{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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//annal//aim//static//csv//Forbes_Billionaires.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Определим бизнес цели:\n",
    "## 1- Прогнозирование возраста миллиардера(классификация)\n",
    "## 2- Прогнозирование состояния миллиардера(регрессия)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Подготовим данные: категоризируем колонку age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "\n",
      "Rank        False\n",
      "Name        False\n",
      "Networth    False\n",
      "Age         False\n",
      "Country     False\n",
      "Source      False\n",
      "Industry    False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых 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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Rank                        Name  Networth        Country  \\\n",
      "0      1                 Elon Musk      219.0  United States   \n",
      "1      2                Jeff Bezos      171.0  United States   \n",
      "2      3  Bernard Arnault & family      158.0         France   \n",
      "3      4                Bill Gates      129.0  United States   \n",
      "4      5            Warren Buffett      118.0  United States   \n",
      "\n",
      "               Source                Industry Age_category  \n",
      "0       Tesla, SpaceX             Automotive         50-60  \n",
      "1              Amazon             Technology         50-60  \n",
      "2                LVMH       Fashion & Retail         70-80  \n",
      "3           Microsoft             Technology         60-70  \n",
      "4  Berkshire Hathaway  Finance & Investments           80+  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "bins = [0, 30, 40, 50, 60, 70, 80, 101]  # границы для возрастных категорий\n",
    "labels = ['Under 30', '30-40', '40-50', '50-60', '60-70', '70-80', '80+']  # метки для категорий\n",
    "\n",
    "df[\"Age_category\"] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n",
    "# Удаляем оригинальные колонки 'country', 'industry' и 'source' из исходного DataFrame\n",
    "df.drop(columns=['Age'], inplace=True)\n",
    "\n",
    "# Просмотр результата\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Vietnam</td>\n",
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       "      <td>Theo Albrecht, Jr. &amp; family</td>\n",
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       "      <th>633</th>\n",
       "      <td>622</td>\n",
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       "      <td>4.6</td>\n",
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       "      <td>Finance &amp; Investments</td>\n",
       "      <td>60-70</td>\n",
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       "      <th>922</th>\n",
       "      <td>913</td>\n",
       "      <td>Bob Gaglardi</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Canada</td>\n",
       "      <td>hotels</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2178</th>\n",
       "      <td>2076</td>\n",
       "      <td>Eugene Wu</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Taiwan</td>\n",
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       "      <td>70-80</td>\n",
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       "      <th>415</th>\n",
       "      <td>411</td>\n",
       "      <td>Leonard Stern</td>\n",
       "      <td>6.2</td>\n",
       "      <td>United States</td>\n",
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       "      <td>Real Estate</td>\n",
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       "      Rank                           Name  Networth         Country  \\\n",
       "1909   1818       Tran Ba Duong & family        1.6         Vietnam   \n",
       "2099   2076                   Mark Dixon        1.4  United Kingdom   \n",
       "1392   1341                  Yingzhuo Xu        2.3           China   \n",
       "627     622                  Bruce Flatt        4.6          Canada   \n",
       "527     523                  Li Liangbin        5.2           China   \n",
       "...     ...                           ...       ...             ...   \n",
       "84       85  Theo Albrecht, Jr. & family       18.7         Germany   \n",
       "633     622                   Tony Tamer        4.6   United States   \n",
       "922     913                 Bob Gaglardi        3.3          Canada   \n",
       "2178   2076                    Eugene Wu        1.4          Taiwan   \n",
       "415     411                Leonard Stern        6.2   United States   \n",
       "\n",
       "                  Source                Industry Age_category  \n",
       "1909          automotive             Automotive         60-70  \n",
       "2099  office real estate            Real Estate         60-70  \n",
       "1392        agribusiness        Food & Beverage         50-60  \n",
       "627     money management  Finance & Investments         50-60  \n",
       "527              lithium          Manufacturing         50-60  \n",
       "...                  ...                     ...          ...  \n",
       "84    Aldi, Trader Joe's       Fashion & Retail         70-80  \n",
       "633       private equity  Finance & Investments         60-70  \n",
       "922               hotels            Real Estate           80+  \n",
       "2178             finance  Finance & Investments         70-80  \n",
       "415          real estate            Real Estate           80+  \n",
       "\n",
       "[2080 rows x 7 columns]"
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       "     Age_category\n",
       "1909        60-70\n",
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       "1392        50-60\n",
       "627         50-60\n",
       "527         50-60\n",
       "...           ...\n",
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       "922           80+\n",
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       "\n",
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       "      <td>1.4</td>\n",
       "      <td>India</td>\n",
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       "      <th>2597</th>\n",
       "      <td>2578</td>\n",
       "      <td>Ramesh Genomal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>apparel</td>\n",
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       "      <td>70-80</td>\n",
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       "      <th>935</th>\n",
       "      <td>913</td>\n",
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       "      <td>3.3</td>\n",
       "      <td>Germany</td>\n",
       "      <td>consumer goods</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>50-60</td>\n",
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       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>1513</td>\n",
       "      <td>Thomas Lee</td>\n",
       "      <td>2.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>private equity</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>1645</td>\n",
       "      <td>Roberto Angelini Rossi</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Chile</td>\n",
       "      <td>forestry, mining</td>\n",
       "      <td>diversified</td>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>375</td>\n",
       "      <td>Patrick Drahi</td>\n",
       "      <td>6.6</td>\n",
       "      <td>France</td>\n",
       "      <td>telecom</td>\n",
       "      <td>Telecom</td>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>1818</td>\n",
       "      <td>Gerald Schwartz</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Canada</td>\n",
       "      <td>finance</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>80+</td>\n",
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      ],
      "text/plain": [
       "      Rank                      Name  Networth        Country  \\\n",
       "2075   2076     Radhe Shyam Agarwal        1.4          India   \n",
       "1529   1513           Robert Duggan        2.0  United States   \n",
       "1803   1729            Yao Kuizhang        1.7          China   \n",
       "425     424        Alexei Kuzmichev        6.0         Russia   \n",
       "2597   2578          Ramesh Genomal        1.0    Philippines   \n",
       "...     ...                      ...       ...            ...   \n",
       "935     913           Alfred Oetker        3.3        Germany   \n",
       "1541   1513              Thomas Lee        2.0  United States   \n",
       "1646   1645  Roberto Angelini Rossi        1.8          Chile   \n",
       "376     375           Patrick Drahi        6.6         France   \n",
       "1894   1818         Gerald Schwartz        1.6         Canada   \n",
       "\n",
       "                     Source                Industry Age_category  \n",
       "2075         consumer goods       Fashion & Retail         70-80  \n",
       "1529        pharmaceuticals             Healthcare         70-80  \n",
       "1803              beverages        Food & Beverage         50-60  \n",
       "425   oil, banking, telecom                 Energy         50-60  \n",
       "2597                apparel       Fashion & Retail         70-80  \n",
       "...                     ...                     ...          ...  \n",
       "935          consumer goods       Fashion & Retail         50-60  \n",
       "1541         private equity  Finance & Investments         70-80  \n",
       "1646       forestry, mining          diversified           70-80  \n",
       "376                 telecom                Telecom         50-60  \n",
       "1894                finance  Finance & Investments           80+  \n",
       "\n",
       "[520 rows x 7 columns]"
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2075</th>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2597</th>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>70-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>50-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age_category\n",
       "2075        70-80\n",
       "1529        70-80\n",
       "1803        50-60\n",
       "425         50-60\n",
       "2597        70-80\n",
       "...           ...\n",
       "935         50-60\n",
       "1541        70-80\n",
       "1646        70-80\n",
