFGkpk5c6Z9XFpammnUqJHx9/e3P08yjsnHx8ccOnTIPnb16tVGkhk8eLBDvRn357p164y/v7/p3LmzSU9Pd6g5s/v4888/N5LMr7/+mu3xAoBVZPyt/+6770yFChWMJNOmTZtMx6alpZmSJUuamjVrmvPnz9uXf/fdd0aSGTFihH1ZVr1h7ty5Dn3zShl/y+Pj4zPdf0bvWLdunX3Z/v37jbe3t3nwwQftyzp27Gg8PT3N7t277cv+/vtvU6hQIdO0aVOn7TZp0sS0aNEi2zqaNWtmatSokWldV9fYv39/p+U56eVpaWmmZs2apmXLlg7LS5UqZSIjIx2WxcbGGknm6NGj2dbTrFkzh9dBxhgzfPhwI8kkJSVds+4MV78myLhevXp1h+PIeF307LPP2pfVqVPHlCxZ0hw7dsy+7I8//jDu7u6mR48eTsfUvn17h33369fPSDJ//PGHQ70ZryuGDRtmPDw8zPz5853qzqyXR0ZGmjvvvDPLYwWA20X//v1NVjHTp59+atzd3c3y5csdlk+ZMsVIMitXrjTGGLNr1y7j7u5uHnzwQaf3UzabzWm7V79/vtL8+fONJPPqq686LH/44YeNm5ub+euvv+zLMnvf3LhxY1O9enX79UuXLpnU1FSHMSdOnDCBgYGmV69e9mXnz5837u7upm/fvg5jc/I+1xhjQkJCTHR0tMOyRx991Pj6+tqv5+Q9eEYfvPp6q1atzKVLl+zLM3rwxIkTjTHX//pMknn66afty2w2m4mKijKenp721xVXvhay2Wyme/fuxtfX16xevdqp7py+ngEyw9QlsKwzZ85IkgoVKpTtuIzbT58+bV9Wv359NWvWzH7dx8dH/fr105EjR+yfsEZERKh06dKaNWuWfdyWLVu0adMm/ec//7EvK1mypJKSkpSWlpZtHT4+PvZ/X7x4UceOHVPFihVVtGjRTD/VPXXqlJKSkrRo0SJFRkY6/ChT8eLF1bNnT61fv16JiYnZ7jenrqwvJSVFycnJaty4sYwx+v333+23nTlzRu7u7td9ht2VTpw4oeTkZO3Zs0fvvPOOPDw8HB6PDMnJyUpOTnb6enFWcvK4/vDDDwoKCrKf4S1JBQsW1MCBA3X27Fn98ssvDtvs2LGjw490NGjQQA0bNtQPP/zgtP89e/YoKipKderU0aeffip3d8c/sVfexxcuXFBycrLuueceScrxJ/sAYBU9e/bUwYMH9eijj2rhwoUO3w7KsG7dOiUlJalfv37y9va2L4+KilLVqlUdvpGVIaM3ZFwyXg/kVqNGjRQWFma/Xq5cOXXo0EE//fST0tPTlZ6eroULF6pjx4723/uQpFKlSunRRx/VihUrHF5jSJe/2pxxRlR20tPT7ceR3euIjJ5x5eXixYtO467sMydOnNCpU6cUHh7u1GPOnDmj4sWLX7O+rFy8eFHJyck6evSoEhIS9PXXX6tWrVr2M66vrvvYsWOy2Ww52nb//v0djqN58+YKCwuzPxf++ecfbdy4UT179lSxYsXs42rVqqXWrVtn2p+vPtP66aeflqRMx06aNElxcXF699131aFDB6fbr6zt1KlTSk5OVrNmzbRnzx6dOnUqR8cIALejuXPnqlq1aqpatapDP2vZsqUk2af/mD9/vmw2m0aMGOH0fup6vwH7ww8/yMPDQwMHDnRY/uyzz8oYox9//NFh+blz55ScnKwjR47oyy+/1B9//KFWrVrZb/fw8JCnp6eky2eoHz9+XJcuXVK9evUcem1KSopsNtsN9drU1FQlJyfbM4Gff/7ZoZaraz5x4kSO50YfMmSIw7fWH3vsMQUGBtp7bW5en105XVnG9GVpaWlavHix09ihQ4dq1qxZ+uKLL9SgQQOn23P6egbIDEE3LCsjwL7WG9zMAvGqVas6jcv4mlPGXJHu7u7q3r275s+fr3PnzkmSZs2aJW9vb3Xu3Nm+XuPGjXXhwgW99NJLOnTokL1hX+38+fMaMWKEfW6wgIAAlShRQidPnsz0jVHHjh0VGBio06dPq0qVKtes90YdOHDA/qbR399fJUqUsIfGV9bXqFEj2Ww2PfPMM9q9e7e9qV6Pu+++WyVKlFCFChU0Y8YMTZo0yanBpaSkqESJEipRooR8fHxUrlw5TZgwIdvt5uRx3b9/vypVquT0oilj3P79+x2WV6pUyWmblStXdrrfU1JSFBkZqcTERB0/fjzTF2HHjx/XM888o8DAQPn4+KhEiRIqX768JPHmGMC/zvHjxzVz5kx9/PHHqlOnjp555hmnv3UZf3Mz63NVq1Z1+pt8ZW/IuPTq1euG6szq7/y5c+d09OhRHT16VOfOncuyF9tsNh08eNBh+cmTJ3M0B+eOHTscel2VKlX02WefOY378MMPnY47Y57SK3333Xe655575O3trWLFiqlEiRJ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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ros = RandomOverSampler(random_state=42)\n",
+    "\n",
+    "X_train_resampled, Y_train_resampled = ros.fit_resample(X_train_df, Y_train_df)\n",
+    "X_val_resampled, Y_val_resampled = ros.fit_resample(X_val_df, Y_val_df)\n",
+    "\n",
+    "analyze_balance(Y_train_resampled, Y_val_resampled, Y_test_df, 'Flexibility Level')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Конструирование признаков. Для начала применим унитарное кодирование категориальных признаков (one-hot encoding), переведя их в бинарные вектора."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Education Level_School</th>\n",
+       "      <th>Education Level_University</th>\n",
+       "      <th>Institution Type_Public</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>Device_Mobile</th>\n",
+       "      <th>Device_Tab</th>\n",
+       "      <th>IT Student_Yes</th>\n",
+       "      <th>Location_Town</th>\n",
+       "      <th>Financial Condition_Poor</th>\n",
+       "      <th>Financial Condition_Rich</th>\n",
+       "      <th>Internet Type_Wifi</th>\n",
+       "      <th>Network Type_3G</th>\n",
+       "      <th>Network Type_4G</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>18</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  Education Level_School  Education Level_University  \\\n",
+       "0   10                    True                       False   \n",
+       "1   18                   False                       False   \n",
+       "2   23                   False                        True   \n",
+       "3   18                    True                       False   \n",
+       "4   23                   False                        True   \n",
+       "\n",
+       "   Institution Type_Public  Gender_Male  Device_Mobile  Device_Tab  \\\n",
+       "0                    False         True           True       False   \n",
+       "1                    False        False           True       False   \n",
