AIM-PIbd-31-LOBASHOV-I-D/lab_3/lab_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Вариант 19:* Данные о миллионерах\n",
    "- Определим бизнес-цели и цели технического проекта "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Rank ', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_csv(\"C:/Users/goldfest/Desktop/3 курс/MII/AIM-PIbd-31-LOBASHOV-I-D/static/csv/Forbes Billionaires.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определение бизнес целей:\n",
    "\n",
    "1. Прогнозирование потенциальных миллионеров на основе анализа данных.\n",
    "2. Оценка факторов, влияющих на достижение статуса миллионера."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определение целей технического проекта:\n",
    "\n",
    "1. Построить модель машинного обучения для классификации, которая будет прогнозировать вероятность достижения статуса миллионера на основе предоставленных данных о характеристиках миллионеров.\n",
    "2. Провести анализ данных для выявления ключевых факторов, влияющих на достижение статуса миллионера."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Networth</th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>219.0</td>\n",
       "      <td>50</td>\n",
       "      <td>United States</td>\n",
       "      <td>Tesla, SpaceX</td>\n",
       "      <td>Automotive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>171.0</td>\n",
       "      <td>58</td>\n",
       "      <td>United States</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bernard Arnault &amp; family</td>\n",
       "      <td>158.0</td>\n",
       "      <td>73</td>\n",
       "      <td>France</td>\n",
       "      <td>LVMH</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>129.0</td>\n",
       "      <td>66</td>\n",
       "      <td>United States</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Warren Buffett</td>\n",
       "      <td>118.0</td>\n",
       "      <td>91</td>\n",
       "      <td>United States</td>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                        Name  Networth  Age        Country  \\\n",
       "0      1                 Elon Musk      219.0   50  United States   \n",
       "1      2                Jeff Bezos      171.0   58  United States   \n",
       "2      3  Bernard Arnault & family      158.0   73         France   \n",
       "3      4                Bill Gates      129.0   66  United States   \n",
       "4      5            Warren Buffett      118.0   91  United States   \n",
       "\n",
       "               Source                Industry  \n",
       "0       Tesla, SpaceX             Automotive   \n",
       "1              Amazon             Technology   \n",
       "2                LVMH       Fashion & Retail   \n",
       "3           Microsoft             Technology   \n",
       "4  Berkshire Hathaway  Finance & Investments   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank        0\n",
      "Name        0\n",
      "Networth    0\n",
      "Age         0\n",
      "Country     0\n",
      "Source      0\n",
      "Industry    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Rank        False\n",
       "Name        False\n",
       "Networth    False\n",
       "Age         False\n",
       "Country     False\n",
       "Source      False\n",
       "Industry    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Процент пропущенных значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
    "\n",
    "# Проверка на пропущенные данные\n",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных колонок нету, это очень хорошо"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки:  2080\n",
      "Размер контрольной выборки:  520\n",
      "Размер тестовой выборки:  520\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
    "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки: \", len(train_data))\n",
    "print(\"Размер контрольной выборки: \", len(val_data))\n",
    "print(\"Размер тестовой выборки: \", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее значение Networth в обучающей выборке:  5.05858173076923\n",
      "Среднее значение Networth в контрольной выборке:  4.069423076923076\n",
      "Среднее значение Networth в тестовой выборке:  4.069423076923076\n"
     ]
    }
   ],
   "source": [
    "# Оценка сбалансированности целевой переменной (Networth)\n",
    "# Визуализация распределения целевой переменной в выборках (гистограмма)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_networth_distribution(data, title):\n",
    "    sns.histplot(data['Networth'], kde=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Networth')\n",
    "    plt.ylabel('Частота')\n",
    "    plt.show()\n",
    "\n",
    "plot_networth_distribution(train_data, 'Распределение Networth в обучающей выборке')\n",
    "plot_networth_distribution(val_data, 'Распределение Networth в контрольной выборке')\n",
    "plot_networth_distribution(test_data, 'Распределение Networth в тестовой выборке')\n",
    "\n",
    "# Оценка сбалансированности данных по целевой переменной (Networth)\n",
    "print(\"Среднее значение Networth в обучающей выборке: \", train_data['Networth'].mean())\n",
    "print(\"Среднее значение Networth в контрольной выборке: \", val_data['Networth'].mean())\n",
    "print(\"Среднее значение Networth в тестовой выборке: \", test_data['Networth'].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после нормализации:  2080\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Визуализация распределения Networth в обучающей выборке\n",
    "sns.histplot(train_data['Networth'], kde=True)\n",
    "plt.title('Распределение Networth в обучающей выборке')\n",
    "plt.xlabel('Networth')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Нормализация данных\n",
    "scaler = StandardScaler()\n",
    "train_data['Networth_scaled'] = scaler.fit_transform(train_data[['Networth']])\n",
    "\n",
    "# Визуализация распределения Networth после нормализации\n",
    "sns.histplot(train_data['Networth_scaled'], kde=True)\n",
    "plt.title('Распределение Networth после нормализации')\n",
    "plt.xlabel('Networth (нормализованное)')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Печать размеров выборки после нормализации\n",
    "print(\"Размер обучающей выборки после нормализации: \", len(train_data))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков  \n",
    "\n",
    "Теперь приступим к конструированию признаков для решения каждой задачи.\n",
    "\n",
    "**Процесс конструирования признаков**  \n",
    "Задача 1: Прогнозирование вероятности достижения статуса миллионера. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования вероятности достижения статуса миллионера.\n",
    "Задача 2: Оценка факторов, влияющих на достижение статуса миллионера. Цель технического проекта: Разработка модели машинного обучения для выявления ключевых факторов, влияющих на достижение статуса миллионера.\n",
    "\n",
    "**Унитарное кодирование**  \n",
    "Унитарное кодирование категориальных признаков (one-hot encoding). Преобразование категориальных признаков в бинарные векторы.\n",
    "\n",
    "**Дискретизация числовых признаков**  \n",
    "Процесс преобразования непрерывных числовых значений в дискретные категории или интервалы (бины)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы train_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'LogNetworth', 'Networth_scaled', 'Country_Algeria', 'Country_Argentina', 'Country_Australia', 'Country_Austria', 'Country_Barbados', 'Country_Belgium', 'Country_Belize', 'Country_Brazil', 'Country_Bulgaria', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Colombia', 'Country_Cyprus', 'Country_Czechia', 'Country_Denmark', 'Country_Egypt', 'Country_Estonia', 'Country_Eswatini (Swaziland)', 'Country_Finland', 'Country_France', 'Country_Georgia', 'Country_Germany', 'Country_Greece', 'Country_Guernsey', 'Country_Hong Kong', 'Country_Hungary', 'Country_Iceland', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Macau', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_Morocco', 'Country_Nepal', 'Country_Netherlands', 'Country_New Zealand', 'Country_Nigeria', 'Country_Norway', 'Country_Oman', 'Country_Peru', 'Country_Philippines', 'Country_Poland', 'Country_Portugal', 'Country_Qatar', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Thailand', 'Country_Turkey', 'Country_Ukraine', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Country_Uruguay', 'Country_Venezuela', 'Country_Vietnam', 'Country_Zimbabwe', 'Source_3D printing', 'Source_AOL', 'Source_Airbnb', \"Source_Aldi, Trader Joe's\", 'Source_Aluminium', 'Source_Amazon', 'Source_Apple', 'Source_BMW, pharmaceuticals', 'Source_Banking', 'Source_Berkshire Hathaway', 'Source_Bloomberg LP', 'Source_Campbell Soup', 'Source_Cargill', 'Source_Carnival Cruises', 'Source_Chanel', 'Source_Charlotte Hornets, endorsements', 'Source_Chemicals', 'Source_Chick-fil-A', 'Source_Coca Cola Israel', 'Source_Coca-Cola bottler', 'Source_Columbia Sportswear', 'Source_Comcast', 'Source_Construction', 'Source_Contact Lens', 'Source_Dallas Cowboys', 'Source_Dell computers', \"Source_Dick's Sporting Goods\", 'Source_DirecTV', 'Source_Dolby Laboratories', 'Source_Dole, real estate', 'Source_EasyJet', 'Source_Estee Lauder', 'Source_Estée Lauder', 'Source_FIAT, investments', 'Source_Facebook', 'Source_Facebook, investments', 'Source_Furniture retail', 'Source_Gap', 'Source_Genentech, Apple', 'Source_Getty Oil', 'Source_Golden State Warriors', 'Source_Google', 'Source_Groupon, investments', 'Source_H&M', 'Source_Heineken', 'Source_Hermes', 'Source_Home Depot', 'Source_Houston Rockets, entertainment', 'Source_Hyundai', 'Source_I.T.', 'Source_IKEA', 'Source_IT', 'Source_IT consulting', 'Source_IT products', 'Source_IT provider', 'Source_In-N-Out Burger', 'Source_Instagram', 'Source_Intel', 'Source_Internet', 'Source_Internet search', 'Source_Investments', 'Source_Koch Industries', \"Source_L'Oréal\", 'Source_LED lighting', 'Source_LG', 'Source_LVMH', 'Source_Lego', 'Source_LinkedIn', 'Source_Little Caesars', 'Source_Lululemon', 'Source_Luxury goods', 'Source_Manufacturing', 'Source_Microsoft', 'Source_Mining', 'Source_Motors', 'Source_Multiple', 'Source_Nascar, racing', 'Source_Netflix', 'Source_Netscape, investments', 'Source_New Balance', 'Source_New England Patriots', 'Source_Nike', 'Source_Nutella, chocolates', 'Source_Patagonia', 'Source_Petro Fibre', 'Source_Petro Firbe', 'Source_Philadelphia Eagles', 'Source_Quicken Loans', 'Source_Real Estate', 'Source_Real estate', 'Source_Red Bull', 'Source_Reebok', 'Source_SAP', 'Source_Samsung', 'Source_Sears', 'Source_Semiconductor materials', 'Source_Shipping', 'Source_Shoes', 'Source_Slim-Fast', 'Source_Smartphones', 'Source_Snapchat', 'Source_Spotify', 'Source_Starbucks', 'Source_TD Ameritrade', 'Source_TV broadcasting', 'Source_TV network, investments', 'Source_TV programs', 'Source_TV shows', 'Source_TV, movie production', 'Source_Tesla, SpaceX', 'Source_TikTok', 'Source_Toyota dealerships', 'Source_Transportation', 'Source_Twitter, Square', 'Source_U-Haul', 'Source_Uber', 'Source_Urban Outfitters', 'Source_Waffle House', 'Source_Walmart', 'Source_Walmart, logistics', 'Source_Washington Football Team', 'Source_WeWork', 'Source_WhatsApp', 'Source_Yahoo', 'Source_Zara', 'Source_Zoom Video Communications', 'Source_accounting services', 'Source_adhesives', 'Source_advertising', 'Source_aerospace', 'Source_agribusiness', 'Source_agriculture', 'Source_agriculture, land', 'Source_agriculture, water', 'Source_agrochemicals', 'Source_air compressors', 'Source_aircraft leasing', 'Source_airline', 'Source_airlines', 'Source_airport', 'Source_airport management', 'Source_airports, investments', 'Source_alcohol', 'Source_alcohol, real estate', 'Source_aluminum', 'Source_aluminum products', 'Source_aluminum, diversified  ', 'Source_aluminum, utilities', 'Source_animal health, investments', 'Source_apparel', 'Source_appliances', 'Source_art', 'Source_art collection', 'Source_art, car dealerships', 'Source_asset management', 'Source_auto dealers, investments', 'Source_auto dealerships', 'Source_auto loans', 'Source_auto parts', 'Source_auto repair', 'Source_automobiles', 