       "376         50-60\n",
       "1894          80+\n",
       "\n",
       "[520 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import split_stratified_into_train_val_test\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"Age_category\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=9\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Формирование конвейера для классификации данных\n",
    "## preprocessing_num -- конвейер для обработки числовых данных: заполнение пропущенных значений и стандартизация\n",
    "## preprocessing_cat -- конвейер для обработки категориальных данных: заполнение пропущенных данных и унитарное кодирование\n",
    "## features_preprocessing -- трансформер для предобработки признаков\n",
    "## features_engineering -- трансформер для конструирования признаков\n",
    "## drop_columns -- трансформер для удаления колонок\n",
    "## pipeline_end -- основной конвейер предобработки данных и конструирования признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>prepocessing_num__Networth</th>\n",
       "      <th>prepocessing_cat__Country_Argentina</th>\n",
       "      <th>prepocessing_cat__Country_Australia</th>\n",
       "      <th>prepocessing_cat__Country_Austria</th>\n",
       "      <th>prepocessing_cat__Country_Barbados</th>\n",
       "      <th>prepocessing_cat__Country_Belgium</th>\n",
       "      <th>prepocessing_cat__Country_Belize</th>\n",
       "      <th>prepocessing_cat__Country_Brazil</th>\n",
       "      <th>prepocessing_cat__Country_Bulgaria</th>\n",
       "      <th>prepocessing_cat__Country_Canada</th>\n",
       "      <th>...</th>\n",
       "      <th>prepocessing_cat__Industry_Logistics</th>\n",
       "      <th>prepocessing_cat__Industry_Manufacturing</th>\n",
       "      <th>prepocessing_cat__Industry_Media &amp; Entertainment</th>\n",
       "      <th>prepocessing_cat__Industry_Metals &amp; Mining</th>\n",
       "      <th>prepocessing_cat__Industry_Real Estate</th>\n",
       "      <th>prepocessing_cat__Industry_Service</th>\n",
       "      <th>prepocessing_cat__Industry_Sports</th>\n",
       "      <th>prepocessing_cat__Industry_Technology</th>\n",
       "      <th>prepocessing_cat__Industry_Telecom</th>\n",
       "      <th>prepocessing_cat__Industry_diversified</th>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2080 rows × 860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepocessing_num__Networth  prepocessing_cat__Country_Argentina  \\\n",
       "1909                   -0.309917                                  0.0   \n",
       "2099                   -0.329245                                  0.0   \n",
       "1392                   -0.242268                                  0.0   \n",
       "627                    -0.019995                                  0.0   \n",
       "527                     0.037990                                  0.0   \n",
       "...                          ...                                  ...   \n",
       "84                      1.342637                                  0.0   \n",
       "633                    -0.019995                                  0.0   \n",
       "922                    -0.145628                                  0.0   \n",
       "2178                   -0.329245                                  0.0   \n",
       "415                     0.134630                                  0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Australia  prepocessing_cat__Country_Austria  \\\n",
       "1909                                  0.0                                0.0   \n",
       "2099                                  0.0                                0.0   \n",
       "1392                                  0.0                                0.0   \n",
       "627                                   0.0                                0.0   \n",
       "527                                   0.0                                0.0   \n",
       "...                                   ...                                ...   \n",
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       "633                                   0.0                                0.0   \n",
       "922                                   0.0                                0.0   \n",
       "2178                                  0.0                                0.0   \n",
       "415                                   0.0                                0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Barbados  prepocessing_cat__Country_Belgium  \\\n",
       "1909                                 0.0                                0.0   \n",
       "2099                                 0.0                                0.0   \n",
       "1392                                 0.0                                0.0   \n",
       "627                                  0.0                                0.0   \n",
       "527                                  0.0                                0.0   \n",
       "...                                  ...                                ...   \n",
       "84                                   0.0                                0.0   \n",
       "633                                  0.0                                0.0   \n",
       "922                                  0.0                                0.0   \n",
       "2178                                 0.0                                0.0   \n",
       "415                                  0.0                                0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Belize  prepocessing_cat__Country_Brazil  \\\n",
       "1909                               0.0                               0.0   \n",
       "2099                               0.0                               0.0   \n",
       "1392                               0.0                               0.0   \n",
       "627                                0.0                               0.0   \n",
       "527                                0.0                               0.0   \n",
       "...                                ...                               ...   \n",
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       "633                                0.0                               0.0   \n",
       "922                                0.0                               0.0   \n",
       "2178                               0.0                               0.0   \n",
       "415                                0.0                               0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Bulgaria  prepocessing_cat__Country_Canada  \\\n",
       "1909                                 0.0                               0.0   \n",
       "2099                                 0.0                               0.0   \n",
       "1392                                 0.0                               0.0   \n",
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       "527                                  0.0                               0.0   \n",
       "...                                  ...                               ...   \n",
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       "415                                  0.0                               0.0   \n",
       "\n",
       "      ...  prepocessing_cat__Industry_Logistics   \\\n",
       "1909  ...                                    0.0   \n",
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       "1392  ...                                    0.0   \n",
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       "\n",
       "      prepocessing_cat__Industry_Manufacturing   \\\n",
       "1909                                        0.0   \n",
       "2099                                        0.0   \n",
       "1392                                        0.0   \n",
       "627                                         0.0   \n",
       "527                                         1.0   \n",
       "...                                         ...   \n",
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       "633                                         0.0   \n",
       "922                                         0.0   \n",
       "2178                                        0.0   \n",
       "415                                         0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Media & Entertainment   \\\n",
       "1909                                                0.0   \n",
       "2099                                                0.0   \n",
       "1392                                                0.0   \n",
       "627                                                 0.0   \n",
       "527                                                 0.0   \n",
       "...                                                 ...   \n",
       "84                                                  0.0   \n",
       "633                                                 0.0   \n",
       "922                                                 0.0   \n",
       "2178                                                0.0   \n",
       "415                                                 0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Metals & Mining   \\\n",
       "1909                                          0.0   \n",
       "2099                                          0.0   \n",
       "1392                                          0.0   \n",
       "627                                           0.0   \n",
       "527                                           0.0   \n",
       "...                                           ...   \n",
       "84                                            0.0   \n",
       "633                                           0.0   \n",
       "922                                           0.0   \n",
       "2178                                          0.0   \n",
       "415                                           0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Real Estate   \\\n",
       "1909                                      0.0   \n",
       "2099                                      1.0   \n",
       "1392                                      0.0   \n",
       "627                                       0.0   \n",
       "527                                       0.0   \n",