+       "2                    False         True           True       False   \n",
+       "3                     True         True           True       False   \n",
+       "4                    False        False           True       False   \n",
+       "\n",
+       "   IT Student_Yes  Location_Town  Financial Condition_Poor  \\\n",
+       "0           False           True                     False   \n",
+       "1           False           True                     False   \n",
+       "2           False           True                     False   \n",
+       "3           False           True                     False   \n",
+       "4           False          False                     False   \n",
+       "\n",
+       "   Financial Condition_Rich  Internet Type_Wifi  Network Type_3G  \\\n",
+       "0                      True                True            False   \n",
+       "1                     False                True            False   \n",
+       "2                     False                True            False   \n",
+       "3                      True               False            False   \n",
+       "4                     False                True            False   \n",
+       "\n",
+       "   Network Type_4G  \n",
+       "0             True  \n",
+       "1             True  \n",
+       "2             True  \n",
+       "3             True  \n",
+       "4             True  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_features = ['Education Level', 'Institution Type', 'Gender', 'Device', 'IT Student', 'Location', 'Financial Condition', 'Internet Type', 'Network Type']\n",
+    "\n",
+    "train_encoded = pd.get_dummies(X_train_resampled, columns=cat_features, drop_first=True)\n",
+    "val_encoded = pd.get_dummies(X_val_resampled, columns=cat_features, drop_first=True)\n",
+    "test_encoded = pd.get_dummies(X_test_df, columns=cat_features, drop_first=True)\n",
+    "\n",
+    "train_encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Применим дискретизацию к числовым признакам."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>Education Level_School</th>\n",
+       "      <th>Education Level_University</th>\n",
+       "      <th>Institution Type_Public</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>Device_Mobile</th>\n",
+       "      <th>Device_Tab</th>\n",
+       "      <th>IT Student_Yes</th>\n",
+       "      <th>Location_Town</th>\n",
+       "      <th>Financial Condition_Poor</th>\n",
+       "      <th>Financial Condition_Rich</th>\n",
+       "      <th>Internet Type_Wifi</th>\n",
+       "      <th>Network Type_3G</th>\n",
+       "      <th>Network Type_4G</th>\n",
+       "      <th>Age_Bin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Education Level_School  Education Level_University  \\\n",
+       "0                    True                       False   \n",
+       "1                   False                       False   \n",
+       "2                   False                        True   \n",
+       "3                    True                       False   \n",
+       "4                   False                        True   \n",
+       "\n",
+       "   Institution Type_Public  Gender_Male  Device_Mobile  Device_Tab  \\\n",
+       "0                    False         True           True       False   \n",
+       "1                    False        False           True       False   \n",
+       "2                    False         True           True       False   \n",
+       "3                     True         True           True       False   \n",
+       "4                    False        False           True       False   \n",
+       "\n",
+       "   IT Student_Yes  Location_Town  Financial Condition_Poor  \\\n",
+       "0           False           True                     False   \n",
+       "1           False           True                     False   \n",
+       "2           False           True                     False   \n",
+       "3           False           True                     False   \n",
+       "4           False          False                     False   \n",
+       "\n",
+       "   Financial Condition_Rich  Internet Type_Wifi  Network Type_3G  \\\n",
+       "0                      True                True            False   \n",
+       "1                     False                True            False   \n",