'Source_automobiles, batteries', 'Source_automotive', 'Source_automotive brakes', 'Source_automotive technology', 'Source_aviation', 'Source_bakeries', 'Source_banking', 'Source_banking, credit cards', 'Source_banking, insurance', 'Source_banking, insurance, media', 'Source_banking, investments', 'Source_banking, minerals', 'Source_banking, oil', 'Source_banking, property', 'Source_banking, real estate', 'Source_banking, tobacco', 'Source_banks, real estate', 'Source_bars', 'Source_batteries', 'Source_batteries, automobiles', 'Source_batteries, investments', 'Source_battery components', 'Source_beauty products', 'Source_beef packing', 'Source_beef processing', 'Source_beer', 'Source_beverages', 'Source_beverages, pharmaceuticals', 'Source_biochemicals', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_biotech investing', 'Source_biotech, investments', 'Source_biotechnology', 'Source_blockchain technology', 'Source_blockchain, technology', 'Source_book distribution, transportation', 'Source_brakes, investments', 'Source_brewery', 'Source_building materials', 'Source_business software', 'Source_cable', 'Source_cable TV, investments', 'Source_cable television', 'Source_call centers', 'Source_cameras, software', 'Source_candy', 'Source_candy, pet food', 'Source_car dealerships', 'Source_car rentals', 'Source_carbon fiber products', 'Source_carpet', 'Source_cars', 'Source_cashmere', 'Source_casinos', 'Source_casinos, banking', 'Source_casinos, hotels', 'Source_casinos, mixed martial arts', 'Source_casinos, property, energy', 'Source_casinos/hotels', 'Source_cement', 'Source_cement, sugar', 'Source_cheese', 'Source_chemical products', 'Source_chemicals', 'Source_chemicals, investments', 'Source_chemicals, logistics', 'Source_chemicals, spandex', 'Source_chewing gum', 'Source_chicken processing', 'Source_cleaning products', 'Source_clinical diagnostics', 'Source_clinical trials', 'Source_cloud communications', 'Source_cloud computing', 'Source_coal', 'Source_coal mines', 'Source_coal, fertilizers', 'Source_coal, investments', 'Source_cobalt', 'Source_coffee', 'Source_coffee, shipping', 'Source_coking', 'Source_commodities', 'Source_communication equipment', 'Source_communications', 'Source_computer hardware', 'Source_computer services, real estate', 'Source_computer services, telecom', 'Source_computer software', 'Source_conglomerate', 'Source_construction', 'Source_construction equipment', 'Source_construction equipment, media', 'Source_construction materials', 'Source_construction, investments', 'Source_construction, media', 'Source_construction, mining', 'Source_construction, mining machinery', 'Source_construction, pipes, banking', 'Source_construction, real estate', 'Source_consumer', 'Source_consumer electronics', 'Source_consumer goods', 'Source_consumer products, banking', 'Source_convenience stores', 'Source_convinience stores', 'Source_copper, poultry', 'Source_cosmetics', 'Source_cosmetics, reality TV', 'Source_cruises', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_cybersecurity', 'Source_dairy', 'Source_dairy & consumer products', 'Source_damaged cars', 'Source_data analytics', 'Source_data centers', 'Source_data management', 'Source_defense', 'Source_defense, hotels', 'Source_dental implants', 'Source_dental products', 'Source_department stores', 'Source_diagnostics', 'Source_diamond jewelry', 'Source_diamonds', 'Source_digital advertising', 'Source_discount brokerage', 'Source_diversified  ', 'Source_drilling, shipping', 'Source_drones', 'Source_drugs', 'Source_drugstores', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_e-commerce software', 'Source_eBay', 'Source_eBay, PayPal', 'Source_education', 'Source_education technology', 'Source_electric bikes, scooters', 'Source_electric components', 'Source_electric equipment', 'Source_electric scooters', 'Source_electric vehicles', 'Source_electrical equipment', 'Source_electrodes', 'Source_electronic components', 'Source_electronic trading', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_email marketing', 'Source_employment agency', 'Source_energy', 'Source_energy drink', 'Source_energy drinks', 'Source_energy drinks,investments', 'Source_energy services', 'Source_energy, banking, construction', 'Source_energy, chemicals', 'Source_energy, investments', 'Source_energy, real estate', 'Source_energy, sports', 'Source_engineering', 'Source_engineering, automotive', 'Source_engineering, construction', 'Source_entertainment', 'Source_executive search, investments', 'Source_express delivery', 'Source_fashion', 'Source_fashion investments', 'Source_fashion retail', 'Source_fashion retail, investments', 'Source_fashion retailer', 'Source_fast food', 'Source_fasteners', 'Source_feed', 'Source_fertilizer', 'Source_fertilizer, real estate', 'Source_fertilizers', 'Source_fiber optic cables', 'Source_finance', 'Source_finance and investments', 'Source_finance, real estate', 'Source_finance, telecommunications', 'Source_financial information', 'Source_financial services', 'Source_financial services, property', 'Source_financial services★', 'Source_financial technology', 'Source_fintech', 'Source_fitness equipment', 'Source_flavorings', 'Source_flavors and fragrances', 'Source_flipkart', 'Source_flooring', 'Source_food', 'Source_food & beverage retailing', 'Source_food delivery app', 'Source_food distribution', 'Source_food processing', 'Source_food service', 'Source_food services', 'Source_food, beverages', 'Source_foods', 'Source_footwear', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture', 'Source_furniture retailing', 'Source_gambling', 'Source_gambling products', 'Source_gambling software', 'Source_game software', 'Source_gaming', 'Source_gas stations', 'Source_gas, chemicals', 'Source_generic drugs', 'Source_glass', 'Source_gold', 'Source_graphite electrodes', 'Source_grocery delivery service', 'Source_grocery stores', 'Source_hair care products', 'Source_hair dryers', 'Source_hair products, tequila', 'Source_hand tools', 'Source_hardware', 'Source_health IT', 'Source_health care', 'Source_health clinics', 'Source_health insurance', 'Source_healthcare', 'Source_healthcare services', 'Source_hearing aids', 'Source_heating and cooling equipment', 'Source_heating, cooling equipment', 'Source_hedge fund', 'Source_hedge funds', 'Source_herbal products', 'Source_high speed trading', 'Source_home appliances', 'Source_home building', 'Source_home building, banking', 'Source_home furnishings', 'Source_home improvement stores', 'Source_home sales', 'Source_home-cleaning robots', 'Source_homebuilder', 'Source_homebuilding', 'Source_homebuilding, insurance', 'Source_hospitals', 'Source_hospitals, health care', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, diversified  ', 'Source_hotels, energy', 'Source_hotels, investments', 'Source_hotels, motels', 'Source_household chemicals', 'Source_hydraulic machinery', 'Source_industrial equipment', 'Source_industrial explosives', 'Source_industrial lasers', 'Source_industrial machinery', 'Source_infant formula', 'Source_information technology', 'Source_infrastructure', 'Source_infrastructure, commodities', 'Source_insurance', 'Source_insurance, NFL team', 'Source_insurance, beverages', 'Source_insurance, investments', 'Source_internet', 'Source_internet media', 'Source_internet search', 'Source_internet service provider', 'Source_investing', 'Source_investment', 'Source_investments', 'Source_investments, art', 'Source_investments, energy', 'Source_investments, real estate', 'Source_jewellery', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_leveraged buyouts', 'Source_lighting', 'Source_lighting installations', 'Source_liquefied natural gas', 'Source_liquor', 'Source_lithium', 'Source_lithium batteries', 'Source_lithium battery', 'Source_lithium-ion battery cap', 'Source_live entertainment', 'Source_live streaming service', 'Source_logistics', 'Source_low-cost airlines', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_magazines, media', 'Source_magnetic switches', 'Source_manufacturing', 'Source_manufacturing, investment', 'Source_manufacturing, investments', 'Source_mapping software', 'Source_materials', 'Source_measuring instruments', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, investments', 'Source_media, real estate', 'Source_medical devices', 'Source_medical diagnostic equipment', 'Source_medical diagnostics', 'Source_medical equipment', 'Source_medical packaging', 'Source_medical patents', 'Source_medical products', 'Source_medical technology', 'Source_medical testing', 'Source_messaging app', 'Source_metal processing', 'Source_metals', 'Source_metals, coal', 'Source_metals, energy', 'Source_metals, mining', 'Source_metalworking tools', 'Source_microbiology', 'Source_microchip testing', 'Source_mining', 'Source_mining, banking', 'Source_mining, banking, hotels', 'Source_mining, commodities', 'Source_mining, copper products', 'Source_mining, metals, machinery', 'Source_mobile games', 'Source_mobile gaming', 'Source_mobile payments', 'Source_mobile phone retailer', 'Source_mobile phones', 'Source_money management', 'Source_mortgage lender★', 'Source_motorcycle loans', 'Source_motorcycles', 'Source_motorhomes, RVs', 'Source_motors', 'Source_movie making', 'Source_movies, investments', 'Source_movies, record labels', 'Source_music, chemicals', 'Source_music, cosmetics', 'Source_music, sneakers', 'Source_mutual funds', 'Source_natural gas', 'Source_natural gas distribution', 'Source_natural gas, fertilizers', 'Source_navigation equipment', 'Source_newspapers, TV network', 'Source_nonferrous', 'Source_nutrition, wellness products', 'Source_office real estate', 'Source_oil', 'Source_oil & gas', 'Source_oil & gas, banking', 'Source_oil & gas, investments', 'Source_oil and gas', 'Source_oil and gas, IT, lotteries', 'Source_oil refinery', 'Source_oil, banking, telecom', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oilfield equipment', 'Source_online dating', 'Source_online gambling', 'Source_online games', 'Source_online games, investments', 'Source_online gaming', 'Source_online marketplace', 'Source_online media', 'Source_online media, Dallas Mavericks', 'Source_online payments', 'Source_online recruitment', 'Source_online retail', 'Source_online retailing', 'Source_online services', 'Source_optical components', 'Source_optometry', 'Source_orange juice', 'Source_package delivery', 'Source_packaged meats', 'Source_packaging', 'Source_paint', 'Source_paints', 'Source_palm oil', 'Source_palm oil, nickel mining', 'Source_palm oil, property', 'Source_palm oil, shipping, property', 'Source_paper', 'Source_paper & related products', 'Source_paper and pulp', 'Source_payment software', 'Source_payments software', 'Source_payments technology', 'Source_payments, banking', 'Source_payroll processing', 'Source_payroll software', 'Source_pearlescent pigments', 'Source_personal care goods', 'Source_pest control', 'Source_pet food', 'Source_petrochemicals', 'Source_petroleum, diversified  ', 'Source_phamaceuticals', 'Source_pharmaceutical', 'Source_pharmaceutical services', 'Source_pharmaceuticals', 'Source_pharmaceuticals, diversified  ', 'Source_pharmaceuticals, food', 'Source_pharmaceuticals, medical equipment', 'Source_pharmaceuticals, power', 'Source_pharmacies', 'Source_photovoltaic equipment', 'Source_photovoltaics', 'Source_pig breeding', 'Source_pipe manufacturing', 'Source_pipelines', 'Source_plastic', 'Source_plastics', 'Source_plumbing fixtures', 'Source_plush toys, real estate', 'Source_polyester', 'Source_ports', 'Source_poultry genetics', 'Source_poultry processing', 'Source_powdered metal', 'Source_power equipment', 'Source_power strip', 'Source_power supply equipment', 'Source_precision machinery', 'Source_price comparison website', 'Source_printed circuit boards', 'Source_printing', 'Source_private equity', 'Source_private equity★', 'Source_pro sports teams', 'Source_property, healthcare', 'Source_prosthetics', 'Source_publishing', 'Source_pulp and paper', 'Source_quartz products', 'Source_readymade garments', 'Source_real estate', 'Source_real estate developer', 'Source_real estate development', 'Source_real estate services', 'Source_real