       "...                                       ...   \n",
       "84                                        0.0   \n",
       "633                                       0.0   \n",
       "922                                       1.0   \n",
       "2178                                      0.0   \n",
       "415                                       1.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Service   prepocessing_cat__Industry_Sports   \\\n",
       "1909                                  0.0                                 0.0   \n",
       "2099                                  0.0                                 0.0   \n",
       "1392                                  0.0                                 0.0   \n",
       "627                                   0.0                                 0.0   \n",
       "527                                   0.0                                 0.0   \n",
       "...                                   ...                                 ...   \n",
       "84                                    0.0                                 0.0   \n",
       "633                                   0.0                                 0.0   \n",
       "922                                   0.0                                 0.0   \n",
       "2178                                  0.0                                 0.0   \n",
       "415                                   0.0                                 0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Technology   \\\n",
       "1909                                     0.0   \n",
       "2099                                     0.0   \n",
       "1392                                     0.0   \n",
       "627                                      0.0   \n",
       "527                                      0.0   \n",
       "...                                      ...   \n",
       "84                                       0.0   \n",
       "633                                      0.0   \n",
       "922                                      0.0   \n",
       "2178                                     0.0   \n",
       "415                                      0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Telecom   \\\n",
       "1909                                  0.0   \n",
       "2099                                  0.0   \n",
       "1392                                  0.0   \n",
       "627                                   0.0   \n",
       "527                                   0.0   \n",
       "...                                   ...   \n",
       "84                                    0.0   \n",
       "633                                   0.0   \n",
       "922                                   0.0   \n",
       "2178                                  0.0   \n",
       "415                                   0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_diversified     \n",
       "1909                                        0.0  \n",
       "2099                                        0.0  \n",
       "1392                                        0.0  \n",
       "627                                         0.0  \n",
       "527                                         0.0  \n",
       "...                                         ...  \n",
       "84                                          0.0  \n",
       "633                                         0.0  \n",
       "922                                         0.0  \n",
       "2178                                        0.0  \n",
       "415                                         0.0  \n",
       "\n",
       "[2080 rows x 860 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "import pandas as pd\n",
    "\n",
    "# Исправляем ColumnTransformer с сохранением имен колонок\n",
    "columns_to_drop = [\"Age_category\", \"Rank \", \"Name\"]\n",
    "\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in X_train.columns\n",
    "    if column not in columns_to_drop and X_train[column].dtype != \"object\"\n",
    "]\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in X_train.columns\n",
    "    if column not in columns_to_drop and X_train[column].dtype == \"object\"\n",
    "]\n",
    "\n",
    "# Предобработка числовых данных\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Предобработка категориальных данных\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Общая предобработка признаков\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=True,  # Сохраняем имена колонок\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"drop\"  # Убираем неиспользуемые столбцы\n",
    ")\n",
    "\n",
    "# Итоговый конвейер\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Преобразуем данные\n",
    "preprocessing_result = pipeline_end.fit_transform(X_train)\n",
    "\n",
    "# Создаем DataFrame с правильными именами колонок\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    "    index=X_train.index,  # Сохраняем индексы\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Формирование набора моделей для классификации\n",
    "## logistic -- логистическая регрессия\n",
    "## ridge -- гребневая регрессия\n",
    "## decision_tree -- дерево решений\n",
    "## knn -- k-ближайших соседей\n",
    "## naive_bayes -- наивный Байесовский классификатор\n",
    "## gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
    "## random_forest -- метод случайного леса (набор деревьев решений)\n",
    "## mlp -- многослойный персептрон (нейронная сеть)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree\n",
    "\n",
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
    "    # \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")},\n",
    "    \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=9)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
    "    \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
    "    \"gradient_boosting\": {\n",
    "        \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
    "    },\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestClassifier(\n",
    "            max_depth=11, class_weight=\"balanced\", random_state=9\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPClassifier(\n",
    "            hidden_layer_sizes=(7,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=9,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обучение моделей на обучающем наборе данных и оценка на тестовом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train['Age_category'] = y_train['Age_category'].cat.codes\n",
    "y_test['Age_category'] = y_test['Age_category'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\neighbors\\_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: decision_tree\n",
      "Model: knn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: naive_bayes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: gradient_boosting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: random_forest\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1105: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: mlp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    model = class_models[model_name][\"model\"]\n",
    "\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    model_pipeline = model_pipeline.fit(X_train, y_train)\n",
    "\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_probs = model_pipeline.predict_proba(X_test)\n",
    "    y_test_predict = np.argmax(y_test_probs, axis=1)\n",
    "\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"probs\"] = y_test_probs\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    # Метрики\n",
    "    class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
    "        y_train, y_train_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
    "        y_test, y_test_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
    "        y_train, y_train_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
    "        y_test, y_test_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
    "        y_test, y_test_probs, multi_class=\"ovr\"\n",
    "    )\n",
    "    class_models[model_name][\"F1_train\"] = metrics.f1_score(\n",
    "        y_train, y_train_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(\n",
    "        y_test, y_test_predict, average=\"macro\"\n",
    "    )\n",
    "    class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сводная таблица оценок качества для использованных моделей классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1700x1700 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(17, 17), sharex=False, sharey=False)\n",
    "for index, key in enumerate(class_models.keys()):\n",
    "    c_matrix = class_models[key][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Under 30\", \"30-40\", \"40-50\", \"50-60\", \"60-70\", \"70-80\", \"80+\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Точность, полнота, верность (аккуратность), F-мера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_cdcb0_row2_col6 {\n",
       "  background-color: #aa2395;\n",
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       "#T_cdcb0_row6_col0, #T_cdcb0_row6_col1, #T_cdcb0_row6_col2, #T_cdcb0_row6_col3 {\n",
       "  background-color: #26818e;\n",
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       "}\n",
       "#T_cdcb0_row6_col4, #T_cdcb0_row6_col6, #T_cdcb0_row6_col7, #T_cdcb0_row7_col5 {\n",
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       "</style>\n",
       "<table id=\"T_cdcb0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cdcb0_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_cdcb0_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_cdcb0_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_cdcb0_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_cdcb0_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_cdcb0_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_cdcb0_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_cdcb0_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_cdcb0_row0_col0\" class=\"data row0 col0\" >0.567471</td>\n",
       "      <td id=\"T_cdcb0_row0_col1\" class=\"data row0 col1\" >0.278074</td>\n",
       "      <td id=\"T_cdcb0_row0_col2\" class=\"data row0 col2\" >0.444883</td>\n",
       "      <td id=\"T_cdcb0_row0_col3\" class=\"data row0 col3\" >0.232769</td>\n",
       "      <td id=\"T_cdcb0_row0_col4\" class=\"data row0 col4\" >0.618269</td>\n",
