AIM-PIbd-31-Makarov-DV/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная 3\n",
    "\n",
    "Датасет: Информация об онлайн обучении учеников"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.model_selection import train_test_split\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",
    "\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": 122,
   "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": 122,
     "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": 123,
   "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": 148,
   "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": 149,
   "metadata": {},
   "outputs": [
    {
     "ename": "IntCastingNaNError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIntCastingNaNError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[149], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m map_flexibility_to_int \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLow\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModerate\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHigh\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m}\n\u001b[1;32m----> 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFlexibility Level\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mFlexibility Level\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_flexibility_to_int\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mint32\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   6637\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   6638\u001b[0m         ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m   6639\u001b[0m     ]\n\u001b[0;32m   6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6642\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6643\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6644\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m   6645\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m    428\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    362\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 363\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    364\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m    366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m    755\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    756\u001b[0m     values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :]  \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[1;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    234\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m    236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 237\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m    239\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m    240\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    179\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 182\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[0;32m     97\u001b[0m         arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m     98\u001b[0m     )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[0;32m    100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_astype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[1;32md:\\ulstu\\cr3\\sem1\\MAI\\AIM-PIbd-31-Makarov-DV\\.venv\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m--> 145\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[0;32m    146\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    147\u001b[0m     )\n\u001b[0;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    149\u001b[0m     \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[0;32m    150\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ]
    }
   ],
   "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": 137,
   "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": 138,
   "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": 139,
   "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": {
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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": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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",
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       "      <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": 140,
     "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": 141,
   "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": 141,
     "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": 142,
   "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": 142,
     "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": 143,
   "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": {
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      "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": 143,
     "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": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 0.22 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": 145,
   "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": 154,
   "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": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "import math\n",
    "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()"
   ]
  }
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