+       "2                     False                True            False   \n",
+       "3                      True               False            False   \n",
+       "4                     False                True            False   \n",
+       "\n",
+       "   Network Type_4G Age_Bin  \n",
+       "0             True   young  \n",
+       "1             True   young  \n",
+       "2             True   young  \n",
+       "3             True   young  \n",
+       "4             True   young  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_features = ['Age']\n",
+    "\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",
+    "age_bins = [0, 25, 55, 100]\n",
+    "age_labels = [\"young\", \"middle-aged\", \"old\"]\n",
+    "\n",
+    "train_encoded = discretize_features(train_encoded, num_features, bins=age_bins, labels=age_labels)\n",
+    "val_encoded = discretize_features(val_encoded, num_features, bins=age_bins, labels=age_labels)\n",
+    "test_encoded = discretize_features(test_encoded, num_features, bins=age_bins, labels=age_labels)\n",
+    "\n",
+    "train_encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Применим ручной синтез признаков. К примеру, для этого датасета, сделаем признак \"соотвествие устройства для обучения\". Мобильные устройства часто менее удобны для учебы по сравнению с планшетами."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Education Level_School</th>\n",
+       "      <th>Education Level_University</th>\n",
+       "      <th>Institution Type_Public</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>Device_Mobile</th>\n",
+       "      <th>Device_Tab</th>\n",
+       "      <th>IT Student_Yes</th>\n",
+       "      <th>Location_Town</th>\n",
+       "      <th>Financial Condition_Poor</th>\n",
+       "      <th>Financial Condition_Rich</th>\n",
+       "      <th>Internet Type_Wifi</th>\n",
+       "      <th>Network Type_3G</th>\n",
+       "      <th>Network Type_4G</th>\n",
+       "      <th>Age_Bin</th>\n",
+       "      <th>Device Suitability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Education Level_School  Education Level_University  \\\n",
+       "0                    True                       False   \n",
+       "1                   False                       False   \n",
+       "2                   False                        True   \n",
+       "3                    True                       False   \n",
+       "4                   False                        True   \n",
+       "\n",
+       "   Institution Type_Public  Gender_Male  Device_Mobile  Device_Tab  \\\n",
+       "0                    False         True           True       False   \n",
+       "1                    False        False           True       False   \n",
+       "2                    False         True           True       False   \n",
+       "3                     True         True           True       False   \n",
+       "4                    False        False           True       False   \n",
+       "\n",
+       "   IT Student_Yes  Location_Town  Financial Condition_Poor  \\\n",
+       "0           False           True                     False   \n",
+       "1           False           True                     False   \n",
+       "2           False           True                     False   \n",
+       "3           False           True                     False   \n",
+       "4           False          False                     False   \n",
+       "\n",
+       "   Financial Condition_Rich  Internet Type_Wifi  Network Type_3G  \\\n",
+       "0                      True                True            False   \n",
+       "1                     False                True            False   \n",
+       "2                     False                True            False   \n",
+       "3                      True               False            False   \n",
+       "4                     False                True            False   \n",
+       "\n",
+       "   Network Type_4G Age_Bin Device Suitability  \n",
+       "0             True   young                Low  \n",
+       "1             True   young                Low  \n",
+       "2             True   young                Low  \n",
+       "3             True   young                Low  \n",
+       "4             True   young                Low  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_encoded['Device Suitability'] = train_encoded['Device_Tab'].apply(lambda x: \"High\" if x == True else \"Low\")\n",