estate, airport', 'Source_real estate, construction', 'Source_real estate, diversified  ', 'Source_real estate, electronics', 'Source_real estate, gambling', 'Source_real estate, investments', 'Source_real estate, manufacturing', 'Source_real estate, media', 'Source_real estate, oil, cars, sports', 'Source_real estate, private equity', 'Source_real estate, retail', 'Source_real estate, shipping', 'Source_refinery, chemicals', 'Source_renewable energy', 'Source_restaurant', 'Source_restaurants', 'Source_retail', 'Source_retail, investments', 'Source_retail, media', 'Source_retail, real estate', 'Source_retailing', 'Source_roofing', 'Source_salsa', 'Source_sandwich chain', 'Source_satellite TV', 'Source_scaffolding, cement mixers', 'Source_scientific equipment', 'Source_security', 'Source_security services', 'Source_security software', 'Source_seed production', 'Source_semiconductor', 'Source_semiconductor devices', 'Source_semiconductors', 'Source_sensor systems', 'Source_sensor technology', 'Source_sensors', 'Source_sensors★', 'Source_shipbuilding', 'Source_shipping', 'Source_shipping, airlines', 'Source_shipping, seafood', 'Source_shoes', 'Source_shopping centers', 'Source_shopping malls', 'Source_silicon', 'Source_smartphone components', 'Source_smartphone screens', 'Source_smartphones', 'Source_snack bars', 'Source_snacks, beverages', 'Source_sneakers, sportswear', 'Source_social media', 'Source_social network', 'Source_soft drinks, fast food', 'Source_software', 'Source_software firm', 'Source_software services', 'Source_software, investments', 'Source_solar energy', 'Source_solar energy equipment', 'Source_solar equipment', 'Source_solar inverters', 'Source_solar panel components', 'Source_solar panel materials', 'Source_solar wafers and modules', 'Source_soy sauce', 'Source_specialty chemicals', 'Source_spirits', 'Source_sporting goods retail', 'Source_sports apparel', 'Source_sports data', 'Source_sports drink', 'Source_sports retailing', 'Source_sports team', 'Source_sports teams', 'Source_sports, real estate', 'Source_staffing & recruiting', 'Source_stationery', 'Source_steel', 'Source_steel pipes, diversified  ', 'Source_steel, coal', 'Source_steel, diversified  ', 'Source_steel, investments', 'Source_steel, telecom, investments', 'Source_steel, transport', 'Source_stock brokerage', 'Source_stock exchange', 'Source_stock photos', 'Source_storage facilities', 'Source_sugar, ethanol', 'Source_sunglasses', 'Source_supermarkets', 'Source_tech investments', 'Source_technology', 'Source_telecom', 'Source_telecom services', 'Source_telecom, investments', 'Source_telecom, oil', 'Source_telecommunication', 'Source_telecommunications', 'Source_temp agency', 'Source_tequila', 'Source_textiles', 'Source_textiles, paper', 'Source_ticketing service', 'Source_tire', 'Source_tires', 'Source_tires, diversified  ', 'Source_tobacco', 'Source_tobacco distribution, retail', 'Source_toll roads', 'Source_touch screens', 'Source_tourism, cultural industry', 'Source_toys', 'Source_tractors', 'Source_trading, investments', 'Source_train cars', 'Source_transportation', 'Source_travel', 'Source_trucking', 'Source_two-wheelers, finance', 'Source_used cars', 'Source_utilities, diversified  ', 'Source_utilities, real estate', 'Source_vaccine & shoes', 'Source_vaccines', 'Source_valve manufacturing', 'Source_valves', 'Source_venture capital', 'Source_venture capital, Google', 'Source_video games', 'Source_video games, pachinko', 'Source_video streaming', 'Source_video streaming app', 'Source_video surveillance', 'Source_videogames', 'Source_vodka', 'Source_waste disposal', 'Source_web hosting', 'Source_wine', 'Source_wireless networking gear', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ']\n",
      "Столбцы val_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'Country_Argentina', 'Country_Australia', 'Country_Belgium', 'Country_Brazil', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Cyprus', 'Country_Denmark', 'Country_Egypt', 'Country_Finland', 'Country_France', 'Country_Germany', 'Country_Greece', 'Country_Hong Kong', 'Country_Hungary', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Liechtenstein', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_New Zealand', 'Country_Norway', 'Country_Philippines', 'Country_Poland', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_St. Kitts and Nevis', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Tanzania', 'Country_Thailand', 'Country_Turkey', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Source_Airbnb', 'Source_Amazon', 'Source_Apple, Disney', 'Source_BMW', 'Source_Berkshire Hathaway', 'Source_Best Buy', 'Source_Cargill', 'Source_Chanel', 'Source_Cirque du Soleil', 'Source_Coca Cola Israel', 'Source_Electric power', 'Source_Estee Lauder', 'Source_Facebook', 'Source_FedEx', 'Source_Formula One', 'Source_Gap', 'Source_Google', 'Source_Hyundai', 'Source_IKEA', 'Source_Indianapolis Colts', 'Source_LG', 'Source_Manufacturing', 'Source_Marvel comics', 'Source_Microsoft', 'Source_Pinterest', 'Source_Real Estate', 'Source_Real estate', 'Source_Roku', 'Source_Samsung', 'Source_San Francisco 49ers', 'Source_Snapchat', 'Source_Spanx', 'Source_Spotify', 'Source_Star Wars', 'Source_TV broadcasting', 'Source_Twitter', 'Source_Uber', 'Source_Under Armour', 'Source_Virgin', 'Source_Walmart', 'Source_acoustic components', 'Source_adhesives', 'Source_aerospace', 'Source_agribusiness', 'Source_airports, real estate', 'Source_alcoholic beverages', 'Source_aluminum products', 'Source_amusement parks', 'Source_appliances', 'Source_art collection', 'Source_asset management', 'Source_auto loans', 'Source_auto parts', 'Source_bakery chain', 'Source_banking', 'Source_banking, minerals', 'Source_batteries', 'Source_beer', 'Source_beer distribution', 'Source_beer, investments', 'Source_beverages', 'Source_billboards, Los Angeles Angels', 'Source_biomedical products', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_budget airline', 'Source_building materials', 'Source_business software', 'Source_call centers', 'Source_candy, pet food', 'Source_car dealerships', 'Source_casinos', 'Source_casinos, real estate', 'Source_cement', 'Source_cement, diversified  ', 'Source_chemical', 'Source_chemicals', 'Source_cloud computing', 'Source_cloud storage service', 'Source_coal', 'Source_coffee', 'Source_coffee makers', 'Source_commodities', 'Source_commodities, investments', 'Source_computer games', 'Source_computer networking', 'Source_computer software', 'Source_construction', 'Source_consumer goods', 'Source_consumer products', 'Source_cooking appliances', 'Source_copper, education', 'Source_copy machines, software', 'Source_cosmetics', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_damaged cars', 'Source_data centers', 'Source_defense contractor', 'Source_dental materials', 'Source_diversified  ', 'Source_drug distribution', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_eBay', 'Source_ecommerce', 'Source_edible oil', 'Source_edtech', 'Source_education', 'Source_electrical equipment', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_energy services', 'Source_entertainment', 'Source_eyeglasses', 'Source_fashion retail', 'Source_fast fashion', 'Source_finance', 'Source_finance services', 'Source_financial services', 'Source_fine jewelry', 'Source_fintech', 'Source_fish farming', 'Source_flavorings', 'Source_food', 'Source_food delivery service', 'Source_food manufacturing', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture retailing', 'Source_garments', 'Source_gas stations, retail', 'Source_generic drugs', 'Source_glass', 'Source_greek yogurt', 'Source_gym equipment', 'Source_hardware stores', 'Source_health products', 'Source_healthcare IT', 'Source_hedge funds', 'Source_home appliances', 'Source_home furnishings', 'Source_homebuilding', 'Source_homebuilding, NFL team', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, investments', 'Source_household chemicals', 'Source_hygiene products', 'Source_imaging systems', 'Source_insurance', 'Source_insurance, NFL team', 'Source_internet and software', 'Source_internet media', 'Source_internet, telecom', 'Source_investing', 'Source_investment banking', 'Source_investment research', 'Source_investments', 'Source_iron ore mining', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_liquor', 'Source_lithium', 'Source_logistics', 'Source_logistics, baseball', 'Source_logistics, real estate', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_manufacturing', 'Source_manufacturing, investments', 'Source_mattresses', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, tech', 'Source_medical cosmetics', 'Source_medical devices', 'Source_medical equipment', 'Source_medical services', 'Source_messaging software', 'Source_metals', 'Source_metals, banking, fertilizers', 'Source_mining', 'Source_mining, commodities', 'Source_mining, metals', 'Source_mining, steel', 'Source_money management', 'Source_motorcycles', 'Source_movies', 'Source_movies, digital effects', 'Source_natural gas', 'Source_non-ferrous metals', 'Source_nutritional supplements', 'Source_oil', 'Source_oil & gas, investments', 'Source_oil refining', 'Source_oil trading', 'Source_oil, banking', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oil, semiconductor', 'Source_online gambling', 'Source_online games', 'Source_online media', 'Source_online retail', 'Source_package delivery', 'Source_packaging', 'Source_paper', 'Source_paper manufacturing', 'Source_payment processing', 'Source_payroll services', 'Source_pet food', 'Source_petrochemicals', 'Source_pharma retailing', 'Source_pharmaceutical', 'Source_pharmaceutical ingredients', 'Source_pharmaceuticals', 'Source_pipelines', 'Source_plastic pipes', 'Source_poultry', 'Source_poultry breeding', 'Source_power strips', 'Source_precious metals, real estate', 'Source_printed circuit boards', 'Source_private equity', 'Source_publishing', 'Source_pulp and paper', 'Source_real estate', 'Source_real estate finance', 'Source_real estate, hotels', 'Source_real estate, investments', 'Source_record label', 'Source_refinery, chemicals', 'Source_restaurants', 'Source_retail', 'Source_retail & gas stations', 'Source_retail chain', 'Source_retail stores', 'Source_retail, agribusiness', 'Source_retail, investments', 'Source_rubber gloves', 'Source_security software', 'Source_self storage', 'Source_semiconductor', 'Source_semiconductors', 'Source_sensor systems', 'Source_shipping', 'Source_shoes', 'Source_smartphone screens', 'Source_smartphones', 'Source_software', 'Source_solar panels', 'Source_soy sauce', 'Source_sporting goods', 'Source_sports', 'Source_sports apparel', 'Source_staffing, Baltimore Ravens', 'Source_stationery', 'Source_steel', 'Source_steel production', 'Source_steel, diversified  ', 'Source_steel, mining', 'Source_supermarkets', 'Source_supermarkets, investments', 'Source_surveillance equipment', 'Source_technology', 'Source_telecom', 'Source_telecom, lotteries, insurance', 'Source_telecoms, media, oil-services', 'Source_testing equipment', 'Source_textile, chemicals', 'Source_textiles, apparel', 'Source_textiles, petrochemicals', 'Source_timberland, lumber mills', 'Source_titanium', 'Source_transport, logistics', 'Source_two-wheelers', 'Source_used cars', 'Source_vaccines', 'Source_vacuums', 'Source_venture capital', 'Source_venture capital investing', 'Source_video games', 'Source_wedding dresses', 'Source_wind turbines', 'Source_winter jackets', 'Source_wire & cables, paints', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ']\n",