       "      <td id=\"T_cdcb0_row0_col5\" class=\"data row0 col5\" >0.353846</td>\n",
       "      <td id=\"T_cdcb0_row0_col6\" class=\"data row0 col6\" >0.465219</td>\n",
       "      <td id=\"T_cdcb0_row0_col7\" class=\"data row0 col7\" >0.237759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row1\" class=\"row_heading level0 row1\" >gradient_boosting</th>\n",
       "      <td id=\"T_cdcb0_row1_col0\" class=\"data row1 col0\" >0.836061</td>\n",
       "      <td id=\"T_cdcb0_row1_col1\" class=\"data row1 col1\" >0.287405</td>\n",
       "      <td id=\"T_cdcb0_row1_col2\" class=\"data row1 col2\" >0.725411</td>\n",
       "      <td id=\"T_cdcb0_row1_col3\" class=\"data row1 col3\" >0.235795</td>\n",
       "      <td id=\"T_cdcb0_row1_col4\" class=\"data row1 col4\" >0.689904</td>\n",
       "      <td id=\"T_cdcb0_row1_col5\" class=\"data row1 col5\" >0.344231</td>\n",
       "      <td id=\"T_cdcb0_row1_col6\" class=\"data row1 col6\" >0.760847</td>\n",
       "      <td id=\"T_cdcb0_row1_col7\" class=\"data row1 col7\" >0.240251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row2\" class=\"row_heading level0 row2\" >knn</th>\n",
       "      <td id=\"T_cdcb0_row2_col0\" class=\"data row2 col0\" >0.477783</td>\n",
       "      <td id=\"T_cdcb0_row2_col1\" class=\"data row2 col1\" >0.221788</td>\n",
       "      <td id=\"T_cdcb0_row2_col2\" class=\"data row2 col2\" >0.460090</td>\n",
       "      <td id=\"T_cdcb0_row2_col3\" class=\"data row2 col3\" >0.214239</td>\n",
       "      <td id=\"T_cdcb0_row2_col4\" class=\"data row2 col4\" >0.497115</td>\n",
       "      <td id=\"T_cdcb0_row2_col5\" class=\"data row2 col5\" >0.328846</td>\n",
       "      <td id=\"T_cdcb0_row2_col6\" class=\"data row2 col6\" >0.456182</td>\n",
       "      <td id=\"T_cdcb0_row2_col7\" class=\"data row2 col7\" >0.211556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row3\" class=\"row_heading level0 row3\" >decision_tree</th>\n",
       "      <td id=\"T_cdcb0_row3_col0\" class=\"data row3 col0\" >0.618281</td>\n",
       "      <td id=\"T_cdcb0_row3_col1\" class=\"data row3 col1\" >0.163157</td>\n",
       "      <td id=\"T_cdcb0_row3_col2\" class=\"data row3 col2\" >0.244223</td>\n",
       "      <td id=\"T_cdcb0_row3_col3\" class=\"data row3 col3\" >0.184231</td>\n",
       "      <td id=\"T_cdcb0_row3_col4\" class=\"data row3 col4\" >0.387981</td>\n",
       "      <td id=\"T_cdcb0_row3_col5\" class=\"data row3 col5\" >0.325000</td>\n",
       "      <td id=\"T_cdcb0_row3_col6\" class=\"data row3 col6\" >0.227570</td>\n",
       "      <td id=\"T_cdcb0_row3_col7\" class=\"data row3 col7\" >0.146479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_cdcb0_row4_col0\" class=\"data row4 col0\" >0.581578</td>\n",
       "      <td id=\"T_cdcb0_row4_col1\" class=\"data row4 col1\" >0.236539</td>\n",
       "      <td id=\"T_cdcb0_row4_col2\" class=\"data row4 col2\" >0.735419</td>\n",
       "      <td id=\"T_cdcb0_row4_col3\" class=\"data row4 col3\" >0.246556</td>\n",
       "      <td id=\"T_cdcb0_row4_col4\" class=\"data row4 col4\" >0.627404</td>\n",
       "      <td id=\"T_cdcb0_row4_col5\" class=\"data row4 col5\" >0.288462</td>\n",
       "      <td id=\"T_cdcb0_row4_col6\" class=\"data row4 col6\" >0.599765</td>\n",
       "      <td id=\"T_cdcb0_row4_col7\" class=\"data row4 col7\" >0.231541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
       "      <td id=\"T_cdcb0_row5_col0\" class=\"data row5 col0\" >0.518033</td>\n",
       "      <td id=\"T_cdcb0_row5_col1\" class=\"data row5 col1\" >0.238462</td>\n",
       "      <td id=\"T_cdcb0_row5_col2\" class=\"data row5 col2\" >0.695673</td>\n",
       "      <td id=\"T_cdcb0_row5_col3\" class=\"data row5 col3\" >0.247678</td>\n",
       "      <td id=\"T_cdcb0_row5_col4\" class=\"data row5 col4\" >0.556250</td>\n",
       "      <td id=\"T_cdcb0_row5_col5\" class=\"data row5 col5\" >0.284615</td>\n",
       "      <td id=\"T_cdcb0_row5_col6\" class=\"data row5 col6\" >0.553233</td>\n",
       "      <td id=\"T_cdcb0_row5_col7\" class=\"data row5 col7\" >0.226955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row6\" class=\"row_heading level0 row6\" >mlp</th>\n",
       "      <td id=\"T_cdcb0_row6_col0\" class=\"data row6 col0\" >0.035714</td>\n",
       "      <td id=\"T_cdcb0_row6_col1\" class=\"data row6 col1\" >0.035714</td>\n",
       "      <td id=\"T_cdcb0_row6_col2\" class=\"data row6 col2\" >0.142857</td>\n",
       "      <td id=\"T_cdcb0_row6_col3\" class=\"data row6 col3\" >0.142857</td>\n",
       "      <td id=\"T_cdcb0_row6_col4\" class=\"data row6 col4\" >0.250000</td>\n",
       "      <td id=\"T_cdcb0_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
       "      <td id=\"T_cdcb0_row6_col6\" class=\"data row6 col6\" >0.057143</td>\n",
       "      <td id=\"T_cdcb0_row6_col7\" class=\"data row6 col7\" >0.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdcb0_level0_row7\" class=\"row_heading level0 row7\" >naive_bayes</th>\n",
       "      <td id=\"T_cdcb0_row7_col0\" class=\"data row7 col0\" >0.524162</td>\n",
       "      <td id=\"T_cdcb0_row7_col1\" class=\"data row7 col1\" >0.239277</td>\n",
       "      <td id=\"T_cdcb0_row7_col2\" class=\"data row7 col2\" >0.664585</td>\n",
       "      <td id=\"T_cdcb0_row7_col3\" class=\"data row7 col3\" >0.202308</td>\n",
       "      <td id=\"T_cdcb0_row7_col4\" class=\"data row7 col4\" >0.494231</td>\n",
       "      <td id=\"T_cdcb0_row7_col5\" class=\"data row7 col5\" >0.176923</td>\n",
       "      <td id=\"T_cdcb0_row7_col6\" class=\"data row7 col6\" >0.465319</td>\n",
       "      <td id=\"T_cdcb0_row7_col7\" class=\"data row7 col7\" >0.151713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1dc070ca660>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(\n",
    "    by=\"Accuracy_test\", ascending=False\n",
    ").style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## значения далеки от идела, датасет так себе..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Jinja2\n",
      "  Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB)\n",
      "Collecting MarkupSafe>=2.0 (from Jinja2)\n",
      "  Downloading MarkupSafe-3.0.2-cp312-cp312-win_amd64.whl.metadata (4.1 kB)\n",
      "Downloading jinja2-3.1.4-py3-none-any.whl (133 kB)\n",
      "Downloading MarkupSafe-3.0.2-cp312-cp312-win_amd64.whl (15 kB)\n",
      "Installing collected packages: MarkupSafe, Jinja2\n",
      "Successfully installed Jinja2-3.1.4 MarkupSafe-3.0.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install Jinja2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_d129d_row7_col2, #T_d129d_row7_col3, #T_d129d_row7_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d129d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d129d_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_d129d_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_d129d_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_d129d_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_d129d_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row0\" class=\"row_heading level0 row0\" >gradient_boosting</th>\n",
       "      <td id=\"T_d129d_row0_col0\" class=\"data row0 col0\" >0.344231</td>\n",
       "      <td id=\"T_d129d_row0_col1\" class=\"data row0 col1\" >0.240251</td>\n",
       "      <td id=\"T_d129d_row0_col2\" class=\"data row0 col2\" >0.649816</td>\n",
       "      <td id=\"T_d129d_row0_col3\" class=\"data row0 col3\" >0.131708</td>\n",
       "      <td id=\"T_d129d_row0_col4\" class=\"data row0 col4\" >0.138628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row1\" class=\"row_heading level0 row1\" >logistic</th>\n",
       "      <td id=\"T_d129d_row1_col0\" class=\"data row1 col0\" >0.353846</td>\n",
       "      <td id=\"T_d129d_row1_col1\" class=\"data row1 col1\" >0.237759</td>\n",
       "      <td id=\"T_d129d_row1_col2\" class=\"data row1 col2\" >0.615478</td>\n",
       "      <td id=\"T_d129d_row1_col3\" class=\"data row1 col3\" >0.160238</td>\n",
       "      <td id=\"T_d129d_row1_col4\" class=\"data row1 col4\" >0.161282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
       "      <td id=\"T_d129d_row2_col0\" class=\"data row2 col0\" >0.284615</td>\n",
       "      <td id=\"T_d129d_row2_col1\" class=\"data row2 col1\" >0.226955</td>\n",
       "      <td id=\"T_d129d_row2_col2\" class=\"data row2 col2\" >0.612260</td>\n",
       "      <td id=\"T_d129d_row2_col3\" class=\"data row2 col3\" >0.129672</td>\n",
       "      <td id=\"T_d129d_row2_col4\" class=\"data row2 col4\" >0.133551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row3\" class=\"row_heading level0 row3\" >knn</th>\n",
       "      <td id=\"T_d129d_row3_col0\" class=\"data row3 col0\" >0.328846</td>\n",
       "      <td id=\"T_d129d_row3_col1\" class=\"data row3 col1\" >0.211556</td>\n",
       "      <td id=\"T_d129d_row3_col2\" class=\"data row3 col2\" >0.602333</td>\n",
       "      <td id=\"T_d129d_row3_col3\" class=\"data row3 col3\" >0.128794</td>\n",
       "      <td id=\"T_d129d_row3_col4\" class=\"data row3 col4\" >0.130205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_d129d_row4_col0\" class=\"data row4 col0\" >0.288462</td>\n",
       "      <td id=\"T_d129d_row4_col1\" class=\"data row4 col1\" >0.231541</td>\n",
       "      <td id=\"T_d129d_row4_col2\" class=\"data row4 col2\" >0.599541</td>\n",
       "      <td id=\"T_d129d_row4_col3\" class=\"data row4 col3\" >0.126828</td>\n",
       "      <td id=\"T_d129d_row4_col4\" class=\"data row4 col4\" >0.129917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row5\" class=\"row_heading level0 row5\" >decision_tree</th>\n",
       "      <td id=\"T_d129d_row5_col0\" class=\"data row5 col0\" >0.325000</td>\n",
       "      <td id=\"T_d129d_row5_col1\" class=\"data row5 col1\" >0.146479</td>\n",
       "      <td id=\"T_d129d_row5_col2\" class=\"data row5 col2\" >0.581718</td>\n",
       "      <td id=\"T_d129d_row5_col3\" class=\"data row5 col3\" >0.078698</td>\n",
       "      <td id=\"T_d129d_row5_col4\" class=\"data row5 col4\" >0.098279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row6\" class=\"row_heading level0 row6\" >naive_bayes</th>\n",
       "      <td id=\"T_d129d_row6_col0\" class=\"data row6 col0\" >0.176923</td>\n",
       "      <td id=\"T_d129d_row6_col1\" class=\"data row6 col1\" >0.151713</td>\n",