+    "val_encoded['Device Suitability'] = val_encoded['Device_Tab'].apply(lambda x: \"High\" if x == True else \"Low\")\n",
+    "test_encoded['Device Suitability'] = test_encoded['Device_Tab'].apply(lambda x: \"High\" if x == True else \"Low\")\n",
+    "\n",
+    "train_encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Конструирование признаков с помощью фреймворка Featuretools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  pd.to_datetime(\n",
+      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  pd.to_datetime(\n",
+      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  pd.to_datetime(\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>Education Level_School</th>\n",
+       "      <th>Education Level_University</th>\n",
+       "      <th>Institution Type_Public</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>Device_Mobile</th>\n",
+       "      <th>Device_Tab</th>\n",
+       "      <th>IT Student_Yes</th>\n",
+       "      <th>Location_Town</th>\n",
+       "      <th>Financial Condition_Poor</th>\n",
+       "      <th>Financial Condition_Rich</th>\n",
+       "      <th>Internet Type_Wifi</th>\n",
+       "      <th>Network Type_3G</th>\n",
+       "      <th>Network Type_4G</th>\n",
+       "      <th>Age_Bin</th>\n",
+       "      <th>Device Suitability</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>young</td>\n",
+       "      <td>Low</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Education Level_School  Education Level_University  \\\n",
+       "id                                                       \n",
+       "0                     True                       False   \n",
+       "1                    False                       False   \n",
+       "2                    False                        True   \n",
+       "3                     True                       False   \n",
+       "4                    False                        True   \n",
+       "\n",
+       "    Institution Type_Public  Gender_Male  Device_Mobile  Device_Tab  \\\n",
+       "id                                                                    \n",
+       "0                     False         True           True       False   \n",
+       "1                     False        False           True       False   \n",
+       "2                     False         True           True       False   \n",
+       "3                      True         True           True       False   \n",
+       "4                     False        False           True       False   \n",
+       "\n",
+       "    IT Student_Yes  Location_Town  Financial Condition_Poor  \\\n",
+       "id                                                            \n",
+       "0            False           True                     False   \n",
+       "1            False           True                     False   \n",
+       "2            False           True                     False   \n",
+       "3            False           True                     False   \n",
+       "4            False          False                     False   \n",
+       "\n",
+       "    Financial Condition_Rich  Internet Type_Wifi  Network Type_3G  \\\n",
+       "id                                                                  \n",
+       "0                       True                True            False   \n",
+       "1                      False                True            False   \n",
+       "2                      False                True            False   \n",
+       "3                       True               False            False   \n",
+       "4                      False                True            False   \n",
+       "\n",
+       "    Network Type_4G Age_Bin Device Suitability  \n",
+       "id                                              \n",
+       "0              True   young                Low  \n",
+       "1              True   young                Low  \n",
+       "2              True   young                Low  \n",
+       "3              True   young                Low  \n",
+       "4              True   young                Low  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ft_data = train_encoded.copy()\n",
+    "\n",
+    "es = ft.EntitySet(id=\"students\")\n",
+    "es = es.add_dataframe(dataframe_name=\"students_data\", dataframe=ft_data, index=\"id\", make_index=True)\n",
+    "\n",