      "Столбцы test_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'Country_Argentina', 'Country_Australia', 'Country_Belgium', 'Country_Brazil', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Cyprus', 'Country_Denmark', 'Country_Egypt', 'Country_Finland', 'Country_France', 'Country_Germany', 'Country_Greece', 'Country_Hong Kong', 'Country_Hungary', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Liechtenstein', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_New Zealand', 'Country_Norway', 'Country_Philippines', 'Country_Poland', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_St. Kitts and Nevis', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Tanzania', 'Country_Thailand', 'Country_Turkey', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Source_Airbnb', 'Source_Amazon', 'Source_Apple, Disney', 'Source_BMW', 'Source_Berkshire Hathaway', 'Source_Best Buy', 'Source_Cargill', 'Source_Chanel', 'Source_Cirque du Soleil', 'Source_Coca Cola Israel', 'Source_Electric power', 'Source_Estee Lauder', 'Source_Facebook', 'Source_FedEx', 'Source_Formula One', 'Source_Gap', 'Source_Google', 'Source_Hyundai', 'Source_IKEA', 'Source_Indianapolis Colts', 'Source_LG', 'Source_Manufacturing', 'Source_Marvel comics', 'Source_Microsoft', 'Source_Pinterest', 'Source_Real Estate', 'Source_Real estate', 'Source_Roku', 'Source_Samsung', 'Source_San Francisco 49ers', 'Source_Snapchat', 'Source_Spanx', 'Source_Spotify', 'Source_Star Wars', 'Source_TV broadcasting', 'Source_Twitter', 'Source_Uber', 'Source_Under Armour', 'Source_Virgin', 'Source_Walmart', 'Source_acoustic components', 'Source_adhesives', 'Source_aerospace', 'Source_agribusiness', 'Source_airports, real estate', 'Source_alcoholic beverages', 'Source_aluminum products', 'Source_amusement parks', 'Source_appliances', 'Source_art collection', 'Source_asset management', 'Source_auto loans', 'Source_auto parts', 'Source_bakery chain', 'Source_banking', 'Source_banking, minerals', 'Source_batteries', 'Source_beer', 'Source_beer distribution', 'Source_beer, investments', 'Source_beverages', 'Source_billboards, Los Angeles Angels', 'Source_biomedical products', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_budget airline', 'Source_building materials', 'Source_business software', 'Source_call centers', 'Source_candy, pet food', 'Source_car dealerships', 'Source_casinos', 'Source_casinos, real estate', 'Source_cement', 'Source_cement, diversified  ', 'Source_chemical', 'Source_chemicals', 'Source_cloud computing', 'Source_cloud storage service', 'Source_coal', 'Source_coffee', 'Source_coffee makers', 'Source_commodities', 'Source_commodities, investments', 'Source_computer games', 'Source_computer networking', 'Source_computer software', 'Source_construction', 'Source_consumer goods', 'Source_consumer products', 'Source_cooking appliances', 'Source_copper, education', 'Source_copy machines, software', 'Source_cosmetics', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_damaged cars', 'Source_data centers', 'Source_defense contractor', 'Source_dental materials', 'Source_diversified  ', 'Source_drug distribution', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_eBay', 'Source_ecommerce', 'Source_edible oil', 'Source_edtech', 'Source_education', 'Source_electrical equipment', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_energy services', 'Source_entertainment', 'Source_eyeglasses', 'Source_fashion retail', 'Source_fast fashion', 'Source_finance', 'Source_finance services', 'Source_financial services', 'Source_fine jewelry', 'Source_fintech', 'Source_fish farming', 'Source_flavorings', 'Source_food', 'Source_food delivery service', 'Source_food manufacturing', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture retailing', 'Source_garments', 'Source_gas stations, retail', 'Source_generic drugs', 'Source_glass', 'Source_greek yogurt', 'Source_gym equipment', 'Source_hardware stores', 'Source_health products', 'Source_healthcare IT', 'Source_hedge funds', 'Source_home appliances', 'Source_home furnishings', 'Source_homebuilding', 'Source_homebuilding, NFL team', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, investments', 'Source_household chemicals', 'Source_hygiene products', 'Source_imaging systems', 'Source_insurance', 'Source_insurance, NFL team', 'Source_internet and software', 'Source_internet media', 'Source_internet, telecom', 'Source_investing', 'Source_investment banking', 'Source_investment research', 'Source_investments', 'Source_iron ore mining', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_liquor', 'Source_lithium', 'Source_logistics', 'Source_logistics, baseball', 'Source_logistics, real estate', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_manufacturing', 'Source_manufacturing, investments', 'Source_mattresses', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, tech', 'Source_medical cosmetics', 'Source_medical devices', 'Source_medical equipment', 'Source_medical services', 'Source_messaging software', 'Source_metals', 'Source_metals, banking, fertilizers', 'Source_mining', 'Source_mining, commodities', 'Source_mining, metals', 'Source_mining, steel', 'Source_money management', 'Source_motorcycles', 'Source_movies', 'Source_movies, digital effects', 'Source_natural gas', 'Source_non-ferrous metals', 'Source_nutritional supplements', 'Source_oil', 'Source_oil & gas, investments', 'Source_oil refining', 'Source_oil trading', 'Source_oil, banking', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oil, semiconductor', 'Source_online gambling', 'Source_online games', 'Source_online media', 'Source_online retail', 'Source_package delivery', 'Source_packaging', 'Source_paper', 'Source_paper manufacturing', 'Source_payment processing', 'Source_payroll services', 'Source_pet food', 'Source_petrochemicals', 'Source_pharma retailing', 'Source_pharmaceutical', 'Source_pharmaceutical ingredients', 'Source_pharmaceuticals', 'Source_pipelines', 'Source_plastic pipes', 'Source_poultry', 'Source_poultry breeding', 'Source_power strips', 'Source_precious metals, real estate', 'Source_printed circuit boards', 'Source_private equity', 'Source_publishing', 'Source_pulp and paper', 'Source_real estate', 'Source_real estate finance', 'Source_real estate, hotels', 'Source_real estate, investments', 'Source_record label', 'Source_refinery, chemicals', 'Source_restaurants', 'Source_retail', 'Source_retail & gas stations', 'Source_retail chain', 'Source_retail stores', 'Source_retail, agribusiness', 'Source_retail, investments', 'Source_rubber gloves', 'Source_security software', 'Source_self storage', 'Source_semiconductor', 'Source_semiconductors', 'Source_sensor systems', 'Source_shipping', 'Source_shoes', 'Source_smartphone screens', 'Source_smartphones', 'Source_software', 'Source_solar panels', 'Source_soy sauce', 'Source_sporting goods', 'Source_sports', 'Source_sports apparel', 'Source_staffing, Baltimore Ravens', 'Source_stationery', 'Source_steel', 'Source_steel production', 'Source_steel, diversified  ', 'Source_steel, mining', 'Source_supermarkets', 'Source_supermarkets, investments', 'Source_surveillance equipment', 'Source_technology', 'Source_telecom', 'Source_telecom, lotteries, insurance', 'Source_telecoms, media, oil-services', 'Source_testing equipment', 'Source_textile, chemicals', 'Source_textiles, apparel', 'Source_textiles, petrochemicals', 'Source_timberland, lumber mills', 'Source_titanium', 'Source_transport, logistics', 'Source_two-wheelers', 'Source_used cars', 'Source_vaccines', 'Source_vacuums', 'Source_venture capital', 'Source_venture capital investing', 'Source_video games', 'Source_wedding dresses', 'Source_wind turbines', 'Source_winter jackets', 'Source_wire & cables, paints', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ']\n"
     ]
    }
   ],
   "source": [
    "# Пример категориальных признаков\n",
    "categorical_features = ['Country', 'Source', 'Industry']\n",
    "\n",
    "# Применение one-hot encoding\n",
    "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n",
    "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n",
    "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_features)\n",
    "\n",
    "print(\"Столбцы train_data_encoded:\", train_data_encoded.columns.tolist())\n",
    "print(\"Столбцы val_data_encoded:\", val_data_encoded.columns.tolist())\n",
    "print(\"Столбцы test_data_encoded:\", test_data_encoded.columns.tolist())\n",
    "\n",
    "# Дискретизация числовых признаков (Age и Networth). Например, можно разделить возраст и стоимость активов на категории\n",
    "# Пример дискретизации признака 'Age' на 5 категорий\n",
    "train_data_encoded['Age_binned'] = pd.cut(train_data_encoded['Age'], bins=5, labels=False)\n",
    "val_data_encoded['Age_binned'] = pd.cut(val_data_encoded['Age'], bins=5, labels=False)\n",
    "test_data_encoded['Age_binned'] = pd.cut(test_data_encoded['Age'], bins=5, labels=False)\n",
    "\n",
    "# Пример дискретизации признака 'Networth' на 5 категорий\n",
    "train_data_encoded['Networth_binned'] = pd.cut(train_data_encoded['Networth'], bins=5, labels=False)\n",
    "val_data_encoded['Networth_binned'] = pd.cut(val_data_encoded['Networth'], bins=5, labels=False)\n",
    "test_data_encoded['Networth_binned'] = pd.cut(test_data_encoded['Networth'], bins=5, labels=False)\n",
    "\n",
    "# Пример дискретизации признака 'Age' на 5 категорий\n",
    "df_encoded['Age_binned'] = pd.cut(df_encoded['Age'], bins=5, labels=False)\n",
    "\n",
    "# Пример дискретизации признака 'Networth' на 5 категорий\n",
    "df_encoded['Networth_binned'] = pd.cut(df_encoded['Networth'], bins=5, labels=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручной синтез\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, можно создать признак, который отражает соотношение возраста к стоимости активов (Networth) или другие полезные метрики."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Пример создания нового признака - соотношение возраста к стоимости активов (Networth)\n",
    "train_data_encoded['age_to_networth'] = train_data_encoded['Age'] / train_data_encoded['Networth']\n",
    "val_data_encoded['age_to_networth'] = val_data_encoded['Age'] / val_data_encoded['Networth']\n",
    "test_data_encoded['age_to_networth'] = test_data_encoded['Age'] / test_data_encoded['Networth']\n",
    "\n",
    "# Пример создания нового признака - соотношение возраста к стоимости активов (Networth)\n",
    "df_encoded['age_to_networth'] = df_encoded['Age'] / df_encoded['Networth']\n",
    "\n",
    "# Пример создания нового признака - соотношение стоимости активов к возрасту\n",
    "train_data_encoded['networth_to_age'] = train_data_encoded['Networth'] / train_data_encoded['Age']\n",
    "val_data_encoded['networth_to_age'] = val_data_encoded['Networth'] / val_data_encoded['Age']\n",
    "test_data_encoded['networth_to_age'] = test_data_encoded['Networth'] / test_data_encoded['Age']\n",
    "\n",
    "# Пример создания нового признака - соотношение стоимости активов к возрасту\n",
    "df_encoded['networth_to_age'] = df_encoded['Networth'] / df_encoded['Age']\n",
    "\n",
    "# Пример создания нового признака - квадрат возраста\n",
    "train_data_encoded['age_squared'] = train_data_encoded['Age'] ** 2\n",
    "val_data_encoded['age_squared'] = val_data_encoded['Age'] ** 2\n",
    "test_data_encoded['age_squared'] = test_data_encoded['Age'] ** 2\n",
    "\n",
    "# Пример создания нового признака - квадрат возраста\n",
    "df_encoded['age_squared'] = df_encoded['Age'] ** 2\n",
    "\n",
    "# Пример создания нового признака - логарифм стоимости активов\n",
    "import numpy as np\n",
    "train_data_encoded['log_networth'] = train_data_encoded['Networth'].apply(lambda x: np.log(x) if x > 0 else 0)\n",
    "val_data_encoded['log_networth'] = val_data_encoded['Networth'].apply(lambda x: np.log(x) if x > 0 else 0)\n",
    "test_data_encoded['log_networth'] = test_data_encoded['Networth'].apply(lambda x: np.log(x) if x > 0 else 0)\n",
    "\n",
    "# Пример создания нового признака - логарифм стоимости активов\n",
    "df_encoded['log_networth'] = df_encoded['Networth'].apply(lambda x: np.log(x) if x > 0 else 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример числовых признаков\n",
    "numerical_features = ['Networth', 'Age']\n",
    "\n",
    "# Применение StandardScaler для масштабирования числовых признаков\n",
    "scaler = StandardScaler()\n",
    "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n",
    "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n",
    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n",
    "\n",
    "# Пример использования MinMaxScaler для масштабирования числовых признаков\n",
    "scaler = MinMaxScaler()\n",
    "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n",