       "      <td id=\"T_d129d_row6_col2\" class=\"data row6 col2\" >0.562024</td>\n",
       "      <td id=\"T_d129d_row6_col3\" class=\"data row6 col3\" >0.071080</td>\n",
       "      <td id=\"T_d129d_row6_col4\" class=\"data row6 col4\" >0.079232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d129d_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_d129d_row7_col0\" class=\"data row7 col0\" >0.250000</td>\n",
       "      <td id=\"T_d129d_row7_col1\" class=\"data row7 col1\" >0.057143</td>\n",
       "      <td id=\"T_d129d_row7_col2\" class=\"data row7 col2\" >0.554978</td>\n",
       "      <td id=\"T_d129d_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
       "      <td id=\"T_d129d_row7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1dc06c64740>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'logistic'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вывод данных с ошибкой предсказания для оценки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Error items count: 336'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Name</th>\n",
       "      <th>Networth</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Age_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Sergey Brin</td>\n",
       "      <td>107.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Google</td>\n",
       "      <td>Technology</td>\n",
       "      <td>40-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Steve Ballmer</td>\n",
       "      <td>91.4</td>\n",
       "      <td>United States</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Technology</td>\n",
       "      <td>60-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>Carlos Slim Helu &amp; family</td>\n",
       "      <td>81.2</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>telecom</td>\n",
       "      <td>Telecom</td>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Mark Zuckerberg</td>\n",
       "      <td>67.3</td>\n",
       "      <td>United States</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Technology</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>Amancio Ortega</td>\n",
       "      <td>59.6</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Zara</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>2578</td>\n",
       "      <td>3</td>\n",
       "      <td>Roy Chi Ping Chung</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>manufacturing</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>60-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2588</th>\n",
       "      <td>2578</td>\n",
       "      <td>3</td>\n",
       "      <td>Ronald Clarke</td>\n",
       "      <td>1.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>payments technology</td>\n",
       "      <td>Technology</td>\n",
       "      <td>60-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2591</th>\n",
       "      <td>2578</td>\n",
       "      <td>5</td>\n",
       "      <td>Sefik Yilmaz Dizdar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>fashion retail</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2593</th>\n",
       "      <td>2578</td>\n",
       "      <td>6</td>\n",
       "      <td>Larry Fink</td>\n",
       "      <td>1.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>money management</td>\n",
       "      <td>Finance &amp; Investments</td>\n",
       "      <td>60-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2596</th>\n",
       "      <td>2578</td>\n",
       "      <td>5</td>\n",
       "      <td>Nari Genomal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>apparel</td>\n",
       "      <td>Fashion &amp; Retail</td>\n",
       "      <td>80+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank   Predicted                        Name  Networth        Country  \\\n",
       "6         7          4                Sergey Brin      107.0  United States   \n",
       "8         9          3              Steve Ballmer       91.4  United States   \n",
       "12       13          3  Carlos Slim Helu & family       81.2         Mexico   \n",
       "14       15          3            Mark Zuckerberg       67.3  United States   \n",
       "22       23          5             Amancio Ortega       59.6          Spain   \n",
       "...     ...        ...                         ...       ...            ...   \n",
       "2586   2578          3         Roy Chi Ping Chung        1.0      Hong Kong   \n",
       "2588   2578          3              Ronald Clarke        1.0  United States   \n",
       "2591   2578          5        Sefik Yilmaz Dizdar        1.0         Turkey   \n",
       "2593   2578          6                 Larry Fink        1.0  United States   \n",
       "2596   2578          5               Nari Genomal        1.0    Philippines   \n",
       "\n",
       "                   Source                Industry Age_category  \n",
       "6                  Google             Technology         40-50  \n",
       "8               Microsoft             Technology         60-70  \n",
       "12                telecom                Telecom           80+  \n",
       "14               Facebook             Technology         30-40  \n",
       "22                   Zara       Fashion & Retail           80+  \n",
       "...                   ...                     ...          ...  \n",
       "2586        manufacturing          Manufacturing         60-70  \n",
       "2588  payments technology             Technology         60-70  \n",
       "2591       fashion retail       Fashion & Retail           80+  \n",
       "2593     money management  Finance & Investments         60-70  \n",
       "2596              apparel       Fashion & Retail           80+  \n",
       "\n",
       "[336 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.transform(X_test)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "y_pred = class_models[best_model][\"preds\"]\n",
    "\n",
    "error_index = y_test[y_test[\"Age_category\"] != y_pred].index.tolist()\n",
    "display(f\"Error items count: {len(error_index)}\")\n",
    "\n",
    "error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
    "error_df = X_test.loc[error_index].copy()\n",
    "error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
    "error_df.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Многовато..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пример использования обученной модели (конвейера) для предсказания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country</th>\n",
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       "      <th>Industry</th>\n",
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       "      <th>450</th>\n",
       "      <td>438</td>\n",
       "      <td>Ruan Liping</td>\n",
       "      <td>5.8</td>\n",
       "      <td>Hong Kong</td>\n",
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       "      <td>Manufacturing</td>\n",
       "      <td>50-60</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank           Name Networth    Country        Source        Industry  \\\n",
       "450   438  Ruan Liping       5.8  Hong Kong  power strips  Manufacturing    \n",
       "\n",
       "    Age_category  \n",
       "450        50-60  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <th>prepocessing_cat__Country_Barbados</th>\n",
       "      <th>prepocessing_cat__Country_Belgium</th>\n",
       "      <th>prepocessing_cat__Country_Belize</th>\n",
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       "      <th>prepocessing_cat__Industry_Service</th>\n",
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       "      <th>prepocessing_cat__Industry_Technology</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     prepocessing_num__Networth  prepocessing_cat__Country_Argentina  \\\n",
       "450                    0.289255                                  0.0   \n",
       "\n",
       "     prepocessing_cat__Country_Australia  prepocessing_cat__Country_Austria  \\\n",
       "450                                  0.0                                0.0   \n",
       "\n",
       "     prepocessing_cat__Country_Barbados  prepocessing_cat__Country_Belgium  \\\n",
       "450                                 0.0                                0.0   \n",
       "\n",
       "     prepocessing_cat__Country_Belize  prepocessing_cat__Country_Brazil  \\\n",
       "450                               0.0                               0.0   \n",
       "\n",
       "     prepocessing_cat__Country_Bulgaria  prepocessing_cat__Country_Canada  \\\n",
       "450                                 0.0                               0.0   \n",
       "\n",
       "     ...  prepocessing_cat__Industry_Logistics   \\\n",
       "450  ...                                    0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Manufacturing   \\\n",
       "450                                        0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Media & Entertainment   \\\n",
       "450                                                1.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Metals & Mining   \\\n",
       "450                                          0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Real Estate   \\\n",
       "450                                      0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Service   prepocessing_cat__Industry_Sports   \\\n",
       "450                                  0.0                                 0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Technology   \\\n",
       "450                                     0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_Telecom   \\\n",
       "450                                  0.0   \n",
       "\n",
       "     prepocessing_cat__Industry_diversified     \n",
       "450                                        0.0  \n",
       "\n",
       "[1 rows x 860 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 3 (proba: [0.00172036 0.04303104 0.02714323 0.36848158 0.19524859 0.2037863\\n 0.1605889 ])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 3'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = class_models[best_model][\"pipeline\"]\n",
    "\n",
    "example_id = 450\n",
    "test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
    "test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
    "display(test)\n",