+    "feature_matrix, feature_defs = ft.dfs(\n",
+    "    entityset=es, \n",
+    "    target_dataframe_name=\"students_data\",\n",
+    "    max_depth=1\n",
+    ")\n",
+    "\n",
+    "feature_matrix.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Featuretools не смог сделать новые признаки.\n",
+    "\n",
+    "Оценка качества набора признаков."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Время обучения модели: 0.11 секунд\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_encoded = pd.get_dummies(train_encoded, drop_first=True)\n",
+    "val_encoded = pd.get_dummies(val_encoded, drop_first=True)\n",
+    "test_encoded = pd.get_dummies(test_encoded, drop_first=True)\n",
+    "\n",
+    "cols = train_encoded.columns\n",
+    "\n",
+    "train_encoded = train_encoded.reindex(columns=cols, fill_value=0)\n",
+    "val_encoded = val_encoded.reindex(columns=cols, fill_value=0)\n",
+    "test_encoded = test_encoded.reindex(columns=cols, fill_value=0)\n",
+    "\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "\n",
+    "start = time.time()\n",
+    "model.fit(train_encoded, Y_train_resampled)\n",
+    "train_time = time.time() - start\n",
+    "\n",
+    "print(f'Время обучения модели: {train_time:.2f} секунд')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature Importance:\n",
+      "                       feature  importance\n",
+      "9     Financial Condition_Rich    0.184028\n",
+      "3                  Gender_Male    0.108992\n",
+      "8     Financial Condition_Poor    0.107030\n",
+      "2      Institution Type_Public    0.095663\n",
+      "10          Internet Type_Wifi    0.089925\n",
+      "7                Location_Town    0.078658\n",
+      "0       Education Level_School    0.061961\n",
+      "6               IT Student_Yes    0.055048\n",
+      "1   Education Level_University    0.049695\n",
+      "12             Network Type_4G    0.044837\n",
+      "4                Device_Mobile    0.042086\n",
+      "11             Network Type_3G    0.038541\n",
+      "13         Age_Bin_middle-aged    0.034876\n",
+      "15      Device Suitability_Low    0.004611\n",
+      "5                   Device_Tab    0.004049\n",
+      "14                 Age_Bin_old    0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Получение важности признаков\n",
+    "importances = model.feature_importances_\n",
+    "feature_names = 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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "RMSE: 0.5652451456569942\n",
+      "R²: 0.22569473420679287\n",
+      "MAE: 0.2697095435684647 \n",
+      "\n",
+      "Кросс-валидация RMSE: 0.5705060311373475 \n",
+      "\n",
+      "Train RMSE: 0.5237418787490223\n",
+      "Train R²: 0.5885416666666667\n",
+      "Train MAE: 0.19791666666666666\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(test_encoded)\n",
+    "\n",
+    "# Анализ важности признаков\n",
+    "feature_importances = model.feature_importances_\n",
+    "feature_names = train_encoded.columns\n",
+    "\n",
+    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
+    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
+    "\n",
+    "rmse = mean_squared_error(Y_test_df, y_pred, squared=False)\n",
+    "r2 = r2_score(Y_test_df, y_pred)\n",
+    "mae = mean_absolute_error(Y_test_df, y_pred)\n",
+    "\n",
+    "print()\n",
+    "print(f\"RMSE: {rmse}\")\n",
+    "print(f\"R²: {r2}\")\n",
+    "print(f\"MAE: {mae} \\n\")\n",
+    "\n",
+    "# Кросс-валидация\n",
+    "scores = cross_val_score(model, train_encoded, Y_train_resampled, cv=5, scoring='neg_mean_squared_error')\n",
+    "rmse_cv = math.sqrt((-scores.mean()))\n",
+    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
+    "\n",
+    "# Проверка на переобучение\n",
+    "y_train_pred = model.predict(train_encoded)\n",
+    "\n",
+    "rmse_train = mean_squared_error(Y_train_resampled, y_train_pred, squared=False)\n",
+    "r2_train = r2_score(Y_train_resampled, y_train_pred)\n",
+    "mae_train = mean_absolute_error(Y_train_resampled, y_train_pred)\n",
+    "\n",
+    "print(f\"Train RMSE: {rmse_train}\")\n",
+    "print(f\"Train R²: {r2_train}\")\n",
+    "print(f\"Train MAE: {mae_train}\")\n",
+    "print()"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/lab_3/requirements.txt b/lab_3/requirements.txt
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index 0000000..f9e243b
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