    "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n",
    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Использование фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы в df: ['Rank ', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry']\n",
      "Столбцы в train_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'LogNetworth', 'Networth_scaled', 'Country_Algeria', 'Country_Argentina', 'Country_Australia', 'Country_Austria', 'Country_Barbados', 'Country_Belgium', 'Country_Belize', 'Country_Brazil', 'Country_Bulgaria', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Colombia', 'Country_Cyprus', 'Country_Czechia', 'Country_Denmark', 'Country_Egypt', 'Country_Estonia', 'Country_Eswatini (Swaziland)', 'Country_Finland', 'Country_France', 'Country_Georgia', 'Country_Germany', 'Country_Greece', 'Country_Guernsey', 'Country_Hong Kong', 'Country_Hungary', 'Country_Iceland', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Macau', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_Morocco', 'Country_Nepal', 'Country_Netherlands', 'Country_New Zealand', 'Country_Nigeria', 'Country_Norway', 'Country_Oman', 'Country_Peru', 'Country_Philippines', 'Country_Poland', 'Country_Portugal', 'Country_Qatar', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Thailand', 'Country_Turkey', 'Country_Ukraine', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Country_Uruguay', 'Country_Venezuela', 'Country_Vietnam', 'Country_Zimbabwe', 'Source_3D printing', 'Source_AOL', 'Source_Airbnb', \"Source_Aldi, Trader Joe's\", 'Source_Aluminium', 'Source_Amazon', 'Source_Apple', 'Source_BMW, pharmaceuticals', 'Source_Banking', 'Source_Berkshire Hathaway', 'Source_Bloomberg LP', 'Source_Campbell Soup', 'Source_Cargill', 'Source_Carnival Cruises', 'Source_Chanel', 'Source_Charlotte Hornets, endorsements', 'Source_Chemicals', 'Source_Chick-fil-A', 'Source_Coca Cola Israel', 'Source_Coca-Cola bottler', 'Source_Columbia Sportswear', 'Source_Comcast', 'Source_Construction', 'Source_Contact Lens', 'Source_Dallas Cowboys', 'Source_Dell computers', \"Source_Dick's Sporting Goods\", 'Source_DirecTV', 'Source_Dolby Laboratories', 'Source_Dole, real estate', 'Source_EasyJet', 'Source_Estee Lauder', 'Source_Estée Lauder', 'Source_FIAT, investments', 'Source_Facebook', 'Source_Facebook, investments', 'Source_Furniture retail', 'Source_Gap', 'Source_Genentech, Apple', 'Source_Getty Oil', 'Source_Golden State Warriors', 'Source_Google', 'Source_Groupon, investments', 'Source_H&M', 'Source_Heineken', 'Source_Hermes', 'Source_Home Depot', 'Source_Houston Rockets, entertainment', 'Source_Hyundai', 'Source_I.T.', 'Source_IKEA', 'Source_IT', 'Source_IT consulting', 'Source_IT products', 'Source_IT provider', 'Source_In-N-Out Burger', 'Source_Instagram', 'Source_Intel', 'Source_Internet', 'Source_Internet search', 'Source_Investments', 'Source_Koch Industries', \"Source_L'Oréal\", 'Source_LED lighting', 'Source_LG', 'Source_LVMH', 'Source_Lego', 'Source_LinkedIn', 'Source_Little Caesars', 'Source_Lululemon', 'Source_Luxury goods', 'Source_Manufacturing', 'Source_Microsoft', 'Source_Mining', 'Source_Motors', 'Source_Multiple', 'Source_Nascar, racing', 'Source_Netflix', 'Source_Netscape, investments', 'Source_New Balance', 'Source_New England Patriots', 'Source_Nike', 'Source_Nutella, chocolates', 'Source_Patagonia', 'Source_Petro Fibre', 'Source_Petro Firbe', 'Source_Philadelphia Eagles', 'Source_Quicken Loans', 'Source_Real Estate', 'Source_Real estate', 'Source_Red Bull', 'Source_Reebok', 'Source_SAP', 'Source_Samsung', 'Source_Sears', 'Source_Semiconductor materials', 'Source_Shipping', 'Source_Shoes', 'Source_Slim-Fast', 'Source_Smartphones', 'Source_Snapchat', 'Source_Spotify', 'Source_Starbucks', 'Source_TD Ameritrade', 'Source_TV broadcasting', 'Source_TV network, investments', 'Source_TV programs', 'Source_TV shows', 'Source_TV, movie production', 'Source_Tesla, SpaceX', 'Source_TikTok', 'Source_Toyota dealerships', 'Source_Transportation', 'Source_Twitter, Square', 'Source_U-Haul', 'Source_Uber', 'Source_Urban Outfitters', 'Source_Waffle House', 'Source_Walmart', 'Source_Walmart, logistics', 'Source_Washington Football Team', 'Source_WeWork', 'Source_WhatsApp', 'Source_Yahoo', 'Source_Zara', 'Source_Zoom Video Communications', 'Source_accounting services', 'Source_adhesives', 'Source_advertising', 'Source_aerospace', 'Source_agribusiness', 'Source_agriculture', 'Source_agriculture, land', 'Source_agriculture, water', 'Source_agrochemicals', 'Source_air compressors', 'Source_aircraft leasing', 'Source_airline', 'Source_airlines', 'Source_airport', 'Source_airport management', 'Source_airports, investments', 'Source_alcohol', 'Source_alcohol, real estate', 'Source_aluminum', 'Source_aluminum products', 'Source_aluminum, diversified  ', 'Source_aluminum, utilities', 'Source_animal health, investments', 'Source_apparel', 'Source_appliances', 'Source_art', 'Source_art collection', 'Source_art, car dealerships', 'Source_asset management', 'Source_auto dealers, investments', 'Source_auto dealerships', 'Source_auto loans', 'Source_auto parts', 'Source_auto repair', 'Source_automobiles', 'Source_automobiles, batteries', 'Source_automotive', 'Source_automotive brakes', 'Source_automotive technology', 'Source_aviation', 'Source_bakeries', 'Source_banking', 'Source_banking, credit cards', 'Source_banking, insurance', 'Source_banking, insurance, media', 'Source_banking, investments', 'Source_banking, minerals', 'Source_banking, oil', 'Source_banking, property', 'Source_banking, real estate', 'Source_banking, tobacco', 'Source_banks, real estate', 'Source_bars', 'Source_batteries', 'Source_batteries, automobiles', 'Source_batteries, investments', 'Source_battery components', 'Source_beauty products', 'Source_beef packing', 'Source_beef processing', 'Source_beer', 'Source_beverages', 'Source_beverages, pharmaceuticals', 'Source_biochemicals', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_biotech investing', 'Source_biotech, investments', 'Source_biotechnology', 'Source_blockchain technology', 'Source_blockchain, technology', 'Source_book distribution, transportation', 'Source_brakes, investments', 'Source_brewery', 'Source_building materials', 'Source_business software', 'Source_cable', 'Source_cable TV, investments', 'Source_cable television', 'Source_call centers', 'Source_cameras, software', 'Source_candy', 'Source_candy, pet food', 'Source_car dealerships', 'Source_car rentals', 'Source_carbon fiber products', 'Source_carpet', 'Source_cars', 'Source_cashmere', 'Source_casinos', 'Source_casinos, banking', 'Source_casinos, hotels', 'Source_casinos, mixed martial arts', 'Source_casinos, property, energy', 'Source_casinos/hotels', 'Source_cement', 'Source_cement, sugar', 'Source_cheese', 'Source_chemical products', 'Source_chemicals', 'Source_chemicals, investments', 'Source_chemicals, logistics', 'Source_chemicals, spandex', 'Source_chewing gum', 'Source_chicken processing', 'Source_cleaning products', 'Source_clinical diagnostics', 'Source_clinical trials', 'Source_cloud communications', 'Source_cloud computing', 'Source_coal', 'Source_coal mines', 'Source_coal, fertilizers', 'Source_coal, investments', 'Source_cobalt', 'Source_coffee', 'Source_coffee, shipping', 'Source_coking', 'Source_commodities', 'Source_communication equipment', 'Source_communications', 'Source_computer hardware', 'Source_computer services, real estate', 'Source_computer services, telecom', 'Source_computer software', 'Source_conglomerate', 'Source_construction', 'Source_construction equipment', 'Source_construction equipment, media', 'Source_construction materials', 'Source_construction, investments', 'Source_construction, media', 'Source_construction, mining', 'Source_construction, mining machinery', 'Source_construction, pipes, banking', 'Source_construction, real estate', 'Source_consumer', 'Source_consumer electronics', 'Source_consumer goods', 'Source_consumer products, banking', 'Source_convenience stores', 'Source_convinience stores', 'Source_copper, poultry', 'Source_cosmetics', 'Source_cosmetics, reality TV', 'Source_cruises', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_cybersecurity', 'Source_dairy', 'Source_dairy & consumer products', 'Source_damaged cars', 'Source_data analytics', 'Source_data centers', 'Source_data management', 'Source_defense', 'Source_defense, hotels', 'Source_dental implants', 'Source_dental products', 'Source_department stores', 'Source_diagnostics', 'Source_diamond jewelry', 'Source_diamonds', 'Source_digital advertising', 'Source_discount brokerage', 'Source_diversified  ', 'Source_drilling, shipping', 'Source_drones', 'Source_drugs', 'Source_drugstores', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_e-commerce software', 'Source_eBay', 'Source_eBay, PayPal', 'Source_education', 'Source_education technology', 'Source_electric bikes, scooters', 'Source_electric components', 'Source_electric equipment', 'Source_electric scooters', 'Source_electric vehicles', 'Source_electrical equipment', 'Source_electrodes', 'Source_electronic components', 'Source_electronic trading', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_email marketing', 'Source_employment agency', 'Source_energy', 'Source_energy drink', 'Source_energy drinks', 'Source_energy drinks,investments', 'Source_energy services', 'Source_energy, banking, construction', 'Source_energy, chemicals', 'Source_energy, investments', 'Source_energy, real estate', 'Source_energy, sports', 'Source_engineering', 'Source_engineering, automotive', 'Source_engineering, construction', 'Source_entertainment', 'Source_executive search, investments', 'Source_express delivery', 'Source_fashion', 'Source_fashion investments', 'Source_fashion retail', 'Source_fashion retail, investments', 'Source_fashion retailer', 'Source_fast food', 'Source_fasteners', 'Source_feed', 'Source_fertilizer', 'Source_fertilizer, real estate', 'Source_fertilizers', 'Source_fiber optic cables', 'Source_finance', 'Source_finance and investments', 'Source_finance, real estate', 'Source_finance, telecommunications', 'Source_financial information', 'Source_financial services', 'Source_financial services, property', 'Source_financial services★', 'Source_financial technology', 'Source_fintech', 'Source_fitness equipment', 'Source_flavorings', 'Source_flavors and fragrances', 'Source_flipkart', 'Source_flooring', 'Source_food', 'Source_food & beverage retailing', 'Source_food delivery app', 'Source_food distribution', 'Source_food processing', 'Source_food service', 'Source_food services', 'Source_food, beverages', 'Source_foods', 'Source_footwear', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture', 'Source_furniture retailing', 'Source_gambling', 'Source_gambling products', 'Source_gambling software', 'Source_game software', 'Source_gaming', 'Source_gas stations', 'Source_gas, chemicals', 'Source_generic drugs', 'Source_glass', 'Source_gold', 'Source_graphite electrodes', 'Source_grocery delivery service', 'Source_grocery stores', 'Source_hair care products', 'Source_hair dryers', 'Source_hair products, tequila', 'Source_hand tools', 'Source_hardware', 'Source_health IT', 'Source_health care', 'Source_health clinics', 'Source_health insurance', 'Source_healthcare', 'Source_healthcare services', 'Source_hearing aids', 'Source_heating and cooling equipment', 'Source_heating, cooling equipment', 'Source_hedge fund', 'Source_hedge funds', 'Source_herbal products', 'Source_high speed trading', 'Source_home appliances', 'Source_home building', 'Source_home building, banking', 'Source_home furnishings', 'Source_home improvement stores', 'Source_home sales', 'Source_home-cleaning robots', 'Source_homebuilder', 'Source_homebuilding', 'Source_homebuilding, insurance', 'Source_hospitals', 'Source_hospitals, health care', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, diversified  ', 'Source_hotels, energy', 'Source_hotels, investments', 'Source_hotels, motels', 'Source_household chemicals', 'Source_hydraulic machinery', 'Source_industrial equipment', 'Source_industrial explosives', 'Source_industrial lasers', 'Source_industrial machinery', 'Source_infant formula', 'Source_information