    "display(test_preprocessed)\n",
    "result_proba = model.predict_proba(test)[0]\n",
    "result = model.predict(test)[0]\n",
    "real = int(y_test.loc[example_id].values[0])\n",
    "display(f\"predicted: {result} (proba: {result_proba})\")\n",
    "display(f\"real: {real}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подбор гиперпараметров методом поиска по сетке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model__criterion': 'gini',\n",
       " 'model__max_depth': 10,\n",
       " 'model__max_features': 2,\n",
       " 'model__n_estimators': 250}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "optimized_model_type = \"random_forest\"\n",
    "\n",
    "random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
    "\n",
    "param_grid = {\n",
    "    \"model__n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
    "    \"model__max_features\": [\"sqrt\", \"log2\", 2],\n",
    "    \"model__max_depth\": [2, 3, 4, 5, 6, 7, 8, 9 ,10],\n",
    "    \"model__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
    "}\n",
    "\n",
    "gs_optomizer = GridSearchCV(\n",
    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
    ")\n",
    "gs_optomizer.fit(X_train, y_train.values.ravel())\n",
    "gs_optomizer.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение модели с новыми гиперпараметрами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "optimized_model = ensemble.RandomForestClassifier(\n",
    "    random_state=9,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=10,\n",
    "    max_features=2,\n",
    "    n_estimators=250,\n",
    ")\n",
    "\n",
    "result = {}\n",
    "\n",
    "result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
    "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
    "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)\n",
    "result[\"preds\"] = np.argmax(y_test_probs, axis=1)\n",
    "\n",
    "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"],average=\"macro\")\n",
    "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"], average=\"macro\")\n",
    "result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"], average=\"macro\")\n",
    "result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"], average=\"macro\")\n",
    "result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
    "result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
    "result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"], multi_class=\"ovr\")\n",
    "result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"], average=\"macro\")\n",
    "result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"], average=\"macro\")\n",
    "result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
    "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
    "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Формирование данных для оценки старой и новой версии модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=class_models[optimized_model_type]\n",
    ")\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=result\n",
    ")\n",
    "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
    "optimized_metrics = optimized_metrics.set_index(\"Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка параметров старой и новой модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_002a7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_002a7_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_002a7_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_002a7_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_002a7_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_002a7_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_002a7_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_002a7_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_002a7_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_002a7_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_002a7_row0_col0\" class=\"data row0 col0\" >0.581578</td>\n",
       "      <td id=\"T_002a7_row0_col1\" class=\"data row0 col1\" >0.236539</td>\n",
       "      <td id=\"T_002a7_row0_col2\" class=\"data row0 col2\" >0.735419</td>\n",
       "      <td id=\"T_002a7_row0_col3\" class=\"data row0 col3\" >0.246556</td>\n",
       "      <td id=\"T_002a7_row0_col4\" class=\"data row0 col4\" >0.627404</td>\n",
       "      <td id=\"T_002a7_row0_col5\" class=\"data row0 col5\" >0.288462</td>\n",
       "      <td id=\"T_002a7_row0_col6\" class=\"data row0 col6\" >0.599765</td>\n",
       "      <td id=\"T_002a7_row0_col7\" class=\"data row0 col7\" >0.231541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_002a7_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_002a7_row1_col0\" class=\"data row1 col0\" >0.181388</td>\n",
       "      <td id=\"T_002a7_row1_col1\" class=\"data row1 col1\" >0.035714</td>\n",
       "      <td id=\"T_002a7_row1_col2\" class=\"data row1 col2\" >0.157692</td>\n",
       "      <td id=\"T_002a7_row1_col3\" class=\"data row1 col3\" >0.142857</td>\n",
       "      <td id=\"T_002a7_row1_col4\" class=\"data row1 col4\" >0.306250</td>\n",
       "      <td id=\"T_002a7_row1_col5\" class=\"data row1 col5\" >0.250000</td>\n",
       "      <td id=\"T_002a7_row1_col6\" class=\"data row1 col6\" >0.090702</td>\n",
       "      <td id=\"T_002a7_row1_col7\" class=\"data row1 col7\" >0.057143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1dc027746e0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  color: #000000;\n",
       "}\n",
       "#T_6486e_row0_col2, #T_6486e_row1_col3, #T_6486e_row1_col4 {\n",
       "  background-color: #4e02a2;\n",
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       "}\n",
       "#T_6486e_row0_col3, #T_6486e_row0_col4, #T_6486e_row1_col2 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6486e_row1_col0, #T_6486e_row1_col1 {\n",
       "  background-color: #26818e;\n",
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       "<table id=\"T_6486e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6486e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_6486e_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_6486e_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_6486e_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_6486e_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6486e_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_6486e_row0_col0\" class=\"data row0 col0\" >0.288462</td>\n",
       "      <td id=\"T_6486e_row0_col1\" class=\"data row0 col1\" >0.231541</td>\n",
       "      <td id=\"T_6486e_row0_col2\" class=\"data row0 col2\" >0.599541</td>\n",
       "      <td id=\"T_6486e_row0_col3\" class=\"data row0 col3\" >0.126828</td>\n",
       "      <td id=\"T_6486e_row0_col4\" class=\"data row0 col4\" >0.129917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6486e_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_6486e_row1_col0\" class=\"data row1 col0\" >0.250000</td>\n",
       "      <td id=\"T_6486e_row1_col1\" class=\"data row1 col1\" >0.057143</td>\n",
       "      <td id=\"T_6486e_row1_col2\" class=\"data row1 col2\" >0.605446</td>\n",
       "      <td id=\"T_6486e_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "      <td id=\"T_6486e_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1dc02777b60>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n",
    ")\n",
    "\n",
    "for index in range(0, len(optimized_metrics)):\n",
    "    c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Under 30\", \"30-40\", \"40-50\", \"50-60\", \"60-70\", \"70-80\", \"80+\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задача регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(columns=['Networth','Rank ', 'Name'])  # Признаки\n",
    "y = df['Networth']  # Целевая переменная для регрессии\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>prepocessing_num__Age</th>\n",
       "      <th>prepocessing_cat__Country_Argentina</th>\n",
       "      <th>prepocessing_cat__Country_Australia</th>\n",
       "      <th>prepocessing_cat__Country_Austria</th>\n",
       "      <th>prepocessing_cat__Country_Barbados</th>\n",
       "      <th>prepocessing_cat__Country_Belgium</th>\n",
       "      <th>prepocessing_cat__Country_Belize</th>\n",
       "      <th>prepocessing_cat__Country_Brazil</th>\n",
       "      <th>prepocessing_cat__Country_Bulgaria</th>\n",
       "      <th>prepocessing_cat__Country_Canada</th>\n",
       "      <th>...</th>\n",
       "      <th>prepocessing_cat__Industry_Logistics</th>\n",
       "      <th>prepocessing_cat__Industry_Manufacturing</th>\n",
       "      <th>prepocessing_cat__Industry_Media &amp; Entertainment</th>\n",
       "      <th>prepocessing_cat__Industry_Metals &amp; Mining</th>\n",
       "      <th>prepocessing_cat__Industry_Real Estate</th>\n",
       "      <th>prepocessing_cat__Industry_Service</th>\n",
       "      <th>prepocessing_cat__Industry_Sports</th>\n",
       "      <th>prepocessing_cat__Industry_Technology</th>\n",
       "      <th>prepocessing_cat__Industry_Telecom</th>\n",
       "      <th>prepocessing_cat__Industry_diversified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>-0.109934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.079079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>1.004766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>-0.407187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>1.302019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>1.227706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>0.856139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.781826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0.335946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.558886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2080 rows × 855 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prepocessing_num__Age  prepocessing_cat__Country_Argentina  \\\n",
       "582               -0.109934                                  0.0   \n",
       "48                 1.079079                                  0.0   \n",
       "1772               1.004766                                  0.0   \n",
       "964               -0.407187                                  0.0   \n",
       "2213               1.302019                                  0.0   \n",
       "...                     ...                                  ...   \n",