technology', 'Source_infrastructure', 'Source_infrastructure, commodities', 'Source_insurance', 'Source_insurance, NFL team', 'Source_insurance, beverages', 'Source_insurance, investments', 'Source_internet', 'Source_internet media', 'Source_internet search', 'Source_internet service provider', 'Source_investing', 'Source_investment', 'Source_investments', 'Source_investments, art', 'Source_investments, energy', 'Source_investments, real estate', 'Source_jewellery', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_leveraged buyouts', 'Source_lighting', 'Source_lighting installations', 'Source_liquefied natural gas', 'Source_liquor', 'Source_lithium', 'Source_lithium batteries', 'Source_lithium battery', 'Source_lithium-ion battery cap', 'Source_live entertainment', 'Source_live streaming service', 'Source_logistics', 'Source_low-cost airlines', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_magazines, media', 'Source_magnetic switches', 'Source_manufacturing', 'Source_manufacturing, investment', 'Source_manufacturing, investments', 'Source_mapping software', 'Source_materials', 'Source_measuring instruments', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, investments', 'Source_media, real estate', 'Source_medical devices', 'Source_medical diagnostic equipment', 'Source_medical diagnostics', 'Source_medical equipment', 'Source_medical packaging', 'Source_medical patents', 'Source_medical products', 'Source_medical technology', 'Source_medical testing', 'Source_messaging app', 'Source_metal processing', 'Source_metals', 'Source_metals, coal', 'Source_metals, energy', 'Source_metals, mining', 'Source_metalworking tools', 'Source_microbiology', 'Source_microchip testing', 'Source_mining', 'Source_mining, banking', 'Source_mining, banking, hotels', 'Source_mining, commodities', 'Source_mining, copper products', 'Source_mining, metals, machinery', 'Source_mobile games', 'Source_mobile gaming', 'Source_mobile payments', 'Source_mobile phone retailer', 'Source_mobile phones', 'Source_money management', 'Source_mortgage lender★', 'Source_motorcycle loans', 'Source_motorcycles', 'Source_motorhomes, RVs', 'Source_motors', 'Source_movie making', 'Source_movies, investments', 'Source_movies, record labels', 'Source_music, chemicals', 'Source_music, cosmetics', 'Source_music, sneakers', 'Source_mutual funds', 'Source_natural gas', 'Source_natural gas distribution', 'Source_natural gas, fertilizers', 'Source_navigation equipment', 'Source_newspapers, TV network', 'Source_nonferrous', 'Source_nutrition, wellness products', 'Source_office real estate', 'Source_oil', 'Source_oil & gas', 'Source_oil & gas, banking', 'Source_oil & gas, investments', 'Source_oil and gas', 'Source_oil and gas, IT, lotteries', 'Source_oil refinery', 'Source_oil, banking, telecom', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oilfield equipment', 'Source_online dating', 'Source_online gambling', 'Source_online games', 'Source_online games, investments', 'Source_online gaming', 'Source_online marketplace', 'Source_online media', 'Source_online media, Dallas Mavericks', 'Source_online payments', 'Source_online recruitment', 'Source_online retail', 'Source_online retailing', 'Source_online services', 'Source_optical components', 'Source_optometry', 'Source_orange juice', 'Source_package delivery', 'Source_packaged meats', 'Source_packaging', 'Source_paint', 'Source_paints', 'Source_palm oil', 'Source_palm oil, nickel mining', 'Source_palm oil, property', 'Source_palm oil, shipping, property', 'Source_paper', 'Source_paper & related products', 'Source_paper and pulp', 'Source_payment software', 'Source_payments software', 'Source_payments technology', 'Source_payments, banking', 'Source_payroll processing', 'Source_payroll software', 'Source_pearlescent pigments', 'Source_personal care goods', 'Source_pest control', 'Source_pet food', 'Source_petrochemicals', 'Source_petroleum, diversified  ', 'Source_phamaceuticals', 'Source_pharmaceutical', 'Source_pharmaceutical services', 'Source_pharmaceuticals', 'Source_pharmaceuticals, diversified  ', 'Source_pharmaceuticals, food', 'Source_pharmaceuticals, medical equipment', 'Source_pharmaceuticals, power', 'Source_pharmacies', 'Source_photovoltaic equipment', 'Source_photovoltaics', 'Source_pig breeding', 'Source_pipe manufacturing', 'Source_pipelines', 'Source_plastic', 'Source_plastics', 'Source_plumbing fixtures', 'Source_plush toys, real estate', 'Source_polyester', 'Source_ports', 'Source_poultry genetics', 'Source_poultry processing', 'Source_powdered metal', 'Source_power equipment', 'Source_power strip', 'Source_power supply equipment', 'Source_precision machinery', 'Source_price comparison website', 'Source_printed circuit boards', 'Source_printing', 'Source_private equity', 'Source_private equity★', 'Source_pro sports teams', 'Source_property, healthcare', 'Source_prosthetics', 'Source_publishing', 'Source_pulp and paper', 'Source_quartz products', 'Source_readymade garments', 'Source_real estate', 'Source_real estate developer', 'Source_real estate development', 'Source_real estate services', 'Source_real estate, airport', 'Source_real estate, construction', 'Source_real estate, diversified  ', 'Source_real estate, electronics', 'Source_real estate, gambling', 'Source_real estate, investments', 'Source_real estate, manufacturing', 'Source_real estate, media', 'Source_real estate, oil, cars, sports', 'Source_real estate, private equity', 'Source_real estate, retail', 'Source_real estate, shipping', 'Source_refinery, chemicals', 'Source_renewable energy', 'Source_restaurant', 'Source_restaurants', 'Source_retail', 'Source_retail, investments', 'Source_retail, media', 'Source_retail, real estate', 'Source_retailing', 'Source_roofing', 'Source_salsa', 'Source_sandwich chain', 'Source_satellite TV', 'Source_scaffolding, cement mixers', 'Source_scientific equipment', 'Source_security', 'Source_security services', 'Source_security software', 'Source_seed production', 'Source_semiconductor', 'Source_semiconductor devices', 'Source_semiconductors', 'Source_sensor systems', 'Source_sensor technology', 'Source_sensors', 'Source_sensors★', 'Source_shipbuilding', 'Source_shipping', 'Source_shipping, airlines', 'Source_shipping, seafood', 'Source_shoes', 'Source_shopping centers', 'Source_shopping malls', 'Source_silicon', 'Source_smartphone components', 'Source_smartphone screens', 'Source_smartphones', 'Source_snack bars', 'Source_snacks, beverages', 'Source_sneakers, sportswear', 'Source_social media', 'Source_social network', 'Source_soft drinks, fast food', 'Source_software', 'Source_software firm', 'Source_software services', 'Source_software, investments', 'Source_solar energy', 'Source_solar energy equipment', 'Source_solar equipment', 'Source_solar inverters', 'Source_solar panel components', 'Source_solar panel materials', 'Source_solar wafers and modules', 'Source_soy sauce', 'Source_specialty chemicals', 'Source_spirits', 'Source_sporting goods retail', 'Source_sports apparel', 'Source_sports data', 'Source_sports drink', 'Source_sports retailing', 'Source_sports team', 'Source_sports teams', 'Source_sports, real estate', 'Source_staffing & recruiting', 'Source_stationery', 'Source_steel', 'Source_steel pipes, diversified  ', 'Source_steel, coal', 'Source_steel, diversified  ', 'Source_steel, investments', 'Source_steel, telecom, investments', 'Source_steel, transport', 'Source_stock brokerage', 'Source_stock exchange', 'Source_stock photos', 'Source_storage facilities', 'Source_sugar, ethanol', 'Source_sunglasses', 'Source_supermarkets', 'Source_tech investments', 'Source_technology', 'Source_telecom', 'Source_telecom services', 'Source_telecom, investments', 'Source_telecom, oil', 'Source_telecommunication', 'Source_telecommunications', 'Source_temp agency', 'Source_tequila', 'Source_textiles', 'Source_textiles, paper', 'Source_ticketing service', 'Source_tire', 'Source_tires', 'Source_tires, diversified  ', 'Source_tobacco', 'Source_tobacco distribution, retail', 'Source_toll roads', 'Source_touch screens', 'Source_tourism, cultural industry', 'Source_toys', 'Source_tractors', 'Source_trading, investments', 'Source_train cars', 'Source_transportation', 'Source_travel', 'Source_trucking', 'Source_two-wheelers, finance', 'Source_used cars', 'Source_utilities, diversified  ', 'Source_utilities, real estate', 'Source_vaccine & shoes', 'Source_vaccines', 'Source_valve manufacturing', 'Source_valves', 'Source_venture capital', 'Source_venture capital, Google', 'Source_video games', 'Source_video games, pachinko', 'Source_video streaming', 'Source_video streaming app', 'Source_video surveillance', 'Source_videogames', 'Source_vodka', 'Source_waste disposal', 'Source_web hosting', 'Source_wine', 'Source_wireless networking gear', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ', 'Age_binned', 'Networth_binned', 'age_to_networth', 'networth_to_age', 'age_squared', 'log_networth']\n",
      "Столбцы в val_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'Country_Argentina', 'Country_Australia', 'Country_Belgium', 'Country_Brazil', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Cyprus', 'Country_Denmark', 'Country_Egypt', 'Country_Finland', 'Country_France', 'Country_Germany', 'Country_Greece', 'Country_Hong Kong', 'Country_Hungary', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Liechtenstein', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_New Zealand', 'Country_Norway', 'Country_Philippines', 'Country_Poland', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_St. Kitts and Nevis', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Tanzania', 'Country_Thailand', 'Country_Turkey', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Source_Airbnb', 'Source_Amazon', 'Source_Apple, Disney', 'Source_BMW', 'Source_Berkshire Hathaway', 'Source_Best Buy', 'Source_Cargill', 'Source_Chanel', 'Source_Cirque du Soleil', 'Source_Coca Cola Israel', 'Source_Electric power', 'Source_Estee Lauder', 'Source_Facebook', 'Source_FedEx', 'Source_Formula One', 'Source_Gap', 'Source_Google', 'Source_Hyundai', 'Source_IKEA', 'Source_Indianapolis Colts', 'Source_LG', 'Source_Manufacturing', 'Source_Marvel comics', 'Source_Microsoft', 'Source_Pinterest', 'Source_Real Estate', 'Source_Real estate', 'Source_Roku', 'Source_Samsung', 'Source_San Francisco 49ers', 'Source_Snapchat', 'Source_Spanx', 'Source_Spotify', 'Source_Star Wars', 'Source_TV broadcasting', 'Source_Twitter', 'Source_Uber', 'Source_Under Armour', 'Source_Virgin', 'Source_Walmart', 'Source_acoustic components', 'Source_adhesives', 'Source_aerospace', 'Source_agribusiness', 'Source_airports, real estate', 'Source_alcoholic beverages', 'Source_aluminum products', 'Source_amusement parks', 'Source_appliances', 'Source_art collection', 'Source_asset management', 'Source_auto loans', 'Source_auto parts', 'Source_bakery chain', 'Source_banking', 'Source_banking, minerals', 'Source_batteries', 'Source_beer', 'Source_beer distribution', 'Source_beer, investments', 'Source_beverages', 'Source_billboards, Los Angeles Angels', 'Source_biomedical products', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_budget airline', 'Source_building materials', 'Source_business software', 'Source_call centers', 'Source_candy, pet food', 'Source_car dealerships', 'Source_casinos', 'Source_casinos, real estate', 'Source_cement', 'Source_cement, diversified  ', 'Source_chemical', 'Source_chemicals', 'Source_cloud computing', 'Source_cloud storage service', 'Source_coal', 'Source_coffee', 'Source_coffee makers', 'Source_commodities', 'Source_commodities, investments', 'Source_computer games', 'Source_computer networking', 'Source_computer software', 'Source_construction', 'Source_consumer goods', 'Source_consumer products', 'Source_cooking appliances', 'Source_copper, education', 'Source_copy machines, software', 'Source_cosmetics', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_damaged cars', 'Source_data centers', 'Source_defense contractor', 'Source_dental materials', 'Source_diversified  ', 'Source_drug distribution', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_eBay', 'Source_ecommerce', 'Source_edible oil', 'Source_edtech', 'Source_education', 'Source_electrical equipment', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_energy services', 'Source_entertainment', 'Source_eyeglasses', 'Source_fashion retail', 'Source_fast fashion', 'Source_finance', 'Source_finance services', 'Source_financial services', 'Source_fine jewelry', 'Source_fintech', 'Source_fish farming', 'Source_flavorings', 'Source_food', 'Source_food delivery service', 'Source_food manufacturing', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture retailing', 'Source_garments', 'Source_gas stations, retail', 'Source_generic drugs', 'Source_glass', 'Source_greek yogurt', 'Source_gym equipment', 'Source_hardware stores', 'Source_health products', 'Source_healthcare IT', 'Source_hedge funds', 'Source_home appliances', 'Source_home furnishings', 'Source_homebuilding', 'Source_homebuilding, NFL team', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, investments', 'Source_household chemicals', 'Source_hygiene products', 'Source_imaging systems', 'Source_insurance', 'Source_insurance, NFL team', 'Source_internet and software', 'Source_internet media', 'Source_internet, telecom', 'Source_investing', 'Source_investment banking', 'Source_investment research', 'Source_investments', 'Source_iron ore mining', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_liquor', 'Source_lithium', 'Source_logistics', 'Source_logistics, baseball', 'Source_logistics, real estate', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_manufacturing', 'Source_manufacturing, investments', 'Source_mattresses', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, tech', 'Source_medical cosmetics', 'Source_medical devices', 'Source_medical equipment', 'Source_medical services', 'Source_messaging software', 'Source_metals', 'Source_metals, banking, fertilizers', 'Source_mining', 'Source_mining, commodities', 'Source_mining, metals', 'Source_mining, steel', 'Source_money management', 'Source_motorcycles', 'Source_movies', 'Source_movies, digital effects', 'Source_natural gas', 'Source_non-ferrous metals', 'Source_nutritional supplements', 'Source_oil', 'Source_oil & gas, investments', 'Source_oil refining', 'Source_oil trading', 'Source_oil, banking', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oil, semiconductor', 'Source_online gambling', 'Source_online games', 'Source_online media', 'Source_online retail', 'Source_package delivery', 'Source_packaging', 'Source_paper', 'Source_paper manufacturing', 'Source_payment processing', 'Source_payroll services', 'Source_pet food', 'Source_petrochemicals', 'Source_pharma retailing', 'Source_pharmaceutical', 'Source_pharmaceutical ingredients', 'Source_pharmaceuticals', 'Source_pipelines', 'Source_plastic pipes', 'Source_poultry', 'Source_poultry breeding', 'Source_power strips', 'Source_precious metals, real estate', 'Source_printed circuit boards', 'Source_private equity', 'Source_publishing', 'Source_pulp and paper', 'Source_real estate', 'Source_real estate finance', 'Source_real estate, hotels', 'Source_real estate, investments', 'Source_record label', 'Source_refinery, chemicals', 'Source_restaurants', 'Source_retail', 'Source_retail & gas stations', 'Source_retail chain', 'Source_retail stores', 'Source_retail, agribusiness', 'Source_retail, investments', 'Source_rubber gloves', 'Source_security software', 'Source_self storage', 'Source_semiconductor', 'Source_semiconductors', 'Source_sensor systems', 'Source_shipping', 'Source_shoes', 'Source_smartphone screens', 'Source_smartphones', 'Source_software', 'Source_solar panels', 'Source_soy sauce', 'Source_sporting goods', 'Source_sports', 'Source_sports apparel', 'Source_staffing, Baltimore Ravens', 'Source_stationery', 'Source_steel', 'Source_steel production', 'Source_steel, diversified  ', 'Source_steel, mining', 'Source_supermarkets', 'Source_supermarkets, investments', 'Source_surveillance equipment', 'Source_technology', 'Source_telecom', 'Source_telecom, lotteries, insurance', 'Source_telecoms, media, oil-services', 'Source_testing equipment', 'Source_textile, chemicals', 'Source_textiles, apparel', 'Source_textiles, petrochemicals', 'Source_timberland, lumber mills', 'Source_titanium', 'Source_transport, logistics', 'Source_two-wheelers', 'Source_used cars', 'Source_vaccines', 'Source_vacuums', 'Source_venture capital', 'Source_venture capital investing', 'Source_video games', 'Source_wedding dresses', 'Source_wind turbines', 'Source_winter jackets', 'Source_wire & cables, paints', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ', 'Age_binned', 'Networth_binned', 'age_to_networth', 'networth_to_age', 'age_squared', 'log_networth']\n",
      "Столбцы в test_data_encoded: ['Rank ', 'Name', 'Networth', 'Age', 'Country_Argentina', 'Country_Australia', 'Country_Belgium', 'Country_Brazil', 'Country_Canada', 'Country_Chile', 'Country_China', 'Country_Cyprus', 'Country_Denmark', 'Country_Egypt', 'Country_Finland', 'Country_France', 'Country_Germany', 'Country_Greece', 'Country_Hong Kong', 'Country_Hungary', 'Country_India', 'Country_Indonesia', 'Country_Ireland', 'Country_Israel', 'Country_Italy', 'Country_Japan', 'Country_Kazakhstan', 'Country_Lebanon', 'Country_Liechtenstein', 'Country_Malaysia', 'Country_Mexico', 'Country_Monaco', 'Country_New Zealand', 'Country_Norway', 'Country_Philippines', 'Country_Poland', 'Country_Romania', 'Country_Russia', 'Country_Singapore', 'Country_Slovakia', 'Country_South Africa', 'Country_South Korea', 'Country_Spain', 'Country_St. Kitts and Nevis', 'Country_Sweden', 'Country_Switzerland', 'Country_Taiwan', 'Country_Tanzania', 'Country_Thailand', 'Country_Turkey', 'Country_United Arab Emirates', 'Country_United Kingdom', 'Country_United States', 'Source_Airbnb', 'Source_Amazon', 'Source_Apple, Disney', 'Source_BMW', 'Source_Berkshire Hathaway', 'Source_Best Buy', 'Source_Cargill', 'Source_Chanel', 'Source_Cirque du Soleil', 'Source_Coca Cola Israel', 'Source_Electric power', 'Source_Estee Lauder', 'Source_Facebook', 'Source_FedEx', 'Source_Formula One', 'Source_Gap', 'Source_Google', 'Source_Hyundai', 'Source_IKEA', 'Source_Indianapolis Colts', 'Source_LG', 'Source_Manufacturing', 'Source_Marvel comics', 'Source_Microsoft', 'Source_Pinterest', 'Source_Real Estate', 'Source_Real estate', 'Source_Roku', 'Source_Samsung', 'Source_San Francisco 49ers', 'Source_Snapchat', 'Source_Spanx', 'Source_Spotify', 'Source_Star Wars', 'Source_TV broadcasting', 'Source_Twitter', 'Source_Uber', 'Source_Under Armour', 'Source_Virgin', 'Source_Walmart', 'Source_acoustic components', 'Source_adhesives', 'Source_aerospace', 'Source_agribusiness', 'Source_airports, real estate', 'Source_alcoholic beverages', 'Source_aluminum products', 'Source_amusement parks', 'Source_appliances', 'Source_art collection', 'Source_asset management', 'Source_auto loans', 'Source_auto parts', 'Source_bakery chain', 'Source_banking', 'Source_banking, minerals', 'Source_batteries', 'Source_beer', 'Source_beer distribution', 'Source_beer, investments', 'Source_beverages', 'Source_billboards, Los Angeles Angels', 'Source_biomedical products', 'Source_biopharmaceuticals', 'Source_biotech', 'Source_budget airline', 'Source_building materials', 'Source_business software', 'Source_call centers', 'Source_candy, pet food', 'Source_car dealerships', 'Source_casinos', 'Source_casinos, real estate', 'Source_cement', 'Source_cement, diversified  ', 'Source_chemical', 'Source_chemicals', 'Source_cloud computing', 'Source_cloud storage service', 'Source_coal', 'Source_coffee', 'Source_coffee makers', 'Source_commodities', 'Source_commodities, investments', 'Source_computer games', 'Source_computer networking', 'Source_computer software', 'Source_construction', 'Source_consumer goods', 'Source_consumer products', 'Source_cooking appliances', 'Source_copper, education', 'Source_copy machines, software', 'Source_cosmetics', 'Source_cryptocurrency', 'Source_cryptocurrency exchange', 'Source_damaged cars', 'Source_data centers', 'Source_defense contractor', 'Source_dental materials', 'Source_diversified  ', 'Source_drug distribution', 'Source_e-cigarettes', 'Source_e-commerce', 'Source_eBay', 'Source_ecommerce', 'Source_edible oil', 'Source_edtech', 'Source_education', 'Source_electrical equipment', 'Source_electronics', 'Source_electronics components', 'Source_elevators, escalators', 'Source_energy services', 'Source_entertainment', 'Source_eyeglasses', 'Source_fashion retail', 'Source_fast fashion', 'Source_finance', 'Source_finance services', 'Source_financial services', 'Source_fine jewelry', 'Source_fintech', 'Source_fish farming', 'Source_flavorings', 'Source_food', 'Source_food delivery service', 'Source_food manufacturing', 'Source_forestry, mining', 'Source_frozen foods', 'Source_furniture retailing', 'Source_garments', 'Source_gas stations, retail', 'Source_generic drugs', 'Source_glass', 'Source_greek yogurt', 'Source_gym equipment', 'Source_hardware stores', 'Source_health products', 'Source_healthcare IT', 'Source_hedge funds', 'Source_home appliances', 'Source_home furnishings', 'Source_homebuilding', 'Source_homebuilding, NFL team', 'Source_hospitals, health insurance', 'Source_hotels', 'Source_hotels, investments', 'Source_household chemicals', 'Source_hygiene products', 'Source_imaging systems', 'Source_insurance', 'Source_insurance, NFL team', 'Source_internet and software', 'Source_internet media', 'Source_internet, telecom', 'Source_investing', 'Source_investment banking', 'Source_investment research', 'Source_investments', 'Source_iron ore mining', 'Source_jewelry', 'Source_kitchen appliances', 'Source_laboratory services', 'Source_liquor', 'Source_lithium', 'Source_logistics', 'Source_logistics, baseball', 'Source_logistics, real estate', 'Source_luxury goods', 'Source_machine tools', 'Source_machinery', 'Source_manufacturing', 'Source_manufacturing, investments', 'Source_mattresses', 'Source_meat processing', 'Source_media', 'Source_media, automotive', 'Source_media, tech', 'Source_medical cosmetics', 'Source_medical devices', 'Source_medical equipment', 'Source_medical services', 'Source_messaging software', 'Source_metals', 'Source_metals, banking, fertilizers', 'Source_mining', 'Source_mining, commodities', 'Source_mining, metals', 'Source_mining, steel', 'Source_money management', 'Source_motorcycles', 'Source_movies', 'Source_movies, digital effects', 'Source_natural gas', 'Source_non-ferrous metals', 'Source_nutritional supplements', 'Source_oil', 'Source_oil & gas, investments', 'Source_oil refining', 'Source_oil trading', 'Source_oil, banking', 'Source_oil, gas', 'Source_oil, investments', 'Source_oil, real estate', 'Source_oil, semiconductor', 'Source_online gambling', 'Source_online games', 'Source_online media', 'Source_online retail', 'Source_package delivery', 'Source_packaging', 'Source_paper', 'Source_paper manufacturing', 'Source_payment processing', 'Source_payroll services', 'Source_pet food', 'Source_petrochemicals', 'Source_pharma retailing', 'Source_pharmaceutical', 'Source_pharmaceutical ingredients', 'Source_pharmaceuticals', 'Source_pipelines', 'Source_plastic pipes', 'Source_poultry', 'Source_poultry breeding', 'Source_power strips', 'Source_precious metals, real estate', 'Source_printed circuit boards', 'Source_private equity', 'Source_publishing', 'Source_pulp and paper', 'Source_real estate', 'Source_real estate finance', 'Source_real estate, hotels', 'Source_real estate, investments', 'Source_record label', 'Source_refinery, chemicals', 'Source_restaurants', 'Source_retail', 'Source_retail & gas stations', 'Source_retail chain', 'Source_retail stores', 'Source_retail, agribusiness', 'Source_retail, investments', 'Source_rubber gloves', 'Source_security software', 'Source_self storage', 'Source_semiconductor', 'Source_semiconductors', 'Source_sensor systems', 'Source_shipping', 'Source_shoes', 'Source_smartphone screens', 'Source_smartphones', 'Source_software', 'Source_solar panels', 'Source_soy sauce', 'Source_sporting goods', 'Source_sports', 'Source_sports apparel', 'Source_staffing, Baltimore Ravens', 'Source_stationery', 'Source_steel', 'Source_steel production', 'Source_steel, diversified  ', 'Source_steel, mining', 'Source_supermarkets', 'Source_supermarkets, investments', 'Source_surveillance equipment', 'Source_technology', 'Source_telecom', 'Source_telecom, lotteries, insurance', 'Source_telecoms, media, oil-services', 'Source_testing equipment', 'Source_textile, chemicals', 'Source_textiles, apparel', 'Source_textiles, petrochemicals', 'Source_timberland, lumber mills', 'Source_titanium', 'Source_transport, logistics', 'Source_two-wheelers', 'Source_used cars', 'Source_vaccines', 'Source_vacuums', 'Source_venture capital', 'Source_venture capital investing', 'Source_video games', 'Source_wedding dresses', 'Source_wind turbines', 'Source_winter jackets', 'Source_wire & cables, paints', 'Industry_Automotive ', 'Industry_Construction & Engineering ', 'Industry_Energy ', 'Industry_Fashion & Retail ', 'Industry_Finance & Investments ', 'Industry_Food & Beverage ', 'Industry_Gambling & Casinos ', 'Industry_Healthcare ', 'Industry_Logistics ', 'Industry_Manufacturing ', 'Industry_Media & Entertainment ', 'Industry_Metals & Mining ', 'Industry_Real Estate ', 'Industry_Service ', 'Industry_Sports ', 'Industry_Technology ', 'Industry_Telecom ', 'Industry_diversified   ', 'Age_binned', 'Networth_binned', 'age_to_networth', 'networth_to_age', 'age_squared', 'log_networth']\n",