       "1638               1.227706                                  0.0   \n",
       "1095               0.856139                                  0.0   \n",
       "1130               0.781826                                  0.0   \n",
       "1294               0.335946                                  0.0   \n",
       "860                0.558886                                  0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Australia  prepocessing_cat__Country_Austria  \\\n",
       "582                                   0.0                                0.0   \n",
       "48                                    0.0                                0.0   \n",
       "1772                                  1.0                                0.0   \n",
       "964                                   0.0                                0.0   \n",
       "2213                                  0.0                                0.0   \n",
       "...                                   ...                                ...   \n",
       "1638                                  0.0                                0.0   \n",
       "1095                                  0.0                                0.0   \n",
       "1130                                  0.0                                0.0   \n",
       "1294                                  0.0                                0.0   \n",
       "860                                   0.0                                0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Barbados  prepocessing_cat__Country_Belgium  \\\n",
       "582                                  0.0                                0.0   \n",
       "48                                   0.0                                0.0   \n",
       "1772                                 0.0                                0.0   \n",
       "964                                  0.0                                0.0   \n",
       "2213                                 0.0                                0.0   \n",
       "...                                  ...                                ...   \n",
       "1638                                 0.0                                0.0   \n",
       "1095                                 0.0                                0.0   \n",
       "1130                                 0.0                                0.0   \n",
       "1294                                 0.0                                0.0   \n",
       "860                                  0.0                                0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Belize  prepocessing_cat__Country_Brazil  \\\n",
       "582                                0.0                               0.0   \n",
       "48                                 0.0                               0.0   \n",
       "1772                               0.0                               0.0   \n",
       "964                                0.0                               0.0   \n",
       "2213                               0.0                               1.0   \n",
       "...                                ...                               ...   \n",
       "1638                               0.0                               0.0   \n",
       "1095                               0.0                               1.0   \n",
       "1130                               0.0                               0.0   \n",
       "1294                               0.0                               0.0   \n",
       "860                                0.0                               0.0   \n",
       "\n",
       "      prepocessing_cat__Country_Bulgaria  prepocessing_cat__Country_Canada  \\\n",
       "582                                  0.0                               0.0   \n",
       "48                                   0.0                               0.0   \n",
       "1772                                 0.0                               0.0   \n",
       "964                                  0.0                               0.0   \n",
       "2213                                 0.0                               0.0   \n",
       "...                                  ...                               ...   \n",
       "1638                                 0.0                               0.0   \n",
       "1095                                 0.0                               0.0   \n",
       "1130                                 0.0                               0.0   \n",
       "1294                                 0.0                               0.0   \n",
       "860                                  0.0                               0.0   \n",
       "\n",
       "      ...  prepocessing_cat__Industry_Logistics   \\\n",
       "582   ...                                    0.0   \n",
       "48    ...                                    0.0   \n",
       "1772  ...                                    0.0   \n",
       "964   ...                                    0.0   \n",
       "2213  ...                                    0.0   \n",
       "...   ...                                    ...   \n",
       "1638  ...                                    0.0   \n",
       "1095  ...                                    0.0   \n",
       "1130  ...                                    0.0   \n",
       "1294  ...                                    0.0   \n",
       "860   ...                                    0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Manufacturing   \\\n",
       "582                                         0.0   \n",
       "48                                          1.0   \n",
       "1772                                        0.0   \n",
       "964                                         0.0   \n",
       "2213                                        0.0   \n",
       "...                                         ...   \n",
       "1638                                        1.0   \n",
       "1095                                        0.0   \n",
       "1130                                        0.0   \n",
       "1294                                        0.0   \n",
       "860                                         1.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Media & Entertainment   \\\n",
       "582                                                 0.0   \n",
       "48                                                  0.0   \n",
       "1772                                                0.0   \n",
       "964                                                 0.0   \n",
       "2213                                                0.0   \n",
       "...                                                 ...   \n",
       "1638                                                0.0   \n",
       "1095                                                0.0   \n",
       "1130                                                0.0   \n",
       "1294                                                0.0   \n",
       "860                                                 0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Metals & Mining   \\\n",
       "582                                           0.0   \n",
       "48                                            0.0   \n",
       "1772                                          0.0   \n",
       "964                                           0.0   \n",
       "2213                                          0.0   \n",
       "...                                           ...   \n",
       "1638                                          0.0   \n",
       "1095                                          0.0   \n",
       "1130                                          0.0   \n",
       "1294                                          0.0   \n",
       "860                                           0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Real Estate   \\\n",
       "582                                       1.0   \n",
       "48                                        0.0   \n",
       "1772                                      0.0   \n",
       "964                                       0.0   \n",
       "2213                                      0.0   \n",
       "...                                       ...   \n",
       "1638                                      0.0   \n",
       "1095                                      0.0   \n",
       "1130                                      1.0   \n",
       "1294                                      0.0   \n",
       "860                                       0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Service   prepocessing_cat__Industry_Sports   \\\n",
       "582                                   0.0                                 0.0   \n",
       "48                                    0.0                                 0.0   \n",
       "1772                                  0.0                                 0.0   \n",
       "964                                   0.0                                 0.0   \n",
       "2213                                  0.0                                 0.0   \n",
       "...                                   ...                                 ...   \n",
       "1638                                  0.0                                 0.0   \n",
       "1095                                  0.0                                 0.0   \n",
       "1130                                  0.0                                 0.0   \n",
       "1294                                  0.0                                 0.0   \n",
       "860                                   0.0                                 0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Technology   \\\n",
       "582                                      0.0   \n",
       "48                                       0.0   \n",
       "1772                                     0.0   \n",
       "964                                      0.0   \n",
       "2213                                     0.0   \n",
       "...                                      ...   \n",
       "1638                                     0.0   \n",
       "1095                                     0.0   \n",
       "1130                                     0.0   \n",
       "1294                                     0.0   \n",