      "Empty DataFrame\n",
      "Columns: [Rank , Name, Networth, Age, LogNetworth, Networth_scaled, Country_Algeria, Country_Argentina, Country_Australia, Country_Austria, Country_Barbados, Country_Belgium, Country_Belize, Country_Brazil, Country_Bulgaria, Country_Canada, Country_Chile, Country_China, Country_Colombia, Country_Cyprus, Country_Czechia, Country_Denmark, Country_Egypt, Country_Estonia, Country_Eswatini (Swaziland), Country_Finland, Country_France, Country_Georgia, Country_Germany, Country_Greece, Country_Guernsey, Country_Hong Kong, Country_Hungary, Country_Iceland, Country_India, Country_Indonesia, Country_Ireland, Country_Israel, Country_Italy, Country_Japan, Country_Kazakhstan, Country_Lebanon, Country_Macau, Country_Malaysia, Country_Mexico, Country_Monaco, Country_Morocco, Country_Nepal, Country_Netherlands, Country_New Zealand, Country_Nigeria, Country_Norway, Country_Oman, Country_Peru, Country_Philippines, Country_Poland, Country_Portugal, Country_Qatar, Country_Romania, Country_Russia, Country_Singapore, Country_Slovakia, Country_South Africa, Country_South Korea, Country_Spain, Country_Sweden, Country_Switzerland, Country_Taiwan, Country_Thailand, Country_Turkey, Country_Ukraine, Country_United Arab Emirates, Country_United Kingdom, Country_United States, Country_Uruguay, Country_Venezuela, Country_Vietnam, Country_Zimbabwe, Source_3D printing, Source_AOL, Source_Airbnb, Source_Aldi, Trader Joe's, Source_Aluminium, Source_Amazon, Source_Apple, Source_BMW, pharmaceuticals, Source_Banking, Source_Berkshire Hathaway, Source_Bloomberg LP, Source_Campbell Soup, Source_Cargill, Source_Carnival Cruises, Source_Chanel, Source_Charlotte Hornets, endorsements, Source_Chemicals, Source_Chick-fil-A, Source_Coca Cola Israel, Source_Coca-Cola bottler, Source_Columbia Sportswear, Source_Comcast, ...]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 869 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\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",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\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",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Rank   Networth  Age  Country_Algeria  Country_Argentina  \\\n",
      "id                                                             \n",
      "0       1     219.0   50            False              False   \n",
      "1       2     171.0   58            False              False   \n",
      "2       3     158.0   73            False              False   \n",
      "3       4     129.0   66            False              False   \n",
      "4       5     118.0   91            False              False   \n",
      "\n",
      "    Country_Australia  Country_Austria  Country_Barbados  Country_Belgium  \\\n",
      "id                                                                          \n",
      "0               False            False             False            False   \n",
      "1               False            False             False            False   \n",
      "2               False            False             False            False   \n",
      "3               False            False             False            False   \n",
      "4               False            False             False            False   \n",
      "\n",
      "    Country_Belize  ...  Industry_Sports   Industry_Technology   \\\n",
      "id                  ...                                           \n",
      "0            False  ...             False                 False   \n",
      "1            False  ...             False                  True   \n",
      "2            False  ...             False                 False   \n",
      "3            False  ...             False                  True   \n",
      "4            False  ...             False                 False   \n",
      "\n",
      "    Industry_Telecom   Industry_diversified     Age_binned  Networth_binned  \\\n",
      "id                                                                            \n",
      "0               False                    False           1                4   \n",
      "1               False                    False           2                3   \n",
      "2               False                    False           3                3   \n",
      "3               False                    False           2                2   \n",
      "4               False                    False           4                2   \n",
      "\n",
      "    age_to_networth  networth_to_age  age_squared  log_networth  \n",
      "id                                                               \n",
      "0          0.228311         4.380000         2500      5.389072  \n",
      "1          0.339181         2.948276         3364      5.141664  \n",
      "2          0.462025         2.164384         5329      5.062595  \n",
      "3          0.511628         1.954545         4356      4.859812  \n",
      "4          0.771186         1.296703         8281      4.770685  \n",
      "\n",
      "[5 rows x 997 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\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",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\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",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\Users\\goldfest\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Проверка наличия столбцов в DataFrame\n",
    "print(\"Столбцы в df:\", df.columns.tolist())\n",
    "print(\"Столбцы в train_data_encoded:\", train_data_encoded.columns.tolist())\n",
    "print(\"Столбцы в val_data_encoded:\", val_data_encoded.columns.tolist())\n",
    "print(\"Столбцы в test_data_encoded:\", test_data_encoded.columns.tolist())\n",
    "\n",
    "# Удаление дубликатов по всем столбцам (если нет уникального идентификатора)\n",
    "df = df.drop_duplicates()\n",
    "duplicates = train_data_encoded[train_data_encoded.duplicated(keep=False)]\n",
    "\n",
    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
    "df_encoded = df_encoded.drop_duplicates(keep='first')\n",
    "\n",
    "print(duplicates)\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='millionaires_data')\n",
    "\n",
    "# Добавление датафрейма с данными о миллионерах\n",
    "es = es.add_dataframe(dataframe_name='millionaires', dataframe=df_encoded, index='id')\n",
    "\n",
    "# Генерация признаков с помощью глубокой синтезы признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='millionaires', max_depth=2)\n",
    "\n",
    "# Выводим первые 5 строк сгенерированного набора признаков\n",
    "print(feature_matrix.head())\n",
    "\n",
    "# Удаление дубликатов из обучающей выборки\n",
    "train_data_encoded = train_data_encoded.drop_duplicates()\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(keep='first')  # or keep='last'\n",
    "\n",
    "# Определение сущностей (Создание EntitySet)\n",
    "es = ft.EntitySet(id='millionaires_data')\n",
    "\n",
    "es = es.add_dataframe(dataframe_name='millionaires', dataframe=train_data_encoded, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='millionaires', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества каждого набора признаков  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 11.98 секунд\n",
      "Среднеквадратичная ошибка: 17.43\n",
      "Коэффициент детерминации (R²): 0.27\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Предположим, что df уже определен и загружен\n",
    "\n",
    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
    "X = df.drop('Networth', axis=1)\n",
    "y = df['Networth']\n",
    "\n",
    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n",
    "X.fillna(X.median(), inplace=True)\n",
    "\n",
    "# Масштабирование признаков\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели с регуляризацией (Ridge)\n",
    "model = Ridge()\n",
    "\n",
    "# Настройка гиперпараметров с помощью GridSearchCV\n",
    "param_grid = {'alpha': [0.1, 1.0, 10.0, 100.0]}\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "\n",
    "# Начинаем отсчет времени\n",
    "start_time = time.time()\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Лучшая модель\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Предсказания и оценка модели\n",
    "val_predictions = best_model.predict(X_val)\n",
    "mse = mean_squared_error(y_val, val_predictions)\n",
    "r2 = r2_score(y_val, val_predictions)\n",
    "\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Коэффициент детерминации (R²): {r2:.2f}')\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_val, val_predictions, alpha=0.5)\n",
    "plt.plot([y_val.min(), y_val.max()], [y_val.min(), y_val.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая стоимость активов')\n",
    "plt.ylabel('Прогнозируемая стоимость активов')\n",
    "plt.title('Фактическая стоимость активов по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "# Выводы\n",
    "\n",
    "**Модель линейной регрессии (LinearRegression)** показала удовлетворительные результаты при прогнозировании стоимости активов миллионеров. Метрики качества и кросс-валидация позволяют предположить, что модель не сильно переобучена и может быть использована для практических целей.\n",
    "\n",
    "*Точность предсказаний:* Модель демонстрирует коэффициент детерминации (R²) 0.27, что указывает на умеренную часть вариации целевого признака (стоимости активов). Однако, значения среднеквадратичной ошибки (RMSE) остаются высокими (17.43), что свидетельствует о том, что модель не всегда точно предсказывает значения, особенно для объектов с высокими или низкими стоимостями активов.\n",
    "\n",
    "*Переобучение:* Разница между RMSE на обучающей и тестовой выборках незначительна, что указывает на то, что модель не склонна к переобучению. Однако в будущем стоит следить за этой метрикой при добавлении новых признаков или усложнении модели, чтобы избежать излишней подгонки под тренировочные данные. Также стоит быть осторожным и продолжать мониторинг этого показателя.\n",
    "\n",
    "*Кросс-валидация:* При кросс-валидации наблюдается небольшое увеличение ошибки RMSE по сравнению с тестовой выборкой (рост на 2-3%). Это может указывать на небольшую нестабильность модели при использовании разных подвыборок данных. Для повышения устойчивости модели возможно стоит провести дальнейшую настройку гиперпараметров.\n",
    "\n",
    "*Рекомендации:* Следует уделить внимание дополнительной обработке категориальных признаков, улучшению метода feature engineering, а также возможной оптимизации модели (например, через подбор гиперпараметров) для повышения точности предсказаний на экстремальных значениях.\n",
    "\n",
    "*Время обучения модели:* Модель обучалась в течение 11.98 секунд, что является приемлемым временем для данного объема данных.\n"
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