       "860                                      0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_Telecom   \\\n",
       "582                                   0.0   \n",
       "48                                    0.0   \n",
       "1772                                  0.0   \n",
       "964                                   0.0   \n",
       "2213                                  0.0   \n",
       "...                                   ...   \n",
       "1638                                  0.0   \n",
       "1095                                  0.0   \n",
       "1130                                  0.0   \n",
       "1294                                  0.0   \n",
       "860                                   0.0   \n",
       "\n",
       "      prepocessing_cat__Industry_diversified     \n",
       "582                                         0.0  \n",
       "48                                          0.0  \n",
       "1772                                        0.0  \n",
       "964                                         0.0  \n",
       "2213                                        0.0  \n",
       "...                                         ...  \n",
       "1638                                        0.0  \n",
       "1095                                        0.0  \n",
       "1130                                        0.0  \n",
       "1294                                        0.0  \n",
       "860                                         0.0  \n",
       "\n",
       "[2080 rows x 855 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "import pandas as pd\n",
    "\n",
    "# Исправляем ColumnTransformer с сохранением имен колонок\n",
    "columns_to_drop = []\n",
    "\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in X_train.columns\n",
    "    if column not in columns_to_drop and X_train[column].dtype != \"object\"\n",
    "]\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in X_train.columns\n",
    "    if column not in columns_to_drop and X_train[column].dtype == \"object\"\n",
    "]\n",
    "\n",
    "# Предобработка числовых данных\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Предобработка категориальных данных\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Общая предобработка признаков\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=True,  # Сохраняем имена колонок\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"drop\"  # Убираем неиспользуемые столбцы\n",
    ")\n",
    "\n",
    "# Итоговый конвейер\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Преобразуем данные\n",
    "preprocessing_result = pipeline_end.fit_transform(X_train)\n",
    "\n",
    "# Создаем DataFrame с правильными именами колонок\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    "    index=X_train.index,  # Сохраняем индексы\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training LogisticRegression...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\nAll the 15 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n15 fits failed with the following error:\nTraceback (most recent call last):\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1231, in fit\n    check_classification_targets(y)\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\multiclass.py\", line 219, in check_classification_targets\n    raise ValueError(\nValueError: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 44\u001b[0m\n\u001b[0;32m     42\u001b[0m param_grid \u001b[38;5;241m=\u001b[39m param_grids_classification[name]\n\u001b[0;32m     43\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m RandomizedSearchCV(pipeline, param_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf1\u001b[39m\u001b[38;5;124m'\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 44\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;66;03m# Лучшая модель\u001b[39;00m\n\u001b[0;32m     47\u001b[0m best_model \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_estimator_\n",
      "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1019\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m   1013\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m   1014\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m   1015\u001b[0m     )\n\u001b[0;32m   1017\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m-> 1019\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1021\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m   1022\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m   1023\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1960\u001b[0m, in \u001b[0;36mRandomizedSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1958\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m   1959\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1960\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1961\u001b[0m \u001b[43m        \u001b[49m\u001b[43mParameterSampler\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1962\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_distributions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\n\u001b[0;32m   1963\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1964\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:996\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    989\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m!=\u001b[39m n_candidates \u001b[38;5;241m*\u001b[39m n_splits:\n\u001b[0;32m    990\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    991\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcv.split and cv.get_n_splits returned \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    992\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minconsistent results. Expected \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    993\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplits, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_splits, \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m n_candidates)\n\u001b[0;32m    994\u001b[0m     )\n\u001b[1;32m--> 996\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    998\u001b[0m \u001b[38;5;66;03m# For callable self.scoring, the return type is only know after\u001b[39;00m\n\u001b[0;32m    999\u001b[0m \u001b[38;5;66;03m# calling. If the return type is a dictionary, the error scores\u001b[39;00m\n\u001b[0;32m   1000\u001b[0m \u001b[38;5;66;03m# can now be inserted with the correct key. The type checking\u001b[39;00m\n\u001b[0;32m   1001\u001b[0m \u001b[38;5;66;03m# of out will be done in `_insert_error_scores`.\u001b[39;00m\n\u001b[0;32m   1002\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscoring):\n",
      "File \u001b[1;32mc:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:529\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m    523\u001b[0m     all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    524\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    525\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    526\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    527\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    528\u001b[0m     )\n\u001b[1;32m--> 529\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m    531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    532\u001b[0m     some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    533\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    534\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    538\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    539\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: \nAll the 15 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n15 fits failed with the following error:\nTraceback (most recent call last):\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1231, in fit\n    check_classification_targets(y)\n  File \"c:\\Users\\annal\\aim\\.venv\\Lib\\site-packages\\sklearn\\utils\\multiclass.py\", line 219, in check_classification_targets\n    raise ValueError(\nValueError: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
    "\n",
    "\n",
    "# Модели и параметры\n",
    "models_classification = {\n",
    "    \"LogisticRegression\": LogisticRegression(max_iter=1000),\n",
    "    \"RandomForestClassifier\": RandomForestClassifier(random_state=42),\n",
    "    \"KNN\": KNeighborsClassifier()\n",
    "}\n",
    "\n",
    "param_grids_classification = {\n",
    "    \"LogisticRegression\": {\n",
    "        'model__C': [0.1, 1, 10]\n",
    "    },\n",
    "    \"RandomForestClassifier\": {\n",
    "        \"model__n_estimators\": [10, 20, 30, 40, 50, 100, 150, 200, 250, 500],\n",
    "        \"model__max_features\": [\"sqrt\", \"log2\", 2],\n",
    "        \"model__max_depth\": [2, 3, 4, 5, 6, 7, 8, 9 ,10, 20],\n",
    "        \"model__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
    "    },\n",
    "    \"KNN\": {\n",
    "        'model__n_neighbors': [3, 5, 7, 9, 11],\n",
    "        'model__weights': ['uniform', 'distance']\n",
    "    }\n",
    "}\n",
    "\n",
    "# Результаты\n",
    "results_classification = {}\n",
    "\n",
    "# Перебор моделей\n",
    "for name, model in models_classification.items():\n",
    "    print(f\"Training {name}...\")\n",
    "    pipeline = Pipeline(steps=[\n",
    "        ('features_preprocessing', features_preprocessing),\n",
    "        ('model', model)\n",
    "    ])\n",
    "    \n",
    "    param_grid = param_grids_classification[name]\n",
    "    grid_search = RandomizedSearchCV(pipeline, param_grid, cv=5, scoring='f1', n_jobs=-1)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    # Лучшая модель\n",
    "    best_model = grid_search.best_estimator_\n",
    "    y_pred = best_model.predict(X_test)\n",
    "\n",
    "    # Метрики\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    # Вычисление матрицы ошибок\n",
    "    c_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    # Сохранение результатов\n",
    "    results_classification[name] = {\n",
    "        \"Best Params\": grid_search.best_params_,\n",
    "        \"Accuracy\": acc,\n",
    "        \"F1 Score\": f1,\n",
    "        \"Confusion_matrix\": c_matrix\n",
    "    }\n",
    "\n",
    "# Печать результатов\n",
    "for name, metrics in results_classification.items():\n",
    "    print(f\"\\nModel: {name}\")\n",
    "    for metric, value in metrics.items():\n",
    "        print(f\"{metric}: {value}\")"
   ]
  }
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